---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@5
- cosine_precision@10
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@5
- cosine_ndcg@10
- cosine_mrr@5
- cosine_mrr@10
- cosine_map@5
- cosine_map@10
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:CoSENTLoss
- dataset_size:102962
- loss:WeightedDenoisingAutoEncoderLoss
- loss:WeightedMultipleNegativesRankingLoss
widget:
- source_sentence: ', antenna, or other sensor to attain mission performance levels
that
currently cannot be achieved by a monolithic satellite. Most aspects of this concept
have been widely studied, but
the first implementation has yet to be realized, with the exception of a few initial
experiments.
A distributed satellite system taxonomy is shown in Fig. 1 with a discussion of
current and planned systems to
follow. At the end of this section, a candidate distributed space mission is presented
as a common reference for
Table 1 presents a selection of current distributed satellite systems, grouped
in the four typical mission
categories'
sentences:
- 'What are the peaks that appear on waterfall plots but not on zero speed curves?
'
- 'What is the main challenge in implementing a distributed satellite system?
'
- 'What are the remaining challenges that need to be addressed for the successful
implementation of optical links?
'
- source_sentence: ':250,000 scale for regional context) . Near-term efforts should
focus on high-priority locations .
[16] Terrain hazard (e .g ., slope, surface roughness), line-of-sight (i .e .,
viewshed), and time-dependent
illumination maps at appropriate scales (e .g ., best-available supported by the
data) are high-priority derived products essential in mission planning, and they
should be made available as soon as possible .
[17] South polar data products could be initially controlled to coarser data and
known surface reference points to support early Artemis missions and other surface
activities, but establishment of a local control network applied to all necessary
data layers would facilitate interoperability and provide more precision for specific
sites .
Higher-order data products are tied to controlled foundational data and are derived
from source data, such as measurements of elemental abundance, temperature or
reflectance at multiple wavelengths, observations of solar illumination, and output
from space weather models . Higher-order data products derived from these source
data will play an essential role in planning and executing south polar missions
. Planning the science activities to be carried out on the lunar surface will
be based on these higher-order data products, and, in turn, the science returned
by those activities will be used to update those same products . For example,
geologic maps based on remotely sensed data prior to early Artemis landings will
be a likely outcome of site assessments and will form the critical basis for traverse
plans and planning of science tasks . The observations, samples, and measurements
made during Artemis surface activities will feed back into updating the geologic
maps, to the benefit of future crewed or robotic missions to the same area . Similarly,
resource maps will drive the selection of landing sites for missions focused on
resource discovery, characterization, and utilization, and the findings of those
missions will be used to iteratively update the resource maps . In these cases,
and others'
sentences:
- 'Who are the authors of the NASA document "Space Radiation Cancer Risk Projections
for Explorative Missions: Uncertainty Reduction and Mitigation"?
'
- 'What is the aggregate data rate of the outputs of the 7-band CCD-in-CMOS TDI
sensor?
'
- 'What are the essential derived products in mission planning, and why are they
crucial for south polar missions?
'
- source_sentence: As LisR as a demonstrator mission under development
sentences:
- ' As LisR only serves as a technology demonstrator, the follow-up mission HiVE
is already under development'
- ' Schematic of the 3 grid extraction system in an ion gridded thruster showing
one ion beamlet and the corresponding axial potential profile (not to scale)'
- ' The diagram of PUC is shown as follows:
The propellent type is Sulfur Dioxide (SO2)'
- source_sentence: Conclusion This provides formation processes application the impact
on, small moon of the Didymos binary
sentences:
- ' The mission network model,
parameters, commodity demand and supply used in this case study are presented
in Fig'
- ' Conclusion
This paper provides an overview of ejecta formation and evolution processes with
specific application to the hypervelo- city impact of the DART spacecraft on Dimorphos,
the small moon of the Didymos binary asteroid system'
- '
vantage points from NASA and non-NASA sources, including in orbit, airborne and
even in-situ sensors to create a more dynamic and complete picture of a natural
physical process'
- source_sentence: Table of
sentences:
- '
[29] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun'
- ' Nevertheless, both formulations used in this work allow enough flexibility to
adapt them to the most common mission requirements, while still being able to
reduce the searching space for the optimization process'
- ' Table 4 offers a description of the selected FoM'
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@5
value: 0.8196517412935324
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8606965174129353
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.16393034825870648
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08606965174129352
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8196517412935324
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8606965174129353
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7116804364524553
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7249564355877233
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.6753316749585401
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6808181315643997
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6753316749585406
name: Cosine Map@5
- type: cosine_map@10
value: 0.6808181315644002
name: Cosine Map@10
- type: cosine_accuracy@5
value: 0.8685897435897436
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9134615384615384
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.1737179487179487
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09134615384615384
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8685897435897436
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9134615384615384
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7286906150877144
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7431205481598633
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.6814636752136752
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6873728123728122
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6814636752136752
name: Cosine Map@5
- type: cosine_map@10
value: 0.6873728123728124
name: Cosine Map@10
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@5
value: 0.8034825870646766
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8470149253731343
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.1606965174129353
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08470149253731342
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8034825870646766
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8470149253731343
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.6967322721990336
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7108049353049597
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.6608001658374787
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6666044776119396
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6608001658374792
name: Cosine Map@5
- type: cosine_map@10
value: 0.6666044776119403
name: Cosine Map@10
- type: cosine_accuracy@5
value: 0.8557692307692307
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.907051282051282
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.17115384615384616
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0907051282051282
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8557692307692307
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.907051282051282
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7241884927554256
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7406789864779515
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.6800747863247864
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6868208180708181
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6800747863247864
name: Cosine Map@5
- type: cosine_map@10
value: 0.6868208180708181
name: Cosine Map@10
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@5
value: 0.777363184079602
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8258706467661692
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.15547263681592036
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08258706467661689
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.777363184079602
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8258706467661692
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.6732366988651133
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.6890994908635195
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.638246268656716
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6448970425649527
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6382462686567164
name: Cosine Map@5
- type: cosine_map@10
value: 0.644897042564953
name: Cosine Map@10
- type: cosine_accuracy@5
value: 0.842948717948718
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8814102564102564
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.16858974358974357
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08814102564102565
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.842948717948718
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8814102564102564
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7221379927354293
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7350654713813302
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.6817307692307693
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6873613654863654
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6817307692307691
name: Cosine Map@5
- type: cosine_map@10
value: 0.6873613654863655
name: Cosine Map@10
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the tsdae and sup datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- tsdae
- sup
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("federicovolponi/BAAI-bge-base-en-v1.5-space-multitask-tsdae")
# Run inference
sentences = [
'Table of',
' Table 4 offers a description of the selected FoM',
'\n[29] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8197 |
| cosine_accuracy@10 | 0.8607 |
| cosine_precision@5 | 0.1639 |
| cosine_precision@10 | 0.0861 |
| cosine_recall@5 | 0.8197 |
| cosine_recall@10 | 0.8607 |
| cosine_ndcg@5 | 0.7117 |
| cosine_ndcg@10 | 0.725 |
| cosine_mrr@5 | 0.6753 |
| cosine_mrr@10 | 0.6808 |
| cosine_map@5 | 0.6753 |
| **cosine_map@10** | **0.6808** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8035 |
| cosine_accuracy@10 | 0.847 |
| cosine_precision@5 | 0.1607 |
| cosine_precision@10 | 0.0847 |
| cosine_recall@5 | 0.8035 |
| cosine_recall@10 | 0.847 |
| cosine_ndcg@5 | 0.6967 |
| cosine_ndcg@10 | 0.7108 |
| cosine_mrr@5 | 0.6608 |
| cosine_mrr@10 | 0.6666 |
| cosine_map@5 | 0.6608 |
| **cosine_map@10** | **0.6666** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.7774 |
| cosine_accuracy@10 | 0.8259 |
| cosine_precision@5 | 0.1555 |
| cosine_precision@10 | 0.0826 |
| cosine_recall@5 | 0.7774 |
| cosine_recall@10 | 0.8259 |
| cosine_ndcg@5 | 0.6732 |
| cosine_ndcg@10 | 0.6891 |
| cosine_mrr@5 | 0.6382 |
| cosine_mrr@10 | 0.6449 |
| cosine_map@5 | 0.6382 |
| **cosine_map@10** | **0.6449** |
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8686 |
| cosine_accuracy@10 | 0.9135 |
| cosine_precision@5 | 0.1737 |
| cosine_precision@10 | 0.0913 |
| cosine_recall@5 | 0.8686 |
| cosine_recall@10 | 0.9135 |
| cosine_ndcg@5 | 0.7287 |
| cosine_ndcg@10 | 0.7431 |
| cosine_mrr@5 | 0.6815 |
| cosine_mrr@10 | 0.6874 |
| cosine_map@5 | 0.6815 |
| **cosine_map@10** | **0.6874** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8558 |
| cosine_accuracy@10 | 0.9071 |
| cosine_precision@5 | 0.1712 |
| cosine_precision@10 | 0.0907 |
| cosine_recall@5 | 0.8558 |
| cosine_recall@10 | 0.9071 |
| cosine_ndcg@5 | 0.7242 |
| cosine_ndcg@10 | 0.7407 |
| cosine_mrr@5 | 0.6801 |
| cosine_mrr@10 | 0.6868 |
| cosine_map@5 | 0.6801 |
| **cosine_map@10** | **0.6868** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8429 |
| cosine_accuracy@10 | 0.8814 |
| cosine_precision@5 | 0.1686 |
| cosine_precision@10 | 0.0881 |
| cosine_recall@5 | 0.8429 |
| cosine_recall@10 | 0.8814 |
| cosine_ndcg@5 | 0.7221 |
| cosine_ndcg@10 | 0.7351 |
| cosine_mrr@5 | 0.6817 |
| cosine_mrr@10 | 0.6874 |
| cosine_map@5 | 0.6817 |
| **cosine_map@10** | **0.6874** |
## Training Details
### Training Datasets
#### tsdae
* Dataset: tsdae
* Size: 95,730 training samples
* Columns: damaged_sentence and orginal_sentence
* Approximate statistics based on the first 1000 samples:
| | damaged_sentence | orginal_sentence |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 3 tokens
- mean: 13.28 tokens
- max: 174 tokens
| - min: 6 tokens
- mean: 30.02 tokens
- max: 374 tokens
|
* Samples:
| damaged_sentence | orginal_sentence |
|:---------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| , the described above allows continue this | However, the modularization into functional units described
above allows to continue this idea and form a well-defined functional hierarchy |
| Solar scientific military and the stage for Change mission technology improvements—continued advances in will mass/volume | Solar sails can perform unique scientific, commercial, and military missions, and the stage is set for near-term
UPGRADE/REPLACE PAYLOADS • Change of mission • Take advantage of technology improvements—continued advances in electronics will cause payload components to shrink in mass/volume, while capabilities increase |
| 4mm Hexcell 5052 aluminum honeycomb with 1 | 4mm thick Hexcell 5052 alloy hexagonal aluminum honeycomb with 1 |
* Loss: losses.WeightedDenoisingAutoEncoderLoss
#### sup
* Dataset: sup
* Size: 7,232 training samples
* Columns: positive and anchor
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 5 tokens
- mean: 354.69 tokens
- max: 512 tokens
| - min: 9 tokens
- mean: 19.21 tokens
- max: 40 tokens
|
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
| , using diverse software or hardware designs may double design and verification costs due to having to build two different components for the same functionality. Hence, although DCLS execution also halves performance efficiency (the corresponding functionality is executed twice), it allows reusing the same design (e.g. the same core design) for the primary and the redundant paths (e.g. with staggered execution), thus containing design and verification costs.
Redundancy can be applied at different granularities accord- ing to the sphere of replication (SoR). Choosing the right SoR depends on several tradeoffs like area overheads, re- design costs, fault detection time, and overall system costs. In the context of DCLS, the SoR is placed at the level of the CPU (core), as done for the AURIX processors. This requires including two replicas of the same core and compare their memory transactions, which requires roughly duplicating com- putational resources in the chip and being able to ensure that replicas can provide independent behavior. On the other hand, storage (memories, caches) and communication means (buses, crossbars) do not need to be fully replicated and can build upon Error Correction Codes (ECC) and Cyclic Redundancy Check (CRC) as a form of lightweight redundancy with diversity.
HPC ASIL-D capable platforms typically combine a low- performance microcontroller amenable for the automotive do- main (i.e. ASIL-D capable) and an HPC accelerator deliv- ering high computation throughput, but whose adherence to ISO26262 requirements is unknown, so its appropriate use for ASIL-C/D systems needs to be investigated. Without loss of generality, we consider an NVIDIA GPU accelerator, thus analogous to those in NVIDIA Drive and Xavier families for the automotive domain. However, the findings in this paper can easily be extrapolated to other products.
Software faults and some hardware faults are regarded as systematic, and it must be proven that their failure risk is residual. However, random hardware faults cannot be avoided, and means are required to prevent them from causing hazards. Those faults can be caused by, for example, voltage droops | What are the advantages of using the same design for the primary and redundant paths in DCLS execution?
|
| : First, the TT&C spectrum requirements of the new satellites shall be assessed. Second, the utilization of existing TT&C frequency allocations and their potential to incorporate the future number of satellites is studied. Only for the case that this study results in the need for new spectrum, the study groups were asked to investigate new potential TT&C frequency allocations in the frequency ranges 150.05-174 MHz and 400.15-420 MHz. The studies shall be completed for WRC-19.
This paper presents the intermediate results of the study groups. A study of the spectrum requirements of small satellites has been completed. The required spectrum for TT&C is expected to be less than 2.5 MHz for downlink and less than 1 MHz for uplink. Consequently, the study groups conducted sharing studies in various bands which will be summarized and evaluated from a satellite developer’s perspective.
After the Cubesat design standard was introduced in 1999 and first satellites of this new class have been launched in the subsequent years, small satellites have become increasingly popular in the past five years. Today not only universities use small satellite platforms for education and technology demonstration, but also commercial operators started to develop and deploy satellites with masses of typically less than 50 kg and reasonably short development times. Currently more than hundred new satellites are currently launched into space per year. The increase of launches was recognized by the International Telecommunication Union (ITU) which is responsible for the coordination of the shared use of frequencies. As the first Cubesats were mainly launched by new entrants into the space sector, mandatory regulatory procedures like frequency coordination were omitted or underestimated by the developers. Additionally, the new developers complaint that the existing regulatory procedures are too complicated and time-consuming for satellites with short development times. The ITU therefore decided at the WRC-12 to study the characteristics of picosatellites and nanosatellites and their current practice in filing satellites to the ITU. The studies were concluded in 2015 with two reports on the characteristics [1] and current filing practice [2]. In these reports it was identified that the characteristics that define small satellites (low mass, small dimensions, low power, …) are not relevant from a frequency coordination perspective and that the short development times are still long enough to properly file the systems to the ITU. As a result | What are the spectrum requirements for TT&C of small satellites?
|
| :287–299, Dec 2019.
[20] Tam´as Vink´o and Dario Izzo. Global optimi- sation heuristics and test problems for prelimi- nary spacecraft trajectory design. Technical re- port, 2008.
[21] Matej Petkovic, Luke Lucas, Dragi Kocev, Saˇso Dˇzeroski, Redouane Boumghar, and Nikola Simidjievski. Quantifying the effects of gyro- less flying of the mars express spacecraft with machine learning. In 2019 IEEE International
[22] Janhavi H. Borse, Dipti D. Patil, Vinod Kumar, and Sudhir Kumar. Soft landing parameter measurements for candidate navigation trajec- tories using deep learning and ai-enabled plan- etary descent. Mathematical Problems in Engi- neering, 2022 | What are some of the research topics and methods explored in the provided references?
|
* Loss: losses.WeightedMultipleNegativesRankingLoss with these parameters:
```json
{
"scale": 20,
"similarity_fct": "cos_sim"
}
```
### Evaluation Datasets
#### tsdae
* Dataset: tsdae
* Size: 10,637 evaluation samples
* Columns: damaged_sentence and orginal_sentence
* Approximate statistics based on the first 1000 samples:
| | damaged_sentence | orginal_sentence |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 3 tokens
- mean: 13.52 tokens
- max: 182 tokens
| - min: 5 tokens
- mean: 30.74 tokens
- max: 452 tokens
|
* Samples:
| damaged_sentence | orginal_sentence |
|:------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
| from providing student licenses the OirthirSAT team |
The authors thank Michael Doherty from Ansys for providing student licenses for STK to the OirthirSAT teamat 205 | 4 as observed by TROPICS Pathfinder at 205 GHz |
| this reason of chemistry needed to radiative heating | For this reason, careful reexaminations of the chemistry models are needed to reduce the uncertainties in the radiative heating |
* Loss: losses.WeightedDenoisingAutoEncoderLoss
#### sup
* Dataset: sup
* Size: 804 evaluation samples
* Columns: positive and anchor
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 4 tokens
- mean: 351.15 tokens
- max: 512 tokens
| - min: 8 tokens
- mean: 19.36 tokens
- max: 45 tokens
|
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|
| , the total number of test thermocouples has been rationalized taking into account redundancy needs, accommodation constraints and hardware passivation needs for flight. The test is subdivided into 19 phases (see Figure 12) with two phases before and after the test for the health check functional tests under room conditions. Functional tests demonstrate anomalies such as the PCDU Reset and operational malfunctions of the RAX instrument at its high temperatures. The PCDU Reset anomaly was solved during the test by a software patch and validated during the final hot and cold plateaus. To address the RAX anomaly at hot, various test configurations were simulated using the thermal numerical model during the test to actually perform RAX functional test at an intermediate plateau facilitating mission operational constraints for flight. Data collected from hot and cold thermal balance test phases, as well as the rover OFF transition from hot to cold, are the inputs for correlation activities conducted post-TV/TB test. The thermal numerical model updates mainly focus on conductive couplings | What was the solution to the PCDU Reset anomaly during the test?
|
| , where +Z axis orients to the earth, and sun pointing attitude mode during day time
orienting -Z axis to the sun. Therefore, attitude control subsystem is required to maneuver the satellite attitude twice per revolution around its pitch axis. Figure 6 shows concept of the attitude maneuverer. Another attitude maneuverer is necessary to perform SAR observation and SAR data download to a to ground station, because X-band transmit antenna is oriented to +Z, so the satellite has to offset its attitude to orient the X-band transmit antenna toward the ground station.
3.4 High pointing accuracy
Disturbance torque and system momentum profiles during few revolutions were estimated as shown in Figure 7 and 8. Four micro reaction wheels, which can respond to these profiles were selected which enable attitude maneuvers within a short period of time. In order to perform a pitch attitude maneuver quickly, two wheels are located on pitch axis while one wheel was located on each of the remaining roll and yaw axes. Figure 9 shows the satellite attitudes during SAR observation. There are three kinds of attitude, strip map mode, sliding spot light mode, and spotlight mode. Large change of momentum is required for pitch axis when the satellite is in spotlight mode. However, two pitch reaction wheels do not generate enough momentum to execute spotlight mode. So, sliding spotlight mode was selected for high resolution SAR observation mode instead of spotlight mode, in order to relax the torque and momentum requirements to the pitch wheels. In addition, two pitch
Figure 7. Disturbance torque profile Figure 8. System momentum profile
reaction wheels are accelerated to plus direction or minus direction by using magnet torque before observation. In order to obtain a high resolution SAR data, high attitude control accuracy is required for spotlight mode observation. To achieve high pointing accuracy against a defined ground target point, the attitude control loop applied feed forward compensation with estimated attitude angle and rate. Figure 10 shows an example of dynamic error during a spotlight mode observation maneuver.[4]
Equipment for SAR mission consumes total large power more than 1300W, therefore PCDU has a risk of causing electrical and RF influence to the bus power and signal line. In order to research the system, electrical interface check was performed using bread board model of PCDU, battery | What is the reason for selecting sliding spotlight mode instead of spotlight mode for high resolution SAR observation?
|
| , body shape and motion assumptions. Then, ORSAT uses DCA to determine the reentry risk posed to the Earth’s
population based on the year of reentry and orbit inclination. It also predicts impact kinetic energy (impact velocity and impact mass) of objects that survive reentry[18]. ORSAT has been in use for the last decade and currently in its 6.0 version. However, unlike DAS, OR-
SAT is not readily available. Only personnel at the Johnson Space Center, Orbital Debris Program Office run ORSAT. ORSAT is limited to ballistic reentry, only tumbling motions or
stable orientations of objects are allowed which produce no lift. Partial melting of objects is considered by a demise factor and almost all materials in the database are temperature de- pendent. Heating by oxidation is also considered [20]. Therefore, ORSAT determines when
and if a reentry object demises by using integrated trajectory, atmospheric, aerodynamic, aero-thermodynamic, and thermal models as outlined in section 3.1 [17, 18, 20].
Reentry demisability analysis using DAS requires the spacecraft to be defined to the level of each individual hardware part constituting the spacecraft. This step facilitates population
of the DAS Spacecraft Definition Module . Section 3.2.1 illustrates a generic spacecraft subdivision approach that can be followed to itemize the individual parts spacecraft parts.
Subsequently, non-demisable parts are identified before or by the actual reentry analysis as explained in section 3.2.2.
Itemization of the demisable spacecraft basic parts can be best approached by decompos- ing the spacecraft according to the Hierarchical System Terminology defined in the NASA Systems Engineering Handbook [14]. Tables 3.2, 3.3 and 3.4 illustrate a generic approach
to decompose a spacecraft into basic parts [29, 30, 9] excluding the payload. Description of the specific product for the basic part identified completes the process. Though slight vari-
ations are likely to occur in the decomposition of different missions, the Generic Spacecraft Subsystems Hierarchical Subdivision approach is robust, hence | What is the limitation of ORSAT in terms of object motion?
|
* Loss: losses.WeightedMultipleNegativesRankingLoss with these parameters:
```json
{
"scale": 20,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 3e-06
- `weight_decay`: 0.001
- `num_train_epochs`: 6
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-06
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional