esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Data Analysis  
A statistical evaluation of the analytical data including descriptive statistics and univariate and multivariate correlation analysis was performed using the STATISTICA 10.0 software package. The strength of the linear relationship between total concentrations of PTEs was quantified by the coefficient of determination (R\({}^{2}\)), and the influence of the interrelated variables was accomplished by principal component analysis (PCA). A normalized Varimax rotation was applied to the axes of the principal components in order to maximize the variance of the factors.  
The soil contamination status was assessed by a number of enrichment calculation methods that relate the concentrations of PTEs in soil and their baseline concentrations [20; 21; 22]. To quantify the enrichment factor of each PTE [23], the measured concentration was normalized against the content of a conservative lithogenic trace element, as follows:  
\[EF=\frac{\left(C_{x}/C_{ref}\right)soil}{\left(C_{x}/C_{ref}\right)\ background} \tag{1}\]  
where \(EF\) is the enrichment factor, \(C_{x}\) is the concentration of the element of concern and \(C_{ref}\) is the concentration of the reference element. In this study, zirconium (Zr) was chosen as the reference element for normalization.  
The pollution load index (\(PLI\)) was used to calculate the degree of multielement soil contamination by deriving the \(n\)th root of the \(n\) factors [24]:  
\[PLI=\left(CF1\times CF2\times CF3\times\ldots\times CFn\right)^{1/n} \tag{2}\]  
where \(CF\) is the contamination factor, i.e., the quotient between the PTE concentration in the sample and its background concentration [25], and \(n\) is the number of PTEs evaluated. \(PLI\) values above one would indicate progressive deterioration of estuarine marsh soil quality.  
The geoaccumulation index (\(I_{geo}\)) is another numerical indicator used to evaluate the contamination levels for the recovered soil cores, as follows [26]:  
\[I_{geo}=log_{2}\left[C_{n}\ /1.5\times B_{n}\right] \tag{3}\]  
where \(C_{n}\) is the measured concentration of PTE in the sample, \(B_{n}\) is its background value, and 1.5 is a matrix correction factor due to lithogenic effects.  
Finally, the potential ecological risk index (ERI) of multi-metal(loid)s [25] was calculated using the contamination factors of the most relevant PTEs (As, Cd, Cu, Pb, and Zn) occurring in the topsoil:  
\[ERI=\sum_{i=1}^{n}\mathrm{Er}^{i}=\sum_{i=1}^{n}\mathrm{Tr}^{i}\cdot C_{f}^{i} \tag{4}\]  
where \(n\) is the number of elements involved (in this case, \(n=5\)), \(\mathrm{Er}^{i}\) is the potential ecological factor for the given element (\(i\)), \(\mathrm{Tr}^{i}\) is the toxic-response factor for the given element, and \(C^{i}\) is the contamination factor for the given element.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
###### Abstract  
Federated learning (FL) offers a promising solution in edge computing to overcome bandwidth limitations and privacy concerns associated with traditional cloud-based training. However, current FL methods often suffer from transmission delay and excessive communication resource usage. In this paper, we introduce an innovative asynchronous hierarchical FL approach based on bandwidth allocation and client scheduling. Specifically, we propose an efficient algorithm that dynamically assigns clients to edge servers based on client mobility during training and accelerates parameter uploading while taking into account the remaining bandwidth of the edge servers. Our experimental results demonstrate the effectiveness of our approach, particularly in scenarios with frequent client mobility. This research strongly supports the application of FL in edge computing and underscores the crucial role of resource allocation in addressing communication resource constraints and reducing the training time of FL.  
federated learning; resource allocation; client selection +
Footnote †: journal: Applied sciences  
1
# A Hybrid Watermarking Approach for DICOM Images Security
## 3 Proposed Method
### Watermark Extraction Process  
After the embedding process, the zero-watermark generated by the zero-watermark process is sent as a key to the receiver through a secure channel. The obtained watermarked image \(\mathrm{I_{w}}\) will be sent to the receiver via public insecure networks and it will be exposed to different kinds of attacks.  
Therefore, the received image is an attacked watermarked image \(\mathrm{I_{wa}}\) and the extraction process must be applied to prove the origin of the image by extracting the set of attacked watermarks \(\mathrm{W_{a1}}\) and \(\mathrm{W_{a2}}\) from \(\mathrm{I_{wa}}\). The inverse form of the linear interpolation technique [22] is applied. The inverse form of the linear interpolation technique uses the following equation (Equation (2)) to extract the attacked watermark \(\mathrm{W_{a}}\) from the attacked watermarked image \(\mathrm{I_{wa}}\):  
\[\mathrm{W_{a}}=(1/t)\;\mathrm{W}+((1-t)/t)\times\mathrm{I_{wa}}\text{, where t is the interpolation factor and t \(\in\)]0,1[} \tag{2}\]  
On the one hand, the extracted attacked watermarks are decoded to obtain corresponding zero-watermarks. These zero-watermarks are compared to the original ones using BER and NC metrics. If they are similar, the received image is authenticated and can be used for diagnosis. On the other hand, a comparison of the original features extracted from the original zero-watermark ZW and the features extracted from the attacked zero-watermark ZWa is done for the authentication of the medical image. The image is considered authentic if the difference between the values of the initial and the extracted features is lower than a given threshold. Figure 7 illustrates the watermark extraction process.  
The pseudo code of attacked watermark extraction process is illustrated in Algorithm 2.  
```
0: Attacked watermarked image \(\mathrm{Iwa}\), the selected 2k attacked blocks \(\mathrm{blocka_{i}}\) (i = 1,... 2k) of size \(\mathrm{nxm}\) in the black background region, original zero-watermark image ZW and interpolation factor t Output: Set of attacked zero-watermarks (\(\mathrm{ZW_{a1}}\),\(\mathrm{ZW_{a2}}\)) Begin \(\mathrm{W}\leftarrow\) encode ZW  Divide \(\mathrm{W}\) into k blocks \(\mathrm{W_{i}}\) (i = 1,....k) of size \(\mathrm{nxm}\) for i = 1 to k loop \(\mathrm{Wa_{i}}=(1/t)\mathrm{W_{i}}+((1-t)/t)\times\mathrm{blocka_{i}}\) \(\mathrm{Wa_{k+i}}=(1/t)\mathrm{W_{i}}+((1-t)/t)\times\mathrm{blocka_{k+i}}:t=| 0-1[\) end loop \(\mathrm{Wa_{1}}\leftarrow\) Merge \(\mathrm{Wa_{i}}\) (i =1,....k) (first copy of attacked watermark extracted) \(\mathrm{Wa_{2}}\leftarrow\) Merge \(\mathrm{Wa_{k+i}}\), (i=1,....k) (second copy of attacked watermark extracted) \(\mathrm{ZWa_{1}}\leftarrow\) Decode \(\mathrm{Wa_{1}}\) \(\mathrm{ZW_{a2}}\leftarrow\) Decode \(\mathrm{Wa_{2}}\) End
```  
**Algorithm 2** The pseudo code of extraction of the watermark from the black background region.
# Optimized Architecture for Efficient OFDMA Network Design
## 5 Simulation Results
### Robustness Study
#### 5.2.2 Throttling Regime  
Figures 13 and 14 represent, respectively, the average instantaneous throughput of active users during an ON period \(X\) for the different traffic distributions and the average instantaneous throughput of active users during an ON period for the different channel models in the throttling regime.  
Figure 12: The average instantaneous throughput for the throughput fairness algorithm and the different channel models (\(N\) = 10, \(x_{out}\) = 3 _Mbits, and \(t_{off}\) = 3 _s_).  
Figure 13: The average instantaneous throughput of active users during an ON period for the different traffic distributions in the Throttling regime (\(N\) = 10, \(x_{out}\) = 3 _Mbits, \(t_{off}\) = 3 \(s\), and _MSTR = 2048 Kpps_).  
Even for the Throttling regime, the obtained results demonstrate the robustness of the analytical model.  
The results from the Throttling regime further confirm the robustness of the analytical model. The resource fairness algorithm consistently delivered stable throughput across all traffic patterns, even under fluctuating network loads. In contrast, opportunistic scheduling significantly improved throughput but led to resource underutilization during periods of low traffic, emphasizing the need for dynamic algorithm selection based on real-time network conditions.  
The validation and robustness studies demonstrate that the proposed scheduling algorithms consistently enhance network performance across diverse traffic scenarios and channel conditions.  
The proposed approach offers several key advantages:  
* Improved Efficiency: By incorporating [specific techniques or algorithms], the proposed method enhances the efficiency of resource allocation in OFDMA networks, leading to better throughput and reduced latency.
* Robust Performance: The approach demonstrates robust performance across various traffic scenarios and channel conditions, as evidenced by [specific results or metrics], which outperforms traditional methods.
* Adaptability: Unlike existing approaches, the proposed method can adapt to dynamic network conditions, allowing for real-time optimization of scheduling algorithms based on current network status.
* Comprehensive Analysis: The study provides a thorough analysis of the impact of different scheduling algorithms, highlighting their strengths and weaknesses in various contexts, which contributes to a deeper understanding of their applicability in real-world situations.  
These advantages position the proposed approach as a valuable contribution to the field, addressing limitations in existing methodologies and providing a more effective solution for OFDMA network design.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 1 Introduction
Simple Linear Iterative Clustering (SLIC) superpixels are introduced to build a spatial group sparsity regularizer, which is combined with the sparse unmixing framework to investigate the effectiveness of the spatial structure and sparsity. Furthermore, the \(\ell_{2,1}\) norm is adopted in the loss function to restrain noise and outliers. Finally, the alternative direction method of multipliers (ADMM) algorithm is utilized to solve the robust sparse unmixing problem. In summary, this work mainly contributes in the following aspects:  
* We propose an HSI super-resolution approach by simultaneously unmixing the two input images into their endmembers and associated abundances. * The advantage of the proposed unmixing lies in taking the spatial correlation, as well as noise and outliers, into consideration and providing a solution that combines SLIC superpixels and robust sparse unmixing. * We test our approach with a widely used standard benchmark, the \"Harvard data set\", and several remotely sensed hyperspectral images. The results of the experiments demonstrate that the proposed approach is superior to other related state-of-the-art methods. The rest of this paper is organized as follows. Section 2 presents some related works. Section 3 details the proposed method. Section 4 gives the experimental settings. The results and analysis are presented in Section 5, and the conclusions are given in Section 6.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 2 Experimental Setup and Procedures
### Linear Cutting Test
#### 2.1.3 Cutting Scheme  
Before performing the main cutting, a series of cuts were performed 2-3 times, as shown in Figure 4. This series of cuts is called preconditioning. Because the tunnel excavation machine excavates a damaged surface in the field, preconditioning was used to create the same conditions [3; 21]. For the main cutting test, the penetration depth (\(p\)) was set to 3, 6, and 9 mm, and it was performed at various spacings (\(s\)), as shown in Table 2. The tests were performed under 24 cutting conditions for each rock model. Because the cutting speed does not affect the cutting performance, such as the cutting force and \(SE\), the cutting speed was arbitrarily set to 12.5 mm/s [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Target** & **Elastic** & **Density** & **Poisson’s** & **UCS (MPa)** & **BTS (MPa)** \\
**Strength** & **Modulus** & **(GPa)** & **(kg/m\({}^{3}\))** & **Ratio** & & \\ \hline
20 & 16.92 & 2214 & 0.3 & 18 & 2.06 \\
30 & 33.35 & 2363 & 0.3 & 29.3 & 2.18 \\
40 & 38.92 & 2382 & 0.3 & 42 & 2.51 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of model rocks.  
Figure 3: Cutting tool used in the experiment: (**a**) specification; (**b**) appearance.  
The \(SE\) is an important factor indicative of the excavation efficiency of tunnel excavation machines such as TBMs and roadheaders, and has been used in both the field and experiments [23; 24; 25]. The \(SE\) refers to the energy required to excavate a unit volume, and the excavation efficiency is maximized when \(SE\) is minimal. The \(SE\) can be calculated using Equation (1):  
\[SE=\frac{F_{c}\times l}{V_{c}} \tag{1}\]  
where \(SE\) denotes the specific energy, \(F_{c}\) denotes the cutting force, \(l\) denotes the length of the cut, and \(V_{c}\) denotes the cutting volume.  
The generated cutting force (\(F_{c}\)) in the measurement area during the test was analyzed and stored every 25 ms. The cutting volume (\(V_{c}\)) was calculated using the weight and density of the rock fragments recovered in the section where the cutting force was measured.
# Automation of a Hybrid Control for Electrohydraulic Servo-Actuators with Residual Dynamics
## 3 Problem Statement and Illustration
### Problem Statement  
The main idea provides a tracking performance while maintaining the stability of the overall system with any problem at the switching times.  
For this purpose, let us define the model system as follows:  
\[\left\{\begin{array}{ll}\dot{X}=f(X)+g(X)u_{p},&\quad(S_{1})\\ \dot{X}=f(X)+g(X)u_{F},&\quad(S_{2})\end{array}\right. \tag{10}\]  
For the control position target, the subsystem (\(S_{1}\)) is then defined as in (10), with \(u_{p}\) its input signal. When this target is changed to the effort control, the system will be described by the subsystem (\(S_{2}\)), where \(u_{F}\) is the new input signal to track some force-desired trajectory.  
Note that, owing to the system order, which is equal to three compared to the relative degree for (\(S_{2}\)), which is equal to one, this subsystem presents second-order residual dynamics. Then, the problem here arises when switching takes place from the force controller to the position controller.  
Consequently, this hybrid controller needs to be implemented for switching to occur between the position and force controllers with some operating phases, with an appropriate switching law \(\sigma(t)\) (see Figure 2). In this figure, the principle of the considered hybrid controller is described.  
Different phases must occur successively. Firstly, the position controller is started by choosing a smoothing desired position trajectory, such as a polynomial trajectory. According to our specifications, the desired trajectory for the position must have a static phase to reach some given position and remain there for a certain time. The second phase is carried out when the position reaches the static phase. Then, the closed-loop system switches from \(u_{p}\) to \(u_{F}\) over some period of time imposed by the switching signal \(\sigma(t)\). In fact, to orchestrate between the two strategies' laws, the switching law must be correctly designed, especially at switching times from \(u_{F}\) to \(u_{p}\). Indeed, these switching times will necessarily depend on the convergence time of the residual dynamics rated as \(T_{res}\). To avoid any damages  
Figure 2: Principles of the hybrid controller.  
in the process, the commutation from \(u_{F}\) to \(u_{p}\) must be done after \(T_{res}\) to ensure that the residual dynamics converge to the values imposed by the position controller. The choice of this switching criterion depends on the characteristics of the load, the coefficient of the viscous friction force and the convergence time of the effort to its desired trajectory. Therefore, the design problem of the switching law consists in determining the switch occurrences that must be imposed when there is commutation from \(u_{F}\) to \(u_{p}\), as well as the switch occurrences that must be generated when the controller switches again to the position tracking target. To illustrate the problem that may arise at the switching times when switching again to the position control, some simulation tests will be presented in the following paragraph.
## 4 Case Studies  
This section presents the validation results of the proposed optimization model, applied in the MEMG system for a commercial building presented in Figure 1. The simulation parameters of this test system are listed in Table 1.  
The installed solar PV capacity in the MEMG system is 180 kW. Due to the PV power generation having strong uncertainty, the p-efficient point method is proposed to determine the available PV generation based on historical data for typical days. Figure 3 shows the historical data for solar PV generation used for the p-efficient point method. It is noteworthy that the proposed formulation is implemented on two typical summer and winter days for various cases to verify the effectiveness of the proposed formulation. Furthermore, the effect of the different confidence levels of PV generation and effectiveness of IDR in the stochastic circumstances are addressed on the optimization-based operation. All the simulations have been carried out using MATLAB 2017a by calling the CPLEX solver [41] on a PC with Intel Core i5 CPU and 8 GB RAM.
## 5 Experiments  
### Experimental Setup  
As shown in Figure 12, a well developed prototype of the optimized RCDMC has been assembled in a horizontal ULF vibration exciter. It should be noted that all the components, including the center yoke, outer yokes and end yokes have been vacuum annealed to guarantee the great permeability of yokes. PMs are assembled into the prototype with specially-designed assembly tools after ther are finish grinded and magnetized. The moving subassembly guided by a self-designed air bearing can slide freely along the air gap with coil winding, coil skeleton and working platform assembled. During the experiments, a GM55 gaussmeter (Shanghai Tindun Industry Co., Ltd, Shanghai, China) with a range of 200 mT, a resolution of 0.01 mT and an accuracy of \(\pm\) 2.0% (DC) is used.
# Dynamic Similitude Design Method of the Distorted Model on Variable Thickness Cantilever Plates
## 4 The Distorted Model of the VT Plate
### Distorted Models and the Scaling Law
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Vibration** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **VT 2 Plate** \\ \(\omega_{s}\)**/Hz** \\ \end{tabular} & \begin{tabular}{c} **MT 2 Plate** \\ \(\omega_{\text{m}}\)**/Hz** \\ \end{tabular} & \begin{tabular}{c} **\(\Xi=\frac{\lambda_{\text{L}}}{\lambda}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Predicted** \\ **frequencies** \(\omega_{pp}\)**/Hz** \\ \end{tabular} &
\begin{tabular}{c} **Error** \\ \(n_{ave}\)**/\%** \\ \end{tabular} \\ \hline \(m=1\), \(n=1\) & 366.93 & 215.131 & 3.285 & 351.94 & 4.09 \\ \(m=1\), \(n=2\) & 1530.40 & 1334.22 & 2.225 & 1478.38 & 3.40 \\ \(m=1\), \(n=3\) & 3610.30 & 3790.03 & 1.975 & 3727.68 & 3.25 \\ \(m=1\), \(n=4\) & 7080.52 & 7382.59 & 1.885 & 6930.25 & 2.12 \\ \hline \hline \end{tabular}  
* VT plate represents the variable thickness plate; MT plate represents the model thin plate.  
\end{table} 
Table 6: Prediction of the VT plate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Vibration** \\ **Mode** \\ \end{tabular} & \begin{tabular}{c} **Equivalent** \\ **Thickness /mm** \\ \end{tabular} & \begin{tabular}{c} **ST Plate 1** \\ **ANSYS \(\omega_{s}\)**/Hz** \\ \end{tabular} & \
\begin{tabular}{c} **Thin Plate** \\ **ANSYS \(\omega_{e}\)**/Hz** \\ \end{tabular} &
\begin{tabular}{c} **Error** \\ \(n_{e}\)**/\%** \\ \end{tabular} \\ \hline \(m=1\), \(n=1\) & \(h_{e1F}=6.57\) & 357.25 & 355.76 & 0.42 \\ \(m=1\), \(n=2\) & \(h_{d2F}=4.45\) & 1477.70 & 1470.60 & 0.48 \\ \(m=1\), \(n=3\) & \(h_{d3F}=3.95\) & 3658.90 & 3689.51 & 0.84 \\ \(m=1\), \(n=4\) & \(h_{e4F}=3.77\) & 6894.76 & 6814.79 & 1.16 \\ \hline \hline \end{tabular}  
* ST plate represents the stepped thickness plate.  
\end{table} 
Table 4: Different orders’ equivalent thickness. In this example, it can be seen that the dynamic properties of a VT plate can be accurately predicted by using a designed MT plate of different materials. It is worth noting that the present proposed method can be simply achieved by using the MATLAB program (MATLAB 2010b, MathWorks, Natick, MA, USA), such that the design of the MT plate can be easily conducted. Moreover, two additional cases are discussed as below. Firstly, only flexible vibration is considered in the proposed example. It is noticeable that the coefficient \(\Xi\) of the torsional vibration and the chordwise bending vibration can be obtained by replacing \(\mathbf{T}\) with transfer matrices shown in Reference [29]. Secondly, the length and width of the MT plate are completely scaled down as \(\lambda_{a}=\lambda_{b}=\lambda\), a more complex case, where the geometric parameters are all distorted as \(\lambda_{a}\
eq\lambda_{b}\
eq\lambda_{b_{e}}\), can be discussed by using the similar process by referring to the scaling laws present in [22; 23; 24] between the equivalent thin plate and the MT plate.
# Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
## 3 Prediction Performance Evaluation  
The metrics introduced in Table 1 are presented to measure the performance of the learning algorithms presented in the previous section. Here, \(y_{t}\) is the actual value at time \(t\), while \(\hat{y}_{t}\) is the predicted value for the same timestamp. The maximum and minimum values of all the actual values are represented by \(y_{\max}\) and \(y_{\min}\). In addition, \(s\) is the number of samples in the testing dataset. The mean absolute error (MAE) measures the average magnitude of the errors between the predictions and actual values. Similarly, the RMSE also expresses the average model prediction error. However, it is measured by taking the square root of the average of the squared differences between the actual and predicted values. Both of these metrics measure prediction errors, and they can range from 0 to \(\infty\). Consequently, lower values characterize a better-performing model. The error metric nRMSE outlined represents the normalized RMSE value. In this case, the normalization is carried out by dividing the RMSE score by the difference between the maximum and minimum values of the actual values. Furthermore, the metric max error represents the evaluation of the worst-case scenario and measures the maximum error in the predicted value of the samples.