esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Time Parameter Selection
* _T_COHER_ (coherence of the arrival times): This parameter assesses the temporal coherence of the Cherenkov photons, with gamma-ray showers typically exhibiting higher coherence compared to hadron-induced showers. * _T_RMS_W_ (weighted RMS of arrival times): This is a weighted version of _T_RMS_, which accounts for the intensity of the signal, providing a refined measure of the temporal spread. * _T_LAC_ (lacunarity of the arrival times sequence): This parameter captures the degree of homogeneity in arrival times, with a sparser distribution that can be indicative of hadron showers. * _LEN_ST_ (length of shower in time): This parameter measures the temporal extent of the shower, helping to differentiate between the typically shorter gamma-ray showers and the longer hadron-induced ones. * _LEN_DT_ (delta time length): This parameter represents the difference in time length across different parts of the shower, providing insights into the temporal asymmetry of the event. * _LEN_ST_NORM_ (normalized shower length in time): This is a normalized version of _LEN_ST_, offering a dimensionless measure that aids in comparing showers of different sizes and intensities. Figures 7 and 8 compare the feature importance of different sets of parameters. Specifically, Figure 7 shows the case where only morphological and stereoscopic parameters are used, without any temporal parameters, achieving a quality factor (QF) of 3.32. The quality factor is defined as \(QF=\frac{e_{\gamma}}{\sqrt{1-e_{\gamma}}}\), where \(e_{\gamma}\) and \(e_{h}\) are, respectively, the number of correctly identified gamma events and the number of correctly identified hadron events. Figures 6 and 8 then compare the importance when using all temporal parameters versus a selected subset. The QF increases from 3.65 when using all temporal parameters to 3.68 when using only the selected parameters. This improvement indicates that the selected parameters not only reduce computational complexity, but also enhance model performance. The correlation matrix (Figure 4), network graph (Figure 5) and feature importance plots (Figures 7 and 8) included in this paper visually support the selection rationale and underscore the efficacy of these parameters in our machine learning models. The integration of these features is anticipated to significantly enhance the discrimination between gamma-ray and hadron showers, contributing to the overall performance of the IACT system. The integration of these temporal parameters into the rejection algorithm significantly improves its performance, as evidenced by the increased Quality Factor (QF) from 3.32 to 3.68, representing an increase of approximately 10.8%. This enhancement underscores the value of these parameters in effectively distinguishing between gamma and hadron events, thereby optimizing the analysis performance. Figure 6: Feature importance of morphological, stereo, and all temporal parameters using random forest (blue for morphological and stereo parameters, light blue for temporal parameters). Figure 7: Feature importance of morphological and stereo parameters without any temporal parameters using random forest.
## 3 Results and Discussion
### Analysis of Microbial Diversity during the Fermentation of Passion Fruit Wine
#### 3.2.2 Bacterial Population Structure  
At the phylum level, 15 phyla were identified in both NF and CF, mainly Proteobacteria and Firmicutes, which were also the main dominant phyla of NF and CF (Figure 3a). [PERSON] et al. [41] found that the dominant bacterial phyla of the light-flavor Baijiu distiller grains were Proteobacteria and Firmicutes, which is consistent with the results of this study. In both CF and NF, the abundance of Proteobacteria gradually decreased, while the change in Firmicutes was the opposite.  
Figure 3: _Cont._  
At the genus level, the NF samples were classified into 130 fungal genera, and the CF samples were classified into 128 fungal genera. These bacteria included _Pauchacter_, _Partoea_, _Achromobacter_, _Escherichia_Shigella_, _Lactobacillus_, _Bifidobacterium_, _Akkernansia_, _Streptococcus_, _Clostridium_sensu_stricto_1_ and _Bacillus_ (Figure 3b). In NF, the dominant bacteria were mainly _Pauchacter_, _Pantoea_, _Escherichia_Shigella_, _Achromobacter_, _Lactobacillus_ and _Bifidobacterium_. The abundance of _Pauchacter_ and _Pantoea_ decreased gradually, and the changes in the abundance of other dominant bacteria were the opposite. According to reports, _Pauchacter_ and _Pantoea_ were also found in fresh water [42] and wine [43], and their abundance decreased gradually, which may be related to the high alcohol environment at the end of fermentation. The abundance of _Lactobacillus_ and _Bifidobacterium_ increased gradually due to their own anaerobic and acid-tolerant characteristics [44, 45], and then growth and reproduction occurred in the late stage of fermentation, and they became the dominant bacteria. In CF, the dominant bacteria were mainly _Pauchacter_, _Achromobacter_ and _Lactobacillus_, the abundance of _Pauchacter_ was modestly increased and then decreased sharply, and the abundance of _Achromobacter_ and _Lactobacillus_ was modestly decreased and then increased sharply. _Lactobacillus_ can tolerate organic acids [46], which inhibit the aerobic metabolism rate of yeast, which is conducive to the growth of beneficial bacteria in fermentation and beneficial to the increase in flavor substances [47, 48]. The CF had fewer dominant genera than the NF, which was speculated to be due to the inhibition of commercial strains (_Saccharomyces cerevisiae_).  
Analysis of Nonvolatile Metabolites and KEGG (Kyoto Encyclopedia of Genes and Genomes) Enrichment during the Fermentation of Passion Fruit Wine
# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 3 Materials and Methods
### Proposed Multimodal Depth-RGB and Thermal-RGB Mosaicking Algorithm  
The deployment of service drones in archaeological aerial monitoring while controlling the altitude produces images with an improved quality compared to the ones produced using the conventional methods and at a lower cost. Despite the advantages obtained, the drone-based approach creates a higher number of images to cover the same area of one satellite or aerial photograph. This creates a need for a stitching algorithm that forms a comprehensive view of the scene. Accordingly, we develop an algorithm that uses the drone flight recordings to provide three orthomosaics of the entire examined area in RGB, depth, and thermal scales. The algorithm begins with creating image sequences from the recorded RGB, depth, or thermal flight footage. Each image frame goes through a preprocessing stage that reduces the computational speed of the mosaic process by resizing the frame. Motion estimation is followed by panorama construction through image warping and blending in our proposed approach.  
We first detect the Speeded-UP Robust Features (SURF) for the current and prior greyscale frame sequences in the motion estimation. The blob features, which are candidate points of interest in a frame, are detected through this approach. Then, for each pair of successive frames, we extract the SURF features to determine the corresponding points between them. To estimate the motion of the unique points between the frames, one-to-one matching between the points on the current frame and the preceding one is required. We use transformation estimation to estimate the transform between the current and prior frames after retrieving the locations of the matched points in the frames.  
Based on the matched pair of points, this algorithm produces a transformation matrix that defines the motion from one frame to the next.  
\[\begin{bmatrix}t_{11}&t_{12}&t_{13}\\ t_{21}&t_{22}&t_{23}\\ t_{31}&t_{32}&1\end{bmatrix} \tag{1}\]  
The more matched pairings with higher confidence, the more accurate the estimated transformation will be at the cost of additional computations. We then use Equation (2), where \"\(n\)\" represents the current frame and \"\(T\)\" represents the transformation matrix between two successive frames, to accumulate the transform from the first frame to the subsequent frames until all transforms for all frames in a flight video are accumulated.  
\[T(n)\times T(n-1)\times...\times T(1) \tag{2}\]  
We use image warping to create the panorama of the full flight video, which applies a geometric change to the image based on the estimated transformation matrix. Each frame can then be relocated to its corresponding position in the panorama, resulting in a fully aligned orthomosaic. The binary mask is then used to apply image blending. The same transformation matrices used for the RGB frame sequences are used to estimate motion on depth, and thermal frames. Figure 6 depicts the whole mosaic pipeline, including intermediate outputs, for an RGB flight video.
# Mechanical Properties Assessment of Low-Content Capsule-Based Self-Healing Structural Composites
###### Abstract  
Microcapsule-based carbon fiber reinforced composites were manufactured by wet layup, in order to assess their mechanical properties and determine their healing efficiency. Microcapsules at 10%wt. containing bisphenol-A epoxy, encapsulated in a urea formaldehyde (UF) shell, were employed with Scandium (III) Triflate (Sc (OTf)3) as the catalyst. The investigation was deployed with two main directions. The first monitored changes to the mechanical performance due to the presence of the healing agent within the composite. More precisely, a minor decrease in interlaminar fracture toughness (G\({}_{\text{IIC}}\)) (\(-14\%\)), flexural strength (\(-12\%\)) and modulus (\(-4\%\)) compared to the reference material was reported. The second direction evaluated the healing efficiency. The experimental results showed significant recovery in fracture toughness up to 84% after the healing process, while flexural strength and modulus healing rates reached up to 14% and 23%, respectively. The Acoustic Emission technique was used to support the experimental results by the onsite monitoring.  
self-healing; capsule based composites; mechanical properties +
Footnote †: journal: Applied Sciences
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment
### Experimental Analysis  
#### 5.4.1 Top-10 Nodes  
In real networks, the nodes in key positions are few compared to the network's size. For easier analysis, the node values obtained by the different algorithms are ranked in descending order, and the top-10 nodes are considered to be the important nodes in the network. Table 2 denotes the results of the top-10 nodes obtained by the different algorithms under the Dolphins network. Nodes that agree with the SIR ranking list are marked in red, nodes that differ are marked in blue, and nodes that do not appear in the SIR ranking list are not colored. Figure 5 summarizes the top-10 important nodes obtained from each comparison algorithm.  
Nodes that are consistent with the SIR ranking are colored dark blue, nodes with inconsistent rankings are colored blue, and nodes that do not appear in the SIR are colored yellow.  
In the heatmap, each row represents the comparison between the important node ranking obtained by the algorithm and the SIR ranking. From the analysis, it can be observed that the LBIA algorithm achieved the highest number of nodes with consistent rankings with SIR on six networks: Dolphins, USAir97, Bio-celegans, Soc-hamser, CiteSeer, and CA-Erdos. It also had the largest number of identical nodes. Although the number of  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**BC** & **CC** & **DC** & **PageRank** & **KSGC** & **LCH** & **NTPR** & **IDME** & **LBIA** & **SIR** \\ \hline
37 & 37 & 15 & 15 & 15 & 15 & 38 & 46 & 15 & 15 \\
2 & 41 & 38 & 18 & 38 & 38 & 15 & 38 & 38 & 38 \\
41 & 38 & 46 & 52 & 46 & 46 & 46 & 15 & 46 & 46 \\
38 & 21 & 34 & 58 & 34 & 34 & 34 & 30 & 34 & 34 \\
8 & 15 & 52 & 38 & 52 & 41 & 51 & 52 & 21 & 30 \\
18 & 2 & 18 & 13 & 30 & 51 & 22 & 34 & 41 & 21 \\
21 & 8 & 21 & 34 & 21 & 21 & 41 & 58 & 30 & 52 \\
55 & 29 & 30 & 30 & 41 & 30 & 14 & 18 & 52 & 22 \\
52 & 34 & 58 & 14 & 58 & 19 & 44 & 21 & 51 & 51 \\
58 & 9 & 2 & 2 & 18 & 52 & 18 & 14 & 19 & 41 \\ \hline \end{tabular}
\end{table}
Table 2: Top-10 nodes for each comparison algorithm on the Dolphins network: nodes that agree with the SIR ranking list are marked in red, nodes that differ are marked in blue, and nodes that do not appear in the SIR ranking list are not colored.  
Figure 5: Top-10 nodes’ heatmaps for different algorithms on different networks and the legend: nodes that are consistent with the SIR ranking are colored dark blue, nodes with inconsistent rankings are colored blue, and nodes that do not appear in the SIR are colored yellow.  
consistent nodes was not the highest on the Eurroad and RT-assad networks, it was second only to the algorithm with the highest count, and the number of identical nodes was the largest among the nine algorithms. On the CA-GrQc network, the LBIA algorithm also performed well for eight nodes and accurately identified the top-two nodes.  
In summary, the LBIA algorithm was better at accurately identifying the top-10 key nodes in the network. Compared to the other algorithms, LBIA performed better in node ranking sequences.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 5 Overview of Reviewed Datasets for ML-Powered IDS
### Ton_IoT  
The TON_IoT dataset [85] was also created by the Cyber Range Lab of UNSW Canberra in 2020. The dataset is labeled, unbalanced, and was generated from an IoT/IIoT network testbed. The dataset contains data from heterogeneous sources, gaining its name (TON) from the data it includes: telemetry, operating systems, and network. The researchers include simulated sensor data from seven IoT/IIoT sensors (weather station, motion-activated lights, garage door, smart fridge, smart thermostat, Modbus service, and GPS) as well as real devices: two phones and a smart TV. Nine types of attacks are represented in the dataset, including Scanning, DoS, DDoS, ransomware, backdoor, data injection, Cross-site Scripting, password cracking attack, and Man-in-The-Middle. There are over 22,000,000 total data records, of which just under 800,000 are normal traffic, which means TON_IoT is also an imbalanced dataset. Over 6,000,000 of the records are from DDoS attacks. Since the dataset contains multiple sub-datasets with unique processed or raw data [25], it is not clear how many features the dataset holds in total, though the combined dataset called combined_IoT_dataset proposed by the original paper [85] uses a total of 22 features.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 4 Experimental Results
### Experimental Conditions  
The test platform is the Ubuntu 22.04.3 with an Intel Xeon(R) CPU E5-2650 processor and 256 GB of memory. All coding experiments follow the V-PCC Common Test Condition (CTC) [37]. Using the five quantization parameters (QP) shown in Table 4, from low bit rate (R01) to high bit rate (R05) [33]. It has good generalization. Moreover, the test sequence is _Loot_, _Redandblank_, _Soldier_, _Longdress_, _Basketball_player_ and _Dancer_ recommended in CTC, where _Basket ball_player_ and _Dancer_ are 11 bit [39; 40]. The remaining test sequences are 10 bit. The encoding configuration is set to all intra. The time-saving ratios \(\Delta T\) are calculated as follows:  
\[\Delta T=\frac{T_{o}-T_{o}}{T_{o}}, \tag{9}\]  
Figure 9: DAPP architecture.  
where \(T_{o}\) and \(T_{e}\) are the coding time consumption of the original version and our method, respectively. For a fair comparison, all test PCs use the same encoding parameters to achieve comparable RD points.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 1 Introduction
### Related Work  
Several surveys have focused on FRS, but they cannot provide comprehensive solutions to address the effects of privacy and security issues in big data applications. In [19], the study investigated the characteristics and challenges of federated learning in detail, but did not provide enough detail on FRSs. In [20], the authors gave an accurate definition of federated learning and its different architectures and applications, such as the FRS. In [21], the authors provided a comprehensive categorization of recommendation methods, as well as discussed their respective limitations. In addition, several surveys were conducted that focused on the privacy and security of federated learning. In [22], the study identified and analyzed the potential privacy threats and security vulnerabilities in federated learning.  
Figure 1: This figure represents the generic training procedure of FRSs, where a set of IoT devices is connected to the cloud server. Firstly, the cloud server synchronizes with the IoT devices and creates personalized recommendations; secondly, those IoT devices train the local learning models individually and then upload those trained learning models to the cloud server. Finally, the cloud server aggregates those local models to generate new recommendations for the next round.  
Similarly, in [23], the authors elaborated on the assumptions, reasons, principles, and differences between the various attacks and defenses in the privacy and robustness fields of federated learning, but none of them particularly focused on FRSs. Despite this, most of these surveys focused on either recommendation systems or federated learning separately, and few surveyed specific problems in FRSs. For example, in [10], the study proposed a classification for FRSs from the perspective of federated learning and investigated the algorithm-level and system-level challenges for FRSs. These surveys may provide insight into the capabilities of FRSs, but they did not offer solutions that can effectively deal with privacy and security within the framework of the system.
## 4 Single parent parallel Lamarckian GA implementation
### Application on silicon germanium core-shell structures  
GA implementation has been applied on the core-shell structures of SiGe heteronuclear clusters when the benchmark on carbon clusters is completed [61]. The main objective of this application is comparing Si-core, Ge-core and mixed structures in order to understand the preference of SiGe clusters when these three alternatives are considered. More information about nanoalloys can be found in the reference [59].  
Figure 4: Optimized structures of carbon clusters. Top line: 11 atoms, 20 atoms (\(I_{h}\) symmetry); Bottom line: 22 atoms, 32 atoms (\(D_{3}\) symmetry)  
Figure 5: Initial core shell geometries of the Si\({}_{50}\)Ge\({}_{50}\) cluster.  
Stillinger Weber PEF [62] has been used to define Si-Si, Si-Ge and Ge-Ge interactions in the clusters. 100 atom and 150 atom clusters have been studied with three different type ratios and subjected three structures which are Si-core, Ge-core and mixed atoms have been used as initial geometries as separate run instances. Si\({}_{25}\)Ge\({}_{75}\), Si\({}_{50}\)Ge\({}_{50}\) and Si\({}_{75}\)Ge\({}_{25}\) clusters have been studied in 100 atom case and Si\({}_{50}\)Ge\({}_{100}\), Si\({}_{75}\)Ge\({}_{75}\) and Si\({}_{100}\)Ge\({}_{50}\) clusters have been studied for 150 atom case. Si-core and Ge-core initial geometries for Si\({}_{50}\)Ge\({}_{50}\) cluster is shown in Figure 5 for exemplification. Population size has been chosen to be 16 and 24 individuals for 100 atom and 150 atom clusters respectively. Value of the shrinkage factor is not crucial for SiGe clusters and thus it has been fixed to the value of 0.7 for all separate run instances.  
Finding global minimum is beyond the capabilities of the described single parent GA implementation when 100 atom and 150 atom heteronuclear clusters are considered. Nevertheless it was helpful to decide about the preference of SiGe clusters between Si-core, Ge-core and mixed geometries. When the population was initialized with Si-core geometry clusters, individual clusters conserved the Si-core geometry during the GA optimization process and final geometries are again totally in the form of Si-core Ge-shell. However, when Ge-core initial geometry was used for individual clusters, initial core-shell structures become distorted during GA optimization and some Ge atoms reach the surface at the end of the optimization process and mixed initial geometry runs concluded with Si rich cores and Ge rich shells. These results have been obtained regardless of the atom type ratios and therefore, it has been concluded that SiGe clusters prefer Si-core Ge-shell structures. Images of the final geometries may be seen in Figure 6 for Si\({}_{25}\)Ge\({}_{75}\) (initial Si-core), Si\({}_{50}\)Ge\({}_{50}\) (initial mixed) and Si\({}_{75}\)Ge\({}_{25}\) (initial Ge-core) clusters. Tendency of Ge atoms for going up to surface can be seen clearly in these images.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 4 Bounds on Orientation Estimation  
In this section, we derive 2-dimensional (2D) and 3-dimensional (3D) CRLBs for an orientation estimator based on the position estimation described process in the previous section.
## 3 An Opportunistic Routing for Data Forwarding Based on Vehicle Mobility Association
### Definition and Analysis
The position relationship of vehicles A and B can be calculated through the Ev vector of the EM message carried by each other to determine whether the two vehicles are traveling in the same direction or in opposite directions. Here we use cosine similarity to calculate the directional relations between vehicles. The cosine similarity is also called the cosine distance. It is utilized as a measure of the magnitude as the difference between two individuals in the vector space. The vector is the direction along the line in the multidimensional space. If the two vectors are in the same direction (that is, the angle is close to zero),then the two vectors are parallel. In order to establish whether the two vectors direction is consistent, it is necessary to use the cosine theorem calculation vector angle. Compared to Euclidean distance, cosine distance pays more attention to the difference between the two vectors in the direction. With the help of the three-dimensional coordinate system to see the difference between the Euclidean distance and cosine distance:  
It can be seen from the Figure 2 that the Euclidean distance is used to measure the absolute distance of each point in space, where each point with coordinates is directly related. The cosine distance measure is the angle between the vector space, more strongly reflecting differences in direction, rather than position. If the position of point A remains the same and point B has moved away from the origin of the axis, then the cosine distance \(\cos\theta\) is kept constant (because the angle does not change), and the distance between A and B is clearly changed. This represents the difference between Euclidean distance and cosine distance. The calculation method is defined as follows:  
\[\eta=\cos\frac{a.v*b.v}{|a.v||b.v|}\]  
\[=\cos\frac{a.v.x*b.v.x+a.v.y*b.v.y}{\sqrt{(a.v.x^{2}+a.v.y^{2})*(b.v.x^{2}+b.v.y^{2})}} \tag{9}\]  
where \(a.v\) and \(b.v\) represent the speed vector of vehicle A and vehicle B. \(a.v.x\) and \(b.v.x\) represent the speed of vehicles A and B in the \(x\)-axis direction vector. a.v.y and b.v.y represent the speed of vehicles A and B in the \(y\)-axis direction vector. \(\sqrt{(a.v.x^{2}+a.v.y^{2})*(b.v.x^{2}+b.v.y^{2})}\) represents the velocity vector inner product. If \(|\eta|<10\) and vehicles in the same information source region indicate that the vehicles are traveling in the same direction, there is no exchange of duplicate information. If \(|\eta|>170\) and vehicles are in different information source regions, as the vehicles are traveling in opposite directions, then duplicate information is exchanged.