esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 2 Related Work  
### Fcn  
As a CNN variant, FCN is widely used for the pixel-level classification. The output of FCN is a category matrix with the semantic information of the image. The core ideas of the FCN model are summarized: (1) converting a fully connected layer to a convolution layer with the kernel size \(1\times 1\); (2) using a deconvolution or upsampling operation to generate a category matrix with the same size as the input image. By this means, the popular FCN-8s, FCN-16s, and FCN-32s are defined in [4], and the generation procedure of them is described in Figure 1.  
Figure 1: Generation of the fully convolutional network (FCN)-8s, FCN-16s and FCN-32s.  
The 1 st row of Figure 1 shows that the feature maps are derived from the input image by repeated convolution calculation and pooling operation. The convolution calculation is used to extract features which cannot change the resolution of the image or feature maps. Each pooling operation can reduce the resolution of the feature map by two times. Therefore, the resolution of the \(n\)th (\(n\) = 1, 2,..., 5) feature map is \(1/2^{n}\) of the input image. Based on these feature maps, the semantic segmentation result, with the same resolution of the input image, can be obtained by using upsampling and multiscale fusion techniques. The upsampling is introduced to increase the resolution of feature map. Multiscale fusion is used to acquire finer details from the feature maps of different scale. Three different combinations of upsampling and multiscale fusion are utilized to implement FCN-8s, FCN-16s, and FCN-32s. As shown in Figure 1, the feature maps used to generate the semantic segmentation results are from pool3, pool4 and conv7, which are 1/8, 1/16, and 1/32 resolution of the input image, separately. The detailed description is as follows:  
* _FCN-32s_ The conv7 feature map is upsampled 32 times directly to obtain the semantic segmentation result.
* _FCN-16s_ The conv7 feature map is upsampled 2 times firstly. Then the two-time-upsampled conv7 feature map and pool4 feature map are fused by using the multiscale fusion technique. Finally, the fusion result is upsampled 16 times to obtain the semantic segmentation result.
* _FCN-8s_ The conv7 feature map is upsampled four times firstly. Then, the pool4 feature map is upsampled two times. Next the four-time-upsampled conv7 feature map, two-time-upsampled pool4 feature map, and pool3 feature map are fused by using the multiscale fusion technique. Finally, the fusion result is upsampled eight times to obtain the semantic segmentation result.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 5 Discussion  
### Effectiveness of the Enhanced ResNet Architecture  
The proposed enhancements to the ResNet backbone--namely multiscale residual blocks and channel attention modules--played a vital role in improving forest carbon stock estimation, particularly in complex mountainous terrains. The multiscale convolutions captured spatial features across varying receptive fields, which is essential for representing canopy heterogeneity and topographic complexity [66]. Meanwhile, the incorporation of Squeeze-and-Excitation (SE) modules enabled the model to learn adaptive spectral weighting, emphasizing informative bands such as the red-edge and shortwave infrared (SWIR), which are known to be sensitive to biomass, leaf area index, and water content [71, 72]. These architectural improvements led to more accurate and spatially coherent predictions compared to standard ResNet and traditional baseline models. The model's demonstrated reliance on red-edge and SWIR bands further validates the structural sensitivity of Sentinel-2 imagery, which plays a vital role in forest heterogeneity modeling.  
It is worth noting that although the RMSE values across models are relatively close (ranging from 23 to 30 Mg/ha), the visual differences in spatial predictions are substantial. This is because RMSE is a global, aggregate metric that captures average predictive accuracy, but it lacks sensitivity to spatial consistency, edge preservation, and fine-scale variation. In contrast, deep learning models such as the Enhanced ResNet not only minimize overall error but also better preserve spatial structure, transitions between vegetation zones, and topographic gradients. These qualities are especially valuable in ecological applications where spatial coherence is critical for decision making and mapping accuracy.  
Compared to traditional machine learning models such as Random Forest and GBDT, which rely heavily on handcrafted features and lack spatial awareness, the enhanced ResNet provides a unified end-to-end solution capable of capturing spatially heterogeneous patterns and nonlinear spectral dependencies. This advantage is especially pronounced in complex mountainous terrain, where conventional models struggle with fragmented predictions [62]. Additionally, in contrast to Transformer-based architectures like ViT, which often require large datasets and exhibit rigid patch encoding, our approach offers a more flexible and lightweight alternative while achieving superior accuracy and spatial coherence [69].
# Enhancing Reliability Analysis with Multisource Data:
## 2 Proposed Method to Mitigate Adverse Selection in Bridge Monitoring and MRR  
The proposed WIM and NBI data driven reliability analysis aims to evaluate probabilities of failure for a large number of bridges with the latest traffic or live load data. Any occurrences where a reliability index is less than the threshold reliability index (2.0)warrant further investigation. Bridge capacities are expected to decrease over time and should reflect changes in conditions. Live loads on the other hand are not expected to significantly change. However, within a 75-year bridge service life, it is possible for bridges to experience heavier-than-expected live loads. For example, rapid changes in autonomous vehicle technologies may change live load patterns. Additionally, advances in data analytics and sensing technologies are expected to provide more information about live loads more rapidly. Therefore, it is possible to synthesize two datasets and perform a reliability analysis of a large number of bridges while more accurately characterizing live load demands (i.e., vehicle weights) collected by WIM sensors and capacities obtained from the NBI's operating load rating (LR) data. LR involves a reliability analysis of bridges to assess failure modes such as girder shear and moment, as well as an evaluation of uncertainties associated with material properties. Such process is tedious and thus is not ideal for assessing a reliability of bridges for a state's inventory. Furthermore, the maximum load carrying capacities are available as a result of a routine LR process. However, LR engineers, who are often contractors to state agencies, tend to be conservative in load-rating because bridges are only becoming old and are associated with public safety and liability. Thus, in the proposed approach, we allow a small amount of uncertainty that may exist in the load rating (or estimating the bridge capacity) process and use field collected live load data to significantly reduce uncertainties in a reliability analysis. Such a tradeoff is expected to assist state agencies to minimize bridge postings which restrict the movement of goods or mobility.
## 2 Preliminaries
### High Degree F-Transform
For r = 1 we have P\({}^{1}_{h}=(x-x_{h})\) and the h-th component of the F\({}^{1}\)-transform is given by the formula:  
\[F^{1}_{h}(x)=c_{h,0}+c_{h,1}\left(x-x_{h}\right)=F^{0}_{h}(x)+c_{h,1}\left(x- x_{h}\right) \tag{12}\]  
If the function \(f\) is known in a set of N data points p\({}_{1}\),... p\({}_{N}\), c\({}_{h,0}\) and c\({}_{h,1}\) can be discretized in the form:  
\[c_{h,0}=\frac{\sum_{i=1}^{n}f(p_{i})A_{h}(p_{i})}{\sum_{i=1}^{n}A_{h}(p_{i})} \tag{13}\]  
\[c_{k,1}=\frac{\sum_{i=1}^{n}f(p_{i})(p_{i}-x_{h})A_{h}(p_{i})}{\sum_{i=1}^{n}A _{h}(p_{i})(p_{i}-x_{h})^{2}} \tag{14}\]  
Likewise, let L\({}_{2}\) ([x\({}_{k1-1}\), x\({}_{k1+1}\)] \(\times\) [x\({}_{k2-1}\), x\({}_{k2+1}\)] \(\times\)... \(\times\) [x\({}_{kn-1}\), x\({}_{kn+1}\)]) be the Hilbert space of square- integrable n-variables functions \(f\): [x\({}_{k1-1}\), x\({}_{k1+1}\)] \(\times\) [x\({}_{k2-1}\), x\({}_{k2+1}\)] \(\times\)... \(\times\) [x\({}_{kn-1}\), x\({}_{kn+1}\)] \(\rightarrow\) R with the weighted inner product:  
\[f_{s}g_{h_{1}h_{2}\cdots h_{n}}=\int\limits_{x_{h_{1}-1}}^{x_{h_{1}+1}}\int \limits_{x_{h_{2}-1}}^{x_{h_{2}+1}}\cdots\int\limits_{x_{h_{n}-1}}^{x_{h_{1}+1}} f(x_{1},x_{2},\cdots,x_{n})g(x_{1},x_{2},\cdots,x_{n})A_{h_{1}}(x_{1})A_{h_{2}}(x_{2}) \cdots A_{h_{n}}(x_{n})dx_{1}\cdots dx_{n} \tag{15}\]  
Two function \(f,g\in L_{2}([x_{h1-1}\), x\({}_{h1+1}\)] \(\times\) [x\({}_{h2-1}\), x\({}_{h2+1}\)] \(\times\)... \(\times\) [x\({}_{kn-1}\), x\({}_{kn+1}\)]) are orthogonal if \(\langle f,g\rangle_{h_{1}h_{2}\cdots h_{n}}=0\). Let \(f\): X \(\subseteq\) R\({}  
are sorted in ascending order to create the Scree Plot; then, the elbow point is set; the features corresponding to the eigenvalues under the elbow point on the y-axis will be deleted. Output of the pre-processing phase is the new reduced dataset in the transformed coordinates. The F1-transform classifier follows the strategy adopted in the MFC algorithm [14]. Initially the classification accuracy threshold is set to \(\theta\) and coarse-grained fuzzy partitions are created (n = 3). After creating the fuzzy partitions of the domains of each feature the algorithm checks that the data are sufficiently dense with respect to the fuzzy partitions; if they are, then the direct and inverse F1-transforms are computed, otherwise, the algorithm terminates because the cardinality of the fuzzy partitions is too fine compared to the density of data points in the feature space and the direct F1-transform cannot be computed. The Calculate CA index component measures the accuracy of the classification. To classify a data point we adopt the following method applied in [9]. Let C be the number of classes and let \(l_{1}\), \(l_{2}\),..., \(l_{\text{C}}\) be the labels of the C classes. Let \(\text{P}_{\text{j}}\) = (\(\text{P}_{\text{j1}}\), \(\text{P}_{\text{j2}}\),..., \(\text{P}_{\text{jn}}\), \(\text{m}_{\text{j}}\)) the _jth_ data point, where \(\text{m}_{\text{j}}\) is the position of the corresponding class.
# Virtual Reality in Space Technology Education
###### Abstract
In several studies, other researchers used VR to simulate the solar system [31; 32; 33]. [PERSON] et al. examine the moon and Mars through observations, interpretations and comparisons. Geological and atmospheric processes are recognized and discussed, as well as astronomy phenomena, and students discover that the same fundamental physical laws apply to all solar system objects [31]. [PERSON] et al. offered a new interactive 3D model of the solar system in VR [32]. In a virtual world, learners can experience the physical world. The created virtual world continues to function as the normal world while the viewer zooms in or out and changes his/her viewpoint. In addition to allowing learners to travel in space, such an education tool creates a unique user experience [33]. No studies were found regarding the use of VR in teaching satellite types, satellite subsystems and the satellite assembly and integration process. This research applies virtual reality technology to space technology education by simulating a space lab to assemble and integrate a satellite subsystem (CubeSat). The virtual space lab is designed to help the users gain a basic understanding about the satellite's subsystems and components in a simplified and attractive way. This research aims to define the main concepts of space segments, satellite subsystems and components, assembly and integration of satellites, launching the satellite, controlling the satellite from the ground station and the resulting remote sensing images from the satellite. All those concepts are illustrated in a simplified way that depends on using virtual reality technology as a modern teaching method based on dazzling and creating suspense for the player using a simulation of the space labs. In this work, gamification was used in learning to improve training outcomes. Gamification is a method for applying game mechanics, elements and principles to non-game contexts to increase user engagement. Therefore, the target users are learners or students.
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 2 Materials and Methods
### Retinal Layer Segmentation and Thickness Computation  
A graph G = (V,E) was constructed for each SD-OCT image. Each pixel within the image was represented by a node V [8; 20]. An edge E defined the connection between a pair of nodes (V\({}_{\mathrm{i}}\), V\({}_{\mathrm{j}}\)). Each edge was assigned a weight W\({}_{\mathrm{ij}}\) based on the normalized vertical image gradients (g\({}_{\mathrm{i}}\) and g\({}_{\mathrm{j}}\)) for the pair of nodes [8; 21]. The vertical gradient adjacency matrices were computed based on the transition from bright to dark layers or dark to bright layers and normalized to values between 0 and 1. These gradient values were determined using [1; \(-\)1] and [\(-\)1; 1] edge maps [8]. Finally, the vertical image gradients were used in the computation of the edge weights within the image graphs. The edge weight W\({}_{\mathrm{ij}}\) was calculated as follows:  
\[W_{ij}\ =\ 2-\left(g_{i}+g_{j}\right)+W_{min} \tag{3}\]  
where W\({}_{\mathrm{min}}\) is a small positive constant value representing the hypothetical minimum weight within the graph.  
A sparse adjacency matrix was generated from the assigned edge weights. The images were converted to grayscale to simplify computation to a single color channel and emphasizing the differences between pixel intensities, thus aiding in the adjacency matrix computation.  
Potential graph cuts within the ROI were defined by edges E\({}_{\mathrm{ij}}\) between a pair of nodes. The lowest-weighted graph cut min (E\({}_{\mathrm{ij}}\)) was used to detect and trace a layer boundary between the nodes (V\({}_{\mathrm{i}}\), V\({}_{\mathrm{j}}\)), maximizing the similarity between all nodes along the boundary [2; 8; 15; 21; 22; 23; 24; 25]. This defined a graph cut that was used to delineate each of the retinal layers within the OCT images.  
Boundary point indices (BPIs) were determined for each delineated boundary. The range of pixels that fell between two boundaries constituted to an intra-retinal layer. Layer thicknesses were computed from the mean difference between the corresponding BPIs of two consecutive boundaries along the same vertical image gradient [8; 26; 27; 28; 29; 30]. Partial results from the pilot study by the authors of this study were previously discussed at a conference [12].  
Figure 3 schematically represents the methodological workflow used to develop the segmentation algorithm. The flowchart details the steps from image preprocessing and denoising to graph construction, boundary detection, and layer thickness computation, culminating in the segmented image with computed layer thicknesses.
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
## 1 Introduction
In fact, our solution leverages the Trusted Computing principles to achieve a level of trust in the behavior of the synchronization distribution network. It is capable to ensure the integrity of the software and configuration of all the nodes and, thus, to avoid the exchange of incorrect synchronization information over secure protocols. For a more complete view on prior works about SW vulnerabilities in other domains, interested readers can also refer to [29; 30; 31] and the references therein. In this sense, the contribution of the paper is twofold:  
* We present our proposed implementation based on Trusted Computing and capable of protecting the integrity of the nodes of a synchronization distribution network. * Next, we propose and describe a new testbed, suitable to emulate the identified attacks and to demonstrate the effectiveness of the proposed solution. After this introduction, the paper is organized as follows. Section 2 provides an overview of the vulnerabilities and solutions related to the GNSS RF spectrum, while Section 3 covers the network attack vector. After that, Section 4 discusses the Trusted Computing paradigm, as a suitable solution to protect the SW integrity of the nodes. Then, Section 5 describes in detail our implementation and Section 6 presents and discusses the obtained results. Finally, Section 7 concludes the paper with the main remarks.
## 2 Materials and Methods
### Analysis of Handover Decision Factors
#### 2.2.1 Remaining Service Time  
When designing the satellite handover algorithm for the power system, it is crucial to fully consider the service time of the satellite. The service time provided by the satellite to grid users is related to the number of service switches. A longer service time of the candidate satellite results in fewer switches for power grid user terminals engaged in communication. The service time of the satellite is closely related to the reliability and stability of the power system. It is crucial for real-time monitoring, control, and other key functions. Frequent handovers may result in long service interruptions, causing loss or delay of information, thereby affecting the system's real-time monitoring and control capability. Therefore, the service time of the satellite is selected as a handover factor. When the grid user is ready to handover to another satellite, the user sends position information to the ground station, which then returns the matrix \(T_{serv}\):  
\[T_{serv}=\begin{bmatrix}t_{1}^{s}t_{2}^{s}t_{1}^{s}\dots t_{N}^{s}\\ t_{1}^{s}t_{2}^{s}t_{1}^{s}\dots t_{N}^{s}\end{bmatrix} \tag{2}\]  
Figure 4: Satellite handover directed graph.  
Figure 3: Satellite service cycle overlap map.  
\(T_{serv}\) is a \(2\times N\) dimensional matrix, where \(t_{i}^{s}\) in the first row denotes the start service time of satellite j to TAs, and \(t_{i}^{e}\) in the second row denotes the end service time of satellite j to TAs (\(1\leq i\leq N\); N is the number of LEO satellites in the satellite network). Therefore, the remaining service time of satellite i to user at moment t can be expressed as  
\[T_{i}(t)=t_{i}^{e}-t \tag{3}\]
## 4 Results and Discussion
### Water Table Drawdown of Underground Powerhouse  
Figure 10 plots the water head distributions of an underground powerhouse of four numerical cases in various working conditions. Overall, the underground caverns significantly change the water head distribution of the study site. The seepage-free surface intersects precisely on the boundaries of the main and auxiliary powerhouse and tailrace gate chamber, while the groundwater surrounding the main transformed cavern is entirely drained. After the lower reservoir impoundment, the seepage-free surface experiences a noticeable uplift. Instead, the seepage control structures lead to an increase in water table drawdown and depression cone.  
Figure 11 shows a comparison between various methods and monitoring data of water table drawdown in Case 1. It can be seen that the water table drawdown is inversely proportional to the distance from the underground powerhouse. The numerical results are approximate to the proposed analytical solutions because of the similar assumptions, but there is quite difference (i.e., the minimum value 82.88%) correlating with the monitoring data. This phenomenon may be caused by the heterogeneity and anisotropy of geological structures and constructive interference. Future work needs to address the gap between research and practice.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Water Inflow into Underground Powerhouse Q (m3/d/m)**} & \multicolumn{3}{c}{**Water Inflow into Drainage Structures (m3/d/m)**} & \multirow{2}{*}{**Drawdown**} \\ \cline{2-2} \cline{5-8}  & **Main and** & **Main** & **Taircase** & **Total** & **Drainage** & **Drainage** & **Total** & **\(s\) (m)** \\ \cline{2-2} \cline{5-8}  & **Auxiliary Powerhouse** & **Transformed Cavern** & **Gate Chamber** & **Total** & **Drainage** & **Drainage** & **Total** & **\(s\) (m)** \\ \hline Case 1 & 753.15 & 0 & 474.70 & 1227.85 & None & None & None & 231.8 \\ Case 2 & 60.48 & 0 & 43.99 & 104.47 & 875.55 & 1035.68 & 1911.23 & 234.3 \\ Case 3 & 820.47 & 0 & 607.69 & 1428.16 & None & None & None & 231.8 \\ Case 4 & 830.23 & 0 & 689.56 & 1519.79 & None & None & None & 231.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Numerical results of four synthetic cases in various working conditions.  
Figure 9: Water inflow rates of underground powerhouse calculated by various methods [3, 8, 9, 10, 31, 33, 34].  
Figure 10: Water head distributions of underground powerhouse in various working conditions: (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, and (**d**) Case 4.
## 4 Framework Components
### Artificial Intelligence  
IBM tries to define AI, stating that it \"is a field, which combines computer science and robust datasets, to enable problem-solving.\" [29]. AI involves the application of algorithms to make predictions and classify risks, enhancing decision making in real-time systems.  
Though artificial intelligence, machine learning, and deep learning are used interchangeably, they are not the same. Figure 4 illustrates the subtle difference succinctly.  
* AI is a field of study or discipline of computer science that consists of processes that help to develop machines that can perform tasks typically requiring human intelligence. For example, machines with AI can learn, reason, perceive, and process natural languages and solve problems as humans do.
* ML  
ML is a subset of AI and consists of algorithms that allow computers to learn from, make predictions, and classify or group information based on data. ML models are trained on labeled (known) data to make predictions or classify unlabeled (previously not known) data to identify patterns to classify the data.
* DL  
Deep learning is a subset of ML, but its algorithms analyze data with a layered structure like how a human would draw conclusions. The design of these algorithms or artificial neural networks (ANNs) is inspired by the biological neural network of the human brain. ANNs are capable of learning that extends far beyond that of standard machine learning models. ANNs automatically learn feature representations from raw data, making DL especially useful for unstructured data like images, video, and text.  
[PERSON] et al. [30] implemented various ML algorithms to predict water adulteration in milk using portable near-infrared spectroscopy data. The authors of this paper discussed a practical and efficient solution for detecting the presence of water adulterants in milk leveraging ML algorithms like Random Forests, K-Nearest Neighbors, and support vector machines.  
[PERSON] et al. [31] reviewed several machine learning techniques to detect bacterial pathogens using Raman spectroscopy data and Surface-Enhanced Raman Spectroscopy (SERS). The authors explored several case studies examining how Raman SERS data and ML methods can enable rapid point-of-care diagnostics and precision-guided food safety.  
Figure 4: AI vs. ML vs. DL.  
In the proposed framework context, AI is a key component that suggests potential food recall scenarios and actions to initiate based on the defined constraints/parameters/thresholds. Once ML/DL models process the incoming data, the AI-driven application further applies the intelligence as humans do and makes decisions that are most relevant to the circumstances.