esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Manually Cleaned Annotated Data (MD)
Evaluating the models with the precision metrics, the BM trained with the RD dataset showed the best performance for all bridges with the exception of the PS/R/D model. The balanced models trained with the BD dataset had the highest TPR values ranging from 0.504 to 0.558, while the augmented models showed the least performance values. Similarly, the balanced models trained with the RD showed the best performance for the TNR and F1-score with the exception of the PS/R/D model. In general, the results for FRNB for the evaluated datasets are presented in Table 7 with a highlight of the highest and lowest performance metrics. The results depicted that the FR/R/D model had the lowest performance for one-out-of-six metrics, whereas the F/R/B model showed the highest performance for three-out-of-six metrics, and FM/M/AB showed three-out-of-six lowest metrics. This suggests that the model trained with background annotated and balanced pixels for the FRNB bridge gave the best performance, while the model trained with MD had the worst performance. This is expected as the number of original pixels had dropped by 38% after manually removing the artifacts and background blocks. Similarly, the other four bridges show the same pattern where the model trained with the RD and balanced pixels depicted the best performance metrics of at least three out of six of the highest metrics, and the worst models are the MD-trained models. If the GA were to be used as the presiding metric for evaluating models' performance, then the models trained with the RD with base hyperparameters showed the highest performance. The augmentation of the pixels had a downtrend effect on the performance of the models. This suggests that local augmentation, as adopted in the models, had no up-side effect on the model's performance. This may imply that the pixel-wise augmentation adopted is not suitable and robust for the semantic segmentation of IRT images.
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 3 Proposed Methodology
### Construct MBRs with Hilbert Curve  
In order to construct the SbMBR tree mentioned above, the key is to construct MBRs with larger maximum error values according to existing MBRs with smaller maximum error values. In this paper, we propose a heuristic method similar to the method used in Hilbert R-tree to realize it [31].  
A Hilbert curve is a kind of space-filling curve which can visit all the points in a \(k\)-dimensional grid exactly once and never crosses. As shown in Figure 2, the basic Hilbert curve, also called the curve of order 1, is on a \(2\times 2\) grid, denoted by \(H_{1}\). The curve of order \(i\) is derived from four curves of order \(i-1\) with different orientations. The second-order and third-order Hilbert curve are also shown in Figure 2. When the order of a Hilbert curve gradually increases until it reaches infinity, the curve becomes fractal [26].  
The path of a Hilbert space-filling curve gives the multi-dimensional grid points a one-dimensional linear order, which makes it as easy to process the data in multi-dimensional space as in one-dimension space.  
Next, we propose a method to construct MBRs with the Hilbert curve. It is assumed that the maximum error value of the MBRs to be constructed has been given. First, we sort the existing MBRs according to their centers in the order of the Hilbert curve. Second, we scan backward from the first MBR until the \(n\)-th MBR is scanned and the maximum error value of the first \(n\) MBRs is just greater than the given maximum error value. Therefore, the first \(n-  
```
0: A set of MBRs with the same maximum error value \(p\), the maximum error of new MBRs to be obtained \(x\)
0: A set of new MBRs with given maximum error value; inclusion relationship between new MBRs to be obtained and existing MBRs
1. \(S\)\(\leftarrow\)\(\varnothing\)
2. \(R\)\(\leftarrow\)\(\varnothing\)
3. sort \(p\) according to their centers with the Hilbert curve
4. \(i\)\(\leftarrow\)0
5. \(j\)\(\leftarrow\)0
6. while\(i<p\).size do
7. while\(j<p\).size do
8. \(y\)\(\leftarrow\)calculate the maximum error of the MBR of \(p_{i},p_{i+1},\ldots,p_{j}\)
9. if\(y>x\)then
10. break
11. end if
12. \(j\)\(\leftarrow\)\(j\) + 1
13. end while
14. \(m\)\(\leftarrow\)calculate the MBR of \(p_{i},p_{i+1},\ldots,p_{j-1}\)
15. \(S\).insert(\(m\))
16. for\(k=i,i+1,\ldots,j-1\)do
17. R.insert(\(p_{k}\))\(\subseteq\)\(m\)
18. end for
19. \(i\)\(\leftarrow\)\(j\)
20. end while
21. return(\(S,R\))
```
**Algorithm 1**Merge MBRs with the Hilbert curve  
The above is mainly about the two-dimensional Hilbert curve. A Hilbert curve can be conveniently extended to three dimensions or higher dimensions. A three-dimensional Hilbert curve is shown in Figure 3. The extension method is described in detail in [32].
###### Abstract  
Spatial location of sugarcane nodes using robots in agricultural conditions is a challenge in modern precision agriculture owing to the complex form of the sugarcane node when wrapped with leaves and the high computational demand. To solve these problems, a new binocular location method based on the improved YOLOv4 was proposed in this paper. First, the YOLOv4 deep learning algorithm was improved by the Channel Pruning Technology in network slimming, so as to ensure the high recognition accuracy of the deep learning algorithm and to facilitate transplantation to embedded chips. Secondly, the SIFT feature points were optimised by the RANSAC algorithm and epipolar constraint, which greatly reduced the mismatching problem caused by the similarity between stem nodes and sugarcane leaves. Finally, by using the optimised matching point to solve the homography transformation matrix, the space location of the sugarcane nodes was for the first time applied to the embedded chip in the complex field environment. The experimental results showed that the improved YOLOv4 algorithm reduced the model size, parameters and _FLOPs_ by about 89.1%, while the average precision (_AP_) of stem node identification only dropped by 0.1% (from 94.5% to 94.4%). Compared with other deep learning algorithms, the improved YOLOv4 algorithm also has great advantages. Specifically, the improved algorithm was 1.3% and 0.3% higher than SSD and YOLOv3 in average precision (_AP_). In terms of parameters, _FLOPs_ and model size, the improved YOLOv4 algorithm was only about 1/3 of SSD and 1/10 of YOLOv3. At the same time, the average locational error of the stem node in the Z direction was only 1.88 mm, which totally meets the demand of sugarcane harvesting robots in the next stage.  
s +
Footnote †: journal: Applied sciences
# Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination
## 3 Results
### Comparison of the Algorithms
#### 3.3.2 Conditions for Successful Diversity Measurements  
For a successful measurement, a correct alignment of the passive measuring heads is essential, otherwise insufficient amounts of light reach the detectors resulting in a correspondingly poor signal quality. Figure 17 shows an example of such a case.  
For this purpose, the measurement object is arbitrary since the aim of this section is only to demonstrate a measurement with incorrect alignment. For this measurement,  
Figure 16: Comparison of the frequency spectra of the four channels CH1–CH4 and combined signal, one section of the spectra is shown smoothed for better visualization; (RBW = 1 Hz).  
Figure 17: Velocity signal of the four channels CH1–CH4 and the combined signal with poor alignment of the measurement heads; (RBW = 2.5 Hz).  
the speaker was removed, and the measurement was conducted on the laboratory table underneath (no active vibration). The resulting poor alignment of the measuring heads results in a poor signal quality for all passive channels, as very little light reaches the sensors of the measuring heads. This causes numerous peaks in the velocity signals of CH1 and CH2. In such a case, the combined signal obtained by our algorithm is largely equivalent to the signal with the highest signal strength (in this case CH4). This is visible in the matching velocity signal in Figure 17 and in the almost identical frequency spectrum, shown in Figure 18.
## 1 Introduction
Flavonoids are a diverse group of phytonutrients (plant chemicals) found in almost all fruits and vegetables, and they have garnered attention for their potential health benefits, including their anti-inflammatory, antioxidant, and anti-diabetic effects [12,13]. Flavonoids have been used for centuries in traditional herbal therapies, particularly in Asia and Europe, where their medicinal properties are valued for managing inflammation, oxidative stress, and diabetes. Commonly found in medicinal plants like green tea, citrus fruits, and various herbs, these compounds play a crucial role in traditional Chinese and Ayurvedic medicine, where flavonoid-rich plant extracts have long been employed to treat chronic conditions such as diabetes, cardiovascular diseases, and inflammation [14,15]. This historical use underscores the therapeutic potential of flavonoids as natural treatments for metabolic diseases, including T2 DM. Several plant-derived flavonoids have been identified as natural AMPK activators, offering a complementary approach to traditional pharmacotherapy in T2 DM management [16]. These compounds not only activate AMPK but also possess a broad spectrum of biological activities, including cardiovascular protection and cancer risk reduction. The use of flavonoids as AMPK activators could provide a safer, more holistic alternative for managing T2 DM, with fewer side effects compared to synthetic drugs. For example, metformin, an oral anti-diabetic drug derived from French lilac, is well known for its safety and efficacy in diabetes treatment [17]. A comparative study found that anthocyanins extracted from blueberries (595 mg/g total anthocyanins) led to a 33% to 51% reduction in blood glucose, compared to a 27% reduction with metformin [18]. Additionally, quercetin hasbeen shown to stimulate an insulin-dependent AMPK pathway, similar to the activity of metforminin [19]. Recent animal studies and preliminary clinical trials have demonstrated the potential of flavonoids for managing T2 DM. For example, quercetin has been shown to improve glucose tolerance and reduce oxidative stress in animal models of T2 DM by activating the AMPK pathway [20]. In another study, genistein, a soy-derived flavonoid, was found to enhance insulin sensitivity and reduce blood glucose levels in diabetic mice, again through AMPK activation [21]. Preliminary clinical trials have also suggested that supplementation with flavonoid-rich extracts can improve insulin resistance and reduce markers of inflammation in individuals with T2 DM, though more extensive research is needed to confirm these findings [22]. The purpose of this review is to explore the role of plant-derived flavonoids as AMPK activators in the management of T2 DM. This review examines the mechanisms by which these compounds activate AMPK, their potential therapeutic benefits, and how they compare to other conventional therapies. The aim is to provide a comprehensive overview of the current understanding of flavonoids in T2 DM therapy and to highlight the potential of these natural compounds as an alternative or complementary treatment strategy.
## 2 Background and Related Work
### Architectural Concepts
#### 2.1.2 Shared Memory  
Programs are loaded from hard disk drives to RAM to be executed, using RAM for both instructions and data, because the access to RAM is much faster than to hard disk. RAM memory is a limited resource, so when there is no free memory available, some data and instructions are moved to virtual memory, which is a copy of RAM stored in the hard disk. Using virtual memory in an application severely affects its performance. It is natural that operating systems employ mechanisms such as memory sharing to reduce memory utilization. Whenever there are two different processes using the same library, instead of loading it twice into RAM memory, sharing memory capabilities allow mapping the same physical memory page into the address spaces of each process.  
Deduplication is a concrete method of shared memory, which was originally introduced to improve the memory utilization of VMMs and was later applied to non-virtualized environments. The hypervisor or the operating system scans the physical memory and recognizes processes that place the same data in memory; that is, pages with identical content. When several pages happen to include the same content, all the mappings to these identical pages are redirected to one of them, and the other pages are released. However, if any change is performed by any process in the merged pages, memory is duplicated again.  
The Linux memory deduplication feature implementation is called KSM (Kernel Same-page Merging) and appeared for the first time in Linux kernel version 2.6.32. KSM is used as a page sharing technique by the Kernel-based Virtual Machine (KVM), which we used as hypervisor within our experiments. KSM scans the user memory for potential pages to be shared, scanning only potential candidates instead of the whole memory continuously [24].  
The deduplication optimization saves memory allowing more virtual machines to run on the host machine. To exemplify this statement, we refer to onl [25], who stated that it is possible to run over 50 Windows XP VMx with 1 GB of RAM each on a machine with just 16 GB of RAM. In terms of performance, deduplication is an attractive feature for cloud providers. However, after several demonstrations of side-channel attacks exploiting page sharing, they are advised to disable this feature and no cloud provider is ignoring this advice.
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 4 Experimental Results
### Ground Truth Labeling  
Here, we shall show ground truth labeling used in our training. According to SPD2010 competition's instructions, for a ground truth singular point \((x_{0},y_{0},t_{0})\), if a detected singular point \((x,y,t)\) satisfies \(t=t_{0}\) and \(sqrt((x-x_{0})^{2}+(y-y_{0})^{2})<10\) at the same time, the point is considered to be truly detected, where \((x,y)\) is the coordinate of detected singular point, \(t\) is the type of the singular point including the core and the delta, and \((x_{0},y_{0},t_{0})\) is the ground truth. According to the competition's instructions mentioned above, the detected singular point is considered as a circular area, which is equal to the singular point area. In the process of our labeling, we use the singular point coordinate as the center and let 10 pixels as the radius to draw a circle. The circle area is set to the foreground while the rest is set to the background. Our labels can be classified into two categories, and thus two sets of labels are generated. One set is the label of cores and the other is the label of deltas. The calibrated samples of labeling can be shown in Figure 6, where (a) is the original fingerprint, (b) is the label of core, and (c) is the label of delta.  
Figure 5: Two examples of the training set of SPD2010: (a) a low-quality fingerprint; and (b) a high-quality fingerprint.  
Figure 6: The calibrated samples of label: (a) the original fingerprint, (b) the label of core, and (c) the label of delta.
# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 3 Related Work
Finally, in [24], the authors notice that, in recent years, the widespread adoption of IoT technology has significantly transformed various industries, driven by enabling technologies that have accelerated this shift. The unprecedented possibilities offered by IoT have led to the development of smart applications integrated into national infrastructure. However, the growing popularity of IoT has also attracted adversaries who exploit the inherent vulnerabilities of IoT devices to launch sophisticated attacks, including _Multi-StageAttacks_ (MSAs), such as IoT botnet attacks. These attacks have resulted in substantial financial losses across industries, amounting to billions of dollars. To address this issue, the study proposes a _two-phase system for IoT botnet detection_. The first phase focuses on _identifying IoT botnet traffic_ by subjecting IoT traffic to feature selection and classification model training to differentiate between malicious and normal traffic. The second phase analyzes the malicious traffic identified in the first phase to _detect various botnet attack campaigns_. This phase employs an alert correlation approach that combines _Latent Semantic Analysis_ (LSA) with graph theory-based techniques. The proposed system was evaluated using a publicly available real IoT traffic dataset and demonstrated promising results.
## 3 Results and Discussion
### Electrical Conductivity  
Table 2 summarizes the electrical conductivity and the apparent density of the compression molded (CM) and 3D manufactured (FDM) samples. The apparent density was estimated from direct measurement of weight square plaques and volume with size of 45 \(\times\) 45 \(\times\) 2 mm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Samples**} & \multicolumn{2}{c}{**Electrical Conductivity (S-cm\({}^{-1}\))**} & \multicolumn{2}{c}{**Apparent Density (g/cm\({}^{3}\))**} \\ \hline \multirow{4}{*}{ABS/GNP} & CM & \((3.06\pm 4.02)\times 10^{-16}\) & 1.05 \(\pm\) 0.02 \\  & FDM-PC & \((4.93\pm 2.89)\times 10^{-16}\) & 0.87 \(\pm\) 0.07 \\  & FDM-HC & \((2.63\pm 3.21)\times 10^{-16}\) & 0.93 \(\pm\) 0.03 \\  & FDM-H45 & \((2.67\pm 3.30)\times 10^{-16}\) & 0.95 \(\pm\) 0.02 \\  & CM & \((1.55\pm 3.67)\times 10^{-14}\) & 1.07 \(\pm\) 0.01 \\  & FDM-PC & \((3.00\pm 4.08)\times 10^{-15}\) & 0.89 \(\pm\) 0.02 \\  & FDM-HC & \((2.34\pm 4.34)\times 10^{-15}\) & 0.97 \(\pm\) 0.02 \\  & FDM-H45 & \((1.89\pm 3.98)\times 10^{-15}\) & 0.98 \(\pm\) 0.02 \\  & CM & \((6.97\pm 1.01)\times 10^{-3}\) & 1.07 \(\pm\) 0.01 \\  & FDM-PC & \((9.17\pm 0.89)\times 10^{-4}\) & 0.87 \(\pm\) 0.03 \\  & FDM-HC & \((2.34\pm 1.11)\times 10^{-4}\) & 0.92 \(\pm\) 0.03 \\  & FDM-H45 & \((1.68\pm 1.21)\times 10^{-4}\) & 0.94 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electrical conductivity and apparent density of ABS and its composites.  
Figure 8: TEM micrograph of ABS/GNP nanocomposite filament: (**a**) Perpendicular and (**b**) parallel.  
Figure 9: TEM micrograph of ABS/CNT nanocomposite filament: (**a**) Perpendicular and (**b**) parallel.  
The presence of MWCNT considerably modified the electrical conductivity of the ABS/CNT composites. Electrical conductivities of about 10\({}^{-3}\) S\(\cdot\)cm\({}^{-1}\) and 10\({}^{-4}\) S\(\cdot\)cm\({}^{-1}\) were achieved for the polymer composites manufactured via compression molding and FDM, respectively. This observation suggests that the addition of 6 wt.% of MWCNT created an efficient conductive network across the ABS matrix offering sufficient mobility to the charge carriers. Differently, the same amount of graphene nanoplatelets did not modify the electrical conductivity of ABS matrix. Both ABS and ABS/GNP nanocomposites were non-conductive materials with electrical conductivity values in the range between 10\({}^{-16}\) and 10\({}^{-14}\) S\(\cdot\)cm\({}^{-1}\), independent of the fabrication route.  
In the case of GNP-based nanocomposites, TEM images (see Figure 8) of extruded filaments indicate that graphene nanoplatelets were oriented along the polymer flow direction. The contacts created between graphene nanoplatelets were not enough to promote a percolative path in ABS/GNP composites resulting in a non-conductive composite. From Figure 9, it can be observed that MWCNTs were not oriented during the extrusion of the filaments. In fact, this filler remained well-distributed across the ABS matrix thus creating an efficient conductive percolated network for carrying charges across the material.
# Effect of Subsequent Subgrade on Seismic Response of the High-Speed Railway Track-Bridge System
## 6 Transfer of Bridge Seismic Risk
### Seismic Response Analysis of Subgrade Section  
Figure 17 shows the effect of the RTCBD on the displacement and internal force of each critical component of the track structure in the subgrade section. It is indicated that the seismic response (longitudinal displacement and axial force) of the base plate, track plate, and rail on the subgrade section increases after the installation of RTCBD. Notably, the closer to the bridge, the greater the seismic response of the base plate, track plate, and rail.  
Figure 16: Differences in internal force and displacement of critical components of bridge section.  
Upon analyzing the seismic response data, we observe notable increases in both longitudinal displacement and axial force across various components of the subgrade section. Specifically, the longitudinal displacement of the base plate experiences an increase ranging from 25% to 46%, while its axial force escalates by 19% to 56%. Similarly, the longitudinal displacement of the track plate rises by 14% to 19%, and its axial force increases by 3% to 24%. For the rail, there is an increase of 12% to 18% in longitudinal displacement and a 14% to 35% rise in axial force.  
In conclusion, the longitudinal displacements and internal forces of the track structure on the bridge section are significantly reduced and more evenly distributed after the installation of the RTCBD. Conversely, there is a significant increase in these parameters in the track structure on the subgrade section. This indicates that the RTCBD is effective in reducing the seismic response of the track structure in bridge section. Furthermore, it can also transfer the seismic response to the subgrade section, thus successfully realizing the design purpose of transferring the bridge seismic damage to the subgrade section. Since the cost of repairing post-earthquake damage to subgrade structures is much less than that of bridge structures, the installation of the risk-transferring connecting beam device can also significantly reduce the seismic maintenance costs of bridge structures [42]. This not only has great economic benefits but also can provide a completely new research direction for future seismic isolation design. The schematic diagram of seismic risk transfer for the risk-transferring connecting beam device is shown in Figure 18.  
Figure 17: Differences in internal force and displacement of critical components of subgrade section.