esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Current Detection Schemes with Reduced Sensors
### (\(N_{p}+1\))/2 Hall Effect Sensors-Based Current Detection Scheme for OMSRM
With \(\mathbf{K}_{\text{Sp,e3}}\), two conductors pass through the sensors \(\mathbf{HS}_{1}\) and \(\mathbf{HS}_{3}\) in a different direction, and thus the measured current ranges are also symmetric with respect to 0 A; two conductors pass through the sensor \(\mathbf{HS}_{2}\) in a direction, and the other one in the other direction, and the measured current range, two-thirds and one-third of which are respectively positive and negative, is not symmetric with respect to 0 A but still matches the measuring range much better than that with \(\mathbf{K}_{\text{Sp,e3}}\). Therefore, \(\mathbf{K}_{\text{Sp,e3}}\) is preferred compared with \(\mathbf{K}_{\text{Sp,e2}}\) since the measuring range can be used much more fully, and thus the current sensing resolution is much higher. By comparatively analyzing these three matrices, we introduce an additional constraint as in (23) and define the number of non-zero elements in the coefficient matrix as the objective function \(J\) expressed as (24). \[sum\left(\mathbf{K}_{\text{Sp}}(j;\cdot)\right)\in\left\{0,1\right\}\text{, }\forall j\in\left\{1,2,3\right\} \tag{23}\]  
\[J=sum\left(abs\left(\mathbf{K}_{\text{Sp}}\right)\right) \tag{24}\]  
In the two equations, \(sum(\cdot)\) denotes the function for calculating the sum of all elements of a matrix, \(abs\left(\cdot\right)\) denotes the function that creates a new matrix consisting of the absolute values of all elements of the input matrix, \(\mathbf{K}_{\text{Sp}}\) (\(j\):) denotes the \(j\)th row vector of \(\mathbf{K}_{\text{Sp}}\). By satisfying the additional constraint, the number of conductors through each sensor in one direction is the nearest to that in the other direction, and thus the measured current range can best fit the measuring range. Finally, the multiplexing scheme for the three sensors in a five-phase SRM drive can be determined by solving the optimization problem represented by \[ \begin{array}{ll}\min&J=sum(abs(\mathbf{K}_{\text{Sp}}))\\ \text{s.t.}&\left\{\begin{array}{l}rank(\mathbf{K}_{\text{Sp}})\!=\!3\\ rank(\mathbf{K}_{\text{SpABC}})=rank(\mathbf{K}_{\text{SpCCD}})=rank(\mathbf{K}_{\text{SpCDE}})= rank(\mathbf{K}_{\text{SpDEA}})=rank(\mathbf{K}_{\text{SpEAB}})\!=\!3\\ sum(\mathbf{K}_{\text{Sp}}(j\!:\!:\!))\in\{0,1\},\,\forall j\in\{1,2,3\}\end{array}\right. \end{array}  \tag{25}\]  
Actually, \(\mathbf{K}_{\text{Sp,e3}}\) is an optimized matrix and is used in this paper. By selecting \(\mathbf{K}_{\text{Sp,e3}}\), the schematic diagram of the AHBC with the three multiplexed sensors is shown in Figure 8.  
Substituting the five submatrices of \(\mathbf{K}_{\text{Sp,e3}}\) back into (13)-(17), the phase currents can be calculated when the corresponding lower switches are turned on. The current sensor multiplexing scheme for other OMSRM with different phase numbers can be formulated by solving an optimization problem similar to (25).
###### Abstract  
The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of Sao Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN.  
wireless sensor networks; internet of things; fault tolerance; disaster forecast; machine learning 17 November 2017; Accepted: 1 March 2018; Published: 19 March 2018
## 1 Introduction
By implementing a VSG control strategy, renewable power plants or DGs can provide system services such as inertia emulation, oscillation damping, and voltage support. In doing so, conventional SM and renewable energy are unified under the same synchronization mechanism and jointly participate in the regulation of system stability and overall energy balance. Furthermore, as the parameters of VSG controlled converters are not fixed but rather tunable without physical limitations, the virtual inertia, damping factor, excitation inductance, and mutual inductance can be designed according to the properties of the integrated network [14]. VSM technology has great prospects in future power systems. It can be used as a low-level control strategy that utilizes autonomous control for the converter-based apparatus, while the communication and information network can be implemented for upper-level control. By utilizing the regulation capability that the VSG enables, the operation center can focus on global system monitoring, scheduling, and dispatch for optimal operation and the electric market [15]. This paper reviews the VSG control-enabled universal compatibility architecture for future power systems with high-penetration renewable energy generation. Section 2 contains traditional operation knowledge and the technical gap for smart grid integration. In Section 3, a mathematical model and the synchronization mechanism of synchronous machines are illustrated for VSG modeling. Then, the applications of the VSM algorithm--wind turbines, photovoltaic, energy storage, electrical vehicles, and demand-side response--are introduced in Section 4. In Section 5, the grid service provided by VSG-based renewable DGs and the microgrid in the transmission and distribution network is introduced. At last, for evolving transition into a power electronic-based future smart grid, the integration of high-penetration renewable energy generation into the current power system under a VSG-enabled universal compatibility architecture is proposed in Section 6.
## 2 Materials and Methods
### Methodology of Experimental Research
These components were combined to analyze human gait. A gyroscope and an accelerometer built into each insole allow the comparison of foot orientation and inertia forces during human walking. The experiment uses special cases of human gait, from a standard walk in a horizontal plane to climbing up a staircase with a damaged knee or hip. The acquired data were used as test data for the performance evaluation of the signal-processing system and the local network. Additional experiments were performed to determine the wireless communication range between ESP32 microcontrollers in real operating conditions. The main goal was to analyze the quality of communication when devices are placed at different distances and rooms separated by different numbers of walls. The purpose of the experiment was to check how an increase in distance and the appearance of some obstacles, like walls between the transmitter and the receiver, will affect the quality of communication. Five different locations were chosen for the study: a straight long corridor, an open field along the busy road, and adjacent rooms separated by one, two, or three solid 250 mm thick concrete walls. The experimental procedure starts with placing the sensor in one room and the receiver in another and transmitting real-time data to monitor the connection. The experiment used specialized software tools to monitor and measure communication quality, including data transmission delay and packet loss. After placing the transmitter and receiver in the desired locations, the data were transmitted in real time, allowing the researchers to monitor the quality of the communication in different conditions. During these experiments, the total delay and a number of successful communications were noted (communication includes the master microcontroller sending out a control command and a request for data and the slave microcontroller sending out a sensor's data message). The delay was measured in the master microcontroller from the moment of sending out the first command message up to receiving and reading data messages from both slave microcontrollers. If a data message is not received from a slave microcontroller during an allowed timespan of 30 ms, communication is considered to have failed. In case of communication with one or both slave microcontrollers failing, the delay is not noted, assuming that communication failed; communication is noted as successful only when data are received from both slave microcontrollers.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 1
#### 5.2.3 Stability Period  
Table 3 and Figure 20 show the network stability period results for EEUC, CHEF, and FQ-UCR. The proposed protocol stood out with a significant improvement in stability. For the network with 100 nodes, the stability period was 380 rounds for EEUC, 357 rounds for CHEF, and 805 rounds for FQ-UCR, which was the longest. FQ-UCR surpassed the others, offering a 262.1% increase over EEUC and a 285.4% increase over CHEF. As the node count grew to 200 and 400, FQ-UCR continued to lead in stability. These results demonstrate the FQ-UCR algorithm's effectiveness in prolonging network stability, thereby enhancing overall performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Sensor Nodes** \\ \end{tabular} } & \multirow{2}{*}{**Protocol**} & \multicolumn{2}{c}{**FND**} & \multicolumn{2}{c}{**HND**} & \multicolumn{2}{c}{**LND**} \\  & & **(First Node** & **(Half of Nodes** & **(Last Node** & **(Last))** & **Stability** \\  & & **Death)** & **Death)** & **Death)** & \\ \hline \multirow{3}{*}{100 Nodes} & EEUC & 380 & 659 & 754 & 380 \\  & CHEF & 357 & 762 & 1170 & 357 \\  & FQ-UCR & 1376 & 1922 & 2477 & 1376 \\ \hline \multirow{3}{*}{200 Nodes} & EEUC & 577 & 800 & 827 & 577 \\  & CHEF & 781 & 980 & 1136 & 781 \\  & FQ-UCR & 1322 & 1876 & 2497 & 1322 \\ \hline \multirow{3}{*}{400 Nodes} & EEUC & 581 & 897 & 938 & 581 \\  & CHEF & 841 & 1079 & 1326 & 841 \\ \cline{1-1}  & FQ-UCR & 1284 & 1879 & 2499 & 1284 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of network stability period of protocols in Scenario 1.  
Figure 19: Network lifetime comparison for 400 nodes in Scenario 1.
# OTFS-Based Handover Triggering in UAV Networks
## 7 Numerical Analysis
### Effect of Channel Estimation Error  
Figure 18 studies the effect of channel estimation error on the average throughput performance of the proposed scheme using 6 UAVs and an HST speed of 500 Km/Hr under different \(P_{th}\) values. In the aforementioned mathematical analysis, we proved that both \(MSE_{\mathcal{H},LS}\) and \(MSE_{\mathcal{H},MMSE}\) are less than \(\sigma_{g}^{2}\). Thus, we will measure the average throughput against the normalized channel estimation error \(\left(MSE_{\mathcal{H}}/\sigma_{g}^{2}\right)\) ranging from 0 to 1. In this context, 0 means exact CIR information without any channel estimation error, and 1 indicates the utmost channel estimation error where \(MSE_{\mathcal{H}}\) reaches \(\sigma_{g}^{2}\), which does not occur, as proven in (42) and (53). From these figures, as the channel estimation error increases, a small degradation in the average throughput is obtained for all tested \(P_{th}\) values. This comes from the marginal effect of the channel estimation error on the performance of the proposed scheme, as proven in (42) and (53) using LS and MMSE OTFS channel estimators. For example, the average throughput only decreases by 4.8%, 3.38%, 2.76% for \(P_{th}=\) 0 dB, 1.5 dB, and 3 dB, respectively, when increasing the normalized channel estimation error from 0 to 1. This figure shows that as \(P_{th}\) increases, the rate at which average throughput decreases slows down.
## 3 Results
### Preliminary Results  
Four preliminary simulations with different particle resolutions have been carried out with \(R_{0}=100\) [\(\mu\)m], \(P_{\infty}=5\) MPa and \(\gamma=1.2\). These values are chosen to match those commonly used in computational studies [14; 17; 52]. Figure 2 shows the total collapse time and the max jet speed (defined as \(V=|\mathbf{v}|\)) for the different resolutions. Particle resolution is specified as the ratio between the initial particle spacing \(dL\) and the initial cavity radius \(R_{0}\).  
Both the collapsing time and max jet velocity converge for \(dL/R_{0}\geq 40\). This resolution value was also independently found by [PERSON] et al. and [PERSON] et al. At this resolution, the model correctly reproduces jet formation [5; 7]. Figure 3 shows that as the collapse proceeds, wall proximity produces a pressure difference between the top and the bottom of the cavity. This pushes down the upper-side of the cavity inducing the formation of a high-velocity/low-pressure jet.  
In Section 3.3 we show simulations at higher resolution. In fact, at higher resolutions, we will be able to uncover more details of the velocity pattern occurring during the collapse.  
Figure 2: Resolution convergences for collapse time (a) and max jet speed (b).
# A Stronger Adaptive Local Dimming Method with Details Preservation
## 3 Method
### Adjustable Backlight Extraction Module
#### 3.1.2 Optimal Backlight Selection
The pixel with luminance less than \(P_{1}\) is considered as dark area and the pixel with luminance larger than \(P_{2}\) is considered as bright area,  
Since the backlight value ranges in 0-255, we use the exponent of 2 as the adjustment step to adjust the target backlight \(\mathbf{BL_{0}}\). The process is expressed as Equation (12). \[ \begin{cases}\mathbf{BL}_{i}\left(m,n\right)=\begin{cases}\mathbf{BL_{0}}\left( m,n\right)-2^{i}\quad\mathbf{BL_{0}}\left(m,n\right)<P_{1}\\ \mathbf{BL_{0}}\left(m,n\right)+2^{i}\quad\mathbf{BL_{0}}\left(m,n\right)>P_{2 }\\ \end{cases}\end{cases}  \tag{12}\]  
\[ \begin{cases} min\left(\mathbf{PA_{max}}\left(m,n\right),\mathbf{BL}_{i}\left(m,n \right)\right)\quad\mathbf{BL}_{i}\left(m,n\right)>\mathbf{PA_{max}}\left(m,n \right)\\ max\left(\mathbf{PA_{min}}\left(m,n\right),\mathbf{BL}_{i}\left(m,n\right) \right)\quad\mathbf{BL}_{i}\left(m,n\right)<\mathbf{PA_{min}}\left(m,n\right) \end{cases}  \tag{13}\]  
where \(i=1,2,\cdots,8\), \(\mathbf{BL}_{i}\) means the \(i_{th}\) adjusted backlight based on \(\mathbf{BL_{0}}\). \(\mathbf{PA_{max}}\) and \(\mathbf{PA_{min}}\) are used to prevent the adjusted backlight from making poor display quality. Contrast Ratio (CR) and Dynamic Range (DR) are two objective indicators of display quality. Both reflect the change of brightness ranging from dark to bright, and the higher are CR and DR, the wider is the brightness range. To select the optimal backlight, the display luminance is first measured under all adjusted backlights. Then, CR and DR of each measured luminance are calculated. The adjusted backlights with the top three performances based on the results of CR and DR are selected. Finally, the optimal backlight is selected from the three adjusted backlights by display quality subjectively. The process is shown in Figure 5.  
The subjective evaluation is set as follows. The three selected adjusted backlights are demonstrated on the display prototype. Observers are asked to vote for the optimal backlight based on visual perception over details, contrast, and brightness. The backlight with the largest number of votes is determined to be the optimal backlight. Given that subjective feeling is susceptible to factors such as gender, age, occupation, and surroundings, the selection was done by 16 observers who are non-experts in image and video processing field. Their ages range from 22 to 30, with eight males and eight females. All of them have normal visual ability, that is, none of them have eye problems such as color blindness, color weakness, shortsighted, etc.
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 2 Related Works  
### Bridge-Damage Identification Using Deep Learning  
One previous study that attempted to develop an automated bridge-inspection system proposed using an unmanned aerial vehicle for bridge inspections and applying deep learning algorithms to the results [13]. Another study used deep learning to detect cracks in concrete structures used CNNs to develop a model for detecting damage in bridge structures, achieving high performance in identifying damaged areas from images of entire bridges [14; 15]. [PERSON] et al. demonstrated that transfer learning could improve bridge-damage-detection models, even with a limited dataset [16]. They proposed a real-time bridge-damage-detection system using deep learning, employing Faster R-CNN to detect damage in bridge images and evaluate structural integrity instantly. Recent studies have also explored using generative adversarial networks for image generation and data augmentation to detect the extent of actual structural damage more accurately [17].  
In addition, research focused on automatic crack detection in concrete using CNNs has led to the development of a deep learning-based method for detecting road defects from ground-penetrating radar images [18]. [PERSON] et al. have discussed a method for identifying structural bridge damage utilizing deep learning with frequency-response-function data [19]. Another proposed model, CrackNet, is a deep learning-based approach for crack detection in structural materials that has produced high-accuracy results for various crack types and defects [20].
## 1 Introduction  
Urinary incontinence is a condition which is characterized as the involuntary loss of urine and affects over 200 million people worldwide [1, 2]. Even though it can be found in both genders, it has twice as much prevalence in women than in men [3, 4]. It affects the muscular and urinary systems as well as quality of life (QoL), including social and sexual life [5, 6]. Most patients prefer to seek conservative treatment, whereas fewer may prioritize surgery [7]. One of the conservative methods widely used is pelvic floor muscle (PFM) exercises, which have long been identified as the first line of treatment for urinary incontinence [8]. This has been supported by several studies, which have found that PFM exercises have a significant positive effect in women suffering from urinary incontinence [9, 10, 11].  
Biofeedback (BF) is regularly used as an adjunct to rehabilitation programs used for urinary incontinence [12]. The role of BF is to convert biological signals into comprehensible visual, auditory and/or vibratory ones. Direct information about the condition of the PFMs is provided, permitting patients to \"see inside their body\" [13, 14, 15]. Biofeedback devices educate women on how to correctly contract their muscles during the exercise [13, 14].  
Indeed, researchers of experimental studies [16; 17; 18; 19; 20] found improvements in symptoms in women with urinary incontinence after PFM exercises with BF, whereas according to the results of one systematic review [21], PFM exercises with BF were not superior to alternative interventions, such as no training, PFM exercises alone or vaginal electrostimulation.  
However, these studies are restricted to women with stress urinary incontinence (SUI) and, as a result, these conclusions cannot be generalized to other types of urinary incontinence (such as urge or mixed urinary incontinence). Furthermore, in [PERSON] et al.'s (2018) systematic review [21], not all of the included studies compared PFM exercises with BF to PFM exercise without BF, as some group interventions consisted of electrical stimulation, kinesthetic training, etc., while other comparative groups included no training at all. Thus, it is not clear whether PFM exercises with BF are better than isolated PFM exercises (without BF). In addition, outcomes such as the endurance and functionality of the pelvic floor muscles, satisfaction and commitment to the treatment have not been explored [21]. No other systematic review was found that clearly investigated, according to the above, the effectiveness of PFM exercises in combination with BF training for the treatment of urinary incontinence across women. Thus, this area merits further research.  
Given the above, urinary incontinence is a condition that negatively affects the muscular and urinary systems as well as the QoL of a large number of women. According to this, we conducted a systematic review investigating the effects of PFM exercises with biofeedback in women with urinary incontinence (stress, mixed and urge) in PFM strength and other muscular variables, QoL and other clinical and functional parameters compared to PFM exercises alone (without BF).
# Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Core
## 3 Application and Analysis
### Prediction Performance Metrics  
The performance of the machine learning model was evaluated using the following statistical metrics: explained variance score (\(EVS\)), mean absolute error (\(MAE\)), mean square error (\(MSE\)), and the determination coefficient (\(R^{2}\)). The value of \(EVS\) is in the range of [0, 1]. Values closer to 1 mean that the independent variable can explain the dependent variable of the variance, and the lower the value, the worse the effect. \(MAE\) is used to assess the degree of closeness between the prediction results and the real dataset, and a lower value indicates a better fit. \(MSE\) is the mean of the errors of the fitted data and the original data corresponding to the sample points, and the lower the value, the better the fit. \(R^{2}\) is the variance score of the explanatory regression model, and its value ranges from [0, 1]. Values closer to 1 mean that the independent variable can explain the variance change of the dependent variable, and lower values mean that the effect is worse. The mathematical expressions of the performance metrics are as follows:  
\[EVS=1-\frac{\mathit{Var}\{y\_p-y\_t\}}{\mathit{Var}\{y\_p\}} \tag{5}\]  
where \(y\_p\) is the predicted output; \(y\) is the true target output; and \(\mathit{Var}\) is variance, the square of the standard deviation.  
\[MAE=\frac{1}{n_{\text{samples}}}\sum_{i=0}^{n_{\text{samples}}-1}\lvert y\_t-yi \_p\rvert \tag{6}\]  
where \(yi\_p\) is the predicted value of the \(i\)-th sample, and \(yi\_t\) is the corresponding true value.  
\[MSE=\frac{1}{n_{\text{samples}}}\sum_{i=0}^{n_{\text{samples}}-1}\left(y\_t-yi \_p\right)^{2} \tag{7}\]  
where \(yi\_p\) is the predicted value of the \(i\)-th sample, and \(yi\_t\) is the corresponding true value.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(yi\_t-yi\_p\right)^{2}}{\sum_{i=1}^{n}\left( yi\_t-\overline{y}\right)^{2}} \tag{8}\]  
where \(yi\_p\) is the predicted value of the \(i\)-th sample, and \(yi\_t\) is the corresponding true value. For \(n\) total samples, \(\overline{y}=\frac{1}{n}\sum\limits_{i=1}^{n}yi\_t\) and \(\sum\limits_{i=1}^{n}\left(yi\_t-yi\_p\right)^{2}=\sum\limits_{i=1}^{n}e_{i}^ {2}\).