esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## Appendix A  
A modified function, \(mReLU(x)\), is proposed, which includes two parameters, \(\alpha\) and \(\beta\), that regulate sensitivity to negative and small positive values:  
\[mReLU(x)=\left\{\begin{aligned} &\alpha\cdot x+\beta\cdot x^{2}, \text{ if }\ x\geq 0\\ &\gamma\cdot x,\text{ if }\ x<0\end{aligned}\right., \tag{1}\]  
where \(\alpha\) is a parameter regulating the linear relations for positive values \(x\); \(\beta\) is a parameter controlling the nonlinear component for small positive values \(x\); and \(\gamma\) is a parameter determining the leakage for negative values \(x\). Parameters \(\alpha\), \(\beta\), and \(\gamma\) are trained to adapt the function to specific sensor data.  
The nonlinear element \(\beta\cdot x^{2}\) in (1) allows the function to be more sensitive to small deviations in positive values, which can be useful for detecting minor but important deviations that may indicate a potential anomaly. Considering that some anomalies may manifest in negative values, the parameter \(\gamma\) enables control over how much the negative values are reduced. It is assumed that anomalies are detected when the function's values exceed the allowable range. In this case, an anomaly function \(A(x)\) can be introduced:  
\[A(x)=\left\{\begin{aligned} & 1,\ \ mReLU(x)\ \ x\geq T_{1}\ \text{ or }\ mReLU(x)\ \ x<T_{2},\\ & 0,\ \text{ if }\ T_{2}\leq mReLU(x)\leq T_{1}\end{aligned}\right. \tag{2}\]  
where \(T_{1}\) and \(T_{2}\) are the upper and lower thresholds for the normal values of the output function.  
If sensor data \(x\) is fed into a neural network activated by the function \(mReLU(x)\), the output \(mReLU(x)\) can be compared to the thresholds \(T_{1}\) and \(T_{2}\) for real-time anomaly detection. Parameters \(\alpha\), \(\beta\), and \(\gamma\) are adjusted during the network training to ensure the function optimally responds to specific anomalies in the data.
## 2 Methods  
### Search Strategy and Data Sources  
The literature review was conducted following the guidelines outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) Statement [14]. The literature search for this literature review was conducted between May 2024 and November 2024. The Web of Science (WOS) and SCOPUS databases were used for the search. In addition, the authors were in the reference list of full-text literature obtained through the original search strategy. To carry out the desired search process, we used the Boolean operators AND and OR, following the search criteria of each database, to concatenate the selected terms. In this paper, we used the PICO method [15] proposed by the Cochrane Library to formulate research questions to establish a search strategy: (1) population: healthy older adults without complications; (2) intervention: stand-alone VR games that do not rely on additional hardware; (3) comparison: regular activities or other game modalities without game intervention; and (4) outcomes: focus on cognitive function and physical health (or one of them). The search strategy used for each database is shown in Table 1.  
Although this study aims to broadly explore the impact of virtual reality exergames on the physical and cognitive health of older adults, we focused on keywords related to 'balance' in our search strategy due to common health issues faced by the elderly, such as fall risks and balance disorders. This choice was based on the fact that the current literature is more concentrated on the effects of VR interventions on balance abilities and fall prevention in older adults. To ensure the scientific rigor and accuracy of the literature selection, we specifically included studies that met these criteria, while subsequent analyses covered other aspects of cognitive function and physical health. This approach does not imply a neglect of broader health topics but rather aims to provide a more targeted analysis within the scope of the literature review.  
In the Web of Science database, the filter settings were as follows: articles published between 2020 and 2024 were selected, with reviews and conference papers excluded. The language filter was set to English. In the Scopus database, the filter settings were articles published between 2020 and 2024, with the article type limited to \"Article\" and the language set to English.
## 3 Results
### Temporal Distribution of Natural and Man-Made (Technological) Disasters
#### 3.2.2 Yearly and Monthly Trends in Consequences of Natural and Man-Made Disasters
Water-related disasters caused 111,237 deaths (0.337%) and USD 77.9 million in economic losses (0.002%), while wildfires resulted in 5366 deaths (0.016%), 15,989 injuries (0.143%), and significant economic losses totaling USD 136.37 billion (3.033%) (Table 11 and Figure 13). When ranking the impact of disasters by fatalities, droughs emerge as the deadliest, causing the highest number of deaths at 11,734,272, accounting for 35.54% of all disaster-related fatalities. Following closely are epidemics, responsible for 9,622,343 deaths, or 29.14%. Floods come in third, resulting in 7,011,404 deaths (21.23%), while earthquakes are fourth, causing 2,407,717 deaths (7.293%). Rounding out the top five, storms contribute to 1,418,647 deaths, making up 4.297% of the total (Table 11 and Figure 13). In terms of injuries, earthquakes lead with the highest number, causing 2,945,052 injuries, representing 26.35% of all disaster-related injuries. Epidemics follow closely with 2,836,719 injuries (25.38%). Extreme temperatures rank third, causing 2,066,936 injuries  
Figure 13: Percentage distribution of natural and man-made (technological) disaster consequences by type (1900–2024). (18.49%). Floods result in 1,398,042 injuries (12.50%), and storms cause 1,411,425 injuries, making up 12.62% of the total. Looking at the number of people affected by disasters, floods have the most significant impact, affecting nearly 4 billion individuals (3,997,629,671), which is 46.039% of the total affected population. Droughs come in second, impacting almost 3 billion people (2,964,996,768), or 34.146%. Storms affect over 1.27 billion people (1,277,565,968; 14.713%), making them the third most impactful. Earthquakes are fourth, affecting over 228 million individuals (228,214,673; 2.628%), while extreme temperatures affect over 107 million people (107,678,810; 1.24%). Regarding economic losses, storms cause the most significant financial damage, with total losses amounting to approximately USD 1.97 trillion (43.748%). Floods rank second, causing over USD 1 trillion in economic losses (1,007,889,805,000; 22.415%). Earthquakes follow, with losses of approximately USD 975.79 billion (21.701%). Droughs come in fourth, with financial damages totaling around USD 257.58 billion (5.728%), and wildfires are fifth, causing economic losses of about USD 136.37 billion (3.033%) (Table 11 and Figure 13). This analysis underscores the profound impact of various disaster types, with droughs and floods being the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses.
###### Abstract  
Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection method combining artificial hummingbird algorithm (AHA) and XGboost has been proposed. According to GB14287.4--2014, an experimental platform for fault arcs was designed and built to collect fault arc signals. By leveraging the global search capability and dynamic adaptive mechanism of AHA, key feature subsets sensitive to arcs are selected from high-dimensional time-frequency domain features. Combining the parallel computing advantages and regularization strategies of XGBoost, a low-complexity, highly interpretable fault classification model is constructed. The hyperparameters of XGBoost are simultaneously optimized by AHA. Experimental results show that the proposed method achieves a fault arc detection accuracy rate of 98.098%, effectively identifying series arc faults.  
arc fault; artificial hummingbird algorithm; feature extraction; XGBoost Article
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
## 3 Dynamic Access Control and Pilot Allocation Algorithm
### Optimization Problem and Algorithm
#### 3.1.2 Access Control Phase  
Since the number of active MTCDs \(K_{i}\) can be estimated, the number of MTCDs that pass the ACB scheme is \(K_{C,i}=p_{\text{ACB},i}K_{i}\). Given \(K_{C,i}\) contenders, the probability of having \(C\) colliding MTCDs that choose the same pilot sequence  
\[p(C|K_{C,i})=\binom{K_{C,i}}{C}\bigg{(}\frac{1}{\tau_{p}}\bigg{)}^{C}\bigg{(}1- \frac{1}{\tau_{p}}\bigg{)}^{K_{C,i}-C},\]  
where \(C\) has a binomial distribution with \(C\sim B(K_{C,i},\frac{1}{\tau_{p}})\). The probability of having \(C\) MTCDs contending to select a pilot sequence, and only one strongest MTCDs repeats this pilot in step 3 is defined by \(P(D_{C}^{1})\), which can be found in ([25], Equation (20)). Then, we can compute the number of successful access MTCDs \(K_{i}^{s}\) during \(i\)th time slot as follows: \(K_{i}^{s}=\tau_{p}\sum_{C=1}^{K_{C,i}}P(D_{C}^{1})P(C|K_{C,i})\). Let \(R_{\text{eff},i}\) be the access success rate during \(i\)th slot. We have  
\[R_{\text{eff},i}=\frac{K_{i}^{s}}{K_{i}},\quad i=1,2,...,I_{A}.\]  
To maximize the access success rate \(R_{\text{eff}}\), the optimum \(p_{\text{ACB},i}^{*}\) can be found via  
\[p_{\text{ACB},i}^{*}=\underset{0\leq p_{\text{ACB},i}\leq 1}{\text{argmax}}R_{ \text{eff},i},\quad i=1,2,...,I_{A}. \tag{4}\]  
The optimization problem in (4) can be solved by the exhaustive search method. This approach limits the number of contending MTCDs for maximizing the access success rate under overload conditions. The dynamic access control and pilot allocation algorithm is summarized in Algorithm 1. Finally, the average access success rate during the observation time \(T_{A}\) can be determined as follows  
\[R_{\text{eff}}=\sum_{i=1}^{I_{A}}R_{\text{eff},i},\quad i=1,2,...,I_{A}.\]  
```
1:\(p_{\text{ACB},1}=1\)
2:\(i=1\)
3:for\(i\leq I_{A}\)do
4: BS counts \(\tilde{S}_{i\text{\emph{s}}\text{\emph{i}}\text{\emph{i}}\text{\emph{e}},i}\)
5: Calculate \(\tilde{p}_{i\text{\emph{i}}\text{\emph{e}},i}\) by Equation (1)
6: Estimate \(\hat{K}_{C,i}\) by Equation (2)
7: Estimate \(\hat{K}_{i+1}\) by Equation (3)
8: Find \(p_{\text{ACB},i+1}\) by Equation (4)
9:\(i=i+1\)
10:endfor
```  
**Algorithm 1** Dynamic Access Control and Pilot Allocation Algorithm
## Materials and Methods
### Restricted Maximum Likelihood (REML) Method in Single-Locus Screening Stage  
#### 2.2.1 Single-Locus Linear Mixed Model  
A standard linear mixed model for association mapping can be expressed as  
\[\mathbf{y}=\mathbf{F}\mathbf{b}+\mathbf{X}\beta+\mathbf{u}+\epsilon \tag{1}\]  
where \(\mathbf{y}\) denotes the \(n\times 1\) observed phenotypic vector of \(n\) individuals, \(\mathbf{F}\) is an \(n\times c\) fixed effect design matrix, including \(\mathbf{1}s\) column vector, \(\mathbf{b}\) is a \(c\times 1\) vector of their corresponding effect sizes, \(\mathbf{X}\) denotes the \(n\times 1\) marker genotype vector of focal variant, \(\beta\) is the random effect of one focal marker with normal distribution \(\beta\sim N(0,\sigma_{g}^{2})\), the variance \(\sigma_{g}^{2}\) is changed with different markers, and \(\mathbf{u}\sim N(0,\sigma_{u}^{2}\mathbf{\Sigma}_{u})\) is an \(n\times 1\) random vector and is typically used to account for polygenic effects or confounding factors; here, \(\sigma_{u}^{2}\) is the variance, and \(\mathbf{\Sigma}_{u}\) is an \(n\times n\) covariance structure defined as \(\mathbf{\Sigma}_{\mathbf{u}}=\mathbf{G}\cdot\mathbf{G}^{\mathbf{T}}/m\); \(\mathbf{G}\) is an \(n\times m\) genotype matrix, and \(m\) is the number of markers; \(\epsilon\sim N(0,\sigma_{n}^{2}\mathbf{I}_{\mathbf{u}})\) denotes an \(n\times 1\) independently and identically distributed (i.i.d.) residual vector, and \(\sigma_{n}^{2}\) is the residual variance. The covariance of \(\mathbf{y}\) can be denoted as  
\[\begin{split} Cov(\mathbf{y})=&\mathbf{X}\mathbf{X}^{T}\sigma_ {g}^{2}+\mathbf{\Sigma}_{\mathbf{u}}\sigma_{u}^{2}+\mathbf{I}_{\mathbf{n}}\sigma_{n}^{2}\\ =&\big{[}\mathbf{X}\mathbf{X}^{T}\omega_{g}+(\mathbf{\Sigma}_{ \mathbf{u}}\omega_{u}+\mathbf{I}_{\mathbf{n}})\big{]}\sigma_{n}^{2}\\ =&\big{(}\mathbf{X}\mathbf{X}^{T}\omega_{g}+\mathbf{P}\big{)} \sigma_{n}^{2}\end{split} \tag{2}\]  
where \(\omega_{g}=\sigma_{g}^{2}/\sigma_{n}^{2}\), \(\omega_{u}=\sigma_{u}^{2}/\sigma_{n}^{2}\), and \(\mathbf{P}=\mathbf{\Sigma}_{\mathbf{u}}\omega_{u}+\mathbf{I}_{\mathbf{n}}\). The estimate of \(\omega_{u}\) can be obtained under the null model, defined as \(\hat{\omega}_{u}\), which only needs to be computed once. Using spectral decomposition, \(\mathbf{\Sigma}_{\mathbf{u}}\) can be expressed as \(\mathbf{\Sigma}_{\mathbf{u}}=\mathbf{Q}\mathbf{\Lambda}\mathbf{Q}^{\mathbf{T}}\), where \(\mathbf{\Lambda}=diag(\lambda_{1},\lambda_{2},\cdots,\lambda_{n})\) is a diagonal matrix of eigenvalues, and \(\mathbf{Q}\) is an \(n\times n\) eigenvector matrix corresponding to these eigenvalues [37; 32; 38].
## 1 Introduction  
Owing to significant progress made in the field of rare earth metals and alloys prepared by molten salt electrolysis in China, this technology has been successfully applied for the production of rare earth aluminum alloys [1; 2; 3]. Al-Cu alloys exhibit high strength, heat resistance, and processability; however, the low corrosion resistance of these materials considerably limits their practical applications [4]. Recent research studies [5; 6] have shown that adding an appropriate amount of rare earth element Y not only improves the high-temperature properties, strength, and conductivity of Al-Cu alloys but also increases the corrosion resistance and acid corrosion resistance of their surfaces. Therefore, the development of Al-Cu-Y alloys with advanced properties is of high practical significance. Previously, the authors of this article prepared an Al-Cu-Y alloy on a consumable copper cathode in the AlF\({}_{3}\)-(Li,Na)F fluoride salt system using Al\({}_{2}\)O\({}_{3}\) and Y\({}_{2}\)O\({}_{3}\) as the raw materials [7]. To further study the migration and transformation of rare earth Y\({}_{2}\)O\({}_{3}\) species, it is necessary to examine the electrical transport properties of Y(III) ions in the AlF\({}_{3}\)-(Li,Na)F salt system. The ion migration number denotes the ratio of the number of migrating ions to the total number of ions in solution. The conductivity of an electrolyte solution strongly depends on the directional migration of ions; therefore, the ion migration number is an important parameter for measuring the ionic conductivity of an electrolyte [8]. Consequently, studying the variation of the migration number of Y(III) ions in the AlF\({}_{3}\)-NaF-LiF molten salt is important for controlling the Y content during the Al-Cu-Y alloy electrolysis process [9; 10]. Common methods for determining the ion migration number include the Hittorf method [11], interfacial movement method [12], and electromotive force method [13]. Among these techniques, the Hittorf method, which calculates the migration number from the concentration change near the electrode surface caused by the current passing through the electrolyte, is most suitable for estimating the migration number of molten salt ions and has been adopted by many researchers [14; 15; 16]. In this study, three slots consisting of an anode chamber, a cathode chamber, and a middle pole chamber were designed by applying the Hittorf method to measure the variation in the migration number of anions and cations. In addition, the migration mechanism of Y(III) ions in the AlF\({}_{3}\)-(Li,Na)F system was elucidated.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 4 Photocatalytic Properties
### Kinetic Study of Photocatalytic Degradation of MO  
As shown in Figure 7, the experimental data are well described with the pseudo-first order kinetic law [21] since the curves of \(Ln(A_{0}/A)\) versus irradiation time are well adjusted with linear fitting. The ZnO-Cr\({}_{1}\) photocatalyst was the most effective with a kinetic rate constant k of 4.451 \(\times\) 10\({}^{-2}\) min\({}^{-1}\) for the photodegradation of MO. The other photocatalysts are less effective and the calculated constant k estimated for the ZnO, ZnO-Cr\({}_{3}\) and ZnO-Cr\({}_{5}\) photocatalysts, respectively, was 1.274 \(\times\) 10\({}^{-2}\), 0.775 \(\times\) 10\({}^{-2}\) and 0.608 \(\times\) 10\({}^{-2}\) min\({}^{-1}\).  
Figure 6: Photodegradation of MO, 10 ppm in aqueous solution, performed under UV irradiation in the presence of the synthesized Cr-doped ZnO nanorods.
# Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring
## 3 Establishment of Simulation
### OD Matrix Traffic Demand  
In the process of generating traffic demand from the OD matrix, the traffic volume from the divided area (with different colors in Figure 2a) is assigned to roads in the area. As the OD matrix data source used in this work describes the total traffic volume in 24 h, there is no more specific data for the traffic volume per hour. Common daily timelines retrieved from cities in West Germany are used [30]. For passenger cars and trucks, timelines named TGw2_PKW and TGw_LKW are used separately for describing the traffic distribution on a workday. The overall process of generating traffic demand from the OD matrix is listed below:  
* **Areas described by the OD matrix transfer into polygons in SUMO:** The areas described by the OD matrix data source are saved in shapefiles (a data format for geometric location information); they are also described by geometric information (longitude and latitude). The most similar data format in SUMO is polygons. The boundary lines can be also described by geometric points in order. In this step, _polyconvert_ in SUMO is used to transfer shapefiles into polygons.
* **Polygons to TAZ:** The Traffic Assignment Zone (TAZ) is a district defined in SUMO and the vehicles can be coded to drive from one TAZ to another within a certain period. The TAZ also comprises districts constituted by points; for better visualization, the geometric coordinate format is transferred to the XY coordinate in SUMO. Figure 4 shows the TAZ distribution of the studied area; all the areas in OD matrix are marked with red boxes.
* **TAZ to edges/roads:** To distribute the traffic demand from the whole zone into edges/roads, a python program called _edgesInDistricts.py_ is used. Depending on the length of the roads, the traffic demand is distributed with different weights.
* **OD matrix to trips:** After the scope-related work is done, the traffic volume is processed. A function called _od2 trips_ has been used. The timeline has been used here to assign traffic into hours. Trucks and passenger cars are processed separately.
* **Trips to routes:** At last, through the network file and trip files in the last step, _duarouter_ is used to generate the route file. The route file describes when and where each vehicle starts on the map, and through which edges/roads it arrives at the destination.  
Figure 4: Traffic Assignment Zone (TAZ) distribution of Duisburg inner ring; each red box shows the roads in one TAZ.
## 4 \"Multistage Anchorage System + CFST\" Combined Support Technology
### Establishment of the New Support Method
#### 4.1.1 Multistage Anchorage System  
The multistage anchorage system in the new support scheme includes short bolts, long bolts, cables and their correlated wire mesh and shotcrete, as follows:  
1. Short bolts: 2.25 m full-length mortar bolts, with spacing of 1 m \(\times\) 2 m, which are applied immediately after the roadway excavation and equipped with a 10 cm concrete spraying layer. The short bolts pass through the first complete rock mass layer, fixing the outermost broken zone to prevent roof falling and collapse during the construction process. This kind of flexible support can also release the original rock stress to some extent, diminishing the pressure for the forthcoming rigid support.
2. Long bolts: 3 m full-length mortar bolts, with spacing of 1 m \(\times\) 2 m. On the basis of the results of the broken rock zone test, the long bolts are able to pass through two broken zones and one intact zone to reach the second intact zone and fix this part of the rock together to completely exploit the rock mass's self-bearing capacity.  
Figure 12: New support scheme layout.  
Cables: 7 m, installed on the haunches, spandrels and corners on which large deformations frequently occur, to fully regulate the horizontal deformation of the roadways. Moreover, cables are connected with the surrounding rock reinforced by shallow combined bolts, forming a support system with a strong anti-deformation ability and greatly improving the stability and integrity of the surrounding rock.  
Shallow broken surrounding rocks have a poor stress transfer ability. Through the multistage anchoring system, effective superposition of the surrounding rock stress is achieved and the synergistic effect is depicted in Figure 13. The multistage anchoring system combines the roadway surrounding rocks to form a reliable bearing structure. It also fully mobilizes the capacity of deep, stable surrounding rocks and restrains the development of harmful deformation. Engineering practice of the study area is taken into account when the length and raw materials of the bolts and cable are selected. For example, the anchor rod used in the study area is made of spiral steel with a 9 m standard length. Therefore, the bolt length (2.25 and 3 m) can effectively reduce the cost because there is no material waste.