esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Relationship between NDVI and Climatic Factors  
#### 3.2.1 Relationship with Climatic Factors at a Country Scale  
The time series of annual average NDVI values from 1982 to 2011 at the country scale was significantly and positively correlated with air temperature (\(r=0.52\), \(p<0.01\)), but insignificantly correlated with annual precipitation (\(r=-0.05\), \(p=0.79\)). Thus, the overall greening trends in the study area are likely caused by the overall rate of increase in temperature (0.019 \(\,\)C per year; \(p=0.004\)) in China. The statistical analysis revealed that inter-annual variation of NDVI was dominantly linked to warming, and less influenced by variations in annual precipitation.
# Development of Optimal Size Range of Modules for Driving of Automatic Sliding Doors
## 2 Solving the Problem
### Stage 7. Algorithmic and Software Support
#### 2.7.1 Full Combination Algorithm  
The complete algorithm can be read in Appendix C.1. Here, the main structure is discussed.  
The necessary input data for the algorithm are the values of the main parameter \(\overline{x}_{n}\) for each element of the initial size range \(\overline{Z}\), and the demand \(\overline{N}^{n}\) for each element \(\overline{x}_{n}\), \(n=1\div\overline{L}\).  
The algorithm iterates over all combinations without repetition of \(l\) elements from \(\overline{L}\), \(l=2\div\overline{L}\), i.e., \(C_{\overline{L}}^{l}\). The first iteration is for the special case of \(l=1\). This is a special case, because the number of possible size ranges is 1, and not \(C_{\overline{L}}^{1}\). This case coincides with the size range composed of only the biggest (in terms of value of the main parameter) element \(\overline{x}_{\overline{L}}\). Next, iterations generate size ranges with number of elements \(l=2\div\overline{L}\). For \(l=2\), there are \(C_{\overline{L}}^{2}\) number of size ranges (combinations of elements \(\overline{x}_{n}\)), for \(l=3\), there are \(C_{\overline{L}}^{3}\) number of size ranges, etc. For each of these size ranges, the following calculations are made in this sequence: calculate demand for each element in the current (current combination of elements) size range; calculate production costs \(\sum_{j=1}^{L}TS(x_{l_{j}})\cdot N^{l_{j}}\cdot\left(\frac{N_{TS}}{N^{l}} \right)^{v_{1}}\); calculate additional costs \(\sum_{j=1}^{L}\sum_{u\in L^{l_{j}}}DR\left(x_{l_{j}},\overline{x}_{u}, \overline{N}^{u}\right)\); calculate total cost \(R\). After calculating the total cost for the current size range, it is compared to the minimum value of \(R\) found so far. If total cost for the current size range is lower than the minimum, then it becomes the current global optimum (minimum); otherwise, iterations continue with the next size range. Additionally, the algorithm searches for local optima, i.e., the size range with \(\min R\) for the combination set \(C_{\overline{L}}^{l}\) for a particular \(l\), \(l=2\div\overline{L}\). For example, the local optimum for the combination set \(C_{\overline{L}}^{2}\) is the size range \(Z=\{\overline{x}_{l_{1}},\overline{x}_{l_{2}}\},l_{1},l_{2}\in\{1\div\overline{L}\}\), which has minimum total cost in comparison to all size ranges with 2 elements that are a combination without repetition from \(\overline{L}\). The local optimum for \(C_{\overline{L}}^{1}\) is the size range \(Z=\{\overline{x}_{\overline{L}}\}\), as it is the only possible one in this combination set.  
After determining if the current size range is globally, locally, or not optimal, the algorithm generates the next combination (next size range) of elements. The algorithm finishes after all combinations are checked, i.e., after \(2^{\overline{L}-1}-1\) iterations.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 3 A Framework of 5G Transportation
### Functionalities of the Framework  
The transportation system has multiple services with many functions and features. Multiple functions dominate the 5G-based MA because many different communications provide the services to users. For instance, vehicular communications such as UAVs (Unmanned Aerial Vehicles) and drones handle many services and applications.  
Table 2 shows the expected functionalities of generic MA, the measurement details of transportation services for managing the data traffic influenced by vehicular communication used in Autonomous Driving Vehicles (ADV). In these selected scenarios, enhanced Mobile Broadband (eMBB), massive Machine-Type Communications (mMTC), millimeter-wave (mmWave), Ultra-Reliable Low-Latency Communications (URLLC), IoT, relay, Long-Term Evolution (LTE), WiFi, and Vehicle-To-Vehicle (V2V) were considered. [PERSON] et al. [20] explain the integrated mmWave-microWave communications and introduce the Ultra-Reliable Low-Latency Communications (URLLC)-aware frame structure. Although mmWave communications alone may not provide enough support, the integrated version improves wireless communications' future challenges. Due to the spectrum scarcity, existing wireless networks do not allow researchers to enhance the requirements of future services. However, this integrated version not only enhances the basic requirements but also provides high-speed communication.  
According to [21], a smart BH solution depends on millimeter-wave (mmW) technologies because this emerging approach allows researchers to enhance the communication requirements of the outdoor urban small cells and 5G cellular spectrum.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments
### Quantitative Evaluation
#### 5.2.1 Ablation Study on Loss Functions  
**Sharpness Losses.** To quantitatively verify the Sharpness Losses, we chose a No-reference autoregressive(AR)-based Image Sharpness Metric (ARISM) which is proposed particularly for assessing image sharpness [69]. The ARISM is established on the hypothesis that AR model parameters estimated from eight connected neighbourhoods of one image pixel tend to be very close to each other when this pixel is located in a comparatively smooth region; otherwise, these parameters are clearly distinct when this pixel is in a sharp region. The ARISM sharpness score is formulated as:  
\[\rho=\sum_{k\in\Omega}\theta_{k}\rho_{k} \tag{13}\]  
where \(\Omega=\{E,C,E^{bb},C^{bb}\}\) and \(\theta_{k}\) are the weights of each component. \(E\) and \(C\) are two classical metrics used to define the difference between the maximum and minimum values of AR parameters at point \((i,j)\) of the input image. \(E^{bb}\) and \(C^{bb}\) are the block-based pooling [70] of \(E\) and \(C\), respectively.  
ARISM has been proved to be robust in assessing colour images with no reference, which is suitable for our case since the outputs of the video style transfer are colourful and also have no reference images. Figure 17 shows the scores of each frame in the Alley_2 scene (MPI Sintel dataset) which are obtained from w/ Perceptual Losses + Pixel Loss (aka, Sharpness Losses blue line) and w/ Perceptual Losses (red line) and w/o Sharpness Losses (magenta line), and **the higher ARISM score is better**. The scores of w/ Perceptual Losses + Pixel Loss are steady around the average score 2.674, while the average score of w/ Perceptual Losses tend to decrease from 2.674 to 2.66, and those of w/o Sharpness Losses are close to 2.651. Note that the scores of w/ Sharpness Losses are always higher than those of w/o Pixel Loss, which indicates that outputs with both Sharpness Losses contain much fewer blurriness artefacts than those without Pixel Loss.  
**Coherent Losses.** We carried out the quantitative ablation study on Coherent Losses in five testing scenes to compare the stability errors of two groups of Temporal Losses: multi-frame RGB-level only and both levels. Table 1 shows the detailed stability errors in the baseline method [10] and two groups. It is noticeable that multi-frame RGB-level only Coherent Loss contributes to a 58.8% improvement compared to the baseline method [10], while multi-frame Feature-level Coherent Loss contributes a further approximately 2.2% improvement which finally leads to 61.0% improvement in total.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 3 Related Work
### Machine-Learning-Based Security Solutions  
Machine Learning plays an important role and has been deeply studied by researchers for providing security solutions in different domains, including IoT [50]. Intrusion detection systems and machine learning approaches are effective from a security perspective, but their efficiency largely depends on provided datasets. IDS-based techniques for detecting routing attacks have certain flaws and limitations. For greater accuracy in attack detection, IDS-based approaches are changing from conventional machine learning to deep learning, particularly in sensor networks. However, none of the existing anomaly detection methods provide a mechanism to secure the overall system via attacks prevention [51]. Furthermore, achieving an effective true-positive-rate (TPR) and false-positive-rate (FPR) in real-time with minimal resource consumption is another intrusion detection challenge in the WSN context, for example, 6 LoWPAN [52].  
A trust-based security solution is feasible due to its lightweight characteristic, low computational cost suitable for IoT devices, and effectiveness for defending WSN-inherited (Blackhole attack) and RPL-specific (Rank attack) attacks [38].
## 3 Results  
Table 2 shows the factor loadings from the \"c\" factor model, including specific, bifactor general (\"p\" and \"g\"), and \"c\" itself. Fit of the model was acceptable: CFI = 0.98, RMSEA = 0.021 \(\pm\) 0.001, and SRMR = 0.030. Interpretation of the specific factor loadings is the correlation between the indicator and the factor after controlling for the covariance explained by \"c\" and the bifactor general factors. Of the specific factors, Externalizing (mean loading = 0.58) and Executive (mean loading = 0.41) had the strongest loadings, indicating moderate inter-variable covariance was left over even after accounting for \"g\", \"p\", and \"c\". The opposite was true for Complex Reasoning (mean absolute loading = 0.21) and Psychosis (mean absolute loading = 0.26), which were weak due to most of their inter-variable covariance being explained by the super-ordinate \"g\", \"p\", and \"c\" factors. The bifactor general factor (\"p\" and \"g\") loadings indicate the inter-variable covariance remaining after controlling for \"c\" only. The \"p\" factor was overall more strongly determined (mean loading = 0.51) by its indicators than was \"g\" (mean loading = 0.36). Within the \"p\" factor, the strongest indicators were Depression (0.71) and the Hallucination/Delusion screen (0.63), followed closely by OC (0.62) and agoraphobia (0.62); within the \"g\" factor, the strongest indicators were Emotion Identification (0.58) and Face Memory (0.57). Finally, the strongest indicators for the \"c\" factor (comprising all variables) were Language (0.74), WRAT (0.70), Nonverbal (matrix) Reasoning (0.52), Emotion Differentiation (0.48), and Line Orientation (0.46). The strongest clinical indicators of \"c\" were Conduct (\(-\)0.31), Oppositional-Defiance (\(-\)0.25), and Agoraphobia (\(-\)0.25). Supplementary Table S2 shows the results of the \"\(c\)\" factor model estimated separately in participants aged 8-12 years and 13-21 years. Fit of both models was acceptable; for the younger and older groups, respectively, CFI = 0.98 for both, RMSEA = 0.023 and 0.019, and SRMR = 0.042 and 0.033. Measurement invariance was assessed by comparing the fit of a multiple-group CFA when loadings and intercepts were freely estimated in each age group to the same CFA when loadings and intercepts are constrained to be equal in the two age groups. Following the guidelines suggested by [PERSON] et al. [53], the differences between these models' RMSEAs (0.003) and SRMRs (0.009) suggest measurement invariance has been satisfied, while the difference between their CFIs (0.011) suggests a possible invariance violation. Examining the loadings in Table S2, where all with absolute value > 0.40 are highlighted, we see there is some variation between age groups in the specific and bifactor general loading patterns. However, the pattern of loadings in the \"\(c\)\" factors are quite
## 4 UTS-mAR Implementation
### UTS-mAR Development  
The implementation of the analytical models used in UTS-mAR is based on a standard programming language, Java. In this study, the Android Studio environment was selected for developing the system because of its open-source nature and the possibility of creating a mobile service for tourists. In this regard, the hotel recommender model was developed in Android Studio using the Java programming language. In addition, supplementary application programming interfaces (APIs) were added to this environment to introduce different functions in the model. Google API (fused location provider) was used to track the user. SpatialLite for Android was used to create the distance and transformation functions. The Google Maps API allowed the system to display the results on Google Maps for the user. In addition, the TOPSIS and MCDM functions were implemented using Native coding in Android Studio, which facilitates the creation of an interface to simplify user interaction. In addition, R was used to implement the improved version of the model proposed for large amounts of data using clustering (Section 3.2). Spatial libraries such as sp, rgeos, rgdal, and matpools were used to analyze geospatial data via R. Additionally, the hierarchical clustering library hclust was used for agglomerative hierarchical clustering analysis.  
The AR visualization model was also developed in the Android Studio environment by adding two important APIs: Google API (fused location provider) was used to track the user, while Google Sceneforum and an (YOLO; (version 5s) object-detection software development kit (SDK) were used to introduce the AR capability. These AR SDKs handles image based and markerless AR respectively. The output of our system is shown in term of markerless AR. In addition, Native coding in the Android Studio was used to develop  
Figure 5: Seoul data created with ArcGIS software.  
the different functions used in the text-size-setting, LOD-setting, and visualization models. The interface designed in this section was flexible, allowing the user to enter the context data and select the type of model for defining the AR property.
## 5 Conclusions  
Based on the measured data, the characteristics of vertical variations of the wind field with time in a typical thunderstorm event in Beijing were explored by applying the directional thunderstorm wind model. The following conclusions considering this thunderstorm event were obtained:  
1. By using the directional thunderstorm wind model, it is possible to achieve the decoupling of longitudinal and lateral fluctuating wind speed components, quantitatively study the wind direction of the thunderstorm, and develop credible comparisons between the properties of thunderstorm and synoptic wind speeds.
2. The wind speed of the thunderstorm event shows a typical transient nose shape profile, and the duration of the jump gradually increases with the increase in height. The wind direction in the upper level is mainly controlled by the moving speed of the thunderstorm, while the wind direction in the lower level is determined by the dispersive outflow caused by the collision between the thunderstorm and the ground, and the moving wind speed.
3. Different from the synoptic wind, the lateral mean turbulence intensity first decreases, then increases, and finally decreases near the time when the peak wind speed occurs. The turbulence integral length scale presents an obvious \"nose\" profile, and the height of the nose tip is similar to that of the wind speed profile.
4. The Von Karman spectrum appears consistent with the measured thunderstorm wind spectrum. Compared with synoptic wind, the gust factor in the process of thunderstorm wind is larger, so it seems important to consider the influence of thunderstorm wind during engineering design. The empirical exponential function model of coherence function for thunderstorms presents a slight deviation with the measured one and deserves further study.  
Conceptualization, S.Z.; methodology, A.Z. and S.Z.; software, S.Z.; data curation, B.L. and S.Z.; writing--original draft preparation, S.Z. and X.X.; writing--review and editing, A.Z. and H.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by Beijing Natural Science Foundation (Grant No. 8222013), National Natural Science Foundation of China (Grant No. 52108431) and the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture (Grant No. JDYC20220806).  
Figure 16: Function of \(k\) and \(\Delta z\).
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### Synergistic Development Characteristics of Logistics and Capital Flows
#### 4.4.2 Synergistic Characteristics of Logistics and Capital Flows
Highly positively correlated city pairs, such as Foshan-Guangzhou and Shenzhen-Dongguan, demonstrate close cooperation within the industrial chain, optimizing supply chain management and enhancing logistics efficiency. This synergy promotes efficient capital flow throughout the industry chain, bolstering the region's shipping competitiveness. Conversely, low or negatively correlated city pairs, like Huizhou, Jiangmen, Zhaoqing, Zhongshan, and Zhuhai, reflect the division of labor and complementary relationships among cities. In this context, cities such as Dongguan, Foshan, Zhongshan, Zhaoqing, and Zhuhai emphasize manufacturing and exports, while Hong Kong, Shenzhen, and Guangzhou focus on financial services and capital flows, enhancing overall economic efficiency. Finally, the strong correlation between logistics and capital flows illustrates their mutual reinforcement within the regional economy. An efficient logistics network facilitates rapid capital movement, while robust capital flows support the development and optimization of logistics infrastructure, crucial for strengthening the Greater Bay Area's shipping economy.
## 4 Conclusions  
The effect of DGS structure on the stub-loaded slot antenna is investigated here and has come to the conclusion that using DGS in conjunction with other performance-enhancing designs can lead to a better solution for the 5G technology. In the present work, it gives a wider bandwidth while maintaining a high gain and better impedance matching. These achievements are appropriate to fulfill the requirements of massive IoT services that will be one of the prime concerns of 5G. Though the miniaturization level is negligible which is shown in Table 2, it resonates in the desired 5G frequency spectrum where both licensed and unlicensed frequency bands are utilized. However, further investigation is needed in the field of DGS to reduce its back radiation and to experiment with other design methodologies so that it can play an important role in 5G technology.  
**Author Contributions:** [PERSON] made significant contributions to this study regarding conception, design, and analysis and writing the manuscript. [PERSON] participated in measurement and provided intellectual suggestions and conceptualization. [PERSON] conceptualized the proposed antenna, participated in revising the article critically for important intellectual contents and supervised the whole study. [PERSON] and [PERSON] also supervised the study.  
**Funding:** This work is supported by Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia under research grant MMUE/180010, TM R&D.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 A Comprehensive Overview of Developments in Vehicle Technology
### All-Electric Vehicle
#### 2.3.2 Fuel Cell Electric Vehicle  
Like BEVs, FCEVs are equipped with an all-electric powertrain, with the difference being that the energy source for an FCEV is a hydrogen-fed fuel cell stack, producing only water and heat as emissions. Because of this, FCEVs are considered zero-emission vehicles.  
In the pursuit of large-scale commercialization of fuel cells, it is crucial to address the careful control of generated water to avoid potential faults and failures, as the mismanagement of water can pose significant challenges to stable operations. Specifically, the scalability of fuel cells necessitates higher power and current densities. However, this scalability introduces a critical concern--operating at elevated current densities can lead to the substantial accumulation of liquid water. This accumulation, if not effectively managed, can result in flooding, impeding gas diffusion and ultimately causing the rapid degradation of cell performance. Therefore, it becomes imperative to focus on enhancing  
Figure 4: Series-parallel HEV configuration.  
water management strategies to achieve and maintain optimal cell output performance for the successful large-scale implementation of fuel cell technology [26].  
Polymer electrolyte membrane fuel cells (PEMFCs) are the best choice for vehicles due to their high-power density, low operating temperature (60-80 \({}^{\circ}\)C), low pressure, and resistance to corrosion over other fuel cell types [27; 28]. The hydrogen used in FCEVs can be obtained either by storing it in a tank utilized on the vehicle or by processing it from fuel. Figure 5 depicts the drivetrain configuration used for FCEVs [29]. FCEVs are adequate for constant power source, except for sudden changes in loading, making them ideal for slower vehicles like forklifts, submarines, trams, buses, and vehicles used for handling materials. Currently, high-speed vehicles are also being manufactured with fuel cells, with notable companies such as Honda, Hyundai, and Toyota creating high-performance FCs to elevate their vehicles' capabilities. Various EMS are being adopted to enhance fuel economy, minimize energy dissipation, and increase the vehicle's efficiency.
## 5 PAPR-Aware Transmission Strategies  
In prior sections, we introduced the capacity of a multisine signal for the scenarios with and without amplitude modulation, and we also discussed that PAPR is important to optimize the PCE of a non-linear rectifier. In this section, we will introduce various pre-equalization strategies that optimize SNR or PAPR, or both. A PAPR-aware pre-equalizer compensates the frequency-selective channel for the modulated multisine SWIPT system. A balanced pre-equalizer is proposed, trading-off between the SNR of each bin and PAPR of the received signal. In Section 6, these pre-equalizers will then be compared.