Model: federicovolponi/arctic-embed-m-space-sup
Author: federicovolponi
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, loss:CoSENTLoss, dataset_size:7232, loss:WeightedMultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-m, base_model:finetune:Snowflake/snowflake-arctic-embed-m, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-m
Card metadata: base_model: Snowflake/snowflake-arctic-embed-m
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:7232, 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 is the precision that the system is aiming for in terms of tracking error?

, What is the main challenge in implementing a distributed satellite system?

, Who are the authors of the NASA document "Space Radiation Cancer Risk Projections for Explorative Missions: Uncertainty Reduction and Mitigation"?

, 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: What are the specifications of the Theia imager that make it suitable for quantitative remote sensing studies?

, Who supported the first study?

, What are the essential derived products in mission planning, and why are they crucial for south polar missions?

, source_sentence: , there are still
some challenges to be overcome it is shown that it is possible to perform such links. Furthermore,
recommendations for future operations of optical links were provided.
FLP is also integrated in the educational aspects of the Institute. Many future aerospace engineers were
trained for satellite operations and Earth Observations and the satellite will be used to train operators
Further investigation of the Attitude Control is required for the stabilization of the optical links on
other G/S as Oberpfaffenhofen. However, future projects might benefit from more standardization on
the side of G/S Feedback for optical links. Overall Flying Laptop is a stable platform for technology demonstration, Earth Observation, and ed-
588. [Online]. Available
sentences: What are the remaining challenges that need to be addressed for the successful implementation of optical links?

, What are the benefits of enhancing the radiometric resolution of VLEO satellite systems?

, What is the reason for using the uncoupled approach for the radiation calculations in this study?

, source_sentence: : they are visible on the waterfall plots with a very high amplitude. Moreover, some peaks appear on waterfall plots while they are not
visible on zero speed curves. These peaks correspond to first order unbalance, engine orders or wheel eigenmodes. By repeating the tests with different configurations (without ventilation, changing the axes, etc...), conclusions have been made and are presented in table 4.
It is necessary to check if the modes presented in table 4 do not cross the order 1 unbalance or the rocking mode. The visible lines starting from the origin and evolving with the rotation speed of the wheel are the engine orders due to the imperfections of the wheel. When they cross modes of the wheel, the amplitudes corresponding to the crossing are much higher as we can clearly see in Table 2, on the x axis waterfall plots at 1050 Hz and 4000 RPM. The waterfall plots allow to have a global view on the wheel structure. By looking at these curves, two wheels can be compared. For example, higher amplitudes on engine orders mean that the wheel has defects. Moreover, a shift of the rocking mode means that the parameters of the wheel are different as shown in equations 4.
Table 3 summarizes the static and dynamic unbalances calculated on three wheels. We notice that they all have the same order of magnitude. Environmental vibration and shock tests can vary this value by damaging the wheel. On the other hand, bearing defects can be reduced when the wheel is continuously rotated due to the running-in process, which can decrease the unbalance value. In general, environmental testing has more impact than running-in.
When the frequencies are low, the wheel has no trouble following the setpoint. At high frequencies, the wheel follows the setpoint but with a loss of amplitude and a phase shift
sentences: What are the peaks that appear on waterfall plots but not on zero speed curves?

, Why is separately scheduling the imaging and download tasks a natural choice for real-world complex systems?

, What are the dominant orbit determination uncertainties?

, source_sentence: : Block diagram of the 7-band CCD-in-CMOS TDI sensor. Each TX slice has two serializers and its own PLL.
The CCD bands operate continuously and time interleaved. The output stages for the CCD arrays are implemented both at the top and bottom of each band to support the bi-directional operation. All 14 output stages in one column are connected to one delta-sigma column-level ADC with digital CDS implemented in the digital decimator. The outputs of every 128 ADCs are serialized to one of 32 LVDS outputs. Two clock signals are also provided via LVDS to synchronize the channels. These outputs are capable of running at an aggregate data rate of >50Gb/s using on-chip PLLs.
The sensor has been processed for Back-Side Illumination and it has been packaged in a custom ceramic PGA package. Figure 15 shows a picture of the sensor with its 7 bands. The figure shows the front-side and back-side versions of the chip side by side.
(a) (b) Figure 15: 7-band CCD-in-CMOS TDI chip photograph. FSI shown only for reference (a) and BSI version (b).
As a proof-of-concept, an RGB butcher-brick filter has been used as glass lid for the sensor, to enable multicolor TDI, although filters may be processed directly on the wafer as well [9]. The sensor,
camera system and a color image captured from the setup are depicted in Figure 16, providing evidence that multispectral TDI is viable with the sensor.
Figure 16: Colour TDI image captured from the sensor, sensor with RGB color filter and camera set-up.
Table 3 below shows a comparison of different TDI sensors, including the first iteration of our sensor.
Integrated drivers
The measurements on the first iteration of the SoC verified
sentences: What is the primary objective of the Zodiac Pioneer Mission?

, What is the main topic of the papers listed in the context?

, What is the aggregate data rate of the outputs of the 7-band CCD-in-CMOS TDI sensor?


model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@5
value: 0.8407960199004975
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8843283582089553
name: Cosine Accuracy@10
verified: no, type: cosine_precision@5
value: 0.16815920398009948
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08843283582089552
name: Cosine Precision@10
verified: no, type: cosine_recall@5
value: 0.8407960199004975
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8843283582089553
name: Cosine Recall@10
verified: no, type: cosine_ndcg@5
value: 0.749593576396566
name: Cosine Ndcg@5
verified: no, type: cosine_ndcg@10
value: 0.7638900783774348
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@5
value: 0.7189676616915421
name: Cosine Mrr@5
verified: no, type: cosine_mrr@10
value: 0.7249965450525153
name: Cosine Mrr@10
verified: no, type: cosine_map@5
value: 0.7189676616915422
name: Cosine Map@5
verified: no, type: cosine_map@10
value: 0.7249965450525152
name: Cosine Map@10
verified: no, type: cosine_accuracy@5
value: 0.9198717948717948
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9551282051282052
name: Cosine Accuracy@10
verified: no, type: cosine_precision@5
value: 0.18397435897435896
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0955128205128205
name: Cosine Precision@10
verified: no, type: cosine_recall@5
value: 0.9198717948717948
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9551282051282052
name: Cosine Recall@10
verified: no, type: cosine_ndcg@5
value: 0.786039298615645
name: Cosine Ndcg@5
verified: no, type: cosine_ndcg@10
value: 0.7975208279742617
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@5
value: 0.740758547008547
name: Cosine Mrr@5
verified: no, type: cosine_mrr@10
value: 0.7455369861619862
name: Cosine Mrr@10
verified: no, type: cosine_map@5
value: 0.740758547008547
name: Cosine Map@5
verified: no, type: cosine_map@10
value: 0.7455369861619863
name: Cosine Map@10
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@5
value: 0.8345771144278606
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8781094527363185
name: Cosine Accuracy@10
verified: no, type: cosine_precision@5
value: 0.16691542288557212
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08781094527363183
name: Cosine Precision@10
verified: no, type: cosine_recall@5
value: 0.8345771144278606
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8781094527363185
name: Cosine Recall@10
verified: no, type: cosine_ndcg@5
value: 0.7384076037005772
name: Cosine Ndcg@5
verified: no, type: cosine_ndcg@10
value: 0.7524024562602603
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@5
value: 0.7060530679933663
name: Cosine Mrr@5
verified: no, type: cosine_mrr@10
value: 0.7117739674642659
name: Cosine Mrr@10
verified: no, type: cosine_map@5
value: 0.7060530679933666
name: Cosine Map@5
verified: no, type: cosine_map@10
value: 0.7117739674642659
name: Cosine Map@10
verified: no, type: cosine_accuracy@5
value: 0.907051282051282
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9519230769230769
name: Cosine Accuracy@10
verified: no, type: cosine_precision@5
value: 0.1814102564102564
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09519230769230767
name: Cosine Precision@10
verified: no, type: cosine_recall@5
value: 0.907051282051282
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9519230769230769
name: Cosine Recall@10
verified: no, type: cosine_ndcg@5
value: 0.7793612708940784
name: Cosine Ndcg@5
verified: no, type: cosine_ndcg@10
value: 0.7942949173487753
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@5
value: 0.7363247863247866
name: Cosine Mrr@5
verified: no, type: cosine_mrr@10
value: 0.7427375864875867
name: Cosine Mrr@10
verified: no, type: cosine_map@5
value: 0.7363247863247864
name: Cosine Map@5
verified: no, type: cosine_map@10
value: 0.7427375864875865
name: Cosine Map@10
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@5
value: 0.8146766169154229
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8631840796019901
name: Cosine Accuracy@10
verified: no, type: cosine_precision@5
value: 0.16293532338308458
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08631840796019902
name: Cosine Precision@10
verified: no, type: cosine_recall@5
value: 0.8146766169154229
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8631840796019901
name: Cosine Recall@10
verified: no, type: cosine_ndcg@5
value: 0.7159371426767726
name: Cosine Ndcg@5
verified: no, type: cosine_ndcg@10
value: 0.731814701526023
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@5
value: 0.6826907131011605
name: Cosine Mrr@5
verified: no, type: cosine_mrr@10
value: 0.6893587617468213
name: Cosine Mrr@10
verified: no, type: cosine_map@5
value: 0.6826907131011608
name: Cosine Map@5
verified: no, type: cosine_map@10
value: 0.6893587617468214
name: Cosine Map@10
verified: no, type: cosine_accuracy@5
value: 0.8846153846153846
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9455128205128205
name: Cosine Accuracy@10
verified: no, type: cosine_precision@5
value: 0.1769230769230769
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09455128205128205
name: Cosine Precision@10
verified: no, type: cosine_recall@5
value: 0.8846153846153846
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9455128205128205
name: Cosine Recall@10
verified: no, type: cosine_ndcg@5
value: 0.7547512036424451
name: Cosine Ndcg@5
verified: no, type: cosine_ndcg@10
value: 0.7747939646301274
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@5
value: 0.7107905982905985
name: Cosine Mrr@5
verified: no, type: cosine_mrr@10
value: 0.7192778286528287
name: Cosine Mrr@10
verified: no, type: cosine_map@5
value: 0.7107905982905982
name: Cosine Map@5
verified: no, type: cosine_map@10
value: 0.7192778286528286
name: Cosine Map@10
verified: no
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Created: 2024-08-18 16:47:02