esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Findings  
This section presents the results in a structured format to ensure clarity and comprehensiveness. The findings are divided into the following subsections:  
* Initial Analysis: A summary of the overall findings from the model validation.
* Algorithm Performance Comparison: A detailed comparison of the NSGAII and MOPSO algorithms.
* Case Study Results: The outcomes from applying the proposed model to a real-world case study.
* Practical Implications: The practical value and potential applications of the results in telecommunication network optimization.  
Based on the mathematical model presented for evaluating the proposed mathematical model, it has been evaluated and analyzed using the MOPSO and NSGAII algorithms. Therefore, in this section, in order to solve sample problems in larger sizes, metaheuristic algorithms NSGA II and MOPSO with priority-based encoding have been utilized. Consequently, at the beginning of this section, initial solutions used in solving the problem and the operators of metaheuristic algorithms are presented. Towards the end of this section, the adjustment of metaheuristic algorithm parameters using the Taguchi method is discussed. Therefore, the mathematical model is initially minimized using the weighted objective function approach for the second and third objective functions.  
The selection of NSGAII and MOPSO was driven by their proven effectiveness in solving multi-objective optimization problems. NSGAII excels in maintaining solution diversity and exploring the Pareto front comprehensively. On the other hand, MOPSO provides a faster convergence rate, making it suitable for scenarios where computational efficiency is critical. These algorithms were chosen based on their performance in similar studies [24; 25; 26].  
These algorithms were compared to other metaheuristic algorithms, such as genetic algorithms (GA) and traditional particle swarm optimization (PSO), which often struggle with either premature convergence or maintaining diversity. The combined strengths of NSGA-II and MOPSO offer an optimal balance of solution quality and computational efficiency, making them ideal for the complex and large-scale nature of the hub location problem in telecommunications.
# Can Blockchain Strengthen the Energy Internet?
## 7 Grid Intelligence
### Role of Blockchain  
Intelligent grids arrive at decisions based on the aggregated data set obtained through IoT devices and incorporation of AI and ML techniques [77]. The integrity of the data used in ML models and preventing data poisoning would be the key consideration, which directly impacts the decision-making process. With blockchain being an immutable DLT, this would ensure data integrity by preventing data manipulation, injection and corruption [70,78]. Privacy preserving of the large data sets can be achieved through distributed edge computing, facilitated by blockchain platforms where raw data will remain closer to its origin, assuring confidentiality [42].  
Nevertheless, the authenticity of the algorithms generated through the incorporation of AI and ML cannot be verified through the blockchain platform. Security and privacy preservation in AI based systems, however, cannot be fully facilitated by blockchain platforms and, thus, will require AI-resilient measures. At the same time, this would affect the control decisions obtained for the stable and reliable operations of the decentralized architecture. Standardization and regulatory enforcement are required to overcome such issues arising in the envisaged electricity grids.
# Influence of Fiber Mixing Process on the Cracking Resistance of Cold Recycled Asphal Mixture
## 3 Experimental Methods
### Crack Resistance Tests
#### 3.1.2 Semicircle Bending Tests  
Considering that the stress state of semicircle bending (SCB) test specimen is closer to that of the actual pavement [22], SCB tests can be used to evaluate the low-temperature anti-cracking performance of asphalt mixture [23]. According to the technical specification for cold recycling construction of emulsified asphalt on highway asphalt pavement (DB13/T-2020) [24], SCB test was conducted. The schematic of SCB test is shown in Figure 7. SCB specimens with a dimension of \(\varphi\) 152.4 mm \(\times\) 105 mm were firstly prepared by large Marshall methods. Then the compacted specimen was cut by a high precision cutting machine into the SCB test specimen (as presented in Figure 7a) whose size diameter and thickness was 150 mm and 50 mm, respectively. The notch depth and width of semicircular specimen was 15 mm and 2.5 mm, respectively. During the test, the SCB test specimen was supported by two fixed rollers with a span of 120 mm (as presented in Figure 7c). The temperature of SCB test was \(-\)10 \({}^{\circ}\)C, the LLD control mode was used for loading, and the loading rate was 1 mm/min. The SCB loading curves is shown in Figure 7d.  
Figure 6: The process and loading curves of IDT test: (a) the test process; (b) the load-displacement curve.  
Figure 7: Semi-circular bending test schematic: (a) compacted specimens and test slices; (b) loading process; (c) key dimensions; (d) SCB loading curves and calculation diagram of fracture energy.  
In the view of the evaluation index, fracture energy can be used to characterize the ability of CRAM to resist low-temperature cracking. Fracture energy (\(G_{f}\)) is defined as the work of SCB specimen generating unit area fracture surface, namely the ratio of force-displacement curve area to specimen fracture area, which could reflect the load and deformation of the specimen at each moment during the SCB test. The larger the fracture energy value, the better the resistance to cracking of asphalt mixture [25; 26]. The calculation diagram of \(G_{f}\) is shown in Figure 6d and the calculation method is presented in Equation (2).  
\[Gf=\frac{\int Pd\mu}{(r-a)t} \tag{2}\]  
where:  
\(G_{f}\)--axial displacement fracture energy;  
\(P\)--test tensile load;  
\(\mu\)--axial displacement;  
\(r\)--radius of the specimen;  
\(a\)--incision length;  
\(t\)--specimen thickness.
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 6 Discussion
### Performance of Multi-Technology Fusion  
As seen in the previous section on Figures 5 and 7, the error obtained by multi-technology fusion improves substantially with respect to single-technology positioning performance. With fusion, more measurements over-determine the WLS algorithm and also the geometry of the reference points enhances from a denser deployment. The over-determination of the localisation problem might not enhance the system performance with the inclusion of MLE if a RP appear intermittently, in which case MLE drastically reduces the impact of this intermittent RP.  
On the other hand, the combination of isolated high-precision ranging technology with low-precision technology such as LTE shows a considerable improvement in all the possible aspects, such as mean, standard deviation, or the magnitude of the outliers, as seen in Figure 7. Despite LTE being a coarse precision ranging technology, it fills the lack of ranging information to solve the WLS algorithm for location augmenting the coverage area where location is provided. Therefore, taking advantage of the fact that several technologies are already deployed in the measurement scenario (and also in many real-world situations), the multi-technology fusion technique can be used to exploit different deployments, improving accuracy, coverage, and reducing the cost of new deployments.
## Appendix B
* Legend for Table 1: Sensor Data Analysis
* N: Newton, a unit of force.
* RH: Relative Humidity, a measure of the amount of moisture in the air.
* cd: Candela, a unit of luminous intensity.
* R.P.M: Rotations Per Minute, a measure of the frequency of rotation.
* \({}^{\circ}\)C: Degrees Celsius, a unit of temperature measurement.
* IP: Internet Protocol, used for IP addresses.
* PIR: Passive Infrared, a type of motion sensor.
* GPS: Global Positioning System, a satellite-based navigation system.
* Time Frame: Duration for which the IP camera is active.
* Legend for Table 2: Sensor Specifications
* FS-500: Model number for force-sensitive resistor.
* TouchSensor_v1.2: Model number for Robot Touch Sensor.
* LM35: Model number for temperature sensor.
* DHT22: Model number for humidity sensor.
* HC-SR501: Model number for PIR motion sensor.
* PV-200: Model number for light intensity sensor.
* ThumbJoystick_v2.0: Model number for Thumb Retail Joystick.
* NEO-6M: Model number for GPS module.
* UltrasonicSensor_v3.1: Model number for tank water level sensor.
* RPM-1000: Model number for Frequency of Rotations Sensor.
* IP Camera: Internet Protocol camera, used for video monitoring.
* SR-1000: Model number for speech recognition sensor.
* Full Definitions of Notations
* Initial Value: The first value recorded at the start of the observation period.
* First Loop Value: The value recorded during the first interval of data collection.
* Second Loop Value: The value recorded during the second interval of data collection.
* Third Loop Value: The value recorded during the third interval of data collection.
* Final Value: The value recorded at the end of the observation period.
* Interval: The time duration between consecutive measurements.
# Effects of Distracted Pedestrian Behavior on Transportation Safety: Causes and Contributing Factors
## 1 Introduction  
Crosswalks at signalized intersections are critical zones of interaction for pedestrians and vehicles and are designed to ensure safe pedestrian walking activities while allowing the uninterrupted flow of vehicles. Pedestrians account for a substantial proportion of road traffic fatalities and injuries, which is increased due to distracted walking. The issue of distraction among pedestrians, owing to several factors, is becoming a concern for the safety and smooth operations at these intersections. Pedestrian distraction occurs when individuals engage in activities that divert their conscious attention away from their environment. These include using smartphones while walking, listening to music, texting, talking on the phone, or being occupied with other electronic devices.  
A study conducted near a school in Austria showed that almost 44% of young pedestrians held their phones in hand while crossing the street [1]. Another study conducted in Leicestershire, United Kingdom, showed that about 42% of pedestrians were visibly distracted while crossing the street [2]. There is growing concern that the impact of distracted walking is most evident in urban areas, where there are more pedestrians and dense traffic flows. As digital devices are becoming more incorporated into daily life, their usage has become a common contributor to distraction, leading to a rise in accidents that involve inattentive pedestrians. Pedestrian distraction poses serious risks by impairing an individual's ability to notice incoming traffic, properly interpret signal timings, or recognize important visual cues, thus heightening the likelihood of collisions and accidents. Other consequences of pedestrian accidents include emotional trauma, legal ramifications, and economic burden for individuals and communities.  
Addressing pedestrian distraction is not only a public safety priority but also a key to preserving the operational efficiency of signalized intersections. Predictable pedestrian behavior plays an important role in the proper functioning of intersections, where distraction creates unpredictability that compromises both safety and traffic flow. Studies have also shown that pedestrian distraction significantly contributes to collision risk [3]. The objective of this study is to identify the primary causes of pedestrian distraction by observing pedestrian behavior and conducting surveys to gauge pedestrian self-awareness at ten signalized intersections in the northwest quadrants of Washington, DC. This study employs binomial regression analysis and Heuristic Bin analysis to identify the sources of pedestrian distractions at signalized intersections and explore potential mitigation techniques.  
In the following sections, a review of four main topics of study is presented. These topics are essential for determining the possible major causes and impacts of pedestrian distraction, along with methodologies that have been used to study distractions and strategies implemented to reduce such distractions.
## 3 Field Monitoring of Shallow Four-Track HSR Tunnels
### Mechanism of Genuine Pressure  
Rock pressure refers to the force exerted on the surrounding rock and support structures during underground excavation, which may lead to deformation or structural failure. Loosening pressure is defined as the load directly imposed on the support structure by detached or collapsed rock masses under the influence of gravity. Genuine pressure occurs when the deformation of the surrounding rock is restrained by closely fitting support systems, leading to a coordinated deformation and load-sharing mechanism between the rock mass and the support. In this context, the rock exerts contact pressure on the supporting structure. With the advancement of tunneling techniques, construction philosophies, support systems, and modern monitoring technologies, the concept of rock pressure in conventional railway tunnels has evolved--from the traditional loosening pressure to what is now termed genuine pressure. Based on field monitoring during the construction of the Zhengzhou-Wanzhou HSR tunnel, [PERSON] et al. [21] determined that the rock pressure in mechanically excavated tunnels corresponded to loosening pressure. The continuous evolution of tunnel engineering practices is expected to reinforce this transformation. It can be concluded that the primary direction in current tunnel engineering is to control the rock pressure within the genuine pressure stage, as illustrated in Figure 10. Based on the compatibility between the tunnel lining and rock, as well as the rock mass quality, this study proposes a conceptual diagram of tunnel bearing states (Figure 11). Assuming the tunnel is initially in a genuine pressure state (point A) upon completion, then if the rock deteriorates, the tunnel may shift to a loosening pressure state (point B). If the lining itself deteriorates, the bearing state may evolve to point C. When both the lining and rock undergo simultaneous degradation, the tunnel's load-bearing state may reach point D.  
Figure 11: Tunnel bearing capacity curve.  
Figure 10: Conceptual transformation of rock pressure states.
###### Abstract  
Parallelization of power electronic converter units is a way to meet the high current requirements of modern electrification applications. In case of voltage source converters, parallel operation can be attained only if the voltages of all the units are equal. In the current state of the art, this voltage synchronization can be achieved at the fundamental frequency, but not at modulation frequency, hence requiring bulky filters to limit circulating currents; this lowers the system performance in terms of cost, volume, weight and sustainability. In this paper, the authors propose a novel approach to synchronization, acting directly at the modulation frequency level, thus removing the need for any filter. This technique relies on the natural parasitic inductance and resistance of the wiring among parallel units. Specifically, this paper presents the first of two synchronization stages required to reach the sub-nanosecond synchronization necessary to completely remove the filters. At start-up, a low-bandwidth industrial communication line, based on the CAN protocol, is exploited to guarantee that the error in the synchronization of PWM signals among all the parallel units is lower than 0.1%. This limits the initial circulating current, supporting the subsequent control stage that achieves sub-nanosecond synchronization. The proposed concept is validated by experiments using a commercial MCU unit with an unadorned CAN peripheral.  
circulating current; initial synchronization; parallel operation of power converters; switched mode DC/AC power converter
# Optimization Design for Sparse Planar Array in Satellite Communications
## 2 Sparse Planar Array Optimization Design Model
### The Main Problems to Be Solved in the Optimization Design of the Sparse Area Array  
From the perspective of the antenna pattern, three types of problems need to be considered.  
The first type of problem is to adjust the excitation amplitude phase weight to determine the array element distribution, such as optimizing peak sidelobe level minimization and main lobe beam width control to obtain the expected antenna pattern.  
The second type of problem is to reduce the number of array elements and optimize the weight and array layout according to the expected antenna pattern without changing the array aperture so as to obtain the expected shaped beam or the reconstructed antenna pattern that best matches the desired pattern.  
The third type of problem is the comprehensive optimization of arrays, which needs to comprehensively consider the joint optimization of multiple parameters such as array element layout, number of elements, array amplitude phase excitation, and peak sidelobe level so as to design a high-performance antenna pattern that meets expectations.  
Satellite communication scenarios are characterized by long transmission distances, complex environmental conditions, numerous influential factors, and strong interference from various sources. Moreover, satellite arrays are constrained by array cost and weight limitations. Consequently, the primary emphasis in such scenarios should be on addressing the third category of issues.  
Since the sparse planar array optimization design problem is a multivariate, nonlinear optimization problem that is difficult to deal with, it is necessary to construct a suitable sparse planar array optimization design method, which is also one of the main difficulties in sparse area array optimization design.
## 5 Case Study and Algorithm Validation
### Effects of Swarm Sizes
The path it generates may not be the best, although a flying path is indeed obtained. It may be suitable for the UAVs to follow but with a much higher cost compared with PSO, standard BAS, and the proposed BSO method in this scenario. The generated UAV paths in a 2D environment by the five algorithms concerning a ground risk map are provided in Figure 12 with different swarm sizes. Theoretically, an optimum UAV path should avoid the ground areas with high risk to make the ground people as safe as possible. As shown in Figure 12, when the swarm size is 10, FA jumps all the time from the beginning to the end. If the UAVs follow the paths provided by FA, they have to turn a lot and frequently. GA is better than FA, with fewer troughs and peaks of waves. PSO and standard BAS are similar to some extent, both of which are much smoother than GA and FA. When there are 20 individuals in a swarm, GA and FA do not change a lot, but there are still many fluctuations. Standard BAS stays the same in the four parameter settings because the swarm size can not affect it at all. PSO becomes more gentle as the swarm size becomes larger and larger. However, the proposed BSO method in this work performs the best with fewer turns on the path and when it is much closer to a straight line that connects the starting point \(S\) and destination point \(D\) in the operation scenario. It is such an expected situation that it is desirable for the UAVs to follow easily and safely. Figure 11: Effects of swarm sizes on path cost. Based on the provided paths in Figure 12, when the swarm size is set as 80 and 90, respectively, the path generated by the proposed BSO method becomes an approximate straight line. It is not only easy for the UAVs to follow it, but it also has a short length to save energy consumption significantly. PSO works a little better with more individuals. The larger swarm sizes would normally make it locate more accurate solutions. Furthermore, GA and FA could also find better waypoints with the help of more individuals. It is obvious that the fluctuations of the two decrease, which means there are fewer turns on the path, which makes it suitable for the UAVs to follow.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 3 Method
### Water Depth Estimation Combining SAR Images and a Digital Elevation Model
This method is based on the law stating that hydraulic energy decreases from upstream to downstream. Using the assumption of one-dimensional (1D) modeling on flow direction, [PERSON] et al. [10] successfully determined a hydraulic hierarchy, composed of an up/downstream relationship between points of water-level estimation, corresponding to the sites of relevant limits. Consequently, following this approach, the water-level estimates in an area with gentle relief imply significant uncertainty. Thus, the method proposed in [10] cannot be applied with good results in extremely flat area where the extent of the flooded area is calculated through water transfer from neighboring cells alongside and perpendicular to the river. In flat topography, the water level usually coincides with terrain elevation on the cells where there is wet-dry shift, i.e., on the relevant flood limits, with gradual floodwater depth increase moving away from these boundary cells. It is particularly true in areas with complex topography where water flow is confined by structural elements (e.g., levees, banks, bridges). In this study, a three-step method is proposed for water-depth assessment in flat areas with complex topography. First, (i) the segmentation of the flood extent extracted from the SAR images is performed to localize the flooded area (segmentation block), which makes it possible to assume uniform flow, i.e., a horizontal water level. While the river channel is characterized by a slope oriented in the direction of the water flow, the complex topography (i.e., levees, embarkments etc.) of the floodplain area can confine water flow, which may be stagnant in some points as identified by the SAR images in different moments of the flood event. Since the segmentation step is not the primary aim of this paper, an expert operator used orthophoto maps to identify the presence of overhead structural elements (typically non-flooded cells) able to confine water flow. Next, (ii) the maximum water level of each segmentation block was estimated as the mean of the terrain elevation values included in a 95-100 percentile range of the DEM cells that are located along the boundaries of the analyzed block; Finally, (iii) the water depth of each pixel within the considered block was evaluated as the difference between the water level value estimated in the previous step and the terrain elevation values of the cells.