esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Grounding Grid Orientation: Transient Electromagnetic Approach
## 2 Related Work
Figure 2: Grounding grid orientation with respect to the substation boundary. (**a**) Grounding grid oriented parallel along the substation boundary. (**b**) Grounding grid with non-parallel orientation along the substation boundary. This paper employs the transient electromagnetic method (TEM) to diagnose the grounding grid orientation without soil excavation. Unlike the derivative method, TEM is independent of the current injection that brings the disturbing inhomogeneity of the surface magnetic flux density. Furthermore, it does not enhance the effect of surrounding EME. The feasibility of the proposed method is also tested for various complex mesh configurations. This incorporates the presence of the diagonal branch and unequally spaced grid configuration. Figure 3: Influence of the surrounding electromagnetic environment (EME) on the derivative method. (**a**) Surface magnetic flux density \(\vec{B_{z}}\) pertaining to the grounding grid in [26]. (**b**) Surrounding EME. (**c**) Mixed signal \(\vec{M_{z}}\) of magnetic flux density \(\vec{B_{z}}\) and the surrounding EME. (**d**) Derivative of mixed signal \(\vec{M_{z}}\). This signal contains fake peaks due to the presence of EME, which causes the identification of true peaks to be impossible.
## 2 The Proposed Method
### Bayesian Neural Network and Improved Variational Inference
* Distribution difference measurement The Kullback-Leibler (\(KL\)) [39] divergence is a method to describe the difference between two probability distributions, \(P\) and \(Q\). In neural networks, if \(P\) is unknown and we want to approximate it with a known distribution \(Q\), the \(KL\) divergence is usually taken as the error function, and the optimal approximate distribution \(Q\) can be obtained by minimizing the \(KL\) divergence, which is calculated as \[KL(Q(w|\theta)\|P(w|X,Y))=\int Q(w|\theta)\log(\frac{Q(w|\theta)}{P(w|X,Y)})d\theta\] (8)  
In this work, a known probability distribution \(Q(w|\theta)\) is directly used to approximate the posterior distribution \(P(w|X,Y)\). We turn the posterior solution problem in Equation (6) into a parameter optimization problem by minimizing the \(KL\) distance between \(Q(w|\theta)\) and \(P(w|X,Y)\). The formula is as follows  
\[\theta^{*}=argminKL(Q(w|\theta)\|P(w|X,Y)) \tag{9}\]  
According to the definition of \(KL\) divergence in Equation (8), we rewrite the formula into the form of an expectation to facilitate the calculation results as  
\[KL(Q(w|\theta)\|P(w|X,Y)) \propto\] \[E_{Q(w|\theta)}log[Q(w|\theta)] - E_{Q(w|\theta)}log[P(w)]-E_{Q(w|\theta)}log[P(Y|X,w)] \tag{10}\]  
Then, the loss function of our network consists of two parts: one is the loss of the network between the real RUL value and the predicted RUL value, which is measured by \(MSE\), and the other one is the loss of uncertainty estimation, which is measured by \(KL\). The final model loss function is defined as  
\[Loss=MSELoss(y_{true},y_{pred})+KL(Q(w|\theta)\|P(w|X,Y)) \tag{11}\]  
where the \(MSELoss\) is a loss function under the Pytorch frame, \(y_{true}\) is the RUL label of the data set, and \(y_{pred}\) is the RUL predicted by the model.
# Modeling and Simulation of Sky Survey
## 4 Design of the Model Solving Algorithm
### Observation Difficulty Statistics  
Different sky areas at different locations have different observation difficulties. According to the known experience of optimization algorithms, the difficult sky areas may have the possibility of not being observed later, so the sky areas with higher observation difficulties should be counted for advance observation arrangements. Algorithm 2 shows the result of observing the difficulty statistics and prioritizing the sky areas with high difficulty. Similar to \(\gamma_{1}\) in Algorithm 1, here, \(\gamma_{2}\) is also a parameter set according to the satellite orbit.
## 3 Field Overvoltage Waveform Acquisition and Statistics
### Statistical Analysis of the Overvoltage Waveform  
Due to the randomness of overvoltage, it is necessary to analyze the amplitude distribution statistically. Through the statistics of the overvoltage waveforms, the overvoltage amplitude was classified into five ranges, as shown in Table 1. The occurrence times of different types of overvoltage are shown in Figure 9.  
The arrester's continuous operating voltage is 34 kV, and the corresponding voltage amplitude is 48 kV, that is, the voltage amplitude does not exceed 48 kV and poses almost no threat to the arrester. Therefore, voltage waveforms with amplitudes greater than 50 kV were counted. As can be seen from Table 1, most amplitudes of the passing section overvoltage are less than 70 kV, and very few overvoltage amplitudes exceed 90 kV. The amplitudes of high-harmonic overvoltage are all less than 60 kV. The amplitudes of steep impulse overvoltage are relatively high; this includes five overvoltage waveforms. Their amplitudes exceed 90 kV, and the maximum voltage amplitude is 138.9 kV. Therefore, in general, steep impulse overvoltage is a relatively great threat to the arrester.  
Figure 9 shows that passing section overvoltage occurs most frequently, and steep impulse overvoltage occurs the least, but its amplitude is high, and multiple steep impulses may occur in one cycle.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{5}{c}{**Overvoltage Amplitude Range (kV)**} \\ \cline{2-6}  & **50–60** & **60–70** & **70–80** & **80–90** & **>90** \\ \hline Passing section overvoltage & 819 & 235 & 89 & 15 & 2 \\ High-harmonic overvoltage & 71 & 0 & 0 & 0 & 0 \\ Steep impulse overvoltage & 6 & 13 & 11 & 8 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overvoltage amplitude distribution.  
Figure 9: Number of three kinds of overvoltage.
## 2 Experimental Section  
The focus of the research study presented in this paper is the UCD process for a telecare service named MedReminder. The MedReminder service is intended to remind people to take their medicines on time or to call a relative or a medical person in an emergency situation [9] as shown in Figure 3. The service can be used in an interactive TV system. It was assumed that MedReminder will be mostly used by adults older than 65. The reason for this assumption is that older adults use more medications than any other age group since most of them live with one or more chronic conditions, such as diabetes, high blood pressure, arthritis or cancer. Additionally, many older adults take multiple medications at the same time. As a result, the UCD process for MedReminder was created from the perspective of older adults.  
The term UCD has its origins in the mid 1980s. One of the best known guides in implementing the UCD approach in practice is ISO 13407, which defines the standards that support the design, development and evaluation of usable products [10, 11]. The standard repres  
Figure 3: Reminder for taking a medicine.  
and describes five main activities for a software life cycle: plan the human-centered process, specify the context of use, specify the user and organizational requirements, and produce the design solutions.  
Taking into account the described activities in ISO 13407 and the criteria for selecting the methods in UCD [12], for the purpose of the MedReminder, the following methods were selected:  
* Wants and needs analysis. This is a quick, inexpensive and brainstorming method for gathering data about users' wants and needs from multiple users simultaneously.
* Focus group. For this method, a group of six to 10 people is brought together to discuss their experiences or opinions about a topic presented by the user researcher.
* Interview. This method is one of the most frequently used methods for gathering user requirements.
* Questionnaire. For the purposes of this study, the system usability scale (SUS) questionnaire was chosen.
## 4 SDR Implementation of FM-ACSK System Prototype
### Slave Chaos Generators  
The receiver contains ACSK demodulator, which is made from two identical slave chaos generators. These generators can synchronize either to inverted or direct chaotic waveform \(R_{in}\), looking at Figure 3 connections from the downsampling block to the generators. Slave chaos generators' internal structure is very similar to the transmitter's master generator's structure that was described in Section 4.2. The main difference between slave and master generators is in the nonlinear function's reconstruction from input signal \(R_{in}\) that was described in Section 3.1. The design of the receiver's slave chaos generator implemented in Simulink is shown in Figure 8.
## 3 Results and Discussion
### SSR Detection Based on Optimal Features
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature Selection**} & \multicolumn{5}{c}{**Classifiers**} \\ \cline{2-6}  & **NB** & **KNN** & **LDA** & **ELM** & **SVM** \\ \hline RF-CA & 86.3 & 89.5 & 88.4 & 90.5 & 89.5 \\ SPA-CA & 86.3 & 86.3 & 89.5 & 93.7 & 94.7 \\ SFS-CA & 88.4 & 85.3 & 89.5 & 93.7 & 94.7 \\ \hline \hline \end{tabular} Note: NB, naive Bayes classifier; KNN, K-nearest neighbor algorithm; LDA, linear discriminant analysis; ELM, the extreme learning machine; SVM, the support vector machine.  
\end{table} 
Table 2: The classification results of models based on the optimal wavelengths. Figure 5: The discriminant results of the extreme learning machine (ELM) model based on optimal wavelengths selected by RF-CA, SFS-CA and SPA-CA under different hidden nodes. methods, respectively (Table 3). Besides the overall accuracy, a lower false positive rate (the infected sample was misjudged as the healthy one) is very critical to find all infected samples and prevent further infection. Although the overall classification accuracies of KNN, LDA, and NB were comparable (Table 2), the false negative rate and the false positive rate for healthy and infected samples were different. For KNN, the healthy samples could be all recognized with 100% accuracy for all three feature selection methods, while the discriminant ability for infected samples was quite poor with accuracies less than 80%. In comparison, the misclassification errors of LDA models were more balanced for both healthy and infected leaves. The number of incorrect/correct detections in healthy and infected leaves obtained from the NB model was similar to those from KNN. SVM models can obtain relatively better overall accuracies, however, the ELM-SFS-CA achieved the lowest false negative rate of 2.4% among 15 different combinations. In addition, the kappa value of SFS-CA-ELM was 0.873, which also indicated a good classification performance. Overall, the results demonstrated that hyperspectral imaging combined with SFS-CA-ELM has the potential for SSR detection in _A. thaliana_ leaves.
# Characterization of Isoclinic, Transversally Geodesic and Grassmannian Webs
## 7 Conclusions  
A \(3\)- web of dimension \(r\) on a manifold \(M\) of dimension \(2r\) is given by \(3\) foliations in general position. Two webs are equivalent at a point \(p\) of \(M\) if there is a germ of local diffeomorphisms at \(p\) that exchanges them.  
Let \(x\in\mathbf{P}^{r+1}\) and \(\hat{x}\) the Schubert manifold of all straight lines intersecting at the point \(x\). \(\hat{x}\) is an \(r-\) dimensional submanifold of \(G(r+1,1)\). A hypersurface \(V\) of \(\mathbf{P}^{r+1}\) defines an \(r\)-dimensional foliation on an open set \(\mathcal{U}\subset G(r+1,1)\): the leaves are \(\hat{x}\) with \(x\in V\). If we consider \(3\) hypersurfaces \(V_{\alpha}\)\((\alpha=1,2,3)\), in a general position in \(\mathbf{P}^{r+1}\), they define a \(3\)-web of dimension \(r\) on an open set \(\mathcal{U}\) of \(G(r+1,1)\) that we call the Grassmann web. A web is said to be Grassmannizable if it is equivalent to a Grassmannian web.  
[PERSON] has shown that Grassmannizable webs are webs that are both isoclinic and transversally geodesic. In this paper, we find three invariants \(\Im,\Re,\Re^{1}\), of type \((2,r)\) of Hangan tensorial structures expressed in terms of torsion and curvature of the unique Chern connection associated with the web as well as in terms of the Nagy's tensors that define the foliations; we show the following results:  
1. \(\mathcal{W}\) is isoclinic if and only if \(\Im=0\).
2. \(\mathcal{W}\) is transversally geodesic if and only if \(\Re^{1}=0\).
3. \(\mathcal{W}\) is Grassmannizable if and only if \(\Im=0\) and \(\Re^{1}=0\).
4. If \(\Im=0\) and \(\Re=0\), then \(\mathcal{W}\) is parallelizable.  
**Author Contributions:** Conceptualization, J.S.; Methodology, J.S. and R.A.; Validation, J.S. and R.A.; Formal analysis, J.S.; Investigation, J.S.; Resources, R.A.; Writing - original draft, J.S.; Visualization, R.A.; Supervision, R.A.; Project administration, R.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Development of a Real Network Encryption Method Based on the TLS Protocol
In the last option, we either allow verification of the peer certificate, or not. Of course, it is better to use the tlsdonverifyserver = no option, but in practice there are many problems with this and the option does not work quite as intended; even if the certificate does not match the verification by ca.crt, Asterisk still allows us to make calls, even though there will be throw up errors. Therefore, it is recommended to use tlsdonverifyserver = yes. Next, we go to the Asterisk console and reboot using the reload command (Figure 18). We can check that the TLS port is working with the command openssl s_client -connect 127.0.0.1:5061. Now, let us make settings in Asterisk on the peer to work with TLS and SRTP, activate tls for the peer in \(\mathrm{sip.conf}\), and enable \(\mathrm{srtp}\) (encryption). To do this, we add the line data in the client fields:  
transport = tls  
encryption = yes  
After the call has been made, another icon appears, a padlock on an orange background, which indicates media traffic encryption (SRTP). Let us check these data in Wireshark (Figure 19). It can be seen from Figures 19 and 20 that the main service information is now transmitted in encrypted form. Several criteria were identified by which it is possible to assess the degree of security of an IP network based on Asterisk PBX and, following them, increase the security of this network:  
1. Authentication and authorization: in our case, users are configured with an access password, so that only authorized users have access to the PBX system and its functions. 2. Encryption: it uses the secure SIP protocol setting over TLS to secure communication between devices. 3. Firewall: the implementation of a firewall blocks unauthorized access to the PBX system. 4. Network segmentation: the segmentation of the PBX network from other networks minimizes the attack surface. Figure 17: Software for generating client certificates and keys. When operating an IP network, we recommend regular updates and maintenance, including keeping the PBX system and its components up to date with the latest security patches and configurations; monitoring and logging system activity to detect and respond to security incidents; regularly scanning the network and PBX systems for vulnerabilities and taking measures to eliminate them; and having a plan for quickly restoring service in the event of a security incident or other disruption. Figure 18: Certificate information software.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### NPs of Other Metals
#### 4.6.3 NiNPs and NiO NPs  
Impact on Plant Growth  
Exposure of _Coriandrum sativum_ L. plants to NiNPs with mean particle size 20 nm, applied at doses 20, 40, and 80 ppm for 22 days, resulted in reduced water content, root, and shoot growth, decreased levels of photosynthetic pigments, lower antioxidant activity and increased NiNPs content in plants, suggesting toxic effect of NiNPs on this medicinal plant [273]. In vitro grown seedlings of _Abelmoschus esculent_ treated with NiO NPs (100-1000 mg/L) showed impaired plant growth, lower Chl content, pronouncedly altered levels of anthocyanin contents, TPC, and TFC and increased ROS and MDA levels, suggesting phytotoxicity of NiO NPs [274].  
Elicitor Effect  
_Nigella arreensis_ plants treated with 50 mg/L NiO NPs resulted in 2.2- and 1.8-fold higher quercetin content in the shoots and roots compared to the control plants. On the other hand, following exposure of black cumin plants to 1000 mg/L NiO NPs up to 3.2-fold higher glaucoma content in shoots, 2.9-fold higher kaempfer content in roots, and considerably higher levels of TPC and TFC were observed compared to the control plants, suggesting that NiO NPs can be used as an elicitor for effective in vitro production of secondary metabolites [148].  
Genotoxicity  
Treatment of _Allium sativum_ L., _Allium schoenoprasum_ L., _Allium porrum_ L., and _Allium fistulosum_ L. growing root tips with NiO NPs, even at a dose of 10 mg/L induced genotoxicity in plants. NiO NPs caused perturbation of biochemical homeostasis and disrupted normal physiology of the cells, which was reflected in enhanced lipid peroxidation, MDA levels, and activities of antioxidant enzymes (CAT, SOD, and GPX) [275].
## 2 Experimental Setup
### Input and Output Data Structuring Method  
Current and voltage measurements were acquired during welding. To appropriately structure the input data, the measurement system and its positions were set as shown in Figure 2. Current and voltage measurements were performed for 30 s at 10 kHz by using a data acquisition (DAQ) module comprising a CompactDAQ chassis (cDAQ-9174, National Instruments, Austin, TX, USA) and analog input module (NI-9229, National Instruments). The current was measured on the ground cable, whereas the voltage was measured between the contact tip and the welded material. The measured current and voltage data were transformed into corresponding dynamic resistance values and processed using STFT, serving as input variables for the CNN model.  
Figure 3 illustrates the method used to measure the penetration condition, which is utilized as output data. A laser vision sensor was used to scan the bottom side of the welded specimens. Based on the scanned results, the classes were categorized. The height (\(h\)) of the weld bead on the underside of the completed weld specimens was measured using a developed laser vision sensor to obtain the output data. A laser vision sensor with a resolution of 0.02 mm along the X- and Z-axes was utilized. Considering the integrity of the back-bead, \(h\) values equal to or greater than 0.2 mm were classified as CJP. The laser sensor was moved along the Y-axis at a speed of 7 mm/s, and \(h\) was measured 100 times/s. \(h\) values below 0.2 mm were classified as class 0 (PJP), \(h\) values equal to or above 0.2 mm were classified as class 1 (CJP), and burn-through instances were classified as class 2. These classes were used as the output data.  
\begin{table}
\begin{tabular}{c c} \hline
**Waveform** & **DC Pulse** \\ \hline WFR & 7.0–12.0 m/min \\ Welding speed & 30 cm/min \\ CTWD & 20 mm \\ Shielding gas (Flow rate) & 90\% Ar + 10\% CO\({}_{2}\) (25 L/min) \\ \hline \end{tabular}
\end{table}
Table 1: Welding parameter.  
Figure 2: Welding current and voltage measurement system and measurement methodology.
# Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
## 1 Introduction  
The proportion of the elderly in the population in most advanced countries now exceeds 15% [1], and the problems associated with aging including dementia and chronic illnesses are also increasing year by year. The care of the elderly is important but is especially difficult for those living alone. The situation is made worse given the current serious shortage of healthcare personnel. The concept of the Activities of Daily Living (ADL) first emerged in the 1950s in the field of healthcare as a tool to assess the daily self-care activities of disabled, and physically or mentally handicapped patients, such as those with dementia or chronic mental health problems. ADL usually include ordinary activities such as eating, shifting position, mobility, bathing, dressing, etc. These are typically rated on some scale such as the Barthel Index, Katz Index or Karnofsky Index, with the Barthel Index being most commonly used as a basis for evaluation of home care [2, 3, 4, 5]. Motion recognition is a common technology today which has found applications in games, interactive computer-human interfaces, telepresence technology, and medical care. In the medical field it is currently used in remote rehabilitation systems, which enable a virtual rehabilitation teacher to evaluate the effectiveness of at home rehab and alleviate the problem of lack of medical resources in remote areas [6, 7]. Motion recognition methods that focus on the description of image features have inspired researchers to improve the efficiency of the technology. ADL recognition for years has been discussed for years and some work has been carried out to improve sensor ability [8]. Recent studies have mostly concentrated on the development of methodologies, including machine learning, probabilistic finite,entropy-based, and knowledge-driven approaches [9; 10; 11]. The processing of data for ADL is also important in order to improving recognition accuracy [12; 13; 14]. It is desirable to deal to use a comprehensive methodology that provides solutions for the recognition of human behaviors, image processing, and movement calculation to achieve the goal of smart care.  
This paper describes a Deep Neural Network (DNN)-based recognition system that combines ADL recognition, image/video processing, movement calculation, and DNN for the purpose of achieving smart care. It utilizes skeletal data obtained from color images from webcams or surveillance records, using the DNN for ADL recognition. The system takes advantage of low-cost and easy-to-install color cameras for monitoring indoor environments and incorporates a feature extraction method for motion recognition.