esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### SLatCoW
Nevertheless, the LT reduces in the general case to  
\[P(\mathbf{s})=L_{\mathrm{s}}^{2}\sum_{m\in\mathcal{M}}\bar{p}_{m}e^{(\mathrm{i }K_{x}^{m}-s_{x})L_{\mathrm{s}}/2}\frac{\sinh\left(\left(\mathrm{i}K_{x}^{m}- s_{x}\right)L_{\mathrm{s}}/2\right)}{\left(\mathrm{i}K_{x}^{m}-s_{x}\right)L_{ \mathrm{s}}/2}e^{\left(\mathrm{i}K_{y}^{m}\right)L_{\mathrm{s}}/2}\frac{\sinh \left(\left(\mathrm{i}K_{y}^{m}\right)L_{\mathrm{s}}/2\right)}{\left(\mathrm{ i}K_{y}^{m}\right)L_{\mathrm{s}}/2}\.\]  
A symmetric structure with respect to \(y=L_{\mathrm{s}}/2\) excited by a plane wave along the \(x\)-axis ensures that \(K_{y}^{m}=0\), \(\forall m\in\mathcal{M}\)[21]. Therefore, the two dimensional LT reduces to  
\[P(s_{x},s_{y}=0)=L_{\mathrm{s}}^{2}\sum_{m\in\mathcal{M}}\bar{p}_{m}e^{( \mathrm{i}K_{x}^{m}-s_{x})L_{\mathrm{s}}/2}\frac{\sinh\left(\left(\mathrm{i}K_ {x}^{m}-s_{x}\right)L_{\mathrm{s}}/2\right)}{\left(\mathrm{i}K_{x}^{m}-s_{x} \right)L_{\mathrm{s}}/2}. \tag{3}\]  
Please note that the symmetry is ensured in the case depicted in Figure 1 along both directions \(\Gamma X\) and \(\Gamma M\), while it is only ensured along the \(\Gamma X\) direction in the case depicted in Figure 1. Along the \(\Gamma M\) direction, the symmetry condition is not satisfied because of the rotation of the quarter-wavelength resonator (QWR) openings. Nevertheless, this will be considered and proven as a side effect in what follows. Note also that Laplace transforming along the \(y\)-direction for \(s_{y}=0\) consists in averaging the pressure field measured along this axis or alternatively Fourier transforming the field with \(k_{y}=0\). The problem becoming also independent from \(y\), we drop the \(x\) dependence on both \(\mathbf{s}\) and \(\mathbf{K}^{m}\), so as \(s_{x}=s=s_{r}+\mathrm{i}s_{i}\) and \(K_{x}^{m}=K^{m}=k_{r}^{m}+\mathrm{i}k_{i}^{m}\). The spatial LT of the field therefore reduces to a form close to the one used in Ref. [11]. The recovery of \(K^{m}\) is performed in a very similar way for each frequency by minimizing the following cost function  
\[ \begin{split} F(\left|\bar{p}^{m}\right|,\phi^{m},k_{r}^{m},k_{i}^ {m},\mathcal{M})=\left\{\sum_{s_{r}}\sum_{s_{i}}\left\|P^{mes}(s)-L_{\mathrm{s} }^{2}\sum_{m\in\mathcal{M}}\left|\bar{p}^{m}\right|\exp\left(\mathrm{i}\phi^{m }\right)\right.\times\\ \left.\exp\left(\left(\mathrm{i}(k_{r}^{m}+\mathrm{i}k_{i}^{m})-s_ {i}-\mathrm{i}\mathrm{s}_{r}\right)L/2\right)\frac{\sinh\left(\left(\mathrm{i} (k_{r}^{m}+\mathrm{i}k_{i}^{m})-s_{i}-\mathrm{i}\mathrm{s}_{r}\right)L/2 \right)}{\left(\mathrm{i}(k_{r}^{m}+\mathrm{i}k_{i}^{m})-s_{i}-\mathrm{i} \mathrm{s}_{r}\right)L/2}\right\}^{1/2},\end{split}  \tag{4}\]  
where \(P^{mes}(s)=P^{mes}(s_{x},s_{y}=0)\) is the spatial LT of the measured field \(p(\mathbf{x})\), and \(\left|\bar{p}^{m}\right|\) and \(\phi^{m}\) are, respectively, the theoretical amplitude and phase of the \(m\)-th mode. The minimization is performed under constraints with the Nelder-Mead simplex algorithm (\(\mathrm{\SIUnitSymbol{R}}\) Matlab function _fminsearchbnd_).
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 3 Proposed Method
### Object Detector  
We noticed that the performance of detection-based tracking is proportional to the performance of the detector. If the object is not detected, it is excluded from consideration, which is highly likely to result in incorrect ID assignment. Thus, we adopted a single-stage detector YOLOv3 model for the detector of the DBT system, which is capable of high accuracy and high speed, to locate the objects in each frame of an image.
## 3 Experimental Measurement Conditions and Methodology
The methodology that is followed in this work is based on the use of the Sonin plot [1; 2; 4; 26; 28], which uses the positive ion current per unit length collected by the probe, \(I_{+}\), when it is biased at a fixed electric potential, \(V_{p}\) in \(k_{B}T_{e}/e\) units, \(e\) being the elementary charge and \(k_{B}\) the Boltzmann constant, referring to the plasma potential, \(V_{plasma}\)[43]. In the Sonin plot, the ordinate of the plot is the non-dimensional ion current,  
\[y_{Sonin}=I^{\prime}(y_{SP})=\frac{I_{+}(y_{SP})}{er_{p}n_{+}}\sqrt{\frac{m_{ +}}{2\pi k_{B}T_{e}}}, \tag{5}\]  
where \(r_{p}\) is the probe radius and \(y_{SP}\) is defined as the non-dimensional probe potential \(y_{SP}=-eV_{p}/k_{B}T_{e}\). The abscissa of the plot has the following expression:  
\[x_{Sonin}={x_{p}}^{2}I^{\prime}(y_{SP})=\frac{I_{+}(y_{SP})er_{p}}{\varepsilon _{0}}\sqrt{\frac{m_{+}}{2\pi k_{B}3T_{e}^{3}}}, \tag{6}\]  
which does not depend on the ion density, with \(x_{p}\) being the non-dimensional probe radius \(x_{p}=r_{p}/\Lambda_{D}\). The \(V_{p}\) value must be chosen carefully, so that it is negative enough to ensure that the current collected by the probe is almost exclusively positive ion current, and the electron current can be neglected. Regarding the other extreme, if the difference between the plasma potential and the probe potential is too high, the emission of secondary electrons from the probe would be accounted for as an increase in the positive ion current collected by the Langmuir probe [1; 2; 4; 5; 26; 28]. As in other articles, we have chosen \(y_{\rm{SP}}=-eV_{p}/k_{B}T_{e}=25\)[1; 2; 4; 26; 28], which accounts for \(V_{p}\) values in the range \(V_{p}(\rm{V})\in[2.15,9.48]\) for the given range of electron temperatures \(T_{e}(\rm{K})\in[1000,4400]\). A single point in the Sonin plot is obtained for each set of plasma conditions--that is, for each set of experimental values for \(I_{+}\big{(}V_{p}\big{)}\), \(n_{e}\) and \(T_{e}\)[1; 4; 5; 26; 28]. The experimental Sonin plot point is placed in the Sonin plot and its position relative to the theoretical orbital and radial curves is analyzed. The plasma conditions are studied in relation to the position of the experimental Sonin plot points.
## 3 Experimental Results
### Quantitative Evaluation
#### 3.4.1 Metrics  
For pixels \(p_{ij}\) in a binary image, \(p_{ij}=1\) means \(p_{ij}\) belongs to the positive class, and \(p_{i,j}=0\) means \(p_{ij}\) belongs to a negative class. Given a ground truth image \(G\) and an image, we predict \(P\). The IOU metric can be defined as follows:  
\[IOU=\frac{|GR\cap PR|}{|GR|+|PR|-|GR\cap PR|}, \tag{8}\]  
where \(GR\) and \(PR\) represent the set of positive pixels in image \(G\) and \(P\), \(|GR|\) and \(|PR|\) represent the number of pixels in \(GR\) and \(PR\). The IOU measures the similarity between a ground truth image and a prediction image by calculating the number of pixels in the intersection set divided by the number of pixels in union set excluding the intersection region.  
We denote \(TP\), \(FP\), \(TN\), \(FN\) as true positive pixels, false positive pixels, number of true negative pixels and false negative pixels, respectively. The Pixel Accuracy (PA) is defined as follows:  
\[PA=\frac{TP}{|P|}. \tag{9}\]  
The PA measures the percentage of true positive pixels from all pixels in an image, providing an overall assessment of prediction.  
Figure 11: Comparison of a segmented tongue image from TongueNet (lowercase) and REF (uppercase).  
Besides the PA, the Precision metric is utilized to measure the accuracy of positive pixels predicted in \(P\), which is defined as follows:  
\[Precision=\frac{TP}{|TP+FP|}. \tag{10}\]  
The Recall metric is utilized to measure the degree to which all the positive pixels are predicted correctly:  
\[Recall=\frac{TP}{|TP+TN|}. \tag{11}\]  
Based on Precision and Recall, the F-score is calculated, to measure the performance comprehensively:  
\[F-score=2\cdot\frac{precision\cdot recall}{precision\cdot recall}=\frac{2 TP}{2 TP+FN+FP}. \tag{12}\]
## 3 Results
### Analysis of Factors Affecting SOM Content
#### 3.3.4 Difference of SOM Content across Different Land Use Patterns  
Land use patterns are an important anthropogenic factor that influence the variability of SOM. Soil properties will change with the changing of land use patterns. The main land use patterns in this study area are forest land, grassland, dry land, paddy field and unused land. As shown in Figure 5, SOM content could be ranked as follows: grassland > forest land > paddy field > dry land > unused land. The maximum (21.71 g\(\cdot\)kg\({}^{-1}\)) was found in grassland, with a possible reason being the high litter yield and low decomposition on the surface of grassland. The minimum (8.38 g\(\cdot\)kg\({}^{-1}\)) was found in unused land, with the former being 2.6 times higher than the latter. As shown in Table 6, SOM has different coefficients under different land use patterns. The variation coefficient of grassland reached 97.29%, which is 1.7 times higher than that of dry land and falls under the strong variability type, while all other land use patterns had moderate degrees of variation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **N** & **Min/(g\(\cdot\)kg\({}^{-1}\))** & **Max/(g\(\cdot\)kg\({}^{-1}\))** & **Mean/(g\(\cdot\)kg\({}^{-1}\))** & **CV/(\%)** \\ \hline Forest land & 13 & 1.20 & 32.42 & 16.63 & 65.11 \\ Grassland & 11 & 2.40 & 74.3 & 21.71 & 97.29 \\ Dry land & 203 & 2.40 & 74.90 & 9.78 & 57.23 \\ Paddy field & 11 & 6.81 & 33.66 & 15.68 & 60.39 \\ Unused land & 19 & 2.30 & 25.74 & 8.38 & 68.55 \\ \hline \end{tabular}
\end{table}
Table 6: SOM content across different land use patterns.  
Figure 5: Difference in SOM content across different land use patterns. Notes: The letters a, b, and c correspond to the same meanings as in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **N** & **Min/(g\(\cdot\)kg\({}^{-1}\))** & **Max/(g\(\cdot\)kg\({}^{-1}\))** & **Mean/(g\(\cdot\)kg\({}^{-1}\))** & **CV/(\%)** \\ \hline Brown soil & 40 & 4.89 & 30.40 & 10.03 & 44.67 \\ Cinnamon soil & 63 & 2.40 & 30.80 & 11.90 & 39.58 \\ Lime connection black soil & 16 & 7.70 & 73.30 & 15.94 & 100.31 \\ Alluvial soil & 78 & 2.40 & 74.90 & 9.88 & 81.38 \\ Saline soil & 18 & 2.30 & 20.30 & 7.38 & 62.33 \\ Paddy soil & 10 & 6.81 & 33.70 & 15.77 & 63.28 \\ Rocky soil & 5 & 4.80 & 17.60 & 11.09 & 48.33 \\ Aeolian sandy soil & 5 & 1.20 & 17.00 & 6.43 & 95.02 \\ Skeleton soil & 16 & 3.50 & 32.40 & 10.79 & 80.07 \\ Red clay soil & 3 & 7.62 & 9.40 & 8.81 & 11.69 \\ Fluvent soil & 3 & 4.30 & 11.10 & 6.67 & 56.82 \\ \hline \end{tabular}
\end{table}
Table 5: Analysis of SOM content in different soil types.
## 7 Security Analysis
### Security Consideration  
The UEs using the proposed D2D security protocols will be able to authenticate each other, share data and delegate their access rights via security tokens in network- and non-network-assisted D2D communications scenarios. The participating entities use ECC, whereby solving the Elliptic Curve Discrete Logarithm Problem (ECDLP) is not feasible. Hence, attackers cannot break the proposed protocols' cryptography, Key Derivation Function (KDF) and find the secret key if a big size of the binary elliptic curve is used as it requires very high computational time. The larger is the curve size, the better is the security level of a protocol. In addition, the use of randomness, hash function, tokens, delegation, ID and certificate digital signature together with Third Generation Partnership Project (3 GPP)'s 5G security standard recommendations harden the security of the protocols.  
The formal analysis proves that the DDSec and DDACap protocols can address 5G-enabled D2D communications security issues as proposed by providing entity authentication, authorization, decentralization, anonymity, scalability, fault tolerance, user permission delegation, secrecy, data authenticity and integrity during D2D data sharing.
## 3 Materials and Methods
### The Considered Performance Metrics  
In a binary classification problem, we are interested in classifying items belonging to a _positive_ class (P) versus a _negative_ one (N). Therefore, there are four basic combinations of actual data category and assigned output category, namely:  
* _True Positive_ (TP): correct positive assignments;
* _True Negative_ (TN): correct negative assignments;
* _False Positive_ (FP): incorrect positive assignments; and
* _False Negative_ (FN): incorrect negative assignments.  
The set of these four metrics is usually arranged in a bi-dimensional matrix layout, called Confusion Matrix (CM), which allows a simple visualization of the performance of a binary classification algorithm. Specifically, each column of the CM represents the instances in a predicted class, while each row represents the instances in an actual class. Moreover, the combination of the previous four numbers in some powerful indicators can be a valid tool to quantitatively measure the performance of a classification algorithm [110]. Among all the possible combinations, in this paper, we focus on the accuracy, precision, recall, and F-measure metrics, whose formal definitions are briefly reported in Table 3. Accuracy is the ratio between the correct identified instances among their total number. Precision is the ratio of relevant instances among the retrieved instances, while the Recall is the ratio of the total amount of relevant instances that were actually retrieved. Finally, precision and recall can be combined in a single measurement, called F-measure, that is mathematically defined as their harmonic mean.  
The state-of-the-art approaches have also been evaluated by the area under the Receiver Operating Characteristic (ROC) curve, abbreviated as AUC. The closer the AUC is to one, the better is the classifier performance. The ROC curve, which is a graphical representation of the performance of a binary classifier, is obtained by plotting the TP rate on the \(y\)-axis against the FP rate on the \(x\)-axis for increasing values of the decision threshold. The TP rate (formally coincident with the recall metric) and the FP rate are, respectively, the ratio of the number of TP to the total positive examples, and the ratio of the number of FP to the total negative examples, respectively. Their definition can be found in the last two rows of Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Computed Tomography**} & \multicolumn{2}{c}{**X-rays**} \\ \cline{2-5}
**Type** & **Target** & **Test** & **Target** & **Test** \\ \hline COVID & 3500 & 500 & 84 & 100 \\ Non-COVID & 3500 & 500 & 580 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of target and test images for each considered class and dataset.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Melt Pool Defect Modeling
#### 3.1.2 Thermo-Mechanical Simulations of LPBF Defects Using FVM  
The FVM can perform similar simulations to FEM, being capable of modeling the continuous powder bed melt pool hydrodynamics and heat transfer behaviors. The FVM can also capture the interdependent melting behavior of each powder grain and its many adjacent grains, as well as the formation of irregularly shaped melt pool shapes and pore defects caused by partially melted powder grains. This is typically accomplished by coupling discrete element method (DEM) and computational fluid dynamics (CFD) codes. Most melt pool simulations using the FVM utilize partially coupled DEM-CFD approaches. Table 2 lists the thermo-mechanical FVM analyses of the melt pool behavior and defect generation. A 3D model using OpenFOAM was developed to model the formation of SS316L defects during the LPBF process [55]. The results indicate that the melt track is driven by surface tension. The porosities are gas bubbles typically observed with high levels of beam energy or a low beam velocity. The wetting behavior fundamentally determines the development of irregularly shaped porosities. The investigated high-power intensity was enough to melt the particle but not the build plate. The wetting behavior is suppressed, resulting in interlayer defects. The roughness of the pre-solidified metal of a given layer affects the following layer's local thickness. Irregular-shaped porosities are due to reduced beam energy and increased scanning velocity.
## 2 Materials and Methods
### Web-Based Platform
#### 2.2.1 Web-Platform Functionality (Use Cases)  
In order to develop a comprehensive and functional web platform, a thorough analysis of use cases was conducted. This involved identifying the key actors and the actions they would perform. To enhance the process, interviews were conducted with stevia farmers, allowing for valuable insights into their needs and experiences with similar technologies.  
The identified actors within the system are as follows: the stevia farmer (user), the administrator, the central nodes, and the backend itself. The user has the ability to authenticate and access the platform's information. They can conveniently review the latest measurements through a spatial map, which displays data node by node. Additionally, the user can visualize historical data using a line chart, with the flexibility to adjust the time range and select specific nodes for visualization. Furthermore, users have the option to export data, allowing for sorting by timestamps and exporting either all nodes or specific ones.  
The administrator possesses the same capabilities as the user, with additional administrative privileges. In addition to the user actions, the administrator can create, disable, or upgrade user accounts to administrator status. They have the authority to create and modify sensor node information, including address, alias, map coordinates, and sensor type (in this case, soil sensors). The administrator is also responsible for adding central nodes and managing their authentication credentials for secure server communication.  
The master node, a central node in the network, has the ability to securely post measurements to the server via HTTP requests. Authentication credentials are required to ensure data security and integrity.  
The backend serves as the core component of the web platform, receiving and processing all user interactions and system actions. It plays a crucial role in executing validation  
Figure 4: Implementation of a sensor node on a Stevia Morita II crop.  
checks to ensure the integrity and security of the data. This includes verifying user authentication, validating input data, and enforcing access control policies. The backend also facilitates seamless communication with the database, allowing for the efficient storage and retrieval of information. It handles complex operations such as data aggregation, analysis, and generating relevant visualizations. Overall, the backend acts as the engine that drives the functionality of the web platform, ensuring smooth and reliable operation for all users and system components.
# Sensory Acceptance and Characterisation of Turmeric- and Black-Pepper-Enriched Ice Cream
## 2 Materials and Methods
### Preliminary Study
#### 2.2.1 Development of Flavoured Ice Cream Formulations  
Nine-point hedonic scale ranging from 1 for \"dislike extremely\" to 9 for \"liked extremely\" was used for the evaluation of ice cream acceptance.  
In the preliminary study ice cream mixes (including the control sample) were initially prepared with 0.25%, 0.5%, 1.0%, 2.0%, 3.0%, and 5.0% turmeric powder. For further study, three levels of turmeric powder were selected by a trained sensory panel (\(N=7\)) based on acceptability as follows: 0.5%, 1.0%, and 2.0%. A higher proportion of turmeric powder (3.0% and 5.0%) was found undesirable due to its over-intense taste and colour, and the lowest proportion (0.25%) had no major impact on evaluating properties; therefore, it was omitted from further research.  
To optimise the black pepper proportion in ice cream, it was added to selected turmeric-enriched samples (0.5%, 1.0%, and 2.0%) at concentrations of 0.01%, 0.02%, and 0.03%, as shown in Figure S1. Nine samples of ice cream were evaluated by trained participants, and the results indicated that the optimal concentration for functional ice cream production with turmeric powder is the addition of 0.02% of black pepper, with a pleasant aftertaste. The overall acceptability of the sample with 0.03% of black pepper was poor, with a too-intense black pepper flavour and too-strong aftertaste, while the one with 0.01% did not differ in attributes from the sample without black pepper. Therefore, seven samples were selected for further consumer testing: standard milk ice cream (control), three turmeric-flavoured samples (0.5%, 1.0%, and 2.0%), and three turmeric-flavoured samples with the addition of black pepper (0.02%) shown in Table 1.
## 4 Result and Discussions
### Feature Importance
Higher \"frequency\" values pushed the path loss values higher as expected, the moderately low values pushed it low, and afew instances in the lowest frequency values pushed the path loss high due to the effect of other factors, such as elevation and/or clutter. The features \"elevation\" and height of the receiving antenna (ht) were observed to push the path loss values low when they are high or extremely low, whereas the upper part of their low values increased path loss values. This could be due to situations when the transmitting antenna is at a higher elevation. image input model, where \"ht\" and \"elevation\" were the last two, in this case, they were the first two because, in the non-image input model, the model was developed without consideration of environmental factors, and its explanation was similar to that of existing empirical models. The inclusion of features of the satellite images enhances the model's ability to differentiate between environments. The model is well fitted to differentiate between the environments based on the importance it gives to the extracted features. Figure 10 is a summary plot of the 20 most important features. It was observed that higher values of the CNN features produce higher path loss values, and lower values produce lower path loss values, with the exceptions of \"f_0\" and \"f_1\". However, the features were extracted by a CNN, and the reason for this behavior cannot be explained as the features are not physical quantities and were not generated by a known equation but a black box model, unlike other quantities such as \"distance\" and \"elevation\" that are physical quantities. However, it is clearly seen that predicted path loss is dependent on these extracted features. Similarly, dependence plots for features \"f_143\" and \"f_2\" are presented in Figures 11 and 12, respectively. Figure 11 showed that \"f_143\" had the highest approximate interaction index with \"f_167\", and it was observed that as \"f_143\" increased, \"f_167\" also increased, as well as path loss but in a stepwise fashion without interactions even though  
Figure 9: Mean feature importances of the top 20 features in the model with a combination of non-image features and CNN + GLCM features. “f_143”, which is a CNN-extracted feature, has the highest importance, while “elevation”, which stands for elevation at receiving antenna position, has the least importance among them. Figure 11: Dependence plot of the feature “f_143” in the model developed with a combination of non-image features and CNN + GLCM features. Feature “f_143” has the highest interaction index with “f_167”. Figure 10: SHAP summary plot of the 20 most important features in the model developed with a combination of non-image features and CNN + GLCM features. It shows the variations in feature values with output path loss value.