esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The Case for Cross-Layer Design
### Justification for Cross-Layer Design  
There are a number of reasons why the traditional layered approach to network design has been questioned and many of these have direct relevance to the specific needs of WBANs:  
_Network Heterogeneity_--The standard layered protocol stack can adequately cater for the requirements of heterogeneous networks such as WBANs in terms of integration into a defined structure, but this may be at the expense of performance and energy efficiency. For example if a routing path is set up between two nodes through different types of network, the routing protocol can simply find an available path considering metrics as shortest distance or lowest cost. However, if nodes want to find a path with enough bandwidth, then the routing protocol needs to consider the interactions with the MAC layer. This cross-layer interaction isn't possible via the standard protocol stack.  
_QoS_--Due to their application, WBANs have a specific requirement for high levels of QoS. High PDR, low levels of packet transmission delay, minimal collisions and retransmissions, optimal balancing of high energy efficiency and reliable transmission are all areas that are keys within the WBAN environment. The traditional layered structure may not be the optimal solution to guarantee these critical levels of service and may limit the potential improvements needed to guarantee energy efficiency and improvements to QoS.  
_Channel Conditions_--The capacity of a link within a WBAN can vary not just due to the standard wireless link issues such as node interference [13], but issues due to the inherent behavior of RF signals in and around the human body such as signal absorption and attenuation effectively resulting in a variance in RF transmission power and path losses depending on the direction of propagation. Channel conditions can change due to factors such as antenna orientation, clothing type, physical stature of the patient, and postural position [69]. These changes in channel conditions can be catered for specifically within cross-layer schemes that incorporate link adaptation to take account of channel variances.  
_Performance_--The closely coupled nature of the standard layered protocol stack means that interaction between protocols is standardized but potentially at the expense of performance. Data communication flows from one layer to another regardless of whether the protocol in any particular layer has any significant role to play in the overall transmission or receipt of data.
# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
###### Abstract  
The paper presents a fast method for 3D point cloud target extraction, addressing the challenge of time-consuming processing in LiDAR-based 3D point cloud data. The method begins with the acquisition of environmental 3D point cloud data using LiDAR, which is then projected onto a 2D cylindrical map. We propose a method for rapid target extraction from LiDAR-based 3D point cloud data, which includes key steps such as projection into 2D space, image processing for segmentation, and target extraction. A mapping matrix between the 2D grayscale image and the cylindrical projection is derived through Gaussian elimination. A target backtracking search algorithm is used to map the extracted target region back to the original 3D point cloud, enabling precise extraction of the 3D target points. Near-field experiments using hybrid solid-state LiDAR demonstrate the method's effectiveness, requiring only 0.53 s to extract 3D target point clouds from datasets containing hundreds of thousands of points. Further, far-field rocket launch experiments show that the method can extract target point clouds within 158 milliseconds, with measured positional offsets of 0.2159 m and 0.1911 m as the rocket moves away from the launch tower.  
hybrid solid-state LiDAR; 3D point cloud; target extraction; 2D grayscale image; target retrospective search algorithm +
Footnote †: journal: Applied sciences  
Article  
[PERSON]  
1 Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China;  
1 [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (H.S.)
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis
### Approximation SWIPT Relay to AF Relay
#### 4.5.2 Average SER and BER Expressions  
The average BERs for the indirect and the combined links can be approximated, respectively, as  
\[\begin{split} P^{\rm AF-APP}_{\rm S,id}&=\frac{1}{ \pi}\int_{0}^{(M-1)\pi/M}\prod_{r=1}^{R}\frac{\pi_{\rm id,r}}{\tilde{\gamma}^{ \rm min}_{r}\mathsf{s}+1}d\theta\\ P^{\rm AF-APP}_{\rm S,cb}&=\frac{1}{\pi}\int_{0}^{ (M-1)\pi/M}\prod_{r=0}^{R}\frac{\pi_{\rm cb,r}}{\tilde{\gamma}^{\rm min}_{r} \mathsf{s}+1}d\theta\end{split} \tag{41}\]  
with \(\mathsf{s}=g_{\rm PSK}/\sin^{2}(\theta)\) and \(g_{\rm PSK}=\sin^{2}(\pi/M)\)[43]. It is noteworthy that (41) with \(M=2\) yields the asymptotic BERs in (35). For the specific case of BPSK (\(M=2\)), the average BERs can also be expressed in a more tractable closed-form as  
\[\begin{split} P^{\rm AF-APP}_{\rm B,id}&=\sum_{r=1}^ {R}\frac{\pi_{\rm id,r}}{2}\left[1-\sqrt{\frac{\tilde{\gamma}^{\rm min}_{r}}{ \tilde{\gamma}^{\rm min}_{r}+1}}\right]\\ P^{\rm AF-APP}_{\rm B,cb}&=\sum_{r=0}^{R}\frac{\pi_{ \rm cb,r}}{2}\left[1-\sqrt{\frac{\tilde{\gamma}^{\rm min}_{r}}{\tilde{\gamma}^{ \rm min}_{r}+1}}\right].\end{split} \tag{42}\]
## 4 Results and Discussion
### Modeling Results  
#### 4.2.1 Classification of Coniferous Tree Species  
After completing the preceding data preprocessing tasks, we began to construct the model using Random Searching and Grid Search to determine the optimal hyperparameters for the employed methods. The method of RF was used for softwood specimen classification. The number of classifiers was 15, and the classification accuracy on the test set was 100%. Figure 15 displays the classification results of three coniferous woods.  
As shown in Figure 15, the RF model can be used to achieve accurate classification of specimens of three coniferous tree species. Therefore, comparison of other modeling methods is not carried out in this part. However, we can learn that the removal of abnormal samples can make the obtained spectra more representative, and the application of NIRS analysis technology can realize the accurate determination of tree species [8].
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## 5 Results and Discussion
### Discussion  
Based on the aforementioned experimental results, the following conclusions can be drawn:  
1. The compiler and matching module design satisfy normal operational requirements under the 100 Gbps test environment, supporting high-throughput line-rate operation. As demonstrated in Section 5.1, we evaluated the impact of average packet size and anomalous traffic ratio on module throughput under 100 Gbps traffic, obtaining the system's throughput performance under extreme conditions. Compared to typical network environments, the results with average packet size exceeding 700 B and anomalous traffic ratio below 10% confirm the system's capability to fulfill practical deployment demands.  
2. The system achieves the predefined targets of supporting 10,000+ rules and 100 Gbps line-rate operation with accuracy exceeding 97%, fulfilling the requirements for large-scale rule set-based IDS in high-throughput networks. As validated in Section 5.2, we tested the system's throughput and accuracy under 100 Gbps traffic with 2000, 6000, and 10,000 rules, respectively. Comparative analyses against software Snort and industry architectures with similar designs (Pigasus and Fidas) comprehensively demonstrate the system's superior performance in throughput, rule set scalability, and detection accuracy.
## 3 System Framework and Model Training
### Datasets Description
#### 4.1.2. UoC Gear Fault Dataset  
According to a benchmark study [26], the CWRU bearing fault dataset is relatively easy to diagnose. However, the University of Connecticut (UoC) gear fault dataset [27; 28] is a challenge for many classical models such as autoencoder (AE) and CNN. Therefore, we use the UoC gear fault dataset to further evaluate the proposed method on different types of rotating machinery.  
As shown in Figure 5, the two-stage gearbox is driven by an electric motor. The first stage consists of a 32-tooth pinion on the input shaft and an 80-tooth gear, and the second stage consists of a 48-tooth pinion and a 64-tooth gear. A magnetic brake is connected to the output shaft as the load. By replacing the pinion on the first stage input shaft, nine different working conditions are introduced into the system, including healthy condition, missing tooth, root crack, spalling, and chipping tip with five different levels of severity, as shown in Table 3 and Figure 6. An accelerometer is used to record the original vibration signals of the gears with a sampling frequency of 20 kHz, and 104 samples are recorded for each working condition, each sample containing 3600 data points.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Condition** & **Defect Diameter (Inch)** & **Label** \\ \hline
1 & Normal & - & Normal \\
2 & Inner race & 0.007 & IR07 \\
3 & Inner race & 0.014 & IR14 \\
4 & Inner race & 0.021 & IR21 \\
5 & Rolling elements & 0.007 & RE07 \\
6 & Rolling elements & 0.014 & RE14 \\
7 & Rolling elements & 0.021 & RE21 \\
8 & Outer race & 0.007 & OR07 \\
9 & Outer race & 0.014 & OR14 \\
10 & Outer race & 0.021 & OR21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CWRU bearing dataset faults.  
As same as the CWRU dataset, we split the original data into segments of 1024 data points in the time domain, then FFT and standardization are performed. Eventually, the gear dataset consists of 312 samples for each category, for a total of 2808 samples.
# Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
## 1 Introduction
Euclidean distance, the well-known dynamic time warp (DTW) measure [7; 8], the soft version of DTW (soft-DTW, [9]) or a kernelization version of DTW (\(\mathcal{K}_{\text{rdtw}}\), [10]); and (ii) four centroid based methods, namely the Euclidean centroid [11], the DTW Barycenter Averaging (DBA) centroid according to the method proposed in [12], the soft-DTW centroid developed by [PERSON] and [PERSON] [9] and the iterative Time Elastic Kernelized Averaging proposed (iTEKA) in [13]. Section 2 introduces the main concepts that are behind the DTW time elastic measure and its kernalization. It also introduces the general purpose (iterative Time Elastic Kernel Averaging (iTEKA) procedure that has been specifically developed to average sets of time series. Section 3 addresses the averaging of sets of GPS trajectories. It details mostly the preprocessing and postprocessing steps as well as the trajectory outlier detection and removal stage. Section 4 presents an experimentation carried out in the context of the \"Averaging GPS segments Competition\" ([[https://cs.uef.fi/sipu/segments/](https://cs.uef.fi/sipu/segments/)]([https://cs.uef.fi/sipu/segments/](https://cs.uef.fi/sipu/segments/))) [14] proposed by University of Eastern Finland. A conclusion ends this study.
# Designing an Interactive Visual Analytics System for
## 5 Interactive Precipitation Data Analysis System
### Station-Specific Analysis Interface
#### 5.1.2 Precipitation Anomaly Detection
After measuring the mean of the Weibull distribution using the gamma function, \(\mathbb{E}[X]=\lambda\cdot\Gamma(1+\frac{1}{k})\), anomalies are determined by evaluating the probability of observing precipitation and comparing it to the threshold (0.05 or 0.01). Figure 4C shows that no precipitation anomaly is detected with the Weibull distribution (\(p<0.01\)). However, when evaluating the data with the statistical significance \(p<0.05\), we found seven precipitation anomalies (see Figure 4D). Figure 4: Visualizations of precipitation anomaly detection using different statistical distributions: (**A**) the normal distribution, (**B**) the gamma distribution, and (**C,D**) the Weibull distribution. Visualizations (**A–C**) are based on precipitation data from 1950 to 2024, while (**D**) represents precipitation data from 2000 to 2024. (**C,D**) applied the Weibull distribution with statistical significance of \(p<0.01\) and \(p<0.05\), respectively. Red-colored glyphs indicate detected anomalies based on distribution-specific thresholds.
# Hardware Update through Digital TV Signals
## 6 Simulation Results
The hardware reconfiguration scheme presented here is an innovative approach compared with the literature on reconfigurable architectures. The main idea of HARD relies on a delivery methodology for pre-synthesized hardware cores. Recent studies (as seen in Section 2) used pre-synthesized cores for runtime reconfiguration and presented similarities with HARD. The main difference is that the former need to maintain several previously stored cores. Then, a resident system must decide when to use each one. HARD, in turn, can broadcast cores to a considerable number of devices, which are then automatically reconfigured. That aside, it could send the pre-synthesized cores of several manufacturers, and each device would then be responsible for accepting or rejecting specific content. Another feature is that a broadcaster can send hardware update data cyclically, allowing all devices in range to perform hardware reconfiguration. Thus, in DTV networks, where receivers are based on replacing hardware modules, new technology advances and enhancements could be immediately incorporated, leading to a flexible DTV environment.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 3 2\(\pi\) Ambiguity Problem
### Simultaneous Dual Frequency Modulation with Sequential Demodulation  
In order to overcome the disadvantages of the sequential dual-frequency modulation method, there is an alternative method which superposes each modulation frequency into a single modulation signal but demodulates it with different frequencies (\(f_{1}\) and \(f_{2}\)) sequentially to get a single phase image [22]. By repeating the procedure using the different initial phase shift, \(\delta_{n}\), \(n=1,2,\,\dots\), \(N\), \(N\) different samples are obtained to extract actual distance. Figure 3 is schematic of simultaneous dual frequency modulation with sequential demodulation, which shows waveform of modulation and demodulation signals [22].  
The main advantage of this method is that it does not significantly increase the total time for one actual distance measurement compared to the single-frequency modulation method described in Section 3.1[22]. One disadvantage of this method, however, is that the demodulation signal must change over time. Moreover, it requires more than five-samples to obtain a single distance [22].  
Figure 3: Schematic of simultaneous dual frequency modulation with sequential demodulation. (**a**) Superposition of two modulation frequencies to modulate the illumination source; (**b**) demodulation signal separated into two parts in sequence.  
Figure 2: Sequential dual-frequency modulation scheme.
# The Inactivation of Microscopic Fungi in Bakery Products Using Hurdle Technology--A Case Study
## 2 Materials and Methods
### Myological Testing of Bakery Products  
The amount of microscopic fungi was also determined in the bakery products. A 10 g sample was taken from each of the four types of bread (Section 2.4) and transferred to 90 mL of 0.1% peptone water (BTL, Lodz, Poland) and placed in stomacher bags with a side filter (MERCK, Germany). The whole sample was homogenized for 5 min in a stomacher BagMixer 400 (Interscience, Saint Nom la Breetche, France) at a speed of 8 beats per second. The resulting solution was diluted by making successive 10-fold dilutions (10\({}^{-1}\)-10\({}^{-5}\)). From each dilution, 1 mL was taken, added to Petri dishes, and dichloran-glycerol agar (DG-18, Merck, Darmstadt, Germany) medium with chloramphenicoloid was poured into the dishes (Angene, London, UK). The plates were incubated at 25 \({}^{\circ}\)C for 3 days and then the grown colonies were counted. Tests were performed on the samples after half baking and 24 h after packaging the products in MAP (70% CO\({}_{2}\)/30% N\({}_{2}\)). The number of microscopic fungi was determined both in the crumb and in the outer crust of the bread. Each test was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Room Type** & **Sample Number** & **Impact Air Sampling Location** \\ \hline Drilling room & 1 & Drilling machine—the troughs on which hot dogs lie during drilling \\ Driller & 2 & Drilling machine—the top at the front on which the hot dogs lie before drilling \\  & 3 & Drilling machine—the surface that presses the hot dog during drilling \\ \hline MAP packing room & 4 & Swab from the surface of the top of the T 10 machine \\
6 & 5 & Swab from the surface of the T 17 machine \\  & 6 & Swab from the bread conveyor belt suspended from the ceiling \\  & & the ceiling \\ \hline \hline \end{tabular}
\end{table}
Table 2: Markings and description of locations where samples were taken from production areas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Room Type** & **Sample Number** & **Impact Air Sampling Location** \\ \hline Drilling room & 1 & On the right side of the hall, at the entrance from the packing room \\ Drilling room & 2 & At drilling machine A \\  & 3 & At drilling machine B \\ \hline \hline \multirow{4}{*}{MAP packing room} & 4 & At the pole located next to the packing \\  & 5 & Between the machines, in the middle of the hall \\  & 6 & In the corner of the hall, under the conveyor \\ \hline \hline \multirow{4}{*}{Atmospheric air} & & Right in front of the entrance to the plant \\  & & through the gate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Designation and description of the locations where air samples were taken.  
performed in triplicate, with the detection level equal to 1.0 \(\times\) 10\({}^{1}\) cfu/g. The result is given in cfu/g of product.
## 3 Results
### N Fluxes and Cumulative Emissions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Oic Incubation Phase (0–6 DAO)**} & \multicolumn{4}{c}{**Anoxic Incubation Phase (47–55 DAO)**} \\ \hline  & **Cumulative NO** & **Cumulative N\({}_{2}\)O** & **NO/N\({}_{2}\)O** & **Cumulative NO** & **Cumulative N\({}_{2}\)O** & **Cumulative N\({}_{2}\)** & **N\({}_{2}\)O(N\({}_{2}\)O+N\({}_{2}\))** \\  & **(ug N\({}_{2}\)-1 day soil)** & **(ug N\({}_{2}\)-1 day soil)** & **(ug N\({}_{2}\)-1 day soil)** & **(ug N\({}_{2}\)-1 day soil)** & **(ug N\({}_{2}\)-1 day soil)** & **(ug N\({}_{2}\)-1 day soil)** & **(ug N\({}_{2}\)-1 day soil)** \\ \hline Control & 24.1 \(\pm\) 2.5 & a & 78.3 \(\pm\) 97.2 & c & 0.37 \(\pm\) 0.19 & a & 0.29 \(\pm\) 0.04 & c & 16.6 \(\pm\) 2.5 & c & 0.88 \(\pm\) 0.33 & c & 0.95 \(\pm\) 0.03 & a \\ Maize Leaves & 20.8 \(\pm\) 1.4 & b & 387.2 \(\pm\) 94.4 & a & 0.05 \(\pm\) 0.02 & b & 0.64 \(\pm\) 0.10 & a & 29.8 \(\pm\) 9.0 & a & 6.75 \(\pm\) 4.28 & ab & 0.83 \(\pm\) 0.04 & b \\ Maize Roots & 22.9 \(\pm\) 2.8 & ab & 319.0 \(\pm\) 81.0 & b & 0.07 \(\pm\) 0.01 & b & 0.41 \(\pm\) 0.06 & b & 21.5 \(\pm\) 1.5 & bc & 3.07 \(\pm\) 1.58 & bc & 0.85 \(\pm\) 0.05 & b \\ Maize Stress & 100.0 \(\pm\) 1.7 & c & 552.2 \(\pm\) 20.7 & a & 0.02 \(\pm\) 0.01 & b & 0.48 \(\pm\) 0.06 & b & 24.8 \(\pm\) 1.0 & ab & 8.36 \(\pm\) 2.06 & ab & 0.75 \(\pm\) 0.05 & c \\ \hline \end{tabular} Values represent means (n = 5, for Control n = 0) standard deviation. Different letters in the same column indicate a significant difference according to the 15D post hoc test at \(p\leq 0.05\). No. different units for gas emissions during ouc and anoxic incubation phases.  
\end{table} 
Table 5: Cumulative NO, N\({}_{2}\)O, and N\({}_{2}\) emissions and ratios of gaseous products during ouc and anoxic incubation. After 47 days, anoxic incubation conditions were induced by flushing the headspace with pure helium gas. N2O, NO, and N2 fluxes strongly increased with onset of anoxic incubation conditions (Figure 2c-e). N2O and NO fluxes peaked on 48 DAO and then decreased until the end of the experiment. N2 fluxes increased after onset of anoxic conditions until the end of the experiment. During the anoxic phase, cumulative N2O, NO, and N2 emissions were higher in litter treatments than in Control, although the effect was not always statistically significant for maize roots (Table 5). The highest cumulative emissions were measured for NO (0.64 mg N kg\({}^{-1}\)) and N2O in Leaf (29.8 mg N kg\({}^{-1}\)), and for N2 in Straw (8.4 mg N kg\({}^{-1}\)). The ratio of the gaseous end products N2O/(N2O+N2) was highest in Control (0.95) and lowest in Straw (0.75).
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 5 Conclusions  
This paper solves the problems of resource pool establishment and performance evaluation in satellite edge computing. When a resource pool is introduced into satellites, the resource pool is unstable due to the orbital motion of satellites and the dynamic change of network topology. Using the predictability of satellite orbit and a search matching algorithm, this paper solves the dynamic satellite selection problem. The queuing theory is used to evaluate the performance of the traditional isolation mode, cooperation mode, and resource pool mode. Compared with the isolated mode, the task success rate of the pooled mode can be increased from 26.21% to 45.73%, and the response time can be accelerated by 19.52% in the case of fewer tasks. The response time is consistent when the number of tasks is high. In terms of resource utilization, the CPU resource utilization of satellites can be improved by 33.27%, and the network usage is significantly increased. However, it also increases the energy consumption, from nearly 26.12% to approximately 37.82%. Compared with the cooperative mode, the average task waiting time, task failure rate, and resource utilization performance are close to those of the pooled mode, but there are two improvements. Firstly, the response of the pooled mode is faster and more stable in the case of a small workload. Secondly, in terms of network and CPU resource utilization, the resource utilization among satellites is more balanced in the pooled mode.  
Therefore, compared with the isolated mode of the traditional satellite, both cooperative mode and pooled mode not only improve the task success rate, but also improve resource utilization at the cost of consuming more energy. However, compared with the cooperative mode, the pooled mode can improve the response time of tasks and improve the load balance of system resources. The pooled mode has a wide application prospect in the battlefield and other scenarios with high task requirements. In future work, the effect of the space-based system network and other factors on the resource pool will be studied to optimize the architecture, such as autonomous decision-making methods for maintaining service performance and communication resource consumption. Further consideration will also be given to issues such as data security in resource sharing in the resource pool.  
Conceptualization, J.Y. and J.Q.; Methodology, X.G.; Software, X.L.; Validation, J.Q. and X.M.; Formal Analysis, J.Q.; Writing, J.Q.; Supervision, J.Y. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.