esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Analysis and Discussion
### Driving Behavior Risk Measurement Model  
#### 3.4.1 Coefficient Normalization Results  
For the convenience of data processing, a normalization processing of the indicators' values was conducted, which did not affect the distribution of the data. The normalization results for each indicator are shown in Figure 5.  
Figure 5 shows the normalized distribution of each indicator after normalization, and the density position of each indicator data aggregation varied. The values of \(R_{1}\), \(R_{2}\), and \(R_{3}\) were mainly distributed in the range of 0-0.6, and the density peak points were concentrated between 0.1 and 0.2. The values of \(R_{4}\) were mainly distributed in the interval [0.0, 0.2]. The data of each indicator showed a trend of normal distribution. Considering the ends of the normal distribution, the left end represents more conservative driving behaviors, whereas the right end represents more dangerous driving behaviors. A higher value of the risk metrics represents a greater risk of driving behavior. The values of the four risk metrics in the interval [0.6, 1.0] of the normal distribution were lower. The reason is that in the driving behavior dataset in this study, the data for dangerous driving behaviors were relatively few. For example, the total number of \(R_{1}\) was 301,111, the number of \(R_{1}<0.4\) was 289,739, whereas the data for other intervals (corresponding to aggressive and dangerous driving behaviors) only accounted for 3.8% of all data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & \(R_{1}\) & \(R_{2}\) & \(R_{3}\) & \(R_{4}\) \\ \hline \(R_{1}\) & 1.000 & 0.160 ** & \(-\)0.040 ** & \(-\)0.036 ** \\ \hline \(R_{2}\) & 0.160 ** & 1.000 & 0.053 ** & 0.043 ** \\ \hline \(R_{3}\) & \(-\)0.040 ** & 0.053 ** & 1.000 & 0.072 ** \\ \hline \(R_{4}\) & \(-\)0.036 ** & 0.043 ** & 0.072 ** & 1.000 \\ \hline \end{tabular} In the table, ** indicates that the correlation is significant at the 0.01 level (two-tailed).  
\end{table}
Table 2: Correlation analysis of the risk measure indexes.
## 4 Driving Simulation Experiment
### Experimental Results  
#### 4.3.1 Reaction Time  
In this study, a driver's reaction time was defined as the duration from the onset of a sudden braking event of the leading vehicle to the moment the driver initiated braking during the simulated driving task. This measure was used to assess changes in driving alertness under different fatigue states. Reaction time data were collected from 30 participants, and a statistical analysis was conducted to examine how the fatigue level and voice stimulus intensity jointly influence drivers' responsiveness to sudden hazards (Figure 3). A two-way repeated-measures ANOVA was employed to systematically evaluate the effects of these variables. The results showed a significant main effect of the fatigue level on the reaction time (\(p<0.001\)). As fatigue severity increased, participants' average reaction time exhibited a marked upward trend, indicating a suppression of driving alertness. This suggests that higher levels of fatigue are associated with delayed recognition and responses to sudden events, thereby posing greater safety risks.  
Moreover, the type of voice interaction also had a significant effect on reaction times (\(p<0.05\)). High-stimulation voice prompts--characterized by a faster speech rate, lively intonation, and active engagement--effectively enhanced driver alertness and reduced the reaction time. In contrast, low-stimulation prompts that merely provided passive information had limited arousal effects, resulting in the longest reaction times.  
The analysis revealed a significant interaction effect between the fatigue level and type of voice interaction (\(p<0.05\)). Specifically, under severe fatigue conditions, high-stimulation voice prompts reduced the reaction time by approximately 300 ms compared to low-stimulation prompts, demonstrating a marked effect in enhancing alertness. In contrast, when participants were in a non-fatigued state, the differences in reaction time across different types of voice prompts were not statistically significant (\(p>0.05\)). These findings indicate that high-stimulation voice interactions offer stronger driving assistance under higher fatigue levels and can effectively counteract fatigue-induced attentional decline.  
Figure 3: Reaction time of drivers under different fatigue states (ns: not significant (\(p>0.05\)); *: \(p<0.05\); ***: \(p<0.001\)).  
Although the main effect of the occupation type was not statistically significant (\(p>0.05\)), preliminary observations suggested trends of differential response patterns among driver groups under specific conditions. Long-haul truck drivers showed the greatest improvement in reaction time under severe fatigue when exposed to high-stimulation prompts, indicating a heightened sensitivity to arousing stimuli. Private car drivers responded most favorably to medium-stimulation prompts under mild fatigue, with notable improvements in reaction time. Taxi drivers exhibited relatively stable responses across different voice interaction types, with medium-stimulation prompts yielding the highest response efficiency.
## 5 Coupling and Solution of Equations in EMS
### Integrated Coupling  
As mentioned before, generally in EMSs, the current is delivered by a voltage source through an external electric circuit. The general next relation between the voltage v and the current I in the external circuit (coil) governs this current:  
\[\mathrm{v}=1/\mathrm{C}.\ \int\mathrm{i}\,\mathrm{d}t+\mathrm{r}\,\mathrm{i}+ \mathrm{L}.\ \mathrm{d}i/\mathrm{d}t+\mathrm{d}\Psi/\mathrm{d}t+\mathrm{s} \tag{8}\]  
In this expression, r is the total resistance of the circuit, L is linear inductance, C is capacitance, s is non-linear voltage drop (typically a semiconductor component, e.g., a diode) in the electrical circuit, and \(\Psi\) is the flux linkage in the coil.  
The EM main domain normally involves electric and magnetic aspects and is represented by Maxwell's equations. Due to fact that current density and hence the current is delivered by a source that we know the voltage value while the value of the current is unknown. Therefore, the equations to solve are (4)-(8). This coupling between the EM domain (4)-(7) and the external electric circuit (8) is particular regarding other couplings with domains other than EM because it represents a \"correction\" inside the EM domain. We call it integrated coupling. Generally, the coupling of the EM domain with the external electric domain needs a simultaneous strong solution of the equations due to non-linearity of behaviors and closeness of the magnetic and electric time constants, see, e.g., [9; 10; 11; 12; 13].
## 1 Introduction
### Hypothesis Testing Method for Interference Monitoring  
The hypothesis testing method establishes hypothesis testing statistics with monitoring indicators, and obtains the threshold of the monitoring indicators with the given significance level. In [2], [PERSON] and [PERSON] proposed a statistical inference technique for GPS interference detection, where the statistical parameters of the signal without interference were obtained and then the presence of interference was tested by comparing these parameters as a null hypothesis with the parametersextracted from a later window of data. The indicators utilized to establish hypothesis testing statistics include power spectral density, the correlator output, code phase, _etc._[2; 3]. The hypothesis testing method requires the predictable interference characteristics when making a hypothesis in order to ensure the rationality of the corresponding statistics.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems  
It has been observed that beyond 5G systems will be based on virtualization due to their ability of better optimization; however, these systems should support URLLC, mMTC, and eMBB. Development of beyond 5G systems not only has larger dimension but it also involves complexity due to the presence of the heterogeneous networks. In order to overcome these issues, a new intelligent architecture needs to be proposed that should offer flexibility and agility. A new intelligent architecture should have strong ability to learn, recognize, and adapt itself according to the application requirements. According to [23], artificial intelligence (AI)-enabled techniques will be applied to meet the above mentioned requirements for a 6G network. Machine learning (ML) and deep learning (DL) algorithms are envisioned as the most popular AI-enabled techniques for beyond 5G networks. It includes supervised learning, unsupervised learning, reinforcement learning, and neural networks. In the following, we provide a brief overview of supervised and unsupervised learning for network slicing.
## 1 Introduction
Recently, there has been rising interest in integrating data from several SAR sensors and SAR with optical sensors to improve the accuracy of biomass estimates [22; 23]. In Malaysia, there are limited studies on the applications of SAR for estimating biomass. Out of many studies conducted worldwide, very few have been done in Malaysia [11; 24; 25]. This indicates that the potential, limitations, and advances of L-band SAR in estimating tropical forest in Malaysia are not extensively explored. Methods of applying this SAR system are also scarcely exploited. Therefore, the objective of this study is to explore the synergy of SAR sensors, i.e., PALSAR-2 L-band and Sentinel-1A C-band for estimation of AGB in inland dry dipterocarp forest in Peninsular Malaysia. This study highlights and discusses advantages and limitations of this technique.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 4 Meshed Network Deployments  
In the first case, we considered the actual network deployment around Phoenix Street, which is shown in Figure 4 and is labelled Topology 1. To build upon this, we then developed two further configurations that were increasingly meshed, yielding more routes between nodes and towards the POPs, i.e., nodes 1 and 2 in Figure 4. The first configuration is illustrated in Figure 5 (labelled Topology 2), while the second is shown in Figure 6 (labelled Topology 3). Then, we compared the above three topologies composed of 22 nodes but an increasing number of links, namely, Topology1 with 40 links (interfaces), Topology 2 with 48 links, and Topology 3 with 58 links, to determine if the network performs better when it is more densely meshed and, if so, to what degree.  
Figure 5: Liverpool 5G Phoenix deployment Topology 2.  
At this stage, we acknowledge that additional deployment and operation overheads would be incurred as a result of this scheme but expect that the benefits in terms of improved resilience, capacity, and performance will ultimately prove that densification is worthwhile (up to a point). The additional links in Topologies 2 and 3 were selected as they represented reasonable meshing opportunities, e.g., at the end of streets, if new interfaces could be added to the nodes. Note that this was performed simply as a proof of concept for this paper and does not benefit from an analysis based on site surveys, power requirements, physical access, etc., which go beyond the scope of the paper.  
In order to determine the optimal degree of meshing for the Liverpool 5G network, we considered a standard version of [PERSON]'s shortest path algorithm [PERSON] (1996), for which the pseudocode implemented for this study is illustrated in Figure 7. In the figure, \(T\) is a graph made up of the nodes in the deployment which serve as vertices, while the links that connect them serve as edges; \(S\) represents a set for each link \(i\) including the source node \(s_{i}\) and the destination node \(d_{i};p_{i}\) is the selected shortest path from \(s_{i}\) to \(d_{i}\); and \(R\) is the output of the algorithm, i.e., a tuple including \(p_{i}\), \(s_{i}\), and \(d_{i}\) for each link \(i\).  
Figure 6: Liverpool 5G Phoenix deployment Topology 3.  
Figure 7: Pseudo-code for the shortest path algorithm.
## 4 Proposed Resource Allocation Scheme
### Incoming Request Prioritization
Now, the weight of each incoming request representing the priority can be calculated as:  
\[w_{r}=\frac{\hat{d}_{r}}{\hat{n}_{r}+\hat{q}_{r}}. \tag{2}\]  
The higher value of \(w_{r}\) denotes the higher priority of an incoming request within a specific scheduling interval. Here, we have generated the priority of each incoming service request based on the required data processing load, the connectivity of the device to the network, and the QoS requirement of the service request. This priority generation technique facilitates the faster execution of the requests with higher data rate requirement from a critical device in terms of signal strength and sending back the result within its QoS requirement. For example, if two requests arrive, one with poor  
Figure 3: Request prioritization model. connectivity and another with strong RSSI (Received signal strength indication) value, although they have same data rate requirement, our proposed system will give faster access of the BBU to the request with poor RSSI value. The same thing will happen to the request with the QoS requirement of less allowable delay to get response.
# An Automated End-to-End Side Channel Analysis Based on Probabilistic Model
## 4 Experiments and Results  
The experiments were conducted under the environment below:  
* The window size \(N_{w}\) is 1000, \(N_{p}\) is dependent on input data, which in our experiment is 1,657,401.
* Hyperparameters were selected as follows: \(\lambda=48/T,\sigma=9.644e-05,V=7.377-e07\), \(\mu=\frac{\sum_{t=1}^{T}y_{t}}{T}\)
* Binary exponentiation of RSA 16-bit algorithm was executed on Samsung Galaxy S3 smartphone. Electromagnetic wave was sampled by oscilloscope. Sampling rate was 500 MS/s.  
As mentioned, \(\lambda\) controls the number of change points. The best guess about the key exponents without any knowledge is a half of 1s and the other half of 0s. The number of change points will then be 48 (= 2\(\times\)(16 + 8)) as bit of 0 leads to only square operation and bit of 1 leads to square and multiplication. The best guess for \(\mu\) is the mean of the time series. Though \(\sigma\) and \(V\) should be optimized for the best inference, we have experimentally chosen the values of \(\sigma\) and \(V\) among the candidates that were sampled during MCMC process.  
Our approach is evaluated with criteria below:  
* To which level of noise added and inserted to signal (Signal to Noise Ratio, SNR) does the approach work
* The quality of clusters with external information  
The first criterion shows the robustness of the approach to the noise whereas adding noise is often suggested as a countermeasure to the side channel analysis. For the second criterion, the external information (information not used for making clusters) we adopt is the very key exponent we want to estimate.
## 7 The Evaluation of BOC-Like Receiving of MBOC
### Performance Evaluation
#### 7.2.3 Anti-Interference Performance  
For the assessment of the code tracking performance under interference, the anti-interference rate Equation (24) can be used, where a larger value means higher sensitivity to a certain kind of interference. The anti-interference performance is evaluated for QMBOC(6,1,1/11) in Figure 8, with respect to the receiving bandwidth, under three types of interference: narrowband, bandlimited and matched-spectrum. The parameters of interference are listed in Table 2. The receiving and despreading strategies include matched receiving and BOC\({}_{11}\)-like receiving.  
From Figure 8 one can see, firstly, under a certain kind of interference, the anti-interference rates \(n\) of matched and unmatched receiving almost coincide at narrow receiving bandwidth of no more than 9.32 MHz. After 9.32 MHz, matched receiving shows an observable advantage towards the BOC\({}_{11}\)-like unmatched receiving, with a difference of 4.35-5.17 dB for the three interference types at the typical wideband receiving bandwidth of 15 MHz. Both BOC(1,1) unmatched users and wideband matched users can achieve good performance from the MBOC signal design. Secondly, from Figure 8 it is easy to compare the impacts caused by different types of interference. Narrowband interference that is near the main lobe of the signal is the most serious, and the bandlimited white noise and matched spectrum interference have almost similar impact on the tracking, under the given conditions. Moreover, further analysis can be conducted on the influencing factors of code tracking performance by observing the anti-interference rates, including the center frequency of the narrowband interference, the bandwidth of the bandlimited interference, etc.  
Above all, by using the proposed anti-interference rate, the unmatched and matched tracking performance of QMBOC is provided under different bandwidth and interference scenarios, which provides useful guidance for the design of MBOC, as well as the anti-interference measures conducted by QMBOC receivers.  
Figure 8: Anti-interference rate of QMBOC(6,1,1/11) with respect of receiving bandwidth, under the matched receiving and BOC\({}_{11}\)-like unmatched receiving. Three types of interferences are considered, including narrowband interference, bandlimited Gaussian interference and matched spectrum interference, of which the parameters setting of this figure is provided in Table 2.
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
## 5 Assessment of Physical Properties Using GPR Data  
[PERSON] [24] discussed the principles of using GPR reflections to compute layer properties in detail. By automatically monitoring the amplitudes and time delays between peaks, it is possible to calculate layer \(\varepsilon\) values using the SR method. The \(\varepsilon_{\mathrm{r1}}\) is calculated by comparing the reflection amplitudes from an asphalt surface and a metal plate surface (100% reflection), as Equation (1) indicates.  
\[\epsilon_{\mathrm{r1}}=\left[\frac{1+\left(\frac{\mathrm{A_{0}}}{\mathrm  
where \(\gamma_{\rm i}\) is the reflection coefficient at the interface between the two following layers, and n stands for the specific layer and can be calculated using Equation (4) [2].  
\[\rm y_{\rm i}=\frac{\sqrt{\epsilon_{r\rm i}}-\sqrt{\epsilon_{r\rm i}+1}}{\sqrt{ \epsilon_{r\rm i}}+\sqrt{\epsilon_{r\rm i}+1}} \tag{4}\]  
where \(\epsilon_{\rm r\rm i}\) is \(\varepsilon\) of the i\({}^{\rm th}\) layer. A function was established for the G\({}_{\rm mb}\) and \(\varepsilon\) of each asphalt layer using density gauge measurements during the asphalt compaction and simultaneous GPR measurements. This function was applied to map the asphalt G\({}_{\rm mb}\) on the investigated road section spatially. The quality assessment of road structures is primarily based on the percent of the air void (AV) of the asphalt layers, and this parameter was estimated using Equation (5).  
\[\rm AV=G_{mm}-G_{mb}/G_{mm} \tag{5}\]  
Laboratory measurements were made on samples from the AC \(11\) (wearing course) and the AC \(22\) (binder and base courses) asphalt mixtures [12]. The measured theoretical maximum G\({}_{\rm mb}\) of the samples was as follows: G\({}_{\rm mm}\) AC \(11=2436\) kg/m\({}^{3}\), G\({}_{\rm mm}\) AC \(22=2572\) kg/m\({}^{3}\).  
G\({}_{\rm mm}\) is the theoretical maximum specific gravity measured in the laboratory of the HMA, and G\({}_{\rm mb}\) is the specific gravity, which is estimated in the field using GPR measurements.  
Finally, the in situ RD of the analyzed layers was calculated using the following formula:  
\[\rm RD=1-AV=G_{mb}/G_{mm} \tag{6}\]  
The obtained values for the above parameters were spatially interpolated using kriging and visualized with Surfer v.16.
## 5 Discussion
### The Influence Mechanism of Cutting Parameters on Cutting Frequency
Therefore, Equation (10) is as follows:  
\[W_{c}=W_{s} \tag{11}\]  
Substituting Equations (7) and (9) into Equation (10), the height of the extrusion zone \(h_{c}\) can be obtained as follows:  
\[h_{c}=\sqrt{\frac{4 hE_{s}}{\tan a\sin\psi E_{c}}} \tag{12}\]  
The cutting speed \(v\) is not included in Equation (12), indicating that the cutting speed does not affect the size of the extrusion zone. Combined with the proportional relationship between cutting speed and cutting frequency shown in Figure 14, we conclude that the cutting speed \(v\) primarily affects the rate of rock-breaking behavior, while it has little impact on the mechanism of tool-rock interaction. Similar viewpoints have been presented in previous studies [13; 45; 46]. Subsequently, \(h_{c}\) in Equation (12) is replaced by Equation (4), and the cutting frequency \(f_{c}\) can be simplified as follows:  
\[f_{c}=\frac{v}{\sqrt{h}}\Bigg{(}\frac{\tan\psi\sqrt{E_{c}\tan a\sin\psi}}{ \sqrt{2E_{s}}}\Bigg{)} \tag{13}\]  
The inner part of the bracket on the right side of Equation (13) is related to rock parameters and take angle. Considering that the square root of \(\tan a\) varies little within the range of \(10^{\circ}\) to \(20^{\circ}\), it is considered a constant value here. Thus, the inner part of the bracket on the right side of Equation (13) becomes a constant term related to the mechanical properties of the rock, which is defined as the cutting coefficient \(K_{c}\). Therefore, Equation (13) can be rewritten as follows:  
\[f_{c}=K_{c}\frac{v}{\sqrt{h}} \tag{14}\]  
Equation (14) reveals the theoretical relationship between cutting frequency, cutting speed, and cutting depth. When the cutting depth is fixed, the cutting frequency is linearly proportional to the increase in cutting depth. However, when the cutting speed is fixed, the cutting frequency decreases nonlinearly with the increase in cutting depth. The variation characteristics of the cutting frequency are consistent with the numerical results obtained in this study (Figures 13 and 15), which validates the theoretical relationship presented in Equation (14).
###### Abstract  
Protection of the grassland's ecological environment and improvement of people's livelihoods are major tasks for the management of pastoral areas in Inner Mongolia. The comprehensive program for grassland conservation in China, the Subsidy and Incentive System for Grassland Conservation (SISGC), was launched in 2011. To comprehend the effects of this major step towards sustainable grassland development, this study focuses on the spatio-temporal development of grasslands in Inner Mongolia since 2011. Through the combination of MODIS (Moderate-resolution Imaging Spectro-radiometer) satellite data with up to date meteorological data, we used the indicators of Fractional Vegetation Cover (FVC) and Net Primary Productivity (NPP) to analyze qualitative and quantitative grassland changes. A classification system on the pixel level, reflecting change trends and fluctuations of both FVC and NPP, was applied to monitor and analyze the grassland development from 2011 to 2019. In particular, the spatial transfer matrix of the recent two years (2018 to 2019) was analyzed to reveal the latest potential issues and random impact factors. The results show a positive overall but spatially unbalanced effect of SISGC, with a prominent positive impact in the semi-desert grassland area. The potential threats from both social and natural aspects as well as the importance of a forecast system for local stakeholders in the pastoral area are discussed.  
Inner Mongolia; grassland cover change; the subsidy and incentive system for grassland conservation; pastoral area +
Footnote †: journal: JANM  
10.3390/Jand10010038