esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Effect of Continuous Low Thrust on Orbital Elements  
The Gaussian-type perturbation equation for the orbital motion considering continuous low thrust is given by  
\[\begin{cases}\begin{array}{c}\frac{\mathrm{d}t}{\mathrm{d}t}=\frac{2}{n \sqrt{1-\epsilon^{2}}}\big{[}a_{\mathrm{R}}e\sin f+a_{\mathrm{T}}\big{(}1+e \cos f\big{)}\big{]}\\ \frac{\mathrm{d}x_{\mathrm{T}}}{\mathrm{d}t}=\frac{\sqrt{1-\epsilon^{2}}}{na} \Big{\{}a_{\mathrm{R}}\sin u+a_{\mathrm{T}}\Big{[}\Big{(}1+\frac{r}{p}\Big{)} \cos u+\frac{r}{p}e_{x}\Big{]}\Big{\}}+e_{y}\frac{\mathrm{d}\Omega}{\mathrm{d} t}\cos i\\ \frac{\mathrm{d}x_{\mathrm{T}}}{\mathrm{d}t}=\frac{\sqrt{1-\epsilon^{2}}}{na} \Big{\{}a_{\mathrm{T}}\Big{[}\Big{(}1+\frac{r}{p}\Big{)}\sin u+\frac{r}{p}e_{ y}\Big{]}-a_{\mathrm{R}}\cos u\Big{\}}-e_{x}\frac{\mathrm{d}\Omega}{ \mathrm{d}t}\cos i\\ \frac{\mathrm{d}i}{\mathrm{d}t}=\frac{r\cos u}{na^{2}\sqrt{1-\epsilon^{2}}}a_{ \mathrm{N}}\\ \frac{\mathrm{d}\Omega}{\mathrm{d}t}=\frac{\sqrt{1-\epsilon^{2}}}{na^{2}\sqrt{1- \epsilon^{2}}}a_{\mathrm{N}}\\ \frac{\mathrm{d}\lambda}{\mathrm{d}t}=n-\frac{n\sin u}{na^{2}\sqrt{1-\epsilon^ {2}}}a_{\mathrm{N}}\end{array}\\ \frac{\mathrm{d}\lambda}{\mathrm{d}t}=n-\frac{\sin u}{na^{2}\sqrt{1-\epsilon^{2}} }a_{\mathrm{N}}\end{cases} \tag{10}\]where \(a,e,i,\Omega,\omega,M\) are the orbital elements, \(a_{\rm T},a_{\rm R},a_{\rm N}\) are the thrust acceleration in the tangential, radial, and normal directions, respectively [18]. Replace \(e\), \(\omega\), and M with \(e_{x}=e\cos\omega,e_{y}=e\sin\omega\), and \(\lambda=M+\omega\), respectively. With the substitution, there is no longer a small eccentricity problem in the differential equation. However, Equation (10) is not applicable to equatorial orbits.
## 5 Experimental Setup
### Training  
For the ML models, we used TF-IDF for feature extraction. TF-IDF operated by extracting the essential words within the corpus, where TF was the term frequency of the words appearing in the ITSM corpus (all incident records in the training set), and IDF was the frequency of the datasets containing those words. The fewer words that appeared in the corpus, the higher the TF-IDF value would be. We used the sklearn library for our analysis to calculate the TF-IDF value. After the feature extraction via the TF-IDF vectorizer, we used the default settings for all conventional ML classifiers, as described in sklearn library [20] for training. The labels for every feature vector available from the raw data (MIR and NMIR) used the sklearn prediction function for evaluation.  
For the DL models, the textual data were transformed into numbers and mapped to the embeddings to achieve better cardinality for each token. The first layer of our DL model was the embedding layer, consisting of three parameters: input dimension, input length, and output dimension). The next layer, called the model layer, contained hidden neurons that helped to model the complex data. It contained two parameters (the number of neurons and the return sequence). Finally, we used a dense layer with parameters such as input shape, batch size, and sigmoid as the activation functions. This layer was also known as the classification layer because it classified the output as MIR and non-MIR values. Compiling a model required parameters such as optimizer and loss function. We used the Adam optimizer and binary cross-entropy as loss functions. For the transformers, we used pre-trained transformers, from which we had already converted the incident reports from the training, validation, and test datasets to integer sequences at a length of 35 tokens each. Next, we converted the integer sequences to tensors. We created dataloaders for both training and validation sets. This dataloaders passed batches of training and validation data as input to the model during the training phase. We implemented the default configurations for BERT, ERNIE 2.0, and RoBERTa. The Figure 5 shows the architecture of our best performing transformer architecture.  
Figure 5: Graphical representation of our best-performing transformer (Roberta) model architecture.
# Design of a Polarization-Insensitive and Wide-Angle Triple-Band Metamaterial Absorber
## 1 Introduction
Subsequently, we study the distribution of the electric field, magnetic field, and surface current at the absorption frequency point, and conclude that the anti-parallel currents on the upper and lower metal surfaces are the main reason for efficient absorption. Next, we studied the influence of the polarization angle and incident angle of the plane wave on the absorption performance of the MMA. Then, by separating and reconstructing the MMA sub-structure, we studied the relationship between the absorption performance of the MMA and the sub-structure parameters. Finally, we use the printed circuit board (PCB) process to produce the sample, and to conduct experimental verification in the 4-8 GHz band. The experimental method is the free space method, and the reflection coefficient is tested for normal incidence and oblique incidence. The MMA presented in this paper can be utilized in inter-satellite communications, enabling functions like electromagnetic shielding and stealth.
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 4 Results and Discussions  
To examine the influences of wave-induced uplift force onto the pipeline buried in sloping seabed, this study involves three aspects. Firstly, the characteristics of nonliear wave-caused oscillatory seabed response near pipelines buried in sloping seabeds is examined. Then, the influencing factors associated with the uplift force onto a pipeline are studied in detail, including wave parameters, soil properties, pipeline characteristics and slope characteristics. Finally, based on the nuemerical results, a simple formula describing the relationship between the wave-induced uplift force onto the pipeline and the influencing factors is proposed. The selection of wave parameters is based on existing literatures [4; 36]. Unless otherwise emphasized, the parameters used in this section are taken according to Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Wave period (T), s & 10 \\ Water depth (d), m & 30 \\ Wave height (H), m & 6 \\ Poisson’s ratio (\(v\)) & 0.3 \\ Seabed porosity (n) & 0.8 \\ Young’s modulus (E), pa & \(5\times 10^{7}\) \\ Soil permeability (k\({}_{\text{s}}\)), m/s & \(5\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used in present study.  
Figure 6: Comparison of wave-induced pore pressure onto pipeline between the numerical model and laboratory experiment [40].
## 3 Profile Fitting and Geometric Modeling
### \"Measurement Unit\" Key Point Detection
#### 3.1.2 HRnet Network Structure  
HRNet is a key point detection network for 2D human pose estimation based on the heatmap approach, which can better extract multi-resolution features of the input image  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Weld Seam** & **Area** & **Feature Points** \\ \hline \multirow{2}{*}{\(i+1\)} & Left fusion line & L\({}_{1}\), L\({}_{2}\), L\({}_{3}\), L\({}_{4}\), L\({}_{5}\) \\  & Right fusion line & R\({}_{1}\), R\({}_{2}\), R\({}_{3}\), R\({}_{4}\), R\({}_{5}\) \\  & Left fusion line & L\({}_{5}\), L\({}_{6}\), L\({}_{7}\), L\({}_{8}\), L\({}_{9}\) \\  & Right fusion line & R\({}_{5}\), R\({}_{6}\), R\({}_{7}\), R\({}_{8}\), R\({}_{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of key points of the “Measurement unit”.  
Figure 5: “Measurement unit” key point labeling diagram.  
and has a powerful feature representation capability [16; 17; 18]. The layout of the 18 key points of the \"measurement unit\" is centrosymmetric with the layout of the human body key points, which has excellent similarity; so, the HRNet network is used as the feature point detection network, and its network structure is shown in Figure 6.  
After inputting the \"measurement unit\" image into the network, the number of channels is first adjusted by four times downsampling through the convolution layer, then through the Layer layer, which consists of repeated stacks of Bottleneck modules, and then through four stage layers for information fusion. Taking Stage 3 as an example, for each branch, the information on different scales is first fused by four Basic Blocks composed of residual blocks, and the output on each branch is obtained by combining the output on all components after Upsamp or Downsamp. Finally, Stage 4 only outputs the output of the downsampled four times branch, and after a \(1\times 1\) convolutional layer to extract features, the heatmap of each key point on the fusion line is obtained to achieve key point detection.
## 6 Algorithms and Frameworks in Federated Learning
### Federated Stochastic Gradient
#### 6.2.1 Algorithmic Steps  
The algorithmic procedure can be described by natural language in terms of the following steps [86].  
1. Initialization: A global model is initialized, and its parameters are shared with all participating clients.
2. Local Computation: Each client computes the gradient of the model on its local data using SGD.
3. Model Aggregation: Clients send their computed gradients or model parameters to the central server.
4. Global Model Update: The central server aggregates these updates (typically by averaging) to adjust the global model.
5. Distribution: The updated global model is shared back with all clients.
6. Iterations: Steps 2-5 are repeated until convergence or for a predefined number of communication rounds.  
While this mechanism may seem straightforward, several sensitivities, such as handling communication inefficiencies and stragglers and ensuring privacy, play a critical role in its actual implementation.
## 4 Implementation and Experiments
### Training Procedure and Evaluation Metrics
#### 4.3.2 Food Volume Estimator Training and Evaluation Framework  
The training process of multi-view stereo utilized the following parameters in Table 7, including batch size, learning rate, number of epochs, choice of optimizer, and the selection of an appropriate loss function.  
In the testing phase, mean absolute percentage error (MAPE) and Mean Absolute Error (L1 loss) were used as the primary performance evaluation metrics for the model, and they are defined as follows.  
MAPE is an indicator that measures the difference between predicted values and actual values by calculating the average of the absolute percentage error for each prediction. It provides the average percentage of prediction errors and is commonly used to assess the accuracy of forecasting models. The main formula is as follows:  
\[MAPE=\frac{1}{N}{\sum}_{i=1}^{N}\left|\frac{y_{i}-y_{i}}{y_{i}}\right| \tag{11}\]  
L1 loss is a method that measures the difference between predicted and actual values by calculating the average of the absolute differences between them. The main formula is as follows:  
\[L1\text{ }Loss=\frac{1}{N}{\sum}_{i=1}^{N}|y_{i}-y_{i}| \tag{12}\]  
where \(N\) is the total number of data points, \(y_{i}\) is the ground truth value, and \(y_{i}\) is the predicted value.
## 2 Materials and Methods
The best way to improve the reliability of an underground mine is to install additional communication equipment at each underground mining shaft. The study is based on the design of the core system of the underground mining communication system Motorola Dimetra, the base station, and the repeaters located inside the underground mine. In this paper, a model was developed based on the core system of the Motorola Dimetra underground mine communication system, the base station and the repeater devices located inside the underground mine and using the reliability availability information of each device. Moreover, the effect of corrective and preventive maintenance on each device of the communication system and on the entire system; the rating of standby systems availability for underground mine communication systems with one shaft, two shafts and three shafts were tested by simulation; and the simulation results were compared. Reliability or probability of communication system availability: The probability of a system's working state is determined by the probability of its availability. According to the definition of a repaired system and Markov analysis, we consider the system as being in one of two states: working or broken. According to Markov analysis, the state of the system is shown in Figure 2. In Figure 2, the system switches from the working state (1) to the broken state (0) and back to the working state due to maintenance. This was considered by utilizing the Markov process. Here, \(\lambda_{10}\) is the failure state and \(\mu_{01}\) is the repair state. The probability \(P_{\text{f}}(\text{t})\) for each state (j) in the given time (t) is expressed by the following differential Equations as (1) and (2):  
\[\frac{\text{dP}_{0}(\text{t})}{\text{dt}}=-\mu_{01}\text{P}_{0}(\text{t})+ \lambda_{10}\text{P}_{1}(\text{t}) \tag{1}\]  
\[\frac{\text{dP}_{1}(\text{t})}{\text{dt}}=-\lambda_{10}\text{P}_{1}(\text{t}) +\mu_{01}\text{P}_{0}(\text{t}) \tag{2}\]  
where \(\text{P}_{1}(\text{t})\) is availability and \(\text{P}_{1}(\text{t})=1-\text{P}_{0}(\text{t})\). Moreover, the initial condition of the system is \(\text{P}_{1}(\text{t})=1,\;\text{P}_{0}(\text{t})=0\). The causal loop diagram of this process is shown in Figure 2.  
As shown in Figure 3, there are two different causal loops, which are normal and fault operation on system availability, and each has negative and positive effects. The failure and normal operation of system availability have both negative and positive effects and that there are two types of causal loops. The loop between normal operation and readiness is positive, while the loop between failure and availability is negative. As the failure and repair functions increase, the failure and normal operation states increase, respectively [5]. The probability of the reliable operation of each underground communication system device is modeled for the first core system device with a single standby core system device and two parallel core system devices. To determine the probability of system availability, this study was developed through the following methodology steps (Table 2 and Figure 4).
## 2 Experimental Method
### Experimental Equipment  
#### 2.2.1 Determination of Open Porosity  
The open porosity in cementitious materials is the main means of chloride ion and water transmission. Therefore, the measurement of the open porosity of cementitious materials can effectively lead to understanding the transmission process of chloride ions and water in the pores of cementitious materials. Based on the mix proportion, the concrete and cement's size was 80 mm \(\times\) 80 mm, and this was cured in a fog chamber with a relative humidity of 95% at 20 \(\pm\) 5 \({}^{\circ}\)C for 28 days based on the GB/T50082-2009 standard for testing methods of the long-term performance and durability of ordinary concrete. The high-precision electronic scale and vacuum saturator are shown in Figure 1.  
According to Equation (1), the open porosity of the cementitious materials was calculated, which can be seen in Table 4:  
\[P_{V}=\frac{(M_{\text{C}}-M_{0})}{V_{\text{C}}\rho_{w}} \tag{1}\]  
where \(P_{V}\) is the open porosity (%); \(M_{\text{C}}\) is the average mass of the sample after vacuum filling (g); \(M_{0}\) is the average mass of the sample after drying (g); \(V_{\text{C}}\) is the volume of the test piece after being full of water (mL); and \(\rho_{w}\) is the water density (g/mL).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & \(\mathbf{M_{\text{C}}}\)**/g** & \(\mathbf{M_{0}}\)**/g** & \(\mathbf{V_{\text{C}}}\)**/mL\({}^{3}\)** & \(\mathbf{P_{\text{V}}}\)**/\%** \\ \hline S-0.35 & 123.782 & 112.349 & 58.1 & 19.67 \\ S-0.45 & 127.331 & 113.864 & 58.6 & 22.98 \\ S-0.55 & 132.574 & 114.352 & 59.4 & 30.67 \\ C-0\% & 128.163 & 114.324 & 59.6 & 23.21 \\ C-20\% & 127.212 & 114.404 & 59.2 & 21.64 \\ C-40\% & 125.759 & 114.196 & 59.0 & 19.59 \\ C-60\% & 129.954 & 116.163 & 59.9 & 23.02 \\ \hline \hline \end{tabular} Note: W/C is the w/c ratio, and C-\% is the content of stone volume in concrete. For example, C-40% means the stone volume is 40%.  
\end{table}
Table 4: Open porosities of cementitious materials.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **W/C** & **Cement** & **Water** & **Sand** & **Stone** & **Stone Volume** \\ \hline S-0.35 & 0.35 & 450 & 158 & 1350 & - & - \\ S-0.45 & 0.45 & 450 & 203 & 1350 & - & - \\ S-0.55 & 0.55 & 450 & 248 & 1350 & - & - \\ C-0\% & 0.50 & 672 & 336 & 1350 & 0 & 0\% \\ C-20\% & 0.50 & 56  
2.2.2. Determination of the Chloride Diffusion Coefficient  
According to GB/T50082-2009, the chloride ion diffusion coefficient can be calculated based on Equation (2), and the RCM rapid chloride analyzer is shown in Figure 2:  
\[D_{RCM}=\frac{0.0239\times(273+T)L}{(U-2)t}\left(X_{d}-0.0238\sqrt{\frac{(273+T) LX_{d}}{U-2}}\right) \tag{2}\]  
where \(D_{RCM}\) is the unsteady ion transfer of specimens (m\({}^{2}\)/s); \(U\) is the absolute voltage used; \(T\) is the temperature (\({}^{\circ}\)C); \(L\) is the thickness of the specimen (mm); \(X_{d}\) is the depth (mm); and \(t\) is the test time (h). The results can be seen in Table 5.  
Figure 1: Test instrument.  
Figure 2: Rapid chloride analyzer (RCM).
## 2 Materials and Methods
### The Database
#### 2.2.3 The Types of Environments  
The environments that housed the wall paintings object of the studies considered were grouped into five categories, based on whether they were hypogean or not, as well as the expected ventilation, confinement, and moisture conditions. The following are the definitions of the categories:  
1. **C-HE**: Confined Hypogean Environment (isolated, without air circulation, with generally high humidity levels and relatively stable and low temperatures) as tumuli, close tombs, or prehistoric caves. They are often characterized by not negligible organic matter inputs from dripping waters, animals, and their fecal pellets and may be influenced by the presence of visitors, promoting the introduction and movement of airborne particulate.
2. **NC-HE**: Non-Confined Hypogean Environment (hypogean environments with reduced air circulation), such as underground crypts, catacombs, rupestrian churches, or Roman houses. These sub-aerial environments offer semi- or non-confined situations that are partially isolated from the external environment, with humidity and temperature comparatively more stable than the outdoor conditions but influenced by external day-night cycles and seasonal fluctuations.
3. **C-NHE**: Confined Non-Hypogean Environment: the sub-aerial environment of churches, monasteries, temples, refectories, and castle chapels represents a confined condition in which the microbial community is enclosed in a mesocosm. They are partially isolated from the external environment and have relatively more stable humidity and temperature than outdoor conditions, but they are influenced by external day-night cycles, seasonal variations in temperature, and relative humidity values. They are more prone to microbial attacks since they include more visited sites.
4. **SC-NHE**: Semi Confined Non-Hypogean Environment (open towards the outdoor environment but protected by wide changes in environmental parameters), such as ancient archaeological buildings and private homes. They are open to the outdoorsbut sheltered from rain and ventilation; they all experience seasonal and daily relative humidity and temperature fluctuations.
5. **O-SPE**: Open and Semi-Protected or Protected Environment; they include buildings' walls or collapsed caves, which are among the most exposed sites to biodeterioration risks.
## 3 Results and Discussion
### Water Sample Analysis  
There are currently many analytical methods for the determination of bisphenol A in actual water samples [32; 33; 34]. The practicality of the BPA-MIPs-SBSE-HPLC analysis method was evaluated by determining the concentration of BPA in real water samples. Under optimized conditions, the BPA content in the real water samples, including deionized water, tap water, bottled water, and reservoir water, was measured using the BPA-MIPs-SBSE-HPLC analysis method. The results showed that BPA was not detected in any of the four water samples. To evaluate the accuracy of the method, spike recovery experiments were conducted by adding BPA to the four types of water samples to create spiked samples at concentrations of 500 \(\mu\)g/L and 2000 \(\mu\)g/L. After the BPA-MIPs-SBSE adsorption process, HPLC analysis was performed, and the results are shown in Table 2. The recovery rates for the two concentrations in the four water samples ranged from 71% to 93%, with relative  
Figure 12: Removal rates of several phenols by BPA-MIPs-SBSE and BPA-NIPs-SBSE.  
Figure 13: Adsorption capacities of BPA-MIPs-SBSE and BPA-NIPs-SBSE for several phenols.  
standard deviations ranging from 4.5% to 8.7%. These experimental results demonstrate the reliability of the method for the analysis of BPA in complex samples.
## 2 Materials and Methods
### Statistical Analysis  
A 2 \(\times\) 3 mixed factorial design was employed, incorporating one between-subjects factor--group (high-intensity interval training [HIIT] vs. control)--and one within-subjects factor--the test phase (R1-R3). For each physiological and cognitive metric, a mixed-design ANOVA was conducted to examine the main effects of the group and the phase, as well as their interaction. To investigate temporal dynamics within each group independently, a repeated-measures ANOVA was performed separately for the HIIT and control groups. Independent samples _t_-tests were additionally applied at each phase (R1-R3) to compare group-level differences.  
To evaluate test-retest reliability, the derived metrics were analyzed using Pearson's correlation-based intraclass correlation coefficients (ICCs), reflecting the stability of each metric over time. Concurrently, measurement variability was quantified via the coefficient of variation (CV%), and the metrics were subsequently classified according to standard interpretive thresholds: the ICC was labeled as excellent (\(\geq\)0.90), good (0.75-0.89), moderate (0.50-0.74), or poor (<0.50); the CV was categorized as very low (<5%), low (5-9.9%), moderate (10-19.9%), or high (\(\geq\)20%). A combined reliability tier designation (high, acceptable, borderline, unreliable, or mixed/needs review) was applied to synthesize the ICC and CV outcomes. Greenhouse-Geisser corrections were implemented where the assumption ofsphericity was violated. Effect sizes were reported using partial eta squared (\(\eta^{2}\)), and the statistical significance was set at \(p<0.05\).  
To examine cross-domain associations, Pearson correlation coefficients were computed between all metric pairs within each group (HIIT, control) and test phase (R1-R3). Comparisons were performed both across time (R1 vs. R3 within group) and between groups at each phase (HIIT vs. control at R3). [PERSON]'s r-to-z transformation was applied to determine the statistical significance of correlation changes across conditions. For each pairwise comparison, the z-statistic was computed, and the corresponding \(p\)-value was derived based on the standard normal distribution [7; 8; 21].  
Statistically significant shifts in correlation (\(p<0.05\)) and absolute delta values (\(\mid\Delta r\mid>0.5\)) were retained for further visualization. To focus on cross-domain dynamics, only interactions between variables of different classifications (e.g., cognitive\(\leftrightarrow\)metabolic) were considered for heatmap generation.  
Heatmaps were created using Seaborn (v0.12) in Python (v3.10), with the color intensity representing \(\Delta r\) values and tick labels color-coded by variable classification. Legends indicating type-pair interactions were constructed to guide visual interpretation. All results, including z-statistics, correlation deltas, and significance levels, were exported to Excel (XLSX) files for reproducibility. Analysis code and output visualizations are available upon request.