esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Supervised Learning across the Spectrum
## 2 Design Motivation and Advantages of S4  
Our design solves multiple challenges unique to SITS segmentation. First, S4 can significantly reduce the need for labeled training data by exploiting abundant unlabeled images. Second, some image modalities such as optical images are obstructed by clouds, leading to missing or incorrect information (see Figure 3). In fact, around 75% of the Earth's surface is covered by clouds at any given point in time [17; 18; 19]. Through pixel-wise alignment of radar and optical image encoders, S4 effectively leverages the rich information provided by radar in cloudy settings (since radar passes through clouds). As a result, S4 pretrains powerful encoders for both modalities that can reduce the negative impact of cloud cover on model performance. Third, for each modality, the reflectance value of each pixel is different. Because of this, certain Earth surface characteristics may be clearly visible in one modality, but not necessarily as visible in the other. S4 solves this through its cross-modal reconstruction network which is able to infer the presence of vegetation (for example) in one modality based on the patterns learned from the other. Intuitively, this enables the model to learn to understand the signatures or indicators that would correspond to certain features across modalities. Lastly, although satellite images are easily aligned spatially, it is difficult to align them temporally, since different imaging modalities are often on separate satellites [20]. As these separate satellites have different orbital patterns, they do not simultaneously pass over the same location at the exact same instant. S4 resolves the temporal heterogeneity of satellite data through temporal alignment across modalities--first through its coarse-grained preprocessing strategy and second through its fine-grained contrastive learning framework.  
Importantly, S4 delivers _single-modality inference_. Single-modality inference is crucial due to two real-world constraints. First, satellites capturing images of different modalities may be operated by different entities. In fact, 95% of Earth Observation satellites are equipped with only a single sensing modality [21]. Thus, while multimodal training data may be available through aggregating public datasets, such data are almost always not available at inference time [20]. Second, requiring both modalities during inference increases the delay of decision-making in response to critical events (e.g., floods and fires), since multiple modalities can be offset in time by several hours to days (depending on satellite orbits) [22]. Hence, while we leverage multimodal data at training time, we limit ourselves to a single modality inference.
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 5 Performance Evaluation
### Performance Metrics
#### 5.2.1 Average Relative Error (ARE)  
_Average relative error_ measures the accuracy of the weights that are estimated by a graph sketch in the edge query task. Given a query \(q\), the _relative error_\(RE(q)\) is formally defined as follows:  
\[RE(q)=\frac{|\hat{f}(q)-f(q)|}{f(q)} \tag{3}\]  
where \(\hat{f}(q)\) denotes the estimated answer of query \(q\), and \(f(q)\) denotes the ground truth answer of query \(q\).  
Given a set of queries \(\mathcal{Q}=\{q_{1},q_{2},\ldots,q_{n}\}\), the _average relative error (ARE)_ is calculated by averaging the relative errors over all the queries in \(\mathcal{Q}\), and it is formally defined as follows:  
\[ARE(\mathcal{Q})=\frac{\sum_{i=1}^{n}RE(q_{i})}{n} \tag{4}\]
## 1 Introduction
The proposed Infrared Image Edge-Enhanced Segmentation Network (IERNet), detailed in Section 2, integrates an encoder-decoder architecture with an edge enhancement module and multi-level fusion strategies to address ship segmentation and temperature anomaly prediction. The model combines gradient perception and deep feature learning to refine boundaries while predicting both spatial locations and developmental trends of thermal anomalies. Section 3 validates IERNet's performance through comparative experiments on the Pearl River dataset, benchmarking segmentation accuracy and temperature prediction robustness. Finally, Section 4 concludes with experimental insights and discusses future directions, including temporal thermal modeling and lightweight deployment for real-time maritime monitoring.
## 3 Methods
### Accuracy Rectification Strategy  
This method integrates the discrimination error with the generator's segmentation error to further enhance CellGAN's ability to recognize cell boundaries and improve performance metrics. The generator and discriminator compete against each other, improving their respective generation and discrimination capabilities. After iterative training, the network generates high-resolution cell segmentation images with clear boundaries, optimizing the segmentation performance. In the discriminator part of the GAN, when the segmentation loss of the improved UNet continues to decrease, the discriminator finds it challenging to provide adequate feedback information due to the lack of prior knowledge. As shown in Figure 6, this study concatenates the generated image and the original image channels, with the real image pair labeled as 1, and the generated image pair labeled as 0, and inputs the concatenated real/fake image pair into the discriminator for discrimination. This approach avoids early convergence of the discriminator during network training. Including the original image provides an additional standard for the GAN discriminator to evaluate the generated results, allowing the discrimination error to be accurately transmitted to the improved UNet, and optimizing the generated image.  
Figure 5: Two-Dimensional Relative Positional Encoding Strategy.  
This study proposes a novel loss function \(L_{Generator}\), composed of a segmentation loss function \(Generator\) and a discriminator loss function \(Discriminator\), to enhance the segmentation capability of the generator. In designing the segmentation loss, we employ cross-entropy loss to compare the predicted probability of each pixel with its actual label, enabling the calculated loss to more directly reflect the quality of the model's output. The mathematical expression for this is given in Equation (7).  
\[\begin{split}& L_{Generator}(G,D)=Discriminator(-\frac{1}{n} \underset{x}{\sum}\left[y\ln a+(1-y)\ln(1-a)\right])\\ &+\alpha Generator(-\frac{1}{m}\underset{pixel}{\sum}\left[c\log(p_ {i})+(1-c)\log(1-p_{i})\right])\end{split} \tag{7}\]  
where \(\alpha\) is the balancing coefficient between the two, \(n\) is the number of discriminator samples, \(y\) represents the true label input to the discriminator, \(a\) represents the predicted output of the discriminator, \(m\) is the number of pixels, \(c\) represents the class of the pixel in the label, and \(p_{i}\) is the output value of the generator at that pixel. By adding the discrimination loss and segmentation loss with the balance of \(\alpha\), the total loss value of the generator is obtained.
## 5 Experimental Evaluation
Thus, in that  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{3}{c}{**E2E Latency (ms)**} & \multicolumn{3}{c}{**Reliability (\%)**} & \multicolumn{3}{c}{**Communication Range (m)**} & \multicolumn{3}{c}{**Data Rate (Gbps)**} \\ \cline{2-11}  & **Slice 1** & **Slice 2** & **Slice 3** & **Slice 1** & **Slice 2** & **Slice 3** & **Slice 1** & **Slice 2** & **Slice 3** & **Slice 1** & **Slice 2** & **Slice 3** \\ \hline Test 1 & [1, 13] & [3, 14] & [18, 20] & [99.989, 99.999] & [99.986, 99.992] & [99.996, 99.998] & [22, 88] & [6, 34] & [35, 74] & [0.4, 0.8] & [0.8, 1.4] & [0.1, 0.6] \\ Test 2 & [2, 6] & [2, 9] & [5, 11] & [99.991, 99.992] & [99.985, 99.993] & [99.989, 99.995] & [60, 82] & [49, 71] & [43, 75]simulation, fuzzy logic was applied to each slice, inserting the randomly selected inputs into the system and obtaining the crisp output of each. By comparing the crisp values of the outputs of each slice, it was possible to determine which was considered better. Finally, a new simulation is started, or if a hundred simulations have already occurred, another test is started. Our experimental strategy presented here can be integrated with orchestration platforms since the _Multi-Provider Network Slice Selector_ may send a JSON array to OSM or ONAP [51,80].
## 5 Performance Evaluation  
In this section, we employ system-level simulations to assess the performance of MDB services. To do so, we use SimuLTE [34], a simulation framework based on OMNeT++[35], which models both eNBs and UEs endowed with a Network Interface Card (NIC) that implements the data plane of the LTE-A protocol stack, from application- to physical-layer. In particular, it allows one to simulate both P2P and P2 MP D2D communications [36]. For the scope of this paper, we enhanced the simulator with a new application module running at the UE side, which is able to send messages to neighboring UEs, leveraging P2 MP D2D communications provided by the underlying LTE-A NIC. The receiving UE can in turn relay the message using again P2 MP D2D communications. In the following, we consider a first scenario where UEs are static and we evaluate the impact of the different application- and MAC-level mechanisms described in Section 4. Then, we will investigate a vehicular network scenario, where UEs are mobile, as a use-case for MDB.
## 4 Analysis of Nonlinear Tuned Mass Damper Systems Based on Different Conditions
### Analysis of NTMD Control Performance Based on Softening Stiffness Conditions  
The previous section focused on studying and analyzing the nonlinear mass damper under the 1:1 resonance condition. In this section, we investigate the control performance of the nonlinear tuned mass damper under the softened stiffness condition. Figure 13 illustrates the amplitude-frequency response curves related to the frequency ratio of the excitation frequency and the primary structure at \(\sigma\
eq 0\). Upon observing Figure 13, it can be observed that the maximum peak of the controlled structure does not occur at the 1:1 resonance; instead, it shifts away from the 1:1 resonance, resulting in double peaks on both sides. In the case of \(\sigma\
eq 0\), the nonlinear NTMD frequency design formulation corresponding to Equation (39) allows for designing such an offset, aiming to identify optimal design parameters that effectively reduce peak displacement.  
When the softening stiffness nonlinearity coefficient \(\alpha_{2}=-0.005\), three design methods with frequency offsets \(\sigma=-10\), \(\sigma=0\) and \(\sigma=10\) are selected, and the amplitude-frequency response curves are obtained for these three cases, as shown in Figure 14. It can be observed from the figure that the maximum peak amplitude of the primary structure obtained when using an offset for the design is smaller than the peak amplitude at 1:1 resonance. Therefore, it can be concluded that the design based on a small offset yields better optimization results for the NTMD when it exhibits softening stiffness nonlinearity. Furthermore, the difference between the optimization results for \(\sigma=-10\) and \(\sigma=10\) is small, and both are more effective than the design at 1:1 resonance.  
By utilizing Equation (39) and selecting different values of \(\sigma\) and \(\alpha_{2}\), we can obtain the design shown in Table 2 for the softening stiffness. The optimal design frequencies of the tuned mass damper under various conditions, as well as the optimal frequencies calculated through numerical methods, are obtained. From Table 2, it can be concluded that the optimal frequency gradually increases with the absolute value of the softening nonlinearity for different frequency offsets in the table. Moreover, if the excitation frequency is unknown, the results obtained by the improved design method based on the offset demonstrate better performance. The optimal NTMD design frequency achieved for an offset of \(\sigma=-10\) and \(\sigma=10\) is closest to the numerical solution.  
Figure 15 compares the amplitude-frequency response curves of the uncontrolled structure, the NTMD-controlled structure in the case of 1:1 resonance, the NTMD-controlled structure with an offset of \(\sigma=10\) and the optimal NTMD-controlled structure calculated  
Figure 13: Amplitude–frequency response curve of the controlled structure.
## 2 Supervisory Control and Data Acquisition Systems
### Case Studies and Applications  
Several case studies highlight the successful implementation of SCADA systems with advanced visualization in water management:  
* Smart Water Networks: Cities like Singapore and Barcelona have implemented smart water networks that leverage SCADA systems with 3D visualization to optimize water distribution and reduce losses [14].
* Flood Management: In the Netherlands, SCADA and 3D visualization are used to monitor and control floodgates, ensuring timely responses to changing water levels [13].
* Remote Monitoring: In rural areas of Australia, SCADA systems with satellite communication and 3D visualization have been deployed to manage water supply in remote communities [8].  
These examples underscore the potential of advanced SCADA systems to transform water resource management, particularly in regions facing unique challenges such as water scarcity, aging infrastructure, and climate change [11].
# Elastic-Plastic Threshold and Rational Unloading Level of Rocks
## 1 Introduction
### Evolution Analysis of Deformation Parameter
The evolution of the deformation parameter during the complete stress-strain process of siltstone and the curve between the axial pressure increment ratio and the stress level are shown in Figure 5.  
As the axial pressure increases, some irreversible deformation gradually occurs within the rock. Continuous pressure loading leads to rock degradation and an accumulation of damage until failure. From the hydrostatic pressure state to failure, several successive stages are observed including linear-plastic-failure-residual. During these stages, the deformation parameter undergoes the following process: \"rapid strengthening, low-speed strengthening, low-speed weakening, drastic drop, and residual strain.\" This finding is consistent with the conclusion obtained from Figure 5. The nominal Poisson's ratio changes in a U-shaped pattern (first decreasing and then increasing) with increasing axial pressure. Conversely, the nominal elastic modulus exhibits an inverted U-shaped pattern (first increasing and then decreasing) with increasing axial pressure. Therefore, we selected the extreme value of the deformation parameter as the elastic-plastic threshold during loading of rocks. Prior to loading at this threshold, the rock is intact with no detected interior damage. Afterwards, varying degrees of damage and failures have been found within rocks. The nominal elastic modulus inflected (from increasing to decreasing) at the 61.17% stress level, and its axial pressure increment ratio, which is sensitive to axial pressure change, showed a pronounced change at the 60.17% stress level. The nominal Poisson's ratio also inflected (from decreasing to increasing) at the 61.17% stress level, but its axial pressure increment ratio presents no pronounced change at stress levels below 86%. Therefore, both the extreme value of nominal elastic modulus (nominal Poisson's ratio) and the stress levels corresponding to the first pronounced change of its axial pressure increment ratio can be taken as the sign of elastic-plastic transition and damage (or not) of rocks.
## 5 RF Feature Extractors
### Kurtosis  
Kurtosis is a measure of the Gaussian behaviour of a random variable and it is defined as  
\[\mathcal{T}_{kurtosis}(r(n))=\mathbf{E}\Bigg{(}\bigg{(}\frac{r(n)-\mathbf{E}(r (n))}{std(r(n))}\bigg{)}^{4}\Bigg{)} \tag{18}\]  
where \(r(n)\) is the complex sampled signal (sampled at sampling times \(nT_{s}\), with \(T_{s}=1/f_{s}\) being the sampling interval, and \(f_{s}\) the sampling frequency); \(\mathbf{E}(\cdot)\) is the expectation operator, and \(std(\cdot)\) is the standard deviation operator. For Gaussian-distributed sequences \(r(n)\), \(\mathcal{T}_{kurtosis}(r(n))\) is close to level 3. For non-Gaussian distributed sequences, this value is higher or larger than 3. Kurtosis was one of the feature extractors selected in our simulations.  
An example of a histogram for the kurtosis results of genuine GNSS transmitter and spoofer is shown in Figure 11. The magenta line represents the threshold to differentiate the spoofer from a genuine GNSS transmitter. It is typically expected that the received GNSS signals in the pre-correlation domain are Gaussian (see blue histogram from Figure 11), due to the fact that the pre-correlation data are dominated by the thermal noise. In the presence of a strong spoofer, this Gaussian property may be lost, due to the fact that spoofer power might become the dominant one.  
Figure 10: Illustrative example of EVM applied on pre-correlation data, in the absence of channel and receiver effects.
# Impact of Fiber Type on Chloride Ingers in Concrete: A MacroXRF Imaging Analysis
## 1 Introduction
Conventional investigations into chloride ingress in fiber-reinforced concrete frequently employ colorimetric tests or concrete powder extraction at different depths to establish chloride profiles [18; 19; 20]. One of these testing methods is presented in ASTM C1152 [21]. However, these techniques are destructive and require special care, as during powder collection, the entire sample is homogenized, preventing the identification of the matrix phase most influential in chloride ingress [22]. Compared to these methods, X-ray macrofluorescence (MacroXRF) offers a non-destructive approach for the identification and quantification of elements within materials. This method is based on the excitation of electrons by X-rays, resulting in the emission of X-ray fluorescence, which enables elemental analysis. Unlike microfluorescence, MacroXRF uses a larger collimator, on the order of millimeters, allowing for the analysis of larger areas [2]. This technique can be applied to concrete, as demonstrated in previous studies conducted by [2; 3; 23; 24]. [PERSON] et al. [3] investigated concrete with varying fly ash contents, comparing chloride diffusion coefficients obtained from powdered samples and XRF mapping. The study revealed that while powder analysis does not account for the influence of aggregates, XRF allows for material isolation and data manipulation, ensuring greater accuracy. As a result, the chloride penetration coefficients calculated from powdered samples were approximately 30% higher than those obtained through fluorescence analysis. Each chemical element emits characteristic X-rays, allowing for the rapid identification and quantification of elements, including external contaminants such as chlorides on the cementitious matrix through pores and cracks. To better understand the behavior of fiber-reinforced concrete under chloride exposure, this study combines accelerated aging tests with a non-destructive analysis technique: X-ray macrofluorescence. The results aim to enhance the understanding of the microstructure of fiber-reinforced concrete by employing a technique that offers greater precision in detecting and quantifying aggressive elements within the cementitious matrix. By evaluating concretes with polypropylene and glass fibers, this study seeks to explore how these additions affect chloride ingress and, consequently, the material's protective capacity. The findings aim to generate valuable data for improving service life predictions and developing more effective durability strategies.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 3 Multiple Relay UAVs Selection Scheme Based on Fuzzy Optimum Selection
### Multiple Relay UAVs Selection Scheme in Cooperative Surveillance  
Based on the four proposed attributes mentioned above, the membership degree to optimal alternative of multiple UAVs can be calculated by fuzzy optimum selection. As final assessment indicator, relay UAVs are correspondingly chosen and communication links are dynamically adjusted by the relationship among UAVs. Then UAVs are appropriately allocated three different roles to guarantee the performance of cooperative surveillance. It is assumed that just only one UAV switches its role at the same time for simplicity. For balancing surveillance payoff and connectivity maintenance, multiple relay UAVs selection stage is performed according to the relevant relay UAVs number \(N_{R}\) ranging from 0 to \(N_{U}-2\). Subsequently, a role conversion stage is developed to dynamically modify the relay UAVs number with surveillance scope adjustment. The relay UAV alternatives sequence is defined as \(\mathbf{V}_{Seq}\), and the one with the \(n\)th highest membership degree to optimum alternative in \(\mathbf{V}_{Seq}\) is defined as the \(n\)th UAV, which not corresponds to its real ID.
## 4 Error Analysis of the Laser Processing Platform for MCR Frequency Modulation
### Accuracy Verification Experiment of the Laser Processing Platform for MCR Frequency Modulation  
The measured positioning deviation of the frequency test probe is shown in Table 3. The average positioning deviation in X direction is 2.955 \(\upmu\)m, and the average positioning deviation in Y direction is 5.816 \(\upmu\)m, which meets the positioning accuracy requirements of the frequency test probe of the laser processing platform for MCR frequency modulation.  
Experiment on Frequency Modulation Characteristics of the Laser Processing Platform for MCR Frequency for MCR  
The target frequency of the MCR studied in this paper is 32.768 kHz. The difference between the processed MCR frequency and the target frequency is the frequency deviation. According to the actual process requirements, the frequency deviation of MCR after rough adjustment should be controlled between \(-900\) Hz and \(-1550\) Hz. In order to verify the frequency deviation range of MCR, the test wafer is fed to the laser processing platform and  
Figure 12: Prototype of the laser processing platform for MCR frequency modulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multicolumn{4}{c}{**Positioning Error of Laser Processing Platform for MCR Frequency**} \\  & & \multicolumn{2}{c}{**Modulation**} & \\  & & \multicolumn{2}{c}{**(\(\upmu\)m)**} & \\ \hline \multirow{2}{*}{X} & 3.086 & 2.845 & 2.786 & 2.945 & 3.102 \\  & 2.675 & 3.112 & 3.026 & 2.915 & 3.056 \\ \hline \multirow{2}{*}{Y} & 5.687 & 5.845 & 5.898 & 5.769 & 5.895 \\  & 5.865 & 5.756 & 5.954 & 5.675 & 5.812 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Positioning error of the laser processing platform for MCR frequency modulation.  
the laser processing test is carried out with the tuning program. The frequency deviation of the MCRs after processing is shown in Figure 13. The frequency deviation of 96% of the MCR products processed by the prototype is within the allowable range. The laser processing platform can basically meet the needs of coarse frequency adjustment of MCR.  
After the tuning of the prototype of the laser processing platform is completed, in order to verify the requirements of the stability and efficiency of the laser processing platform, the equipment is further tuned, and the frequency modulation performance is tested through trial production. The number of unqualified products caused by poor contact between the frequency test probe and the MCR circuit during the trial production of each wafer is counted. The effective processing rate in the trial production is shown in Figure 14. The average effective processing rate reaches 99.375%, meeting the processing requirements.
# Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces
## 2 The Proposed Method
### Tool Path Planning  
The presented path planning for a multifaceted eyeglass frame consists of three major parts: surface division, path planning for each part surface, and stitching of all part surfaces in one trajectory.