esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Online Optimized Control for Underwater Pipe-Cleaning Robots
## 3 Fusion Bat Algorithm-Optimized Fuzzy Controller
### Fuzzy Controller Design  
The inputs of the fuzzy controller were chosen as \(e_{pos}\) and \(e_{ori}\), while the output was chosen as the turning angle \(\alpha_{t}\). Their domains of discourse were all [6; 7; 8; 9], and the fuzzy sets selected for them were named Negative-Big (NB), Negative-Medium (NM), Negative-Small (NS), Zero (ZE), Positive-Small (PS), Positive-Medium (PM), and Positive-Big (PB). The membership functions for the selected fuzzy sets are shown in Figure 7.  
Figure 6: Schematic diagram of the fuzzy controller with fusion bat algorithm (BA).  
Figure 7: Membership functions of the fuzzy controller: **(a)** Inputs, **(b)** Output.  
Triangular membership functions were adopted as they are the piecewise linear functions proven to be efficient due to their computational simplicity [30]. The fuzzy rules for path tracking were generated by the experiences of manual control, as shown in Table 1. The max-min inference method and the Mamdani method were used in the process of fuzzy inference, and a centroid defuzzifier was applied to obtain the actual turning angle, which was constrained in the range of \([-25^{\circ},25^{\circ}]\).
###### Abstract  
The ocean is filled with mesoscale eddies that account for most of the oceanic kinetic energy. The importance of eddies in transporting properties and energy across the ocean basins has led to numerous efforts to track their motion. Here, we implement a computer vision technique--the optical flow--to map the pathways of mesoscale eddies in the South Atlantic Ocean. The optical flow is applied to the pairs of consecutive sea surface height maps produced from a nearly 30-year-long satellite altimetry record. In contrast to other methods to estimate the eddy propagation velocity, the optical flow can reveal the temporal evolution of eddy motion, which is particularly useful in the regions of strong currents. We present the time-dependent estimates of the speed and direction of eddy propagation in the Eulerian frame of reference. In an excellent agreement with earlier studies, the obtained pattern of eddy propagation reveals the interaction of eddies with the background flow and the bottom topography. We show that in the Antarctic Circumpolar Current, the variability of the eddy propagation velocity is correlated with the variability of the surface geostrophic velocity, demonstrating the robustness of the optical flow to detect the time-variable part of eddy motion.  
mesoscale eddies; eddy propagation; Rossby waves; optical flow; computer vision; sea surface height; satellite altimetry; South Atlantic Ocean 2023
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 3 Webmail XSS
### Email XSS Injection Type  
Referring to the research on Webmail XSS vulnerabilities [16], we concluded that there are several types of Webmail XSS injections. We concluded that there are several types of Webmail XSS injections. Table 1 shows the examples of various Email XSS injection types.  
* Sender injection. If the Webmail is available for registration and its filtering is not stringent, an attacker might try to register a username or sender name that contains malicious code. When the victim loads the sender information on the website, the XSS code will be executed.
* Receiver injection. If the Webmail server does not carefully detect the receiver name, the attacker can attempt to construct an illegal receiver. This kind of vulnerability often appears in the email group-send function. If all receivers of the group are displayed on the victim's page, the XSS code will also be performed.
* Subject injection. The subject injection is the attack type in which hackers are most interested, because it will not only appear on the email browsing page but will also appear in the email list bar. Once the malicious XSS code is injected into the subject, the browser will directly execute the XSS code when loading the email or email list.
* Content injection. The content of the email can be inserted into images, links, and other controllable components, which makes the content most susceptible to the XSS vulnerabilities. If the email content editor is not designed properly, the attacker can directly edit the HTML code. Therefore, filtering the content of the email has always been a struggle for the designer, and is the injection point where the attacker spends the most time working.
* Attachment injection. Attachment injection is divided into the name of the attachment (named filename injection) and the attachment content injection (named attachment injection). Filename injection seeks to include the malicious XSS code in the name of the attachment and also attached to the email. If the attachment name is displayed directly on the web page, the XSS code will be executed. Attachment injection injects malicious XSS code into the content of the attached file [23]. If the Webmail has preloaded functions, which display part or all of the attachment content on the page, it will also trigger an XSS vulnerability.
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
## 1 Introduction
A wide-band antenna array based on MTM was invented to cover the frequency band in 5G systems [22]. Another design was proposed based on flexible transparent wideband antenna elements to form a MIMO antenna array to operate at sub-6 GHz [23]. An electromagnetic band gap structure was introduced between antenna elements to diminish the effects of mutual coupling among antennas in a single array form to suit the applications of 5G systems [24]. A meander line-based antenna array was developed for MIMO applications by separating the antenna elements using an electromagnetic band gap structure at sub-6 GHz bands [25].
## 5 Discussion
In particular, obtaining high-resolution datasets, optimizing the real-time processing, enhancing the model diversity and flexibility, and addressing privacy and data security concerns are vital issues to be addressed in future research. Through these efforts, the proposed system is expected to be effectively applied to a broader range of real-world environments.
# Low-Profile Dual-Polarized Antenna Integrated with Horn and Vivaldi Antenna in Millimeter-Wave Band
## 5 Conclusions  
In this paper, a low\(-\)profile dual\(-\)polarized antenna integrated with an SIW horn antenna and Vivaldi antenna is proposed. The dual\(-\)polarized antenna uses an SIW horn antenna to achieve VP, and HP is gained by a Vivaldi antenna integrated on the surface of the horn antenna. By integrating two common wideband antennas, the antenna obtains wide impedance bandwidth while maintaining a simple configuration. The isolation between two ports of the antenna is also optimized by a different feeding structure. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Published Year**} & \multirow{2}{*}{**Antenna Type**} & \multirow{2}{*}{**No. of Element**} & \multicolumn{2}{c}{**Impedance**} & \multirow{2}{*}{**Gain/dBi**} & \multirow{2}{*}{**Isolation/dB**} \\  & & & & & & \\ \hline
[14] & 2021 & ME\(-\)dipole & \(8\times 8\) & 34.7–39.7 GHz, 14.4\% & 24.8 & 27 \\ \hline
[18] & 2022 & SIW & \(1\times 4\) & 25.8–28.8 GHz, 11.0\% & 10.5 & 31.5 \\ \hline
[20] & 2023 & Patch & \(1\) & 24.4–28 GHz, 13.7\% & 6 & 15 \\ \hline
[27] & 2021 & Metasurface & \(1\) & 7.66–11.33 GHz, 38.65\% & 7.68 & 35 \\ \hline
[28] & 2017 & AMC planar & \(1\) & 2.34–2.70 GHz, 14.29\% & 7.8 & 25 \\ \hline
[29] & 2018 & Slotted SIW & \(8\times 8\) & 55.7–66.1 GHz, 17.1\% & 22.3 & 34.7 \\ \hline
[30] & 2018 & ME\(-\)dipole & \(1\times 8\) & 53.3–63.7 GHz, 18\% & 16.1 & 45 \\ \hline This work & 2023 & Horn and Vivaldi & \(1\) & 20.04–25.5 GHz, 23.97\% & 8.2 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with some previous dual\(-\)polarized antennas.  
Figure 15: Fabricated prototype of the proposed dual\(-\)polarized antenna.  
proposed antenna achieves an overlapped impedance bandwidth of 23.97% from 20.04 to 25.5 GHz for both polarizations. The Vivaldi and SIW horn antenna achieve a gain of 8.2 dBi and 5.2 dBi at 22.5 GHz, respectively. The isolation between the two ports is better than 36 dB in the operating band. The antenna can find potential applications in mm\(-\)wave communication systems. In the future, the antenna can be utilized to form an antenna array, which can increase spectrum efficiency and further enhance the directivity.  
Conceptualization, X.Y.; Methodology, X.Y. and Y.Y.; Project administration, [PERSON] ([PERSON]) and J.H.; Software, X.Y. and J.L. ([PERSON]); Supervision, [PERSON] ([PERSON]), [PERSON], Y.Y. and S.Y.; Writing--original draft, X.Y.; Writing--review & editing, [PERSON] ([PERSON]), [PERSON], and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under Grant 2020 YFB1806102, in part by the \"Siyuan Scholar\" Fellowship of XJTU, and in part by the Shaanxi Province Innovation Capability Support Program under Grant 2023 KJXX-130.  
Not applicable.  
All data are available from the corresponding author.  
The authors declare that they have no conflicts of interest to report regarding the present study.
# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
## 2 Materials and Methods
### Image Recognition
#### 2.2.12 Breast Cancer Validation Model  
Due to personal information attached to the mammograms; data collection was difficult. As a result, the amount of data obtained was relatively small, and when the data is small, training the network from scratch can easily cause overfitting and affect the generalization of the model. Therefore, transfer learning will be ideal for training the image recognition model.  
Since the number of mammograms in this study was very minimal, among which the benign tumor images are the least, the diagnostic results of benign and normal were combined into one category (B or N), and malignant (M) into another category, this reduces the occurrence of overfitting.
## 1 Introduction
For meaningful word segmentation features, we first calculate the context-sensitive word embedding features based on ELMo [39] after meaningful word segmentation, which is similar to the method of [PERSON] et al. [20]. To measure the word correlations in a domain name, we calculate the standard deviation (Std) of each dimension of word features in addition to the mean value of each dimension, and the concatenation of mean and Std features are used as the final word distribution features. Statistical features are widely used in the detection of AGDs, and the part-of-speech distribution features of words are valuable for the detection of dictionary-based AGDs [25]; therefore, we incorporate 3 additional kinds of statistical features, with a total of 11 dimensions, including domain length features, character ratio features, and word statistical features. For the n-gram sequence features, to mutually ensure the detection effects of character-based and dictionary-based AGDs, we compare the performance of 1-gram, 2-gram, and 3-gram sequence features and demonstrate that the combination of 1-gram and 3-gram sequence features exhibit the best detection performance through experiments. Finally, we perform feature fusion of the 4 kinds of features and perform classification to achieve end-to-end detection of the two kinds of AGDs and effectively improve the detection performance. In summary, our method makes the following contributions:  
1. Based on meaningful word segmentation methods, we propose using the standard deviation and mean value of word embedding features to form the word distribution features. We also design multiple-dimensional statistical features and integrate n-gram sequence features to improve the detection performance of dictionary-based AGDs. 2. We determine the best combination of n-gram sequence features in experiments, use a 1-gram sequence to describe the character sequence features of character-based AGDs and use a 3-gram sequence to describe the character fragment sequence features of dictionary-based AGDs. By combining 1-gram and 3-gram sequence features, the detection effects of character-based and dictionary-based AGDs can be jointly considered. 3. Our method achieves accuracies of 96.33% and 98.64% in the dictionary-based AGD dataset and character-based AGD dataset, respectively, and achieves an accuracy of 97.24% in the mixed dataset, exhibiting significantly better performance than the existing methods. The remainder of the paper is organized as follows. Section 2 summarizes the previous work related to DGA attack detection. Section 3 introduces the motivation and framework of the detection method based on the feature fusion of meaningful word segmentation and n-gram sequences. In Section 4, we explain how the datasets were constructed and describe the application of the model to the datasets, comparing our method with the state-of-the-art method. Section 5 concludes the paper.
## 2 Materials and Methods
### Challenge Test--Experimental Design
#### 2.3.2 Laboratory-Scale Fresh Cheese Model  
Pasteurized cow's milk fresh cheese, typical for Catalonia, called \"mato\", commercially available at local retailers, was selected as a food matrix and used at the early stage of its shelf-life. The labelled cheese composition was 10% fat (including 6.9% saturated fat), 8.3% protein, 3.1% carbohydrates (being 1.3% free sugars), 0.1% NaCl, 250 mg/100 g calcium (31% Nutrient Reference Values (NRV)), and 165 mg/100 g phosphorus (23% NRV).  
The antimicrobial effect of _E. selaginoides_ against _B. cereus_ was tested in two independent trials performed on two different days using two batches of fresh cheese and newly dissolved crude extract and subfractions. Cheeses were prepared in miniaturized format in 12-well plates in a sterile environment into a biosafety cabinet (Scalaf Mars Pro, Labogene, Allerod, Denmark). Three grams of fresh cheese was weighed in each individual well and each concentration of extract was added (0.45 \(\
u/w\)) in two wells (duplicate) into the food matrix. Afterwards, each well was inoculated (1% \(v/w\)) with 10\({}^{4}\)-10\({}^{5}\) cfu/g of _B. cereus_ and properly mixed with a sterile stick until complete homogenization. One well without _B. cereus_ inoculum or extracts was also included to verify the hygienic conditions of handling and storage.  
Once performed, the plates were covered and sealed in polyamide/polyethylene (PA/PE) bags (oxygen permeability of 50 cm\({}^{3}\)/m\({}^{2}\)/24 h and water vapor permeability of 2.8 g/m\({}^{2}\)/24 h) (Sistemvac, Estudi Graf S.A, Girona, Spain). Sealed 12-well plates were stored for 10 days at 8 \({}^{\circ}\)C. The temperature was chosen to assess a worst-case scenario, with a slight abuse of the refrigeration temperature corresponding to a reasonably foreseeable condition at the consumer stage [36; 37]. Temperature was constantly recorded by a real-time wireless LABGUARD(r) system (BioMerieux, Marcy I'Etoile, France).
## 3 Results
### Qualitative Visual Characterization of the WG Coated Specimens  
As a result of the firing treatment, none of the WG types reached the melting point up to the temperature of 700 \({}^{\circ}\)C. At 850 \({}^{\circ}\)C, WG1 and WG2 types created a dense coat that did not adhere to the substrate, whereas WG3 showed more densification; however, it did not cover the entire surface. At 1000 \({}^{\circ}\)C, all types of WG reached the melting point, and a dense coat structure was formed. The tiles specimens fired at 1000 \({}^{\circ}\)C showed better properties in terms of material adhesion when compared to the 700 and 850 \({}^{\circ}\)C temperature cases. Hence, this temperature was set up, and according to the structure and aesthetical perspective, 27 specimens fired at 1000 \({}^{\circ}\)C (Figure 4a-c) were selected for further tests. All the samples had a homogeneous visual appearance to ensure the reproducibility of data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **SiO\({}_{2}\)** & **Na\({}_{2}\)O** & **CaO** & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **K\({}_{2}\)O** & **TiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **BaO** & **PbO** \\ \hline Clay & 44.46 & 0.69 & 11.21 & 3.63 & 16.08 & 5.48 & 3.34 & 0.66 & 0.14 & - & - \\ WG1 ** & 73.2 & 11.4 & 10.8 & 1.35 & 2.03 & 0.31 & 0.88 & 0.066 & \textless{}0.04 & - & - \\ WG2  & 72 & 13 & 9 & 2 & 1.75 & \textless{}0.1 & 0.55 & - & - & - \\ WG3  & 52.5 & 6 & 3 & 1.75 & 2.25 & 0.15 & 7.5 & 0.075 & - & 2 & 20.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average chemical composition in wt.% of WG and clay obtained from FRX analysis. Please note that they are average values and do not necessarily add up to 100% for each element.  
Figure 4: Tiles specimens fired at 1000 \({}^{\circ}\)C during **(a)** 20 min, **(b)** 40 min, and **(c)** 60 min. In each figure, the first row shows WG1, the second row WG2, and the third row WG3 coated samples respectively. In addition, in each figure, the first column shows Q1 samples, the second column shows Q2 samples, and the third column shows Q3 samples according to Table 1.  
The coating structure of the three types of WG was different. According to Figure 4a, WG1 presented a weak transition zone characterized by some cracking, and an unevenly distribution of melted WG due to particle shrinkage for small quantity of WG that improved with the increase of WG quantity. WG2 showed a porous surface and more roughness than the other types, and these qualities were notably reduced at a holding time of 60 min (Figure 4c).  
WG1 and WG2 coatings had the appearance of an opaque surface lying on the substrate, but the WG3-coated samples had a glossy surface that is notably transparent; in fact, some breaks or rifts can be observed at the transition zone between the substrate and the coating. Moreover, WG3 coating contained air bubbles, and some pinholes occurred due to reactions of the oxides (Figure 4a). These effects decreased with the increase of the holding time (Figure 4c).
# Modeling Firm Search and Innovation Trajectory Using Swarm Intelligence
## 3 Materials & Methods
### Data
We summarize the process for obtaining a firm's position in topic space from patent data in Figure 1. \begin{table}
\begin{tabular}{c c} \hline
**3-Digit Code** & **Description** \\ \hline
340 & Communications: electrical \\ \hline
341 & Coded data generation or conversion \\ \hline
342 &
\begin{tabular}{c} Communications: directive radio wave systems and \\ devices (e.g., radar, radio navigation) \\ \end{tabular} \\ \hline
343 & Communications: radio wave antennas \\ \hline
367 &
\begin{tabular}{c} Communications, electrical: acoustic wave systems \\ and devices \\ \end{tabular} \\ \hline
370 & Multiplex communications \\ \hline
375 & Pulse or digital communications \\ \hline
379 & Telephonic communications \\ \hline
398 & Optical communications \\ \hline
455 & Telecommunications \\ \hline
719 &
\begin{tabular}{c} Electrical computers and digital processing systems: \\ interprogram communication or interprocess \\ communication (ipc) \\ \end{tabular} \\ \hline \end{tabular} Source: United States Patent Office.  
\end{table} 
Table 1: Technology classes in sample. To create a tractable sample of firms for demonstration purposes, we included only public firms that were in the top 20 in terms of patenting in each of the 11 technology classes. This yielded a total of 32 large firms that are extremely active in the communication technologies space. Note that such a sample should bias our model findings away from follow-the-leader and imitative strategies, as these firms are the leaders who will most likely follow their own feedback. The resulting sample has 11 separate topic landscapes (one for each technology class). The number of topics varies from 10 to 47 depending on the technology class. The time dimension (\(t\)) was set to a year, and we calibrated the parameters using the 1991-2007 period. Note that we started our analysis in 1991 when communication technology patenting had more density.
## 3 Case Study  
### Data Source and Preparation  
To validate the proposed IKQT algorithm, the Shanghai Qiangsheng Taxi GPS tracking trajectory data acquired during April 2018 were used in this case study. The Shanghai Qiangsheng Taxi Company in China, a major public transit provider, operated approximately 11,000 taxis, which constituted 25% of the total taxi fleet in Shanghai. The data were collected through the GPS installed in each Qiangsheng taxi. The GPS trajectory data, with a sampling interval of approximately 10 s, included detailed information such as taxi ID, vehicle service status, timestamps, and vehicle latitude and longitude.  
Data preprocessing was conducted to extract demand data for further analysis. This involved removing abnormal data caused by either human error (e.g., accidental start or end button presses) or technical errors resulting in incomplete or inaccurate service data. The filtering criteria included no missing items in the data, a minimum of four GPS satellites available at any given time, and an average speed between two consecutive track points not exceeding 140 km/h. Approximately 4.9% of the trajectory data was filtered out based on these criteria, resulting in a remaining service quantity of 6,770,383.  
Another aspect of data preprocessing involved identifying each taxi's charging demands by calculating their maximum daily travel distance. According to the information provided by the Shanghai electric taxi company in 2020, electric taxis can travel approximately 350 to 400 km on a full battery charge. However, taxi drivers had flexibility in choosing their charging times based on remaining battery power and the availability of nearby passengers. This study focused on daytime charging demands, assuming one night-time charge after work hours as the default behavior.  
Taxis IDs were then grouped based on their maximum daily travel distance, which leads to different charging demands: Group 1 comprised taxi IDs with distances ranging from 200 to 300 km and had a 25% probability of requiring charging; Group 2 consisted of taxi IDs with distances from 300 to 400 km and had a 50% probability of needing charging; and Group 3 included taxi IDs with distances exceeding 400 km, requiring one daytime charge.  
The next step was converting the taxi IDs into the charging demand points. A list of taxi IDs was randomly extracted from the total travel demand, with sampling probabilities tailored to each group: 25% for Group 1, 50% for Group 2, and 100% for Group 3. This resulted in a sample of 15,760 services, reflecting the anticipated daytime charging demands.  
Following this, charging demand points were randomly sampled among all services based on the list of taxi IDs. The latitudes and longitudes of the service destinations in the service data served as the input charging demand points for the IKQT and are presentedin Figure 2. The background of Shanghai map in Figure 2 was captured from an online Geographic Information System (GIS) platform, utilizing the tool's ability to retrieve and visualize geospatial data based on specific latitude and longitude coordinates.