esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of Proposed Method
### Proposed Method
#### 4.2.1 Signal Decomposition Using VMD  
To break down the noisy signal \(\mathbf{x}(n)\) into the \(K\) predetermined BLIMFs \(\{\mathbf{u}_{k}(n)\}_{k=1}^{K}\), VMD uses a completely non-recursive method. This is accomplished by first determining the center frequencies \(\mathbf{\omega}_{k}(n)\) and then solving the following constrained variational problem [16]:  
\[\min_{\{\mathbf{u}_{k}(n)\},\{\mathbf{\omega}_{k}\}}\ \sum_{k=1}^{K}\bigg{\|} \partial_{n}\bigg{[}\bigg{(}\mathbf{\delta}(n)+\frac{j}{\pi n}\bigg{)}*\mathbf{u}_{k}( n)\bigg{]}e^{-j\omega_{k}n}\bigg{\|}_{2}^{2}, \tag{3}\]  
subject to  
\[\sum_{k=1}^{K}\mathbf{u}_{k}(n)=\mathbf{x}(n), \tag{4}\]  
where \(\mathbf{\delta}(n)\) is the Dirac delta function, \(*\) denotes convolution, \(\partial_{n}\) is the gradient operator with respect to \(n\), and \(\|\cdot\|_{2}\) denotes the \(L_{2}\) norm. By incorporating a quadratic penalty term and Lagrange multipliers, the corresponding unconstrained augmented Lagrangian \(L(\cdot)\) is constructed as  
\[L(\{\mathbf{u}_{k}(n)\},\{\mathbf{\omega}_{k}(n)\},\lambda) =\alpha\sum_{k=1}^{K}\bigg{\|}\partial_{n}\bigg{[}\bigg{(}\mathbf{ \delta}(n)+\frac{j}{\pi n}\bigg{)}*\mathbf{u}_{k}(n)\bigg{]}e^{-j\mathbf{\omega}_{k}( n)}\bigg{\|}_{2}^{2}\] \[+\bigg{\|}\mathbf{x}(n)-\sum_{k=1}^{K}\mathbf{u}_{k}(n)\bigg{\|}_{2}^{2}+ \bigg{\langle}\mathbf{\lambda}(n),\bigg{(}\mathbf{x}(n)-\sum_{k=1}^{K}\mathbf{u}_{k}(n) \bigg{)}\bigg{\rangle} \tag{5}\]  
where \(\langle\cdot,\cdot\rangle\) denotes the inner product, \(\mathbf{\lambda}(n)\) values are the Lagrange multipliers, and \(\alpha\) is the penalty coefficient determining the relative significance of the first term compared to the remaining two (where the second term enforces the constraint (4) via quadratic  
Figure 3: Block diagrams for (**a**) the proposed DFA-based metric calculation and (**b**) the overall proposed VMD-based denoising method.  
penalty and the last term enforces the constraint (4) by applying the Lagrange multiplier). Using the alternating direction method of multipliers (ADMM), the optimization problem is iteratively solved by updating \(\mathbf{u}_{k}(n)\), \(\mathbf{\omega}_{k}\), and \(\mathbf{\lambda}(n)\) until convergence. The output of VMD is a set of \(K\) modes \(\{\mathbf{u}_{k}(n)\}\), which we denote as \(\{\mathbf{d}_{k}(n)\}\) for consistency.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 3 Electromagnetic Irradiation
### Gamma-ray Irradiation  
It is generally accepted that MoS\({}_{2}\) macroscopic materials are stiff under gamma-ray irradiation. There is little literature on \(\gamma\)-irradiated bulks. It was reported that MoS\({}_{2}\) powders were resistant to a \(\gamma\)-ray irradiation with a dose of \(5\times 10^{9}\) R (\(1.29\times 10^{6}\) C/kg) [105].  
[PERSON] et al. [106] mechanically exfoliated MoS\({}_{2}\) 2D layers (50-132 layers) and exposed them to 5000 \(\gamma\)-ray photons with 662 keV. It was found that the resonance frequency of the layers upshifted immediately after \(\gamma\)-ray exposure and returned to their initial frequency after 60 h. The procedure was repeatable. It was assumed that \(\gamma\)-ray photons generated charges on MoS\({}_{2}\) and caused electrostatic forces between MoS\({}_{2}\) and substrates, resulting in electrostatic tension and deflection of MoS\({}_{2}\) layers. The MoS\({}_{2}\) multi-layers were stiff under the \(\gamma\)-ray and showed no irradiation damage.  
[PERSON] et al. [107] prepared MoS\({}_{2}\) multi-layers (5-8 layers) through the vapor phase sulfurization and exposed the layers to a \(\gamma\)-ray irradiation of \({}^{60}_{27}\)Co source (\(\gamma\)-ray energy: 1.1732 MeV and 1.3324 MeV) with a dose of 120 Mrad. The irradiation was carried out at room temperature in an ambient atmosphere. The X-ray photoelectron spectra indicated that MoS\({}_{2}\) layers were converted to molybdenum oxide (MoO\({}_{x}\)) after the irradiation. It is plausible that the \(\gamma\)-ray displaced or knocked out \(S\) atoms while leaving molybdenum atoms unaffected. Maybe the as-produced sulfur vacancies were filled with oxygen atoms to form MoO\({}_{x}\).
## 2 System Framework for GIS-Based Map Construction
### Road Layer  
The road layer describes the topological relationship among roadway segments and intersections. A road section line between two adjacent intersections is defined as a road segment, which describes a one-way or two-way connection between two adjacent intersections. Thus, the model of the road layer can be represented as Equation (1):  
\[R_{r}=\{P_{r},S_{r},E_{r}\} \tag{1}\]  
where \(R_{r}\) is the road layer network, \(P_{r}\) is all the intersection nodes in the network, \(S_{r}\) is the centerline of all the road segments on the road, and \(E_{r}\) is the road edge line. Road edge data describes the road width along with the road segment.  
A road segment is composed of one or more lanes. For each segment \(s_{r}\) in the segment set \(S_{r}\) is identified by its ID and can be expressed as Equation (2):  
\[s_{r}=(S_{l},P_{r},Q_{r}) \tag{2}\]  
where \(S_{l}\) is the set of lane centerlines contained in the road segment, \(P_{r}\) is defined as the road-level intersection node, and \(Q_{r}\) is the attributes of the road segment, including the  
Figure 1: Architecture of the map construction model.  
road segment ID, the name of the road where the road segment is located, the road width, the length of each road segment, the number of lanes, and the direction of the road segment.
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 2 Related Works
### UAV Routing Problem for Logistics
Inspired by density-based clustering methods, they first designed an algorithm to classify regions into clusters and obtained approximate optimal point-to-point paths for UAVs, such that the coverage task is performed correctly and efficiently. In the other study by [PERSON] et al. [27], they focused on the coverage path planning problem of heterogeneous UAVs, and present an ant colony system (ACS)-based algorithm to obtain good enough paths for UAVs and fully cover all regions efficiently. These models are limited in assuming items only be transported by UAVs without considering a concept of equity. In the transportation of items at the time of a disaster, often referred to as post-disaster HL, it is necessary to consider equity in the distribution of relief supplies as an important issue [1,44]. In addition, existing heuristic approaches are not yet efficient enough to solve large-scale problems or problems in dynamic environments. Therefore, this study focuses on a typical reinforcement learning method, QL, which is a powerful approach to complex sequential decision-making problems with large or continuous state and action spaces [45]. UAVRPs studied in this study is a multi-period sequential decision problem with a large number of states and actions, which is suitable for QL. RL method can be used to model the decision-making of agents that can adapt to dynamic environments based on learning from previous knowledge, and, thus, has potential for application to dynamic real-world environments in post-disaster situations. For example, RL have been studied in HL, such as the deployment of emergency infrastructure, the selection of rescue paths and the prediction of relief demand [46]. However, there are few studies in which reinforcement learning has been applied to UAVRPs in post-disaster HL, so this study developed a QL method for planning the transportation of supplies. This study aims to optimize transport in static scenarios where the state of the environment does not change, but the methods in this study can be extended and applied to optimization in dynamic scenarios.
## 4 Methods for Improved Reliability and Robustness
### Quality Control Techniques  
QC techniques grade the quality of either input CMR images or segmentation outputs, allowing for recognizing anomalous scenarios, but without performing any action to correct the identified problem. Therefore, they improve reliability and/or robustness by signalling the identified anomalies to the users for them to act upon the problem. Most of these frameworks are not conceived to depend on a specific segmentation architecture, but they can adapt to the different segmentation pipelines available in the literature.  
We identify two types of QC techniques, depending on when they are used. We denote as _pre-analysis QC_[71; 72; 73; 74; 75; 76; 77] those methods that act exclusively on the inputs of a DL-based model, i.e., before the model is executed, thus aiming specifically to improve robustness. _Post-analysis QC_[78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88] refers to those methods that act on the outputs of the model todetect a malfunction, thus addressing reliability. Pre- and post-analysis mechanisms are not mutually exclusive. They can be combined in an end-to-end framework. Moreover, pre-analysis QC tools can be combined with further processing steps that mitigate the erroneous detected inputs.
## 3 Results and Discussion
### Analysis of the Effects of YLDI on the Volatile Compounds of Green Tea Using GC-MS/MS
#### 3.3.5 Analysis of the Metabolic Pathways of Key Volatile Compounds
CDVs such as \(\beta\)-ionnone, nerolidol, and \(\beta\)-damascenone primarily originate from the oxidative degradation of carotenoids (Figure 5). Specifically, \(\beta\)-damascenone is predominantly produced through the degradation of neoxanthin, whereas \(\beta\)-ionone is generated via the oxidation of \(\beta\)-carotene. Nerolidol is derived from the photo-oxidation of phytofluene and is synthesized via the nerolidol synthase gene [32; 40]. In our study, these compounds exhibited divergent responses to light intensity, with variations observed across different treatments. Nerolidol and \(\beta\)-ionone attained their peaks under 4000 Lux treatment (Figure 6D). [PERSON] et al. [40] have found that increasing light intensity can enhance the formation of CDVs, which corroborates our findings. The interconversion of zeaxanthin, antheraxanthin, and violaxanthin, known as the xanthophyll cycle, is regulated by light. Zeaxanthin can be catalyzed into CDVs through the action of carotenoid cleavage dioxygenases. Stronger light intensity promotes the accumulation of zeaxanthin, potentially explaining the highest concentrations of nerolidol and \(\beta\)-ionone under 4000 Lux treatment. In contrast, \(\beta\)-damascenone peaked at 1000 Lux and decreased markedly at higher intensities, which may be attributed to its susceptibility to photo-oxidative degradation. These results imply that although stronger light intensity can enhance the formation of certain CDVs, it may simultaneously accelerate the loss of others, indicating that individual CDVs respond differently to light intensity. Figure 5: Possible formation pathways of 21 important volatile components with OAV \(>\) 1 in tea samples exposed to yellow light with different intensities. Figure 6: Dynamics changes of key differential volatile compounds in tea samples exposed to yellow light with different intensities. (**A**) Fatty acid-derived volatiles; (**B**) Glycoside-derived volatiles; (**C**) Amino acid-derived volatiles; (**D**) Carotenoid-derived volatiles.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 3 Materials and Methods
### The Idea of Recognizing the Pattern of the Road  
The process of recognizing fragments of the route on which the machine moves can be presented in the following steps:  
1. Creating a catalog of patterns concerning various types of road fragments.
2. Iterative comparison of successive cut signal fragments with the directory using DTW.
3. Recording the value of the normalized DTW distance for each of the patterns from the catalogs in the subsequent fragments of signals.
4. Choosing the best match among the patterns in the catalog to the tested signal.  
This concept is also presented in Figure 3.  
Figure 3: Concept of road pattern recognition from accelerometer readings.  
The way in which the described method can be used in the process of correcting the vehicle location is shown in Figure 4.
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 4 IP Mobility Management in MANET and Comparative Summary
### Hybrid Gateway Discovery-Based Mobility Management Solutions
The second table is used by each MANET node to store available gateways in the network. A drawback of the system is that it propagates gateway advertisement to one hop neighbors only, and for faraway nodes it uses the ring search method strategy, similar to [48], to discover a gateway. A distant MANET node will thus experience longer delays to discovering routes to a gateway. This section has introduced six proposals that use Mobile IP and follow the hybrid gateway discovery approach and they are summarized in Table 3.
## 4 Double-Criteria Drift Streaming Peaks over Threshold (DCDSPOT)
### Probability Weighted Moments for Parameter Estimation of GPD  
When we employ DCDSPOT for anomaly detection, it is inevitable to set three different thresholds for the target parameter and the WSP. Hence, the running time is 6 times that of the original biDSPOT. To shorten the running time of our method, parameter estimation needs to be improved.  
Parameter estimation using the Maximum Likelihood Estimation (MLE) method is time-consuming. In our method, we applied Probability Weighted Moments (PWM) for parameter estimation of GPD instead of MLE by Grimshaw trick [26]. PWM is based on the basic principle of the moment estimation method, which regards the probability distribution as the weights. The formula of PWM is described in Equation (13):  
\[w_{r}(\theta)=E[YF^{r}(Y;\theta)] \tag{13}\]  
\(w_{r}\) denotes \(r\)-order probability weighted moment, and \(\theta\) denotes the parameter of the distribution function, and \(E(\cdot)\) means to find the expected value. Substituting the GPD function into Equation (13), the \(r\)-order PWM of the GPD distribution can be calculated via Equation (14):  
\[w_{r}=E(YG^{r})=\frac{\sigma}{\gamma}\times[\frac{r!}{(r+1-\gamma)(r-\gamma)... (1-\gamma)}-\frac{1}{r+1}] \tag{14}\]  
\(w_{r}\) can be estimated by Equation (15) using the sample:  
\[w_{r}=\frac{1}{N_{t}}\sum\limits_{i=1}^{N_{t}}\frac{(i-1)(i-2)...(i-r)}{(N_{t} -1)(N_{t}-2)...(N_{t}-r)}Y_{(i)} \tag{15}\]  
where \(Y_{(1)}\leq Y_{(2)}\leq...\leq Y_{(N_{t})}\), that is, \(Y_{(i)}\) denotes the \(i\)-th data in the sequence after arranging the sample data from small to large. Then \(w_{0}\) and \(w_{1}\) can be computed via Equation (16):  
\[\begin{array}{l}w_{0}=\frac{1}{N_{t}}\sum\limits_{i=1}^{N_{t}}\frac{i}{N_{t} }Y_{(i)}=\frac{1}{N_{t}^{2}}\sum\limits_{i=1}^{N_{t}}Y_{(i)}i\\ w_{1}=\frac{1}{N_{t}}\sum\limits_{i=1}^{N_{t}}\frac{i-1}{N_{t}-1}Y_{(i)}=\frac{ N_{t}}{N_{t}-1}w_{0}-\frac{1}{N_{t}(N_{t}-1)}E(Y)\end{array} \tag{16}\]  
Finally, the sample PWM is used instead of the population PWM to estimate parameters, namely:  
\[\left\{\begin{array}{l}w_{0}=\frac{\sigma}{1-\gamma}\\ w_{1}=\sigma\frac{3-\gamma}{2(1-\gamma)(2-\gamma)}\end{array}\right.\to\left\{ \begin{array}{l}\widehat{\sigma}=\frac{2w_{0}(w_{1}-w_{0})}{w_{0}-2w_{1}}\\ \widehat{\gamma}=2+\frac{w_{0}}{w_{0}-2w_{1}}\end{array}\right. \tag{17}\]  
In our method, we use Algorithm 3 to calculate thresholds of POT.  
```
0: time series \(X=(x_{1},x_{2},...,x_{n},x_{n+1},...,x_{N})\), risk parameter \(q\), quantile \(\alpha\)
0:\(z_{q},t\)
1:function POT using PWM(\(X,q,\alpha\))
2:\(t\gets SetInitialThreshold(X_{1},X_{2},...,X_{n})\) # set by low \(\alpha\) quantile
3:\(Y_{t}\leftarrow\{X_{i}-t|X_{i}>t\}\)
4:\(\widehat{\gamma},\widehat{\sigma}\gets PWM(Y)\) Equation (17)
5:\(z_{q}\gets t+\frac{\widehat{\sigma}}{\gamma}[(\frac{qk}{N_{t}})^{-\widehat {\gamma}}-1]\)
6: return \(z_{q}\)
7:endfunction
```  
**Algorithm 3** Peaks over threshold Using PWM
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 4 Position Solution
### Stand-Alone mmWave Positioning  
The position and rotation estimation for a 5G mmWave system is based on the following non-linear-weighted-least-squares (NWLS) optimization problem.  
\[\left[\hat{\mathbf{p}},\hat{\alpha},\hat{\beta}\right]=\underset{\mathbf{p}, \hat{\alpha},\hat{\beta}}{\text{argmin}}\left\|\bar{\mathbf{\eta}}-\hat{\mathbf{f}} (\mathbf{p},\alpha,\beta)^{\text{T}}\right\|_{\Xi}, \tag{32}\]  
where the observation vector \(\bar{\mathbf{\eta}}\triangleq[\eta_{1}^{\text{T}},\cdots,\eta_{N_{a}}^{\text{T}}] ^{\text{T}}\), and \(\Xi\) is the weighing matrix including estimates of the error variances of the observables, \(\hat{\mathbf{f}}(\mathbf{p},\alpha,\beta)\triangleq[\mathbf{f}_{1}^{\text{T} }(\mathbf{p},\alpha,\beta),\cdots,\mathbf{f}_{N_{a}}^{\text{T}}(\mathbf{p}, \alpha,\beta)]^{\text{T}}\) and \(\mathbf{f}_{i}(\cdot)\) is the vector function including the LoS and NLoS relationships between the user's location-rotation information and the channel parameters corresponding to the link between the \(i\)-th base station and the user, i.e., Equations (9)-(18) \(\forall i=1,\ldots,N_{a}\).  
In the simple case of LoS (or dominant LoS) channel condition, the above optimization includes solely the LoS equations yielding to a non-linear system of \(N_{a}\times 5\) equations. The solution is sought with a standard iterative algorithm (e.g., the Levenberg-Marquardt), which can be initialized with  
\[\hat{\mathbf{p}}_{0}=\frac{1}{N_{a}}\sum_{i=1}^{N_{a}}\hat{\mathbf{p}}^{i}, \tag{33}\]\[[\hat{a}_{0},\hat{\beta}_{0}]^{\mathrm{T}}=\frac{1}{N_{a}}\sum_{i=1}^{N_{a}}[\hat{ \alpha}^{i},\hat{\beta}^{i}]^{\mathrm{T}}, \tag{34}\]  
and defined as  
\[\tilde{\mathbf{p}}^{i} =\mathbf{q}^{i}+\tilde{d}_{0}^{i}\begin{bmatrix}\cos\tilde{\theta }_{0}^{r,i}\cos\tilde{\theta}_{0}^{r,i}\\ \cos\tilde{\theta}_{0}^{r,i}\sin\tilde{\theta}_{0}^{r,i}\\ \sin\tilde{\theta}_{0}^{r,i}\end{bmatrix}, \tag{35}\] \[\tilde{\alpha}^{i} =\pi+\tilde{\Phi}_{0}^{t,i}-\tilde{\Phi}_{0}^{r,i},\] (36) \[\tilde{\beta}^{i} =\tilde{\theta}_{0}^{r,i}-\tilde{\theta}_{0}^{t,i}, \tag{37}\]  
where the \(i\) superscript indicates a measurement or estimation respect to the \(i\)-th base station.
# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
## 3 Materials and Methods
### Geo-Environmental Factors: Definition and Statistical Analysis
In this context, the stream power index should be considered an important factor for LSMs. **Land use:** This is a geo-environmental factor, categorized based on the evaluation of previous landslides. While factors such as settlement areas, agricultural areas, forest areas, and areas where vegetation is dense or sparse do not cause the formation of landslideson their own, they are among the factors frequently used in LSMs because resistance to landslide formation will differ under different effects [60]. **Distance from stream:** As distance to a stream decreases, the increase in groundwater and erosion caused by the bearing power of the stream heightens the probability of landslide formation [99]. Therefore, distance from a stream is a frequently used factor in LSMs. The slope map was created using ArcMap software's [95] Spatial Analysis Tools and divided into five classes: \(<\)10\({}^{\circ}\), 10-20\({}^{\circ}\), 20-30\({}^{\circ}\), 30-40\({}^{\circ}\) and \(>\)40\({}^{\circ}\). The slope aspect map was created with ArcMap software's Spatial Analysis Tools and divided into nine classes: flat, north, northwest, west, southwest, south, southeast, east and northeast [95]. When preparing the LSM, use of a general curvature map was preferred, since this contains both plan and profile curvature. With the help of ArcMap's [95] Spatial Analysis Tools, a general curvature map was created and divided into three classes: convex, flat and concave. The elevation map was prepared using ArcMap software [95] with the help of a digital elevation model (DEM), and divided into nine classes: \(>\)50 m, 50-100 m, 100-150 m, 150-200 m, 200-250 m, 250-300 m, 300-350 m, 350-400 m and \(>\)400 m. The discontinuity map was prepared using remote sensing methods, utilizing using aerial photographs of the geology and GDMRE active fault line maps [75]. The density-of-discontinuities map was prepared with the help of ArcMap software [95] and divided into four classes: low, medium, high and very high density. The SPI map was created with ArcMap software's [95] Hydro Tools and divided into four classes: low, moderate, high and extremely high. The land use map produced for this study divided the study area is divided into four classes: forest, arid land, alluvial plain and settlement areas. To prepare the distance from stream map, first a drainage map was prepared and then areas that were 0-100 m, 100-200 m, 200-300 m, 300-400 m and more than 400 m were reclassified according to the distance to these streams.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
### Light Gas Gun  
The compressed gas gun and the two-stage light gas gun are two types of light gas guns. The compressed gas gun is used for providing a dynamic response behavior to materials undergoing low-pressure impact. By accelerating projectiles to high velocities, the two-stage light gas gun simulates the impact effect of orbital debris on spacecraft and satellite components. In comparison to explosive-driven flyer technologies, the light gas gun offers the advantages of precise projectile velocity adjustment, smooth flyer flight, superior data repeatability, and high measurement accuracy.  
The powder chamber, pump tube, coupling section, and launch tube are the primary components of a two-stage light gas gun, as illustrated in Figure 3. The propellant in the powder chamber is ignited during the experiment cycle. At a moderate pressure, the burning propellant produces a high molecular weight gas that drives a large piston down the smooth pump tube. Initially, this pump tube is a closed system containing appropriate helium or hydrogen. The light gas is compressed to high pressure as the piston moves  
Figure 1: Schematic diagram of explosive-driven flyer.  
Figure 2: Schematic view of flyer driven by micro-charge detonation.  
down the pump tube and into the coupling section. The opening of a burst diaphragm permits this gas to propel a small projectile down the launch tube at high speeds.  
[PERSON] et al. optimized the two-stage light gas gun by adjusting the pump tube length to the diameter ratio, the piston mass, and the hydrogen pressure, allowing a projectile of known shape and mass to be propelled at speeds of up to 15 km/s for debris shield design [6]. [PERSON] et al. used a two-stage light gas gun to perform hyper-velocity impact studies on solar cell arrays and assessed the main mechanical damage characteristics [7].