esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Implementation
### Radar Simulation Setup  
For the simulation of radar, the setup from [PERSON] et al. was used [24]. The antenna patches are squared and have an edge length of \(I_{patch}=3\,\mathrm{mm}\). The antennas are etched on the substrate RO4350B with a dielectric constant of \(\epsilon_{r}\,=\,3.55\,\mathrm{\SIUnitSymbolMicro 10}\,\mathrm{GHz}\). The antenna is excited by a coaxial probe, whose position was chosen such that the input impedance matched at \(50\,\mathrm{\SIUnitSymbolOhm}\). Radar antennas were placed on the top and bottom of the domain \(\Omega\). The bottom antennas are used as transmitters, and the antennas on the top are used as receivers. A pulse excitation was used for the stimulation of the antennas. One excitation cycle was composed of a sawtooth signal with an up-chirp time of \(433\,\mathrm{\SIUnitSymbolMicro s}\), a down-chirp time of \(100\,\mathrm{\SIUnitSymbolMicro s}\), and a waiting time in between the chirps of \(100\,\mathrm{\SIUnitSymbolMicro s}\). The simulation was carried out as follows.  
The described radar antennas were only used on the bottom and top of the domain. While a fixed distance between the radar antennas is required for the above-described radar setup, in reality, it is more practical to produce a circuit board with embedded antennas already on it. Since breasts have different sizes, the placing of radar antennas on the sides of the domain requires strenuous effort, to still ensure the correct placement of the antennas. EIT electrodes, on the other hand, can be placed manually on the sides, as they can be designed to stick on the skin.  
As in the EIT setup, the skin was modeled with a thickness of \(2\,\mathrm{mm}\). For the skin, the complex dielectric properties of the [PERSON] database were used [32]. The breast fat and tumor tissues were modeled from the values of [PERSON] et al. of the subgroup with high adipose tissue [34]. The radar simulations were performed in CST Microwave Studio 2019.
## 6 Benchmarks
### Secure Federated \(t\)-Test  
We tested our secure federated \(t\)-test protocol against a polygenic risk score validation use case using the 1000 Genomes dataset [26]. We conduct a \(t\)-test based on the polygenic risk score for cholesterol (PGS000192 [27] from the PGS Catalog) against three pairs of classes--American and East Asian (Data ID 1, 1075 samples), European and African (Data ID 2, 1526 samples), and Male and Female (Data ID 3, 3202 samples). We similarly compare the communication overhead (MB), runtime (s), and relative error (%) of computing the \(t^{2}\) value and degree of freedom across the three pairs of classes in Tables 4-6, respectively.  
It is clear that even when working with a small number of samples as in the American and East Asian setting, the calculation of the mean and variance of the two groups creates a huge bottleneck for the SMPC method, which has to exchange \(50\times\) more data amongst the two parties, thus resulting in a \(2\times\) slowdown in runtime when compared to our MHE method. This effect is further exacerbated in the other settings with the SMPC method requiring 12 GB of communication between the parties in the Male and Female setting. This  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(N=2\) & \multicolumn{2}{c}{\(N=128\)} & \multicolumn{2}{c}{\(N\)= 16,384} \\ \hline MHE & SMPC & MHE & SMPC & MHE & SMPC \\ \hline
65.5 & 46.5 & 65.5 & 1852 & 65.5 & 234,743 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication overhead (MB) of performing a batch of \(N\) divisions where numerators and denominators are randomly chosen from \([1,1\times 10^{4}]\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(N=2\) & \multicolumn{2}{c}{\(N=128\)} & \multicolumn{2}{c}{\(N\)= 16,384} \\ \hline MHE & SMPC & MHE & SMPC & MHE & SMPC \\ \hline
7.54 & 0.73 & 7.54 & 9.75 & 7.54 & 1232 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Runtime (s) of performing a batch of \(N\) divisions where numerators and denominators are randomly chosen from \([1,1\times 10^{4}]\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(a_{i_{r}}b_{i}\in[1\times 10^{-4},1]\) & \multicolumn{2}{c}{\(a_{i_{r}}b_{i}\in[1,1\times 10^{4}]\)} \\ \hline MHE & SMPC & MHE & SMPC \\ \hline
0.0316 & 0.0126 & 0.0424 & 0.00294 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relative error (%) of performing secure batch division where numerators (\(a_{i}\)) and denominators (\(b_{i}\)) are randomly chosen from various ranges.  
clearly shows that for even medium scale computations, the SIMD property of MHE vastly improves performance and can potentially overcome the efficiency losses of performing more complicated operations such as division.  
Overall, both methods have roughly the same relative error with our MHE method being more accurate in calculating the degrees of freedom whereas the SMPC method is more accurate in calculating the \(t\) statistic. On the whole, our MHE method has an average relative error of 2.14% compared to the SMPC method's 2.45%, which shows that both methods are relatively accurate.
## 3 Analytical Modeling of the 2-DOF CPM
### Input and Output Stiffness  
The stiffness model of the 2-DOF CPM is given in Figure 8a, and the input and output stiffness matrices will be derived first. Because the stiffness modeling is related to specific coordinate frames, the globe coordinate frame is defined at the center of the terminal platform and the local coordinate systems of each chain are shown in Figure 8b.  
Figure 7: Detailed structure of 2-DOF CPM.  
\[\mathbf{K}_{i-2}^{l}=\left[\begin{array}{ccc}\frac{3 EDt_{2}}{L}&0&0\\ 0&\frac{3 EDt_{2}^{3}}{L^{3}}&\frac{3 EDt_{2}^{3}}{2L^{2}}\\ 0&\frac{3 EDt_{2}^{3}}{2L^{2}}&\left(6\left(\frac{d}{L_{2}}\right)^{2}+3\right) \cdot\frac{EDt_{2}^{3}}{3L}\end{array}\right] \tag{18}\]  
The chain of the CPM is composed of Parts I and II connected in series; therefore, its compliance matrix under its local coordinate frames \(\{O_{i}\}\) can be calculated as  
\[{}^{O_{i}}\mathbf{C}_{i}=\left({}^{O_{i}}\mathbf{K}_{i-1a}+{}^{O_{i}}\mathbf{K}_{i-1b} \right)^{-1}+{}^{O_{i}}\mathbf{K}_{i-2}^{-1} \tag{19}\]  
The CPM is composed of the four identical chains connected in parallel type, and its output stiffness can be obtained as  
\[\mathbf{K}_{\rm output}=\sum_{i=1}^{4}{}^{O}\mathbf{T}_{i}^{-1}\left({}^{O_{i}}\mathbf{K }_{i}\right)^{O}\mathbf{T}_{i}^{-\rm T} \tag{20}\]  
where \({}^{O}\mathbf{T}_{1}=\left[\begin{array}{ccc}-1&0&0\\ 0&-1&0\\ 0&W_{1}&1\end{array}\right]\), \({}^{O}\mathbf{T}_{2}=\left[\begin{array}{ccc}0&1&0\\ -1&0&0\\ -W_{1}&0&1\end{array}\right]\), \({}^{O}\mathbf{T}_{3}=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&-W_{1}&1\end{array}\right]\), and \({}^{O}\mathbf{T}_{4}=\left[\begin{array}{ccc}0&-1&0\\ 1&0&0\\ W_{1}&0&1\end{array}\right]\).  
The input stiffness of the 2-DOF CPM along its two axes is identical. The input stiffness model of the CPM in the \(x\)-axis is depicted in Figure 10, where \({}^{O}\mathbf{K}_{\rm T1}\) is the total stiffness of three chains from the second to the fourth chain and can be obtained as  
\[{}^{O}\mathbf{K}_{\rm T1}=\sum_{i=2}^{4}{}^{O}\mathbf{T}_{i}^{-1}\left({}^{O_{i}}\mathbf{ K}_{i}\right)^{O}\mathbf{T}_{i}^{-\rm T} \tag{21}\]  
The input stiffness of the CPM can be obtained according to Figure 11 as  
\[\mathbf{K}_{\rm Input}={}^{A_{1}}\mathbf{K}_{1-1a}+{}^{A_{1}}\mathbf{K}_{1-1b}+\left({}^ {A_{1}}\mathbf{K}_{1-2}^{-1}+{}^{A_{1}}\mathbf{T}^{\rm T}\left({}^{O}\mathbf{K}_{\rm T1}^{ -1}\right)^{A_{1}}\mathbf{T}\right)^{-1} \tag{22}\]  
where \({}^{A_{1}}\mathbf{T}=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&L+W_{1}+2W_{2}&1\end{array}\right]\).  
Figure 10: Input stiffness model of CPM in the \(x\)-axis.
# Utilization of Partially Liquefied Bark for Production of Particleboards
## 3 Results and Discussion
### Characterization of Bark and Solid Residues of PLB
The water vapor sorption isotherms of bark, PLB and purified PLB are presented in Figure 5a. PLB and purified PLB showed considerably lower EMC than bark in the RH range of 0% to 75%. A strong upward bend was observed in the EMC of PLB and purified PLB from 75% to 95% and surpassed bark at 95% RH. PLB showed an EMC of 33% at 95% RH, while the EMC values of purified PLB and bark at this RH level were 30% and 24%, respectively. Moisture increment (MI) of bark was found to vary little over the entire RH range (Figure 5b), as it was slightly decreased from 15% to 45% RH, then gradually increased from 45% to 90% RH, and then decreased from 90% to 95% RH. PLB and purified PLB, however, showed a different MI trend. The MI of PLB gradually increased with increasing the RH from 15% to 75% and then sharply increased from 75% to 95%. MI was decreased in purified PLB at the RH range of 15% to 60% but then increased greatly after 60% RH. PLB and purified PLB illustrated a more hydrophilic behavior than bark at the higher RH range over 75%. This can be related to more accessible hygroscopic hydroxyl sites after partial liquefaction from the degradation of cellulose due to alcohol hydrolysis catalyzed by strong acids [48; 49]. It was reported previously that liquefaction of lignocellulosic materials in polyhydric alcohols initially hydrolyzes the glucoside linkage of the cellulose to produce glucoside monomers, which further decomposed to levulinic acid esters [41; 50]. During the liquefaction process, the polymeric structure of lignin was degraded, and the obtained lignin monomers reacted with ethylene glycol to form a condensed lignin-based polymeric material with predominant aromatic hydroxyl groups, which can enhance the hydrophilicity of PLB [51]. The higher moisture sorption of PLB as compared to purified PLB can be attributed to the remaining ethylene glycol that provided extra hydroxyl groups in the PLB. Some part of these hydroxyl groups may have also been removed in purified PLB by the solvent during the purification step.
## 3 Results and Discussion
### Practical Application of the Data-Mining Hub
In the same way, a high percentage detection of dairy cows with risk of subclinical mastitis (50.0%) was observed in the data-mining hub using a lower number of data parameters (LO:8), while using a data-mining hub with a higher number of data parameters (HI:21) detected only 18.75%. This finding signifies that using a lower number of data parameters in the data-mining hub overly detect dairy cows being in a subclinical mastitis risk state, which are in fact in normal condition, as verified by the 80.1% precision of the LO:8 group. It is worthy to note that the MID:13 group detected 59.38% heat stress and 28.13% subclinical mastitis in dairy cows. Based on the overall accuracy and precision of classification, the MID:13 group showed moderately high accuracy and fairly high precision of classifying heat stress and subclinical mastitis in dairy cows. It should be noted that the combined effects of 13 parameters could reasonably classify and detect heat stress and subclinical mastitis in dairy cows, although 21 parameters still showed the highest accuracy and precision. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Parameters**} & \multicolumn{2}{c}{**Heat Stress**} & \multicolumn{2}{c}{**Subclinical Mastitis**} \\ \cline{2-5}  & **Accuracy, \%** & **Kappa Coefficient** & **Accuracy, \%** & **Kappa Coefficient** \\ \hline HI (21) & 94.5 & 0.92 & 93.2 & 0.90 \\ MID (13) & 88.6 & 0.83 & 87.9 & 0.86 \\ LO (8) & 84.8 & 0.80 & 83.7 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The overall accuracy and Kappa coefficient of using the data-mining hub with a different number of parameters: HI (21 parameters), MID (13 parameters), and LO (8 parameters). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data parameters**} & \multicolumn{2}{c}{**Precision, \%**} & \multicolumn{2}{c}{**Precision, \%**} \\ \cline{2-5}  & **Normal** & **Heat Stress** & **Normal** & **Subclinical Mastitis** & **Others** \\ \hline HI (21) & 92.8 & 93.7 & 93.7 & 94.2 & 89.8 \\ MID (13) & 85.7 & 86.7 & 82.3 & 81.8 & 82.2 \\ LO (8) & 79.9 & 80.2 & 78.5 & 80.1 & 79.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The precision of the effect of classification using the data-mining hub with a different number of parameters: HI (21 parameters), MID (13 parameters), and LO (8 parameters). The precision of classification and overall accuracy of detecting heat stress and subclinical mastitis increased with the increasing number of data parameters used, signifying that the combined information such as the environmental condition, physiological and behavioral state of the cows, milk performance indexes, and feed intake could improve the precision of classification of conditions and accuracy of detection in dairy cows. The combination of the 21 parameters in detecting heat stress and subclinical mastitis in dairy cows improved the precision of classification and the accuracy of detection as compared to the data-mining hub that used only 13 and 8 parameters.
###### Abstract  
A new multi\(-\)carrier orthogonal double bit rate differential chaotic shift keying (MC\(-\)ODBR\(-\)DCSK) communication system is presented in this paper. With two composite signals generated by an orthogonal chaotic signal generator as reference signals, 2M bits of information data are transmitted on M\(-\)channel subcarriers, improving transmission speed and energy efficiency. In addition, the receiver does not require a radio frequency (RF) delay circuit to demodulate the received data, which makes the system easier to implement. This paper analyzes Data\(-\)energy\(-\)to\(-\)Bit\(-\)energy Ratio (DBR) of the system. The bit error rate performance of the system is simulated to verify the impact of parameters such as chaotic maps, semi-spread spectrum factor, and sub-carrier number. At the same time, the bit error rate performance of the MC\(-\)ODBR\(-\)DCSK system is compared with traditional DCSK systems in Rician fading and additive Gaussian white noise (AWGN) channels.  
differential chaotic shift keying; multi\(-\)carrier differential chaotic shift keying; orthogonal double bit rate differential chaotic shift keying; bit error rate Article
## 3 Results and Discussion
### Micro-Infrared Spectroscopy (micro-IR)  
The results of the micro-IR test are shown in Figure 5. The main absorption peaks of the five samples were basically the same. They mainly included the strong absorption peaks at 2350, 2157, and 2030 cm\({}^{-1}\) and the absorption peaks at 1282 and 1175 cm\({}^{-1}\). For samples R-YG-1, R-Y-1, and R-B-1, the absorption peaks of 1282 and 1175 cm\({}^{-1}\) were relatively obvious, and the peak intensity corresponding to 1282 cm\({}^{-1}\) was greater than that of 1175 cm\({}^{-1}\). The two absorption peaks in sample R-Y-2 and R-P-1 were weak, and some samples showed absorption peaks at 1358 cm\({}^{-1}\).  
Combining the analysis of the diamond microscopic infrared spectrum by [PERSON] et al. [25], the infrared spectrum of the sample can be divided into two parts: (1) The absorption peak of below 1332 cm\({}^{-1}\) is a one-phonon absorption region, where the one-phonon absorption caused by the A nitrogen center is located near 1282 cm\({}^{-1}\), and the photon absorption caused by the B nitrogen center is located near 1175 cm\({}^{-1}\). It can be judged that the five diamond samples are all IaAB diamonds. (2) Next are the absorption peaks around 1973-2500 cm\({}^{-1}\), such as the absorption caused by C-C lattice vibration near 1973, 2022, and 2159 cm\({}^{-1}\).
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Security Requirements
#### 3.2.1 Authenticity  
Authentication is required for sensing devices, users, and portal nodes before access to a limited resource is enabled or essential information is disclosed [23]. In addition, for communicating entities to have mutual authentication, two of the communicating entities have to be a monitoring drone and a ground-control station. To guarantee full forward secrecy, it is essential to employ a secure key exchange using a method that produces session keys that are impossible to recover [24].
# Composition Diversity and Expression Specificity of the _Tps_ Gene Family among 24 _Ficus_ Species
## 4 Materials and Methods
### Positive Selection Analysis  
We performed gene positive selection analyses using the branch-site model and branch model in the PAML package version 4 [69]. A concatenated _TPS_ gene topology phylogenetic tree was constructed as the guide tree using RAxML v8.2.12 software [63] under the parameters \"rxmI HPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -# 1000 -m GTRGAMMA\". Species _Morus notabilis_ C. [PERSON] was used as the outgroup. Because the PAML documentation recommends that the absolute minimum be four or five if the sequence divergence is optimal, the _TPS_ genes that occurred in fewer than four species were ignored from further processing.  
In the branch model, the one-ratio model was the null model, which means that all branches had the same \(\omega\) value, while the free-ratio model considered that each branch had a different \(\omega\) value. We compared the likelihood ratio of the two models and calculated the \(p\)-value using the LRT test.  
In order to find genes that potentially experienced positive selection, the branch-site model (model = 2 and NSsites = 2) of the PAML package was used, with each branch specified as the foreground. To avoid false positive results in the branch-site model, the following rigorous criteria were used: a dN/dS ratio (\(\omega\)) greater than 1 on the foreground branch, and positively selected sites with a posterior probability calculated by the Bayes empirical Bayes (BEB) method greater than 0.95 and a \(p\)-value \(\leq\) 0.05 in the likelihood ratio test [70].
## 1 Introduction
### Contribution  
In [14], a practical iterative algorithm was proposed to optimize the grouping scheme that maximizes the sum-rate, and in [15], a grouping method that maximizes the cosine similarity of the channel between the BS and RIS and the channel between the RIS and UE was proposed. In contrast, this paper proposes RSMA transmission-based RIS impedance network grouping in an MU-MIMO system through user scheduling. Furthermore, a simple scheme for obtaining the complicated optimal RS power-splitting ratio [18] is proposed. The main contributions of the paper are summarized as follows:  
* First, the impedance network grouping scheme of the RIS is proposed for the MU-MIMO system. We obtained the existing RIS scattering matrix that maximizes the sum-rate. Then, the user scheduling was determined according to the effective channel gain of each user. To maximize the sum-rate, RIS elements having a high channel gain were grouped in the order of user scheduling.
* Second, to reduce the computational complexity, the scattering matrix of the impedance network grouped by the proposed method was successively optimized for each group using the quasi-Newton method. Therefore, the computation of the quasi-Newton method is effectively reduced.
* Finally, zero-forcing (ZF) precoding was used for the private data to cancel inter-user interference, and singular-value decomposition (SVD) precoding was considered for the common data. To find the optimal RS power-splitting ratio that maximizes the sum-rate, the problem derived by fractional programming was differentiated.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Fault Detection in Substations and Customer Facilities
#### 3.2.2 Fouling Detection in Heat Exchangers  
When focusing on operational faults specifically related to heat exchangers, the literature shows that most of them commonly involve fouling formation [118]. This can be described as the accumulation of deposits on heat transferring surfaces, which cause a higher thermal and hydraulic resistance in the heat exchanger. An automatic method using the usual meteringvariables such as volumetric flow and temperatures in the substation primary and secondary circuits has been developed by [PERSON] and [PERSON] (2020) [120]. It consists of the indirect calculation of the global heat transfer coefficient and monitoring its change during the fouling process. This method is easy to implement but involves important calibration challenges when it comes to different kinds of heat exchangers.
## 3 Probabilistic Seismic Demand Model and Definition of Limit States  
### Probabilistic Seismic Demand Model  
As stated above, the seismic fragility analysis of steel moment-resisting frames (SMRF) has been studied for a long time, and various methods have been proposed to form the fragility curves of the structure. In this paper, the method proposed by [PERSON] et al. [36] was used to develop the fragility curves of the gantry crane. The seismic demands were deemed to obey a logarithmic normal distribution in this method. Hence, the fragility description can be written as:  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Parameters** & **Trolley Ginder** & **Flexible Leg** & **Rigid Leg** & **Description** \\ \hline \(b_{t}\) (mm) & 1535 & 1333 & 3000 & Width of flange slab \\ \(t_{t}\) (mm) & 14 & 8 & 10 & Thickness of flange slab \\ \(F_{y}\) (MPa) & 235 & 235 & 235 & Yield stress of steel \\ \(h\) (mm) & 1987 & 1258 & 1500 & Distance between two flange slabs \\ \(t_{w}\) (mm) & 10 & 10 & 10 & Thickness of web slab \\ \hline \hline \end{tabular}
\end{table}
Table 1: Section properties of the gantry crane.  
Figure 4: 3D model of the gantry crane.  
Figure 5: Spring model of the structure.  
Figure 6: Constitutive model of the spring.  
\[P(D\geq d\ |\ IM)=1-\phi(\frac{\ln(d)-\ln(S_{D})}{\beta_{D|IM}}) \tag{1}\]  
in which,  
\(\phi(\cdot)=\) the standard normal cumulative distribution function;  
\(D=\) the structural demand;  
\(d=\) the specified threshold values for different limit states;  
\(S_{D}=\) the median value of the structural demand in terms of a seismic intensity measure;  
\(\beta_{D|IM}=\) the logarithmic standard deviation.  
In light of [36; 37], the association between \(S_{D}\) and the intensity measure (IM) can be described as a power model:  
\[S_{D}=aIM^{b} \tag{2}\]  
To complete a linear regression analysis, the above equation is generally revised as follows:  
\[\ln(S_{D})=b\ln(IM)+c \tag{3}\]  
where \(b\) and \(c\) are the coefficients acquired by regression analysis.  
The raw statistics used to form fragility curves were acquired by performing increment dynamic analyses (IDAs). Then, \(S_{D}\), the median value of the structural demand in terms of a seismic intensity measure, and \(\beta_{D|IM}\), the logarithmic standard deviation, could be calculated for each analysis. \(\beta_{D|IM}\) can be calculated by the following equation:  
\[\beta_{D|IM}\cong\sqrt{\frac{\sum(\ln(d_{i})-\ln(aIM^{b}))^{2}}{N-2}} \tag{4}\]  
in which \(d_{i}\) is the \(i^{\text{th}}\) seismic demand from the non-linear time history analyses and \(N\) represents the number of total simulation cases, which was twenty in this research.
## 8 Discussion
### Challenges
#### 8.1.8 Intelligence  
Because future cellular networks will be more dynamic, system knowledge of potential congestion and environmental changes will be necessary to provide qualified services, with three obstacles. To begin, the communication system necessitates the ability to federate learning in order to meet the changing service requirements of mobile users in a timely manner. Second, automated and unexpected system upgrades are essential for adapting to new conditions. Third, in order to fully realise the potential of machine learning, the difficulties of heterogeneity in software and hardware must be addressed.