esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optical Diagnosis of the Plasma Source
#### 3.4.2 OES Measurements
Moreover, using simulation software like _Liftbase_[86] and _Spectrum Analyzer_[87], we determined from the acquired  
Figure 10: The emitted light in the UV-Vis-NIR from CAP in Ar (red line) and He (blue line), acquired at 5 mm from the nozzle exit, in mid air, 8 kV\({}_{pp}\) applied voltage. plasma emission spectra the characteristic plasma temperatures, such as the rotational temperature of OH radicals (T\({}_{rot}\)(OH)), the rotational temperature of nitrogen molecular ions N\({}_{2}^{+}\) (T\({}_{rot}\)(N\({}_{2}^{+}\))) and the vibrational temperature of nitrogen molecules N\({}_{2}\) (T\({}_{p}\)(N\({}_{2}\))). As a result, we estimated for Ar-CAP a T\({}_{rot}\)(OH) around 420 \(\pm\) 25 K (\(\sim\)146 \({}^{\circ}\)C), T\({}_{rot}\)(N\({}_{2}^{+}\)) 450 \(\pm\) 50 K (\(\sim\)176 \({}^{\circ}\)C), and T\({}_{p}\)(N\({}_{2}\)) 1766 \(\pm\) 167 K. Moreover, for He-CAP, the T\({}_{rot}\)(OH) around 420 \(\pm\) 20 K (\(\sim\)146 \({}^{\circ}\)C), T\({}_{rot}\)(N\({}_{2}^{+}\)) 425 \(\pm\) 25 K (\(\sim\)151 \({}^{\circ}\)C), and T\({}_{p}\)(N\({}_{2}\)) 2381 \(\pm\) 603 K. In most cases, cold atmospheric pressure plasmas are considered non-thermal plasmas, and thus the rotational temperatures are often equivalent to those of the working gas. These estimated values of the rotational temperatures of OH radicals and molecular ions of N\({}_{2}^{+}\) are slightly higher than those measured using FOT optical fiber. No significant rotational temperature difference was to be observed between the Ar and He-CAP, this leading to the idea that these two plasmas are at the same temperature, when ignited at the same parameters. However, it must be borne in mind that these values correspond to '_spectroscopic temperatures_' referring to the energies that those plasma species (OH, N\({}_{2}\) and N\({}_{2}^{+}\)) can be achieved by various processes (dissociation, Penning, etc.) and can be further used in the plasma environment to initiate various physico-chemical processes that can take place on the surface or in the plasma volume. The noticeable difference is within the vibration temperature, when He-CAP T\({}_{p}\) is almost 35% higher than that estimated for Ar-CAP. In addition, it might be that a higher T\({}_{p}\)(N\({}_{2}\)) found for He-CAP to be to blamed for the small or even non-existent production of NO\({}_{x}\) and O\({}_{3}\).
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 2 Related Work
### Grid-Based Downsampling  
Grid-based downsampling methods [21; 22] are another class of commonly used downsampling methods, first proposed by [PERSON] and [PERSON] [29]. The principle is to grid the point cloud space and then use the average or weight of the points in each grid to replace all the points in the original grid, thus reducing the number of points. [PERSON] [30] proposed a point cloud simplification algorithm based on voxelized grid downsampling. This method divides the point cloud space into different subspaces, then samples in each subspace, and finally combines them into a complete sampled point cloud. This method is efficient and scalable, with the capability to cope with large-scale point cloud sampling problems. In experiments, this method showed better sampling effects and faster calculation speed than other point cloud sampling methods. [PERSON] [31] proposed an adaptive triangular mesh model redistribution algorithm that could preserve the details and overall shape of the point cloud at the same time. Grid subsampling fits the point cloud plane, but it is easy to blur and stretch the shape of the original 3D point cloud image. The comparison before and after grid subsampling is shown in Figure 1.
## 3 System Model and Problem Statement  
As shown in Figure 1, we consider a scenario where a group of UAVs equipped with solar panels take off from the same location and achieve fair coverage of the target area. The UAVs are represented by the set \(\mathcal{N}=\{1,\ldots,N\}\), and serve as height-adjustable aerial base stations to support ground users with coverage services in an \(A\times A\) meter area. All UAVs are limited by a connectivity constraint and lose connection with the swarm of UAVs when there are no other UAVs within communication range. Since UAVs may move vertically, their coverage range \(R_{n}\) changes with movement, where \(n\in\mathcal{N}\).  
The flight cycle \(D\) is composed of \(M\) equally long time slots, denoted by \(\mathcal{M}=\{1,\ldots,M\}\). We set the time slot \(\Delta\tau\) to meet \(D=M\Delta\tau\). In this paper, \(\Delta\tau\) is set to be small enough so that the position of the UAV can be considered as constant during each time slot. Multiple ground users are randomly distributed in the target area. In the current time slot, if a user is within the coverage range of an UAV, the swarm of UAVs is considered to be providing coverage for the user. Our task is for all UAVs to move around the target area and simultaneously provide network coverage services to ground users within the flight cycle \(D\). The position of the UAV in time slot \(t\) is represented by the 3D Cartesian coordinate system \([x[t],y[t],z[t]]^{\mathrm{T}}\), where \(t\in\mathcal{M}\). \(x[t]\), \(y[t]\), and \(z[t]\), respectively, represent the horizontal x-coordinate, horizontal y-coordinate, and vertical z-coordinate  
Figure 1: The structure of our network model.  
of a UAV. Since the flight of UAVs in this paper is divided into horizontal flight and vertical flight, the horizontal coordinates of the UAV need to be separately represented as \(\omega[t]=[x[t],y[t]]^{\mathrm{T}}\), where \(t\in\mathcal{M}\). Users are represented by the set \(\mathcal{K}=\{1,\dots,K\}\), and their positions are represented by horizontal coordinates \(q[k]=[x[k],y[k]]^{\mathrm{T}}\), where \(k\in\mathcal{K}\).
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 2 Ensemble Machine Learning (ML) Models
### Stacking Strategy  
The stacking strategy was first proposed by [PERSON] and further developed by [PERSON], who introduced 'Stacked Regressions' using generalized linear models [34]. Unlike bagging and boosting algorithms, the stacking algorithm combines multiple base learners using a nonlinear integration method to form a multilayered strong learner. These base learners can be different types of heterogeneous learners. For example, in a two-layer learner structure, the stacking algorithm first trains the base learners in the first layer. The  
Figure 1: Schematic diagram of the bagging and boosting algorithms: (**a**) the bagging algorithm; (**b**) the boosting algorithm.  
results from the first layer are then used as a new training and testing set for the second layer (meta-learners) to train and predict. The basic principle of the stacking strategy is shown in Figure 2.  
The blending algorithm is very similar to the stacking algorithm, as both of them are effective ensemble learning methods designed to enhance prediction performance. Compared to the stacking algorithm, the blending algorithm directly uses non-overlapping data to train different base learners. Therefore, when sample data are insufficient, the blending method uses fewer data in the second layer, making it prone to overfitting. In contrast, the stacking method uses all data at each training layer, resulting in a more robust forecasting ability. In this study, with only 97 sets of prediction data, the sample data are relatively insufficient. Thus, the stacking method was chosen for model fusion.
## 3 Case Study
### Drainage System and Rainfall Data  
Since the rainwater channel system has been installed in the study area, the system concentrated on the existing blocks and the gutters of the road system in the nearby local area to determine the drainage area (shown in Figure 3. After some flooding events, the flooding project was examined and found that in the past 10 years, there have been no serious typhoon disasters and serious casualties in the study area, and only local inundations have occurred. For the simulation of rainfall events, we referred to the historical precipitation records of the Wugu Station of Taiwan's Central Meteorological Bureau, which is the nearest to the base center. They are respectively 112 mm/1 h and 234.5 mm/3 h. In addition, the extreme events caused by global climate change, lead to frequent heavy rains, and in the future, groundwater resources will become increasingly serious, simultaneously. The quantitative precipitation simulation scenario of the 350 mm/6 h, using empirical methods, was based on historical flooding and rainfall data to develop a flooding scenario as rain in the stormwater management model of this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Content** & **Current Pavement** & \begin{tabular}{c} **General Permeable** \\ **Pavement** \\ \end{tabular} & \begin{tabular}{c} **JW Ecological** \\ **Technology** \\ \end{tabular} \\ \hline Surface & \begin{tabular}{c} Roughness \\ Slope \\ \end{tabular} & 0.01 & 0.02 & 0.012 \\ \hline \multirow{3}{*}{Pavement} & \begin{tabular}{c} Thickness \\ Void Ratio \\ Permeability \\ \end{tabular} & \begin{tabular}{c} 500 mm \\ 0.03 \\ 15 mm/h \\ \end{tabular} & \begin{tabular}{c} 500 mm \\ 0.12 \\ 1250 mm/h \\ \end{tabular} \\ \hline Soil & \begin{tabular}{c} Thickness \\ Perosity \\ Field Capacity \\ \end{tabular} & \begin{tabular}{c} 45 mm \\ 0.05 \\ 0.08 \\ \end{tabular} & \begin{tabular}{c} 45 mm \\ 0.15 \\ 0.08 \\ \end{tabular} \\ \hline \multirow{3}{*}{Storage} & \begin{tabular}{c} Thickness \\ Void Ratio \\ Seepage Rate \\ Clogging Factor \\ \end{tabular} & \
\begin{tabular}{c} 100 mm \\ 0.23 \\ 0 mm/h \\ 0 \\ \end{tabular} &
\begin{tabular}{c} 1000 mm \\ 0.35 \\ 0 mm/h \\ \end{tabular} \\ \hline Underdrain & Flow Coefficient & 0 & 0 & 0 \\ \hline \hline \end{tabular}  
Data sources: Low-impact development manual [21] and CHY1-SHYANG CO., LTD. [20].  
\end{table}
Table 1: The vertical construction parameters of the different impermeability zones.  
Figure 2: Schematic diagram of elevation and the main stormwater server.
## 2 Related Work
### Video Query System  
A video query system aims to find the most relevant videos given a user query [50]. Among the processing activities, the most important are ingesting and preprocessing, which require advanced computer vision algorithms to find and identify relevant content, such as shapes, objects, colors, and events [33]. Then, each object and event must be properly labeled and stored in a data structure or database. Finally, an engine is needed to retrieve videos and events given a user query. For this reason, video search frameworks are usually all-in-one solutions that handle data ingestion, preprocessing, object detection, labeling, storage, and retrieval [4; 51; 52; 53; 4; 6; 10; 32; 43; 48; 51; 53].  
From the user's perspective, few of the mentioned works provide a query language or human-computer interaction (HCI) [4; 5; 10; 33; 51; 53; 54]. Some current query languages extend traditional SQL with specific operators related to video events, such as objects (e.g., a car), attributes of objects (e.g., a red car), and spatiotemporal relationships (e.g., a car to the left of a bus, a truck that appears before the car) [10; 13; 53; 54].
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Network Layer
#### 3.2.3 Packet Manipulation  
Defined as a modification of the header or payload of packets transmitted over the network, packet manipulation consists of a packet-stealing phase and a packet-modification phase. In the packet-stealing phase, the attacker accesses the network and deceives communication nodes, including physical systems and the computing system, using vulnerabilities in the network protocol, CPS implementation, and communication nodes, etc. Then, the packets on the network are forwarded to the attacker. After the packet-stealing process, the attacker modifies the header or payload of the packets received from the legitimate source node and forwards the modified packets to the original destination.  
When the attacker manipulates the error-checking field in the header during the modification phase, such as the checksum under the user datagram protocol (UDP), the destination node discards the manipulated packet because it determines that the received packet has an error due to the modified checksum. Therefore, the packet manipulation attack can also have a DoS attack effect. In other words, if the attacker modifies the payload while strictly adhering to the network protocols, then the destination nodes are deceived by the packet manipulation. When the sensor measurements or the control input signal are modified, this packet manipulation attack has an effect equal to physical layer attacks.  
The packet manipulation attack can occur in various CPS fields. For a CBTC system, a packet manipulation attack targeting train control command packets is introduced with address resolution protocol (ARP) spoofing [16; 32]. Due to the security vulnerability in the ARP process, the attacker can steal communication authority between the train and ground station; train collisions can occur due to control command manipulation. In [55], a sensor data-spoofing attack and various packet injection attacks targeting a vehicular ad-hoc network (VANET) are presented. Specifically, a Sybil attack, which deceives a number of nodes on the road in a VANET, makes it appear that traffic congestion occurs, although it does not, by sending incorrect messages, which results in inconvenience for traffic.
## 3 Simulation Setup and Validation
### Numerical Engine Model
#### 3.2.2 Model Validation  
Figure 6 shows the comparison of the simulation and test results of in-cylinder pressure with different PODE blending ratios, and both the experiment and simulation are conducted under 1400 rpm and a BMEP of 1.1 MPa. It is clear from Figure 6a-c that the simulated cylinder pressure of P0, P10, and P15 matches with the measured in-cylinder pressure very  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mathematical Physics Model** & **Type** \\ \hline Turbulence model & standard k-\(\epsilon\)[26] \\ Atomization model & 3D Coupling method [23] \\ Secondary crushing model & Wave [27] \\ Bump wall model & Wallijet0 [28] \\ Spontaneous combustion model & Auto-Ignition [29] \\ Combustion model & ECFM-3Z [30] \\ NOx emission model & Extended Zeldovich [31] \\ Soot emission model & Lund Flamelet Model [32] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of models used in the combustion calculation process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Value**} \\ \hline
**Engine Speed (rpm)** & \multicolumn{2}{c}{**1400**} \\ \hline BMEP (MPa) & \multicolumn{2}{c}{1.1} \\ \cline{2-4} Fuel & P0 & P10 & P15 \\ Injection quantity (mg/cycle) & 492.62 & 512.79 & 525.57 \\ Injection start point (\({}^{\circ}\)CA) & 713.9 & 713.8 & 713.7 \\ Injection duration (\({}^{\circ}\)CA) & 11.6 & 11.9 & 12.4 \\ Single-hole injection quantity (mg/cycle) & 10.26 & 10.68 & 10.95 \\ \hline \end{tabular}
\end{table}
Table 5: Detailed fuel-injection-related parameters for high-load conditions at 1400 rpm.  
well. Therefore, the numerical model developed and the model parameters selected during the numerical calculation process are reasonable.  
Figure 5: Mesh model of the combustion chamber.  
Figure 6: Comparison of simulation and experiment results of in-cylinder pressure with different PODE blending ratios.  
Table 6 shows the comparison of the NO\({}_{\rm x}\) emission simulation and test results for different PODE/diesel blends. The value of the NO\({}_{\rm x}\) emission error is the ratio of the error between the simulated and experimental results to the simulated results. Although the error is inevitable, the numerical results can predict the influence of the blending ratio on the NO\({}_{\rm x}\) emission well.
## 3 Results
### Chemical Analysis Results
#### 3.6.4 MTI Soil  
The secondary electron image (Figure 12A) shows that the particles in the raw fraction have a grain-like structure.  
Microanalysis of the X-ray spectrum allowed the determination of the percentages of the elements O (65.7%) and Si (34.3%).  
These images show us quartz grains for the MTI soil. The B42, Morne Coco, and Cabale soils contain grains of quartz and iron oxide coated by clay minerals. The clay minerals have nucleus habits in the Morne Coco and Cabale soils and a clay network structure in B42.  
Figure 13 shows the mid-infrared absorption spectra of the four soils. The mineralogical interpretation of the IR spectra reveals the presence of two mineral species (kaolinite and gibbsite) and three oxides (quartz, hematite, and goethite).  
The IR spectra of the three soil samples (Figure 13A-C) show the same well-defined characteristic bands of kaolinite at 3695 (strong), 3667 (weak), 3654 (weak), and 3620 cm\({}^{-1}\) (strong). Only the MTI soil (Figure 13D) suggests traces of this mineral species at 3695 and 3620 cm\({}^{-1}\) but shows mostly characteristic quartz peaks: 1057 (broad); 795, 779, 694 (medium); 521 and 450 cm\({}^{-1}\) (strong) [42; 43; 44]. The BE42, Cabale, and Morne Coco soils (Figure 13A-C) show bands at 1113 (weak), 1094 (weak), 1029 (strong), and 1008 (strong) cm\({}^{-1}\) characteristic of kaolinite as well as the band at 797 cm\({}^{-1}\) (medium) characteristic of goethite, while the bands at 912 (medium), 520 (medium), and 464 cm\({}^{-1}\)(medium) are characteristic of hematite [45].  
Only the spectra of the Cabale and Morne Coco soils (Figure 13B,C) show an intense band characteristic of gibbsite at 3620 cm\({}^{-1}\), which has its other bands in common at 3526, 3447 (broader), 3395, and 3373 cm\({}^{-1}\)[46]. In addition, the broad band with a maximum of 3447 cm\({}^{-1}\) also coincides with goethite [47; 48].  
Figure 13: FTIR spectra of the 4 soils: (**A**) BE42, (**B**) Cabale, (**C**) Morne Coco, and (**D**) MTI. The IR bands, characterised for mineral species and oxides, are marked by a colour for simplified interpretation. Overlapping colours mean that bands are labelled for more than one mineral.
The proposed technique proves the stability of the motor's operation at low speeds and low torque ripples. In [22], the authors provide an FTC drive system that can be used for the sensorless speed-controlled Y-connected three-phase induction motor during both normal and single-phasing conditions. In [23], the research is focused on fault detection based on the comparison between predicted and actual values of stator currents. In the proposed FTC method, an asymmetrical current transformation is used for stator current components, and the conventional symmetrical voltage transformation is employed for stator voltage components. It is important to mention here that most of the recent publications on single-phasing fault present algorithms and techniques for fault-tolerant control on three-phase induction motors under single-phasing fault, ignoring the effect of the rising stator winding temperature and ignoring the main recommendation under this fault, which is to turn off the three-phase induction motor. This recommendation is crucial for preventing motor damage and ensuring safety. This study attempts to answer the question: should the three-phase induction motor be turned off under the single-phasing fault? To do this, the single-phasing fault of the induction motor is presented and investigated theoretically and practically through the following:  
* Analysis of the single-phasing fault and derivation of the electrical model of the three-phase induction motor. * Verification of the results obtained under different load conditions. * Measure the stator winding temperatures and motor vibration. * Conduct extensive experiments on a three-phase induction motor to determine the most effective protection settings. The main contribution of this paper is to investigate the operation of a three-phase induction motor under a single-phasing fault and make new or innovative recommendations for the operation of a three-phase induction motor under this fault. The paper is organized as follows: after the introduction in Section 1, Section 2 presents the theoretical analysis of single-phasing faults, and proposes the basic idea of this research. Section 3 contains practice tests and theoretical calculations of the three-phase induction motor under various load conditions as well as the simulation results. Section 4 presents the proposed motor protection scheme. Section 5 discusses the obtained results and gives recommendations for motor operation. Section 6 contains the conclusion.
## 3 Performance in Selective Fading Conditions
### Test Setup
Figure 9: The effect of the two-ray propagation on FSK (**a**) and FM-CSK (**b**).
## 4 Finite Element Analysis (FEA)
### Finite Element Type and Mesh  
The FEA model was divided into four parts: the soil, the pipeline, the drop hammer, and the rigid cushion. According to References [41; 42], four-node shell elements with reduced integration (S4R) were selected to simulate the pipeline, while eight-node brick solid elements with reduced integration (C3D8R) were used to simulate the soil. In addition, the drop hammer and the rigid cushion were simulated by the four-node three-dimensional rigid elements (R3D4). To ensure the simulation's effectiveness, a fine mesh was adopted to develop the model by controlling the mesh density, which enhanced the convergence of the FEA result in the algorithm, as shown in Figure 12. In addition, seeds should be scattered on parts by controlling approximate size, then by selecting the element type of mesh parts with a structured mesh method. Based on the global mesh model, the number of elements was 127,300, including 123,252 hexahedral elements (C3D8R and R3D4) and 4048 quadrilateral elements (S4R).