esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Deployment of WDM-PON Protection Schemes
### Reliability Diagrams for WDM-PON Protection Schemes  
The network reliability analysis is based on Reliability Block Diagram (RBD) representations due to their significant advantages including an accuracy, flexibility, simplicity and visual impact [3; 6]. They allow a determination the total network availability for a given protection scheme. In the RBD scheme, each component is included as a simple block in a correlation with adjacent blocks. Connections in a given protection scheme illustrate unprotected (in series) and/or protected (parallel) components.  
For better representation, RBD block schemes are realized for considered WDM-PON protection schemes (Table 1) involved in the performance analysis in Figure 1. RBD diagrams (a)-(d) represent the P2 MP WDM-PON protection scenarios according to the ITU-T standard for the TDM-PON network. RBD diagrams (e)-(h) specify RING WDM-PON protection scenarios.  
Figure 1: RBD diagrams for WDM-PON protection schemes (**a**) the unprotected P2 MP network, (**b**) Type B protection, (**c**) Dual-parented Type B protection, (**d**) Type C protection, (**e**) the unprotected RING access network, (**f**) the protected RING access network, (**g**) the unprotected RING metro-access network, (**h**) the protected RING metro-access network.
# Adapting Mechanisms for In-Pipe Inspection Robots: A Review
## 3 Analysis of Adapting Mechanisms
### Adapting Mechanisms Based on Combined Linkages
The sliders of the adapting mechanisms are moved simultaneously by means of a screw driven by a DC motor. Controlling each track module independently, the robot is able to move through elbows and Y- or T-branches. The length of the proposed robot is approx. 410 mm, and the adaptation mechanism makes it possible to move through pipes with a diameter between 300 and 450 mm. [PERSON] et al. in [54] propose a design method for an intelligent pipe inspection robot with the ability to adjust its diameter to fit different pipes. As in the previous examples, the inspection robot consists mainly of three track-based modules connected to the robot body through combined linkages. As a novelty, the authors introduced a spring damper in the structure of the adapting mechanism. A certain degree of flexibility of the mechanism would make it possible for the robot to adapt more easily to small variations in pipe diameter, surface unevenness due to welded seam or corroded pits. [PERSON] et al. in [55] present the design and stability analysis of a crawler-type inspection robot with radial adjustment ability. The adapting mechanism can realize the active adjustment of the outer diameter of the robot so that the track modules can be in contact with the pipe wall as much as possible. Each track is driven independently, thus the robot can move in the pipe through so-called three-shaft differential driving. Figure 18 shows the schematic diagram of the active adapting mechanism. The robot is adapted to different diameters by moving slider \(1\) by means of a ball screw driven by a stepper motor. The adapting mechanism is equipped with a force sensor for active control of the wall pressing force. The mathematical model that describes the relationship between the pressing force, the geometry of the mechanism and the torque developed by the stepper motor is presented in [55]. The total length of the robot is 380 mm, and it can inspect straight curved or inclined pipes with a diameter between 220 and 320 mm. Figure 18: Active adapting mechanism with track module.
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 3 Main Factors That Affect the Permeation and Absorption of Solutes in Hair
### Hair
#### 3.1.2 Charge Characteristics of Hair  
Keratin is the most important component of hair, and its isoelectric point is approximately 3.67 [11]. When the solvent pH is \(<\)3.67, the amino and carboxyl groups in the amino acids that make up keratin are protonated, and the hair appears to be positively charged externally, which increases the absorption of anionic solutes and decreases the absorption of cationic solutes. When the solvent pH = 3.67, the degree of protonation of the free amino groups is equal to the degree of dissociation of the carboxyl groups, the net charge is zero, and the hair is not charged. When the solvent pH is higher than 3.67, the free carboxyl group dissociates, the protonated amino group becomes deprotonated, and the hair appears to be negatively charged externally. Consequently, the absorption of cationic solutes increases while the absorption of anionic solutes decreases [23; 26]. Therefore, the charge force can be considered one of the main driving forces for solute permeation and absorption in hair.
## 2 Theoretical Analysis
### Transmission from the Target to the Receiver Plane
After measuring the \(\sigma_{in},\delta_{xx},\delta_{xy},\sigma_{xyout},P_{out}(0,z,\omega)\) in the experiment, we obtain the two sets of solutions for \(w_{R}^{2},l_{c}^{2}\) by applying Equations (37), (38), (43) and (44). Then, we can substitute these values into Equations (25) and (26) to calculate the values of \(\sigma_{xyout},P_{out}(0,z,\omega)\), and these are the correct solutions, the obtained values of which are equal to the measured values of \(\sigma_{xyout}\) and \(P_{out}(0,z,\omega)\). Let us consider an example, the target is set 500 m away from the light source, when the parameters of source beams and the turbulence are \(\lambda=1550\quad\text{nm},\sigma_{in}=2.5\quad\text{cm},\delta_{xx}=5\quad \text{mm},\delta_{xy}=6\quad\text{mm}\), \(C_{n}^{2}=10^{-15}\quad\text{m}^{-2/3}\), the parameters of the received beam are \(\sigma_{xyout}=7.5\quad\text{cm}\), \(\text{P}_{out}(0,z,\omega)=0.32\). Through applying Equations (37), (38), (43) and (44), we get the information for the target surface \(w_{R}=1\quad\text{m},l_{c}=3\quad\text{mm}\) without considering other invalid solutions. After calculating Equations (34) and (35), the values of \(\sigma_{xyout},P_{out}(0,z,\omega)\) are the same as the measured values. Based on the parameter measurement system of EGSM beams proposed in [8], the experimental measurement of the received beam radius \(\sigma_{xyout}\) can be carried out easily and efficiently. Moreover, according to [61], by using the polarizer and quarter-wave plate, we can also measure the degree of polarization in the receiver plane \(\text{P}_{out}(0,z,\omega)\).
## 1 Introduction  
Reservoir-triggered seismicity (RTS) has been the subject of many scientific papers in recent times due to its ability to cause damage to buildings and infrastructure and, in the worst case, to kill people. The main cause of RTS is additional static loading due to the weight of water reservoirs, tectonic faults, liquefaction, and variations in pore pressure. Several studies of RTS have been carried out worldwide [1; 2; 3; 4; 5; 6; 7], and all of them reached the same conclusion that reservoir-filling influences the seismic regime of areas with active tectonic faults. The operational regime of a dam is generally oscillating and driven by the seasonal cycle of the water level [3]. Pore fluid pressure diffusion underneath the reservoir or the gravitational loading of the reservoir [8; 9; 10] are considered the main mechanisms of RTS. [PERSON] et al. [5] proposed that seismic events are triggered by \"ambient stress field conditions (as a result of the water load which may lead to a failure), fracture occurrence, hydromechanical properties of the rocks beneath the reservoir, geology of the area, dimensions of the reservoir and lake-level fluctuation.\" [PERSON] et al. [11] suggested that RTS originates in the violation of equilibrium due to the \"reduction in the strength of the fault zones.\" This finding was later refined in [PERSON] and [PERSON] [1], who concluded that \"water passes through the basalts broken by the meridional faults and penetrates into the fault zone with the highly permeable\" layers. There have been several cases reported worldwide where earthquakes, even large ones, occurred years after the impoundment of the reservoir in areas that were practically aseismic before the impoundment [12].  
Among the various ways to characterize the dynamics of a time series is the amount or degree of uncertainty or disorder; this is quantified by Shannon entropy (SE), which is large for very disordered series and low for very organized ones.  
SE has found successful application in the investigation of various complex nonstationary time series [13] and, in particular, in the study of the dynamics of earthquakes. Previous studies investigated the relationship between the SE and the progressive failure processes of the rocks leading to earthquakes, greatly depending on the heterogeneities of the crust [14]. [PERSON] et al. [15] analyzed the pattern of the microfractures occurring before the final crack on heterogeneous materials by recording the acoustic emissions under progressive load; they found a decrease in the SE. [PERSON] et al. [16] found that SE decreases before a large event by investigating the micro-seismicity preceding a rock burst in a galena mine. Similar results were discussed in [PERSON] et al. [17] in connection with the magnitude series of large Caucasian earthquakes. Recently, [PERSON] et al. [18] applied the concept of SE to seismic nowcasting. In this context, in our study we perform an entropic analysis of seismicity triggered by the Song Tranh 2 reservoir by using SE, which, to our knowledge, has not been used to investigate the time dynamics of reservoir-triggered seismicity so far.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 2 Preliminary
### Codeword Likelihood in BIAWGN Channels  
Given a received codeword \(Y^{N}\), the log-likelihood of a codeword \(X^{N}\) is the logarithm of the probability of sending \(X^{N}\) and getting \(Y^{N}\). In BIAWGN channels with noise variance \(\sigma^{2}\) and BPSK modulation, we can express the log-likelihood of a codeword \(X^{N}\) given that \(Y^{N}\) was received from the channel as \[\log\Pr(Y^{N}|X^{N}) =\log\prod_{i=1}^{N}\Pr(y_{i}|x_{i})\] \[=\log\prod_{i=1}^{N}\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\left(-\frac {(y_{i}-x_{i})^{2}}{2\sigma^{2}}\right)\] \[=-\frac{N}{2}\log(2\pi\sigma^{2})+\sum_{i=1}^{N}-\frac{(y_{i}-x_{ i})^{2}}{2\sigma^{2}}\] \[=-\frac{N}{2}\log(2\pi\sigma^{2})-\frac{1}{2\sigma^{2}}\sum_{i=1 }^{N}y_{i}^{2}-\frac{N}{2\sigma^{2}}+\frac{1}{\sigma^{2}}\sum_{i=1}^{N}x_{i}y _{i} \tag{1}\]  
In the final equation in Equation (1), only the last part is variable. Therefore, to find the ML codeword, we need to find \(X^{N}\) that maximizes \(\sum_{i=1}^{N}x_{i}y_{i}\). Further, if \(\overline{X}^{N}\) is the hard decision codeword derived from \(Y^{N}\), maximizing \(\sum_{i=1}^{N}x_{i}y_{i}\) is equivalent to minimizing \(\sum_{i\in Flipped}|y_{i}|\) where \(Flipped\) is the set of bits of \(X_{N}\) that are different from \(\overline{X}^{N}\). We use \(f\) to denote this discrepancy according to  
\[f=\sum_{i:X_{i}^{N}\
eq\overline{X}_{i}^{N}}|y_{i}| \tag{2}\]
## 2 Materials and Methods
### Principles of the Present Algorithm  
A natural easy choice could be to generalize (3) by preserving the second-order accuracy. As we have mentioned, we cannot just provide a simple formula for this. Instead, we could perform an optimization procedure to determine the coefficients, which approximate the Laplacian operator as accurately as possible.  
Before digging into the details, we immediately reduce the setup of this problem:  
* One can obviously assume the translation-invariant property of the approximation. Accordingly, we may assume that \(\mathbf{x}_{0}=(0,0)\).
* Also, any physically meaningful approximation should be scale-invariant. In concrete terms, if we have the optimal coefficients \(\{a_{j}\}_{j=0}^{4}\) for some configuration \(\{\mathbf{x}_{j}\}_{j=1}^{4}\), then we should have \(\{\frac{1}{r^{2}}a_{j}\}_{j=0}^{4}\) for the configuration \(\{r\cdot\mathbf{x}_{j}\}_{j=1}^{4}\).  
However, a separate optimization procedure in all of the grid points would be extremely time-consuming. This is the point where artificial neural networks come into the picture and the following main principle is established.  
An ANN should be trained to learn this optimization in the sense that it should give the coefficients \(\{a_{j}\}_{j=0}^{4}\) in (1) using the positions \(\{\mathbf{x}_{j}\}_{j=1}^{4}\) as an input.  
Formally, we associate the mapping \(\mathcal{N}\) to the ANN, such that  
\[\mathcal{N}:(\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{x}_{3},\mathbf{x}_{4}) \rightarrow(a_{0},a_{1},a_{2},a_{3},a_{4})\]  
First, we reduce the problem again by requiring that the Laplacian of the constant function is computed in \((0,0)\) exactly. This means that \(\sum_{j=0}^{4}a_{j}=0\), so that we have  
\[a_{0}=-\sum_{j=1}^{4}a_{j}.\]  
With this simplification, we have to optimize only the set of parameters \(\{a_{1},a_{2},a_{3},a_{4}\}\).  
Note that a second-order approximation can reconstruct the Laplacian for all at most second-order polynomials.  
\[\begin{split}& p_{0}(x,y)=1,\quad p_{1}(x,y)=x,\quad p_{2}(x,y)=y, \\ & p_{3}(x,y)=x^{2},\quad p_{4}(x,y)=y^{2},\quad p_{5}(x,y)=xy. \end{split} \tag{9}\]
# A Wearable Microphone Array Helmet for Automotive Applications
## 2 Materials and Methods  
### Array Helmet Design  
The first step of the development consisted of the choice of the helmet. It must be as spherical as possible, with a smooth surface without holes, grids, vents, knobs, and protruding parts to avoid any practical limitation for capsule mounting. This led to the choice of the model X3000 by the Italian manufacturer AGV (Vicenza, Italy).  
The microphonic capsule chosen to build the array is AOM-5024L-HD-R from PUI Audio (Fairborn, OH, USA), whose technical specifications are reported in Table 1. It has a very low self-noise of just 14 dB(A) and more than 90 dB(A) of dynamic range. In Figure 1 (provided by the manufacturer), one can note that the frequency response is almost flat in the interested frequency range, which is 20 Hz-1 kHz.  
A 3D model of the helmet was acquired with the laser scanning technique by employing a Shining 3D EinScan H2. The result was stored as a stereolithography file, namely stl (also referred to as standard triangle language or standard tessellation language). In this way, it was possible to easily import and manipulate the geometry in MATLAB (R2024b). To define the optimal distribution of the capsules, the nearly uniform sampling was adopted. First, the geometrical center of the model was calculated, then 32 straight lines were propagated from the center in the same directions of a spherical design of degree \(t=7\), with N = 32 points. This configuration, as well as many others up to \(t=21\) and \(N=240\), can be found in [47]. The bundle of lines was rotated to minimize the intersections with the areas where it was not possible to mount any capsules, namely the visor and the hole for the neck. The minimum number of unallowed points was six: two on the visor and four on the neck. These points were manually repositioned. Finally, 10 mm diameters holes were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline Diameter & 9.7 mm \\ Height & 5 mm \\ Type & Analogue, electret \\ Directivity & Omnidirectional \\ Freq, range & 20 Hz-10 kHz \\ Self-noise & 14 dB (A) \\ Max SPL & 110 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microphonic capsule specification (AOM-5024L-HD-R).  
Figure 1: Frequency response of the PUI Audio AOM-5024L-HD-R capsule.  
drilled by using a computer numerical control (CNC) machine and the capsules were fixed in positions by using a special adhesive modeling paste.
## 2 Materials and Methods
### Equipment and Data Processing  
Data collection was realized using the WIMU PRO(tm) device (RealTrack Systems, Almeria, Spain). The inertial measurement unit combines various sensors in a lightweight 70 g unit of 81 \(\times\) 45 \(\times\) 16 mm. Four 3D accelerometers with various ranges (\(\pm\)16 G, \(\pm\)16 G, \(\pm\)32 G, and \(\pm\)400 G) provide thorough detection of movement at varying intensities. Three gyroscopes (two of which are \(\pm\)2000\({}^{\circ}\)/s and one of which is \(\pm\)4000\({}^{\circ}\)/s, both at 1000 Hz sampling) measure rotational movement, augmented by a 3D magnetometer (\(\pm\)8 Gauss at 160 Hz) and a barometer (\(\pm\)1200 mbar at 100 Hz).  
To enable accurate spatial localization and speed measurements, the device uses GNSS/GPS technology at 10 Hz with a UWB positioning system at 18 Hz. Data transmission is enabled by various channels, such as WiFi 802.11 b/g/n, Bluetooth, ANT+, and USB 2.0, and all data are recorded in 8 GB internal flash memory. The 4 h battery life of the device enables efficient analysis of training sessions.  
Primary data processing was performed through SPRO(tm) software (Version 2023.1, RealTrack Systems, Almeria, Spain), which offers large-scale data analysis and the possibility to export raw data for subsequent use of custom-designed algorithms. The analysis process included generic and sport-specific software tools. Generic analysis was conducted by Microsoft Excel (Version 16.0, Microsoft Corporation, Redmond, WA, USA) and IBM SPSS Statistics (Version 28.0, IBM Corp., Armonk, NY, USA). Custom-made algorithm development and signal processing were performed with MATLAB (Version R2023b, MathWorks Inc., Natick, MA, USA).  
All tests were conducted using ICF standard C1 (single-person canoe) watercraft. The canoes used in this study complied with International Canoe Federation (ICF) regulations for sprint competitions: minimum length of 5.20 m, minimum width of 0.35 m, minimum weight of 14 kg, and no rudder. Each athlete used their personal competition canoe to ensure familiarity with the equipment during testing, though all canoes met the standardized ICF specifications. These competition vessels were selected to ensure result validity specifically for C1 sprint applications, which represents one of the core Olympic disciplines in sprint canoeing.
## 2 Experimental Study
### Material Properties  
All specimens were fabricated with Chinese Standard Q235 steel (nominal yield stress \(f_{y}=235\) MPa). Tensile coupon tests were conducted according to the test procedures given in the Chinese Standard of Metallic Materials (GB/T 228-2002) [62] to determine the mechanical properties of carbon steel CHS tubes. The material properties obtained from the tensile coupon tests are the elastic modulus (\(E\)), tensile yield stress (\(f_{y}\)), ultimate tensile stress (\(f_{u}\)), and elongation. The concrete-filled Warren CHS tubular trusses were fabricated by filling the concrete with nominal cube strength of 25 MPa in the compression top chord members and the intension bottom chord members. The material properties of concrete cubes with the nominal length of 150 mm were prepared and tested based on the recommendations of the Chinese Standard on Ordinary Concrete (GB/T 50081-2002) [63]. The measured concrete cube strength (\(f_{cu}\)) is 18.3 MPa and the measured elastic modulus (\(E_{C}\)) is 27.7 GPa. The obtained material properties data are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Specimen ID**} & \multicolumn{2}{c}{**Chord**} & \multicolumn{2}{c}{**Brace**} & \multicolumn{2}{c}{**Lateral Bracing**} & \multicolumn{3}{c}{**Dimensionless Parameters**} \\ \cline{2-9}  & \(\mathbf{D\times T\) (mm)} & \(\mathbf{d\times t\) (mm)} & \(\mathbf{d_{1}\times t_{1}\) (mm)} & \(\mathbf{\theta}\) & \(\mathbf{\beta}\) & \(\mathbf{\gamma}\) & \(\mathbf{\tau}\) & \(\mathbf{O_{v}}\) \\ \hline T-HW & 60 \(\times\) 2.5 & 33 \(\times\) 2 & 22 \(\times\) 2 & 60\({}^{\rm o}\) & 0.58 & 12 & 0.8 & 39.3\% \\ T-HN & 60 \(\times\) 2.5 & 33 \(\times\) 2 & 22 \(\times\) 2 & 60\({}^{\rm o}\) & 0.58 & 12 & 0.8 & 39.3\% \\ TS-AS & 60 \(\times\) 2.5 & 33 \(\times\) 2 & 22 \(\times\) 2 & 60\({}^{\rm o}\) & 0.58 & 12 & 0.8 & 39.3\% \\ TS-FC & 60 \(\times\) 2.5 & 33 \(\times\) 2 & 22 \(\times\) 2 & 60\({}^{\rm o}\) & 0.58 & 12 & 0.8 & 39.3\% \\ \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of specimens.  
Figure 1: Warren circular hollow section (CHS) tubular truss. (**a**) Front view of the tubular truss (mm); (**b**) Top view of the tubular truss (mm); (**c**) joints detail of the tubular truss.
## 4 Experimental Results
### Measures  
Performance assessment of the proposed method in this study relied on three well-known measures: accuracy (ACC), sensitivity (SEN), and specificity (SPEC). Plus, we also present precision or positive predictive value (PPV), negative predictive value (NPV), f1-score, the area under the curve (AUC), and processing time to give a comprehensive view about the advantage of the proposed method and for other research reference purposes. The calculation of the measure can be explained as follows in Equations (3)-(8):  
\[ACC=\ \frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]  
\[SEN=\ \frac{TP}{TP+FN} \tag{4}\]  
\[SPEC=\ \frac{TN}{TN+FP} \tag{5}\]  
\[PPV=\ \frac{TP}{TP+FP} \tag{6}\]  
\[NPV=\ \frac{TN}{TN+FN} \tag{7}\]  
\[F1-score=\ \frac{2 TP}{2 TP+FP+FN} \tag{8}\]  
where:  
True positive (TP) presents the number of caries images classified correctly as caries;  
True negative (TN) presents the number of non-caries images classified correctly as non-caries;  
False-positive (FT) means the number of non-caries images classified wrongly as caries;  
False-negative (FN) means the number of caries images classified wrongly as non-caries.