esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Stimuli  
The stimulus materials were banner ads placed on the homepage of e-commerce websites. The banner ads were evaluated separately and not as a part of such websites in order to eliminate the effect of browsing and the position on the page. The online banner ads used in this study were related to the apparel sector, and consisted of three main components: (1) 'Brand'--the brand associated with the discount; (2) 'Discount rate'--the discount rate related to the brand; (3) 'Image'--the photo of a garment or the picture of a model/models wearing a garment of the corresponding brand. They were collected from the 50 most popular e-commerce websites in Turkey--such as Hepsiburada, Boyner and Trendyol--according to Alexa (alexa.com). We analyzed various banner ads in these websites, and identified the four most widely-used unique banner layouts comprising three content elements, which are image, discount rate, and brand. Hence, a total of eight banner ads were selected, with two different versions for each of four layouts. All of the banner ads selected for this study are presented in Appendix A, and the layout details of them are also listed in Table 1.  
As shown in Table 1, the selected banners are pertain to four layouts that differ with respect to the positions of three content elements. For example, in 'banner 1a' the image is placed in the middle, the discount rate is positioned on the left, and the brand name is located on the right-hand side. Moreover, for each layout, there are two versions of the banner advertisement. One contains object image (the photo of a garment), while the other contains a picture of models (the photo of models wearing garments). For instance, 'banner 1a' has a model image, whereas 'banner 1b' has an object image (please see Appendix A, Figure A1). In addition to these, banners for distinct brands were chosen, and they were selected to investigate the effect of the discount rate, as the pairs of each combination have different discount rates.  
The banners were typically a horizontal rectangle, and were fixed at same the original sizes as those in the corresponding websites from which they retrieved. In order to avoid capturing biased eye-movement data, as in these banners, for the banners with the model image, we selected the ones with two models (a woman, and a man), whereas we selected ones with sports shoes for the banners with the object image (please see Appendix A). In this regard, we also selected banners from the brands with products for both genders.
# Iterative Receiver Design for the Estimation of Gaussian Samples in Impulsive Noise +
## 4 Signal Estimation
### Hard Decisions and the Parallel Iterative Schedule Algorithm  
Substituting (16) and (40) in (21), it is clear that the resulting message:  
\[p_{u}(s_{k})=\underset{i_{k}}{\sum}P_{u}(i_{k})g(y_{k};s_{k},\sigma_{i_{k}}^{2}) \tag{41}\]  
is a Gaussian mixture and not a single Gaussian distribution, as assumed in (27).  
The generation of a Gaussian mixture, at every vertical edge of the FG, exponentially increases the computational complexity. Thus, the use of mixture reduction techniques is unavoidable. For that purpose, one radical approach is to make a hard decision on every \(i_{k}=\text{argmax}\{\hat{P}(i_{k})\}\), selected as the modal value of the estimated PMF, \(\hat{P}(i_{k})=P_{u}(i_{k})P_{d}(i_{k})\). The mixture (41) is thus approximated by its most likely Gaussian term, so that the assumption of having Gaussian priors for each \(s_{k}\) (i.e., the information on the individual sample that does not depend on the correlated sequence), as carried by the upward messages \(p_{u}(s_{k})\), is respected and the algorithm in Section 4.1 can be applied.  
With this approximation, we can then establish a \"parallel iterative schedule\" (PIS) in the loopy FG. The upper and lower parts of the FG work in parallel, at every iteration, according to their forward-backward procedure (corresponding, as seen, to the Kalman smoother and the BCJR, respectively). At the end of a forward-backward pass, the two halves of the FG exchange information through messages sent along the vertical edges of the graph. Namely, \(p_{d}(s_{k})\) is convolved with the corresponding Gaussian observation (16), so as to get \(P_{d}(i_{k})\) in (22), while \(P_{u}(i_{k})\) is used to update the impulsive noise state PMF, \(\hat{P}(i_{k})=P_{u}(i_{k})P_{d}(i_{k})\), hence the estimated \(i_{k}\), at every iteration. Such a scheduling and the approximate hard decision strategy, which is an essential part of it, shall guarantee the convergence of the overall iterative algorithm, as shown in Section 5.
## 2 Decoupling Techniques (DTs) in Ultra-Wideband MIMO Antennas (UMAs)
### Parasitic Element  
In this DT, a conducting structure was placed between AEs, which acts as a reflector to increase isolation. In article [68], two square-shaped radiators with W-shaped and inverted U-shaped slots on the patch were used to notch WiMAX and WLAN frequency bands, and the AEs were arranged in 90\({}^{\circ}\) orientation to form the MIMO antenna topology, as presented in Figure 8a. A parasitic structure was embedded between two monopole antennas to enhance isolation. In paper [69], the AE was formed by a patch (square-shaped) with microstrip line feed and a defect in the ground surface, as illustrated in Figure 8b. A rectangular cut was added to the ground surface to widen the bandwidth. AEs 1 and 3 were printed on top, and AEs 2 and 4 were printed on the base of the substrate. Likewise, AEs 1 and 3 were placed orthogonally to AEs 2 and 4 to reduce mutual coupling. To further enhance isolation, two four-directional staircase-shaped patches (identical and symmetrical) were introduced in the center of the upper plane and lower plane of the MIMO antenna. In order to enhance isolation, the decoupling structure (staircase-shaped) was separated by orthogonal slots. An L-slot was added to the ground surface to reject the X-band. The HL-shaped slot was printed on the square-shaped radiator to produce a notch at the WLAN band. Also, a U-slot was introduced on the ground surface to reject the WiMAX band. In reference [70], the AEs were developed from a modified rectangular patch, and a decoupling network was designed on the lower side of the substrate to achieve high isolation, as shown in Figure 8c. A dumbbell-shaped stub is also added to the partial ground surface to suppress inter-element coupling.  
Figure 7: Metamaterial: (**a**) [65], (**b**) [66], and (**c**) [67].
## 6 Security Analysis and Results
### Detection Time and Accuracy
This is because, in practice, it is possible that a node disseminates the duplicate node identity in the same cycle that it learned about it, for which the theoretical analysis fails to account. What we can glean from this analysis is that, for the smallest cache size \(c=5\), all of the nodes in the home zone of the duplicated node (regardless of where the duplicate is deployed) learn about the replication within seven time cycles. The largest gain is towards the end of the time period, as one would expect from this randomized process. Observe that only about 50% of the nodes in the home zone are made aware of the replica after five time cycles where Time 0 corresponds to the time instant that the adversary inserts one of his duplicates into the network (either inside or outside the home zone). This implies that the adversary may be able to outsmart **Disccard** for no more than seven time cycles before being detected. This advantage can clearly be controlled by increasing the cache size. Of course, realizing the importance that the time period plays in the adversary's advantage, the network architects would need to decide on an appropriate time period \(\delta\), as well. We would like to note that while the prior work results are meticulous in presenting their detection accuracy, they do not, however, present the relationship with time, as we show in our analysis.
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion
### Visual Effect  
In order to ensure that the MS Circle Map function effectively protects any information contained in an image, all of the information in the encrypted image must be completely obscured and visually unreadable. For example, see Figure 5, where (a) and (d) are the original color and grayscale images, respectively, (b) and (e) represent the results of the encrypted color image and the encrypted grayscale image. In both images, all information is completely scrambled and absolutely unreadable. Using the same parameter values during the encryption process, Figure 5c,f show the decrypted color and grayscale images, respectively. Visually, from both images, all information can be restored as in the original images. The examples described above show that the key stream generated by the MS Circle Map function has excellent image encryption and decryption capabilities and can thus provide effective protection of the information contained in the image.  
Figure 5: Example of (**a**) original color image, (**b**) color image encrypted, (**c**) color image decrypted, (**d**) original grayscale image, (**e**) grayscale image encrypted, and (**f**) grayscale image decrypted.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Data** & **Grayscale Image** & **Size (pixels)** \\ \hline
16 & & 256 \(\times\) 256 \\
17 & & 512 \(\times\) 512 \\
18 & & 1024 \(\times\) 1024 \\ \hline
19 & & 768 \(\times\) 1024 \\
20 & & 1536 \(\times\) 2048 \\
21 & & 3072 \(\times\) 4096 \\ \hline
22 & & 2304 \(\times\) 1536 \\
23 & & 3456 \(\times\) 2304 \\
24 & & 4608 \(\times\) 3072 \\ \hline
25 & & 512 \(\times\) 512 \\
26 & & 1024 \(\times\) 1024 \\
27 & & 2048 \(\times\) 2048 \\ \hline
28 & & 512 \(\times\) 288 \\
29 & & 1024 \(\times\) 576 \\
30 & & 2048 \(\times\) 1152 \\ \hline \end{tabular}
\end{table}
Table 3: Grayscale image test data.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
###### Abstract  
Multirotors play an important role in electric power inspection, border control, modern agriculture, forest fire fighting, flood control, disaster prevention, etc. Multirotor failures, such as a communication fault, a sensor failure, or a power system anomaly, may well lead to mission interruption, multirotor crashes, and even casualties. To ensure flight safety, a multirotor decision module should be established to prevent or reduce the adverse effects caused by failure. Therefore, this paper proposes a real-time safety decision-making method for multirotor flight strategies based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, the flight of the multirotor was simulated based on the Rflysim UAV flight simulation platform, and a fault-injection module was constructed to simulate different types of faults, so as to realize real-time monitoring of the flight status of the multirotor, and to collect flight data under various faults to establish condition assessment information sources. Then, based on the random forest algorithm, a failure level classification model of the multirotor was constructed, the model was trained and verified by inputting flight data of three types of safety level failures, and the model effectively classified the failure levels of the multirotor. Under this framework, a real-time safety decision-making model for the multirotor based on the TOPSIS model was constructed to realize the flight safety decision-making of the multirotor under different faults. This method can effectively realize the real-time decision-making for the flight strategy of a multirotor. By comparison with other models, the classification accuracy of the failure level classification model is higher, and the consideration of flight decision-making is more comprehensive and accurate, thus effectively ensuring the flight safety of the multirotor.  
failure level classification; multirotor; random forest; real-time safety decision-making; TOPSIS +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 4 Trials in the Baltic Sea Corridor (Tallinn-Helsinki)
### Description of Planned Satellite Connectivity Trials  
Currently, at the time of writing, the set-up for the implementation of satellite access on the ferry as a transport network is being prepared. It basically consists of an integrated satellite antenna/modem assembly that needs to be connected to the ferry power supply and via ethernet to the (backhaul) router of the ferry's 5G cell. This assembly will be installed on the upper deck of the ferry with the Ku-band antenna pointing towards zenith--one of the terminal options currently being considered is the Kymeta Hawk u8 [35]. The satellite link is planned to be provided by the OneWeb constellation with the access link from the ferry to the satellite in Ku-Band (at 10.7-12.7 GHz for DL and 14.0-14.5 GHz for UL) and the feeder link from the satellite to the gateway--that then seamlessly interfaces with the onshore 5G core network--in Ka-Band (at 17.8-20.2 GHz for DL and 27.5-30.0 GHz for UL).  
As described in general in the section below, the planned testing campaign will initially measure the performance of the satellite link--i.e., throughput, latency and availability--in a standalone mode. Then, the final field trials will assess the overall connectivity performance for the entire ferry trip based on the above KPIs. Based on these results,  
Figure 12: Overview of the trial architecture.  
recommendations for a prototypical and ultimately full operational implementation will be derived.
## 3 Target Image Optimization Processing  
### Image Gray Scaling  
In order to achieve the desired effect in stereo matching, it is necessary to first exclude the interference of noise, illumination, pixels and other factors as much as possible, so the image needs to be grayed out and image-enhanced first, which can reduce the computation of the program processing procedure while still retaining the complete two-dimensional information of the image. In the RGB model, if \(R=G=B\), then the color indicates a grayscale color, where the value of \(R=G=B\) is called the grayscale value; therefore, the grayscale image is only one byte per pixel to store the grayscale value for a grayscale range of 0-255: when the grayscale is 255, it means it is the brightest; when the grayscale is 0, it means it is the darkest.  
The benefits of grayscale are as follows: compared to color images, grayscale images take up less memory and run faster; after, the grayscale image can visually increase the contrast and highlight the target area.  
In this paper, the weighted average method is used to weight the \(R\), \(G\) and \(B\) components according to the more suitable weights, as shown in Equation (4). The effect is shown in Figure 2.  
\[Gray=\frac{W_{R}\times R+W_{G}\times G+W_{B}\times B}{3} \tag{4}\]
# A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load
## 2 New Statistical Model Updating Method
### Statistical Model Updating Equation
Letting \(\mathbf{K}_{ti}=\left.\frac{\partial\mathbf{K}_{mt}}{\partial\alpha_{i}} \right|_{\alpha_{i}=0}\) and \(\Delta\mathbf{x}_{t}=\mathbf{x}_{at}-\mathbf{x}_{mt}\), Equation (9) can be rewritten as:  
\[\sum_{i=1}^{n}\alpha_{i}\mathbf{K}_{ti}\mathbf{x}_{mt}=\mathbf{K}_{at}\Delta \mathbf{x}_{t} \tag{10}\]  
Actually, Equation (10) is the model updating equation with respect to the element updated factors \(\alpha_{i}\) (\(i=1,\,\ldots,\,n\)). If the measured displacements or \(\mathbf{x}_{mt}\) contain the measurement errors, and these errors are considered as random quantities, the element updated factors will also be random. In this regard, Equation (10) is named as the statistical model updating equation in this paper.
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Privacy-Preserving Federated Learning  
In the privacy-preserving federated learning process for space-air-ground integrated networks, terminal devices (such as drones, user equipment, etc.) conduct training using local data and send the resulting model parameters to edge nodes for local aggregation. Edge nodes then upload these locally aggregated results to the central server for global model aggregation.
# Biotin Auxotrophy and Biotin Enhanced Germ Tube Formation in _Candida albicans_
## 3 Results
### Biotin Enhances Germ Tube Formation in Serum  
Typically, concentrations of 10%-20% serum are used to induce GTF [1; 32]. However, measuring the concentration of biotin in human serum is technically challenging [33]. It has been estimated to be ca. 150-700 ng/L [34] but RPMI 1640, a growth medium designed to replicate serum, contains 200 ug/L of biotin. To determine whether biotin contributes to the ability of serum to trigger GTF, we identified bovine serum concentrations that produced both high and low % GTF for 2 \(\times\) 10\({}^{7}\) cells at 37 \({}^{\circ}\)C after 5 h (Figure 4).  
Serum percentages as low as 0.5% achieved ca. 40% GTF and we chose 1% and 5% serum for low and high GTF, respectively (Figure 4). Significantly, the addition of 1-4 uM biotin doubled the % GTF in 1% serum (Figure 5a) while the addition of the biotin binding protein neutravidin (10-20 ug/mL) decreased the % GTF in 5% serum by 40% (Figure 5b).  
These data support the idea that biotin is one of the factors in serum which enhances GTF. This emphasis on the importance of biotin does not, of course, mean that biotin is the sole trigger in serum. As a technical point, the cells used as inocula for these experiments on sera had been washed three times, as per our standard protocol. However, we also noted that the number of washes can be important. An experiment using sheep serum showed that the threshold for stimulating GTF was only 0.1% serum with the interesting feature that this low threshold could only be achieved with cells which had been washed six to eight times (Table 3). One possibility is that these extra washes act by removing cytoplasmic and membrane-bound farnesol which is still present after three or less washes [35].  
Figure 4: Effect of serum concentration on % GTF in _C. albicans_. Three times washed inocula were incubated at 37 \({}^{\circ}\)C with rotary agitation at 220 rpm. Cultures were observed for 5 h, and plotted as, no serum (water control);,, 0.5% serum;, 1% serum; and, 5% serum. Values shown are the average of triplicate experiments \(\pm\) SD.  
Figure 5: Effects of increased and decreased available biotin on GTF. Three times washed inocula were incubated at 37 \({}^{\circ}\)C with rotary agitation at 220 rpm. (**a**) Exogenous biotin increases serum induction of GTF. Cultures were observed for 5 h, and plotted as:, no additions;, 100 nM biotin; and, 4 uM biotin. (**b**) Neutravidin reduces serum induction of GTF. Symbols:, no additions;, 10 ug/mL. neutravidin;, 20 ug/mL neutravidin. Values shown are the average of triplicate experiments \(\pm\) SD.
###### Abstract
The rest of the paper is organized as follows: Section 2 summarizes relevant research and findings. Detailed methodologies used in this research work are depicted in Section 3. Section 4 describes the experiment setup components, trajectory data logging, and applied jamming scenarios. Results discussions are split over Sections 5 and 6, where the former is dedicated to the simulated jamming results and the latter to the actual jamming results analysis. Further discussion of all test results is given in Section 7. Section 8 wraps up the paper with conclusions and remarks about the research findings.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 3 Experiments
### Experiment I: Block Recognition Algorithm  
After obtaining the fabric dataset, we attempted to use high-resolution images to train the proposed model; however, the detection results were extremely poor and the model could not complete the detection task. Thus, we proposed a multiscale defect recognition algorithm. In this study, Cascade R-CNN with the proposed block recognition algorithm was used as the baseline model.  
In the experiment, large fabric images were cropped into small images to train the model, and the detection results of small images were merged to obtain those of the large images in the inference stage. Considering the inference time and detection accuracy, a large image was divided into six small images of resolution 1460 \(\times\) 960 to train the model.  
The IoU threshold and shape similarity of two detection boxes were the two criteria used to determine whether two detection results should be merged. As shown in Figure 15, we only used the IoU threshold as the merge criterion in Experiment 1-1 and included shape similarity as the merge criterion in Experiment 1-2. The mAP value increased from 49.44% to 50.33%, while the AP value of stop_marks defects increased from 37% to 51% after implementing the proposed block recognition algorithm.  
Figure 14: AP curve.
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 3 Antimicrobial Activities of D-Limonene
### AntibioticMim Activity of D-Limonene  
It has been widely reported that biofilms formed by fungi or bacteria are found on the surfaces of foods, medical equipment, soil, and other substrates, thus posing serious threats to public safety [101; 102]. Biofilms are large numbers of microorganisms aggregated together, attached to the surface of organisms (e.g., meat tissue) or abiotic (e.g., processing equipment) [103] and enclosed in an extracellular matrix [104] (Figure 1). The biofilm matrix is generally composed of water and extracellular polymeric substances (EPSs), mainly composed of polysaccharides, proteins, nucleic acids, lipids, dead microbial cells [105; 106]), flagella, and other adhesive fibers [103], thereby enhancing their resistance to drying, liquid flow, antimicrobial agents, disinfectants, and other methods [106]. Biofilm formation is an adaptation strategy of microorganisms to their environment and it also increases their virulence, resulting in greater pathogenicity [107]. Statistics have shown that more than 80% of recurrent microbial diseases and chronic infections are associated with biofilm formation [108]. Therefore, it is important to investigate biofilm inhibition and removal.  
Table 3 lists some of the reports on the D-limonene inhibition of microbial biofilm formation, indicating that the inhibitory activity on biofilm is related to the microbial species, number of viable microorganisms and strains, and D-limonene concentration. The biofilm inhibition of _Streptococcus mutans_ has been reported to be 94.88% and 46.62% at D-limonene concentrations of 10.5 mg/mL and 2.625 mg/mL [81]. However, for _Streptococcus uboris_, the inhibition rate was 88.25% at a concentration of 3.3 mg/mL, thus indicating that the biofilm inhibitory activity of D-limonene on different microorganisms is variable [80].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & **Strain** & **CFU/mL** & **MBIC** & **Inhibition Rate** & **Ref.** \\ \hline _Aerononas hydrophilla_ & MF 372510 & \(1\times 10^{5}\) & 51.2 mg/mL & - & [21] \\ \hline _Escherichia coli_ & O157:H7 & - & 25 \(\mu\)L/mL & 92\% & [74] \\ \hline \multirow{3}{*}{_Streptococcus pyogenes_} & SF 370 & \(2\times 10^{3}\) & 400 \(\mu\)g/mL & 83\% & [109] \\ \cline{2-6}  & St 38 & \(2\times 10^{3}\) & 400 \(\mu\)g/mL & 95\% & [109] \\ \hline _Streptococcus uboris_ & - & \(1\times 10^{6}\) & 3.3 mg/mL & 88.25\% & [80] \\ \hline _Streptococcus mutans_ & UA 159 & \(1\times 10^{8}\) & 10.5 mg/mL & 94.88\% & [81] \\ \hline _Candidia albicans_ & ATCC 90028 & \(1\times 10^{7}\) & 300 \(\mu\)g/mL & 87\% & [84] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The antibiofilm activity of D-limonene.