esa-sceva/satcom-chunk-collection huggingface dataset:
# Computation of Optimized Electrode Arrays for 3-D Electrical Resistivity Tomography Surveys
## 1 Introduction
[28] presented a novel algorithm combining the optimization of measurement configuration and electrode placement both for the 2-D and 3-D case. The present study is focused on generating the optimum 3-D tomographic resistivity data sets based on the sensitivity matrix and employing two different formulations. At first, a 2-D optimization algorithm was the basis to extract optimum arrays along a single section and the optimized 3-D data set resulting from cloning these parallel 2-D optimized sections. The second approach involved the extension of an experimental algorithm in a 3-D context and the extraction of the optimized arrays from 3-D tomographic data sets composed of a dense network of parallel 2-D lines. The resolving capabilities of the optimized configurations produced by these two different strategies were compared using synthetic data generated from the extension of a known published 2-D resistivity model in the 3-D space. In either case, the comprehensive data set was composed of a mixture of commonly known electrode arrays. Both optimization techniques are compared with [PERSON]'s approach. The efficiency of the 3-D optimized protocols in imaging the subsurface resistivity structure was also verified with two real field data examples from Greece.
# Automatic Age and Gender Recognition Using Ensemble Learning
###### Abstract  
The use of speech-based recognition technologies in human-computer interactions is increasing daily. Age and gender recognition, one of these technologies, is a popular research topic used directly or indirectly in many applications. In this research, a new age and gender recognition approach based on the ensemble of different machine learning algorithms is proposed. In the study, five different classifiers, namely KNN, SVM, LR, RF, and E-TREE, are used as base-level classifiers and the majority voting and stacking methods are used to create the ensemble models. First, using MFCC features, five base-level classifiers are created and the performance of each model is evaluated. Then, starting from the one with the highest performance, these classifiers are combined and ensemble models are created. In the study, eight different ensemble models are created and the performances of each are examined separately. The experiments conducted with the Turkish subsection of the Mozilla Common Voice dataset show that the ensemble models increase the recognition accuracy, and the highest accuracy of 97.41% is achieved with the ensemble model created by stacking five classifiers (SVM, E-TREE, RF, KNN, and LR). According to this result, the proposed ensemble model achieves superior accuracy compared to similar studies in recognizing age and gender from speech signals.  
ensemble learning; hyperparameter tuning; majority voting; speaker age and gender recognition; stacking Article
# Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
## 1 Introduction  
The automated extraction of road networks and their attributes is crucial for the success of challenging applications like dynamic routing, autonomous driving or artificial intelligence (AI)-driven urban planning. The pure size and rapid changes of the infrastructure make it impossible to survey road networks manually in required spatial resolution, level of detail, and update frequency. Data-driven approaches have been developed to tackle the automated generation or to update existing road networks. A typical strategy is to use remote sensing data [1; 2] or to gather information from traffic participants in the form of trajectories which then can be processed to obtain road segments. Trajectories are defined as a list of waypoints in metric space. Sometimes the waypoints include further information such as the traveling speed or the type of traffic participant which can be included in the processing. There are several ways to obtain trajectories of traffic participants. One way is to use external sensors [3], like mounted cameras that track the location of traffic participants. Another way is to use data, captured by vehicles that log their position and possibly additional information [4]. Similarly, the position of pedestrians, bikers or car drivers is often logged by mobile devices tracking the positions of their users. Since commodity devices with localization functionality are nowadays widely spread, the resulting data becomes more and more popular for the task of generating road networks. Nevertheless, the quality of trajectories captured with mobile devices in urban regions is often not good enough to obtain precise information about the real position of the user in order to reconstruct the road network. On the one hand, this is due to low-cost sensors, on the other hand, the movement of traffic participants such as pedestrians usually does not correspond directly with the middle of the road. Furthermore, the data can be obfuscated to reduce privacy risks [5]. The idea to overcome these problems is to use multiple trajectories and predict the real position of the road. This task is called trajectory averaging.  
Furthermore, since a lot of data is available, fast processing of the data using efficient algorithms is often as important as achieving accurate results. With these challenges in mind, the \"averaging GPS segments\" contest [6] was organized with the task of averaging segments of GPS trajectories to predict road segments while including the algorithms speed in the final evaluation. This work is mainly based on our submission of this contest.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 2 Related Work
### Data-Driven Detection Methods  
Deep learning has emerged as a powerful alternative for OTFS signal detection due to its capability to learn complex mappings without explicit channel modeling. These methods utilize large-scale training data to learn the delay-Doppler structure and corresponding symbol representations, making them well-suited to dynamic and non-stationary channel conditions.  
Notable architectures include underwater acoustic communication (UWA)-OTFS [22], deep neural networks (DNN) [22; 23], 2D-CNN [24], reservoir computing (RC)Net [25], ViterbiNet [26], and OTFS-MatNet [27]. CNN-based models exploit spatial correlation in the DD domain, while RCNet introduces residual connections for training stability. Models like UAM and DNN have been applied to various scenarios including UWA, V2X, and multiple-input multiple-output (MIMO) systems, demonstrating strong generalization across environments. ViterbiNet integrates the classic Viterbi decoding process with learnable neural networks to enable data-driven path selection under low-SNR conditions. 2D-CNN exploits the delay-Doppler channel by learning the MIMO-OTFS input-output relationship. However, the use of 5 \(\times\) 5 and 7 \(\times\) 7 convolutional kernels results in increased complexity.  
Despite their high accuracy, deep learning-based detectors often involve large parameter sets and intensive computation during inference, limiting their deployment in real-time or embedded systems. As a result, designing lightweight neural architectures, optimizing model compression, and leveraging feature sharing mechanisms are becoming key research directions in this field.
## 5 Experimental Validation
### Experimental Results
#### 5.2.1 Signal Time-Frequency Analysis  
Firstly, the time-frequency waveform of the signal is plotted with the 40 m/s@300 Hz test condition as an example. The microphone signal at the center of the microphone array is selected to plot the time-frequency spectrum as shown in Figure 11. It shows that in the case of a 40 m/s flow rate, the aircraft standard model produces a wide frequency distribution of aerodynamic noise dominated by low- and medium-frequency energy, and the energy shows a gradual attenuation trend as the frequency rises, while the 300 Hz single-frequency noise produced by the loudspeaker can be highlighted, and the SNR of the signal satisfies the analysis requirements.
## 3 Experiments
### Experimental Setup
#### 3.1.2 Experimental Scenario Visualization and Equipment  
To provide a more intuitive representation of the experimental environment, Figure 7a shows the actual scenario for testing the BBCN model. As illustrated, the experimental platform was deployed in a simulated substation environment featuring complex ground structures and surrounding obstacles. The test site was paved with permeable bricks to simulate the complex terrain inside substations, facilitating the evaluation of the navigation model's adaptability under various surface conditions. The surrounding area also included static obstacles (such as parked vehicles) and designated zones for dynamic obstacle movement, providing a realistic environment for testing the system's obstacle avoidance capabilities.  
The experiment utilized a robotic platform equipped with sensor modules and a data processing system. The sensor modules included a RTK Beidou navigation receiver; the  
Figure 7: The experimental setup. (**a**) The simulated substation environment used for testing. (**b**) The robotic platform equipped with the Beidou receiver, binocular camera, and IMU.  
binocular vision module was equipped with an Intel RealSense D455 camera, providing left and right images as well as depth maps for local environment modeling and dynamic obstacle avoidance. An IMU sensor (MPU-9250; InvenSense Inc., San Jose, CA, USA) was used to provide attitude estimation data. It is important to note that the magnetometer integrated within the IMU was deliberately disabled. Its data were excluded from the fusion algorithm because substation environments contain strong, fluctuating magnetic fields from high-voltage equipment, which introduce significant and unpredictable errors into the heading estimation, making magnetometer data unreliable for this specific application. The experimental equipment is shown in Figure 7b. The performance of the robotic platform in real-world environments directly impacts the efficiency of data acquisition and the control system. The experimental control system was based on the ROS 2 framework, responsible for real-time collection and processing of sensor data, as well as path planning and motion control.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 1 Introduction
### Aim and Contents of the Paper  
The present investigation was carried out in order to identify the main physic-chemical characteristics of Moroccan gypsum rocks sampled from Agadir (AG) and Safi (SG) regions, located in southwestern Morocco, on the Atlantic Ocean. In order to study the structural and physical properties, the sampled gypsum minerals were dried and heated at different temperatures.  
The powder obtained from the minerals was used for manufacturing plaster samples, which were characterized at Ibn Zohr University in Agadir-Morocco and at University of Perugia-Italy in terms of thermal and acoustic performance.  
The following measurements were carried out: thermal conductivity by means of a hot disk apparatus, and absorption and insulation acoustic performance, by means of a Kundt's tube.  
The thermal conductivity measurements were carried out with thermal flux meter methodologies. However, in many studies [15; 16; 17] the hot disk apparatus is used in order to evaluate the thermal properties of the materials, because of the small dimensions required for the samples and the quick tests, even if it is a more approximate approach with respect to the thermal flux meter method.  
A precise measurement of the acoustic properties is normally performed in reverberating rooms, under sound diffuse field conditions, but it is very expensive and time-consuming, due to the amount of material needed for fabricating large samples. As an alternative, the acoustic tests can be carried out at normal incidence, in a traditional impedance tube. The two (absorption coefficient \(\alpha\)) and the four (transmission loss) microphones configuration were followed for the measurements on the Moroccan plasters, ranging from 100 Hz to 1600 Hz (large tube) and from 400 Hz to 5000 Hz (small tube) and the results were compared with traditional plasters.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 1 Introduction
### Contributions  
This article presents a systematic review of programmable data plane applications in 5G and beyond networks along with relevant commercial implementations. This work targets applications that use P4, which is the most widely used language in both academia and industry. The contributions of this survey can be summarized as follows:  
* Providing the first review that exclusively focuses on programmable data plane implementations on 5G and beyond architectures.
* Offering a comprehensive and up-to-date review of research work on these noveltechnologies.
* Proposing a classification of programmable data plane implementations based on 5G and beyond architectural components, as well as their use cases, categorizing 59 research papers and 2 commercial solutions.
* Identifying open challenges and future research directions in the field.
* Providing information about the implemented device and the code availability for each of the surveyed articles.
## 3 Results and Discussion
### Oil Repellency  
Oil test results performed according to AATCC test method 118 are reported in Figure 8. Treated fabric with PA1 did not show any oil repellency. In contrast, application of PA2 by admicellar polymerization yielded an initial oil repellency of grade 7. This value dropped to grade 5 after half a dozen home launderings and then remained at grade 5 after many cycles. In fact, it was in these oil repellency tests where the effects of the longer chain fluoroalkyl moiety in polyacrylamide PA2 were most evident in its effect on oil repellency. Both off-the-shelf reference samples were rated initially at grade 5. However, interlock sample Ref. 1 degraded rapidly to ratings of 3 and 2. The performance of the woven fabric Ref. 2 paralleled that of PA2, only 2 grades lower throughout. With the exception of PA1, the oil test results for durability corresponded to findings by the water repellency tests.  
Figure 7: Comparison of spray test of treated fabric and commercial samples.
## 2 Material and Methods  
### Location of the Field Trials and Soil Conditions  
Field trials were conducted at six locations in 2018-2020 in Central Poland and in two regions, i.e., Kuyavian-Pomeranian and Greater Poland. Research and development works were carried out under the project name 'Moja Soja' (My Soybean), which operated on the basis of the Consortium Agreement signed on 27 July 2017. The members form the operational group, belonging to the European Innovation Partnership (EIP), referred to in Art. 56 of the Regulation of the European Parliament and of the Council of Europe of 17 December 2013, for the purpose of joint implementation of the operation involving the development and implementation of innovative solutions in selected farms cultivating and feeding soybeans. An operation called \"Soybeans in the Kuyavian-Pomeranian and Greater Poland provinces-innovative solutions in cultivation and feeding for farms\". The first part of the study concerns the production results of the soybean crop, protein and oil contents based on experimental work carried out at three experimental stations (Mochelek, Minikowo and Grocholin) and on the farms participating in the consortium (Rakowo, Witrogoszcz, Jedrzejewo) (Table 1).  
Field trials were conducted on 36 square-meter experimental plots, repeated three times at each site in blocks. Cultivar plots contained 12 rows, each 12 m long with 0.25 m row spacing. The harvested area was limited to the 10 rows, excluding 0.5 m from the edges.  
The soil conditions with particle-size distribution exhibited sandy loam at three locations (Grocholin, Rakowo and Jedrzejewo) and loamy sand at another three locations (Mochelek, Minikowo and Witrogosz). The soil showed a neutral pH (in 1 M KCl 6.2-6.9) (PN-ISO 10390:1997P). The soil was characterized by medium to very high availability of phosphorus (158-238 mg P kg\({}^{-1}\) soil) (PN-R04023:1996P) and high potassium availability (196-235 mg K kg\({}^{-1}\) soil) (PNR-04022:1996/ Az1:2002P). The humus content was at a level of 1.2-2.2% (KQ/PB-34), whereas the nitrogen content ranged from 0.12 to 0.70% (Table 1). These data represent the averaged status of macronutrient content in the spring.
## 6 Experimental Results
### Results of the Mathematical Model  
The proposed mathematical model is used to obtain optimal routes on the presented dataset. Optimal results are obtained with the CPLEX solver for the generated ESOGU dataset. Table 3 shows the mathematical model results for CEVRP without including the time window constraint for some instances in the dataset. The execution time, the minimum distance, the number of routes, and the optimal route for solved instances are shown in Table 3. The mathematical model can produce results in a reasonable time for small-sized problems involving 5, 10 customers. For the solution of large-sized problems involving 20, 40, and 60 customers, the model is run for approximately 3 h and results are given in Table 3. Optimal results could not be obtained in a reasonable time for large-sized problems. \begin{table}
\begin{tabular}{l l l l} \hline
**Instance Name** & **Distance (m)** & **CPLEX Time (s)** & **Routes** \\ \hline ESOGU\_C5 & 3956.00 & 0.50 & R1: Depot-35-cs7-115-2-32-Depot \\  & & R2: Depot-42B-Depot \\ \hline ESOGU\_R5 & 4977.00 & 0.53 & R1: Depot-31-32-19-cs10-75-60A/2-Depot \\  & & & \\ ESOGU\_RC5 & 4944.00 & 0.41 & R1: Depot-31-42B-115-32-Depot \\  & & & R2: Depot-75-Depot \\ \hline ESOGU\_C10 & 5646.00 & 124.33 & R1: Depot-31-19-34-24-22A-Depot \\  & & & R2: Depot-45-13-14-cs8-113-26-Depot \\  & & & \\ ESOGU\_R10 & 4889.00 & 389.74 & R1: Depot-56-122-70-74-77-78-60A/2-Depot \\  & & & R2: Depot-22E-21-22A-Depot \\  & & & \\ ESOGU\_RC10 & 5843.00 & 345.63 & R1: Depot-13-14-22E-21-24-22A-Depot \\  & & & R2: Depot-56-122-74-45-Depot \\  & & & \\ ESOGU\_C20 & * 9125.00 & 11,160.69 & R1: Depot-30-115-26-116-27-37B-Depot \\  & & & R2: Depot-44-58/1-58/1-43C-42B-31-39-Depot \\  & & & R3: Depot-49-2-8-cs8-9-50-51-Depot \\  & & & R4: Depot-153-Depot \\ \hline ESOGU\_R20 & * 9974.00 & 11,134.66 & R1: Depot-35-119-116-115-cs8-10-45-Depot \\  & & & R2: Depot-152-22D-76-Depot \\  & & & R3: Depot-154-44-55-43-C-Depot \\  & & & R4: Depot-60A/2-62-74-82-64-61B-Depot \\ \hline ESOGU\_RC20 & * 9161.00 & 11,113.25 & R1: Depot-35-115-2-cs8-10-22D-Depot \\  & & & R2: Depot-44-43C-42B-39-Depot \\  & & & R3: Depot-152-153-51-50-45-Depot \\  & & & R4: Depot-58/1-58/1-61B-64-82-62-Depot \\ \hline  & & & R1: Depot-25-1-5-2-8-15-Depot \\  & & & R2: Depot-38-35-116-27-28-29-30-39-Depot \\  & & & R3: Depot-44-60A/2-58/1-58/1-57-55-Depot \\ ESOGU\_C40 & * 16,102.00 & 10,800.05 & R4: Depot-49-120-18-17-22B-22C-Depot \\  & & & R5: Depot-52-51-50-3-37A-Depot \\  & & & R6: Depot-117-16-9-cs8-113-115-Depot \\  & & & R7: Depot-152-42C-42B-41-Depot \\ \hline \end{tabular}
\end{table} 
Table 3: Optimal solutions of test problems without time windows for full-charging strategy using CPLEX solver. Video in reference [38] shows the optimal route obtained by the mathematical model, for ESOGU_C10 instance in the dataset. The obtained optimal routes are visualized in SUMO. The routes for the ESOGU_C10 are as follows: R1: D-45-22A-CS8-14-D R2: D-CS4-26-113-CS6-24-13-D R3: D-119-34-31-D. In video the route shown in yellow represents R1, the route shown in red represents R2, and the route shown in blue represents R3.
## 1 Introduction
The selection of cluster heads and gateways in cluster-based schemes have a significant impact on the number of rebroadcasts [8; 9]. Hence, this research work aims to design a new clustering algorithm that groups the vehicles based on dominating set and set theory to achieve an optimal number of cluster heads and gateways in a fully distributed manner by considering the local neighborhood information and adapting an event-triggered approach that can achieve high reachability with reduced number of rebroadcasts. The significant contributions of the paper are catalogued as follows:  
* **To mitigate broadcast storm problem**: Cluster heads are selected based on minimum dominating set and gateways are selected using set cover that minimizes the number of vehicles involved in rebroadcasting the alert message. * **To increase high reachability**: During low-traffic density to address intermittent connectivity problem, vehicles moving in the opposite direction will carry the live messages and rebroadcast it, when a new vehicle within the zone of relevance moving towards the region of interest whose cluster state is not determined. * **To ensure high stability of clustering**: The degree of cohesion (DC) of a vehicle among the neighborhood is computed using its relative speed, and the relative distance between vehicles and the vehicle with the highest DC is selected as a cluster head. Also, if a vehicle can hear from a gateway node, but not currently clustered, it is allowed to be un-clustered untill a vehicle with a high degree of cohesion with neighboring vehicles is found. The proposed algorithm ensures the reachability of the message to all vehicles with a minimum number of rebroadcasts and less message overhead for selection of cluster head and gateway vehicles. The proposed scheme is compared with DTA (Density and Topology-based Approximation) [22] and DWCM (Distributed and Weighted Clustering based on Mobility Metrics) [23] protocols in terms of cluster stability: the average number of state changes per vehicle, cluster density: the average number of members per cluster, cluster head density: the average number of cluster heads per kilometer, cluster state distribution: the percentage of vehicles in each state per kilometer and reachability: the percentage of vehicles that receives the message. The simulation result shows that high reachability of messages was achieved with a minimum number of rebroadcasts.
# Autonomous Driving Control Based on the Perception of a Lidar Sensor and Odometer
## 2 Preliminary
### Deep Q-Network (DQN)  
Q-learning is commonly used in RL problems. It attempts to evaluate the action value in order to take the best possible action during the procedure. In particular, the state-action-reward-state tuples are used as experience to implement the estimation [5]. Specifically, an agent with this approach interacts with an environment that is external to where the agent resides and is represented by states. At time \(t\), after taking an action \(a\) in state \(s\), the agent is given a reward \(r\) and transfers to the next state \(s^{\prime}\). According to each state and action in the future \(Q_{(s,a)}\), called the \(Q\)-value, the expected total reward can be calculated recursively. Furthermore, it is updated as follows:  
\[Q_{(s,a)}\leftarrow(1-\alpha)Q_{(s,a)}+\alpha(r+\gamma\underset{a^{\prime}}{ \text{max}}Q_{(s^{\prime},a^{\prime})}), \tag{1}\]  
where \(\alpha\in(0,1]\) and \(\gamma\in[0,1]\) are the learning rate and discount rate. The \(Q\)-value of each state and action will be kept in a table, i.e., the \(Q\)-table. Thus, the main purpose in an RL problem is determining how to choose a fitting action at a specific state to accumulate the highest possible total reward during the entire procedure.  
DQN is inspired by Q-learning and the concept that the human visual cortex area understands the images received by the eyes [5]. Note that the term \"deep\" in DQN refers to the utilization of a \"deep convolutional neural network\" (DCNN). For image inputs, the state should be abstracted, due to the efficiency of realization in the Q-learning approach. Hence, the agent will be intelligent enough to make sense of the visual states by simplifying the states of raw image pixels using DCNN. In summary, DQN is an algorithm with model-free, off-policy, and value-based (discrete action space) properties.