esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 4 Results
### Comparison of Spectral and Geometrical Features from Multi-Wavelength LiDAR  
Airborne LiDAR records both 3D coordinates and reflected signals, and the LiDAR points can be used to generate the spectral features (i.e., intensity and vegetation index) and geometrical features (i.e., height and roughness). One characteristic of multi-wavelength LiDAR is providing spectral features at different wavelengths. The spectral feature of multi-wavelength LiDAR is not only the intensity, but also the vegetation index from the infrared and green channels. To compare the capability and benefits of spectral and geometric features for different land cover classification, we designed three combinations (Table 6): spectral features included intensity and NDFIs; geometrical features included nDSM and curvature; and the third merged the features of previous two combinations. For these three cases, the input features for classification were also the features for segmentation, and we used equal weight when combining all these input features in segmentation. We summarized the results of these three cases (Figure 8) and the completeness and correctness for each land cover in different combinations  
The completeness from spectral features (Case 2-1) was higher than 80% because road, soil, and grass have different spectral signatures [37]. In addition, the results of spectral features were significantly better than for the geometrical features (Case 2-2) for road, soil, and grass. The geometrical features showed higher accuracy for buildings and trees because these two objects have different roughness (i.e., curvature). Because the heights of grass, road, and soil classes are similar; these geometrical features are not easily discriminated among those objects. The integration of spectral and geometrical features (Case 2-3) improved the accuracy of these three classes from 1.7 to 75.5 percentage points.  
In correctness analysis, the integration of all features (Case 2-3) improved the accuracy from 1.1 to 65.6 percentage points. Most accuracy indices from spectral features were higher than those from geometrical features (Table 7); in other words, the results indicate that the use of LiDAR spectral features contributes more than the geometrical features. The OA of spectral features reaches 93% but only 64% for geometrical features (Figure 10) because of the higher accuracy for buildings and trees but lower accuracy for grasses, roads, and soils. The results demonstrate that spectral features from LiDAR could be useful features in land cover classification when multi-wavelength intensities are available.
## 2 System Model and Transmission Process
### Transmission Process
#### 2.2.2 Cooperative Transmission Stage of the Second Time Slot  
In the second time slot, the cooperative links are activated. An optimal relay is selected to forward the signal from BS by using adaptive power allocation coefficients. There are two possible scenarios:  
1. Relay \(R_{n}\) is selected as the optimal relay. \(R_{n}\) will re-encode the decoded signal and generate a new superimposed signal, \(x_{R_{n}}^{\Theta}=\sqrt{\beta_{n1}^{\Theta}}x_{1}+\sqrt{\beta_{n2}^{\Theta}}x _{2}\), according to SC. Here \(\beta_{n1}^{\Theta}\) and \(\beta_{n2}^{\Theta}\) denote the power allocation coefficients at the optimal relay for messages \(x_{1}\) and \(x_{2}\) with \(\beta_{n1}^{\Theta}\geq 0\), \(\beta_{n2}^{\Theta}\geq 0\) and \(\beta_{n1}^{\Theta}+\beta_{n2}^{\Theta}=1\), respectively. U\({}_{2}\) is not affected by PT because it is far from PT. Therefore, the received signal at U\({}_{2}\) and U\({}_{1}\) can be expressed as \[y_{R_{n}U_{2}}=\sqrt{P_{R}}h_{R_{n}U_{2}}x_{R_{n}}^{\Theta}+\eta_{U_{2}}\] (5) and \[y_{R_{n}U_{1}}=\sqrt{P_{T}}h_{TU_{1}}x_{P}+\sqrt{P_{R}}h_{R_{n}U_{1}}x_{R_{n} }^{\Theta}+\eta_{U_{1}}\] (6) respectively, where \(P_{R}\) is the transmit power of relays and U\({}_{1}\).
2. U\({}_{1}\) is selected as the optimal relay. Then, U\({}_{1}\) will forward \(x_{2}\) with full power because it has successfully decoded both \(x_{1}\) and \(x_{2}\). The received signal at U\({}_{2}\) is \[y_{U_{1}U_{2}}=\sqrt{P_{R}}h_{U_{1}}u_{2}x_{2}+\eta_{U_{2}}\] (7) and the achievable rate of \(x_{2}\) at U\({}_{2}\) is given by \[R_{U_{1}U_{2}}^{x_{2}}=\frac{1}{2}log_{2}\Big{(}1+\rho_{R}\big{|}h_{U_{1}U_{2 }}\big{|}^{2}\Big{)}\] (8) where \(\rho_{R}=P_{R}/\sigma^{2}\) is the transmit SNR for relays and U\({}_{1}\).
## 3 Coordinate Transformation of Structural Plane Information
### Establishment of a Spatial Coordinate System  
The coordinate system established in this study is a local project-specific system aligned with the tunnel cross-section design. It is not referenced to a global geodetic coordinate system such as WGS84 or UTM. The origin and axis directions are defined based on known geometric markers in the image (yellow origin and green scale bar), with a physical-to-pixel conversion ratio derived from field measurements. The expected positioning accuracy of the coordinate origin is within \(\pm\)5 pixels, which corresponds to an average physical deviation of less than 2 cm in both X and Z directions, defined as follows:  
X-axis: Defined by the line connecting the intersection points of the lower arcs in the inner contour drawn using the three-center circle method. The direction to the right is taken as positive. This axis represents the lateral position of the tunnel cross-section.  
Z-axis: Defined as the perpendicular bisector of the X-axis line, with the upward direction being positive. It indicates the vertical elevation of the cross-section.  
Y-axis: Oriented in the tunnel excavation direction, with forward advancement as positive. This axis describes the spatial location of the structural plane along the tunnel's longitudinal direction.  
This coordinate system not only conforms to conventional tunnel engineering practices but also facilitates data sampling and 3D reconstruction.
###### Abstract  
Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990-2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfull temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash-Sutcliffe efficiency coefficient (NSE), Kling-Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.  
parameter sensitivity; SWAT; UHRB; Morris; Sobol; FAST; EFAST +
Footnote †: journal: Applied Sciences
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
###### Abstract  
In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource Allocation (SRA) considering the channel and queue state, while supporting fairness. In this paper, we extend the SRA algorithm to consider 5G use-cases, namely enhanced Machine Type Communication (eMTC), Ultra-Reliable Low Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB). In a simulation study, we evaluate the performance of our SRA algorithm in comparison to an advanced greedy cross-layer algorithm for eMTC, URLLC and LTE (long-term evolution). For eMTC and URLLC, our SRA method outperforms the greedy approach by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. In the case of LTE, our approach outperforms the greedy method by 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness compared with tested baseline.  
5G wireless technology; massive multiple-input-multiple-output (MIMO) communications; scheduling and resource allocation (SRA); orthogonal frequency division multiplexing (OFDM); filter bank multi-carrier (FBMC) +
Footnote †: This paper is an extended version of our paper published in IEEE 5G World Forum (5 GWF), Santa Clara, CA, USA, 9–11 July 2018.
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 4 Experiments and Analysis of Results
### Comparison with Other Algorithms
Furthermore, the detailed comparison presented in Table 6 elucidates the superior performance of our model over a range of defect types. Specifically, our model demonstrated exceptional precision in identifying 'B-leakage' and 'B-pit' defects, with an AP of 97.3% and 99.9% respectively, eclipsing the other YOLO variants by a significant margin. These findings are not just statistical victories; they translate into tangible benefits in the manufac \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters/M** & **FPS/f.s-1** & **Precision/P** & **Recall/R** & **[EMAIL_ADDRESS]** \\ \hline SSD [39] & 26.29 & 63 & 72.8 & 66.5 & 70.1 \\ Faster RCNN [40] & 134.08 & 16 & 80.1 & 72.4 & 76.4 \\ YOLOv5n [41] & 7.05 & 58 & 86.7 & 86.8 & 88.8 \\ TPH-YOLOv5 [42] & 46.3 & 61 & 92.7 & 82.3 & 88.4 \\ YOLOv7-tiny [43] & 6.05 & 120 & 73.5 & 81.2 & 81.8 \\ YOLOv8n [44] & 3.01 & 96 & 90.3 & 80.7 & 86.3 \\ Gold-YOLO [45] & 5.61 & 95 & 83 & 87.9 & 83.5 \\ Ours & 3.37 & 78 & 97.9 & 91.2 & 95.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results from different algorithms. turing process, in which early and accurate defect detection is crucial to maintaining the integrity of the production line. In instances of 'C-mushy spot' and 'C-chipping' defects, our model again surpassed the YOLOv5n and YOLOv7-tiny, with APs of 91.0% and 91.3%, respectively. This high level of accuracy in defect detection ensures that even the most subtle irregularities are captured, which is vital for the longevity and reliability of electronic components. For 'D-defect' and 'D-soiling', the improved model maintained its lead with an AP of 75.5% and 95.6%. These scores are not only reflective of the model's ability to detect a wide variety of defect types but also its adaptability to different defect characteristics, which is essential for a comprehensive quality control system.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 1 Introduction
However, in each coherence block, there are a limited number of pilot signals available, with the exact number depending on the pilot scheme and how much of the block is dedicated to channel estimation via pilot signals versus transmission of data. Pilot signals then determine which user devices can estimate their channels and thus be spatially multiplexed in the same coherence block, allowing them to make use of the time and frequency resources in the block to transmit simultaneously. In this paper, we consider the case with both deadline-based industrial control traffic and rate-based background traffic in the system, and develop a scheduling solution for dynamic RAN slicing that accommodates both simultaneously. In previous work [16], we examined the scheduling problem with the objective of overall pilot usage minimization. In a networking slicing context, this would correspond to static slicing, in which each slice is given a fixed allocation and no sharing of resources between slices is possible. With dynamic slicing, unused pilots allocated to one slice can be borrowed by lower-priority slices, and it is desirable to minimize the actual number of occupied pilot resources, not just the overall size of the slice. This adds considerable complexity to the optimization problem but yields a more efficient use of scarce radio resources. In this paper, we formulate two different mixed-integer programming (MIP) optimization problems, for cases where the frame length for scheduling is constant or variable, where the objective is to minimize the maximum number of pilots per time slot. The industrial control slice takes priority as it serves critical traffic, and so, by minimizing the number of pilots allocated to this slice, we maximize the resources left available for other slices. We have tested our formulations with numerical experiments with up to 32 nodes and a range of different traffic patterns. The formulation for fixed frame length is unsurprisingly faster to solve than that with variable frame length. Minimizing the number of pilots used per slot is a more difficult objective than minimizing a static slice allocation as we did in our previous work, and so, it results in slower solution times. However, with the help of an iterative solution procedure that we developed, we were able to solve all test cases in less than 1 s. This means that our scheduling solutions are feasible to implement in real-world scenarios, where the active time for a schedule will typically be much longer than the solution times we obtained. The rest of this paper is organized as follows. Section 2 describes existing work on scheduling and RAN slicing for industrial communications using massive MIMO. Next, Section 3 details the smart factory scenario we target, and Section 4 gives our system model. In Section 5, we then elaborate our optimization formulations. Section 6 outlines the numerical experiments we conducted, and their results are then given in Section 7. Finally, Section 8 concludes this paper.
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 3 Molecular Mechanisms of T3 SS Deployment in Response to Environmental Stimuli
### Negative Regulation of the T3 SS by the RhpSR Two-Component System  
The RhpSR two-component system, characterized primarily in the model pathogen _P. syringae_ pv. _phaseolicola_ 1448A (alternatively _P. savastanoi_ pv. _phaseolicola_ or _P. amygdala_ pv. _phaseolicola_), also regulates the expression of T3 SS-associate genes. In its phosphorylated form, the response regulator RhpR represses the T3 SS through binding to a semi-conserved inverted repeat element in the promoters of _hrpR_ and T3 SS effector gene _hopR1_[144; 145; 146]. Phosphorylated RhpR additionally downregulates the T3 SS through the activation of the promoter of the _lon_ protease gene, promoting Lon-mediated HrpR degradation [144; 145; 146; 147]. The sensor histidine kinase of this two-component system, RhpS, is capable of autokinase activity, and exhibits both kinase and phosphatase activity towards RhpR [146]. Under T3 SS-repressive environmental conditions, such as nutrient abundance in rich culture medium, RhpS functions as a kinase to promote T3 SS repression through the phosphorylation of RhpR, while T3 SS-inducing conditions favor RhpS phosphatase activity towards RhpR, thereby decreasing RhpR activity at target promoters [144; 146].  
RhpR is also phosphorylated independently of RhpS, potentially by physiological phosphodonors such as acetyl phosphate and/or by sensor kinases from other two-component systems [146]. In addition to the repression of the T3 SS, RhpR broadly regulates central cellular maintenance, including ribosomal protein synthesis and various cell envelope-related processes, in a phosphorylation state- and culture medium-dependent manner [144; 146; 148]. RhpR similarly activates its own promoter, thereby maintaining T3 SS repression under non-permissive conditions by a negative feedback loop [146; 147]. Co-regulation of T3 SS and cellular maintenance by the RhpSR system may facilitate a general tradeoff between pathogen virulence and metabolism according to environmental conditions such as nutrient availability [145; 146]. However, the specific environmental stimuli that govern RhpSR activity remain unknown.
## 3 Application Examples and Comparative Analysis  
### Selection of Relevant Parameters and Calculation Points  
A tunnel in Fujian Province is a double-track highway tunnel with a 10 m net spacing that was constructed using the upper- and lower-step method, with simultaneous construction of the left and right tunnels. The surrounding rock is weathered granite with well-developed joint fissures; its physical and mechanical properties are listed in Table 1. Engineering practice indicates that slotted holes in a single section carry a high charge, producing the greatest stress and vibration. Therefore, as an example, eight secondary slotted holes were detonated simultaneously on the upper steps. Each borehole (dot) has a diameter of 40 mm, an explosive charge diameter of 32 mm, a hole depth of 3 m, a charge of 2.4 kg per hole, and bottom initiation. The layout is shown in Figure 6. The explosion has a detonation velocity of 3200-3800 m/s, a density of 1200 kg/m\({}^{3}\) and a detonation product pressure coefficient (n) of 10 [31]. Due to the extensive jointing in the rock mass, the rock damage factor is taken as D = 0.2.  
Figure 5: Schematic diagram of simultaneous explosion of multiple blastholes at the same point.  
According to existing studies [39], the blast-facing sidewall of the adjacent tunnel is a critical risk area. To evaluate the stress magnitude and distribution in the rock mass at this location, six calculation points (Points A-F) are placed at 1-m intervals along a horizontal line within the sidewall (see Figure 7). The first point (Point A) is located 11.42 m from the center of the cut holes. According to reference [17], a single cylindrical charge with an aspect ratio of 10 was divided into 15 short cylindrical charge units (m = 15), each 16 cm long with a base radius of 1.6 cm. These parameters were then used in Equations (20)-(22) and (25) for calculation.
## 4 Engineering Example Validation
### Data Collection
#### 4.1.1 Data Input  
The data obtained for road design contains a series of parameters, encompassing stake numbers, horizontal and vertical alignment elements, as well as a variety of road structure  
Figure 4: The experimental road.  
elements. Examples of these data are shown in Table 3 to meet the specific requirements of the study.  
The study collects a total of 1001 cases of traffic accident data, of which the accident data under adverse weather conditions such as snow, rain, and haze account for a total of 206 cases, amounting to 20.58% of the overall accident data. Traffic accident data information mainly includes the time of traffic accidents; weather data at the time of traffic accidents; traffic accident stateout locations; traffic accident patterns and causes; and types of vehicles involved in accidents, and the specific data collection format is shown in Table 4.  
The meteorological data information collected for the study was obtained from the Mindful Weather platform, including all weather conditions from June 2018 to August 2021 at the start and end of the highway. The platform data characteristics provide high spatial and temporal resolution meteorological observations for this study. With the real-time weather data at 1 km \(\times\) 1 km grid points updated 5-min by 5-min, and the day-by-day and hour-by-hour historical meteorological data this study is able to capture subtle weather changes and analyze their immediate impacts on traffic safety accordingly. The accuracy of the data meets the requirements of each meteorological vector within the scope of the subsequent study.
###### Abstract  
Due to the interconnected scheme of multiple components, such as distributed generators, storage systems, and loads through converters to a common bus in DC microgrids, the possibility of fault occurrence is increasing significantly. Meanwhile, due to the huge and rapid increase of short-circuit currents, the development of a small- and large-scale DC system requires a reliable and fast protection system to ensure fault clearance and maintain safety for the rest of the system. Thus, fault protection has been focused on as one of the most critical issues in a direct current network. The application of traditional circuit-breakers for DC fault protection has the drawback of slow operation, which requires a high rating power equipment. Recently, the high speed and excellent performance capabilities of semiconductor breakers have attracted a lot of attention and been considered as an optimal solution for fast DC fault interruption. In this study, a bidirectional Insulated-Gate Bipolar Transistor (IGBT) semiconductor breaker, suitable for the fault protection of low-voltage DC networks, is proposed. The operating characteristics of this breaker are based on changes in the circuit current and terminal voltage of IGBTs. It detects the abrupt change of the terminal voltage as an abnormal condition and isolates the faulted branch in a short time to prevent the operation disturbance in the healthy part of the network. Therefore, for the entire protection of a typical 400V DC-microgrid cluster, breakers need to be integrated and examined in each branch and the interconnected lines. The proposed protection method in this study is examined in a Simulink\({}^{\copyright}\)/MATLAB environment to analyze and assess its operation.  
DC-Microgrid Cluster; PV System; Battery Arrays; DC Converters; Short-Circuit Protection; Solid State Circuit Breaker