esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 3 Results
### Evaluation  
We trained the full-path prediction architecture using K-fold cross-validation (see Appendix B) with \(k=5\), meaning that in each epoch, the model was trained on 80% of the dataset, while the remaining 20% was used for testing. Each fold underwent training for20 epochs. To evaluate performance, we measured the model's F1 score (see Appendix C) and ROC-AUC score (see Appendix D) for each fold. K-fold cross-validation enhances the robustness of performance evaluation by ensuring that every data point is used for both training and validation. By averaging the results across all folds, this approach mitigates the impact of data variability and reduces the risk of biased performance estimates that could arise from a single train-test split. Additionally, it helps to assess the model's generalizability, as it is trained and evaluated on multiple subsets of the dataset. The results are illustrated in Figure 6.  
The following parameters were set: \(\Phi=1\), \(\Psi=1\), \(\alpha=1\), \(\beta=10^{-3}\), \(\zeta=1\), batch size \(N=64\), and initial learning rate \(l=10^{-3}\). As the loss component tends to be larger than the two others, setting \(\beta\) to reduce its contribution to the loss helps to stabilize training. We used an exponential decay learning rate scheduler with decay rate \(r=0.97\). This scheduler was responsible for reducing the learning rate every epoch (see replication package for details). We set the mask coefficient \(w_{c}=10^{-3}\), and the dropout probability in BMM SAGEConv layers was set to \(=0.2\), meaning that each neuron had an equivalent probability of being \"dropped out\" of the loss computation during training, which helped to avoid overfitting. These hyperparameters were found to be optimal using the grid search strategy [104]. We trained all models on an NVIDIA GeForce GTX 1660 Ti GPU with 6 GB of memory.  
Having retrieved the ROC curves of the four models, we could compute [PERSON]'s J statistic [99], which indicates the optimal threshold with respect to the prediction task. The thresholds obtained are shown in Table 1. Finally, the thresholds were set for each of the models, and we could measure the F1 score (see Appendix C for details).  
For path prediction (\(\mathcal{M}_{1}\left(\Psi=1\right)\)), we obtained a final evaluation F1 score of 0.9308 and ROC-AUC \(=0.9533\), which is superior to the EPSS performance. For start/end node classification (\(\mathcal{M}_{2}\), \(\mathcal{M}_{3}\)), we obtained an F1 score of 0.9780 and ROC-AUC of 0.9037 for start node prediction and F1 score of 0.8214 and ROC-AUC \(=0.8081\) for end node prediction.  
Figure 6: (_left_) Plot showing the ROC curves of the four models. \(\mathcal{M}_{1}\) is the path prediction architecture with (\(\Psi=1\)) and without the (\(\Psi=0\)) \(\mathcal{L}_{\text{pinn}}\) component, and \(\mathcal{M}_{2}\) and \(\mathcal{M}_{3}\) are the node classification models for start and end node classification, respectively; (_right_) the quadrant shows the confusion matrix for the corresponding models.
## 1 Introduction
To this end, joining experiments are performed with a defined setup of reactive foil, solder and surface metallisations for a set of four typical substrate materials for microelectronics, covering an extended range of thermal conductivities from extremely low (i.e., borosilicate glass) to extremely high (i.e., oxygen-free high conductivity Cu; see Table 1). Due to the different thermal properties of the materials, within the joining zone, variations in peak temperatures by several hundred degrees and in melting durations from less than a millisecond to several milliseconds can be expected according to model predictions [11]. The microstructure evolving in the joining zone as a function of the different time-temperature-profiles is studied by use of high-resolution scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS), and computer tomography (CT) for non-destructive analysis of the joint quality. In particular, the interaction of the solder with the reactive foil and the surface metallisation, the ensuing formation of intermetallic phases, and the formation of porosity in the solder layer are scrutinised. Finally, the microstructural features observed for the different component materials as a function of their specific thermal properties, hence time-temperature-profiles, are related to the mechanical performance of the reactive joints upon shear testing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Borosilicate Glass** & **Al2O3** & **Si** & **Cu** \\ \hline thermal conductivity/(Wm\({}^{-1}\)K\({}^{-1}\)) & 1.2 [16] & 26 [17] & 150 [18] & 402 [19] \\ \hline volumetric isobaric heat capacity/(10\({}^{6}\)Jm\({}^{-3}\)K\({}^{-1}\)) & 1.9 [16] & 3.0 [17] & 1.7 [20] & 3.4 [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal conductivities and volumetric heat capacities at room temperature of the different substrate materials used in the present work.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 3 Image Segmentation Algorithm Based on Edge Detection and K-Means Clustering Algorithm  
Images collected by the liquid level acquisition system were \(1024\times 768\) pixels with three RGB channels. The original image offers high resolution, which is helpful for improving accuracy but increases runtime. From the figure above, it is evident the effective information in the green area includes the scale and liquid indicator. As such, the acquired images were segmented to improve algorithm efficiency and extract the useful information. Segmentation, particularly for color images, is a critical component of image processing and analysis [7]. Common algorithms include the threshold, clustering, region growth, and edge detection techniques. For example, K-means clustering has successfully been applied in fruit segmentation, medical image processing, and other fields. [PERSON] et al. used clustering in color space to segment images of Bayberry plants [8]. [PERSON] et al. used a graph cut method based on K-means clustering to subdivide nuclear magnetic resonance images of the brain [9]. Edge detection can also be used to segment images by finding high-contrast boundaries between adjacent pixels. For example, [PERSON] et al. used edge detection and automatic seed region growing to segment target objects [10].  
The target area in the image exhibits obvious linear features. The vertical edges can be divided into four structures: the left edge of the scale, the right edge of the scale, the left edge of the tube, and the right edge of the tube. The K-means clustering algorithm can classify multidimensional data using the shortest distance between subclass information and cluster centers, based on the number of clusters. As such, a hybrid methodology, combining image segmentation based on edge detection and K-means clustering, is proposed in this study for automated liquid level identification.  
Prior to segmentation, a series of image processing steps were performed. This consisted of denoising the original image with a filter, using the Sobel operator to extract edge information in the  
Figure 5: A general block diagram for the liquid level recognition algorithm.  
vertical direction, and applying the Hough straight-line detection model to identify edges. The K-means clustering algorithm was then used to classify the resulting line information, locate the edge of the target area, crop the original image, and achieve precise segmentation of the scale and liquid tube images.
## Materials and Methods
### Atlas-Based FEA Method  
The atlas-based FEA workflow utilized has been described in detail in previous studies [19; 21; 25]. Here, we present a summary of the main points in the workflow. Subject-specific knee joint geometries for FEA models were generated using an atlas-based workflow for all eligible knees using MRI and X-Ray data (N = 2092 MRI-based models + 1117 X-Ray-based models = 3209 FEA models). To scale the existing atlas model based on the MRI image data, the following anatomical dimensions of the knee were measured (Figure 2 (bottom)):  
1. The tibofemoral joint spaces (total cartilage thickness) at the medial compartment (J\({}_{\text{MED-MRI}}\)).
2. The tibofemoral joint spaces (total cartilage thickness) at the lateral compartment (J\({}_{\text{STAT-MRI}}\)).
3. The maximum anterior-posterior dimensions at medial femoral condyles (AP\({}_{\text{MED-MRI}}\)).
4. The maximum anterior-posterior dimensions at lateral femoral condyles (AP\({}_{\text{LAT-MRI}}\)).
5. The medial-lateral condyle distance measured as the distance between the medial and lateral contact area (CD\({}_{\text{MRI}}\)).  
Similarly, the following anatomical dimensions were measured from the X-Ray images (Figure 2 (bottom)):  
1. The tibofemoral joint spaces in the medial compartment (J\({}_{\text{MED-Xray}}\)).
2. The tibofemoral joint spaces in the lateral compartment (J\({}_{\text{STAT-Xray}}\)).
3. The full medial-lateral width of the distal femur (W\({}_{\text{Xray}}\)).  
Based on the measured anatomical dimensions, 3D subject-specific knee FEA geometries for medial and lateral compartments were generated and simulated under gait loading conditions. See the Supplementary Material for a more detailed description of the template scaling and the material model used.
###### Abstract  
The objective of the present study was to determine the effect of mixed plant extracts on chemical (pH, total volatile base nitrogen (TVB-N), trimethylamine nitrogen (TMA), thiobarbituric acid reactive substances (TBARS), biogenic amines, relative fatty acid composition) and microbiological quality indicators of vacuum-packed fish burgers stored at 0 \(\pm\) 2 \({}^{\circ}\)C over 13 days. Three mixtures of common juniper by-product and blackberry leaves extracts (JB), _Padina paonica_ and prickly juniper needles extracts (PCJ), and blackberry leaves extract with catechin and vanillic (BCV) were tested. At the end of storage, TVB-N (15.38-20.03 mg/100 g) and TMA (10.64-15.63 mg/100 g) of burgers with extracts were significantly lower than those of the control group (22.77 mg TVB-N/100 g, 18.37 mg TMA/100 g). The TBARS values in the control burger reached 2.62 \(\pm\) 0.02 mol malondialdehydedehyde (MDA)/100 g, while in burgers with extracts, final values were in the range of 0.62 \(\pm\) 0.01 to 0.80 \(\pm\) 0.02 mol MDA/100 g. The extracts showed no effect on biogenic amine formation (tryptamine, putrescine, and cadaverine levels increased during the storage, being the lowest in BCV) or microbial counts, with the exception of the _Pseudomonas_ sp. counts that were significantly lower in JB and PCJ in comparison to the control, reaching 4.1, 4.1, and 5.0 log CFU/g in JB, PCJ, and control, respectively.  
plant extracts; seaweed; _Padina paonica_; juniper; blackberry leaves; fish quality; antioxidants; antimicrobial agents +
Footnote †: journal: Applied sciences
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology
### LSTM Based Train Dynamic Model: Problem Statement  
To overcome the aforementioned disadvantages of the parametric approach, we propose a data-driven approach to address the issue of train dynamic model construction, where the LSTM network is applied. Besides, by deliberately designing the lagged features and statistical features, we extend the proposed LSTM based algorithm to predict train speed for multi-step ahead. To be more specific, the train dynamic model can be considered as an approach to get the train speed of the next time step. Since LSTM networks have the capacity to model long-term dependencies, our proposed model can obtain the train speeds for next \(n\) time steps.  
Figure 1: Parametric based train dynamic model.  
For clarity, in this subsection, we review the problem of the train dynamic model. Note that the model inputs are exactly the same as the aforementioned parametric approach. Formally, let \(I\) and \(O\) denote the sets of observable features and target outputs, respectively. Similar to Section 3.1, all features (model inputs) can be instantly seized at any time step. \(T_{prev}\) represents the set of previous time steps, which can be written as \(T_{prev}=\{-K_{prev},\cdots,0\}\). For \(x\in I\) and \(k\in T_{prev}\), we express the feature vector \(\mathbf{x}_{k}=(x_{k,1},x_{k,2},\cdots,x_{k,n})\) as the values of features seized at \(|k|\)th previous time step, where \(n\) denotes the number of features. The feature matrix can be formulated as  
\[\mathbf{X}=\left[\begin{array}{c}\mathbf{x}_{-K_{prev}}\\ \mathbf{x}_{-K_{prev-1}}\\ \vdots\\ \mathbf{x}_{0}\end{array}\right]=\left[\begin{array}{cccc}x_{prev,1}&x_{ prev,2}&\cdots&x_{prev,n}\\ x_{prev-1,1}&x_{prev-1,2}&\cdots&x_{prev-1,n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{0,1}&x_{0,2}&\cdots&x_{0,n}\end{array}\right] \tag{4}\]  
where \(x_{i,j}\) means the \(j\)th feature captured at \(i\)th time step.  
In addition, we denote \(T_{post}=\{0,\cdots,Q_{post}\}\). For \(\hat{g}\in O\) and \(q\in T_{post}\), \(\hat{g}_{q}\) is defined as the value of model output at \(q\)th time step in the future. \(\hat{\mathbf{y}}=(\hat{y}_{1},\hat{y}_{2},\cdots,\hat{g}_{q})_{g\in O\
otin T_ {post}}\) is defined as the output vector. Using a deep learning method, a regression function \(h\) is trained, which can be written as  
\[\hat{\mathbf{y}}=h(\mathbf{X};\Theta) \tag{5}\]  
where \(\Theta=\{\theta_{1},\theta_{2},\cdots,\theta_{n}\}\) represents the set of algorithm parameters.  
With the purpose of reducing the gap between the predicted outputs and the actual values, the loss function \(L(\Theta)\) should be minimized  
\[\min_{\Theta}L(\Theta)=\frac{1}{l}\sum_{i=1}^{l}L(h(\mathbf{X};\Theta), \mathbf{y}) \tag{6}\]  
where \(l\) represents the number of samples. \(\mathbf{y}=(y_{1},y_{2},\cdots,y_{q})\) denotes the vector of actual values. In our scenario, according to Figure 1, using feature matrix \(\mathbf{X}\) generated by \(x_{m}\), \(v_{m}\), \(F_{m}\) and \(B_{m}\), the proposed approach is capable of getting the predicted train speeds \(\hat{\mathbf{y}}\).
## 4 Security Threats
### Implementation  
#### 4.4.1 Timeslots Usage Violation  
A large number of approaches found in the literature adopt methods for sharing the available spectrum in the time domain [55, 56]. The procedure followed is more or less the same for most of them, with the very first task to be carried out being that of time division into periods of fixed, or of dynamic length. Afterwards, a cognitive mechanism is assigned the task of optimally allocating these slots to the competing nodes in order not only to satisfy their needs, but also to increase the overall spectrum utilization and maximize social welfare. Each and every node of a DSA network must abide by these allocations and more generally its rules. Unfortunately, nodes may misbehave, causing a number of irregular conditions to arise.  
To make things clearer, a non-conformant node may carry out transmissions during timeslots not allocated to him (or which he is not allowed to use) [57]. Such timeslots may be already assigned to one of his competitors, or be unavailable due to Primary User appearance. Illegally transmitted packets may collide with legal ones, resulting in (unexpectedly) increased packet loss rate (for the latter). QoS degradation would unavoidably be suffered by legitimate nodes, while the profits gained by attackers would increase steeply (since such nodes will gain access to the spectrum during periods they should otherwise refrain from using). Social fairness as well as welfare would also be affected.  
Moreover, a misbehaving node may carry out such an attack in order to lower the value--and consequently the price--of particular timeslots and, especially, those considered highly profitable by his competitors [49]. Attacks against such high-quality transmission opportunities can render them automatically less attractive and, thus, effectively mislead more and more nodes to falsely turn their interest to alternative (more expensive or of lower quality) opportunities. Since the really valuable ones will remain unexploited, or characterized by lower competition, the attacker will be able to gain the right of accessing them much easier and at a much lower price than before, thus increasing his profit.  
The reason why this attack can cause such negative results in a DSA network is twofold. Firstly, there is no technique for alleviating--at least to some extent--the aftermath of such a misbehavior, given that in such strictly scheduled environments there is no need for applying collision avoidance mechanisms (under normal conditions). Then, there are no mechanisms proposed to date for detecting and identifying these simultaneous transmissions carried out by misbehaving nodes (and at least in their simplest form, they are undetectable).
## 3 Modeling and Simulations of the Assembly Error Effect on the Stability Accuracy
### Modeling and Simulation of the Assembly Error Effects on Stability Accuracy  
From Equation (6) and Equation (7), it can be seen how the UAV movements and the system attitude generally affect the stability accuracy. However, considerations of other error-causing sources, including environment disturbances, component noises, or assembly errors, are neglected. Those will deteriorate the overall stability accuracy index once they are coupled to the system movements, which cannot be ignored in practice. In this research, major error sources including assembly errors of axis perpendicularities, run-outs and gyroscope locations are modeled. Those analysis results will provide guidance for future possible improvements.
## 4 Results and Discussion
### Relationships between Landslides and the Related Factors

Table 1: _Cont._The FR values in the study area were higher than 1 for elevations of 941-1193 m and 1193-1416 m. This is due to the fact that the areas of high elevation in the study area are generally covered by vegetation, while the areas of low elevation have a flat topography. Therefore, landslides are frequent in the range of 941-1193 m and 1193-1416 m. The FR values were 1.94 and 1.24 for distances of \(<\)500 m and 500-1000 m to rivers, respectively. When the distance to rivers was greater than 1000, FR value was less than 1. The topographic changes caused by river erosion can affect the initiation of landslides [52]. Regarding the distance to roads, a maximum FR value of 1.37 was achieved when the distance to roads was less than 800 m. Therefore, we can conclude that the FR always decreases with distance from roads. This can be attributed to the destruction of the slope's stability caused by road construction [53]. For slope aspect, the FR value was greater than 1 for five directions: east, southeast, south, southwest and northwest. The smallest FR value of 0.55 was found for north-facing slopes, which indicated that they are less prone to landslides. Regarding SPL the FR values were greater than 1 for the ranges of 1.5-2.5, 2.5-4.5 and \(>\)4.5, and reach a maximum value of 6.15 within the 2.5-4.5 range. For TWI, the FR values were greater than 1 for the categories of \(<\)5 and 5-7. This also means that landslides occur more frequently in these areas. The relationship between NDVI and FR showed that the FR values in the range of \(<\)0.15 and 0.15-0.23 were greater than 1. The minimum FR value was obtained for NDVI values greater than 0.36, with a value of 0.14. This is mainly because vegetation can reinforce the soil by increasing cohesion to some extent [54]. Therefore, areas with strong vegetation are less prone to landslides. For the slope, gentle slopes do not have enough shear stress and gravity to create landslides, whereas steep slopes lack sufficient soil thickness to create landslides, so landslides mostly occur in the range of 3-13, and the FR value in this range was greater than 1. Moreover, for plan curvature, the FR values for the ranges of \(-\)0.7 to \(-\)0.3 and \(-\)0.3 to 0.03 were greater than 1. The FR values of surface cutting depth and terrain relief were greater than 1 in the range of 3.5-9.5 and 7-28, respectively. The FR values of the terrain roughness index were greater than 1 in the range of 1.01-1.11. These findings are in accordance with previous results [55; 56].
# _Oncom_: A Nutritive Functional Fermented Food Made from Food Process Solid Residue
## 6 A Potential Functional Food? Health-Promoting Properties of _Oncom_
### Reproductive Health Benefits of Oncom  
_Oncom_ is known for its rich isoflav content, particularly daidzein and genistein, which are related to reproductive health and menopausal symptoms [103; 104]. Studies on reproductive-age white rats showed that the consumption of black and red _oncom_ prolonged their oestrous cycles, indicating elevated oestrogen levels compared to the control group [25]. Isoflavones, a class of phytoestrogens predominantly found in soy products such as _oncom_, exhibit significant biological activity due to their structural similarity to oestrogen. This similarity allows isoflav to bind to oestrogen receptors (ERs) in the body, particularly ER-alpha and ER-beta, thereby exerting oestrogenic or anti-oestrogenic effects defending on the physiological context [105; 106].  
The mechanism through which isoflav exerts its effects involves several pathways. Firstly, isoflav can act as selective oestrogen receptor modulators (SERMs), meaning they can mimic oestrogen in tissues where oestrogen is beneficial while blocking its effects in tissues where oestrogen may be harmful [105; 107; 108]. For instance, in postmenopausal women, isoflav moves have been shown to alleviate menopausal symptoms such as hot flashes, providing a non-hormonal alternative to hormone replacement therapy [103]. This is particularly important as many women seek alternatives due to the risks associated with traditional hormone therapies [109; 110]. The preference for such alternatives is growing due to the associated risks of traditional hormone therapies [111].  
The metabolism of isoflav in the human body also plays a crucial role in their efficacy. Upon ingestion, isoflaves are metabolized by gut microbiota into more bioactive forms such as equal, which has been shown to have a higher affinity for oestrogen receptorsand greater antioxidant activity than its parent compounds [112; 113]. This conversion is variable among individuals, with some populations exhibiting a higher capacity for equal production, which may influence the health benefits derived from dietary isoflaves [114]. This variability highlights the importance of individual differences in gut microbiota composition in determining the effectiveness of isoflaves.  
The presence of free and residual glutamic acid in _oncom_ plays a significant role in its flavour profile and nutritional value. Glutamic acid, an amino acid, is known for its contribution to the umami taste. This characteristic is indicative of rich amino acid content [23]. These nutrients are crucial for maintaining hormonal balance and supporting reproductive functions in both men and women [115; 116]. [PERSON] et al. demonstrated that amino acids play critical roles in the synthesis of hormones and neurotransmitters, which are essential for reproductive functions [117]. Furthermore, [PERSON] et al. emphasized that dietary sources of amino acids can modulate responses to oestrogen, thereby influencing reproductive health and hormonal regulation [118].