esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Influence of Valve Stroke Parameters  
Figure 8e,f illustrates that the single-cycle motion of the RC-DTH hammer piston is segmented into five distinct stages by \(F_{1}\), \(F_{2}\), \(R_{1}\), and \(R_{2}\). During these stages, the pressure and velocity within the upper and lower chambers exhibit pronounced and characteristic changes.  
The intake stroke in the lower chamber (\(R_{1}\)) is instrumental in extending the stroke length and rapidly boosting the piston's speed during its movement. However, as the piston stroke length extends, the rate of acceleration progressively declines, and the incremental advantage of increasing \(R_{1}\) diminishes. An enhancement in \(R_{1}\) leads to a lengthening of the single cycle time, which in turn decreases the frequency of impacts. During the piston's return movement, a deceleration point, N1, is identified within the 0 to \(R_{1}\) stroke range. At this specific point, the piston's speed reaches its maximum absolute value. Following N1, the speed starts to decrease until it reaches the final impact speed. As such, the value at the N1 node is inversely related to the final impact speed; a higher value at N1 corresponds to a lower final impact speed. Moreover, the value at N1 has a direct relationship with \(R_{1}\). In essence, within a specific range, the impact performance of the piston shows a negative correlation with the value of \(R_{1}\).  
The outgassing stroke in the upper chamber (\(F_{2}\)) marginally reduces the overall stroke during the piston's forward movement, slightly diminishing the final impact velocity while slightly increasing the impact frequency. During the piston's return phase, the downward acceleration decreases within the \(F_{2}\) to \(R_{1}\) range, affecting the return speed. Consequently, an extension of the \(F_{2}\) stroke negatively impacts the piston's overall impact performance.  
The intake stroke in the upper chamber (\(F_{1}\)) establishes the location of the deceleration node N2 during the piston's forward stroke, and the total stroke length increases substantially as \(F_{1}\) is augmented. During the piston's return movement, an increase in \(F_{1}\) leads to a slight decrease in the piston's speed, although this effect is relatively minor. Overall, the impact performance of the piston exhibits a positive correlation with the value of \(F_{1}\).  
The outgassing stroke in the lower chamber (\(R_{2}\)) contributes to an increase in the piston's stroke during the stroke process. As the piston returns, the acceleration benefit derived from the stroke diminishes as the stroke length decreases. Consequently, the value of \(R_{2}\) is directly proportional to the impact performance of the piston, indicating that an increase in \(R_{2}\) generally enhances the piston's impact performance.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 3. Maritime Federated Learning Use Cases
### Maritime Transportation  
As shipping is responsible for the vast majority of global trade, reductions in fuel consumption and CO\({}_{2}\) emissions are necessary to reduce costs and improve environmental sustainability. Conventional data-driven and centralized learning approaches have been proposed in various works. For example, the authors in [24] focused on energy-efficient ship management, in terms of shaft power and fuel consumption rate per unit time. Then, the authors in [25] presented a collision avoidance system relying on data analysis and risk modeling. In inland waterways, a recent study [26] developed a data-driven solution leveraging precise position, navigation, and timing data to provide bridge-collision warnings, mooring, and autonomous guidance.  
However, centralized approaches have limited scalability, since vessels may be geographically dispersed and so centralized solutions might be infeasible due to high communication overheads and increased delays. In this context, a small number of works have recently presented FL-based techniques to improve the performance and sustainability of maritime transportation. The study in [15] introduced a two-phase FL-based algorithm to predict ship fuel consumption and optimize ship sailing speed. Based on the experimental results, the initial phase of the algorithm led to fuel consumption prediction with improved accuracy. In the second phase, this prediction model was leveraged to derive the optimal ship speed. The experiments revealed a reduction in ship fuel consumption between 2.5% and 7.5% when compared to models relying on individual initial data. In addition, ref. [16] developed a maritime FL framework for decentralized on-ship intelligence to predict the primary engine power (PEP) of large cargo vessels, being directly related to their fuel consumption. By using real datasets containing both weather- and ship-related data, various ML models were assessed as local learners. Among these models, artificial neural networks (ANNs) provided the best PEP prediction accuracy. Finally, comparisons were included among FL, centralized ML, and decentralized transfer learning, with FL offering the best prediction accuracy, data privacy, and communication efficiency balance.
# Metaheuristic with Cooperative Processes for the University Course Timetabling Problem
## 4 Computational Results
### SACP Efficiency  
Figure 15 presents the efficiency of SACP evaluated for large instances. It can be seen that, up to 21 processes, efficiency presents a superlinear speedup above the ideal. This is because the first 12 processes in SACP are executed on a single node of the computational cluster and does not require communication with the other three nodes of the cluster. From 21 processes onwards, efficiency begins to decrease considerably. This is due to two things. First of all, that communication between nodes is performed with an Ethernet 100 BASE-TX, 100 Mbs connection, which slows down the sending of messages from SACP between nodes. Second, that SACP carries out cooperation between processes, which implies that in each iteration there is always communication between all the processes that are executed at all nodes of the cluster, applying collective communication and point-to-point communication.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Rank** \\ \end{tabular} } & \multicolumn{2}{c}{**SACP \(p\)-Values**} \\ \cline{3-3}  & & \multicolumn{1}{c}{**Post Hoc**} \\  & & \multicolumn{1}{c}{**Wilcoxon–Mann–Whitney**} \\ \hline SACP & 1 & - \\ \hline SA & 2 & 0.074910 \\ \hline ILS & 3 & 0.008816 \\ \hline GA & 4.6 & 0.008816 \\ \hline TS & 4.8 & 0.008816 \\ \hline ACO & 5.6 & 0.008816 \\ \hline \end{tabular}
\end{table}
Table 7: Post hoc test and average ranking for multiple \(p\)-values comparisons between SACP vs. other algorithms taken only data from medium instances.  
Figure 14: Graph of the \(p\)-values and average ranking between the compared algorithms. (**a**) Small instances. (**b**) Medium instances.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 4 Discussion  
### The European, National, and Local Context of Floods: Incidence, Trends, and Impact  
Although progress has been made in flood forecasting, warning, and risk management, as well as in the implementation of protection measures [62], catastrophic floods continue to pose a major threat at both global and European levels [63], as highlighted by the devastating events of recent decades [64; 65; 66].  
Economic and environmental losses caused by floods have significantly increased over recent decades [67; 68] for two main reasons: the rising frequency and intensity of precipitation due to global warming [7; 69; 70] and the growing population and the value and \"density\" of anthropogenic assets exposed to floods, as socio-economic development progresses [71].  
From a non-economic perspective, research in the field suggests that in developed countries, floods with multiple fatalities are becoming increasingly rare [72; 73], while in developing countries, the average number of fatalities remains high [72; 74]. Economic development can reduce vulnerability to natural hazards, although the relationship between these factors varies [75; 76].  
Over the past two decades, Europe has been affected by more than 400 major floods [69], with some of the most devastating being the floods of 1993 and 1995 in Germany, the Netherlands, and France [77], the 2002 floods in the Elbe River basin [78], the 2010 floods in central and eastern Europe [79], and the 2014 floods in the United Kingdom [80]. In Romania, many river basins continue to lack adequate flood protection measures. Generally, the existing defense infrastructure and hydraulic structures, most of which were built in the 1970s, have undergone very few improvements in the subsequent decades [81]. In this context, catastrophic floods with severe environmental and population impacts were recorded in 2005, 2008, 2010, 2014, and 2020 [69], as well as in 2024, particularly in the northeastern part of the country (Siret and Prut river basins).  
In the study area, specifically the Cigher River sub-basin, part of the Crisul Alb River basin, numerous hydrological events of varying intensity have been recorded. Among the major ones (severe floods and flash floods) with significant environmental and population impact were those of 1970, 1981, 1995, 2000, 2005, 2006, 2008, 2014, 2020, and 2022 [54]. Even minor floods, occurring annually over small areas, particularly on agricultural land, affect local communities given the rural nature of the area, where agriculture represents an important source of subsistence.
# Development of Wireless Power Transmission System for Transfer Cart with Shortened Track
## 4 Test Method and Result
In our WPT system, AC to DC conversion is performed twice: On the primary coil and on the pickup sides, as shown in Figure 6. In our system, the voltages used for both the three-phase power source as an input and the coil for the transmission were 380 and 670 V, respectively. If it was more than 200 V, the efficiency would generally be 99% when considering the loss of the voltage drop caused by the diode used in the rectifier. Therefore, the effect of the performance of the rectifiers used in our system on the overall system would be negligibly small. In Figure 6b, we are looking at two places, labeled as 1 and 2, to check the wireless charging efficiency (see Table 2). Figure 4: Three candidates of modified pickup modules to enhance thermal performance: (**a**) Module A, (**b**) module B, and (**c**) module C.  
Figure 5: Experimental results of temperature measurement for three candidates: (**a**) Module A, (**b**) module B, and (**c**) module C.  
Specifically, its reference condition includes L = 9 uH for the module, and the coils are composed of single-turn Litz wire passing through it. The insulation resistance test and dielectric withstanding voltage test were passed. An aging experiment was conducted at 3 kW and the saturation temperature was kept below 50 \({}^{\circ}\)C except for the core part. We commissioned a performance evaluation experiment to the \"Korea Electronics Technology Institute (KETI, Test Report: T2016-04-005, Tel: +82-63-219-0114, Fax: +82-63-219-0099).\" We were able to obtain the results that the performance evaluation of the WPT system showed that at a 21.54 kW power level, the transfer distance of the WPT was above 15 mm with a charging efficiency above 83.24% (see Figure 6). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **No. 1** & **No. 2** \\ \hline Voltage (V) & 318.06 & 287.80 \\ Current (A) & 81.34 & 74.83 \\ Power (W) & 25,871.0 & 21,546.07 \\ Charging efficiency (\%) & & 83.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters related to power level between No.1 and No.2 shown in Figure 6a, and charging efficiency (83.24%), which was measured based on 20 kW. Figure 6: The actual experiment of power transfer capacity of L-module: (**a**) Instrument system setup (NCP: Non-contact power supply), (**b**) schematic diagram for overall setup of experiment (ATS: Automatic transfer switch), (**c**) effective distance of wireless power (15 mm) with charging efficiency of 83.24%.
# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 2 Materials and Methods  
### Shared Control Architecture  
In the proposed architecture, three possible operational modes are taken into account:  
* _no-contact_ mode: hand-guidance of the end-effector in the free space to allow the operator to move the end-effector towards, e.g., a workpiece;
* _contact_ mode: hand-guidance of the end-effector while in contact with a stiff surface to allow performing operations like, e.g., carving, welding or drawing;
* _hold_ mode: in case of external disturbances, the manipulator is designed to elastically return the end-effector to a fixed position; this position is the same at which the switch to the _hold_ mode occurs. This allows for the temporary relocation of the robot away from the point of interest, facilitating the placement of a workpiece that the robot is then responsible for maintaining in that position.  
The proposed approach allows for the tackling of a wide range of common tasks in industrial scenarios through the combination of these three operational modes. To achieve this, the gains of the admittance controller of the manipulator are changed among three sets corresponding to each operational mode. A finite state machine algorithm handles the switching among these sets based on the movements or contractions of the operator's arm, which are recognized through an EMG-based classifier.  
Figure 1 shows the block diagram of the entire shared control architecture. The _High-level layer_ includes all the functional blocks that address the generation of a certain set of admittance gains, while the _Low-level layer_ includes all the blocks that concern the motion control of the manipulator. The raw EMG signals are used as the input to an SVM classifier that recognizes three possible classes of motions/contractions of the operator. The finite state machine algorithm receives this information and configures a set of admittance gains on the base of the operational mode; then, an admittance controller [39] outputs a desired trajectory for the end-effector, which is finally tracked by computing the needed joint velocity with an inverse kinematics algorithm [40]. In the following sections, details about all the functional blocks are given in a bottom-up order.
## 5 Experiments
### Comparing with DRL Baselines  
We then compare our approach ms-SDRGN with three DRL baselines, including DGN [36], DQN [24] and MAAC [52]. DQN is a simple and efficient single-agent DRL approach, but it is still applicable for multi-agent tasks. Based on DQN, DGN uses GAT for modeling and exploiting the communication between agents. MAAC integrates self-attention mechanism with MADDPG [47], and provides agents with fully observable information to learn decentralized stochastic policy using a centralized critic. Thus, we compare ms-SDRGN with DGN to show the effectiveness of the multi-scale encoder and memory unit. Then, we compare with MAAC to validate the necessity of communication for the multi-agent swarm, especially in a partially observable environment.  
Figure 5: (**a**) The episodic reward learning curves of DRL algorithms. (**b**) The global metrics learning curves of ms-SDRGN.  
We have evaluated the converged methods for 1000 episodes, and taken the mean value and standard deviation of all metrics, as shown in Table 1. Note that for a fair comparison, we also provide fuzzy global observations for the baselines, to ensure the raw observation inputs are the same.  
The evaluation results are presented in Table 1. Then, we conduct a independent T-test between our approach and other three DRL baselines on every evaluation metric. It can be concluded that ms-SDRGN has a significant difference comparing to the baselines (\(p<0.05\)). We can obtain the following observations from Table 1:  
Firstly, the proposed approach ms-SDRGN outperforms all other baselines in terms of reward and coverage index significantly. It demonstrates that with the help of multi-scale convolutional encoder and graph-based communication, ms-SDRGN achieves better data collection and energy management efficiency in SAG-MCS scenario. Compared with DQN and DGN, ms-SDRGN can better sense the surrounding environment from previous experiences in the memory unit, and make decisions more efficiently between seeking for more PoIs or returning for charging.  
Secondly, from the perspective of fairness and energy, MAAC improves 0.005 fairness and 0.0075 energy index than ms-SDRGN. As a fully observable algorithm, we believe that MAAC can achieve similar cooperative exploration as ms-SDRGN using the observation embeddings from the whole UAV swarm. Regardless of extracting features from neighboring UAV nodes or from the memory unit, MAAC has a simpler objective to reduce its energy consumption for getting a higher reward.  
Furthermore, the reward standard deviation of ms-SDRGN is higher than other methods, which may be attributed to randomness generated by the complex MADRL framework.
## Security Analysis
### Against Known Attacks
#### 4.5.2 Replay Attack  
An attacker intercepts the data from the communication, then sends the same data to the receiver, pretending to be the sender. In our method, we use a timestamp mechanism in every communication between the sender and receiver to prevent this kind of attack.  
For example, in the authentication phase, the accessing party is encrypted at a timestamp \(T_{1}\) in the cipher message \(C_{AP_{1}}\) and sends it to the SCC. The SCC decrypts the message and generates a timestamp \(T_{2}\) that represents the received time, then checks the time difference between \(T_{2}\) and \(T_{1}\). If the time is over the parameter \(\tau\), that means that the message sending time is too long and it may have been intercepted by an attacker. The related formula is as follows:  
\[C_{AP_{1}}=E_{Puk_{SCC}}(T_{1}||R_{AP_{1}}||i_{AP_{1}}||Sig_{AP_{1}}||Sig_{AP_{1} }||M_{Access}||H(M_{Access})) \tag{140}\]  
\[(T_{1}||R_{AP_{1}}||i_{AP_{1}}||Sig_{AP_{1}}||Sig_{AP_{2}}||M_{Access}||H(M_{ Access}))=D_{Prk_{SCC}}(C_{AP_{1}}) \tag{141}\]  
\[(T_{2}-T_{1})\stackrel{{?}}{{\leq}}\tau \tag{142}\]  
Our method also added a sequential number in the message from a sender; for example, in the authentication phase, a sequential \(ID\)\(Seq_{i}\) generated randomly from the \(AP\), in the next round of messages from the \(SCC\) will increase the \(Seq_{i}\) to \(Seq_{i+1}\). It is bound with the message and sent to the \(AP\); the \(AP\) receives the message and validates the sequential \(ID\) and checks if it is in the same sequence.
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 4 Experiments, Results and Discussion
### Baseline Models  
We compare the performance of the proposed models against four state-of-the-art deep learning-based models, specifically, Fully Convolutional Networks (FCN) [9], U-Net [16], ResU-Net [17], and DeepLabV3 [15]. FCN is considered as a milestone in deep-learning based semantic segmentation models. In addition, FCN is one of the first models built on only convolutional layers in the semantic segmentation domain. Although U-Net was originally proposed for medical image processing, several variations of it were successfully deployed in different domains. In addition to that, both of our models are built based on U-Net architecture. Thus, comparing the proposed models' performance against U-Net and FCN would be appropriate. Furthermore, ResU-Net is one of the variations of U-Net implemented in the realm of road detection from remote sensing imagery. On top of that, the parameter size of ResU-Net is relatively smaller than other baselines. Finally, DeepLabV3 is one of the recently developed networks in the general semantic segmentation research area, and it would be interesting to check the performance of this model on the binary segmentation task that we are dealing with in this study. It should be noted that the backbone network of both DeepLabV3 and FCN is ResNet50 pretrained on ImageNet-1000 dataset [34]. All baseline models were trained from scratch with the same parameters used in the training of the proposed models.
## 1 Introduction
### Aircraft Maintenance Categories  
[PERSON] et al. [5] explained how aircraft maintenance can be categorised into two distinct types: Hangar Maintenance (H.M.) and Line Maintenance (L.M.). H.M. is scheduled for extensive maintenance in a hangar that requires specialised facilities and equipment. H.M. usually takes days or months, and multiple engineers from different areas of expertise work together in some cases simultaneously to perform activities like overhauls, checks, structural repairs, and modifications or upgrades. One of the challenges is the time pressure, as the aircraft must be delivered without any delays to avoid extra losses. Another challenge can be the working conditions, as, in many cases, there are temperature, lighting and noise restrictions. On the other hand, Line Maintenance (L.M.) ensures the aircraft's airworthiness and refers to essential routine maintenance tasks that must be performed daily between flights. These tasks can be performed at the airport gate and mainly involve inspections, minor repairs, and light troubleshooting. If L.M. cannot resolve the issue, H.M. will take over.  
In a detailed study of aircraft maintenance personnel's work hours, the Civil Aviation Authority (CAA) highlighted the critical issue of a shortage of licenced engineers and their excessive working hours, which can raise safety concerns. The proposed solution is for the maintenance organisation to implement 24 h shifts, either in three 8 h shifts or two 12 h shifts [11]. As explained earlier, both cases can be stressful and challenging.  
The Federal Aviation Administration (FAA) obliges airlines to prepare a mandatory maintenance programme called the Continuous Airworthiness Maintenance Program (CAMP). This process covers day-to-day and detailed inspections called \"checks\". From lighter to most thorough, there are four levels of checks: A, B, C, and D [12]. [PERSON] et al. [13] provided a summary table (Figure 3) of all four checks, including a brief description, the location, and the frequency/duration for each, arranged in order of increasing complexity.  
Figure 2: Installation Error and Associated Factors [10].  
Aircraft maintenance can be further classified into two sub-categories: airframe and engine maintenance. Airframe maintenance focused on electronic systems is referred to as non-structural, while maintenance addressing defects in the structural components, such as fuselage panels, is known as structural airframe maintenance [14].  
This research is oriented toward structural airframe maintenance, focusing on Hangar Maintenance checks C and D and improving inspection procedures.