esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Manual Methodology for Measuring Roundness and Cylindicity Tolerances
## 2 Materials and Methods
### Theoretical and Analytical Fundamentals and Development
#### 2.1.1 Roundness Tolerance
\[r_{i}=\sqrt{\left(R+e_{i}\right)^{2}-c^{2}\sin^{2}(\theta_{i}-\alpha)}+c\cos (\theta_{i}-\alpha) \tag{2}\]  
In addition, according to the industrial application of the roundness tolerance concept, the LSC center is very close to the origin, as the roundness tolerance is 2 or 3 orders of magnitude smaller than the dimension of the parts. For that reason, the distance \(c\) is very small and the term \(c^{2}\) is negligible. Consequently, Equation (3) is obtained. \[r_{i}=R+c\cos(\theta_{i}-\alpha)+e_{i} \tag{3}\]  
Figure 2: Geometric representation of a section (red line) and the least squares circle (in black line). Moreover, taking into consideration the definition of LSC circle, the sum of the squares of the radial errors between the circle and each point, \(S\), Equation (4), must be minimized. The full derivation of the expression for \(S\) is shown in Appendix A.  
\[S=\sum\limits_{i=1}^{n}{{{e_{i}^{2}}}}=\sum\limits_{i=1}^{n}{{(R+a\cos{\theta_{i }}+b\sin{\theta_{i}}-r_{i})^{2}}} \tag{4}\]  
For that purpose, partial derivatives with respect to the position of LSC center and the radius, i.e., with respect to \(a\), \(b\), and \(R\), must be calculated. The detailed mathematical procedure is depicted in the Appendix A. Thus, considering that the measured points of the profile are equidistant, the solution is found using Equations (5) and (6). \[ \begin{array}{l}{a=\frac{{{2\sum\limits_{i=1}^{n}{{{r_{i}}\cos{\theta_{i}}}}} }}{{n}}=2\overline{x}}\\ {b=\frac{{{2\sum\limits_{i=1}^{n}{{{r_{i}}\sin{\theta_{i}}}}}}}{{n}}=2 \overline{y}}\end{array}  \tag{5}\]  
Note that the coordinates of the center, \(a\) and \(b\), are the double of the mean values of the \(x\) and \(y\) coordinates of the profile points. Once the center has been obtained, the radius of LSC is equal to the average of the distances between each point on the profile and LSC center, Equation (6). \[R=\frac{{\sum\limits_{i=1}^{n}{{\sqrt{{{{({x_{i}}+a)}^{2}}}+{{({y_{i}}+b)}^{2}} }}}}}{{n}}=\frac{{\sum\limits_{i=1}^{n}{{{R_{i}}}}}}{{n}}=\overline{R} \tag{6}\]  
As observed, the coordinates of the LSC center and its radius are easily determined from the cartesian coordinates of the points of the profile. Finally, the roundness tolerance, \(T_{round}\), is obtained, according to Equation (7), i.e., it is the difference between the maximum and minimum distances from LSC center and the measured points on the profile. \[T_{roundness}=R_{\text{max}}-R_{\text{min}} \tag{7}\]
## 3 Results and Discussion
### X-Ray Diffraction  
The X-ray diffraction (XRD) patterns of natural lignocellulosic fibers displays a typical crystal lattice of cellulose type I, with the main diffraction signals at 20 values of 15\({}^{\circ}\), 16\({}^{\circ}\), 22.5\({}^{\circ}\) and 34\({}^{\circ}\)[50] for all cellulose preparations (Figure 4) consistent with cellulose I. The absence of doublet in the main peak at 20 = 23\({}^{\circ}\) indicates the absence of any cellulose II [51]. This indicates that the cellulose structure did not endure any morphological change by chemical treatment, and most of the crystallinity is observed. The sharpness for the diffraction peak at 23\({}^{\circ}\) for all cellulose I forms indicates an increase in crystallinity. Sample (A) from Figure 4 shows the highest crystallinity. The increase of crystallinity could be due to the removal of hemicellulose and lignin, which exist in amorphous regions, leading to the realignment of cellulose molecules. Furthermore, during the hydrolysis process, the hydrophilic ions could penetrate into the amorphous regions of cellulose causing hydrolytic cleavage of glycogen bonds and release of the crystallites [52]. The decrease in the crystallinity could also be due to the breakage of hydrogen bonds in the crystalline part of cellulose chains during pulp treatment. The overall resolution of CMSC peaks indicates an amorphous region rather than highly crystalline region after the carboxymethylation process as expected. This is due to the breakage of inter- and intra-molecular hydrogen bonds in the crystalline part of the cellulose together with an increase in the electrostatic interactions between the CMSC as a result of the anionic -CH2 COO\({}^{-}\) groups substituted with the hydroxyl groups of cellulose [30]. A shifting of peaks of 002, 101 in 20 degree occurred and this could be due to the broadening the distance between cellulose polymer molecules. As the obtained cellulose was solubilized in isopropanol, an organic solvent and also swelling the cellulose in the concentrated sodium hydroxide during carboxymethylation process could have caused the conversion of polymorphs cellulose I to polymorphs cellulose II. Thus, the substitution of carboxymethyl group to the backbone of cellulose leads to the transformation of polymorphs cellulose I in cellulose to polymorphs of cellulose II in CMSC [22].  
Figure 3: DSC (Differential Scanning Calorimetry) thermogram of cellulose A (preparation 1) (**A**); cellulose B (preparation 2) (**B**); cellulose C (preparation 3) (**C**); cellulose D (preparation 4) (**D**); cellulose E (preparation 5) (**E**); and CMSC (**F**).
## 2 Principle of Multi-Twin-SSB PON Downlink Transmission
The third term is the SSBI of the desired ONU signal. The fourth term is the beat interference between different ONU signals. Note that the third and fourth terms are the PD-induced nonlinear distortions which occupy the same bandwidth as the signals. An illustration of the interference effect is shown in the inset (II) (e) of Figure 1 where the signals with spectrum closer to the DC component are expected to have more serious interference than the signals away from the DC. Effectively eliminating the nonlinear SSBI becomes crucial for successful signal separation and detection on the ONU side. For the following signal processing after PD in the proposed scheme, Equation (1) is rewritten as:  
\[I(t)=\left|A+m_{SSB}(t)\right|^{2} \tag{2}\]  
where the sum of \({s_{i}}(t)\) is represented as a sum signal \(m_{SSB}(t)\) which could be either LSB or RSB signal. If \(\left|A\right|\) is large enough, which means that the minimum phase condition is satisfied, the KK scheme can be utilized in our scheme to reconstruct the SSB signal as [16]:  
\[\phi(t)=\frac{1}{\pi}p.v.\int\limits_{-\infty}^{+\infty}{dt^{\prime}}\frac{ \ln\left[\sqrt{I(t^{\prime})}\right]}{t-t^{\prime}} \tag{3}\]  
\[E_{m}(t)=\left\{\sqrt{I(t)}\exp[\pm j\phi(t)]-E_{0}\right\} \tag{4}\]  
where \(\phi(t)\) is the phase part of the minimum phase signal and _p.v._ denotes the Cauchy principal value of the integral. \(E_{m}(t)\) is the reconstructed complex SSB signal containing information from all sub-bands as illustrated in the inset (II) (f) of Figure 1. Note that '+' should be used for the RSB signal and '-' should be used for LSB signal in Equation (4). After that, the data of each user can be recovered using a matched filter (MF). Note that the MFs should also be dynamically set via the SDN-controller together with the corresponding shaping filters (SFs) on the OLT side.
###### Abstract  
Disaster Area Wireless Networks (DAWNs) are widely deployed in natural or man-made disaster scenes, since the communication infrastructure may be completely destroyed by the disaster. This paper proposes a hybrid network architecture for DAWNs due to the mobility of first responders and refugees. Based on the link characterization of DAWNs, we choose four essential criteria and propose a multi-criteria decision-making method for emergency communication protocol (MCDM-ECP), which utilizes the analytic hierarchy process (AHP) method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to find the optimal next-hop node in DAWNs. Routing discovery and routing maintenance processes are included in the novel protocol. The simulation results show that MCDM-ECP performs better than other classical protocols both in energy consumption and packet received rate (PRR) for long-term emergency communications.  
Keywords:DAWNs; AHP; TOPSIS; MCDM-ECP; emergency communication +
Footnote †: journal: Applied Physics Letters
# Bilateral Matching Decision Model and Calculation of High-Speed Railway Train Crew Members
## 2 Problem Description and Modeling
### Construction of the Train Crew Matching Model
#### 2.2.1 Establishing the Complete Preference Order Matrix  
The complete preference order vector of stewards for chief stewards is \(R_{i}=\{r_{i1},r_{i2},\ldots,r_{in}\}\), where \(i\in M,j\in N\), and \(r_{ij}\) represents the ranking of chief steward \(B_{j}\) in the preference order of steward \(A_{i}\). Similarly, the complete preference order vector of chief stewards for stewards is \(S_{j}=\{s_{1j},s_{2j},\cdots,s_{mj}\}\), where \(s_{ij}\) represents the ranking of steward \(A_{i}\) in the preference order of chief steward \(B_{j}\). The complete preference order matrices are denoted by \(R=\left[r_{ij}\right]\) and \(S=\left[s_{ij}\right]\). A smaller value of \(r_{ij}\) or \(s_{ij}\) indicates a higher satisfaction level of the matching [1]. All ranking values in this paper follow a strict ordering: if \(j\
eq k\), then \(r_{ij}\
eq r_{ik}\); if \(i\
eq l\), then \(s_{ij}\
eq s_{lj}\).
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.6 Altimeter  
Altimeters are mainly used to determine the topography of the SI surface, which can be used to calculate SI thickness [85]. The calculated SI thickness from altimetry data is invaluable for the SI volume change monitoring [85]. Cryosat, the European Space Agency (ESA) mission, launched in April 2010, is specifically designed to provide polar ice data, including SI altimetry. Additionally, other altimetry missions, such as Joint Altimetry Satellite Oceanography Network (JASON) satellites, NASA Radar Altimeter (NRA) on board of the TOPEX-Poseidon, and Synthetic Aperture Radar Altimeter (SRAL) on Sentinel-3 missions, have provided altimetry data for SI studies. Finally, laser altimeter instruments, which use visible pulses for altimetry measurements, have also been employed for SI studies. The data acquired by the Ice, Cloud, and land Elevation Satellite (ICESat-1) and ICESat-2 are the most popular laser altimetry data for SI studies [85].  
Figure 3: Different properties of SI in different channels of SAR images [94].  
In terms of ML algorithms, the SVM algorithm has shown a high potential for SI classification [81]. Additionally, rule-based ML models, including the decision tree and RF algorithms, have been utilized for deriving melt pond statistics and detection [82], as well as for SI thickness estimation and leads detection [83]. [PERSON] et al. [84] also implemented an automated processing chain using content-based ML algorithms to analyze and interpret the specific ice-related parameters using high-resolution SAR images.
## 1 Introduction
### Research Significance  
Presently, sustainable and affordable housing is still a topical issue in the building construction industry of most developing nations. The mass production of robust prefabricated bricks made of geopolymers and natural fibers has the potential to meet increasing housing demands. The findings of this study will benefit the construction industry if the compressive and flexural strength of geopolymer composite meets minimum ASTM requirements adequate for construction applications such as low-tech building material (clay bricks). In addition, it will present sustainability benefits when this geopolymer material is used as an alternative to ordinary Portland cement. This research will also help improve the understanding of using coir fiber as a reinforcing material in geopolymer composite production. The use of coir fibers as a reinforcement element in the production of geopolymer composites has not been fully reported in previous studies. Incorporating coir fibers in metakaolin geopolymer to improve the mechanical properties may be another waste disposal solution for sustainable development.  
Specifically, this study evaluated the (i) modification in the physical property (bulk density), and ultrasonic pulse velocity (UPV) values of metakaolin-based geopolymer at a varying weight percent of coir fibers; (ii) changes in strength (compressive and flexural) of metakaolin-based geopolymer due to the inclusion of varying weight percent of coir fibers; and (iii) the effect of coir fibers on the phase composition, bonding mechanism, and microstructure of metakaolin-based geopolymer. This investigation will play a role in sustainability efforts and the preservation of the ecosystem.
## 5 The Simulation Results and Discussion
### Simulation Setup
#### 5.1.2 The Parameters of the Waypoints  
First, a zigzag path is used as a standard complex path for the optimization process of the FLC. Then, several popular defined paths will be used to test the robot motion control by using suboptimal FLC that have been found in the optimization process. These paths are defined by the several given waypoints in a unit of a meter as listed in Table 7.
## 5 System Evaluation
### Usability Testing  
The evaluation of kClusterHub's usability employs the widely recognized System Usability Scale (SUS) questionnaire [36] ([[https://forms.gle/dUSecKe1](https://forms.gle/dUSecKe1) pgE566128]([https://forms.gle/dUSecKe1](https://forms.gle/dUSecKe1) pgE566128), accessed on 15 September 2023). More specifically, SUS was used to measure the users' overall satisfaction with kClusterHub, as well as their perception of its effectiveness, efficiency, and ease of use. SUS is a widely used tool for measuring the usability of web applications. It consists of ten questions that participants complete to rate their experience. The questions in the questionnaire are as follows:  
1. I think that I would like to use this website frequently;
2. I found the website unnecessarily complex;
3. I thought the website was easy to use;
4. I think that I would need the support of a technical person to be able to use this website;
5. I found the various functions in this website were well integrated;
6. I thought there was too much inconsistency in this website;
7. I would imagine that most people would learn to use this website very quickly;
8. I found the website very cumbersome to use;
9. I felt very confident using the website;
10. I needed to learn a lot of things before I could get going with this website.  
Participants rate each question on a 5-point Likert scale, ranging from \"strongly disagree\" (1) to \"strongly agree\" (5). Calculating the SUS score involves subtracting 1 from the scores of odd-numbered questions and subtracting their value from 5 for even-numbered questions. The resulting scores are then summed and multiplied by 2.5 to yield a final score between 0 and 100. A SUS score of 80 or above is typically considered excellent.  
A total of 37 respondents, primarily computer science undergraduate students enrolled in a data mining course, completed the questionnaire upon our request. The distribution of responses for each rating range is provided in Table 3. The resulting SUS score was 83.58, indicating a high level of user satisfaction with the kClusterHub experience. These results affirm the positive usability of kClusterHub as perceived by its users.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Question** & **1** & **2** & **3** & **4** & **5** \\ \hline
1. & 0 & 2 & 4 & 17 & 14 \\ \hline
2. & 23 & 11 & 2 & 0 & 1 \\ \hline
3. & 0 & 0 & 1 & 7 & 29 \\ \hline
4. & 23 & 7 & 3 & 4 & 0 \\ \hline
5. & 0 & 0 & 4 & 11 & 22 \\ \hline
6. & 27 & 7 & 2 & 1 & 0 \\ \hline
7. & 2 & 0 & 4 & 9 & 22 \\ \hline
8. & 16 & 9 & 9 & 2 & 1 \\ \hline
9. & 0 & 0 & 1 & 14 & 22 \\ \hline
10. & 11 & 10 & 11 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of SUS questionnaire.
## 3 Results and Discussion
### Fatty Acid Profile and Lipid Composition  
The fatty acid (FA) composition of both full-fat soy (FFS) and expanded soybean cake (ESC) was analyzed across a 6-month storage period (Table 8). The processing stages and subsequent oil removal significantly altered the FA profiles between the two products, while temporal storage effects remained limited. Saturated fatty acids (SFA) were substantially lower in ESC compared to FFS. In FFS, SFA content ranged from 33.70% to 34.55% across storage time, while in ESC, values increased slightly from 15.88% at one month to 22.00% after six months. The consistently lower SFA fraction in ESC enhances its lipid quality profile, in alignment with nutritional recommendations favoring lower saturated fat intake [45]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & \multicolumn{2}{c}{**2022**} & \multicolumn{2}{c}{**2023**} & \multicolumn{2}{c}{**2024**} \\ \cline{2-6}
**Content** & **FFS** & **ESC** & **FFS** & **ESC** & **FFS** & **ESC** \\ \hline CP & 36.12 \(\pm\) 0.39 b,* & 41.99 \(\pm\) 0.25 a & 36.10 \(\pm\) 0.95 b & 39.82 \(\pm\) 0.24 a & 34.16 \(\pm\) 0.71 b & 39.42 \(\pm\) 0.43 a \\ CO & 24.13 \(\pm\) 0.04 a & 14.78 \(\pm\) 0.05 b & 24.90 \(\pm\) 0.30 a & 16.77 \(\pm\) 0.64 b & 26.16 \(\pm\) 0.63 a & 15.07 \(\pm\) 0.31 b \\ Ash & 6.31 \(\pm\) 0.00 a & 6.24 \(\pm\) 0.00 a & 6.64 \(\pm\) 0.59 b & 6.77 \(\pm\) 0.16 a & 6.73 \(\pm\) 0.46 a & 6.17 \(\pm\) 0.19 b \\ T-NSP & 10.34 \(\pm\) 0.01 b & 12.90 \(\pm\) 0.08 a & 10.89 \(\pm\) 0.01 b & 12.43 \(\pm\) 0.08 a & 10.69 \(\pm\) 0.01 b & 12.71 \(\pm\) 0.08 a \\ UA & 2.53 \(\pm\) 0.01 a & 2.52 \(\pm\) 0.04 a & 2.67 \(\pm\) 0.01 a & 2.43 \(\pm\) 0.04 b & 2.61 \(\pm\) 0.01 a & 2.48 \(\pm\) 0.04 b \\ KL & 1.20 \(\pm\) 0.01 a & 1.33 \(\pm\) 0.03 a & 1.26 \(\pm\) 0.01 a & 1.28 \(\pm\) 0.02 a & 1.24 \(\pm\) 0.01 b & 1.31 \(\pm\) 0.02 a \\ RFO & 3.93 \(\pm\) 0.04 a & 4.11 \(\pm\) 0.03 a & 4.14 \(\pm\) 0.04 a & 3.96 \(\pm\) 0.04 b & 4.06 \(\pm\) 0.03 a & 4.05 \(\pm\) 0.04 b \\ DF & 17.98 \(\pm\) 0.00 b & 20.99 \(\pm\) 0.00 a & 18.94 \(\pm\) 0.39 b & 20.22 \(\pm\) 0.68 a & 18.58 \(\pm\) 0.86 b & 20.68 \(\pm\) 1.14 a \\ PA & 1.40 \(\pm\) 0.01 a & 1.43 \(\pm\) 0.01 a & 1.47 \(\pm\) 0.01 a & 1.38 \(\pm\) 0.01 b & 1.45 \(\pm\) 0.01 a & 1.41 \(\pm\) 0.01 b \\ TPC & 2.49 \(\pm\) 0.03 a & 2.43 \(\pm\) 0.02 a & 2.62 \(\pm\) 0.03 a & 2.34 \(\pm\) 0.02 b & 2.57 \(\pm\) 0.03 a & 2.39 \(\pm\) 0.03 b \\ \hline \hline \end{tabular}
*–small superscript letters (a, b) indicate significant differences between FFS and ESC within each year according to [PERSON]’s HSD test (\(p\) < 0.05). T-NSP—total non-starch polysaccharides, UA—uronic acid, KL—Klason lignin, RFO—affinose family oligosaccharides, DF—ditary fiber, PA—phytic acid, TPC—total phenolic content.  
\end{table} 
Table 7: Anti-nutritional and dietary fiber components (% dry matter) in FFS and ESC, 2022–2024.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 3 Theoretical Foundations of Roll Stability Control
### Roll Dynamic Model
\(F_{\rm{ylf}}\), \(F_{\rm{yrf}}\), \(F_{\rm{ylr}}\), and \(F_{\rm{yrr}}\) are the lateral forces at the four wheels, respectively. \(I_{z}\) is the vehicle moment of inertia about the vertical axis. a and b are the distances from the center of mass to the front and rear axles, respectively. \(B_{\rm{f}}\) and \(B_{\rm{r}}\) are the front and rear track widths. \(I_{x}\) is the vehicle roll inertia about the longitudinal axis. \(\varphi\) is the roll angle. \(h_{x}\) is the distance from the center of mass to the roll axis. \(K_{\varphi}\) and \(C_{\varphi}\) are the suspension roll stiffness and damping coefficients, respectively. Numerous studies have developed dynamic models to analyze vehicle rollover behavior and support control strategy design. [PERSON] et al. [59] proposed a nonlinear 8-DOF model for ME-wheel off-road vehicles, and introduced a predictive load transfer rate (PLTR) index that improves early rollover risk detection. [PERSON] et al. [60] developed a 7-DOFmodel to analyze the influence of suspension parameters on vehicle response, supporting simulation-based testing. [PERSON] et al. [61] combined roll dynamics with an unscented Kalman filter to estimate the dynamic center of mass, and proposed a roll angle-roll rate phase-plane method for accurate, real-time rollover evaluation. [PERSON] et al. [62] built a Human-Vehicle-Road model for tankers, and identified the curvature radius, speed, and load rate as key rollover factors. [PERSON] et al. [63] developed a complex model with 4D visualization to assess the impact of vehicle height and speed on roll angle and tire forces in high-CG vehicles. Furthermore, to systematically reveal the roll dynamics characteristics of vehicles, various studies have adopted different modeling methods and analytical perspectives, continuously advancing research into vehicle roll dynamics and rollover behavior. Table 1 summarizes representative modeling methods and their applications in this field in recent years. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} Influence of CG height and lateral acceleration \\ \end{tabular}  &
## 6 Conclusions and Future Work  
OppNets present unique challenges due to their intermittent connectivity and dynamic network topologies. Traditional routing protocols struggle in these environments, often leading to high resource consumption and inefficient performance. This paper proposes a novel Adaptive Clustering-based Routing Protocol (ACRP) specifically designed for OppNets. ACRP leverages a member-based adaptive dynamic clustering approach to create a structured network that facilitates efficient routing. Our simulation results demonstrate that ACRP outperforms existing clustering protocols in terms of packet delivery ratio (increased by 28%), end-to-end delay (reduced by 42%), throughput (increased by 45%), hop count (reduced by 44%), and network reachability (increased by 80%). These improvements highlight the effectiveness of ACRP in enhancing communication performance within OppNets.  
In our future work, we aim to reduce energy consumption and improve important network parameters using machine learning algorithms.  
Conceptualization, M.S.S.; Methodology, M.S.S., S.I. and H.S.; Validation, F.T.; Formal analysis, M.S.S.; Investigation, S.I. and H.S.; Writing--original draft, M.S.S.; Writing--review & editing, S.I., F.T. and R.R.; Visualization, M.S.S.; Supervision, S.I. and R.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data are contained within the article.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Technologies
#### 3.1.5 Near Field Communication  
For wireless connectivity within a short range, near-field communication (NFC) is used with a 4 cm range. With NFC, data can be transmitted swiftly and effortlessly across devices with one touch. To transfer data between two NFC-enabled devices, they must be close to each other and must be paired. There are two modes of operation in NFC technology, active and passive mode. In active mode, there is no need for pairing between devices, while in passive mode it needs pairing.