esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Design Methodology and Numerical Simulation of BLDC Motor for Power Loss Reduction
## 4 Experimental Setup  
The experimental setup of the Landsman Converter based BLDC motor is represented in Figure 8. The experimental setup consists: MOSFET, BLDC motor, control inverter, RF transmitter, fluffy rationale controller, and RF receiver units. Three signs are produced by inherent lobby sensors. A single-stage AC source has been passed to the stage-controlled rectifier to convert AC to DC. Single-stage to three-stage converters are used to convert uncontrolled DC into controlled or pulse DC. The pulse DC is to run the BLDC motor. The Hall effect sensor senses the position of the rotor, and the flag is opened, which is passed to the microcontrollers. The implanted C coding program is scorched in IC 16F877A using the CCS compiler. The RF transmitter transmits the set speed, and the beneficiary gets and passes this information to the decoder. The speed of the real and the reference set  
Figure 5: Simulation output of rotor speed for PI controller.  
Figure 6: Simulation output of stator current for PID controller.  
Figure 7: Simulation output of electromagnetic torque for PID controller.  
is compared by the controller. The controller determines the error speed and the control signals are created. These control signals are sent to the inverter MOSFET circuit. These signals energize suitable windings by flipping the necessary switches in the inverter. In the experiment, a 2.2 kW BLDC motor is used. It is intended for low-power and variable-speed domestic applications. The motor's performance has been found to be quite good for its operation at varying speeds over a wide range of supply. Figure 9 depicts the input waveform of the Landsman Converter. Experimental tests to show the control output of the BLDC motor's Fuzzy Controller. In the experiment, two test cases are given, which are the sudden case of load variation and the fixed case of speed variation. Figure 10 shows the drive circuit input gate pulse 4. Figure 11 represents the output waveform for the phases R and Y of BLDCM. From the output of the experimental setup and simulation, the fuzzy controller gives better performance.
## 2 Background and Definition of CIM
### Technology Foundation of CIM
#### 2.2.3 Internet of Things (IoT)  
The IoT serves as a pivotal enabler in CIM by facilitating the real-time monitoring of diverse dynamic urban elements through an extensive network of interconnected sensors and smart devices. This integration elevates CIM from a static geographic data framework to a dynamic, continuously evolving data ecosystem, thereby significantly advancing the development of smart cities. By capturing and processing the real-time data on critical urban metrics such as traffic flow, energy consumption, and environmental changes, IoT empowers more responsive and data-driven urban management strategies [27]. For example, in the context of intelligent transportation systems, the deployment of IoT-enabled traffic sensors and surveillance cameras allows for the real-time monitoring of traffic conditions, enabling the adaptive control of traffic signal cycles to mitigate congestion dynamically. Similarly, in the domain of energy management, IoT systems provide real-time insights into the energy consumption patterns across urban buildings, assisting city administrators in optimizing energy distribution and reducing inefficiencies. Through these applications, the IoT not only enhances the operational efficiency of urban systems but also lays the foundation for sustainable and intelligent urban development, reinforcing its critical role in the evolution of CIM.  
At the same time, the implementation of the IoT within CIM presents several significant challenges that must be addressed to ensure its effectiveness and reliability. Foremost among these are data security and privacy concerns, as the extensive network of urban sensors collects vast quantities of personal and sensitive information. Robust encryption, access control mechanisms, and the compliance with data protection regulations are essential to mitigate the risks of breaches and unauthorized access. Another critical issue is ensuring the accuracy and reliability of sensor data. Environmental factors, device malfunctions, and signal interference can introduce errors, potentially undermining the integrity of decision-making processes. Rigorous calibration, fault detection algorithms, and redundancy measures are necessary to maintain the data quality. Furthermore, the complexity of integrating IoT systems poses a substantial challenge. Achieving interoperability across diverse devices, systems, and platforms requires addressing compatibility issues and developing standardized protocols to facilitate seamless communication and data exchange [28; 29]. Despite these challenges, the IoT holds immense potential to transform CIM, particularly in enhancing urban emergency response, optimizing resource management, and strengthening the environmental monitoring. As technological advancements continue, the IoT is poised to further refine urban management systems, accelerating the realization of smart cities and fostering more efficient, sustainable, and resilient urban development.
## 2 Energy Conservation Schemes in WSN
### Objectives and Contributions  
In this paper we investigate a quantized CS framework, aiming at reducing the amount of transmissions in a WSN. In doing so, one expects that network nodes save energy, thus increasing WSN autonomy. We consider different environmental data: temperature, humidity, and illumination [7].  
For reconstruction, we investigate the performance of three distinct CS reconstruction schemes. We also evaluate the impact of packet loss in the rate-distortion performance of that encoding scheme.  
As the transmitted CS measurements are quantized, the rate-distortion behavior of the reconstruction of monitored signals must be evaluated. This is done considering an exhaustive combination of uniform quantizers with distinct amounts of CS coefficients. The analysis generates an RD operational curve that goes over the best compromises between rate and distortion, allowing to reduce energy consumption in transmissions (bits, packets, and bursts) carried out by each sensor node, while keeping the rate-distortion performance sufficiently close to the optimal.  
We propose to employ quantization refinements to incrementally transmit CS measurements, resulting in a successive approximation framework for CS. Consequently, at any operational RD point, an increment in rate is mapped into how many extra coefficients the sensor shall transmit or how many extra bits shall be used to improve previously transmitted CS measurements or both simultaneously. In practice, this scheme allows sensor nodes to first encode signals with a coarse quality and let the sink (the application layer) to ask for refinements without requiring significant extra sensor node resources, thus providing a sink-based control of the RD operational point of sensor nodes. It is worth to mention that while the RD control is coordinated by the sink, each sensor can be at different operational points. To the best of our knowledge, this is the first time that the incremental CS quantized measurements is discussed.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 4 Simulation Setup for TSN Synchronization over 5G Scenarios
### OMNeT+ Based Simulation Framework
#### 4.1.1 Updated 802.1 AS Model  
The 802.1 AS model in the INET framework implements the time synchronization mechanism exactly as mentioned in the previous section. The TSN nodes which forward TSN messages into the 5G network and vice versa need to be updated. The encapsulation and decapsulation of time synchronization messages into layer 3 (UDP/IP) PDUs have to be implemented. The sync frames are forwarded, encapsulated within IP PDUs in the 5 GS, and once they reach the 5G TSN boundary, decapsulation is performed, and further forwarding in the TSN network relies on layer 2 (MAC) addressing. This means that encapsulation is performed at the ingress 5G TSN boundary node and decapsulation is performed at the egress boundary node. Furthermore, depending on the TSN Grand Master positioning in the simulation scenario, 5G TSN boundary nodes can be set up as ingress or egress boundary nodes in the updated 802.1 AS model. This is used in simulating time synchronization scenarios, where the TSN Grand Master can be located in either uplinkor downlink; thus, the synchronizing device would be located on the opposite side of the 5G network.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Ablation Experiment and Analysis
#### 4.4.1 The Role of Dual Mixing Attention Module  
We evaluated the performance of different components of our proposed DMAM on the VeRi-UAV-based dataset in Table 3. The different results of using only CMA, SMA, and DMAM are listed separately. We made the following three observations:  
(1) First of all, the results show that adding CMA to the baseline results in 1.20%, 1.62%, 0.55%, and 4.04% improvements in the assessment over the baseline on Rank-1,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline BOW-SIFT [45] & 36.2 & 52.6 & 61.0 & 9.0 \\ LOMO [46] & 69.3 & 77.8 & 82.3 & 34.1 \\ VGGNet [47] & 56.0 & 72.4 & 78.6 & 44.4 \\ ResNet50 [48] & 58.7 & 74.0 & 79.5 & 47.3 \\ VD-CML & 62.5 & 76.2 & 81.3 & 49.7 \\ (VGGNet) [37] & 67.3 & 78.8 & 83.0 & 54.6 \\ (ResNet50) [37] & 77.1 & 79.2 & 84.9 & 58.5 \\ EMRN [32] & 87.6 & 88.9 & 92.4 & 65.9 \\ CANet [1] & 94.4 & 95.0 & 95.8 & 77.9 \\ HPGN [30] & 94.7 & 95.6 & 97.4 & 78.4 \\ HSGNet [6] & 94.8 & 95.7 & 97.6 & 78.5 \\ GiT [31] & 95.3 & 95.9 & 97.9 & 80.3 \\ \hline Baseline & 95.1 & 95.6 & 97.5 & 79.6 \\
**Ours** & **97.0** & **98.7** & **98.8** & **87.0** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different methods on VeRi-UAV (%).  
Rank-5, Rank-10, and mAP, respectively. CMA introduces the attention mechanism, which can pay more attention to features with high distinction and importance to improve the feature judgment ability. Finally, the results show that CMA can indeed improve the model performance. (2) First, adding SMA to the baseline alone results in an improvement of 5.37% over the baseline on mAP. The addition of SMA improves the mAP by 1.33% compared with the addition of CMA. The difference is that SMA divides different subspaces along the space, and each subspace fuses after the attention mechanism. In vehicle ReID, there is a strong correlation between different vehicle parts. Each spatial position of the feature map can be weighted adaptively by the subspace through the attention mechanism so that the regional correlation between features can be better modeled and utilized. (3) First of all, adding DMAM, which combines CMA and SMA on top of the baseline, we can find another 1.9%, 3.02%, and 1.35%. Adding DMAM, which combines CMA and SMA on top of the baseline, we can find another 1.9%, 3.02%, 1.35%, and 7.40% improvement on Rank-1, Rank-5, Rank-10, and mAP, respectively. The addition of DMAM improves mAP by 2.03% compared with the addition of SMA. Both CMA and SMA have some limitations, and the fusion can complement each other's shortcomings and improve the robustness and generalization ability of the model. Ultimately, DMAM performs better than CMA and SMA.
## 2 ARD Framework  
The stages of the proposed framework are presented in Figure 1: (i) selection of ROI; (ii) configuration of dates of interest, maximum cloud coverage threshold, Sentinel-1 and Sentinel-2 bands of interest; (iii) selection of satellite products; (iv) download of products; (v) processing and finally (vi) cropping of the image in patches. All output images are geo-referenced and saved using the GeoTIFF file format in a folder following the naming convention described in A. The framework is made publicly available (on GitHub: [[https://github.com/cidcom/Sentinel1-Sentinel2-ARD](https://github.com/cidcom/Sentinel1-Sentinel2-ARD)]([https://github.com/cidcom/Sentinel1-Sentinel2-ARD](https://github.com/cidcom/Sentinel1-Sentinel2-ARD)) (accessed on 31 January 2022)) and is implemented in the Python programming language, with processing performed using the Graph Processing Tool (GPT) from the ESA SNAP toolbox [39].  
Figure 1: Overview of the proposed ARD framework.  
The processing steps for Sentinel-1 and Sentinel-2 products vary depending upon the application but certain common steps need to be performed to render the data available for immediate analysis. The CEOS Analysis Ready Data for Land (CARD4L) initiative defines the minimum set of requirements for both radar and optical sensors [40; 41]. In summary, general metadata, quality metadata, measurement-based or radiometric calibration and geometric calibration are required for all sensors. Solar, view-angle and atmospheric correction are required for optical sensors, while radiometric correction for both incidence angle and topography is required for radar sensors.
# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
## 2 Materials and Methods
### Performance of Machine Learning Techniques  
#### 2.4.1 Confusion Matrix  
To assess the performance of a classifier, we considered the following results reported by WEKA: true positive (TP), true negative (TN), false positive (FP), and false negative (FN) [12]. TP and TN are correct identification by the classifier. An FP is an outcome predicted incorrectly as being positive when it is actually negative. An FN is an outcome predicted incorrectly as being negative when it is actually positive. The quality of a classifier is judged by the following formulas [12; 19].  
True positive rate: the number of TP is divided by the total number of actual positives, also called sensitivity:  
\[\text{True positive rate}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]False positive rate: the number of FP is divided by the total number of actual negatives, also equal to 1--specificity:  
\[\text{False positive rate}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{7}\]  
Specificity: the number of TN is divided by the total number of actual negatives, also equal to 1--false positive rate:  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{8}\]  
Precision: the number of true positive predictions divided by the total number of positive predictions, also referred to as positive predictive value (PPV):  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
Accuracy: the probability that the model prediction is correct:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{10}\]  
Area under the receiver operating characteristics curve (ROC curve): the ROC curve is created by plotting the true positive rate (also called sensitivity) against the false positive rate (also called 1-specificity) at various threshold settings. The area under the ROC curve is also called area under curve (AUC), or ROC area. The AUC can be used to evaluate the prediction performance of a classifier system. The value of AUC varies between 0 and 1. If the AUC is 0, all the predictions will be wrong. If AUC = 1, the classifier can predict perfectly. If 0.7 < AUC \(\leq\) 0.8, it is defined as acceptable discrimination If 0.8 < AUC \(\leq\) 0.9, it is defined as excellent discrimination. If 0.9 < AUC \(\leq\) 1.0, it is defined as outstanding discrimination [26].
## 6 Conclusions  
This paper proposed an accurate and stable fault diagnosis framework for multi-degree tooth faults in the gearbox under variable speed conditions. The raw vibration signal obtained from the gearbox contains fault-related information and background noises. To obtain information related to multi-degree tooth faults from the vibration signal, the proposed method preprocesses the raw vibration signal by using the newly developed localized adaptive denoising technique. The localized adaptive denoising technique results in optimized vibration sub-bands with reduced noise. To obtain fault-related information in the form of a time-frequency scale image, a wavelet-based vibration imaging approach is applied to the denoised vibration signal. Finally, these wavelet-based vibration images are provided as an input to a deep convolutional neural network model for fault classification. The deep convolutional neural network is specifically developed for fault diagnosis purposes. To verify the effectiveness of the proposed method, the proposed method was applied to two different datasets. The first dataset had a fixed speed; however, the second dataset consisted of variable speed conditions. On both datasets, the proposed method  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Experiment**} & \multicolumn{6}{c}{**Average Classification Accuracies of Frameworks (\%)**} \\ \cline{3-8}  & & **Fw1** & **Fw2** & **Fw3** & **Fw4** & **Fw5** & **Proposed Fw** \\ \hline Scenario 1 & Experiment 0 & 62.18 & 65.13 & 54.71 & 83.50 & 91.68 & 100 \\ \hline \multirow{4}{*}{Scenario 2} & Experiment 1 & 53.43 & 57.35 & 72.54 & 86.65 & 88.82 & 100 \\ \cline{2-8}  & Experiment 2 & 45.31 & 51.43 & 68.78 & 81.51 & 86.85 & 100 \\ \cline{2-8}  & Experiment 3 & 57.62 & 67.71 & 74.19 & 88.30 & 90.21 & 100 \\ \cline{2-8}  & Experiment 4 & 50.17 & 58.69 & 72.70 & 85.90 & 89.49 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The overall identification accuracies of the compared frameworks through two scenarios.  
outperformed the existing state-of-the-art methods with an average classification accuracy of 100%. In the future, the goal is to apply the proposed fault diagnosis technique to the fault diagnosis of complex rotating machinery, such as centrifugal pumps.  
Conceptualization, C.D.N., Z.A. and J.-M.K.; data curation, C.D.N. and Z.A.; formal analysis, C.D.N., Z.A. and J.-M.K.; funding acquisition, J.-M.K.; methodology, C.D.N., Z.A. and J.-M.K.; software, C.D.N. and Z.A.; supervision, J.-M.K.; validation, C.D.N., Z.A. and J.-M.K.; visualization, C.D.N., Z.A. and J.-M.K.; writing--original draft, C.D.N. and Z.A.; writing--review and editing, J.-M.K. All authors have read and agreed to the published version of the manuscript.  
Following are results of a study on the \"Leaders in Industry-university Cooperation +\" Project, supported by the Ministry of Education and National Research Foundation of Korea.  
The data are publicly available.  
The authors declare no conflict of interest.
## 3 Theoretical Foundations  
### Fundamentals of the RPCA Algorithm  
Robust Principal Component Analysis (RPCA) is an algorithm capable of extracting principal components from data containing outliers. Unlike traditional Principal Component Analysis (PCA), RPCA separates outliers and noise from the data while preserving the component information. The fundamental concept of RPCA involves decomposing the original data matrix into the sum of a low-rank matrix \(L\) and a sparse matrix \(S\).  
\[\mathbf{M}=\mathbf{L}+\mathbf{S} \tag{5}\]  
where \(L\) contains the principal component information of the original data and \(S\) contains the outliers and noise in the data.  
[PERSON] et al. [23] initially formulated the optimization model of the RPCA algorithm as follows:  
\[\min_{L,S}rank(\mathbf{L})+\left\|\mathbf{S}\right\|_{0}\quad s.t.\mathbf{M}=\mathbf{L}+\mathbf{S} \tag{6}\]  
\(rank(\mathbf{L})\) represents the rank of the matrix \(L\), and \(\left\|\mathbf{S}\right\|_{0}\) denotes the number of nonzero in the matrix \(S\) and also the sparsity of the matrix \(S\).  
Since the \(\ell_{0}\) norm problem has been proven to be an NP-hard problem, there is currently no direct optimal solution algorithm available. As a result, this problem has been relaxed into the following form:  
\[\min_{\mathbf{L},\mathbf{S}}\left\|\mathbf{L}\right\|_{*}+\lambda\left\|\mathbf{S}\right\|_{ 1}\quad s.t.\mathbf{M}=\mathbf{L}+\mathbf{S} \tag{7}\]  
where \(\lambda>0\) is the regularization parameter used to control the impact of the low-rank and sparse components on the objective function. \(\left\|\cdot\right\|_{*}\) represents the nuclear norm, which approximates the size of the matrix rank using a norm involving the singular values of the matrix. Computing the nuclear norm involves adding the absolute values of the singular values obtained after performing singular value decomposition. If the singular value vector is denoted as \(\delta\), then \(\left\|\cdot\right\|_{*}=\left\|\delta\right\|_{1}\). \(\left\|\mathbf{S}\right\|_{1}=\sum_{i=1}^{m}\sum_{j=1}^{n}\mid s_{i,j}\mid\) represents the \(\ell_{1}\)-norm of \(S\), which is defined as \(\left\|\mathbf{S}\right\|_{1}=\sum_{i=1}^{m}\sum_{j=1}^{n}\left|s_{i,j}\right|\).
## 2 The Fabrication and Sensing Principle of OMCI  
### Structure and Fabrication  
In this work, the OMC is fabricated by laterally fusing and tapering two twisted silica glass single-mode fibers (SMFs, the core diameter is 10 \(\upmu\)m and the cladding diameter is 125 \(\upmu\)m) [18; 19]. As schematically shown in Figure 1a, OMC is mainly composed of three parts, including a uniform waist area, two tapered transition areas, and four input and output ports (P1, P2, P3, P4). The waist region can be regarded as the coupling between two microfibers (OMs). By connecting fiber mirrors to the two ports of the microfiber coupler respectively, the optical microfiber coupler interferometer structure is constructed, as shown in Figure 1b. Comparing with the conventional optical fiber interferometer, we used OMC as a coupling beam splitting device. Its transmission spectrum not only has interference phenomena which is similar to the conventional interferometer, but also is modulated by OMC's spectral transmission characteristics. Figure 1c shows the microscope images of OMCI uniform waist region.  
Due to the existence of birefringence effect and environmental disturbances in ordinary single-mode optical fibers, the construction of interferometers with ordinary fiber end mirrors will cause the problem of polarization fading, and then causing interference instability. Utilizing the Faraday rotator mirrors (FRMs) instead of fiber mirrors can play a role in suppressing polarization fading [20]. Therefore, it is better to choose FRM as the reflector of the interferometer in sensing applications.