esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Analysis
### Comparison Experiments with Other Models  
To validate the effectiveness of our model in improving detection rates in SAR images, we compared it against eight widely used models in industrial scenarios, including YOLOv5n/s, YOLOv6n [30], YOLOv8n, YOLOv9t [31], RT-DETR-I [32], YOLOv10n [33], YOLO11n [34], and four fine-tune models that are more adaptive to SAR images including GS-YOLO [35], DGSP-YOLO [36], SHIP-YOLO [37], and ODConv2-YOLO-SAHI [38]. We used evaluation metrics such as precision (P), recall (R), mAP@50, mAP@50:95, parameters, FLOPs, and F1 score. As shown in Table 8, MDD-YOLOv8 achieves the highest scores across all metrics, with a precision of 87.7%, recall of 76.1%, mAP@50 of 78.9%, mAP@50:95 of 47.0%, and F1 score of 0.81. These results indicate that MDD-YOLOv8 outperforms other models due to its enhanced FPN-PAN network, which incorporates four detection headers to better distinguish tiny targets in complex backgrounds, leading to significantly higher recall rates without requiring extensive fine-tuning. Furthermore, the deformable large kernel attention (DLKA) module expands the receptive field of the model, resulting in an 8.1% improvement in precision over the base model. Simultaneously, the DynamicConv module increases the flexibility of YOLOv8, enabling it to better adapt to the multi-scale variations in targets in SAR images. Comparing the first and second lines, it is clear that the performance of YOLOv5 improves with an increase in parameters, albeit at the cost of higher computational resources and longer processing times. For a better balance between speed and accuracy, we optimized YOLOv8n. The results also show that modifying the network structure adds approximately 0.6 million parameters to MDD-YOLOv8, which achieves an 8.1% improvement in precision. In contrast, YOLOv5s only improves precision by 1.7%, despite adding 5.63 million parameters compared to YOLOv5n. In addition, the fine-tune models are more accurate than the common models correspondingly; however, their generalization abilities are weaker than MDD-YOLO. Therefore, by using the MSAR-1.0 dataset, which has four classes to train the model, the SOTA of one-class SAR object detection models is not good enough to fit the new scene.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **P (\%)** & **R (\%)** & **mAP@50 (\%)** & **F1** \\ \hline DLKA & 84.8 & 68.2 & 74.2 & 0.75 \\ PPA & 77.2 & 70.4 & 74.2 & 0.73 \\ EMA & 80.2 & 70.5 & 73.9 & 0.74 \\ DAttention & 75.8 & 72 & 72.6 & 0.73 \\ MLCA & 73.9 & 69.6 & 72.1 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of attention mechanisms comparison.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead
### RIS for FSO-Based NTN
They enhanced communication between the satellite and terrestrial sources using an UAV as a relay. The RIS was employed in the RF channel to reflect terrestrial user signals to the relay with a fixed amplification gain, while FSO technology was employed for high-speed relay-satellite connections.
## Appendix B Linblad Equation Derivation in Brief  
Lindblad master equation is obtained following different assumptions in order to remove the details of the bath system but leaving sufficient information to predict the effect of it on the main system. The interaction between the system and the bath is assumed bilinear in the form of \(\hat{H}_{S-B}=\sum_{i=1}^{N^{2}-1}\gamma_{i}\tilde{S}_{i}\otimes\tilde{B}_{i}\).  
In the Lindblad model, the integrals in (17) are performed by assuming that the operators of the bath have correlations satisfying \({\rm Tr}(B_{i}(t)\rho_{B}B_{j}(t^{\prime}))=\delta(t-t^{\prime}){\rm Tr}(B_{i }(t)\rho_{B}B_{j}(t))\), which conducts to the non-diagonal form of Lindblad equation (\(h_{ij}=2\gamma_{i}\gamma_{j}{\rm Tr}(\tilde{B}_{i}\tilde{\rho}_{B}\tilde{B}_{j})\)):  
\[\dot{\tilde{\rho}}_{S}(t) = \frac{1}{\hbar^{2}}\sum_{i,j=1}^{N^{2}-1}h_{ij}\left(\tilde{S}_{i} \tilde{\rho}_{S}\tilde{S}_{j}-\frac{1}{2}\{\tilde{S}_{j}\tilde{S}_{i},\tilde{ \rho}_{S}\}\right) \tag{13}\]  
which by diagonalizing \(h_{ij}=\sum_{\alpha,\beta}T_{i\alpha}J_{\alpha}\delta_{\alpha\beta}T^{ \dagger}_{\beta j}=\sum_{\alpha}T_{i\alpha}J_{\alpha}T^{\dagger}_{\alpha j}\) and returning to the Schrodinger picture can be expressed as:  
\[\dot{\rho}_{S}=-\frac{i}{\hbar}[H_{S},\rho_{S}]+\frac{1}{\hbar^{2}}\sum_{\alpha }\left(L_{\alpha}\rho_{S}L^{\dagger}_{\alpha}-\frac{1}{2}\{\rho_{S},L^{\dagger }_{\alpha}L_{\alpha}\}\right) \tag{14}\]  
where \(L_{\alpha}=J_{\alpha}\sum\tilde{S}_{i}T_{i\alpha}\), represents the so called Lindblad operators. Nonetheless, the relation of \(L_{\alpha}\) with the physical arrangement is not always direct because it is based in generic operators \(S_{j}\) or \(L_{\alpha}\) stating a basis.
# Split-And-Delay Unit for FEL Interferometry in the XUV Spectral Range
###### Abstract  
In this work we present a reflective split-and-delay unit (SDU) developed for interferometric time-resolved experiments utilizing an (extreme ultraviolet) XUV pump-XUV probe scheme with focused free-electron laser beams. The developed SDU overcomes limitations for phase-resolved measurements inherent to conventional two-element split mirrors by a special design using two reflective lamellar gratings. The gratings produce a high-contrast interference signal controlled by the grating displacement in every diffraction order. The orders are separated in the focal plane of the focusing optics, which enables one to avoid phase averaging by spatially selective detection of a single interference state of the two light fields. Interferometry requires a precise relative phase control of the light fields, which presents a challenge at short wavelengths. In our setup the phase delay is determined by an in-vacuum white light interferometer (WLI) that monitors the surface profile of the SDU in real time and thus measures the delay for each laser shot. The precision of the WLI is 1 nm as determined by optical laser interferometry. In the presented experimental geometry it corresponds to a time delay accuracy of 3 as, which enables phase-resolved XUV pump-XUV probe experiments at free-electron laser (FEL) repetition rates up to 60 Hz.  
Keywords:white light interferometry; split-and-delay unit; XUV optics characterization; pump-probe +
Footnote †: journal: Applied Sciences
## 2 DC Fault Current Analysis
### DC Fault Analysis of the Test System
#### 2.2.1 Submodule Capacitor Discharging Period
Namely, the maximum \(C_{eq}\) equals \(1.473\), since the fault time of the simulation is set at 1.2 s.  
\[C_{eq}=\frac{2C_{0}}{[1+m^{2}\sin^{2}(\omega t)]N}+\frac{2C_{0}}{[1+m^{2}\sin^ {2}(\omega t-\frac{2}{3}\pi)]N}+\frac{2C_{0}}{[1+m^{2}\sin^{2}(\omega t+\frac {2}{3}\pi)]N} \tag{1}\]  
Figure 4: (**a**) Equivalent AC grid feeding current circuit, (**b**) equivalent SM capacitor discharging current circuit, (**c**) simplified SM capacitor discharging current circuit. Figure 3: Direct current pole-to-pole fault circuit diagram before blocking submodules. During the SM capacitor discharging period, the AC feeding current can be ignored, because the discharging current is dominant. For example, the Phase-A discharging circuit can be regarded as a second-order RLC circuit (circuit consisting of a resistor, an inductor and a capacitor), as displayed in Figure 4c. Here, the resistance in the arms is neglected. The initial voltage and current of the capacitor discharging circuit are expressed by Equations (3) and (4), respectively. \[u_{c}(0_{+})=u_{c}(0_{-})=U_{dc} \tag{3}\]  
\[i_{dis}(0_{+})=i_{dis}(0_{-})=-C_{eq}\frac{du_{c}}{dt}=I_{dis0} \tag{4}\]  
The equivalent capacitor, inductance, and resistance presented in Figure 4c are obtained as follows:  
\[C_{eq}=\frac{\sigma C_{0}}{N} \tag{5}\]  
\[L_{eq}=\frac{2}{3}L_{0}+2L_{dc} \tag{6}\]  
Figure 2:0  
The second-order equation of the RLC circuit is expressed in Equation (8) as follows:  
Figure 2:1  
Figure 2:2  
Figure 2:3  
Figure 2:4  
Figure 2:5  
Figure 2:6  
Figure 2:7 Relationship between discharging coefficient and fault time (m = 0.93). where \(\tau\) is the attenuation coefficient of the capacitor discharging current, \(\omega_{1}\) is the oscillating current angular frequency, \(\omega_{0}\) is the inherent angular frequency of the circuit, and \(\theta\) is the initial phase angle of the initial current. The capacitor discharges until the capacitor voltage decreases to 0. The maximum discharging current can be obtained at \(t_{dis\_peak}\) without blocking IGBT. Figure 2:8  
Figure 2:9  
The AC side can be considered a three-phase short-circuit fault under the DC pole-to-pole fault, and the DC fault current only consists of the discharging currents of the three converter units, given the balance fault on the AC side. Thus, the fault current through DC line is defined as  
Figure 3.0  
For example, Phase-A arm current during this period can be decomposed using Equations (17) and (18). Figure 3.1  
Figure 3.2  
The Phase-A voltage on the AC side is set as  
Figure 3.3  
where \(\theta_{a}\) is the phase angle (taken as 0 for convenience in calculation afterward), and \(\omega_{s}\) is the fundamental angular frequency. Then, the Phase-A current can be obtained using Equation (20). Figure 3.4  
where \(\varphi\) is the impedance angle. The AC feeding current in each phase is equally divided into upper and lower arms. \[i_{pa\_ac}(t)=i_{na\_ac
# Modeling Photovoltaic Potential for Bus Shelters on a City-Scale: A Case Study in Lisbon
## 2 Materials and Methods  
### Study Area and Dataset  
The methodology presented applies to any city. To demonstrate its implementation, Lisbon, capital of Portugal (Figure 1), was selected. Located on the Atlantic coast of Portugal, the city has an average of 8 h of sunshine per day, one of Europe's capital cities with the highest solar insolation. Lisbon's metropolitan area has 2.8 million inhabitants, 550,000 of whom inhabit the city (2011 national census). However, the number of city users almost doubles due to commuting movements.  
The city is served by a wide public transportation network. The bus network in 2016 was composed of a fleet of 758 buses covering 88 routes and served 140.6 million passengers [17]. Daily bus services are used by residents, workers, students, city visitors and tourists. Nevertheless, and in line with other cities, the number of passengers has been declining. In 2009, a series of surveys were conducted by the bus service operator to identify some of the causes of the shift from public transport to private cars. One of the reasons pointed out was that the bus was perceived as being an outdated and old-fashioned form of transport. Based on these findings, several measures were implemented to improve its image, from renewing part of the fleet to offering free Wi-Fi internet on two routes.  
For studying the PV potential of the bus shelters in Lisbon, three main data sources were used: (1) an ortho-photo (with a 50 cm spatial resolution) for characterizing the urban context; (2) a point  
Figure 1: Location of the study area, Lisbon, Portugal (**a**), and the city’s land use land cover map (**b**).  
shapefile with the locations of the 1880 bus stops (with and without shelters), available at the city hall; and 3) a Digital Surface Model (DSM) obtained from a Light Detection And Ranging (LiDAR) flight mission over Lisbon in 2006 to model the sun radiation available at each location in the city (Figure 2). From the original LiDAR point cloud (with a density of 1-2 points per m\({}^{2}\)), a surface image was produced based on the second return, with a 1 m resolution, representing the DSM of the city.  
For studying the social demand for bus shelters, two types of users were selected: tourists and daily commuters. The users' preferred locations were used as a proxy for local energy demand. These were obtained from a dataset including sightseeing bus tours collected from local tourist companies, and open geographic data from Lisbon City Council, including tourist attractions, schools, health care facilities and metro and train stations.
## 1 Introduction
The complexity of inverse kinematics usually makes it more difficult to find solutions than with forward kinematics; therefore, researchers have developed different approaches for inverse kinematics. The robot arm system in [8] was designed to implement the elevator button-pushing behavior by using an inverse kinematics technique. Recently, some research efforts have been directed to solving the inverse kinematics problem using fuzzy logic [9; 10]. For the combining of image processing with the robot arm, some papers [11; 12] presented the use of a camera on the robot arm to identify the target and find its location, and then the arm can find the target successfully. The aim of this study was to provide vision ability to the robot arm through the use of character recognition techniques, as the robot arm must be able to recognize characters on the control panel and the smartphone. [PERSON] _et al._[13] presented an approach making it possible for an autonomous mobile robot to read characters, using characters printed on a colored sheet and a neural network trained to identify characters in different conditions to take into consideration the various viewpoints possible. [PERSON] _et al._[14] proposed an image processing technology which used the number license plate to identify the vehicle, called Automatic Number Plate Recognition (ANPR). The ANPR system robustly detects and recognizes the vehicle using the license plate under different lighting conditions, and it can be implemented at the entrance of highly restricted areas. Finally, template matching and probability classifications can be used to recognize the characters. In this study, a multi-degree-of-freedom manipulator, vision sensor, image processing and fuzzy theory were applied to the control scheme design. Through position control and image processing, the proposed control scheme proved capable of recognizing characters and driving the robot arm to press the desired button of the tested smartphone. Besides the Introduction section, this paper is organized as follows. Section 2 gives the experimental setup and the kinematic model of the robot that are used in this study. Section 3 describes the methods of image processing for characters recognition. Section 4 proposes the control sequence and a fuzzy controller to the robot arm control. Section 5 presents two experiments to confirm the proposed control scheme. Section 6 concludes this research.
## 3 Fatigue Life Prediction Modeling
### Modeling Process  
Step 1: Prepare the source dataset. The source dataset is constructed based on dataset 1 in Section 3.1. The dataset contains a total of 27 sets of data, and each set of data consists of fatigue life values (output variables) and corresponding 24 pore structure parameters (input variables). At the same time, the dataset is divided into 70% training data and 30% test data to verify the effect of data enhancement as a blank control.  
Step 2: Prepare the augmented datasets. According to the experience of the literature [34], we use the GRW-DBA method described in Section 2.1 to perform data augmentation on the source dataset, and we enhance the 27 sets of data of the source data to 10 times, 20 times, 50 times, 100 times, 200 times, 500 times, and 1000 times. These augmented datasets are all split into 70% training data and 30% validation data.  
Step 3: Determine hyperparameters. First, to determine the optimal factor of augmentation, the effect of augmented data with 10, 20, 50, 100, 200, 500, and 1000 times are compared. Then, in order to make the model achieve fast and accurate prediction, it is necessary to select the appropriate number of training iterations, the number of hidden layers, and the learning rate constant \(\alpha\). With reference to the literature [41], the number of iterations was chosen to be 100, 500, 800, 1000, and 1200. The number of hidden layers is generally chosen from small to large and should not be too large [42], so they are chosen as follows: 1, 2, 3, and 4. In practice, the learning rate constant is generally taken to be \(10^{-n}\), with \(n\) being a positive integer [43]. Thus, the rate constants \(\alpha\) are chosen as: 0.01, 0.001, 0.0001, and 0.00001. The modeling flowchart is shown in Figure 3. In the experiments, Origin software was used to generate and export the images.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 5 Multi-Level Adaptation Policy  
The architecture of E2 DF identifies different macro-components (i.e., AM-OM and IM-RM) that cooperate to adapt the deployment of DSP applications and infrastructures at run-time. The resulting architecture is general enough to not limit the specific internal policies and goals for these components. By properly selecting the internal policy for each component, the proposed solution can work in different execution contexts, which may encompass applications with different requirements, infrastructures with heterogeneous resources, and different user preferences. As a result, the system components can work under different degree of coupling. To favor decentralization and scalability, we design internal control policies that pursuit a strict separation of concerns among the different system components.  
Since the control components (i.e., AM, OMs, IM, and RMs) work at different abstraction layers, we need two-layered control policies as well. Specifically, we consider _local policies_, associated with each RM and OM, that are concerned about low-layer adaptation actions and exploit a fine-grained view on the controlled entities (i.e., computing resources and DSP operators). The local policies do not directly enact planned adaptation actions, which instead are communicated to the higher level components (i.e., IM or AM). Each of these components is equipped with a _global policy_ that works at the granularity of the whole application/infrastructure and thus exploits a global level view. Based on the overall monitored performance and the application requirements (i.e., its target response time \(R_{max}\)), the global policies identify the most effective reconfigurations proposed by the decentralized agents, providing an implicit coordination mechanism among the independent local policies.
## 3 Methodology and Models  
This section provides detailed information about the GREENIA methodology, with an emphasis on the models and metrics employed within the framework. The high-level methodology of the GREENIA framework, as illustrated in Figure 1, encompasses a comprehensive pipeline for RES placement optimization. The figure provides a visual representation of the interconnected processes, which include data preprocessing, ML-based energy prediction, optimization, explicability, and the generation of natural language explanations for actionable insights.  
Figure 1: GREENIA high-level architecture.  
The framework begins with the collection and preprocessing of meteorological and energy production data. This includes features such as wind intensity, humidity, temperature, and energy production (in MWh), ensuring high-quality inputs for the predictive model. The core predictive model is a GRU-based neural network enhanced with attention mechanisms and time mixing layers. The GRU layer captures temporal dependencies within the sequential data, while the attention mechanism emphasizes critical features or time steps. A series of fully connected layers with Swish activation transforms the outputs, leading to precise energy production forecasts. The trained ML model outputs are optimized using the Java algorithm. The optimization module refines the input solution iteratively to maximize RES production. The flowchart includes decision nodes for evaluating and improving solutions based on predefined criteria. To ensure transparency, Shapley additive explanations (SHAPs) were applied to analyze the influence of input features (e.g., temperature, humidity) on model predictions. This provided stakeholders with an interpretable understanding of the forecasting process. Outputs from the SHAP analysis were translated into user-friendly, human-readable recommendations using GPT-3. These explanations enhanced accessibility for non-technical stakeholders, such as policymakers and energy planners. The framework culminated in actionable insights and strategic recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
###### Abstract  
This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.  
Keywords: enemy avoidance; reinforcement learning; decision making; hardware-in-the-loop simulation; unmanned aerial vehicles +
Footnote †: journal: Applied