esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Convolutional Neural Network (CNN)-Based Object Detection  
With the rapid growth of CNN technologies recently, a large variety of CNN-based approaches have facilitated object detection to a new stage. CNN-based object detectors are generally grouped into two main categories. The first category is termed as two-stage approaches, including fast region-based CNN (Fast R-CNN) [14] and Faster R-CNN [15]. The two-stage methods include two parts. The first part aims to generate a sparse series of candidate object proposals, and the second part refines the candidate proposals to determine the accurate objects locations and its corresponding class label. Although the aforementioned two-stage detectors achieve desirable accuracy, the computational speed is slow. By contrast with two-stage detectors, one-stage methods such as SSD [16] and YOLO [20; 35] avoid generating region proposals and resampling features by encapsulating all operations in a single network, which performs better in speed than the two-stage detectors. In CNN-based methods, the high-level layers which contain sufficient semantic features lack the detailed spatial features of the objects because of the striding operations in pooling and convolutional layers. This makes it hard to extract discriminative features. Consequently, it is difficult to identify target locations. On the contrary, the features from the low-level layers which contain enough spatial information but lack the semantic information are not robust enough to the challenges of appearance variation and occlusions. To address these issues, some networks [18; 36] try to observe and utilize the pyramidal features to a large extent by building an encoder-decoder architecture with lateral connections. These networks show dramatic improvements in accuracy when compared with conventional detectors. However, effective methods to combine the information from the encoder and decoder modules for good performance still need to be explored. Motivated by the aforementioned works, we introduce a novel encoder-decoder module that takes an IR image as an input to the encoder module to capture richer semantic information and adopts a decoder module to progressively recover the spatial information. In our proposed encoder-decoder module, a new skip-connection block is used to fuse the information from the encoder and decoder module. The encoder-decoder structure can efficiently capture the long-range context information while keeping the spatial information of IR objects. This owes to the expressiveness of the encoder-decoder layers, which are originated from the combinatorial nature of Rectified Linear Unit (ReLU) for decomposition and reconstruction.
## 3 Stress-Strain Relationship of Basalt-Fiber-Reinforced Loess
### Microstructure of Basalt-Fiber-Reinforced Loess  
To analyze the reinforcement mechanism of fiber-reinforced soil, the scanning electron microscopy (SEM) tests were carried out using SEM equipment (EVO 10 type, ZEISS, Jena, Germany), and Figure 6 shows the microstructure of basalt-fiber-reinforced loess. As shown in Figure 6a, it can be observed that the fiber was surrounded with soil particles, and soil particles had an anchoring effect on the fiber. The shear strength of the reinforced soil depended on the cohesion and friction between fibers and soil particles. When the sample was subjected to shear loading, the external force was transferred from soil particles to fibers between the interfaces. The anchoring effect increases the friction force, prevents the pullout of fibers, and improves the integrality of composite material. Therefore, the peak strength of the reinforced soil was significantly higher than that of the unreinforced soil. As shown in Figure 6b, it can be found that the soil particles were connected by fibers, and fibers played a bridging effect. Due to the high tensile strength of fibers, the bridging effect can restrain the displacement of soil particles and improve the cohesion force of composites, thereby the sample can resist larger deformation and destruction under shear loading. On the whole, the anchoring effect and the bridging effect between fibers and soil particles contributed to the cohesion and friction of composites, resulting in the improvement in the shear strength of the reinforced soil.  
Figure 5: Effect of cell pressure on stress–strain curves: (**a**) \(FL\) = 4 mm; (**b**) \(FL\) = 8 mm; (**c**) \(FL\) = 12 mm; (**d**) \(FL\) = 16 mm.
## 1 Introduction  
The increasing frequency of earthquakes worldwide has brought significant attention to the importance of soil-structure interaction (SSI) in seismic design. SSI is described as a dynamic interaction where the inertial forces of a structure affect the surrounding soil, and the deformed soil, in turn, influences the structural behavior [1]. This interaction modifies the structure's natural frequencies and damping ratios, making predicting its dynamic responses more complicated. Depending on the frequency components of seismic waves, this can also result in resonance, requiring careful analysis to consider these effects [2, 3].  
Many countries' current seismic design standards recommend using fixed-end models, based on multiple previous research studies showing that ignoring SSI produces conservative design results, ensuring safer structural systems. Internationally, only a few design codes require the use of an SSI analysis. Moreover, differences in application criteria and analysis methods across the design codes also create challenges in achieving consistency within the design process [4, 5].  
Previous studies have attempted to overcome the limitations of fixed-end models by incorporating SSI; however, most analyses have been limited to two-dimensional approaches or simplified the foundation soil using spring elements, failing to fully capture the complex interactions between the superstructure, basement, and soil [6; 7; 8; 9]. A two-dimensional analysis cannot capture three-dimensional dynamic effects, such as torsional displacement caused by the mismatch between the structural centroid and the center of stiffness. Previous studies have demonstrated that 2D models are inadequate for asymmetric 3D conditions, highlighting the necessity of a 3D finite element analysis [10].  
In this study, three-dimensional numerical analyses were performed using LS-DYNA R12.0.0, a general-purpose finite element analysis software, to overcome the limitations of previous studies. The input ground excitation was the 2016 Gyeongju earthquake in Korea, dominated by high-frequency components [11]. Four typical types of soil in Korea, Fill (FI), Alluvial Soil (AS), Weathered Soil (WS), and Weathered Rock (WR), were considered. The target structures were selected as a high-rise structure with 40 stories and a low-rise structure with 5 stories, and the effects of SSI on the dynamic response of structures with high natural frequencies (low-rise structures) and low natural frequencies (high-rise structures) were comparatively analyzed.
# Surface Quality Evaluation in the Milling Process Using a Ball Nose End Mill
## 2 Materials and Methods/Research Methodology
### Surface Roughness Analysis Methodology  
The values of the observed surface roughness parameters were evaluated (in accordance with ISO 25 178) from the extracted surface after the removal of its nominal shape (polynomial function of the paraboloid). For the purpose of the analysis, measurements were taken at three locations at specified heights. All measurements were taken at a position of 0 degrees with respect to the specimen axis. The measured areas for surface roughness assessment are shown in Figure 6. The area measured was 6 \(\times\) 6 mm.  
The following surface roughness parameters were evaluated in the experiment:  
* S\({}_{10\mu}\)--Sensitive to changes in the topography of the observed surface; an important parameter in evaluating the surface functionality (affects dimensional accuracy of fitted surfaces, tightness of joints, etc.).
* Sa--A powerful statistical parameter that is used to regulate and control production.
* Ssk--Gives us information about the protrusions and depressions of the topography of the observed surface. If it takes a positive value, protrusion dominates, and if it takes a negative value, depression dominates.
## 4 Experimental Results
The architecture of the proposed ensemble fusion system is detailed in Figure 5. The diagram outlines the prediction flow, starting from input preprocessing through base model inference, class-specific weighting, and final meta-classification. After preprocessing, each base model (VitBase, DeiT, MaxViT, FasterVIT, and XCIT) generated _logit_ as the prediction for each class, which was then weighted with a coefficient from the weight matrix. The weighted logits were scaled and fed into the chosen meta-classifier, which calculated probabilities for each class. The meta-classifier thus combined the information from all the base models, following the better-performing models for certain classes. The final predicted class had the highest probability, providing a more robust and accurate prediction. The summary of this process involved processing the image  
Figure 5: Architecture of the proposed ensemble system. _Logits_ from five ViT models were weighted by class-specific \(F1\) scores and combined via a logistic regression meta-classifier to produce the final prediction. by several models, combining and weighting predictions, and using an ensemble model to generate the final prediction. The ensemble's performance exceeded that of the individual models. This denotes that each architecture, with good metrics for the chosen datasets, had a significant decision factor, overcoming the limitations of the individual models. Table 3 shows the metrics obtained for the ensemble architecture. It can be observed that the performances were better for the D0 dataset than IP102. It is important to clarify that the high \(F1\) score of 99.82% corresponded to the ensemble model evaluated on the D0 dataset, as presented in Table 3. This dataset was well-balanced and less complex than IP102, which facilitated better generalization. In contrast, Figure 4 illustrates the performance on the IP102 dataset, where the larger number of classes and unbalanced distribution made overfitting more likely. The ensemble approach, based on logit-level weighting and logistic regression, played an important role in reducing overfitting and enhancing robustness, particularly in the case of the D0 dataset. The reported metrics were computed on a single fixed split of 70% training, 20% validation, and 10% test data for each dataset. This split was randomly generated but held constant across all models to ensure comparability. Due to the size and diversity of the datasets, especially IP102, this split provided meaningful evaluation. However, no cross-validation or repeated random sampling was applied, which could provide more insight into model variance and stability.
# Extraction of Catastrophe Boundary and Evolution of Expressway Traffic Flow State
## 4 Analysis of Evolution Law of Traffic Flow State
### Dissipation Law of Traffic Flow Congestion  
Congestion dissipation is an essential process in the evolution of traffic flow, and its law is the primary basis for studying dissipation control strategies. In order to explore the dissipation law of traffic flow congestion, the input traffic volume was set as 4000-1500 pcu/h, and the simulation data of the intermediate section for a total of 6 h were selected as the research object. The results are shown in Figure 9.  
Figure 9a-f show the distribution relationships between traffic volume and occupancy rate collected in each period (1 h). As shown in Figure 9a, at the beginning of the simulation, only a few vehicles were in the free flow state, before quickly entering the congestion state due to a speed catastrophe. Almost all the traffic volume was distributed in the congestion flow area. Then, with the decrease in input traffic volume, as shown in Figure 9b, the traffic flow congestion was alleviated; however, most vehicles were still in the congested flow state, with a few vehicles reaching the free flow state. With a further reduction in the input traffic volume, as shown in Figure 9c, traffic volume and occupancy distribution returned to the average level, and most of the traffic volume was in the free flow state.  
Figure 8: Distribution of traffic flow parameters in cusp catastrophe model. (**a**) Traffic volume—occupancy distribution; (**b**) Speed—occupancy distribution; (**c**) Speed—traffic volume distribution.  
Then, with the continuous reduction in the input traffic volume, the traffic flow state was further optimized until Figure 9f, where the traffic flow distribution was entirely in the free flow state.  
Figure 9: Distribution of traffic volume as a function of occupancy during congestion dissipation. (**a**) 0–1 h\({}_{2}\) (**b**) 1–2 h; (**c**) 2–3 h\({}_{2}\) (**d**) 3–4 h\({}_{2}\) (**e**) 4–5 h\({}_{2}\) (**f**) 5–6 h.  
Comparing Figure 9b,c, it can be seen that, during the process of congestion dissipation, the distribution of traffic volume changed significantly, indicating that the catastrophe of the traffic flow state would still occur. There was a noticeable data gap between the two steady states of congested flow and free flow, indicating the synchronized flow region. There were very few data points in this region, verifying the inaccessibility of the cusp catastrophe theory. At the same time, comparing the evolution law of congestion formation, it can be seen that the two parameters of traffic volume and occupancy were generally consistent before congestion formation and after congestion dissipation. However, the traffic volume and occupancy during system catastrophe were different, highlighting the hysteresis of cusp catastrophe theory.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 4 Technology Trends to Achieve the Requirements in the 5G Network
### Device-To-Device (D2D) Communications  
D2D communication is another trend for the next-generation network that is 5G [73]. The said technique is usable in two different methods. The first way is where one terminal collaborates with others in a manner that enhances the communication features between the base station and terminals. The second method is where one terminal communicates with another terminal directly without including the base station. Device-to-device communication can improve channel reliability, system throughput, operation cost reduction, and energy efficiency. Figure 8 shows both of the approaches. Direct communication in D2D allows for numerous D2D links concurrently to share the same bandwidth, which leads to increasing the cell traffic capacity. Furthermore, the SNR can be improved with direct communication approach (as compared to the communication through the base station), due to which transmission power is minimised to save energy or maximise the link capacity. At last, the radio link latency is reduced by D2D direct communication [74].  
Figure 7: Heterogeneous network.
## 3 Time-Dependent Reliability Analysis of an Aqueduct Based on Dual Power Transformation  
Time-dependent reliability analysis is a critical approach for evaluating the long-term performance of structures, particularly those subjected to environmental factors such as carbonation, which causes gradual deterioration in reinforced concrete. Over time, carbonation lowers the alkalinity of concrete, leading to reinforcement corrosion, cracking, and overall structural degradation. Understanding these time-dependent processes is essential for ensuring the safety and functionality of key infrastructure, such as aqueducts.  
The higher-order moment method plays a significant role in the time-dependent reliability assessment of aqueducts. This method enables a detailed analysis of the probability distributions of structural responses, allowing for precise predictions of failure probabilities. Furthermore, the proposed method innovatively tackles the challenge of calculating small failure probabilities using the direct fourth-moment method, enhancing the accuracy of reliability predictions for rare events. An additional key component of this study is the application of dual power transformation, a mathematical technique that adjusts the distribution of random variables through power transformations. This approach is particularly effective for addressing the nonlinear behaviors and skewed distributions often encountered in time-dependent reliability assessments, improving the robustness of the analysis.
###### Abstract  
Major challenges are presented when managing a large number of heterogeneous vehicles that have to communicate underwater in order to complete a global mission in a cooperative manner. In this kind of application domain, sending data through the environment presents issues that surpass the ones found in other overwater, distributed, cyber-physical systems (i.e., low bandwidth, unreliable transport medium, data representation and hardware high heterogeneity). This manuscript presents a Publish/Subscribe-based semantic middleware solution for unreliable scenarios and vehicle interoperability across cooperative and heterogeneous autonomous vehicles. The middleware relies on different iterations of the Data Distribution Service (DDS) software standard and their combined work between autonomous maritime vehicles and a control entity. It also uses several components with different functionalities deemed as mandatory for a semantic middleware architecture oriented to maritime operations (device and service registration, context awareness, access to the application layer) where other technologies are also interweaved with middleware (wireless communications, acoustic networks). Implementation details and test results, both in a laboratory and a deployment scenario, have been provided as a way to assess the quality of the system and its satisfactory performance.  
autonomous underwater vehicles; cyber-physical systems; middleware +
Footnote †: journal: Sensors
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Comparison to Other Alternative Approaches  
To verify the superiority of the proposed waterline detection approach, we carry out an experimental comparison between relevant alternative approaches and ours.  
Current vision-based waterline detection primarily resorts to non-deep-learning methods. Specifically, they generally apply a non-deep-learning paradigm to focus on waterline-relevant feature representation or final discriminative strategy. Among them, edge detection is such a classic method that has been extensively applied in applications based on waterline detection. Thus, in this subsection, we compare the representative method with ours on their robustness and stability in the presence of environmental noises.
## 3 Results and Discussion
18% for the HCS and ca. 12% for the NHCS materials). The TGA data for the SCSs, HCSs, and NHCSs materials are presented in Figure 5. The data for the SCSs show the expected loss of graphitic-like carbon with the derivative peak maximum between 600 \({}^{\circ}\)C and 750 \({}^{\circ}\)C, with no residual material remaining after the carbon oxidation. Interestingly, the HCSs show two derivative maxima, one at ca. 380 \({}^{\circ}\)C and the other at ca. 680 \({}^{\circ}\)C. The peak at 380 \({}^{\circ}\)C is suggestive of the loss of amorphous (or polymeric) carbon while the weight loss at 680 \({}^{\circ}\)C is consistent with loss of graphitic type carbon. The broader derivative peak observed for the solid spheres is expected. However, doping with N normally leads to a decrease in carbon stability that is not observed here. This could relate to the amorphous/polymeric material that was removed from the HCSs that led to easier oxidation of the remaining carbon in this material. Raman data were also recorded on the three spherical carbon nanomaterials (Figure 6). The typical carbon D and G bands were observed at ca. 1280 cm\({}^{-1}\) and ca. 1600 cm\({}^{-1}\) that indicate the amorphous/graphitic (and sp\({}^{2}\)/sp\({}^{3}\)) content of the carbon [38]. The G band position was invariant in the three samples while the D band showed an increase in peak position for the HCS and NHCS samples. The I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratios for these bands gave values of 0.51 (SCSs), 0.50 (HCSs) and 0.54 (NHCSs) showing good aromatic content. The addition of N to the HCS indicated a modest increase in disorder, as expected for N-doped carbons. These values can be compared to HCSs (and NHCSs) made from traditional carbon sources such as acetylene and acetonitrile where more disordered graphitic structures were observed (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)\(>\) 1) [39; 40]  
XPS spectra were recorded for the three samples (Figure 7a; Supplementary Figures S3-S5 and Tables S1-S3). In all cases C and O were observed and some residual F (1%-2%) was noted in the NHCS and HCS materials. The residual F was readily removed by further washing with water. The C and O content of the SCS and HCS is given in Tables S2 and S3, and are typical of these types of materials. The N content was found to be 3.8%--again a common dopant loading found for NHCSs. Deconvolution of the N peak indicated four types of N atoms [41; 42; 43]: pyridinic N (21 %; 397,90 eV), pyrollic N (17%; 399.0 eV), graphitic N (45%; 400.82 eV), and N-O (17 %, 402.9 eV). These types of N atoms are found in varying ratios in N-doped graphitic carbon materials, and their ratios were determined by the nitrogen source, the carbon source, and the reaction temperature. Figure 5: TGA and derivative curves for the SCSs, HCSs, and NHCSs.
# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
###### Abstract  
By modulating the optical power of the light-emitting diode (LED) in accordance with the electrical source and using a photodetector to convert the corresponding optical variation back into electrical signals, visible light communication (VLC) has been developed to achieve lighting and communications simultaneously, and is now considered one of the promising technologies for handling the continuing increases in data demands, especially indoors, for next generation wireless broadband systems. During the process of electrical-to-optical conversion using LEDs in VLC, however, signal distortion occurs due to LED nonlinearity, resulting in VLC system performance degradation. Artificial neural networks (ANNs) are thought to be capable of achieving universal function approximation, which was the motivation for introducing ANN predistortion to compensate for LED nonlinearity in this paper. Without using additional training sequences, the related parameters in the proposed ANN predistorter can be adaptively updated, using a feedback replica of the original electrical source, to track the LED time-variant characteristics due to temperature variation and aging. Computer simulations and experimental implementation were carried out to evaluate and validate the performance of the proposed ANN predistorter against existing adaptive predistorter schemes, such as the normalized least mean square predistorter and the Chebyshev polynomial predistorter.  
artificial neural networks; digital predistorter; LED nonlinearity; visible light communications