esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Time-Resolved Photoluminescence Measurements
In Table 2, there is also a difference in the radiative rate \(A_{\text{r}}\) between the low and high Er-concentration samples. In Samples B and C, the experimentally deduced \(A_{\text{r}}\) almost agrees with the theoretically estimated values [34; 35] which was obtained by using the Judd-Ofelt parameters \(\Omega_{t}\) (\(t=2\), 4, 6) in (Er\({}_{x}\)Sc\({}_{1-x}\))\({}_{2}\)O\({}_{3}\) transparent ceramics with \(x=0.003\)[36]. On the other hand, a larger \(A_{\text{r}}\) was observed in Sample A, and it suggests the increase in the _effective_ transition dipole moment. The smaller inter-Er\({}^{3+}\) distance achieved in high Er-concentration crystals enhances the Er-Er interaction and may induce a larger coherence volume [37] as one plausible candidate. It can lead to a larger effective dipole moment, and thus, to a larger radiative relaxation rate compared with those in low Er-concentration systems. Figure 5: Time-resolved-PL signals of the Y\({}_{1}^{\prime}-\)Z\({}_{1}^{\prime}\) transition at (a) 6 K and (b) 20 K. Insets are the logarithmic plots of PL intensities in Sample A. The signals of Sample B are shifted down for eye-friendly plots. The double exponential decays with characteristic relaxation times, \(\tau_{\text{fast}}\) and \(\tau_{\text{slow}}\), are clearly observed for all the samples and temperatures. The solid curves are the calculations based on the population dynamics model in Section 3.3. Next, we mention the origin of the fast, temperature-insensitive decay. Since the radiative relaxation process is included in the \(\tau_{\rm slow}\), the \(\tau_{\rm fast}\) is determined only by the non-radiative relaxation process. At this stage, we suppose that the non-radiative energy dissipation from the \(\rm Y_{1}^{\prime}\) level due to the multi-phonon (MP) emission process to the quenching centers is one candidate for the \(\tau_{\rm fast}\) component. The MP relaxation time \(\tau_{\rm MP}\) ordinarily depends on the sample temperature, and it is proportional to the following factor, \(\left[1-\exp(-E_{p}/k_{\rm B}T)\right]^{-m}\) in the single frequency model, where \(E_{\rm p}\) is the phonon energy and \(m\) is the number of phonons, respectively [38]. Since the \(E_{\rm p}\) in \(\rm Sc_{2}O_{3}\) is about 74 meV [18] and the corresponding one in (Er, Sc)\({}_{2}\)O\({}_{3}\) crystal seems to be close with that, the MP relaxation rate for \(m=1\)\(-\)15 is insensitive to the change in temperature below 100 K, and it agrees qualitatively with the behavior of the experimentally obtained \(\tau_{\rm fast}\).
## 4 Proposed Model
### Feature Extraction using KPCA  
Feature extraction is a process in which the most relevant features are extracted from the original dataset. In the literature, many methods have been proposed for feature extraction. In this research, we use KPCA for feature extraction. It extracts useful information from the entire dataset as much as possible, without losing important information. It is a variant of Principal Component Analysis (PCA) with kernel function. It uses a kernel function to project the dataset into a higher-dimensional feature space, where it is linearly separable. To implement the KPCA algorithm, the following steps are involved.  
* The first step is the choice of kernel mapping \(k(x_{m},x_{n})\).
* Based on training data \(\{x_{n},(n=1,\cdots,N)\}\), we get \(K\).
* To get \(\lambda_{i}\) and \(a_{i}\), solve eigenvalue problem of \(K\).
* For each given data point \(x\), obtain its principal components in the feature space: \((f(x)\cdot\phi_{i})=\sum_{n=1}^{N}a_{n}^{(i)}k(x,x_{n})\)
# AIS Data Vulnerability Indicated by a Spoofing Case-Study
## 6 Conclusions
This paper identifies the AIS vulnerabilities that impact maritime security and recommends that the maritime community implement a robust cybersecurity framework, use multiple GNSS constellations and encrypted signals that increase protection against spoofing and other maritime cyber threats. GNSS is not easily vulnerable, but we must be wary of those who say the risks are overstated. Conceptualization, A.A., I.P., J.M. and M.P. ; methodology, A.A. and M.P. ; data collection, A.A., I.P., J.M. and M.P. ; validation, A.A. and M.P. ; formal analysis, A.A., M.P. and I.P. ; investigation, A.A. and M.P. ; data curation, I.P., J.M. and M.P. ; visualization, A.A. and M.P. ; supervision, M.P. ; writing--original draft preparation, A.A., I.P., J.M. and M.P. ; internal review, M.P. All authors have read and agreed to the published version of the manuscript. The publication of the paper is partially financed by the research project (L7-1847; Developing a sustainable model for the growth of the \"green port\") and the research group (P2-0394; Modelling and simulations in traffic and maritime engineering) at the Faculty of Maritime Studies and Transport, financed by the Slovenian National Research Agency. Not applicable. The data analyzed in this study was a reanalysis of existing data from the Mediterranean AIS Regional Exchange System (MARE\(\underline{\square}\)) stored at the Slovenian Maritime Administration. We thank the Italian Coastguard for providing the auxiliary sets AIS, which contain the VHF signal information (VSI) with signal strength (SS) and signal-to-noise ratio (SNR) at the receiving station. We thank Elman Srl and Wartsila Vessel Traffic Services. The authors declare no conflict of interest.
# Bias Effects on g- and s-Factors in Westcott Convention
## 3 Results and Discussions
### Bias on the Reaction Rate by the Joining Function  
In order to estimate the bias on the reaction rate originating in a joining function, Equation (1) is used. The size of the bias on the reaction rate depends on the value of the epithermal index r. Figure 15 shows biases on the reaction rate originating in a joining function in the case of r = 0.1; the value of r depends on the irradiation facility and irradiation position and ranges typically from 0 to 0.2. The ratios \(R({\rm s}_{4}^{W})/R({\rm s}_{2}^{W})\) and \(R\big{(}{\rm s}_{4}^{H})/R({\rm s}_{2}^{W})\) are shown by black bars and red-hatched bars. The results in the cases of neutron temperatures of 300 K, 600 K, and 1000 K are shown in Figure 15a-c, respectively.  
Figure 15: _Cont._  
As shown in Figure 15a, large biases exceeding 10% occur only in the cases of \({}^{226}\)Ra at the neutron temperature of 300 K, for \(R\left(\mathrm{s}_{4}^{H}\right)/R\left(\mathrm{s}_{2}^{W}\right)\). At the neutron temperature of 600 K, the largest deviation from the unity is also observed in \({}^{226}\)Ra, but for \(R\left(\mathrm{s}_{4}^{W}\right)/R\left(\mathrm{s}_{2}^{W}\right)\), that exceeds 20%. In the case of the neutron temperature of 1000 K, the largest deviation from the unity is again observed in \({}^{226}\)Ra for \(\mathrm{s}_{4}^{W}/\mathrm{s}_{2}^{W}\) and it is about 26%.  
The reason that the largest bias is not observed in \({}^{151}\)Eu but in \({}^{226}\)Ra is explained as follows: for the reaction rate, the bias is moderate due to the opposite effect exerted by the g-factor and s-factor on the reaction rate. This is demonstrated for \({}^{151}\)Eu, by comparing Figures 5a and 7a.  
The height of a bump in a joining function at its peak position is about 26-31%, as shown in Figure 1. The peak position of the bump increases as neutron temperature increases. When an energy of a first huge resonance is close to the peak position, a large bias is anticipated in the s-factor.  
In this work, a bias originating in neutron nuclear data is not considered. It should be noted here that there is sometimes an uncertainty in the order of 10% even in well-investigated neutron nuclear data, as demonstrated in recent works on \({}^{241}\)Am [10; 17; 18].
## 4 Experiment and Discussion
### Image Segmentation
#### 4.1.2 Comparison of Image Segmentation Results  
In order to train the neural network with the gathered images, it is imperative to generate suitable labels for the dataset. First, we must select different thresholds for each image to make coarse binary images. Then, the binary images are used as a mask for manually segmenting the veins, removing the veins that are too thin to be injected. The labels can be viewed in Figure 9.  
Then, we place the dataset and the labels in the network to train it and observe the impact of the Gabor kernel on the segmentation results. In this paper, we have conducted experiments using a network with Gabor kernels, Sobel kernels, and no kernels, respectively. The obtained segmentation results are shown in Table 3. It can be observed that embedding Gabor kernels in the shallow convolutional networks significantly improves the segmentation results.  
Figure 8: Comparison of infrared images after Gabor kernel filtering with and without DC components. **(a)** The NIR vein image, **(b)** the image filtered by Gabor, and **(c)** the image filtered by Gabor without a DC component.  
Figure 9: Binary labels after segmentation.  
In order to assess the performance of the improved network, comparative experiments were carried out using VGG16, Res-UNet, Dense-UNet, MobileNetv2 [37], and the proposed approach. Before conducting the experiment, a total of 108 dorsal hand vein images were collected, and, after simple data augmentation, they were divided into 432 training samples, 54 validation samples, and 54 test samples. The model was trained with a batch size of 4 for 200 epochs, and the loss functions for each model are shown in Figure 10. After training completion, the test dataset was fed into the neural network for segmentation. Each network model was trained five times. The average accuracy, size, and time taken for image segmentation by the different networks were recorded, as shown in Table 4.  
From the table above, it can be observed that, whether the IOU value or precision is used as the standard, the proposed method requires less time and takes up a smaller storage space, while preserving the segmentation efficacy. The segmented results are shown in Figure 11. By examining the disparity between the segmented result and the labels, it is discernible that the error mainly exists at the ends of veins or at the connection points of two comparatively slender veins, which do not fall within the designated area for venipuncture.
# Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
## 3 Materials and Methods
### Materials
#### 3.1.2 Data Preprocessing  
In the preprocessing stage, the dataset is prepared to ensure it meets the input requirements for the hybrid model, which combines a Siamese network and the VGG16 architecture. This process is outlined in Algorithm 1 and involves several key steps to handle the image data effectively. The images, consisting of both normal and cataract-infected eye samples, are first loaded from the respective folders for training and testing. Each image is converted to grayscale using the PIL library and then resized to a uniform dimension of 30 \(\times\) 30 pixels to maintain consistency across the dataset. Grayscale conversion reduces computational complexity, allowing the hybrid Siamese-VGG16 model to process a single-channel image input. Following this, the pixel values of the images are normalized by scaling them to the range [0, 1], which is a standard step to enhance the model's training performance and improve convergence. The dataset is then split into training and testing subsets, ensuring an independent evaluation of the model's performance. In the hybrid model, the VGG16 architecture was employed for feature extraction, while the Siamese network architecture was used to measure similarity between image pairs. To prepare the data for this setup, the labels (\"normal\" or \"cataract\") are encoded using LabelEncoder and then converted into one-hot encoded vectors, aligning with the model's classification output layer. Finally, Grad-CAM (Gradient-weighted Class Activation Mapping) was integrated into the framework to provide explainability. This allows the visualization of the regions in the input images that the model focuses on during its prediction, offering insights into the decision-making process of the hybrid network.  
```
Input: Image Data Output: Preprocessed Data dataPreprocessing(folder_path) Load the dataset from specified folder paths (train and test sets) Convert each image to grayscale Resize each image to 30 \(\times\) 30 pixels Normalize the pixel values to the range [0, 1] Append each image to the image list and its corresponding label to the label list Encode the labels using LabelEncoder One-hot encode the labels for the hybrid Siamese-VGG16 model Return the processed dataset (X_train, X_test, y_train, y_test) end dataPreprocessing
```  
**Algorithm 1**Data Preprocessing  
This preprocessing pipeline was established to ensure that the dataset was appropriately formatted for the hybrid Siamese-VGG16 model. The resizing and normalization  
Figure 2: Cataract infected eye images.  
of images, combined with encoded labels, facilitate efficient training. Additionally, the integration of Grad-CAM enhances interpretability by highlighting the image regions that are most influential in the model's predictions.
## 4 Results
### Chlorophyll Content Estimation  
Chlorophyll is a critical compound in leafy greens, playing an essential role in the process of photosynthesis by absorbing light energy and converting it into chemical energy for the plant [46]. It directly influences plant health, development, and vigor while also serving as a key indicator of nutritional quality [47]. Moreover, chlorophyll content correlates with the levels of other important compounds such as nitrogen, making it a valuable metric for assessing plant health and productivity [47].  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline \multirow{3}{*}{**Study**} & **How Clearly the Spectral Imaging Methodology Was Described?** & **How Clearly the Reference Biochemical Methods (e.g., Standard Laboratory Measurements) Were Described?** & **If Validation Procedures (Cross-Validation, Independent Test Sets) Were Clearly Stated?** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Risk of Bias.  
Both hyperspectral imaging systems and spectrometers operate between 305 nm and 2500 nm, with the most common range used in all studies being 400 nm to 1000 nm. Those setups enabled the researchers to capture the detailed spectral signatures associated with chlorophyll content. In spinach, a spectrometer demonstrated significant utility for capturing chlorophyll-related reflectance. Additionally, high-throughput hyperspectral analysis techniques for lettuce utilized advanced spectral configurations, offering comprehensive data for chlorophyll quantification and enabling precise characterization of chlorophyll variations. Tables 3-6, contain comprehensive overview of the studies.  
A variety of machine learning and statistical models were applied to process spectral data and predict chlorophyll content. Techniques included Partial Least Squares Regression (PLSR), Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and advanced methods like Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs). For baby leaf lettuce (Table 4), models such as PLSR and PCR achieved high accuracy, with R-values of 0.9099 and 0.9094, respectively, and normalized RMSE values as low as 0.4147. CARS-PLSR was notably effective for spinach, achieving an R\({}^{2}\) of 0.89 and an RMSE of 0.10. Similarly, automated machine learning techniques (e.g., XGBoost version not specified [30]) used with hyperspectral data demonstrated superior performance for lettuce, yielding an R\({}^{2}\) of 0.93.
# Maritime Supply Chain Optimization by Using Fuzzy Goal Programming
## 3 Proposed Model  
### Definition and Formulation of Problem  
In this study, the optimization of the maritime supply chain for containerized special cargoes is studied as a theoretical perspective. Three different special cargoes coming from three different (feeder ports) suppliers to the hub-port, are shipped to the five different destination ports for six months. These cargoes are primarily stored in certain warehouses of the hub-port. These cargoes are then containerized in a standard form. This standard form is predetermined in line with the needs of the destination ports. Fuzzy goal programming model is set for the solution of this problem. A model is established to minimize the returns of customers with the total cost of maritime supply chain and container losses and losses in the network. Demands and objective functions are set as fuzzy numbers.
## 3 RCU-Style Non-Blocking Synchronous Algorithms
### Read Copy Update (RCU)
#### 3.1.1 Procedure ofa Node Update  
We begin by introducing the RCU procedure with an example of a node update in a singly linked list. The operating procedure is discussed using the example of Figure 1a.  
1. Thread R1 enters the critical section with negligible synchronization overhead, traverses the linked list, and reads node \(B\) ( in Figure 1a).
2. Thread W creates a copy of node B, node \(B^{\prime}\), and updates the value to change node \(B\) (). Thread R1 is able to read concurrently without interfering with the node modification operation of thread W.
3. Thread W updates the next node pointer of node A to node \(B^{\prime}\) (). At this stage, threads that read the original and the copied nodes are split.
4. Thread R2 reads a new node \(B^{\prime}\) via node \(A\) by traversing the nodes ().
5. Thread W defers memory release until R1 has completed reading node \(B\). Thread W releases node \(B\) and completes the update process once it is confirmed that no other thread is accessing node \(B\).
# Monitoring of Recovery Process at Yeongildae Beach, South Korea, Using a Video System
## 4 Discussion
Because Hwajin Beach is located on the open coast outside Yeongil Bay, its wave force is much greater than that observed at Yeongildae Beach despite, the difference in water depth. As a result, the sediments in this site could respond more easily at lower wave energy levels, which might lead to a different pattern of erosion and recovery process. For example, the sand size in the study site ranged from 0.15 to 0.35 mm, as it is categorized as fine to medium sand, which was smaller than the sand size measured in the two beaches located  
Figure 11: Comparison of the observed wave power between the two beaches of (**a**) Yeongildae Beach (this study site) and (**b**) Hwajin Beach, located in the open coast outside Yeongil Bay. Although the distance between the two beaches is only ~20 km, the wave power in Hwajin Beach is much greater than that in Yeongildae Beach. in the open coast outside the bay, where it was \(\sim\)0.5 mm at both beaches (categorized as medium to coarse sand) [30,31]. The criteria for erosion/accretion suggested in this study can hardly be determined in other beaches on the open coast, where nearshore crescentic sandbars are actively developed. Because the horns of the sandbars are coupled with the shoreline pattern [32], the erosion/accretion of the shoreline has a strong locality rather than showing the general trend as observed in this study site, where no crescentic sandbars developed. For this reason, the information on the locality from these study results should be also confined to this specific site.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Results
#### 4.4.2 Comparative Analysis 2  
This case is based on the comparison of the propagation models with constant routing protocol, interfacing protocol, and varying simulation time. The realistic scenario is taken with 71 vehicles communicating by sending BSM. Figure 6 shows that the Nakagami fading model has the worst performance of all. FRIIS and the Two-Ray Ground model show similar performance. The Log-Distance model shows minimum delay and maximum packet delivery ratio depicted in Figure 6a,b. MAC/PHY overhead is highest in the case of the Nakagami model and lowest for FRIIS and Two-Ray Ground, as shown in Figure 6e. FRIIS and Two-Ray Ground show high throughput, which drastically increases after a specific time and shows better average routing goodput compared to the other two models as shown in Figure 6c,d, respectively. Finally, to sum up, the Log-Distance outperforms other models in terms of end-to-end delay and packet delivery ratio. Still, FRIIS and Two-Ray perform better for throughput and average routing goodput as simulation time increases.  
Figure 4: VANETs Traffic Scenario. (**a**) Map of Rourkela by OSM; (**b**) Simulation in SUMO; (**c**) NetAnim Representation.
## 3 Methodology
### Preparation of Training and Testing Datasets  
The total working dataset comprised 6212 sample points, which included both stable and unstable locations (Figure 3). A set of 3106 unstable sites was obtained from pixels within each of the 257 landslides, with an average of 12 to 13 points sampled per instability area. Additionally, a set of 3106 random samples was generated in areas where seismic-induced landslides did not occur with the aim of balancing the dataset. This entire process was carried out using ArcGIS with a pixel size of 5 m. The total working dataset was subsequently split into a training dataset and a testing dataset to train and verify the models, respectively. A random split is performed by assigning 70% of the data to the training dataset and the remaining 30% to the testing dataset.
### Simulation Test #3
The average error in a time window composed by \(n_{k}\) time steps is  
\[\mu_{i}^{\eta}=\frac{1}{n_{k}}\sum_{k=1}^{n_{k}}e_{i}^{\eta}(k) \tag{52}\]  
The mean absolute error \(MAE_{i}^{\eta}\) made by the \(i\)-th UAV is defined as follows:  
\[MAE_{i}^{\eta}=\frac{1}{n_{k}}\sum_{k=1}^{n_{k}}e_{i}^{\eta}(k) \tag{53}\]  
Similarly, it is possible to define the standard deviation \(\sigma_{i}^{\eta}\) with respect to the average error as follows:  
\[\sigma_{i}^{\eta}=\sqrt{\frac{1}{(n_{k}-1)}\sum_{k=1}^{n_{k}}\left(e_{i}^{\eta }(k)-\mu_{i}^{\eta}\right)^{2}} \tag{54}\]  
Table 7 shows mean absolute error and standard deviation computed by UAVs for any component \(\eta\) of the target position vector. Figures 12 and 13 show the uncertain target volume detected by the proposed algorithm with respect to the centralized KF and the real target position. \begin{table}
\begin{tabular}{c c c} \hline
**Performance Index** & **Proposed HCMCI Model** & **Centralized Model** \\ \hline \(\sigma_{1}^{X_{t}}\) (m) & 0.6250 & 0.6251 \\ \(\sigma_{1}^{X_{t}}\) (m) & 0.5972 & 0.6223 \\ \(\sigma_{1}^{X_{t}}\) (m) & 1.0847 & 0.7911 \\ \(MAE_{1}^{X_{t}}\) (m) & 0.2013 & 0.1515 \\ \(MAE_{1}^{\eta}\) (m) & 0.2839 & 0.1586 \\ \(MAE_{1}^{\eta}\) (m) & 0.0438 & 0.1851 \\ \hline \end{tabular}
\end{table} 
Table 7: Simulation Test #3—Results comparison in terms of standard deviation and Mean Absolute Error. Figure 11: Simulation Test #3—Comparison of the estimates of the target position. \(Z_{t}\) represents the real \(Z\)-coordinate of the target, while \(\hat{Z}_{t}^{1}\) and \(\hat{Z}_{t}^{con}\) are, respectively, the estimate made by \(UAV_{1}\) that uses the HCMCI algorithm, and that of the centralized scheme.