esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Test and Result Analysis
### Experimental Test
#### 3.4.1 Model Training  
The computer configuration used for model training is shown in Table 1.  
In this paper, a series of training parameters and strategies are used to ensure that the model can learn efficiently and stably. The weight used for training is vSlite-s.pt. The specific file and label path are input through the yaml file, in which the Batch_size is set to 16, the iteration period epochs are set to 150, the confidence threshold is 0.45, and the iou threshold is 0.65. The warmup learning rate method is used for training. When the model is in the initial epochs, a smaller learning rate value will be selected to increase the stability of the model in the initial training stage. After stabilization, the training will be continued  
Figure 7: Workflow of object detection function.  
with a preset cyclic learning rate to improve the convergence speed. The hyperparameter settings in the training process are shown in Table 2.
# Allocating Small Transporters to Large Jobs
## 2 Literature Review
### Multiple Knapsack Problems  
Even though it is not immediately obvious from a practical point of view, \((\mathrm{TAP})\) is rather strongly related to multiple knapsack problems. Recall that the knapsack problem [20] is a popular optimization problem, with the objective to select items with maximum total value without exceeding a certain capacity. The multiple knapsack problem considers several knapsacks with potentially different capacities. The objective is to assign items to knapsacks such that the total items value is maximized without exceeding any knapsack capacity. In comparison, for \((\mathrm{TAP})\), consider each job as a knapsack with a capacity given by the total size of transporters needed before all idle times vanish (note that this capacity is not fixed, as it depends on the largest transporter assigned, cf. Equation (2)). Then, a good allocation of transporters is one where each \"knapsack\" is as close to exactly filled as possible.  
Many knapsack-related problems are known to be NP-hard [11]. Most algorithms for the multiple knapsack problem are either greedy or meta-heuristics such as in [21; 22], polynomial approximation schemes such as in [23; 24], or exact methods using mathematical programming as in [25; 26].  
For our paper, a special kind of knapsack problem studied in [27] is of particular interest. In this version, so-called assignment restrictions are given, such that each item is only allowed to be assigned to as subset of the knapsacks. This is interesting, since in \((\mathrm{TAP})\), jobs may become ineligible for further transporters once they have reached their minimum possible completion time. In [27], the authors propose several approximation algorithms, some with a simple, greedy structure. We will later (cf. Section 5) see that two of these algorithms can be adjusted to work well as heuristics for \((\mathrm{TAP})\).
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 5 Experimental Results
### F1-Score Enhancement via CycleGAN-Augmented Training Sets
For instance, in the rainy-day condition, the F1-score improves from 91.74% (Real 500) to 95.79% (Real 500 + Gen 500) in the independent set, and from 94.32% to 97.17% in the dependent set--closely matching the 97.88% and 98.42% achieved with 1000 real images. Similar trends are observed in the other scenarios, including rainy night, sun glare, and high-beam glare, where the inclusion of high-quality synthetic images notably boosts detection performance. These results reinforce the effectiveness of CycleGAN-generated images in augmenting training data under conditions where real-world image acquisition  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Scenario** & **Real 500** & **Real 500 +** & **Real 500 +** & **Real 500 +** & **Real 500 +** & **Real 1000** \\  & & **Gen 250** & **Gen 500** & **Gen 750** & **Gen 1000** & **Real 1000** \\ \hline \multirow{2}{*}{Night} & 94.38\% & 94  
is limited. They also suggest that a balanced augmentation ratio--specifically 1:1 between real and synthetic samples--can yield near-optimal detection performance, providing a practical trade-off between data availability and model accuracy. To further illustrate these findings, Figure 10 visualizes the dependent-testing-set F1-scores across different training configurations. The bar chart clearly shows that the Real 500 + Gen 500 configuration consistently outperforms the Real 500 baseline and approaches the performance of the Real 1000 model across all the scenarios. This visual evidence complements the tabulated data and emphasizes the practical value of synthetic augmentation in challenging environments. In this experiment, the F1-score was evaluated using an IoU threshold of 0.5, which is commonly adopted as a baseline in object detection benchmarks. As demonstrated in Section 5.1 (Figure 9), increasing the IoU threshold generally results in lower F1-scores due to stricter localization requirements. Therefore, for the purpose of analyzing performance variation with different quantities of synthetic images, we selected IoU = 0.5 to ensure clearer differentiation and consistent comparison across configurations. Extending this analysis to higher IoU thresholds is a valuable direction for future work to further evaluate model robustness under stricter evaluation criteria.
## 4 Performance Comparison  
There are some differences in the performance of DETLs with different tuning methods and structures. The primary liquid lens parameters reported publicly are listed and compared, which is of some reference significance for readers. The parameters compared in this review include tuning principle, lens structure, dielectric field strength, focusing ability, response time, DEA material, and other key indices. Among them, the dielectric field strength refers to the maximum electric field strength that the lens can withstand before breakdown; the focusing capability describes the physical range or the zoom ratio (the ratio of the maximum focus to the minimum focus) that the lens system achieves for the focus adjustment. Response time is then defined as the drive time required for the focal length to reach 90% of the change in focal length from voltage application when a square wave drive voltage is applied and the recovery time required to achieve 90% of the change in focal length after the voltage is withdrawn. The specific parameters are detailed in Table 2.  
Figure 12: Dielectric elastomer-driven metalenses: (**a**) top view of the lens object; (**b**) schematic diagram of lens zoom, where the upper and lower diagrams show the change in focal length before and after applying voltage, respectively [77].
## 2 Materials and Methods
### Autoclave Molding  
The production of the hybrid composites has been carried out by using state-of-the-art materials, equipment, and procedures of the aeronautic sector. The CFR plies and rubber  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Stacking Sequence** & **Elastomer** \\ \hline C3-E1-C3 & C/C/C/E/C/C & Rigid, Soft \\ C3-E2-C3 & C/C/C/E/E/C/C/C & Rigid, Soft \\ C1-E1-C2-E1-C2-E1-C1 & C/E/C/C/E/C/C/E/C & Rigid, Soft \\ C1-E1-C4-E1-C1 & C/E/C/C/C/E/C/E/C & Rigid, Soft \\ C2-E1-C2-E1-C2 & C/C/C/C/E/C/E/C & Rigid \\ E1-C6-E1 & E/C/C/C/C/C/C/E & Rigid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stacking sequence of the molded plates.  
Figure 2: Architectures of the hybrid composite structures (C = CFR ply, E = elastomer layer).  
layers were cut in the shape of 300 \(\times\) 300 mm\({}^{2}\) square sheets, and laminated according to the architectures of Figure 2. A vacuum bag was used, and all 10 plates cured contemporarily in autoclave for 3 h at the temperature of 180 \({}^{\circ}\)C at the pressure of 6 bar, according to the datasheets of the raw material manufacturers, with a heating and cooling rate of 2 \({}^{\circ}\)C/min. The measured cured ply prepreg thickness was 0.24 mm and the measured areal weight of the prepreg was 335 g/m\({}^{2}\). During the cure, CFR plies and elastomer layers consolidated and co-bonding at the CFR/elastomer interface occurred. After molding, rectangular samples were cut for testing from all the hybrid laminates, far from the edges.  
Average thickness and density of molded plates are reported in Table 2. Sandwich densities are lower than conventional CFR laminates with epoxy matrix, ranging between approximately 1.4-1.5 g/cm\({}^{3}\), because of the lower density of the elastomer layers. In fact, by reducing the number of elastomer layers, the density reduces. For the same reason, by increasing the thickness, the density reduces as well. By comparing hybrid laminates with rigid and soft elastomer layers, the thickness of the soft class is 60% thicker than the rigid, on average, because of the initial thicker uncured plate.  
Density and thickness are important parameters to determine the dynamic behavior of the hybrid laminates, as natural frequencies increase by reducing the density, and increase by increasing the thickness.  
The appearance of the cross-section of hybrid plates is shown in Figure 3 by Leica S9i stereoscope. Magnifications of some interfaces are proposed. As expected, a very good adhesion was found between adjacent rubber layers and adjacent CFR plies. Moreover, the same optimal adhesion was observed at the hybrid interface between elastomers and CFR plies. Co-bonding was very effective thanks to the common thermosetting nature and the chemical affinity of the components. Moreover, the rubber resin bleeding, and the similar processing window for curing, aimed for this high adhesion.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 3 Experimental Validation
### Diagnosis Results and Comparative Analysis  
The comparison experiment was split into two parts. The proposed model was first compared with SAE, ANN, and DBN. Then, it was compared with standard CDBN.
## 4 Integrated Energy System Optimization Scheduling
### Optimal Scheduling Model for Energy Storage Life Decay and Calculation  
4.2.1. Objective Function  
When taking into account the decay of energy storage life, the system in operation, energy storage will be continuously charged and discharged resulting in the decay of capacity, resulting in life loss costs, and the capacity of energy storage operations and maintenance costs will also change accordingly.  
\[\begin{split}& F_{ES}(t)=\frac{\Delta C(t)}{C_{R}}F_{R}\\ &\Delta C(t)=C(t)-C(t-1)\\ &{f_{ES}}^{\prime}(t)=\sum\limits_{t=1}^{24}{[K_{om1}^{ES}\cdot C (t)+(K_{om2}^{ES}+C_{dp}^{ES})|P_{ES}(t)|]}\end{split} \tag{22}\]  
where \(F_{ES}(t)\) is used for the consideration of energy storage life decay after the cost of life loss, \(\Delta C(t)\) represents the decay of energy storage capacity from the moment \(t-1\) to the moment \(t\), and \({f_{ES}}^{\prime}(t)\) is the energy storage operation cost after the operation and maintenance costs of energy storage has changed. When the cost of energy storage life loss and the cost of operation and maintenance are taken into account, the operation goal of the system is as follows Equation (23):  
\[\min F^{\prime}=F_{e}(t)+F_{g}(t)+{F^{\prime}}_{OC}(t)+F_{q}(t)+F_{R}+F_{ES}(t) \tag{23}\]  
where \({F^{\prime}}_{OC}(t)\) represents the system operating cost after the change in energy storage operations and maintenance costs and \(F^{\prime}\) represents the total system cost after taking into account the decay of energy storage life.  
4.2.2. Constraints  
The charge state of the energy storage will change when the energy storage life decay is taken into account; therefore, the operating constraint of energy storage becomes Equation (24):  
\[\begin{split}& 0\leq P_{ES,C}\leq P_{ES,C}^{\max}\\ & 0\leq P_{ES,D}\leq P_{ES,D}^{\max}\\ & SOC_{ES}^{\min}\leq SOC^{\prime}_{ES}\leq SOC_{ES}^{\max}\\ \end{split} \tag{24}\]  
The rest of the constraints are the same as in Section 4.1.2.
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 2 Video Monitoring Platform Description
### Video Monitoring Platform Architecture
More specifically, the acquirer splits video streams, which have to be stored, in sub-streams that can be saved on different drones of the backbone mesh network, named _storage drones_. In order to increase the storage capacity of the ssS, a set of fixed storage servers are installed on the ground to store old flows that will likely not be requested frequently. In our prototype, the acquirers use Forward Error Correction (FEC) to encode video streams to introduce some redundancy, in such a way that, even if some drones used for storing sub-streams of the same main stream are temporarily not available when a Client request to watch a flow, this flow can be rebuilt. For each flow to be stored, the choice of the number of involved storage drones and fixed storage servers is in charge of the orchestrator.
###### Abstract  
According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1 FIS) using Distance to Closest Point of Approach (\(D_{CPA}\)) and Time to Closest Point of Approach (\(T_{CPA}\)) computed via the radar has been largely used for assessing the collision risk. However, the T1 FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2 FIS) as follows: (i) the T1 FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1 FIS into the IT2 FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2 FIS using the FOU size \"\(\pm\)5%\" (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.  
Keywords:collision risk; fuzzy inference system; type-2 fuzzy logic; footprint of uncertainty; near-collision; ship safety domain +
Footnote †: journal: Applied
## 4 Proposed Method
### New Fitness Function  
The fitness function is used to evaluate the patch using the output from the validation process. This helps to identify whether the patch is a solution or not. Also, the incorrect patch with the best fitness value (low value) will undergo the repairing process once again. So, there is a chance of producing a test adequate patch during the re-repairing process.  
In Astor [11; 12; 13], whenever a patch encounters a failed test case, it sums up the failed test case count and sets it as a fitness value. If there are no failed test cases, the patch is considered to be a test adequate patch. Following the same fitness function in our approach will not be accurate and efficient as we run test cases in subsets from the prioritized test suite. For example, in our approach, one failed test case out of 50 test cases is different from one fail test case out of 100 test cases. So, we are presenting a new concrete fitness function to alleviate the fitness function efficiency. The fitness function will be calculated based on the following formula,  
\[F_{s}=\frac{T_{E}}{T_{T}}-\frac{T_{F}}{T_{E}} \tag{1}\]  
where,  
\(T_{T}\)--Total Test Cases Count  
\(T_{E}\)--Executed Test Cases Count  
\(T_{F}\)--Failed Test Cases Count  
**Condition.** Let there exist two patches: p1, p2, which have the same number of test cases. Patch 'p1' provides the best fitness, if \(p1(T_{E})>p2(T_{E})\) and \(p1(\frac{T_{E}}{T_{E}})<p2(\frac{T_{E}}{T_{E}})\).  
By calculating the fitness score using the above formula, the efficiency of fitness scores can be maintained. As the most likely to fail test cases are expected to be present in the initial subsets of the prioritized test suite, calculating fitness based on this formula would help in maintaining and improving repair efficiency.
# ADS-B Like UTM Surveillance Using APRS Infrastructure
## 2 Materials and Methods
### Ground Transceiver Station
It is quite useful to give an early warning to pilots if battery power supply is not sufficient at this point. Figure 13 shows the tracking of 6-DoF UAV during the flight. The UTM controller can check the UAV's status including position and 6-DoF flight data by using the 90 byte surveillance data from APRS OBU. From the yaw in Figure 13, it is obvious that the UAV have a right turn in flight according to data no. 36 and 37. Moreover, the flight altitude is held in flight since the value of Vz was kept around 0.  
system. Figure 16 shows the UAV surveillance data of 6-DoF UAV. The turning in the flight can be easily identified by observing the change of yaw angle. In addition, the NED-based velocities can be used to determine the flight direction. For example, the velocities of data no. 25 indicate the UAV flight direction is in southwest by south. In this test, the GTS receives all of the APRS OBU reporting surveillance data with the complete 90 bytes. The UAV attitude can be shown on UTM display during flight performance, these are data no. 36 and 37 in test case 2 in Figure 17.  
Figure 16: Surveillance data of UAV 6 DoF for test case 3. Figure 14: OBU surveillance data for cruise altitude and current. Figure 15: In-flight UAV displays on UTM surveillance system. Using a different presentation, the UAV attitude can be displayed as a primary flight display (PFD) in Figure 18 while the UAV is turning. This is more convenient for observing the UAV's in-flight situation.
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Mechanisms by Which Walnut Contributes to the Management of NAFLD
#### 3.2.2 Effects of Walnut on Lipotoxicity (Oxidative Stress)  
Lipotoxicity refers to cellular damage caused by the aberrant accumulation of lipid compounds in the cell, resulting in oxidative damage to tissue and organs through the formation of ROS [75]. In NAFLD patients, abnormal lipid metabolism causes damage to mitochondrial DNA and mitochondrial respiratory chain proteins, thereby increasing ROS formation. This phenomenon is particularly important for the progression of NAFLD to NASH [75]. In NASH, the levels of toxic palmit are elevated in the plasma and palmitate accumulates is observed in the liver, where it can activate apoptosis process in hepatocytes [75].  
Wahnut has a very high content of antioxidants. A randomized crossover study demonstrated that walnut meal increased postprandial plasma concentrations of \(\gamma\)-tocopherol, gallocatechin gallate, epicatechin gallate and epicallocatechin gallate and increased hydrophilic and lipophilic oxygen radical absorbance capacity in healthy individuals [76].  
In a high-fat diet animal model with C57 BL/6] mice, walnut supplementation not only decreased hepatic fat accumulation but also significantly decreased the hepatic levels of cytochrome P450-2E1, an important source of oxidative stress, nitrated proteins--which are markers of nitrosative stress and are involved in the development and progression of NAFLD--and lipid peroxidation, a metabolic process wherein ROS result in the oxidative deterioration of lipids. Furthermore, walnut supplementation decreased the levels of activated cell death-associated p-JNK and p-p38K, a phenomenon that was accompanied by increased hepatocyte apoptosis in the high-fat-diet group. These results collectively suggested the beneficial effects of dietary walnut, i.e., attenuating high-fat diet-induced hepatic steatosis and apoptosis could be partially attributed to the anti-oxidant ingredients [77].  
In a transgenic mouse model of Alzheimer's disease, transgenic mice fed a control diet showed significant age-dependent increase in ROS levels, lipid peroxidation, and protein oxidation, coupled with impaired activities of anti-oxidant enzymes such as super oxide dismutase, catalase, and glutathione peroxidase compared with those in wild-type mice. Transgenic mice fed diets containing walnuts exhibited significantly reduced oxidative stress, as demonstrated by decreased levels of ROS, lipid peroxidation, and protein oxidation and enhanced activities of anti-oxidant enzymes compared with those in the control diet mice. Long-term supplementation with walnuts was more effective in reducing oxidative stress in this animal model [78]. These observations indicate that walnut can reduce oxidative stress, not only by scavenging free radicals, but also by maintaining the anti-oxidant status. Walnut has potent anti-oxidant properties and may reduce the oxidative stress associated with lipid accumulation in NAFLD.
###### Abstract  
Massive multi-input multi-output (massive MIMO) technology offers significant multiplexing gains and enhances transmission rates by efficiently utilizing available airspace resources. However, it requires each antenna to be paired with a separate radio frequency (RF) chain, which leads to the need for numerous RF chains in the system, resulting in high hardware costs, increased computational complexity, and elevated power consumption. To address this, antenna selection technology reduces the number of RF chains required, activating only the antennas that correspond to the available RF chains. Moreover, user scheduling provides multi-user diversity in multi-user massive MIMO systems. Therefore, this paper introduces a joint antenna selection and orthogonality-based user scheduling (JAS-OUS) algorithm aimed at maximizing the system sum rate. Furthermore, to tackle the issue of fairness, which is often overlooked by traditional user scheduling algorithms, a proportional fairness user scheduling (PFUS) approach is proposed. In this scheme, user weights are updated based on proportional fairness, ensuring a fair selection of users for communication in each time slot. Simulation results demonstrate that the JAS-OUS algorithm achieves robust performance across various configurations of transmitting antennas and users. Additionally, when combined with PFUS, the joint algorithm ensures more equitable user participation in communication without compromising the system sum rate.  
amssive MIMO; antenna selection; user allocation; proportional fairness 2025