esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Sensor Networks
### WSN Applications
#### 3.3.4 Commercial Applications  
Some of the commercial applications are monitoring material fatigue; building virtual keyboards; managing inventory; monitoring product quality; constructing smart office spaces; environmental control in office buildings; robot control and guidance in automatic manufacturing environments such as interactive toys; interactive museums; factory process control and automation; monitoring disaster area; smart structures with sensor nodes embedded inside; machine diagnosis; transportation; factory instrumentation; local control of actuators; detecting and monitoring car thefts; vehicle tracking and detection; as well as instrumentation of semiconductor processing chambers, rotating machinery, and wind tunnels.
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques
### Complementary Split Ring Resonators (CSRR)  
This method is one of the good solutions for mutual coupling reduction in which magnetic and electric coupling are produced from an LC resonator. This method is useful in filtering and improving isolation [38]. The impedance bandwidth is broadened through the proper placement of CSRRs on the patch at a symmetrical position of the MIMO antenna in which the two printed dipole antennas are perpendicular to each other [39]. Since the CSRRs behaves as negative permittivity band-stop filters, higher-order modes are suppressed. In [40], a slotted CSRR etched in the ground plane between the antennas achieves a good isolation. In this configuration, the length of slotted CSRR etched in ground plane is adjusted to improve the isolation. This technique is also known as space diversity utilization.
## 3 Methodologies
### User Preference Prediction Based on Psychographic Segmentation and Neural Network
#### 3.2.1 Psychographic Segmentation Based on DBSCAN  
Clustering-based segmentation is often used to categorize consumers on the basis of the relative importance they place on various product attributes, benefits, personality, and value [45; 65]. DBSCAN cluster analysis does not require one to specify the number of clusters in the data a priori, as opposed to k-means. DBSCAN can find arbitrarily shaped clusters. It can even find a cluster completely surrounded by, but not connected to, a different cluster. We conduct DBSCAN on the consumers' psychographic scores, namely, the SVS and BFF scores (see Section 3.1). In the subsequent e-commerce consumer preference-predicting process, we predict the preferences based on the positive and negative consumer/product psychographic scores and the segmented groups they belong to.
# Antifungal Effects of Fermented _Sophora flavescens_ and _Eleutherococcus sessiliflorus_ Extract
## 3 Results and Discussion
### Antifungal Effects of the Fermented and Unfermented Plant Extracts
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Target Fungi**} & **Incubation** & \multicolumn{4}{c}{**Antifungal Effects**} \\  & **Time (h)** & **SE a** & **FSE b** & **EE c** & **FEE d** \\ \hline _P. cactorum_ & & - e & - & - & - \\ _B. cinerea_ & & - & + & - & + \\ _C. fructicola_ Sau-3 & 24 & - & ++ & - & ++ \\ _C. gloeoporoides_ & & - & ++ & - & ++ \\ _P. cactorum_ & & + & + & - & + \\ _B. cinerea_ & & - & + & - & + \\ _C. fructicola_ Sau-3 & 48 & - & ++ & - & ++ \\ _C. gloeoporoides_ & & - & ++ & - & ++ \\ _P. cactorum_ & & + & + & - & + \\ _B. cinerea_ & & + & ++ & - & ++ \\ _C. fructicola_ Sau-3 & 72 & - & ++ & - & ++ \\ _C. gloeoporoides_ & & - & ++ & - & ++ \\ \hline _P. cactorum_ & & + & ++ & - & ++ \\ _B. cinerea_ & & + & ++ & - & ++ \\ _C. fléncola_ Sau-3 & 96 & - & ++ & - & ++ \\ _C. gloeoporoides_ & & - & ++ & - & ++ \\ _P. cactorum_ & & + & ++ & - & ++ \\ _B. cinerea_ & 120 & + & ++ & - & ++ \\ _C. fructicola_ Sau-3 & - & - & ++ & - & ++ \\ _C. gloeoporoides_ & & - & ++ & - & ++ \\ \hline \end{tabular}  
* SE means _Sophora flavescens_ extract, b FSE means fermented _Sophora flavescens_ extract, c EE means _Eleutherococcus sessiliflorus_ extract, and d FEE means fermented _Eleutherococcus sessiliflorus_ extract. e The effectiveness of antifungal activities is expressed depending on the degree of inhibition of fungi growth. The inhibition is displayed as +, ++, and ++, referring to less than 2 mm, less than 3 mm, and more than 3 mm in comparison to no inhibition, respectively.  
\end{table} 
Table 1: Inhibitory effects of targeted fungi by the fermented and unfermented _Sophora flavescens_ and _Eleutherococcus sessiliflorus_ extract.
## 3 Mobile Communications Background
### Evolution of Mobile Technology
#### 3.1.5 Fifth Generation (5G)  
In comparison with all previous mobile generation networks, 5G is a significant advancement and a digital transformation foundation. The 5G brings three new services to users: extreme Mobile Broadband, extreme Machine Type Communication, and Ultra-Reliable Low-Latency Communication. eMBB provides users with fast internet, increased bandwidth, lower latency, UltraHD streaming movies, and media for VAR, among several other benefits. In the same vein, the eMTC offers highly fast, wide-bandwidth machine-type communication across long distances, with minimal power use at an affordable price. For 5G to provide low latency and highly reliable communication, the URLLC is the proposed 5G feature that will guarantee excellent QoS and extremely high dependability, which ordinarily are not feasible with the previous mobile technologies [2]. 5G mobile communications technology provides unlimited internet connectivity to users while improving reliability, scalability, and energy efficiency (EE). 5G uses two main frequency ranges: Frequency Range 1 (FR1), also known as sub-6 GHz, and Frequency Range 2 (FR2), also known as millimeter wave, to effectively supply the wide range of services. A 5G connection is best suited to the sub-6 GHz frequency spectrum since it balances capacity and coverage. For the following generation of mobile networks, mmWave will instead deliver ultra-wide bandwidth without compromising the necessary high-speed transmission. However, molecular absorption and distance long-distance transmission are key issues posing limitations to the use of mmWave in 5G.
## 3 Materials and Methods  
Navigating and tracking PVI and objects inside buildings have many obstacles. While Global Positioning System (GPS) is used for outdoor navigation with good results, it is not suitable for indoor use, as it may give inaccurate results. For example, it is impossible to use a GPS to automatically determine which floor the user is currently on in a tall building. Using the mobile device's accelerometer and compass to track users showed better results and proved to be more available than GPS tracking but, requires a wireless connection or General Packet Radio Service (GPRS) service to function. Tags can be used to ensure that even when the PVI deviate from the correct path, they will immediately allow the system to adjust the exact location and continue navigating. This manuscript proposed a system to help PVI navigate indoors using markers. At first, tags are printed on multiple pieces of paper and placed in the specified location of interest. Then, a graph is created where nodes represent the markers' position. Edges are labelled with the number of steps and the direction between nodes are connected to it. The system starts by requesting the PVI to select their starting point based on the surrounding tags. When a marker is detected by the smartphone camera, the system will use this marker as a starting position.  
To start navigation, the system asks the PVI to choose their destination using voice commands. Then, it searches in the database for the shortest path from this point to the destination. This path is a list of checkpoints that the PVI should pass to arrive at their destination. During navigation, continuous guidance is given to them when moving from one point to the next. Figure 2 shows a diagram to illustrate these steps.  
The authors found that the essential components of this prototype are: 1. How to accurately find PVI location at any time based on the installed tags so, they can continue navigating safely. 2. How to navigate from the starting point to the destination based on these tags. Therefore, the authors divided the proposed system into two parts: Identifying PVI location and navigating to a destination.
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 3 Combination of Face Detection with EVM
### General Design of MC-EVM
Where:  
* Input: the recorded video frames;
* Output: the calculated pulse;
* _fps_: the frames per second value of the video;
* _croppedVideo_: contains the frames of the cropped face images (needs to have the same size as the first detected face);
* _isFaceDetectionBusy_: a function that checks if the face detection process is currently busy or not;
* _getFaceCoordinates_: starts the face detection process on the input frame;
* _getLatestFaceCoordinates_: returns the latest face coordinates from the shared variable memory;
* _performEVM_: starts the EVM process on the cropped video frames;
* _meanRed_: an array of the mean values of the red part of the RGB frame image;
* _findPeaks_: a function to detect the peaks from the input data array;
* _Pulse_: the resulting pulse which is calculated as: \[p=\frac{s_{p}\times 60}{\left(\frac{s_{m}}{fps}\right)}\] (1) where: \[\bullet\] \[s_{p}\] : presents the size or the number of detected peaks; \[s_{m}\] : presents the size or the number of magnified frames; \[s_{p}\] : presents the number of frames per second in the input video, and \[s_{p}\] : presents the size of the detected peaks; \[s_{p}\  
\(p\): presents the calculated pulse. The original limitations of the EVM process are still valid. The EVM process can have bad results in the case of changing the lighting conditions, outdoor recording in daylight, or very low/bad light conditions. However, all the light condition changes need to deal with the magnification factor (the alpha parameter from the EVM process). The low and high expected frequencies of the results also need to be monitored to give the best results from the detected pulse (changing these parameters results in a different pulse and may result in a bad and noisy signal).
# A 5G-Based VR Application for Efficient Port Management
## 2 Smart Ports' Demand Analysis
### VR Development  
Virtual reality technology originated in the 1960s and has become a technology of concern to the scientific and engineering communities [8; 9]. It involves multiple disciplines, such as computer science, mathematics, mechanics, acoustics, optics, mechanics, biology, and even aesthetics and social sciences [10]. Essentially, the 3D virtual environment allows people to feel the immersive space with natural functions such as sight, hearing, touch, and smell, just like being on the scene. In this way, people can use the abilities and methods that people are accustomed to observing, analyzing, manipulating and controlling the generated objective world, and, finally, immerse in it. VR is not creating a real-world, but an alternate environment. People can enter and interact with the environment through various media of the computer. As a systematic technology, VR has already made a qualitative leap in the way of thinking. It does not consider the problem from only one aspect as in a single technology but requires all components to pursue the best overall performance of the system in content acquisition, processing, transmission, and rendering processes.  
VR applies many next-generation information and communication technologies such as near-eye display, perception interaction, rendering processing, network transmission, and content production, and building a virtual immersive and interactive environment for users through somatosensory interaction. It constructs a business form spanning the  
Figure 2: PaaS and IaaS layers.  
cloud-edge-things continuum [4] to meet the needs of the immersive movement of the user's viewport, and to promote information consumption. An effective virtual realization experience in the smart port will make port operators unaware of the real environment and focus on the existence of objects in the virtual environment. The use of VR technologies can help improve the effectiveness of the virtual reality experience and achieve system goals. The pivotal technologies of the 5G network include mobile edge computing, network slicing, and large-scale antennas, which can provide QoS guarantees for smart ports. Smart ports based on a 5G network and VR can provide a variety of different business opportunities.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 1 Introduction  
Brain-Computer Interface (BCI) systems can interact directly with external devices without using the brain's regular output circuit, while enables conscious control of brain activity terminal equipment. BCI offers significant scientific study value and broad application potential in various domains, including the operation of an intelligent EEG wheelchair via an electroencephalography (EEG) signal, rehabilitation training and the creation of wearable devices [1, 2, 3].  
Three paradigms for brain-computer interfaces are frequently used: Motor Imagery, P300 signal [4, 5], and Steady-State Visual Evoked Potential (SSVEP) [6, 7]. SSVEP is a potential created in the brain's visual cortex or the occipital region outside the skull when the human eye is stimulated with a specific frequency of flash or image [8, 9]. A BCI system based on SSVEP has garnered considerable interest due to its advantages, including high classification accuracy, short training time and fast pace of Information Transfer Rate (ITR) [10, 11]. [PERSON] et al. [12] used ensemble learning to improve the performance of ear-EEG in SSVEP-based BCI through re-implementing a compact convolutional neural network. [PERSON] et al. [13] presented the Spatio-Spectral canonical correlation analysis (SS-CCA) algorithm, which is inspired by common spatio-spectral patterns algorithm. It is used as the base model of filter bank canonical correlation analysis (FBCCA), which can help increase the frequency recognition performance. [PERSON] et al. [14] proposed a novel multidomain signal-to-signal translation method to generate artificial SSVEP signals from resting EEG. [PERSON] et al. [15] presented a recursive Bayes-based technique for enhancing the classification effectiveness of high-frequency SSVEP. [PERSON] et al. [16] suggested a Volterra-filter-based nonlinear model of SSVEP. The training cost is minimized in this model by transfer learning and recreating stable reference signals using a small number of training targets.  
This paper focuses on the SSVEP algorithm. SSVEP typically places a premium on the simplicity of use of brain-computer interface devices in practical applications. In certain application cases, obtaining sufficient individual data to pre-train the BCI system is challenging. Therefore, an unsupervised fast SSVEP classification algorithm based on FBCCA was developed. Simultaneously, the minimum time required to collect EEG data from 30 subjects with a 0.2 Hz frequency variation of 40 targets can be lowered to 1.5 s. Additionally, the maximum accuracy can reach 89.1% regardless of the sampling period when the sampling time approaches 3.5 s. Each set of trials' processing time is less than 2.5 s, which bodes well for the use of some brain control devices. When the data length is 2 s, the classification accuracy may rise by more than 20% when compared to the standard canonical correlation analysis (CCA) algorithm.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 5 Linear Programming-Based Mathematical Formulation
### Feasible Region Energy Minimization
#### 5.1.1 Graphical Analysis  
For clear visualization of the objective function of energy minimization, graphical analysis is presented to compute all the values within the feasible region. Assuming \(Packet_{size}=888\) bits,\(Data_{rate}\) = 16,000 bps, \(P_{trans}=\{12.5,25,\ldots,50\}\) W and \(P_{rcp}=\{0.0395,0.079,\ldots,0.158\}\) W, the feasible solution for energy minimization is computed as:  
\[0.693\leq E_{tx}\leq 2.775 \tag{9}\]  
\[0.002\leq E_{rx}\leq 0.0087 \tag{10}\]  
\[0.695\leq E_{tx}+E_{rx}\leq 2.7837 \tag{11}\]  
The feasible region of energy minimization is shown in Figure 5 using points extracted from Equations (9)-(11), and the points on the boundary of this feasible region are:  
\(P_{1}\left(0.693,0.002\right)=0.695\) J  
\(P_{2}\left(0.693,0.0087\right)=0.7017\) J  
\(P_{3}\left(2.775,0.0087\right)=2.7837\) J  
\(P_{4}\left(2.775,0.002\right)=2.777\) J  
Hence, selecting any value from these points results in minimum energy consumption in the network during communication.
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 6 Conclusions  
This paper addresses the bandwidth limitation of traditional molecular electron transfer electrochemical vector hydrophones (METHs) in the low-frequency range by proposing a frequency response extension method based on a dynamic feedback network. By introducing a dynamic force balance negative feedback system based on a digital signal processor (DSP), the operational bandwidth of the electrochemical vector hydrophone is extended, and its low-frequency signal detection capability is enhanced. The proposed system features field adjustability and can adaptively compensate for long-term frequency response drift. Simulation and experimental results demonstrate that the proposed method significantly improves the frequency response consistency and sensitivity of the electrochemical vector hydrophone in the low-frequency range, providing a reliable technical solution for applications in low-frequency underwater noise monitoring and underwater target radiation noise detection.  
Conceptualization, F.B. and D.Y.; methodology, F.B.; software, F.Z.; validation, F.B., H.Y. and F.Z.; formal analysis, F.B.; investigation, F.B., A.L., H.Y., H.Z. and L.Z.; resources, F.B.; data curation, H.Y., H.Z. and L.Z.; writing--original draft preparation, F.B.; writing--review and editing, F.B., A.L., H.Y., F.Z., D.Y. and R.L.; visualization, R.L.; supervision, D.Y.; project administration, F.B.; funding acquisition, F.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Project supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. SML2022 SP201).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
In this paper, all models are simulated by using JMAG finite element analysis software (v.21.0). Under no-load conditions, the permanent magnet flux linkage of the proposed model is significantly shifted compared with the conventional model, and the flux aggregation effect of the permanent magnet arrangement combined with a Halbach array is significant. The air-gap flux density and the back electromotive force (EMF) are significantly shifted and increased compared to the conventional model. The torque decomposition of the conventional and proposed models is conducted using the frozen permeability method under loaded operating conditions. The proposed model has the MAS effect, and the permanent magnet torque and the reluctance torque can be maximized at similar current phase angles. Compared to the conventional model, the proposed model has higher permanent magnet torque utilization and reluctance torque utilization. Next, this paper optimizes the rotor topology of the proposed model based on the FAST multi-objective optimization algorithm in modeFRONTIER. The optimized model has higher average torque and lower torque ripple than the conventional and the proposed models. Finally, the constant power speed range (CPSR) performance and anti-demagnetization performance are analyzed for the three models. The comparison results of CPSR performance show that the proposed model and optimized model have better CPSR performance. In the anti-demagnetization performance analysis, it is shown that all three models have good anti-demagnetization performance. In comparison, the permanent magnets of the conventional model have better anti-demagnetization performance, while the permanent magnets of the proposed model have worse anti-demagnetization performance. Due to the low average torque of conventional PMA-SynRM, in this paper, an asymmetric-rotor PMA-SynRM motor model combined with a Halbach array is proposed based on the above study. The sections of the article are organized as follows: In Section 2, the conventional model as well as the motor topology of the proposed model are presented. A comparative analysis of the two models at no-load and load conditions is also carried out to demonstrate the torque characteristics and torque improvement of the proposed model. In Section 3, the torque performance of the motor is optimized by changing the rotor topology of the proposed model, and the optimal design, sensitivity analysis, and multi-objective optimization are carried out. In Section 4, the CPSR performance and anti-demagnetization performance of the three models are analyzed. In Section 5, a short summary is provided.
# Effect of Parameters on Vapor Generation in Ship-to-Ship Liquefied Natural Gas Bunkering
## 2 Description of Liquefied Natural Gas (LNG) Bunkering System  
In general, a ship-to-ship LNG bunkering system consists of a bunker tank, cargo pump, liquid bunker line, hose, emergency release coupling (ERC), quick-connector/dry disconnect coupling (QC/DC), receiving (fuel) tank, and vapor return lines. There are four types of tanks used for gas carriers: three self-supporting types (IMO type A, B, and C tanks) and a non-self-supporting type (membrane tank). IMO type A, B, and membrane tanks are atmospheric tanks, which have a design pressure less than 0.7 barg. These tanks require partial or full secondary barriers. The IMO type C tank is a cylindrical pressure tank with the maximum allowable relief valve setting of 10 bar and does not require a secondary barrier. It is widely used as a cargo and fuel tank in existing LNG bunkering ships and LNG-fueled ships, as this type of tank has an advantage in regards to holding time with pressure accumulation [3]. To maintain the LNG cold temperature and minimize boil-off, a good-quality insulation system is essential. Polyurethane foam (PUF) is normally used for the IMO type C bunker tank [3], and vacuum perlite insulation is mostly used in IMO type C fuel tanks [22]. A deep well or submerged pump is used for the cargo pump. A variable-speed pump is recommended for controlling the bunkering time by regulating the bunkering flow rate [23]. The liquid lines for bunkering the LNG and vapor return lines for regulating the pressure in the receiving fuel tank comprise austenite stainless-steel type 316 or 304 (ASTM A312 TP316 or 304). These are suitable for cryogenic temperatures and are applied with polyurethane or double-walled vacuum insulation. The hoses are typically made of composite multi-layer thermoplastic and are flexible, allowing for relative movements between ships from wind, waves, draft changes, etc. [3]. The hoses are connected with QC/DC and ERCs, allowing hose separation when necessary. Each separated section includes a self-closing shut-off valve to avoid dripping LNG [14]. It is recommended that the flow velocity of the LNG line does not exceed 10 m/s, to avoid additional surge pressures generated by friction and cavitation [14]. A schematic isometric view of the ship-to-ship bunkering adopted in this study is shown in Figure 2.