esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 5 Discussion and Conclusions  
In this study, a new landslide displacement prediction method was proposed by combining the VMD, GA, and Elman models. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as a case study, the trend, periodic, and random term components of the cumulative landslide displacement were obtained separately using VMD. The GA-Elman model was used to conduct an accurate prediction of landslide displacement, and the optimal combination model under different training sets was obtained. At the same time, the applicability of the model was verified in comparison with the basic Elman and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **MAPE (\%)** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline Elman & 372.55 & 98.6357 & 9.3709 & 0.3633 \\ GA–Elman & 153.04 & 23.1281 & 3.8904 & 0.8507 \\ VMD–GA–Elman (Combined model) & 0.3493 & 1.1635 & 0.9001 & 0.9895 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the prediction accuracy of different prediction models.  
Figure 13: The cumulative displacement prediction results of the validation set.  
GA-Elman models. However, due to the classification of different training sets, some training sets exhibited an overfitting phenomenon in the process of prediction. Therefore, in the future landslide displacement prediction, the type of training set and the influencing factors of landslides should be selected reasonably to avoid the overfitting phenomenon and obtain the best landslide displacement prediction effect.  
In this paper, VMD was used to achieve the effective decomposition of landslide displacements, solving the modal mixing problem of traditional empirical modal decomposition. The Elman neural network was optimized using GA, which effectively solved the problem posed by the difficulty of determining the weights, thresholds, and neurons of the Elman neural network; moreover, it effectively improved the model's prediction accuracy.  
This study accounted for the internal and external factors that influence landslide deformation, such as past cumulative displacement, precipitation, and the reservoir water level. The changes in monitoring data were analyzed in detail, in conjunction with previous research, and four influencing factors were ultimately identified. The gray correlation among these four influencing factors and the displacement of the fluctuating term was greater than 0.5, indicating that the influencing factors were selected effectively.  
The prediction results showed that the model had high prediction accuracy and prediction capabilities with the effective acquisition of early monitoring data of landslides. This study, therefore, provides a new basis for predictions in the study of similar landslides.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 4 Proposed MS-FL Framework
### Intra-Server Training Phase
#### 4.2.2 Vehicles' Models' Aggregation at Server  
For simplicity, we assume that the Federated Averaging (FedAvg) [5, 6] algorithm is employed at each server to efficiently merge local updates from vehicles; however, other client aggregation approaches would work as well. Once local training is finished, each client \(\mathrm{i}\in\mathrm{K}\) sends its updated model \(\mathrm{w_{i}(t+1)}\) to its associated server. The server aggregates these updates through FedAvg, a weighted average that factors in each client's proportion of the total training data \(\left(\frac{\mathrm{n}}{\mathrm{n}}\right)\)[5]. This process produces an updated server model \(\mathrm{w^{(s)}(t+1)}\), which is then distributed back to the clients for the next training round.  
\[\mathrm{w^{(s)}(t+1)}\ \leftarrow\ \sum_{\mathrm{i}=1}^{\mathrm{K}}\frac{ \mathrm{n_{i}}}{\mathrm{n}}\mathrm{w_{i}(t+1)}. \tag{12}\]  
In the proposed MS-FL, each server collects both server model updates from its peer servers, and local model updates from vehicles within its coverage area, and uses these updates to refine models. A key element of this process is a model aggregation algorithm that consolidates parameters from different servers to form a comprehensive server model.  
In the intra-server training phase, each server enforces a round deadline \(\tau\). At \((t+\tau)\), it aggregates whatever subset of updates has been received; late arrivals are discarded for that round.  
During the inter-server phase, models forwarded by server \(\mathrm{s^{\prime}}\) arrive at server \(\mathrm{s}\) after a deterministic backhaul delay \(\delta_{\mathrm{s}\longrightarrow\mathrm{s^{\prime}}}\ll\tau\). If \(\delta_{\mathrm{s}\longrightarrow\mathrm{s^{\prime}}}>\tau\), that update is applied in the next round.
## 2 Study Area and Datasets
### Satellite Imagery  
The (model predictor) image used for this study was acquired by the RapidEye satellite constellation. RapidEye imagery was selected because it was the most suitable in terms of spatial and spectral resolution and also time of image capture. Figure 2 displays the RGB RapidEye image of the study area, characterized by calm sea conditions and a cloud free atmosphere (2% cloud-cover).  
Figure 1: Study Area—Dublin Bay, East coast of Ireland. The Liffey channel is clearly centrally located.
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 5 Conclusions and Future Work  
This paper investigates network connectivity issues against the mobility of nodes in beyond 5G D2D network, which is receiving increasing interest for supporting future-driven smart applications such as smart factory, the 5G based social group networking, and so on. The fast connectivity construction framework is proposed for time-sensitive smart applications to achieve LLC properties. The proposed framework is composed  
Figure 5: (**a**) Total Delivery times. (**b**) Packet Delivery Ratio.  
Figure 6: (**a**) Number of Delivered Packets. (**b**) Total Delivery Times.  
of two phases: the training phase and connecting phase. In the training phase, data about D2D network context are acquired and trained based on on-node deep learning, named LMK. To calculate the networking path, the mobile device uses the LMK in the connecting phase. Based on the performance analysis, the proposed D2D network scheme can maintain its networking connectivity over the best MAC layer link at 11.3 s faster than general connectivity construction. In addition, we demonstrate that the proposed scheme can transmit data faster than the general connectivity scheme while maintaining 100% of the packet delivery ratio. To improve performance metrics on latency suffering from the handover in D2D networks, the hierarchical deep learning architecture based on mobile edge computing and cloud computing are taken into account as future work. Furthermore, the battery power consumption of mobile devices has increased with a lot of data in the 5G network system. In future works, we will consider the battery power consumption of mobile devices.  
Conceptualization, S.P. and S.S.; methodology, S.-H.L. and T.-S.K.; experiments and simulation, S.S. and S.P.; writing S.P. and S.-H.L.; supervision, S.P. and T.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Controller Design
### Model Predictive Controller
\begin{array}{cccc}\textbf{H}_{m}\boldsymbol{\Gamma}_{m}&0 &\cdots&0\\ \textbf{H}_{m}\boldsymbol{\Phi}_{m}\boldsymbol{\Gamma}_{m}&\textbf{H}_{m} \boldsymbol{\Gamma}_{m}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ \textbf{H}_{m}\boldsymbol{\Phi}_{m}^{N_{p}-1}\boldsymbol{\Gamma}_{m}&\textbf{ H}_{m}\boldsymbol{\Phi}_{m}^{N_{p}-2}\boldsymbol{\Gamma}_{m}&\cdots&\textbf{H}_{m} \boldsymbol{\Phi}_{m}^{N_{p}-N_{c}}\boldsymbol{\Gamma}_{m}\end{array} \right] \tag{22}\]  
The optimizer calculates the optimal \(\textbf{U}_{p}\) to minimize the cost function \(l_{p}\) as shown below. \[ \begin{array}{c}-\\ \textbf{y}_{p}=\left(\textbf{R}_{s}-\textbf{Y}_{p}\right)^{T}\textbf{Q}\left( \textbf{R}_{s}-\textbf{Y}_{p}\right)+\textbf{U}_{p}^{T}\textbf{R}\textbf{U}_{p} \end{array}  \tag{23}\]  
where \(\textbf{R}_{s}\) is the reference input in the prediction horizon; **Q** and **R** are weighting matrices. They are defined as follows. \[\textbf{R}_{s}=\left[\textbf{r}_{r,k,i+1}^{T}\ \ \ \textbf{r}_{r,k,i+2}^{T}\ \ \ \cdots\ \ \textbf{r}_{r,k+N_{p}}^{T}\right]^{T} \tag{24}\]  
\[\textbf{r}_{r,k,i+1}=\left[y_{r,k,i+1}\ \ \ \psi_{r,k,i+1}\right]^{T} \tag{25}\]  
\[\textbf{Q}=\left[ \begin{array}{cccc}Q_{1}&0&\cdots&0&0\\ 0&Q_{2}&\cdots&0&0\\ \vdots&\ddots&\vdots\\ 0&0&\cdots&Q_{1}&0\\ 0&0&0&Q_{2}\end{array} \right]_{2N_{p}\times 2N_{p}} \tag{26}\]\[\mathbf{R}= \begin{bmatrix}R_{1}&0&\dots&0&0\\ 0&R_{2}&0&0\\ \vdots&\ddots&\vdots\\ 0&0&\dots&R_{1}&0\\ 0&0&0&R_{2}\end{bmatrix} _{2N_{c}\times 2N_{c}} \tag{27}\]  
where \(y_{r}\) is the desired lateral displacement; \(\psi_{r}\) is the desired heading angle; \(Q_{1}\) is the weighting of lateral displacement error; \(Q_{2}\) is the weighting of heading angle error; \(R_{1}\) is the weighting of the steering angle; \(R_{2}\) is the weighting of the DYM. These weightings are designed using [PERSON]'s rule [43] as shown below. \[Q_{1}=\frac{1}{y_{e,max}^{2}};\ Q_{2}=\frac{1}{\psi_{e,max}^{2}} \tag{28}\]  
\[R_{1}=\frac{1}{\delta_{f,max}^{2}};\ R_{2}=\frac{1}{M_{z,max}^{2}} \tag{29}\]  
where \(y_{e,max}\) is the maximum allowable lateral displacement error; \(\psi_{e,max}\) is the maximum allowable heading angle error; \(\delta_{f,max}\) is the maximum allowable steering angle; \(M_{z,max}\) is the maximum allowable DYM. The optimal \(\mathbf{U}_{p}\) can be obtained as follows. \[\mathbf{U}_{p}=\left(\mathbf{G}^{T}\mathbf{Q}\mathbf{G}+\mathbf{R}\right)^{-1 }\mathbf{G}^{T}\mathbf{Q}(\mathbf{R}_{s}-\mathbf{F}\mathbf{x}_{k,i}) \tag{30}\]  
The current optimal control input \(\mathbf{u}_{k,i}\) is equal to the vector that consists of the first and second components of \(\mathbf{U}_{p}\) as follows. \[\mathbf{u}_{k,i}=\left[\mathbf{U}_{p}(1)\quad\mathbf{U}_{p}(2)\right]^{T} \tag{31}\]
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 4 Discussion
### The Advantages of DTW  
Selection of the appropriate classifier is very important in any image classification scenario [75]. The workflow described here used the DTW method due to its already demonstrated advantages to identifying classes of interest from SITS [11; 18; 26; 76]. Firstly, DTW can deal with temporal variations of phenological patterns caused by differences in the agricultural practices or by different weather conditions. This is a very important asset when vegetation dynamics, i.e., phenology, are used for the classification purpose [31]. Secondly, DTW is not very sensitive to the number of reference samples, as already proven by Belgium and Csillik [26], as long as their temporal pattern is characteristic for each target class. No training samples are needed if one performs clustering of temporal patterns into a predefined number of clusters and then use expert knowledge or other rules to assign meaningful crop classes. These are large advantages for mapping crops at larger extents or for countries with a lack of reference samples [77].
## 1 Introduction
### Power Limiting Control (PLC)  
The concept presented in the following is the basis for all the power curtailment methods. It is based on the imposition of a certain level of active power required from the plant (\(P_{\textit{res}}\)), that has to be reached by modifying the voltage applied to the PV panels (\(v_{PV}\)*), taking into account the power-voltage curve, as shown in Figure 1.  
It is evident that, given a certain \(P_{\textit{res}}\), it is fundamental to characterize the irradiance (\(G\)) and the temperature (\(T\)) in order to establish the shape of the power curve. In this sense,  
Figure 1: Power-voltage curve of a PV module for two different ambient conditions, showing the maximum power point (MPP) and the possible reference operating points for a 20% reserve.  
this raises another problem: the choice of the method to obtain the power curve. Different solutions are available, for example, the direct measurement of \(G\) and \(T\), a non-linear least-square curve fitting or using solar forecasting methods. The choice represents a trade-off between cost and accuracy, due to the fact that the most accurate methods are also the most expensive to be implemented. Once the actual current-voltage curve is determined, all the inputs required for the algorithm are available:  
* If \(P_{res}\geq P_{MPP}\rightarrow\) MPP operation: the PV system will operate at its maximum power point, so \(v_{PV}\star=v_{MPP}\) and \(P_{PV}=P_{MPP}\).
* If \(P_{res}<P_{MPP}\rightarrow\) curtailed operation: the PV system will operate at a voltage \(v_{PV}\star=v\) and \(P_{PV}=P_{res}\).  
It can be noticed that, in the curtailed case, two voltage levels are possible for the same \(P_{res}\), respectively, on the left and on the right of the MPP: a comparison between both options is provided in the following.
## 3 Experiments and Results  
In this section, we apply our calibration method to a real robot and sensor and present the results. The mobile manipulator used is the Moby manufactured by Neuromeka (Seoul, Republic of Korea), as shown in Figure 1, which consists of a 7-DOF manipulator and a mobile 4 WIS-4 WID platform. The FT sensor used is the AFT200 manufactured by AIDIN ROBOTICS (Anyang, Republic of Korea), with detailed specifications provided in Table 1. The end tool used is the Robotiq's 3-Finger Adaptive Gripper (Levis, QC, Canada).  
The data acquisition postures for calibration are shown in Figure 3. In our case, the CoM of the end tool is the furthest along the \(z\)-axis in the \(\{f\}\) coordinate system, resulting in the greatest torque generation. Therefore, data were collected from the 10 postures that produced the highest torque. Depending on the shape of the end tool, users may choose different postures for calibration.  
To assess the accuracy of the base incline estimation, we first adjust the inclination of the mobile platform as shown in Figure 4 and compare the results. The estimated base incline angles are then compared with the geometric analysis, and the results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Value** & **Unit** \\ \hline Type & capacitance & - \\ Nominal force range & 200 & N \\ Nominal torque range & 15 & Nm \\ Limit force (\(f_{xyz}\)) & 300 & N \\ Limit torque (\(\tau_{xyz}\)) & 25 & Nm \\ Resolution (\(f_{xyz}\)) & 0.15 & N \\ Resolution (\(\tau_{xyz}\)) & 0.015 & Nm \\ Maximum sample rate & 1000 & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of AFT200 FT sensor from ADIN ROBOTICS.  
To analyze the impact of each calibration parameter, we define the calibration levels for the following experiments as follows: cases where only bias calibration is performed are labeled as _No Calibration_; cases where CoM and crosstalk are calibrated using Equations (7) and (8) with the LSM are labeled as _LSM Calibration_; and cases where floor incline calibration using Equation (10) is additionally performed using Equation (3) are labeled as _Full Calibration_.
# Parameter Optimization Method for Metal Surface pBRDF Model Based on Improved Strawberry Algorithm
## 4 Simulation and Experiment
### Experimental Verification  
In order to further validate the performance of the proposed method for parameters retrieval of the metal pBRDF model, several experiments were conducted in a dark room and the results by the proposed method were compared with a nonlinear least squares algorithm (LM), particle swarm optimization algorithm (PSO) and original strawberry algorithm (SBA).  
A standard 45# steel gauge block with a roughness of 0.67 \(\upmu\)m was selected and irradiated with incoming light at a wavelength of 546 nm at the incident angles of 30\({}^{\circ}\), 40\({}^{\circ}\) and 50\({}^{\circ}\). The outgoing light from the block was detected at different observing angles ranging from 20\({}^{\circ}\) to 65\({}^{\circ}\) by a step of 5\({}^{\circ}\) with an SALSA liquid crystal polarization camera and the polarization reflection images of the target were obtained. Then the DOP values of the reflected beam were calculated, which was repeated 10 times and the average was selected as the final measured value. The schematic diagram of the experimental setup is shown in Figure 10.  
The retrieving pBRDF parameters for the 45# steel surface with three algorithms of PSO, LM, SBA and the proposed algorithm ISBA are compared and shown in Figure 11. The simulation accuracy of DOP is evaluated using the root mean square error (RMSE), and the results are shown in Table 2.  
From Figure 11 and Table 2, it can be seen that under different incident angles, the pBRDF model established by the proposed method agrees better with the experimental data with higher accuracy; the fitting accuracy of the proposed method in this paper is significantly higher than those by SBA, LM and PSO. It can be explained through the fact that the weakness of the original SBA's insufficient search ability at the early stage was overcome by introducing the strategy of chaotic initialization and the Levy flight, and the global search and local exploration capabilities were balanced, which can accelerate the convergence and improve the data fitting accuracy. From the retrieving process of the pBRDF parameters, it can be concluded that the ISBA algorithm is more efficient and accurate in solving the parameter optimization problem of the pBRDF model for metal surfaces.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Incident Angle** & **LM** & **PSO** & **SBA** & **ISBA** \\ \hline
30\({}^{\circ}\) & 0.86\% & 0.16\% & 0.23\% & 0.09\% \\ \hline
40\({}^{\circ}\) & 0.70\% & 0.39\% & 0.21\% & 0.02\% \\ \hline
50\({}^{\circ}\) & 0.26\% & 0.40\% & 0.23\% & 0.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE values by different methods with different incident light.  
Figure 10: Schematic diagram of the experimental setup.
# Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances +
## 3 Reliable Computing Environments
### Achieving Computational Reliability  
This paper proposes containers in Linux to construct reliable virtual computing environments. Container-based virtualization implements isolation of processes at the operating system level, thereby avoiding the overhead typically imposed by hypervisor-based virtualization [16; 33; 34]. Each container has its own process and network space. Linux containers are implemented primarily via cgroups and namespaces in recent Linux kernels. While the cgroups mechanism allows resource manipulation (e.g., limits and priorities) for groups of processes, namespaces provides a private, restricted view towards certain system resources within a container (i.e., a form of sandboxing) [35]. Several container-based solutions exist nowadays, such as LXC [36], Docker [16], OpenVZ, and CoreOs Rocket [34]. As containers on a host share the underlying operating system kernel, container migration is much more lightweight than a VM migration. Previous works have shown the effectiveness of said tiering approach (i.e., running containers within VM instances) in terms of performance [37], and as a first solution with which to implement fault-tolerance mechanism to run the applications on spot VMs [24]. Moreover, in [38; 39; 40] it is shown that migration of containers running various real applications is accomplished within a few tens of seconds at most. These results are confirmed by [PERSON] and [PERSON] in their work on handling VMs spot price fluctuations on EC2 [24]. More specifically, authors refer to the following key aspects regarding migration of containers: (1) the time to transfer a container's memory state and restore it as a function of its memory footprint; (2) memory-to-memory stop-and-copy transfers (i.e., copies the source container's memory state to the memory of the destination container without saving it to stable storage) are near linear in the amount of data transferred; (3) memory-to-memory transfer enables migrations of up to 32 GB in \(\sim\)30 s using EC2's 10 Gbps interfaces; (4) the time needed to transfer the container's disk and network is \(\sim\)28 s, in average. In turn, a couple of seconds is sufficient to deploy and boot up a container, which remains essentially constant with respect to the image size [41]. Since the application downtime is a function of the time to disconnect and reconnect the container's disk and network interfaces, and physically transfer the container's memory state, the average time to migrate a container ranges from 30 to 90 s [24], depending on the size of the memory state. Therefore, the time needed to complete the migration of a container is much less than the two-minute SI interruption notice.
# Educational Escape Rooms as an Active Learning Tool for Teaching Telecommunications Engineering
## 2 Methodology
### Escape Room 2
#### 2.2.1 Hamming  
Hamming codes are error correcting codes which allow single-bit errors in messages to be corrected based on redundancy encoded within the message. This puzzle presents students with a series of 7-bit binary strings (representing some even hamming(7,4) encoded data) and Table 2.  
Hamming(7,4) encodes 4 data bits (3, 5, 6 and 7) with 3 parity bits (1, 2, 4) to allow the receiver to work out which bit, if any, have been flipped, and hence correct the error. To solve the puzzle students need to compute the parity for all three parity bits and deduce which bit (1-7) is in error for each of the encoded strings. Hence, the string '1010111' would result in the answer 6 as D3 is in error.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 6 Numerical Results
### Convergence Study  
First, a convergence study is performed to determine the mesh size and the element degree needed to obtain the eigenvalues (i.e., natural frequencies) with good accuracy. In this work, square meshes are used for all the cases studied. In general, an element with polynomial degree \(p=4\) is used and, if the convergence is slow, the polynomial degree is increased to \(p=8\). In this study, the relative error between each mesh size is calculated as follows:  
\[\epsilon_{r}=\left|\frac{\omega^{i-1}-\omega^{i}}{\omega^{i}}\right|\times 100, \tag{33}\]  
where \(\epsilon_{r}\) is the relative error in percentage, \(\omega^{i-1}\) is the natural frequency obtained with the previous mesh, and \(\omega^{i}\) is the natural frequency calculated with the current mesh \(i\). This study is divided in two parts. The first one presents results for plates and the second one for shells.