esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 5 Conclusions  
Data from InSAR, seismic profiles, and gravity were used to map the surface deformation caused by subsidence and earthquake swarms in the Quetta Valley and surrounding vicinities.  
* InSAR results from 2003-2010 show a rate of 5.5 mm/year for subsidence in the Quetta Valley and a rate of 1.8 mm/year for uplift in the mountain ranges surrounding the valley. A median filter was applied to the InSAR velocity curve to separate the subsidence caused by tectonic processes from subsidence caused by anthropogenic processes. The results indicate subsidence in the Quetta Valley is mainly caused by anthropogenic processes followed by movement along thrust faults.
* Multi-temporal InSAR analysis shows a sinusoidal displacement pattern for the swarm of earthquakes that occurred in 2008 along the Harnai and Karahi strike-slip faults.
* This study identified a high risk for potential fissures.  
**Supplementary Materials:** The following are available online at www.mdpi.com/2072-4292/8/11/956/s1, Figure S1: DInSAR co-seismic wrapped phase interferogram (11 November 2008-29 December 2008) covers EQ 5, 6, 7, and 8 (Figure 1). Based on the location of earthquakes, a half fringe (a bright yellow dot) on the west side of Harnai Fault may correspond to ~55 mm co-seismic movement of the EQ 5, 6, 7, and 8. While there is another fringe present on the north side of the Harnai Fault, the time and location do not correspond to any of the eight earthquakes. This fringe may be caused by slow uplift of the Takatu Mountain Range (Figure 8).  
**Acknowledgments:** This work is funded by the National Academy of Science and the Higher Education Commission of Pakistan through Pak-US Science and Technology Cooperation Program. We thank [PERSON] of the Icelandic Meteorological Office for providing the python code for GPS pole-of-rotation calculation. We thank five reviewers and editor-in-chief for very thorough and detailed reviews.  
**Author Contributions:** [PERSON] contributed to the InSAR data processing, data analysis and wrote the draft version of the manuscript. [PERSON] designed the work and provided the analytical framework and edited and contributed to writing of the manuscript. [PERSON] contributed to the InSAR data processing and analysis. [PERSON] contributed to the GPS data processing. [PERSON] contributed to the regional geology and tectonic interpretations. [PERSON], [PERSON], [PERSON], [PERSON] contributed to GPS data acquisition in the field.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 6 Evaluation
### Estimation Schemes  
The following two simple prediction procedures are employed as the reference baselines:  
1. A stationary predictor using the previous value in the time series: \[y_{\tau}^{p}=y_{\tau_{1}}\] (15) which is equivalent to assuming an MC model with \(\Pi=I_{N}\) (i.e., to assign probability 1 to the self-links in the graph).
2. An MC predictor assuming a transition matrix \(\overline{\Pi}\), whose random column vectors follow a uniform distribution over the non-zero elements of the adjacency matrix of the graph: \[y_{\tau}^{p}=\overline{\Pi}\cdot y_{\tau_{1}}\] (16)
3. A predictor that is based only on the second step of the GNN approach (Equation (14)) without calculating \(h\). This has been labeled NoGNN in the evaluation. The motivation for this predictor is to evaluate the effect of message passing in the prediction error for the selected architecture.  
Alternatively, the three more elaborated approaches proposed in this paper are as follows:  
4. A more elaborated MC predictor according to (1) where \(\widehat{\Pi}\) is obtained from (10). Considering the model used in the generation of the evaluation datasets detailed in Section 6.2, this predictor should achieve very high accuracy given enough training data, as its underlying Markovian model is the same as that used for generating the datasets in Section 6.2.
5. A set of predictors using an MLP architecture to be trained in the spirit of (11) (with a proper use of training and validation sets as explained below), and to be employed according to (2).
6. A set of predictors based on a GNN according to (3), where several parameters can be adjusted. First, different number of characteristics \(W\), whether the one-hot representation of the node id has been used (using the Id suffix in the model name) and whether the number of time steps is included in the feature vector (using the T suffix in the model name). For the sake of simplicity, our evaluation uses the same hyperparameters for both \(\text{MLP}_{\boldsymbol{\Theta_{1}}}\) and \(\text{MLP}_{\boldsymbol{\Theta_{2}}}\): two hidden layers to allow for more expressiveness and two possible configurations for the number of neurons in each layer (64 and \(N\)) to evaluate the effect of dimensionality in both training and prediction error.  
Note that predictors 1, 2 and 4 rely on models based on homogeneous (i.e., time-invariant) MCs. Alternatively, predictors 5 and 6 allow modeling time-varying behaviors in some of its configurations.
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 3 Fusion of Lane Location Information and SLAM Positioning Algorithm  
The above-mentioned lane line detection and representation are sufficient to enable unmanned vehicles to complete lane keeping and steering functions, but it is still impossible to determine the specific global position of the lane where the unmanned vehicle is located. Therefore, the global consistent positioning method of SLAM needs to be considered. This paper uses a SLAM system that combines vision and inertial navigation. Based on the algorithm [15], IMU and monocular camera are used to complete the front-end output process of SLAM. Different from the indoor SLAM algorithm, it is difficult to have enough loop information during the driving process of the unmanned vehicle. Therefore, combining the timely position information which is provided by the SLAM and the map information is an important way to create an artificial loop. In this paper, by correlating the detected lane and position information, a prediction-update model is used to realize the fusion of SLAM positioning information and vehicle map lane position.  
At each moment of fusion positioning, the result of the above local positioning \(d_{lane}\) is used to obtain the virtual trajectory of the vehicle parallel to the lanes of the map. Each point stored in the directed graph of the map is translated to this virtual trajectory in the same way. After the line localization result is updated, this operation performs the same processing on the virtual trajectory and the map point.  
Figure 3: Schematic diagram of lane virtual candidate points and feature matching.  
Figure 2: Representing map information that is difficult to use directly through directed graphs.  
In the rest of this section, the map point mentioned refers to the point information stored in the directed graph on the map, and the candidate point refers to the map point where the vehicle has a greater probability of being at this position. The candidate points are on the virtual track obtained after each update of the distance from the lane line. The core of this method lies in the update of candidate points. Probability is used to update the candidate points and realize the positioning function.
# Natural Frequencies of a Tethered Satellite System
## 2 Natural Frequencies of the Dumbbell Model
When \(m_{1}=m_{2}\), as observed in Equations (21)-(23), the natural frequencies \(\omega_{1}\) and \(\omega_{2}\), along with the orbital angular velocity \(\omega_{c}\), are independent of the satellite masses. In this case, they are determined by the orbital radius of satellite \(m_{1}\) and tether length. However, if the satellites have different masses, i.e., if \(m_{1}\
eq m_{2}\), the frequencies are influenced by the masses. We analyzed the dynamic responses computed by applying the Newmark time integration method [17] to the nonlinear equations given by Equations (1)-(3). The physical  
Figure 4: Natural frequencies of the dumbbell model for the variation of the satellite mass, \(m_{1}\): (**a**) first natural frequency and (**b**) second natural frequency. \[a>0,\ b>0,\ a^{2}-4b>0 \tag{20}\]0 Natural frequencies of the dumbbell model for the variation of the satellite mass, \(m_{2}\): (**a**) first natural frequency and (**b**) second natural frequency. properties used for these computations are based on the reference values mentioned above. The dynamic responses for initial libration angles of 1\({}^{\circ}\), 10\({}^{\circ}\), and 30\({}^{\circ}\) are displayed in Figure 6a,b,c, respectively. As shown in this figure, the initial values of the libration angle correspond to the amplitude of each dynamic response. It is interesting that as the amplitude of the libration angle increases, the libration period becomes longer. Specifically, as the libration amplitude increases from 1\({}^{\circ}\) to 10\({}^{\circ}\) and 30\({}^{\circ}\), the libration period extends to approximately 3083.3, 3106.7, and 3307.5 s, respectively. The corresponding libration frequencies are \(2.0378\times 10^{-3}\), \(2.0225\times 10^{-3}\), and \(1.8997\times 10^{-3}\) rad/s, respectively. Recalling that the computed natural frequencies are \(\omega_{1}=1.1761\times 10^{-3}\) rad/s and \(\omega_{2}=2.0371\times 10^{-3}\) rad/s, the libration frequency of \(2.0378\times 10^{-3}\) rad/s, corresponding to the 1\({}^{\circ}\) amplitude case, is very close to the second natural frequency \(\omega_{2}\). This implies that, for small amplitudes, the tethered satellite system behaves almost linearly. However, as the amplitude increases, the nonlinearity of the satellite system becomes more pronounced, causing the libration frequency to decrease further below the second natural frequency of the linearized system and resulting in a greater deviation from the natural frequency. The growing deviation from the natural frequency means an increasing nonlinearity in the motion of system.
## 2 Algorithm Principle
### INS+LEO 3-Satellite Alternate Switching Ranging Integrated Navigation Algorithm
#### 2.2.1 Algorithm Principle  
The idea of the algorithm is as follows: according to the satellite selection algorithm of GDOP, three satellites that are always visible during the flight of the carrier aircraft are selected. However, the difference is that, at any time, the aircraft can obtain the true ranging values of two satellites and they will also change with the switching of satellites. The true ranging values of one satellite will not participate in the switching and the true ranging value of another satellite is alternately switched with the virtual ranging value of the remaining satellite. The virtual ranging value also changes with satellite switching, but the three ranging values also participate in INS-integrated navigation filtering.  
For 2-satellite alternate switching ranging/INS under the LEO 3-satellite algorithm, the algorithm can be divided into four scenarios:  
Scenario 1: The three satellites participating in the alternation are all located on the same orbital plane; one of the satellites uses the continuous true value and the other two satellites alternate ranging with real ranging value and virtual ranging value. This algorithm positioning scenario is shown in Figure 2(a).  
Scenario 2: Alternately switch satellites in the same orbit and continuous ranging satellites in different orbits (which can be adjacent). This algorithm positioning scenario is shown in Figure 2(b).  
Scenario 3: The continuous ranging satellite is in the same orbit as an alternate satellite and the other alternate satellite is in a different orbit (it can be adjacent). This algorithm positioning scenario is shown in Figure 2(c).  
Scenario 4: The three satellites are in different orbits (can be adjacent) and the continuous ranging satellite can be in any orbit. This algorithm positioning scenario is shown in Figure 2(d).  
We give a specific schematic diagram of scenario 1. As shown in Figure 4, scenario 4 is similar to scenario 1. Due to space limitations, it is not reported in this paper.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 4 Generic Header Compression
### Compression  
How to compress is not defined in the standard, but the chosen compression algorithm should generate a compressed payload that conforms to the decompression rules. By leaving the choice or design of the compression algorithm open, it can be adapted and tuned to specific use cases. To better explain how a compression algorithm can operate, an example is provided in Figure 3. First, a buffer is initialised with the dictionary, as described in Section 4.1. We iterate over the uncompressed packet and calculate the number of consecutive zeros. We also look for a match between the uncompressed data and the dictionary. When there are no consecutive zeros present and no match is found, the data are just copied into the compression buffer, as depicted in Figure 3 with steps 1 and 3. The copied data are prepended with the copy instruction, which contains the number of bytes to be copied when decompressing.  
When a match is found, the backreferencing instruction is used. If the length of the match is higher than 6 or the match is more than 6 bytes to the left, then the extended backreferencing instruction needs to be used. This is shown in Figure 3 with steps 2 and 5. Step 2 completely elided the source address and step 5 found a partial match with the destination address. When a long sequence of zeros is found, it can be elided using the zero instruction, as shown in step 4, where nine zeros are replaced by only one instruction.  
In case of IPv6 extension headers, a stop instruction will be appended. This instruction is needed to show where the compression of each intermediate header ends. This is not needed for the upper-layer headers, since they always appear at the end of the packet.
## 5 Simulation Results
As expected, the detection performance is influenced by the presence of the STO, especially in the case of the RCSD method. More importantly, it is shown that the performance degradation of the proposed RCJD scheme is negligible compared to the HCJD method, regardless of the SNR and SFO valUE. Figure 4: Performance of integer carrier frequency offset and sector identity detectors in the Pedestrian A channel when \(\tau_{max}=0\): (**a**) integer carrier frequency offset detector (**b**) sector identity detector. Figure 5: Performance of the joint detection methods in the Pedestrian A channel: (**a**) \(\tau_{max}=0\); (**b**) \(\tau_{max}=5\). Figure 6 depicts \(P_{f}\) of existing and proposed detectors in the Vehicular A channel with mobile speed of 60 km/h. The simulation condition is identical to that in Figure 5. It is immediately seen that the trend of the probability curves is basically identical to that in Figure 5. Apparently, the increased frequency selectivity of the channel is harmful to the performance of existing and proposed estimation methods, which is because both methods are based on the differential correlation from adjacent PSS subcarriers. When compared to the observations in the Pedestrian A channel, the HCJD and RCJD methods still require at least \(N_{r}=140\) PSS repetition for 90% probability of successful joint detection. Due to its high sensitivity to STO and fading impairments, the RCSD scheme does not work properly in dispersive channel environment. Regarding the number of flops, the RCJD scheme reduces the complexity by 31.8% and 38.4% over the HCJD and RCSD methods, respectively. It is summarized that the proposed RCJD method provides almost the same detection capability to the HCJD scheme in addition to having lower complexity. Since the proposed scheme is based on the PSS with a fixed length of \(N_{p}=62\) subcarriers regardless of the system bandwidth, so it can be straightforwardly extended to the ordinary LTE device with larger system bandwidth.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Overall Framework  
Our model UC-CMAF can be represented in the architectural Figure 1, including four key sub-processes: multimodal feature extraction, cross-modal adaptive collaborative attention fusion for news text and images, cross-modal attention fusion of user comments with news text and images, and fake news detection.  
The UC-CMAF framework consists of four integrated components working synergistically to achieve accurate fake news detection. The framework begins with text and image encoding networks that leverage state-of-the-art pre-trained models to extract rich semantic representations from textual and visual modalities of the provided news content. These encoded representations are then processed through the cross-modal adaptive collaborative attention (CMA) component, which implements sophisticated attention mechanisms that  
Figure 1: UC-CMAF architectural diagram.  
capture fine-grained relationships between textual and visual content while dynamically adjusting attention weights based on content characteristics. The User Comment Integration (UCI/UCT) component introduces specialized networks to process and integrate user comments with news content, enabling the model to leverage social signals and collective intelligence for authenticity assessment. Finally, the fake news detector serves as the aggregation component that combines all multimodal features and produces authenticity predictions with associated confidence scores, completing the comprehensive framework for detecting fake news through the synergistic integration of content analysis and social validation signals.
## 4 Experiments and Results
### Baseline Systems
#### 4.3.1 Frame-Based Model  
The frame-based model was trained [11; 12] to generate only \(F_{0}\) because the performance of the \(F_{0}\) model degrades when \(F_{0}\) and the spectral parameters are modeled simultaneously [55]. The input features used consisted of the same set employed on the model for generating speech, whereas the output features consisted of log \(F_{0}\) with dynamic features and a voiced/unvoiced flag. Similar to [11], we included silence in all frames to maintain the continuity of the \(F_{0}\) contour within a sentence. The hyper-parameters were tuned as in the proposed model. The network structure with the best performance consisted of three feedforward layers with 256 nodes per layer, where the top two hidden layers had a BLSTM structure, and each combined 128 forward units with 128 backward units. The model was trained with a learning rate of 0.0001 and had a mini-batch size of 64.
## 2 Kinematics Analysis
### Newton-Raphson Method for FKP  
The Newton-Raphson method is a traditional numerical method to solve multivariate nonlinear equations. It turns the nonlinear equations to linear equations through Taylor formula expansion, and gradually gains on the solution of the equations through continuous iteration of approximate solutions. The Jacobian matrix of nonlinear equations is  
\[J\Big{(}X^{k}\Big{)}=\frac{\partial\Big{(}F(X^{k}\Big{)}}{\partial X^{k}} \tag{5}\]  
When \(J\) is reversible, te root formula is:  
\[X^{k+1}=X^{k}-J\Big{(}X^{k}\Big{)}^{-1}F\Big{(}X^{k}\Big{)} \tag{6}\]  
The steps of Newton-Raphson method to numerically solve the nonlinear equations are as follows:  
1. Set the initial value \(X^{0}\), maximum iterations \(N\), and the target accuracy \(\varepsilon\). Set \(k=0\).
2. Solve \(d^{k}=-J\Big{(}X^{k}\Big{)}^{-1}F\Big{(}X^{k}\Big{)}\).
3. If \(\left|d^{k}\right|<\varepsilon\), stop the iteration, or set \(X^{k+1}=X^{k}+d^{k}\).
4. If \(k=N\), stop the iteration, or set \(k=k+1\), turn to (2).  
An important condition of using Newton-Raphson method is that the Jacobian matrix of nonlinear equations at the operating point exists and the matrix shall be nonsingular. Moreover, the computation of the Jacobian matrix and its inverse is complex. This method's convergences rely on the selection of initial values, while the inappropriate selection of initial values will make the solving progress difficult to converge.
## 5 Numerical Experiments
### Results and Discussion  
We compared the performance of the algorithms by comparing the values of the objective function and the number of iterations for the algorithms to converge.  
Figure 6 shows the evolution curves of the algorithms for each benchmark problem. For the sake of clarity, only 50 iterations are shown in Figure 6. Although a maximum of 1000 iterations were run in our experiments, the objective values stay unchanged beyond 50 iterations. As shown in Figure 6, our algorithm can find better solutions earlier than other algorithms for most of the benchmark problems.  
Figure 6: Evolution curves of benchmark functions.  
Table 4 shows the ability of the algorithms to find the global optimal solutions. Notice that since all benchmark problems require the objective functions to be minimized, the smaller the value of the objective function for the solution obtained by the algorithm, the more accurate the solution is. In Table 4, the columns labeled with \(F_{ave}\) and \(F_{best}\) are, respectively, the average and best values of the objective function at the termination of the algorithm over 50 runs. The best obtained results are highlighted in bold.  
From these results, we can see that our algorithm outperforms the other algorithms in all benchmark problems, except for \(f_{8}(\mathbf{x})\) in both the average and the optimal cases. This clearly indicates that our algorithm can find solutions that are much closer to the global optimal solution than other algorithms, especially for optimization problems in high dimensional space. The slightly worse performance than DE for problem \(f_{8}(\mathbf{x})\) was due to the difficulty caused by thousands of local optima in the solution space. However, this result of our algorithm is still much better than those of GA and PSO.  
Table 5 shows the speed at which the algorithms converge. We measured the maximum, minimum, and average number of iterations executed until the algorithms converged to a solution.  
From Table 5, we can see that our algorithm converges much faster than other algorithms on all benchmark problems except \(f_{8}(\mathbf{x})\).
## 5 Simulation and Performance Analysis  
In this study, we utilized STK and NS2 to construct the simulation system for the Iridium satellite network, enabling us to simulate network packet loss scenarios and generate a comprehensive dataset. Subsequently, feature selection was performed, followed by training of a heterogeneous ensemble learning model for packet loss differentiation to validate the feasibility and effectiveness of our proposed method. Additionally, optimal parameters for relevant algorithms were determined and their rationality in terms of parameter settings is verified. Finally, in order to assess the performance advantages of our proposed method, we compared it with the LDM-satellite [11] and LLD [12] loss differentiation models under identical conditions.