esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
## 3 Algorithm Simulation and Results Analysis
### Simulation Setup  
We assume that there are three RSUs in the simulation scenario, namely RSU1, RSU2, RSU3. Then we consider a group of VUEs that are randomly deployed in the OFDMA wireless network within the coverage of RSUs. The total spectrum resources of the resource pool \(B^{sum}\) is 15 MHz. The minimum transmission rate requirement \(R^{min}\) for RSUs are \(R_{1}^{min}=20\) Mbps, \(R_{2}^{min}=15\) Mbps, \(R_{3}^{min}=10\) Mbps, respectively. The bit error rate of each RSU is initialized to \(10^{-2}\). The average transmission power of each RSU are 50 mW, 20 mW, 20 mW, respectively. The average communication radius of each RSU are 200 m, 150 m, 100 m, respectively. we assume that the value of the \(\beta\) is 3.  
For analysis purposes, we assume that there are three types of VUEs within the coverage of RSUs. VUE1 primarily requests voice service from the RSU. VUE2 primarily requests a text service from the RSU. VUE3 mainly requests video services from the RSU. Obviously, different VUEs have different requirements for the service characteristics provided by RSU. Latency and jitter may be more important than throughput for a VUE requesting online game services. In addition, in order to facilitate the calculation, we use the delay, jitter and packet loss rate to measure the performance of RSUs. For example, the minimum requirements for delay, jitter and packet loss rate for VUE1 are 100 ms, 25 ms and 4% respectively, while the best performance of RSU1 for delay, jitter and packet loss rate is 98 ms, 27 ms and 5%, respectively. Hence, according to the analysis above, we can find that \(SF_{1,o}^{1,1}=1\), \(SF_{2,o}^{1,1}=0\), \(SF_{3,o}^{1,1}=0\). Then, we can calculate the matching degree between VUEs and RSUs. The performance parameter values of each RSU are shown in Table 2.  
As we know, RSU with low latency is better suited to provide voice services, text services should prefer RSU with low packet loss rates, video services should prefer RSU with less jitter. The minimum performance requirements of VUEs for delay, jitter and packet loss rate are shown in Table 3. According to Equation (14), we can get the matching degree between each RSUs and VUEs, as shown in Table 4. Then we can find that RSU1 is suitable for voice transmission services, RSU2 and RSU3 are suitable for text services.  
After the spectrum resources allocation to RSUs is completed, we consider the allocation of resources for VUEs in the second layer, including power allocation and subchannel allocation. Consider the space limitation of this paper, we just study the resources allocation problems in RSU2. We assume that there are three types of VUEs within the coverage of RSU2, which are VUE1, VUE2 and VUE3, respectively. The value of minimum rate requirement \(r_{1}^{min}=0.1\) Mbps, \(r_{2}^{min}=0.5\) Mbps and \(r_{3}^{min}=1.0\) Mbps. The subchannel bandwidth \(\omega_{0}^{k}=20\) kHz. The maximum bit error rate that VUEs can tolerate is \(10^{-3}\).
## 5 Simulation and Experiment  
### Simulation Results  
In this section, the simulation results of the proposed control system are presented. The simulation primarily focused on the control of a DC-DC converter. Therefore, the input voltage in the simulation was set independently of the TENG's input voltage. The simulation method involved implementing the control system designed in Section 4 and substituting the control input into the plant model expression for the input \(u\), as described in Equation (10), to observe how the output changes. A numerical analysis was performed using the Runge-Kutta method. The input voltage was configured to change every 10 s as follows: from 2 V to 6 V, from 6 V to 8 V, and from 8 V to 3.5 V. This was carried out to examine whether the output voltage could still track the target value despite rapid fluctuations in the input voltage. The parameters used in the simulation are listed in Table 1. The simulation results of the proposed control system are illustrated in Figure 11. In Figure 11, it can be observed that, despite significant fluctuations in the input voltage,the system follows the reference value. However, it is important to note that the plant model used in the simulation as the control target is the same as the model used in the control system design, and there is no uncertainty involved.
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
## 2 Materials and Methods  
### Materials  
The main materials used in the study were coarse aggregate, cement, silica fume, water, and superplasticizer. The physical properties of coarse aggregate are shown in Table 1. Portland cement (P.O 42.5) was obtained from Jilin Yatai Cement Co., Ltd. (Jilin, China), and its physical properties and chemical composition are shown in Tables 2 and 3, respectively. Silica fume was provided by Changchun Siao Technology Co., Ltd. (Jilin, China), and its chemical composition is shown in Table 3. The density of silica fumes is 2178 kg/m\({}^{3}\), and its average particle size is 0.1-0.3 \(\upmu\)m. The polycarboxylic acid superplasticizer produced by Shanxi Qinfen Building Material Co., Ltd. (Shanxi, China), was used to improve the workability of pervious concrete.  
\begin{table}
\begin{tabular}{c c} \hline
**Physical Property** & **Index** \\ \hline Type & natural granite coarse aggregate \\ Size (mm) & 4.75–9.5 \\ Apparent density (kg/m\({}^{3}\)) & 2786 \\ Bulk density (kg/m\({}^{3}\)) & 1534 \\ Bulk porosity (\%) & 44.9 \\ Crushing value (\%) & 9.7 \\ Needle-like particle content (\%) & 7.1 \\ Water absorption (\%) & 1.63 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of coarse aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
\begin{tabular}{c} **Strength (MPa)** \\ \end{tabular} \\
**(g/cm\({}^{3}\))** & **(m\({}^{2}\)/kg)** & **Initial Setting** & **Final Setting** & **3d** & **28d** & **3d** & **28d** \\ \hline
2.96 & 342 & 182 & 251 & 21.8 & 47.6 & 4.7 & 7.5 \\ \hline \end{tabular}
\end{table}
Table 2: Physical properties of Portland cement.
###### Abstract  
Non-orthogonal multiple access (NOMA) systems are being considered as candidates for 5G wireless systems due to their promise of improved spectral efficiency. NOMA schemes are being combined with popular multicarrier schemes such as orthogonal frequency division multiplexing (OFDM) to take advantage of the benefits of multicarrier signals. A variant of the power domain NOMA is Layer Division Multiplexing (LDM). The most commonly deployed power domain LDM scheme involves successive interference cancellation (SIC) based decoding at the receiver. Fast convolution based filtered-OFDM (FC-F-OFDM) systems are becoming popular among 5G wireless access technologies due to their ability to process 5G physical layer signals efficiently. In this work, firstly, a cognitive multicarrier non-orthogonal multiplexed system based on the concept of LDM is discussed, which uses FC-F-OFDM and conventional OFDM as its component layers. Secondly, cyclostationary FREquency SHift (FRESH) filter based SIC decoding is used at the receiver side, which also utilizes artificial neural network (ANN) processing. Computer simulations indicate that the system provides good bit error rate (BER) performance under frequency selective Rayleigh fading channels.  
multicarrier; NOMCR; cyclostationarity; ANN +
Footnote †: journal: Sensors
## 2 Methodology
### Fault Detection and Isolation (FDI)  
#### 2.3.1 Test Degradation Scenarios  
Benchmarking was carried out using a series of test datasets recorded to assess the performance of FDI algorithms in the context of multi-component degradation. Table 4 below describes the nature of the test data with the components under consideration for the multi-component degradation scenarios at different pump speeds as well as their levels of severity. For example, T14 captured data from a scenario where the system (running at 950 rpm) was affected by a combination of a degraded pump and nozzle; the pump's level of severity was emulated by setting the DPV2 at 45% open, whereas the degradation of the nozzle was emulated by swiping the entire opening envelope of the DPV4 (100% open highlighting a healthy nozzle and 30% opening emulating a high-severity level of degradation). Also, Appendix A.1 (Figure A1) shows the distribution of the MCD test degradation scenarios in Table 4 used to test the performance of the FDI algorithms. The levels of severity were categorised into five groups, with below 20% defined as healthy, between 20% and 40% defined as low severity, between 40% and 60% defined as medium severity, between 60% and 80% defined as high severity, and above 80% defined as high failure risk. The FDI algorithms were expected to classify or predict any instances above 20% severity as faulty in both multi-component and single-component scenarios. To better clarify how the deviation from normality occurred, Figures 4 and 5 show how the distribution of the process parameters of the water distribution system deviated in test degradation scenarios T13 and T17, respectively.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Other Considerations
#### 5.3.6 Deliberate Interference  
While seemingly not addressed in the literature (to the best of the authors' knowledge), one risk that needs to be thought through with BVLOS operations in forest environments is the potential for deliberate interference with the unmanned aircraft being operated. Anecdotally, the authors are aware of such instances within New Zealand. The potential for this is that criminal activity in cultivating illegal drugs such as marijuana and cocaine often takes place in remote forest environments on public lands [108; 109; 110]. As unmanned aircraft become more commonly used by enforcement powers and government authorities, it is possible that an unmanned aircraft being used for legitimate purposes in a forest environment may be confused with law enforcement unmanned aircraft. In such instances, it is possible that the unmanned aircraft may be shot down. This is a risk that needs to be considered when operating over forest environments where it is known that there may be cultivation of illegal crops. Options include not conducting the operation, publicising the operation and its purpose in the hope of getting the word out, or even direct contact with organisations that are known to be involved with such activities. The latter option does also present ethical issues. However, those are outside of scope for this paper. As this risk appears to be undocumented in the literature, it would be useful to see research on the frequency of illegal interference with unmanned aircraft operations and also what measures operators are using the mitigate the likelihood of such occurrences.
# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
## 5 Results and Discussion
### Nominal Performance  
FC with unity feedback, FLC, ANFIS, and PID controllers were simulated for a fixed load. The output voltage offered by these controllers is shown in Figure 13. The PID parameters used were summarized in Table 3.  
Figure 12: New rules surface, after training in ANFIS.  
Figure 13: The output voltage of FC with unity feedback, FLC, ANFIS, and proportional–integral–derivative (PID) controllers.  
The response parameters extracted from Figure 13 are tabulated in Table 4. From Table 4, it can be deduced that the ANFIS-based feedback controller had the least rise time, settling time, percentage overshoot, peak voltage, peak time, and steady-state error at 0.8827 ms, 6.3 ms, 0.5603%, 24.1345 V, 1.8 ms, and 1.04%, respectively, as compared to FLC and PID. Only the percentage undershoot was less, 0.0149%, in the case of FLC. The ANFIS controller completely outshone the other controllers.
## 6 Discussion
### Weighing Up Latency vs. Security  
An important observation in our experiments was the compromise between security and performance. While the system effectively blocked malicious transactions, the presence of faulty or malicious nodes increased latency, especially in the consensus process. With one to three faulty nodes, the consensus time increased by up to 25%, and with a higher number, a consensus could no longer be reached.  
This raises the important question of how much performance degradation is acceptable in the pursuit of strong security measures. In critical applications such as autonomous driving or real-time traffic management, even small increases in latency can have serious consequences. Therefore, optimizing the balance between safety and real-time performance remains a challenge that future research must address.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Idealized Conditions** &
\begin{tabular}{l} **Dynamic Conditions** \\ **(Mobility \& Jitter)** \\ \end{tabular} \\ \hline Sybil attack detection rate & 100\% & 97\% \\ DoS attack mitigation rate & 95\% & 90\% \\ Consensus latency & 100 ms & 120 ms (average), peaks of \\ Throughput reduction (DoS) & 20\% & 150 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis: idealized vs. dynamic conditions.
# Acute Effects of Static Stretching on Submaximal Force Control of the Ankle
## 2 Materials and Methods  
### Participants  
Seventeen healthy young male participants were enrolled in the study (age: 23.5 \(\pm\) 2.0 years; height: 172.6 \(\pm\) 4.5 cm; body mass: 65.9 \(\pm\) 6.1 kg). Individuals with musculoskeletal impairments or neurological conditions affecting the lower extremities were excluded from the study. To control for potential confounding factors, all participants were instructed to avoid strenuous physical activity, alcohol consumption, and caffeine intake for at least 24 h before testing. Female participants were excluded to minimize the effects of hormonal fluctuations related to the menstrual cycle, which can influence muscle stiffness and neuromuscular control [25; 26]. All experimental sessions were conducted at a consistent time of day for each participant within a \(\pm\) 1 h window, to minimize the influence of circadian variation. The testing limb was selected based on limb dominance. All participants were right-limb dominant in this study, so the measurements were performed on the right leg. Ethical approval was obtained from the Institutional Review Board of the affiliated university (approval number: 23-31). All participants were given a comprehensive explanation of the study purpose and procedures and provided written informed consent before participation. Prior to recruitment, a priori power analysis was performed using G*Power 3.1 (Heinrich Heine University, Dusseldorf, Germany) to determine the minimum sample size. Based on a two-way repeated-measures ANOVA (condition \(\times\) time) with an assumed medium effect size (f = 0.25, alpha = 0.05, power = 0.80), the required sample size was estimated to be 17.
## 3 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Average Daily** & **Average Daily** & **Average Daily** & **Average Daily** & **Average Daily** \\  & **Air Flow Rate** & **Ammonia** & **TOC Loading** & **TSS Loading** & **BOD Loading** \\  & **(SCFH)** & **Loading (lb/day)** & **(lb/day)** & **(lb/day)** & **(lb/day)** \\ \hline January 2018 & 16,585.06 & 7369.25 & 43,030.87 & 86,180.39 & 79,483.35 \\ \hline February 2018 & 16,063.90 & 7611.25 & 40,614.63 & 81,647.46 & 75,244.57 \\ \hline March 2018 & 15,039.29 & 7591.59 & 49,229.47 & 93,405.89 & 91,720.42 \\ \hline April 2018 & 14,385.80 & 7870.27 & 50,195.34 & 88,808.87 & 93,561.69 \\ \hline May 2018 & 16,656.15 & 8409.86 & 50,370.99 & 85,384.35 & 93,917.26 \\ \hline June 2018 & 18,400.43 & 7197.76 & 45,202.81 & 85,852.81 & 84,007.68 \\ \hline July 2018 & 18,389.68 & 7651.13 & 51,501.27 & 93,583.23 & 91,876.91 \\ \hline August 2018 & 17,563.42 & 7430.12 & 43,342.82 & 90,191.13 & 80,313.99 \\ \hline September 2018 & 16,210.81 & 7617.75 & 41,670.15 & 90,990.46 & 77,187.15 \\ \hline October 2018 & 16,891.28 & 7697.64 & 42,066.05 & 87,970.58 & 73,818.29 \\ \hline November 2018 & 15,101.90 & 8291.36 & 41,600.87 & 88,441.15 & N/A \\ \hline December 2018 & 16,855.85 & 8560.39 & 41,727.03 & 85,673.32 & N/A \\ \hline January 2019 & 17,333.03 & 9090.04 & 44,214.72 & 94,205.88 & N/A \\ \hline February 2019 & 20,994.76 & 8765.44 & 42,461.94 & 89,715.26 & 78,538.47 \\ \hline March 2019 & 22,322.08 & 7722.53 & 41,135.69 & 83,236.07 & 77,308.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Correlation Calculation Results. Figure 41: Average daily ammonia loading measured in pounds per day (lbs/day) as compared to average daily air flow rate measured in standard cubic feet per hour (SCFH). Using a correlation function, a 25% correlation is found between both parameters. Calculation Appendix B: Correlation of ABAC DO Set Point and Measured DO Equation (A2):  
\[\text{Correlation }(\text{X},\text{Y})\ =\ \frac{n\ (\sum\ xy)-(\sum\ x)(\sum\ y)}{ \sqrt{\left[n\sum\ x^{2}-(\sum\ x)^{2}\right]\left[n\sum\ y^{2}\ -(\sum\ y)^{2}\right]}}\] (A2)  
where X and Y are the calculated means for the data sets. In this case, X is ABAC DO Set Point (mg/L) and Y is the measured DO level (mg/L).
# Optimization Method of Customized Shuttle Bus Lines under Random Condition
## 2 Problem Formulation
### Model Establishment
#### 2.2.3 Mathematical Formulation  
We aim at deciding routes of vehicles reasonably to meet demands while the number of stops served by each vehicle is unknown. The optimization objective is to minimize the operation cost of all vehicles under a vehicle capacity constraint.  
Based on the above, we now introduce the objectives and constraints.  
\[\begin{split} minZ=\sum_{b=1}^{k}\biggl{\{}\bigl{[}\bigl{(}L_{b}+L^{ \prime}_{b}\bigr{)}\times c_{b}+c^{\prime}_{b}\bigr{]}-\sum_{s=1}^{m_{b}}f_{s} \times d_{s}\biggr{\}}\end{split} \tag{4}\]  
s.t.  
\[\sum_{v=1}^{m}X_{s,v}^{b}=1,\forall b\in B,s\in S \tag{5}\]  
\[\sum_{s=1}^{m_{b}}X_{s,v}^{b}=1,\forall b\in B,v\in V \tag{6}\]  
\[W_{1}^{b}=\sum_{v=1}^{m}\Bigl{(}Q_{v}\times X_{1,v}^{b}\Bigr{)},\forall b\in B \tag{7}\]  
\[W_{2}^{b}=W_{1}^{b}-\sum_{v=1}^{m}\left(P_{v}-Q_{v}\right)\times X_{2,v}^{b},\forall b\in B \tag{8}\]  
\[W_{s}^{b}=W_{s-1}^{b}-\sum_{v=1}^{m}\Bigl{[}\bigl{(}P_{v}-Q_{v}\bigr{)}\times X _{s,v}^{b}\Bigr{]},\forall b\in B,s\in S \tag{9}\]  
\[W_{s}^{b}\leq r_{b},\forall b\in B,s\in S \tag{10}\]  
\[T_{0}^{b}=t_{0}^{b},\forall b\in B \tag{11}\]  
\[T_{1}^{b}=t_{0}^{b}+\sum_{v=1}^{m}t_{0,v}^{b}\times X_{1,v}^{b},\forall b\in B \tag{12}\]  
\[T_{v}^{b}=t_{0}^{b}+\sum_{v=1}^{m}\Bigl{(}t_{v-1,v}^{b}\times X_{s,v}^{b} \Bigr{)}+\sum_{v=1}^{m}\Bigl{(}t_{v-1}^{b}\times X_{s-1,v-1}^{b}\Bigr{)},\forall b \in B,s\in S \tag{13}\]  
\[T_{v}^{b}\in[a_{v}-d_{v}^{-},a_{v}+d_{v}^{+}],\forall b\in B,v\in V\text{ or }a_{v}-d_{v}^{-}\leq T_{v}^{b}\leq a_{v}+d_{v}^{+},\forall b\in B,v\in V \tag{14}\]  
The objective function (4) minimizes the cost of all the vehicles. Constraint (5) means that each vehicle cannot serve more than one stop simultaneously. Constraint (6) ensuresthat each stop can only be served once for each vehicle in one trip. Constraint (7)-(10) are the vehicle capacity constraints. Constraint (7) represents the number of passengers at the first stop in each vehicle. Constraint (8) indicates the number of passengers aboard when the vehicles departure from the second stop. Constraint (9) is the recursion formula to calculate the number of passengers in each vehicle at each stop. Constraint (10) sets the limit of the number of passengers in each vehicle. Constraint (11)-(14) are time constraints. Constraint (11) states the departure time of each vehicle. Constraint (12) represents when each vehicle arrives at the first stop served. Constraint (13) indicates when each vehicle arrives at other stops served. Constraint (14) states the uncertainty for vehicles' arrival time at stops. \(d_{v}^{+}\) and \(d_{v}^{-}\) are the definitions of positive and negative deviation of arrival time at stop \(v\) for vehicle \(b\), respectively.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 2 Base Model and Proposed Model
### Base Model  
Currently, there are numerous improvements to U-Net, but they perform poorly in detecting crack integrity. To address this issue, this paper introduces a dynamic snake convolution module to enhance crack detection integrity. DSC is a type of dynamic convolution [27] that, unlike standard convolution, employs variable convolution kernels. This strategy enhances its capability to adapt and detect objects of various sizes more  
\begin{table}
\begin{tabular}{c c c c} \hline
**Network Layer** & **Network Module** & **Number of Downsampled Channels** & **Number of Upsampled Channels** \\ \hline First layer & Standard convolution & 64 & 64 \\ Second layer & CBAM + DSC & 192 & 192 \\ Third layer & CBAM + DSC & 384 & 384 \\ Forth layer & CBAM + DSC & 786 & 786 \\ Fifth layer & DSC & 1536 & 1536 \\ \hline \end{tabular}
\end{table}
Table 1: ADSC-U-Net network structure and number of channels.  
Figure 1: The architecture of ADSC-U-Net.  
Figure 2: Downsampling process of the feature maps.  
effectively. Typically, road cracks are surrounded by fine fissures and can appear indistinct amid complex backgrounds such as shadows, lane markings, and water stains. Standard convolutional neural networks struggle to detect these subsidiary cracks. In contrast, DSC adjusts its convolution kernel shape and size dynamically, adapting to irregular cracks. This flexibility of dynamic convolution enables it to better capture local features in irregular data compared to standard convolution, making it more adept at handling complex noise influences [28]. Furthermore, DSC is specifically enhanced for learning tubular structures, which are common in irregular shapes of road cracks. As shown in Figure 3, adding biases to the input enables the convolution kernel to extract features beyond those captured by standard kernels. Through bias-constrained networks, it achieves adaptive learning within a certain range. After passing through the DSC module, the original feature maps are no longer output in standard square grid shapes but rather in tubular shapes, which better conform to the shape of road cracks.  
This study integrates the DSC module into the U-Net network, enabling the network to adaptively focus on local features that are slender and curved. When road defect images are input into the DSC, the variable receptive field of DSC allows for the better fitting of irregular crack shapes during the downsampling feature extraction process. As shown in Figure 3, employing DSC enables the convolution kernel to vary horizontally and vertically, thereby capturing more useful information. In contrast, using standard convolutional kernels, the receptive field is limited to the surrounding pixels in the horizontal and vertical directions, whereas road cracks often exhibit tubular features that are more likely to extend horizontally or vertically rather than in the surrounding directions.