esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The main strengths of this study are the adoption of a reliable and validated skin marker-based multi-segment foot model, which included the tracking of the midfoot and analysis of a large sample of naturally FFS or RFS runners. In particular, by not imposing any footstrike pattern on the participants, we avoided any potential adaptation that might have occurred in converting their footstrike pattern. However, some limitations should be considered when assessing the results of this study. Foot joint kinematics were collected barefoot, which differ from the shed condition; this is indeed difficult to implement using any skin-marker-based multi-segment foot model. However, both groups were subjected to the same testing conditions; thus, if there was any significant change in foot kinematics due to the barefoot condition, this likely affected both groups. Although runners were assessed on a treadmill, the literature shows that most of the spatiotemporal, kinematic, and kinetic parameters are similar between treadmill and overground running [34]. [PERSON] et al. [35] examined differences in multi-segment foot kinematics during treadmill and overground running and found only one difference at the ankle and not in the other foot joints: greater plantar flexion at footstrike in treadmill running. Because of the barefoot condition, it is possible that the typical footstrike technique declared by the participant and checked on the treadmill while running barefoot would not be reproduced when running shed in real-world environments. However, [PERSON] et al. [36] suggested that the ability of runners to accurately self-report the footstrike pattern is poor, even when shed. Despite these limitations, runners could clearly be classified by their strike patterns regarding their foot joint motion. Future prospective studies should address the correlations between running-related injuries and footstrike patterns to potentially improve injury prevention and rehabilitation. The kinematic findings of the present study may be of interest to clinicians and other health professionals to support strategies to prevent or to rehab specific types of injury associated with FFS or RFS, such as plantar fascitis, Achilles tendinopathy, and metataralsals stress fractures [1], through biomechanics guidance regarding the footstrike patterns.
## 2 Theoretical Framework
### Soil Resistivity  
To calculate and design a GS, the soil resistivity in the desired location of the connection must be investigated to determine the general composition and degree of uniformity of the soil. Several tables of the resistivity ranges for various soils and rocks are available in the engineering literature, such as those found in [3; 7; 8; 9; 10]. However, resistivity should not be estimated based on soil classification because these tables provide only approximate values. Accordingly, resistivity tests must be performed [3; 11].
# Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia
## 3 Methodology
### Station Demand
It probably represents a higher concentration of EV usage and charging demands, offering insightful information for planning urban EV infrastructure. The estimated future number of EVs in Hail City was also calculated. This study determined the impact of technical factors affecting the NSs using MATLAB, including route length, EV consumption rate, car BC, car slots per station (CSs), and CP. PV*Sol 2022 R6 was also used to design the solar system required for the station as a model. The four primary parameters of the study fluctuated [41]:  
* EV consumption rate, R: ranged from 0.05 to 0.40 kWh/km in steps of 0.05 kWh/km. * Car BC: varied from 25 to 100 kWh in increments of 5 kWh, reflecting the energy storage capacity of EVs. * CS per station: Ranged from 50 to 500 slots in increments of 50. This indicates the capacity of each charging station in terms of the number of cars it can accommodate. * CP: varied from 20 to 75 kWh in increments of 5 kWh, indicating each charger's power output. In total, 15,360 examples for all possible combinations of these factors were investigated in this study. This wide range of situations allowed for a thorough understanding of how various factors influence the infrastructure needs of EVCSs. A standard set of assumptions was used to analyze the need for EVCSs throughout Hail City's 17 zones. This methodology offers a fundamental synopsis of the charging station requirements in every zone, enabling thorough comprehension of the infrastructure requirements. The following assumptions were made when determining the NS' required for the 17 zones: R = 0.20 kWh/km; BC = 75 kWh; CSs = 200; and CP = 45 kWh. A crucial practical aspect was added to these conventional assumptions to represent actual charging behaviors: Based on assumptions, [PERSON] et al. [29] and [PERSON] [28] suggested that 60% of EVs are charged at home or by using private chargers. This illustrates the expanding popularity of at-home charging options and how they affect the need for public charging infrastructure. Calculating the remaining BC for different trip lengths while considering the various battery capacities and energy consumption rates typical of various vehicles, such as compact cars, sedans, and luxury SUVs, is part of the methodology for analyzing battery performance across different vehicle types. This method makes it easier to comprehend how various cars lose battery power in other ways over predetermined distances. The primary equation used to calculate the remaining battery life is as follows:  
\[Battery\;Remaining=BC-(R\times L)\]  
The results can inform urban planning, infrastructure development, and policy decisions related to sustainable transportation and EV charging infrastructure, enabling decision-makers to make informed plans and policies with which to support the transition to electric mobility and address future transportation needs.
## 5 Field Experimental Studies
### Point Cloud Filtering and Denoising Process  
When acquiring depth images and point cloud data, due to the limitations of equipment accuracy, environmental factors, experimenters' operating experience, changes in the surface properties of the measured object, and the influence of data splicing and alignment operation process, some noise points affecting the experimental results will appear in the point cloud data, so that the original point cloud data contains a large number of hash points and isolated points. Only in the filtering preprocessing will noise points, outliers, holes, data compression, and other filtering processing be used in order to better visualize and perform other subsequent applications.  
The processing principle of filtering denoising is to fit a plane in the local range of the model and set a reasonable threshold to delete the points away from the plane as outliers so as to get a more accurate 3D model point cloud [58; 59; 60]. The specific filtering effect is shown in Figure 16.
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 4 Proposed Solution  
The approach presented in this paper is based on the following two main steps:  
1. Transformation of the continuous optimisation problem into a (discrete) search problem over a specially constructed graph;
2. Application of the proposed optimal path search algorithm to find a good approximation of the set of Pareto optimal control input sequences: \[\widehat{\mathcal{P}\mathcal{S}}=\Big{\{}\mathbf{u}_{i}=\Big{(}[c_{1},v_{s_{ 1}}]^{T},[c_{2},v_{s_{2}}]^{T},\dots,[c_{N},v_{s_{N}}]^{T}\Big{)}\Big{\}}_{i}\] (5)  
**Remark 2**.: _The sequence \((c_{k})_{k=1}^{N}\) represents a continuous piecewise-linear sailing path (formed by the sequence of the graph edges that correspond to \((c_{k})\)), whilst \((v_{s_{k}})_{k=1}^{N}\) is the sequence of relative ship speeds (along each linear path segment), which forms a piecewise-constant function._  
Figure 1: Conceptual diagram of the first ship route (\(\widetilde{AB}\)) optimisation problem ([_Prob1_]) under consideration. (**left**) the tidal stream at time \(t_{A}+3t\); the ship—represented as the black triangle—sails _with_ the current (as indicated by arrows). (**right**) the tidal stream at time \(t_{A}+9t\); the ship has to sail _against_ the current. The colour bar shows absolute values, \(|\mathbf{v}_{t}|\), of the tidal stream.  
The key elements of the proposed algorithm are as follows:  
* A _3D-graph_-based solution (design) space and the computational topology it generates (for dynamic programming (DP)-based local search);
* Adaptive Markov chain-based path generator (directing the search process);
* The search process augmented by the DP-based pruned local search. They are discussed in the following subsections.
## 1 Introduction
### Motivation  
As discussed in the introduction, when each network device is directly connected to the gateway, numerous energetic and radio channel access problems are faced, especially in dense and large networks [33]. Clustering the network by using direct connections among nodes and their CH engenders a high number of small clusters [34]. K-hop intra-clustering has demonstrated many advantages to avoid the congestion problems and prolong the network lifetime [5; 35]. However, k-hop intra-clustering protocols are mostly centralized and only consider the node direct neighbors information, which lacks robustness. Indeed, with a centralized k-hop scheme, the BS is a central point of failure, i.e., a potential BS failure will obstruct the execution of the whole protocol. A loss of a critical node or a communication error could eventually cause a severe clustering failure because some data are usually of higher importance in a centralized approach. Moreover, when the network scales up, the BS may become a performance bottleneck. In this regard, this paper presents a new distributed k-hop intra-clustering protocol that takes into consideration the two-hop neighbors connectivity information. The main objective is to optimize the set of CHs in order to reduce the number of long-range communication channels to avoid congestion issues and extend the network lifetime. The main contributions of this work are as follows:  
* A new connectivity metric is introduced to elect the set of appropriate CHs. The novelty of this metric is taking into account the two-hop connectivity of the current node and its surrounding neighborhood (instead of using the traditional direct neighbors connectivity), in order to strengthen the clusters stability. * The design of the algorithm is inspired from the distributed self-stabilizing systems. We prove that the algorithm converges within \(O(n+k)\) rounds, which represents the upper bound of the time complexity, \(n\) is the number of network nodes and \(k\) is the depth threshold of the clusters. This perspective allows network devices to efficiently tolerate potential failures that can occur locally in the dynamic topology. * The proposed approach generates clusters with an energy efficient topology by reducing the distance between nodes and their respective CH. The adopted approach is peculiar in that it constructs the intra-cluster links in a distributed manner rather than using a centralized algorithm executed by the CH. The remainder of this paper is structured as follows: Section 2 describes some related works. The network model and the algorithm objectives are presented in Section 3. Section 4 is dedicated to the presentation of our contribution. The energy model and transmission reliability are presented in Section 5. Section 6 is devoted to the convergence proof of the proposed scheme. The complexity of the algorithm is analyzed in Section 7. Section 8 describes the experimental settings and the simulation results. We conclude our work in Section 9.
## 3 Heliostat Curvature
### Parabolic Curvature  
The focal length (\(f\)) and the curvature (\(a\)) of the paraboloid, as a function of the heliostat projected half-diagonal (\(x\)) and deflection (\(z\)), are given by the following (see Figure 3):  
\[a =\frac{z}{x^{2}} \tag{1}\] \[f =\frac{1}{4a} \tag{2}\]  
Figure 3: (**a**) Paraboloid curvature \(a\) and focal length \(f\) (see [19] for details) are functions of heliostat deflection \(z\) shown in (**b**) and heliostat projected half-diagonal \(x\) depicted in (**c**). Random reflection points and incident sun rays are generated in the rectangle (in red) at height 0 (lateral and top views) before intercepting the heliostat curvature (in black) below, where the reflection actually occurs.  
Figure 2: Comparison between parabolic (blue) and spherical (red) curvatures for a typical 200 cm \(\times\) 200 cm (diagonal \(\times\) diagonal) heliostat as a function of curvature deflection (all distances in cm). For deflections below 30%, parabolic and spherical curvatures are identical.
## 2 Preliminaries
### Basic Notions  
A finite set \(V\) of symbols is called an alphabet. A word or a string \(\alpha=a_{1}a_{2}\ldots a_{m}\), \(a_{i}\in V,1\leq i\leq m\), \((m\geq 1)\) of length \(m\) over an alphabet \(V\) is a finite sequence of symbols belonging to \(V\). The length of a word \(\alpha\) is denoted by \(|\alpha|\). The set of all words over \(V\), including the empty word \(\lambda\) with no symbols, is denoted by \(V^{*}\). For any word \(w=a_{1}a_{2}\ldots a_{n}\), \({}^{t}w\) is the vertical word with the word \(w\) written vertically. For example, if \(\alpha=bab\) over the alphabet \(\{a,b\}\), then \({}^{t}\alpha\) is \(a\). If \(w\) is a single symbol of the alphabet, then we write \({}^{t}w\) as \(w\) itself. A rectangular \(p\times q\) array (also called picture array) \(X\) over an alphabet \(V\) is of the form  
\[\begin{array}{ccccc}&a_{11}&\cdots&a_{1q}\\ X=&\vdots&\ddots&\vdots\\ &a_{p1}&\cdots&a_{pq}\end{array}\]  
where each \(a_{ij}\in V,1\leq i\leq p\), \(1\leq j\leq q\). The number of rows of \(X\) and the number of columns of \(X\) are respectively denoted by \(|X|_{r}\) and \(|X|_{c}\). The set of all rectangular arrays over \(V\) is denoted by \(V^{**}\), which includes the empty array \(\lambda\). \(V^{++}=V^{**}-\{\lambda\}\). A picture language is a subset of \(V^{**}\).  
The column catenation \(X\circ Y\) of two arrays \(X\) and \(Y\) with the same number of rows is formed by juxtaposing \(Y\) to the right of \(X\). The row catenation \(X\circ Y\) of two arrays \(X\) and \(Y\) with the same number of columns is formed by juxtaposing \(Y\) below \(X\). For example, if  
\[X=\begin{array}{ccccc}a&b&a&a&b\\ b&b&b&a&a\\ a&a&b&a&b\\ a&a&b&b&b\\ a&a&b&b&b\\ \end{array},Y=\begin{array}{ccccc}b&b&a\\ b&b&b\\ a&a&b&b\\ a&b&b\\ \end{array},Z=\begin{array}{ccccc}b&b&a&b&b\\ b&a&b&b\\ a&b&a&b&b\\ a&a&b&b\\ \end{array},\]then  
\[X\circ Y=\begin{array}{cccccccc}a&b&a&a&b&b&b&a\\ b&b&b&a&a&b&b&b\\ a&a&b&a&b&a&a&b\\ a&a&b&b&b&a&b&b\end{array},X\circ Z=\begin{array}{cccccccc}a&b&a&a&b\\ b&b&b&a&a\\ a&a&b&a&b\\ a&a&b&b&b\\ b&a&b&a&b\\ a&a&b&b\end{array}.\]
## 2 Materials and Methodology
### Fault Diagnosis Methodology
#### 2.2.1 Vibration Signal Processing  
An illustrative example of the vibration signals in the time domain is presented below in Figure 4, specifically for a 0.007\" diameter flaw.  
Figure 3: Outline of the methodology.  
A more detailed analysis of these signals, in addition to observing the presence of noise in them, shows that the signals affected by the fault have a duration of 10 s, in contrast to the signals of bearings without fault, which have a duration of 20 s. For this reason, a normalization process is carried out, and the signals are segmented into 0.1 s intervals. In this way, 200 segments are obtained from the signals with a duration of 20 s, while 100 segments are generated from those with a duration of 10 s, thus allowing a homogenization in the analysis, as can be seen in Figure 5.  
Given the limited availability of ball bearing failure data in the database, it has been decided to avoid their inclusion in the study. This choice is based on the search for greater effectiveness in the training of the neural network, allowing it to concentrate on the classification of failures already identified and documented with sufficient representation in the available data. By reducing the complexity of the problem by focusing on specific failures with sufficient data, the aim is to improve the accuracy and robustness of the classification model, thus more effectively fulfilling the main objective of this research work, which is the correct identification and classification of the existing failure signals in the conditions of this database.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 4 Aeronautical Mobile Telemetry
### Channel Coding in the 21 st Century
#### 4.2.1 Enhanced Flight Termination System (Efts)
The code was obtained from the (31,13) Reed-Solomon code by puncturing. After the addition of a 19-bit frame synchronization pattern to identify the start of the codeword, the message is transmitted using bi-\(\phi\) PCM/FM. Messages are sent continuously at a rate of 50 messages/s, equivalent to an over-the-air bit rate of 7.2 kbits/s. Because a secondary goal was to reuse as much of the installed FTS infrastructure as possible, the bi-\(\phi\) pulse was used to remove the DC componentfrom the FM modulator input because high-power FM modulators are usually AC-coupled to their inputs. The frequency deviation was set to a rather large value of 300 kHz (the instantaneous frequency was the carrier \(\pm\) 300 kHz) to compensate for receiver/demodulator phase noise caused by shock and vibration on the airborne test article. EfTS was successfully tested at Eglin AFB, FL, USA, and Tyndall AFB, FL, USA in 2007 [198]. In the end, EETS was not included in the IRIG 319 Standard. Just after the EETS tests, the number of experimental UAVs that required testing declined. Accompanying this decline was the introduction of autonomous flight termination systems (AFTS) [199; 200] primarily for weapons and launch vehicles. Together, these trends reduced the perceived need for the advantages of E  
Figure 23: A block diagram of the FTS dating from the 1950s: (**a**) the (ground-based) transmitter; (**b**) the (airborne) demodulator/decoder.
## 5 Conclusions  
The pose estimation of long-distance CubeSat is a difficult problem for autonomous rendezvous and track maintenance. In this paper, in order to achieve high-precision motion estimation, we propose an approach for the pose measurement and motion estimation of CubeSat based on the micro-Doppler effect by a laser radar system. Parameters such as spin rate and attitude angle can be estimated using data extracted from the echo signal. By utilizing adaptive threshold processing based on the least square method, edge extraction in the TF image can be achieved effectively. When the target size is unknown, the double edge ratio method (DERM) is proposed to obtain the attitude angle \(\alpha\). The simulation results show that when the noise is weak enough, the spin rate and the attitude angle can be acquired precisely. Our experiment results show that the accuracy of spin frequency  
Figure 11: TF diagram of experimental results. (a) Attitude angle = 15\({}^{\circ}\). (b) Attitude angle = 30\({}^{\circ}\). (c) Attitude angle = 45\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Control**} & **Attitude** & \multirow{2}{*}{\(f_{max}\)} & **Attitude** & \multirow{2}{*}{**Absolute**} & \multirow{2}{*}{**Relative**} \\
**Voltage (V)** & **Angle** & & & & \\
**Standard** & **Value (\({}^{\circ}\))** & & & & \\
**Value (\({}^{\circ}\))** & & & & & \\ \hline
15 & 15 & 0.042 & 15.2475 & 0.2475 & 1.65 \\
15 & 30 & 0.082 & 30.8942 & 0.8942 & 2.98 \\
15 & 45 & 0.118 & 47.6458 & 2.6458 & 5.88 \\ Mean & - & - & - & 1.2625 & 3.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cube target attitude angle estimation results.  
measurement with a relative error of 2.41% and an attitude angle measurement accuracy with a relative error of 3.5% can be acquired. This means that the estimation of spin and pose by our approach can fulfill the demand in practice.  
In the future, we will continue and focus on further work on non-cooperative targets such as tumbling satellites, space debris, or asteroids, which are central concerns of the national aerospace community. We hold a positive attitude towards the application of this technology in the field of space security control in the future.  
Conceptualization, Y.Z.; Validation, R.Y.; Visualization, Y.H.; Writing--original draft, Y.H., R.Y. and Z.W.; Writing--review & editing, Z.Y., Z.W. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Moisture Measurement in Paper and Cardboard Packaging Waste Bales for Recycling
## 3 Results and Discussion
### Voltage Measurement Variability Regarding Bale Characteristics  
Volts (V) were the measurement unit of the equipment in operation in this study. Defining which parameters can interfere with the voltages becomes important for performing the correct operation of this type of equipment.  
Taking the voltage data as the basis of the equipment operation, the study of the R\({}^{2}\) between equipment operation and the measured parameters of each bale is presented  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Bale**} & **Cardboard** & **Moisture Content** & \multirow{2}{*}{**Voltage (V)**} & \multirow{2}{*}{**Weight (kg)**} & \multirow{2}{*}{**Density (kg/m\({}^{3}\))**} & **Compression** \\  & **Content (\%)** & **in Over (\%)** & & & & **Density  
in Table 3. Thus, this is intended to verify the parameter's influence on the equipment in operation.  
It should be noted that the moisture content of the bales was shown to be the variable that most influenced the voltage measurements' behavior, as expected, since the operation of the equipment was based on the difference in the dielectric constants of water and paper and cardboard, as previously explained. No significant correlation was found between those parameters and the measured voltage; thus, the moisture content was effectively the parameter that made the voltage vary. [PERSON] et al. (2010) reached similar results.  
The variable compression density, shown in Equation (1), presented a poor correlation with the measurements in volts. The analyzed bales had similar compression ranges, since they were subjected to similar pressures used in the presses by the waste operators. It would be expected that this parameter would be less significant to the present analysis, and that the same would be true for the density.  
Overall, it was not expected that the physical characteristics of the bales would interfere in the dielectric principle regarding the indirect moisture measurements.