esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Naive Bayes  
Ref. [2] introduced the application of the Naive Bayes classifier and vessel trajectory clustering in a maritime surveillance system for detecting abnormal vessel behavior. Data from the Automatic Identification System (AIS) was employed to compare hierarchical and k-medoids clustering methods to model and understand normal vessel sailing patterns in port waters. Unusual ship behavior was detected and classified using the Naive Bayes classifier. Using vessel trajectories obtained from AIS data, the proposed approach was tested and confirmed in the seas of Xiamen Bay and Chengsanjiao, China. The results demonstrated the feasibility of the recommended technique in enhancing marine situational awareness in coastal seas, as supported by statistics analysis.  
Ref. [3] also proposed a Naive Bayes Network anomaly detection model as applied to the catastrophic impact of intrusions on computer systems and networks. The automated intrusion detection strategies were successful to reduce the risk associated with intrusion attacks to some level. They further employed a novel Machine Learning strategy that  
Figure 9: Basic architecture model for anomaly detection in automated system level.  
combined the Naive Bayes and weighted radial basis function Network approaches. While experimenting on the KDDCup'99 data set in detecting intrusions, the proposed schema showed highly promising results in comparison to several earlier techniques.  
[PERSON]. [4] introduced OCPAD, an innovative approach for detecting suspicious payload content in network packets, specifically targeting HTTP attacks. This approach combines the strengths of single-class classification and short sequence frequency analysis through a Multinomial Naive Bayes classifier, leveraging one class as an anomaly detector. OCPAD calculates the likelihood of recurrence for each short sequence within the payload of known non-malicious packets. During the training phase, OCPAD establishes the probability range for the occurrence of each sequence based on a unique data structure called the Probability Tree. A brief sequence is regarded anomalous in the testing phase if it is not found in the database or if its probability of recurrence in a packet exceeds the training phase's range. It creates a sequence depending on how often short sequences are likely to occur. When evaluated on a huge dataset of 1 million HTTP packets received from an academic network, OCPAD had a high detection rate (up to 100%) and a low false positive rate (less than 1%).  
Ref. [5] devised a comprehensive framework for distinguishing between normal and abnormal monitoring signals. The primary concept was to create a large number of binary indicators that make full use of expert knowledge. Each indicator was a fully parametrized anomaly detector that was created using expert-designed parametric anomaly ratings.
# Seasonal Variability of Volatile Components in _Calypogeia__integristipula_
## 2 Results and Discussion
### Statistical Analysis of the Obtained Results
The analysis of the heat map showed that the chemical compounds detected in the _Calypogeia integristipula_ samples analysed form two separate groups, the content of which in the plants tested changes clearly depending on the time of collection of the material in the field (Figure 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Compounds** & **Spring** & **Summer** & **Autumn** & **In Vitro** \\ \hline \multicolumn{6}{c}{Highest loading (\(\geq\)95\%) in PC1} \\ \hline
1 & 2-propanol & 0.95–1.19 & 0.29–0.48 & 1.21–1.45 & 0.29–0.34 \\
10 & benzeneacetaldehyde & 1.09–1.24 & 0.18–0.25 & 1.63–1.75 & 0.23–0.27 \\
23 & anastreptene & 17.87–18.23 & 25.06–25.41 & 15.27–16.01 & 29.98–31.14 \\
30 & (-)-aristolene & 0.91–1.18 & 0.63–0.80 & 1.11–1.25 & 0.75–0.82 \\
42 & \(\delta\)-selinene & 1.24–1.58 & 2.01–2.25 & 0.96–1.14 & 2.47–2.63 \\
45 & bicyclogermacene & 8.24–8.51 & 16.27–18.09 & 6.99–7.87 & 12.91–13.01 \\
52 & 200[M+](54) 185(100) 143(91) & 0.10–0.21 & 0.00–0.00 & 0.16–0.25 & 0.00–0.00 \\
59 & palustrol & 8.14–8.46 & 5.09–5.58 & 9.78–9.94 & 4.95–5.06 \\
65 & global & 2.87–3.32 & 1.07–1.52 & 2.94–3.15 & 1.41–1.51 \\
67 & 220[M+](2) 145(100) 200(93) & 1.11–1.31 & 0.06–0.11 & 1.99–2.22 & 0.11–0.13 \\
78 & 1,4-dimethyl-7-(1-methylethyl)-azulene & 2.59–2.77 & 1.06–1.33 & 3.02–3.56 & 0.59–0.64 \\ \hline \hline \multicolumn{6}{c}{Highest loading (\(\geq\)75\%) in PC2} \\ \hline
2 & pentanal & 0.25–0.39 & 0.26–0.35 & 0.36–0.47 & 1.57–1.76 \\
3 & hexanal & 0.16–0.23 & 0.04–0.09 & 0.38–0.45 & 3.14–3.26 \\
9 & benzenemethanol & 1.03–1.21 & 0.96–1.11 & 0.97–1.11 & 5.12–5.47 \\
16 & phenoxyethanol & 0.87–1.01 & 0.37–0.45 & 1.11–1.35 & 4.47–4.61 \\
39 & 204[M+](18) 128(100) 143(95) & 0.26–0.37 & 0.25–0.44 & 0.29–0.40 & 0.06–0.09 \\
57 & 204[M+](18) 143(100) 157(98) & 0.04–0.12 & 0.02–0.07 & 0.03–0.08 & 0.18–0.23 \\ \hline \hline \multicolumn{6}{c}{Highest loading (\(\geq\)85\%) in PC3} \\ \hline
4 & 1-hexanol & 0.41–0.56 & 0.29–0.35 & 0.25–0.33 & 0.33–0.37 \\
13 & benzeneethanol & 0.09–0.16 & 0.63–0.75 & 0.54–0.62 & 0.15–0.18 \\
76 & germacra-4(15),5,10(14)-trien–1-\(\alpha\)-ol & 0.47–0.65 & 0.83–0.95 & 0.83–1.03 & 0.23–0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ranges of variability of the main compounds indicated by PCA in _Calypogeia integristipula_ samples across seasons.
# Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy
## 3 Improved A* Algorithm
### Improvement of the Heuristic Function
#### 3.1.2 Introduction of Environmental Constraints  
Environmental constraints, i.e., the percentage of obstacles, are also critical factors that affect the efficiency of path planning. When the percentage of obstacles in the environment is high, the planned path is prone to local optimization issues, leading to path search failures. Conversely, when the percentage of obstacles is low, the search speed may decrease due to the broader path search range. To address these issues, this paper introduces an obstacle percentage coefficient \(O_{\text{s}}\) to adaptively adjust the heuristic function. Additionally, to ensure data smoothness, the obstacle percentage coefficient \(O_{\text{s}}\) is first logarithmically transformed and then taken in its absolute value to ensure non-negativity. The improved heuristic function formula is as follows:  
\[ht(n)=\alpha h(n) \tag{5}\]  
\[\alpha=\left|\ln O_{\text{s}}\right| \tag{6}\]  
\[O_{\text{s}}=\frac{N}{(\left|x_{g}-x_{s}\right|+1)\times(\left|y_{g}-y_{s} \right|+1)} \tag{7}\]  
Here, \(N\) is the number of obstacle grids between the starting point and the endpoint and \((x_{s},y_{s})\) and \((x_{g},y_{g})\) are the coordinates of the starting point and the endpoint, respectively.  
From the improved heuristic function, it is evident that as the percentage of obstacles increases (i.e., the occupancy factor of obstacles becomes larger), \(\alpha\) decreases. A smaller \(\alpha\) indicates a reduced weight of the heuristic function in the evaluation function. At this point, the A* algorithm tends to behave more like the Dijkstra algorithm, expanding its search range and thus avoiding local optimization problems. Conversely, when the percentage of obstacles decreases (i.e., there is a smaller occupancy factor of obstacles), \(\alpha\) increases. A larger \(\alpha\) indicates a greater weight of the heuristic function in the evaluation function. In this scenario, the A* algorithm leans towards behaving more like the BFS algorithm, narrowing its search range and resulting in a faster search.  
The test results of the algorithm before and after the introduction of environmental constraints on a grid map of 20 \(\times\) 20 are shown in Figure 7. Figure 7a shows the test results before introducing environmental constraints and Figure 7b shows the results after their introduction. From the figures, it can be observed that the nodes expanded by the algorithm increase after the introduction of environmental constraints, resulting in a larger search range. However, the final path length obtained is shorter than that achieved without environmental constraints.  
Figure 7: Simulation comparative testing: (**a**) without environmental constraints; (**b**) with environmental constraints.
# Metaheuristic with Cooperative Processes for the University Course Timetabling Problem
## 4 Computational Results  
To perform the efficiency and efficacy tests of the metaheuristic with cooperative processes SACP, a computational cluster, consisting of four nodes, each node with a motherboard with two processors, Intel Xeon six core 3.06 GHz, with a total of 48 cores, 96 GB RAM, Ethernet connection 100 BASE-TX, 100 Mbs, O.S. Centos 5.5. MPI libraries GNU. The instances of the university course timetabling problem are those presented by the Metaheuristic Network group [26]. These are the so-called small01, small02, small03, small04, small05, medium01, medium02, medium03, medium04, medium05, large01 and large02. Table 2 presents the characteristics of these instances, which can be downloaded from [42]. Computational experiments are limited to a few instances because they are the only ones found in the literature for the optimization model that is presented in this work.  
To carry out the tests with SACP, a sensitivity analysis of the main SACP parameters was performed. Table 3 presents the tuned values of the parameters by means of a sensitivity analysis, where \(NP\) indicates the number of processes running in SACP. The sensitivity analysis was applied according to the procedure presented in [43], which performs a sweep of values for each parameter (\(T_{i}\), \(T_{f}\), \(MCL\), \(\alpha\)) in interval from a lower bound up to an upper bound. These limits defined taking as the center of these intervals the value of the parameter that is frequently applied to SA in optimization problems.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Instance** & **Small** & **Medium** & **Large** \\ \hline Events & 100 & 400 & 400 \\ Rooms & 5 & 10 & 10 \\ Features & 5 & 5 & 10 \\ Approx\_features\_per\_room & 3 & 3 & 5 \\ Percent\_feature\_use & 70 & 80 & 90 \\ Students & 80 & 200 & 400 \\ Max\_events\_per\_student & 20 & 20 & 20 \\ Max\_students\_per\_event & 20 & 50 & 100 \\ Periods & 9 & 9 & 9 \\ Days per week & 5 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the instances.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Tuned Value** \\ \hline \hline \(T_{i}\) & 2 \\ \hline \(T_{f}\) & 0.001 \\ \hline \(MCL\) & 400 \\ \hline \(\alpha\) & 0.98 \\ \hline \(NR\) & 12 \\ \hline \(NP\) & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tuned simulated annealing values.  
For the \(NR\) parameter, it was tuned according to the execution time limit defined for SACP, and for the \(NP\) parameter, it was tuned according to the best results obtained by SACP.
# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 2 Preliminaries
### Open Set Domain Adaptation by Backpropagation  
Traditional transfer learning is faced with the closed set recognition task, that is, the source domain and the target domain contain exactly the same data types. However, in practical problems, it is more often the case that the target domain contains the target types that do not exist in the source domain, and the data types in the source domain do not all appear in the target domain. Therefore, the open set domain adaptive method is proposed. The method mainly faces two problems: one is that the unknown class cannot be separated from the target domain due to lack of prior knowledge; the other is that the difference between the source domain and the target domain cannot be solved due to drift, and the domain feature alignment method cannot be used to solve the problem.  
Among these solutions, Open Set Domain Adaptation by Backpropagation is very representative, and its core idea is to separate known classes and unknown classes in the target domain. The feature extractor wants the data distribution to be consistent, while the classifier wants to separate the target domain and source domain data, and classify the unknown category into the same class. Obviously, there is an adversarial relationship between the feature extractor and the classifier.  
Therefore, the method uses an adversarial training approach to obtain a classifier and a generator, where the classifier is trained to form a boundary between the source and target samples, while the generator is trained to keep the target samples away from the boundary. And the classifier is set to an output \(K+1\) dimensional vector, where \(K\) represents the classification probability of the known class, and the last dimension refers to the probability of the unknown class, that is, to unify all unknown types into one class.  
In the field of bearing fault diagnosis, this problem can be described as follows: the source domain and the target domain contain different types of bearing faults, among which different types can include different working conditions, different fault types and different fault size. In actual working conditions, only partial bearing fault data can be obtained in most cases. Sometimes it is possible to obtain all fault class data, but it is difficult to obtain all fault size and working conditions for each fault class, and sometimes it is impossible to obtain all fault class data.  
We explain this problem more clearly in Figure 3, where different shapes represent different fault categories, different colors represent different fault sizes, and the color shades represent load conditions. Among them, the four figures a, b, c, and d show the four cases where the source domain and target domain contain the same fault type, the target domain contains unknown fault class, the target domain contains an unknown fault size, and the target domain contains both an unknown fault class and fault size. To solve this practical problem in the field of bearing fault diagnosis, we use the OSDABP method to solve and improve it.
## 2 Forward Modeling of Pavement Structures
### Forward Model Validation  
First, the accuracy of SEM was verified by selecting a three-layer pavement structure model placed on a homogeneous semi-infinite soil. [PERSON] et al. [27] studied a general pavement in Quebec, Canada, including a 150 mm thick asphalt layer, a 300 mm thick granular material base course, and a 600 mm thick subphase sand layer set on the semi-infinite soil, as shown in Figure 3. The generic pavement was isotropic, and its material parameters, including its Young's modulus, Poisson's ratio, mass density, and damping ratio, are reported in Table 1.  
The FWD load applied on the pavement is shown in Figure 4; the load amplitude was 40 kN, the duration was 30 ms, the number of Fourier transform samples \(N=2048\), and the number of wavenumber samples \(M=200\). The load acted within a circle of radius 0.15 m, i.e., the radius of the drop hammer was 0.15 m. The displacement of the deflection point was calculated at the radial distances from the load center point \(r=0\), 0.25, 0.375, 0.50, 0.625, 0.75, 1.00, 1.25, 1.50, and 2.00 m. The oscillation dissipation distance was assumed to be \(R=150\) m. The calculated displacement time-history curve is shown in Figure 5a, and the calculated maximum displacement curve at the deflection point is shown in Figure 5b. The calculation results were compared with the published results in the reference [27]. The figure reveals that the two results were basically completely matched, thus verifying the accuracy of the calculation results of the proposed algorithm in the time domain.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & \(h\) **(m)** & \(E\) **(MPa)** & \(\mu\) & \(\rho\) **(kg/m\({}^{3}\))** & \(\zeta\) **(\%)** \\ \hline Asphalt & 0.15 & 3000 & 0.35 & 2400 & 5 \\ Base course & 0.30 & 300 & 0.35 & 2300 & 2 \\ Subbase & 0.60 & 150 & 0.35 & 1900 & 2 \\ Subgrade & - & 75 & 0.35 & 1800 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The properties of the generic pavement.  
Figure 4: The FWD load pulse on the circular disk.  
Figure 3: A generic multi-layered pavement structure resting on a homogeneous half-space.
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures
### Services for NTN
High-definition (HD) and ultra-HD video channels can be broadcast or multicast to people subscribing the service. The average video data rate varies according to the image resolution, frame rate, coding and quality level, having, for example, a bit rate of about 7 Mbit/s for 4K (i.e., \(3840\times 2160\)) using HEVC x.265 codec, 50 Hz, 8/10 quality level [31]. The _throughput enhancement_ can be obtained above all in the case of limited terrestrial connectivity (e.g., fiber) or for live events. NTN can easily interconnect the final user with the content provider network, bringing the contents to the edge of the network. Table 3 summarizes the NTN services, the most-suited NTE and related requirements in terms of data rates. We also consider possible 5G categories where they can fall: eMBB, mMTC, and uRLLC. Note that uRLLC in Table 3 should not be considered as the classical way in 5G services (i.e., requiring connectivity lower than 5 ms). For NTN, we consider only the reliability concept based on the provision of coverage and then connectivity in the (even wide) service area with limited requirements on latency.
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 5 Conclusions and Future Scope  
The interest in automatic screening and diagnosis of DR is encouraged because of the very large diabetic and hypertensive population and the prevalence of retinopathy cases among them. Early screening and diagnosis are crucial in decreasing the risk of vision loss. This paper emphasizes the effective DR and HR grading schemes. Different methods have been developed to provide ophthalmologists with a mass screening solution, tracking the disease's course, and minimizing the risk of vision loss. In this paper, the proposed pseudocode and algorithms have been implemented and evaluated on MESSIDOR and ODIR datasets. The analysis of the performance in the form of visual and graphical analysis is performed. For validation of the proposed algorithms, PSNR is used for image enhancement, P, N, T values for image segmentation and the accuracy for classification of DR and HR have been performed. Transfer learning is utilized for the classification of DR and HR. The proposed approach achieves an accuracy of 98.72 %. The future work focuses on diagnosing various retinal diseases such as glaucoma, AMD and RoP. However, multiscale classification of HR can be developed to assist ophthalmologists for the correct diagnosis of HR.  
Conceptualization, methodology, writing--original draft, results analysis, D.N.; data collection, data analysis, writing--review and editing, results analysis, N.A.; methodology, writing--review and editing, design and presentation, references, B.O.S. and M.S.A.; methodology, writing--review and editing, M.A.; methodology, writing--review and editing, H.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R321), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU) for funding this research through the Research Group Program Under the Grant Number: (R.G.P.2/451/44).  
Not applicable.  
The datasets used during the current study are available from the corresponding author upon reasonable request.  
This research was financially supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R321), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU) for funding this research through the Research Group Program Under the Grant Number:(R.G.P.2/451/44).  
The authors declare no conflict of interest.
## 6 Discussion  
The rapid evolve of FSO market size motivates the development of new methods and technologies that provide robust, reliable, fast, and secure FSO systems. This work focused on the security issue of a well-researched FSO technology that uses structured light as bit pattern codes. The demonstrated system is based on two main parts, the first is the jammer signal which is a simple off-the-shelf laser source. The second part is the data communication link which comprises an SLM (at the transmitter-side) and a CCD camera (at the receiver-side).  
The practical implementation of the data communication link is limited by the cost and the switching speed (i.e., data transmission rate) of the structured light beam generator. Complex SLM can be replaced by 3D-printing microscale spiral phase plates that can generate pure structured light beams and are easily integrated with laser sources [25]. This increases the proliferation of the structured light beam generator in future FSO nodes. On the other side, the direct-detection method using a CCD camera and a robust ML algorithm reduce the hardware complexity at the receiver nodes, as they eliminate any modal decomposition process [7]. However, the limiting factors of the CCD camera are its sensitivity and frame capture rate. The former controls the free space distance; the latter limits the communication data rate.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## Materials and Methods
### 3D CAD Modelling of the Customized Template  
The 3D modelling of the customized template is the crucial step of the methodology; it has to fit perfectly with the vertebra to direct the surgeon correctly during screw insertion.  
The spine segment has to be imported into the Rhinoceros(r) software to check potential interferences between vertebra and the template (during the surgery) while also neglecting the vertebral natural distraction due to excision of the supraspinous ligament (which usually is foreseen in this kind of surgery or is needed in decompressive laminectomy surgery and usually is performed for vertebral distraction) that usually helps avoid interferences.  
The template interfacing shape is obtained by creating a negative solid from the lamina of the vertebra through a Boolean difference operation between a sufficiently big prism and the verteb itself. While doing that, the modelling procedure takes into consideration the spinous process to prevent under-cuts that could negatively affect the template positioning.  
Therefore, it is appropriate to cut the 3D model of the template in its internal surfaces so as not to generate interference and under-cuts with the surfaces of the spinous process. On the solid, tubes and holes have to be created along the already verified trajectories for creating the guides for the screws. Figure 2 shows the final shape of the solid.