esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Conclusions  
### Summary of Key Findings  
This review highlights the complexity of managing PST at the PTI, emphasizing that effective crowd management must go beyond static metrics and instead adopt dynamic, context-sensitive approaches. The findings from empirical studies reviewed throughout this paper demonstrate that variables such as infrastructure layout, passenger behavior, accessibility elements, and interaction patterns have nonlinear and interdependent effects on PST. The evidence underscores that operational delays are not merely a function of demand volume but are shaped by platform geometry, vehicle design, and passenger heterogeneity. These insights should motivate transit agencies to reconsider one-size-fits-all policies and invest in interventions grounded in human-centered design and empirical observation [15; 72].  
From a practical standpoint, the integration of experimental methods--such as real-scale mock-ups and sensor-based motion tracking--has produced robust, actionable data that can directly inform design standards and operational practices. For instance, targeted infrastructure changes such as widening doors, repositioning vertical handrails, or implementing visual cues at the platform have demonstrated measurable reductions in PST and improved pedestrian stability [23; 28]. Moreover, experiments involving passengers with reduced mobility (PRMs) reinforce the importance of designing for inclusivity: accessibility-enhancing features not only support vulnerable users but also improve overall efficiency and comfort for the entire system [56]. Consequently, public transport authorities should embed these experimental insights into planning processes, particularly when retrofitting aging infrastructure or designing new metro lines (see Table 3).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** \\ \hline Vertical (V) and horizontal (H) gaps & Ideally level access; tolerated V = 2–5 cm and H = 2–5 cm; not exceed 10 cm for V or H. \\ \hline Yellow safety line & The yellow safety line should have a minimum width of 20 cm. In addition, a tactile texture should be included, especially for PRMs. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recommended design for the PTI based on empirical studies.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN  
[PERSON]  
1 National Key Laboratory on Electromagnetic Environment Effects, Army Engineering University of PLA, Shijiazhuang 050003, China; [EMAIL_ADDRESS] (TX. Many deep learning models can learn a single task, such as spectrum recognition, threat assessment, modulation recognition, radio frequency fingerprint recognition, etc., and can achieve good performance. However, the previous methods used in EMI signals classification and threat assessment often ignored the connection between the two tasks, so it needs to model each task separately, resulting in a significant increase in the calculation and storage costs. To the best of our knowledge, this is the first work to apply multi-task learning to EMI identification and threat assessment of UAV data link. As a typical application of deep learning in computer vision, convolutional neural network (CNN) is a data-driven deep neural network structure. Because of its excellent feature extraction ability, CNN is applied extensively in electromagnetic signal classification and threat assessment [14; 15; 16]. In this paper, we design a multi-task convolutional neural network with multi-input (MIMT-CNN) for electromagnetic environment perception of the data link of a UAV. Firstly, the visualized performance parameters (VPP), the short-time Fourier transform (STFT) spectrograms, and the normalized density constellation (NDC) are used as the inputs of the multi-input CNN for feature extraction. Among them, the NDC is acquired through the feature enhancement of the constellation diagram. Then based on multi-task learning [17], the EMI signal classification and threat assessment are put into a shared parallel structure for learning simultaneously. In addition, in order to improve the performance of the obtained model further, Bayesian optimization is used to optimize the structure of the parallel part of the network as well as the training hyperparameter. The main contributions of this paper are as follows:  
1. The data is constructed based on the EMI injection test rather than simulation to solve the real-world problem. 2. A graphical preprocessing and enhancement method for EMI signals is proposed to fuse the heterogeneous information of EMI signal and data link performance. 3. Based on the series-parallel structure and the balance loss function, the proposed MIMT-CNN can achieve a balance between interference identification and threat assessment performance. This paper is organized as follows: In Section 2, we introduce the acquisition process of the input data, as well as the prediction process based on the MIMT-CNN. Section 3 presents the visualization process of the initial input data. Then, the structure of MIMT-CNN, as well as its evaluation method, is proposed in Section 4. Section 5 conducts hyperparameters optimization and network training. In Section 6, different network structures are tested to verify the generalization ability of the network, as well as find the best input structure of the network. Finally, the conclusions are drawn in Section 7.
## 4 Problem Formulation  
This section focuses on the mathematical modeling of the network slicing-planning problem in packet-switched Xhaul networks. The optimization problem considered concerns (a) the placement of DU and CU entities at selected PP nodes for processing of radio traffic related to different 5G services provisioned by a given set of RUs, according to the specific requirements of particular network slices realized in the network, and (b) the routing of FH and MH flows over the packet Xhaul network. As in [29; 31], the optimization objective is minimizing the number of PP nodes required to support the demands. The problem constraints are as follows:  
1. The DU processing for a cluster of RUs is performed at the same PP node.
2. The CU processing for network slices realizing eMBB services is performed at the hub node.
3. The CU processing for a network slice realizing URLLC services is performed at a selected PP node, where an MEC server is also colocated.
4. The overall load related to DU and CU processing at a PP node cannot exceed the available PP capacity.
5. The FH and MH flows are non-bifurcated and should be realized over single routing paths in the network.
6. The overall volume (bit rate) of traffic in a network link cannot exceed the link capacity.
7. The maximum one-way latency of an individual flow must not exceed the latency limit specific for this flow.  
In the following, the notation used in problem modeling is introduced, and afterward, the problem is formulated as a MILP optimization problem. The MILP model is an extension of the model formulated in [20] for the network scenario supporting a single network slice. The purpose of presenting a similar MILP model here is twofold. First, it shows its applicability in a network with multiple slices, each with specific DU/CU placement requirements, which introduces additional problem constraints. Second, the model variables and constraints are directly referred to and used in developing the optimization method in Section 5.
## 3 Results
### Impact of Radiation Changes on GPP
#### 3.4.2 Spatial Distribution of Radiation Effects on GPP  
The average GPP in the period 1981-2012 simulated in S2 (Figure 9a) and S3 (Figure 9c) were similar to the distribution for S1 (Figure 6). The difference (Figure 9b) between the multi-year mean GPP simulated in S1 and S2 showed that any reduction in \(R_{\rm g}\) would result in a reduction in GPP over most of China. In North China, the decrease in \(R_{\rm g}\) was significant, simultaneously reducing the GPP by more than 50 g C m\({}^{-2}\) y\({}^{-1}\). The \(R_{\rm g}\) over Central and Southwest China showed no significant decrease, but these areas experienced a decrease in GPP that was higher than 50 g C m\({}^{-2}\) y\({}^{-1}\), which was predominantly because of the high LAI in these regions (see Supplementary Materials, Figure S5). Compared with areas with lower LAI, the reduction in \(R_{\rm g}\) substantially reduced GPP for regions with higher LAI. During the period 1981-2012, the areas that showed the highest GPP increase were along the southeastern coast, northern Sichuan, as well as northeastern, northern, and some western parts of Xinjiang.  
Figure 8: Interannual variation in total annual GPP for the period 1981–2012 in China for three simulations (GPP_Scenario 1; GPP_Scenario 2; and GPPsi GPP_Scenario 3). Scenario 1 assumed that the global radiation and diffuse radiation ratio changed with time; Scenario 2 assumed that global radiation remained unchanged from 1981 to 2012; and Scenario 3 assumed that the diffuse radiation fraction remained unchanged from 1981 to 2012.  
The difference in GPP values between S1 and S3 are shown in Figure 9d. In most regions of China, the enhanced diffuse radiation fraction led to an increase in GPP, especially in Central China and Yunnan Province. The significant increase in GPP in Central China mainly resulted from the increase in \(R_{\rm d}\). In Yunnan Province, although the increase in diffuse radiation fraction was not significant, the GPP increased because of the high LAI (see Supplementary Materials, Figure S5). On the Qinghai-Tibet Plateau, although the diffuse radiation fraction significantly increased, the increase in GPP was not as strongly significant as that in Central China because of the scarcity of vegetation in the former region. Similarly, along the southeastern coast, in Xi'an and western parts of Xinjiang, the reduction in diffuse radiation fraction led to a reduction in GPP.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 4 Results and Discussion  
### Parametric Analysis  
The steady-state moment was identified to be a major feature for calculating the operation of the DLTS hinge. The geometric parameters \(R\), \(t\), and \(\theta\) were examined and modified for the apprehension of steady-state moment variation tendencies. \(\sigma_{\rm v}\) variations were explored in the stowed and deployed state to identify irreversible plastic deformation.  
A parametric study was conducted by controlling and fixing \(t\) (\(t\) = 0.1 mm), \(L\) (\(L\) = 2.45 mm), and \(\theta\) (90\({}^{\circ}\)), whereas \(R\) was alternated from 6 mm to 12 mm. From Figure 7a, it is observed that the steady-state moment values fluctuated irregularly in relation to the radius changes, which is an indication of sensitivity. The \(\sigma_{\rm v}\) increased relative to \(R\), with a maximum \(\sigma_{\rm v}\) of 2.43 \(\times\) 10\({}^{3}\) N/mm\({}^{2}\) and only a slight decrease of 17.49% between 9 and 9.5 mm.  
Figure 7: _Cont._  
Figure 7b showcases the parametric analysis that was implemented by modifying \(t\) from 0.06 mm to 0.24 mm and keeping constant \(L\) (2.45 mm), \(\theta\) (90\({}^{\circ}\)), and \(R\) (6 mm). It is observed that the \(\sigma_{\rm v}\) and steady-state moment results were linearly increased with respect to the thickness increase. As the moment was raised, the local \(\sigma_{\rm v}\) increased, causing a slight permanent deformation in the center of the DLTS; thus the local \(\sigma_{\rm v}\) levels were considered in the optimization analysis to ensure they are kept in a safe limit without causing design failure. The safe limit was specified to be less than the ultimate tensile strength of the manufacturing material of the DLTS hinge.  
Figure 7c displays the effect analysis that was carried out by maintaining constant values of \(t\) (0.1 mm), \(L\) (2.45 mm), and \(R\) (6 mm) and alternating \(\theta\) from 54\({}^{\circ}\) to 94\({}^{\circ}\). The \(\sigma_{\rm v}\) and steady-state moment exhibited a comparable relation with the different subtended angles. The highest peak for the \(\sigma_{\rm v}\) moment was acquired at a subtended angle of 86\({}^{\circ}\), followed by a decrease in both slopes.  
The steady-state moment gradients for \(R\) and \(\theta\) were noted to fluctuate more irregularly and presented similar maximum values, with a percentage difference of 12.96% compared to \(t\). The \(\sigma_{\rm v}\) slopes linearly increased for all three varying parameters, although there were slight drops in the slope for \(R\) and \(\theta\) due to non-linearity and deformation. The maximum peaks were found to be from 1.98 \(\times\) 10\({}^{3}\) to 2.61 \(\times\) 10\({}^{3}\), with the highest percentage difference of 24.18%.
# Effect of Surface Roughness on Aerodynamic Loads of Bluff Body in Vicinity of Smoothed Moving Wall
###### Abstract  
This paper contributes to a new Lagrangian vortex method for the statistical control of turbulence in two-dimensional flow configurations around a rough circular cylinder in ground effect when considering higher subcritical Reynolds numbers, namely \(3\times 10^{4}\leq\text{Re}\leq 2\times 10^{5}\). A smoothed moving wall (active control technique) is used to include the blockage effect in association with the variation in cylinder surface roughness (passive control technique), characterizing a hybrid approach. In contrast with the previous approaches of our research group, the rough cylinder surface is here geometrically constructed, and a new momentum source term is introduced and calculated for the investigated problem. The methodology is structured by coupling the random Discrete Vortex Method, the Lagrangian Dynamic Roughness Model, and the Large Eddy Simulation with turbulence closure using the truncated Second-Order Velocity Structure Function model. This methodological option has the advantage of dispensing with the use of both a refined near-wall mesh and wall functions. The disadvantage of costly processing is readily solved with Open Multi-Processing. The results reveal that intermediate and high roughness values are most efficient for Reynolds numbers on the orders of \(10^{5}\) and \(10^{4}\), respectively. In employing a moving wall, the transition from the large-gap to the intermediate-gap regime is satisfactorily characterized. For the conditions studied with the hybrid technique, it was concluded that the effect of roughness is preponderant and acts to anticipate the characteristics of a lower gap-to-diameter ratio regime, especially with regard to intermittency.  
drag reduction; Venturi effect; LES theory; Lagrangian Dynamic Roughness Model; panel method; Discrete Vortex Method +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Comparison with State-of-the-Art Methods  
We perform both quantitative and qualitative comparisons between our proposed method and several existing SISR and RefSR methods. The SISR methods include SRCNN [10], EDSR [12], RCAN [15], SRGAN [16], Enet [37], ESRGAN [18], and RankSRGAN [19]. The RefSR methods include CrossNet [1], SRNTT [3], TTSR [4], SSEN [2], MASA [5], C\({}^{2}\)-Matching [6], and RRSR [26]. We train two sets of parameters: one using all loss functions and another using only the reconstruction loss (denoted as -rec).
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 3 Integrated System and Algorithm Design
### Throughput Optimization Model
#### 3.3.1 Throughput with CCLF  
Assuming that the consumer requests Data at a high rate and the CS has sufficient storage space, utilizing the CCLF method involves scheduling the forwarding of all Data packets. For any link \(i\), the maximum throughput capacity that the link can provide within the time interval \((0,t)\) is  
\[q_{i}(t)=\int_{0}^{t}b_{i}(\tau)\mathrm{d}\tau \tag{3}\]  
For the overall network throughput, which is the traffic volume \(q(t)\) of the consumer, it will not exceed the throughput capacity of any single link.  
\[q(t)\leq q_{i}(t),\forall i\in[1,N] \tag{4}\]  
It means that \(q(t)\) is limited by the throughput capacity of the links: \(q(t)\leq\min_{i}\{q_{i}(t)\}\).  
Assuming the bottleneck link \(c=\arg\min_{i}\{q_{i}(t)\}\), then \(\min_{i}\{q_{i}(t)\}=q_{c}(t)\). Due to the high transmission rate, the bottleneck link has no idle capacity. For downstream links \(\forall i>c\), their average bandwidth exceeds that of the bottleneck link, resulting in minimal backlog of Data packets received from upstream. Assuming that the downstream link \(i\) delivers less Data traffic than \(\min_{i}\{q_{i}(t)\}\) during the time interval \((0,t)\) due to Data backlog \(\epsilon_{i}(t)\), then  
\[\lim_{t\rightarrow\infty}\frac{\epsilon_{i}(t)}{q_{c}(t)}=0 \tag{5}\]  
The network throughput in time \((0,t)\), that is, the data transfer \(q(t)\) of the consumer, can be expressed as  
\[q(t)=q_{c}(t)-\sum_{i=c}^{N}\epsilon_{i}(t) \tag{6}\]  
\[\frac{q(t)}{q_{c}(t)}=1-\sum_{i=c}^{N}\frac{\epsilon_{i}(t)}{q_{c}(t)} \tag{7}\]  
According to Formula (5), the throughput can be simplified to \(\lim_{t\rightarrow\infty}\frac{q(t)}{q_{c}(t)}=1\). Therefore, using the CCLF method, the end-to-end throughput of the network is optimized as  
\[q(t)\sim q_{c}(t)=\min_{i}\left\{\int_{0}^{t}b_{i}(\tau)\mathrm{d}\tau\right\} \tag{8}\]  
In this paper, the symbol \"\(\sim\)\" denotes approximation: \(a(t)\sim b(t)\Leftrightarrow\lim_{t\rightarrow\infty}\frac{a(t)}{b(t)}=1\). Thus, the average bandwidth within time \((0,t)\) can be expressed as  
\[b_{\text{CCLF}}(t)=\frac{q(t)}{t}=\min_{i}\left\{\frac{1}{t}\int_{0}^{t}b_{i}( \tau)\mathrm{d}\tau\right\} \tag{9}\]  
Using the CCLF method, the equivalent average bandwidth of the network is determined by the minimum average bandwidth among all links. In contrast, using existing congestion control schemes, the equivalent bandwidth of the network is the bandwidth of the bottleneck link at every moment. We prove the optimization in the following equation:  
\[\min_{i}\left\{\frac{1}{t}\int_{0}^{t}b_{i}(\tau)\mathrm{d}\tau\right\}\geq \frac{1}{t}\int_{0}^{t}\min_{i}\{b_{i}(\tau)\}\mathrm{d}\tau \tag{10}\]
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
###### Abstract  
With the rapid development of renewable energy technologies, large numbers of photovoltaic (PV) and battery energy storage systems (BESS) have been connected to distribution networks. However, both PV and the BESS are inverter interfaced power sources, which may cause the traditional protection relays to mis-operate or mal-operate. Moreover, according to the latest grid connection specifications, PV and BESS are required to absorb negative sequence current during asymmetric faults of distribution networks, indicating that they both must adopt new control strategies during the fault ride through period. In response to the above challenges, this work first studies the fault ride through control strategies of PV and BESS when different phase-to-phase faults occur according to the latest grid connection requirements. Second, it analyzes the negative sequence impedance characteristics of PV and BESS under asymmetric faults and quantitatively calculates its variation range. Third, during symmetric faults, the differences in fault current provided by PV and BESS and those provided by the large power grid are compared. Then, this work proposes a fault direction detection principle for the distribution network with PV and BESS. For asymmetric phase-to-phase faults, this principle detects the fault direction by using the negative sequence power angle; for symmetric faults, it detects the fault direction by using the reactive current and active current. Finally, simulation tests are carried out to verify the operation performance of the proposed principle.  
relay protection; PV; distribution network; BESS; directional element; fault direction detection Article
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 3 Improved YOLOX Detection Algorithm
### Introduction of Attention Mechanism  
In order to obtain richer image semantic information in vehicle/pedestrian target features, an attention mechanism (CA, Coordinate Attention) [65] that fuses coordinate information is introduced to the YOLOX backbone network, added after the CSP component, and the specific structure is shown in the subdiagram of the coordinate attention module in Figure 1. The CA module decomposes the channel attention into two parallel 1D feature encoding operations, which can effectively fuse spatial coordinate information into the generated attention map, enhance the utilization of cross-channel information, direction-aware information, and position-aware information, and promote the model to locate and identify targets more accurately.  
Based on the YOLOX model, the CA module aggregates the vertical and horizontal input features into two independent direction-aware feature maps using pooling kernels of dimensions \((H,1)\) and \((1,W)\). Then, the two feature maps embedded with specific direction information are encoded into two attention maps by \"cascading, convolving, slicing, and reconvolving\". In this way, long-range dependencies can be captured along one spatial direction and meanwhile precise positional information can be preserved along the other spatial direction. Finally, the two attention maps are multiplied onto the input feature map to enhance the representation of the feature map. The final output of the CA module can be expressed as shown in Equation (1).  
\[y_{c}(i,j)=x_{c}(i,j)\times g_{c}^{h}(i)\times g_{c}^{w}(j) \tag{1}\]
## 3 Results
In focusing the attention on the thermal performance of the whole system, it can be observed that, at large plate spacings, the heat transfer rate is almost independent of the plate staggering as each plate tends to behave as a single plate, whereas, at intermediate separation distances, the average Nusselt number increases as the plate staggering is decreased, which is due to the aiding contribution of the chimney effect. Conversely, at close spacing between the plates, the average Nusselt number enhances as the plate staggering is increased due to the larger portion of each plate being exposed to the undisturbed fluid reservoir. Such a behavior inversion occurs at a plate spacing \((W/H)_{\rm inv}\), which decreases with increasing the Rayleigh number. The distributions of the optimal dimensionless separation distance between the plates \((W/H)_{\rm opt}\) for the maximum heat transfer rate from the pair of staggered plates, which are plotted against the Rayleigh number \(Ra_{\rm H}\), are displayed in Figure 12 using the dimensionless vertical alignment \(L/H\) as a parameter. It can be observed that \((W/H)_{\rm opt}\) decreases both as \(Ra_{\rm H}\) increases due to the decrease of the boundary layer thickness and \(L/H\) decreases due to the reduced interaction occurring between the bottom portion of the boundary layer adjacent to the upper plate and the top portion of the boundary layer adjacent to the lower plate. Figure 11: \(Nu\) vs. \(W/H\) for \(Ra_{\rm H}=10^{6}\) using \(L/H\) as the parameter. The whole set of numerical results obtained for the optimal distance \((W/H)_{\text{opt}}\) and the corresponding peak-value of the average Nusselt number, denoted as \(Nu_{\text{opt}}\), can be correlated by using the following pair of dimensionless correlating equations obtained using a multiple regression method:  
\[\left(\frac{W}{L}\right)_{\text{opt}}=8\cdot Ra_{\text{H}}{}^{-0.23}\left(1+ \frac{L}{H}\right)^{-0.9} \tag{8}\]  
\[Nu_{\text{opt}}=0.69\cdot Ra_{\text{H}}{}^{0.23}\left(1+\frac{L}{H}\right)^{0. 16} \tag{9}\]  
where the former has a 2.4% standard deviation of error and the latter with a 1.9% standard deviation of error, as shown in Figures 13 and 14, respectively. Figure 12: \((W/H)_{opt}\) vs. \(Ra_{\text{H}}\) using \(L/H\) as the parameter. Figure 2:0 Comparison between Equation (8) and the numerical data obtained for \((W/H)_{\text{opt}}\).