esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
## 2 The Effect of Internal Structure on Damping
The study shows that proper layer composition and fiber treatment can significantly enhance the mechanical and damping properties of natural composites. Chemical treatment improves the adhesion between the fibers and the polyester matrix, thereby increasing load transfer and overall strength [14]. One of the challenges in manipulating composite structures to improve damping properties is that increased damping often results in decreased structural strength. The study by [PERSON] et al. presents experimental results examining the damping properties of carbon fiber composites using flax fibers and carbon nanotubes (CNT). Their combination provides multi-level reinforcement of the composite, enhancing its ability to absorb and dissipate mechanical energy. The improvement in damping did not lead to a significant loss of strength and stiffness of the composites [15]. A study by [PERSON] et al. showed that the carbon composite bar investigated exhibited a hysteretic rather than viscous damping character. The investigated material was produced by a pultrusion process where dry long fibers were drawn through a resin bath [16].
# Numerical Simulation of Seepage Surface and Analysis of
## 3 Results
### Seepage Stability Analysis  
The seepage stability of a tailings dam refers to the deformation and failure characteristics of the tailings under the action of seepage. If not handled in a timely manner, it can cause major safety accidents and affect normal production. The foundation of the tailings dam was moderately weathered marble, and there was no possibility of seepage deformation damage in the dam base layer. Therefore, this article mainly analyzes the seepage stability of the tailings dam body. According to the three-dimensional seepage calculation results of tailings dams under various working conditions, the seepage gradient of the starter dam at the inner slope foot was the largest, and this area was the most unfavorable position. There were silt tailings distributed at this position, with a specific gravity value of (G\({}_{\rm S}\)) 2.78 and a pore ratio of (e) 0.735. According to the critical slope calculation Formula (1), the critical seepage gradient of the silt tailings at the foot of the starter dam slope was 1.02.  
\[\rm{i}_{cr}=\frac{G_{s}-1}{1+e} \tag{1}\]  
In the equation:  
\(\rm{i}_{cr}\)--Critical seepage gradient of silt tailings.  
\(\rm{G}_{\rm S}\)--Ratio of particle density of silt tailings to the density of water.  
e--Pore ratio of silt tailings.  
The results of the seepage gradient at the foot of the starter dam slope under normal water level and flood-level conditions at a height of 210 m are shown in Figure 9, and the corresponding relationship between the calculated seepage gradient and the critical seepage gradient is shown in Table 5.  
Figure 9: Area of maximum seepage gradient of the dam body at a height of 210 m. **(a)** Normal water-level conditions (i = 0.679). **(b)** Flood-water-level conditions (i = 0.690).  
According to the analysis results, it can be seen that:  
1. The seepage gradient of the dam body slightly increased under flood-level conditions compared to normal water-level conditions, which was due to the shortening of the seepage diameter under flood-level conditions.
2. Whether under flood-level conditions or normal water-level conditions, the seepage gradient of tailings near the starter dam slope was less than the critical seepage gradient of 1.02, indicating that there would be no seepage deformation or seepage damage, and the stability of seepage of the tailings dam body was safe.
## 6 Novel mHealth Predictive Outbreak for COVID-19 (mPOC) Algorithm and IoMT Protocol
### The mPOC System Methodology  
The mPOC algorithm uses an iterative and recursive approach to provide continuous monitoring and adaptive capability to support specific users. Below is a detailed description of each stage aligned with the system's methodology and algorithm.  
1. Stage 1 (Initial Stage): - Devices: Smartphone and Bio-Watch - Sensing/Information Input: The smartphone collects location data (suburb, city, country), BMI, pre-existing conditions, job information, and the number of members in the household. The Bio-Watch receives sensory data input from the smartphone and calibrates sensors to learn user's physiology. - Monitored CPPs/Health Conditions and User Inputs: Users input their age, gender, ethnicity, height, weight, and pre-existing conditions, including cancer, organ implants, hypertension, diabetes, and pulmonary, renal, CS, and cardiovascular diseases. The algorithm calculates BMI and risk factors based on age and gender, resulting in the calculation of DRI.
2. Stage 2 (IoMT Stage): - Devices: Smartphone and Bio-Watch - Sensing/Information Input: The smartphone gathers data from various sensors, such as microphone (voice/sound), camera (video/light), gyroscope, accelerometer, compass, and GPS. The Bio-Watch collects data from sensors, including temperature, accelerometer, gyroscope, compass, ECG, EMG, EEG, SpO2, BVP (Blood Velocity Pulse), SCL (Skin Conductance Level), and THz (Terahertz). - Monitored CPPs/Health Conditions and User Inputs: Users provide information about dry cough and nasal congestion through the microphone; shortness of breath through the microphone and SpO2 reading; hypertension through BVP sensor; pain level through BVP, ECG, and SCL sensors; fever through the temperature sensor; and tachycardia through ECG. Various diseases can also be monitored, such as restlessness/fatigue/insomnia using PSG (Polysomnography), confusion using EEG, diabetes using THz, heart conditions using ECG, and diarrhea and vomiting using the microphone. - Data Exchange: All the collected data is sent to the PRAaS stage, where the PEI is calculated.
3. Stage 3 (PRaaS Stage): - Devices: Smartphone and Bio-Watch - Sensing/Information Input: The smartphone uses LTE-Direct (D2D), connects with PHBA (Public Health Broadcasting Agency), and exchanges anonymous health details with users within wireless range. The PEI value is calculated and DRI and PEI values are recursively updated and sent to the OTC.
# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Other Problems
#### 4.3.3 Problem 3.3  
For Category 3: A Push-Broom 2D LiDAR, Which Is Used for the Detection of the Obstacles in Front of the Vehicle  
For the studies discussed in this subsection, a push-broom 2D LiDAR is used for the detection of obstacles in front of the vehicle, rather than the construction of the 3D maps of the environment. As for the studies focus on the construction of the 3D maps of the environment by using a push-broom 2D LiDAR, see Section 3.2 for more details. In [58], a 2D LiDAR is mounted in front of the robot and tilted downward at a certain angle. Line segments are extracted from 2D point cloud collected by 2D LiDAR in real time. These line segments are divided into two categories, which corresponding to roads and obstacles respectively. Note that the discrimination of the roads and obstacles here is based on 2D point cloud rather than 3D point cloud, which is conducive to improving the real-time performance of the discrimination. Similar research has been done in [59], a 2D LiDAR is mounted obliquely downward at a certain angle, by extracting line segments from the 2D data of the LiDAR, the road areas, and obstacle areas in front of the robot are discriminated. Compared with the study in [58], multiple 2D LiDARs are used in [59], with different downward angles, which are used to detect short-range and long-range roads respectively.  
In [60], the obstacle detection is mainly done by monocular cameras, and the 2D LiDAR plays an auxiliary role. Two monocular cameras are mounted at different angles. The low-angle camera is used to divide the road areas and non-road areas in front of the robot, and the high-angle camera is used to estimate the direction of the road. The 3D point clouds collected by the push-broom 2D LiDAR are fused with vision data collected by monocular cameras to improve the reliability of road recognition and build accurate road boundary models. In [61], a push-broom 2D LiDAR is used for the road edge detection.
## 10 Current Performance  
Performance is defined in various ways [3], but most simply by how far the algorithm deviates from perfect performance. Perfect performance of an algorithm on a test problem means that an algorithm detects all isotopes that are present in the source and does not detect any isotope that is not in the source. An example of a more specific performance metric is \"conditionally correct,\" which means that at least one of the most abundant isotopes in the sample was correctly identified, but with less confidence than some isotope that was not in the source.  
Several studies have demonstrated lower-than-desired performance as characterized by various measures of correct isotope identification. For example, [3] reported only approximately 30%-50% correction identification among medical, industrial, NORM, and threat isotopes and [31] reported a high rate of correct identification for several RIID algorithms for bare sources, but a very low rate of correct identification when the same sources where shielded by 2 mm of steel.  
Largely because of inadequate RIID algorithm performance, the U.S. Department of Energy (DOE) has created a program requiring trained \(\gamma\) -ray spectroscopists to be on call every day all day to resolve alarms as rapidly as possible. That is, human experts outperform the best RIID algorithm. The Domestic Nuclear Detection Office and the Department of Homeland Security within the DOE, and other interested organizations continue to sponsor dedicated experiments to evaluate NaI \(\gamma\) detectors as well as lower and higher resolution detectors.
## 2 Modeling of Rubber Pad with Spring Coil Set  
### Mass-Spring Series Model with Rubber Pad  
A time-domain analysis was used to simulate the rail vehicle dynamic performance. However, the connection between the spring dynamic stiffness and input frequency is a function of the frequency domain. The continuous coil spring was replaced by a mass-spring series model with several DoF to represent the frequency-dependent characteristics of springs in the time domain. N mass bodies in series and N \(-\) 1 linear supporting force elements are used to develop the equivalent model. To express the characteristics of a frequency-dependent spring in the time domain, the continuous coil spring was replaced by a mass-spring series model with multiple DoF. The schematic diagram of a helical coil spring used for Linke Hofmann Busch (LHB) coaches is shown in Figure 1[14,15] and presents the coil spring set's outer and inner springs from the primary suspension (Figure 2). The springs are fitted on a rubber pad.  
Figure 1: Faiit bogie with primary and secondary spring.  
Figure 2: A cross-sectional view of the secondary spring and the primary spring.  
The bogie frame is bolted towards the top of the spring; the rubber pad's bottom is attached to the upper surface of a radial arm. The active coils are at the two ends, and they do not touch the bearing surfaces [45; 46]. The innerspring is made of a thinner wire with a smaller diameter and has a higher number of coils. The primary suspension analyzed here is installed with a spring set (two springs) that may bear a larger load than a single spring. Thus, the condition in Equation (1) must be satisfied to ensure that the static stiffness and modality of the equivalent model are equal to those of the actual spring [39].  
\[k_{i}\;=\;(N\;-\;1)k_{s} \tag{1}\]  
\[k_{s}\;=\;\frac{G\text{d}^{4}}{8 nD^{3}} \tag{2}\]  
where \(K\)s is the static stiffness, \(G\) is the modulus of rigidity, d is the wire diameter and \(n\) is the effective turns of the coil spring. The rubber pad vertical stiffness (\(kr\)) is calculated as shown in Equation (3):  
\[k_{r}\;=\;\frac{A_{r}\mu_{r}E_{r}}{h_{r}} \tag{3}\]  
where \(\mu_{r}\;=\;1\cdot 2\times\left(1\;+\;1\cdot 65\text{g}_{r}^{2}\right)\), \(A_{r}\;=\;\frac{\pi\left(D_{1}^{2}-D_{2}^{2}\right)}{4}\), \(S_{r}\;=\;\frac{D_{1}-D_{2}}{4h_{r}}\); \(A_{r}\) is the surface area (upper), \(\mu_{r}\) is the vertical form coefficient, \(D_{1}\) is the outer diameter of the hollow pad (225 mm), \(D_{2}\) is the inner diameter (90 mm), \(E_{r}\) is the modulus of elasticity and \(h_{r}\) is the height of the filled rubber pad. According to Equation (3), the vertical rigidity \(k_{r}\) is 7.0 MN/m. This value is as high as 20 times that of the combined static stiffness of the coil spring. To achieve damping, a complex stiffness type and multiplication of the constant of the spring by (\(1+i\eta_{r}\)) can be utilized. In this factor, \(i\) denotes an imaginary unit, and \(\eta_{r}\), which denotes the factor of loss in damping, has been set to 0.25 [39], keeping in mind the physical properties of the rubber.
## 2 Structure and Working Principle  
### Sensor Structure  
Figure 1a illustrates the in situ deployment of the sensor. A measurement-while-drilling (MWD) system is mounted above the downhole power drilling tool within the borehole. The sensor is sealed within the MWD system and connected to the downhole power drilling tool, enabling rotational speed measurement, so the sensor does not come into direct contact with the external environment. Figure 1b presents a schematic diagram of the sensor's structure. The sensor is a cylindrical structure with dimensions of \(\varphi\)75 mm \(\times\) 130 mm, primarily comprising a stator and a rotor. The stator remains stationary, while the rotor rotates relative to the stator. Both stator and rotor are 3D printed from polylactic acid (PLA), and the printing temperature was set to 210 \({}^{\circ}\)C. The rotor consists of a rotation shaft and a rotating body, with a flexible arch ring attached to the end of the rotating body. A 0.1 mm thick Kapton polyimide film is adhered to the surface of the arch ring to form a friction layer. The stator is a cylindrical tube with an outer diameter of 75 mm, and two 0.1 mm thick copper foil layers are affixed to its inner wall to serve as electrodes. Kapton and copper foil are both adhered to the corresponding positions by applying a layer of  
pressure-sensitive adhesive on the back. Figure 1c shows the unfolded electrode layers. Both electrode layers exhibit a sawtooth pattern, each being divided into six segments, resulting in a 60 degrees rotational angle per segment. The electrode width is 25 mm, equal to the height of the rotor's arch ring, and the distance between the two electrode layers is 1 mm. The sawtooth base of electrode layer one and the sawtooth apex of electrode layer two are aligned horizontally.
## 3 Classification of Attacks on RPL-Based IoT
### Wireless Sensor Network-Inherited Attacks  
#### 3.1.1 Black Hole Attacks  
A black hole attack, a type of Denial of Service (DoS) attack, occurs when a malicious node intentionally discards all received packets instead of forwarding them as shown in Figure 1. An advanced variation, known as a Selective-Forwarding attack, involves malicious nodes selectively forwarding RPL control messages while discarding other packets. These malicious nodes typically participate in the creation and maintenance of DODAG, mimicking the behavior of benign nodes.  
The malicious node propagates false information in the network, thereby misleading nearby nodes about its capabilities. This includes false advertisements claiming to have the shortest path to the destination, thus convincing the neighboring nodes to route their packets through the malicious node [38]. Upon joining the routing path, the malicious node intentionally drops all received packets, disrupting communication among legitimate nodes.  
The consequences of a black hole attack are significant, leading to disruption either in a specific network segment or across the entire network, especially with the presence of multiple malicious nodes. Conversely, Selective Forwarding is a less aggressive form of attack, occasionally disrupting routing paths. The propagation mechanism of a black  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & **Neighbor** & \multirow{2}{*}{**Leaf Node**} & \multirow{2}{*}{**False Alarm**} & **Faulty** \\  & **Overhearing** & & & **Reporting** \\ \hline
[8] & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline
[18] & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline
[19] & \(\surd\) & \(\surd\) & \(\surd\)  
hole attack causes the legitimate nodes to unknowingly route their packets through the malicious node. This disruption adversely impacts network performance and can lead to an overall degradation of system functionality [12].
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 1 Introduction
This work is an extension of previous work [10]. The biggest difference between this article and the Reference [10] is that this article considers a more general full MIMO relay model, including the use of multi-antenna reception and transmission at the relay node instead of a single antenna relay as in Reference [10]. This paper focuses on the impact of the diversity-multiplexing performance of the overall link when the number of RF link and FSO link antennas are different. Our analysis provides a generalized framework of a multi-antenna mixed relay system, so that the designer can have an intuitive understanding of the transmission rate and stability requirements of the entire system. The rest of the paper is organized as follows: Section 2 gives the model of RF and FSO channels, and we will derive the probability density functions (PDF) and CDF of each link. In Sections 3-5, we will derive the closed form end-to-end outage probability, average bit error probability and average ergodic capacity of the considered system, respectively. Further, we will analyze the diversity order and diversity-multiplexing tradeoff of the proposed system in Sections 6 and 7. Section 8 conducts numerical simulations and analyses. Finally, Section 9 presents the conclusions.
## 3 Proposed Fault Detection and Classification Algorithm
### Fault Index  
The result computed by multiplying the W-index with the \(S\)-index is designated as the proposed fault index (FI).  
\[FI=(S-index).*(W-index) \tag{3}\]  
A threshold (TH) value of \(5\times 10^{9}\) is selected for the FI to identify the fault phase or type of a fault. The values of the FI are compared with the TH; if the value of the FI for a phase is high compared to the TH, then the phase is a fault, else the phase is healthy in nature. In identifying the number of fault phases, the types of faults are identified. Further, the threshold is selected by testing the algorithm on 20 datasets of each fault obtained by changing the parameters such as the location of the fault on the transmission line, the fault incidence angle, the fault impedance, the reverse power flow, the presence of noise, the hybrid combination of the underground (UG) cable and overhead line, etc. Hence, the threshold (TH) value of \(5\times 10^{9}\) is effective to identify the types of faults and the fault phase during all operating scenarios of the power system.
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### Second Stage: Path Planning
#### 3.2.1 Detecting Tree Lines
\[\mathit{distance}_{c,i}=\|\rho_{c}-\rho_{i}\| \tag{2}\]  
\[\Gamma_{3}=12\cdot(1+\|\theta_{c}-\theta_{i}\|) \tag{3}\]Hough Transform has been commonly used in the literature related to detection tasks in agricultural environments. Recent research shows that it is useful to process local imagery [25; 26] and remote imagery [27; 28].
## 5 Orbital State Vectors (OSVs)  
To meet a high standard, geolocation requires not only accurate knowledge of the imaging system and Earth environment, but commensurately accurate knowledge of the Orbital State Vectors (OSVs) as well (_i.e._, the platform positions). Four sources for the OSVs were available: GNSS internally annotated in the source packets, _predicted_ (S1A_OPER_MPL_ORBPRE files), near-real-time _restituted_ (S1A_OPER_AUX_RESORB files), and _precise_ (S1A_OPER_AUX_POEORB files). Predicted OSVs are generated separately about once a day, so their accuracy depends mainly on the interval between the  
Figure 5: Calculation of the bistatic residual for a point target in an S1A StripMap product (**a**) nominal mid-range bistatic correction applied for a StripMap product by the S1 IPF as of v2.43; (**b**) bistatic correction needed for a point target PT at slant range srpt.  
file generation and the acquisition times. The precise OSVs are only currently available with a latency of three weeks, so many applications employ the restituted OSVs, which were found to be nearly as accurate.  
The case of the internal OSVs is complicated. Although the GNSS state vectors injected into the source packets are inherently accurate, they are provided within the boundaries of the acquisition segment. In order to perform proper interpolation, the EO_CFI (used also by the IPF) requires \(\sim\)6 state vectors before and after the product. This requirement is even more stringent in the case of products generated from one of the TOPS modes (Terrain Observation with Progressive Scan products, _i.e._, IW and EW), where the focused burst is extended, requiring more OSVs than are available. Simple extrapolation methods were tested but none were sufficiently robust. Consequently, given that GNSS OSV interpolation was always possible, the IPF employed the EO_CFI library in propagation mode based on a single ascending node crossing (ANX) OSV. This method is inherently less accurate; its quality quickly decreases with distance from the ANX point. Our results presented in the coming sections also confirmed that if restituted or even precise OSV files were annotated, then the geolocation quality was nearly equivalent to using the corresponding external files, as long as the orbit interpolation was sufficiently reliable. Since these initial tests, a more robust approach for using the GNSS OSVs has been implemented in the S1 IPF and is currently undergoing testing, with initial ALE estimates within the expected limits.  
For the best geolocation accuracy, it is recommended that precise orbit files be used, (AUX_POEORB auxiliary files). If near-real-time is required, then the restituted AUX_RESRB orbit files were demonstrated to be nearly as accurate. The structure of these auxiliary files is described in [17]. These were our initial source of orbital state vectors, interpolated using ESA's EO_CFI library [18]. A comparison of the various state vector source qualities is shown in the latter part of Section 7 below.