esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 4 System Model
### Base Station's Sleep Modes  
Sleep modes reduce the power consumption of a base station during the idle period by deactivating various hardware resources (see Figure 11). They are classified into four modes based on the minimum sleep duration and the activation-deactivation time of the associated hardware resources. Deeper sleep modes (\(SM_{2}\), \(SM_{3}\), and \(SM_{4}\)) reduce the base station's power consumption by a large extent as more hardware components are deactivated. However, the increased burstiness in the traffic demand during peak hours makes it difficult to switch down to deeper sleep levels having long transition times. Moreover, with the maximum periodicity of the synchronization signal block (SSB) transmission in 5G being 160 ms [29], the base station cannot switch to \(SM_{4}\) with a transition time of 1 s. Therefore, we restricted our study to the first three sleep levels. The transition times for the sleep levels \(SM_{1}\), \(SM_{2}\), and \(SM_{3}\) were taken as 0.5 us, 35 us, and 15 ms, respectively [30]. The base station's power consumption model considering the various sleep modes can be written as Equation (14). Here, \(N_{s}\) is the number of sectors, and \(P_{B}\) is the no-load power consumption of the base station given in Equation (15).  
\[P_{BS}^{SG}=N_{s}*\begin{cases}P_{total}&\text{if $p>0$}\\ P_{B}&\text{if $p=0$ without sleep}\\ \delta_{1}P_{B}&\text{if $p=0,72\text{u}$\leq T_{sleep}<1\text{ms}$}\\ \delta_{2}P_{B}&\text{if $p=0,1\text{ms}$\leq T_{sleep}<10\text{ ms}$}\\ \delta_{3}P_{B}&\text{if $p=0,T_{sleep}\geq 10\text{ms}$}\end{cases} \tag{14}\]  
\[P_{B}=M_{i}\Big{[}\frac{1}{(1+c)\eta}\left(eP_{max,PA}\right)\Big{]}+P_{gsm}+M_ {i}P_{bs}+P_{fixed} \tag{15}\]
## 3 Results and Discussion
### Simultaneous Quantification of the 14 Marker Components in Freeze-Dried SDGT Using HPLC-PDA  
The established and validated HPLC assay was then applied to the simultaneous analysis of the 14 marker components (LIQA, LIQ, NOD, NAR, NARG, HES, NPON, PON, API, GLY, HON, DEC, DECA, and PRAA) for the quality control of SDGT. Each sample was analyzed in triplicate across three batches. Quantification of each marker component was conducted with a PDA detector, which was then scanned simultaneously in the range of 190-400 nm; the detection wavelengths for quantification  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Analytic**} & \multirow{2}{*}{**Conc.**} & \multicolumn{3}{c}{**Intraday (_m = 9_)**} & \multicolumn{3}{c}{**Interday (_m = 5_)**} & \multicolumn{3}{c}{**Repeability (_m = 6_)**} \\ \cline{2-10}  & & **Measured** & **Precision** & **Accuracy** & **Measured** & **Precision** & **Accuracy** & **RSD (\%) of** & **RSD (\%) of** \\  & & **Conc.  
were 254 nm (GLY), 275 nm (LIQA and LIQ), 280 nm (NAR, NARG, HES, NPON, and PON), 290 nm (HON), 320 nm (PRAA), 330 nm (DEC and DECA), and 335 nm (NOD and API). The concentrations of the 14 markers were found to range from 0.05 mg/g to 5.25 mg/g (Table 4). Of these markers, PON, the marker component of Ponciri Fructus Immaturus, was found to be the most abundant in SDGT (5.20-5.25 mg/g).
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Economic RS-based Proxies
#### Logistics  
Logistics based on transport networks and facilities are vital aspects of a strong economy in urban areas. Consequently, their state and performance are important for urban DRM economic assessment (Table 5). [PERSON] and [PERSON] [40] compute the impact of a simulated disaster on the performance of the street networks that are not directly affected by a flooding scenario in London. The potential of street network analysis is to provide objective indicators of indirect impacts of flooding on urban street networks in unaffected areas. The indirect effects of business interruption, particularly in a city center, play a key role in assessing an economic performance after an adverse event. In a similar study, [PERSON], et al. [127] propose spatial connectivity as a proxy for a post-earthquake recovery assessment. Variables, such as distance, travel time, and quality of public transportation are used to compute this metric. These authors conclude that an efficient spatial connectivity of areas to the central business district of L'Aquila after an earthquake can decrease the recovery time due to an agile return of economic functioning. [PERSON], et al. [128] indicate that an accessibility and commuting intensity within an urban area--computed based on resources availability, vulnerability, and flexibility of the transportation system--significantly boost economic activities. Accordingly, [PERSON] and [PERSON] [129] propose a model for accessibility analysis as a proxy to compute an economic vulnerability of an urban are. This proxy is mostly based on streets connectivity in an urban road network. However, the proposed connectivity-based approaches need traffic data in addition to RS data to compute street network performance. In a different study, [PERSON] and [PERSON] [109] show that a higher ratio of transportation lands (e.g., roads) and facilities reduces the economic vulnerability of urban areas. However, this proxy also disregards the functional analysis of the roads and transportation facilities focusing on the physical infrastructure side only.
# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 2 Materials and Methods
### Proposed Methods
#### 2.1.4 LSTM Structure  
The study utilized LSTM, a type of RNN that can handle long-term dependencies and mitigate gradient vanishing problems. This approach was applied to detect arcing and non-arcing events in both time and frequency domains by replacing the hidden layers of RNNs with LSTM units that have memory blocks and gates. The LSTM architecture consists of memory cells (C) that are controlled by gates such as input gates, output gates, and forget gates [51]. These gates allow the data to be stored or retrieved from the memory cell. LSTM has been successfully used in sequential modeling tasks such as text categorization and time series modeling [52]. The LSTM architecture is illustrated in Figure 3 and Equation (4) through (9) provide the mathematical formula for an LSTM block.  
\[i_{t}=\sigma\left(x_{t}U^{i}+h_{t-1}W^{i}+b_{i}\right) \tag{4}\]  
\[f_{t}=\sigma\left(x_{t}U^{f}+h_{t-1}W^{f}+b_{f}\right) \tag{5}\]  
\[O_{t}=\sigma\left(x_{t}U^{o}+h_{t-1}W^{o}+b_{o}\right) \tag{6}\]  
\[C_{t}=\sigma\left(f_{t}\odot C_{t-1}+i_{t}\odot\tilde{C}_{t}U^{i}+b_{c}\right) \tag{7}\]  
\[\tilde{C}=tanh(x_{t}U^{g}+h_{t-1}W^{g}) \tag{8}\]  
\[h_{t}=tanh(C_{t})\odot O_{t} \tag{9}\]  
where \(x_{t}\) is the network input matrix, \(h_{t}\) is the hidden layer output, and \(\sigma\) shows the SoftMax function. Since important case is decided by Equation (7), \(C\) serves as a memory cell that acts to retain this information. The outdated cell case \(C_{t-1}\) is updated to the modern cell case \(C_{t}\). New potential values \(\tilde{C}\) and output of present LSTM block \(h_{t}\) tangent function in hyperbolic space are shown in Equations (8) and (9). Weights make up the learnable parameters [(\(W^{i}\), \(W^{f}\), \(W^{o}\), \(W^{g}\)) and (\(U^{i}\), \(U^{f}\), \(U^{o}\), \(U\), \(U\), \(U\))] which organize the internal parameters and bias vectors of the neural network (\(b_{i}\), \(b_{f}\), \(b_{o}\), \(b_{c}\)). The model updates weights and biases by reducing the cost function. The operator \(\odot\) denotes element-by-element multiplication.  
Figure 2: The fundamental design of 1D CNN.
# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 4 Experiments and Results
### Results and Analysis
#### 4.3.2 Visualization  
The attention model can process a large amount of data information and generate weight probability information. The weight information can reflect the different degrees of importance of local regions, so as to allow the network to focus on those regions that are of great importance and more interesting to us. The Attention V-Net in this paper can use the interdependence between channel mappings to emphasize the interdependent feature mapping, and improve the feature representation of specific semantics. Therefore, the model pays more attention to the salient features that are meaningful for specific tasks, while suppressing any insignificant parts. Finally, the Attention V-Net plays a role in enhancing the ability of image feature representation.  
We use the Attention V-Net model based on DTC to randomly segment four 3D datas in the test dataset and reconstruct the left atrium image. Figure 3 is the reconstructed image of ground truth and prediction. Comparing the 2D visualization in Figure 3(1) and the 3D visualization in Figure 3(2), it can be seen that the overall prediction result of the Attention V-Net model is very close to the manual annotation. In contrast, DTC often misses the internal area of the target object, resulting in irregular shapes, while the model with the added attention mechanism can better simulate the segmentation results of the left atrium, improving the accuracy of the integrity of the internal area and boundary. Compared with other methods, our results have a higher overlap ratio with the ground truth, produce fewer false positives, and preserve more details, which further indicates the effectiveness, generalization, and robustness of our proposed method. The 3D representation of our structure is closer to the real left atrium model, but there are still deviations in the details, and cannot be completely consistent with the real shape.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Metrics**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{2-5}  & **Dice (\%)** & **Jaccard (\%)** & **ASD (Voxel)** & **95 HD (Voxel)** \\ \hline Entropy Mini (CVPR 2019) & 88.45 & 79.51 & 3.72 & 14.14 \\ UA-MT (MICCAI 2019) & 88.88 & 80.21 & 2.26 & 7.32 \\ SASNet (MICCAI 2020) & 88.78 & 80.04 & 2.96 & 10.30 \\ DTC (AAAI 2021) & 88.52 & 79.74 & 2.08 & 8.54 \\ \hline Attention V-Net & 89.08 & 80.48 & 1.94 & 8.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with Other Semi-supervised Methods.  
Figure 3: _Cont._
# Storage and Processing of Big Data for Geomagnetic Support of Directional Drilling
## 3 Materials and Methods
The following actions were performed in stages:  
* Preparing the environment on the master and segment nodes, including installing the CentOS 7 operating system (open source software), creating users, and configuring access rights for them;
* Downloading and installing ADCM on the manager node, which allows managing the cluster via a graphical interface or API;
* Creating a new Arenadata DB cluster in ADCM via a graphical interface and specifying the necessary parameters, such as the number of nodes, their configuration, and network settings;
* Automatic configuration of all necessary operating system services and Arenadata DB components was performed;
* Starting the cluster and checking its operability. Test queries were executed, the node status was checked, and a test database was created. For the MAGNUS BD system built on Big Data technology, the structure shown in Figure 4 was proposed, including data sources, a data collection and storage unit, and a data analytics and visualization unit. The following data were loaded into the MAGNUS BD system:  
* Raw minute and second data from 22 ground-based geomagnetic observatories, over 300 GB in volume;
* Processed minute and second data from 22 ground-based observatories, over 35 GB in volume;
* Second and 50 Hz data from the Swarm satellite constellation, over 7 TB in volume. As a result, in 2024, a fully operational pilot version of the MAGNUS BD system was created at the GC RAS. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Host Name**} & **Central Processing Unit (CPU)** & **Random Access Memory (RAM)** & **Read-Only Memory (ROM)** \\ \hline \multirow{3}{*}{master} & 8\(\times\) core & \multirow{3}{*}{16 GB} & \multirow{3}{*}{240 GB SSD} \\  & Intel(R) Xeon(R) & & \\  & CPU E5450 3.00 GHz & & \\ \hline \multirow{3}{*}{seg0} & 8\(\times\) core & \multirow{3}{*}{16 GB} & \multirow{3}{*}{1 TB SSD} \\  & Intel(R) Xeon(R) & & \\  & CPU E5345 & & \\  & 2.33 GHz & & \\ \hline \multirow{3}{*}{seg1} & 8\(\times\) core & \multirow{3}{*}{16 GB} & \multirow{3}{*}{1 TB SSD} \\  & Intel(R) Xeon(R) & & \\  & CPU E5345 & & \\  & 2.33 GHz & & \\ \hline \multirow{3}{*}{ftp} & Intel Core i7-2600 & \multirow{3}{*}{16 GB} & \multirow{3}{*}{14 TB HDD} \\  & 3.40 GHz & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware of the MAGNUS BD pilot system.
## 5 Test-Result Analysis
### Prediction-and-Evaluation System of Mechanical Properties of Steel Anchor Box Based on PSO-BP
#### 6.2.2 PSO-BP Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{2}{c}{**Input Variable Array 1**} & \multicolumn{2}{c}{**Input Variable Array 2**} & \multicolumn{2}{c}{**Input Variable Array 3**} \\ \cline{3-8}  & & **Calculated** & **Predicted** & **Calculated** & **Predicted** & **Calculated** & **Predicted** \\  & & **Value** & **Value** & **Value** & **Value** & **Value** & **Value** \\ \hline \multirow{3}{*}{N1–F1} & \(\mu_{1}\) & 1.902 & 1.915 & 1.865 & 1.867 & 1.840 & 1.839 \\  & \(p_{1}\) & 23.87\% & 23.88\% & 23.69\% & 23.65\% & 24.57\% & 24.60\% \\  & \(\tau_{\text{max1}}\) & 32.433 & 32.314 & 35.444 & 35.387 & 36.223 & 36.059 \\ \hline \multirow{3}{*}{N1–F2} & \(\mu_{2}\) & 2.068 & 2.069 & 2.010 & 2.009 & 2.036 & 2.032 \\  & \(p_{2}\) & 22.56\% & 22.60\% & 22.62\% & 22.60\% & 23.12\% & 23.11\% \\  & \(\tau_{\text{max2}}\) & 33.665 & 33.699 & 36.941 & 36.781 & 37.592 & 37.505 \\ \hline \multirow{3}{*}{N2–F1} & \(\mu_{3}\) & 2.091 & 2.064 & 1.974 & 1.982 & 2.007 & 2.010 \\  & \(p_{3}\) & 23.17\% & 23.17\% & 23.39\% & 23.45\% & 24.04\% & 24.09\% \\  & \(\tau_{\text{max3}}\) & 33.806 & 33.817 & 37.354 & 37.430 & 38.484 & 38.598 \\ \hline \multirow{3}{*}{N2–F2} & \(\mu_{4}\) & 1.975 & 1.979 & 1.881 & 1.880 & 1.893 & 1.891 \\  & \(p_{4}\) & 22.78\% & 22.72\% & 22.70\% & 22.74\% & 23.16\% & 23.14\% \\  & \(\tau_{\text{max4}}\) & 31.864 & 31.983 & 34.863 & 34.882 & 35.010 & 34.845 \\ \hline \multirow{3}{*}{_P_} & \multicolumn{2}{c}{92.38\%} & 92.40\% & 92.40\% & 92.44\% & 94.89\% & 94.90\% \\ \cline{2-8}  & & & & & & & \\ \end{tabular} Note: input-variable array 1—X1 = 30, X2 = 1900, X3 = 38, X4 = 4000; input-variable array 2—X1 = 32, X2 = 1710, X3 = 40, X4 = 4600; input-variable array 3—X1 = 34, X2 = 1800, X3 = 42, X4 = 5000.  
\end{table} 
Table 11: Comparison of calculated values and predicted results.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 4 Experiments  
In order to investigate the performance of the proposed gas leak detection method and provide a comprehensive analysis of the candidate stabilization methods, three experiments are conducted which are described thoroughly in the following sections. The first experiment involves the data acquisition of gas emanation in an isolated environment. The second experiment is an online dataset of gas leaks and gas emanations in a real scenario. Finally, the third experiment is a drone-based inspection that aimed to be used for evaluating the stabilization technique as well as the system performance.
## 3 Evaluation of the Measured Values
### Monitoring of Friction Force  
Another monitored parameter was friction force, which was directly measured and read from the tribometer. The course of friction force depends on many parameters such as the material's mechanical properties, contact pressure value, the coefficient of friction, contact surface roughness, mutual dissolution of materials, contact time, tribosystem elasticity, and the presence of foreign bodies in the contact zone. The results of the friction forces as a function of the path traveled for the X and Z-orientations are shown in Figures 10 and 11.  
The graph in Figure 10 shows that the friction force increases from the initial value at the beginning of the experiment and stabilizes over a 50-60 m long path, where it then oscillates around a certain value until the end of the experiment. The friction force was lower at the beginning of the testing procedure than at the end of the testing procedure. The lowest friction force at the beginning of the experiment can be observed in Sample 5, which remains the lowest at the end, together with the friction force in Sample 4.  
Figure 9: Difference in weight loss in Samples 6–10, represented graphically.  
The highest friction force in samples with X-orientation (Samples 1-5) was recorded in Sample 3, where the fiber deposition orientation was parallel to the direction of wear (under 0\({}^{\circ}\) angle with respect to tested sample wall) and Sample 2, where the fiber orientation was under 45\({}^{\circ}\) angle and in both layers. Samples with a lower friction force followed, such as Sample 1, with a 45\({}^{\circ}\) orientation but the opposite tilt in the even and the odd layer.  
The graph in Figure 11 shows a similar course of friction as in the X-oriented samples, i.e., that the friction force increases from the initial value at the beginning of the experiment and stabilizes along a 40-50 m long path, where it then oscillates around a certain value until the end of the experiment. The lowest friction force at the beginning of the experiment can be observed in Sample 10, which remains the lowest at the end, together with the  
Figure 11: Plotted coefficient of friction depending on the path of Samples 6–10.  
Figure 10: Plotted coefficient of friction depending on the path of Samples 1–5.  
friction force in Sample 6. The X-oriented samples show greater variance in values, i.e., more pronounced jumping as compared with the Z-oriented samples. In addition, we also found lower coefficients of friction as compared with the Z-oriented samples.  
In terms of achieving the optimal parameters for the tribological properties of surfaces of models produced by the FFF technique from ULTEM 9085 material, it is advisable to use the layer deposition strategy, such as in the case of the X-oriented Samples 4 and 5 and the Z-oriented Samples 6 and 10. Using these strategies, a lower friction force (or its course) was found, as well as less weight loss.
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 2 Positioning Model and Inequality Constraints
#### 2.2.3 Distance Constraint  
The above-mentioned constraints employ information that is only related to the operating smartphone. In this section, we consider a smartphone CP application that can utilize inter-smartphone information. Assuming that devices are connected to a central system, the appropriate inter-device distance can be estimated using extra information, such as road width or dedicated short-range communication (DSRC) measurements [38]. This distance range can also be applied by the estimator. When the extra information is employed, the positioning performance can be improved for all participating smartphones [26].  
Assuming that two smartphones are connected, the distance range is determined as follows:  
\[D\in\begin{bmatrix}D_{l}&D_{u}\end{bmatrix} \tag{19}\]  
where \(D\) represents the distance between two smartphones in meters, and \(D_{l}\) and \(D_{u}\) represent the lower and upper bounds of the distance range. In this paper, we achieve the distance constraint using three steps.  
The first step involves computing the initial distance between two smartphones, i.e., \(D_{0}=\parallel\mathbf{r}^{0}-\mathbf{r}^{1}\parallel\), where \(D_{0}\) is the initial distance, \(\mathbf{r}^{0}\) and \(\mathbf{r}^{1}\) are the positions of the two smartphones, and \(\parallel\parallel\) is the 2-norm symbol. After linearizing the equation, we can obtain the distance \(D_{0}\) and its variance \(Q_{D}^{0}\). The second step is to check whether the computed distance is within range. When the result fulfills the condition, the constraint will prevent the task from being performed. Additionally, if the distance is out of range, we can apply the inequality constraint estimator to calculate the constrained distance \(\hat{D}_{i}\) and its variance \(\hat{Q}_{D}^{i}\), in which \(i\) represents the iteration time. Then, the constrained distance can be applied to re-estimate the positions of the smartphones in the system. This calculation ends when the distance is inside the given range or the difference between \(D_{i-1}\) and \(D_{i}\) is within a certain range \(\sigma_{D}\). The steps carried out in the distance constraint are introduced in Refs. [39; 40]. Additionally, a flowchart of the calculation is shown in Figure 4.
## 3 Results
### Influence of the Feature Variables on the Classification Accuracy
#### 3.2.3 Influence of the Feature Variables on the PA of the Different Land Cover Types  
Table 2 (Appendix A) shows the producer accuracies (PAs) of the different land cover types obtained using the different remote sensing classification algorithms when different feature variables participated in the classification. The PAs of the forests, grasslands, water bodies, bare lands, and permanent snow and ice were generally higher and were less affected by the inputted feature variable combinations, while the land cover types of croplands, shrublands, wetlands, and construction land were greatly affected by the feature variable combinations. Specifically, when the RF classifier was used and the feature variable combination was spectral bands + spectral indices + terrain features + radar features, the PA value of the cropland reached the highest value (71.25%). When all of the feature variables were inputted into the RF classifier, the PA of the forest was the highest (94.13%). The PA of the grassland reached the maximum value of 98.61% when only the spectral bands were inputted into the SVM classifier. When the CART classifier was used and the feature variable combinations was spectral bands + spectral indices + terrain features, the PAs of the shrublands and water bodies reached the highest values (66.67% and 97.03). When the RF classifier was used and the spectral bands, spectral indices, terrain features, and radar features were involved in the classification, the PA of the wetlands was the highest (49%). When the SVM classifier was used and all of the feature variables were involved in the classification, the PA of the construction land reached the highest value of 90%. When the spectral bands and spectral indices were inputted into the SVM classifier at the same time, the PA of the bare lands reached the highest value of 99.4%. The PA of the permanent snow  
Figure 5: The influences of the different feature variable combinations on the overall accuracy (OA) of the classification results. Feature variable combination 01: spectral bands; feature variable combination 02: spectral bands + spectral indices; feature variable combination 03: spectral bands + spectral indices + terrain features; feature variable combination 04: spectral bands + spectral indices + terrain features + radar features); feature variable combination 05: spectral bands + terrain features + radar features + texture features.  
and ice reached the maximum value of 100% when only the spectral bands were inputted into the SVM classifier.