esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### GC-MS Analysis of Wild Strawberry Fatty Acids in Waste and SCO\({}_{2}\) Extract  
The fatty acids profiles of wild strawberry waste and WSWSCO\({}_{2}\) extract are reported in Table 2. The fatty acids amount in supercritical CO\({}_{2}\) extract is almost 50 times higher compared to waste, as expected, confirming the efficacy of the extraction approach. Both in waste and in supercritical CO\({}_{2}\) extract, unsaturated fatty acids (UFAs) are prevalent compared to saturated fatty acids (SFAs), with linoleic (C18:2) and linolenic acid (C18:3) being the most representative compounds, followed by oleic acid (C18:1). Palmitic acid (C16:0) and stearic acid (C18:0) are major saturated fatty acids. In addition, SFA:UFA ratio is 0.11 and 0.16 in waste and supercritical CO\({}_{2}\) extract, respectively.  
An exemplificative chromatogram of fatty acids profile of wild strawberry waste is reported in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**R.T (min)** & **Abbreviation** & **Fatty Acid Identification** & **WS Waste mg/g** &
\begin{tabular}{c} **WSWSCO\({}_{2}\) Extract** \\ **mg/g** \\ \end{tabular} \\ \hline
14.51 & C12:0 & Lauric acid & ND & 0.63 \(\pm\) 0.03 \\ \hline
19.18 & C14:0 & Myristic acid & ND & 0.61 \(\pm\) 0.08 \\ \hline
23.88 & C16:0 & Palmitic acid & 0.88 \(\pm\) 0.04 & 23.71 \(\pm\) 0.85 \\ \hline
25.05 & C16:1 & Palmitoleic acid & ND & 0.84 \(\pm\) 0.04 \\ \hline
26.15 & C17:0 & Heptadecanoic acid & ND & 0.82 \(\pm\) 0.03 \\ \hline
28.35 & C18:0 & Stearic acid & 0.51 \(\pm\) 0.03 & 27.63 \(\pm\) 0.96 \\ \hline
29.42 & C18:1 cis (n9) & Oleic acid & 1.80 & 105.10 \(\pm\) 1.14 \\ \hline
29.55 & C18:1 trans (n9) & Elaidic acid & ND & 4.11 \(\pm\) 0.31 \\ \hline
31.15 & C18:2 cis (n6) & Linoleic acid & 4.63 \(\pm\) 0.31 & 242.32 \(\pm\) 1.76 \\ \hline
32.48 & C20:0 & Arachidic acid & ND & 17.54 \(\pm\) 0.79 \\ \hline
33.10 & C18:3 (n6) & Linolenic acid & 6.31 \(\pm\) 0.45 & 331.30 \(\pm\) 1.44 \\ \hline
33.35 & C20:1 (n9) & cis-11 Eicosenoic acid & ND & 4.05 \(\pm\) 0.24 \\ \hline
35.01 & C20:2 (n6) & cis-11,14 Eicosadiene acid & ND & 1.27 \(\pm\) 0.11 \\ \hline
36.25 & C22:0 & Behenic acid & ND & 5.81 \(\pm\) 0.03 \\ \hline
39.82 & C24:0 & Lignoceric acid & ND & 30.72 \(\pm\) 0.64 \\ \hline  & & Total & 14.13 & 796.45 \\ \hline  & & Total saturated (SFA) & 1.39 & 108.31 \\ \hline  & & Total unsaturated (UFA) & 12.74 & 688.14 \\ \hline  & & Total n6 & 10.94 & 574.89 \\ \hline  & & Total n9 & 1.80 & 113.26 \\ \hline  & & SFA:UFA & 0.11 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fatty acids profiles of wild strawberry (WS) waste and wild strawberry supercritical CO\({}_{2}\) (WSWSCO\({}_{2}\)) extract. Both full names and abbreviations of each identified compound are reported in this Table. ND: not detected.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Case Two--Multi-Hop (Mesh)  
The SMs are interconnected in a partial mesh or a full-mesh topology. Each group in the NAN consists of a GW and its nodes. The GW nodes are, in turn, interconnected to the NOC in a star configuration, _i.e._, all the GW nodes are one-hop away from the star point, the NOC. Nodes within a group will require multiple hops to reach either the GW or the NOC. A full-mesh topology is where every SMs has a circuit connecting it to every other SMs in a group. Figure 6 illustrates this topology. In this case, there are six SMs that form a partial mesh topology between them with a multi-hop path to the NOC. A pre-installation phase comprises storing the shared key between the SM and the NOC, \(K_{sm,noc}\), the ID of the device \(SM_{n,g}\), a group ID \(G\), and a serial number \(SN\) on the devices.  
Figure 5: Authentication for NAN in a star topology.  
Figure 6: Authentication for a multi-hop NAN topology.
## 9 Significance of Smart Healthcare during COVID-19 Pandemic  
Coronavirus disease (COVID-19) is an infectious disease caused by SARS-CoV-2. The statistics have shown that approximately 300 million cases and 5.5 million deaths have been registered to date. To control the spread of SARS-CoV-2 and reduce COVID-19, the strategies such as contract tracing, telemedicine, and the Internet clinic are expedited. Smart healthcare facilitated more significant support in monitoring the remote patients during COVID-19 quarantine. With the deployment of smart health applications, efficient and effective health monitoring can be enabled through remote communication of COVID-19 symptoms for doctors' assistance and diagnosis. The primary symptoms of COVID-19 are cough, fever, headache, mylalgia, sore throat, breathing difficulty, diarrhea, etc. Prescriptions can be drawn by remotely interpreting the severity of a patient's symptoms. Thus, smart healthcare services are beneficial during the pandemic to improve the standard of living for COVID-19 patients. Physical devices such as sensors (pulse sensor), Arduino boards (Arduino Uno), and gateways (Bluetooth module) collect the data from COVID-19 patients. In addition, the wearables such as COVID-19 smart bracelets can detect some of the symptoms and collect the data. Communication technologies such as Wi-Fi and Bluetooth can transmit collected data for diagnosis. Mobile applications or web applications are used for data visualizations by doctors and alert generation for patients. The results can be seen in the respective health management applications. The usage of smart healthcare applications and their services has seen a drastic increment during the COVID-19 pandemic. The adaption of digital health technologies increased from 19% in 2018 to 57% in 2021 after the pandemic. Medical 4.0 is the fourth medical revolution representing the extensive utilization of intelligent healthcare applications through the Internet of Medical Things. COVID-19 pandemic needs the comprehensive utilization of IoT and AI-enabled smart devices. Medical 4.0 will adequately handle the ongoing and upcoming situation of the COVID-19 pandemic [132].  
During the post-pandemic period, the patients need not to physically attend the clinical trial sites, and the diagnosis can be carried out remotely to prevent any further spread of COVID-19. In future, we will develop a clear protocol and flow using SHRA framework for designing any smart healthcare application that is useful for post-pandemic situations.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 3 Methodology
### Enhanced Word Embedding Based on Word2 Vec
#### 3.1.4 Embedding Vector Generation  
We first apply the Jieba tool to perform word segmentation on short text inputs, resulting in a token sequence \(W_{1:n}=\{w_{1},w_{2},\cdots,w_{n}\}\). Each word is then mapped to a 300-dimensional initial embedding vector \(PV_{1:n}=\{pv_{1},pv_{2},\cdots,pv_{n}\}\) using a pre-trained Word2 Vec model. Subsequently, we compute the TF-IDF-CDW weight for each word \(TIC_{1:n}=\{tic_{1},tic_{2},\cdots,tic_{n}\}\) as described in Equation (6). POS encoding vectors \(POS_{1:n}=\{pos_{1},pos_{2},\cdots pos_{n}\}\) are derived using the approach in Section 3.1.3.  
Ultimately, the enhanced embedding vector \(V_{1:n}\) for each word is constructed by scaling the Word2 Vec vector \(pv_{i}\) with its corresponding TF-IDF-CDW weight \(tic_{i}\) and concatenating it with the 10-dimensional POS encoding vector \(pos_{i}\), yielding a 310-dimensional representation:  
\[V_{1:n}=\{pv_{1}\cdot tic_{1}+pos_{1},pv_{2}\cdot tic_{2}+pos_{2},\cdots,pv_{n} \cdot tic_{n}+pos_{n}\} \tag{7}\]  
The newly generated enhanced word embedding vectors reflect the importance differences of the same word across different texts and also capture POS information. The generated embedding matrix highlights key semantic elements while reducing noise interference in the classification process.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 2 Materials and Methods  
### Literature Search Strategy  
To conduct a comprehensive review of ship wake detection methods, a systematic literature search was performed across multiple academic databases, including IEEE Xplore, ScienceDirect, SpringerLink, SPIE Digital Library, and Google Scholar. The search was conducted up to April 2024, to capture the most recent developments in the field. We used a combination of keywords and phrases related to ship wake detection and maritime surveillance. The primary search terms included: \"ship wake detection\", \"wake detection\", \"maritime surveillance\", \"synthetic aperture radar\", \"SAR\", \"optical imagery\", \"multi-spectral\", \"deep learning\", \"machine learning\", \"object detection\", \"Radon transform\", \"Kelvin wake\", and \"ship wake characteristics.\"
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 5 Simulation Results
Figure 4: BER as a function of the number of pilots for CP-OFDM with a transmitter-receiver distance of 50 m: (**a**) 64 subcarriers; (**b**) 128 subcarriers; (**c**) 256 subcarriers; (**d**) 512 subcarriers. Figure 5: BER as a function of the pilot energies for CP-OFDM with 64 subcarriers, a transmitter-receiver distance of 50 m, and the number of pilots as 16. Figure 8 shows the BER performance for the LS and ZF equalizers when the transmitter-receiver distance was varied from 50 m to 1250 m. The number of subcarriers used was  
Figure 6: BER as a function of the equalizer used for QPSK CP-OFDM with 128 subcarriers, a transmitter-receiver distance of 800 m, and the number of pilots as 32. Figure 7: BER of CP-OFDM when using the LS and ZF equalizers as a function of the transmitter-receiver distance with 64 subcarriers and 16 pilots. 128 with 32 pilots and BPSK was used in the mapper. The behavior that was observed in Figures 6 and 7 was also noted in these figures. However, the BER was marginally inferior in this case owing to a higher number of subcarriers. Figure 9 shows the BER performance for the LS and ZF equalizers when the number of subcarriers was varied from 64 to 512 and QPSK was used in the mapper. The number of pilots in both cases was one-fourth the number of subcarriers and the transmitter-receiver distance was kept constant at 500 m. It was observed that the performance with the ZF equalizer was marginally better than that of the LS equalizer. Moreover, as the number of subcarriers increased, the BER degraded owing to an increased ICI. We also provide a summary of our findings in Table 2 for a fixed SNR of 40 dB. It was evident that the BER deteriorated with the number of subcarriers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equalizer** & **N = 64** & **N = 128** & **N = 256** & **N = 512** \\ \hline LS & 0.002729 & 0.006434 & 0.0133 & 0.02782 \\ \hline ZF & 0.003521 & 0.007205 & 0.01396 & 0.02955 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BER performance for 64, 128, 256, and 512 subcarriers at a fixed distance of 500 m.  
Figure 8: BER of CP-OFDM when using the LS and ZF equalizers as a function of the transmitter-receiver distance with 128 subcarriers and 32 pilots.
# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 4 The Proposed System's Goals, Approach, and Architecture
As demonstrated in this thesis, this combined approach ensures that anomalies are detected with greater precision, preventing unauthorized actions from going unnoticed and strengthening overall energy distribution security. The figures above represent two different cases of energy consumption anomalies for Client V and Client VI:  
Client V Anomalous Ratio between Gas and Electricity Consumption:  
* From January to September 2020, both gas and electricity consumption show a stable and proportional decline. However, from October to December 2020, electricity consumption drops significantly, while gas consumption remains stable, indicating potential tampering or a reduction in electricity meter accuracy. * This type of anomaly signals that electricity usage is being reduced intentionally while gas remains constant, which is not typical for regular energy consumption behavior. Client VI Electricity Consumption Drops to Zero:
* From January to August 2020, both gas and electricity consumption decrease gradually, which appears normal. * Starting in September 2020, electricity consumption drops to zero, while gas consumption continues to decrease at a regular pace. This could indicate meter tampering or the disconnection of electricity measurement. * The complete cessation of electricity usage while gas remains in use is a strong indicator of fraud, especially in the absence of alternative energy sources. These graphs illustrate how comparing gas and electricity consumption over time can reveal potential fraudulent activity. The analysis of such anomalies is crucial for effective energy fraud detection and can be integrated into monitoring systems for continuous oversight. Client VII Consistent Low Gas Usage with a Drop in Electricity Consumption:
* Gas consumption remains low and stable throughout the year. However, electricity consumption shows a significant drop, especially from October to December 2020, reaching zero by the end of the year. This sharp decline indicates potential meter tampering or disconnection of electricity while gas consumption remains unaffected. Client VIII Gradual Decline in Both Gas and Electricity Usage: Both gas and electricity consumptions follow a gradual decline throughout the year, but electricity shows a sharper decline compared to gas, reaching near-zero by the end of the year. This behavior could be a sign of either intentional underreporting of electricity consumption or manipulation of the electricity meter while still maintaining gas usage. These graphical representations illustrate how prolonged low consumption, followed by irregular spikes or complete drops, can reveal hidden fraudulent behavior. These types of anomalies should trigger further investigation into potential energy fraud cases.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 2 Related Works  
### Avatars in Virtual Meetings  
Many existing 3D video conference systems that pursue realism use RGBD cameras to capture a user's 3D geometry and texture. For example, VirtualCube [3] adopts a method that uses multiple Azure Kinect RGBD cameras [4] to capture humans and display remote participant videos at full scale on monitors to improve meeting realism. Other depth sensors, such as Intel RealSense [7], can also visualize humans in 3D using depth maps. However, it is difficult for users to use multiple sensors for video conferencing because high-precision depth sensors have not been widely adopted. Therefore, we aimed to achieve similar depth sensor results for generating 3D point cloud avatars using a general monocular webcam. Spatial [2] utilizes a Metaverse virtual space, where users can create 3D virtual avatars containing their faces and collaborate anywhere using VR, desktop, and mobile devices. The face of the avatar in the virtual space is based on 3D reconstruction from a single photo and facial expressions are simple preset animations. The hand movements of VR users are captured by the sensors in Oculus Quest [8] VR glasses, and those of users on other platforms are simply preset animations. The body skin tone and clothing of avatars in the 3D space do not reflect the actual conditions of users.  
AudienceMR [9] provides a multi-user mixed-reality space that extends the local user space into a large, shared classroom for lecturers. A user's full-body avatar in AudienceMR is a 3D avatar whose face is generated from an image of the user. Analysis in AudienceMR has revealed that 3D avatars provide a more realistic feeling in an actual lecture compared to a 2D video stream, similar to our study on virtual conferences. However, user avatars in AudienceMR cannot reflect their clothes, expressions, movements, etc., and are not generated in real-time.  
[PERSON] et al. [10] summarized 36 comparable human-like virtual avatars used in augmented reality, VR, and virtual spaces, and proposed that the photorealism of an avatar increases the feeling of co-presence. Although we recognize that full-body avatars are the most popular choice for representing users, because we aim to improve traditional 2D video conferencing, where most user cameras only show the upper body, our avatars only include the head, arms, and torso, according to the field of view of a typical webcam. Our work does not use sensors, VR headsets or other special equipment, such as VirtualCube [3] and Spatial [2], except for a common webcam, but is able to provide a similar interactive experience.
# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 1 Introduction
from the German Space Center installed a camera on a aircraft to record the relative motion of the parafoil and the aircraft, and then calculated the flight attitude of the parafoil using digital image technology according to the recorded relative motion information [9]. [PERSON] and [PERSON] designed an automatic tracking system that tracked the position and flight attitude of parachutes and dropped objects during an airdrop [10]. In summary, parachute tracking has received a significant amount of attention from various countries. After years of continuous exploration, researchers have proposed and implemented a variety of tracking methods. Each tracking method has different advantages and disadvantages, but in general, the degrees of automation of these tracking methods are generally not high, and the system is not intelligent enough. In the tracking process, experimenters are required to participate throughout the whole process, and there is still a certain distance from the actual artificial intelligence. Therefore, in this paper, we mainly utilized the computer vision method to track the aircraft and parachutes automatically. Specifically, we designed an turntable servo system; its movement was controlled by means of machine learning algorithms to track the aircraft and parachutes. In addition, we also propose an effective multi-target tracking  
Figure 1: Parachute movement stage: (**a**) free fall stage; (**b**) straightening stage; (**c**) inflatable stage; (**d**) steady descent phase. switch algorithm based on the image frame difference and optical flow, realizing real-time switching from the aircraft to the parachutes in the airdrop tests. Our main contributions are as follows:  
* We propose an turntable servo system named TuSeSy, which can track the aircraft and parachutes in airdrop tests automatically. TuSeSy calculates the differences between the taken images by cameras and the inferred images by tracking algorithms, and then generates the control commands to track the aircraft and parachutes. * To achieve real-time switching from the aircraft to the parachutes in the airdrop tests, we designed an effective multi-target tracking switch algorithm based on the image frame difference and optical flow. * We conducted extensive experiments; the results show that TuSeSy cannot only solve the problem of wrong target tracking, but also reduce computational overhead. Moreover, the multi-target tracking switch algorithm has higher computing efficiency and reliability, ensuring the practical applications of the turntable servo system. The rest of this paper is organized as follows. Section 2 presents a review of the related works. We present the mechanical structure, the architecture, and the hardware parameters of the turntable servo system in Section 3. The main tracking algorithm is presented in Section 4. The performance evaluation of TuSeSy is presented in Section 5. Finally, the conclusion is presented in Section 6.
## 1 Introduction
### What is SDN?  
Software defined networking (SDN) is a recent trend of a network that introduces the idea of eliminating tight coupling between the control and the forwarding plane. The network can be broadly divided into three components: a centralized controller, hosts, and switches that connect a pair of hosts, a pair of switches, or a host-switch pair. Northbound and southbound application programming interfaces (APIs) are there to establish communication between various network entities. The centralized controller has the responsibility of configuring forwarding planes (popularly termed as flow tables) according to which switches forward the packets during communication. The basic structure of an SDN appears in Figure 1.  
SDN is deployed in various types of networks, such as LAN, MAN, WAN, data center networks, etc. The advantages are flexible network control without sacrificing forwarding performance, ease of implementation, administration, and ample opportunities of reducing energy consumption [44; 45]. SDN has proven to be extremely successful in highly scalable data centers extending from private enterprises to public sectors where managing big data is not the only issue [8; 9; 10]; thousands of new data are being generated every day to satisfy the requirements of various applications initiated by different levels of users. OpenFlow-based SDN data centers have set a new trend as far as performance efficiency is concerned. Scheduling the flows to various ports is a very important component of network flow control. Also, the occasional deactivation of switches is required to preserve energy.  
Reduction of energy consumption is an important aspect of efficiency in SDN.
# A Study on the Improvement of Filter Performance to Remove Indoor Air Pollution
## 2 Simulation Method and Result
### Sub-Filter Simulation Result  
Figure 8 shows the pressure distribution and vector component of the filter. By examining the direction of the vortex generated in the wake of the sub-filter, it can be seen that the sub-filter acts as a turbulence generator at the entrance of the main filter. The increase in efficiency as the speed increases confirmed that the Stokes number increases in proportion to the speed in the physical filter dust collection of the inertial collision method.  
Figure 6: Analytical boundary conditions for sub-filters mounted under the filter.  
Figure 7: Three features proposed as sub-filters. (**a**) Circle; (**b**) Droplet; (**c**) Cone.  
After applying the sub-filter to Table 3, the filter's dust-collection efficiency, pressure loss, and flow velocity inside the filter are shown Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Trapped Particle (ea.)**} & \multirow{2}{*}{**Efficiency (\%)**} & \multicolumn{2}{c}{**Pressure Loss (mmAq)**} & \multirow{2}{*}{**Velocity (m/s)**} \\  & & & & \\ \hline N/A & 3680 & 36.80 & 0.938 & 1.24 \\ \hline Circle & 6409 & 64.09 & 1.261 & 1.45 \\ \hline Droplet & 6378 & 63.78 & 1.142 & 1.44 \\ \hline Cone & 6132 & 61.32 & 1.140 & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Efficiency by sub-filter.  
Figure 8: (**a**) Circle; (**b**) Droplet; and (**c**) Cone pressure and vector contours.  
As a result of the analysis, by applying the sub-filter, the dust-collection efficiency was high in the order of circle > droplet > cone, and the pressure loss was up to 1.261 mmAq. Even when the sub-filter was applied, it did not have a significant effect on the pressure loss. It is recommended that the pressure loss required by general companies is less than 3.5 mmAq. This is to control noise and vibration. In this respect, the Coanda effect and the sub-filter satisfy the pressure-loss condition. The reason for the high efficiency of the circular shape is that the turbulent component of the circular sub-filter is higher than that of other shapes.  
Figure 9 shows the efficiencies of the three proposed filters by particle size. The graph shows that the circular sub-filter has a good effect between 4 \(\upmu\)m and 7 \(\upmu\)m.