esa-sceva/satcom-chunk-collection huggingface dataset:
# Nitrogen-Related Defects in Crystalline Silicon
## 1 Introduction
Experimentally, there have been a variety of techniques employed to study defects in semiconductors and, especially in this work, N-related defects in Si: a) those studying the electrical properties of defects [32; 33; 34; 35] use, for instance, the deep-level transient spectroscopy (DLTS) technique which basically investigates the properties of deep levels in the forbidden gap of semiconductors; b) those studying the magnetic properties of defects [36; 37; 32; 38] use, for instance, the electron paramagnetic resonance technique (EPR) spectroscopy which is used to identify the electronic structure of defects and their charge states; and c) those studying the optical properties of defects use, for instance, infrared spectroscopy (IR) which mainly investigates using infrared absorption of the localized vibrational modes (LVMs) of the defects [39; 40; 41; 42; 43], as well as photoluminescence (PL) where light separates charge carries within the band or impurity structure of a semiconductor, and whose later recombination produce characteristic emissions [44; 45; 46]. Theoretically, a lot of works have been published employed DFT as well as semi-empirical calculations to gain insight into the structure, binding energies, electronic and vibrational properties, and the behavior of various nitrogen-related defects in Si [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. In the present review, we focus on the most important nitrogen-related defects and their properties in Si. We consider both the experimental and theoretical studies on these technologically important nitrogen-related defects in Si.
# Narrowband Internet-of-Things to Enhance the Vehicular Communications Performance
## 2 Modified V2V Benchmark
The imposed wavelet stage in Figure 6 allows multi-parallel processing in the new MIMO deployment depicted in Figure 2. In such multi-parallel processing, the following procedure is as follows:  
1. Eight decomposition wavelet levels are used in processing the transmitted OFDM signal. 2. Based on distributed energy, the new signal is decomposed based on the wavelet's parameters. Figure 5: Suppressing the side-lobes based on the W-DFT process. Figure 6: The communication link modification based on the wavelet-stage. Three criteria are used to check the performance of the reconstructed signals namely: \begin{tabular}{l l} (a) & \(\quad\)CMSE: the mean-squared error. \\ (b) & \(\quad\)C\({}_{\text{Mcorr}}\): the correlation matrix. \\ (c) & \(\quad\)C\({}_{\text{SQAD}}\): the sum of the absolute difference. \\ \end{tabular}   
Figure 7 depicts the decomposition performance enhancement based on the Complementary Cumulative Distribution Function (CCDF). CCDF is defined as the probability of the power in any OFDM symbol exceeding a certain threshold. In this comparison, two different wavelet criteria have been used to check the decomposition criteria performance; the harmonics wavelets and Daubechies wavelets. The results are aligned with the achieved ones in [24], which stated that the harmonic wavelets are the fastest wavelets, in addition to outperforming our previously published work in [25], which is based on Daubechies wavelets. The results show that the peak-to-average power ratio has been reduced to 6.2 dB for the probability of 10%, in addition to reducing the SNR by a factor of 33% over the conventional OFDM work. As a result, it has been shown that significant performance improvement can be achieved with Daubechies wavelets in the V2V physical layer. This clearly supports the 5G and beyond technologies.
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 1 Introduction
Kafka has several advantages, including real-time processing, strong scalability, low delay, and high flexibility [16]. This paper proposes a distributed real-time monitoring scheme for air pressure stream data based on Kafka. This scheme can customize Kafka clusters according to the number of sensor nodes in the working environment and can transmit real-time air pressure data to Kafka clusters via producers. These clusters are then read and processed by consumers and fed back to the client. The data are stored in a database for historical-record-based decision analyses. Through this scheme, users can obtain high throughput, low delay, and reliable real-time air pressure monitoring services. The main contributions of this paper are as follows:  
* We integrate the advantages of Apache Kafka and the characteristics of distributed processing, leading to the proposal of a distributed real-time SP scheme for air pressure monitoring that is tailored to handle massive data. This integration allows the system to efficiently handle large-scale stream data and provide reliable real-time monitoring. The reliability and efficiency of this scheme in multi-sensor working environments are validated through performance comparisons with communication via ActiveMQ pipelines and existing systems, further demonstrating its practical feasibility. * We construct a low-power sensor network using low-cost microcontrollers, pressure sensors, and communication modules to establish a reliable hardware foundation for the practical application of the scheme. * We provide a visualization interface and interactive data feedback platform through web pages and apps, allowing users to intuitively understand pressure monitoring data and perform real-time data interaction and analysis. The processed data are persistently stored in the MongoDB database, which enables users to conduct decision analyses based on historical data. This provides users with deeper and more comprehensive data analysis capabilities to better support decision making and the application of real-time monitoring systems. This paper is organized as follows: Section 2 discusses the literature, providing the background and an overview of the field; Section 3 introduces the overall architecture of the scheme, including the hardware for the sensor devices and system architecture and the functionalities of each component; Section 4 tests the throughput and transmission delay of Kafka under a large-scale data background and makes comparisons to ActiveMQ message brokers; finally, Section 5 summarizes the paper, provides conclusions, and suggests potential future research directions.
# Thurstonian Scaling for Sensory Discrimination Methods
## 4 Discussion  
Thurstonian modeling of sensory discrimination methods is highly effective in enhancing sensory evaluation's role in food safety by quantifying perceptual differences (\(\delta\) or \(d^{\prime}\)) between safe and potentially contaminated food products. Sensory evaluation is an essential frontline tool used primarily to detect and assess potential issues with food quality, spoilage, and contamination, thereby offering valuable insights for early contamination detection and maintaining high food safety and quality standards. Safety concerns for sensory evaluators are of utmost importance. Sensory testing of foods may only be carried out if it is confirmed that the food tested does not have any health hazards (toxins, heavy metals, microbial contamination, etc.). If disposition or handling of the product is unknown or suspect, it should not be consumed for test purposes. Sniff vs. taste testing may be appropriate in certain instances.
# Challenges Associated with Implementing 5G in Manufacturing
## 4 Infrastructure Requirements for 5G
### Fog and Edge Computing
#### 4.2.1 Edge Computing  
The IIoT edge gateway, or aggregation point (AP), primarily provides connectivity to networks with a large number of sensors, controllers and actuators, all of which can use different wireless/wired technologies. It is located on the edge of the network close to the data provider and the edge of the system leading from the machine to the cloud [2,22].  
In an original conceptual IoT topology, smart sensors would collect data, and send it back to the cloud for processing. One of the critical enablers for the IoT market is the proliferation of sensors and the actuator. These sensors need to be capable of many features whilst meeting the industry demands, which are mainly; the reduction the price of sensors, adding compute capability and connectivity capabilities through either their wired (RS232/422/485 etc.), wireless (Wi-Fi, SigFOX, Lora, BLE, ZigBee etc.) or radio (3G and 4G/LTE etc.) technologies. These features will considerably increase the value of these devices, and more importantly enable real-time management of the devices especially in large sensor arrays.  
In manufacturing environments, programmable logic circuits (PLCs) already undertake the context and compute capabilities, however frequently do not have connectivity capabilities or if they do, they are proprietary in nature [23]. Conversely one of the main reasons the Internet of Things is becoming more prevalent is due to the reduced price of commodity sensors and actuators [24] adding connectivity and compute capabilities would provide pricing and implications for large sensor arrays.  
Edge devices provide capabilities, correlating data from multiple input protocols and mediums, undertaking local processing, such as data compression, or decision making using artificial intelligence algorithms [25], while providing connectivity to TCP/IP via IP Networks, Wi-Fi or radio access networks (RAN) and interfacing to proprietary networking and industrial connectivity protocols. Another crucial role of edge compute devices, or gateways, is to provide continuous operation, running rulesets locally even during outages or when latency issues occur on the WAN link back to the Cloud infrastructure [26].
## 4 Interfacing with External Control Platforms
### Robust Servomechanism Controller  
The robust servomechanism controller of [40] is employed to regulate the voltage of the islanded system, whereas an internal oscillator is used to set the frequency of the islanded system (Figure 6). The frequency of the internal oscillator is set at the system nominal frequency.  
As proposed on [40], a robust controller is designed to solve the robust servomechanism problem (RSP) [41; 42; 43; 44; 45] for the case of constant signals, in conjunction with the new proposed robustness constraint in [46]. Since the plant is minimum phase [40], in principle, we can achieve \"perfect control\" [43] for the system [40].  
**Controller Design Procedure:** Consider the following \"cheap control\" performance index [44]:  
\[J_{\epsilon}=\int_{0}^{\infty}(e^{\prime}e+\epsilon\
u^{\prime}\
u)d\tau, \epsilon>0 \tag{7}\]  
that should be minimized for the system  
\[\dot{\tilde{x}} =\left[\begin{array}{cc}A&0\\ \mathscr{B}_{c}C_{m}&\mathscr{A}_{c}\end{array}\right]\tilde{x}+\left[ \begin{array}{c}B\\ 0\end{array}\right]\
u \tag{8}\] \[e =\left[\begin{array}{cc}0&\mathscr{C}_{c}\end{array}\right] \tilde{x}\]  
where \(\tilde{x}=[x^{\prime}\eta^{\prime}]^{\prime}\), \(e\) is the error in the system, and  
\[\dot{\eta}=\mathscr{A}_{c}\eta+\mathscr{B}_{c}e \tag{9}\]  
is the servo-compensator [41] for the system, where  
\[\mathscr{A}_{c} =\left[\begin{array}{ccc}0&I&0\\ 0&0&I\\ 0&-\omega^{*2}I&0\end{array}\right]_{6\times 6}, \mathscr{B}_{c} =\left[\begin{array}{c}0\\ 0\\ I\end{array}\right]_{6\times 2} \tag{10}\] \[\mathscr{C}_{c} =\left[\begin{array}{cc}I&0&0\end{array}\right]_{2\times 6}, \omega^{*} =12440.7\]  
\(\omega^{*}=12440.7\) is the frequency of switching harmonics generated by the VSC. Since Equation (8) is stabilizable and detectable, the optimal controller which minimizes Equation (7), subject to Equation (8), is a stabilizing controller. In this case, a standard observer [43] can be used to implement the resultant optimal state feedback controller obtained in minimizing Equation (7) as given by:  
\[\
u=K_{0}x+K_{1}\eta \tag{11}\]  
where \(K_{0}\) and \(K_{1}\) are constant gain matrices [43]. In particular, as \(\epsilon\to 0\), zero channel interaction and arbitrary fast speed of response occurs, while no unbounded peaking occurs in the error response.  
Assume now that the controller (11) is implemented by using an observer so that controller (11) becomes:  
\[\left[\begin{array}{c}\dot{\xi}\\ \dot{\eta}\end{array}\right]=\left[\begin{array}{cc}A-\Lambda C_{m}+BK_{0}&BK_{1 }\\ 0&\mathscr{A}_{c}\end{array}\right]\left[\begin{array}{c}\xi\\ \eta\end{array}\right]+\left[\begin{array}{c}\Lambda\\ \mathscr{B}_{c}\end{array}\right]e \tag{12}\]  
where \(e=y-y_{ref}\) is the error in the system, and where \(\Lambda\) is the observer gain found so that \((A-\Lambda C_{m})\) is stable, which can be represented as:  
\[\dot{\beta}=\overline{\mathscr{A}}_{c}\beta+\overline{\mathscr{B}}_{c}(y-y_{ ref}) \tag{13}\]  
Performing the design procedure of [40], the \(12^{th}\) order strictly proper controller is obtained.
## 2 Materials and Methods
### Learning and Forgetting Phenomena
#### 2.2.1 LAF Models  
It is believed that the first application of learning curves in production and operations management was published by [PERSON] in 1936 [46]. The learning-curve representation suggested by [PERSON] is a geometric progression which expresses the decreasing cost required to accomplish a repetitive operation. According to his theory: as the total quantity of units produced doubles, the cost per unit declines by some constant percentage [47]. [PERSON]'s learning curve (WLC) can be classified as a log-linear model and can be represented as:  
\[y_{x}=y_{1}x^{-b}, \tag{1}\]  
\(y_{x}\)--the time to produce the \(x\)th unit,  
\(y_{1}\)--the time to produce the first unit,  
\(x\)--the production count,  
\(b\)--the learning curve exponent.  
Usually, the \(b\) parameter value is equal to \(-\log(LR)/\log(2)\), where \(LR\) is a learning rate.  
Over the years [PERSON]'s learning curve model was modified in numerous ways--for example, by including prior experience level [48], by mixing manual and automated operations [49], by introducing a mixed model assembly (with two or more items being repetitively assembled) [50], or by introducing restoring interruptions [51].  
Various studies tackled advantages and disadvantages of WLCs, and its modifications. This led numerous researchers (including [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]) to study and develop exponential and hyperbolic models for learning curves [46].  
The models can be further divided into categories according to their characteristics, for example, by task duration (continuous or interrupted tasks); learning individuals (single workers, groups, or organizations); type of learning (motor or cognitive); and learning drivers (e.g., working time, cumulative number of produced units, number of setups, number of detected problems, completed projects, etc.) [52].  
Many models of forgetting have also been developed. Although the phenomenon is the opposite of learning, it is usually not modelled as simply the mirror image of learning (Figure 1). Some of the most renown forgetting models include: the variable regression variable forgetting model (VRVF) [53], the variable regression invariant forgetting model (VRIF) [54], the learn-forget curve model (LFCM) [55], the power integration diffusion model (PID) [56], and the depletion power integration latency model (DPIL) [57].  
Both phenomena (learning and forgetting) are being studied both mathematically and empirically.
# Dempster-Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
## 1 Introduction
Moreover, the confidence increases for a given DBM estimate are clearly observed [16] to solve uncertainty to boost the advantage of each DBM classifier and compensate the limitations. * Our methodology provides upper and lower bounds from the clustered BP measurements using the \(k\)-medoids algorithm [18] to reduce the BP estimation uncertainty. * This approach can mitigate the standard deviation of errors (SDEs) of SBP and DBP by 10.4% and 10.1% contrasted to the DBM single estimator [12]. This finding indicates that the DBM-based DS fusion estimator is superior to that of the single DBM estimator in terms of addressing the BP measurement uncertainty. In Section 2, signal processing and feature data selection from oscillometric BP measurements are expressed. The proposed DBM-based DS fusion method with artificial features is presented in Section 3. Section 4 describes the experimental scenario, measurement methodology, and results. Finally, discussion and conclusions are provided in Section 5.
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results
### Scalability to High Resolution Imagery  
The proposed scheme provides a single platform for the compression of various forms of 2-D data and images including imagery acquired from large resolution sensors. 4K images, HDR images, MRI/CT image volumes are our future work directions.  
Since there is too much redundancy in these datasets, one method that we are currently investigating involves applying MTF transform encoding prior to BBWCA. Figure 11 shows the amount of data that can be reduced to long runs of zeros by applying MTF transform on the pristine image before encoding it with BBWCA. Figure 11a shows the original image and its MTF encoded version is given in Figure 11b. Figure 11c shows the histogram of both images. Large number of zeros in the MTF encoded version is beneficial in data reduction prior to BWT and its successive stages.
# Production of a Human Metabolite of Atorvastatinin by Bacterial CYP102A1 Peroxygenase
###### Abstract  
Atorvastatinin is a widely used statin drug that prevents cardiovascular disease and treats hyperlipidemia. The major metabolites in humans are 2-OH and 4-OH atorvastatinin, which are active metabolites known to show highly inhibiting effects on 3-hydroxy-3-methylglutaryl-CoA reductase activity. Producing the hydroxylated metabolites by biocatalysts using enzymes and whole-cell biotransformation is more desirable than chemical synthesis. It is more eco-friendly and can increase the yield of desired products. In this study, we have found an enzymatic strategy of P450 enzymes for highly efficient synthesis of the 4-OH atorvastatinin, which is an expensive commercial product, by using bacterial CYP102A1 peroxygenase activity with hydrogen peroxide without NADPH. We obtained a set of CYP102A1 mutants with high catalytic activity toward atorvastatinin using enzyme library generation, high-throughput screening of highly active mutants, and enzymatic characterization of the mutants. In the hydrogen peroxide supported reactions, a mutant, with nine changed amino acid residues compared to a wild-type among tested mutants, showed the highest catalytic activity of atorvastatinin 4-hydroxylation (1.8 min\({}^{-1}\)). This result shows that CYP102A1 can catalyze atorvastatinin 4-hydroxylation by peroxide-dependent oxidation with high catalytic activity. The advantages of CYP102A1 peroxygenase activity over NADPH-supported monooxygenase activity are discussed. Taken together, we suggest that the P450 peroxygenase activity can be used to produce drugs' metabolites for further studies of their efficacy and safety.  
cry102A1; atorvastatinin; 4-hydroxy atorvastatinin; hydrogen peroxide; P450 peroxygenase; NADPH 7 January 2021  
D 2021
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
## 5 Conclusions  
Depending on the miniaturization development trend of 5G/6G, a pattern-corrected decoupling technique was proposed for the H-plane parallel patch antenna with a spacing of about \(\lambda_{0}/20\). The decoupling structure consisted of a single substrate and two symmetrical T-type metals etched on it where the vertical strip must connect to the ground plane of the patch antenna. This structure will partly prevent the radiated wave, surface wave, and surface current from different parts of the fed antenna. Properly designing the proposed wall to form a resonator reduces the mutual coupling to lower than \(-20\)dB in the desired bandwidth, and the pattern at the concerned frequency is corrected to the broadside direction.  
Conceptualization, Y.W. and B.W.; methodology, Y.W., X.Y. and J.M.; validation, S.Z. and D.L.; writing--original draft preparation, Y.W.; writing--review and editing, A.A.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, grant number GXXL06200101, GXKL0620011, and GXKL06220111; National Natural Science Foundation of China, grant number 12161025; National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics of China, grant number HTKJ2020 KL014003.  
Not applicable.  
The data are contained within the article. The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 6 Experimental Results
### Model Comparison  
The results in Tables 2 and 3 show that only 2% percent of accuracy is lost, on the other hand, the complexity of the network is reduced greatly by decreasing the parameters and the feature layers by half. The model size drops from 2225 KB to 1068 KB. The comparison between the two models is shown in Table 4. The inference time is directly related to computation cost in the network. The proposed model's inference time decreased about 18%, although the number of parameters decreased significantly (see Table 4 and Figure 10). There are two tasks performed by the GPU: creating the kernel and evaluating the model. The proposed model only decreases the evaluation time which is why the inference time does not decrease at the same rate. In other words, the inference time depends greatly on other computation factors rather than just the evaluation of the neural network model (i.e., the cost of launching the GPU kernel starts to dominate the computation time).  
Table 5 shows the means, standard deviations, and t-test results which indicate that the average inference time per frame has reduced from 44.7 ms to 36.5 ms per frame. This reduction in the inference time means that the maximum input frame rate for real-time performance can be increased by 5 fps (from 22 fps in ENet to 27 fps in LNSNet). The \(p\)-value of \(4.06\times 10^{-51}\) indicates that the difference is statistically significant at \(\alpha=0.01\). Although the proposed segmentation method has a lower inference time compare to the ENet method, the main contribution of the proposed method is reducing the model size (more than 50%). This reduction enables multiple modules to be run on the same Jetson TX1, which will reduce the latency caused by integrating multiple Jetson TX1s through the wired network.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **LNSNet** & **ENet** \\ \hline Average inference time per frame (ms) & 36.5 & 44.7 \\ Total parameters & 227,393 & 371,116 \\ Max input frame rate for real-time performance & 27.4 & 22.4 \\ Model size (KB) & 1068 & 2225 \\ \hline \hline \end{tabular}
\end{table}
Table 4: LNSNet vs. ENet.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **ENet** & **LNSNet** \\ \hline Recall & 0.9890 & 0.9908 \\ Precision & 0.8855 & 0.8384 \\ Accuracy & 0.9435 & 0.9231 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proposed vs. ENet Accuracy in Test Data.  
Figure 9: Precision-Recall curves: (**a**) training time, (**b**) test time.