esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 4 Automatic Geometry Simplification Method for FVM
### Polyhedron Generation  
After the faces are classified, the base solid of simplified model is generated as a bounding volume. The base solid need to have buffer space outside the original model (i.e., bigger than minimum bounding volume), since new solid might be generated in the simplified model as shown in Figure 1(d). In this study, a solid that is twice the length of each dimension of the minimum bounding box was generated.  
The base solid is split by each plane containing a major face as shown in Figure 4. Among the solids split by the planes, the solid that will form the simplified model is selected by comparing it with the original model (Figure 4(a)). For each split solid (\(S_{i}\)), the ratio of shared volume of the solid and the original solid (\(S_{i}\ \cap\ S_{target}\)) over volume of the solid (\(S_{i}\)) is calculated. The solid with a volume ratio of 0.5 or more is selected. Figure 4(b) shows the calculated volume ratio of part of solids. Finally, the selected split solids (Figure 4(c)) were united to generate simplified building geometry. Algorithm 2 is the pseudocode for the polyhedron generation process.  
Input:  
\(S_{target}\): Set of geometry solids of the target model  
\(F_{major}\): Set of major faces of target geometry \(S_{target}\)  
Output:  
\(S_{simplified}\): Solids of simplified geometry  
Algorithm:  
\(P[\text{Plane containing }f|f\in F_{major}]\)  
\(S_{base}\) Bounding volume of \(S_{target}\)  
Split \(S_{base}\) by each plane in \(P\) into \(S_{split}=\{S_{1},\ S_{2},S_{3},\cdots\}\)  
\(S_{selected}\)/\(\varnothing\)  
FOR EACH solid \(S_{i}\) in \(S_{split}\)  
\(r\)(Volume of \(S_{i}\ \cap\ S_{interest}\))/(Volume of \(S_{i}\))  
IF 0.5 \(\leq\) r  
\(S_{selected}\gets S_{selected}\ \cup\ [S_{i}]\)  
\(S_{simplified}\) Union of solids in \(S_{selected}\)  
RETURN \(S_{simplified}\)  
Figure 3: Classification of minor face on 3D building model; \(\overrightarrow{c}_{i}\) is the center, \(\overrightarrow{n}_{i}\) is the unit normal of face \(f_{i}\), \(\overrightarrow{p}\) is the foot of perpendicular from \(c_{2}\) to \(f_{1}\).
## 4 Conclusions  
A simple protocol for the immobilisation of Con A on a quartz substrate has been developed. The binding activity of the immobilised Con A was assessed by measuring the intensity from fluorescent labelled dextran over a glucose concentration range from 0-40 mM whereas any structural changes to the surface topography were investigated using AFM. Complementing measurements from both the AFM and fluorescence intensity verified that a change in the intensity was accompanied with a change in the average surface roughness and thereby the composition (addition and loss of dextran) at the sample surface. Controls performed with no immobilised Con A verified that any unspecific binding of fluorescent labelled dextran did not take place. The binding of Alexa 488 dextran with a dimension smaller than Con A was found to represent a simple and rapid method to estimate the number of bound fluorescent labelled dextran, and hence, functional Con A molecules attached to the sample surface. This work has shown that Con A, immobilised on a quartz surface using the simplified immobilisation protocol described in this paper, is able to bind to a larger dextran molecule as well as to a smaller glucose molecule in a competitive manner. The substitution of dextran with glucose and vice versa will result in a mass or viscoelastic change at the interface between the immobilised Con A and the surrounding media. Although the Alexa 488 dextran exhibited larger numbers and fluorescence intensities, the FITC dextran is predicted to give a larger absolute mass change. This change as a function of glucose concentration will be explored in the design of a new, miniaturised gravimetric glucose sensor based on acoustic wave resonators.  
The authors have made the following contributions to the paper: Conceptualization, formal analysis, validation and investigation, all authors; Methodology and Data Curation, T.B.H. and B.T.S.; Software and Original Draft Preparation, T.B.H.; Review & Editing, Supervision, A.J., U.H., B.T.S. and E.A.J.; Visualization, T.B.H., B.T.S. and E.A.J.; Project administration, resources and funding: A.J.  
The Research Council of Norway is acknowledged for the support to the Norwegian Micro and Nano-fabrication facility, NorFab (197411/V30) and the Norwegian Ph.D. Network on Nanotechnology for Microsystems, Nano-Network (221860/F40). We would also like to thank technical staff at the University of South-Eastern Norway for the help and support, and Mrs. [PERSON] at the Norwegian University of Science and Technology (NTNU) for the assistance in developing the immobilisation protocol and for taking the AFM images.  
The authors declare no conflict of interest.
## 6 Results
### Data Association  
The data association functional block is subdivided into three kernels as discussed in Section 5.3. Therefore, the experimental results are shown for the three kernels separately in Sections 6.3.1-6.3.3. The sensitivity analysis is performed based on the number of particles and landmarks on the map. Since our implementation deals with the observations sequentially, this parameter is kept constant at 16.
## 2 Review: Regional-Scale Assessment of Buildings' Seismic Vulnerability
### Empirical Vulnerability Assessment  
Vulnerability curves established by post-seismic observations of buildings by type can serve as a simplified and cost-efficient means of vulnerability assessment, allowing reasonable estimations of expected damage levels at given seismic intensities [6; 7; 8]. However, despite their great simplicity, such empirical approaches have drawbacks in terms of both flexibility and accuracy. Firstly, since vulnerability curves are determined based on post-event surveys of damaged buildings in an affected area, they cannot be fairly applied to any building types that differ markedly from those already affected by earthquakes or that have been affected but not surveyed systematically. As such, the empirical approach is not suitable to areas of low to moderate seismic hazard or where no accurately recorded historical damage data exists. Secondly, a measurement of ground-motion intensity, such as peak ground acceleration (PGA), is commonly used as the explanatory variable in regression models that have building damage as the dependent variable, without taking into consideration the relationship between the frequency content of the ground motion and the fundamental period of vibration of buildings. Specifically, PGA is the maximum ground response of an earthquake, not the maximum response of a structure; such use of PGA can therefore lead researchers to ignore the possible effects of resonance between the frequency of an earthquake and a structure and thereby decrease the accuracy of vulnerability curves. For example, a high-rise building will have a greater response to a low-frequency earthquake than to a high-frequency one with the same PGA.
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
###### Abstract  
Wireless Sensor Networks (WSNs) have been extensively applied in ecological environment monitoring. Typically, event boundary detection is an effective method to determine the scope of an event area in large-scale environment monitoring. This paper proposes a novel lightweight Entropy based Event Boundary Detection algorithm (EEBD) in WSNs. We first develop a statistic model using information entropy to figure out the probability that a sensor is a boundary sensor. The EEBD is independently executed on each wireless sensor in order to judge whether it is a boundary sensor node, by comparing the values of entropy against the threshold which depends on the boundary width. Simulation results demonstrate that the EEBD is computable and offers valuable detection accuracy of boundary nodes with both low and high network node density. This study also includes experiments that verify the EEBD which is applicable in a real ocean environmental monitoring scenario using WSNs.  
Keywords:event boundary detection; information entropy; wireless sensor networks; determining rules; ecological environment monitoring +
Footnote †: journal:
# Design Procedure for Motion Profiles with Sinusoidal Jerk for Vibration Reduction
## 2 General Formulation of Motion Profiles  
### Review of Seven-Segment S-Curve Profile  
To avoid acceleration discontinuities, the S-curve velocity profile, also known as the third-order motion profile, has been extensively studied and applied in recent years. Due to the characteristic of finite jerk spread over time, this profile enables the generation of smooth motions with small residual vibrations. For the rest-to-rest motion, a complete profile consists of seven symmetrically distributed segments, in which the jerk remains constant at zero or its peak value, as shown in Figure 1, where \(t_{i}\), \(i=0,\ 1,\ \ldots,\ 7\) are the time boundaries of each segment; \(T_{1}\), \(T_{2}\), and \(T_{3}\) represent the time periods for the constant jerk segment, constant acceleration segment, and constant velocity segment, respectively. The first three segments constitute the acceleration phase, while the last three segments constitute the deceleration phase.  
Let \(J_{peak}\) denote the peak value of the jerk; then, the jerk profile can be expressed as follows:  
\[j(t)=\begin{cases}J_{peak},&t_{0}\leq t<t_{1}\\ 0,&t_{1}\leq t<t_{2}\\ -J_{peak},&t_{2}\leq t<t_{3}\\ 0,&t_{3}\leq t<t_{4}.\\ -J_{peak},&t_{4}\leq t<t_{5}\\ 0,&t_{5}\leq t<t_{6}\\ \par_{peak},&t_{6}\leq t<t_{7}\\ \par\end{cases} \tag{1}\]  
As a result, the acceleration changes linearly, reaching its maximum magnitude in the second and sixth segments. It takes a trapezoidal form during the acceleration phase \([t_{0},\ t_{3}]\) and an inverse trapezoidal form during the deceleration phase \([t_{4},\ t_{7}]\). In the intermediate phase, the velocity maintains its maximum value, while the acceleration turns to zero. Accordingly, the displacement curve is depicted by connected polynomial segments up to the third order. However, step jumps in the jerk degrade the performance to some extent. More details about this motion profile can be referred to in [38; 39; 40].
## 7 Testing and Evaluation  
The analytical model was validated using simulation. Two simulations were used, SUMO and VISSIM. Both models were used to simulate the Leicester testbed as described in Figure 3. This was a three-junction scenario. Each junction had four exits. Link 1 between Junction 1 and 2 was 2000 m long while Link 2 between Junction 2 and Junction 3 was 2500 m long. For each junction, the number of cars approaching the junction was measured, as was the number of cars turning left, going straight on and turning right. The remaining details are provided in [38].
## 3 Results  
The insertion of AlfaGate implants took up to three times longer as compared to NobelActive implants due to their smaller thread pitch. In sinuslifting, both implant types reached mean insertion torques greater than 40 Ncm and mean primary stability also exceeded ISQ values of 40 (Table 2). In extraction sites, mean insertion torque values were 14.29 Ncm for NobelActive and 16.43 for AlfaGate with latter ones reaching mean primary stability of ISQ 47.80 (Table 2).  
The Shapiro-Wilk-Tests (Table 2) could be assumed to be normally distributed measurement values for all parameters with the exception of torque values recorded for NobelActive in sinuslift situations (\(p\) = 0.0298). Given the large differences in mean torque values between the two implant types inserted in sinuslift situations, the non-normal distribution could be neglected.  
For simulated sinuslift procedures (Figure 4), NobelActive implants required significantly higher insertion torques as compared to AlfaGate (\(p\) = 0.000) but did not achieve greater implant stability as measured by resonance frequency analysis (\(p\) = 0.076).  
In extraction sites, AlfaGate implants showed both, significantly higher insertion torques (\(p\) = 0.004) and significantly greater implant stability (\(p\) = 0.000).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{3}{c}{**NobelActive**} & \multicolumn{3}{c}{**Shapiro-Wilk-Test**} & \multicolumn{3}{c}{**AlfaGate**} & \multirow{2}{*}{**Shapiro-Wilk-Test**} \\ \cline{3-3} \cline{5-8} \multicolumn{2}{c}{} & **Mean** & **SD** & & \(\langle p\)-Value\(\rangle\) & **Mean** & **SD** & \(\langle p\)-Value\(\rangle\) \\ \hline \multirow{3}{*}{**Sinuslift**} & Maximum insertion & \multirow{2}{*}{48.49} & \multirow{2}{*}{1.965} & \multirow{2}{*}{**0.0298**} & \multirow{2}{*}{40.42} & \multirow{2}{*}{1.460} & \multirow{2}{*}{0.1877} \\  & torque [Ncm] & & & & & & \\ \cline{2-8}  & Ossetll [ISQ] & 40.65 & 5.874 & 0.8498 & 44.70 & 3.164 & 0.6327 \\ \hline \multirow{3}{*}{**Extraction site**} & Maximum insertion & \multirow{2}{*}{14.29} & \multirow{2}{*}{1.244} & \multirow{2}{*}{0.4605} & \multirow{2}{*}{16.43} & \multirow{2}{*}{1.578} & \multirow{2}{*}{0.2829} \\  & torque [Ncm] & & & & & & \\ \cline{1-1} \cline{2-8}  & Ossetll [ISQ] & 41.80 & 3.225 & 0.9252 & 47.80 & 2.541 & 0.5593 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean values and standard deviations recorded for insertion torque and primary stability for both implant types. Results of Shapiro-Wilk-Tests on normality of distribution of measurement values are given as \(p\)-values; significant differences (\(p\) < 0.05) are written in bold.
## 4 Conclusions  
This paper proposes a hybrid framework for an efficient reliability analysis of 3D slopes incorporating soil spatial variability, validated through two benchmark cases. The principal findings are summarized as follows.  
The PSO algorithm, when coupled with the 3D Bishop method, reduces computational demands by 97.5% compared to the entry and exit approaches, while maintaining sub \(-0.33\%\) relative error in failure probability (\(P_{f}\)) calculations. This demonstrates its superior capability in balancing accuracy and efficiency for layered slope reliability assessment.  
Spatial variability exerts a pronounced influence on the reliability assessment of 3D slopes. With other parameters held constant, the derived failure probability exhibits a positive correlation with the varying parameters. The mean safety factor diminishes as spatial variability coefficients increase, decreases with the increase in the failure surface width, escalates with the enhancement of the horizontal correlation length, and gradually declines as the vertical correlation length increases. This aligns with well-established findings in probabilistic geotechnical engineering.  
Conceptualization, X.C., Y.W. and D.W.; methodology, X.C.; software, J.X. and R.Y.; validation, X.C., Y.W. and D.W.; formal analysis, Y.W. and R.Y.; investigation, J.X. and R.Y.; data curation, Y.W. and J.S.; writing--original draft, J.X. and R.Y.; writing--review & editing, X.C., Y.W. and D.W.; visualization, R.Y.; supervision, X.C.; project administration, X.C. and J.S.; funding acquisition, X.C. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52208361; the Natural Science Foundation of Jiangsu Province, grant number BK20220638; and the Natural Science Foundation of Ningxia, grant number 2024 AAC03421.  
Not applicable.  
If required, the data supporting the conclusions of this research can be obtained from the corresponding author, [PERSON].  
The authors declare no conflict of interest.
## 6 Experimental Evaluation and Results
### Validation in Smart Grid Voltage Control Systems
The fundamental advantage of our approach lies in its ability to mask erroneous results through online executor operation, effectively shielding the system from false data injection attacks when executors do not share common-mode vulnerabilities. Only when all online executors possess common-mode vulnerabilities does our method degrade to performance levels equivalent to SOTA MTD approaches, as extensively analyzed in Section 6.4. The results clearly show that our method can effectively mitigate false data injection attacks that produce erroneous outputs under differential-mode attack scenarios, while maintaining high system availability even under challenging attack conditions.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Scientific and Technological Challenges
#### 3.2.3 Land Surface Emission  
Accurate estimations of land surface emissivity (LSE) is another crucial issue for MW-based retrievals of precipitation over land and of atmospheric profiles, and for numerical weather prediction (NWP) model data assimilation. Without adequate maps of surface emissivity from observations or near-real-time LSE models, the retrievals of precipitation over land will always be affected by substantial errors, especially when using MW channels whose weighting functions peak close to the ground. Several groups have been working on this problem, and datasets are available (e.g., [195; 196; 197]). LSE models also exist for use in NWP applications, and are useful also for precipitation retrievals; two examples are the Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) [198] and the Ringerud et al. [199] model. Model evaluations through observations are being conducted [200; 201; 202] to ensure consistency and adequate error models for precipitation retrieval applications. One more aspect is the detection of cloud contamination in PMW measurements over land, which may adversely affect the quality of brightness temperature retrievals, especially in the presence of convective clouds and/or precipitation [203].
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 4 Ergodic Capacity
### Ergodic Capacity in the Presence of Interference Leakages  
With the aid of [PERSON]'s inequality and (20), ergodic capacity in the presence of interference leakages can be expressed as:  
\[c_{erg}^{\text{up}},\mathcal{I}=\frac{1}{K}\log_{2}\left[\mathbb{E}\bigg{(}1+ \min\bigg{(}\frac{\mathcal{P}_{\mathbb{S}}\frac{\|\mathbf{H}_{\mathbb{S}- \mathbb{R}_{1}}\|^{2}}{\sigma_{\mathbb{R}}^{2}}}{1+\frac{\epsilon_{\mathbb{S} \rightarrow\mathbb{I}}^{\sum_{i}\mathcal{P}}\|\mathbf{H}_{\mathbb{S}-\mathbb{R} _{i}}\|^{2}}{\sigma_{\mathbb{R}}^{2}}},\frac{\mathcal{P}_{\mathbb{R}_{1}}\frac {\|\mathbf{H}_{\mathbb{S}-\mathbb{R}_{2}}\|^{2}}{\sigma_{\mathbb{R}}^{2}}}{1+ \frac{\epsilon_{\mathbb{S}\rightarrow\mathbb{I}}^{\sum_{i}\mathcal{P}}\| \mathbf{H}_{\mathbb{S}-\mathbb{R}_{2}}\|^{2}}{\sigma_{\mathbb{R}}^{2}}},\dots, \frac{\mathcal{P}_{\mathbb{R}_{K-1}\frac{\|\mathbf{H}_{\mathbb{S}-\mathbb{R} _{i}-\mathbb{R}}\|^{2}}{\sigma_{\mathbb{R}}^{2}}}}{1+\frac{\epsilon_{ \mathbb{S}\rightarrow\mathbb{I}}^{\sum_{i}\mathcal{P}}\|\mathbf{H}_{\mathbb{ S}-\mathbb{R}_{i}}\|^{2}}{\sigma_{\mathbb{R}}^{2}}}\bigg{)}\right]. \tag{25}\]
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC)  
Calorimetric data were obtained by differential scanning calorimetry using a Mettler Toledo DSC 820 series (Columbus, OH, USA). The samples (4 mg) were placed in aluminum crucibles (40 \(\upmu\)L). Three successive heating/cooling/heating segments were applied, with a heating/cooling rate of 10 \({}^{\circ}\)C/min between 0 and 200 \({}^{\circ}\)C, using N\({}_{2}\) as protective gas at a flow of 50 mL/min. The samples were analyzed in triplicates, and averages and deviations were taken as representatives. The degree of crystallinity (\(X_{c}\)) was calculated through Equation (1), where \(\Delta{h_{m}}^{\circ}\) is the value of the melting enthalpy of a perfect crystal of infinite size (93 J/g) [28]; \(\Delta{h_{m}}\) is the melting enthalpy; \(\Delta{h_{cc}}\) is the cold crystallization enthalpy; and \(f\) is the fraction weight of PLA in the nanocomposites. The lamellar thickness (\(l_{c}\)) was calculated by applying the Thomson-Gibbs equation (Equation (2)) based on the temperatures associated with the melting transitions [63,64]. In this equation, \(T_{m}\) is the melting peak temperature; \(T_{m}\)\({}^{0}\) is the equilibrium melting temperature of an infinite crystal (480 K); \(\sigma_{c}\) is the surface free energy of the basal plane where the chains fold (60.89 \(\times\) 10\({}^{-3}\) J/m\({}^{2}\)); and \(\Delta{h_{m}}_{V}\) is the melting enthalpy per volume unit (111.083 \(\times\) 10\({}^{8}\) J/m\({}^{3}\)) [65].  
\[X_{c}(\%)=\frac{\Delta{h_{m}}-\Delta{h_{cc}}}{f\times\Delta{h_{m}^{{}^{-}}}} \times 100 \tag{1}\]  
\[l_{C}(T_{m})=\left[\left(1-\frac{T_{m}}{T_{m}^{0}}\right)\times\frac{\Delta{ h_{m}}_{V}}{2\times\sigma_{c}}\right]^{-1} \tag{2}\]
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 5 Proposed Hardware Realization of Flexible 5G NR LDPC Encoder
### LDPC Encoder Architecture
The entire architecture is pipelined to obtain high operating frequency. Therefore, it is necessary that the encoding schedule does not use core parity bits for the calculation of additional parity bits until the former are ready. Ideally, the first rows that are used for additional parity bits calculation should not contain CPMs in the columns related to the core parity bits. This is provided by giving the additional constraint to the processing order optimization. In the end, it should be mentioned that almost identical architecture could be applied for encoding of IEEE 802.16e (WiMAX) [38] LPDC codes. There are two main differences. First is that WiMAX lifting sizes are between 24 and 96. Therefore, the main CS would be designed using four CS24 components, instead of CS96s. The second difference is based on the fact that WiMAX has only core parity bits which can be calculated recursively in a way similar to (15) and (16). The proposed 5G NR encoder calculates all core parity bits in parallel, but for WiMAX, it may be more efficient to conduct that serially because the number of parity bits is not the same for all codes. Additional CS needed for parity bits calculation should support slightly more than only two shift values as it is in the 5G NR encoder.