esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### Materials  
Two different materials were employed in this work, both being commercially available 316L grade stainless steel. All samples were the same size: 10 mm \(\times\) 10 mm \(\times\) 1 mm. The first sample was produced by LPBF by Croft Additive Manufacturing Ltd., Warrington, UK, who is an industry supplier of LPBF components for filtration applications. Pre-alloyed 316L powder was used, alongside standard manufacturing settings. The relative density of the LPBF samples was measured as 98% (equivalent to 7.85 g/cc) from optical micrographs of the polished surface. Images were automatically segmented into solid and void using the Otsu method prior to quantification. The other sample was wrought 316L stainless steel sheet supplied by Merseyside Metal Services Ltd., Birkenhead, UK to EN10088 2:2005 1.4404 with surface finish grade 2B. The intention is thus to compare the corrosion performance of industry standard stainless steel. All samples were polished to mirror finish with the following polishing process: First, the samples were ground with silicon carbide abrasive papers of P400 and P1200 grades. Then, they were polished with polycrystalline diamond pastes of 3 \(\upmu\)m and 1 \(\upmu\)m sizes. The polished plane of the LPBF samples was XY. The smooth polishing was carried out with colloidal silica gel dissolution at 50% in volume in distilled water. After each stage, the samples were firstly cleaned with commercial detergent and soaked with fresh water. Distilled water was employed in the next step to rinse the samples. The isopropanol spray and dryer were used in the last cleaning step. All grinding and polishing consumables were provided by Struer Ltd., Glasgow, UK, and the chemical agent was provided by RS Components Ltd., Corby, UK. The polishing process was carried out to avoid the influence of roughness on the corrosion resistance of the samples.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 4 Methodology  
The overall architecture of our method is illustrated in Figure 2. The model for learning passenger travel behavior can be divided into three components: temporal sequence construction, dynamic representation learning, and future behavior prediction. Specifically, given a predicted interaction \(e_{k}\) at time \(t_{k}\), we first fetch the recent historical interaction sequence and capture the temporal features of passenger \(u_{k}\) and station \(s_{k}\) via the first component. The second component learns the dynamic behavior pattern for the passenger and passenger flow trend for the station before time \(t\). Then, a prediction layer is utilized  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meaning** \\ \hline \(u_{i}\) & The \(i^{th}\) passenger \\ \(s_{i}\) & The \(i^{th}\) station \\ \(\mathcal{U}\) & Collections of passengers \\ \(\mathcal{S}\) & Collections of stations \\ \(l_{k}\) & The \(k^{th}\) label, which is \(0\) when the passenger gets on the train and \(1\) when they get off the train \\ \(t_{k}\) & The \(k^{th}\) timestamp \\ \(e_{k}\) & The \(k^{th}\) interaction \\ \(\mathcal{G}^{t}\) & The dynamic graph at time \(t\) \\ \(x_{u_{i}}\) & Feature of the passenger \(u_{i}\) \\ \(x_{s_{i}}\) & Feature of the station \(s_{i}\) \\ \(\hat{p}_{u,s}^{t}\) & The probability of interaction between passenger \(u\) and stations \(s\) at time \(t\) \\ FC & Fully connected layer \\ ReLU & Linear rectification function, a type of activation function in neural networks \\  & A common S-shaped function in biology, it also acts as the activation function for \\  & neural networks, mapping variables between \(0\) and \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations and their meanings.  
to estimate the probability that passenger \(u_{k}\) would pass through station \(s_{k}\) based on their dynamic representations. Finally, we present a time-batch algorithm to enhance the model efficiency by creating batches based on time intervals, ensuring consistency with the passenger flow over time.
## 4 Discussion
The _Pinus sylvestris_ trees from a raised bog located in the Kashubian Lakeland (near the Baltic Sea) reach an age of over 140 years and a TRW equal to 0.87 mm/year. The performed dendroclimatic analyses indicate neither a dominant relationship period nor a weather index that would influence TRW. Based on the variability in the chronology, however, changes in the bog hydrology were reconstructed [54]. The _Pinus sylvestris_ trees growing on three peatlands in Lithuania also cannot be used for climatic reconstructions. They do have, however, potential for the reconstruction of multi-annual-to-decadal hydrological fluctuations. These pine trees are characterized by mean TRW values in the order of 0.48, 0.54, and 0.79 mm/year [59]. Also, TRW values from pine trees growing in four peatlands in Southern Sweden are low, equaling, on average, 0.52 to 1.85 mm/year. Tree ring widths were found to have a relationship with precipitation or with the discharge of a nearby river, thus reconstructing the degree of peatland hydration [60]. A notable application of pine in environmental reconstruction is the study by [PERSON] [61], focusing on a mite in Southern Sweden. Here, trees were used to determine the age of the peatland's origin, to describe the first years of its existence, and to determine the period of climate humidification. Similar questions regarding Lower Saxony (NW Germany) are addressed by [PERSON] et al. [56]. Passive nature protection often fails in raised bogs. Progressive habitat degradation, increased desiccation, and forest succession onto previously unforested land are observed in such areas. The measures that are undertaken the most frequently in order to retain/improve the habitat state include construction of locks, thresholds, dikes, or culverts designed to increase the level of groundwater [3, 8, 11, 62, 63, 64]. The restoration process shown here, involving the construction of ridges, dams, locks, and dikes, filling drainage trenches dug by people, and clearing most of the tree stand, is an example of active nature protection. The aim of all these measures is to retain water within the bog and a resumption/intensification of peat-forming processes. Such drastic steps taken within a nature reserve may cause public outrey, which is why continued educational efforts are being undertaken, involving the construction of a learning trail ending in an observation deck, as well as conducting classes and field lessons. A successful conclusion to such active protective efforts will not only be the restoration of natural peat-forming processes within the reserve but also the acceptance of these efforts in the public eye.
## 4 Qualitative and Quantitative Analyses  
### Qualitative Analysis  
The morphological and regional features of slope units provide a qualitative basis for evaluating segmentation accuracy. The HPAM relies on watershed segmentation principles, involving depression filling and river network extraction. It is imperative to ensure the continuity of the regional hydrographic network for HPAM-based flow direction computations. This necessitates the systematic identification and comprehensive rectification of all topographic depressions during the depression filling phase. During the HPAM process, inverted depressions formed by elevation reversal require artificial filling, which may transform mountain peaks into depressions and generate extensive flat areas. River networks extracted from these plains often exhibit artificial geometric patterns such as parallel lines, right angles, or false channels (Figure 6b). In contrast, the SUD-CGHP method integrates terrain undulation with hydro-mechanical parameters through watershed merging to achieve more physically meaningful segmentation results (Figure 6c). Figure 6a shows the Longyuan Reservoir in Yangloushi Town, which corresponds to the slope value of \(-1\) in Figure 6b,c. Comparative analysis reveals that SUD-CGHP demonstrates superior precision in identifying flat regions (a slope gradient of \(-1\)): it treats entire planar areas as coherent slope units, while the HPAM produces fragmented rectilinear patterns (including intersecting horizontal/vertical lines) that necessitate extensive manual correction.  
Figure 5: Slope unit division diagram.  
The comparison demonstrates that SUD-CGHP achieves three critical advancements through its innovative approach: First, it preserves spatial coherence in flat terrain by employing holistic segmentation, treating planar areas as unified slope units rather than fragmented networks. Second, it eliminates artificial river network artifacts inherent in HPAM, a common issue caused by elevation reversal-induced depressions and rigid flow threshold constraints. Third, it enhances the automation capacity by reducing reliance on manual corrections, particularly in planar regions where HPAM produces intersecting horizontal/vertical lines that require extensive post-processing. These methodological breakthroughs collectively enable SUD-CGHP to deliver more physically realistic and operationally efficient slope unit delineations, holding significant implications for landslide risk assessments in mountainous regions through improved spatial accuracy and processing efficiency.
# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 3 Experimental Results
### Comparison between Existing Studies and the Proposed Model  
In this subsection, we conducted performance comparisons with existing methods. [PERSON] et al. [34] performed various tasks using the PTB-XL ECG dataset based on deep learning. For classification tasks, CNN-based methods and ensemble methods demonstrated superior performance. They obtained classification AUC and F1-score of 93.4% and 82.5% for superclass, 93.0% and 76.6% for subclass, respectively. [PERSON] et al. [35] performed classification for NORMAL, AF, I-AVC, LBBB, and PAC classes in the PTB-XL ECG dataset. The inception-ResNet-v2 model [36] was used, and classification was performed by converting the ECG signal into a 2-dimensional texture image. As a result of classification using 5 classes, a F1-score of 88.62% was obtained. [PERSON] et al. [37] performed classification using 6 public ECG data. Classification was performed on 27 classes using SE-ResNet, and 0.885 was achieved in the challenge score, the evaluation criterion of the PhysioNet/Computing in Cardiology Challenge 2020 [38]. Although there are various ECG classification results, it is difficult to compare directly due to labeling methods and evaluation metrics are different, in addition, there is a difference in the purpose of classification. Nevertheless, our method has been found to be superior in terms of F1-score.
## 3 Methodology
### Loss Function  
In this subsection, the loss function used in this paper will be introduced.  
\[\left\{\begin{aligned} \mathcal{L}&=\lambda_{box} \cdot\mathcal{L}_{box}+\lambda_{cls}\cdot\mathcal{L}_{cls}+\lambda_{obj} \cdot\mathcal{L}_{obj}\\ \mathcal{L}_{box}&=1-\text{IoU}+\frac{\rho^{2}( \mathbf{b},\mathbf{b}^{d})}{c^{2}}+\alpha v\\ \mathcal{L}_{cls}&=-\sum_{i=1}^{\mathbb{C}}[y_{i} \cdot\log(\hat{y}_{i})+(1-y_{i})\cdot\log(1-\hat{y}_{i})]\\ \mathcal{L}_{obj}&=-\sum_{j=1}^{N}\left[t_{j} \cdot\log(p_{j})+(1-t_{j})\cdot\log(1-p_{j})\right]\\ \text{IoU}&=\frac{\text{Area}(\text{Intersection})}{ \text{Area}(\text{Union})}\\ \alpha&=\frac{v}{(1-\text{IoU})+v}\\ v&=\bigg{(}\frac{4}{\pi^{2}}\bigg{)}\bigg{(} \arctan\frac{w^{8}}{h^{8}}-\arctan\frac{w}{h}\bigg{)}^{2}\end{aligned}\right. \tag{2}\]  
In the object detection loss function, multiple symbols work together to optimize the model performance: \(\lambda_{box}\), \(\lambda_{cls}\), and \(\lambda_{obj}\) are the weight coefficients of each part of the loss function, which are used to balance the impact of bounding box regression, classification, and objectness loss. \(\hat{y}_{i}\) is the true class label for the \(i\)th class (which takes the value 0 or 1), and \(p_{j}\) is the probability of the \(j\)th object being an object that the model predicted this class. \(t_{j}\) is an indication of the presence of the JTH target (1 is present, 0 is absent), and \(p_{j}\) is the probability that the model predicts the presence of the target. \(\mathbf{b}\) is the center coordinate of the predicted box, and \(\mathbf{b}^{d}\) is the center coordinate of the true box. \(w^{8}\) and \(h^{8}\) are the width and height of the true box, respectively, while \(w\) and \(h\) are the width and height of the predicted box. Together, they are used to calculate the aspect ratio loss and intersection over union (IoU) metrics, which help the model better locate the boundary of the object.
## 3 Evaluation Criterion
### Multi-Path Error Envelopes  
The multi-path errors is one of the dominant error sources in GNSS. The multi-path error envelopes and average multi-path errors are valuable indexes to evaluate the multi-path mitigation ability. The received base-band signals disturbed by other reflected signals can be expressed as [19]:  
\[r(t)=a_{0}e^{j\
u_{0}}x(t-\tau_{0})+\sum_{n=1}^{N}a_{n}e^{i\
u_{n}}x(t-\tau_{ n}) \tag{12}\]  
where \(a_{0}\), \(\tau_{0}\) and \(\psi_{0}\) are the amplitude, delay and phase of the direct signal. Likewise, \(a_{n}\), \(\tau_{n}\) and \(\psi_{n}\) are the amplitude, delay and phase of reflected signals, and \(N\) denotes the number of reflected signals. If we only consider one reflected path and use a coherent EML discriminator, the discriminator output can be described as follows [20]:  
\[D(\epsilon)=a_{0}\bigg{[}\Re(\epsilon-\frac{d}{2})-\Re(\epsilon+\frac{d}{2}) \bigg{]}+a_{1}\bigg{[}\Re(\epsilon-\Delta\tau-\frac{d}{2})-\Re(\epsilon- \Delta\tau+\frac{d}{2})\bigg{]}\times\cos(\Delta\psi)\equiv 0 \tag{13}\]  
where \(\Delta\tau\) and \(\Delta\psi\) are the delay and carrier phase difference between the multi-path and direct signals with \(\Delta\tau=\tau_{1}-\tau_{0}\) and \(\Delta\psi=\psi_{1}-\psi_{0}\) separately, and \(\epsilon\) denotes the multi-path errors. To explore the theoretical lower bound of the multi-path errors, the cases \(\Delta\psi=0\) and \(\Delta\psi=\pi\) corresponding to the worst multi-path errors are considered. By the definition of the Maclaurin series, the Equation (13) can be simplified as:  
\[D(\epsilon)\sim D(0)+D\,\text{'}(0)\epsilon \tag{14}\]  
According to the Wiener-Khintchine theorem, \(D(0)\) and \(D\,\text{'}(0)\) can be obtained by substituting '0' into Equation (13) and the corresponding first-order derivative, _i.e._,  
\[D(0)=\pm 2a_{\text{i}}\int_{-B/2}^{B/2}G(f)\sin(-2\pi\!f\Delta\tau)\sin(\pi\!f \!d)df \tag{15}\]  
\[D\,\text{'}(0)=4\pi a_{\text{o}}\int_{-B/2}^{B/2}fG(f)\sin(\pi\!f\!d)df\pm 4 \pi a_{\text{i}}\int_{-B/2}^{B/2}fG(f)\cos(-2\pi\!f\Delta\tau)\sin(\pi\!f\!d)df \tag{16}\]  
By combining Equations (14)-(16), the multi-path error envelopes can be estimated eventually by:  
\[\epsilon(\Delta\tau)=\frac{\pm\widetilde{a}\,\int_{-B/2}^{B/2}G(f)\sin(2\pi\!f \!\Delta\tau)\sin(\pi\!f\!d)df}{2\pi\!\int_{-B/2}^{B/2}fG(f)\sin(\pi\!f\!d)[1 \pm\widetilde{a}\cos(2\pi\!f\!\Delta\tau)]df} \tag{17}\]  
with \(\widetilde{a}\) multi-path to direct ratio (MDR) namely \(\widetilde{a}=a_{\text{i}}\,/\,a_{\text{o}}\,\). The corresponding average multi-path errors can be given by:  
\[\epsilon_{\text{\tiny{av}}}(\Delta\tau)=\frac{1}{\Delta\tau}\int_{0}^{\Delta \tau}\frac{\left\|\epsilon_{\text{o}}(\Delta\tau)\right\|+\left\|\epsilon_{ \text{\tiny{x}}}(\Delta\tau)\right\|}{2}d\Delta\tau \tag{18}\]  
where \(\epsilon_{\text{o}}(\Delta\tau)\) and \(\epsilon_{\text{\tiny{x}}}(\Delta\tau)\) are the multi-path errors under the conditions \(\Delta\psi=0\) and \(\Delta\psi=\pi\).
# The CYGNSS Mission: On-Going Science Team Investigations
## 2 Methodology, Results, and Discussions
### Land Surfaces
It was concluded that the extension to a fleet of regional aircrafts will generate an unprecedented GNSS-R scientifically valuable dataset.
# Multiparameter Approach for Damage Propagation Analysis in Fiber-Reinforced Polymer Composites
## 2 Experimental Procedure
### Fuzzy c-Means Data Clustering Results
The symmetrical mode AE signals carry a lower frequency component but have less dispersion in their energy during propagation. The low amount of dispersion means that the R-Frequency can be high for the symmetrical mode. This is because the signal duration in the symmetrical mode of propagation is very low, which leads to the higher R-Frequency. The AE signals generated from matrix cracking and delamination release AE signals, which propagate in symmetric mode. In contrast, the AE signals, which are propagating in asymmetric mode, have a higher frequency and disperse more during propagation. The asymmetric mode AE signals can have a lower R-Frequency compared to the symmetric mode AE signals. These asymmetric AE signals mostly have fiber breakage or interlaminar crack growth as their AE source.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Result Analysis
#### 4.4.3 Correct Detection Using the Improved YOLOv11 Algorithm  
After improving the YOLOv11 model, a noticeable reduction in false positives was observed, as presented in Figure 9. The enhanced model shows a significantly better ability to differentiate visually similar characters. These improvements were achieved through a combination of advanced feature extraction techniques, optimized training procedures, and more effective data augmentation strategies.  
Although the model consistently reports high confidence scores (approximately 0.99 for each class), occasional misclassifications persist, indicating the need for further refinement. Notable enhancements include the integration of label smoothing to mitigate overconfidence, the use of GIoU and DIoU loss functions to improve bounding box localization, and the implementation of refined attention mechanisms (such as C2 PSA) to better  
Figure 8: False detection using the original YOLOv11.  
Figure 9: Correct detection using improved YOLOv11.  
capture subtle visual distinctions. Moreover, addressing class imbalances through dataset balancing and targeted augmentation significantly boosted the model's generalization capabilities, leading to more robust and accurate Bangla character recognition.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### An Overview of the Evolutionary Algorithms
#### 2.2.1 Genetic Algorithm (GA)  
GA is a robust technique based on natural biological evolution which has proven to be an efficient method to optimize satellite constellations [34]. With the principle of \"survival of the fittest\", the GA is initialized with a population of candidate individuals. These individuals are represented by chromosomes that perform selection, crossover, and mutation to form the next generation of the population. In each generation, the fitness of every chromosome is evaluated with the fitness function, and the evolution process terminates when a maximum number of generations or a satisfactory fitness level has been reached. See [35; 36] for details.  
To deploy GA, 30 individuals, i.e., 30 different constellation configurations of 2D-LFC and 3D-LFC, are created randomly as the initial population. Each individual is represented in the form of chromosomes with the same length in the GA process. It is essential that the constellation parameters of the 2D-LFC and 3D-LFC are encoded as chromosomes of a GA represented with a bit string. For the 2D-LFC, the inclination and the semi-major axis are represented with 8 bits, while the number of orbital planes, the number of satellites in each orbital plane, and the phasing parameter are all encapsulated by applying 4 bits. For the 3D-LFC, the inclination and the semi-major axis are also represented with 8 bits, while the eccentricity and the phasing parameters are represented with 4 bits. After the initial population is determined, different operations of GA, such as selection, crossover, and mutation, are carried out to obtain the best constellation configuration. Thus, each individual is evaluated with the fitness function, and two individuals are chosen as the parents producing the offspring with the roulette wheel approach. The fitness function used in this simulation is defined in Section 2.3. The elitist selection strategy is applied to guarantee the solution quality obtained by the GA process. Particularly, the one-point crossover approach and the mutation processes are applied to generate next generations of population. In order to avoid a premature convergence of GA, which leads to the convergence of the solution to a local maximum, the value of the one-point crossover probability and mutation probability is set as 0.85 and 0.2, respectively [26; 34]. The GA process is terminated when the process is iterated for 100 generations. The optimal constellation configuration found is the solution for the GA optimization process.
## 3 Results and Discussion
### Hardened-State Properties of Mortars
#### 3.2.2 Strength Activity Index  
The strength activity indices (SAI) of the mortars, between 7 days and 365 days of curing, are shown in Figure 4b. Although the replacement of OPC with RC or FA in binary mortars, and with RC and FA in ternary mortars, generally leads to a decrease in compressive strength after 7 days of curing in relation to the conventional mortar, all the mortars studied showed SAI values higher than 75%. The latter value is required by the ASTM C618-19 [41] standard for cements containing up to 20% FA or natural pozzolans after 7 days of curing; the exception was the mix that contained 10% of RC and 20% of FA. In this way, the SAI after 7 days of curing (SAI\({}_{\text{7 days}}\)) (i) of the binary mortars produced with RC (5-10%) was between 80% and 83%, (ii) of the binary mortars produced with FA (10-20%) was between 78% and 93%, and (iii) of the ternary mortars, with the simultaneous incorporation of RC and FA, was between 68% (M-5/10) and 94% (M-10/20). These results are in agreement with those of [PERSON] [42], who found values of SAI\({}_{\text{7 days}}\) greater than, or equal to, 89% in mortars containing up to 20% of recycled concrete powder, as well as with the results of [PERSON] et al. [43] who determined that the SAI\({}_{\text{7 days}}\) of ternary mortars that simultaneously incorporate limestone powder (10%) and FA (20%) was also higher than 85%. At 365 days of curing, the highest value of SAI can be observed for the ternary mix with the simultaneous incorporation of 5% of RC and of 10% of FA, reaching a value of SAI\({}_{\text{365 days}}\) = 98%. These results again reveal the positive synergies of jointly using RC and FA, compared to binary mortars that only incorporate RC or FA, in the improvements of the mechanical properties of mortars.