esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Border Irregularity Assessment with Application in Skin Pathology
## 2 Material and Methods
### Determining the Epidermis \"Outer\" Edge  
To determine the \"outer\" edge of the epidermis (the edge adjacent to stratum corneum) for each border point \(P_{i}\) we cast two rays--in the same direction as its normal (\(\vec{r}_{\rm n}\)) and in the opposite direction (\(\vec{r}_{\rm o}\)). For each ray we compute the distance along the ray to its first intersection with the slide background, \(d_{\rm BgN}\) and \(d_{\rm BgO}\) (Figure 9a). If the ray does not cross the background, we consider the distance to be infinitely large. Since in the dermis there are empty spaces between collagen fibers, to compute the distance to the background we do not use the \"raw\" mask, \(M_{\rm bg}=\overline{M_{\rm fg}}\), but \(M_{\rm bg}\circ\text{SE}_{D15}\) (where \(SE_{Dn}\) is a disk-shaped structuring element of radius \(n\)).  
The point \(P_{i}\in L_{b}\) is initially considered as belonging to the \"outer\" edge if the following conditions are met: (1) \(d_{\rm BgN}<d_{\rm BgO}\), and (2) \(\vec{r}_{\rm n}\) does not cross epidermis (Figure 9a). The results of  
Figure 8: Approximating a normal to the epidermis border \(\vec{n}_{i}\) at point \(P_{i}\). The approximated normal is orthogonal to the vector \(\overline{P_{i-d}P_{i+d}}\) (the point distance was set to \(d=4\)).  
such a test for all border points form a vector of logical values, \(M_{\text{Out0}}\), which might be interpreted as a 1D binary mask. We will denote the results of the test \"\(\vec{r}_{\text{n}}\) vector crosses the epidermis\" as \(M_{\text{EpX}}\).  
Since the initial criteria fail to correctly classify \"pockets\" in epidermis (Figure 9b), we refine the \(M_{\text{Out0}}\) mask in the following three steps. Firstly, for each sequence of 0s in \(M_{\text{EpX}}\) (from \(i\)th to \(j\)th element) we check if elements \((i-1)\)th and \((j+1)\)th in \(M_{\text{Out0}}\) are both 1s; if it is the case, we consider such a sequence as a \"pocket\", which should be part of the valid \"outer\" edge (Figure 9c). Then, to fill tiny gaps between parts of the valid \"outer\" edge and to suppress artifacts, we perform the following sequence of morphological closings and openings:  
\[M_{\text{Out}}=\left(\left(\left(M_{\text{Out0}}^{\prime}\bullet\text{SE}_{L5 0}\right)\circ\text{SE}_{L500}\right)\bullet\text{SE}_{L1500}\right)\circ \text{SE}_{L2000} \tag{6}\]  
where \(\text{SE}_{Ln}\) is a line-shaped structuring element of length \(n\). Finally, we retain only the largest segment (Figure 9d).
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 3 Delay-/Disruption-Tolerant Networking
### LTP Performance  
Recent studies of this protocol focus on its performance in the presence of link disruptions [50], which is of crucial relevance for its implementation for FSO communications, where the links suffer from burst fading and high delays during adverse weather conditions. In this study, the performance of transmission is predicted for reliable file delivery by an analytical method, focusing on the delay prediction. The research is conducted over communication channels with random link disruptions, long link delays, and high data loss rate.  
With the analytical and experimental findings of Reference [50], it was shown that the total file delivery time decreases exponentially with increases in LTP block size. The higher delivery time suffered when using smaller blocks is justified by an increment of latency with each transmission round, worsened when the channel has a higher Bit-Error-Rate.  
The study of the memory dynamics in LTP-based communication systems for a deep-space network is presented in Reference [51]. The links emulated are characterized by extremely long delays and asymmetric data-rates. The conclusions presented on the effect of LTP block size show that smaller block sizes allow for a quicker release of occupied memory.
## 4 System Model
### DMPF Scheduler Module  
DMPF Scheduler Module (DSM) runs as an application over the SDN controller. This allows the DASH clients to stream adaptively over HTTP. In this research study, we assume that DASH client requests are provided by local HTTP media servers. In Figure 5, we provide a brief description of the operation of the proposed approach and also illustrate the three-layer SDN architecture model containing DSM. DSM considers the received requests and available network resources, for example, link bandwidth, to determine an optimal solution. The DSM consists of three components: the request analyzer, resource monitor, and flow setup component.
# Aptamer-Based Biosensors for the Analytical Determination of Bisphenol A in Foodstuffs
## 5 Conclusions and Future Perspectives  
Several studies have been successfully carried out to obtain aptasensors with high sensitivity. In December 2021 the EFSA proposed to considerably lower the TDI for BPA. This can lead to a change of BPA SMLs in the future. In this framework, aptasensors can represent a promising, useful, and reliable tool to monitor BPA levels in food samples. However, some improvements are required.  
First of all, aptasensors have been tested on a limited number of food types. Further efforts are needed to evaluate the performance of the same or new BPA aptasensors for a broader selection of food categories and for more complex matrices than liquid foods. Regarding the latter, it should be pointed out that the binding of the aptamer to a target molecule depends on its folding in a unique structure, which, in turn, is strongly influenced by the environment where the analyte recognition occurs. In complex food matrices, molecules other than the target one can affect the aptamer/BPA complex formation and consequently the reproducibility of the results. Therefore, the need for pre-treatment of the sample cannot be ruled out _a priori_.  
Secondly, all studies lack a complete validation process in accordance with the specific guidelines for the validation of analytical methods in food safety studies. Then, the concrete applicability of aptasensors in food analyses for BPA risk assessment needs to be verified.  
Finally, it is important to note that BPA analogs are now used to replace BPA in PCMs, although several studies have highlighted that they have a toxicity comparable to that of BPA. As a result, the monitoring of their levels is also mandatory to protect consumers' health. For this reason, another challenge of the future is the development of new sensitive aptasensors that are able to rapidly, simply, and sensitively quantify both BPA and its substitutes in a multi-analyte simultaneous analysis.  
Writing--review and editing, M.E.S., A.A., M.V. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Research on Multi-Scale Fusion Method for Ancient Bronze Ware X-ray Images in NSST Domain
## 2 Materials and Methods
### Non-Subsampled Shearlet Transform  
In 2005, [PERSON] et al. [14] introduced a synthetic dilation affine system in their research. They utilized multi-resolution and geometric analysis to construct shearlet-wave transformations and developed the basic framework for continuous and discrete shearlet-wave transformations. On this basis, [PERSON] et al. [15] proposed he Non-Subsampled Shearlet Transform (NSST) method. NSST has numerous advantages in the field of image processing, making it one of the most frequently used methods. It not only inherits the flexibility of shearlet-wave but also offers a better choice of spatial orientation, compared to  
Figure 1: Flowchart of the fusion method. A, B is the input image, F is the fusion result image.  
other methods. This implies that NSST has the capability to efficiently capture and describe the intricate details of an image, thereby enhancing the quality of the fused image and significantly minimizing the probability of the Gibbs effect. NSST achieves an optimal sparse representation of an image, which is highly important in image processing. This means that NSST can accurately represent image features and reduce redundancy by selectively representing crucial information in the image.  
First, the high-frequency subbands and the low-frequency subbands of the image are obtained by multi-scale decomposition of the source image using a non-subsampled pyramid filter (NSPF). Then, the high frequency subbands of the image are decomposed in multiple directions using a shearlet filter (SF). Finally, the image is reconstructed by inverse NSST operation after completing the processing of the corresponding subbands. This process is shown in Figure 2, which presents a schematic diagram of the three-level NSST decomposition.
## 2 Path Loss Simulation for Optimal Relay Position
For the candidate position \(p_{1}\), since \(p_{1}\) is the  
Figure 4: Path loss distributions according to rocket altitude: (**a**) at 0 m; (**b**) at 1000 m.  
closest point to the rocket, the lowest path loss is observed at \(p_{1}\) as indicated with a blue line when \(h\) is near an altitude of 0 m. However, the path loss increases rapidly with the rocket altitude, and the crossing altitude is relatively low at 217 m. Thus, it is more advantageous to receive the rocket status from the relay station than the main station when the rocket altitude is under \(h=217\) m. On the other hand, in case of the position \(p_{4}\), which is the farthest point from the rocket, the pass loss increases slowly according to \(h\). Thus, the relay station at \(p_{4}\) is more suitable for reception than the main station when the rocket altitude is under 1000 m. \(p_{2}\) and \(p_{3}\) are placed between \(p_{1}\) and \(p_{4}\) so that the intersection altitudes at these positions are 432 m and 619 m, respectively. The relay stations at \(p_{2}\) and \(p_{3}\) have are advantageous than the main station when rocket altitudes are less than 432 m and 619 m, respectively. Therefore, once the rocket altitude to be covered by the relay station is determined, it is possible to find the optimum position of the relay station. In our rocket launching event, the rocket altitude covered by the relay station is assumed to be 430 m. Thus, \(p_{2}\) is found to be the optimum position of the relay station. Figure 5: Path loss according to rocket’s altitude. Figure 6: Running averaged path loss according to rocket’s altitude.
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Pharmacokinetics from Oral Intake
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Dose (mg)**} & \multirow{2}{*}{**C\({}_{max}\) (\(\upmu\)g/mL)**} & \multicolumn{2}{c}{**AUC**} & \multirow{2}{*}{**t\({}_{max}\) (min)**} & \multirow{2}{*}{**t\({}_{2/2g}\) (min)**} \\  & & & & & \\ \hline \multirow{3}{*}{6-gingerol, total} & 1000 & 0.4 \(\pm\) 0.2 & 12.6 \(\pm\) 6.4 & 55.0 \(\pm\) 7.7 & — \\  & 1500 & 1.69 \(\pm\) 2.31 & 75.6 \(\pm\) 110.3 & 60.0 \(\pm\) 0.0 & — \\  & 2000 & 0.85 \(\pm\) 0.43 & 65.6 \(\pm\) 44.4 & 65.5 \(\pm\) 22.6 & 110.0 \(\pm\) 34.9 \\ \hline \multirow{3}{*}{6-gingerol glucoronide} & 1000 & 0.16 \(\pm\) 0.15 & — & — & — \\  & 1500 & 0.62 \(\pm\) 0.62 & — & — & — \\  & 2000 & 0.62 \(\pm\) 0.56 & — & — & — \\ \hline \multirow{3}{*}{6-gingerol sulphate} & 1000 & 0.02 \(\pm\) 0.03 & — & — & — \\  & 1500 & 0.04 \(\pm\) 0.04 & — & — & — \\  & 2000 & 0.33 \(\pm\) 0.41 & — & — & — \\ \hline \multirow{3}{*}{8-gingerol, total} & 1000 & 0.1 \(\pm\) 0.1 & 2.1 \(\pm\) 2.2 & 52.5 \(\pm\) 8.7 & — \\  & 1500 & 0.1 \(\pm\) 0.1 & 2.6 \(\pm\) 2.0 & 60.0 \(\pm\) 0.0 & — \\  & 2000 & 0.23 \(\pm\) 0.16 & 18.1 \(\pm\) 20.3 & 73.1 \(\pm\) 29.4 & 113.5 \(\pm\) 41.1 \\ \hline \multirow{3}{*}{10-gingerol, total} & 1000 & 0.1 \(\pm\) 0.1 & 2.9 \(\pm\) 3.2 & 60.0 \(\pm\) 0.0 & — \\  & 1500 & 0.1 \(\pm\) 0.02 & 7.7 \(\pm\) 5.3 & 80.0 \(\pm\) 34.6 & — \\  & 2000 & 0.53 \(\pm\) 0.4 & 50.1 \(\pm\) 49.3 & 75.0 \(\pm\) 27.8 & 128.7 \(\pm\) 38.8 \\ \hline \multirow{3}{*}{6-shogaol, total} & 1000 & 0.1 \(\pm\) 0.1 & 0.8 \(\pm\) 1.5 & 55.0 \(\pm\) 8.7 & — \\  & 1500 & 0.4 \(\pm\) 0.08 & 1.6 \(\pm\) 2.8 & 60.0 \(\pm\) 0.0 & — \\  & 2000 & 0.15 \(\pm\) 0.12 & 10.9 \(\pm\) 13.0 & 65.6 \(\pm\) 22.6 & 120.4 \(\pm\) 42.0 \\ \hline \hline \end{tabular} Note: C\({}_{max}\) = maximum plasma concentration, AUC = area under the concentration-time curve, t\({}_{max}\) = time point at which C\({}_{max}\) is observed, t\({}_{1\beta\beta}\) = elimination half-life.  
\end{table} 
Table 1: Pharmacokinetic parameters for the main active components of ginger. lowest doses of ginger tested, 100 and 250 mg, the Cmax of 6-gingerol conjugates had values of 0.3 and 0.4 ug/mL, respectively. Ginger was well tolerated in all subjects. Ginger metabolism and excretion is still not fully determined. Studies using 6-gingerol as a model show that this molecule is extensively metabolised in the liver by enzymes of the uridine diphosphate glucuronosyltransferase family to form glucuronide conjugates [20; 23]. Roughly, half of the administered 6-gingerol is excreted through the bile as a glucuronide, with only 2-3% appearing in the urine in the free form [20]. Other metabolites, formed by hepatic oxidation and eliminated in the urine, include 9-hydroxy-6-gingerol, vanillic acid, ferulic acid, (S)-(+)-4-hydroxy-6-oxo-8-(4-hydroxy-3-methoxyphenyl)octanoic acid, and 4-(4-hydroxy-3-methoxyphenyl)butanoic acid.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Evaluation on Stage 1: OCSVM  
Based on the results of detection stage 1 using OCSVM, we analyze the metrics depicted in confusion matrix Figure 8 for CSECIC-IDS2018 and Figure 9 for CIC-IDS2017. The model calculates the performance metrics as illustrated in Figure 10. The significant count of true negatives (2,316,485) demonstrates the model's effectiveness in identifying normal traffic accurately as non-attacks, highlighting its strong capability in distinguishing non-malicious activities. The false negatives (12,624) indicate cases where the model failed to detect actual attacks. While relatively low compared to true positives and true negatives, this number remains critical as missed detections could lead to undetected attacks.  
The model's high number of true positives (212,268) demonstrates its capability to accurately identify a substantial portion of attacks. This positive aspect highlights the model's reliability in identifying malicious activities, thereby contributing significantly to its high recall rate. The false positives (59,166) represent cases where normal traffic was mistakenly classified as an attack. This is problematic because false positives can result in unnecessary alerts, leading to potential alarm fatigue and challenges in resource allocation.  
The higher incidence of false positives suggests that although the model is adept at detecting attacks, it tends to be overly cautious, labeling benign traffic as malicious. The assessment of detection accuracy in OCSVM relies on the \"nu\" parameter, which regulates the balance between model complexity (flexibility) and the acceptance of margin violations. The current \"nu\" parameter (set to 0.95) in the OCSVM model creates a decision boundary  
Figure 8: Confusion matrix of detection stage 1 OCSVM on CSECIC-IDS2018.  
Figure 9: Confusion matrix of detection stage 1 OCSVM on CIC-IDS2017.  
that is overly stringent, leading normal traffic patterns to be misclassified as attack patterns. To optimize performance, we adjusted the \"nu\" parameter to 0.1, thereby relaxing the model's strictness and enhancing precision and specificity. Although the precision is moderately high, there is still room for improvement to meet elevated standards. However, this adjustment does affect other metrics. To mitigate false negatives overlooked by the OCSVM model and decrease the false positive rate, we implemented a hybrid approach incorporating a deep autoencoder (dAE) in the subsequent detection stage. The dAE model effectively identifies normal patterns, thereby enhancing overall accuracy, recall, and specificity.
## 5 Publicly Available Datasets and Benchmarks
### Comparison of Various Panoptic Segmentation Models  
Table 1 illustrates the performance of various panoptic segmentation models using different backbone networks found in the literature. On the Cityscapes dataset, the panoptic FPN comparison is performed with DIN [54]. Panoptic FPN surpasses DIN with a PQ margin of 4.3 points. DIN (Detect-to-Instance Network) is a substitute for region-based instance segmentation, initiating with pixelwise SS and performs grouping to retrieve instances [34]. Likewise, AUNet is compared with leading bottom-up methods (such as DWT [55], SGN [8]) and Mask R-CNN achieving a persistent improvement in accuracy with MS-COCO, and hence, serve as a new approach. The design of Panoptic DeepLab is straightforward and requires only three loss functions while training as discussed in [5], and incorporates minimal parameters to a contemporary semantic segmentation model. Note: the results of each performance metric presented in Table 1 are the results extracted from the respective models presented in [5; 34; 35; 36]. To achieve seamless scene segmentation [36], semantic and instance segmentation methods are combined and jointly operate on top of a single network backbone. UPSNet [7] makes use of three large datasets such as COCO, Cityscapes, and UPSNet, whose empirical results illustrate that UPSNet attains SOTA performance with quicker inference in comparison to other models [57]. On the other hand, a one-stage EPSNet [37] was presented that achieves significant performance on COCO dataset and outperforms other one-stage approaches. Therefore, EPSNet is significantly faster than other existing PS networks.
# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 3 Preprocessing
For better contrast of an image cumulative histogram equalization and CLAHE were used. Cumulative histogram equalization alters the histogram intensity distribution to improve image contrast and the appearance of MAs in retinal images is enhanced by CLAHE [11; 49]. The retinal capillaries are eliminated from the image using morphological techniques then perform the CLAHE operation on it. After applying the preprocessing technique to the IDRiD dataset the model gives an accuracy of 83.84% [50]. Gabor filters are used to extract textural information from fundus images to identify MAs [51]. High-pass and top-hat filter is used with themorphological operator to create the binary images for blood vessel segmentation [52]. After resizing the image channel splitting approach is implemented on the retinal image to split image patches into red, blue, and green colors. This technique is applied to IDRiD dataset for the detection of exudates and to obtain an accuracy of 96.95% [53]. The result of preprocessing methods is visualized in Figure 5 in which different preprocessing techniques such as CLAHE, grayscale conversion, gaussian smoothing, Gabor filter and cumulative histogram equalization are applied on Messidor images [54]. The retinal images is transformed into a lab image then apply histogram equalization approach on it to improve the brightness of scans. The adaptive filter is employed to improve the segmentation of blood arteries [55]. The Gaussian blur mask is applied on the publicly accessible APTOS-2019 datasets for noise reduction. Later average color local filter is employed to enhance the retinal and obtained 90% accuracy [56]. The transformation of retinal images into grayscale images was performed after resizing the retinal images; then, the green channel was utilized for preprocessing. The top-hat-transform operation was utilized to improve the low-severity regions such as MAs, HMs, and blood vessels. The applied technique achieved 87% sensitivity on DIARETDB0 and 93% specificity on the DIARETDB0 dataset. Non-local mean filter (NLFM), CLAHE, 2D gaussian, and top-hat transform are applied to smooth the image quality that provides help for the recognition of the dark retinal lesions such as MAs and HMs. These preprocessing techniques are implemented on three publicly available datasets and give better accuracy of 96.95% on e-Ophtha, 97.95% on DIARETDB0, and 97.35% on DIARETDB1 dataset [57]. Average filter is utilized to eliminate the micro blood vessels from retina images. This technique is applied to the publicly available Kaggle dataset [58]. The wavelet transform approach is employed to strengthen and enhance the HE image [59]. The overview of the preprocessing techniques is described in Table 3.  
Figure 5: Image preprocessing techniques applied on Messidor dataset [54].
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 3 Methods
### Data Selection and Preprocessing
This included MMSI, time, longitude, latitude, speed over ground, and course over ground. For experimental convenience, the trajectories were segmented into 60 consecutive points, as mentioned in Step 3, followed by the application of the cubic spline interpolation method in Step 4. The AIS trajectory interpolation is shown in Figure 6, where the blue points represent the original data, and the red points represent the interpolated data. Figure 6: Illustration of Trajectory Interpolation. The final processed dataset contains 3011 sets of navigation trajectories, with 2409 sets selected as the training set and 602 sets chosen as the testing set. For a single vessel, its behavior at time \(i\), denoted as \(B_{i}\), can be expressed as:  
\[B_{i}=\{m_{i},e_{i},n_{i},t_{i},s_{i},c_{i}\} \tag{11}\]  
In Equation (11), \(m_{i}\) represents the vessel's MMSI call sign, \(e_{i}\) is the time information for the \(i\)-th trajectory point, \(n_{i}\) denotes the longitude information for the \(i\)-th trajectory point, \(t_{i}\) represents the latitude information for the \(i\)-th trajectory point, \(s_{i}\) is the speed over ground for the vessel at the \(i\)-th trajectory point, and \(c_{i}\) denotes the heading of the vessel at the \(i\)-th trajectory point. This paper employed a sliding window approach, with a fixed input window of 20 trajectory points and an output of 40 trajectory points. This configuration allowed for the prediction of a vessel's future dynamic trajectory over a 40 min period, which met the demand for safe navigation in the Bohai Strait. The operation of the sliding window is illustrated in Figure 7.  
The input consisted of \(B_{i-9}\), \(B_{i-18}\), \(B_{i-17}\ldots B_{i}\), and the output was the longitude and latitude in \(B_{i+1}\) used to train the hybrid network. Consequently, the expression for the ship trajectory prediction model was as follows:  
\[B_{i+1}=F(\{B_{i-39},B_{i-38},B_{i-37}\ldots B_{i}\}) \tag{12}\]  
In Equation (12), the function \(F\) denotes the activation function. In the PSO-CNN-RGRU-Attention network, the tanh function was used as the activation function for the neurons. The function expression is shown in Equation (13):  
\[f(x)=\frac{(e^{x}-e^{-x})}{(e^{x}+e^{-x})} \tag{13}\]  
The tanh function has the following characteristics: it is a continuous, smooth, and strictly monotonic function; its output range is \((-1,1)\), with \((0,0)\) as the center of symmetry and a mean value of 0; the output changes significantly as the input approaches 0; as the input moves away from 0, the output changes gradually decrease and eventually approach 1. The symmetry of the output about the origin, combined with the mean value of 0, helps alleviate the vanishing gradient problem, positively impacting the learning process of the neural network.