esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Test Results for Diversion Function Pipe Segment Models
### Analysis of Total Stresses on Both Sides of the Pipe Segment under Different Differential Pressures  
#### 4.4.1 Distribution and Variation in Total Stress on Both Sides of the Pipe Segment  
The quantitative analysis of the change in total stress at each measurement point as a function of the pressure difference between the two sides of the pipe segment is required. Figure 19 illustrates that as the differential pressure rises from 0.5 kPa to 2.5 kPa, the total stresses on the left side of the pipe segment at 0.3 m, 0.5 m, and 0.7 m from the bottom increase by 1.5%, 1.6%, and 1.5%, respectively. Notably, the increase in the total stress on the left side of the pipe segment is comparatively less pronounced. The total stress around  
Figure 18: Variation in pore pressure. (**a**) Pressure difference 0.5 kPa. (**b**) Pressure difference 1.0 kPa. (**c**) Pressure difference 1.5 kPa. (**d**) Pressure difference 2.0 kPa. (**e**) Pressure difference 2.5 kPa. (**f**) Changes in pore pressure difference on both sides of pipe segments at different depths.  
the pipe segment at 0.3 m, 0.5 m, and 0.7 m from the bottom on the right side decreases with the increase in differential pressure. The total stress decreases by 33.6%, 34%, and 33.5%, respectively. The change in the total stress on the right side of the pipe segment is more affected by the increase in differential pressure.  
When the pressure difference between the two sides is altered, the sand and water levels on the left side remain largely unchanged, while the total stress on the left side is only minimally affected. In contrast, the water level on the right side declines, resulting in a notable increase in the effective stress on the right side and a reduction in the pore water pressure. However, the decline in the pore water pressure is greater than the rise in the effective stress, leading to a decrease in the total stress on the right side as the pressure difference increases.
## 2 Various Types of Flexible Sensors (Temperature, Pressure, Humidity and Chemical)
### Overview of Current Materials and Fabrication Methods
The current research area involves enhancing the materials used in fabricating these sensors. These include nanomaterials such as graphene [11; 17], carbon nanoparticles, MXene [20], cellulose nanocrystals [20], copper nanowire [21], silicon nanomembranes [11], cellulose nanofibrils [9], silver nanomaterials [3; 22], gold nanoparticles [3], nickel nanoparticles [23] and polymers, including polyimide, Kapton [17], polyglycerol sebacate [24], polyethylene glycol, polyvinyl alcohol [25], polydimethylsiloxane (PDMS), polyurethane (PU), polyethylene terephthalate and hydrogels [25]. Various colloidal nanocrystal-based sensors have been developed including pressure, temperature and strain gauge sensors [11].
## 3 Experimental Setup  
To test the proposed approach, a real transformer under different SCT conditions is employed. The experimental setup is presented in Figure 5, where the transformer, sensors, and data acquisition system (\(DAS\)) are shown. A 1.5 kVA single-phase transformer is operated at 120 V with 135 turns in its primary winding. To produce SCT damage in a controlled way, the transformer was intentionally modified. It was unwinding and rewinding; during the rewinding, various taps were obtained every five turns. Each tap generally represents a reduction in the winding, simulating a short-circuit condition. In a short-circuit condition, the length of the wire that makes up the winding is reduced. In this way, when the connection is made at the ends of the transformer primary, a healthy connection is represented, meaning 0 SCTs. On the other hand, when the connection is made at the first tap, the first faulty condition is produced, indicating a reduction of 5 turns and, consequently, simulating a faulty condition of 5 SCTs. These connections are also made for the second, third, fourth, fifth, and sixth taps, representing 10, 15, 20, 25, and 30 SCTs, respectively. It is worth noting that the higher the number of SCTs, the greater the severity of the damage. Additionally, an autotransformer is used to de-energize the transformer.  
A Fluke measures the current i200s current clamp, and the vibrations are measured by a tri-axial accelerometer KISTLER, model 8395A with a resolution of 400 mV/g until \(\pm\)10 g. The \(DAS\) is a National Instrument NI-USB 6211 board that allows for converting the sensor data into 16 bits for storage on a PC with a frequency sample of 6000 samples/s during 2.3 s in a stable state. Twenty files that contain one current signal and three vibration signals were obtained to test the QSA method with 0, 5, 10, 15, 20, 25, and 30 SCT. The 0 SCT case represents the healthy transformer. Table 1 presents a summary of the equipment used Examples of the current and vibration signals for the healthy transformer and 30 SCT are shown in Figure 6.  
Figure 4: KNN principle. (**a**) Calculation of distance between the data and the categories. (**b**) Category assignation of the data.  
Figure 5: (**a**) Diagram of connections, (**b**) experimental setup.  
Figure 6: Current and vibration signals of 0 SCT and 30 SCT.  
Figure 7 shows the graphical interface to apply the proposed methodology, it is divided into three sections, signals, analyzed signals, and diagnosis. In the signals section, the captured vibration and current signals are displayed. The user can choose between reading a previously stored file or performing a new signal acquisition. This section also requests the sampling frequency as well as the acquisition time. In the analyzed signals section, the window of the signal to be analyzed is shown, the user can choose between 100, 500, 1000, or 8000 samples. They can also select which sample to start analyzing from the original signals. Finally, in the diagnosis section, the user selects which classifier to use, and the result is shown through an LED light that indicates any of the SCT severities.
## 5 Simulation Model and Results
### Maximum Achievable Throughput Efficiency  
The improvement in throughput efficiency and energy efficiency due to an increase in the number of _CNodes_ is shown in Figure 6a,b, respectively. More number of _CNodes_ increases the probability of successfully delivering all the FEC packets to the destination, thus improving the performance. Figure 6a gives an idea of the minimum number of _CNodes_ required to achieve the maximum achievable throughput efficiency, _i.e._, \(nCN=9\). For \(nCN>9\), the extra _CNodes_ are unable to enhance the throughput efficiency further. Rather, they decrease the energy efficiency by adding to the overall energy consumption of the network, as clearly depicted in Figure 6b. For very dense networks, these results can be utilised to set the limit for the _Look-Up_ list length, defined earlier, so that once the optimum number of _CNodes_ become available the _CNode_ discovery process is skipped to directly go into cooperation, thus avoiding time and energy wastage.
## 2 Separation of the Multichannel Active Noise Control Problem into Two Subproblems
### Separating the Active Noise Control Problem  
The aim of ANC is to obtain optimal control filters that yield the optimum performance based on certain objective functions [22]. Before deriving the optimal solution for the control filters, we delve into the formation of the multichannel ANC problem. As shown in Figure 2, the ANC problem can be readily divided into 2 subproblems. In the subproblem 1, our aim is to obtain the optimal secondary outputs \(\mathbf{Y}\) from input signals \(\mathbf{D}\) and secondary paths \(\mathbf{S}\) so that the cost function can be optimized. In the subproblem 2, based on optimal secondary outputs \(\mathbf{Y}\) and reference signals \(\mathbf{X}\), we derive the optimal control filters \(\mathbf{W}\). It is intuitive that the performance of the ANC system is only directly related to secondary outputs, whereas the control filters do not contribute directly as long as desired secondary output can be obtained with the control filters. This separation helps to simplify the multichannel ANC problem as follows.  
According to [22], a multichannel ANC system usually has more or at least an equal number of error microphones (or control points) as compared to the secondary sources, i.e., \(M\geq K\), casting problem 1 into either an overdetermined or fully-determined problem. In this case, we can define the cost function as the sum (or using a weighted sum [23]) of the squared error signals:  
\[\xi=\mathbf{E}^{\mathrm{H}}\mathbf{E}, \tag{2}\]  
where superscript \({}^{\mathrm{H}}\) is the Hermitian operator. The optimal secondary output \(\mathbf{Y}_{\mathrm{opt}}\) can thus be calculated by equating the derivative of (2) to zero, which is expressed as  
\[\mathbf{Y}_{\mathrm{opt}}=-{\left(\mathbf{S}^{\mathrm{H}}\mathbf{S}\right)}^{ -1}\mathbf{S}^{\mathrm{H}}\mathbf{D}. \tag{3}\]  
If we are to add a constraint to limit the secondary output power, the objective function becomes \(\xi=\mathbf{E}^{\mathrm{H}}\mathbf{E}+\beta\mathbf{Y}^{\mathrm{H}}\mathbf{Y}\), where \(\beta\) is the regularization parameter [22]. In this case, we can obtain a more generalized solution for the regularized optimization problem as \(\mathbf{Y}_{\mathrm{opt}}=-{\left(\mathbf{S}^{\mathrm{H}}\mathbf{S}+\beta \mathbf{I}\right)}^{-1}\mathbf{S}^{\mathrm{H}}\mathbf{D}\). Using (3), we can further compute the optimal residue error energy as  
\[\xi_{\mathrm{min}}=\mathbf{D}^{\mathrm{H}}\mathbf{D}-\mathbf{D}^{\mathrm{H}} \mathbf{S}{\left(\mathbf{S}^{\mathrm{H}}\mathbf{S}\right)}^{-1}\mathbf{S}^{ \mathrm{H}}\mathbf{D}. \tag{4}\]
## 4 Results and Discussions
### Initial Identification
The measurements of the surface roughness for the finished surface are implemented by using the ZYGO-Newview 8200, a white light interferometer, and the surface roughness R\({}_{a}\) of  
Figure 6: Vibration displacement signal obtained in 3D-EVC process (spindle speed was 90 rpm/min, the feed rate was 10 mm/min, misting). Figure 7: Frequency obtained by fast Fourier transform (FFT). the finished surface is used to estimate the three cutting states under various cutting conditions. In the condition of stable cutting, as shown in Figure 8a, the finished surface has a clear and regular elliptical trajectory, owing to the small cutting parameters in 3D-EVC process, and the value of the surface roughness \(\mathrm{R_{a}}=0.046~{}\upmu\)m. In the condition of a transient state, with the increase of cutting depth, the cycle of each elliptical motion starts to appear slight chatter marks, owing to the additional components corresponding to undesired vibration frequencies (150 Hz and 200 Hz) as shown in Figure 8b. Meanwhile, the value of surface roughness also increased with the augment of cutting depth \(\mathrm{R_{a}}=0.245~{}\upmu\)m. In the condition of a chatter state, the elliptical motion trajectory becomes irregular in the finished surface, as shown in Figure 8c, the undesired vibration causes the discontinuous contact between the tool tip and the work-piece in the cycle of each elliptical motion, and the intermittent chopping-elliptical-vibration cutting is formed in the process of 3D-EVC, which corresponds to the large value of surface roughness \(\mathrm{R_{a}}=0.853~{}\upmu\)m.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 3 Methodology
### Feature Extraction  
In the domain of NLP tasks, the step of text feature extraction represents a critical part [47]. Textual data are not computable and cannot be directly processed by learning models. In fact, data must be expressed numerically so that they can be processed using different learning models. Therefore, we must somehow convert the given textual data into numerical values and a computer-readable format. This is implemented in the feature extraction step using various techniques. A meaningful feature can be extracted using these techniques to transform texts into numerical vectors. We used the word embedding method to convert the words in our dataset into numerical vectors.  
Word embedding is a technique used to represent words. It works by capturing the relationships between words in textual data. These relationships suggest that words that occur in similar contexts have similar semantics. Therefore, words with similar meanings are represented [28]. To construct this representation, each word is individually mapped into one embedding. This embedding is represented as a d-dimensional vector with numerical values. We conducted this process by using an embedding layer, which is typically included as part of deep neural network models [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]. There are three parameters that must be specified for the embedding layer, namely, the input dimension, the output dimension, and the input length, and they are defined as follows:  
* Input dimension: This is the number of all unique words in textual data, usually called the vocabulary size.
* Output dimension: The dimension of the generated vector is determined empirically, and it is usually set from 100 to 300.
* Input length: This is the number of words in each input sequence that has the maximum length.  
We performed the following sequential steps while preparing the textual data to be input into the embedding layer:  
Figure 3: Data preprocessing steps.  
We applied tokenization to each input sequence; this is the process of splitting a text sequence into separate words or tokens.
2. We built an indicating dictionary using all the vocabulary from the whole input dataset to be assigned into unique indices. As a result, we obtained a known vocabulary size that defines the input dimension parameter of the embedding layer.
3. We applied the padding method, as the length of each input sequence in the dataset was expected to differ. For consistency, we padded certain additional tokens at the end of each input sequence to unify their lengths to the maximum length. As a result, the lengths of all input sequences were equal to the maximum length that defines the input length parameter of the embedding layer.  
Next, these padded sequences with fixed lengths were ready to be passed to the embedding layer. Then, the embedding layer delivered its output as a two-dimensional numerical vector for each word in the input sequence.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### SI Applications in Sensor Deployment  
Reference [22] proposed a method based on fuzzy clustering and PSO to reduce network interruption. Compared with the traditional FCM method, GA and PSO algorithms were introduced to optimize FCM. The hybrid FCM-PSO algorithm is repeatedly executed until the optimal topology of the sensor is determined. Simulation results showed that this method can reduce energy consumption and improve the connection rate of CH to BS, non-CH to CH.  
Literature [36] proposed a PSO-based coverage algorithm in static WSNs with randomly deployed sensors. This method partitions networks into grids and calculates the coverage rate of each grid. Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP-complete [37]. Then, it adjusts the node sensing range until the coverage rate is higher than 90% and also adjusts the sensing radius with PSO. Compared with traditional PSO-based coverage algorithm methods, this method improves coverage and reduces energy consumption. However, this method may not be suitable for networks where obstacles exist.  
In [38], an ACO deployment problem-solving method combined with local search (LS) was proposed. This approach considers the deployment of a WSN that meets the specified minimum reliability level within the task time at the minimum network deployment cost. In their paper, it proposes the problem of a minimum cost reliability constraint for node deployment, proves that the problem is NP-complete, and proposes a local search algorithm. It proved that the ACO solution exhibits better quality than the greedy algorithm.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 2 Experimental Method
### Data Analysis  
To study the effect of load variation on gait recognition, the BPNN models were trained and tested with the sEMG data from four different loads under three different speeds (12 trials for each subject and 120 trials in total). The inputs of the BPNNs models were the \(RMS\) and \(iEMG\) of the four muscles, and the outputs were the five gait phases. In each experiment, 80% of the data was randomly selected for use in training the BPNN models, and the remaining 20% of the data was used to test the models. In the training phase of BPNNs, a 10-fold cross-validation method was employed to test the accuracy of the model, in which the selected data was randomly divided into 10 parts, with nine parts being used to train the model, and the remaining part being used for validation [17; 32]. The average recognition accuracy of a single trial was acquired by averaging the accuracy of the five gait phases of the trial, which were gained separately. For a comparison, the SVM and k-NNs models were used to deal with the same data.  
As for the accuracy of the gait recognition, univariate analysis of variance (UNIANOVA) was employed to analyze the effect of the load variation and speed on the accuracy of the gait recognition. If the effect was significant, the least significant difference (LSD) post hoc test was carried out to fully explore this effect. All data were analyzed by SPSS version 24.0 for Mac (SPSS Inc., Chicago, IL, USA), and the statistical significance was determined at \(p\) < 0.05.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 4 Sensing Algorithms Generation: Target Detection and Localization
### Target Detection Algorithm  
A target or user is detected when the energy of the effective channel \(\hat{\text{h}}_{\text{eff}}(n)\) at any given temporal sample \(n\) exceeds a pre-established threshold, denoted by \(\xi\). A Boolean variable \"withTarget\" is set to true in the algorithm when a target is detected and is false otherwise.  
The determination of the threshold is of paramount importance and requires meticulous calibration. In this work, the threshold has been chosen as \(\xi=\Delta_{\xi}\hat{E}\), where \(\hat{E}=\frac{1}{K}\sum_{t=1}^{K}||\hat{\text{h}}_{\text{eff}}(n)||^{2}\) represents the mean energy of \(\hat{\text{h}}_{\text{eff}}(n)\) over time and \(\Delta_{\xi}\) corresponds to a selected constant factor. This formulation dynamically adjusts the threshold \(\xi\) to match the scenario's noise power. For the sake of clarity, a summary of the above steps is provided in Algorithm 2.  
```
Input:\(\hat{\text{f}}_{\text{eff}}(n)\), \(\Delta_{\xi}\) Output:withTarget
1. withTarget = false
2. \(\hat{\text{E}}=\frac{1}{k}\sum_{n=1}^{K}\|\hat{\text{h}}_{\text{eff}}(n)\|^{2}\)
3. \(\xi=\Delta_{\xi}\hat{\text{E}}\)
4. for\(n=1\)to\(N\)do if\(||\hat{\text{h}}_{\text{eff}}(n)||^{2}>\zeta\)then  withTarget = true break endfor
```  
**Algorithm 2** Target detection algorithm
## 2 Materials and Methods  
### Materials  
Ion exchange membranes that were used during the experiments were supplied by Fujifilm Manufacturing Europe B.V (Tilburg, The Netherlands). Sodium chloride (NaCl, \(\geq\)99.5%), magnesium chloride (MgCl\({}_{2}\), \(\geq\)99.8%), sodium sulfate (Na\({}_{2}\)SO\({}_{4}\), \(\geq\)99%), trimesoyl chloride (TMC, \(\geq\)98%), m-phenylenediamine (MPD, \(\geq\)99%), and hexane (anhydrorous, 95%) were acquired from Sigma-Aldrich (Overijse, Belgium). Basolite(r) Z1200 (ZIF-8) was produced by BASF (Antwerpen, Belgium) and acquired from Sigma-Aldrich. Deionized (DI) water was applied throughout the experiments. All of the reagents and solvents were commercially available and were used as received.
# Improving Decodability of Polar Codes by Adding Noise
## 2 Related Works
### GA Based Decoding Algorithms  
Since the overall search strategy in the optimization process of the GA does not depend on gradient information or other auxiliary knowledge [43], it provides a general framework for solving complex system problems in various areas, such as combinatorial optimization, machine learning, signal processing, and adaptive control.  
The GA has been used in LDPC decoding [43; 44]. However, its application in polar decoding is still in the early stages. The authors in [33; 35] employed the GA to generate perturbation noise, so as to perturb the received signal that failed to decode. Inspired by the GA of AI technology, the authors in [45] used the mean of the log-likelihood ratio (LLR) distribution as the fitness function of each individual to update the successive cancellation flip (SCF) decoding algorithm of the population. Simulation results verified that the improved SCF decoding algorithm based on new constructed candidate flipping positions sets can achieve competitive decoding performance compared with some state-of-the-art SCF decoding algorithms.  
In addition, the GA has also achieved good results in polar code constructions [46; 47; 48; 49]. In [46], the authors proposed a GA-based framework to construct polar codes using the BER as the fitness function of the GA, thereby reducing the size of the SCL list and the number of iterations. In [47], the authors proposed a scheme for constructing polar codes based on a hash table update population, which reduced the computational complexity of repeated candidates. Aiming at the joint detector and decoder over the resistive random-access memory (ReRAM) channel model, effective polar codes were constructed using the GA in [48].
## 6 Simulation
### Target Localization Error
#### 6.2.2 TOA/AOA with/without ANN Refinement  
Compared with Figure 13, Figure 14(left) shows that the average localization error is significantly suppressed by applying the TOA information. Considering \(n=2\) and \(\theta_{th}=9.98\) degree, the average localization error of TCAT using AOA/TOA information with ANN refinement is about \(36\%\) less than that of TCAT using AOA information with ANN refinement. Nonetheless, as the deviation of TOA measurement increases, the localization performance of TCAT with joint TOA/AOA is approaching to that of TCAT with AOA only (Figure 14(right)). This is attributed to the fact that a noisy TOA measurement may lead to a broader sample area (_i.e._, a lower particle density) for Bayesian filtering, which enlarges the localization error.  
Figure 14: The comparison of target localization error with/without ANN refinement (**left**); the performance comparison of TCAT with joint AOA/TOA and TCAT with AOA only with varying the deviation of TOA measurement (**right**).  
Figure 13: The comparison of target localization error with/without ANN refinement.  
Given the variance of angle estimation \(\sigma_{\theta}=0.5\) and with varying the uncertainty of distance estimation \(\sigma_{d}\), here we consider the best achievable performance and the tracking performance with TCAT using TOA/AOA information. Notice that the CRLB and the performance of the proposed method tend to merge together with an increasing measurement uncertainty \(\sigma_{d}\). Due to a small sample size for particle filtering and the limited capability of a sensor, as shown in Figure 15, fundamental problems when locating mobile target in a network are to estimate the distance between the reference sensors and the target and to determine the angle of arrival of the signals since accurate location estimates highly rely on precise TOA/AOA measurements and the processing capability of a sensor node.