esa-sceva/satcom-chunk-collection huggingface dataset:
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 4 Problem Formulation
\[\underset{x,y}{min}\ \ \mathbb{D}(\lambda,\mathbf{R})=\sum_{i=1}^{N_{K}}w_{i}/N_{K}\] (7) subject to: \[\sum_{j=1}^{N_{P}}x_{ij}+y_{i}\leq 1 \forall i \tag{8}\] \[x_{ij}\cdot c_{K_{i}}\leq b_{P_{j}} \forall i\] (9) \[x_{ij}\in\{0,1\} \forall i,\forall j\] (10) \[y_{i}\in\{0,1\} \forall i \tag{11}\]  
In Equation (7), we minimize the summation of the weighted penalty points of all tasks. Equation (8) ensures that only one vehicle processor or cloud is assigned to a task. There is inequality because some tasks may not be completed due to the randomness of processor availability and task owner departure times. The task completion time slot, \(c_{K_{i}}\) on the left-hand side of Equation (9) is the sum of the task arrival time, \(a_{K_{i}}\) and \(t_{total,i}\). Equation (9) guarantees that the task should be completed before the processor departs. Because the departure times are not known during the run-time, only our quasi-optimal algorithm can use this constraint to guide decisions. For heuristics, a task is sent back to the pool if the constraint happens to fail. Equations (10) and (11) represent the possible values of the decision variables. Task offloading decisions significantly affect multiple delay results, especially when related vehicles share the same APs or links for connectivity since available data rates for those vehicles drop. Thus, each possible decision combination should be evaluated. This multi-user, multi-task problem is proved to be NP-hard [32], while the penalty scoring in our system model increases the complexity due to its nonlinear behavior upon task owner departures.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 1 Introduction  
Within the framework of ongoing global climate change, significant scholarly attention has increasingly focused on disaster mitigation and emergency evacuation within built environments, particularly in historical districts. These areas present dual challenges: their unchangeable physical state as heritage sites and their heightened susceptibility to disasters [1, 2], which are exacerbated by dense tourist populations [3]. The inherent resistance of historical structures to modernization further complicates disaster preparedness and evacuation planning [4, 5]. Consequently, systematic evaluations of built environment responses, along with detailed evacuation analyses under dynamic disaster conditions in historical districts, are imperative. These evaluations are critical for both heritage preservation and public safety [6, 7, 8].  
Existing scholarship on historical districts and similar environments predominantly investigates four principal research dimensions: disaster impact assessments, encompassing scenarios such as fires [9], post-earthquake conflagrations [10,11], floods [12], intense precipitation [13], and landslides [14]; evacuation behavior studies that explore the physical and psychological factors [15] influencing human responses in densely populated traditional settings [16, 17, 18]; advancements in disaster and evacuation simulations, including agent-based modeling (ABM) for environmental impacts and social force models (SFMs) for pedestrian behavior analysis [19]; and the development of integrated evacuation planning tools as multi-objective strategies [20], smart planning [21], and algorithms such as 2 SFCA [22], 3 SFCA [23], and GA2 SFCA [24].  
Nevertheless, the current literature exhibits several shortcomings. Foremost among these is the lack of sufficient observation and reflection on how distinctive spatial characteristics inherent to historical districts, such as cul-de-sacs, confined pathways, and combustible building materials, may affect evacuation behaviors [25]. Additionally, most studies adopt static analytical frameworks, overlooking the nonlinear and temporal dynamics inherent to evacuations in evolving disaster scenarios [26,27], neglecting critical variables such as congestion dynamics, detours, psychological influences, and real-time information exchange [28]. Furthermore, analyses frequently omit complete evacuation processes extending from historical districts to designated shelter facilities [29,30].  
This research, therefore, aims to evaluate evacuation in fire, one of the most destructive hazards for timber structures, using dynamic holistic evacuation modeling. The outcomes are expected to yield pragmatic enhancements, including refined evacuation route planning, heightened preparedness protocols, and improved emergency communication mechanisms. These advancements will substantially elevate resilience and safety outcomes in historical districts.
## 3 Results
### Muscle Activation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Walking Condition**} \\ \hline \hline  & \multicolumn{2}{c}{**FW**} & \multicolumn{2}{c}{**BW**} & \multicolumn{2}{c}{**FT-W**} & \multicolumn{2}{c}{**BT-W**} \\ \hline
**Muscle** & **Pre** & **Post** & \(p\)**-Value** & **Pre** & **Post** & \(p\)**-Value** & **Pre** & **Post** & \(p\)**-Value** & **Pre** & **Post** & \(p\)**-Value** \\ \hline \multirow{2}{*}{RRF} & 0.74 & 0.76 & & 0.71 & 0.73 & & & & & & & \\  & (0.04) & (0.06) & & (0.13) & (0.15) & & & & & & & \\ \hline \multirow{2}{*}{LRF} & 0.7 & 0.76 & & 0.83 & 0.72 & 0.78 & 0.79 & 0.67 & 1.05 & 0.43 & 0.68 & 0.71 & \multirow{2}{*}{0.15} \\  & (0.05) & (0.06) & & & (0.01) & (0.08) & & & (0.08) & (0.79) & & & \\ \hline \multirow{2}{*}{RTA} & 0.75 & 0.77 & & 0.73 & & 0.74 & & & & & & & \\  & (0.06) & (0.04) & & & (0.02) & (0.06) & & & & & & & \\ \hline \multirow{2}{*}{LTA} & 0.75 & 0.75 & & 0.71 & & 0.84 & 0.48 & 0.67 & 0.67 & 0.62 & 0.71 & 1.02 & \multirow{2}{*}{0.08} \\  & (0.05) & (0.07) & & & (0.06) & (0.33) & & & & & & & \\ \hline \multirow{2}{*}{LBF} & 0.71 & 0.73 & & 0.21 & 0.72 & 0.72 & 0.43 & 0.72 & 0.68 & 0.87 & 0.65 & 0.32 & \multirow{2}{*}{0.1} \\  & (0.05) & (0.05) & & & (0.05) & & & & & & & \\ \hline \multirow{2}{*}{RBF} & 0.64 & 0.73 & & 0.7 & & & & & & & & & \\  & (0.05) & (0.05) & & & (0.06) & & & & & & & & \\ \hline \multirow{2}{*}{LGG} & 0.69 & 0.69 & & 0.66 & 0.75 & 0.83 & 0.36 & 0.67 & 0.73 & 0.67 & 1.1 & \multirow{2}{*}{0.18} \\  & (0.07) & (0.14) & & & (0.08) & & & & & & & \\ \hline \multirow{2}{*}{RMG} & 0.64 & 0.75 & & 0.69 & & & & & & & & & \\  & (0.06) & (0.05) & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Healthy participants’ pre- compared to post-training muscle activation for walking conditions (FW, BW, FT-W, BT-W). Means are shown with standard deviation in parentheses. The color-coding highlights significance level: \(p\) < 0.05 (blue), \(p\) < 0.005 (green), \(p\) < 0.001 (yellow). Table 5 shows the effect size calculations using the [PERSON]'s \(d\) equation. [PERSON] classified effect sizes as small (d = 0.2), medium (d = 0.5), and large (d \(\geq\) 0.8); the larger the effect size, the more powerful it is. Using this calculation, the effect size quantifies the difference between two means. A smaller effect size indicates that the means between the pre- and post-training values (e.g., 0.3) only differ by 0.3 standard deviations, so the differences are minimal. A medium effect size (e.g., 0.6) indicates that the two means differ by 0.6 standard deviations, which can be considered non-trivial, but significance may be questioned. A larger effect size (e.g., \(>\)0.8) signifies a larger difference between the means and is confirmed by the results of the \(p\)-value. A smaller [PERSON]'s \(d\) value reported in this study indicated the potential need for a larger sample size required to make statistically significant differences; however, medium and large [PERSON]'s \(d\) values were observed in both forward and backward walking conditions.
# Circle-U-Net: An Efficient Architecture for Semantic Segmentation
## 2 Related Work  
[PERSON] et al. (2018) present U-Net, which consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. U-Net is fast, and the segmentation of a 512 \(\times\) 512 image takes less than a second on a recent GPU. [PERSON] et al. (2018) present Eff-UNet, which combines the effectiveness of compound scaled Efficient Nets and the encoder for feature extraction with U-Net decoder for reconstructing the fine-grained segmentation map. The Eff-UNet combines high-level feature information, as well as low-level spatial valuable information, for precise segmentation. [PERSON] et al. (2018) propose a Squeeze U-Net inspired version of U-Net for image segmentation. The Squeeze U-Net is efficient in both low MACs and memory use. [PERSON] et al. (2018) propose that U-Net-based architecture provides detailed per-pixel feedback to the generator, while maintaining the global coherence of synthesized images by providing global image feedback. [PERSON] et al. (2018) propose Feedback U-Net using Convolutional LSTM, the segmentation method using the Convolutional LSTM and feedback process. [PERSON] et al. (2018) introduce a novel recurrent U-Net architecture that preserves the compactness of the original U-Net. [PERSON] et al. (2018) propose an enhanced Rotation-Equivariant U-Net for Nuclear Segmentation. RUNet (2018) can learn the relationship between a set of degraded low-resolution images and their corresponding original high-resolution images. [PERSON] et al. (2018) put forward a bi-directional Co-nvLSTM (BConvLSTM) U-Net with densely connected convolutions for medical image segmentation, taking full advantage of U-Net, bi-directional ConvLSTM, and the mechanism of dense convolutions. Additionally, BConvLSTM combines the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. W-Net (2018) is a reinforced U-Net for density map estimation. Retina U-Net (2018) is the one-stage detector, which combines detection with an auxiliary segmentation. The adaptive triple (2018) U-Net with a test-time augmentation can segment common and internal carotid arteries more efficiently and accurately. R2U-Net (2018) realizes the power of Residual Network, U-Net, as well as RCNN, which are several advantages for segmentation tasks. [PERSON] et al. (2018) propose Attention U-Net, which is assessed on CT datasets and is for multi-class image segmentation. UNet++ (2018) is a nested U-Net architecture which gets an average IoU gain of 3.9 points over U-Net.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 3 Monotonic Shear Tests
### Analysis and Discussion
#### 3.2.2 Initial Shear Stiffness  
Figure 6 shows the results of initial shear stiffness of loose sand and dense sand with different interface roughness. The abscissa is \(F(e)P_{a}(\sigma/P_{a})^{0.5}\), and the ordinate is \(K_{in}\). It can be found that the fitting results of three interface roughness are three parallel lines. With reference to the empirical formula for the initial shear modulus of sand [29], a formula for calculating the initial shear stiffness of the interface between sand and structure is proposed:  
\[K_{in}=AF(e)P_{a}(\frac{\sigma}{P_{a}})^{0.5}+B \tag{2}\]  
where \(F(e)\) is the void ratio function, \(F(e)=(2.17-e)^{2}/(1+2e)\), \(\sigma\) is the normal stress, and \(P_{a}\) is atmospheric pressure, which is 101 kPa. \(A\) and \(B\) are fitting parameters to the test results, both of which are related to the interface roughness. \(A\) is the amplitude of initial shear stiffness increasing with \(F(e)P_{a}(\sigma/P_{a})^{0.5}\), reflecting the influence of interface roughness on shear stiffness when shear stress is present. \(B\) represents the influence of interface roughness on shear stiffness when there is no shear stress. This may be due to the presence of similar bite forces after the sand surface is in contact with the steel surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Interface Strength Type**} & \multicolumn{1}{c}{**Peak Friction Angle**} & \multicolumn{1}{c}{**Residual Friction Angle**} \\ \cline{3-4} \multicolumn{1}{c}{} & \(\varphi_{0}\) [\({}^{\circ}\)] & \(\varphi_{r}\) [\({}^{\circ}\)] \\ \hline \multirow{2}{*}{\(R_{n1}\)} & \(D_{r}\) = 0.3 & 24.70 & 24.50 \\  & \(D_{r}\) = 0.9 & 30.28 & 29.64 \\ \hline \multirow{2}{*}{\(R_{n2}\)} & \(D_{r}\) = 0.3 & 30.24 & 30.07 \\  & \(D_{r}\) = 0.9 & 38.76 & 35.43 \\ \hline \multirow{2}{*}{\(R_{n3}\)} & \(D_{r}\) = 0.3 & 33.54 & 33.23 \\  & \(D_{r}\) = 0.9 & 39.18 & 33.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strength parameters of monotonic shear at the sand–steel interface.  
It can be seen from Figure 6 that the value of parameter \(A\) is almost unchanged, and they are all around 0.8 mm\({}^{-1}\). The main factor that affects the roughness of the interface is parameter \(B\). The greater the value of \(B\), the greater the roughness of the interface, resulting in greater initial shear stiffness. It can also be found that as the normal stress or the relative density of sand increases, both affect the initial shear stiffness of the sand-steel interface. This is consistent with the law observed in the shear stress-shear displacement curve above.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 4 Hybrid VLC
### LED-to-LED System
[PERSON] et al. [92] have developed a carrier sensing multiple access/collision detection and hidden avoidance (CSMA/CD-H) communication protocol, which provides reliable transmission for VLCnetworks consisting of nodes with different Field of View (FOV). Hidden node problem arises due to diverse FOVs of LEDs. CSMA/CA-H protocol enables intra frame bidirectional communication in the LED network. The proposed protocol is implemented using an OpenVLC platform consisting of beagleBone Black (BBB) board and a front-end transceiver. Experiments show that the CSMA/CD-H protocol can reduce hidden node collisions significantly while increasing throughput by 50% and 100% for two and four nodes networks respectively. [PERSON] et al. [93] have addressed the issues of flickers elimination and efficient collision-free medium access control in bidirectional LED-to-LED networks. The system also consists of a method to measure the incoming light during data reception and transmission. Research showed that LED sensitivity, physical rate and flicker elimination are tightly correlated and lead to a tradeoff scenario. The proposed system is evaluated experimentally and shown that it can address flickers eliminations and collision-free communication with a throughput of 780 b/s. The prevailing assumption in the VLC community is that LEDs emit constant light and can provide flicker-free constant throughput. Many of the current deployed LEDs are smart, dynamically adjusting emitted light to ambient conditions. Dynamic change can lead to fluctuating throughput and flickering, resulting in bad user experience and awkward lighting scenarios. [PERSON] et al. have proposed a smartVLC system based upon the investigation of the trade-off between the fine-grained dimming level and high data rate throughput [94]. In order to achieve this trade-off, a new modulation scheme adaptive multiple pulse position modulation (AMPPM) is proposed which multiplexes individual symbols to achieve dimming granularity. Experiments show that an inexpensive smartVLC system provides flicker-free communication up to a distance of 3.5 m with improved throughput. These studies have led to the development of very low-cost LED-to-LED VLC systems which can be readily applied is providing connectivity on the very low scale and cater to some specific applications. However, the reliability, scalability and robustness of such VLC based communication networks regarding handling interference, inter/intra LED-to-LED VLC communication and its energy efficiency are still less explored. Nevertheless, a LED-to-LED VLC system would be the end goal to tap into the large chunk of unregulated visible light spectrum. Some point to point applications would be potential applications for a full functional LED-to-LED VLC communications such as vehicle to vehicle and vehicle to infrastructure communication.
# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 3 System Model and Problem Description  
### System Overview  
Consider the edge-computing application illustrated in Figure 1, with N available edge devices represented as set \(\mathcal{N}=\{U_{1},U_{2},...,U_{i},...,U_{N}\}\). Each edge device runs the same DNN model, \(\mathbb{G}\), consisting of L layers, excluding the fully connected layers, expressed as \(\mathbb{G}=\{\mathcal{L}_{1},\mathcal{L}_{2},...,\mathcal{L}_{j},...,\mathcal{ L}_{L}\}\).  
When an edge device initiates an inference task, it is designated as the master device (MD), whereas the others serve as worker devices (WDs). The general process of DNN inference in this situation proceeds as follows: the MD comprehensively considers the computational capabilities, as well as the computation and transmission power of heterogeneous edge devices. It assigns a greater workload to WDs with higher computational capabilities and lower computation and transmission power. After the allocation, sub-tasks are sent to the WDs for parallel execution. The WDs then send their results back to the MD, which processes the data to complete the inference task.  
The workload-partitioning decision made by the MD is represented as vector \(\pi=\{a_{1},a_{2},...,a_{i},...,a_{N}\}^{T}\), where \(a_{i}\in Z^{+}\) denotes the portion of the workload allocated to edge device \(U_{i}\). Vector \(\pi\) is considered to be an effective workload-partitioning decision if the conditions defined in Equations (1) and (2) are met:  
\[a_{i}\geq 0,a_{i}\in\mathbb{Z},\forall i\in N \tag{1}\]  
\[\sum_{i\in N}a_{i}=H \tag{2}\]  
Equation (1) requires that \(a_{i}\) must be a non-negative integer, and Equation (2) ensures that the sum of all elements in the partitioning strategy is equal to the height, \(H\), of the output feature map produced by layer \(\mathcal{L}_{j}\) of model \(\mathbb{G}\).  
The key symbols used throughout this paper are defined in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Symbol** & **Meaning** & **Variable Symbol** & **Meaning** \\ \hline \(U_{i}\) & Edge device number & \(C_{in}\) & The number of input channels for the \(\mathcal{L}_{j}\)’s input feature map \\ \(\pi\) & workload-partitioning decision & \(C_{out}\) & The number of output channels for the \(\mathcal{L}_{j}\)’s output feature map \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variable symbols and their meanings.  
Figure 1: System overview.
## 4 Discussion
### Feasibility and Rationality of Evaluation Methods
#### 4.1.2 Stability of Principal Component Analysis as Indicator Weighting  
The contribution rates of the first principal component obtained after applying PCA to all three urban agglomerations exceeded 83%, with BTH at about 83.1%, and CYRD and PRD reaching 86.9% and 87.8%, respectively. To assess the applicability and stability of the PCA method in this study, we analyzed the influences of each variable in the three urban agglomerations on the first three principal components. However, it is important to note that the values presented here represent the scores of each variable, not the loadings. To obtain the loadings, the score matrix needs to be transposed and multiplied by the eigenvalues after taking the square root. In our calculations, the loadings of the majority of variables surpassed 0.4, indicating the significance of the selected variables. The selection of the first three principal components was based on the total explanatory power exceeding 95%.  
As shown in Figure 9, the contributions of various indicators in different urban agglomerations remain relatively stable, indicating the effectiveness of the calculated weights across different urban agglomerations. Additionally, the symbols and sizes of each indicator in the first principal component, which has the highest explanatory power, are consistent, further affirming the reliability of the weights obtained through PCA (refer to the left row of all subfigures, PC1) [98]. Variables such as HD and GDP exhibit a certain negative contribution in ecological quality assessment. By comparing the performance of a single indicator in the first principal component across different urban agglomerations, valuable information can be gleaned. For instance, the contribution of PM2.5 gradually decreases with decreasing latitude, reflecting the diverse ecological challenges faced by the three urban agglomerations [99; 100]. Previous studies have also indicated that PCA demonstrates stable and outstanding performance in identifying critical factors after dimension reduction [101].
## 4 Results and Analysis of Impact Tests
### Mechanical Performance of Samples under Dynamic Loading
This is attributed to the interaction of the holes, that is, the right hole on the same level is located in the unloading area of the left hole, so that the concentrated dynamic tensile stress around the hole is relatively small. This deeply explains that the strength of the samples of G3 is greater than that in G2. With regard to the samples with two openings with different configurations, the strength of the sample with double horizontal holes is the highest, followed by the sample with two vertical holes, and finally the sample with two oblique holes. This phenomenon may be related to the distribution of dynamic stress around the holes since the strength change law of samples with holes of different numbers and configurations under uniaxial loading are also the same [33; 34]. As can be observed in Figure 9, the dynamic deformation process can be parted into three phases, namely the elastic deformation stage OA, plastic deformation stage AB and the post-peak stage BC. In comparison with the steep post-peak stage of the intact specimens, the slope of the after-peak curve of the pre-holed specimens is small. This indicates that the pre-holed samples present plastic failure at the last stage rather than the brittle failure of the intact specimens. In comparison with the deformation stages of the specimens under uniaxial loading, it is found that the initiation compaction stage of the specimens under impact loads is absent. This is resulted from the strain rate of the samples under impact being very high (51-81 s\({}^{-1}\) in this work), that is, the deformation process is so fast that time is short for the initial compaction stage to occur under dynamic loading. According to the dynamic stress versus strain curve, we can solve the elastic modulus of these specimens, as presented in Table 2 and Figure 10. In conclusion, the elastic modulus of the complete samples under impact loads is larger than that of the samples embedded with openings, but it showed no regularity among the samples with holes. The peak strain of the samples also exhibits similar characteristic. In addition, we find that the hit velocity range is 9.83-10.18 m/s, indicating that the reliability of this testing system is very high and the experimental error is very small. Figure 10: Dynamic parameters of samples.