esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Application of the Method on Two Earthquakes in Greece
### Station STRF:  
Figure 5 shows the time series of coordinates with a total length of 10 to 12 days. The anharmonic spectral analysis is shown to detect a maximum of one major period, which are represented by the blue curves. The co-seismic displacements are determined from the differences between the averaged values (red lines). The calculated variances are characterized by the pink belts.  
Figure 5: Results of the time series analysis during the earthquake. The horizontal red lines are approximations of averaged positions before and after the earthquakes.  
Subsequently derived co-seismic displacements from the approximation curve differences \"before and after\" can be found in Table 2 with different computations of RMSE using both approaches (see Section 5.2).  
The RMSEs from the 1 st approach are less than 3 mm, and on average 1.4 mm. This is more than in the previous example and this is because the processed time series of coordinates are shorter (5 days vs. more than 30 days). RMSEs from the 2 nd approach (from 0.9 to 7.5 mm, with an average of 3.6 mm) are 2 to 3 times greater than the RMSEs from the 1 st approach. RMSE from the 2 nd approach better corresponds to the usual accuracy of determining the difference from PPP positions.  
Considering the 99.7% confidence level, the deviation limit is calculated as 2.95 times the RMSE. The components N and E at all of the stations are greater than the deviation limit and can be considered as determined. The opposite is the case for the Up components. Their limit deviations (1.12, 2.21, 1.65 cm for the STRF, ZAKY, and PYRG stations respectively) are larger than the absolute values of the Up components of the co-seismic displacement (0.66, 0.70, 0.35 cm for the STRF, ZAKY and PYRG stations respectively). Therefore, we do not consider them to be reliably determined.  
The distribution of the stations with the resulting vectors are shown in Figure 4. Values of 2.95 sigma (2 nd approach) are depicted at the end of the vector of horizontal shifts.  
As we have already stated, the 2 nd approach for RMSE shows that the vertical component of the co-seismic displacement cannot be detected accurately. [PERSON] et al. [14] compared the horizontal components of co-seismic displacement (computed from GNSS data) with seismic source model results for the Zakynthos earthquake. They state that the horizontal displacement of the ZAKY and PYRG stations agrees with the seismic source model. The horizontal displacement determined at the STRF station corresponds in orientation, but its amplitude is four times larger than the corresponding seismic source model. However, from the RMSE, it is evident that RMSE estimated by the 2 nd approach is more realistic.
Therefore, the routing schemes should avoid communication through void region and balance the energy by distributing the data broadcast load evenly on different nodes in the network. The lowest depth nodes with small residual energy must not be penalized. In this manner, we can delay the creation of the void region. However, if there is already a void region in the network, then the routing scheme must also divert the communication around the void area. By keeping all those features under consideration, we have proposed an Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) scheme in this paper. EDOVE uses the depth difference information between the data packet receiving node (simply we call it _receiver_ node in this context) and the sender data, forwarding or relaying node (call it _Sender_ node in the context of this paper.) as well as the neighbors of the _receiver_ node. EDOVE prioritize the receiver node to become the potential forwarding candidate, if:  
* it has maximum residual energy among its one hop neighbors. * it has one or more than one neighbors with less depth. * it has more number of neighbors with depth less than the _receiver_ node and their normalized depth variance should be larger. The above mentioned characteristics select a potential forwarder that has multiple nodes with lower depth to avoid data packet collision at the next hop, does not penalize the low battery power nodes, and avoids the potential void region. The rest of the manuscript is organized in the following manner: The literature review that is related to the proposed EDOVE is summarized in Section 2. In Section 3, holding time and working principle of EDOVE is described. Simulation analysis of EDOVE is compared to the state of the art scheme in terms of end-to-end delay, energy consumptions, the number of copies of data packets, and hop count, packet delivery ratio, dead nodes, etc., are discussed in Section 4. Finally, the overall conclusion extracted from the discussion is presented in Section 5.
## 4 Model Design
### Mathematical Description  
Predicting network traffic load within the context of a Space Air Ground Integration Network (SAGIN) is an intricate and multifaceted challenge. This process entails forecasting future network traffic load by drawing from historical data and pertinent parameters that are unique to such network environments. It can be envisioned as the task of predicting network traffic patterns within a specified geographical area and time horizon, with the primary objective of resource allocation optimization and the promotion of efficient communication. Below, we provide a mathematical delineation of this problem.  
Let \(T\) represent time, where \(t_{1},t_{2},\cdots,t_{n}\) are discrete time points. Let \(L(t)\) represent the traffic load at time \(t\) in the SAGIN. This load could be measured in bits per second (bps), packets per second (pps), or other relevant units. Let \(X(t)\) represent a vector of predictor variables at time \(t\), which could include factors such as satellite bandwidth, air-to-ground link conditions, ground station congestion, and the number of active users.  
Given historical data of traffic load and associated predictor variables, the problem is to find a function \(f\) that predicts the traffic load \(L(t)\) at a future time \(t+\Delta t\) based on the predictor variables \(X(t+\Delta t)\). Similar to the general network traffic prediction problem, this can be framed as a regression problem. A common approach is linear regression, where \(f\) can be represented as  
\[L(t+\Delta t)=\beta_{0}+\beta_{1}\cdot X_{1}(t+\Delta t)+\beta_{2}\cdot X_{2} (t+\Delta t)+\cdots+\beta_{p}\cdot X_{p}(t+\Delta t)+\varepsilon \tag{1}\]  
\(\beta_{i}\) are the coefficients to be estimated. \(X_{i}(t+\Delta t)\) are the predictor variables at the future time \(t+\Delta t\). \(\varepsilon\) is the error term.  
Historical data specific to the SAGIN environment are used to train the model. Coefficients \(\beta_{i}\) are estimated to minimize the prediction error, often by minimizing the mean squared error (MSE) or another suitable loss function. The model's performance is assessed using metrics like Root Mean Squared Error (RMSE) on a validation dataset. Once trained and evaluated, the model can be deployed to predict future traffic loads based on the predictor variables. Traffic load prediction in SAGIN, as in other network contexts, may require periodic model updates as new data become available. This iterative process ensures that the predictions remain accurate in a dynamic network environment.  
The specifics of the mathematical model and predictor variables should be tailored to the unique characteristics and objectives of a Satellite-Air-Ground Network. Factors like satellite positioning, link quality, mobility patterns, and ground station capacities are crucial variables to consider when building and training the prediction model for SAGIN.
## 4 Results
### MAHNOB Dataset  
The MAHNOB dataset was used to further evaluate the robustness of the proposed model. As shown in Figure 7, the proposed method exhibited a notable improvement in average accuracy using method 3.  
A comparison of the proposed method's performance with KNN and CNN on the MAHNOB dataset is shown in Table 3. The results reveal that the proposed method 3, achieved an average accuracy of 56% and 60% for valence and arousal, respectively. These results are comparable to or higher than those of state-of-the-art methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Proposed Methods**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{1}{c}{**CNN**} \\ \cline{2-6}  & **M1** & **M2** & **M3** & **K = 5** & **EEGNet** \\ \hline Valence & 54.94\% & 55.25\% & 56\% & 54.4\% & 55.34\% \\ Arousal & 52.16\% & 54.32\% & 58.64\% & 58\% & 56.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: DREAMER classification results.  
Figure 6: DREAMER Dataset classification results: (**a**) classification result comparison of the proposed methods. (**b**) Valence classification confusion matrix (method 3). (**c**) Arousal classification confusion matrix (method 3).  
The accuracy of the classification results for high valence and arousal is demonstrated in the confusion matrix presented in Figure 7a,b, with an accuracy of 61% and 62%, respectively. The confusion matrix of the proposed method 3 exhibited a distinct diagonal pattern, indicating that the model accurately identified the participant's emotional state for the arousal class. However, for the valence class, the model exhibited higher precision for high valence. The precision for low valence was relatively lower, as the model misclassified some instances of low valence as high valence. These results suggest that the model is better at identifying high-valence emotions and may require further improvement to distinguish low-valence emotions more accurately.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 4 Discussion
### Scattering Window Classification Performance  
The classification using scattering features showed high accuracy for every scenario. This result can be explained by wavelet scattering's ability to represent time and frequency domain features at different scales with each scattered window [52], and then to be fed to a classifier that can take large feature vectors to learn and generate the output classification. Moreover, the original signal segment classification based on the majority vote of the assigned class to each scattered window may inherit the high accuracy of scattered window classification. This method yields a better classifier performance with the drawback of having a tie in some cases as the algorithm would not be able to determine the final class. However, this can be avoided by selecting an odd number of scattered windows and using only two classes.
## 4 Implementation and Results  
### Validation  
The database of Taiwan's National Center for Research on Earthquake Engineering (NCREE) contains thousands of school building images in different sizes and resolutions taken in Taiwan by various devices. The authors selected images that were taken indoors and outdoors from the front view and the side view of buildings from the database of NCREE and implemented these images in MATLAB R2017a (The MathWorks, Natick, MA, USA) to validate the performance of the proposed method. Finally, the detection precision and recall were used respectively to assess the detection accuracy and detection efficiency of the proposed method. Part of the column and wall detection results are shown in Tables 1 and 2 respectively. Precision and recall are calculated by Equations (14) and (15), respectively:  
\[Precision=\frac{TP}{TP+FP}, \tag{14}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{15}\]  
where \(TP\) (true positive) is the number of target objects (i.e., columns or walls in this study) that are correctly detected; \(FP\) (false positive) is the number of detected objects that are not target objects; \(FN\)(false negative) is the number of target objects that are undetected; \((TP+FP)\) is the total number of objects that are detected; \((TP+FN)\) is the total number of target objects to be detected. The values of \(TP\), \(FP\), and \(FN\) are judged manually from detection results. High precision means that most detected elements are target elements, and high recall indicates that most target elements in images are correctly detected.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline \multirow{2}{*}{**Number of the Image**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{1}{p{28.5 pt}}{**Recall**} \\ \cline{5-8}  & & & & & **TP/(TP+FP)** & **TP/(TP+FN)** \\ \hline  
Total & 76 & 0 & 21 & 100.0\% & 78.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The column detection results for 20 column images.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline \multirow{2}{*}{**Number of the Image**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{1}{p{28.5 pt}}{**Recall**} \\ \cline{5-8}  & & & & **TP/(TP+FP)** & **TP/(TP+FN)** \\ \hline
2 & 2 & 0 & 1 & 100.0\% & 66.7\% \\
3 & 1 & 0 & 1 & 100.0\% & 50.0\% \\
4 & 2 & 0 & 0 & 100.0\% & 100.0\% \\
5 & 1 & 0 & 1 & 100.0\% & 50.0\% \\
6 & 1 & 0 & 0 & 100.
# Synchronizing Many Filesystems in Near Linear Time
## 3 Filesystem Synchronization
### Synchronization
The first replica deletes the file and all directories above it. The second replica creates a file below /a/b. The third replica creates the same file but with different content, and also creates a copy of that file below /a. The canonical sequences describing these changes are  
\(A_{1}=\{\sigma_{1},\sigma_{2},\sigma_{3}\}\), where \(\sigma_{1}=\langle\texttt{a}/\texttt{b}/\texttt{c},\texttt{f}_{\texttt{o}}, \texttt{O}\rangle\), \(\sigma_{2}=\langle\texttt{a}/\texttt{b},\texttt{D},\texttt{O}\rangle\), \(\sigma_{3}=\langle\texttt{a},\texttt{D},\texttt{O}\rangle\);  
\(A_{2}=\{\tau\}\), where \(\tau=\langle\texttt{a}/\texttt{b}/\texttt{z},\texttt{O},\texttt{f}_{\texttt{z}}\rangle\);  
\(A_{3}=\{\rho_{1},\rho_{2}\}\), where \(\rho_{1}=\langle\texttt{a}/\texttt{z},\texttt{O},\texttt{f}_{\texttt{u}}\rangle\), and \(\rho_{2}=\langle\texttt{a}/\texttt{b}/\texttt{z},\texttt{O},\texttt{f}_{\texttt{ u}}\rangle\). It is clear that \(\sigma_{3}\) is in conflict with all commands in \(A_{2}\) and \(A_{3}\); \(\sigma_{2}\) is in conflict with \(\tau\) and \(\rho_{2}\). Commands \(\tau\) and \(\rho_{2}\) are also in conflict; and a merger containing \(\sigma_{3}\) must also contain \(\sigma_{2}\). \(\sigma_{1}\) is compatible with all other commands, and thus it will be in each possible mergers. If \(\sigma_{3}\) is in the merger, then it can contain no commands from either \(A_{2}\) or \(A_{3}\). If \(\sigma_{2}\) is not present, then only the conflict \(\tau\) vs. \(\rho_{2}\) remains. Thus, there are four mergers, namely  
\[M_{1}=\{\sigma_{1},\sigma_{2},\sigma_{3}\},\ \ \ M_{2}=\{\sigma_{1},\sigma_{2}, \rho_{1}\},\ \ \ M_{3}=\{\sigma_{1},\tau,\rho_{1}\},\ \ \text{and}\ \ \ M_{4}=\{\sigma_{1},\rho_{1},\rho_{2}\}.\]  
Each merger describes a possible synchronized state, and each one can be the desired one under the right circumstances.
## 6 Conclusions  
In this paper, we demonstrate that a UGS is a low-rank feature. This implies that the accurate determination of whether a location is UGS can be achieved with just one or a few pixels around it, and the segmentation accuracy is not highly dependent on the depth of the model. Excessive down-sampling, which means a large receptive field, does not contribute significantly to accuracy. In contrast, preserving more texture and spectral features aids in enhancing the model's accuracy. Therefore, we propose a novel UGS segmentation network, named GSRPnet, which, with a small number of parameters, improves the accuracy of UGS segmentation. The feature extraction backbone network used in GSRPnet, i.e., RPS-ResNet, is an enhancement of ResNet-50. Particularly, it replaces the original down-sampling convolutional layers and max-pooling layers with the reverse Pixel Shuffle method, transforming relationships between adjacent pixels into relationships between layers. This minimizes the loss of UGS feature information caused by the down-sampling process. Experimental results on GaoFen-2 remote sensing imagery show that, with a parameter count of only 17.999 M, GSRPnet outperforms U-net, PSPnet, Segnet, DeepLab V3+, and SGCN-Net in terms of precision, the F1-score, IoU, and OA. This strongly validates the correctness of our proposed approach.  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] provided the original remote sensing data. [PERSON] and [PERSON] made the dataset and wrote this paper. [PERSON] and [PERSON] revised this paper and gave some appropriate suggestions. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China (2022 YFF0711600) and the Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province (Grant No. BE2022612).  
**Data Availability Statement:** The GaoFen-2 satellite data and the WHDLD data used in this study can be accessed from [24; 26], respectively.  
**Acknowledgments:** The authors acknowledge the data support from Yangtze River Delta Science Data Center, National Earth System Science Data Center, and the National Science & Technology Infrastructure of China ([[http://geodata.nnu.edu.cn/](http://geodata.nnu.edu.cn/)]([http://geodata.nnu.edu.cn/](http://geodata.nnu.edu.cn/)), accessed on 30 September 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
## 2 Monitoring Methods Used in Valleebre and Sample Results
### In-Hole (or Geotechnical) Instrumentation  
Between July 1996 and March 1997, 14 boreholes were drilled in the slope for reconnaissance; 3 more were drilled in 2005 (Figure 10). Some of them were equipped with inclinometers, wire extensometers and open standpipe piezometers. Inclinometers and piezometers were standard devices. As an example, Figure 11 shows one inclinometer head and its typical record, where the slip surface is clearly marked. Measurements were  
Figure 9: Examples of results of the GPS monitoring (**a**) planimetric displacements from the initial position in the period 1995–1998 (**b**) planimetric movement of one point during the yearly campaigns.  
made every 2-3 weeks until the casing deformation prevented the safe and proper sliding of the probe.  
On the other hand, the extensometers were wire-type, specially built following a design described in [20]. They consist of a protected steel wire anchored to the bedrock, below the slip surface, inside a piezometric pipe (Figure 12). After some computation, the wire displacement at the pulley can be related with the horizontal displacement of the landslide (Figure 13). We can quote two major advantages of this device. Firstly, with a potentiometer, it allows the continuous recording of the displacement, particularly necessary to collect information during the concentrated rainfall periods, characteristic of the Mediterranean climates (Figure 14). Secondly, the wire extensometer works properly with big landslide displacements, much larger than the 20-30 cm that would typically break the inclinometric pipe; in fact, in 2012, 16 years after their installation, 3 wire extensometers were still working, with a cumulated total wire displacement of up to 6 m. Full details of the wire extensometer can be found in [4].  
Figure 11: **Left**: view of the head of a borehole during the measurement with the inclinometer. **Right**: graph with the successive displacement profiles from August 1996 to December 1997. The main slip surface appeared to be 34 m deep. The pretty constant deformation profile corresponds with a translational slide.  
Figure 10: NE-SW geological cross-section of the Vallcebre landslide according to [1,2]. Some of the 17 boreholes drilled are shown, which were equipped with inclinometers, wire extensometers (for shear displacement) and vibrating wire piezometers.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 4 Discussion  
According to the results, the PPP-B2b orbit and clock offset are in good agreement with the GBM product. Compared with the CNES orbit, the PPP-B2b orbital accuracy is poor, which may be caused by the regional monitoring stations of PPP-B2b. For PPP-B2b, the application of ISL makes the orbit and clock accuracy of BDS-3 higher than GPS. In addition, according to the results, PPP-B2b DCB and CAS DCB also have good consistency, while B1I-B3I DCB has a relatively large deviation. Thus, the accuracy of PPP-B2b DCB needs to be further improved.  
The B2b-RTPPP time transfer accuracy has a strong correlation with the baseline length. The longer the baseline length, the worse the time transfer accuracy. This is mainly related to the satellite orbit errors and clock errors observed by the two stations. With a short baseline, more common satellites can be observed, and the effects of orbit errors and clock errors on time transfer can be eliminated. For long baselines, these errors cannot be eliminated.  
DCB has a great influence on time transfer. After DCB is corrected, the time transfer accuracy of B2b-RTPPP is improved significantly. DCB has a certain influence on frequency stability: the pseudorange residuals of the uncorrected DCB are greater causing the receiver to absorb part of the error, which in turn leads to a decrease in frequency stability.  
The cut-off elevation angle also has a great influence on the time transfer. At a large cut-off elevation angle, the time transfer performance of B2b-RTPPP will be reduced. When the angle reaches 40\({}^{\circ}\), the B2b-RTPPP time transfer is not continuous, and the B2b-RTPPP time transfer is not discontinued, and frequent interruptions and reconvergence occur due to the sharp decrease in visible satellites.
###### Abstract  
This study investigates the development of pore-filling anion-exchange membranes (PFAEMs) for water-electrolysis applications. Ionomers using two different cross-linking monomers, namely hydrophilic C10 and hydrophobic C11, along with a common electrolyte monomer, E3, were compared in terms of through-plane ion conductivity, hydrogen permeability, mechanical and chemical stability, I-V polarization, and water-electrolysis durability. The results revealed that the E3-C10 PFAEM exhibited 40% higher OH\({}^{-}\) conductivity (98.7 \(\pm\) 7.0 mS cm\({}^{-1}\)) than the E3-C11 PFAEM with a similar ion-exchange capacity. This improvement was attributed to improved separation of hydrophobic and hydrophilic domains, creating well-connected ion channels by the hydrophilic C10. Alkaline stability tests demonstrated that the E3-C10 retained higher ion conductivity compared to E3-C11, due to the absence of ether linkages and increased resistance to nucleophilic attack. During water-electrolysis operations, the E3-C10 PFAEMs showed 10% better durability and 87% lower hydrogen permeability, confirming their suitability for anion-exchange-membrane water electrolysis (AEMWE). Despite the higher ion conductivity of the E3-C10 PFAEM, performance was limited by interfacial resistance. It is suggested that ionomer-coated electrodes could further enhance AEMWE performance by leveraging the higher ion conductivity of the E3-C10. Overall, this study provides valuable guidance on strategies for utilizing pore-filling membranes in water electrolysis.  
water electrolysis; anion-exchange membrane; pore-filling membrane; performance; durability +
Footnote †: journal: Applied Sciences  
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