esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Tool Wear in Electrochemical Discharge Machining
## 4 Discussion
### Average Current of Spark Discharge  
The magnitude and stability of the average current of spark discharge reflects the spark machining ability and the stability of gas film. As can be seen from Figure 8, the average current of spark discharge shows a trend to decrease slightly and then increase as the voltage increases. According to the research of [PERSON] et al. [27], it is known that in the process of electrochemical discharge, the current of the electrochemical reaction is much higher than the spark discharge current. When the voltage is low, the system has not entered the spark state of stable discharge, and the spark discharge is mixed with the electrochemical reaction. Therefore, the average current of the spark discharge is higher. During the gradual increase in voltage, the ratio of electrochemical reaction gradually decreases, the average current also decreases accordingly, and the average current slightly decreases. When the voltage continues to increase, the average current begins to increase gradually, and the spark discharge also becomes more intense.  
However, the effect of tool wear on the average current of spark discharge does not seem to show a certain pattern, and the variation range before and after the loss is also very small. The average current decreases between 62 V and 64 V. In the experiments at these two voltages, the cathode showed significant loss, which led to the shortening of the blade length, and the depth of cathode immersed in electrolyte also changed accordingly, which reduces the average current as a result. Finally, the tool wear has little effect on the average current of spark discharge, which mainly depends on the voltage.  
Figure 8: Relationship between the voltage and average current of the spark discharge before and after machining.
## 4 Experiment and Analysis
### Performance Comparison: With VG vs. without VG
Specifically, during the global time period, the VG scheme reduced the RMSE of the horizontal attitude from 0.149 degrees to 0.081 degrees, and during the period containing only GNSS denial, it was reduced from 0.240 degrees to 0.098 degrees. The optimizations in these two types of periods correspond to improvements of 45.63% and 59.17%, respectively.
# Identification by GC-MS Analysis of Organics in Manufactured Articles through a D-Optimal Design
###### Abstract  
Many manufactured articles are made of composite materials often bonded by a phenolic resin. Through a D-optimal design, we optimized a method to characterize phenolic resins after the extraction process by GC-MS analysis. The study was conducted on three different phenolic resins and four manufactured articles with the same inorganic composition and different analyzed binders. Moreover, three cardand resins that differ in their production systems were analyzed to see if there were differences between them. Through Soxhlet extraction with dichloromethane or acetone, it is possible to differentiate the raw materials through characteristic compounds and to identify them in the manufactured articles.  
p 2022 2  
Despite the complexity of these materials, it could be possible to identify the inorganic components by different techniques such as Scanning Electron Microscope (SEM), X-ray Diffraction (XRD), or X-ray Fluorescence (XRF) [19; 20; 21]. Moreover, spectroscopic analyses (e.g., Raman and FTIR spectroscopy) could also be used to characterize inorganic components [22].  
On the contrary, regarding organic components, it is not possible to carry out a complete characterization using these techniques, as only the spectroscopies could give some general information. The major problems come from binders; the most used are phenolic resins that, once cross-linked, are difficult to recognize and characterize. This is a limit for companies, which results in them being \"blind\" to the organic binder of their composite materials. For this reason, finding a characterization method for organic components, more specifically for phenolic resin, is essential for the quality control sector of the industries, since these analyses could give information about the quality and the properties of their articles.  
In this study, a valid method of qualitative characterization of thermo-curing phenolic resin present in some different manufactured objects is described. The objects are produced by binding inorganic materials with phenolic resins cross-linked at high temperatures. The resins were dissolved in different solvents and the extracts were characterized by GC-MS analyses. Through comparison with analyses of raw materials, it is possible to determine whether there is a specific compound in the matrix. To accomplish this, a Design of Experiments (DOE) study was carried out to understand which was the best extractive method. This model was only used on raw materials without treatment, since they should have the highest content of compounds, to choose which analysis parameters to adopt for all samples. This study can make an important contribution to organic characterization in this type of material for companies, as there is currently no similar work in the literature.
# Natural Extracts from White Common Bean
## 3 Results and Discussion
### mRNA and Protein Expression of LPL and ap2  
**4. Conclusions**  
In conclusion, among the three tested natural extracts from white common bean, CBP and \(\alpha\)-AIE inhibit 3T3-L1 adipocytes differentiation evidenced by the results of Oil red O staining. In addition, CBP and \(\alpha\)-AIE downregulate the mRNA and protein expression level of PPARy, C/EBP\(\alpha\), SREBP-1c, ap2, and LPL. However, CBP shows stronger inhibitory effects and \(\alpha\)-AIE only show a slight inhibition. These findings indicate that CBP, from white common bean, might be the major component responsible for the inhibitory effects on adipocyte differentiation in vitro.  
Conceptualization, Z.S., Y.Z. and Y.Y.; Methodology, Z.S.; Software, Z.S., X.Z.; Validation, G.R.; Formal Analysis, G.R.; Investigation, X.Z., Z.S. and Y.Y.; Resources, G.R.; Data curation, Z.S. and Y.Z.; Writing--Original Draft Preparation, Z.S.; Writing--Review and Editing, X.Z., Y.Z. and Y.Y.; Supervision, G.R.; Project Administration, G.R. and Y.Y.; Funding Acquisition, X.Z. and G.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
The present study was supported by the earmarked fund for the National Natural Science Foundation of China (31571737). This work was supported by the earmarked fund for the Modern Agro-industry Technology Research System (CARS-08-G20) and China Agriculture Research System (31701597). We also thank for the support of China Scholarship Council (CSC) and National Crop Germplasm Resources Center.  
Figure 4: Effects of CBP and \(\alpha\)-AIE on the protein expression level of PPARy, C/EBP\(\alpha\), SREBP1c, LPL, and ap2. The data were expressed as mean \(\pm\) SD. Different letters mean significantly different (\(p<0.05\)).
## 2 Preliminaries
### Basic Ide of Weighted Group Sparse Classification (WGSC)
[11] proposed a weighted GSC (WGSC) model as follows, by involving the information of the similarity between query sample and each class as well as the distance between query sample and each training sample,  
\[\underset{\mathbf{x}\in\mathbb{R}^{n}}{\text{minimize}}\ \frac{1}{2}\|\mathbf{y}-\mathbf{A}\mathbf{x}\|_{2}^{2}+ \lambda\sum_{i=1}^{g}w_{i}\|\mathbf{d}_{i}\odot\mathbf{x}_{i}\|_{2}, \tag{7}\]  
where \(\mathbf{d}_{i}=[d_{i1},d_{i2},\cdots,d_{in_{i}}]\in\mathbb{R}^{n_{i}}\) penalizes the distance between \(\mathbf{y}\) and each training sample of \(i\)-th class, \(w_{i}\) is set to assess the relative importance of training samples from \(i\)-th class for representing the test sample, and here \(\odot\) denotes element-wise multiplication. Specifically, the weights are computed by  
\[d_{ij}=\exp\biggl{(}\frac{\|\mathbf{y}-\mathbf{a}_{ij}\|_{2}}{\sigma_{1}}\biggr{)}\quad \text{and}\quad w_{i}=\exp\biggl{(}\frac{r_{i}-r_{\text{min}}}{\sigma_{2}} \biggr{)}, \tag{8}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) are bandwidth parameters, \(\mathbf{x}_{i}^{*}=\arg\min_{\mathbf{x}_{i}}\|\mathbf{y}-A_{i}\mathbf{x}_{i}\|_{2}^{2}\), \(r_{i}=\|\mathbf{y}-A_{i}\mathbf{x}_{i}^{*}\|\) computes the distance from \(\mathbf{y}\) to the individual subspace generated by \(A_{i}\), and \(r_{\text{min}}\) denotesthe minimum reconstruction error of \(\{r_{i}\}_{i=1}^{g}\). The regularizer in (7) can be written as a non-separable weighted \(\ell_{2,1}\), i.e., \(\|\mathbf{W}\mathbf{x}\|_{2,1}\), where  
\[\mathbf{W}=\text{BlockDiag}(\mathbf{W}_{1},\mathbf{W}_{2},\cdots,\mathbf{W}_{g})\quad\text{and} \quad\mathbf{W}_{i}=\text{Diag}(w_{i}d_{i1},w_{i}d_{i2},\cdots,w_{i}d_{in_{i}}). \tag{9}\]  
For the aforementioned methods, after obtaining the optimal solution (denoted by \(\hat{\mathbf{x}}=[\hat{\mathbf{x}}_{1}^{\top},\hat{\mathbf{x}}_{2}^{\top},\cdots,\hat{\mathbf{ x}}_{g}^{\top}]^{\top}\)), they assign \(\mathbf{y}\) to the class that minimizes the class reconstruction residual defined by \(\|\mathbf{y}-\mathbf{A}_{i}\hat{\mathbf{x}}_{i}\|_{2}\). Although \(\ell_{2,1}\) regularizer and its weighted variants are widely used in GSC and WGSC-based methods, they not only suppress the number of selected classes, but also suppress significant nonzero coefficients within classes. The later may lead to underestimation of high-amplitude elements and adversely affect the performance. The nonconvex regularizers such as \(\ell_{2,p}\) (\(0<p<1\)) [37] and group MCP [38] make the corresponding optimization problems nonconvex. Therefore, we hope to use a regularizer which can reduce the bias and approximate \(\ell_{2,0}\) better than \(\ell_{2,1}\) while ensuring the overall convexity of the problem.
## 2 Background
### An Overview of Intrusion Detection Systems
#### 2.3.1 Types of Intrusion Detection Systems  
IDSs can be classified based on their focus areas, deployment strategies, and detection techniques. The two primary types of IDS are the host intrusion detection system (HIDS) and the network intrusion detection system (NIDS) [45].  
* HIDS: A HIDS is a critical cybersecurity component that focuses on monitoring and protecting specific hosts or devices within a network. It operates directly on endpoints such as servers, workstations, or other devices, analyzing local activities and configurations. HIDS identifies signs of malicious actions by comparing observed activity to predefined security regulations or baselines [46]. These activities may include unauthorized access attempts, file alterations, and unusual processes. HIDS employs methods like log analysis, file integrity verification, and real-time system monitoring to detect potential security issues. If suspicious actions are detected, HIDS generates notifications, alerting administrators to investigate and take appropriate actions to protect individual devices and their stored data. HIDS is particularly useful in environments where safeguarding specific hosts from internal and external threats is paramount.
* NIDS: NIDS is a cybersecurity solution that monitors and analyzes network traffic for indicators of malicious activity or potential security concerns. Unlike host-based systems, NIDS operates at the network level, analyzing data packets as they traverse the network. NIDS is strategically placed at critical points throughout the network, passively observing and analyzing all incoming and outgoing traffic in real time. It generates alerts when it detects suspicious trends, allowing security teams to promptly investigate and respond to potential security incidents. NIDS is especially beneficial for securing large and complex networks  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Approach to Outlier Detection** & **Advantages** & **Disadvantages** & **Typical Applications** \\ \hline \multirow{4}{*}{Robust ML Models} & Models that are less sensitive to outliers, like Random Cut Forest or models with regularization. & Can handle outliers while performing the primary learning task. & May require careful tuning; could ignore subtle but important anomalies. & Scenarios where model robustness is crucial. \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._
## 1 Introduction
The third principal stress component, i.e., the axial stress, corresponds to the initial in situ stress, and, hence, it is the intermediate principal stress. At least, that is the situation away from the face or borehole bottom and for linear elastic calculations. Hence, as illustrated in Figure 1c (black arrow), the stress path is a combination of an increase in one principal stress component and the decrease in  
Figure 1: Illustration of stress path for an initial isotropic stress state around a circular excavation. (**a**) Initial (ini) stress state; (**b**) stress state on circumference after excavation; (**c**) stress path with a support pressure equal to S(supp), presented by dark green arrow, and comparison with stress paths for conventional loading experiments (uniaxial (red arrow) and triaxial (blue arrow) loading); not to scale. another principal stress component. This is a different path in comparison to conventional uniaxial or triaxial loading experiments. Around the face or borehole bottom, the stress variation is more complex [17; 18]. As a function of the initial in situ principal stress ratios, the stress paths are more complex and are different at each point of the circumference. The tangential stress component may decrease for a certain combination of in situ principal stresses and support pressures. This component can even become tensile. When a rectangular cavity is excavated (e.g., in room and pillar workings), all of the stress components of the rock at the sidewall (e.g., in the middle part of the sidewall) may decrease to zero due to the redistribution of the stress. Similar changes may occur in open pit mines depending on the geometry of the slope. In conclusion, at least one stress component decreases around an excavation. When looking at the design of foundations, it could be that the stress state is only increased, but if the volume of the foundation is excavated initially, the stress state decreases first before it continues to increase. The aim of this paper was to evaluate with an open mind if different stress paths could lead to different failure envelopes when using the distinct element code UDEC (see further). Hereby, it is essential that (micro-)fracturing is integrated into the simulated failure process. At this stage of the research, no specific laboratory experiments are modelled. The study focusses on a small representative volume element (RVE). However, the values of the various input parameters are based on past calibration of laboratory experiments. (See further for more details about the code and model). The behaviour of the RVE model was also verified by modelling published laboratory experiments (See paragraph 5). The loading of the RVEs was not by the use of platens, which undergo a displacement, but by applying directly external stresses. The main conclusion of the presented results is that the chosen stress path has a significant effect on the strength and failure envelopes of rock material. Hence, one should further investigate this finding and complement the research by conducting lab experiments. All this could lead to adapted procedures for characterising rock material.
## 5 Building a Mining Field Exploitation Model--Schedule, Parameterization
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Means of Transport 1**} \\ \hline \multirow{2}{*}{Parameter name} & \multirow{2}{*}{LHD4} & \multirow{2}{*}{LHD3} & \multirow{2}{*}{LHD2} & 1\(\times\)LHD2 + & 1\(\times\)LHD2 + & 1\(\times\)LHD2 + & 1\(\times\)LHD2 + & \\  & & & & 1\(\times\)HT & 2\(\times\)HT & 3\(\times\)HT & 4\(\times\)HT \\ \hline \(M\)—mass of the transported the & & & & & & & & \\ winning (Mg) & 10.67 & 8.12 & 4.64 & 13.92 & 27.84 & 41.76 & 55.68 \\ \hline \(t_{\textit{zat}}\)—loading time (min) & 1.0 & 0.5 & 0.5 & 1.5 & 1.5 & 1.5 & 1.5 \\ \hline \(t_{\textit{max}}\)—unloading time (min) & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 \\ \hline \(V_{\textit{v}}\)—speed of travel with the & & & & & & & \\ winning (km/h) & 7.0 & 6.0 & 5.0 & 8.0 & 8.0 & 8.0 & 8.0 \\ \hline \(V\)—speed of travel without the & & & & & & & \\ winning (km/h) & 9.0 & 8.0 & 7.0 & 10.0 & 10.0 & 10.0 & 10.0 \\ \hline Operating cost per 1 shift (EUR) & 840 & 680 & 525 & 1050 & 1575 & 2100 & 2625 \\ \hline \hline \end{tabular}  
* The data was collected during the R&D project implementation “Adaptation and implementation of Lean in the copper mines”, financed by NCBiR (No. 09-0011-10/2011).  
\end{table} 
Table 3: List of computational parameters of loaders and haul trucks for the needs of simulation experiments. A slight fluctuation in the rate of increase in the total costs of transporting the excavated material and the amount of HC+NO\({}_{\text{x}}\) emissions during the exploitation of the mining field, shown in Figure 8, results from the increase in the distance between the mine faces and discharge points as the extraction front line moves. At the moment of launching the next discharge point, the distance for which the winning is transported gets reduced, thus the rate of increase in costs and the amount of HC+NO\({}_{\text{x}}\) emissions decreases. Then, the distance begins to increase again, together with moving away from the discharge point. The costs of transporting the excavated material are limited by the mechanism of selecting the least expensive means of transport; the obtained results are shown in Figure 9.  
Figure 8: The cost of transporting the excavated material with wheeled haulage during the exploitation of the mining field in a room and pillar mining system (means of transport, complying with the Stage IV standard, selected according to the minimum cost). Figure 9: Selection of the means of transport for the winning, depending on the stage of exploitation of the mining field. For the cross-cutting stage, the most frequently chosen means of transport is the set of 1\(\times\)LHD2 + 2\(\times\)HT (62%) and LHD4 (12%), while during the retreating stage these proportions change: LHD4 (45%) and 1\(\times\)LHD2 + 2\(\times\)HT (37%). Other types of means of transport are chosen much less frequently. The crucial difference between these two stages while selecting the means of transport is the smaller amount of the winning to be obtained from the pillars at the retreating stage.
## 3 Communication Delay Issues with Combined TSC and TCR Controller
### Method Using FLC  
A two-input-based FLC method was used in this work to reduce the negative impacts of time delays on the TSC performance. The FLC inputs were the voltage-level fluctuations at the PCC and V, as well as the latency of the control input (D). The controller's output was alpha (\(\alpha\)), which was the thyristor's firing angle. The signal delays ranged from 100 to 900 ms during the controller design phase, which covered all the practical scenarios [40; 42]. The following is a description of the planned FLC.  
During the fuzzification process, the membership grade was determined. Both the inlet and outlet triangles formed control parameters, as depicted in Figures 6 and 7, and were obtained after a sequence of trial and error. It is worth mentioning that, when additional fuzzy measures were set, such as nonlinear regression features, and were examined and tried, the system functioned admirably. Unfortunately, in several cases, the input and output membership algorithms produced great outcomes. This is why triangle membership functions were used for this study. Figure 6 depicts the symbols NB (negative large), NM (negative medium), Z (zero), PM (positive medium), PB (positive big), N (negative), and P (positive). In Figure 7, the membership functions of the thyristors' firing angle (alpha), are  
Figure 5: GPS-enabled shuttered monitoring system.  
NB, NM, Z, PM, and PB. Throughout this work, one continuity formula [41] was used to determine the degree of crisp values:  
\[\mu_{Ai}(x)=\frac{1}{b}(b-2|x-a|) \tag{4}\]  
where \(\mu_{Ai}(x)\) is the value of the membership grade; \(x\) is the value of V and D; \(a\) is the position where the membership grade is one; and \(b\) is the length. In this work, Table 2 shows 15 guidelines for items such as the independent variables, V, and the outcome variable. The management guidelines were published with a certain system functioning, as well as a defect-detecting technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{D}\) & \(\Delta\mathbf{V}\) & \(\mathbf{NB}\) & \(\mathbf{NM}\) & \(\mathbf{Z}\) & \(\mathbf{PM}\) & \(\mathbf{PB}\) \\ \hline N & PB & PM & Z & NM & NB \\ Z & PB & PM & Z & NM & NB \\ P & PB & PM & Z & NM & NB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Membership functions of input and output.  
Figure 6: Fuzzy controller input membership function: (**a**) \(\Delta\)V and (**b**) delay.  
Formula (5) indicates the extent of conformance \(W_{i}\) of every fuzzy set using [PERSON]'s technique [41]:  
\[W_{i}=\mu_{Ai}(\Delta V)\times\mu_{Bi}(D) \tag{5}\]  
where \(\mu_{Ai}(\Delta V)\) and \(\mu_{Bi}(D)\) are membership grade values, and \(i\) is the provision of the section. The following middle-area approach was used to compute the thyristor firing angle, \(\infty\):  
\[\propto=\frac{W_{i}C_{i}}{W_{i}} \tag{6}\]  
where \(C_{i}\) is the fuzzy-logic-based table's value for \(\infty\).
## 2 Materials and Methods
### Analyzed Methods
#### 2.1.6 Transformer Network for Anomaly Detection (TranAD)  
TranAD [13] is an anomaly detection method based on the Transformer model [37], which learns to reconstruct an input by applying several attention-based transformations. The model proposed by [PERSON] et al. [13] uses two-phase training. In the first phase, the model learns an approximate reconstruction of the whole time series \(\mathcal{T}\) to capture long-term trends and uses the deviation from the true time series as a _focus score_. In phase two, the focus score is used to find those subsequences where the deviation in phase one is high. Similar to other encoder-decoder models, the reconstruction loss is used as the anomaly score. We included TranAD, as it was one of the most recent publications by the time of its selection.