esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Model Application
### Metrics Evaluation  
Table 8 presents the results of the main detection and segmentation metrics for the test dataset. It is possible to notice a similarity with the values obtained for the validation dataset which proves the robustness of the model. Additionally, Table 9 presents the results for each classified instance of the test dataset, most of them are true positives, showing the assertiveness of the model. From these numbers were obtained a recall and precision equal to 85.8% and 84.0%, respectively. The inference total time per image, for CPU and GPU, where 7.64 s and 0.49 s, respectively, which are competitive values envisaging real time applications.
# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 7 Limitaion  
We use political bias labels based on the US political system to categorize news from other countries. This simplified approach is common in previous datasets, as manually labeling the political stance of media in each country is challenging. Additionally, we observed that some non-English media included translated English news, suggesting possible consistency in bias.  
Our labeling relies solely on MBFC for three main reasons: first, it is a recent, publicly available, large-scale source evaluation label set; second, other agencies like NewsGuard and Allsides do not offer free services; third, many previous media evaluations have utilized MBFC's factual and political bias labels, indicating their credibility. However, we recognize that MBFC's labels may not be entirely accurate, as they are primarily derived from labelers in English-speaking countries, which can introduce biases. Furthermore, the evaluation framework is based on the US political spectrum, which may not be applicable to all countries. We aim to address these issues in the future.  
Conceptualization, C.L. (Chichen Lin) and Q.W.; methodology, C.L. (Chichen Lin); software, C.L. (Chichen Lin); validation, C.L. (Chichen Lin); formal analysis, C.L. (Chichen Lin); investigation, C.L. (Chichen Lin) and C.L. ([PERSON]); resources, C.L. (Chichen Lin) and Q.W.; data curation, C.L. (Chichen Lin) and J.X.; writing--original draft preparation, C.L. (Chichen Lin) and C.L. ([PERSON]); writing--review and editing, W.F. and Q.W.; visualization, C.L. (Chichen Lin) and C.L. ([PERSON]); supervision, Q.W. and Y.W.; project administration, Q.W. and Y.W.; funding acquisition, Q.W. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant number 2021 YFF0901602) with funding from [PERSON] (10,000 RMB), the Fundamental Research Funds for the Central Universities (Grant number CUC23 ZDTJ005) with funding from [PERSON] (10,000 RMB), and the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China) with funding from [PERSON] (20,000 RMB).  
Not applicable.  
The dataset is available at [[https://zenodo.org/record/8340085](https://zenodo.org/record/8340085)]([https://zenodo.org/record/8340085](https://zenodo.org/record/8340085)), accessed on 24 October 2023. Our data should be used only for academic research. Using crawlers to collect data may raise some ethical issues. Before crawling data, we checked the robots.txt file of the target website to understand the restrictions and requirements of the website for crawlers. The news data we crawled will be provided in a restricted access form, and the source and author of the news will be accurately marked. We will ask users not to use these data for harmful purposes. If there are sensitive data in the news, we will remove these parts.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### Tracking Orientation
#### 4.2.1 Ground-to-Air Tracking Antenna  
Ground-to-air antenna tracking systems relies on the determination, measurement, and comparison of signal strength gradient from the GCS to the airborne platform. This scheme motivates the search for the optimal elevation and azimuth angle between the two antennas involved [86; 87]. The ground-to-air tracking antenna system is a very simple and efficient approach of tracking a target without knowledge of the air vehicle attitudes. The use of a circularly polarized microstrip antenna array to increase the bandwidth in \(\mathsf{S}\)-band for UAV ground to air transmission has been explored [88]. The simulation results of the array element showed that its relative bandwidth is 21.7% with gain greater than 5 dBi and \(\text{VSWR}\leq 1.25\). Similarly, reference [31] built an antenna tracker using GPS and a 32-bit microcontroller, which is of two degrees of freedom. The system took into consideration the altitude of the vehicle and was able to provide an azimuth angle of 360 degrees and a pitch angle of 90 degrees. This approach will be ideal for a UAV case, considering the fact that airborne platform is constrained by weight, size and power (swap).
# Multicell Interference Management in Device to Device Underlay Cellular Networks
## 1 Introduction
### Paper Contributions  
In this paper, we address the joint problem of optimal RB and power allocation in the uplink communication of multicell D2D enabled networks, where the optimization objective is the minimization of the total interference of the network. Due to the inherent complexity of the nature of the combined problem, a two step approach is followed. At the first step the RB allocation problem is solved considering that all users of the network transmit at fixed power. At the second step, taking the RB allocation vector of the first stage as an input, the total interference minimization problem is decomposed into multiple interference minimization problems per available RB.  
Specifically, at the first step the problem is formulated as a non-cooperative game which is proven to be a bilateral symmetric interaction game. This type of games are exact potential games. Every player of the game is one of the potential receivers of the system. The receiver of the network can be either an eNB or a D2D receiver. The eNB is in charge of deciding the optimal RB assignment to the cellular users who belong to the respective cell. The D2D receiver decides the optimal RB for the respective D2D transmitter. Leveraging the highly desirable properties of potential games, we prove that at least one NE of the game exists. A best response algorithm is proposed which converges after a finite number of iterations to the identified NE point of the game.  
It is noted that in our approach and methodology, in contrast to the majority of existing literature, we do not restrict a priori the number of D2D pairs that can reuse a RB, allocated to a cellular link. This is determined dynamically by the objective of interference minimization. Thus, a RB reserved by a cellular link can be potentially reused multiple times by different D2D links in order to increase spectrum efficiency.  
During the second step of our approach each uplink power allocation problem per RB is formulated as a linear programming problem. This optimization problem is shown that is equivalent to the total power minimization problem for every available RB. For this reason, the well known Foschini-Miljanic algorithm [29], which solves the total power minimization problem [30], is properly adopted to solve our problem in a distributed way. This algorithm finds either a feasible solution or pushes the users who cannot satisfy their SINR requirement to the maximum transmission power level in order to achieve a SINR level as close as possible to their SINR requirement.  
Finally, the efficiency of the overall proposed framework is evaluated through numerical simulations, especially with respect to significantly decreasing the total power consumption in the network, while increasing the network capacity in terms of number of users who are able to satisfy their SINR requirement.
## 1 Introduction
These advantages make MAML-based algorithms particularly suitable for combating malicious intelligent jamming and attacks with high dynamic characteristics. Also, they can overcome the limitations of existing deep reinforcement learning anti-jamming algorithms in terms of algorithm robustness and generalization abilities. In this paper, an intelligent anti-jamming algorithm based on the integration of the principles of deep reinforcement learning and meta-learning is proposed and verified by computer simulations. First of all, a base learner is employed by the algorithm to derive optimal strategies from a series of known, similar anti-jamming tasks. Then, a meta-learner is used to abstract the general characters and rules of all the previous tasks and acquire the general knowledge about the jamming and attacks. This can provide a more universal strategy to be used by the intelligent anti-jamming algorithms to finish their learning process for new tasks. Thus, the proposed algorithm, starting from this initial strategy, can swiftly adapt to new and more dynamic jamming scenarios. The main contribution of this paper is as follows:  
We propose the Meta-PPO intelligent anti-jamming algorithm to address the issue where the communication party is unable to use known anti-jamming experience and thus led to slow strategy convergence when faced with malicious jamming that dynamically and randomly modifies jamming parameters. When the external jammer randomly changes jamming parameters, the algorithm uses the MAML thought to update the initial model parameters in real-time. This enables the communication party to utilize known historical anti-jamming experience and converge more quickly to the optimal anti-jamming strategy. To validate the effectiveness of the Meta-PPO algorithm proposed in this paper, we simulated scenarios of wireless communication interaction using Pytorch software (version 2.0). In these scenarios, the communication party employed both Meta-PPO and other algorithms for decision making, and their normalized throughput was compared. Simulation results show that the proposed algorithm can consistently maintain a high throughput performance for data transmission, especially in a jamming environment where jamming parameters are frequently changing. Also, the proposed algorithm shows enhanced generalization ability and robustness compared with conventional reinforcement learning algorithms, resulting in superior anti-jamming capabilities. For future research directions, we plan to integrate actual hardware and conduct experimental simulations of the Meta-PPO algorithm in specific wireless communication environments. The organization of this paper is as follows: The components of the system and the formulation of the problem are presented in Section 2. The Meta-PPO-based intelligent anti-jamming algorithm for wireless communication is detailed in Section 3. The simulation outcomes and analysis of the algorithm are discussed in Section 4. Finally, the paper is concluded in Section 5.
# Towards Sustainable Regional Planning: Potential of Commuter Rail in the Madrid Urban Region
###### Abstract
In the same year, the amount of trains-km was 62 million for AVE and 81 million for commuter rail, and the offered seats-km were 21,220 million in AVE and 29,970 in commuter rail [31]. If public transport corridors, such as those along commuter rails, are effective for daily trips, mainly along the axis where the transport network runs [32], how has the model of mobility and occupation evolved on a metropolitan scale? Is progress being made towards a mobility model based on walking, cycling and public transport to the detriment of the use of private vehicles? Is there an urban culture aimed at promoting a denser and more compact occupation model, or has urban growth been favoured by leaps and bounds based on road infrastructure? Assuming the hypothesis that the more people live or work in the vicinity of a station, the greater the possibility of getting to it and leaving from it walking, cycling, or using other public transport modes (e.g., the bus), to what extent has growth and development been favoured in the municipalities with a commuter station compared to the municipalities that do not have a commuter station? Thus, given an existing infrastructure such as the commuter rail network and being aware that the urban area around the station is the \"feeder\" of mobility, what has been the land use in the surroundings of commuter stations and what room is there for growth in these areas? This work addresses three objectives: (i) to study and describe the urban land expansion of the Madrid urban region and the mobility patterns of its population, with a focus on the commuter rail network; (ii) to analyse and characterise the municipalities of the urban region--with and without commuter stations--in terms of population, employment, and urban land, and investigate the relation between this characteristics and the use of commuter rail; and (iii) to investigate the urban development potential around the commuter station in the municipalities of the Madrid urban region. According to the TOD concept, the analysis of the surroundings of the transit stations is common to most studies. Most of them, however, concentrate on the study of their current urban characteristics. Lately, in light of the 15 min city proposal [33; 34], researchers repeatedly analyse the station surroundings in terms of accessibility with regard to daily destinations [35]. Our proposal, on the other hand, focuses not on the urban component of the areas around the stations, but on undeveloped land, to evaluate their potential to densify the areas in terms of people and destinations.
## 6 Case Study  
### Simulation Parameters  
The modified IEEE-RBTS Bus-6 medium-voltage distribution network was utilized in this study [25]. The network is of 10 kv voltage level with 53 lines and is connected to 23 medium-voltage consumers through fuses and transformers. Isolation switches, along  
Figure 2: Algorithm flow chart.  
with DG units incorporating PV and WT systems, are integrated between nodes 9 and 11, as well as between nodes 19 and 21. In the event of a line fault, the isolation switches disconnect to achieve fault isolation, allowing the DG units with PV and WT to continue operating in island mode. For computational simplicity, each MVDN is assumed to have five connected low-voltage (LV) users, resulting in a total of 115 LV users across the entire network, as shown in Figure 3. The simulated problem is solved using gurobi 10.0.3 on a PC with Intel Core i5-12500 3.0 GHz CPU and 16 GB RAM.  
The LP1 to LP_n_ in the figure represents medium voltage users 1 to n. It is assumed that at \(t=0\) a line fault occurs between nodes 6 and 7 with a repair time of 3 h, and another fault occurs between nodes 11 and 18 with a repair time of 2 h. During the first 2 h, since the lines have not been repaired, the network topology remains unchanged, as shown Figure 4.  
Figure 4: Topology of the distribution network in the first 2 h.  
Figure 3: Improved IEEE-RTS Bus-6 MVDNs.  
In Figure 4, the yellow area represents normal operation; the gray area represents the fault area, where the load is completely shed; and the green area represents the island operation area. Because there is a disconnector between nodes 2 and 3, the disconnector blows to achieve fault isolation when a fault occurs, which enables LP1 to continue normal operation. The gray area contains faults that cannot be isolated, so it cannot operate normally. The green area is isolated from the distribution network faults by switching isolation switches. During this period, power is supplied by DG with PV and WT units, although their output fluctuates. Therefore, selective load shedding is required for the LV users of LP15-LP23.  
Once the fault between nodes 11 and 18 has been repaired at t = 3 h, the distribution network topology is as shown in Figure 5.  
At this time, the LP8-LP14 area does not contain any faults and can achieve fault isolation through isolation switches, which enables it to operate normally. Both sets of PVs and WTs can simultaneously supply power to users in LP8-LP23, which necessitates selective load shedding for the downstream LV distribution network users in LP8-LP23.
## 4 Experimental Design
### Result Analysis
#### 4.3.1 Classification Results of MLP Model Based on Different Feature Combination
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Combination** & **Precision\_uf** & **Recall\_uf** & **F\({}_{1}\)** & **Precision** \\ \hline TF-IDF & 0.647 & 0.546 & 0.592 & 0.599 \\ PDP & 0.916 & 0.864 & 0.889 & 0.896 \\ DG & 0.792 & 0.732 & 0.761 & 0.779 \\ IC & 0.821 & 0.769 & 0.794 & 0.805 \\ PE & 0.896 & 0.735 & 0.808 & 0.847 \\ PE-N\({}_{\text{PSP}}\) & 0.875 & 0.724 & 0.792 & 0.832 \\ PE-N\({}_{\text{PP}}\) & 0.885 & 0.728 & 0.799 & 0.84 \\ PD & 0.845 & 0.723 & 0.779 & 0.805 \\ PB & 0.912 & 0.851 & 0.88 & 0.889 \\ PB-D\({}_{\text{rc}}\) & 0.894 & 0.847 & 0.87 & 0.876 \\ PB-D\({}_{\text{fa}}\) & 0.893 & 0.832 & 0.861 & 0.87 \\ PB-N\({}_{\text{b}}\) & 0.802 & 0.769 & 0.785 & 0.796 \\ PB-N\({}_{\text{g}}\) & 0.813 & 0.771 & 0.791 & 0.799 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition results of the MLP neural network classifier. As can be seen from Table 3, when the feature combination is N\({}_{\text{B}}\) (the number of comment to be viewed), N\({}_{\text{G}}\) (the number of likes on comments), N\({}_{\text{PSP}}\) (the number of other products to be viewed in the store where the target locates), D\({}_{\text{FA}}\) (the days between first comments and additional comments), N\({}_{\text{PP}}\) (the number of similar products of the target to be viewed), N\({}_{\text{CV}}\) (the number of comments viewed), D\({}_{\text{FC}}\) (the days between receiving goods and commenting), D\({}_{\text{P}}\) (the number of days the product was added to the cart), the MLP classifier model has the best recognition effect, with the F\({}_{1}\) value reaching 89.3%; when the feature combination is the feature of the whole process of consumer purchase decision, the F\({}_{1}\) value decreases to 88.9%. It can be seen that the addition of N\({}_{\text{kr}}\) (keyword retrieval number) is not conducive to the model's recognition of the usefulness of online reviews. In the decision-making process of the consumer, the stage of demand generation is far from the writing stage of online comments after the final deal is reached, which may be affected to some extent, resulting in deviation. However, except for the demand generation stage, the addition of each feature index in other stages all improves the classification and recognition effect of the model, which can prove the effectiveness of the feature extraction method proposed in this paper considering the purchasing decision-making process of consumers.
## 3 Experimental Methodology  
Two experimental campaigns were carried out to investigate the thermal response of a reinforced concrete (RC) wall subjected to microwave heating, using a setup designed to closely replicate the conditions modeled in the numerical simulation. The RC specimen featured exposed ends of steel reinforcement bars extending from the top surface, enabling accurate identification and tracking of each bar throughout the experiment. This configuration was essential for analyzing the thermal behavior of the embedded reinforcement. The experimental apparatus is depicted in Figure 6.  
A 2.45 GHz microwave system, matching the configuration of the numerical model, was employed to heat the RC wall specimen. The test was conducted exclusively in modulation mode, applying an average power of 600 W over 300 s. The wall incorporated nine steel bars (12 mm diameter) spaced at 100 mm intervals within the concrete, enabling the assessment of thermal effects across different reinforcement positions. The concrete cover facing the antenna measured 15 mm, reflecting common structural configurations and allowing the evaluation of microwave penetration. Microwave energy was delivered via a pyramidal horn antenna positioned 40 cm from the wall surface. Two antenna orientations were tested to investigate directional heating effects: 30\({}^{\circ}\) (\(\alpha\)) and 45\({}^{\circ}\) (\(\beta\)), as shown in Figure 7.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Aluminum** & **Steel** & **Concrete** \\ \hline Heat capacity: Cp [J/(kg\(\cdot\)K)] & NA & 475 & 880 \\ Density: \(\rho\) [kg/m\({}^{3}\)] & NA & 7850 & 2300 \\ Relative permittivity: \(\epsilon\) & 1 & 1 & 8.5 \(-\) 0.86j \\ Thermal conductivity: k [W/(m\(\cdot\)K)] & NA & 44.5 & 0.8 \\ Electrical conductivity: \(\sigma\) [S/m] & 3.77 \(\times\) 10\({}^{7}\) & 4.032 \(\times\) 10\({}^{6}\) & 0.1 \\ Relative permeability: \(\mu\) & 1 & 1 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Thermal and electrical parameters of chosen materials used in the numerical model.  
Figure 6: The experimental setup.  
Thermal data were captured using an infrared camera operating at 5 frames per second, ensuring precise monitoring of temperature evolution. For each antenna orientation, the camera was placed in two distinct positions to ensure full surface coverage. When the antenna was set at 30\({}^{\circ}\) (\(\alpha\)), the camera was located 2.11 m away along the 45\({}^{\circ}\) (\(\beta\)) direction; conversely, for a 45\({}^{\circ}\) (\(\beta\)) antenna position, the camera was placed 2.32 m away in the 30\({}^{\circ}\) (\(\alpha\)) direction. This arrangement provided comprehensive and angle-sensitive thermal imaging. In the experimental investigations, only one microwave heating mode was employed--modulated heating--where the periodic function matched that used in the simulations (see Figure 3).
## 3 Results
### Impermeable Surface Accuracy Verification
#### 3.2.2 Impervious Surface Area Error  
Figure 7 shows the relative error between the extracted and actual impervious surfaces for different landform types. Statistically, the impervious surface area extracted from the Luojia1-01 nighttime light image is more accurate than the actual impervious surface area.  
Figure 6: Comparison results of spatial accuracy of impermeable surfaces.  
Furthermore, the impervious surface extracted using the NPP/VIIRS nighttime light image does not contain any information about the rest of the landform types, except for the Karst fracture basin. The impervious surface extracted using the Flint nighttime light images is the least accurate as it is significantly larger than the actual impervious surface area.  
It can be gathered from the results of the different landform types that the errors in the extraction of the impermeable surfaces using the three nighttime light images in the Karst gorge and Karst fracture basin landform types are higher than those in the other flat and contiguous areas. This indicates that the topography influences the application of nighttime lighting, with the error in extracting impervious surface area in broken topography regions being significantly higher than that in flat and contiguous topography regions. The use of NPP-VIIRS images results in a lower impervious surface area error for the Crested Depressions (error = \(-\)16.31%) and Karst Trough Valley (error = \(-\)17.55%). The use of Flint images resulted in the least errors for impervious surface area extraction for Karst Trough Valley (error = 154.00%), Non-Karst Landscapes (error = 171.89%) and Karst Plateau (error = 187.84%). This indicates that the Luojia1-01 data provide a greater advantage in extracting impervious surface in economically developed, light-concentrated, and contiguous areas, while the NPP-VIIRS and Flint images are more influenced by topography than the Luojia1-01 images for impervious surface extraction.