esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contribution to Global Change Studies and Outlook  
This study extends results identified by previous studies. First, compared with studies that exclusively targeted monotonic trends [12, 13], the presented approach provides a more sophisticated insight in the way vegetation activity varied during the past decades. The accuracy of previous magnitude estimates is not debated, but the pathway to arrive at the considered magnitude appeared non-monotonic for 74% of the grid cells with trends. This, in combination with the increased turnover rate, implies that the complexity of biosphere alterations and responses to various external forces has increased. This should be addressed in more detail by studying randomness in the occurrence of trend shifts, since an increase would indicate accelerated changes in vegetation processes. Accounting for trend shifts, while analyzing GIMMS or equivalent time series, provides a better representation of multi-decadal biosphere dynamicsand the drivers of alteration herein. The main challenge is to incorporate these dynamics in models that provide physical links with human and climatic drivers. Many factors prevent this from being a straightforward exercise, including lag effects in the response of ecosystems, multi-actor systems with mutual influences and also the nature of broadband indices like NDVI, which aggregate a multitude of effects into a single metric. Second, the observed dynamics may provide a means to compare against modeled biosphere dynamics [3,9]. At global scale and long temporal scales, field validation of both types of products is not feasible, calling for careful interpretation. This previously resulted in a debate about the sensitivity of applied models to certain parameters, notably temperature [10,11]. The current study is based on observations and revealed actual dynamics recorded by satellites, which, with the benefit of hindsight, may serve to verify model simulations. The current study investigated type and timing of trend shifts; further work should investigate the associated changes in the rate of gradual change induced by these trend shifts.
## 4 Results and Discussion
### Detailed Mechanical Characterization of the 100 mm Diameter Membrane
#### 4.3.1 Measured Displacement Fields
Both moir methods correctly located the position (\(\mathrm{x_{w,max}}\);\(\mathrm{y_{w,max}}\)) of the membrane point \(\mathrm{P_{w,max}}\) with the maximum out-of-plane deformation. This point does not coincide with the center of the membrane: \(\mathrm{x_{w,max}}\) and \(\mathrm{y_{w,max}}\) coordinates are positive and almost equal to (\(\mathrm{x_{m}}+\mathrm{x_{M}}\))/2 and (\(\mathrm{y_{m}}+\mathrm{y_{M}}\))/2, respectively. This is consistent with the position indicated in Figure 8c for the maximum w-deformation point. Furthermore, the average difference between the w-displacements measured along the vertical and horizontal diameters of the specimen at each fixed distance from the pole of the membrane (this point corresponds to the origin of the reference system) is 2.795% (with a standard deviation of 0.181%) for the one-shot projection moirte technique and 1.355% (with a standard deviation of 0.0687%) for the two-projectors, one-camera IMPM technique. This confirms that the proposed one-shot projection moirte setup using cross-gratings can capture asymmetries in the membrane's displacement field in a more effective way than a double illumination projection moirte projecting only vertical lines. All displacement components increased slightly in magnitude passing from the left side (i.e., x \(<\) 0) to the right side (i.e., x \(>\) 0) of the horizontal diameter of the membrane and from the bottom side (i.e., y \(<\) 0) to the top side (i.e., y \(>\) 0) of the vertical diameter. Furthermore, measured displacements along the horizontal diameter control path were slightly smaller than their counterparts relative to vertical diameter. This is consistent with the in-plane tests results that indicated the membrane to be stiffer in the X-direction (see Section 4.2). Interestingly, both the one-shot projection moirte proposed here and the two-projectors, one-camera IMPM obtained smoother distributions for out-of-plane displacements than for in-plane displacements. Such a behavior occurred for at least two reasons: (i) projection moirte techniques are inherently sensitive to w-displacements; and (ii) out-of-plane dis  
Figure 10: Comparison of out-of-plane displacements obtained by the proposed one-shot projection moirte setup and the two-projectors, one-camera IMPM setup for the natural rubber membrane inflated at the maximum pressure of 2.13 kPa. placements undergone by the inflated membrane are much larger than the corresponding in-plane displacements.
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 4 Experiments and Results  
For evaluating the proposed method, we have compared the LSTM-based speech/music classification algorithm with the EVS method. The evaluation was implemented on the TIMIT speech database [16], Billboard year-end CDs from 1980 to 2013, and classical music CDs. From the music CDs, different genres (jazz, hip-hop, classic, blues, etc.) of music were collected. The entire music database was 221 h long and a large amount of data was employed in the experiment. For training the LSTM-based speech/music classifier, 60 h of speech signal and 160 h of music signal were randomly selected from database. The length of each speech segment ranged from 6 to 12 s, and the length of each music segment ranged from 3 to 12 min. To create noisy environments, we added babble, car, white, pink, factory1, and factory2 noises from the NOISEX-92 database to the clean speech data at 5, 10, 15, and 20 dB SNR. As initial parameters of the proposed LSTM, we have used the parameter setting listed in Table 1.  
For testing, we randomly chose 20 h of speech data and 61 h of music data, which were separated from the training data. The data were sampled at 16 kHz with a frame size of 20 ms. To calculate the accuracy of the algorithm, each frame was manually labelled and compared to the corresponding classification results of the classifier. For proper understanding of the performance difference, the results of the speech/music classification in conjunction with the test speech/music segment are shown in Figure 4. It can be observed from this figure that the proposed method has effectively classified speech and music, according to manual marking (silence = 0, speech = 1, music = 2), and has yielded better results in white noise (5 dB SNR) conditions when compared with the EVS based algorithm and RNN based algorithm. To appraise the performance of the proposed algorithm, the speech/music classifier accuracy of the algorithm was investigated. The results are shown in Table 2. The test results verified that the proposed LSTM based method effectively improves the performance of the EVS. In particular, in the case of pink and factory1 noise environments at 5 dB SNR, the accuracy of the proposed method is significantly improved when compared with that of the EVS-based method. In the comparison between RNN and LSTM, the performance of LSTM is shown to have better performance than the RNN in all conditions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of first hidden layer units & 200 \\ Number of second hidden layer units & 600 \\ Number of third hidden layer units & 200 \\ Learning rate & 0.00006 \\ Dropout rate & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter setting of the proposed LSTM.
# Directed Network Disassembly Method Based on Non-Backtracking Matrix
###### Abstract  
Network disassembly refers to the removal of the minimum set of nodes to split the network into disconnected sub-part to achieve effective control of the network. However, most of the existing work only focuses on the disassembly of undirected networks, and there are few studies on directed networks, because when the edges in the network are directed, the application of the existing methods will lead to a higher cost of disassembly. Aiming at fixing the problem, an effective edge module disassembly method based on a non-backtracking matrix is proposed. This method combines the edge module spectrum partition and directed network disassembly problem to find the minimum set of key points connecting different edge modules for removal. This method is applied to large-scale artificial and real networks to verify its effectiveness. Multiple experimental results show that the proposed method has great advantages in disassembly accuracy and computational efficiency.  
directed network dismantling; non-backtracking matrix; spectral partition; minimal dismantling set +
Footnote †: journal: 2022, 12, 12047. [[https://doi.org/10.3390/app122312047](https://doi.org/10.3390/app122312047)]([https://doi.org/10.3390/app122312047](https://doi.org/10.3390/app122312047))
## 2 Description of the Equipment, Facilities, and Data Collection

Table 1: Grouping sensors by wiring connection and nominal values of each variable. Figure 2: Current and improved information processes. Controlled and emergency stops are the two types of stoppages that an operator can perform. The latter is carried out in a shorter time and can cause the greater degradation of the compressor components. In addition, the training processes for control operators are extensive due to the absence of any technological support for decision-making. Every action requires collaboration with highly experienced operators until trainees attain the necessary expertise and confidence to accurately assess the situation, thereby minimizing the risk of a full system shutdown. The data presented in Figure 3 reflect fifteen-minute averages of both the outlet hydrogen pressure and the main motor's current consumption. As illustrated, five scheduled equipment shutdowns--associated with preventive maintenance--are evident, with the most prolonged interruption occurring during the summer months. The maintenance team focuses on inspecting and replacing components according to established work protocols and standards. If the increased deterioration of any component is observed during a planned maintenance intervention, adjacent components that may be affected are also inspected and repaired. However, there are currently no dedicated teams that systematically investigate the root causes of these issues following a defined methodology.
## 7 Conclusions  
In this paper, we have proposed a novel scheme to implement the fundamental concept of CF. We have proposed an integer forcing precoder to precode the signals, such that the signal obtained at the relay is naturally a linear combination of original signals. The IFP effectively reduces the channel into an AWGN channel. The proposed scheme can remove the self-noise, which arises in CF, due to the approximation of the channel by an integer, but it incurs the penalty of extra power usage at the transmitter. We have shown that this extra power used is in a finite range for most channels.  
Further, we have made an end-to-end analysis of the CF scheme by observing the performance of CF at the destination. The aim of the destination is to decode the original source signals from the linear combinations obtained from the relay. We have provided a two level decoding at the destination: firstly, the destination decodes the linear combinations from the noisy signal sent from the relay. Secondly, after obtaining \(L\) such linear combinations, the destination obtains the original source signals using the linear combinations.  
To analyze the performance of our proposed scheme, we have also derived the expression for probability of error of the proposed scheme. We have also compared our proposed scheme to the existent  
Figure 5: Comparison of achievable rates for various schemes for \(L=2\). The average value is taken over 10,000 different random channel realizations. The rates are measured in bits per channel use (bpcu).  
schemes using numerical simulations. To this end, we have obtained the performance of the proposed scheme at the relay, as well as at the destination, making an absolute end-to-end analysis. We have used [PERSON]'s theorem to derive a closed-form expression for probability of error for the case of cubic lattices. In addition, we have also demonstrated the outage formulation of the proposed scheme, which arises due to the use of extra power at the transmitters. Concluding the analysis, we study the performance of IFP in terms of achievable rates and show that the use of channel state information at transmitters to design IFP helps attain considerable gains in terms of rates over traditional relaying scheme, like decode and forward.  
CF is a natural transmission framework for upcoming vehicular networks. Achieving practically the theoretical limits of performance in such networks is a challenge, and CF is a promising approach to improve this performance. The precoder introduced in this work can be optimized according to specific scenarios, and such designs are required to be explored further.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 4 Model Realization and Result Analysis
### Experimental Parameter Optimization and Training Process
#### 4.1.2 Validation Process  
* Data partitioning: Stratified 60-10-30% split (training-validation-testing)
* Early stopping: Triggered after 5 epochs of non-improving validation loss
* Performance tracking: Validation metrics monitored every 30 iterations
## 2 Mobility Models in FANETs
### Path Planned Mobility Models  
In these path-planned models, a predetermined trajectory is generated ahead of time and stored into each UAV, forcing it to track without making random movements. The UAV could change random patterns or repeat the operation at the end of this predetermined trajectory.  
Figure 14: The trajectory of FANETs using ST models.  
Figure 15: The trajectory of FANETs using EGM models.
## 4 Empirical Analysis
### Comparison of Analysis Results Using Learning Variables and Observation Conditions  
We conducted four experiments to find the optimal combination of learning variables and observation conditions for PM image analysis. The data collection period for Experiments 1 and 2 was from 21 April 2021 to 19 July 2021, and that for Experiments 3 and 4 was from 21 April 2021 to 14 February 2022. The data collection time was from 8:00 to 22:00 only in Experiment 1, while it was from 8:00 to 18:00 for the other experiments. The datasets varying from 95,039 to 178,316 were constructed for each experiment to apply distributions per time and PM class. All conditions were controlled except for the key factors per the purpose of each experiment. The experiment with a deep learning model was also conducted based on ResNet, as in Section 4.2, but a single DL model per experiment was employed, and the hybrid DL-ML model had SVR and XGBoost applied differently.  
In Experiment 1, we compared the predictive power of the dataset for the entire time between 8.00 and 22.00 and the dataset for the daytime (8.00-18.00), excluding the nighttime between 18.00 and 22.00. As a result, the dataset for the daytime had an RMSE of 3.12 and MAPE of 38.67%, and the entire time (08.00-22.00) had an RMSE of 3.30 and MAPE of 41.02%. In other words, the daytime dataset analysis slightly improved the predictive power over the entire dataset analysis. In Experiment 2, we compared the predictive power of the addition status of solar incidence angle variables (i.e., solar azimuth and solar altitude values) to the input. As a result, the dataset with solar incidence angle added had an RMSE of 2.94 and MAPE of 36.21%, slightly higher than the existing dataset with an RMSE of 3.12 and MAPE of 38.67%. In Experiment 3, we compared the predictive power of status by adding a dataset with a distribution ratio per PM class to simulate real atmospheric conditions. The dataset with the distribution ratio per PM class had an RMSE of 4.18 and a MAPE of 25.19%, slightly more than the existing dataset with an RMSE of 4.42 and a MAPE of 27.43%. In Experiment 4, we compared the predictive power of the addition status of a dataset with the outlier removal algorithm. The dataset with the outlier removal algorithm had an RMSE of 3.61 and a MAPE of 19.74%, slightly higher than the existing dataset with an RMSE of 4.01 and a MAPE of 21.70%. Table 5 shows the results of the analysis by learning variable and observation condition.
## 2 Proposed Radio Link Model for Underground Mines
### Path Loss Model
For multi-mode propagation zones, the path loss is calculated as follows:  
\[L_{MM}=20\text{log}_{10}\left(\sqrt{10^{2\alpha_{H}d}+10^{2\alpha_{V}d}}\right) +20\text{log}\left(L_{roughness}\right)+20\text{log}(L_{obstacle}) \tag{6}\]  
where,  
\[\alpha_{H}=4.343\lambda^{2}\Bigg{(}\frac{\varepsilon_{r}(h)}{w^{3}\sqrt{ \varepsilon_{r}(h)-1}}+\frac{1}{h^{3}\sqrt{\varepsilon_{r}(v)-1}}\Bigg{)} \tag{7}\]  
\[\alpha_{V}=4.343\lambda^{2}\Bigg{(}\frac{1}{w^{3}\sqrt{\varepsilon_{r}(h)-1}} +\frac{\varepsilon_{r}(v)}{h^{3}\sqrt{\varepsilon_{r}(v)-1}}\Bigg{)} \tag{8}\]  
\(\varepsilon_{r}\)_(h)_ and \(\varepsilon_{r}\)(\(v\)) are considered to be 5-6 for salt mines [3]. The tilt angle loss is neglected in the multi-mode propagation zone due to the attenuation caused by higher-order Transverse Electric (TE) modes.
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
###### Abstract  
This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.  
System Wide Information Management; ant colony optimization algorithm; hardware performance quality index; load standard deviation function; load balancing +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 90; doi:10.3390/f11040090
# A Constrained Louvain Algorithm with a Novel Modularity
## 3 Materials and Methods
### A Novel Objective Function
\begin{array}{l}F2_{merge}=[\frac{n_{1}(n_{1}-1)p}{n_{1}(n_{1}-1)p+n_{1}n_{2 }p}]^{2}+[\frac{n_{2}(n_{2}-1)p}{n_{2}(n_{2}-1)p+n_{1}n_{2}p}]^{2}\\ =(\frac{n_{1}-1}{n_{1}+n_{2}-1})^{2}+(\frac{n_{2}-1}{n_{2}+n_{1}-1})^{2}\\ =1-\frac{2n_{1}n_{2}-1}{(n_{1}+n_{2}-1)^{2}}<F2_{single}\end{array}  \tag{10}\]  
Similarly, when a complete network is a single community, we have \(F2_{single}=1\). If the network is divided to any two communities which include \(n_{1}\) and \(n_{2}\) nodes, respectively, the \(F2\) is  
\[ \begin{array}{l}F2_{merge}=[\frac{n_{1}(n_{1}-1)}{n_{1}(n_{1}-1)+n_{1}n_{2}} ]^{2}+[\frac{n_{2}(n_{2}-1)}{n_{2}(n_{2}-1)+n_{1}n_{2}}]^{2}\\ =1-\frac{2n_{1}n_{2}-1}{(n_{1}+n_{2}-1)^{2}}<F2_{single}\end{array}  \tag{11}\]  
In a word, \(Q\) has a resolution limit problem; however, \(F2\) has not had this problem on the considered networks. The main reason is that \(Q\) is related to the number of links of the whole network. It is not scale-independent as a global index. The resolution limit of \(Q\) results from the comparison between the number of links of the interconnected communities and the total number of links of the whole network. However, \(F2\) is a local index, which is unrelated to the whole network. It is only related to the local structure. Therefore, \(F2\)  
Figure 1: Performances of different objective functions on several representative networks. (**a**) Several 3-cliques connected to a ring through single edges (Reprinted/adapted with permission from Ref. [36], Copyright (2007) National Academy of Sciences, U.S.A.). On this network, two candidate community structures are generated. One is to identify each \(q\)-clique as a single community. The other is to merge each pair of adjacent cliques into one community. The values of modularity functions \(Q\), \(M\) and \(F2\) for these two structures are listed as ‘single’ and ‘merge’ when the network contains 10 3-cliques in the inserted table. (**b**) A network consisting of two 6-cliques and two 3-cliques (reprinted/adapted with permission from Ref. [36], Copyright (2007) National Academy of Sciences, U.S.A.). ‘Single’ in the inserted table refers to identifying each clique as an individual community, while ‘merge’ refers to the integration of the two 3-cliques. (**c**) A well-connected network which is more compact than networks of Figure 1 in Ref. [40]. “single” splits the network into two communities, while “merge” identifies the whole network as one community. overcomes the resolution limit problem to certain extent. Moreover, \(F\) and \(M\) may detect unreasonable community partition. However, \(F2\) can provide a reasonable community partition. As a result, compared with the other objective functions, the modularity \(F2\) can identify the partition of communities correctly. It is better than the other three functions. Therefore, \(F2\) has many advantages for community detection.
## 4 Data Set Weighting
### gABIC Impact  
It is essential in the joint inversion to determine the relative weights between the different data sets and the constraint to properly balance the contributions of each of them. The difference with previous studies is the use of the generalized formula of the ABIC method ([31]) from Reference [29] (gABIC) to be able to take into account any type of data sets without need of specification and different type of constraints. The point of using gABIC is to obtain a precise value for the weight of each data set that is used for the estimation of the slip distribution. The main issue with this method is that it is computationally intensive. In order to reduce the processing time, the following strategy was followed. First each data set was run separately with the GPS as reference to estimate the relative weight of this data set in relation to the GPS. Then, when a first estimation was made, we could run all data sets together by restricting the search intervals.  
The first row of Figure 9 presents the slip distribution obtained from GPS and the three interferograms where first all the weights were set to 1 and secondly the weights were estimated by gABIC. The second row is presenting the slip distribution obtained from all the data sets used jointly following the same steps: first the weights were set to 1, then weights were estimated by gABIC.  
Figure 8: Slip distribution model for 4 sub-faults geometry for the Chelungpu fault (scale bars in m). Inversion (Correlation coefficient, RMS (m)) based on (**A**) GPS only (0.96, 0.39); (**B**) GPS, benchmarks and levelling (0.57, 3); (**C**) One interferogram (15 July 1999) (0.90, 0.03); (**D**) SPOT offset only (0.91, 0.90). The colour scale saturates at 12 m. \(M_{0}\) is the total seismic moment for each model.  
After the estimation of the relative weights by gABIC, we can see that the slip distributions are smoother, with a maximum of slip at the curve of the fault of about 12 m. No unexpected slip is present on the edge of the fault. The weights for the benchmarks and levelling is low because the spatial extent of the data sets is small in comparison with the other data sets even though their precision is better.