esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Experiments on Synthetic Dataset  
We evaluated the effect of rain streak removal in the synthetic dataset both on qualitative and quantitative metrics. The results with the Rain100 L dataset are shown in Figure 6.  
Figure 5: The partial images of the real dataset.  
Figure 6: The de-rained results on Rain100 L dataset. From the first column to the sixth column: Rainy image, GMM [9], UGSM [8], DCNN [16], FS-GAN [28], Ours.  
When there were few rain streaks in an image (as shown in the fifth row of Figure 6), all methods achieved results. When the rain streaks were medium in number (as shown in the first row of Figure 6), the GMM and UGSM yielded worse results than the other methods. In the case of a large number of rain streaks in the image (as shown in the third row of Figure 6), our method, which yielded images barely containing any visible streaks, delivered the best visual effect. The images generated by the GMM contained prominent streaks, and those obtained by the DCNN and FS-GAN contained blur in regions containing the rain streaks. In particular, when the streaks were similar to the background image, such as in the image of white sky region in the second row of Figure 6, the image obtained using the DCNN and FS-GAN contained residual streaks. In comparison, our method removed sparse rain streaks to a greater extent and retained background information, as shown in the fourth row of Figure 6. We also tested the results of some nighttime rain image experiments, as shown in the last three rows of Figure 6. Our approach yielded the best results.  
We used Mean Squared Error (MSE) [34], the Structural Similarity Index Method (SSIM) [35], the Peak Signal to Noise Ratio (PSNR) [36], Visual Information Fidelity (VIF) [37], and Learned Perceptual Image Patch Similarity (LPIPS) [38] as performance metrics for quantitative evaluations.  
MSE is the most common estimator of image quality measurement metric. PSNR is used to calculate the ratio between the maximum possible signal power and the power of the distorting noise, which affects the quality of its representation. The ratio between two images is computed in decibel form. SSIM is a perception-based model that considers image degradation as the change in perception in structural information. VIF is derived from the quantification of two mutual information quantities, measured for the goal of the quality assessment of image. LPIPS is a perceptual distance that measures how similar two images are in a way that coincides with human judgment.  
Table 3 shows that our method achieved the best values in the MSE, PSNR, SSIM, VIF, and LPIPS. The results were normalized and shown in a histogram in Figure 7. A lower value of the MSE and higher values of the SSIM, PSNR, and VIF indicated images of a higher quality. The results on the synthetic dataset thus verified that our method could adapt to different scenes featuring rain and yielded a better visual effect and higher image quality than the other methods.
## 5 Damage Characterization, Objective Functions, and Optimization Algorithm
### Optimization Algorithms
#### 5.4.1 Gradient-Based Optimization Algorithms  
One type of mathematical optimization methods is the gradient-based method that determines the search direction by using the gradient information, offering the best convergence behavior [176]. The gradient-based algorithm starts from a predefined initial parameter set and uses line search algorithms such as quasi-Newton methods [177] to ensure the stepwise convergence to an optimum. For unconstrained optimizations, two popular methods are the Fletcher-Reeves and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods. For nonlinear constrained optimization problems, the three algorithms commonly encountered in engineering are the sequential linear programming (SLP), the modified method of feasible directions (MMFD), and the sequential quadratic programming (SQP). Among all the gradient-based methods, the SQP algorithm is the state-of-the-art algorithm for solving differentiable nonlinear programs and has gained great attention in the recent years due to its superior rate of convergence [177; 178; 179]. The formulation of an SQP is based on [PERSON]'s method and Karush-Kuhn-Tucker optimality conditions for constrained problems that are changed to the unconstrained optimization [180]. With respect to the finite element based problems that involve relatively large number of degrees of freedom and design variables, the SQP is a quite effective option.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 3 Proposed Method
### Cluster Head Selection Phase
#### 3.3.3 Network Coverage (NC)  
One of the important factors in overcoming the challenges of WSNs is network coverage [15; 27]. If the aim is to reach higher scalability, then the network coverage should be increased [27]. Thus, finding a CH for the nodes that have no CHs (non-CHs) can effectively cover the network. In this paper, the network coverage is evaluated as follows:  
\[\text{Network Coverage }(\text{NC})=\frac{(\text{N}^{*}-\text{K})-\sum_{k=1}^{ \text{K}}||\text{CM}_{k}||}{\sum_{k=1}^{\text{K}}||\text{CM}_{k}||} \tag{17}\]  
where \(||\text{CM}_{k}||\) represents the \(kth\) total number of CMs in the cluster, K is the number of CHs, and N* is the total number of nodes (N) minus the number of nodes annihilated in each round. Minimizing the number of non-CHs is perfromed through NC [15; 27]; thus, by minimizing the \(EE\), \(CC\), and \(NC\) parameters, the best set of CHs is selected. A novel fitness function of the clustering phase is formulated as shown in Equation (18):  
\[\min\text{F}_{\text{clustering}}=\alpha_{1}\times\text{EE}+\alpha_{2}\times \text{CC}+\alpha_{3}\times\text{NC} \tag{18}\]  
subject to EE\(>\)E (threshold), CC\(<\)T (maximum), \(\sum\limits_{i=1}^{3}\alpha_{i}=1\) and \(\alpha_{i}\in[0,1]\).  
Here, nodes die if their energy is less then E (threshold), and T (maximum) is the maximum range of node transmission.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 3 An RL-Based Framework of Congestion and Awareness Control
### Elements of the RL Framework for Congestion and Awareness Control
Note that the vehicle density cannot be changed based on the action but only calculated based on the BSMs received from the neighbors, so the whole state space will consist of 500 individual states. At each state, the vehicle selects a new transmission rate from 10 possible rates and updates its state accordingly, based on the information received from its neighbors in the form of BSMs. * The reward: A reward is a scalar value that measures the quality of an action taken by an agent. The agent uses the rewards provided by the environment after each action to learn and improve its behavior over time [13]. In the context of V2V communication, the reward is calculated based on observations from the environment and the goals of the vehicle. The reward calculation is performed using a reward function, which should be designed to meet the desired learning objectives. The goal of our proposed approach is to maintain the CBR below a predefined threshold \(\eta\) while simultaneously maximizing the number of BSMs transmitted. To accomplish this, we have defined the reward function as follows: \[r(CBR,BR)=BR\cdot CBR\cdot sign(\eta-CBR)\] (5) where sign is the signum function shifted by the target value \(\eta\). Any action that causes the CBR to exceed \(\eta\) will have a negative reward, which can speed up the learning process [13]. A very low transmission rate is not encouraged because the resulting reward will be lower. In this paper, we have used \(\eta=0.6\) as the target channel load. For different learning objectives, this value can be modified as needed or a different reward function can be implemented.
## 2 Materials and Methods
### Test Stand and Sensor Integration  
The gear wheel sensor utilizes the gear wheel of a gearbox as a material measure. When a passive anisotropic MR sensor is radially positioned towards the gear wheel, the ferromagnetic teeth passing by the sensor generate a sine-like signal [15], with the spacing between two teeth equaling one sine-like wave, creating an angle reference. The working distance between the anisotropic MR sensor and the tooth tip is one quarter of the tooth pitch, which is 1.6 mm. To measure the rotational direction and to increase the measurement accuracy, two passive anisotropic MR sensors are integrated, which have a sine-wave phase-shift of one fourth. Due to the integration space, the spacing between the two sensors equals a teeth spacing of 1.75. The variations in the shape of the gear teeth distort the signal compared to an ideal sine wave, which causes fluctuations of the acquired rotational angle [15]. The sensors are integrated in a one-stage gearbox, shown in Figure 1. As a reference angle sensor, the pole ring sensor is used, consisting of an incremental pole ring integrated to the gear wheel shaft, measuring the same rotational angle as the gear wheel sensor, and an active anisotropic sensor. The gear wheel of the gear wheel sensor has 51 teeth, and the pole ring of the pole ring sensor has 256 poles. An acceleration sensor, as the most used sensor in gear condition monitoring, glued to the outside of the gearbox adapter, serves as an additional reference. The gear wheel and the pinion of the sensory-enhanced gearbox have a standard profile with a module of 2 mm, a normal pressure angle of \(\alpha=20^{\circ}\), and a helix angle of \(\beta=15^{\circ}\).  
The back-to-back test rig consists of two 30 kW Siemens asynchronous motors (Siemens, Munich, Germany); one is the speed-controlled drive and the other is load-controlled (see Figure 2). The gearboxes are connected with each other via their low-speed shafts. The load gearbox has a transmission ratio of \(i=0.31\), and the sensory-enhanced gearbox has a transmission ratio of \(i=2.43\). Therefore, they have different gear mesh frequencies (GMF) and rotational speeds at the high-speed shafts. A temperature sensor measures the oil temperature of the sensory-enhanced gearbox. The data acquisition system, a cDAQ 9178 with NI9205 and NI9215 modules for the analogue voltage signals of the MR sensors, as week as an NI9234 module for the IEPE signals of the acceleration sensor, acquire the signals throughout the tests. This guarantees the time-synchronous sampling of the data. The signals are sampled at 40 kHz. The test data is published and accessible [17].  
Based on the test stand, the further test design is developed in the following section. The artificial gear damage is applied to the pinion of the sensory-enhanced gearbox, which meshes with the gear wheel sensor.  
Figure 1: Sensory-enhanced, one-stage gearbox with integrated sensors, according to [16].  
Figure 2: Gearbox test stand, according to [16].
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 4 Experimental Validation  
To experimentally validate the proposed power control, wind tunnel tests were conducted. The wind tunnel experiments utilized a scaled wind turbine with a rated power of 39.7 W, which was designed and developed through previous research [30]. The blades, hub, tower, and other components of the scaled wind turbine model used in this study were manufactured in South Korea [31]. The wind turbine model features three blades and has a mechanism similar to that of conventional large wind turbines. Considering the operating mechanism akin to that of large wind turbines, the scaled wind turbine model was deemed suitable for experimentally validating the proposed control algorithm. Figure 9 shows the shape of the scaled wind turbine, while Table 6 presents a summary of its specifications.  
In this study, to accommodate the PCS torque control, the control system was separated to allow for the independent calculation of torque commands based on the generator speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Unit** & **Value** \\ \hline Wind Turbine Type & - & HAWT, VSVP, Upwind \\ Rated Power & W & 39.7 \\ Rotor Diameter & m & 1.1 \\ Hub Height & m & 0.9 \\ Rated Wind Speed & m/s & 5.5 \\ Rated Rotor Speed & rpm & 678 \\ Fine Pitch Angle & deg & 0.5 \\ Optimal Tip Speed Ratio & - & 7.1 \\ Max-Cp & - & 0.4 \\ Gear Ratio & - & 1:14 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Specification of the scaled wind turbine.  
Figure 8: Performance comparison of the power control considering the mode switch and Set Point Smoother in the independent generator torque control. (**a**) Simulation results under average wind speed conditions of 10 m/s; (**b**) Simulation results under average wind speed conditions of 12 m/s.
# Home Automation System Based on Intelligent Transducer Enablers
## 8 Results  
In this section, we present the results of several tests conducted to evaluate the performance of HASITE in real scenarios. Two different sets of tests were conducted. The first set of tests was aimed at measuring the time that went by since an ITN was plugged in until it is was accessible to an end user. The test was designed to isolate easily the times depending on the self-configuration and self-registration protocols from the ones dependent on the physical and data link layers (in this case, Wi-Fi). The second set of tests was aimed at determining whether HASITE's hardware and software were fast enough to satisfy the response times and processing capabilities desired in a home automation application.  
In order to perform the different tests, we modified an ITN to register several relevant parameters (for instance, the time spent at each of the steps of the self-configuring and self-registration protocols). In this way, the ITN was programmed to record and dump the results onto a computer via a serial TTL communication.  
The tests were conducted inside GTEC's Lab, in the Scientific Area of the University of A Coruna. Figure 11 shows the testing environment, where L denotes the position of the laptop that ran the tests and R was the location of the Wi-Fi router used as the access point. The ITN and the laptop were connected via Wi-Fi to the router. Such a router was placed at a height of 80 cm above the floor, while the ITN was moved through the building and positioned in four different relevant locations:  
* Scenario A (short distance, line-of-sight): the ITN was located 1 m from the router at the same height.
* Scenario B (medium distance, line-of-sight): the ITN was located 5 m from the router at the same height.
* Scenario C (medium distance, non-line-of-sight): the ITN was located 8 m from the router at the same height with a closed plywood door in between.
* Scenario D (medium distance, non-line-of-sight): the ITN was located 10 m from the router, upstairs and with both a closed door and stairwells in between.  
Such scenarios were selected to have a set of diverse data samples, minimizing any bias introduced by the distance between the AP and the ITN or by interferences from any Wi-Fi network operating in a frequency close to the one used in the tests.  
Figure 11: Testing environment. (**A**) Short distance, line-of-sight; (**B**) Medium distance, line-of-sight; (**C**) Medium distance, non-line-of-sight; (**D**) Medium distance, non-line-of-sight, upstairs. (**L**: Laptop; **R**: Router).
## 4 Experimental Setup and Result Analysis
### System Testing  
#### 4.6.1 Real-Time Dual-Camera Image Stitching Implementation  
The real-time image stitching system was deployed on the Jetson TX2 NX edge computing platform, utilizing the DeepStream framework to capture dual-camera video streams. The system acquires the initial frame from each camera and performs pre-alignment, during which the computed homography matrices and optimal seam lines are stored in GPU memory. Upon completion of the initialization process, the real-time stitching module is activated, and the stitched output is streamed to a designated URL.  
Without the integration of an object detection network, the system achieves a frame rate of 36 FPS, with six-core CPU utilization exceeding 50%, GPU usage at 19%, and memory consumption of approximately 250 MB. After incorporating the object detection network, the frame rate decreases to 27 FPS, CPU utilization increases to over 65%, GPU usage rises to an average of 68.7%, and memory usage reaches approximately 678 MB. Experimental results are illustrated in Figures 20 and 21.
# QoE Modeling on Split Features with Distributed Deep Learning
###### Abstract  
The development of Quality of Experience (QoE) models using Machine Learning (ML) is challenging, since it can be difficult to share datasets between research entities to protect the intellectual property of the ML model and the confidentiality of user studies in compliance with data protection regulations such as General Data Protection Regulation (GDPR). This makes distributed machine learning techniques that do not necessitate sharing of data or attribute names appealing. One suitable use case in the scope of QoE can be the task of mapping QoE indicators for the perception of quality such as Mean Opinion Scores (MOS), in a distributed manner. In this article, we present _Distributed Ensemble Learning (DEL)_, and _Vertical Federated Learning (vFL)_ to address this context. Both approaches can be applied to datasets that have different feature sets, i.e., split features. The _DEL_ approach is ML model-agnostic and achieves up to 12% accuracy improvement of ensembling various generic and specific models. The _vFL_ approach is based on neural networks and achieves on-par accuracy with a conventional _Fully Centralized_ machine learning model, while exhibiting statistically significant performance that is superior to that of the _Isolated_ local models with an average accuracy improvement of 26%. Moreover, energy-efficient _vFL_ with reduced network footprint and training time is obtained by further tuning the model hyper-parameters.  
Quality of Experience (QoE); machine learning; distributed split learning; distributed ensemble learning; decentralized learning +
Footnote †: 2021: 1, 165–190. [[https://doi.org/10.3390/network1020011](https://doi.org/10.3390/network1020011)]([https://doi.org/10.3390/network1020011](https://doi.org/10.3390/network1020011))  
Footnote †: 2  
From generation to generation, every network technology is expected to outperform its predecessor in terms of capacity, coverage and QoE, accompanied by increased demands for sustainability [3]. Furthermore, networks are expected to become increasingly dense [4], thus requiring greater adoption of the edge computing paradigm to reduce latency and protect data privacy. Consequently, end-to-end data collection, network performance monitoring and development of forecasting models for network performance prediction and/or QoE estimation, will be even harder due to the inherently distributed observations at different parts of the network path such as RAN (Radio Access Network), transport, and applications, which are expected to be provisioned by different business segments such as operators and service providers. Eventually, all measurements performed on different segments need to be shared to compute overall end-to-end QoE. This necessitates efficient and automated enablers such as distributed machine learning on multiple non-shared datasets that have potentially different _feature sets_. This way, a complete QoE model can be assembled leveraging end-to-end distributed single observations from different applications, networks, and energy performance sensors.
# RMS Modeling and Control of a Grid-Forming E-STATCOM for Power System Stability in Isolated Grids
## 4 Energy Storage System (ESS) Modeling and Control
### Supercapacitor Model  
Supercapacitors (SCs) are energy storage devices that offer high power density but lower energy density compared to batteries. Due to their ability to deliver stored energy rapidly and withstand tens of thousands of charge/discharge cycles, they are well suited for power applications in energy storage systems (ESSs). Figure 9 shows the conventional equivalent circuit model of an SC, which consists of an equivalent resistor \(R_{s}\) connected in series with a capacitor \(C_{sc}\). Additionally, a parallel resistor \(R_{p}\) is included at the capacitor terminals to represent the SC's self-discharge.  
This work introduces a novel control strategy that dynamically adjusts the DC voltage in response to active power exchanges between the E-STATCOM and the grid. While most RMS-based grid-forming models assume a constant DC bus, typically modeled as an ideal DC source, this assumption does not accurately reflect real-world conditions, where energy storage systems (ESSs) exhibit dynamic behavior. By integrating a control mechanism that allows the DC voltage to vary according to power exchanges, this approach enhances the accuracy of the SC representation while maintaining computational efficiency for large-scale stability studies.  
The dynamic equation for the SC describes the relationship between the voltage \(e_{dc}\) and the current \(i_{sc}\) in the capacitor \(C_{sc}\), as given by Equation (23):  
\[C_{sc}\frac{de_{dc}}{dt}=i_{sc} \tag{23}\]  
Expressing Equation (23) in p.u. and calculating \(E_{dc}\) as a function of \(I_{sc}\) yields Equation (24),  
\[E_{dc}(t)=E_{dc}(0)+\frac{1}{\tau_{sc}}\int I_{sc}dt \tag{24}\]  
so that \(E_{dc}\) acts as the state of charge (SoC) of the SC, with the SC time constant in Equation (24), \(\tau_{sc}\), defined as the product of \(C_{sc}\) and the DC impedance base \(Z_{b,dc}\).  
\[\tau_{sc}=C_{sc}Z_{b,dc} \tag{25}\]  
Figure 9: SC equivalent circuit.  
The impedance base \(Z_{b,dc}\) is calculated as \(V_{DC,b}^{2}/P_{DC,b}\). It is worth noting \(\tau_{sc}\) can also be defined as the ratio between the energy (in MJ) and the nominal power (in MW) of the SC.  
In Equation (24), \(I_{sc}\) is obtained as the sum, with a negative sign, of the current \(I_{dc}\) and the self-discharge current \(E_{sc}/R_{p}\), with \(R_{p}\) in p.u., as given by Equation (26):  
\[I_{sc}=-\left(I_{dc}+\frac{E_{sc}}{R_{p}}\right) \tag{26}\]  
Likewise, from \(I_{dc}\) and \(R_{s}\) (p.u.), the DC voltage \(V_{dc}\) is equal to  
\[V_{dc}=E_{dc}-R_{s}I_{dc} \tag{27}\]  
Figure 10 shows the SC block diagram in accordance with the previous equations, where \(I_{dc}\) is the input and \(V_{dc}\) and \(SoC\) are the outputs, all expressed in p.u.
## 2 Piecewise Regularized Inversion Method
In the case where \([h_{i,\min},h_{i,\max}]\) is known, it is only necessary to select the inner layer thickness range, i.e., \([\delta_{i,\min},\delta_{i,\max}]\), to obtain the number of inner layers, \(J_{i}\), in the \(i\)-th segment. As depicted in Figure 3, for the selection of \([\delta_{i,\min},\delta_{i,\max}]\), it primarily depends on two factors: one is selecting appropriate upper and lower boundary values based on the required layering accuracy in actual exploration, and the other considers the fact that the inversion accuracy of transient electromagnetic data gradually decreases with increasing depth. Hence, the threshold value for the change in layer thickness is set to increase  
Figure 1: Geoelectric structure model diagram. Figure 2: Comparison of initial model building methods. with depth (e.g., during synthetic data simulation, it is set as within 200 M, more than 200 M, and more than 400 M of predicted depth, and the layer thickness thresholds are set to [5; 25], [10; 50], and [20; 100], respectively). Second, since transient electromagnetic inversion exhibits highly nonlinear characteristics, introducing a regularization constraint for model smoothing is crucial to reduce false anomalies and enhance the model fitting effect. Therefore, segmental regularization constraints are incorporated to construct the objective function, expressed as follows:  
\[F=\phi_{d}+\sum_{i=1}^{N-1}\left(\lambda_{i}\psi_{i}\right) \tag{5}\]  
where \(\psi_{i}\) is the vertical model constraint inside the model in the segment, and \(\lambda_{i}\) is the regularization weight of the segment model.