esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The specific aims of this work were: (1) to evaluate the feasibility of multi-category models (LDA, PCA-SVM, and PCA-GRNN) to classify honey pomelo fruits into five granulation levels; (2) to compare the model accuracy rate (_ARM_) and predictive performance (_CA_, _CS_, and _CSP_) of three multi-category models to discriminate five granulation levels of samples. To assess internal fruit quality, different methods have been applied; however, methods with limited penetration depth are limited in their ability to accurately assess internal quality. The predictive accuracy of measurement of internal quality attributes by transmittance spectroscopy is especially low for thick-skinned fruit, such as watermelon and pomelo [15]. To enhance the predictive performances of models, the goal of the research was to combine visible and near-infrared transmittance spectroscopy with machine vision technology to assess the degree of granulation in samples. In recent years, machine vision technology has been widely applied in the agricultural and food industries, including for the assessment of fruit characteristics, such as mass and volume [16; 17; 18; 19]. Fruit volume can be used to predict harvest time [20], and differences in volume and mass are used for fruit quality testing by correlation with granulation in citrus [21]. Machine vision technology presents an effective and relatively inexpensive approach for accurate measurement of fruit characteristics, and could be applied to test large numbers of samples quickly and non-destructively. In multi-source data fusion, character-level fusion is used to extract the principal component of preprocessed spectra data, and data-level fusion is used to combine the external characteristics with the principal components of the preprocessed spectra data. We propose multi-source data fusion and a multi-category classification model with a low number of input layers and acceptable high classification accuracy for on-line fruit assessment. The goal of this research was to develop a rapid, accurate, and non-destructive method for online grading and detection of fruits with granulation using visible and near-infrared spectroscopy combined with machine vision.
## 2 Materials and Methods
### Laser Total Stations for 3D Terrain Modeling  
Laser total stations are currently among the most widely used instruments for conducting the types of measurements described above, offering high precision in determining horizontal and vertical angles as well as distances. Classical tacheometry involves polar coordinate-based surveying to determine the planimetric and altimetric position of detail points, using trigonometric leveling. Based on the collected point cloud data, a 3D model of the bench face or its section can be generated in appropriate processing software. Figure 1 presents a sample 3D model visualized as a point cloud, showing the roof, bench face, and floor of the excavation wall along with the planned layout of the blasthole pattern.  
In the case of highly irregular bench faces, there is a high probability that an optimal measurement position for the instrument cannot be found--one that would enable complete geometric reconstruction of the entire face. As seen in Figure 1, the perspective from which the scan was conducted limited the ability to capture the full dimensions of individual surfaces--roof, floor, and face. In such cases, supplementary scans must be carried out from additional vantage points to dimension surfaces that were not visible during the initial measurement.  
Another issue involves the coordinate systems required by the instrument operator. While mapping the model in a standard Cartesian coordinate system (x, y, z) is generally unproblematic for commercially available devices, mapping into custom or local coordinate systems requires the establishment of well-defined reference points. These must precisely define geographic length, width, and elevation. Moreover, these reference points must be visible from all scanning perspectives used during the survey of a given area of the excavation site.  
These considerations highlight the fact that measurements performed using stationary laser-based instruments may, under certain conditions, prove insufficiently functional. This can directly affect the quality and completeness of the collected data, which are subsequently used to determine the input parameters for blast design.
# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 6 Experiments and Results
### Results  
Table 6 summarizes the different evaluation indexes of all the methods used on the test dataset for all three metrics. The processed dataset was used to train CNN, LSTM, and hybrid CNN-LSTM. The obtained results are shown in Figures 8-11. It can been seen that the hybrid CNN-LSTM has the closest-fitting degree of prediction when compared to the other two methods. The evaluation index of each method is calculated and the comparison results can be seen in Table 6 and Figure 9. It can be seen from Table 6 that the hybrid CNN-LSTM model has the lowest MAE and RMSE values, and the R-squared accuracy is almost close to 1. MAE decreases from 32.985 to 30.125 by 9.084% for LSTM as compared to CNN, R-squared accuracy increases by 2.123% with the \(p\)-value < 0.05, and RMSE decreases from 45.012 to 42.98 by 4.618%, hence LSTM performs better than CNN. The evaluation index of MAE and RMSE for the hybrid CNN-LSTM are the lowest and the R-squared accuracy is highest. The MAE and RMSE values of hybrid CNN-LSTM model improves by 10.863% and 10.477%, and R-squared accuracy improves by
# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 3 Materials and Methods
### Methods
#### 3.1.4 Long Short-Term Networks  
Long short-term memory (LSTM) networks, one of the earliest approaches to RNN architectures with nontrivial state control [46], in addition to the recurrent synapses containing the short-term memory, has an additional memory cell that stores and releases the long-term information through a system of subnets called gates. Combining these two types of memory, LSTMs have achieved excellent results in several fields. [PERSON] et al. [53] offered an exhaustive overview of LSTM architectures.  
Mathematically, denoting with \(W^{(p)}\in\mathbb{R}^{q\times d}\), \(U^{(p)}\in\mathbb{R}^{q\times q}\), and \(\mathbf{w}_{0}^{(p)}\in\mathbb{R}^{q}\) the weights associated to each subnet \(p\in\{i,o,f,z\}\), the output of the LSTM layer with \(q\in\mathbb{N}\) can be described as follows:\[\dot{\mathbf{r}}^{(t)} = \sigma\bigg{(}\boldsymbol{w}_{0}^{(i)}+W^{(i)}\boldsymbol{x}^{(t)} +U^{(i)}\boldsymbol{z}^{(t-1)}\bigg{)} \tag{3}\] \[\boldsymbol{o}^{(t)} = \sigma\bigg{(}\boldsymbol{w}_{0}^{(o)}+W^{(o)}\boldsymbol{x}^{(t) }+U^{(o)}\boldsymbol{z}^{(t-1)}\bigg{)}\] (4) \[\boldsymbol{f}^{(t)} = \sigma\bigg{(}\boldsymbol{w}_{0}^{(f)}+W^{(f)}\boldsymbol{x}^{(t )}+U^{(f)}\boldsymbol{z}^{(t-1)}\bigg{)}\] (5) \[\boldsymbol{z}^{(t)} = \tanh\bigg{(}\boldsymbol{w}_{0}^{(z)}+W^{(z)}\boldsymbol{x}^{(t) }+U^{(z)}\boldsymbol{z}^{(t-1)}\bigg{)}\] (6) \[\boldsymbol{c}^{(t)} = \boldsymbol{c}^{(t-1)}\odot\boldsymbol{f}^{(t)}+\boldsymbol{i}^ {(t)}\odot\boldsymbol{z}^{(t)}\] (7) \[\boldsymbol{z}^{(t)}(\boldsymbol{x}) = \phi\big{(}\boldsymbol{c}^{(t)}\big{)}\odot\boldsymbol{o}^{(t)} \tag{8}\]  
where \(\sigma(\cdot):\mathbb{R}\ \mapsto(0,1)\) and \(tanh(\cdot):\mathbb{R}\ \mapsto(-1,1)\) are the sigmoid and the tanh activation function, respectively, and \(\boldsymbol{c}^{(t)}\) is the state memory cell at time \(t\) (with an initial value \(\boldsymbol{c}^{(0)}=\boldsymbol{0}^{\emptyset}\)). The forget input and output gates regulate the mechanism of storing and releasing the information.  
Chronologically, the forget gate is the first gate to act on the memory cell (Equation (5)). It uses the sigmoid function to delete a percentage of information considered obsolete. Next, the input gate (Equation (3)) adds to the output of the forget gate (Equation (7)) the new useful information extracted from the data input. Finally, the state of the memory cell \(c^{(t)}\) is combined with the output gate (Equation (6)) to compute the output value of the LSTM cell (Equation (8)).  
The number of weights in an LSTM layer with \(q\) units is equal to \(4\times q\times(d+q+1)\). A graphical illustration of the LSTM cell is showed on the left side of Figure 1.
## 4 Performance Evaluations and Results Analysis  
### Simulation Environment  
Experiments were conducted by dividing the prediction of network traffic and transmission time into four sections: packet products per node, transmission generation by zones, estimation of parameters for rush hour prediction, and congestion prediction.  
Instead of building an actual network, the prediction model was tested by implementing a WSN using the IEEE 802.15.4 ns-2 package. The distance of one zone is 100 m, the total number of zones is 11, and the transmission radius of the nodes is set to 20 m. Between 10 and 20 nodes were placed randomly. When the node energy is 100%, the basic node energy is assumed to 1 J. It is assumed to be possible to operate for 100 min at 100%, and the nodes were randomly divided into three equal energy groups: 100%, 70%, and 40%. The node transmission time was set to be once per 10 s, and the event occurrence frequency was set to 30%-35%, 10%-15%, and 0% based, on 100 min. Events were  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Speed** & **Number of Channel** & **Optimal \(\alpha\), \(\beta\)** \\ \hline High & =1 & \(\alpha=3.931\), \(\beta=5.316\) \\ High & =2 & \(\alpha=1.459\), \(\beta=1.943\) \\  & \(\geq\)3 & \(\alpha=3.210\), \(\beta=5.936\) \\  & =1 & \(\alpha=0.152\), \(\beta=4.020\) \\ Middle & =2 & \(\alpha=0.136\), \(\beta=3.984\) \\  & \(\geq\)3 & \(\alpha=0.581\), \(\beta=2.450\) \\  & =1 & \(\alpha=1.896\), \(\beta=3.894\) \\ Low & =2 & \(\alpha=0.430\), \(\beta=3.566\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal \(\alpha\), \(\beta\) in network types.  
Figure 7: Parameter optimization.  
randomly distributed with a Poisson model as shown in Figure 8. It was calculated under the same conditions for the proposed prediction model.
# Privacy-Preserving Data Analytics in Internet of Medical Things
###### Abstract
The proposed privacy-preserving scheme for healthcare data will contribute in the following ways:  
1. By employing differential privacy techniques on publicly available healthcare datasets to demonstrate the practical feasibility and effectiveness of preserving patient privacy;
2. By demonstrating that differential privacy can effectively balance privacy and utility, guaranteeing that converted results can still be used for insightful analysis and research;
3. By examining the impact of varying values of the privacy budget (epsilon) on both privacy protection and data utility;
4. By conducting a comparative analysis of the Gaussian and Laplace mechanisms within the differential privacy framework and evaluating the performance and usefulness of these mechanisms, emphasising the situations in which each mechanism operates at its best;
5. By analysing the time complexity of applying differential privacy techniques, focusing on computational efficiency as the parameters of user queries increase, and providing insights into the scalability of differential privacy methods, offering guidance on their practical implementation in real-world healthcare data systems. The rest of this paper is organized as follows. Section 2 describes the theoretical background of early privacy preservation techniques. A review of related work is presented in Section 3, while Section 4 provides a comprehensive overview of the proposed method for privacy preservation using differential privacy. Experimental results are reported and discussed in Section 5. Concluding remarks and directions for future work are presented in Section 6.
## 4 Conclusions  
The conclusions regarding the mechanical properties and hydration behavior of SS-RM-EMR-based composite mortar are as follows:  
1. Compared with the OPC group specimens, 30% SS alone will inhibit the hydration reaction of the slurry, resulting in the decrease of the mechanical properties of the mortar, while the compound mortar can improve the mechanical properties with the appropriate amount of RM and EMR. The 28 d flexural and compressive strengths of the composite mortar were the highest when 15% of SS, 12% of RM, and 3% of EMR were mixed, which were 7.2 MPa and 41.4 MPa, respectively. Compared with the test group with 30% SS alone, the 28 d flexural strength increased by 18.0% and the compressive strength increased by 25.5%.
2. Compared with the OPC group specimens, 30% SS alone reduced the hydration reaction rate and accumulated heat release of the slurry, while the compounding of appropriate amounts of RM and EMR can improve the hydration reaction rate and accumulated heat release of the slurry. The accumulated heat release of the slurry was 246.2 J/g when 15% of SS, 12% of RM, and 3% of EMR were mixed.
3. Compared with the OPC group specimens, 30% SS alone reduced the content of hydration products Ca(OH)\({}_{2}\), Aft, and C-S-H gels, while compounding with appropriate  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Porosity/\%**} & \multicolumn{4}{c}{**Pore Size Distribution/\%**} \\ \cline{3-6}  & & **\textless{}20 nm** & **20–50 nm** & **50–200 nm** & **\textgreater{}200 nm** \\ \hline OPC & 18.25 & 9.31 & 8.02 & 9.18 & 73.49 \\ A0 & 15.90 & 21.81 & 15.88 & 27.97 & 34.34 \\ A1 & 14.13 & 35.48 & 30.45 & 3.28 & 30.79 \\ A2 & 16.80 & 26.22 & 27.89 & 4.27 & 41.62 \\ A3 & 14.98 & 25.26 & 32.45 & 10.94 & 31.35 \\ A4 & 18.21 & 24.58 & 23.02 & 10.10 & 42.30 \\ A5 & 21.13 & 13.45 & 19.87 & 12.82 & 53.86 \\ A6 & 19.16 & 19.73 & 23.29 & 19.31 & 37.67 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Composite mortar distribution of the pore structure.  
Figure 8: The principal diagram of the experiment.  
amounts of RM and EMR can promote the slurry to produce more AFt and C-S-H gels, which are favorable for microstructure development and form a more dense hardened body structure. Compared with the specimens of 30% SS group, when 15% of SS, 12% of RM, and 3% of EMR are mixed, it can promote the generation of hydration products AFt and C-S-H gel and form more dense hardened body structure, which makes the overall decrease of harmless and harmful pores 26.35% and the overall increase of harmless and less harmful pores 43.57% in the composite mortar specimens.  
Conceptualization, L.Z.; methodology, L.Z.; investigation, L.Z., P.C. and C.H.; resources, L.Z.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z, C.H. and L.Z; visualization, L.Z.; supervision, H.X. and Z.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare that they have no conflict of interest.
## 4 Performance Analysis
### Analysis of Experimental Results
#### 4.4.2 F13 (2D Composition Function 3)  
Figure 8h shows F13, which is a more complex multimodal optimization function than F7, consisting of six basic functions, including the Weierstrass function and Griewank's function. The peaks in 2D Composition Function 3 include both coarse and fine peaks. The fine peaks display a remarkable level of irregularity, justifying this function's classification as a classic complex multimodal problem. Similar to the procedure used for the 2D Vincent function, the ranges of potential peaks accurately predicted in rounds 1, 10, 20, and 30 were selected. As shown in Figure 12, the TCDE algorithm finds all coarse peaks, as well as some of the fine peaks at the beginning. As the algorithm iterates, the taboo areas of the coarse peaks gradually expand, forcing the population to stay away from the peaks that have been found. This is shown in Figure 13. Finally, as the TCDE algorithm iterates, the topographic contours of 2D Composition Function 3 become increasingly obvious. Very small peaks that were previously difficult to explore are also successfully discovered, which clearly illustrates the superiority of the TCDE algorithm.  
Figure 12: Four stages of optimizing Composition Function 3 (**a–d**).  
Figure 13: Individual distribution of Composition Function 3 with TS for topographical exploration when applying DE (**a–d**).
## 3 Results and Discussions
### Fracture Surface Analysis  
To confirm the brittle failure of the adhesives in the SLS specimens and to identify the peeling and fiber breakage failure in JLS specimens, the fractured surfaces were observed under optical microscope. The fractographic results of specimen JLS is presented in Figure 7.  
In Figure 7a, the adhesive layer peeling off at the knee of the curvature in the joggled overlapping region can be observed. The debonding of the laminates along the applied adhesive layers can be viewed in Figure 7b. The adhesive not only peeled off from the lamina but also inflicted fiber/matrix debonding in the same lamina. Figure 7c is the zoomed view of the circled region in Figure 7b. From Figure 7c, the ruptured fiber at the end of the lamina can be observed. Figure 7d shows the presence of more fiber/matrix debonding and fiber breakage near the knee of the adhesive joint. This consolidates the explanations provided in Section 3.2 regarding the failure modes by acoustic emission parameters.  
Similarly, the optical images of SLS specimens are presented in Figure 8. Figure 8a shows the complete lack of matrix/fiber debonding in the area where the adhesive is applied. The fibers are left unharmed in the fractured region of the adhesive. The adhesive has delaminated from the outermostply, but it did not affect the fiber/matrix integrity. Figure 8b shows the brittle fracture and the direction of the crack grew along the width of the adhesive under loading. Figure 8c shows the fiber breakage at the end of the specimen. Figure 8d is the zoomed image of the ruptured fibers. So, the fiber breakage has occurred in the SLS specimens, nonetheless, only at the edge of the lamina.  
Figure 7: Fractographic analysis of JLS specimens. (**a**) Peeling at the knee of adhesive; (**b**) Debonding of laminates; (**c**) Ruptured Fiber; (**d**) Fiber/Matric Debonding.  
Therefore, by comparing Figures 7 and 8, it can be concluded that the crack growth initiated at the knee of the joggled lap in the JLS specimens, which in turn results in the peeling. The peeling also has induced the debonding between the fiber and matrix in the area where the adhesive is applied. However, in SLS specimens, the crack opened in the adhesive region and travelled through the width of the specimen resulting in a brittle failure.  
The fractographic results are sufficient to prove the damage modes explained in the acoustic emission result sections. Thus, the AE proves to be a powerful tool, not only in predicting the failure in an adhesive bonded FRP but also in identifying the different damage modes in the failure. By setting up a proper experimental campaign and understanding the different AE descriptors, it is efficient to analyse the failure modes in an entire structure comprising adhesive-bonded FRP.
## 4 Experimental Results
### Results of Visual Complexity Measurement  
To compare the visual complexity of Korean and English, the perimetric complexity and algorithmic complexity, as proposed in [20], were measured. The results are shown in Figure 9. Figure 9 presents a graph displaying the average perimetric and algorithmic complexities calculated using the training data from Table 1, categorized by the number of pixels corresponding to the text regions in the images. The results indicate that both perimetric and algorithmic complexities are generally higher for Korean text than for English, with a 95% confidence interval confirming this trend. This difference arises because English is structured with 26 alphabet letters arranged linearly from left to right, contributing to a regular pattern. In contrast, Korean is composed of 14 consonants and 10 vowels, which combine in various ways to form syllabic blocks, some of which include final consonants, making it challenging to identify a consistent structure. Additionally, the complex forms created by combining consonants, vowels, and sometimes final consonants result in higher visual complexity for Korean text.  
Figure 9: Results of visual complexity: **(a)** perimetric complexity and **(b)** algorithmic complexity.
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 5 Conclusions  
In this paper, an sub-aperture recursive LASSO method is proposed for the online DOA estimation of TDM-MIMO radar using sub-aperture data blocks. The proposed method has two advantages. First, it makes full use of the TDM-MIMO reception time to improve the real-time performance of the DOA algorithm (i.e., online DOA estimation), and the complexity and memory usage per update are remarkably reduced when compared with traditional methods. Second, the proposed method is superior to the traditional DAS and IAA methods with respect to the DOA resolution because it can reach the DOA estimation accuracy of traditional LASSO methods. Simulations and experimental data verify the effectiveness of the proposed method. The online DOA estimation of actual TDM-MIMO radar will be realized through hardware programming in the future.  
Conceptualization, J.L. and Y.Z. ([PERSON]); methodology, J.L.; software, Y.Z. ([PERSON]); validation, J.L., Y.Z. ([PERSON]), and Y.Z. ([PERSON]); formal analysis, J.L.; investigation, J.L. and D.Z.; resources, J.L.; data curation, Y.Z. ([PERSON]); writing--original draft preparation, J.L.; writing--review and editing, J.L.; visualization, Y.Z. ([PERSON]); supervision, Y.Z. ([PERSON]) and A.J.; project administration, Y.Z. ([PERSON]) and A.J.; funding acquisition, Y.Z. ([PERSON]), Y.Z. ([PERSON]), Y.H., and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under grants 61901092, 61901090, and in part by the Special Science Foundation of Quzhou.  
Not applicable.  
This study did not report any data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TDM & time-division multiplexing \\ MIMO & multiple-input multiple-output \\ DOA & direction-of-arrival \\ LASSO & least absolute shrinkage and selection operator \\ \end{tabular}
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 2 An Overview of Literature on Denial-of-Service in Control Systems
### Networked State Estimation Problem
In the setting in [65; 66], the players attempt to optimize not only the timing but also the signal and the interference power levels of transmissions and attacks. In the multi-channel estimation problem considered in [67], the sensor aims to optimally select the wireless channels that will be used to transmit packets, and, moreover, the attacker wants to optimally select the channels that will be blocked. For the state estimation problem  
Figure 2: Operation of networked state estimation subject to DoS attacks. with multiple sensors and multiple channels, the optimal strategies of the attacker are also discussed in [68]. We note that estimation problems under different attack types have also been studied. For instance, [PERSON] et al. [69] investigated optimal false data injection attacks in state estimation problem. Furthermore, [PERSON] and [PERSON] [70] explored state estimation under jamming attacks as well as false data injections and developed a threshold-based detection method.