esa-sceva/satcom-chunk-collection huggingface dataset:
### Review on Groundwater Level Prediction Using a Deep Learning Technique  
In recent studies, deep learning (DL) techniques have shown an extensive prospect in the prediction field by refining the predictive parameters that have the ability to assess  
Figure 1: State–wise groundwater level fluctuation (2010–2020).  
enormous amounts of data. [PERSON] et al. [11] compared the performance of ML with DL techniques and found that long short-term memory (LSTM) produced better predictions for groundwater recharge. [PERSON] et al. [12] studied the existence of potential groundwater levels contrary to the high demand for renewable groundwater levels and presented better validation accuracy. [PERSON] et al. [13] partitioned the available dataset into pre-monsoon, post-monsoon, and combined annual datasets for measuring the groundwater level with a seasonal auto-regressive integrated moving average (SARIMA) model with LSTM. [PERSON] et al. [14] proposed a data-driven model for groundwater level prediction with practical significance using ARIMA and LSTM. [PERSON] et al. [15] developed a DL framework that is tuned by a genetic algorithm to produce a generalized prediction over daily pricing. [PERSON] et al. [16] designed a non-parametric framework to estimate the statistical significance for classification by combining an auto-encoder with a support-vector machine (SVM).  
Even though deep learning has the ability to process huge data, its prediction depends on various influential parameters, which may affect the prediction accuracy of using a particular classifier. Additionally, it suffers from the limitation of high computation cost with data inconsistency, leading us to take steps for the data acquisition process. Furthermore, overfitting is one of the significant issues to be taken care of with a better algorithm selection process with an efficient hyperparameter tuning process. Thus, combining more than one classifier may improve the prediction process and pave the way for the ensemble learning mechanism.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 5 Evaluation  
We conduct testbed experiments to study the performance of our ECDedup. The main results of our evaluation are summarized as follows:  
* ECDedup achieves the decentralized server-aided encryption with a better encryption (including the key/matrix generation and encoding) performance than the traditional decentralized server-aided encryption schemes.
* ECDedup can directly index the ciphertext chunk during deduplication on the cloud server, which is not achievable for the traditional decentralized server-aided encryption schemes, so that it can reduce the indexing time.
* ECDedup improves the upload and download performance and can speed up the whole encrypted deduplication storage system.
## 3 Materials and Methods
### Voice Communication  
The basic function of collaboration is the two-way transmission of voice, with each application using voice as the basis for communication. For the large-scale organization of complex integrated test environments, it is first necessary to command the test personnel of the whole environment at the control end as a means of improving the global control capability. In the case of multi-task, multi-team work in the parallel and irregular working area, a multi-node voice communication system is needed to ensure that any member of any team can join the communication group of this team at any location. Additionally, a multi-node system that is designed based on data distribution service ensures that communication between groups does not interfere with each other. A Mirror Networking framework is used for voice transmission, and the call quality can be improved by echo cancellation and noise suppression technology. The communication module that supports multiplayer real-time voice on multi-terminal devices (such as MR headset Microsoft HoloLens2 and VR headset HTC VIVE) is deployed, which can realize multi-platform real-time voice communication.  
The Mirror Networking framework consists of a series of layers that add functionality. This networking framework has message handlers, generic high-performance serialization, distributed object management, state synchronization, and network classes: server, client, connection, etc. The network architecture diagram is shown in Figure 4.  
Figure 3: Collaborative data transfer process based on restriction program permissions.  
The relationship between the server, local client, and client is shown in Figure 5. Note that the client also acts as the host, which means that the client himself is the \"local client\". The local client is also connected to the host server, with both running on the same computer. The other two remote clients, each located at different terminals, can connect to the host server at the same time in order to achieve multi-platform voice communication to the terminals.
## 2 Study Area and Data  
### Study Area  
The TP, which covers an area of about 2.5 \(\times\) 10\({}^{6}\) km\({}^{2}\), is located in southwestern China (Figure 1). Our study focuses on the area within China (73\({}^{\circ}\)-105\({}^{\circ}\)E, 25\({}^{\circ}\)-40\({}^{\circ}\)N), which encompasses six provinces, namely the entire Tibet Autonomous Region and the Qinghai Province, and parts of the Xin Jiang Uygur Autonomous Region, Gansu, Sichuan, and Yunnan Provinces. In the TP, topography is extremely complicated, but it has an overall descending tendency from west to east. Specially, the highest mountain in the world, Mount Everest, stands on the southern fringe of the TP. Due to its unique geographical location and high elevation, the TP has great influence on regional and even global energy and water cycles through thermal and dynamical forcings [43]. Climate on the TP is influenced by multiple climatic systems, which are characterized by the Indian monsoon and East Asian monsoon in the summer and Westerlies in the winter. The monsoon-dominated rainfall varies greatly in space with an obvious southeast to northwest gradient, and annual precipitation ranges from over 1500 mm to less than 100 mm [44]. In the summer, the southeast monsoon produces heavy precipitation, specifically in southeastern Tibet. In the winter, the snow brought by westerly winds is the main precipitation form in western regions [45]. The TP has an overall low annual mean temperature below 0 \({}^{\circ}\)C, with the warmest month in July. Overall, the annual mean temperature decreases from east to west across the TP. Correspondingly, vegetation types also exhibit a spatial transition from forests in the southeast, to temperate shrubland or meadow in the middle, to desert land in the northwest regions [46]. Many of Asia's major rivers, such as the Indus, Yangtze, Yellow, Brahmaputra, Mekong, and Salween rivers, all originate from the TP. In this study, the headwater region of the Yellow River, with the outlet of the Tangnaihai hydrological station, was selected for hydrological evaluation of the satellite precipitation products.
## 2 Background and Related Work
### Nonlinear Optimization
Since the sufficient decrease condition is not enough to rule out unacceptably short step lengths, we normally can use the curvature condition, defined by Equation (3) for \(0<c_{1}<c_{2}<1\). Both the sufficient decrease condition and curvature condition are collectively known as the Wolfe conditions or the weak-Wolfe conditions [31]. In addition, we can modify the curvature condition to find a broad neighborhood of a local minimizer or stationary point of \(\phi(\cdot)\), as described in Equation (4). The conditions satisfying both Equations (2) and (4) are called the strong-Wolfe conditions. \[\
abla f(x_{k}+\alpha_{k}d_{k})^{T}d_{k}\geq c_{2}\
abla f_{k}^{T}d_{k} \tag{3}\]  
\[|\
abla f(x_{k}+\alpha_{k}d_{k})^{T}d_{k}|\geq c_{2}|\
abla f_{k}^{T}d_{k}| \tag{4}\]  
There are various algorithms for line search. Armijo line search (Armijo) [32], bisection method for weak-Wolfe conditions (BWW), and More-Thuente line search (MT) [33] find a step length that satisfies the sufficient decrease, weak-Wolfe, and strong-Wolfe conditions, respectively. All three methods use iterative steps to find the appropriate step length. BWW and MT need to recalculate both the objective function \(f\) and its gradient \(g\) for every iteration. However, Armijo requires only a new calculation of the objective function \(f\).
## 4 Methodology
### 8-Parameter Transformation  
In the previous section, where the 7-parameter transformation was defined, the same scale factor for all axes was assumed. However, in the case of the 5-parameter transformation, it has been demonstrated that the theoretical and realized ellipsoid parameters are not identical, which is significant because measured ellipsoidal coordinates are converted to Cartesian coordinates using ellipsoid parameters to perform the calculation of transformation parameters. Of course, it is not possible to define by how much they differ or whether the semi-major and semi-minor axes are proportionally different. For this reason, it makes sense to use a transformation that will take into account the error in the semi-major axis with one scale factor and the error in the semi-minor axis with another scale factor, as is the case in the 8-parameter transformation (Figure 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Standard Deviation** \\ \hline \(t_{x}\) (m) & 546.509 & 0.350 \\ \(t_{y}\) (m) & 162.269 & 0.387 \\ \(t_{z}\) (m) & 469.395 & 0.346 \\ \(\mu\) (ppm) & \(-4.418\) & 0.044 \\ \(r_{x}\) (\({}^{\prime\prime}\)) & \(-5.906\) & 0.011 \\ \(r_{y}\) (\({}^{\prime\prime}\)) & \(-2.075\) & 0.013 \\ \(r_{z}\) (\({}^{\prime\prime}\)) & 11.507 & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of 7-parameter transformation from HDKS to HTRS96.  
Figure 6: The 8-parameter transformation.  
General expression for the 8-parameter transformation is similar to the 7-parameter transformation, except the scale factor is not the same for all axes, so it can be written as [8]  
\[\mathbf{X}_{o}=\mathbf{t}+\mathbf{R}\cdot\mathbf{S}\cdot\mathbf{X}_{i} \tag{56}\]  
or, in matrix form,  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{o}=\begin{bmatrix}t_{X}\\ t_{Y}\\ t_{Z}\end{bmatrix}+\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}\cdot\begin{bmatrix}\mu_{xy}&0&0\\ 0&\mu_{xy}&0\\ 0&0&\mu_{z}\end{bmatrix}\cdot\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{i} \tag{57}\]  
where \(\mathbf{S}\) represents matrix of the scale change, and other elements are same as those in Equation (29). In Equation (57), the abridged form of the rotation matrix is given, whereas, for the calculation of transformation parameters, rotation matrix from the CFR convention (Equation (39)) are used.
## 4 Feature Extraction Method for Sparse Trajectories
### Feature Extraction by GMM
#### 4.1.1 Determining RPs via GMM  
Generally, it is desirable to select densely populated locations, such as stations and commercial facilities, as RPs; however, this method requires an analysis of the regional characteristics of a target area and thus would not result in a general framework. Instead, we use the GMM to aggregate a target area into a number of RPs. The GMM is a clustering method that represents data points scattered in space as overlapping \(n\)-Gaussian distributions.  
In this study, because the target space is two-dimensional, the GMD is represented by an overlapping bivariate Gaussian distribution as follows:  
\[N(\mathbf{x}\mid\mathbf{\mu},\mathbf{\Sigma})=\frac{1}{2\pi|\mathbf{\Sigma}|^{1/2}}\exp\left\{ -\frac{1}{2}(\mathbf{x}-\mathbf{\mu})^{\top}\mathbf{\Sigma}^{-1}(\mathbf{x}-\mathbf{\mu})\right\},\]  
where \(\mathbf{\mu}\) and \(\mathbf{\Sigma}\) are the mean vector and variance-covariance matrix, respectively. Thus, the GMD is defined as follows:  
\[p(\mathbf{x}) =\sum_{i\in I}\pi_{i}N(\mathbf{x}\mid\mathbf{\mu}_{i},\mathbf{\Sigma}_{i}),\] \[N(\mathbf{x}\mid\mathbf{\mu}_{i},\mathbf{\Sigma}_{i}) =\frac{1}{2\pi|\mathbf{\Sigma}_{i}|^{1/2}}\exp\left\{-\frac{1}{2}(\bm {x}-\mathbf{\mu}_{i})^{\top}\mathbf{\Sigma}_{i}^{-1}(\mathbf{x}-\mathbf{\mu}_{i})\right\},\] \[\sum_{i\in I}\pi_{i} =1,\]  
where \(I\) is the set of bivariate Gaussian distributions and \(\mathbf{\mu}_{i}\) and \(\mathbf{\Sigma}_{i}\) are the \(i\)-th mean vector and variance-covariance matrix, respectively. In addition, \(\pi_{i}\) is the mixing coefficient that expresses the weight of the \(i\)-th Gaussian distribution. The obtained means \(\mathbf{\mu}_{i},i\in I\) of \(n\) bivariate Gaussian distributions are regarded as the coordinates of the RPs with which we  
Figure 2: Imputation method based on the assumption as described in Section 3.3. Data between two temporally continuous nodes are linearly imputed.  
approximate the target space. The number of overlapping Gaussian distributions, i.e., the number of components \(n\), can be determined arbitrarily. In this context, because the lengths of the location data are not necessarily the same for any ID \(k\), we use the raw location data \(\{\mathbf{x}\mid\mathbf{x}\in L_{k},\forall k\in K\}\) as samples for the GMD.
## 2 Experimental Study
### Remote Real-Time Monitoring System
#### 2.2.1 Network Architecture  
As the underground tunnel project is located in a reclamation area, a remote data processing platform was established in this study for data collection using an FBG demodulator (SM130-700) which transmits signals by combining a GPRS (General Packet Radio Service) DTU (data terminal unit) with an industrial PC (IPC-810-a), as shown in Figure 6. As shown in Figure 7, the platform consisted of two main modules (i.e., information query and data management).
## 5 Conclusions  
In this paper, an improved magnetic hysteresis model for PWM excitation is presented, which can consider the local magnetic hysteresis loop and calculate the magnetic flux density waveform. On this basis, a grain-oriented silicon steel sheet measurement platform is designed. According to the experimental data, the accuracy of the magnetic hysteresis model is verified. By comparing the two methods, we find that the improved model error is lower than the classical model, that is, the improved magnetic hysteresis modeling result is more accurate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modulation Ratio K** & **Method** & \(\mathbf{B_{r}/T}\) & \(\mathbf{e_{B_{r}\%}}\) & \(\mathbf{B_{m}/T}\) & \(\mathbf{e_{Bm}\%}\) \\ \hline \multirow{3}{*}{\(K=1.5\)} & Experimental results & 1.087 & / & 1.479 & / \\ \cline{2-5}  & Classical Preisach model & 1.119 & 2.94 & 1.548 & 4.67 \\ \cline{2-5}  & Improved Preisach model & 1.103 & 1.472 & 1.514 & 2.366 \\ \hline \multirow{3}{*}{\(K=1.3\)} & Experimental results & 1.115 & / & 1.456 & / \\  & Classical Preisach model & 1.149 & 3.050 & 1.524 & 4.67 \\  & Improved Preisach model & 1.132 & 1.525 & 1.491 & 2.404 \\ \hline \multirow{3}{*}{\(K=1.1\)} & Experimental results & 1.171 & / & 1.415 & / \\  & Classical Preisach model & 1.228 & 4.867 & 1.481 & 4.664 \\  & Improved Preisach model & 1.183 & 1.024 & 1.448 & 2.332 \\ \hline \multirow{3}{*}{\(K=0.9\)} & Experimental results & 1.185 & / & 1.365 & / \\  & Classical Preisach model & 1.251 & 5.495 & 1.524 & 11.60 \\  & Improved Preisach model & 1.201 & 1.332 & 1.403 & 2.564 \\ \hline \multirow{3}{*}{\(K=0.7\)} & Experimental results & 1.216 & / & 1.359 & / \\  & Classical Preisach model & 1.148 & 5.592 & 1.427 & 11.11 \\ \cline{1-1}  & Improved Preisach model & 1.204 & 1.002 & 1.397 & 2.796 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Residual and saturated magnetic flux error comparison between the two models.  
**Author Contributions:** Conceptualization, N.D.; data curation, X.G.; formal analysis, X.G.; funding acquisition, N.D.; investigation, X.G.; methodology, X.G. and S.W.; project administration, N.D.; resources, N.D., L.Z. and W.X.; software, X.G.; supervision, N.D.; validation, X.G. and N.D.; visualization: X.G.; writing--original draft, X.G.; writing--review and editing, M.L. and S.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grants 52077161 and 52007141.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company State Grid Liaoning Electric Power Supply Co., Ltd. Authors [PERSON] and [PERSON] were employed by the company State Grid Shaanxi Electric Power Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Performance Evaluation  
### Outline of Experiments  
In order to evaluate the performance of each RNN using the recurrence plots and the model constructed in Section 3, experiments were performed using a computer. Here, PyTorch 1.7.1 which is a machine learning library, cuda11 and Python 3 (version 3.8) was used. The execution environment was Ubuntu 18.04.4 (64 bit) and we used i7 8700 (RAM 16 GB) and gtx1080 of GPU (VRAM 8 GB) for single precision calculations. For the processing of voice data, a python library named Pydub [25] was used. Some of Pydub functions depend on FFmpeg [26]. In addition, when using GPU on PyTorch, it was necessary to set CUBLAS_WORKSPACE_CONFIG =: 16:8 as an environment variable for reproducibility due to use of cuda11. In the experiments, all random seeds were initialized to 10.
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### Cluster Head Selection
#### 3.3.4 Distance  
The distance between the cluster head i and member node j, \(d_{x_{i},x_{j}}\), is determined by the number of hops for the shortest path. Thus, the cost for distances of nodes in the cluster is an important factor. The distance cost between nodes is measured from the cluster head to member nodes. Here, the distance for the clusterhead \(x_{i}\) is defined by the Equation (5):  
\[d(x_{i})=\sum_{x_{j}\in[\text{cluster members of $x_{i}$}]}d_{x_{i},x_{j}} \tag{5}\]
## 4 Results
### Impact of Design Cues on Sextortion Susceptibility
#### 4.3.2 Non-Parametric Testing
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**Design Cues (I)**} & \multicolumn{1}{c}{**Design Cues (J)**} & \multicolumn{1}{c}{**Mean Difference (I–J)**} & \multicolumn{1}{c}{**Std. Error**} & \multicolumn{1}{c}{**Significance b**} & \multicolumn{1}{c}{**Confidence Interval for**} \\  & & & & & & **Difference b** \\ \hline No cue & Email source & \(-0.088\) \({}^{\star}\) & \(0.034\) & \(0.011\) & \(-0.156\) & \(-0.021\) \\  & Grammar and spelling & \(-0.073\) & \(0.033\) & \(0.028\) & \(-0.138\) & \(-0.008\) \\  & Urgency cues & \(-0.096\) & \(0.040\) & \(0.018\) & \(-0.175\) & \(-0.017\) \\  & Title/subject line & \(-0.131\) \({}^{\star}\) & \(0.040\) & \(0.001\) & \(-0.210\) & \(-0.052\) \\ Email source & No cue & \(0.088\) \({}^{\star}\) & \(0.034\) & \(0.011\) & \(0.021\) & \(0.156\) \\  & Grammar and spelling & \(0.015\) & \(0.029\) & \(0.598\) & \(-0.042\) & \(0.073\) \\  & Urgency cues & \(-0.008\) & \(0.032\) & \(0.811\) & \(-0.072\) & \(0.056\) \\  & Title/subject line & \(-0.043\) & \(0.032\) & \(0.180\) & \(-0.105\) & \(0.020\) \\ Grammar and spelling & No cue & \(0.073\) & \(0.033\) & \(0.028\) & \(0.008\) & \(0.138\) \\  & Email Source & \(-0.015\) & \(0.029\) & \(0.598\) & \(-0.073\) & \(0.042\) \\  & Urgency cues & \(-0.023\) & \(0.029\) & \(0.420\) & \(-0.080\) & \(0.034\) \\  & Title/subject line & \(-0.058\) & \(0.028\) & \(0.042\) & \(-0.114\) & \(-0.002\) \\ Urgency cues & No cue & \(0.096\) & \(0.040\) & \(0.018\) & \(0.017\) & \(0.175\) \\  & Email source & \(0.008\) & \(0.032\) & \(0.811\) & \(-0.056\) & \(0.072\) \\  & Grammar and spelling & \(0.023\) & \(0.029\) & \(0.420\) & \(-0.034\) & \(0.080\) \\ Title/subject line & \(-0.035\) & \(0.034\) & \(0.312\) & \(-0.103\) & \(0.033\) \\ Title/subject line & No cue & \(0.131\) \({}^{\star}\) & \(0.040\) & \(0.001\) & \(0.052\) & \(0.210\) \\  & Email source & \(0.043\) & \(0.032\) & \(0.180\) & \(-0.020\) & \(0.105\) \\  & Grammar and spelling & \(0.058\) & \(0.028\) & \(0.042\) & \(0.002\) & \(0.114\) \\  & Urgency cues & \(0.035\) & \(0.034\) & \(0.312\) & \(-0.033\) & \(0.103\) \\ \hline \hline \end{tabular} Note. Based on estimated marginal means. b Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). \({}^{\star}\) The mean difference is significant at the 0.0125 level.  
\end{table} 
Table 4: ANOVA post hoc comparison test results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(N\) & **CHI-SQUARE** & _df_ & **Asymp. Significance** \\ \hline
86 & 13.080 & 4 & 0.011 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [PERSON] nonparametric test results. specified, \(z\) (\(N=86\)) = 2.47, \(p=0.014\), \(r^{2}=0.04\), nor when participants focused attention on urgency cues compared to when attention to urgency cues was not specified, \(z\) (\(N=86\)) = 2.47, \(p=0.014\), \(r^{2}=0.04\). Wilcoxon Signed-Rank test results are shown in Table 6.
# Ex Vivo Exposure to Soft Biological Tissues by the 2-um All-Fiber Ultrafast Holmium Laser System
## 1 Introduction
Such a precise effect is achieved due to the nonlinear absorption that occurs when the incident power intensity is sufficient to induce simultaneous absorption of multiple photons [28]. Thus, the volume into which the laser energy is deposited decreases and, consequently, there is less damage to the surrounding tissues [29]. The first studies on the medical application of femtosecond laser pulses dealt with effects on the retina and skin [30; 31]. In [32], [PERSON] et al. showed that ablation and the plasma optical breakdown threshold strongly depends on the tissue linear absorption at the laser focus for the nanosecond pulses, while for femtosecond pulses it is practically independent of the target tissue linear absorption. This, in turn, simplifies targeting of small individual cellular structures [33; 34]. Thus, ultrashort pulse (USP) (10 fs-100 ps) sources allow more precise non-thermal exposure, in contrast to continuous-wave radiation [35; 36]. Moreover, in [37] [PERSON] et al. showed that the damage is noticeably less and the healing process without scarring is much faster after exposure to a picosecond infrared laser, in contrast to the conventional surgical laser or a scalpel. Thus, the use of this type of radiation can lead to a more comfortable environment for patients by reducing healing time and reducing the risk of infections during surgery. Significant progress has been made since the first use of lasers for medical purposes as an alternative to mechanical surgical instruments [38]. The design of laser systems has been greatly improved and changed towards compactness and simplicity. Due to the numerous advantages, particular attention is paid to the medical application of fiber lasers performing precise impact with minimal collateral damage to surrounding tissues [39; 40]. Additionally,the use of fiber lasers simplifies the task of delivering laser radiation during minimally invasive operations using surgical endoscopes. In this paper, we present the results of ex vivo exposure on soft tissues while using an ultrafast all-fiber Ho-doped laser system and compare them with the results obtained earlier in [41] with a CW Ho-doped fiber laser under the same conditions and applied energy. A simple, unique Ho-doped laser system generated ultrashort pulsed radiation with less than 1 ps pulse width, with a repetition rate of 20 MHz at a central wavelength of 2.06 \(\upmu\)m. The main objectives were to evaluate the depth and diameter of ablation (AZ), coagulation (CZ) zones and the heat-affected zone (HAZ) after ex vivo experiments of single-spot ablation of porcine longissimus muscle tissue by 2-\(\upmu\)m ultrashort pulsed radiation with an average power of 0.3 W in the applied energy range of 1.5-18 J. Furthermore, we aimed to quantify the ablation efficiency (AE) for the Ho-doped fiber laser used in the study.