esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Structure and Principle of the Antenna Sensor
### Theory Estimation of the Antenna Sensor Parameters  
The relevant parameters of the antenna sensor are estimated by calculating the parameters of its circumcircle microstrip antenna. Then, these parameters of the antenna are optimized with the aid of a electromagnetic simulation software named Ansoft HFSS (Ansoft Corporation, Pittsburgh, PA, USA). The snowflake patch and its circumcircle with radius \(r\) are illustrated in Figure 4. Coaxial probe-feed is adopted, and the radius of the feed position and the diameter of the feed probe are represented by \(r_{0}\) and \(d_{0}\), respectively, while \(e_{r}\) and \(h\) represent the relative dielectric constant and the dielectric layer thickness. The circular microstrip antenna is equivalent to an RLC resonant circuit (A circuit structure that is composed of resistance (R), inductor (L), and capacitance (C))according to circuit model analysis, as shown in Figure 5.  
Figure 3: Iterative process of Koch snowflakes structure.  
Figure 2: Iterative process of Koch curve.  
Considering the inductive reactance \(X_{L}\) caused by the coaxial feed probe and the resonant resistance \(R\), the input impedance \(Z_{in}\) of the circuit can be defined as follows [20]:  
\[Z_{in}(f)=\frac{R}{1+Q_{T}^{2}(\frac{f}{f_{s}}-\frac{f}{f})^{2}}-\bar{j}[\frac{ RQ_{T}(f/f_{s}-f_{s}/f)}{1+Q_{T}^{2}(f/f_{s}-f_{s}/f)^{2}}-X_{L}] \tag{1}\]  
\[X_{L}=\frac{377\bar{f}h}{c}\text{ln}(\frac{c}{\pi fd_{0}\sqrt{\varepsilon_{r}}}) \tag{2}\]  
\[R=\frac{h^{2}E_{0}^{2}j_{1}^{2}(1.841r_{0}/r)}{2P_{T}} \tag{3}\]  
where \(Q_{T}\) is the quality factor of the resonance circuit, \(f_{s}\) is the resonance frequency, \(P_{T}\) is the total power loss, and \(J_{I}(\) ) is first-order Bessel function of the first kind.  
The resonance frequency of the circular microstrip antenna can be approximated as follows:  
\[f_{s}=\frac{1.841c}{2\pi r\sqrt{\varepsilon_{r}}} \tag{4}\]  
Based on Equations (1)-(4), the input impedance \(Z_{in}\) of the antenna can be estimated. The reflection parameter \(\Gamma\) can be derived by using the Equation (5), where \(Z_{c}\) is the system characteristic impedance. An impedance value of 50 Ohm is used for \(Z_{c}\) in this study:  
\[Z_{in}=Z_{c}\frac{1+\Gamma}{1-\Gamma} \tag{5}\]  
Eventually, the return loss \(RL\) and voltage standing wave ratio VSWR can be calculated using the following equations:  
Figure 4: (**a**) Snowflake patch, (**b**) its circumcircle.  
Figure 5: Equivalent circuit of the circular microstrip antenna.  
\[RL=-20\text{log}_{10}|\Gamma|,\quad\text{VSWR}=\frac{1+|\Gamma|}{1-|\Gamma|} \tag{6}\]
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 6 Evaluation
### Compared with Best Individual Formats  
We have mentioned that there are two general solutions to the \"one-size-fits-all\" issue: selecting the best individual format and using the hybrid solution. Figure 10 compares the performance of these two solutions. From Figure 10a,b, we see that, for the small matrices (i.e., with few non-zero elements), the performance of HYB5 is worse than that of the best individual format. However, the performance of HYB5 is competitive for large sparse matrices. Figure 10c also shows that HYB5 performs better than the best individual formats, especially for the large matrices. Overall, we summarize that using the best individual formats generally outperforms using a hybrid solution. However, programmers have to maintain various individual storage formats and endure the converting overhead across formats. By contrast, we have to maintain only one hybrid format during software development. This particularly helps when developing large-scale applications. Therefore, using a hybrid solution wins in maintenance cost, but suffers a loss in performance. It is up to the programmers to make the final choice.  
Figure 7: Comparing HYB5 performance to state-of-the-art formats. The violin diagrams show the reduction ratio of the compared formats for the relatively optimal format of SpMV performance for each matrix. The shape of the violin represents the distribution of the acceleration ratio over the data set, and the bold black line is the location of 50% of the data set.  
Figure 8: SpMV performance of SELL-8-512, CSR5, and CVR with matrices of different variations. The x-axis is variation (vardata), which represents the regularity of a given matrix. As var increases, the regularity degree of matrices decreases.  
Figure 9: The violin diagram shows the comparison of three methods of obtaining parameter K by showing its corresponding SpMV performance on the three target architectures.
## 1 Introduction
* Proposed a privacy-aware secure routing using elliptical curve cryptography with optimal RSU distribution to improve the performance of VANETs
* Through the elliptical curve cryptography, the trusted authority becomes highly secured, which leads to improving the authentication and confidentiality of VANETs. * To control the computational overhead of VANETs optimal RSU distribution is performed using a Genetic Algorithm. * Thread models such as Sybil attacks and black hole attacks are introduced to analyze the performance of the network. Figure 1: VANETs Communication. This paper is organized as follows. In Section 2, the related works are discussed in terms of optimization and security-based approaches. In Section 3, the network model, the thread model, the background of ECC, and the genetic algorithm are discussed. In Section 4, privacy-aware secure routing using elliptical curve cryptography with optimal RSU distribution is elaborated. In Section 5, results are analyzed in terms of the number of vehicles and speed. Section 6 concludes the research with future directions.
## 3 Results and Discussion
### In Vitro Microbiological Study  
#### 3.2.1 Strain Selection Based on Tolerance Tests and Enzymatic Activities  
The range of salts tested and the NaCl tolerance of the eight bacterial strains studied are shown in Table 14. Strains P6 and B38 were classified as moderately halophilic microorganisms, whereas the remaining strains were halotolerant.  
Considering water stress tolerance (Table 15), strains N3, B23, and B38 exhibited the highest growth at low PEG concentrations (0-15%). In contrast, strains 8C, 11C, 14C, and 15C demonstrated a broader tolerance range, as they were able to grow--albeit weakly--even at PEG concentrations up to 25%.  
The enzymatic activities related to PGP properties are shown in Table 16. Strains B23, 8C, 11C, 14C and 15C showed positive results for five out of seven biochemical tests, suggesting the highest PGP properties. These strains belong to the genera _Kushmeria_ (B23), _Bacillus_ (8C, 11C and 14C) and _Pseudomonas_ (15C). Previous studies have reported PGP activity in the genera _Bacillus_ and _Pseudomonas_, supporting our results [75; 76].  
Of the five aforementioned strains, Gram-positive (11C, _Bacillus cabrialesii_) and Gram-negative bacteria (15C, _Pseudomonas neuropathica_) were selected to perform growth tests with soil/biochar substrates (described in Section 2.3.4). According to [PERSON] et al. (2023) [77], _Bacillus cabrialesii_ is involved in phosphate solubilisation in soil, increases chlorophyll content in plants, and can grow under saline and water stress conditions. Additionally, these authors highlight its Nase and ACC deaminase activities, as well as the production of hydrolytic enzymes such as amylase and caseinase. Although no previous studies have demonstrated PGP activity in _Pseudomonas neuropathica_, it was selected for this assay due to its broad tolerance to salinity and water stress, as well as its enzymatic activity (Tables 14 in Appendix A)
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Self-Localization
#### 3.3.1 Localization Inside Tunnel Nodes  
Inside tunnels the LHD's pose is defined as the one-dimensional distance or trajectory \(\rho\), measured from the beginning of the tunnel. This distance is updated incrementally as:  
\[\rho_{t+1}=\rho_{t}+v_{t}\cdot dir_{t}\cdot\Delta t \tag{1}\]  
where \(v_{t}\) is the current linear speed of the LHD, and \(dir_{t}\) is the current direction of movement, with the value 1 meaning that the LHD is oriented to the tunnel orientation and \(-1\) if it is not. The values of \(\rho_{t}\), \(v_{t}\), and \(dir_{t}\) are estimated using a standard Kalman filter. The filter is updated using two different observations sources: first, using the LHD wheel's odometry obtained directly from the LHD encoders, and second, using the LIDAR point cloud. In the latter case, the ICP algorithm is used to match the current LIDAR point cloud with the LIDAR-landmarks that are near the current LHD position and the key LIDAR-landmarks within 50 [m] of distance. The result of the ICP matching is the distance to the most similar LIDAR-landmark, which is then used to estimate the relative position of the LHD in the node's coordinates, and its orientation. Both the wheel's odometry, and the ICP-estimated position and orientation, are used in the corrective stage of the Kalman filter asynchronously, i.e., every time they arrive.  
It is important to mention that the ICP matching is local, not global, meaning that the matching is made only with LIDAR-landmarks that are near the current position of the LHD. The main reasons for this decision are time efficiency and to avoid wrong matching due to the similarity that different LIDAR-landmarks may have. (Mining tunnels have regular surfaces and LIDAR-landmarks computed in different tunnels may be similar). This also prevents the self-localization from changing drastically in its localization estimation.
# Heat Transfer Performance of Fruit Juice in a Heat Exchanger Tube Using Numerical Simulations
## 3 Results and Discussion  
### Rheological Measurements  
Figure 4 shows the rheological behaviour of the fruit juice at the different tested temperatures obtained in the laboratory, where the rheogram shows the non-linear Power-Law fit. The results for the fitting parameters are given in Table 2. In all cases, as expected, an increase in temperature resulted in a decrease in viscosity. Moreover, the flow behaviour index is lower than 1, thus the fruit juice can be defined as a pseudoplastic fluid, with the viscosity decreasing as the shear rate increases. This trend is mainly noticeable at shear rates lower than 150 s\({}^{-1}\).  
The effect of the temperature on the viscosity of the fruit juice at a specified shear rate was determined using the Arrhenius equation. Equation parameters and the apparent viscosity are shown in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{T}\)\({}^{\circ}\)C & \(\mathbf{K(\text{Pa}\cdot\text{s}^{\text{n}})}\) & \(\mathbf{n}\) & \(\mathbf{r^{2}}\) \\ \hline
24 & 0.247 & 0.539 & 0.987 \\ \hline
30 & 0.234 & 0.529 & 0.984 \\ \hline
35 & 0.237 & 0.514 & 0.982 \\ \hline
40 & 0.239 & 0.503 & 0.980 \\ \hline
45 & 0.238 & 0.495 & 0.978 \\ \hline
50 & 0.233 & 0.491 & 0.976 \\ \hline
55 & 0.222 & 0.493 & 0.975 \\ \hline \end{tabular}
\end{table}
Table 2: Power-Law equation fitting parameters of the fruit juice.  
Figure 4: Shear rate vs. Shear stress curves of fruit juice at several temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(\mathbf{T}\)\({}^{\circ}\)C & \(\mathbf{24}\) & \(\mathbf{30}\) & \(\mathbf{35}\) & \(\mathbf{40}\) & \(\mathbf{45}\) & \(\mathbf{50}\) & \(\mathbf{55}\) \\ \hline Apparent viscosity (\(\eta\), Pa\(\cdot\)s) & 0.0296 & 0.0268 & 0.0254 & 0.0242 & 0.0232 & 0.0223 & 0.0215 \\ \hline \end{tabular}
\end{table}
Table 3: Apparent viscosity at 100 s\({}^{-1}\) of fruit juice.
## 3 Efficient Network AOS Comprehensive Multiplexing Algorithm
### Synchronous Traffic
#### 3.1.2 Virtual Channel Scheduling Algorithm  
In the virtual channel multiplexing layer, a cyclic polling scheduling algorithm is employed. In this algorithm, each VC in the system takes turns occupying the physical channel and transmitting data frames in a strict sequence. If the current VC does not have valid synchronized data, the VC with the maximum number of pending frames in the other synchronous VCs is granted the scheduling priority. Otherwise, a padding frame is transmitted. At any given scheduling moment \(\left(r,k\right)_{sche}\), the virtual channel scheduling algorithm can be expressed as follows:  
\[Z_{t}(k)=\left\{\begin{array}{ll}n_{t,q}(k),&c(n_{t,q}(k))>0\\ n_{t,m}(k),&c(n_{t,q}(k))=0\end{array}\right. \tag{2}\]  
In the equation, \(Z_{t}\) represents the VC channel number that ultimately obtains the scheduling authority at that moment. \(c(y)\) denotes the number of frames waiting for scheduling in the \(y\)-th VC at that moment. \(n_{t,q}\) indicates the VC channel number that is currently being polled, and \(n_{t,m}\) represents the VC channel number with the maximum number of frames waiting for scheduling at that moment.
## 2 Experimental System
### AE Monitoring System  
The monitoring system consists of AE sensors, amplifiers, and data collectors, which can realize multi-channel, high acquisition frequency, and full-waveform data acquisition simultaneously. The response frequency range of the AE sensor is 50-400 kHz, and the input and output impedances are above 10 M\(\Omega\) and 50 \(\Omega\), respectively. Its magnification can be adjusted by 20, 40 and 60 dB. A total of 16 AE sensors in 4 rows are arranged linearly in the longitudinal direction on the outer surface of the cross-section at 45\({}^{\circ}\), 135\({}^{\circ}\), 225\({}^{\circ}\) and 315\({}^{\circ}\), respectively, and the arrangement adopts a uniform staggering of adjacent angles, which can ensure the integrity of the acquisition effect. Figure 3 shows the arrangement.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Inner Diameter**} & \multirow{2}{*}{**Thickness**} & \multirow{2}{*}{**Outer Diameter**} & \multirow{2}{*}{**Skeleton Layer Position**} & \multicolumn{4}{c}{**Circumferential Reinforcement Bars**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Longitudinal Reinforcement** \\ \end{tabular} } \\ \cline{4-9}  & & & & **Diameter** & **Inner Diameter** & **Inner Rings** & **Pitch** & **Diameter** & **Quantity** \\ \hline
1000 & 140 & 1140 & Single & 3 & 321 & 13.3 & 75 & 5 & 3 \\ \hline
1500 & 165 & 1665 & Single & 3 & 321 & 13.3 & 75 & 5 & 3 \\ \hline
2000 & 210 & 2210 & Single & 3 & 321 & 13.2 & 75 & 5 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reinforced concrete sewer pipe parameters.  
Figure 2: Experimental loading system.
# Effect of Ajwa Date and Germinated Barley on Lipid Profile in Rats Fed High-Fat Diet
## 3 Results
### Liver Function  
In the current data, ALT and AST values were significantly increased (\(p\leq 0.05\)) in rats fed a HFD and treated with different ABM (Table 6). Rats fed a HFD treated with ABM had significantly lower values of ALT than the PC group. ABM-treated groups significantly reduced the ALT levels. All ABM groups had greater AST values than the PC group, while the lowest values of AST were detected in the NC group. Figure 1: The effect of a mixture of Ajwa date and germinated barley on body weight changes (IBW; initial body weight, FBW; final body weight, BWG; body weight gain, and FI, food intake) in rats fed a high-fat diet. NC = Negative control, PC = Positive control, ABM20 = Ajwa barley mix 20%, ABM30 = Ajwa barley mix 30%, ABM40 = Ajwa barley mix 40%. %. \({}^{\text{a}}\)\({}^{\text{-c}}\) Values don’t have the same letter are different (\(p\leq 0.05\)); values are expressed as mean \(\pm\) standard error. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Items \({}^{*}\)**} & \multicolumn{4}{c}{**Treatments \({}^{**}\)**} \\ \cline{2-5}  & **NC** & **PC** & **ABM20** & **ABM30** & **ABM40** \\ \hline
**ALT (U/L)** & 27.18 \(\pm\) 3.388 \({}^{\rm b}\) & 54.34 \(\pm\) 3.41 \({}^{\rm a}\) & 37.76 \(\pm\) 2.71 \({}^{\rm ab}\) & 40.12 \(\pm\) 4.88 \({}^{\rm ab}\) & 33.32 \(\pm\) 4.13 \({}^{\rm ab}\) \\
**AST (U/L)** & 55.53 \(\pm\) 14.68 \({}^{\rm b}\) & 91.82 \(\pm\) 34.29 \({}^{\rm ab}\) & 98.25 \(\pm\) 21.41 \({}^{\rm ab}\) & 100.94 \(\pm\) 6.72 \({}^{\rm a}\) & 105.15 \(\pm\) 9.78 \({}^{\rm a}\) \\ \hline \end{tabular}  
* AST, aspartate aminotransferase; ALT, alanine aminotransferase. \({}^{**}\) NC = Negative control, PC = Positive control, ABM20 = Ajwa barley mix 20%, ABM30 = Ajwa barley mix 30%, ABM40 = Ajwa barley mix 40%. \({}^{\rm ab}\) Values in a row without the same superscript are different (\(p\leq\) 0.05); values are expressed as mean \(\pm\) SE.  
\end{table} 
Table 6: The effect of Ajwa date and germinated barley on liver function in rats fed a high-fat diet. Figure 2: Effect of Ajwa date mixture and germinated barley on blood lipid profile in rats fed a high-fat diet. TC, total cholesterol; TG, total glycerides; LDL, low-density lipoprotein; HDL, high-density lipoproteins; VLDL, very-low-density lipoproteins. NC = Negative control, PC = Positive control, ABM20 = Ajwa barley mix 20%, ABM30 = Ajwa barley mix 30%, ABM40 = Ajwa barley mix 40%. \({}^{\rm a-c}\) Values don’t have the same letter are different (\(p\leq\) 0.05); values are expressed as mean \(\pm\) SE.
## 5 Denial-of-Service Attack on Node to Node Key Agreement Protocol
### Preventing the DoS Attack  
The intruder is successful at generating the attack because the identity of the node (_i.e._, node A's identity) that commenced the message exchanges is not authenticated. Therefore, we propose a modified version of the protocol, which is presented in Figure 16. In this modified version, in the first message, [PERSON] appends the MAC of the message (_i.e._, \(N_{A},A\)) at the end of the message. While generating this MAC, A uses the MAC key, \(K^{\prime}_{AS}\), shared between \(A\) and \(S\). The second message, which is sent to \(S\) from \(B\), has the following three components:  
* the first message it receives from \(A\), _i.e._, \(N_{A},A,MAC(K^{\prime}_{AS},N_{A}||A)\);
* \(B\) adds its nonce and identity, _i.e._, \(N_{B},B\);
* finally, \(B\) appends the MAC of the first two components using the MAC key, \(K^{\prime}_{BS}\), shared between \(B\) and \(S\).  
When \(S\) receives this message, \(S\) authenticates the messages generated by \(A\) and \(B\). Since the MAC key, \(K^{\prime}_{AS}\), is shared between \(A\) and \(S\), only \(A\) can generate the first message. As a result, no intruder is able to masquerade as the identity of \(A\). In this modified version, any attack generated by the intruder will be detected in the second message, and thus, \(S\) will not generate any new symmetric key (\(SK_{AB}\)) for \(A\) and \(B\). Thus, the third and the fourth messages will not be forwarded by \(S\). This will definitely prevent the potential DoS attack on the key distribution protocol.  
Note that there is a replay attack when the adversary intercepts the first message, keeps a copy of it and then replays it to \(B\). Here, the adversary masquerades in the role of \(A\). This attack is only possible if the nonce \(N_{A}\) in the first message is not fresh, _i.e._, not generated from a random or pseudo-random  
Figure 15: Protocol simulation for DoS attack on the node to node key agreement protocol.  
number. If the replay attack is not able to be prevented through the nonce, an increasing counter might be used instead of the nonce (note the similar technique being used in the counter exchange protocol in Section 4.3).  
\[A\to B:N_{A},A,MAC(K^{\prime}_{AS},N_{A}||A)\] \[B\to S:N_{A},A,,MAC(K^{\prime}_{AS},N_{A}||A),N_{B},B,MAC(K^{\prime}_{ BS},N_{A}||A||MAC(K^{\prime}_{AS},N_{A}||A)||N_{B}||B)\] \[S\to A:\{SK_{AB}\}_{K_{SA}},MAC(K^{\prime}_{SA},N_{A}||B||\{SK_{AB}\}_{ K_{SA}})\] \[S\to B:\{SK_{AB}\}_{K_{SB}},MAC(K^{\prime}_{SB},N_{B}||B||\{SK_{AB}\}_{ K_{SB}})\]
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 8 Prototype Implementation and Results
### Phase Disambiguation
The utility of the approach proposed here is subtle, but important. By trapping an abstract description of the DCP at the emulated smart meter and transmitting it to the substation for comparison with the UCP, determination of the specific phase of the feeder which services the meter where the DCP originated is very simple. In effect, the proposed approach disambiguates which feeder is servicing each meter in the service territory, and does so in real time. This is an element of situational awareness that utility operators do not currently enjoy, as described in Sections 3.2 and 3.3. This element of situational awareness, including real-time disambiguation of feeder/phase and the set of meters being serviced by specific feeder/phase combinations, can be directly leveraged in optimizing voltage and implementing a more complete \(\overline{\text{DVO}}\) system. Figure 8: Spectrogram of downstream current disturbance (DCP) captured at the substation (UCP). The DCP originated on a lateral serviced by Phase A, so the figure shows the fullband (unfiltered) spectrum of Phase A.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 5 Methods  
As depicted in Figure 2, TT&C signals exhibit modulation characteristics on the spectrogram amplitude envelope in a manner distinct from background signals. Under conditions where phase information is constrained, careful utilization and exploration of these features contribute to signal detection and identification. Therefore, departing from traditional signal detection algorithms, we transform the 1D amplitude sequence into a 2D spectrogram, which provides an intuitive representation of envelope variations. Such 1D spectrum amplitude sequences contain only signal frequency information, making it challenging to identify envelope changes. In contrast, 2D spectrogram images not only incorporate frequency contextual information but also reveal spatial information about the amplitude envelope. This spatial information is crucial for uncovering modulation features on the envelope.  
The overall framework of the proposed TT&C signal detection network TATR under restricted conditions is illustrated in Figure 4. Initially, we design the ResTA backbone based on a residual structure and triplet attention mechanism. By extracting deep features, the ResTA backbone captures global spectral attention through a multi-level triplet attention. Subsequently, drawing inspiration from the architecture of DETR [37], we integrate signal position embedding with the features extracted by the backbone, and feed them into the  
Figure 3: Spectrogram of satellite downlink signal reception in complex scenarios, which contain diverse 3G/4G signals as well as burst and frequent interference signals.  
Figure 2: PCM-BPSK-PM signal spectrogram characteristics of satellite TT&C signals under different modulation sensitivities. (**a**) \(K_{p}=0.1\). (**b**) \(K_{p}=1\). The spectral characteristics of the inner modulation signal hinge on the phase modulation sensitivity. Low sensitivity yields a spectrum dominated by a strong carrier component, while high sensitivity reveals the inner modulation signal, its carrier frequency, and symbol rate information.  
TATR encoder-decoder module. This module employs a multi-head attention mechanism to correlate contextual local spectral information. The decoder module establishes pairing relationships between the queried signal objects and the encoded features, producing features of the detected signal set of interest. Finally, employing the Hungarian algorithm, our signal detection module is designed as an anchor-free framework. Through bipartite graph matching, we obtain signal detection results.