esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 5 Discussion  
The research described here was aimed at developing a warning system for high-temperature threats using deep learning methods. The main element was to carry out an automatic analysis of thermal images based on convolutional neural networks. The warning concept is based on the detection of high-temperature objects and hands or arms located near these objects. Experimental results and comparison with other hand detection methods indicate a high effectiveness of the proposed solution. Optimization of the model structure further reduces the hardware requirements needed to run it on mobile devices. The proposed model achieved an accuracy and sensitivity of 99.5% and 99.7%, respectively (Table 2). By optimizing the network structure, it was possible to reduce the memory demand (over four times less memory) and shorten image analysis time (by 20%)--Table 2--No. 7. The main goal of the proposed method is to quickly detect situations that are dangerous to the user and may cause burns. Therefore, from the point of view of user safety, the number of FN cases (when hands are not detected), which is very low in the examined set--less than 0.2%--is particularly important. The low optical resolution of thermal images (compared to visible light images) is sufficient for hand detection and at the same time allows for quick analysis. Thus, we can conclude that the proposed method will work in practical applications. Further development may involve expanding the set of images to include other cases and situations to make the method even more universal. Taking into account the fact that the system is to be ultimately used on mobile devices, further optimization of the network structure is also justified. In the future, it seems important to use new thermal imaging cameras or integrated devices that will appear on the market and provide greater user comfort.
## 7 Experimental Evaluation
### Results for the Full Request Satisfaction Mode  
#### 7.2.1 V-DPAP Results Analysis for \(n_{p}=2\)  
Figure 10(a) compares the normalized utility obtained by each algorithm. As expected, the utilitarian allocation algorithm (util) returns the best global utility. Such a utility is nevertheless quite low, as it does not reach 0.4 on average. The leximin allocation algorithm (lex) is the second in terms of the normalized utility. Its normalized utility score is slightly lower than that of the util. The approximate leximin (a-lex) algorithm's utility is around 0.3. The fact that a-lex's utility is lower than lex's comes from the fact that a-lex cannot backtrack on its decision on the agent's order in the leximin vector, which is prejudicial in the case of the utility's equality within agents. Greedy allocation (greedy) and path round-robin allocation (p-\(r\)) have almost the same global utility (around 0.2) and finally, node round-robin allocation (n-\(r\)) has a global utility lower than 0.1. In terms of global reward, this corresponds to 1000 for the best algorithm (util) and around 150 for the worst one (n-\(r\)).  
The time required by each algorithm is reported in Figure 10(c). The most time-consuming approaches are a-lex and lex (around 20 s). In fact, they have to call the MILP solver (CPLEX) as many times as the number of agents (here four). Algorithms greedy, n-\(r\), and p-\(r\) are the fastest ones as they return a solution in less than a second. Algorithm util returns a solution in approximately 10 s.  
The top line of Figure 12 displays the average utility profiles involving two orbital planes. The best served agent has a utility very close to 1. Such radars show that the worst served and second worst served agents all have a null utility. This comes from the fact that the corresponding instances are very conflicting. Once two agents receive a path with a utility strictly greater than 0, this prevents the others from satisfying their requests. We can notice that algorithms util and lex have very similar utility profiles on average. Algorithm a-lex does not perform as well for fairness, specifically for the second best served agent. Algorithms n-\(r\), p-\(r\), and greedy serve only one agent.
# Content of Biogenic Amines and Physical Properties of
## 3 Results and Discussion  
### Changes in the \(p\)H Value and the Number of LAB during Mushroom Fermentation  
The use of starter cultures is recommended in the process of lactic fermentation of the fruiting bodies of edible mushrooms. Most studies in this field were based on the use of _L. plantarum_ strains as starter cultures [1]. The probiotic _L. plantarum_ 299v strain and the _L. plantarum_ EK3 isolate were used in the present study. The number of LAB introduced with the starter cultures was 7.08 \(\pm\) 0.03 and 7.01 \(\pm\) 0.05 log CFU/mL in the _L. plantarum_ 299v and _L. plantarum_ EK3 fermentation variants, respectively, in the white mushroom samples, and 7.11 \(\pm\) 0.04 and 7.05 \(\pm\) 0.03 log CFU/mL of _L. plantarum_ 299v and _L. plantarum_ EK3, respectively, in the brown mushroom samples (Figure 1a).  
The highest number of LAB was recorded after the one-week fermentation time, i.e., 7.95 \(\pm\) 0.05 and 8.63 \(\pm\) 0.04 log CFU/mL in the _L. plantarum_ 299v and _L. plantarum_ EK3 variants, respectively, in the white mushroom samples, and 8.14 \(\pm\) 0.05 and 8.53 \(\pm\) 0.04 log CFU/mL, respectively, in the _L. plantarum_ 299v and _L. plantarum_ EK3 variants in the case of the brown mushroom samples. During the refrigerated storage period, the number of LAB systematically decreased and was within the range of 7.10-7.80 log CFU/mL on day 42 of the experiment. It was higher in the samples fermented with the EK3 isolate in both raw materials. As reported by [PERSON] et al. [17], the probiotic 299v LAB population in the fermented white button mushrooms increased rapidly and reached a maximum on day 4 of the experiment, while the EK3 isolate reached the maximum count on day 2.  
The pH value is an important indicator of the fermentation progress. As shown in Figure 1b, the initial pH value in all the experimental combinations was in a similar range of 6.72-6.78. During the first days of fermentation, the pH declined sharply and its value stabilised at a value below 4 (\(\leq\)3.75) on day 7 of the experiment, which evidenced a correct course of the process. Similar values were recorded during the consecutive days of refrigerated storage of the samples. Slightly lower pH values in the final product were recorded in the white and brown button mushrooms fermented with the EK3 isolate (3.58 \(\pm\) 0.05 and 3.53 \(\pm\) 0.03, respectively) than in the 299v strain variant (3.65 \(\pm\) 0.05 and 3.69 \(\pm\) 0.04, respectively). This correlated with the amount of LAB present in the fermented product. Similar pH values were reported in previous studies on _A. bisporus_[15; 16; 17].  
A low pH value indicates the production of large amounts of lactic acid in the lactic fermentation process, which has a considerable effect on the organoleptic characteristics and primarily determines the storability of fermented products.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 6 Proposed Methodology
### Long Short-Term Memory Networks (LSTMs)  
LSTMs are a specific kind of RNN that are especially proficient at taking care of consecutive information and gaining from long-range conditions [62]. This trademark is important in the clinical field, where patient information is in many cases transient, for example, changes in glucose levels, BMI after some time, or varieties in pulse [63]. LSTMs are fit for handling these time-series inputs successfully, catching the movement and worldly elements that might show an expanded gamble of stroke [64]. They are intended to hold data over lengthy success, making them appropriate for displaying advancing ailments and distinguishing patterns that may not be apparent in static datasets [65].  
Because LSTM networks can model long-term dependencies and assess time-series health data, including patterns in blood pressure or heart rate, they are very successful for both detecting and predicting brain strokes [66]. They may choose to store and discard information because of their unique cell structure, which includes forget, input, and output gates as shown in Figure 4. This ensures that important temporal patterns that affect stroke risk are recorded [67]. Sequential input data are processed by LSTMs using memory cells, and the output layer predicts the low, medium, and high-risk levels for stroke [67; 68]. LSTMs provide accurate and context-aware predictions by learning these temporal patterns as sown in Figure 4, which makes them perfect for risk assessment and early detection [68; 69].  
Figure 3: Basic recurrent neural network architecture.
## 3 Results and Discussion
### The Catalytic Suzuki-Miyaura Cross-Coupling Reaction
#### 3.2.2 Effect of Catalyst  
When compound **3** was coupled with compound **4** during fresh catalysis, the activities of various Pa catalysts showed the order PdCl\({}_{2}\)(dtbpf) (averaged yield 86.66%, entries 13-15, Table 1) \(>\)PdCl\({}_{2}\)(dppf) (averaged yield 86.33%, entries 10-12, Table 1) \(>\)Pd(OAc)\({}_{2}\) (averaged yield 42.33%, entries 4-6, Table 1) \(>\)PdCl\({}_{2}\) (averaged yield 38.00%, entries 7-9, Table 1) \(>\)Pd(PPh)\({}_{4}\) (averaged yield 16.33%, entries 1-3, Table 1). Furthermore, the catalytic cross-coupling of compounds **3** with **7** and **10** showed much the same order (Tables 2 and 3). Overall, the divalent Pd complexes (PdCl\({}_{2}\)(dtbpf) and PdCl\({}_{2}\)(dppf)) appeared better than the simple divalent Pd salts, such as Pd(OAc)\({}_{2}\) and PdCl\({}_{2}\), while the activity of the zero-valent Pd precursor (Pd(PPh)\({}_{4}\)) was the lowest.  
It was previously reported that the typical Suzuki-Miyaura catalytic cycle undergoes oxidative addition, transmetallation, and reductive elimination [21; 47]. The catalytically active Pd\({}^{(0)}\) species are first generated in situ, coming from the Pd\({}^{(0)}\) or Pd\({}^{(\rm II)}\) precursor (raw catalyst), and then oxidative addition of aryl halide ArX (electrophile) provides the Pd complex [ArPdXLn]. Next, the addition of [ArPdXLn] with the pre-added base (RO\({}^{-}\) or OH\({}^{-}\)) gives an [ArPdORLn] intermediate, which is quickly reacted with an organoboron compound Ar'B(OH)\({}_{2}\), leading to the diaryl complex [ArPdAr'Ln]. This procedure is summarized as transmetallation. Lastly, reductive elimination of the diaryl complex produces the biaryl product Ar-Ar', along with Pd\({}^{(0)}\) for recycling [21; 47].  
In association with the typical Suzuki-Miyaura cross-coupling mechanism, it can be seen that the Pd(PPh)\({}_{3}\)4, a zero-valent palladium precursor, could not afford sufficient catalytically-active Pd\({}^{(0)}\) species, probably because the PPh\({}_{3}\) ligands were not able to stabilize the [ArPdXLn] intermediate complex very well in the present work [47]. In comparison, employment of OA\({}^{-}\) and Cl\({}^{-}\) as ligands of Pd raw catalysts yielded better outputs (respective entries 4-9 vs. 1-3, Tables 1-3). However, the ferrocene-based ligands derived from PdCl\({}_{2}\)(dppf) and PdCl\({}_{2}\)(dtbpf) had the best effect on the stabilizing [ArPdXLn] intermediate, finally leading to much improved catalytic efficiencies (respective entries 10-15 vs. 1-9, Tables 1-3).
# Flexible DEM Model Development and Parameter Calibration for Rape Stem
## 3 Results and Discussion
### Flexible DEM Model  
#### 3.2.1 Bonding Parameter Box-Behnken Simulation Test  
The result of the bonding parameters' Box-Behnken simulation is shown in Table 7. A quadratic multivariate fitting of the experimental results was carried out by applying Design-Expert 12 software, and an ANOVA and a regression coefficient significance test were carried out for the regression model. The result is shown in Table 8. \begin{table}
\begin{tabular}{c c c c} \hline
**Test No.** & **G/N\(\cdot\)m\({}^{-3}\)** & **H/N\(\cdot\)m\({}^{-3}\)** & **I/mm** & **y\({}_{2}\)/N** \\ \hline
1 & \(5.50\times 10^{9}\) & \(1.00\times 10^{9}\) & 2.0 & 54.80 \\
2 & \(5.50\times 10^{9}\) & \(1.00\times 10^{9}\) & 1.0 & 16.16 \\
3 & \(1.00\times 10^{10}\) & \(1.00\times 10^{8}\) & 1.5 & 35.90 \\
4 & \(1.00\times 10^{9}\) & \(1.00\times 10^{8}\) & 1.5 & 8.72 \\
5 & \(5.50\times 10^{9}\) & \(5.50\times 10^{8}\) & 1.5 & 27.60 \\
6 & \(1.00\times 10^{9}\) & \(5.50\times 10^{8}\) & 1.0 & 4.91 \\
7 & \(5.50\times 10^{9}\) & \(5.50\times 10^{8}\) & 1.5 & 27.90 \\
8 & \(1.00\times 10^{10}\) & \(5.50\times 10^{8}\) & 2.0 & 86.50 \\
9 & \(5.50\times 10^{9}\) & \(1.00\times 10^{8}\) & 2.0 & 47.80 \\
10 & \(1.00\times 10^{10}\) & \(5.50\times 10^{8}\) & 1.0 & 19.26 \\
11 & \(5.50\times 10^{9}\) & \(5.50\times 10^{8}\) & 1.5 & 25.30 \\
12 & \(1.00\times 10^{10}\) & \(1.00\times 10^{9}\) & 1.5 & 41.80 \\
13 & \(5.50\times 10^{9}\) & \(1.00\times 10^{8}\) & 1.0 & 14.51 \\
14 & \(1.00\times 10^{9}\) & \(5.50\times 10^{8}\) & 2.0 & 31.20 \\
15 & \(1.00\times 10^{9}\) & \(1.00\times 10^{9}\) & 1.5 & 12.43 \\ \hline \end{tabular} Note: \(G\) is normal stiffness per unit area, N\(\cdot\)m\({}^{-3}\); \(H\) is shear stiffness per unit area, N\(\cdot\)m\({}^{-3}\); \(I\) is bonded disk radius, mm; \(y_{2}\) is maximum damage force, N.  
\end{table} 
Table 7: Bonding parameter Box–Behnken simulation result. According to Table 8, the regression model's p-value is less than 0.01, which indicates that the model is very significant. The lack of fit F-value is more than 0.5, which implies that the model is not significant relative to the pure error. It indicates that the model can correctly reflect the relationship among \(y_{2}\) and \(G\), \(H\), and \(l\) as well as predict the test results. Among them, \(G\), \(I\), and \(GI\) are highly significant model terms and \(I^{2}\) is a significant model term. After excluding the insignificant factors, the quadratic regression model (actual factor) of the maximum damage force \(y_{2}\) is:  
\[y_{2}=57.64-3.32\times 10^{-9}G-88.40I+4.55\times 10^{-9}GI+34.91I^{2} \tag{13}\]  
The effect of the interaction factors on the maximum damage force is shown in Figure 12. When \(H\) is at the center level (5.50 \(\times\) 10\({}^{8}\)), \(y_{2}\) increases with the increase of \(G\) and \(I\). The response surface curve changes faster along the \(I\) direction, and the effect of the bonded disk radius on the maximum damage force is more significant than that of normal stiffness per unit area.
## 4 Intraoral Molar Distalization Methods
### Tooth Tissue-Supported Intraoral Molar Distalization Methods
#### 4.1.6 First Class Appliance  
The First Class appliance was developed by [PERSON] et al. [32] to address the issue of anchorage loss associated with the Distal Jet appliance. This device comprises two components, palatal and buccal, which are connected by Ni-Ti springs positioned between the premolars and molars. [PERSON] et al. [31] compared the maxillary dentoalveolar changes in patients treated with three distalization force systems (Jones Jig, Distal Jet, and First Class appliances) using digitized models. In conclusion, similar amounts of distalization were promoted, with some degree of undesirable effects. The First Class promoted the smallest rotation of maxillary molars and had the shortest treatment time.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 3 Compute the Reward (\(r_{v+1}\))  
After executing an action, the system received a reward (\(r_{v+1}\)) based on the outcome, as shown in Figure 2.  
The agent is motivated to learn how to choose small cells that maximize network performance and minimize low-quality handovers using this incentive mechanism:  
* Successful HO (\(S_{HO}\)) represents the positive rewards of better QoS: +20 points.
* Failed HO (\(F_{HO}\)) represents the negative rewards of worse performance: \(-10\) points.
* The ping-pong effect (\(P_{HO}\)) represents the negative rewards of unnecessary handover: \(-20\) points.
* Blocked HO (\(B_{HO}\)) represents the positive rewards where the Stay decision is better: +5 points. \[r_{v+1}=-P_{r}+A_{r}\left\{\begin{aligned} +S_{HO}+B_{HO}\\ -F_{HO}-P_{HO}\end{aligned}\right.,\] (2) where \[P_{r}\] represents the penalties.
* \[A_{r}\] represents the adding points.
* \[r_{v+1}\] is the set of possible rewards.  
The reward function reinforces favorable decisions and assigns positive points for success and the prevention of unnecessary HOs, eliminating the need for weight adjustments. The penalties are the inefficient handovers caused by failed and ping-pong effect, and the negative points are assigned to trigger an adjustment of the weights. As a result, this procedure modifies the state to obtain a more favorable outcome in the next handover.
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 3 Challenges and Research Gap
### Research Problem in Transfer Learning  
It is difficult to obtain good performance with a deep learning approach due to the massive number of images required for training. In image recognition and classification, a deep convolutional neural network (DCNN) with many layers can achieve excellent results, sometimes better performance than a human, if an enormous volume of data is obtainable [35; 60; 61]. However, these applications demand large datasets to prevent overfitting and generalize DCNN models properly.  
There is no minimum size for the dataset in training a DCNN, but training with small datasets or using a DCNN with fewer layers prevents the model from being highly accurate because of under or overfitting issues. Models with fewer layers are less accurate because they are unable to use the hierarchical features of large datasets. Collecting labeled datasets is extremely cost-effective in fields such as environmental science and medical imaging etc. [62].  
In particular, in the field of medical image analysis, most crowdsourcing workers do not have the required medical/biological knowledge to accurately annotate medical/biological images. For that, machine-learning researchers frequently depend on the field specialists for labeling these images. Indeed, this is an unproductive and costly process. Thus, producing enough amount of labels to flourish deep networks becomes impracticable.  
Researchers have used various techniques to overcome the lack of training data. One of the most common techniques is data augmentation, where data is created virtually [63]. Although such techniques enhance the data by creating further images, convolutional neural network (CNN) models still struggle with overfitting issues due to repeated images in data augmentation. In recent years, many researchers have employed a TL technique where deep learning models are trained on a large dataset, then fine-tuned to train on a smaller targeted dataset [27]. Although TL improves performance in many CV and pattern recognition tasks [64; 65], it still has a fundamental challenge which is the type of source data used for TL compared to the target dataset. For example, DCNN models trained on the ImageNet dataset [60], which comprises of natural images, are utilized to enhance the performance of the medical image classification task. These images of the ImageNet dataset are quite different from medical images, which would not improve the performance of medical image classification. It has been proven that different domain TL does not significantly affect performance on medical imagingtasks, with lightweight models trained from scratch perform nearly as well as standard ImageNet transferred models [66].
## 2 System Model
### Collision Detection and Alert System  
In addition to clustering, the system continuously analyzes vehicle data to detect potential collision risks. This is achieved by comparing the relative speeds and positions of vehicles within each cluster. If the system determines that two or more vehicles are on a likely collision path, based on their proximity and velocity, it triggers an immediate alert. This alert is shared with all vehicles in the cluster to take preventive measures, such as slowing down or changing lanes.  
Mathematically, the system assesses collision risk based on the predicted future positions of vehicles. For two vehicles \(v_{i}\) and \(v_{j}\), their future positions after time \(t\) are calculated as:  
\[P_{i}(t)=P_{i}(0)+v_{i}\cdot t\] \[P_{j}(t)=P_{j}(0)+v_{j}\cdot t\]  
where \(P_{i}(0)\) and \(P_{j}(0)\) are the current positions of the vehicles, and \(v_{i}\) and \(v_{j}\) are their respective velocities. If the predicted distance between the two vehicles at time \(t\) falls below a predefined safety threshold \(d_{\text{threshold}}\), a collision alert is generated:  
\[\|P_{i}(t)-P_{j}(t)\|<d_{\text{threshold}}\]
## 3 Research Methodology
### Svm  
SVM is to use the kernel function to map the sample data eigenvalues in the low-dimensional space to the corresponding high-dimensional space. And the optimal hyperplane is determined in the high-dimensional space based on the structural risk minimization principle [36]. The expression of the classification hyperplane is shown in Equation (9), the objective function is shown in Equation (10), and the constraints are shown in Equation (11).  
\[f(x)=w\cdot x+b \tag{10}\]  
\[\min\frac{1}{2}\|w\|^{2}+g\sum_{i=1}^{m}\,(\xi_{i}+\xi^{*}) \tag{11}\]\[\text{s.t.}\left\{\begin{array}{l}y_{i}-(w\cdot\varphi(x_{i})+b)\leq\varepsilon+ \xi_{i}\\ \left(w\cdot\varphi(x_{i})+b\right)-y_{i}\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i}\geq 0,\xi_{i}^{*}\geq 0\ (i=1,2,\cdots,m)\end{array}\right. \tag{12}\]  
where \(w\) is the normal vector of the hyperplane, \(x\) is the input vector, \(b\) is the hyperplane translation distance, \(\xi\) is the relaxation factor, \(g\) is the penalty factor, \(m\) is the number of samples, \(\varepsilon\) is the insensitivity coefficient factor, and \(\varphi\) is the mapping function.  
The output function of the SVM can be expressed as  
\[g(x)=\sum_{i=1}^{m}a_{i}K(x_{i},x_{j})+b \tag{13}\]  
where \(a_{i}\) is the Lagrange multiplier, and \(K\) is the kernel function, and the radial basis kernel is chosen as the kernel function in this paper, as shown in Equation (13):  
\[K(x_{i},x_{j})=\exp(\frac{-\left\|x_{i}-x_{j}\right\|^{2}}{2c^{2}}) \tag{14}\]  
where \(c\) is the kernel function parameter.  
For machine learning models, optimizing hyperparameters is decisive for the model performance [37]. A reasonable choice of hyperparameters is beneficial to improve the performance of the model. Studies have shown that the optimization of penalty factor g and kernel function parameter c in SVM is an effective way to improve the accuracy of SVM [38]. To obtain reasonable \(g\) and \(c\) and avoid overfitting of the model. In this study, we choose the method of K-fold Cross Validation (CV) to obtain reasonable parameters, and the pseudo-code of K-fold CV optimize \(g\) and \(c\) is shown in Algorithm 1.  
```
1:begin
2:bestaccurcy; \(c_{\text{best}}\); \(g_{\text{best}}\);
3:for\(c=c_{\text{min}}\)\({}^{2}\):\(c_{\text{max}}\)\({}^{2}\)
4:for\(g=g_{\text{min}}\)\({}^{2}\):\(g_{\text{max}}\)\({}^{2}\)
5: divide the dataset equally into \(K\) groups;
6:for\(=1\):\(K\)
7: divide the dataset equally into \(K\) groups;
8:\(\text{train}(K)\) as the test set, the rest as the training set;
9: record the accuracy of the test set ace(\(K\));
10:end;
11:\(\text{cv}=(\text{ace}(1)+\text{ace}(2)+\cdots+\text{ace}(K))/K\);
12:if\(\text{cv}>\text{bestaccuracy}\)
13:\(\text{bestaccuracy}=\text{cv}\); \(c_{\text{best}}=c\); \(g_{\text{best}}=g\);
14:end;
15:end;
16:end;
17:end;
```  
**Algorithm 1** K-fold Cross Validation