esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison of Landslide Detection Results  
In this paper, the deformation-rate results obtained by InSAR technology are superimposed on the three-dimensional Google images, and the three-dimensional optical image is used to assess the disaster-producing conditions and deformation signs of the deformed area. Usually, a landslide will result in local changes in optics, and there will be ground fissures, small collapses, and bare areas. The vegetation index is significantly reduced. If the optical signs conform to the characteristics of the landslide and there are certain indicators of risk, the area is preliminarily judged as a potential landslide, more than 80% of the which are verified in the field. Finally, a total of 88 landslides were identified in the study area (Figure 8), and 66 of them were investigated in the field; others that have not  
Figure 7: Diagram of deformation-rate statistics. (**a**,**b**) represent the deformation-rate statistics of Sentinel\(-\)1A and ALOS/PALSAR\(-\)2 data in the LOS direction, respectively; (**c**) represents the deformation-rate difference between Sentinel\(-\)1A and ALOS/PALSAR\(-\)2, and (**d**) is the corresponding statistical graph.  
been verified also show obvious deformation traces in the optical time-series images. It is clear IPTA\(-\)SBAS is able to detect landslides in the Qinling-Daba Mountains. Through analysis, we found that landslides in this area mostly occurred on both sides of the river. First, the river is a densely populated area, which indicates the influence of engineering [64]; second, long-term erosion by the river will also reduce the stability of the slope [65,66].  
Landslides in the Qinling-Daba Mountains are mainly shallow, small and medium-sized, with clear seasonality [33]. The superior penetration and high spatial resolution of ALOS/PALSAR\(-\)2 enabled that system to identify 80 landslides in this area (Figure 8). Accounting for 90.9% of the total number of detected landslides, it shows excellent detection performance. However, Sentinel\(-\)1A detected only 38 landslides in this area, accounting for 43.2% of the total number. Furthermore, 50 landslides were detected only by ALOS/PALSAR\(-\)2; most of these are small, seasonal, and located near rivers. Additionally, eight landslides were detected only by Sentinel\(-\)1A, most of which are distributed in the alpine and canyon areas with dense vegetation and steep slopes. Generally, ALOS/PALSAR\(-\)2 has absolute detection advantages in areas of dense vegetation [67,68], but it still missed eight landslides in our experiment.  
Figure 8: Inventory map of detected landslides. HP 1, HP 2 and HP 3 are Huijayuan landslide, Lianfengçun landslide and Sanxingwan landslide, respectively; In this figure, Zone 1, Zone 2 and Zone 3 correspond to those in Figure 6.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### BLE-Based Positioning
#### 3.2.1 Traditional Centroid Algorithm  
Suppose \(M\) iBeacon tags are deployed in the target indoor area with known positions \((x_{i},y_{i}),i=1,2,\ldots,M\). Let \(N_{th}\) and \(P_{th}\) be the number threshold, and power threshold, respectively, and \(P_{i}\) be the RSS at the smartphone from iBeacon \(i\). Denote the number of iBeacon signals with the RSS exceeding the threshold \(P_{th}\) by \(N(P_{i}>P_{th},i=1,2,\ldots,M)\). If \(N>N_{th}\), the estimated position of the smartphone is  
\[(\hat{x},\hat{y})=\bigg{(}\frac{x_{1}+\ldots+x_{N}}{N},\frac{y_{1}+\ldots+y_{N }}{N}\bigg{)}. \tag{1}\]
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology
### Earthwork Virtual Model Construction  
The purpose of virtual models in earthwork construction is to develop a virtual environment containing a digital representation of the physical entities and their characteristics. The realism, accuracy, and computation of the simulation are highly dependent on the model's level of detail (LOD), which is directly correlated with its representation. For example, the as-planned model represents excavation materials to be removed, which requires a detailed representation with a higher LOD. Each earthwork model employs distinct geometric modeling to balance computational efficiency with fidelity to actual earthwork conditions.
## 1 Introduction
This places higher demands on the design of box-type substations, requiring consideration not only of their seismic performance but also an assessment of their performance under combined loading conditions. Given the limitations of the existing research, this study employs finite element simulation methods, integrating static and dynamic analyses, to systematically investigate the performance of box-type substations under extreme wind, snow, and seismic loads, exploring structural optimization strategies. Firstly, the seismic response spectrum analysis method [9] used in this study calculates the structure's frequency-domain response under seismic actions, combined with its dynamic characteristics, to evaluate its response to seismic loads. It involves determining seismic inputs, calculating seismic response spectra, obtaining the structure's natural vibration modes, matching the seismic response spectrum with the structural modes, computing structural responses, and ultimately assessing structural safety. This method, considering both structural and ground dynamic characteristics, is accurate and widely applied in assessing the seismic performance of power facilities [10]. Secondly, through simulation iterations and comparative analyses, an optimized design solution balancing structural strength and stiffness is proposed. This solution not only reduces the weight and thickness of the enclosure but also ensures that the equivalent stress is below the material's yield strength. The findings of this study not only provide scientific evidence and practical demonstrations for enhancing the seismic performance and reliability of box-type substationsbut also demonstrate the feasibility of a lightweight design in ensuring structural safety, offering valuable theoretical and practical guidance for similar power facility designs and emergency management.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### Satellite Grid Visibility Algorithm  
The basic requirement for communication between satellites is that they are visible to each other. The main factors influencing satellite visibility include geometric visibility, antenna visibility, and the influence of distance between satellites [14,15]. Geometric visibility means that the two satellites are not blocked by the earth and the atmosphere, whereas antenna visibility implies that the angle range of the two satellites is within the angle range of each other's antenna. The influence of distance refers to satellite communication being affected by power and other aerospace factors, resulting in the signal not being transmitted.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 2 General Background
### Fuzzy Logic  
Fuzzy logic has been used as a term to refer to degrees of something [43], rather than a Boolean value (i.e., 0 or 1). It is helpful to handle imprecision, and it gives more flexibility in the systems [44]. In 1965, the concept of fuzzy logic theory was introduced by [PERSON] [45]. The idea came while working on a problem with natural language, which is complicated to translate to binary values because not everything can be described as absolutely true or absolutely false.  
The main purpose of this approach was to overcome many of the problems found in classical logic theory [46], especially the limitations presented with crisp values. With a fuzzy approach, the items have a membership grade value that goes from a lower limit to an upper limit for a given class A; typically, it goes in an interval from [0,1]. The lower limit (membership value zero) represents no membership to class A, while the upper limit (membership value one) represents full membership to class A. The other possible numbers mean that there is a partial degree of membership of the corresponding class. The membership grade is assigned by a membership function that possesses the quality of shape adaptability according to a specific need. Elements with zero degree of membership are usually not listed.  
A fuzzy set is defined as:  
\[\tilde{A}=\{(o,\mu_{\tilde{A}}(o))|oeO\}.\]  
where \(\tilde{A}\) is the fuzzy set, \(O\) is a set of objects, \(o\) is the object, and \(\mu_{\tilde{A}}(o)\) is the membership grade of \(o\) in \(\tilde{A}\)[47].  
As we mentioned before, fuzzy logic has the primary purpose of overcome the limitations presented with crisp values. This problem is extended to classifiers where the hard cut of the data can result in an abrupt classification of the objects. A common solution is to apply fuzzy logic to different stages of the classifier.  
A fuzzy classifier calculates a membership vector \(M=\{m_{1},m_{2},m_{3},...,m_{n}\}\) for an object \(o\). The vector element \(m_{i}\in[0,1]\) is the membership grade value of \(o\) in the class \(C_{i}\)[48]. The object \(o\) is represented by a vector of t-dimensional features from a universe of discourse \(U\) and \(C_{1},C_{2},C_{3},...,C_{n}\) is a set of given classes or classes to be discovered.  
Since fuzzy set theory is a broader topic, more information can be consulted in [47]. It details definitions, algebraic operations with fuzzy numbers, fuzzy functions, and applications. We also suggest the work of [PERSON] et al. [49] and [PERSON] [50], where they explained in detail the process of developing complex systems in real-world environments with a fuzzy approach.
## 3 Methods
### Time Series-Based Methods
#### 3.3.1 Long Short-Term Memory (LSTM)  
LSTM networks are designed to address the vanishing gradient problem in traditional RNNs by incorporating memory cells and gating mechanisms. The LSTM unit comprises several key components that work together to manage information flow over time [28].  
The Input Gate is responsible for controlling how much of the current input \(x_{t}\) should be passed to the cell state. The mathematical formulation of the input gate is given by  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{8}\]  
In Equation (8), \(i_{t}\) represents the activation of the input gate. The sigmoid function, denoted as \(\sigma\), squashes the values into the range \([0,1]\), determining the extent to which each component of the input contributes to the cell state. \(W_{i}\) is the weight matrix for the input gate, \(h_{t-1}\) is the hidden state from the previous time step, \(x_{t}\) is the input at the current time step, and \(b_{i}\) is the bias term associated with the input gate.  
The Forget Gate plays a crucial role in deciding which information from the previous cell state should be retained or discarded. The forget gate is computed as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{9}\]  
Here, \(f_{t}\) is the activation of the forget gate, which, like the input gate, uses the sigmoid function \(\sigma\). The weight matrix \(W_{f}\) and the bias term \(b_{f}\) control the influence of the previous hidden state \(h_{t-1}\) and the current input \(x_{t}\) on the forget gate's decision.  
The Cell State Update mechanism is used to update the cell state \(C_{t}\) based on the candidate cell state \(\widetilde{C}_{t}\) and the output of the forget gate. The updated cell state is calculated as  
\[C_{t}=f_{t}*C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{10}\]  
In Equation (9), \(C_{t}\) is the cell state at the current time step, \(C_{t-1}\) is the cell state from the previous time step, and \(\widetilde{C}_{t}\) is the candidate cell state generated by applying the hyperbolic tangent function tanh to the input and previous hidden state, as follows:  
\[\widetilde{C}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{11}\]  
\(W_{c}\) is the weight matrix used to compute \(\widetilde{C}_{t}\), and \(b_{c}\) is the associated bias term.  
Finally, the output gate determines the final output of the LSTM unit. The output gate is defined as  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{12}\]  
where \(o_{t}\) is the activation of the output gate. The output \(h_{t}\) of the LSTM unit is then obtained by applying the output gate's activation to the updated cell state \(C_{t}\) using the hyperbolic tangent function, as follows:  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{13}\]  
In Equation (13), \(h_{t}\) is the hidden state (or output) of the LSTM block, which is subsequently passed on to the next time step or the next network layer.
## 3 Methodology
### Proposed Method
#### 3.5.5. PSSP Model Training  
The Bi-LSTM network is trained using the output of the autoencoder-compressed subsequences as input. Each subsequence of shape \(L\times D\) (where \(L\) is the fixed length and \(D\) is the reduced dimension) is passed through the Bi-LSTM layers. The output layer employs a softmax function to generate class probabilities for each residue:  
\[p_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{C}e^{z_{j}}} \tag{3}\]  
Here, \(p_{i}\) denotes the predicted probability for class \(i\), \(z_{i}\) is the raw logit, and \(C\) is the number of classes (3 for Q3, 8 for Q8).  
For the Q3 and Q8 secondary structure classification tasks, we adopt the categorical cross-entropy loss, a standard objective function for multi-class classification. It is defined as follows:  
\[\mathcal{L}_{\text{CCE}}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{c=1}^{C}y_{ic}\log( \hat{p}_{ic}) \tag{4}\]  
where \(y_{ic}\in\{0,1\}\) denotes the ground-truth label for residue \(i\) and class \(c\), \(\hat{p}_{ic}\in(0,1)\) is the predicted probability output by the softmax function, \(C\) is the number of classes (3 for Q3, 8 for Q8), and \(n\) is the number of residues in the batch. This loss encourages the model to maximize the log-likelihood of the correct class and is theoretically justified as  
Figure 4: Dimensionality reduction of ProtBERT embeddings using stacked autoencoders.  
Figure 5: Subsequence generation strategy. Proteins are split into fixed-length segments, minimizing padding/truncation compared to traditional approaches.  
the negative log likelihood of the multinomial distribution under maximum likelihood estimation (MLE).  
Categorical cross-entropy has favorable convergence properties in deep networks when paired with softmax output layers, especially under stochastic optimization settings like Adam. It has also been shown to yield well-calibrated probabilistic outputs, making it suitable for biological tasks where prediction confidence is important [41]. In our experiments, early stopping based on validation loss was used to mitigate overfitting and ensure generalization to unseen sequences.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 1 Introduction
EO also currently plays an important role in the fields of education [20; 21; 22], forestry [10; 23; 24], ecosystem services [13; 25; 26; 27; 28], agriculture [29; 30], geology [31; 32], and disease and public health [33; 34; 35; 36; 37; 38] as well as in the monitoring of land degradation [39], ocean [40], biomass and carbon [11; 41], coastal areas [42], urban areas [43; 44], natural disasters [45; 46; 47], land use and land cover [48; 15; 49], cold regions [50; 51; 52], the atmosphere [53], biodiversity [54], and water resources [55; 56]. The development of EO satellites in the past 60 years has also led to advances in our understanding of Earth-system processes, and the information derived from satellite observations has become the best basis for responding to changes in human society and the natural environment [18]. However, more support and initiatives are needed to promote open access to remote sensing data, which is significant for the achievement of the SDGs [4]. There are still challenges related to the storage, processing, dissemination, and analysis of EO big data as well as in relation to the uncertainties in the data and the fusion of data from multiple sensors [57; 58; 59; 60; 61]. In this review, the progress of major EO satellite remote sensing applications are tracked and the main user countries, research areas, journals, and changes are tracked over the period of operation of individual satellites, which demonstrate the current impacts to the remote sensing community of different EO satellite data. Then, a new impact index, called remote sensing impact factor (RSIF), was constructed to measure the current impacts of EO satellites and to predict future trends in applications of their data. Thiscomprehensive evaluation will help with the exploration of the full potential of EO data in monitoring, assessing, and modeling our world and contribute to the construction of a sustainable and prosperous human society and planet.
## 4 Method and Discussion
### The Bistatic Radar Equations
In this study, the following CYGNSS L1 variables were used for the calculation of the surface reflectivity:  
- \(\mathrm{ddm\_snr}\) (\(SNR_{dB}=10\log(S_{max}/N_{avg})\) with \(S_{max}\) being the maximum value in a single DDM bin and \(N_{avg}\) is the average raw noise counts per-bin  
- \(\mathrm{gps\_tx\_power\_db\_w}\) (\(P_{R}^{t}\))  
- \(\mathrm{gps\_ant\_gain\_db\_i}\) (\(G^{t}\))  
- \(\mathrm{sp\_rx\_gain}\) (\(G^{r}\))  
- \(\mathrm{rx\_to\_sp\_range}\) (\(d_{sr}\))  
- \(\mathrm{tx\_to\_sp\_range}\) (\(d_{ts}\))  
The parameter \(\lambda\) is the wavelength of the GPS L1 carrier (\(\sim\)0.19 m). We converted all the values to the dB scale (some of them were already in dB within the CYGNSS files).
## 5 Case Study
### Sensitivity Analysis for Budget Limitation in Model-II  
To analyze the influence of the budget ceiling on the scheduling results in the budget evaluation model, this section conducts a sensitivity analysis on the budget ceiling for this case study. First, the budget is fine-tuned from NTD 12.6 million (USD 420 thousand) to NTD 12.59 million (USD 419.67 thousand), with the results shown in Tables 13 and 14.  
The slight reduction in the budget can still keep all the facilities in working condition during the plan cycle. However, the superstructure of bridge eight cannot undergo maintenance operation in the first year due to the insufficient annual budget; thus, maintenance will have to be postponed. The new result is to change the plan selection to the middle plan and act during the third year, as shown in Tables 13 and 14.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Facility** & **1 sup** & **1 sub** & **2 sup** & **2 sub** & **3 sup** & **3 sub** & **4 sup** & **4 sub** \\ \hline Period & E & E & M & E & E & E & E & M \\ \hline Cost of the period \({}^{*}\) & 0.67 & 1 & 43 & 0.33 & 1.33 & 1.33 & 0.67 & 58 \\ \hline Years remaining of & 1 & 2 & 0 & 1 & 6 & 3 & 5 & 0 \\ effect period & & & & & & & & \\ \hline
**Facility** & 5 sup & **5 sub** & **6 sup** & **6 sub** & **7 sup** & **7 sub** & **8 sup** & **8 sub** \\ \hline Period & E & M & M & M & M & M & M & E \\ \hline Cost of the period \({}^{*}\) & 0.33 & 36.67 & 70 & 80 & 33.33 & 33.33 & 26.67 & 0.33 \\ \hline Years remaining of & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 2 \\ effect period & & & & & & & & \\ \hline \hline \end{tabular} \({}^{*}\)Unit: 1 thousand (USD).  
\end{table}
Table 14: Current status impact of budget ceiling (from USD 420 thousand to USD 419.67 thousand).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Facility** & **1 sup** & **1 sub** & **2 sup** & **2 sub** & **3 sup** & **3 sub** & **4 sup** & **4 sub** \\ \hline  
\hline \end{tabular}
\end{table}
Table 13: Plan selection impact of budget ceiling (from USD 420 thousand to USD 419.67 thousand).  
From Table 15, when the budget ceiling is gradually adjusted to NTD 11.6 million (USD 386,670), maintenance for the superstructure of bridge eight will have to be postponed due to the gradual reduction in the budget. The plan changes to the middle plan in the first plan period there is a delay of 2 years.  
Continuously, when the budget ceiling is down to NTD 10.61 million (USD 353,670), the superstructure of bridge seven must be postponed.  
Until the budget ceiling is adjusted to NTD 8.55 million (USD 285,000), there is no solution to the model, indicating that the budget value must be at least more than NTD 8.56 million (USD 285,330) if all facilities are to remain in an effect condition.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 4 Experimental Results and Analysis
### Positioning Performance Evaluation Metrics  
When evaluating the positioning performance, we assumed that the personnel speed predicted by the network model was \(\widetilde{\mathbf{v}}(\widetilde{v}_{x},\widetilde{v}_{y})\), and the personnel estimated position \(\widetilde{\mathbf{p}}(\widetilde{x},\widetilde{y})\) was obtained through calculation. The root mean square error (RMSE) and relative localization accuracy (RTE) were chosen as evaluation metrics. RMSE was used to quantify the overall deviation between the estimated trajectory and the true trajectory with the following calculation formula:  
\[RMSE=\sqrt{\frac{1}{k}[\sum_{t=1}^{k}{(x_{t}-\widetilde{x}_{t})^{2}+(y_{t}- \widetilde{y}_{t})^{2}}]} \tag{11}\]  
Here, \(\widetilde{\mathbf{p}}_{t}(\widetilde{x}_{t},\widetilde{y}_{t})\) represents the coordinates of the position point at time \(t\) in the estimated trajectory, while \(p_{t}(\mathrm{x}_{t}\mathrm{y}_{t})\) represents the coordinates of the position point at time \(t\) in the true trajectory, and \(k\) is the total number of points in the trajectory. On the other hand, RTE is used to reflect the overall positioning accuracy of the estimated trajectory with the following calculation formula:  
\[RTE=\frac{\sum_{t=1}^{k}{\sqrt{{(x_{t}-\widetilde{x}_{t})}^{2}+(y_{t}- \widetilde{y}_{t})^{2}}}}{k*I} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimenter** & **Height (cm)** & **Weight (kg)** \\ \hline A & 171 & 82 \\ B & 178 & 84 \\ C & 158 & 49 \\ D & 183 & 80 \\ E & 175 & 75 \\ F & 166 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimenter information sheet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \multicolumn{4}{c}{**Value**} \\ \hline Camera internal parameter matrix & \begin{tabular}{c} \(654.45291494\) \\ 0 \\ 0 \\ \end{tabular} & \begin{tabular}{c} 0 \\ 0 \\ 0.127 \\ \end{tabular} & \begin{tabular}{c} 644.9591719 \\ 405.31468142 \\ 1 \\ \end{tabular} \\ Camera distortion coefficient matrix & \begin{tabular}{c} \([0.2486\) \\ \end{tabular} & \begin{tabular}{c} \(-0.7481\) \\ \end{tabular} & \begin{tabular}{c} \(0.0127\) \\ \end{tabular} & \begin{tabular}{c} \(-0.0044\) \\ \end{tabular} & \begin{tabular}{c} \(0.7095\) \\ \end{tabular} \\ Rotation matrix & \begin{tabular}{c} \([-0.40250731\) \\ \end{tabular} & \begin{tabular}{c} \(-1.36022442\) \\ \end{tabular} & \begin{tabular}{c} \(-2.09998569\) \\ \end{tabular} \\ Translation matrix & \begin{tabular}{c} \([-1.611763\) \\ \end{tabular} & \
\begin{tabular}{c} \(3.62288654\) \\ \end{tabular} &
\begin{tabular}{c} \(9.40672029\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera parameter.  
Here, \(I\) represents the length of the true trajectory.