esa-sceva/satcom-chunk-collection huggingface dataset:
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 4 Results
### Token Aggregation and Post-Processing Techniques
#### 4.1.2 BERT versus Random Embeddings  
The most dramatic increase in performance due to the two techniques is observed for the model of random embeddings. For STS tasks, the average Spearman correlation rises from 50.3 to 66.4, while for clustering tasks, it increases from 36.3 to 49.5. Although accuracy for the latter tasks is still very low, for STS, it is just a mere 3.4 points below what BERT managed to accomplish with both techniques applied.  
If we were to look at the detailed performance on individual tasks in Appendix A.1, we would find several where random embeddings with the help of the two shaping techniques score higher than the also-shaped BERT representations. For STS14 (Table A3), it is 68.8 versus 68.3 Spearman correlation, while for the googleTS (Table A11), stackoverflow (Table A13), and tweet (Table A14) datasets, the clustering accuracy is, respectively, 69.5 vs. 68.5, 70.6 vs. 59.9, and 58.5 vs. 55.1. This may indicate a smaller complexity of these particular datasets, where the task can largely be solved based on a small set of keywords. Also, if the texts do not contain the natural language of the type the BERT was pre-trained on (e.g., they contain code), the model cannot properly contextualize the tokens, and the random embeddings without contexts work better. Yet it is important to note that it is achieved only with the help of aggregation and post-processing methods on top of the random embeddings.  
The largest clustering accuracy difference between BERT and random embeddings is for the agnews and searchsnippets datasets, with the best scores of 86.8 versus 43.4 and 82.9 versus 36.6, respectively. We think that the observed performance gap may be related to the vocabulary and text sizes of the datasets. Stackoverflow and tweet datasets, with random embeddings ahead of the BERT, have the smallest vocabulary sizes of 7332 and 8091 (see Table 2), while agnews and searchsnippets have the largest vocabulary sizes of 16,140 and 16,334 unique tokens, respectively. Because random embeddings rely only on distinctness of tokens, with the increased amount of them, the probability of having exactly the same keywords in two texts drops. BERT embeddings having non-identical but semantically similar tokens and similar representations helps in this case.  
Having shorter text lengths (average text sizes of 12 and 11 for stackoverflow and tweet, respectively, versus 26 and 24 for agnews and searchsnippets, respectively) may also help the random embeddings, because they do not average away so easily, and there is less context to consider.  
In contrast, other static models have much more comparable scores. For example, the Avg. clustering accuracy is 83.8 (agnews) and 74.2 (searchsnippets).
## 4 Discussion
The AM/PM diets contained two different formulae of nutrients in the present study. Based on the sampling time (early morning before the AM diet was swapped to the PM diet), we assumed only the PM diet was present in the ileal digesta of the sampled hens fed the AM/PM diet in this study. Thus, the lower ileal digestible energy in the hens fed the AM/PM diet compared with those fed the control diet might be attributed to the lower energy level in the PM diet. Moreover, the metabolic thresholds for Ca in hens are determined by a multitude of dietary and physiological factors that affect the digestion and absorption of nutrients [41]. Excess dietary Ca reduces enzymatic activity in the intestinal tract by increasing intestinal pH, leading to the precipitation of essential cationic minerals with counter anions [42; 43]. This may lead to a reduction in the digestibility of protein and energy in hens fed the PM diet, possibly through the formation of indigestible Ca soaps [44; 45]. Determining nutrient digestibility in the AM and PM diets separately is necessary to fully evaluate the nutrient digestibility of hens offered the AM/PM feeding regime over the entire day. Alternatively, the utilisation of a total excrete collection method to measure nutrient digestibility may help to solve this issue. Additionally, special attention should be paid to maximising the level and particle size of Ca during the formulation of AM/PM diets to increase the nutrient digestibility in hens, which warrants further investigation. A crucial aspect of this study revealed a greater return on investment from the hens fed the AM/PM diet compared with the control hens, primarily attributed to the enhanced feed efficiency associated with the AM/PM diet. The improved feed efficiency underscores the cost-effectiveness of the AM/PM feeding strategy, which arises from strategically optimising nutrient utilisation in line with the metabolic needs of the hens. Furthermore, the positive outcomes, including improved FCR and egg mass, reflect improved productivity with lower costs due to savings in feed costs. Collectively, these findings endorse the AM/PM strategy as a favourable practice for egg producers, providing a cost-efficient and resource-effective approach to layer production. Although the AM/PM strategy requires two feed silos, a feed weighing system, and an automated mechanism for twice-daily ration changes, and thus entailing initial investments for existing and new housing setups; nevertheless, growing confidence in advancing equipment and IT solutions supports its adoption.
# RTACompensator: Leveraging AraBERT and XGBoost for Automated Road Accident Compensation
## 4 Research Methodology
### Arabic Corpus of Responsibility
#### 4.1.1 Data Collection Process  
To build the corpus, we used judgments from the traffic accident chambers of Moroccan courts rather than police reports. Police reports only contain descriptions of the accident, without any reference to responsibility. In contrast, judgments are already classified by the degree of responsibility, making them more suitable for our purposes. A judgment is a document produced after a court hearing that contains detailed information about the trial. As illustrated in Figure 6, a typical judgment is structured into five key sections:  
* The first section provides details of the court staff, including the names of the head of the court, the court clerk, and the prosecution representative. It also includes the address of the court and the case number.
* The second section provides information about the victim and the offender, address, date of birth, name, etc.
* The third section describes the accident and the circumstances leading up to it.
* The fourth section presents the legal texts on which the judge based his decision.
* The final section presents the judge's decision.  
Figure 7 shows the original Arabic judgment on the left, with a translated English version on the right.  
We used 1000 judgments from traffic accidents that occurred in different Moroccan cities between 2004 and 2019. These judgments are classified into seven folders, each referring to a category of responsibility. We focused only on the third section, which describes how the accident happened. This section is identical to what is found in police reports and it is the part judges use to determine responsibility. To automate data extraction, we developed a Python script, _description_extractor.py_, which extracts the accident description from each judgment and stores it in a CSV file. The CSV file has two columns: the first contains the accident description, and the second contains the corresponding responsibility label. The pipeline for creating the corpus is illustrated in Step 1 of Figure 8.  
Figure 6: Judgment structure with an example of each part of it.  
Figure 7: Example of a judgment in Arabic with its English translation.
## 5 Conclusions  
This study introduces a novel approach for cognitive workload classification using EEG signals and advanced deep learning techniques. Leveraging the STEW dataset, comprehensive preprocessing and correlation analyses were conducted, followed by detailed short-time Fourier transformation analysis to extract relevant features. Our proposed deep gated neural network (DGNN) model, integrating Bi-LSTM and GRU layers, achieved an impressive accuracy of 99.45%. Precision, recall, and F1 score metrics further validated the efficacy of our model in effectively classifying cognitive workload across distinct categories. A comparative analysis with existing methodologies underscores the superior performance and robustness of the DGNN model in this domain.  
To validate the proposed approach, this study conducted the cognitive workload monitoring experiment (CWME), deploying the DGNN model on a classification server. The deployment leveraged Raspberry Pi Zero W modules as gateways, employing the constrained application protocol (CoAP) for efficient data transfer to the Things Board monitoring platform. Things Board facilitated real-time visualization and analysis of critical parameters such as end-to-end latency, AI model classification processing time, and workload levels, providing actionable understandings into system performance. This setup enhances operational efficiency by enabling prompt cognitive workload assessments, supporting dynamic decision-making processes. The proposed research provides practical  
Figure 16: Gateway-004 cognitive workload monitoring data graphs.  
solutions for managing cognitive workload in industrial settings amidst ongoing digital transformation and automation [69].  
The successful implementation and validation of the DGNN model in real-time cognitive workload monitoring hold significant implications for various fields, particularly in industrial settings. By accurately assessing cognitive workload levels using EEG signals, organizations can proactively manage workforce health and safety. This approach enables timely interventions to mitigate cognitive fatigue, thereby reducing the risk of errors and accidents in high-risk environments. Moreover, the ability to monitor workload levels in real-time fosters adaptive work environments, potentially enhancing overall productivity and employee well-being.  
Despite the promising results achieved in this study, transitioning from virtual users to real human participants poses several significant challenges. Variability in EEG signal quality and ensuring participant comfort with wireless headsets are critical considerations that can impact data accuracy and participant engagement. Adhering to study protocols and ethical guidelines for EEG data collection is essential to maintain data integrity and participant trust. Additionally, the wide cost range of wireless EEG devices, spanning from 150 to 30,000 USD, and the variability in battery life (3 h to 10 h) present financial constraints and usability issues that must be carefully managed [70; 71].
## 2 Materials and Methods
### ANN Model and Hyperparameter Tuning  
Based on the literature review, it was decided to use three hidden layers for this case. Initially, to build the base ANN model, grid search was employed to find the best hyperparameters, including the number of neurons in the hidden layers, epochs, dropout rates, learning rate, activation functions, optimizers, and batch size. However, this process proved to be extremely time-consuming, taking approximately 24-48 h to obtain the optimal results. This considerable duration highlights the inefficiency of grid search, particularly for high-dimensional hyperparameter spaces, prompting the need for a more efficient optimization method.  
Hyperparameters are essential for the training and performance of neural networks. Accurately adjusting these parameters is crucial for attaining an ideal model which is both precise and capable of being applied to many scenarios. After exploring various sources and research, the decision was made to utilize the \"gp_minimize\" function from the Bayesian optimization framework to identify the best hyperparameters using the same search space [28]. Gaussian process (GP) optimization significantly reduces the time required to converge to optimal hyperparameters by leveraging probabilistic models to predict the performance of different hyperparameter settings as shown in Figure 5. This provided us with an approximation of how the performance changed when the hyperparameters were updated. When we assessed the real performance of a given collection of hyperparameters, we had a clear understanding of the performance, protecting against certain potential random variations. Subsequently, we could request the Bayesian optimizer to provide us with a novel recommendation for hyperparameters inside an unexplored part of the search space (Table 5) or hyperparameters which the Bayesian optimizer deemed most likely to yield significant enhancements. Subsequently, we iterated through this  
Figure 4: (**a**) Current probe on mold temperature controller device. (**b**) Current probe on melt temperature controller and motor controller device.  
procedure multiple times until the Bayesian optimizer constructed a robust model which captured the relationship between performance and various hyper-settings. This enabled us to select the optimal parameters. This method balances exploration and exploitation more effectively, ensuring a faster and more reliable search for optimal configurations [29].
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
By adjusting the angle of the carbon-fiber-reinforced polymer (CFRP) laminate, we were able to design a type of composite laminate with extremely low thermal expansion, which was applied to the support structure design of an off-axis space remote sensing camera, and the thermal stability of the support structure in the asymmetric di-rection was thus realized. Finally, the effectiveness of the thermal stability design for an off-axis camera was verified using finite element thermal deformation analysis and optical analysis.
## Real-World Oriented Smartphone AR Learning System
### Learning Goal for the Seasonal Constellation in the Planetarium  
We proposed a mobile learning system for supporting seasonal constellation observation in a real-world environment. We designed the functions of the learning system are based on the planetarium contents regarding constellations from its projection learning program. The following contents are included on the topic of the seasonal constellation in the planetarium.  
1. Observe constellation (Leo, Scorpius, Aquila, Pegasus, Orion) to determine its azimuth and altitude in the real sky. The projector in the planetarium simulates the actual sky above the horizon on the dome. There are grid lines of latitude and longitude in the dome, as shown in Figure 1. There are four notations: east, west, north, and south that indicate the direction in the dome is pointing towards in the real world environment. These four notations that combine with the latitude and longitude line in the dome can assist learners in observing and locating the azimuth and elevation angle of planets and stars while curators teach astronomical concepts with dome projection.  
Figure 1: The grid line of latitude and longitude in the planetarium dome interface, and the interface of demonstrating the elevation angle between horizon and a start using a hand image.  
2. Search and locate typical seasonal constellation (Leo, Scorpius, Aquila, Pegasus, Orion) with body scale guidance in the starry sky. In planetarium learning, curators utilize the body scale image method such as hand to demonstrate how to measure the elevation angle of stars above the horizon for visitors, as shown in Figure 1. After visitors go outside to observe stars, they can use the same method to search for their target stars or constellations in the starry sky. Anyone can use the body scale method to search for targeted stars or planets while observing stars in the night sky.
3. Observe spring constellations, summer constellations, autumn constellations and winter constellations, spring triangle, the summer triangle, autumn quadrangle, and winter triangle in the south starry sky as shown in Figure 2. The triangle consists of three of the brightest stars in the spring starry sky, each the brightest star in its own constellation. The same as the quadrangle consists of four of the brightest stars in the starry sky, each the brightest star in its own constellation. As shown in Figure 2, the spring triangle consists of the Denebola star in Leo constellation, Arcturus stars in Bootes constellation, and Spica star in Virgo constellation.
4. Observe typical season constellations (Leo, Scorpius, Aquila, Pegasus, Orion) diurnal motion orbit over one day, as shown in Figure 3.
5. From the outside of earth viewpoint, namely universe viewpoint to obtain the mechanism of seasonal constellations.  
Figure 2: Planetarium materials of seasonal constellations in the south starry sky: (**upper left**) spring constellations; (**upper right**) summer constellations; (**bottom left**) autumn constellations; (**bottom right**) winter constellations.
## 1 Introduction
Thus, it is important that comprehensive studies taking the all-important parameters be carried out to collect a significant amount of information about the material and to expand this relatively new material technology to field and site applications. This research work aims to provide a comprehensive study on the main factors affecting the workability and mechanical properties of fly ash and slag based geopolymer concrete (FS-GPC). The mechanical properties studied herein were compressive strength, tensile strength, flexural strength, and modulus of elasticity. There were five parameters/factors considered in this study viz. the effect of ground granulated blast furnace slag (SG) content (10%, 20%, 30%, and 50%), amount of alkaline activator solution (35% and 40%), sodium silicate to sodium hydroxide ratio (SS/SH = 2.0, 2.5 and 3.0), sodium hydroxide molarity (10 M, 12 M, and 14 M), and additional water to improve workability. The optimal mixtures of FS-GPC were proposed for the general concreting applications, i.e., columns, beams, and foundations. The data from this research can be utilized to design a mix of FA and SG blended GPC with a specified strength. The influence of all mix design parameters can be utilized to control the desired fresh and strength properties of FS-GPC. This study will help construction practitioners to achieve the desired fresh and mechanical properties of FS-GPC by controlling different mix design parameters. Further, mathematical equations based on the compressive strength were also proposed to predict the splitting tensile strength, flexural strength, and static modulus of elasticity of ambient cured FS-GPC. These equations can be used to provide prediction of tensile, flexural strengths, and modulus of elasticity of FS-GPC mixes having 28 days compressive strength in the range of 30-60 MPa.
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 6 Numerical Simulation Results Analysis  
The simulated shallow-water waveguide environment is depicted in Figure 1. The 100 m water column overlies a 10 m sediment layer, with the sound speed profile annotated in the figure. Key environmental parameters affecting acoustic propagation, including sound speed, density, and attenuation coefficients, are specified in the diagram. The radiated noise is assumed to be a single-frequency signal at 750 Hz with the power of 90 dB. The sound source marked by a black pentagram in the figure is located at a depth of 50 m. The receiving hydrophone array marked by a set of black solid circles is a 31-element VLA with half-wavelength inter-element spacing. The central depth of the array is 50 m, and it is horizontally situated 100 m away from the radiated noise source. The ambient noise is set as white Gaussian noise with a power of 60 dB.  
In the following simulations, we compare the proposed MLE method against conventional beamforming approaches: CBF, NFBE, MFP, and an RPCA-based denoising method. Performance is systematically evaluated across varying conditions using the estimated variance, calculated as  
\[\begin{array}{ll}\text{Variance}&=\dfrac{1}{N-1}\sum\limits_{n=1}^{N}\left( \sigma_{s\,n}^{2}-\overline{\sigma_{s}^{2}}\right)^{2}\\ \overline{\sigma_{s}^{2}}&=\dfrac{1}{N}\sum\limits_{n=1}^{N}\sigma_{s\,n}^{2} \end{array} \tag{31}\]  
where \(\sigma_{s}^{2}\) represents the average of \(N\) estimated values, and \(N\) is the number of Monte Carlo trials. In the following simulations, the number of Monte Carlo trials \(N\) is taken as 10,000. For the convenience of numerical display, the logarithm of the calculated variance is represented in the picture.
## 2 Monostatic-Bistatic Geometry for Stereo-Radargrammetric Reconstruction
### 2.2 Methods for Monostatic-Bistatic DEM Generation
#### 2.2.2 Bistatic Rigorous SAR Stereo Problem  
The equations defining the rigorous SAR stereo problem in bistatic geometry are [17]:  
\[r^{\prime}=\left|\vec{R}^{\prime}-\vec{T}\right| \tag{10}\]  
\[r^{\prime}=\left|\vec{R}^{\prime}+\vec{B}-\vec{T}\right| \tag{11}\]  
\[f^{\prime}_{DC}=2\frac{\left(\vec{V}^{\prime}_{\oplus}-\vec{V}_{T}\right) \cdot\left(\vec{R}^{\prime}-\vec{T}\right)}{\lambda\left|\vec{R}^{\prime}- \vec{T}\right|} \tag{12}\]  
\[f^{\prime}_{DC}=\frac{1}{\lambda}\Bigg{[}\frac{\left(\vec{V}^{\prime}_{ \oplus}-\vec{V}_{T}\right)\cdot\left(\vec{R}^{\prime}-\vec{T}\right)}{\left| \vec{R}^{\prime}-\vec{T}\right|}+\frac{\left(\vec{V}^{\prime}_{\oplus}+\vec{ V}_{\oplus 0}-\vec{V}_{T}\right)\cdot\left(\vec{R}^{\prime}+\vec{B}-\vec{T}\right)}{\left| \vec{R}^{\prime}+\vec{B}-\vec{T}\right|}\Bigg{]} \tag{13}\]  
where \(\vec{T}\) and \(\vec{V}_{T}\) are the target position and velocity vector, respectively; \(\vec{V}_{\oplus}\) is the ECEF baseline vector time derivative, \(\lambda\) is the radar wavelength, and \(f^{\prime}_{DC}\) and \(f^{\prime}_{DC}\) are the monostatic and bistatic Doppler centroid frequencies. Eqs. (10)-(11) define monostatic and bistatic range spheres, while Eqs. (12)-(13) individuates monostatic and bistatic Doppler cones. Differently from classic photogrammetry where triangulation equations are applied on corresponding targets identified in photographic images by means of the optical stereo reconstruction obtained thanks to stereocorparators, monostatic and bistatic slant ranges and Doppler centroid frequencies are derived for each image point of co-registered data, as in numerical photogrammetry. As for monostatic radargrammetry least square methods can be used to derive target positions [29; 31], and target height too, accounting for spherical zero relief surface and assuming a static scene (\(\vec{V}_{T}=0\)).
## 3 Results and Discussions
### Raman Spectroscopy  
Raman scattering spectrometer was used to obtain the Raman spectra, which were recorded between 130 and 700 cm\({}^{-1}\) (Figure 6). Six Raman bands are identified at 189.3, 345.2, 478.3, 522.6, 615.6, and 655.8 cm\({}^{-1}\). According to the research on Co\({}_{3}\)O\({}_{4}\) NPs characterization [69; 70], these peaks indicate the Co\({}_{3}\)O\({}_{4}\) spinel structure with an average shift of \(\Delta\)v\(\sim\)9 cm\({}^{-1}\) (191-470-510-608-675 cm\({}^{-1}\)). This shift may be attributed to surface strain or size effects. The notable Raman peaks correspond to the F2g (189.3 cm\({}^{-1}\)) Eg (345.2  
Figure 4: TEM images of the Co\({}_{3}\)O\({}_{4}\) NPs at a resolution of (a) 1 \(\upmu\)m, (b) 500 nm, (c,d) 200 nm, (e) 100 nm, and (f) 20 nm; (g) diffraction pattern.  
Figure 5: EDS spectra of Co\({}_{3}\)O\({}_{4}\) NPs.  
and 478.3 cm\({}^{-1}\)), F2g (522.6 and 615 cm\({}^{-1}\)), A1g (655.8 cm\({}^{-1}\)) modes of the Co\({}_{3}\)O\({}_{4}\) NPs crystalline phase. The Raman mode at 655.8 cm\({}^{-1}\) is attributed to characteristics of the octahedral sites (CoO\({}_{6}\)) associated with the A1g symmetry, whereas the Eg and F2g modes are likely related to the combined vibrations of the tetrahedral site (CoO\({}_{4}\)) and octahedral oxygen motions.