esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 4 Hybrid Path Planning Algorithm
### Mechanism of Algorithm Fusion
#### 4.2.1 Objective Function (Fitness Function)  
The objective function is used to evaluate the path quality, enabling the LOA to identify superior paths when optimizing the improved hybrid A* algorithm. This function typically incorporates multiple critical factors, such as the path length, smoothness, and safety (obstacle avoidance), as detailed in Equation (25):  
\[f(\theta)=\left\{\begin{array}{ll}L(\theta)&\quad\text{$If$ the path is valid and safe}\\ \omega_{1}\cdot\text{L+}\omega_{2}\cdot\text{C+}\omega_{3}\cdot\text{D}&\quad \text{$If$ the path is invalid or collides}\end{array}\right. \tag{25}\]  
where \(\theta=[\theta_{1},\theta_{2},\theta_{3}]=[\text{min\_r,step},P_{\theta}]\) is the parameter vector to be optimized, and \(L(x)\) is the path length; \(C\) is the curvature penalty (smoothness); \(D\) is the obstacle distance penalty (safety); and \(j\); \(\omega_{1},\ \omega_{2}\) and \(\omega_{3}\) are the weighting factors.  
Each of these sub-items plays a different role in the algorithm, as shown in Table 5.  
In hybrid path planning algorithms, kinematic constraints play a crucial role in both the global search and local optimization stages, working together to ensure that the generated paths comply with the vehicle's physical characteristics while meeting the actual driving requirements. The following is a stage-by-stage analysis of their importance and technical implementation.  
Global search phase (exploring diversity)  
The parameters are primarily constrained by their ranges, with upper and lower bounds (lb, ub) used to limit them. A minimum turning radius (min_r) \(\in\) [0.8, 1.2] is suitable for vehicle kinematics; the step size of the path search \(\in\) [0.4, 0.6] balances precision and computational efficiency; and the weight of the curvature penalty P\({}_{\theta}\)\(\in\) [0.005, 0.015]. Initial solutions are generated around the standard parameters following a normal distribution, balancing diversity and feasibility.  
Local search phase (developing optimality)  
Kinematics feasibility verification: Each time the hybrid A* algorithm is called, the isPathClear function is used to check whether the path meets the following conditions: minimum turning radius--the path curvature is constrained by min_r; continuous curvature--ensures path smoothness through the curvature penalty term C. Post-processing optimization (path post-processing) further smooths the path while checking the kinematic constraints. During multiple optimization attempts (num_trials), the system dynamically narrows the parameter range (e.g., lb_trial, ub_trial) to focus on a more optimal solution space.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 1 Introduction
### Related Studies
The concept of relaying was thoroughly studied in a number of works on communication systems that exploit unmanned aerial vehicles (UAV). Accordingly, in [19], the end-to-end performance of such a system where a relay UAV connects mobile users was evaluated in terms of the ergodic sum rate. The authors analyzed the trade-offs affecting the optimum position of buffer-aided (BA) and non-BA relay UAVs. The achieved results revealed that the random variations of the free-space optical backhaul link caused by the UAV's instability can be mitigated by BA relaying which results in a larger achievable ergodic sum rate compared to non-BA relaying at the expense of introducing an additional delay into the system. The authors of [20] investigated the possibility of using unmanned aerial vehicles (UAV) as mmWave mobile base stations. They considered the air-to-ground signal propagation model in line of sight, as well as in the presence of blockers. The authors analyzed signal propagation using direct and 3D beamforming. The results showed that 3D shaping improves the overall quality of the channel. In [21], the authors proposed a system design concept for multiple connected UAVs which form relaying bridges for offloading data traffic from an overloaded cell. By utilizing stochastic geometry and renewal theory, they obtained the offloading gain of the multi-hop UAV bridges. The obtained numerical results demonstrated that the use of buffering at the UAV side drastically improves the achievable offloading gains. Summarizing the related work, we note that most of the studies on V2X communications performed so far concentrated on a single-hop characterization such as the choice of the propagation paths, the impact of blockage, etc. The system-level studies assessing the performance of multi-hop communications and providing insights on the BS deployment density along the roads are missing. In addition, there are no studies addressing the selection of the last hop in multi-hop communications scenarios.
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 4 Proposed Method
### Long Short-Term Memory Cell  
However, 1D-CNNs have some limitations, such as their inability to capture long-term dependencies due to fixed-size windows, a tendency to overfit due to numerous parameters, and insufficient recognition of sequential data relationships. To tackle these challenges, the LSTM network, introduced by [PERSON] and [PERSON] [37], provides an effective solution. Illustrated in Figure 6, the LSTM network, a variant of RNN, adeptly handles time-series sequence issues by incorporating memory blocks. These blocks contain a forget gate, an input gate, a cell state update, and an output gate. In the 1D-CLANet model, the deep spatial features extracted by the 1D-CNN are flattened by Flatten1 and then fed into an LSTM with 512 hidden units to produce spatiotemporal features (sequences). This architecture allows the network to effectively manage temporal dependencies and complex sequences, thereby alleviating the computational constraints and error propagation issues found in traditional feedforward neural networks [31; 38].  
Figure 5: The structure diagram of 1D-CNN.  
The calculation for the memory block depicted in Figure 6 is as follows:  
\[f_{t}=\sigma\Big{(}w_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{12}\]  
\[i_{t}=\sigma(w_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{13}\]  
\[\hat{C}_{t}=\tanh(w_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{14}\]  
\[\hat{C}_{t}=f_{t}\cdot\hat{C}_{t-1}+i_{t}\cdot\hat{C}_{t} \tag{15}\]  
\[O_{t}=\sigma(w_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{16}\]  
\[h_{t}=o_{f}\cdot\tanh(C_{t}) \tag{17}\]  
where \(f_{t}\) represents the forget gate's activation vector, \(i_{t}\) denotes the input/update gate's activation vector, \(\hat{C}_{t}\) is cell input activation vector, \(\hat{C}_{t}\) is current cell memory, \(O_{t}\) stands for the output gate's activation vector, and \(h_{t}\) is current cell output. The bias vector and weight matrices for the input gate (\(i\)), output gate (\(o\)), forget gate (\(f\)), and memory cell (\(c\)) are denoted by \(b\), \(w\). \(h_{t-1}\) is previous cell output, \(\hat{C}_{t-1}\) is previous cell memory, \(\sigma\) signifies the sigmoid function, and \"\({}^{,}\)\" indicates the Hadamard product [38].
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 2 Methods
### VLSM-Net Model  
In the LSM, the selection of the initial contour has a crucial role in the curve evolution to reach the correct segmentation boundary, and the initial contour contains information about the location, size, and shape of the ROIs. If the initial contour is set randomly, it is possible that it will be set too far from the target; the segmentation result may miss the target completely. If the initial contour is set close to the target, the result can be more accurate. Therefore, the VLSM-Net model uses the segmentation results of the V-Net model as a priori for the LSM, sets accurate initial contours, and uses the LSM to further refine the segmentation results. A robust and accurate segmentation model of CT images can be obtained.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 2 Method
### Generation of Yield Gain Surfaces from Spectral Models
#### 2.6.1 Image Acquisition  
High-resolution multispectral orbital images were obtained through remote orbital sensors from the PlanetScope satellite constellation two months before harvest during the floury grain production stage.  
The choice of this phase is justified due to the greater correlation between the image bands and yield for this period compared to other phenological phases. The tests were per formed in the preliminary stages of defining the final proposed method, such as described previously [19].  
Regarding the sensor characteristics, PlanetScope satellite images have a spatial resolution of 3 m and radiometric resolution of 12 bits. The Bayer Mask CCD type PS2 sensor used in this constellation of satellites captures the wavelengths of blue (455-515 nm), green (500-590 nm), red (590-670 nm), and near-infrared (780-860 nm). The images are configured in the Universal Transverse Mercator projection system and World Geodetic System-84 horizontal datum and correction level 3B. The atmospherically corrected images are converted to surface reflectance.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 3 Related Research
### Comparison of Related Studies  
As can be seen from the above evaluations, the results of the continuous authentication performance depend heavily on the used dataset. For example, the Touchalytics database (see [26]) only considers horizontal and vertical features. However, it has to be taken into account how the system behaves, in case of usage scenarios not handled by the original developers. Since the device was only trained on horizontal and vertical swipe gestures, it remains undetermined how the system would behave when when using primarily single touch inputs.  
While a lot of approaches use traditional classifiers, the studies presented in [28; 29] rely on HMM based implementations. The advantage of training this classifier is that it utilizes only data of the person of interest. This means that the training process, as well as the authentication process, only requires data from one user. Other classifiers, such as the SVM, require data from multiple users to generate a working machine learning model. Another advantage, when the training process only relies on data from the correct user is, that the model can be retrained with new data, without relying on a connection to fetch data from other users to rebuild the model. The whole training process can be done offline,resulting in allowing the system to be privacy-preserving since usage data doesn't need to be shared.  
The authentication performance and used performance metrics also differ across the presented studies. While most of the studies rely on the EER metric to compare the performance of their approaches, some (for example [1]) just report the FAR and FRR. The number of consecutively used inputs differs between the proposed solutions. A higher number of consecutive touch inputs usually decreases the EER of the system, but also extends the time it takes for the authentication to take place. Therefore, there is always a trade-off between security and usability.  
Furthermore, none of the studies mention an implementation on Android devices for the authentication process, therefore it might have been done directly on the development machine, where the model was trained. In our study we are evaluating and implementing the recording as well as the evaluation process necessary, for continuous authentication to happen on mobile devices, running the Android operating system.
## 4 Results and Discussion
### Classification of Articles by Application Domain and Field
#### 4.2.1 Medical Applications  
In the medical field, EEG-based emotion detection systems are used for assisting, monitoring, enhancing, or diagnosing patients' affective states. These medically adapted EEG systems are also used to analyze different types of neurodevelopmental disorders. Some of these neurodevelopmental disorders affect the memory, emotion, learning ability, behavior, and communication of persons  
Figure 4: Classification of articles by field and publication year. Figure 5: Treemap classification of articles by application domain and field (= non-medical = medical). suffering from these conditions. The most common cases are Schizophrenia, Autism, Depression, Huntington's disease (HD), and a myriad of psychiatric and neurological diseases. Table 6 shows a classification of medical articles into areas of application, including Assessment (22 articles, 32.8%), Assistance (13 articles, 19.4%), Diagnosis (8 articles, 11.9%), Monitoring (20 articles, 29.9%), and Other (4 articles, 6%). Generally, neurological studies seek to understand how defects in neurobiological processes result in problems associated with neural functioning. These studies are conducted with the goal of understanding how individual differences in brain structure and function influence affective, cognitive, and behavioral outcomes. Different approaches have been proposed, and several research groups have developed EEG-based BCI systems that aim to detect these affective states. Examples of medical applications are hereunder identified. Depression is a mental disorder that is related to a state of sadness and dejection. It affects the emotional and physical state of a person. Using EEG-based emotion recognition as a diagnostic test for depression produces conclusive results. A number of studies are based on the automated classification of normal and depression-related EEG signals. This proposed automatic classification system could serve as a useful diagnostic and monitoring tool for the detection of depression [20; 79; 133; 135; 226; 228; 231]. Persons with schizophrenia exhibit drab facial expressions and rarely show positive emotions. These abnormal personality traits may impact social functioning and communication. Whether these deficits reflect an aberrant sensory anomaly, an inability to retain information in their memory, or a dysfunctional integration of these two functions remains unclear. However, studies have shown that sensory processing and memory functioning may be affected in schizophrenic patients [5; 17; 18; 54; 62; 66; 72; 73; 90; 94; 98; 180; 233; 236]. These studies' experimental protocols were intended to clarify patient deficits in processing emotional faces.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 4 Simulation Results  
In this section, the performance of jamming detection and suppression is verified through simulations. We consider an uncoded OTFS system with \(M=256\), \(N=64\), and QPSK modulation.
# A Novel Way to Enhance the Spark Plasma-Assisted Ignition for an Aero-Engine Under Low Pressure
## 3 Results and Discussion
### Effect of the MDJS on the Ignition Process and the Ignition Limit Extension
With respect to the kinetic pathway, the MDJS produces active radicals and takes them to a deeper area of the combustor where it is more likely that the spark kernel will meet the gas-oil mixture at stoichiometric ratio. For the transport effects of the MDJS, the high-velocity jet expelled through the orifice helps change the distribution of kerosene and improve mixing to some extent. A novel multichannel discharge jet-enhanced spark (MDJS) ignition method for aero-engines was proposed. Compared to the conventional SI, the higher total arc discharge energy and the larger plasma volume driven by the MDJS significantly deepened the spark plasma penetration depth and broadened the activated area which helped induce a greater and faster-growing initial flame kernel. The MDJS and the SI were both activated by the custom ignition power supply system instead of a specific power supply with an extra gas source. Furthermore, the MDJS was the same size as the SI, which means that the MDJS can replace the conventional SI quite conveniently. The larger initial spark kernel and the higher spark penetration depth are the key facts for using a multichannel discharge jet-enhanced spark to improve ignition capability. An extension of the ignition limit in a lean-burn model swirl combustor was obtained with the MDJS under low temperature. This can largely improve high-altitude secondary ignition capability of long-endurance aircraft, which helps to promote the energy efficiency of the aero-engine combustor in the future. In brief, the MDJS, activated by the same condition power supply as the SI, could produce a stronger arc discharge energy (for its higher energy utilization) and induce a larger flame kernel which spreads at a faster rate. With the same cost, the MDJS could be a potential way of enhancing the spark plasma-assisted ignition for an aero-engine under low pressure.
###### Abstract  
In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot's steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (_TAI_), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.  
deep reinforcement learning; task allocation; navigation; multi-robot systems +
Footnote †: journal: Applied Sciences  
allocation problem [9]. On the other hand, the use of DRL to solve the navigation problem has been given more attention by researchers due to DRL's ability to learn decentralized policies that can drive an MRS towards its goal positions [10; 11; 12; 13]. As far as we know, there is a gap in the literature where there have not been any proposed methods that combine both MRTA and navigation.  
In this paper, we propose a novel method that combines both MRTA and navigation for a homogeneous multi-robot system. The method is a decentralized deep reinforcement learning policy whose inputs are the raw sensor measurements and whose outputs are the robot's steering commands. The output of the policy should drive the robot from its initial position to a unique goal position without colliding with other robots or obstacles. We demonstrate that our method learned to perform both MRTA and navigation from end-to-end effectively, and we compare our method to two methods from the literature that only perform decentralized navigation for a multi-robot system. Thus, this paper is a first step to address performing MRTA and navigation from end-to-end and shows promising results that can be built on to enhance the current state of multi-robot systems.  
The main contributions of this paper are:  
* An introduction of a novel method that performs MRTA and navigation for a homogeneous multi-robot system from end-to-end using deep reinforcement learning.
* An introduction of a new metric called the Task Allocation Index (\(TA1\)), which measures the performance of navigation in MRTA.
## 3 Results
In Figures 4 and 7, the maximum alternate-current magnetic susceptibility and surface roughness had a relation, which indicates that the increased surface roughness can lead to the pinning effect of the domain wall, which has difficulty moving, resulting in the increase in coercivity and the decrease in the \(\chi_{\text{ac}}\) value. Figure 5: Optimal resonance frequency for films of various thicknesses. Figure 8a-d showed the contact angles (\(\theta\)) with DI water: 73.85\({}^{\circ}\), 72.92\({}^{\circ}\), 79.44\({}^{\circ}\), and 70.34\({}^{\circ}\) for the 10, 20, 30, and 40 nm films, respectively. Figure 8e-h shows the contact angles (\(\theta\)) with glycerol as 62.59\({}^{\circ}\), 74.27\({}^{\circ}\), 59.64\({}^{\circ}\), and 60.23\({}^{\circ}\) for 10, 20, 30, and 40 nm films, respectively. The contact angle results from DI water and glycerin are shown in Figure 8. The CoFeVB films can be used as a seed or buffer  
Figure 6: In-plane magnetic hysteresis loop of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films. Figure 7: AFM 3D images showing different surface roughness of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films with various thicknesses: (**a**) 10 nm, (**b**) 20 nm, (**c**) 30 nm, and (**d**) 40 nm. layer, so the surface energy and adhesion are very important. The results show that the water drops of CoFeVB film are almost spherical. The contact angles of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) film were less than 90\({}^{\circ}\), which indicates that the film has a good wetting effect and is hydrophilic. When the surface free energy is high, the liquid absorption is large and the liquid absorption area is also large, which leads to the decrease in contact angle [24; 25; 42]. The surface energy of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) films was obtained by measured contact angles, as shown in Figure 9[24; 25; 26; 27; 28; 29; 32]. High surface energy corresponds to strong adhesion [43]. It was found that the surface energy tended to increase with the increase in thickness. Figure 9 shows that when the thickness increased from 10 nm to 40 nm, the surface energy range increased from 34.2 mJ/mm\({}^{2}\) to 51.5 mJ/mm\({}^{2}\), which indicates that the surface energy is larger and the adhesion is enhanced. The surface energy is the key factor affecting the adhesion of the film. As CoFeVB is compatible with MTJ applications, it can also be used as a free layer and in combination with other layers. Figure 8: Contact angles of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films with DI water: (**a**) 10 nm, (**b**) 20 nm, (**c**) 30 nm, and (**d**) 40 nm. Contact angles of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films with glycerol: (**e**) 10 nm, (**f**) 20 nm, (**g**) 30 nm, and (**h**) 40 nm.