esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### The Motivation  
The strength of the electric field and its potential health effects, particularly concerning radio frequency (RF) and nonionizing radiation, remain subjects of ongoing research [1,2]. Electric fields can generate currents within the human body, and while low levels are generally considered harmless, higher levels associated with exposure to RF and microwaves can result in tissue heating [3,4]. The potential link between long-term high-level exposure to RF and cancer, especially brain tumors, continues to be the focus of active study [5]. The International Agency for Research on Cancer (IARC) has classified RF electromagnetic fields as \"possibly carcinogenic to humans\" (Group 2B). Furthermore, research suggests a possible association between exposure to electromagnetic fields (EMFs) and neurological problems such as headaches, sleep disturbances, and cognitive impairment, although the evidence remains inconclusive [6]. Some individuals report symptoms such as headaches, fatigue, and stress, which they attribute to exposure to EMF. Although the World Health Organization (WHO) does not recognize electromagnetic hypersensitivity (EHS) as a medical diagnosis, the reported symptoms are real and can be severe for those affected. Regulatory organizations, including the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Federal Communications Commission (FCC), have established safety guidelines for exposure based on scientific evidence. These guidelines are periodically reviewed to incorporate new research findings [7]. The expansion of 5G technology has also raised public concerns about its potential health impacts [8,9]. Consequently, public health agencies continue to emphasize the need for ongoing research into the long-term health effects of RF exposure and advocate for evidence-based guidelines to ensure public safety.
## 6 Conclusions  
In this paper, four existing pipelines adjacent to the blasting of a subway station were taken as a case study to analyze the dynamic responses induced by the arch cover method. When compared with the ground surface monitoring data, our numerical simulation was verified. The dynamic blasting characteristics of the pilot tunnels, existing pipelines, secondary lining were studied and analyzed. The findings are as follows:  
1. When using the arch cover method for construction, the effect of blasting the later pilot tunnel on the earlier pilot tunnel will be pronounced if the clearance between the later and earlier pilot tunnels is smaller. Extensive blasting area and many charges will result in large peak velocity in a short time;
2. The maximum peak velocities of the existing pipelines induced by blasting the pilot tunnels decreased with the increase in the relative distance. The effect of blasting the pilot tunnel on the far pipelines was insensitive. However, the hollow effect can amplify the pipeline vibration to change the characteristics;
3. For blasting in the arch cover method, installing the secondary lining can control the deformation and reduce the effect of blasting on the existing pipelines. Setting up the secondary lining promptly can guarantee the safety of the subway station and adjacent structures;
4. When blasting the lower rock mass, the vibration velocity of the arch foot was larger than that of the arch crown and arch waist. The arch foot is in the most adverse position. The blasting of the lower rock mass should be carried out at a suitable age for the secondary lining concrete, and more attention should be paid to protect the built-up arch structure.  
Conceptualization, X.L. and A.J.; methodology, X.L.; software, X.G.; validation, L.H. and X.G.; formal analysis, X.L.; investigation, X.L.; resources, A.J.; data curation, L.H. and X.G.; writing--original draft preparation, X.L.; writing--review and editing, A.J.; supervision, A.J.; project administration, A.J.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese National Natural Science Foundation, grant number 52108363 and 52078093, China Postdoctoral Science Foundation, grant number 2021M/700654, and LiaoNing Revitalization Talents Program, grant number XLYC1905015.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
# Triangular Sierpinski Microwave Band-Stop Resonators for K-Band Filtering
## 1 Introduction
An uplink frequency higher than the downlink one is justified by a lower noise contribution and lower losses in the atmosphere for the propagation of waves with lower frequency. Using two different frequencies helps prevent oscillations generated by the satellite amplifier when simultaneously processing both signals. In this framework, the novelty of this paper lies in the systematic investigation of fractal Sierpinski resonators excited by a coplanar waveguide and coupled structures made by the side coupling of individual triangles instead of using the microstrip-fed resonators, already studied and currently available in the literature. Moreover, we focus our attention on the guided propagation response, while many papers have proposed structures useful for free propagation (antennas). Considerations are drawn regarding the spectrum interpretation and the necessity of refinement in determining the frequency of resonance, which cannot only be predicted by the effective side length of the triangle, such as in the case of microstrip excitation [12]. A simple perturbation approach is not enough to predict the excited modes when considering the increasing complexity of the Sierpinski triangles.
## 2 Calculation Model  
Figure 1a shows a schematic calculation model, based on a figure in a previous study [1]. The values of design parameters, such as the length of the tubes, were set based on those of [PERSON]'s experimental set up [1]. The total length of the looped tube \(L_{loop}\) was set as 2.8 m, its inner diameter was set as 40 mm, and the looped tube was filled with a 501 kPa helium gas. One of the stacks was sandwiched by hot and ambient heat exchangers, and it worked as an engine stack. The other stack was sandwiched by ambient and cold heat exchangers, and it worked as a cooler stack. The length of both the stacks corresponded to 40 mm. The distance between the two stacks is denoted as \(L\) (see Figure 1a) and was used as one of the parameters with respect to which the performance of the system was varied. Both the stacks were modelled as an array of circular channels. The radius of the circular channels in the engine stack is denoted as \(r_{e}\), while the radius in the cooler stack is denoted as \(r_{c}\).  
The temperatures of the hot, ambient, and cold heat exchangers are denoted as \(T_{H}\), \(T_{A}\), and \(T_{C}\), respectively. The values of \(T_{A}\) and \(T_{C}\) in the numerical calculation were fixed at 301 K and 251 K, respectively. The temperatures (\(T_{A}\) and \(T_{C}\)) are close to the temperatures obtained by [PERSON] et al. [1]. However, the value of \(T_{H}\) was determined as one of the calculation results. Each heat exchanger was modelled as a series of parallelly stacked flat plates, with the space between plates corresponding to 1.0 mm and the height corresponding to 10 mm. Two thermal buffer spaces existed along the looped tube. One of the spaces was located at the vicinity of the hot heat exchanger and the temperature changed from \(T_{H}\) to \(T_{A}\). The other space was located at the vicinity of the cold heat exchanger and the temperature changed from \(T_{C}\) to \(T_{A}\) (see Figure 1).
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 3 System Modeling and Problem Description
### Network Model  
This study considers a scenario where end devices directly send task data to cloud or edge servers, as shown in Figure 1. We consider \(M\) static edge servers \(E_{t}^{s}\), which are placed in densely populated areas, where \(i=\{1,2,\ldots,M\}\). Additionally, \(N\) dynamic edge servers\(E_{j}^{d}\) are used to improve the system performance, where \(j=\{1,2,\ldots,N\}\). Furthermore, there are \(Z\) users \(D_{k}\) randomly placed in the scenario, where \(k=\{1,2,\ldots,Z\}\).
# End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control
## 4 Proposed Method
### Network Architecture  
In general, the training of a neural network can be affected by various aspects, especially the network architecture including the number of the network layers, the structure of each layer, the selection of the activation function and so on. As the number of network layers increases, the complexity of the architecture may be beneficial for identifying complex features. However, the variation of input distribution resulting from the subtle changes in the parameters at each layer may be amplified layer by layer and the upper layer network has to adapt continuously to these distribution changes, increasing the difficulty of training. Additionally, different structures of each layer have their own properties. For instance, the fully connected network can be used to fuse the features, the convolutional  
Figure 3: The image-based RL framework for a UAV control problem.  
Figure 2: Comparison of the traditional control and end-to-end control pipelines.  
network is used to extract the spatial features, and the recurrent network is appropriate for temporal sequence processing. The activation functions result in non-linearity, improving the representational ability of the network. In addition, their selection in each layer is related to the attributes of the input and output of the layer.  
The architectures of the neural network designed for the DDPG agent in this paper are shown in Figure 4. The input and the output variables are noted in blue. The actor and the critic have a similar network architecture that consists of convolution layers and fully connected layers. The inputs of both of the networks consist of the pre-processed grey image from the downwards camera with the size of 128 \(\times\) 128. To extract the features of the runway from the whole scene, 3 convolutional layers are used with max-pooling and the _ReLU_ activation function. The size of the filter in each layer is 5 \(\times\) 5. To reduce computation, the stride is set as 2. The output of the convolutional layers is finally given to the fully connected layer and then activated by _tanh_ function for the output layer to keep the control commands within a fixed range. For the actor-network, its output is an \(m\)-dimensional vector in which each element corresponds to the control command of the respective actuators. The value of \(m\) is determined according to the specific mission and controlled object. For the critic-network, the current action vector is used as an additional input for the fully connected layer. In addition, its output is activated with _ReLU_ function to produce a scalar that represents the estimated Q-value.
## 4 Results
### Review Papers and Application Fields
#### 4.6.1 MCDA and Energy Management
Furthermore, it has not fully addressed the challenges facing the bioenergy sector. Simultaneously, applying an MCDA methodology to specific problems in the bioenergy industry might be simpler, but the hybrid MCDA approach is more effective in producing an optimal design from the perspective of all stakeholders. [PERSON] and [PERSON] [67] conducted an analysis of case studies pertaining to renewable energy investments and proposed that fuzzy theory, hybrid MCDA, and the development of new user-friendly methods are gaining traction and are anticipated to emerge as upcoming trends in energy planning. [PERSON] et al. [65] examined energy management issues and concluded that MCDA can aid decision-makers and stakeholders in addressing specific problems in environmental decision-making amidst uncertainty. The authors also noted a rising trend among researchers in employing these techniques at different points in the environmental decision-making process, with hybrid and fuzzy approaches being the most popular. [PERSON] et al. [74] focused on urban energy system planning and found that many of the analysed publications justified their use of the MCDA approach based on its popularity. This suggests that decision-makers are more inclined to trust an approach that has been thoroughly researched and tested by peers. Additionally, they found that, while there has been an upsurge in the use of MCDA, it may impede beginners' comprehension and entry into the area. [PERSON] et al. [79] investigated the selection of passive energy consumption optimization strategies for buildings. Theyconcluded that MCDA can be useful in selecting the best passive strategy. Cost and energy saving potential were commonly used as selection criteria, with AHP being the most popular method, but it may not be suitable for all scenarios. [PERSON] et al. [88] studied the MCDA's application in energy storage systems for grid purposes and revealed that it is a useful tool in this field, and most studies concentrate on handling excess energy from renewable sources.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 3 Methods
### Contour Constraint  
Due to the complexity of the geometric structure of the building surface, extracting the geometric features of the building surface using deep learning requires a large amount of data for training, but training the deep learning model is complex and takes a long time. Therefore, we use the RANSAC [15] to fit the building's point cloud into multiple surface polygons and divide it into one or more building blocks \(\{Z_{1},\cdots,Z_{a}\}\). Then, we extract contour constraint regions for each group to address the missing parts. The extracted polygons are denoted as \(V=\{V_{1},V_{2},\cdots,V_{n}\}\), and the normal vectors for each polygon point outward are denoted as \(\overrightarrow{n}=\left\{\overrightarrow{n}_{1},\overrightarrow{n}_{2}, \cdots,\overrightarrow{n}_{n}\right\}\) The centers of each polygon are represented as \(G=\{G_{1},G_{2},\cdots,G_{n}\}\). Figure 2 illustrates the process of constructing _Contour Constraint_.
## 3 All-Fiber Lasers Mode-Locked with m-SWCNT Saturable Absorber
### Thulium-Doped Fiber Laser  
A thulium-doped fiber laser operating at 1938.7 nm was pumped by an erbium-doped fiber laser. At an average output power of 2.5 mW (pump power of 960 mW), a stable mode-locked operation was observed. The 6.1 nm wide optical spectrum exhibited some \"dips\" that were a result of strong water absorption lines present in the 1.9 \(\upmu\)m region (Figure 5a). Pulse duration was of 713 fs, as shown in the autocorrelation trace presented in Figure 5b. The time bandwidth product of the pulses was equal to 0.36.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Group 1**} & \multicolumn{4}{c}{**Group 2**} & \multicolumn{4}{c}{**Group 3**} & \multicolumn{4}{c}{**\(p\)-Value**} & \multicolumn{2}{c}{**
\begin{tabular}{c} _Significant p-Value_** \\ **Group Differences** \\ \end{tabular}} \\ \hline  & Median & 1.Q & 3.Q & Median & 1.Q & 3.Q & Median & 1.Q & 3.Q & & & \\ \hline Slence (without & & & & & & & & & & & \\ headphones & 87 & 73.5 & 99 & 76 & 64.25 & 88 & 84 & 77 & 93 & **0.05** & 1 vs. 2.05 \\ \hline Headphones without & & & & & & & & & & & \\ noise canceling & 90 & 77 & 103.5 & 75.5 & 65 & 86 & 81 & 74 & 92 & **0.01** & 1 vs. 2.001 \\ \hline Headphones with & & & & & & & & & & & \\ noise canceling & 86 & 71 & 102 & 78.5 & 67 & 83.8 & 81 & 71 & 95 & **0.02** & 1 vs. 2.001 \\ \hline Pink noise & 92 & 81 & 104.5 & 78.5 & 67.3 & 88.8 & 82.5 & 73.5 & 91.5 & **0.001** & 1 vs. 2.001 \\ \hline Relaxation music & 90 & 79.5 & 104.5 & 77 & 64 & 84 & 80 & 75 & 93 & **0.001** & 1 vs. 2.001 \\ \hline Classical music & 86 & 77.5 & 108 & 76 & 65.5 & 83.8 & 81 & 75 & 91 & **0.001** & 1 vs. 2.001 \\ \hline Heavy metal music & 88 & 77.5 & 101.5 & 77 & 67.5 & 85.5 & 80 & 74 & 91 & **0.04** & 1 vs. 2.003 \\ \hline
117 bpm & 91 & 81.5 & 106.5 & 80 & 65 & 88 & 80 & 73 & 91.3 & **0.001** & 1 vs. 2.001 \\ \hline
139 bpm & 88 & 79.5 & 99 & 78.5 & 64.3 & 86.8 & 81.5 & 73.3 & 91.5 & **0.03** & 1 vs. 2.03 \\ \hline
174 bpm & 90 & 75.5 & 105.5 & 76.5 & 66.3 & 86.5 & 82 & 74.3 & 91.8 & **0.02** & 1 vs. 2.01 \\ \hline \end{tabular}
\end{table} 
Table 2: Median, 1 st and 3 rd quartiles of each group for all measurement conditions. Between-group differences: \(p\)-values of the Kruskal-Wallis test for comparing the three groups per measurement condition with each other are given after each condition as well as the \(p\)-values of the Conover-Iman test (Bonferroni-Holm, corrected) regarding the pairwise comparisons. Significant \(p\)-values are indicated in bold.
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 3 Results and Discussion
### MIMO Performance  
Different performance metrics of MIMO antennas, such as ECC (envelope correlation coefficient), DG (diversity gain), and TARC (total active reflection coefficient), should be examined to ensure their efficient operation.  
The ECC value is a critical parameter for evaluating the performance of the radiation patterns of MIMO radiators. A lower ECC value indicates a lower effect on other antennas while working alone, and greater efficiency. To guarantee effective operation of each antenna, the specified ECC value of a MIMO system in wireless communication networks is generally less than 0.5 [18]. The ECC can be calculated from the S-parameters using  
Figure 11: Calculated radiation efficiency and peak gain.  
Figure 10: Simulated and measured far-field patterns on XOZ and YOZ planes at (**a**) 5.4 GHz, (**b**) 6 GHz, (**c**) 8 GHz, (**d**) 11.2 GHz, and (**e**) 14.6 GHz.  
Equation (1). The results of simulated and measured ECC are displayed in Figure 12, which shows an acceptable isolation performance.  
ECC can be computed from the S-parameters using the following equation.  
\[\text{ECC}=\frac{\left|\text{S}_{11}^{*}\text{S}_{12}+\text{S}_{21}^{*}\text{S}_ {22}\right|^{2}}{(1-\left|\text{S}_{11}\right|^{2}-\left|\text{S}_{21}\right|^ {2})(1-\left|\text{S}_{22}\right|^{2}-\left|\text{S}_{12}\right|^{2})}, \tag{1}\]  
Another key MIMO performance metric is DG, which describes how effective the diversity is. ECC is used to represent its value, which can be computed by Equation (2).  
\[\text{DG}=10\times\sqrt{1-\left|\text{ECC}\right|}, \tag{2}\]  
The value of DG, calculated from the S-parameters of the proposed MIMO antenna system, is shown in Figure 13. In the working frequency band, DG is larger than 9.96 dB, thereby indicating a strong MIMO diversity performance.  
Furthermore, the statistic TARC is related to the total reflected power and total incident power, and it is used to assess the MIMO system's effectiveness. TARC should ideally be zero, which indicates that the antenna receives all of the incident power [19]. TARC can be computed using Equation (3) for a two-port system. Figure 14 displays a comparison  
Figure 12: Simulated and measured ECC.  
Figure 13: Calculated DG.  
between simulated and measured TARC values, which reveals that TARC is less than \(-20\) dB in operating frequency range.  
\[\text{TARC}=-\sqrt{\frac{\left(\text{S}_{11}+\text{S}_{12}\right)^{2}+\left(\text {S}_{21}+\text{S}_{22}\right)^{2}}{2}}, \tag{3}\]
## 7 Integrated Localization and Communication
### Joint Localization and Communication  
From the wireless communication perspective, localization is important for improved connectivity and communication [177]. Localization of UEs helps in CSI estimation [178], UE allocation to cellular networks and avoiding intercell interference, secure communication against eavesdropping, etc. [179]. Notably, lately, position-aware beamforming with directional beam-steering is essential for dynamic environments with mobile UEs such as intelligent vehicles and vehicular networks [180]. Further, object sensing and detection using RF signals [181] is a practical technology as being used in radar sensors in intelligent vehicles [182]. In this context, the integrated sensing and communication (ISAC) concept has been introduced to maximize the energy and spectral efficiency of wireless channels [183].  
On the other hand, from the vehicular navigation perspective, accurate localization is critical for autonomous navigation and unmanned driving for intelligent vehicles [184; 185]. In outdoor environments, localization is implemented with the aid of a global positioning system (GPS). However, the meter-scale accuracy of the GPS signals is not sufficient for the safe driving of unmanned vehicles. Particularly, GPS signals are not available in indoor areas and their interruptions in dense environments, such as urban areas, pose fatal autonomous-vehicles safety risks. Therefore, sensor fusion base localization is performed to achieve more robust and reliable localization for safe unmanned driving [185].  
Autonomous mobile robots and vehicles normally rely on onboard sensors for localization such as inertial navigation systems and odometry, radar, light detection and ranging (LiDAR), and cameras, among others. To minimize localization errors, the simultaneous localization and mapping (SLAM) concept has been very popular in the robotic community [186]. In SLAM, by combining the localization information given by the inertial navigation system (odometry) with the sensing and measurement data given by sensors, the localization error due to odometry drift or sensor inaccuracy is minimized. Also, simultaneously, a map of the environment is created. Lidar-based SLAM, although more accurate, is expensive and suffers from its dependency on mechanical processes to sendlight signals in the 360-degree direction. Recently, visual SLAM using pixels has become very popular, particularly in autonomous vehicles, thanks to the developments in computer vision and deep learning techniques for extracting geometry information from pixels [187]. Nevertheless, still, visual-based localization is not robust [188], as it is impacted by weather conditions and technical aspects such as depth detection [189].