esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
To the best of our knowledge, this is the first study focused on the suspension of EFLPs in groundwater systems over loess regions. EFLPs with 1.1 \(\upmu\)m, 1.9 \(\upmu\)m and 3.4 \(\upmu\)m D\({}_{50}\) had large \(\zeta\) values and a relatively high potential to be dispersed and exist in the studied groundwater. The conventional hydrochemical components and their concentrations and particle size had significant impacts on the AS and suspension of EFLPs, and their impacts changed along the groundwater flow path. Meanwhile, both anions and cations had impacts and interactions regarding this behavior and exhibited a combined effect, whereby the effect of component concentration strengthened while that of hydrochemical component got weakened along the path. \(\zeta\) was employed to represent electrostatic repulsion to determine the CCC value for the EFLPs in the studied systems based on the DLVO theory, and the CCC values were determined to be 0.12 mmol/L and 50.04 mmol/L for Cu(NO\({}_{3}\))\({}_{2}\) and NaF, almost consistent with the results of 0.1 mmol/L and 50 mmol/L, respectively. These CCC values were greater than their relative concentrations in the studied groundwater. All these imply that the hydrogeochemical conditions are beneficial to the suspension of EFLPs, with effects being enhanced along the groundwater flow. EFLPs can be present, suspended and then transported and even cotransported with adsorbed pollutants in the groundwater over Guanzhong Plain, China. These results deepen our understanding of the behavior mechanisms of fine soil particles and further help in predicting their environmental behaviors in groundwater systems.  
**Author Contributions:** Conceptualization, Y.W. and R.S.; methodology, R.S., X.S. and S.H.; formal analysis, Z.Z., X.W., Z.W. and H.L.; investigation, Z.Z., X.W., Z.W. and H.L.; writing--original draft preparation, Z.Z., Z.W. and Y.W.; writing--review and editing, X.S., S.H. and R.S.; supervision, Y.W.; funding acquisition, Y.W., R.S. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (no. 42077283), the Key Research and Development Program Projects in Shaanxi Province (no. 2023-YBNY-271) and the Innovation and Entrepreneurship Training Program for College Students (2024).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Acknowledgments:** Many thanks are given to the reviewers and the editor for their useful comments and suggestions. Our special thanks are also given to many colleagues who conducted related tests in our laboratory and made contributions to the present paper, including [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Condition Monitoring of the Impedance Bonds
### Condition Monitoring Device and Sensors Used for Monitoring the Impedance Bonds
#### 3.2.2 Current Transducers with Hall Effect  
In order to interface the current transformer for measuring high currents, within the device, two inputs characterize the current transducers. Current transducers (with Hall effect) are placed on the main board of the device. They have a rated current of 6A and can measure both direct current and alternating current. In this case, the measurement is AC and acquires currents from the secondary windings, from the current transformer described above.  
The characteristics of LEM LTS 6 transducer at 25 \({}^{\circ}\)C are primary nominal RMS current, 6 A (I\({}_{\text{P}}\)); primary current, measuring range 0... \(\pm\)19.2 (I\({}_{\text{pn}}\)); output voltage (analog) of 2.5 \(\pm\)(0.625 \(\times\) I\({}_{\text{P}}\)/I\({}_{\text{pn}}\)) V; sensitivity, 104.16 mV/A; internal measuring resistance, 208.33 \(\Omega\) (\(\pm\)0.5%); accuracy I\({}_{\text{pn}}\), \(\pm\)0.2%; supply voltage 5 Vdc.
## 3 LoRa and Localization Algorithms
### Overview of LoRa  
LoRa is a form of robust low-power wireless networking that is used for long-distance communication. LoRa utilizes chirp spread spectrum (CSS) modulation to modulate the Industrial, Scientific and Medical (ISM) bands [29]. Modulation of the chirp spread spectrum broadcasts a narrow band signal over broader channel bandwidth. LoRa operates on ISM bands such as 868 MHz in Europe, 995 MHz in North America, and 433 MHz in Asia [30]. LoRa can communicate over distances of between 10 and 40 km in rural areas, and urban area coverage is between 1 and 5 km. The LoRa protocol specification was developed by the LoRa Alliance, as shown in Figure 1. The LoRaWAN protocol comprises the MAC Layer and the Application Layer, and operates based on the LoRa physical layer. LoRaWAN is a network standard for telecom operators founded on the LoRa physical layer (PHY). It enables network services and encourages systems to transfer data to gateways wirelessly over a long range. LoRaWAN communicates between LoRa gateways and IoT devices via a star-network topology, and single hopping is allowed between a gateway and a LoRa device.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  
The analysis of LoRa communication in distinct applications can be undertaken based on the parameters of spreading factor (SF), link budget, signal-noise ratio (SNR), bandwidth (BW), link budget, receiver sensitivity, bit error rate (BER), and packet error rate (PER).
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 3 Algorithm Framework and Modules
### Image Fusion Module  
The input to the image fusion module consists of registered homogeneous feature pairs \(f_{h}^{x}\), \(f_{h}^{yy}\) and isolated feature pairs \(f_{i}^{x}\), \(f_{i}^{yy}\). Its goal is to obtain the desired fused image \(f^{f}\). According to the hypothesis, the feature extraction network can separate and extract the required feature information for the image fusion module. Therefore, the image fusion module must perform targeted operations on each feature. As a result, we introduce the Gated-Dconv Block from the Transformer model [20], which, through its dynamic gating properties, can adaptively process the weights of each feature layer, achieving maximal information retention while removing interference. The feature extractor can be formalized as follows:  
\[w_{GD}=G\cdot(x*w_{b}),\quad G=\big{(}A*w_{g}\big{)}, \tag{7}\]  
where \(w_{b}\) and \(w_{g}\) are the dynamic convolution filters; \(G\) is the gating matrix, and \(A\) is the activation function. After the feature pairs are concatenated, the image fusion module processes them using the dynamic gated convolution block. A single convolution layer is employed for channel reduction, which can be formalized as follows:  
\[f_{f}=Conv\Big{(}w_{GD}*CAT\Big{(}f_{h}^{x},f_{h}^{yy},f_{i}^{x},f_{i}^{yy} \Big{)}\Big{)}, \tag{8}\]  
where \(Conv(\cdot)\) exhibits the convolution operation; \(GD(\cdot)\) represents the dynamic gated convolution block, and \(CAT(\cdot)\) highlights the channel concatenation operation.
## 4 Results and Discussion  
### Slot Direction  
Figure 11 shows the comparison of the hysteretic loops of the BRB-RCB with the welded gusset and BRB-RCBs with the bolted gusset connections. Both specimen TG and specimen NG-H exhibited stab hysteretic behaviors, while the pinching effects were observed for both specimens NG-V and NG-S. Specim NG-V even experienced buckling failure under compression at the last cycle. As seen in Figure 12, a large slip between the bolts and the gusset was found for both specimens TG and NG-H at the fourth cycle, when the lateral restoring force was close to zero. This was the main reason for their decreasing lateral stiffnesses and pinching phenomenon, which also had a negative effect on their energy dissipation capacities. The displacements of the bolts and the gusset were coordinated for specimen NG-H in vertical and horizontal directions (Figure 12). The corresponding elastic stiffness was increased by 2.4% and the cumulative energy dissipation grew by 9.7% compared with specimen TG.  
Figure 13 demonstrates the Von Mises stress distributions on the gusset interfaces at \(\pm\)2% drift. The maximum stresses on the gusset-to-column and gusset-to-beam interfaces of the BRB-RCB with the welded gusset plate all exceeded the yield strength of 345 MPa, while the stress distribution at the gusset interfaces were relatively uniform for the other specimens with bolted gusset connections, and the peak stresses of the three slotted gusset plates (NG-V, NG-H and NG-S), compared with the specimen TG, were reduced by 40.7%, 9.2% and 67.1%, respectively. It should be noted that the peak stress had a shift tendency from the gusset corner to the gusset tip at the gusset-to-column interface for the specimen NG-H, which could cause a premature fracture at the gusset tip under greater lateral drift.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Yielding Load** & **Lateral Stiffness** & **Cumulative Energy** & \(\mathbf{V_{\text{max}}}\) & \(\mathbf{V_{\text{min}}}\) \\  & **(kN)** & **(kN/mm)** & **Dissipation (kN\(\cdot\)m)** & **(kN)** & **(kN)** \\ \hline TG & 537.69 & 89.37 & 189.1 & 579.06 & \(-\)596.08 \\ NG-V & 482.94 & 80.72 & 79.17 & 575.88 & \(-\)616.19 \\ NG-H & 526.34 & 91.49 & 207.4 & 680.43 & \(-\)647.58 \\ NG-S & 531.53 & 79.58 & 142.9 & 586.83 & \(-\)560.25 \\ NG-VH-1 & 565.28 & 82.39 & 203.1 & 670.13 & \(-\)645.73 \\ NG-VH-3 & 550.16 & 88.29 & 208.4 & 684.24 & \(-\)640.23 \\ NG-VH-5 & 573.75 & 95.43 & 215.8 & 687.42 & \(-\)658.82 \\ NG-H-B & 539.73 & 95.  
Figure 11: Comparison of hysteretic response of the BRB-RCB with welded gusset plate and two-sided slotted gusset plate with unidirectional sliding bolts.  
Figure 12: The (**a**) vertical and (**b**) horizontal displacement contours of specimens NG-V, NG-H and NG-S.  
Figure 13: The Von Mises stress responses along the gusset interfaces at 2% drift: (**a**) Joint closing; (**b**) Joint opening.
# Two-Dimensional Scanning of Circularly Polarized Beams via Array-Fed Fabry-Perot Cavity Antennas
## 5 Conclusions  
In this work, a compact, planar, Fabry-Perot cavity antenna is studied as a possible launcher for electronically steerable, circularly polarized pencil beams. A versatile and original application of the reciprocity theorem allowed for optimizing the unit-cell parameters. This latter strategy, though unusual for periodic metasurface design, constitutes a fast and effective way to account for multiple bounces, which otherwise may considerably affect the stability of the axial ratio.  
Here, due to structure of the unit-cell, which is a multilayered stack of dielectrics and metallic patches, the in-cavity optimization process allows for evaluating and mitigating resonance effects on polarization degradation. The axial ratio is hence kept under the 3 dB threshold in the half-power beamwidth from 27 GHz corresponding to broadside radiation, to 28.5 GHz, where a conical, azimuthally invariant beam is obtained with the radiation maximum set on almost 40\({}^{\circ}\) off-broadside on the elevation plane.  
Inserting a theoretical planar array of 40 equispaced, uniform sources accounting for horizontal electric dipoles, allows us to demonstrate that the associated array factor is capable of transforming the omnidirectional conically shaped element pattern to a circularly polarized pencil beam. Taking advantage of the frequency sweep and properly phasing the elements in the array, elevation and azimuth planes are scanned, respectively.  
Obtained results clearly confirm that the reciprocity-based design is valid for dimensioning compact, planar, and electronically scanning CP pencil-beam launchers, with limitations appearing when the off-broadside angle steers to considerable values. Furthermore, this approach also demonstrates that, with respect to more classical solutions, based on free-space arrays, the problem of grating lobes appears to be considerably mitigated due to the directive nature of the element pattern.  
Although the covered elevation angles are already sufficiently large for many practical applications, the obtained results clearly show that, in future works, further improvements in the unit-cell design could potentially extend the region in which elevation scan is possible in the CP regime.  
Conceptualization, M.M.; Methodology, E.N., W.F., D.C., A.G. and P.B.; Software, M.M.; Formal analysis, M.M., E.N., W.F., D.C., A.G. and P.B.; Writing--original draft, M.M.; Writing--review & editing, E.N., W.F., D.C., A.G. and P.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union - Next Generation EU under the Italian National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.3, CUP B53C22004050001, partnership on \"Telecommunications of the Future\" (PE00000001 - program \"RESTART\").  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 4 Results
### Statistical Validation  
Given that all results were obtained through simulation-based experiments, statistical validation was performed to confirm the significance of the observed improvements:  
A \(t\)-test was conducted to compare the platoon-based approach against the actuated signal-control method, analyzing differences in both delay and number of stops. Confidence intervals (95%) were calculated to account for variability across multiple simulation runs. Results showed statistically significant improvements (\(p<0.05\)) for all penetration levels of connected vehicles (CVs), confirming that the observed reductions in delay and stops were not due to random variations.  
The following sections present a detailed analysis of HCV delay and total number of stops, highlighting the advantages of the platoon-based approach over conventional signal-control strategies.
# Differential Analysis of a Cryptographic Hashing Algorithm HBC-256
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Hashing Function** & **Keys Generation** \\ \hline Stage 1 & a \(\rightarrow\) a + b & a \(\rightarrow\) b \\ Stage 2 & 0 \(\rightarrow\) 0 & a + b \(\rightarrow\) a + b \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Difference conversion scheme for the HBC-256 hashing function. Figure 7: Difference conversion scheme for the HBC-256 hashing function. Similarly, we find chains for the hashing function (Table 8). We use as input differences only the input differences from Table 7 and look for such chains that at the output the difference corresponding to the difference (a + b) is formed. In Table 8, all values are one byte of difference, written in decimal form, the notation corresponds to the scheme: Input difference \(\rightarrow\) Difference after Stage 1 \(\rightarrow\) Difference after Stage 2 \(\rightarrow\) Difference after Stage 3. Tables 7 and 8 show that the probability of converting one difference byte ranges from \(\frac{1}{2^{6}}\) to \(\frac{1}{2^{1}}\). In order to determine the final probability of conversion of one round, it is necessary to calculate the number of non-zero difference conversions through S-boxes of replacement. There will be 4 non-zero S-boxes involved for one round of key conversion. Since the generation of one key occurs for 8 rounds, the generation of the first subkey will require the conversion of non-zero bytes through 32 non-zero S-boxes. As a result, the probability of difference conversion when generating the first key can be from \(\frac{1}{2^{12}}\) to \(\frac{1}{2^{13}}\). The generation of the second-round key will have the same probability. The one-round conversion for the squeeze function will also involve 4 non-zero S-boxes and will have a probability in the range from \(\frac{1}{2^{6}}\) to \(\frac{1}{2^{4}}\). Thus, combining the obtained probabilities according to Figure 7, we obtain that the conversion probability as a result of the hypothesis will have a probability of \(\frac{1}{2^{30}}\) in the worst case, and \(\frac{1}{2^{30}}\) in the best case. The results of the differential analysis of the HBC-256 algorithm are based on the S-box analysis performed in Tables 2-5, scheme of analysis is presented at Figures 3 and 7. As a result of the analysis of the obtained data, the possibility of building paired chains of difference transformation (for text conversion and key generation) was hypothesized, which is theoretically confirmed by the data in Tables 7 and 8. The biggest limitation of the experimental proof of research hypothesis is the difficulty of using full-size inputs and outputs for the developed hashing algorithms, because in this case the analysis becomes time-consuming, uses huge computational resources, and a has high time complexity.
# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 2 Methodology
### Physics-Inspired Metaphor Algorithms
#### 2.3.3 Sine Cosine Algorithm  
The Sine Cosine Algorithm (SCA) [31] has a very unique source of inspiration. It utilizes two sine and cosine functions to update the position of solutions when searching the space to find the global optimum. The position-updating model of this algorithm is very simple and is formulated below:  
\[X_{i}^{t+1}=\begin{cases}X_{i}^{t}+r_{1}\times\sin(r_{2})\times\bigl{|}r_{3} P_{i}^{t}-X_{i}^{t}\bigr{|},&r_{4}<0.5\\ X_{i}^{t}+r_{1}\times\cos(r_{2})\times\bigl{|}r_{3}P_{i}^{t}-X_{i}^{t}\bigr{|},&r_{4}\geq 0.5\end{cases} \tag{10}\]  
In the above equation, \(X_{i}\) denotes a solution in the \(i^{\text{th}}\) dimension, and \(P_{i}\) denotes the global best solution, namely, the destination solution in the paper. The above equation involves a few other variables that are defined below.  
\(r_{1}\) is an adaptive parameter that is linearly reduced with the course of iterations. It starts from a prefixed value and linearly decreases in each iteration. It is computed as follows:  
\[r_{1}=\alpha-t\frac{\alpha}{T} \tag{11}\]  
where \(\alpha\) is constant.  
* \(r_{2}\) is randomly generated in the range of 0 to 2\(\pi\).
* \(r_{3}\) is also a random number that is generated in the range of 0 to 2.
* \(r_{4}\) is also a random number that is generated in the range of 0 to 1, and based on its value, it is decided whether to use the sine function or the cosine function in updating the position of the current solution.  
When multiplied by \(r_{1}\), the range of values provided by \(\sin(r_{2})\) and \(\cos(r_{2})\) shifts from [\(-1\), 1] to [\(-2\), 2]. Due to a linear decrease in the values of the parameter \(r_{1}\), the range begins at [\(-2\), 2] and linearly declines to [0, 0] during iterations. The position-updating equation of SCA creates two regions around the destination P: an inner region that promotes exploitation and an outer region to promote exploration. The precondition to search the inner region is \(\{-1<=r_{1}\ X\ \cos(r_{2})<=1\}\) or \(\{-1<=r_{1}\ X\ \sin(r_{2})<=1\}\),and the precondition to search the outer region is \(\{r_{1}\ X\ \cos(r_{2})\}\), or \(\{r_{1}\ X\ \sin(r_{2})\}\) gives a value greater than 1 or lesser than \(-1\).
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results
### Traffic Sign Classification
Therefore, building a more large-scale classification database is crucial to the improvement of the traffic sign recognition rate. The detailed information of the last 25 epochs in the green dotted line box in the top right corner of Figure 16 was magnified to the inside figure, which the green arrow points to in the lower right corner. In Figure 17, the classification rates of our method and the other-state-of-the-art deep learning methods based on CNN are displayed, including RGB-CNN, multi-scale CNN [42], cascaded CNN [43], CNNaug/MLP [34], and single-scale CNN [15]. All the above methods were based on the dataset of GTSRB. Our method achieved superior performance as per the data shown. For further comparison, we designed an RGB-CNN. The architecture of the RGB-CNN was the same as the CNNs that we proposed in our method, except for the input, which was three matrices of red, yellow, and blue. Figure 14: Visualization of the transformation and the first convolution layers of Reference [41]. Figure 13: Visualization of the traffic sign transformation and the first convolution layers of the CNN. As shown in Table 7, there were 25, 61, and 163 misrecognized traffic signs in the methods of CNN for the triangle, circle, and overall, respectively. Some misclassification samples are shown in Figure 18. The main reasons for classification errors were because the size of the traffic signs was too small, the occurrence of severe occlusion, interference from the environment by shadows, and motion blur. Some error samples could not even be identified by human beings. The average processing time of our system included SGW filtering, region proposal, and super class classification by SVM, including preprocessing the HOG feature extraction, and finally, the classification of traffic signs. Details of the above are shown in Table 8. Through the timesaving  
Figure 17: The comparison of the classification rate of ours and the state-of-the-art methods. Figure 18: Some misclassification of the test samples. SGW feature extraction, the processing times of this stage were reduced to 17 and 15 milliseconds, for the GTSDB and CSTD, respectively. The HOG feature extraction time stayed at 93 and 79 for the two databases, as shown in Table 8, through parameter optimization. As mentioned above, in real-time applications, the features used by the classification are shared from the detection stage, so double feature extraction is avoided, saving processing time. The average processing time of the whole process is 159 milliseconds, and about 6.3 frames per second. Our method met the needs of real-time processing. Figure 19 shows the results of traffic sign recognition under different road conditions using our method. The four images in Figure 19 were successfully detected and recognized, including prohibitory, danger, and mandatory traffic signs, and multiple traffic signs in one image.