esa-sceva/satcom-chunk-collection huggingface dataset:
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 1 Introduction  
The permanent magnet synchronous motor (PMSM) is an important type of synchronous motor. Compared with the conventional synchronous motor, its stator is also the symmetrical three-phase windings, but for its rotor, a special-shaped rare-earth permanent magnet is used instead of the field windings [1]. This means that the motor has a simple structure, small size, light weight and large overload capacity, which means that the PMSM is compact, efficient, and has a high torque density and high dynamic performance [2]. Because of these advantages, PMSM have been widely applied in many fields, such as manufacturing systems, electric vehicles [3], wind generators, ship propulsion engines and so on [4].  
However, since the motor is working in the industrial environment with different stresses, affected by the power supply and load conditions, various faults will inevitably occur in the motor during the course of long-term continuous operation [5]. These faults can seriously affect the reliability and safety of the motor operation. If they cannot be diagnosed and corrected in time, it may cause serious equipment and property damage [6]. Therefore, it is especially important to study the fault detection and diagnosis technology of PMSM.  
In this paper, a review of faults and diagnosis methods of PMSM is presented. In Section 2, the electrical fault, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. In Section 3, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven intelligent diagnostic algorithms proposed recently are enumerated.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Immersive Technologies
#### 6.1.3 Mixed Reality (MR)  
Mixed reality (MR) is an advanced technology that seamlessly blends the physical and digital worlds, creating environments where real and virtual elements coexist and interact in real-time [171]. Unlike VR, which immerses users in a completely virtual environment, or AR, which overlays digital information in the real world, MR allows for more complex interactions between physical and virtual objects. MR is typically achieved using advanced sensors, cameras, and displays, often incorporated into headsets like Microsoft's HoloLens or Magic Leap. These devices track the user's position and surroundings, enabling virtual objects to be anchored in the real world and interact with them naturally and intuitively [172].  
The potential applications of mixed reality span numerous fields, enhancing productivity, creativity, and learning [8]. MR can be used in industry and manufacturing for remote collaboration, allowing engineers to visualize and manipulate 3D models of equipment or structures as if they were physically present [23]. This can improve design accuracy and speed up problem-solving processes. MR can provide immersive learning experiences in education, such as virtual laboratories where students can conduct experiments without the risk or expense associated with physical setups. MR can assist in medical training in the healthcare sector by simulating complex surgical procedures with real-time feedback and guidance. As MR technology continues to evolve [173], it promises to revolutionize how we interact with both the digital and physical worlds, providing a more integrated and interactive experience.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 2 Literature Review
### Research Status of Water Quality Prediction  
Previously proposed water quality prediction models are based on qualitative analysis. Water quality prediction was first studied in 1925, when [PERSON] and [PERSON] proposed the S-P model to track BOD-DO changes in water quality. Since then, with the worsening global water pollution and drinking water crises, an increasing number of water quality prediction models have been proposed. However, due to the complexity of water environments, it is difficult to obtain accurate prediction results with traditional mathematical models, so scholars began to use neural networks to predict water quality. [PERSON] et al. (2009) used the monthly data of 11 water quality indicators from 8 different monitoring points over 10 years to establish two BP neural network models, 11-23-1 and 11-11-1, to calculate the levels of dissolved oxygen and biochemical oxygen demand of Gomti River in India and indirectly judge the water quality [12]. [PERSON] et al. (2016) used an artificial neural network model to predict eight water quality indicators downstream of Cheongpyeong Dam [13]. [PERSON] et al. (2013) established a GM(1,1) model to predict the water quality of the Yellow River estuary from 2012 to 2015 [14]. [PERSON] et al. (2015) took the section of Shanghai Qingpu Urgent Water Port in Taihu Lake Basin as an example and applied the gray model after residual correction of an extreme learning machine regression model for the prediction of water quality indicators. They used the data of the first 100 days of 2013 of six water quality indicators, including dissolved oxygen and chemical oxygen demand, to predict the data of the 101 st to 110 th days [15]. [PERSON] et al. (2007) proposed a weighted support vector regression model to predict the concentration of permanganate in Taihu Lake Basin using the same method; the results showed that the prediction accuracy of this algorithm was higher than that of SVM and RBF neural network alone [16]. According to the idea of neural network weighting introduced in the abovementioned literature, in this study, we designed and implemented a mixed model to predict water quality.
## 3 Distributed Base Station  
In the radio access network (RAN) [21], the cellular network is modelled with Base Stations (BS)s that are responsible for providing coverage and resource assignment to the users. The default architecture is three sectors implementation, in which the BS is transmitting with directional antennas in three directions. All antennas are co-located at the BS location, the term CBS will be used for future representation of Co-located BS. An alternative approach is DBS network architecture. DBS splits the BS into two parts: the BBU part located at the centre and RRUs part that are mounted on remote towers apart from their corresponding BBUs.  
In this scheme, the RRUs are connected with fibre links to their corresponding BBUs. Fibre links are used to power the RRUs and carry the signalling.  
The DBS network fits well with C-RAN network architecture. In C-RAN, BS comprises a number of distributed RRUs that are connected with high speed fibre links to their BBUs, where all data processing is handled. Signalling is carried over these dedicated links--called fronthaul--which connect the RRUs to the BBU [22]. RRUs have the ability to improve the signal to interference plus noise ratio (SINR) in their deployment area. The SINR mapping is shown in Figure 3, where DBS architecture is used, representing three BBUs and for each BBU there are two RRU, the path loss and  
Figure 2: Connected Health (CH), where Machine Type Communication (MTC) devices transmit to the Gateway through 5G cellular network.  
shadow fading map is shown here, comparing default CBS architecture with no remote antennas with a DBS architecture with two remote antennas. As shown in this figure, SINR is improved in the regions where RRUs are existed. RRU system design includes transceivers, duplexers, analogue to digital conversion (ADC), filtering processes and power amplifier (PA) stage. DBS network architecture paves a new paradigm for 5G UDN deployment, by making the next generation network architecture efficient, flexible and scalable.  
The adoption of RRUs in next generation network has many advantages, such as [23]:  
* Smaller footprint and higher software flexibility.
* Easier installation, lower site rental costs, remote and easier upgrades.
* Optimized coverage, with potential multipoint transmission enabled by runtime precoding, which support spatial multiplexing [9].
* Higher energy and spectral efficiency network.  
In this work, RRUs are exploited to forward traffic to the cell edge users, where remote RRUs share the resources with the BBU using the same centralised scheduler. RRUs have been located apart from the BBU in order to provide optimum coverage and capacity to all surrounding users [24].
# DDRCN: Deep Deterministic Policy Gradient Recommendation Framework Fused with Deep Cross Networks
###### Abstract
The dynamic recommendation process based on reinforcement learning is shown in Figure 1, which includes three parts: the user, the agent (recommendation system), and the item list. Users first view the recommended items in the list and then give feedback according to their preferences, including clicks, favorites, forwarding, etc. In particular, the items operated by users are included in the order. According to the user's feedback, the agent constantly learns to adjust the recommendation actions, and predicts the items that the user may be interested in, to make recommendations. Figure 1: Dynamic recommendation process for reinforcement learning. In our study, we provide a deep deterministic policy gradient-based reinforcement learning recommendation method called DDRCN, which includes Actor and Critic dual networks for recommendation action generation and action value evaluation, respectively. In particular, we use a deep cross network to process the basic features of the user data, and to fit a state representation for strategy selection and value estimation. During the action selection, the Actor policy neural network may fit the policy function through the deep cross network, which directly outputs the recommended actions saved to the project recommendation pool. The Critic network is a value estimation of the current action and state of the user, and the model is trained by two networks together. Through a large number of experiments, the proposed recommendation framework achieves good recommendation results. The contributions of this paper are as follows:  
* We provide a deep deterministic policy gradient recommendation framework, DDRCN, that fuses deep cross networks. The framework uses the Actor-Critic approach, which maximizes the cumulative reward of recommendations through the continuous exploration and exploitation of the Actor and Critic networks, combined with greedy strategies. * In this recommendation framework, we fit the data features between users and items, utilizing a deep cross network. The deep cross network consists of a Deep network and a Cross network, and the two networks work together to compute the cross relationship between the features. * We conducted relevant recommendation experiments on the real movie and music datasets, and the experimental results show that our proposed model outperforms its competitors regarding recall and ranking effects. The rest of the sections are organized as follows. Section 2 presents the related work. Section 3 introduces the preliminary knowledge. Section 4 elaborates on the proposed recommendation framework. Section 5 discusses the experimental results, and the paper is concluded in Section 6.
## 1 Introduction
### Deep Drawing Behaviour of Sandwich Materials
For the non-cured thermoplastic resin-based FML, a thermoformed laminate structure can be produced by combining polymer injection moulding, where the metal cover and fibres are formed first, then thermoplastic polymer is injected from the edges between the formed metal laminates at a high temperature and pressure, and, finally, the thermoplastic resin is cooled and polymerised to produce a 3D sandwich structure through a combination of deep drawing and thermoplastic resin transfer moulding (T-RTM) [17]. However, moulding techniques using semi-cured or non-cured resin-based FML require high equipment accuracy and control. Moreover, semi-cured resins still require 60-90 min at RT to fully cure at the end of the process. Thus, the efficiency of this approach does not meet the conditions for mass production. Therefore, a new forming technology was introduced based on traditional sheet stamping technology combined with temperature-controlled forming, called thermoforming. For example, the deep drawing behaviour of flat thermoplastic-based FML semi-products based on steel/GF-PA6)/steel was studied [18], where the sample size was \(\O\)140 mm and the deep drawing ratio was 1.9. The study showed that defect-free samples could only be obtained at very high holding forces of 200 kN to 300 kN for drawing depths of less than 17 mm. Due to the molten state of the core matrix and the reduced resistance to circumferential pressure between the laminates and the inner side of the thermoplastic-based FML, severe wrinkles were formed [18]. Further research was published using Al/unidirectional-GF-PA6/Al laminates while forming at a tool temperature of 270 \({}^{\circ}\)C (greater than the melting point at 220 \({}^{\circ}\)C of PA6) so that PA6 was squeezed out during deep drawing and the PA6 turned brown due to the thermal degradation at this temperature [19]. In addition, a removable base was added to the bottom of the die in order to ensure the bond between the layers at the bottom of the cup, thus ensuring the bond strength of the sample. However, wrinkles in the inner aluminium layer into the soft matrix of GF-PA6 could not be avoided. Wrinkling of the inner Al sheet on the sidewalls led to the accumulation and uneven thickness distribution of the GF-PA6 core. A number of similar studies were performed [20; 21; 22; 23; 24; 25; 26; 27] wherein the formability of sandwich materials at RT and elevated temperatures through a combination of conventional sheet stamping and thermoforming was the focus. The influence of the core layer on the formability of thermoplastic-based FMLs, mainly strain evolution, and the resulting defects were analysed, highlighting the approaches intended to achieve improved deep-drawing results.
# Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection
## 5 Simulation Results and Statistical Analysis  
The simulation parameters used in this section are as follows: 384,192 LDPC codes with a code rate of 0.5 and a column weight of three. The matrix is generated by the progressive edge growth (PEG) algorithm [34; 35], divided into 4-ary (Code 1) and 16-ary (Code 2) simulation, which has a maximum number of iterations of 100. Under the AWGN channel conditions and using BPSK modulation, at least 1000 error bits are collected at each SNR. The link level simulation block diagram is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SF Algorithm** & **Addition Operations** & **Multiplication Operations** & **Division Operations** \\ \hline
**WSF** & \(Mq(d_{c}-1)+Nq(d_{v}-1)+(N-1)\) & **0** & **0** \\  & \(+(N-1+d_{c}d_{v})(A{I_{1}}-1)\) & & \\ \hline
**SMWSF** & \(Mq(2d_{c}-1)+Nq(d_{v}-1)+(N-1)\) & **0** & **0** \\  & \(+(N-1+d_{c}d_{v})(A{I_{2}}-1)\) & & **0** \\ \hline
**MSMWSF** & \(Mq(2d_{c}-1)+Nqd_{v}+(N-1)\) & **0** & **0** \\  & \(+(N-1+d_{c}d_{v})(A{I_{3}}-1)\) & & **0** \\ \hline
**LUDSMWSF** & \(Mq(2d_{c}-1)+Nq(d_{v}-1)+(N-1)\) & & **0** \\  & \(+(N-1+d_{c}d_{v})(A{I_{4}}-1)\) & & **0** \\ \hline
**LUDSMWSF** & \(Mq(2d_{c}-1)+Nqd_{v}+(N-1)\) & & **0** \\  & \(+(N-1+d_{c}d_{v})(A{I_{5}}-1)\) & & **0** \\ \hline
**FF-BP** & \(AI_{6}\left[\begin{array}{c}2 Nd_{v}q\log_{2}q\\ +2 Nd_{v}(q-1)+M(d_{c}-1)\end{array}\right]\) & \(AI_{6}\left[\begin{array}{c}Nd_{v}q(d_{c}+2d_{v}-1)\\ +Md_{c}\end{array}\right]\) & \(AI_{6}[Nd_{v}(q+2)]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average total number of real operations in each algorithm.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
###### Abstract  
The trihedral corner reflector (TCR) is widely used as the calibration device in monostatic synthetic aperture radar (SAR) calibration, and the performance of the TCR in radiometric calibration has been studied and verified in depth. As for the bistatic SAR system calibration problem, there have been few published studies. There is a lack of knowledge regarding the exact bistatic radar cross-section (RCS) pattern of TCR with different bistatic angles, and it is also not clear whether the TCR can be used as the calibration target in bistatic SAR. Moreover, the bistatic and monostatic radar cross-section (RCS) characteristics of the TCR are different, even if the bistatic angle is very small. Therefore, the feasibility, design, and deployment requirements of the TCR for bistatic SAR calibration should be carefully investigated. In this paper, we outline the theoretical and practical requirements that need to be satisfied when choosing appropriate calibration devices for bistatic radiometric calibration. Based on these requirements, we analyzed the bistatic RCS patterns using electromagnetic simulation, and concluded that the TCR is feasible for bistatic SAR calibration under relatively small bistatic angles (less than 6\({}^{\circ}\)). The change of TCR boresight with the bistatic angle is not considered generally. However, we found that the TCR boresight and peak RCS will change with the bistatic angle. We have also proposed that the bistatic angle can be extended to 20\({}^{\circ}\) by taking the change of the TCR boresight into account. In this condition, we should get the TCR boresight according to the bistatic angle and then align it during the deployment. Both of these two conditions have their own unique advantages. Different error sources of TCR RCS from manufacture, misalignment, and deformation were investigated quantitatively with simulations, which can provide a theoretical basis for how to design a suitable TCR and guarantee the calibration accuracy for bistatic calibration. In addition, simulation results are different from those of monostatic calibration. Through experiments, we have further verified the feasibility by comparing the quality of bistatic SAR images and point target energy with several typical bistatic angles as the TCR boresight is considered or not. If the bistatic angle is larger than 6\({}^{\circ}\), taking the TCR optimum boresight into account can improve imaging quality and point target energy.  
b  
b  
b
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 3 Hardware Design
### Power Consumption and Optimization
In that case, other SDR RFFEs that already includes an FPGA, e.g., the Lime SDR mini (Lime Microsystems, Guildford, UK, [[https://limemicro.com/products/boards/limesdr-mini/](https://limemicro.com/products/boards/limesdr-mini/)]([https://limemicro.com/products/boards/limesdr-mini/](https://limemicro.com/products/boards/limesdr-mini/)), accessed on 23 March 2023), could be a more efficient approach. The remainder of the power consumption, about one-sixth, is used for the GNSS receiver, ML inference, WiFi communication, and running the web server. It shows that the usual arguments that communications (i.e., WiFi) and ML are issues for power efficiency are not valid in this case. The recorded data were sent once per minute in the test configuration, which is a fairly low rate. However, as most of the data are pre-processed with spectral features, the data upload requirements of the interference monitoring station are low (see the software and algorithmic Sections 4 and 5). Nevertheless, other more power-efficient communication protocols could be considered. Alternatively, the upload rate can be optimized to limit power consumption and data throughput. For example, only when the initial detection raises, is the alarm data sent to the server. However, as the WiFi power consumption is insignificant to other requirements on the system, such optimizations have lower priority. The shortest day of the year in Nuremberg, Germany, is slightly longer than 8 h, and the mean power consumption of the monitoring station is slightly below 6 W. This indicates that at least an 18 W solar panel is required for fully autonomous operation over 24 h. Accounting for power conversion losses, battery losses in charging and discharging, and sub-optimal panel orientation losses, a standard 50 W solar panel with a battery system suffices (i.e., accepting a 36% efficient system). With current technologies, such a solar panel would require a surface area of approximately 0.64 m\({}^{2}\) and cost about EUR 200. Alternatively, if the system runs from the power grid, the yearly power costs assuming the current 0.30 \(\in\) /kWh costs in Germany is:  
\[\text{Yearly Energy Cost} =365\text{ days}\times 24\text{ h}\times 0.30\in/\text{kWh}\times 6.107\cdot 10^{-3} \text{ kW} \tag{5}\] \[=16.05\in \tag{6}\]  
Both solar panel and yearly operational energy costs are relatively low, indicating low operating costs compared to hardware costs. However, if presented with the option, using power from the power grid is better. It has lower upfront costs, requires less maintenance (e.g., replacing batteries or cleaning the solar panels), is more reliable, and is less prone to theft. Furthermore, this analysis provides an initial expectation for the final deployment.
## 3 Nutritional and Bioactive Components of Sprouted Grains
### Macnontrient Modifications During Sprouting
#### 3.1.3 Modification of Lipids and Fatty Acid Profiles  
Storage lipids in cereals, primarily stored as triacylglycerols, undergo enzymatic hydrolysis during germination, generating non-esterified fatty acids and glycerol through the action of lipase [49]. These metabolites are subsequently oxidized via \(\beta\)-oxidation and the glyoxylate cycle, supplying energy for seedling growth. In sprouted oats, enzymatic activity shifts to favor proteolysis and amylolysis, while lipase activity declines [26].  
Fatty acid composition also changes significantly during germination. In barley, sprouting results in increased levels of stearic acid (C18:0) and \(\alpha\)-linolenic acid (C18:3n-3),along with a concurrent decrease in oleic acid (C18:1n-9), indicating a shift toward a more unsaturated lipid profile [36]. A comprehensive comparative study of eight sprouted grain varieties--including millet (_Panicum miliaceum_), quinoa (_Chenopodium quinoa_), rye (_Secale cereale_), wheat (_Triticum aestivum_), barley (_Hordeum vulgare_), oat (_Avena sativa_), buckwheat (_Fagopyrum esculentum_), and amaranth (_Amaranthus_ spp.)--confirmed that germination not only increases total lipid content but also enhances the proportion of polyunsaturated fatty acids (PUFAs) [37]. Across all grain types, PUFA content ranged from 46.9% to 75.6% of total fatty acids, while saturated fatty acids comprised between 10.1% and 25.9%. Millet displayed the most favorable lipid profile, characterized by the highest PUFA and lowest saturated fat content. Sprouted oats demonstrated a 54.3% increase in total fat compared to their unspotuted controls, reflecting a substantial nutritional enhancement [37]. Furthermore, the omega-6 to omega-3 fatty acid ratios were particularly desirable in sprouted rye and barley, measured at 7:1 and 8:1, respectively. The dominant fatty acids present in the sprouted grains included linoleic, oleic, palmitic, \(\alpha\)-linolenic, and stearic acids. These compositional changes not only improve the lipid quality of grains but also support their use as functional ingredients in dietary interventions aimed at improving lipid metabolism and insulin sensitivity in individuals with obesity-associated IR [37].