esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 2 Problem Identification--Materials and Methods
### Results of the Field Measurement and Numerical Simulation
This way, the M4 and M5 measurements are compared to the simulation to validate the results (Figure 4). For M4, the clearance was calculated to be 8.9 m. In Figure 4a, this is compared to the simulated curve at 9 m clearance. The curves' general shape matches each other, such as the local peaks' position and value. In COMSOL, we simulated the instantaneous values of the electric field formed with phase shifting of the voltages to illustrate the real field values around the line. The same situation is presented in Figure 4b; the only difference is that the calculated clearance was 8.7 m, and the curve was compared to the simulation at 8.8 m clearance. According to the results, the field measurement and the numerical simulation are consistent with each other. The detailed comparison is provided in Table 4.  
Based on Table 4, the field strength maximum values are the same with a good approximation. According to the simulation results, it is reasonable to say that the measurement results are a sound basis for further discussion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Clearance** & **Peak Value** & **Position of the Peak** \\ \hline M4 & 8.9 m & 7.150 kV/m & 8.20 m \\ S4 & 9.00 m & 7.276 kV/m & \(-8.57\) m \\ Relative error & 1.1\% & \(-0.02\%\) & 0.04 \% \\ \hline M5 & 8.7 m & 7.420 kV/m & 8.10 m \\ S5 & 8.80 m & 7.400 kV/m & \(-8.06\) m \\ Relative error & 1.1\% & \(-0.01\%\) & 0.01 \% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the measurement and the simulation. Figure 4: Comparing simulated and measured electric field distribution. (**a**) M4 measurement curve and E-field simulation at clearance 9 m; (**b**) M5 measurement curve and E-field simulation at clearance 8.8 m.
## 4 Results
### Simplification Model Based on Azimuth Difference and Side Length (SM-ADSL)
#### 4.1.2 Building the Simplification Model  
Based on the two principles of sample vertex selection, we identified two characteristics. The first was the side length between adjacent sample vertices, which determined the simplification rate, such as side lengths a\({}_{0}\)a\({}_{1}\) and b\({}_{0}\)b\({}_{1}\) in Figure 4. The second was the azimuth difference between adjacent sample vertices, which determined the deformation between the original and simplified envelopes. The azimuth of a vertex is defined as the direction of the ray passing through that vertex, which is tangent to the distribution envelope and biased towards the next vertex to be selected. For example, the ray starting at b\({}_{0}\) in Figure 4B is the azimuth of b\({}_{0}\), and angle \(\alpha_{1}\) is the azimuth difference between the two selected vertices b\({}_{0}\) and b\({}_{1}\).  
Applying the 177 sample vertices on the distribution envelope on 17 May, we calculated the azimuth differences and side lengths of adjacent vertices. Figure 6 is the scatter plot of the sample vertices, where the x-coordinate is the azimuth difference, and the y-coordinate is the side length. The following two rules can be discerned: (1) When the azimuth difference varies between 0 and 60\({}^{\circ}\), there is a negative correlation between azimuth difference and side length in general, and almost all side lengths are larger than 3 km. (2) When the azimuth difference is larger than 60\({}^{\circ}\), the corresponding side length varies between 3 km and 4 km.  
Figure 5: Schematic diagram of sample vertices selection in green tide distribution envelope on 17 May 2021.  
Azimuth difference x refers to the absolute value of the azimuth difference between the currently selected vertex and the candidate vertex. There are four situations encountered in the calculation of azimuth difference: (1) When the azimuth of the candidate vertex changes in the direction of increase, the azimuth difference x = x\({}_{2}\) - x\({}_{1}\). (2) If the azimuth of the candidate vertex is close to 360\({}^{\circ}\) and crosses 360\({}^{\circ}\) during iteration, the azimuth difference is x = x\({}_{2}\) - x\({}_{1}\) + 360\({}^{\circ}\). (3) When the azimuth of the candidate vertex changes in the direction of decrease, the azimuth difference x = x\({}_{1}\) - x\({}_{2}\). (4) If the azimuth decreases to 0\({}^{\circ}\) and crosses 0\({}^{\circ}\) during iteration, the azimuth difference x = x\({}_{1}\) - x\({}_{2}\) + 360\({}^{\circ}\). Here, x\({}_{2}\) represents the azimuth of the candidate vertex, and x\({}_{1}\) represents the azimuth of the previously selected vertex.
## 5 Simulation Results
The advanced hardware designed specially for neural network operations can significantly help the deployment of such technology. For the follow PMI, the parameters required are almost negligible, since the DFT codebook can be generated whenever required. Nonetheless, the complexity from a FLOPs perspective is almost zero at the base station; however, at the user side it can be quite significant, since it requires a full search over all the DFT beam-forming codewords in each frequency position to maximize the capacity. Thus, the number of FLOPs can be estimated as the multiplication between two complex matrices of size \(M\times N\) and \(N\times C_{S}\): \(FLOP_{followpMI}=8 MC_{s}NL\); where \(C_{s}\) is the codebook size and \(L\) is the number of frequency bins where the PMI is computed. In our tests, a different PMI is required for each PRB; thus, \(L=25\) and we use as the codebook a DFT beamspace with an oversampling factor \(O_{F}=4\); therefore, \(C_{s}=O_{F}N=32\). The final number of FLOPs with follow PMI is equal to \(FLOP_{followpMI}=102.400\). The use of a system such as NR-CsiNet instead of the simpler follow PMI demands higher complexity; however, unlike follow PMI, it can allow the BS to apply advanced precoding techniques that exploit complete CSI, which is not achievable through simplified codebooks (e.g., in the case of users in NLOS and/or who could interfere with each other). We aim to investigate NR-CsiNet capabilities for more sophisticated MIMO scenarios in future studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**K** & **Parameters** & **FLOPs** \\ \hline
1/120 & 5.381 & 179.200 \\
1/60 & 10.725 & 243.200 \\
1/30 & 21.411 & 371.200 \\
1/15 & 42.785 & 627.200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Complexity of the encoder in terms of learnable parameters and FLOPs.
## 2 Chemistry and Structure-Activity Relationship of Styrylquinolines _in vitro_  
3'-Processing and strand transfer reactions are both essential for the life cycle of the virus. In both cases the key step is the hydrolysis of a phosphodieser bond. However, because of the high stability of the DNA phosphodieser bond, a highly efficient catalytic process is required to provide the critical rate enhancement needed to cleave this bond [10]. Polynucleotidyl transferases, including HIV-1 IN take advantage of the catalytic effect of two-metal-ion operating in concert to achieve the tremendous rate enhancement required. For instance, in the case of the 3'-5' exonuclease reaction of _Escherichia coli_ DNA polymerase I, [PERSON] and [PERSON] [11] have suggested that the phosphodieser bond cleavage is promoted by two divalent cations separated by about 3.9 A, the first one (M\({}^{1}\)) initiates the formation of the hydroxide ion, whereas the second (M\({}^{2}\)) directly ligates the leaving group atom, thereby facilitating the bond breakage step by neutralizing the developing negative charge (Figure 2). Although the exact nature of the metal cations is unknown in the case of HIV-1 integrase the crystal structure of Mg(II)-complexed core domain of HIV-1 IN shows that one Mg\({}^{2+}\) ion is coordinated by two of the three critical active site residues (Asp-64 and Asp-116) [12]. It is probable that the second ion binds only in the presence of the DNA substrate [13].  
On this basis, polyhydroxylated SQLs which embedded in their structure two chelating subunits were designed to chelate divalent metal ion(s) present in the catalytic core domain of HIV-1 IN. SQLs,  
Figure 2: The “two-metal-ion” enzymatic mechanism for the 3’-5’ exonuclease reaction of _Escherichia coli_ DNA polymerase I according to the [PERSON] and Steitz hypothesis [11] and general structure of styrylquinoline inhibitors.  
exemplified by **1**, (Figure 1) were found to be potent HIV-1 IN inhibitors in _in vitro_ experiments. On the other hand they block the replication of HIV-1 in cell culture and are devoid of cytotoxicity [14]. Following this initial finding, a program of systematic chemical modification of the hit compounds was undertaken since receptor based design of novel HIV-1 inhibitors was hampered by the lack of complete three-dimensional bioactive structure of HIV-1 integrase. More than 150 new analogues were prepared and biologically investigated in an attempt to delineate the structure-activity relationship of this family. Modifications were initially performed on the quinoline ring since it was recognized that this moiety constituted the main pharmacophore. Further modifications and elaborate substitutions at various sites of the ancillary ring were next studied. Finally some modulations of the spacer were examined.
## 2 Results and Discussion
### S3 Device  
Data were processed by PCA (principal component analysis) with Matlab software. Information thus obtained (Figure 2) with PC1 and PC2 were completely explained to differentiate the two present clusters [6; 7; 8; 9; 10; 11; 12].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \# Peak & Retention Time (RT) & Compound & Crust & Pulp \\ \hline
1 & 1285 & 2-heptanone & x & – \\
2 & 1745 & 2-octanone & x & – \\
6 & 1997 & 5-Methyl-2-hexanol & – & x \\
3 & 2160 & 2-nonanone & x & – \\
4 & 3803 & phenylethyl alcohol & – & x \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the selected chemicals compounds.  
Figure 1: SPME-GC-MS, 5-Methyl-2-hexanol identified in 6 different percentages of grated PR.  
The figure shows three different percentages, 100% pulp, 90%, and 95%. Therefore, the S3 device was able to differentiate samples containing crust or only pulp.  
Figure 3 shows three different clusters formed by three different percentages of blend crust/pulp (10% black stars, 15% blue circle, and 19% green cross). PCA clearly explains the efficiency of S3 technology for differentiation.  
PCA obtained with our S3 (Figures 3 and 4) is extremely significant because it compares two similar percentages close to the legal limit: PG5 81%-19% CG3 (not legal), PG5 82%-18% CG3 (legal limit).  
The two groups of samples are very compact and clearly recognized by the nanowire sensors fraud recognition index.  
The latest PCA, especially, are very accurate in identifying samples of similar percentages. This confirms the fact that S3 can be used easily and quickly on the production line to make sure the 18% crust limit is not exceeded.  
Figure 3: PCA Score PLOT for different crust percentage (10%, 15%, and 19%).  
Figure 2: PG1 and CG2 PCA Score PLOT with different crust percentages.
## 2 Principles
### Schematic of the Multi-Band Phased Array Radar
Signals from all receiving elements are summed by combiners as \(S_{cH1}=\Sigma S_{m\_cH1}\) and \(S_{cH2}=\Sigma S_{m\_cH2}\) and input into ADCs. Finally, the signals are synchronously acquired and digitized. The two digitized signals are interleaved as [\(S_{cH1}(1)\), \(S_{cH2}(1)\), \(S_{cH1}(2)\), \(S_{cH2}(2)\), \(\ldots\), \(S_{cH1}(n)\), \(S_{cH2}(n)\)]. The time interval of the interleaved digital signal is \(1/(2f_{s2})\), which means the sampling rate and instantaneous bandwidth of the receiver is doubled. The maximum value of \(B_{0}\) and \(k_{0}B_{0}\) are \(2\,|f_{s2}-f_{s1}/2\,|\) and \(f_{s2}\), respectively. Therefore, the total band number and the bandwidth of each individual band are effectively expanded. As illustrated in Figure 2, the proposed architecture innovatively generates and receives multi-band signals through a unified optical link. Compared to traditional architectures, the number of required RF components is reduced. This hardware simplification not only cuts system complexity but also significantly lowers manufacturing costs and power consumption, making it a more cost-effective and practical solution for multi-band phased array radar applications.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 6 Discussion  
In this section, we discuss the parameter settings of the model. Considering that \(\alpha\), \(\beta\), and \(d\) are three crucial parameters in our model, determining the weights of various components in the model, we have conducted extensive experiments by carefully adjusting the proportional relationships among these three parameters for video datasets. The effects of \(\alpha\), \(\beta\), and \(d\) on PSNR and SSIM at various sampling rates are shown in Figure 6. We can observe that the best completion performance in terms of PSNR and SSIM is achieved when \(\alpha=5\), \(\beta=5\), and \(d=20\).  
In addition, we observed that our method occasionally performed worse than traditional linear transformation-based methods such as FTNN and UTNN in terms of SSIM, particularly at higher sampling rates. Additionally, we noticed that NTTNN, which also employs nonlinear transformations, exhibited lower performance compared to the traditional methods. This could be attributed to the fact that SSIM places particular emphasis on the preservation of structural information when evaluating image or video restoration quality. For such cases, simpler linear models may yield better results.  
Figure 5: The results of MRI data using different methods at different sampling rates (SR = 2.5%, 5%, 10%, 15%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**SR = 2.5\%**} & \multicolumn{2}{c}{**SR = 5\%**} & \multicolumn{2}{c}{**SR = 10\%**} & \multicolumn{2}{c}{**SR = 15\%**} \\ \cline{3-10}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{MRI} & Observed & 19.448 & 0.4877 & 19.559 & 0.4959 & 19.796 & 0.5124 & 20.043 & 0.5289 \\  & TNN & 22.387 & 0.5085 & 25.030 & 0.6113 & 27.445 & 0.7155 & 29.074 & 0.7769 \\ \cline{1-1}  & UTNN & 23.503 & 0.5412 & 25.851 & 0.6424 & 28.261 & 0.7450 & 30.146 & 0.8092 \\ \cline{1-1}  & FTNN & 26.834 & 0.6993 & 29.142 & 0.7746 & 31.668 & 0.8500 & 33.526 & **0.8924** \\ \cline{1-1}  & NTTNN & 27.139 & 0.5996 & 28.400 & 0.6660 & 29.469 & 0.7307 & 29.986 & 0.7633 \\ \cline{1-1}  & OUR & **29.073** & **0.7833** & **30.039** & **0.7971** & **31.932** & **0.8549** & **33.638** & 0.8795 \\ \hline \hline \end{tabular}
\end{table}
Table 3: TNN, UTNN, FTNN, NTTNN, and our model’s PSNR and SSIM results for the completion of MRI data at different sampling rates, with the optimal values highlighted in bold.
## 2 Materials and Methods
### Mesh Independence Test  
High-quality mesh is a prerequisite for accurate simulation results. To test the mesh independence, three meshes with different densities were generated. They were named as \"medium\", \"better\", \"fine\", and \"finer\" meshes and the corresponding mesh quantities are 54,743, 91,001, 186,460 and 479,228, respectively (Figure 10). The mesh quality was carefully inspected. Nine velocity measuring points (Figure 11a) were selected evenly along the radial direction and were plotted in Figure 11b.  
Figure 10: Meshes with different densities: (**a**) medium mesh, (**b**) better mesh, (**c**) fine mesh and (**d**) finer mesh.  
Figure 9: Physical geometry and boundary conditions for a 3D model with a circular simulated microchannel with a cross-sectional area of 64 pixel\({}^{2}\) (0.2273 mm\({}^{2}\)). (**a**) physical geometry, (**b**) boundary conditions.  
As can be seen from Figure 11b, the coarser mesh has a bigger difference when compared with the finer mesh at the same position (e.g., 0.01065). Theoretically speaking, the finer mesh is the ideal option for this simulation. However, it takes a significantly longer computation time than the fine mesh. Additionally, the relative error of fine mesh is 1.28%. Considering both computational cost and error, the fine mesh was selected in this study.
# Online EEG Seizure Detection and Localization
## 2 Materials and Methods
### Dataset  
Two datasets were used for evaluating the performance of the proposed approach. The Physionet dataset, provided by the Children's Hospital Boston (CHB-MIT) [35], is comprised of 980 h of EEG records, obtained from 24 patients with a total of 185 seizure attacks and is used for the detection of seizures. Since the CHB-MIT dataset does not provide localization information about the seven seizures, a second dataset obtained from the Karunya University EEG database [36] was used, which contains 10 s EEG seizure activity. This dataset was used to evaluate localization performance. Both datasets are publicly available. Table 2 contains the descriptions for each patient obtained by the CHB-MIT dataset, sampled at 256 Hz, and includes the length of the EEG signals, the number of seizures, and the average seizure durations. The Karunya dataset also has a sampling rate of 256 Hz, but is additionally filtered through an analog bandpass filter with cutoff frequencies of 0.01 Hz and 100 Hz. This dataset contains a total of 175 10-second EEG seizure sequences obtained from patients with an age range spanning from 1 to 107 (\(33\pm 22\)) years old, involving 88 generalized and 87 focal seizures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patient** & **Gender** & **Age** & **\# of Seizures** & **Av Length of Seizures (s)** & **Length (h)** \\ \hline  
(\underline{1}\) & \(\underline{1}\) & 16 & 31.94 & 21.30 \\ \hline \hline \end{tabular}  
* The gender and age of patient 24 are not available.  
\end{table}
Table 2: Children’s Hospital Boston (CHB-MIT) patients record description.
## 5 Conclusions and Future Work  
This work introduced two models (Model_I and Model_II) for the early analysis and detection of diseases. Both models are based on the model the authors published previously (Model_0). Model_I introduced the miRNA fragmentation method combined with Model_0 to retain high accuracy of disease detection while reducing the needed processing time and consequently the required processing specifications. On the other hand, Model_II introduced an LSTM model combined with the preprocessing pipeline of Model_0 and the fragmentation method of Model_II. Additional experimental work was applied on the three different datasets. The reported accuracy of MS and RA biomarker detection using Model_I was 96.5 and 95.7, respectively. Additionally, the study findings denote that the fragmentation size of 0.2 and the first subset of the fragmented dataset retain the same accuracy as when analyzing an entire miRNA sequence, which can reduce the execution time by 22.4% compared to Model_0 and by 87% compared to Model_II. In addition, the experimental results imply that NEXTFLEX improves disease detection when processed using the introduced models.  
Furthermore, the additional experimental work applied on Model_0 supported the previously published results, with an early MS detection accuracy of 96.5. Finally, the results obtained by the deep learning model (Model_II) indicate its high potential for early MS detection, considering the size of the studied dataset's limitation, resulting in a detection accuracy of 0.71. In our future work, further experimental work will be applied on Model_II with a higher number of samples in the dataset that is relatively high considering the number of the extracted features. In addition, the introduced three models will be studied with other diseases.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 2 Related Works
### IoT System Architecture
This interconnection of heterogeneous objects requires ad-hoc networks, which most of the time are neither scalable nor interoperable. [PERSON] et al. [43; 44] proposed a top-down approach to developing an IoT application for rapid deployment. Their proposed TinyLink system enables application developers to write their hardware independent application code and generate a configuration for hardware platforms and hardware dependent binary code ready for porting. This can be thought of as an analog of optimizer function used in compilers for hardware. Software optimizers are great and useful but they do have limitations, and certain high-level optimization always requires human intervention. However, this can reduce the time and cost of developing a new application. This also provides an idea that the system architecture of IoT can be broken up into functional blocks and can be made modular with some high-level perspective and understanding of each layer as in Figure 1.  
The first layer consists of sensors that collect environmental parameters and actuators that serve as feedback devices, and are aggregated by the embedded processor in the second layer. The third layer is the wireless transceiver module such as Bluetooth Low Energy (BLE), Wi-Fi, ZigBee, which provides wireless connectivity to the sensor nodes. Typically, these wireless devices create a type of mesh network for covering a large area. In some applications, the embedded hardware layer has an integrated network stack so the second and third layer is merged into one unit. While a few wireless transceivers follow specific protocols of communication and conform to security requirements, the majority do not. Therefore, the data is vulnerable when it is transiting from the wireless transceiver layer to the internet gateway layer. The fourth layer is the central internet gateway device, which sends the sensor data to the next layer, the cloud server via Wi-Fi, 3G/4G or Ethernet connectivity, where the data is stored for future analysis.