esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Profile Wideband Linear-to-Circular Polarization Conversion Slot Antenna Using Metasurface
## 3 Parametric Analysis  
The dimensional parameters of the metasurface and the source antenna should be optimized to realize wideband circular polarization characteristics of the proposed antenna. Through simulation analysis, it was found that the two main parameters of the metasurface (a,w) and the thickness of the top substrate (h\({}_{1}\)) have an obvious influence on the axial ratio performance of the antenna. As illustrated in Figure 6a, when a is decreased from 3.63 mm to 3.23 mm, the frequency dip shifts up to 6.4 GHz with AR = 0 dB. However, from 5.25 GHz to 6.3 GHz, the AR value fluctuates around 3 dB, which leads to a narrow ARBW. As a is increased to 4.03 mm, the frequency dip drops to 5.45 GHz, and its ARBW is about half that at a = 3.63 mm. Figure 6b shows that when the value of the unit cell spacing w is larger than or less than 1 mm, the axial ratio values increase, and the ARBWs narrow. The ARBWs can be adjusted by changing the values of a and w, mainly because the equivalent impedance of the metasurface equivalent circuit is thus changed. When the value of the thickness of the top substrate h\({}_{1}\) is larger than or less than 3 mm, the ARBWs are narrowed, as illustrated in Figure 6c. When the value of p is larger than or less than 1.1 mm, the ARBW is narrower than that at p = 1.1 mm, as shown in Figure 6d. In addition, when p = 2.2 mm, the shape of the middle metal plate groove is a rectangle. A hump band (with an axial ratio value of \(>\)3 dB) appears around 6.1 GHz, which makes the ARBW narrow. When the parameter values of a, w, h\({}_{1}\), and p are adjusted to optimize ARBW, the corresponding S\({}_{11}\) bandwidth experiences only little change. Therefore, the optimum ARBW combination of a = 3.63 mm, w = 1 mm, h\({}_{1}\) = 3 mm, and p = 1.1 mm was chosen for a wide ARBW and good impedance matching.  
Figure 5: Phases of \(\mathbf{E}_{1}\), \(\mathbf{E}_{2}\), and their differences in (**a**) the metasurface with four parasitic square cross gaps and (**b**) the metasurface with seven parasitic square cross gaps.  
Figure 6: _Cont._
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 3 Experiment and Results
### Results and Discussion  
The ablation experiment adopted the YOLOv8 Nano algorithm as the baseline model. (Referred to as YOLOv8n for short in the following text.) Known for its fast and accurate object detection capabilities, YOLOv8n served as the starting point for evaluating other improvement strategies. To ensure the practical applicability of our experimental results, we selected two different datasets for evaluation: SSDD and SSDv0.  
The SSDD likely includes target detection samples from various complex environments, whereas the SSDv0 focuses specifically on ship detection tasks. This provides a diverse testing environment to assess the performance of ST-YOLOv8 across different scenarios.  
During the experiments, certain components of YOLOv8n were systematically removed or replaced to observe how these changes affected the performance of ST-YOLOv8. Comprehensive tests were conducted on each improvement strategy, including but not limited to detection speed (time required for the model to process an image and produce predictions), accuracy (the proportion of correctly identified targets), recall (the model's ability to identify all targets), and IoU (Intersection over Union, measuring the overlap between predicted and ground truth bounding boxes). Multiple iterations were performed for each strategy to ensure the stability and reliability of the results. Experimental results were organized to show in Tables 3-8 form for easier comparison and analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **OPERA** & **SA** & **ASPP** & **W-IoU** & **P** & **R** & **F1** \\ \hline SSDD & & & & & 0.891 & 0.767 & 0.823 \\ SSDD & \(\surd\) & & & & 0.911 & 0.782 & 0.841 \\ SSDD & \(\surd\) & \(\surd\) & & & 0.923 & 0.787 & 0.849 \\ SSDD & \(\surd\) & \(\surd\) & \(\surd\) & & 0.931 & 0.80 & 0.861 \\ SSDD & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.941 & 0.821 & 0.876 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment progressive module integration analysis (SSDD).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **OPERA** & **SA** & **ASPP** & **W-IoU** & **P** & **R** & **F1** \\ \hline SSDD & & & & & 0.891 & 0.767 & 0.823 \\ SSDD & \(\surd\) & & & & 0.921 & 0.776 & 0.842 \\ SSDD & & \(\surd\) & & & 0.913 & 0.832 & 0.870 \\ SSDD & & & \(\surd\) & & 0.918 & 0.819 & 0.866 \\ SSDD & & & & \(\surd\) & 0.926 & 0.812 & 0.865 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment single-module impact assessment (SSDD).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **OPERA** & **SA** & **ASPP** & **W-IoU** & **P** & **R** & **F1** \\ \hline SSDv0 & & & & & 0.833 & 0.760 & 0.794 \\ SSDv0 & \(\surd\) & & & & 0.889 & 0.762 & 0.820 \\ SSDv0 & & \(\surd\) & & & 0.917 & 0.754 & 0.827 \\ SSDv0 & & & \(\surd\) & & 0.864 & 0.852 & 0.857 \\ SSDv0 & & & & \(\surd\) & 0.903 & 0.821 & 0.853 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment single-module impact assessment (SSDV0).
## 3 Results and Discussion
### XRD Results
#### 3.2.2 XRD for the SSW Sample  
Figure 4b presents the XRD patterns of the SSW sample at 7 and 28 days. Here, calcium silicate hydrate I (C-S-H(I)), which is a more crystalline form of C-S-H, is found as a main reaction product [31; 45]. Earlier studies have showed that C-S-H in concrete has the capacity to adsorb chloride ions on its surface area, referred as physical Cl-binding in hydrated portland cements [46; 47; 48]. Although the peaks of C-S-H(I) have not much grown after 7 days, C-S-H(I) would adsorb chloride to some extent.  
In XRD, Cl-hydrocalumite and Cl-hydrotalcite are also identified. In hydrated portland cements, AFm phases, which belong to layered double hydroxides (LDH), play important roles in chloride chemical binding. In general, Mg, Ca, and Al sources in raw GGBFS lead to the formation of hydrotalcite (Mg-Al LDH) and hydrocalumite (Ca-Al LDH) in alkali-activated GGBFS [39]. Both hydrotalcite and hydrocalumite have LDH structures with interlayered anions, which can be exchanged with chloride ions [40; 49]. In the present study, Figure 4b illustrates the formation of Cl-exchanged hydrotalcite (i.e., Cl-hydrotalcite) and Cl-exchanged hydrocalumite (i.e., Cl-hydrocalumite) in the SSW sample. [PERSON] et al. recently found [PERSON]'s salt as a chloride \"sink\" in four M-NaOH-activated GGBFS prepared with 2.47% NaCl solution [22]. Although [PERSON]'s salt and Cl-hydrocalumite are often mistaken as an identical phase [50; 51], their XRD patterns [38] are not the same to each other even though these phases have a great similarity in their structures (i.e., LDH); Cl-hydrocalumite contains chloride as a dominantly interlayered anion with a small quantity of OH ions in its interlayer spaces, while [PERSON]'s salt has only Cl ions. Due to their structural and compositional similarities, Cl-hydrocalumite may have a similar binding capability for chloride. However, as shown in Figure 5, which is the detailed XRD figure that shows the strongest peaks of Cl-hydrotalcite and Cl-hydrocalumite in 8\({}^{\circ}\) to 15\({}^{\circ}\) 20 [38], only Cl-hydrotalcite seems to have significantly grown in its peak intensity from 7 to 28 days compared to Cl-hydrocalumite. Given that the bound chloride content in the SSW sample considerably increased from 7 to 28 days (Figure 3), Cl-hydrotalcite would play a more important role in binding chloride rather than Cl-hydrocalumite in this study.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 4 Mathematical Model
### Energy Consumption  
To calculate the energy consumption of our model, we have considered the parameters that are discussed in Table 2.
# Real-Time Psychological Stress Detection According to ECG Using Deep Learning
## 2 Materials and Methods
### Conventional Methods  
To compare the conventional methods to the deep learning model proposed in this article, conventional methods were conducted, the characters of conventional algorithms were manually extracted from the time domain and frequency domain of the RR interval or HRV. HRV was defined as the change in the difference of successive RR intervals. To get the RR interval, the R-peaks' position was detected from the ECG signal using the Pan Tompkins algorithm [19]. To reduce the impact of individual differences, this article used the z-score algorithm as Formula (1) below to normalize the RR interval, so that its average would become 0 and the variance becomes 1.  
\[\text{z}=((\text{x}-\text{\mu}))/\delta \tag{1}\]  
The time domain features influenced by momentary ANS (autonomic nervous system) activities were extracted. Such statistical features included mean-RR (the mean value of RR interval), SD (standard deviation of HRV), MED (the median value of HRV), QD (quartile deviation of RR-interval), percent 20 th (the 20 th percentile's value of RR-interval), percent 80 th (the 80 th percentile's value of RR-interval) and average heart rate. As the research [20] has shown that the high-frequency energy of HRV is related to the activity parasympathetic nerve and the low-frequency energy is related to the activity of the sympathetic nerve, then HRV was computed from the RR intervals time-series and the features of HRV were extracted, including HF (high-frequency energy value of HRV signal), LF (low-frequency energy value of HRV signal) and LF_HF (the ratio of HF power and LF power) as shown in Figure 8. Finally, the 10 features would be trained and tested using nine machine learning classifiers (XG-boost [21], logistic regression [22], RBF Radial Basis Function-SVM Support Vector Machine [23], random forest, decision tree, Linear SVM, K-nearest neighbors, Ada Boost, Naive byes) as Table 1 shows. The performance would be further illustrated in the results section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Machine Learning Methods**} \\ \cline{2-3}  & **Probabilistic Model** & **Non-Probabilistic Model** \\ \hline RR-interval’s statistic features & Decision Tree, Logistic regression, Naive & K-Nearest Neighbors, Ada Boost, \\  & Bayes byes, Random forest, XG-boost & Linear-SVM, RBF-SVM \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features and the methods used in classification.  
Figure 8: Signal preprocessing and extraction of statistical features from time domain and frequency domain.
## 3 Results and Discussion
### Effect of Organic Content on Biogas Production and COD Removal  
The impact of the organic loading rate was studied on the response of the digester using the HRT for 21 days and a magnetite load of 4 g. The two streams compared were municipal wastewater and a mixture of municipal and sugar refinery streams with high organic content and both being nutrient-rich, as presented in Table 1. Figure 7 shows a 300% increase in cumulative biogas with the increase in organic content from the mixed stream. This is a result of high organic content (CODin of 9590 mg/L) and total dissolved solids exposed to microorganisms for degradation. Compared to municipal wastewater, this stream achieves a low reduction in contaminants even though it has a high biogas production. An increase in organic content results in a decrease in pH due to the accumulation of VFAs and long-chain fatty acids (LCFA) [32]. The study by [33] also reveals that LCFA and the presence of organics that are difficult to degrade are inhibitory to all groups of microorganisms, hence, the AD of high organic content results in 70-80% removal of contaminants.  
Figure 5: Comparison of the quality of biogas produced with and without magnetite for the same stream of wastewater.  
Figure 6: Effect of magnetite load on (**a**) color removal (%); (**b**) turbidity removal (%).  
Effect of Organic Content in the Treatability Efficiency for the Digester  
The effect of organic content in the removal of water contaminants for the digester is presented in Figure 8. The digester with high organic content achieves 65% in the removal of color and 75% in the removal of turbidity. This confirms that this stream has highly dissolved and suspended solids, with dark-yellow color and high viscosity. Although biogas production increases exponentially for this digester, a post-treatment step is necessary for further reduction in contaminants to produce cleaner water and to meet wastewater discharge standards.
# Numerical Study of the Toughness of Complex Metal Matrix Composite Topologies
## 3 Materials and Methods  
### Theory on the Determination of the Fracture Toughness  
The determination of the fracture toughness \(K_{Ic}\) is based on the load-crack mouth displacement curve (Figure 2). The crack mouth opening displacement (CMOD) corresponds to the load-line displacement. This is therefore measured with a clip gauge on the loading line (Figure 3b).  
Figure 2: Type-I load-displacement curves for the determination of the critical load \(P_{Q}\) then the calculation of the stress intensity factor \(K_{Q}\)[17].  
The aim is to determine a load \(P_{5}\) which is the intersection between the load-displacement curve and a line \(5^{\circ}\) secant to the tangent line of the curve which is the initial elastic loading slope (OA line in Figure 2). For a type-I load-displacement curve, the critical load \(P_{Q}\) is equal to \(P_{5}\) (Figure 2). If the ratio \(P_{\max}/P_{Q}\) (with \(P_{\max}\) the maximum recorded load) does not exceed 1.10 (_i_), then a stress intensity factor \(K_{Q}\) can be calculated using Equation (2). This stress intensity factor \(K_{Q}\) is equal to the critical stress intensity factor \(K_{Ic}\) if the criterion \(2.5(K_{Q}/R_{e})^{2}\leq B,b_{0}\) (_ii_) with \(b_{0}=W-a_{0}\) is satisfied. If the different conditions (_i_ and _ii_) are not satisfied, \(K_{Ic}\) cannot be calculated because the test is a size-dependent \(K_{Ic}\) test.  
\[K_{Q}=\frac{P_{Q}}{\sqrt{B^{2}W}}g_{2}(\frac{a_{0}}{W})10^{\frac{3}{2}} \tag{2}\]  
\(B\) is the specimen thickness, \(b_{0}\) is the length of the uncracked part of the specimen, \(W\) is the length from the bottom of the specimen to the center of its holes, \(a_{0}\) is the initial crack length taking into account the length from the center of the specimen holes to the notch tip and the pre-cracking (Figure 3a). \(g_{2}(a_{0}/W)\) is a stress intensity factor coefficient [17]. \(R_{e}\) is the yield strength.
## 4 Outline of the Approach
### Integration and Consumption of Profile Information
```
0: U2 MIO ontology
0: Profile consumer s required values delivered
1: Receive \(cd_{i}\) from profile information consumer request
2:if consumer id is new to U2 MIO then
3: create a new concept scheme \(X_{i}\), identify \((C_{i},H_{i},V_{i}\) ) and insert an instance with \(V_{i}\) values in U2 MIO
```
4: Extract \(C_{T}\) from the ubiquitous user model concept scheme \(U\)
5: Calculate the relation \(R_{0}\) of every triplet \((c_{s},c_{t},R_{0})\)\(\forall c_{s}\in C_{s},\forall c_{t}\in C_{T}\)
6: comment: Structure level matching
7: Select the set of neighbors \(N_{S},\forall c_{s}\in C_{s}\) and \(N_{T},\forall c_{t\bar{b}}\in C_{T}\)
8: Calculate relation \(R_{I}\) of every triplet\((c^{\prime}_{s},c^{\prime}_{t\bar{b}},R_{1})\)\(\forall c^{\prime}_{s}\in N_{s},\forall c^{\prime}_{t\bar{b}}\in N_{t}\)
9: Apply IF THEN rules given \(R_{0}\) and \(R_{1}\forall c_{s}\in C_{s}\) and obtain \(M^{X_{i}U}=map(c_{s},c_{t\bar{b}})\)\(\forall c_{s}\in C_{s},\forall c_{t\bar{b}}\in C_{T}\) and U2 MIO enhance recommendations
10: Add \(M^{X_{i}U}\) to U2 MIO
11:endif
12: Get concept requested \(V_{i}\) from concepts with _exactmatch_ in U2 MIO
13: Select \(V_{i}\)* with conflict resolution process
14: Deliver \(V_{i}\)*
15:if consumer is not also supplier
16: Save \(cd_{i}\) with values, timestamp and consumer id in Service usage historical data repository
17:else
18: Save \(cd_{i}\) with values, timestamp and consumer id in raw data repository
19:endif ```  
**Algorithm 2** Consumption of profile information from the ubiquitous user model. A conflict resolution process must select the best suited value to deliver (statement 12). For the time being, the best value is selected taking only into account the effective date of extraction from the original source, delivering the most recent value.
## 3 Case Study
### Data Preprocessing  
(1) Data Cleaning  
To address missing values and anomalies in the recorded ledger data, data cleaning was performed to remove or correct erroneous, duplicate, or inconsistent entries in the dataset.  
1 Samples with numerous missing input parameters were removed.  
2 For samples with a few missing parameters, mean values from complete samples were used to fill the gaps.  
3 For anomalous data points, mean values from complete samples were also used as replacements.  
Anomalies were identified using the boxplot method based on interquartile ranges (IQRs), as shown in Figure 8. Anomalous values were defined as those less than Q1 - k \(\times\) IQR or greater than Q3 + k \(\times\) IQR, where Q1 and Q3 represent the first and third quartiles, respectively, and k was set as 1.5.  
(2) Data Normalization  
The min-max normalization method was applied to scale all features to the [0, 1] range, ensuring that differences in feature scales did not impact algorithm performance.  
The normalization formula is  
\[x_{\text{norm}}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}, \tag{3}\]  
where \(x_{\text{norm}}\) represents the normalized value, \(x\) represents the original value, \(x_{\text{min}}\) represents the minimum value in the dataset, and \(x_{\text{max}}\) represents the maximum value in the dataset.
## 4 Comparison and Discussion
### Signal Processing Approach  
CDMA is not considered as a promising scheduling technique to avoid interference between WBASNs [10]. However, in CDMA-based WBASNs combined with the multiuser detection, the interference from other systems can be mitigated and, thus, the BER performance of the on-body transmission is acceptable [48]. Another technique which is robust to interference is the WUR [49]. According to the simulation results, WUR not only filters interference but also reduces the power consumption.
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
## 3 Experimental Results and Analysis
### Comparison with Rice Panicle Image Detecting and Segmenting Methods  
The proposed method was compared with existing rice panicle detection and segmentation methods, as shown in Table 4. The table lists the specific algorithms used and their evaluation indexes, and summarizes the advantages and disadvantages of each method. In addition to the evaluation indicators already listed above, root mean square error (RMSE), also called standard error, is a measure of how close the calculated value is to the true value; the smaller the value, the higher the accuracy of the model. Accuracy (acc) refers to the proportion of the correct quantity to the total quantity. As can be seen from Table 4, the method proposed in this paper is obviously better than the method proposed by [PERSON] and slightly worse than that propose by [PERSON] [11; 32]. However, the images of rice in reference [32] were taken from the top with a Nikon D40 digital camera, and the number of rice panicles in each image was small. In this paper, rice images were taken from the side with the help of field cameras, and there were many rice panicles in each picture, which was more complex but more practical. Compared with the method proposed by [PERSON], the RMSE value of the number of rice panicles is slightly lower; however, the method in reference [14] has higher requirements for the flight height of the UAV when collecting images, and is not suitable for the small range in the rice field environment [14]. Compared with the method proposed by [PERSON], this method in reference [33] was used for the experiment and counting of grain per ear of rice panicle in a laboratory environment [33]. Although the result in this paper was worse than that of reference [33], as the accuracy of the number of rice panicles with the proposed method in this paper was 83.27%, it was more practical for a real field environment. It can be seen from Table 4 that the method proposed in this paper has good results in an actual field environment.
## 4 Results  
During the walkthrough on the websites, there were a lot of controversies regarding the number of news websites for this study. As a result, there were a lot of changes in order to secure the sample. Throughout the search of the top international news websites, the news aggregators and social media websites were removed from the sample. Additionally, many websites could not be accessed (many of them were from the United States of America) due to the new regulation of the General Data Protection Regulation (GDPR). Consequently, from the 500 most visited news websites of the globe, the final sample was 353. From the overall 353 websites, only 130 allowed users to comment on news articles via their social media accounts (36.82%). In addition, the percentage of news websites that use multiple comment platforms for accepting users' comments was 16.43% (58). Additionally, 64 (18.13%) were using Facebook and 3 (0.84%) were using the commenting platform Disqus (Figure 4) [46].  
Figure 3: Example acquired from www.timesleader.com.  
Figure 2: Example acquired from www.islandpacket.com.  
Regarding the most popular news websites in Greece, the list of 500 top visited news websites of alexa.com was used. Again, the social media platforms and the news aggregators were removed. However, in this case, there were not so many websites excluded due to the GDPR regulations. The main problem with this sample was that the provided list did not include only the news websites of the examined country, but also commercial, marketing, e-shops, and other websites. Consequently, the final number of the examined websites was 175. From the overall 175 websites, only 76 (43.42%) gave to the user, the option of login via their social media accounts and to comment on news articles. On the other hand, the Greek news websites who accepted comments from multiple platforms was 33 (18.85%). The number of news websites that used Facebook was 39 (22.28%), and 2 (1.14%) were those that used the commenting platform Disqus (Figure 5).  
Figure 4: The Results of the Study for the International News Websites for the Commenting Platforms (n = 353).  
Figure 5: The Results of the Study for the Greek News Websites for the Commenting Platforms (n = 175).  
The sample with the news websites of Cyprus had the same problems or characteristics as the previous one. However, there was no problem with the GDPR regulations and no one news website was removed due to this reason. Additionally, an important characteristic emerged from this sample for the Cypriot media system. In the list of the most visited news websites provided by alexa.com, there was a large number of Turkish news websites, which were offering content in both the Turkish and English language. The overall number of the examined news websites was 128. From this number only 51 (39.84%) allowed the users to use their social media accounts in order to comment the provided news articles. The number of news websites with multiple comment platforms was 22 (17.18%). In addition, 26 (20.31%) of the news websites were using Facebook, and only 2 (1.56%) the commenting platforms used Disqus (Figure 6).