esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation
### Experimental Results and Analysis
Therefore, automatic feature extraction based on an ANN can more adequately extract the feature information of the fault than the traditional feature extraction method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline \multirow{2}{*}{**Fault Code**} & **Proposed** & **Traditional EWT +** & \multirow{2}{*}{[8]} & \multirow{2}{*}{[10]} & \multirow{2}{*}{[13]} \\  & **Method** & **Multi-Input ResNet** & & & \\ \hline F0 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F1 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F2 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F3 & 100.00 & 100.00 & 100.00 & 98.00 & 100.00 \\ F4 & 100.00 & 92.00 & 100.00 & 96.00 & 100.00 \\ F5 & 98.00 & 100.00 & 82.00 & 96.00 & 98.00 \\ F6 & 100.00 & 96.00 & 98.00 & 98.00 & 82.00 \\ F7 & 100.00 & 100.00 & 100.00 & 100.00 & 98.00 \\ F8 & 100.00 & 100.00 & 100.00 & 100.00 & 98.00 \\ F9 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F10 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F11 & 100.00 & 100.00 & 100.00 & 98.00 & 98.00 \\ F12 & 98.00 & 96.00 & 98.00 & 98.00 & 80.00 \\ F13 & 100.00 & 100.00 & 100.00 & 100.00 & 96.00 \\ F14 & 100.00 & 100.00 & 100.00 & 98.00 & 98.00 \\ Average & 99.73 & 98.93 & 98.53 & 98.80 & 96.53 \\ accuracy & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fault diagnosis result of the CSTV filter circuit. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline \multirow{2}{*}{**Fault Code**} & **Proposed** & **Traditional EWT +** & \multirow{2}{*}{[8]} & \multirow{2}{*}{[10]} & \multirow{2}{*}{[13]} \\  & **Method** & **Multi-Input ResNet** & & \\ \hline F0 & 100.00 & 100.00 & 34.00 & 68.00 & 98.00 \\ F1 & 100.00 & 98.00 & 100.00 & 98.00 & 100.00 \\ F2 & 100.00 & 98.00 & 94.00 & 100.00 & 100.00 \\ F3 & 100.00 & 96.00 & 70.00 & 98.00 & 100.00 \\ F4 & 100.00 & 98.00 & 64.00 & 96.00 & 98.00 \\ F5 & 100.00 & 100.00 & 100.00 & 92.00 & 98.00 \\ F6 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F7 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F8 & 100.00 & 100.00 & 74.00 & 100.00 & 100.00 \\ F9 & 100.00 & 94.00 & 86.00 & 94.00 & 100.00 \\ F10 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F11 & 100.00 & 92.00 & 34.00 & 100.00 & 100.00 \\ F12 & 100.00 & 98.00 & 100.00 & 94.00 & 100.00 \\ Average & 100.00 & 98.00 & 81.23 & 95.38 & 99.54 \\ accuracy & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fault diagnosis result of the Sallen–Key bandpass filter circuit. In Tables 4-6, the average diagnosis accuracy of the analog circuit fault diagnosis method proposed in this paper is higher than that of the method in [8; 10; 13] and the method based on the combination of traditional EWT and multi-input ResNet. In summary, the proposed method in this paper can effectively obtain the response signal characteristics of analog circuits under different faults, and it performs well in analog circuit fault diagnosis.
## 3 Results  
### Characterization of Carboxen  
The details of the spectral components obtained by deconvolution of C\({}_{\rm 1s}\) and O\({}_{\rm 1s}\) spectra are shown in Figure 2. The peak assignments are reported in the figure legend, in which black dots and red lines represent the experimental data and the best-fit curves, respectively.  
The C\({}_{\rm 1s}\) spectrum exhibits a prominent peak around 284.51 eV, which corresponds to \(sp^{2}\) carbons in C=C bonds within aromatic regions of unoxidized carbon rings and in aliphatic chains [7; 9; 15]. Additional components associated with \(sp^{3}\) and oxidized carbons appear on the higher-binding-energy side: the feature at 288.29 eV is attributed to HO-C=O [7; 9; 13; 15], the peak at 286.12 eV is linked to epoxides and C-OH groups [7; 9; 13; 15], and the peak at 284.93 eV corresponds to C \(sp^{3}\) and C-C(O) [7; 13; 15].  
Figure 2: (a) C\({}_{\rm 1s}\) and (b) O\({}_{\rm 1s}\) high-resolution XPS spectra obtained from the surface of oxidized Carboxen. The C\({}_{\rm 1s}\) and O\({}_{\rm 1s}\) core-level spectra were recorded at photon energies of 400 eV and 650 eV, respectively.  
On the lower-binding-energy side, a component at 283.89 eV is associated with carbon vacancies [7; 9; 15].  
The O\({}_{\rm 1s}\) spectrum shows three prominent peaks: The main peak at 532.28 eV is related to photoemitted electrons from epoxy oxygen [7; 9; 15]. A peak at 533.39 eV on the higher-binding-energy side is attributed to ether and hydroxyl groups [7; 9; 15], while the component at 531.02 eV on the lower-binding-energy side is ascribed to carbonyl, carboxyl, and lactone groups [7; 9; 15]. Finally, a very weak component at higher binding energy is assigned to H\({}_{2}\)O, likely resulting from the adsorption of hydroxyl groups in the pore structure [25].
## 7 Conclusions  
A novel and low-computational-complexity algorithm for Doppler estimation and signal recovery in bi-phase radar systems has been proposed. The algorithm is designed to accurately estimate the Doppler frequency and remove the Doppler effect from the received signal, allowing for precise range and velocity calculations.  
The algorithm consists of several sub-modules, including a median filter, nonlinear transformation, local minmax detector, Doppler calculation module, Doppler checker, phase corrector, and bi-phase signal recovery. These sub-modules work together to iteratively estimate and reduce the Doppler effect from the received signal until the Doppler residue is zero or sufficiently small.  
The excellent performance of the proposed system in estimating the Doppler effect and recovering the signal at different SNRs and Doppler values has been demonstrated. In addition, the implementation using SDR technology showed great potential.  
Investigating the possibility of dynamically adapting the radar parameters, expanding the algorithm to handle scenarios with multiple targets simultaneously, and testing the algorithm under different background effects would be valuable extensions that will be the focus of our work in the near future.  
Conceptualization, A.Y. and B.M.; methodology, A.Y. and B.M.; software, A.Y.; validation, A.Y.; formal analysis, A.Y., B.M. and P.F.D.; investigation, A.Y., B.M. and P.F.D.; writing--original draft preparation, A.Y.; writing--review and editing, A.Y., B.M. and P.F.D.; visualization, A.Y., B.M. and P.F.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
We would like to express our sincere gratitude to the anonymous reviewers for their valuable feedback and constructive comments, which greatly contributed to improving the quality of this paper. Their insightful suggestions were instrumental in shaping the final version of the manuscript. We would like to thank the Digital Research Alliance of Canada for their valuable computational resources and services.  
The authors declare no conflict of interest.
In this regard, the exploitation of automatic inclinometers can be very useful in gaining knowledge about the preparatory factors controlling the temporal trends in displacement [51; 52; 53; 54]. This piece of equipment helps to overcome some of the limitations of traditional ones, such as the low temporal resolution of measurements, enabling a more accurate examination of the kinematics behaviour of slow landslides, especially if displacement data are coupled with other continuously monitored controlling factors. As claimed by some authors [10; 40], thorough examination of representative case studies provides valuable insights for deciphering general principles on the basis of the patterns of movement of slow-moving landslides. In light of this, the main goal of this work is to investigate the kinematics controlling factors of a reactivated slow-moving landslide located in the eastern Liguria region (north-western Italy) through extensive field-based measurements. Such a landslide is partially urbanized, and in recent years, significant accelerations of the movements have resulted in extensive damage and the evacuation of several residential buildings. The main engineering-geological and kinematics characteristics of the landslide were outlined by [55]. In this study, based on analysis and comparison of new and previous monitoring data, further aspects on the preparatory and triggering factors conditioning the patterns of landslide activity are presented. To fulfil this study's goal, a detailed analysis of continuous hydrological and geotechnical monitoring data series has been performed. Specifically, displacement and pore water pressure trends have simultaneously been obtained by means of fixed-in-place inclinometric probes and a pressure transducer, respectively. All these sensors have been positioned at the main deformation zones known from previous geotechnical monitoring campaigns performed through mobile inclinometers, enabling the examination of the connections between pore water pressure and deep displacements over a period of two and a half years.
# Accurate Landmark Localization for Medical Images Using Perturbations
## 4 Results  
The proposed method was evaluated on hand radiograph data and cephalometric X-ray data to compare with papers [10; 11] that achieved the state-of-the-art. The network was trained and tested in PyTorch with Intel Xeon Gold 6126 2.6 GHz CPU, 64 GB of memory and RTX 2080 Ti GPU with 11 GB RAM. For all datasets, we resized input images to 640x800] using the bilinear method. Since our network is based on U-net, it produces heatmap outputs of 640x800 sizes.  
The performance of landmark localization methods in Table 3 was evaluated with point-to-point error (PE) and error detection rate (EDR). PE is defined as the Euclidean distance between the ground truth landmark point and the predicted landmark point. The metric \">n mm\" in EDR means the ratio of landmarks whose PE is greater than n millimeter (mm).
# Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation
## 3 Methods
### Preliminaries  
As shown in Figure 1, the image \(F\) is first divided into several \(16\times 16\) grids. For every pixel \(p\) in image \(F\), our goal is to introduce an associate map \(M\) to assign the \(p\) to one of the surrounding nine grids \(S_{i}\), just as is shown in Figure 1. Mathematically, deep-learning-based methods feed the \(F\) to the network and output the associate map \(M\in R^{H\times W\times 9}\). The \(H\) and \(W\) stand for the height and width. The 9 means the nine surrounding grids \(S\). And we see the \(M_{s}(p)\) as the probability of the pixel \(p\) belonging to the seeds \(S\). However, there is no label to compute the loss directly. Therefore, we serve the map \(M\) as an intermediate variable to reconstruct the pixel-wise label, i.e., the property label \(P_{g}\), and the location label \(I_{g}\).  
First, we need to compute the center of the superpixel \(S_{c}=(P_{s},I_{s})\), where \(P_{s}\) is the property vector and \(I_{s}\) is the location vector. The calculation can be written as:  
\[P_{s}=\frac{\sum_{p:S\in N}\cdot P_{g}\cdot M_{s}(p)}{\sum_{p:S\in N}M_{s}(p)} \tag{1}\]  
\[I_{s}=\frac{\sum_{p:S\in N}\cdot I_{g}\cdot M_{s}(p)}{\sum_{p:S\in N}M_{s}(p)} \tag{2}\]  
where \(P_{g}\) and \(I_{g}\) are the property vector and location vector of the image \(F\), which is also the property vector that we want to preserve. The \(M_{s}(p)\) is the probability that the pixel \(p\) belongs to the seed \(S\). After, we compute the property vector and location vector of the center of the superpixel \(S_{c}\). We can reconstruct the property vector \(P_{r}\) and location vector \(I_{r}\) because the pixels in the superpixel have the same low- and mid-level properties. And we can compute the \(P_{r}\) and \(I_{r}\) as follows:  
\[P_{r}=\sum_{S\in N}P_{s}\cdot M_{s}(p) \tag{3}\]  
\[I_{r}=\sum_{S\in N}I_{s}\cdot M_{s}(p) \tag{4}\]  
where \(N\) is the surrounding nine superpixels. \(P_{r}\) and \(I_{r}\) are the reconstructed property and location vectors, respectively. We can obtain the loss by computing the distance between the groundtruth property vector \(P_{g}\) and location vector \(I_{g}\) and the reconstructed property vector \(P_{r}\) and location vector \(I_{r}\). The calculation of the loss can be written as:  
\[L=dist(P_{g},I_{g})+\frac{m}{s}dist(I_{g},I_{r}) \tag{5}\]  
where \(L\) is the loss that we want to obtain. The \(dist(.)\) is the loss function, and we introduce the CrossEntropy loss function. The \(m\) and \(s\) are the balance weight and superpixel sampling interval, respectively. The first part of Equation (5) can encourage the model to group the pixels with the same property. The second can help the model produce a more compact superpixel map.
## 4 Discussion
### Water Saving Potential
Therefore, both \(\mathrm{P_{y}}\) and \(\mathrm{S_{p}}\) were the lowest in the JG treatment; however, the WUE and water saving capacity of the JG treatment were significantly higher than that of the NF and WG treatments. Photosynthetic production potential represents the maximum crop yield achieved only under light conditions. \(\mathrm{Y_{c}}\) was the maximum yield of crop subjected to both light and temperature constraints [35]. Regions that experienced a rapid decrease in the climate production potential of maize over the last 30 years show a dramatic increase in \(\mathrm{Y_{c}}\), resulting from a dry climate [32]. Radiation and temperature are the most critical factors affecting \(\mathrm{Y_{c}}\); these factors decreased by \(-12.70\%\) and \(-6.10\%\), respectively, when solar radiation of maize decreased by 10% or temperature increased by 1 \({}^{\circ}\)C during the growing season [35]. Under micro-spray irrigation, maize yield was the highest in the JG treatment, in which \(\mathrm{Y_{c}}\) was considerably lower than that in the NF and WG treatments, although no significant differences were detected in \(\mathrm{ET_{c}}\) among the three treatments. The photosynthetic production potential of the yield has been the main focus of research in northeast China [36]. The decrease in solar radiation is primarily responsible for the decrease in the photosynthetic production potential of maize. Spatial variation characteristics of the photosynthetic production potential of maize were similar to those of the surface solar radiation, both of which showed a decreasing trend from the southwest to the northeast [37]. [PERSON] et al. systematically analyzed global maize yields and concluded that the maize yield would increase by 50%, if 95% of the maize cultivation areas worldwide met the climate potential. However, the difference between climate production potential and actual production was approximately 20% because of improvements in traditional farming patterns, economic costs, and technological measures, and the yield potential of maize was mainly affected by unreasonable farmland management and low technical levels. Therefore, conclusions explored in our study on the water saving potential of maize using different planting patterns and micro-spray irrigation are extremely conducive to the rational planning of the layout of planting areas, effectively improving maize yield and ensuring food security.
## 3 Overview and Mechanism of Mining-Induced Earthquake Events at the 3308 Working Face
### Analysis of the Mechanism of High-Energy Mining-Induced Earthquake Events
In summary, the 20 December high-energy mining-induced earthquake event was primarily caused by the combined effects of mining activity and stress concentration leading to roof movement within the coal seam. Although this event did not result in casualties or equipment damage, it warrants attention and preventive measures to mitigate subsequent mining-induced earthquake events and rockbursts that could affect the 3308 working face. As the goaf area of the 3308 working face continues to expand, risks of high-energy mining-induced earthquake events persist due to factors such as high ground stress (especially structural stress), fault influence, incompletely caved roofs, fault-protected coal pillars, and mining disturbances. These risks are primarily associated with roof fracturing under high static and dynamic loads. As the working face moves farther from the DF60 fault, faults on the belt roadway side, such as DF56 and DF34, may influence the scale of mining-induced earthquake events. Additionally, lateral support pressure from shallow goaf areas and the incompletely caved roof may impact the safe extraction at the 3308 working face. In conclusion, while microseismic and stress monitoring each have strengths in identifying mining-induced earthquake events and analyzing stress distribution, relying on a single method presents clear limitations in mining-induced earthquake prediction and early warning. Microseismic monitoring cannot directly reflect dynamic stress concentration, and stress monitoring is unable to capture the spatiotemporal characteristics of microseismic activity. These limitations hinder the accurate delineation of risk areas and the effectiveness of control measures. Therefore, integrating multiple monitoring and analysis methods--including microseismic, stress, and source mechanism analyses--is essential to enhance the reliability and accuracy of mining-induced earthquake predictions and support the prevention of rockbursts during subsequent extraction at the working face.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 3 Trade-offs in Wireless Network Design
### Latency  
Latency is reduced by increasing the data rate, in turn enabled by more complex codings and larger bandwidths. Furthermore, multi-hop topologies increase the latency considering that forwarding and routing introduce additional delays. In addition, computing a new route when a link fails also introduces delay which can render multi-hop topologies useless in low-latency time-critical applications. Medium Access Control (MAC) design has a significant impact on latency as well, especially in IoT technologies where devices aim to sleep as long as possible to save energy, therefore delaying transmissions and receptions. MAC protocols can be classified into four classes: (1) Fixed Assignment Protocols where resources are divided among the nodes for a defined time duration, (2) Demand Assignment Protocols where resources are provided to a node on demand, (3) Random Access Protocols where resources are divided randomly and (4) Hybrid Protocols that combine fixed or demand assignment with random access. Fixed Assignment Protocols such as Time Division Multiple Access (TDMA) introduce determinism and achieve lower latency than random access protocols under very high load, but under low load they waste resources by inefficient usage of the channel time,  
Figure 4: Network design choices (white rectangles) influence several performance properties simultaneously (grey rectangles), thus creating the trade-offs between them.  
where random access protocols achieve lower latency. Demand-based protocols are not suitable for low-latency time-critical communications given that explicitly asking for resources every time takes up bandwidth and adds up to latency. For heterogeneous industry applications, hybrid approaches are the most promising given that they aim to combine the benefits of both fixed assignment and random access protocols, while surpassing their limits at the same time and adapting to the network conditions. In addition, retransmissions need to be kept a minimum as they also increase the latency.
## 2 Materials and Methods  
### Study Sites  
Two study sites were selected in this study. The first study site was the Washington Park Arboretum (WPA), which is located at the south of the University of Washington campus in Seattle, Washington, USA. It is an urban heterogeneous forest park. The dominant tree species include Douglas fir (_Pseudotsuga menziesii_), western hemlock (_Tsuga heterophyllla_), western red cedar (_Thuja placata_), bigleaf maple (_Acer macrophlylhum_), monkey puzzle (_Araucaria araucana_), southern magnolia (_Magrolia grandiflora_), and New Mexican locust (_Robinia Neomexicana_). The terrain slope throughout the WPA site is less than 15%. The second study site was the Panther Creek (PC) watershed, which is a natural forest located at the southeast of the city of Portland, Oregon, U.S.A. It is a managed natural forested area. The Douglas fir (_Pseudotsuga menziesii_), western hemlock (_Tsuga heterophyllla_), western red cedar (_Thuja placata_), and bigleaf maple (_Acer macrophlylhum_) are dominant tree species (Figure 1). The slope of the PC site was up to 51.3\({}^{\circ}\).  
We established four circular plots (i.e., WPA-P1, WPA-P2, WPA-P3, and WPA-P4) with a radius of 30 m in the WPA site, and three square plots (i.e., PC-P1, PC-P2, and PC-P3) with a side length of 40 m in the PC site based on the ground measurement data. According to forest densities' variations, we selected four plots (i.e., WPA-P1, WPA-P2, WPA-P3, and PC-P1) to validate the ALS-based four forest component results. Meanwhile, three plots (i.e., WPA-P4, PC-P2, and PC-P3) were selected to explore the effects of ALS data characteristics on the estimation of the four forest component proportions resulting from a range of acquisitions of ALS data in these plots. Moreover, to investigate the effects of surrounding forest stands on the four forest component proportions estimation of a given forest stand, we constructed a plot (i.e., WPA-P2E) by extending the radius of plot WPA-P2 to 90 m. Based on the values of normalized difference vegetation index (NDVI) of pixels of the HyMap hyperspectral imagery, these forest plots were divided into three levels: Low density (i.e., 0.1 \(<\) NDVI \(<\) 0.3), medium density (i.e., 0.3 \(<\) NDVI \(<\) 0.5), and high density (i.e., NDVI \(>\) 0.5).More detailed characteristics of the plots can be found in Table 1.  
Figure 1: The original aerial laser scanning (ALS) colored by height and corresponding ALS data overlaid with true-color aerial photos in the urban heterogeneous forest of Washington Park Arboretum (WPA) in Washington state, USA, and natural homogeneous forest of Panther Creek (PC) site in Oregon state, USA. Seven forest plots in total (i.e., WPA-P1, WPA-P2, WPA-P3, WPA-P4, PC-P1, PC-P2, and PC-P3) were set up in these two study sites.