esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Electrical Signal Classification Based on Waveform Similarity
## 2 Related Works
### Time Series Waveform Recognition and Classification
#### 2.2.3 Features Extraction and Classification
A smaller value of SampEn indicates more self-similarity in data set or less noise [63]. Correlation dimension (CD) is employed initially to determine whether the time series is a chaotic process. The CD gives the minimum number of independent variables to describe the dynamical behavior of the system. To calculate CD, it is needed to select proper embedded dimension m, time delay t, and r for the distance between two vectors of m-dimensional state space. We can estimate the CD by correlation integral. Commonly, if CD is infinite, then time series is not chaotic process. However, a finite CD does not necessarily imply having chaos, it provides a method to detect the difference of dynamical behavior [64]. Lyapunov exponents provide a quantity that characterizes the rate of separation of infinitesimal close trajectories to a stationary solution. It is related to divergence or convergence of exponents in phase space. If there is one more positive Lyapunov exponent in the time series, it is defined as chaotic. A positive LLE indicates chaos. The LLE may be non-positive, implying regular system dynamics [65; 67]. A negative Lyapunov exponent measures exponential convergence of two nearby trajectories [66]. Hurst exponent can be used to quantify the long-time \"memory effect\". It can classify a random or non-random self-similar time series. The range of Hurst exponent is from 0 to 1. H = 0.5 means random time series. If H > 0.5, there is an increasing statistical dependence of future increments on past increments. If H < 0.5, the time series is anti-persistent [68]. The common dimensionality reduction methods [69] include Principal Component Analysis (PCA), ICA, and linear discriminant analysis (LDA), etc. They can help reduce the feature numbers and complexity of classifying. Common classification method includes ANN [70; 71; 72; 73], SVM [70; 71], LDA [69], pattern matching [74; 75], and k-nearest neighbors [76], etc. In our research, we compared four methods for classifying AP signals. Unfortunately, plant electrical signals are very different from EEG or ECG. It is easily affected by environment factors, e.g., light, temperature, water, and nutrition, which lead to large variation in generated AP. To our knowledge, there is still not an effective and automatic detection and classification method for plant AP. Hence, we proposed our solutions in the paper.
###### Abstract  
The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach's decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the Z axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results.  
fall risk; community service; inertial sensor; multiple fall risk assessment Article
## 3 Results and Discussion
### Evaluation of the Extraction Process
The RSDs of each measurement are reported as error bars. consisted of changing the solution polarity by salt addition. This event modified the ionic strength of the solution and supported the separation between the polar phase and the apolar phase. At the same time, the time required for separation decreased. The use of a stirring magnetic plate stressed and increased the result. For this purpose, it was necessary to choose a strongly dissociated salt. Among the different salts tested at different concentrations (i.e., NaCl, KCl, CH\({}_{3}\)COONa, NH\({}_{4}\)Cl), sodium chloride was chosen because it allowed the best recoveries. Figure 4 reports the PAE/BPA recoveries (in percentage) based on the different NaCl amounts added. Looking at Figure 4, the major recoveries were obtained by adding 15 g L\({}^{-1}\) NaCl, ranging between 98 and 102%. The additions of smaller NaCl amounts were not adequate to break up the dispersion and the analyte recoveries were not so good (<90% using 5 g L\({}^{-1}\) and <95% using 10 g L\({}^{-1}\)), whereas higher NaCl amounts did not increase the analyte recoveries, which remained constant around the levels obtained using 15 g L\({}^{-1}\) (between 97 and 102% using 20 g L\({}^{-1}\)). After the formation of the two phases, the extraction solvent recovery was recovered using a special homemade laboratory glassware (Figure 5). This particular lab glassware, sealed, was built in our laboratory, with the apolar solvent being collected in the thin-diameter glass tube, in particular a special fitting with sintered glass. The deionized water, which was added to the flask on the right side, was used to grow the solvent level in the tube to recover the extraction solvent, which was then injected directly into the GC-MS.  
For a correct risk assessment of chemicals in each matrix, especially with respect to issues related to public health problems, it is necessary to have a satisfactory analytical procedure. One of the most important points is to examine the application of each procedure on a blank sample and on a real sample. In this case, the authors applied the entire procedure to a distilled water sample (\"blank solution\") and to a tap water sample (\"real sample\"), both solutions being spiked with the mixed PAE/BPA  
Figure 4: Phthalate (or phthalates ester, PAE) and bisphenol A (BPA) recoveries obtained varying the NaCl amount added for breaking the dispersion. The RSDs of each measurement are reported as error bars. Figure 5: Close-up view of the extraction solvent recovery in a special homemade laboratory glassware. standard solution (80 ng mL\({}^{-1}\) of each compound) and reaching comparable recoveries in both cases. Table 1 shows the recoveries (as %) obtained by analyzing the two matrices; they range between 97.2 and 99.6% for the blank solution with a relative standard deviation (RSD) below 4.4 and between 95.1 and 98.8% for the real sample with an RSD below 8.7 (RSDs are reported in brackets). The good agreement between the two datasets shows that the matrix effect is to be considered negligible.
## 3 Background
### Security Requirements
#### 3.3.2 Confidentiality  
Sensitive information passes through the communication channels in IoV networks. This information can have serious effects if it gets into the wrong hands. As such, an efficient system should ensure that sensitive information is secure and only the intended users can access it. Confidential information should be protected at all costs. Encryption has proven to be an effective solution in this endeavor.
## 2 Model Building
### Soft Constraints  
1. Constraint Equation (12) specifies that a course should be distributed as evenly as possible throughout the week. \[\min_{c\in C}(\sum_{w\in W}\sum_{k\in K}x_{w,k,c}-\sum_{w\in W}\sum_{d\in D} \sigma_{w,d,c})\] (12)
2. Constraints Equations (13) and (14) specify that the courses for each administrative class should be distributed as evenly as possible. \[\min_{c\in E}\frac{1}{4}\sum_{d\in D}(Y_{c,d}-\bar{Y}_{c})^{2}\] (13) \[\bar{Y}_{c}=\frac{1}{5}\sum_{d\in D}Y_{c,d},\forall e\in E\] (14)
3. Constraints Equations (15) and (16) specify that a teacher's teaching times should be distributed as evenly as possible. \[\min_{t\in T}\frac{1}{4}\sum_{d\in D}(Z_{t,d}-Z_{t})^{2}\] (15) \[\bar{Z}_{t}=\frac{1}{5}\sum_{d\in D}Z_{t,d},\forall t\in T\] (16)
4. Constraint Equation (17) specifies that the teaching time preferences of teachers should be met as closely as possible. \[\min(\sum_{t\in T}(Q_{t}-V_{t}))\] (17)
5. Constraint Equation (18) specifies that the utilization of classrooms and equipment should be maximized as much as possible. \[\max(\frac{1}{\sum_{r\in R}\sum_{k\in K}\theta_{r,k}}\sum_{r\in R}\sum_{w\in W }\sum_{k\in K}\frac{\theta_{r,k}\delta_{r,w,k}}{20N_{r}})\] (18)  
Hard constraints refer to conditions that must be strictly met; any violation of these constraints would render the scheduling plan invalid. Soft constraints refer to conditions that are desirable to meet as closely as possible; although violations of these constraints do not invalidate the scheduling plan, they do reduce the quality of the plan. Our primary goal is to adhere to all hard constraints while minimizing violations of the soft constraints.  
However, due to the complexity of combined and independent scheduling, the need to process large-scale data, and the constraints of costs and resources, most popular algorithms have faced difficulties in practical applications. For example, combined scheduling requires the coordination of multiple class and teacher schedules, which increases the complexity of the scheduling process. Additionally, processing a large volume of scheduling data requires efficient algorithms and optimization strategies, which imposes high demands on the existing methods. Traditional heuristic and greedy algorithms have often struggled to handle such complex problems and have been prone to local optima. Furthermore, schools typically face challenges of limited resources, such as insufficient classroom availability and teacher time conflicts, which further increase the difficulty of achieving an optimized scheduling solution. Therefore, the next section describes a more effective approach.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 3 Methodology
### Underwater Channel Model
#### 3.3.2 Neighborhood Information Acquisition Phase
The neighborhood table contains IDs of the neighboring nodes \(y\), the total hop count to the UW-Sink through any particular node \(y\), the residual energy of node \(y\), the expected received power at node \(y\) and the distance between node \(x\) (neighborhood table host) and that particular node \(y\). The table is sorted in ascending order of hop count subject to link condition, i.e., if the expected received power at the destination node \(x\) is above a certain threshold and it is the minimum hop count node, node \(x\) goes top of the list. The topmost node is used as the next hop relay. However, if there are more than one nodes that fulfill the above-mentioned criteria, residual energy becomes the deciding factor. That is, the node with highest residual energy among the least hop count nodes is selected as relay. This helps in delaying connectivity hole creation and in achieving low end-to-end delay. It is important to note that a node with estimated received power below the required threshold will not be considered as the relay irrespective of its hop count and residual energy. Figure 7 elaborates upon the relay selection process. Figure 6: Neighborhood information acquisition phase. Data Transmission  
During this phase, all the nodes that have data send their packet to their next hop nodes. We use the Distributed Coordination Function (DCF) to deal with the contention related issues. (Besides providing a solution to the hidden node problem, DCF also helps in dealing with the harsh underwater environment through the ACK mechanism. The ACK function in the DCF ensures that packet delivery to the next hop is confirmed. If ACK is not received the packet is resent). Every node that has a data packet to send forwards the packet to its predetermined next hop by reserving the channel through RTS/CTS exchange. A successful reception at the destination is acknowledged with an ACK frame from the receiver. As every node takes the shortest possible path, certain nodes with higher traffic volume may drain their energy early. Consequently, such sensor nodes die out early during the sensing mission, thus reducing the number of sensor nodes deployed for the sensing mission. To deal with this problem, we propose that every node maintains a minimum residual energy threshold. Upon reaching the threshold, the node refuses to act as relay for other nodes. In response, the neighboring nodes, whose next hop is a node that refused to act as relay node, reroute their traffic through other neighboring nodes. The process is explained as follows.
## 4 Results
### Adhesion  
Point and interval estimates of the parameters of the regression model (Model III or Model IV) for the variables Y\({}_{3}\) and Y\({}_{4}\) are shown in Table 9.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Estimate**} & \multicolumn{2}{c}{**The Lower Bound of**} & \multicolumn{2}{c}{**The Upper Bound of**} & \multirow{2}{*}{\(p\)**-Value**} & \multicolumn{2}{c}{**Statistical Significance of**} \\  & & **the 95\% Estimate** & & & & \\ \hline Intercept & 9.035 & 8.102 & 9.968 & \(1\times 10^{-15}\) & \\ X\({}_{1}\) & \(-\)0.016 & \(-\)0.018 & \(-\)0.014 & \(8\times 10^{-15}\) & Significant \\ X\({}_{2}\) & \(-\)0.006 & \(-\)0.011 & \(-\)0.0001 & \(5\times 10^{-2}\) & Non-significant \\ \hline \hline \end{tabular}
\end{table}
Table 8: Estimates of regression model parameters (Model II, \(\alpha\) = 0.05).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Estimate**} & \multicolumn{2}{c}{**The Lower Bound of**} & \multicolumn{2}{c}{**The Upper Bound of**} & \multirow{2}{*}{\(p\)**-Value**} & \multicolumn{2}{c}{**Statistical Significance of**} \\  & & **the 95\% Estimate** & & & & \\ \hline \multicolumn{5}{l}{_Adhesion in the direction of grain growth (Y\({}_{3}\))—Model III_} \\ Intercept & 63.08 & \(-\)21.364 & 147.524 & 0.136 & \\ X\({}_{1}\) & 0.280 & 0.116 & 0.444 & \(2\times 10^{-3}\) & Significant \\ X\({}_{2}\) & \(-\)1.112 & \(-\)1.605 & \(-\)0.620 & \(1\times 10^{-4}\) & Significant \\ \hline \hline \multicolumn{5}{l}{_Adhesion against the direction of grain growth (Y\({}_{3}\))—Model IV_} \\ Intercept & 48.113 & \(-\)30.020 & 126.246 & 0.215 & \\ X\({}_{1}\) & 0.257 & 0.105 & 0.409 & \(2\times 10^{-3}\) & Significant \\ X\({}_{2}\) & \(-\)0.972 & \(-\)1.428 & \(-\)0.516 & \(2\times 10^{-4}\) & Significant \\ \hline \hline \end{tabular}
\end{table}
Table 9: Estimates of regression model parameters (Model III, Model IV, \(\alpha\) = 0.05).  
Figure 9: Experimental [5] and theoretical values for the regression model Model II—Ti layer thickness.  
The results of the testing show that the regression model Model III (or Model IV) is statistically significant (\(p\)-value = 3 \(\times\) 10\({}^{-5}\) \(<\) \(\alpha\), or \(p\)-value = 5 \(\times\) 10\({}^{-5}\) \(<\) \(\alpha\)). The results show that both independent variables are statistically significant and significantly affect adhesion. The value of the multiple coefficient of determination is 0.718 (or 0.759).
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 5 Guide for Choosing a Correct Accelerometer
### Technology  
The first step in technology selection involves determining the measurement type. There are three popular technologies that people use to measure acceleration.  
* Piezoelectric accelerometers are useful tools for measuring shocks and vibrations. They can detect a wide range of frequencies, from very low (a few times per second) to quite high (up to 30,000 times per second). This makes them great for capturing different types of movements and impacts.
* Piezoresistive accelerometers are sensitive devices that measure changes in movement. They are particularly good for detecting sudden shocks and can effectively monitor changes over long periods of time. These accelerometers can capture a wide range of motion, but their ability to detect very low frequencies, including steady situations, diminishes over time.  
Variable capacitance accelerometers, also known as VC or MEMS accelerometers, are a type of technology used to measure movement and vibrations. They are known for being very sensitive, which allows them to detect subtle changes, and they perform well even when temperatures vary. This makes them ideal for monitoring slow movements and steady changes in acceleration.  
To effectively identify different kinds of irregular movements, it is important to use the right accelerometer along with some noise reduction techniques and digital processing. This combination helps in accurately measuring and analyzing data related to motion.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 2 Filters Integration, Topologies, and Impact in the Power Grid
### Integration of Power Line Filters in the Grid  
Typically, power line communication filters are used in the smart grid in the following cases:  
* Between the SM and the fuse box for filtering the conducted noise that is generated by the devices in the house [29]. If the filter does not require a ground connection or if the main fuse is not differential, it can be installed after the main fuse without any issues. However, if the filter does require a ground connection or if the main fuse is differential, installing the filter after the main fuse may cause the fuse to trip. This filter use case is labeled as Filter Type 1 in Figure 2;
* Between two sections of the power-line for separating NB-PLC devices from two areas which are connected to the same voltage transformer. The division in two areas ensures that the SM using NB-PLC from each area connects to the PLC-DC (power line communication data concentrator) they are supposed to. This type of filter installation is required when there are many SM connected to a branch and an additional DC is installed so that the data-rate is improved [25]. This filter use case is labeled as Filter Type 2 in Figure 2.  
Figure 2 represents the two general use cases of PLC filters in the low voltage smart grid. For simplicity reasons, only single-phase devices are shown.
## 4 Application
### Method Comparison
The Box Morphing approach requires much more manual work. Like [3] observed, the definition of the control boxes is quite time-consuming [3]. Especially for such a complex component as the used knuckle, creating the Box Morphing structure took an estimated time of 100 h. Furthermore, the whole implementation of the code including the identification of splitting factors of plane and Box Morphing as well as smoothing operations took another 100 working hours. That must almost be completely repeated if a different part should be morphed, since the morphing scripts refer to specific control points with values that were evaluated specifically for the used knuckle. Nevertheless, schemes like the rotational corrections might be applied easily on different parts, making at least parts of the morphing script reusable. The extra effort needed for the generation of the Box Morphing structure comes with greater possibilities to control the morphing behavior. The preservation of axes or surface shapes is easily  
Figure 11: Three different optimization methods and simulation results obtained by different morphing approaches. (**a**) Manual Optimization via separate metamodels in Figure 10. (**b**) Optimization via _Optislang_ metamodel resulting from Direct Morphing data. (**c**) Optimization via _Optislang_ metamodel resulting from Box Morphing data. possible in contrast to the Direct Morphing approach. Recent research identifies the Box Morphing approach as less suitable when it comes to how precisely the wanted geometry is achieved than morphing methods that act directly on the FE nodes [28]. Since this paper evaluates the influence of the location of specific hard points on the FRF, only the displacements of these points are relevant. The exact shape of the geometry is less important and unknown, at least in the early design phase. This prevents us from applying an exact shape on the nodes in the Direct Morphing approach. In our scenario, the averaged deviation for the morphing accuracy of the Box Morphing is 0.02 % after the first morphing iteration. The Direct Morphing approach converges towards the target coordinate, but due to the regular grid it will never reach the coordinate exactly. As the interpolation inside the regular grid is linear though, it is possible to alter the target coordinate so that the morphed shape matches the desired one. With two iterations, we reach an accuracy of 0.2 %. The mesh quality is automatically checked before each FE calculation and does not violate any quality criteria, used for regular FE investigations, neither for the Direct Morphing approach, nor the Box Morphing approach. The limitation to small geometry changes prevents different control nodes from morphing into each other, which would result in erroneous meshes. Visually, the morphed geometries are smooth with preserved features, meaning that we consider the received designs close to designs generated in CAD.
# Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment
## 2 Materials and Methods
### Functions and Processes of System Software and Data Acquisition Systems
#### 2.2.3 Noise-Suppression Technology
These filters can typically be designed with linear phase characteristics, where all frequency components of the signal pass through the filter at the same rate, effectively reducing phase distortion. The method presented in this paper uses variable IIR digital bandpass and bandstop filters by independently controlling \(\alpha\) and \(\beta\), two adjustable parameters, to achieve a \(-3\) dB bandwidth bw and the stopband frequency \(\mathrm{f_{N}}\). The parameter \(\alpha\) originates from the frequency domain transformation from a low-pass to a low-pass filter and controls the \(-3\) dB bandwidth. The parameter \(\beta\) controls the stopband frequency \(\mathrm{f_{N}}\) and is derived from the frequency-domain transformation from a low-pass to a high-pass filter. Digital bandpass and bandstop filters are obtained by transforming a half-band digital low-pass filter using digital-domain frequency transformations. Variable digital filters have fewer coefficients, which means there are fewer multipliers, adders, and delays in the implemented circuit compared to previous designs. The design steps of the method proposed in this paper are very simple in terms of computation, as it involves only two adjustable parameters. A new formula for the damping factor \(\zeta\) with \(\alpha\) and \(\beta\) is introduced, and the root locus of the second-order variable digital Butterworth bandpass and bandstop filters with a damping factor is analyzed. Figure 10 shows the results using an FIR filter.