esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 3 Modeling
### Modeling Assumptions  
1. A maximum bit-rate capacity is assumed for the whole cell. However, as per the zone structure, the capacity is subject to the modulation level and the number of users in the cell. Only licensed bands employ LTE technology, however, LTE is still able to share the unlicensed bands' resources provided by Wi-Fi technology, taking into consideration that the admission of the request is still eliminated by the minimum bit-rate threshold.
2. The Opportunistic Supplemental Downlink method is proposed [25] to assure a flexible evolution and deployment between LTE and Wi-Fi technologies without degrading the network performance. Based on this method, the unlicensed band can be perceived as an opportunistic carrier to be utilized when the licensed carrier is completely utilized. Therefore, the limitation on accessing each technology is based on the minimum bit-rate threshold to be satisfied.
3. According to the modulation principle of LTE, which is based on OFDM, two zones are assumed in this study, as shown in Figure 2. According to the OFDM signal, there is a potential to select between the modulation types of LTE signals [26; 27; 28; 29]. Zone 1,  
Figure 1: LTE/Wi-Fi integration/coexistence model.  
with 64 QAM, represents the secondary zone (inner zone) and zone 2, with 16 QAM, represents the primary zone (outer zone), as shown in Figure 2.
4. For the arrival rate to the cell, this is assumed to be the Poisson arrival with the inter-arrival time \(\lambda\), and it is related to the areas of the zones (\(\alpha i\)\(\lambda\), \(i\) = 1,..., zone \(i\)), the density of the users in zone \(i\) (\(\alpha i\)), and the modulation schemes used in each zone.
5. For a specific QoS assurance, a user admits to the cell if a minimum-threshold bit rate is satisfied. This condition is for both the LTE and Wi-Fi technologies.
## 1 Introduction
Comparative analysis across different network scenarios, vehicle densities, and UAV configurations further validates the crucial role of the innovative state space design and reward shaping mechanisms in training stability and algorithm convergence speed. These detailed results provide strong quantitative support for our solution's practicality and superiority. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **[4]** & **[9]** & **[22]** & **[23]** & **[24]** & **[25]** & **[27]** & **[28]** & **[31]** & **[32]** & **Ours** \\ \hline UAV-Assisted Vehicle Networks & & & ✓ & ✓ & & & & ✓ & ✓ & ✓ \\ Multi-UAV Cooperation & ✓ & ✓ & & & & ✓ & ✓ & ✓ & ✓ & ✓ \\ Service Caching & & & ✓ & ✓ & & & & ✓ & ✓ & ✓ \\ Content Caching & & & ✓ & ✓ & & & & ✓ & ✓ & ✓ \\ Computation Offloading & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ UAV Trajectory Optimization & ✓ & ✓ & & & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ DRL-based Solution & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of our contributions to the state-of-the-art related literature. The remainder of this paper is organized as follows: Section 2 details the system model and problem formulation. Section 3 presents the MAPPO-based joint optimization algorithm. Section 4 evaluates performance through experiments, and Section 5 concludes the paper and discusses future directions.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 6 Conclusions  
In this paper, a feature system integrating rock mechanics and slope stability influencing factors is constructed based on the molar Coulomb criterion. Four hundred fifty-eight datasets are calculated as input, and the slope safety factor is taken as output. A new slope stability prediction method is proposed, and the main structural parameters of the multilevel sensing neural network are optimized by the BKA algorithm. The optimal model is used for simulation tests, and the results are compared with those of other commonly used models. Finally, SHAP is used to analyze the mechanism of the influence of each feature on slope stability. The main conclusions are as follows:  
1. Based on the M-C criterion, \(\tau\) and \(\sigma^{\prime}\) are extracted as key features to construct an index system, which has more direct physical significance and interpretability.
2. The BKA-MLP model performs well in slope stability prediction, with an MAE value of 0.0124, RMSE of 0.0241, and R\({}^{2}\) of 0.9499, all of which are better than other prediction models.  
Figure 10: Feature Importance Analysis Using SHAP Values.  
3. SHAP analysis revealed the influence mechanism of each feature on the optimal BKA-MLP prediction model from the global and local and found that the effective stress, slope inclination, and shear strength have a great influence on the occurrence of landslide; the results can provide some references for practical slope engineering applications.
4. In this study, the model primarily focuses on geotechnical mechanical properties for predicting slope stability while other nonmechanical factors are not considered. Future research could be enhanced in two main directions. Firstly, incorporating real-time variables such as rainfall intensity, seismic activity, and soil moisture content can improve the model's adaptability to transient conditions. Secondly, exploring the feasibility of hybrid algorithms may optimize prediction accuracy, computational efficiency, and model interpretability.  
Methodology, Y.L.; resources, H.Z.; writing-original draft, Y.L.; data curation, Y.L.; writing--reviewing and editing, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Science and Technology Project of the Ministry of Emergency Management of the People's Republic of China, grant number 2024 EMST080802, and by the National Key R&D Program of China, grant number 2022 YFB4703701.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors thank the anonymous reviewers, who provided valuable suggestions that improved the manuscript.  
The authors declare that they have no conflicts of interest.
# Model Parallelism Optimization for CNN FPGA Accelerator
## 3 Approach
### Other Layers  
Except for the convolutional layer and channel shuffle layer, CNNs contain other layers, such as BN, DWconv, and Maxpool. Looking closely at the computation of these layers, it is found that each input feature map is computed with weight parameters to obtain the output feature map, and the data of other feature maps are not needed during this period. Unlike convolution layers, the feature maps of these layers do not closely relate to each other. Therefore, it can be seen that model parallelism can be applied directly to these layers. Depending on the number of devices, the input feature maps and parameters are evenly spread out and computed on each device alone.
## 4 Experiments and Discussions
### Image Acquisition Experiment
#### 4.2.2 Collect Images Manually  
To obtain the contrast dataset, under the ideal conditions (the industrial camera was fixed vertically at a certain distance above the fabric, and the ambient light was uniform when collecting the image), the image was collected by manually controlling the camera. Part of the image collected by the system is shown in Figure 13. Fifty images were collected for each class of fabric. Images were expanded to 500 through image processing to keep consistent with the number of images collected by the system. Since the image enhancement technology based on deep learning [28] will increase the calculation time to a certain extent, and the quality of the generated image is difficult to control, it is difficult to meet the needs of automatic training. Therefore, this paper uses the traditional data augmentation technology to expand the collected data. Typical traditional data augmentation techniques include flipping, clipping, rotation, translation, scaling, histogram equalization, enhancing contrast or brightness, random erasure, etc., [18; 19; 20; 21]. In order to ensure that the augmented data are consistent with the real data as much as possible, combined with the actual environmental conditions in the fabric detection process, this paper finally used rotation, stretching, brightness transformation, and other methods to augment the data.  
Table 5 shows the collection methods and collection time-consuming of the three types of datasets in image acquisition experiment. The experimental results show that the system takes about 9 min to collect 500 images (a kind of fabric) and about 3 min to collect 50 images (a kind of fabric) manually. On average, the image acquisition speed of the system is faster than that of manual acquisition obviously, and the angle and illumination of the manual acquisition are single. The image shooting angle and illumination obtained by the system are variable, and the image richness is higher.  
Figure 12: Part of the data collected by the system: (**a**) Fabric 4; (**b**) Fabric 26.  
Figure 13: Part of data collected manually: (**a**) Fabric 4; (**b**) Fabric 26.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 4 Results and Discussion
### Simulation of Scratch Width Recognition
#### 4.3.2 Recognition Error Analysis for the Scratch Cross-Section Shape Deviation  
For a standard v-shaped cross-section scratch, the DRT has good recognition accuracy, as shown in Table 2. However, the real cross-section shape of the scratch may not be so standard in practice. Hence, the recognition accuracy of the DRT needs to be further discussed. Here, we use the Monte Carlo method to generate a series of random like v-shaped scratches to verify the reliability of the DRT. Firstly, a rectangle ABCD is made in FDTD solution software as the original cross-section shape of the scratch. The original refractive index of the rectangle is set as \(n_{\text{Air}}\). Then, the rectangle ABCD is divided into a large number of squares. Each square edge is 0.05 \(\upmu\)m. Next, the built-in script in the software based on the Monte Carlo method is carried out to select some squares from the bottom edge DC to the top edge AB and override the refractive index of the selected squares with \(n_{\text{SiO}_{2}}\). Finally, the scratch cross-section AOB is obtained. A total of 150 random v-shaped cross-section scratches are generated. The width \(|\)AB\(|\) is set in the range of 0.5 \(\upmu\)m to 5 \(\upmu\)m. The DRT width recognition result is, as shown in Figure 9. For the scratch width of 0.5 \(\upmu\)m, 1 \(\upmu\)m, 2 \(\upmu\)m, 3 \(\upmu\)m, 4 \(\upmu\)m, and 5 \(\upmu\)m, the interquartile range (IQR) of the recognition absolute error is 0.17 \(\upmu\)m, 0.34 \(\upmu\)m, 0.41 \(\upmu\)m,0.20 \(\upmu\)m, 0.11 \(\upmu\)m, and 0.08 \(\upmu\)m, respectively. The scratch width of 1 \(\upmu\)m and 2 \(\upmu\)m has a relatively high IQR, and the IQR of the remaining width scratches is within 0.2 \(\upmu\)m. It indicates that the inconsistency between the ideal and real shape of the scratch in the width of 1 \(\upmu\)m and 2 \(\upmu\)m has more influence on the scratch width recognition than other scratch width ranges.
## 1 Introduction
### Our Contribution  
Motivated by the above issues, this paper aims at contributing with investigations on the performance of eigenvalue-based spectrum sensing algorithms in view of two important issues, namely, the effect of impulsive noise and the behavior in a realistic implementation oriented model.  
Although it is known that IN can severely degrade the performance of communication receivers, little effort has been put into investigations about the influence of IN in cognitive radio receivers in the context of spectrum sensing. This paper also aims to contribute with such investigations.  
This paper presents a unified analysis about the influence of IN in four important detection techniques for data-fusion cooperative spectrum sensing, namely, GLRT, MMED, MED, and ED, not only regarding the conventional model that is often adopted in the literature, but also considering a more realistic approach in which an implementation-oriented CR receiver model is taken into account.  
We show that GLRT and MMED are quite robust in the IN environment, while MED and ED performance is drastically affected. We also show that the sensing performance under the conventional model can be rather pessimistic if IN is present, while it can be optimistic in the absence of such impairment. We further show that the implementation-oriented model is intrinsically able to combat IN.  
Given the large differences in performance attained by these models in some situations, our main conclusion is that the implementation-oriented model should be preferred for spectrum sensing design and assessment, as it more closely reflects the reality. Furthermore, this model shows that sensing can be more robust than expected with the conventional model under impulsive noise circumstances. To the best of our knowledge, no publication so far has considered such a unified approach.  
Many papers in the literature, such as [12; 13; 14], deal with the detection of samples affected by impulsive noise. Supposing that these samples can be perfectly identified, we also investigate some simple techniques to mitigate the impact of IN in spectrum sensing. These techniques consist in simply ignoring the affected samples or the affected receivers when performing detection algorithms. We show that these simple procedures can help improving the sensing performance.  
The rest of the paper is organized as follows. Section 2 presents the system model for the eigenvalue-based sensing techniques and for IN generation. Section 3 describes the simulation setup, and Section 4 presents simulation results and discussions concerning the influence of the system parameters on spectrum sensing performance. Section 5 does the same as section 4, but now considering the influence of IN. The effect of some countermeasures added specifically to combat IN is also analyzed in section 5. Finally, Section 6 concludes the paper.
## 3 RFI Detection and Mitigation Algorithm in Synthetic Aperture Microwave Radiometry Imagery
### Dual-Polarization Mode Algorithm
The user-defined factor \(N\) is selected in such a way that the noise spikes characterized by the radiometric sensitivity \(\Delta\)T(\(\xi\), \(\eta\)) at a given pixel (\(\xi\), \(\eta\)) in the BT image have a low probability of occurrence. In our case \(N\) is set to 3, then the probability that BT(\(\xi\), \(\eta\)) \(>\) background + 3\(\cdot\)\(\Delta\)T (\(\xi\), \(\eta\)) is equal to:\[\frac{1}{\sqrt{2\pi}\sigma}\int_{3\sigma}^{*}e^{-\frac{x^{2}}{2\sigma^{2}}}dx= \frac{1}{2}\textit{erfc}\bigg{(}\frac{3}{\sqrt{2}}\bigg{)}=0.0013\] (2) This probability is basically the probability of false alarm (\(P_{fa}\)) associated to noise spikes (\(P_{fa}\!=\!0.13\%\)). In a non-uniform background situation, spikes may appear in strong BT gradients, which will increase \(P_{fa}\). 6. The modified Sun cancellation algorithm (step #3) only works for circular or quasi-circular RFI sources (shape of the impulse response) and not for extended RFI sources such as those that can be found, for example, in large metropolitan areas. The criterion to define whether a region detected in step #5 is a quasi-circular region is the following: \[\textit{bound}_{1}\leq\frac{4\pi\cdot\textit{area}}{\textit{perimeter}^{2}} \leq\textit{bound}_{2}\] (3)
7. Ideally, \(\textit{bound}_{1}\) and \(\textit{bound}_{2}\) should be 1, since only circles satisfy that \(4\!\cdot\!\pi\!\cdot\!\textit{area}/\textit{perimeter}^{2}=1\). However, due to discretization effects in the (\(\xi\), \(\eta\)) grid it has been empirically found that many RFI sources are properly cancelled if the bounds are extended up to \(\textit{bound}\)\(1=0.2\), and \(\textit{bound}\)\(2=4\). 8. For regions satisfying step #5 and #6, the procedure explained in step #3 is applied. The performance of this algorithm is qualitative shown with real SMOS data in Figures 13 and 14, corresponding to Figures 9 and 10. It works reasonably well for most quasi-point sources, even if they are in the alias regions. On some occasions it tends to over-compensate the RFI sources, while on others it fails to compensate them completely due to errors in the location of the sources and/or estimation of the background. One of the drawbacks is that it cannot handle extended targets as the ones in Figure 13c, even though they are not too extended. The algorithm explained in Section 3.2 shows how to handle such cases. Figure 14: RFI in SMOS (dual-pol mode): Scenario 4 with tens of RFI sources in the whole BT image. (left) SMOS L1b image with RFI, (center) SMOS RFI-cleaned L1b image, and (right) detected RFI map. Note that most RFI sources have been attenuated even outside the alias-free FOV. Many RFI sources have been attenuated, but there are some that are not completely removed or they have been over-compensated (dark blue dots in Figure 14-central panel). Note: title corresponds to the SMOS data filename including the date of acquisition and the snap-shot number. Figure 13: _Cont._
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 4 The Proposed Method
### Example Analysis  
The higher the average clustering coefficient of the network, the more connected edges exist between nodes in the network, and the more closely connected they are. Due to the ease of visualization of the Dolphins network as well as its high average clustering coefficient, in this section, the Dolphins network is taken as an example to introduce the computation process of the LBIA algorithm. Figure 4 depicts the Dolphins network, which includes 62 nodes and 159 edges. Node 33 was selected as the source node, and its first- and second-order neighbors were generated, where purple indicates the source nodes, green is the first-order neighbor node, and orange is the second-order neighbor node.  
First, the information value of the source node 33 was calculated according to Equation (7):  
\[M(33)=\log(1+3/12+0.42)\approx 0.58\]  
Next, we took the first-order neighbor node 10 as an example:  
\[LB_{v}(10)\approx\frac{6}{3.58+0.52+3.06}+1.93\approx 2.76\]  
Then, \(LB_{v}(14)\approx 2.90\), \(LB_{v}(61)\approx 0.33\), which were summed to obtain \(AI_{33}\approx 6\), and the final result is \(LBIA_{33}\approx 7.91\) using Equation (10). Similarly, we found the impact value of the other nodes, and arranged all the nodes in descending order according to their impact values, thereby obtaining a ranked list of the nodes, where a higher rank indicates that the node occupies a more important position in the network.  
Figure 4: Dolphins network visualization: where the number represents the node ID. Purple node is source node, green nodes are first order neighbours, orange nodes are second order neighbours.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 1 Introduction
Metasurfaces are thin sheets that can be fabricated as flexible sheets depending on the choice of base material, thus allowing them to be installed along the structure. This allows for control of radio waves' scattering properties while maintaining the structure's existing geometry. In general, the IRS is expected to adaptively control the radio environment by periodically repeating the following two actions. The first action is estimating the radio channel state information necessary to determine the IRS operation. The second one is controlling the scattering intensity and phase distribution on the IRS surface based on the estimated information to obtain the desired propagation channel. This is the first paper to integrate the IRS into UAV-BS networks for coverage extension. Specifically, it applies realistic space-time UE distributions extracted from field data and reproduces existing building structures into our simulation environment for strictly judging blocking conditions between the transmitter and receiver for the sake of enhancing reality. The contributions of this paper are as follows:  
* We propose a system model for multi-IRS-assisted UAV-BS networks to extend the coverage of mmWave communications. * We construct a three-dimensional environment based on actual field data and evaluate the proposed system through numerical analysis. The rest of this paper is structured as follows. Section 2 introduces previous research on the IRS. Section 3 states the overall architecture of the proposed system and the system model. Section 4 describes the proposed IRS selection algorithm with the actual environment. Section 5 presents numerical analysis results and discussions. Finally, Section 6 summarizes this paper and mentions future works.
## 2 Materials and Methods
### Micro-Computed Tomography (Micro-CT)  
After 8 weeks of light exposure, the beagle was humanely euthanized using a lethal dose of sodium pentothal. The mandibular blocks were then extracted and immersed in a 5% formaldehyde solution at pH 7.0. The upper and lower jaws were incised separately and immediately fixed in a 4% formaldehyde solution for 2 weeks. The mandibular blocks were then examined using a micro-CT scanner (SkyScan 1172 X-ray Microtomography, Bruker, Antwerp, Belgium). The scanner is equipped with an X-ray tube operated at settings of 80 kV and 125 \(\upmu\)A. It has the ability to adjust specimen orientation and is complemented by a 12-bit digital CCD camera. This camera's pixel resolution is 11.2-11.6 \(\upmu\)m and is amplified using optical fiber. The specified distance maintained was 105.95 mm from the specimen to the X-ray source and 217.9 mm between the camera and the light source. A 0.5 mm thick aluminum filter was integrated during the scanning process.  
The mandibular block was mounted within a 7 mm diameter plastic container on the scanning platform, as detailed in a previous study [19]. The X-ray beam was rotated 180\({}^{\circ}\) in 0.4\({}^{\circ}\) increments throughout the scanning process. It took approximately 60 min to scan each sample. Raw scan data were converted to digital images using reconstruction software (NRecon Server, Ver 2.0). Initially, images were captured with standard settings. The histogram was then adjusted to enhance the contrast in the region of interest to ensure optimal image processing. Cross-sectional images were reconstructed using a modified  
Figure 1: Experimental setup demonstrating PBM in the beagle model. (**a**) PBM was performed on the left teeth and compared with untreated right teeth (Control). (**b**) FET (Si2305; P-Channel 1.25-W, 1.8-V MOSFET) was employed in the switch mechanism. (**c**) An LED array system consisting of nine LEDs was placed between the gingival tissue and teeth to cover most areas of the teeth.  
Feldkamp cone-beam algorithm, producing images with an average pixel size of 11.6 \(\upmu\)m and dimensions of 900 \(\times\) 900 pixels.