esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Final Remarks and Challenges  
The inactivation of PPO enzymes in fruits and the energy requirements for various treatments were evaluated. PPO in different fruit juices was effectively inactivated by thermal, HPP, and ultrasound treatment, with residual enzyme activity decreasing with increasing temperature, pressure, or ultrasound intensity and treatment time. PPO in different fruits showed first-order kinetics in many studies of kinetic inactivation. However, kinetic inactivation by HPP exhibited non-linear kinetics in some fruits, which fit better with first-order biphasic models. The activation energy data, D-values, and z-values summarised from this review can be used to better understand inactivation kinetics and to develop an industrial process that can produce stable products with longer shelf life. The estimation of energy calculated in this study based on Equations (1) and (3) can be used to compare the energy consumption and requirements for different types of food processing technologies. Estimation of energy is important, especially for food industries to develop solutions towards sustainable food production and more efficient energy use. Most food processing operations involve adding heat energy to ensure microbiological safety, improve quality, and extend product shelf life. Furthermore, most alternative processes (HPP and ultrasound) use electricity, so energy calculation allows the estimation of energy efficiency and comparison of novel food processing technologies. Researchers could explore more in this area, as it is significant to determine the cost effectiveness of fruit processing.  
Some challenges and limitations during this study should be noted. It was difficult to find temperature data that combined thermal and non-thermal treatments in previous studies. Additionally, for thermal treatment, the calculation is based on the heat absorbed by the sample rather than the energy provided by the equipment based on the power supply, and this could provide an underestimation of energy supplied as heat losses were not included. Aside from that, few studies have used PPO enzyme inactivation as a baseline comparison to estimate the energy requirements for fruit processing using thermal and non-thermal technologies, particularly HPP and ultrasound. More research on energy estimation is needed, particularly in the food industry, to develop solutions for sustainable food production and more efficient energy use.  
Conceptualisation, A.S. and F.V.M.S.; methodology, N.A.F.Z. and A.S.; validation, M.S.H. and N.A.M.H.; writing--original draft preparation, N.A.F.Z. and A.S.; writing--review and editing, N.A.F.Z., A.S., F.V.M.S., M.S.H. and K.K.; supervision, A.S.; project administration, A.S. All authors have read and agreed to the published version of the manuscript.  
This work received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to acknowledge the technical facilities and financial support from the Universiti Putra Malaysia (UPM).  
The authors declare no conflict of interest.
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 2 Numerical Model
### Body-Force Model  
Figure 6 presents the simplified geometry applied in the body-force model, in which the impeller and guide vane blades are removed from the original waterjet system geometry, while the waterjet shaft and hub are required to remain in the simulation. The absence of a hub will result in an increase in cross section, which can decrease flow momentum and induce a recirculation zone.  
In the body-force model, the flow head is increased by applying a uniform axial body-force to the volume surrounding the impeller. Ideally, its initial value is set equal to the axial force acting on the impeller and the guide vanes computed from the MRF model. However, the axial force is unknown a priori. Thus, the value of body-force is initially set as the bare hull resistance. Then, for a self-propelled ship, this value has to be adjusted repeatedly to achieve resistance/thrust balance. It should be noted that the axial body-force in self-propulsion must be equal to the resistance of the entire ship (including hull, ducting channel, shaft, hub, etc.).  
The axial body-force per unit volume, \(B_{a}\), is defined as follows [34]:  
\[B_{a}=\frac{R_{T}}{\
abla_{B}} \tag{6}\]  
where \(R_{T}\) is the accumulated resistance of the waterjet-hull system, and \(\
abla_{B}\) is the volume of the region subjected to body-force.  
The body-force model program is based on a User-Defined Function (UDF) on the ANSYS Fluent platform. At the same time, considering the impeller tip clearance size in the actual pump geometric model, the volume \(\
abla_{B}\) of the region subjected to body-force can be defined. The body-force model UDF algorithm consists of the following steps.  
Step 1: Determine whether the X coordinate of element A (\(x\), \(y\), \(z\)) falls between that of the front and rear end faces of the impeller. (\(x_{1}\leq x\leq z_{2}\));  
Step 2: Judge whether the element A\({}_{1}\) (\(x\), \(y\), \(z\)) obtained in Step 1 falls between the impeller hub radius and impeller radius. (R = sqrt(\((y-y_{0})\)*\((y-y_{0})+(z-z_{0})\)*\((z-z_{0})\)), \(R_{1}\leq R\leq R_{2}\));  
Step 3: Apply the axial body-force per unit volume \(B_{a}\) to element A\({}_{2}\) (\(x\), \(y\), \(z\)) obtained in Step 2 (source = \(-B_{a}\)).  
Here, \(y_{0}\) and \(z_{0}\) denote the Y and Z coordinates of the impeller axis, respectively. \(R_{1}\) and \(R_{2}\) are the impeller hub radius and impeller radius, respectively. \(R_{2}\) can be modified easily and directly in the body force UDF code to fit different impeller tip clearance sizes, which reduces the meshing workload of the body-force model corresponding to the actual pump geometric model.
# Experimental and Numerical Dynamic Identification of an Aerostatic Electro-Spindle
## 4 Numerical Results
### Choice of the Time Step  
The optimal time step, \(dt\), must be chosen for simulations in order to guarantee a stable solution and independence of the dynamic solution on the time step. The entity of the damping factor was found to be dependent on the time step, \(dt\), and on the grid refinement. Figure 11 compares the values obtained for the damping factor at different rotational speeds and time steps, \(dt\), with a 31 \(\times\) 96 grid. As shown, the convergence is reached with a time step of \(dt=10^{-8}\) s, and a further decrease does not significantly change the damping factor. The effect of the grid on the damping factor is visible in Figure 12. When doubling the grid nodes along the circumferential direction, it is sufficient to halve the time step, \(dt\), in order to obtain a very similar solution. As a result of these considerations, a 31 \(\times\) 48 grid with \(dt=2\times 10^{-8}\) s was chosen for dynamic simulations, as it was found to be a good compromise between accuracy and computational time.  
Figure 11: Damping ratio vs. rotational speed obtained with different time steps; the grid is 31 \(\times\) 96.  
Figure 12: Cont.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(p_{s}\) (MPa)} & \multicolumn{2}{c}{\(h_{f}\) = 16 \(\mu\)m, \(h_{r}\) = 20 \(\mu\)m} & \multicolumn{2}{c}{\(h_{f}\) = 19 \(\mu\)m, \(h_{r}\) = 23 \(\mu\)m} \\ \cline{2-5}  & \(\omega_{cpl}\) (k\(\mathbf{r}\)pm) & \(\omega_{cem}\) (k\(\mathbf{r}\)pm) & \(\omega_{cpl}\) (k\(\mathbf{r}\)pm) & \(\omega_{cem}\) (k\(\mathbf{r}\)pm) \\ \hline
0.3 & 46.6 & 58.9 & 48.2 & 57.7 \\
0.5 & 72.4 & 86.2 & 67.8 & 78.0 \\
0.7 & 89.2 & 102.8 & 79.1 & 88.9 \\
0.9 & 101.1 & 114.3 & 86.7 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Undamped natural frequencies at different supply pressures and radial clearances.
# Ontology with Deep Learning for Forest Image Classification
## 2 Related Works
However, future research will need to compare various CNN architectures to determine the best model for solving the problem at hand. Recent trends have shown that image collection has significantly increased, thereby activating further research in image classification and annotation. A technique based on bag of visual words (BoVW), which relies on ontology, has been widely used in this area. However, problems relating to ambiguities between image categories have posed challenges with regard to image classification and annotation. A study in [26] proposed a hierarchical max pooling (HMAX) model based on ontology to classify images of animals into their respective categories. The contribution of their model was the exploitation of semantic relationships between image categories as a way of eliminating the problem of ambiguity between image categories. The model performed well as it achieved an accuracy of 80%. However, HMAX is not a desirable technique for producing features; hence, our study has used an ensemble of CNNs for feature production. CNNs have been widely employed to solve image classification problems, attributed to their power in extracting features and always making continuous breakthroughs in the field of image recognition. However, they suffer from a huge overhead, requiring a lot of time for the training process. To alleviate this challenge, a study [27] developed a hybrid of deep learning and random forest algorithm to solve an image classification problem. The sole purpose of the CNN is for feature extraction, and the classification process is handled by the random forest algorithm. Random forest (RF) has the advantages of fast training speed and high classification accuracy. The model was effective as it produced a low error rate of 9.18%. The model did not carry out a comparative assessment against other baseline classifiers, which is accounted for in our study. A supervised deep-learning approach based on a stacked auto-encoder was used in [28] for the classification of forest areas. The study used unmanned aerial vehicle (UAV) datasets because they have been found to be quite useful for forest feature identification due to their relatively high spatial resolution. Through cross-validation, the model achieved an accuracy of 93%. However, one significant limitation of deep learning is that it requires more computing power than other machine learning algorithms.
## 2 Methodology
### Removing the Outliers  
The random sample consensus (RANSAC) algorithm is commonly used to remove incorrect matches obtained using the SIFT algorithm [37]. The RANSAC algorithm was proposed by [PERSON] and [PERSON] in 1981 [38]. It is an outlier detection method [39] that iteratively derives the parameters of a mathematical model from a set of observed data that contains outliers. However, because of the randomness of RANSAC, the effect of removing incorrect matches depends on the selection of sample points. To achieve robust performances, the RANSAC algorithm was combined with the homography matrix to remove outliers in this work. The homography matrix is a transformation matrix that maps the points in one image to the corresponding points in the other image. It can be represented using Equation (11).  
Figure 6: The feature descriptor of a feature point.  
\[H=\left[\begin{array}{ccc}h_{00}&h_{01}&h_{02}\\ h_{10}&h_{11}&h_{12}\\ h_{20}&h_{21}&h_{22}\end{array}\right], \tag{11}\]  
The matched tie points are denoted as {(\(x_{w}\), \(y_{w}\)), (\(x\), \(y\))}, where (\(x_{w}\), \(y_{w}\)) are the coordinates of the feature point in the input image to be registered, and (\(x\), \(y\)) are the coordinates of the corresponding feature point in the reference image. The incorrectly matched tie points were excluded using the following steps.  
First, a homography matrix \(H\) between the input image and the reference image was initially estimated by the RANSAC algorithm using all the matched tie points.  
Then, the transformed coordinates of each feature point to be registered were estimated using the initial homography matrix \(H\), according to Equation (12).  
\[\left[\begin{array}{c}X\\ Y\\ 1\end{array}\right]=H\left[\begin{array}{c}x_{w}\\ y_{w}\\ 1\end{array}\right]=\left[\begin{array}{ccc}h_{00}&h_{01}&h_{02}\\ h_{10}&h_{11}&h_{12}\\ h_{20}&h_{21}&h_{22}\end{array}\right]\left[\begin{array}{c}x_{w}\\ y_{w}\\ 1\end{array}\right], \tag{12}\]  
where (\(x_{w}\), \(y_{w}\)) is the coordinates of the feature points of the input image and (\(X\), \(Y\)) is the transformed coordinates of (\(x_{w}\), \(y_{w}\)).  
After that, the residual error and root-mean-square error (\(RMSE\)) of the \(i\)th tie point were obtained using Equations (13) and (14), respectively,  
\[\Delta x_{i}=x_{i}-X_{i} \tag{13}\]  
\[\Delta y_{i}=y_{i}-Y_{i} \tag{14}\]  
\[RMSE_{i}=\sqrt{\Delta{x_{i}}^{2}+\Delta y_{i}^{2}}, \tag{15}\]  
where \(\Delta x_{i}\) and \(\Delta y_{i}\) are the residual errors of the \(i\)th feature point and \(x_{i}\) and \(y_{i}\) are the coordinates of the \(i\)th feature point in the reference image. Tie point pairs that have (\(\Delta x_{i}\), \(\Delta y_{i}\)) and \(RMSE_{i}\) values higher than an initial threshold, which is denoted as \(T_{R}\), are considered outliers and removed.  
Finally, \(H\) and \(RMSE_{i}\) were estimated iteratively based on the remaining tie points and the outliers were removed until either the \(\Delta x_{i}\), \(\Delta y_{i}\) and \(RMSE_{i}\) values were lower than the threshold \(T_{R}\) or the number of iterations was higher than a set value denoted as \(N_{iter}\).
# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 5 Experiments
### Metrics and Baselines
#### 5.2.2 Baselines  
We compare asGCL with popular baselines and describe them briefly as follows:  
* BPRMF [3]. This is a fundamental matrix factorization algorithm, and the objective of BPR (Bayesian Personalized Ranking) is to maximize the score difference between items that have been interacted with and those that have not. In straightforward terms, it aims for the items that users have accessed to receive higher ratings. This algorithm has become a foundational concept in the subsequent development of recommendation systems.
* NeuMF [4]. This algorithm specializes in matrix factorization and extends it to Generalized Matrix Factorization (GMF). Building upon this foundation, it explores deep learning by employing a multi-layer perceptron to learn the interaction function between users and items. This approach integrates the linear advantages of matrix factorization with the nonlinear benefits of multi-layer perceptrons.
* GCMC [35]. The link prediction problem can be framed as a matrix completion task. The Graph Convolutional Matrix Completion (GCMC) method designs a differentiable message-passing-based graph autoencoder framework that generates latent features within a bipartite interaction graph. This approach effectively integrates interaction data with edge information.  
GAT [36]. The model computes the corresponding latent information for each node and incorporates a single-layer feedforward neural network into the graph structure through masked attention. This mechanism focuses solely on calculating the first-order neighboring nodes of the target node.
* NGCF [6]. It uncovers collaborative signals in the interactions between users and items, integrating these signals into the embedding process. The model introduces encoding methods for node message construction and message aggregation, whereby stacking multiple message aggregation layers enables the extraction of higher-level collaborative information.
* LightGCN [13]. It is an embedding-based model that inherits and enhances the principles of NGCF (Neural Graph Collaborative Filtering). By eliminating the complex design of multi-layer nonlinear feature transformations, it employs a simple weighted sum aggregator to effectively capture the effects of graph convolution and self-connections. This lightweight and efficient approach has made it quite popular.
* SGL [19]. It generates multiple views of the same node through data augmentation, maximizing the similarity between different views while minimizing the similarity between representations of different nodes. This approach aids in uncovering challenging negative sample information, leading to improved recommendation performance, particularly for long-tail items.
* LightGCL [21]. LightGCL posits that heuristic-based augmentation techniques for generating contrastive views do not effectively preserve the intrinsic semantic structure. Consequently, it specifically employs singular value decomposition for contrastive enhancement, thereby modeling collaborative relationships more effectively.
## 3 Artificial Neural Networks  
### Artificial Neural Network Performance for Islanding Detection  
As noted in the previous section, the IDM should be as fast and precise as possible and the non-detection zone should be as small as possible. Also, the method should be simple to implement in existing equipment (protection systems) with as little additional investment as possible. Regarding the criteria of cost, implementation complexity, and minimal impact on power quality, passive islanding detection methods have an advantage over active ones. Direct passive methods are easy to use and usually do not require additional investments in equipment; however, they have lower accuracy and a large non-detection zone. Setting the appropriate detection threshold for direct passive methods represents a special challenge on which the accuracy of the method depends. In some situations (especially when, in the grid island, the energy that is produced is almost or completely equal to the energy that is consumed), the changes in electrical quantities during islanding operation are minimal and will not exceed the set threshold, and therefore, IDM will not detect the islanding operation properly. In order to improve the performance of the direct passive methods (especially the threshold-setting problem), researchers have started to modify input signals using signal processing methods. The goal is to find some hidden features of the signal that can improve islanding detection. Further improvement is carried out by adding an intelligent classifier that will based on the extracted features to classify the occurrence of the islanding operation. Further improvement is accomplished by adding an intelligent classifier that will based on the extracted features to classify the occurrence of the islanding operation. According to [5], commonly used intelligent classifiers are ANNs, Fuzzy Logic Control (FLC), Adaptive Neuro-fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Decision Tree (DT). As for accuracy, according to [5], ANN-based methods show high accuracy (up to 98.19%), although ANFIS and DT-based methods show 100% accuracy in some applications [5]. Another advantage of ANN-based methods is their wide application possibilities to different kinds of real-life situations. They are capable of handling situations with DGs based on synchronous generators as well as multi-inverter DGs (photovoltaics) [6]. Since the ANN makes a decision based on the data that were used for training, setting the activation threshold is no longer necessary, but choosing the appropriate dataset for training is crucial. A review of the literature reveals that the ANN as a classifier for islanding detection, despite many advantages, also has its drawbacks. According to the literature [6,7], authors discuss the quality of the input data and the potential shortcomings of the extensive training with huge and irrelevant data. This all can lead to bad performance of the ANN.
# Characterization of Asphalt Binders Modified with Bio-Binder from Swine Manure
## 3 Results
### Viscosity Test  
Viscosity was studied in order to evaluate the effect of adding bio-binder on the workability of the bio-modified asphalt binder. In Figure 5, the evolution of the bio-modified asphalt binder viscosity can be observed as a function of the percentage of added bio-binder and temperature. In all cases, a linear decrease in viscosity is observed as the content of bio-binder increases, indicating that it is possible to reduce the manufacturing temperature of the asphalt mixture. Additionally, the reduction in viscosity could reduce the energy required during the manufacturing and installation process of bituminous mixtures, which could lead to economic and environmental benefits by reducing fuel consumption. These results are consistent with those obtained by [PERSON] et al. [56; 57; 58; 59]. The difference in viscosity values between the reference binder and the modified binder decreases as temperature increases, becoming negligible at a temperature of 180 \({}^{\circ}\)C.
# Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks
## 6 Self-Adjustment Deployment Protocol
### Self-Adjustment Deployment Protocol Description  
In Figure 4, we show the difference between the calculated new position which is \(B\) (calculated with the 3D-DVFA algorithm), and the direction and distance that should be traveled to reach the position \(B\) considering water currents. The idea is the same idea of the boat navigation used to reach a specific destination. In fact, based on the speed and the direction of the water currents, a new direction is calculated to reach the destination. Hence, the trajectory is not a line matching the starting point \(A\) and the arriving point \(B\). Similarly, in the sensor network, in each deployment algorithm iteration, the sensor will define its direction and the distance traveled based on the position calculated by the 3D-DVFA algorithm and the water currents and its direction.  
Figure 3: Total distance traveled by the underwater sensor nodes.  
The new direction of the sensor node is defined by the angle \(\theta\). As shown in Figure 4, we have  
\[V_{ss}=V_{is}+V_{inc}^{*} \tag{3}\]  
where \(V_{ss}\) is the sensor arrival speed, \(V_{is}\) is the the sensor initial speed, and \(V_{inc}^{*}\) is the water currents speed.  
To determine the new direction, \(\theta\), we use the sine rule:  
\[\frac{V_{ss}}{\text{sin}\omega}=\frac{V_{inc}}{\text{sin}\gamma}=\frac{V_{is}} {\text{sin}(\alpha-\beta)} \tag{4}\]  
\[\text{sin}\gamma=\frac{V_{inc}\text{sin}(\alpha-\beta)}{V_{is}} \tag{5}\]  
\[\gamma=\text{sin}^{-1}\frac{V_{inc}\text{sin}(\alpha-\beta)}{V_{is}} \tag{6}\]  
\[\theta=\gamma+\alpha \tag{7}\]  
where \(\beta\) is the water direction, \(\alpha\) is given by the 3D-DVFA algorithm based on the calculated new position of the sensor node. Hence, to reach its calculated new position, the sensor node will move using the new direction \(\theta\) and its speed \(V_{is}\).