esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Analysis and Features Extraction
### Health Features Extraction
#### 2.2.2 Energy Health Features Extraction  
As the number of charge-discharge cycles increases, the available chargeable and dischargeable energy in the battery gradually decreases. The energy value can reflect the electrochemical reaction activity of the battery during the charging process. As shown in Figure 2, with battery aging, the electrochemical reaction activity declines, leading to a reduction in the constant-voltage charging energy. To reflect the SOH of the battery, the energy difference during constant-voltage charging (EDCV) is used as health feature 4 (HF4).  
\[EDCV=\int_{t1}^{t2}V_{\text{CC}}\cdot I_{\text{CC}}(t)dt \tag{2}\]  
where \(t_{1}\) and \(t_{2}\) represent the times when the charging voltage reaches 4.2 V and when the charging current drops to 0.05 A, respectively. VCC and ICC (\(t\)) represent the 4.2 V charging voltage and charging current within the time interval [\(t_{1},t_{2}\)], respectively.
## 3 Experiment and Results Analysis  
### Data Processing and Annotation  
#### 3.1.1 Data Preprocessing  
To address the data scarcity issue in coral reef ecosystem research, this study implemented a series of steps to construct a high-quality coral reef ecosystem corpus, as illustrated in Figure 6. This process includes data collection, cleaning, annotation, and data quality control, aimed at ensuring the accuracy and usability of the corpus.  
Figure 6: Flowchart for constructing a high-quality marine coral reef ecosystem data corpus.  
Initially, our research focused on the Coral Reef System Status Bulletins published between 1999 and 2022, which formed our preliminary corpus. This time period was selected because it encompasses significant environmental change events, which is crucial for understanding the evolution of coral reef ecosystems. These bulletins serve as vital resources for comprehending the changes in coral reef ecosystem statuses. Next, to enhance data quality, we used regular expression techniques to precisely remove spaces, emoticons, and other irrelevant content from the text, ensuring data cleanliness and professionalism. Additionally, non-textual content that was not beneficial to the research was excluded to ensure the data's relevance and specificity. At this stage, we obtained approximately 180,000 characters of purified text data, laying the foundation for subsequent annotation work. Following this, based on extensive literature research and consultations with domain experts, we developed a specialized labeling system and meticulously annotated the data to ensure the professionalism and accuracy of the annotations. The main categories of annotation included coral species, biological species, geographical locations, environmental factors, etc., aiming to comprehensively capture the key information of the coral reef ecosystem. To enhance the accuracy and consistency of the annotations, we employed a statistical strategy to correct mislabeled entities and used text matching techniques to supplement missed entities, ensuring all relevant entities were accurately identified. Finally, we validated the accuracy and consistency of the annotation results through random sampling and comparison with the original text, ensuring high data quality standards. This step is crucial in assuring the quality of the corpus, directly impacting the effectiveness of model training.  
Through the efforts described above, we successfully created a valuable resource for coral reef ecosystem research. To foster research collaboration and knowledge sharing, we meticulously documented the annotation rules and methods and plan to share the corpus and its annotated data. This will significantly advance coral reef ecosystem research.
## 2 Method
### Definition of the Spectra of Geophysical Phenomena and the Error Curves of MOCAST+ and GRACE
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name Legend in Figure 8** & **Configuration** \\ \hline MOCAST+ Bender 2 couples 4 Tzz+ Cl nominal & Bender configuration (2 couples, polar+inclined, 4 Tzz gradiometers) D = 100 km; 0.1 Hz clocks; \\  & optimal noise PSD for the gradiometers \\ \hline MOCAST+ Bender 2 couples 4 Tzz + Cl improved & Bender configuration (2 couples, polar+inclined, 4 Tzz gradiometers), D = 1000 km, 1 Hz clocks, \\  & optimal noise PSD for the gradiometers \\ \hline MOCAST+ Bender 2 triplets 6 Tzz + Cl improved & Bender configuration (2 triplets, polar+inclined, 6 Tzz gradiometers), D = 1000/2000 km, 1 Hz clocks, \\  & optimal noise PSD for the gradiometers \\ \hline MOCAST+ Bender 2 triplets Cl improved & Bender configuration (2 triplets, polar+inclined),  D = 1000/2000 km, 1 Hz clocks, clock-only solution \\ \hline MOCAST+ Bender 2 triplets 6 Tyy & Bender configuration (2 triplets, polar+inclined, 6 Tyy gradiometers), D = 1000/2000 km, 1 Hz clocks \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mission configurations simulated in the frame of the MOCAST+ project. D = distance between couples or triplets of satellites. All the error estimates have been simulated for a timespan of one month [2]. In the case of the \"MOCAST+ Bender 2 couples 4 Tzz+ Cl nominal\" configuration, we were also provided with the results of simulations covering two and four months. Apart from this specific case, timespan scaling was applied to account for the reduction in error due to longer observation intervals. This reduction, arising from the improved coverage and stacked measurements, was modelled with the following scaling relationship:  
\[\sigma^{2}(l)_{\Delta t_{1}}=\sigma^{2}(l)_{\Delta t_{0}}\cdot(\Delta t_{0}/ \Delta t_{1}) \tag{2}\]  
where \(\sigma^{2}(l)\) is the error degree variance at SH degree \(l\) formulated for the solution timespan \(\Delta t_{0}\) (original time interval) and \(\Delta t_{1}\) (time interval we are scaling to). To simplify the figures, hereinafter, the sensitivity to the geophysical signal is determined by employing only the best configuration (blue curve in Figure 8).
## 5 Experimental Evaluation
### Security Enhancement Validation
#### 5.4.2 ROC Curve Analysis  
Receiver Operating Characteristic (ROC) analysis for critical attack scenarios demonstrates excellent discrimination capability with Area Under the Curve (AUC) exceeding 0.92 for all attack types, confirming superior detection performance with optimal trade-offs between true positive and false positive rates. As shown in Figure 8, the ROC curves for three critical attack scenarios demonstrate excellent discrimination capability with AUC values exceeding 0.92 across all attack types, providing strong evidence of our system's robust detection performance.  
Average AUC across all attacks: 0.88, correlating with the 88.3% overall detection rate.
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 2 Experimental Datasets and Methods
### Experimental Methodology
#### 2.2.5 Baseline Models  
Random Forest (RF) is an ensemble learning method that predicts by constructing multiple decision trees and aggregating their results [41; 42]. It uses bootstrapping to sample the original dataset with replacement, creating a distinct subset for each tree. During tree construction, nodes split on features randomly selected from a subset, enhancing tree independence and diversity. This method is robust, handles high-dimensional data and large datasets well, and is tolerant to missing and abnormal values. It also ranks feature importance, aiding in understanding their contribution to predictions. In this study, RF serves as a baseline model, offering an ensemble-based performance reference with rich parameters for subsequent tuning and comparison.  
Support Vector Machine (SVM) is a supervised learning algorithm. Its core idea is to find an optimal hyperplane in feature space to maximize the separation between different classes [43]. For linearly separable data, SVM solves a convex quadratic programming problem to find this hyperplane. For linearly non-separable data, SVM uses kernel functions to map low-dimensional features to a high-dimensional space, making the data linearly separable. Common kernels include linear, polynomial, and radial basis function kernels, each suited to different data types and problems. SVM excels in handling small samples and high-dimensional data, has strong generalization ability, and effectively avoids overfitting. In this study, SVM is chosen as a baseline model due to its good performance in classification and regression tasks. Its decision boundaries, based on geometric and optimization theories, offer a predictive perspective different from Random Forest.  
All baseline models are optimized using Bayesian optimization with 5-fold cross-validation conducted solely on the 80% training set. Performance evaluation and analysis of these baseline models help better understand the data and problem, providing a reference for comparing the improved model and assessing its performance gains.
## 5 Engineering Applications of Lattice Structures Built with Polygon Hollow Shapes
### Applications in Aerospace Industries  
Polygonal lattice designs are advantageous for aerospace uses because of their high strength-to-weight ratios. These structures are capable of reducing the overall weight of aircraft, spacecraft, and drone parts, such as wing ribs, landing gear parts, and fuse -large frames, while preserving their structural integrity [17; 52]. This in turn, reduces the consumption of fuel and also improves the mechanical performance [8; 17]. Lightweight structures play a crucial role in the design of satellites and spacecrafts to reduce costs associated with launches as well as improve the success rate of missions. Polygon hollow structures are often used to reduce the weight in satellite structures, antenna supports, and other parts [8; 53]. The structures are additionally used in the design of rocket parts, such as the rocket body and engine parts. Their lightweight characteristics assist in improving the cargo capacity and mechanical performance [8; 54; 53]. Heat management is also crucial in spacecraft and aeronautical applications. Lattice parts with polygon hollow shapes are typically built with channels for the passage of coolant or heat transfer fluids, making them therefore ideal for lightweight, high-efficiency heat exchangers [48; 49]. Polygon hollow structures are used for building radiation barriers, which act as protective covers for antennas and sensors on aircraft and spacecraft. These structures offer electromagnetic transparency whilst remaining lightweight [48]. Furthermore, antennas on aircraft and spacecraft are supported by lightweight lattice structures, allowing the transmission of signals and reception while minimising weight and aerodynamic effects [55].  
Solar arrays for spacecraft and satellites are readily designed using lattice architectures. These structures support solar panels while minimising the weight, resulting in an improved power generation in space [56; 57; 58]. In addition, lightweight polygon hollow structures are particularly useful for unmanned aerial vehicles (UAVs), wherein a balance of the structural integrity and weight is crucial for increased flight periods and the performance of missions [57]. These structures are also used in composite materials, particularly for aerospace uses that enhance the structural performance while retaining the lightweight property [56]. Integrating AM methods, such as selective laser sintering (SLS) and fused deposition modelling (FDM), into the design of lattice parts allows for the manufacture of complicated polygon hollow structures that can be tailored to specific applications in the aerospace sector [12; 59].
## 3 Results and Discussion
### Analysis of Phase Three  
Figure 8 depicts the distribution of indoor illuminance using three different internal curved LS angles (10%, 15%, and 25%) compared to horizontal LS on all design days. The results revealed that the use of an internal curved LS with 10\({}^{\circ}\) tilt angle recorded the highest value of WPI and uniformity index at a distance of 7 m from the window. The back daylight area saw an increase of up to 5-11% on these parameters during the summer and winter solstices; however, the increase was less than 4% at the spring equinox. The uniformity index also saw an improvement for all the seasons. The WPI value at about a 4 m distance from the window (middle area) and the front area is within the recommended range, except at midday, where it is consistently higher than 750 lux. The LSC3 configuration with 25\({}^{\circ}\) tilt angle presents the least optimal case since the WPI increased for the front area but decreased for the back area, which results in less uniformity in daylight distribution. Thus, the optimal tilt angle of LS is 10\({}^{\circ}\) (LSC1) for all seasons. In contrast, [PERSON] et al. [35] found that the optimal specifications for external curved light shelves are different depending on the season, but are effective at improving the indoor uniformity ratio compared to a flat light shelf during summer.  
Overall, the observations from the optimized light shelf configurations after the three phases (height, reflector, curved internal light shelf) were compared with the reference model (without the light shelf). The proposed model illustrated a significant improvement considering the uniformity index on all design days, especially during midday. Furthermore, there was an increase of about 13-20% in the absolute WPI value concerning the back daylight area during the summer season for the entire day. At the same time, the absolute WPI value reduced for the middle (63\({}^{\circ}\)) and low (39\({}^{\circ}\)) sun path altitudes, as specified in Appendix B.  
Figure 7: The performance of a reflector on the top of window combined with external and internal light shelves.
## 6 Open Challenges, Future Prospects and Research Directions
### Hybrid Adaptive Optics Techniques
#### 6.4.9 Hybrid FSO Systems  
Hybrid FSO systems are poised to revolutionize optical communication by leveraging the unique strengths of FSO alongside other complementary technologies. These systems address individual limitations, offering enhanced performance in various environmental conditions. The future of hybrid FSO systems will revolve around several key research areas aimed at optimizing integration, enhancing reliability, and supporting the next generation of communication technologies.  
Exploring Seamless Communication Solutions  
The exploration of hybrid FSO-RF systems offers a promising avenue for ensuring seamless communication across diverse environments. By combining the high-capacity and low-latency characteristics of FSO with the robustness and reliability of RF technologies, these systems can maintain connectivity even in the face of adverse atmospheric conditions. Future research should prioritize the development of intelligent algorithms that can determine the most suitable transmission medium based on environmental assessments, user demands, and application requirements.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 2 Satellite Constellations in IoT
Microsatellites weighing 10 and 100 kg are ideal for testing new technologies and capabilities before they are used for larger missions. They benefit scientific and exploration missions, where their small size and ability to be launched in groups provide flexibility and the capacity for simultaneous measurements at different points on Earth. The combined use of mini- and microsatellites is a growing trend in space exploration, providing a viable alternative to conventional large satellites. Nanosatellites belong to the smallest satellite category, weighing between 1 and 10 kg. They are mainly used for technology demonstrations and educational missions due to their low cost, rapid development, and ease of deployment in small constellations. However, they are rapidly gaining popularity as an alternative to conventional satellites [23]. Among nanosatellites, CubeSats are the tiniest, measuring 10 cm \(\times\) 10 cm \(\times\) 11.35 cm and weighing less than 1.33 kg. Despite their small size, they include all the basic subsystems of larger satellites. Other satellite variants with even smaller sizes, such as PocketQubes, are also available [24]. Picosatellites weighing less than 1 kg are mainly used for technology demonstrations and educational missions. However, some satellite companies are presently using them to deploy low-cost constellations and offer commercial services. FOSSA Systems [25] is one such example. On the other hand, femtoseconds weigh less than 0.1 kg. They are primarily used for space fragmentation detection tests, the evaluation of tracking capabilities of various sensors used for space surveillance, and the detection of tiny objects [26]. The form factor, denoting a satellite's size and shape, is another classification criterion. CubeSats are measured in units (U), with 1U equivalent to a cube with an edge of 10 cm. This measurement is essential for planning and designing small satellites [24]. CubeSats are available in different sizes, ranging from 1U to 6U. However, even larger sizes, such as 12U and 16U CubeSats, are expected to be developed. Conversely, pocket tubes have an even smaller form factor than CubeSats [24].
## 3 Experiments
### Evaluation  
We evaluated the proposed method in terms of five metrics, including overall accuracy (OA), raw pitch accuracy (RPA), raw chroma accuracy (RCA), voicing detection rate (VR), and voicing false alarm rate (VFA). The measures are defined as below:  
\[\text{RPA, RCA}=\frac{\#\{\text{voice frames for which (pitches, chromas) are predicted correctly}\}}{\#\text{ of voiced frames}} \tag{6}\]  
\[\text{OA}=\frac{\#\{\text{frames for which pitches and voicing are predicted correctly}\}}{\#\text{ of all frames}} \tag{7}\]  
\[\text{VR}=\frac{\#\{\text{voiced frames for which voicing are predicted correctly}\}}{\#\text{ of voiced frames}} \tag{8}\]  
\[\text{VFA}=\frac{\#\{\text{frames that are predicted as voiced, but not actually voiced}\}}{\#\text{ of voiced frames}} \tag{9}\]  
The evaluation consists of two main parts: voice detection determining whether the voice is included in a particular time frame (VR and VFA) and pitch estimation determining the melody pitch for each time frame (RPA, RCA). OA is the combined accuracy of pitch estimation and voice detection. We converted the quantized pitch labels in 6.25 cents (1/16 semitone) to frequency scales (Hz) to compare them with the ground truth.  
\[f=2^{(m-69)/12}\times 440\ (\text{Hz}) \tag{10}\]  
where \(m\) is the MIDI note number estimated by the main network for melody extraction. The pitch at each frame was considered correct if the difference between the estimated pitch and the ground-truth pitch was within a tolerance of \(\pm 50\) cents. We computed them using _mir-eval_, a Python library designed for objective evaluation in MIR tasks [41].
## 3 Methodology
### Fault Tree Analysis (FTA)
#### 3.2.1 The Quantitative Analysis  
FT quantitative analysis is to calculate the occurrence probability of the TE if all the failure probabilities of the BEs are available. The FTA method is based on Boolean logic [54], and the calculation of FT uses Boolean mathematics [55]. An FT can be modeled by a set of AND gates and OR gates connected between BEs and IEs. The AND gate and OR gate with m BEs are given in Equations (1) and (2), respectively:  
\[Q(t)=\prod_{j=1}^{m}Q_{j}(t) \tag{1}\]  
\[Q(t)=1-\prod_{j=1}^{m}(1-Q_{j}(t)) \tag{2}\]  
where \(Q_{j}(t)\) is a probability of failure of BE \(j\) (\(j=1\) to \(m\)), \(Q(t)\) is probability of failure of the top gate event due to all BEs.  
Each MCS represents a combination of some BEs, which may cause the TE. It is assumed that all the \(r\) BEs in the CS \(j\) are independent; the probability that the CS \(j\) fails at time \(t\) is:  
\[Q_{o}(t)=\prod_{r=1}^{n}Q_{r}(t) \tag{3}\]  
where \(Q_{r}(t)\) is the probability of failure of BE r (r = 1 to \(n\)) leading to CS, \(Q_{0}(t)\) is the probability of failure of CS o due to all BEs.  
The probability of the TE failure is determined by Equation (4).  
\[Q_{\text{TE}}(t)=1-\prod_{o=1}^{k}(1-Q_{o}(t)) \tag{4}\]  
where \(Q_{o}(t)\) is the probability of failure of CS o (o = 1 to \(k\)), \(Q_{\text{TE}}(t)\) is the probability of failure of TE due to all CSs. If the r BEs in the CS \(j\) are not independent, the probability of the TE failure in a complex system will become extremely complicated, and Equation (4) cannot find an accurate solution. However, generally, the approximate solution is still obtained by Equation (4) [56].
# A Robust Localization System Fusion Vision-CNN
## 3 System Overview and Methodology
### Scan Matching Optimization
The score corresponding to each grid is \(M(P(x_{0},y_{0}))\),\(M(P(x_{1},y_{0}))\),\(M(P(x_{1},y_{1}))\), and \(M(P(x_{0},y_{1}))\); and, the score at \(P(x,y)\) can be obtained by Lagrangian interpolation:  
\[\begin{array}{l}M(P(x,y))\approx \begin{array}{l}\frac{y-y_{0}}{y_{1}-y_{0}}( \frac{x-x_{0}}{x_{1}-x_{0}}M(P(x_{1},y_{1}))+\frac{x_{1}-x}{x_{1}-x_{0}}M(P(x _{0},y_{1})))+\\ \frac{y-y}{y_{1}-y_{0}}(\frac{x-x_{0}}{x_{1}-x_{0}}M(P(x_{1},y_{0}))+\frac{x_{1 }-x}{x_{1}-x_{0}}M(P(x_{0},y_{0})))\end{array}  \tag{13}\]  
The partial derivative of \(M(P(x,y))\) with respect to \(x\) is:  
\[\begin{array}{l}\frac{\partial M(x,y)}{\partial x}= \begin{array}{l}\frac{y- y_{0}}{(y_{1}-y_{0})(x_{1}-x_{0})}(M(P(x_{1},y_{1}))-M(P(x_{0},y_{1})))+\\ \frac{y_{1}-y}{(y_{1}-y_{0})(x_{1}-x_{0})}(M(P(x_{1},y_{0}))-M(P(x_{0},y_{0})) )\end{array}  \tag{14}\]  
The partial derivative of \(M(P(x,y))\) with respect to \(y\) is:  
\[\begin{array}{l}\frac{\partial M(x,y)}{\partial y}= \begin{array}{l}\frac{x -x_{0}}{(x_{1}-x_{0})(y_{1}-y_{0})}(M(P(x_{1},y_{1}))-M(P(x_{1},y_{0})))+\\ \frac{x_{1}-x}{(x_{1}-x_{0})(y_{1}-y_{0})}(M(P(x_{0},y_{1}))-M(P(x_{0},y_{0})) )\end{array}  \tag{15}\]  
Substituting Equations (14) and (15) into Equation (11), we can complete the calculation of the Jacobian matrix \(J_{k}\). After obtaining the Jacobian matrix \(J_{k}\), we conveniently use the Ceres library [40] to solve the nonlinear least squares problem (8). Figure 5: Bilinear interpolation.
## 2 Model Test
### Result Analysis
#### 2.2.2 Longitudinal Temperature Variation  
Test data with a time interval of 6 h were selected to give the temperature changes of the longitudinal measurement points (i.e., along the tunnel line direction) with time, as shown in Figure 6.  
Obviously, the temperature distribution is high in the middle and low at both ends. It can be seen from Figure 6 that within 24 h of heating, the temperature values of measuring points \(Z_{1}\), \(Z_{2}\), \(Z_{8}\), and \(Z_{9}\) at both ends of the model fluctuated up and down. This is because the two ends are temperature boundary conditions, while the ambient temperature changes periodically and is fitted as a sine function, as shown in Equation (1). After heating for 36 h, the temperature of the \(Z_{1}\) and \(Z_{9}\) measuring points rose slowly, basically around 15 \({}^{\circ}\)C, indicating that the heat conduction reached a balance with the convection at the temperature boundary at both ends. After 78 h of loading, the temperature at the middle measuring point 1 reached the highest level, i.e., 34.5 \({}^{\circ}\)C, while the temperature at measuring points 1 and 9 was only approximately 15 \({}^{\circ}\)C, with a difference of nearly 20 \({}^{\circ}\)C. It can be concluded that the ambient temperature has a great influence on the temperature field near the entrance.  
Figure 6: Longitudinal temperature curve with time.