esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Sensor
#### 2.3.4 Data Server  
The server had a microservice architecture running MySQL and Java. Every time the processing unit registered an event, it pushed the information to the server. A so-called heartbeat message, an ID and an IP address to access the device remotely was sent every time the system was powered on and a \"counter + 1\" message was sent after an acoustic detection.
# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 2 Related Work
### Deep Learning based Methods  
Deep learning-based methods have gained much popularity and success in a variety of high-level computer vision tasks in the recent past [26; 27; 28] as well as in image processing problems [29; 30; 31]. Deep learning was introduced for de-raining in [11] where a three-layer CNN was used for removing rain streaks and dirt spots in an image that had been taken through glass. In [12], a CNN was proposed for video-based de-raining, while a recurrent neural network was adopted by [PERSON] in [32]. The authors in [33] proposed a residual-guide feature fusion network for single image de-raining. A pyramid of networks was proposed in [17], which used the domain-specific knowledge to reinforce the learning process.  
CNNs learn to minimize a loss function, and the loss value itself decides the quality of output results. Significant design efforts and domain expertise are required to define an effective loss function. In other words, it is necessary to provide the CNN with what the user requires to minimize. Instead, if it is possible to set a high-level, general goal such as \"make the output image indistinguishable from the target images\", then the CNN can automatically learn a loss function to satisfy the goal. This is the basic underlying concept behind generative adversarial networks (GANs).
## 1 Introduction
Unlike fixed configurations, our system makes adaptive choices between ABCS and SWIPT modes based on instantaneous environmental factors, allowing for flexible and optimal performance in diverse contexts. This approach not only enhances adaptability but also provides a practical framework for making energy-rate trade-offs in real time, ensuring that IoT devices maintain functionality even under fluctuating network demands. Additionally, we maintain a strong focus on practical implementation, considering real-world constraints and challenges specific to industrial IoT deployments. This ensures that our proposed solution is not only theoretically sound but also feasible in actual industrial settings. The structure of this paper is as follows: Section 2 presents the significance and contributions of this study, while Section 3 introduces the system model. The IoT transmission scheme and the proposed switching algorithm are detailed in Section 4 and Section 5, respectively. Section 6 evaluates system performance, focusing on data rate and energy efficiency, Section 7 discusses the trade-off between energy efficiency and data transmission, and Section 8 discusses the research findings.
## 3 Mathematical Formulation of the Problem  
This study aims to determine for all periods, \(H\), firstly, the economic plan of production, \(u(k)^{*}\), to meet the varying customer demand and the optimal delivery quantities, \(Q_{i}{}^{*}\), to all the warehouses, with service and quality levels. Secondly, according to the obtained production planning, to establish the optimal strategy of maintenance considering the optimum number of preventive maintenance activities, \(N^{*}\), and the most appropriate time, \(t_{p}{}^{*}\), as well as the optimal parameters of the control chart. These are the number of quality inspection, \(m\), the size of sample, \(n\), the interval of control, \(h\), and the coefficient of control limits, \(k_{p}\). The prime aim is to minimize the total cost associated with production, inventory/holding, maintenance, and quality, comprising sampling, false alarm, and rejection costs over the finite horizon time, \(H\). It is assumed that the production horizon is divided equally into \(H\) production periods with a \(\Delta t\) length. Furthermore, we assume that demand variation is followed by a normal distribution, with mean \(\hat{d}_{k}\) and variance \(V_{dk}\).  
In this study, to resolve the joint optimization problem, we use a sequential scheme by proposing two sub-models (production, inventory, and delivery model, and maintenance and quality model), which is defined as a successive optimization of two sub-models, with the first sub-model as a constraint to optimize the second. The optimization problem is described by a random parameter of our problem (forecasting demand and production, and stochastic constraint of service level, etc.) and also the dependence of the decision variables.
# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 5 Uncertainty Detection and Processing
### Abnormality Detection and Processing of Monitoring Data
When the probability of conflict reaches the robust threshold, it is determined as abnormal data, and an abnormal event report is then sent to the construction party. (4) The monitoring value of PM10 is less than the value of PM2.5  
PM10 is the inhalable particulate matter with an aerodynamic equivalent diameter less than or equal to 10 microns. On the other hand, PM2.5 refers to the particulate matter in the ambient air with an aerodynamic equivalent diameter less than or equal to 2.5 microns [57]. Hence, the monitoring value of PM10 should be greater than that of PM2.5. If \(r_{PM10}(t_{i})<r_{PM2.5}(t_{i})\), the probability value of abnormal data is accumulated. When the probability value of abnormal data reaches the robust threshold, it is judged as abnormal data. (5) PM10 monitoring value is abnormal on rainy and snowy days  
The laser backscatter method is performed to measure PM10. The built-in high-stability laser signal source irradiates the dust particles. The irradiated dust particles will then reflect the laser signal, and the reflected signal intensity is positively associated with the dust concentration. Therefore, the dust concentration can be calculated using a specific algorithm. Due to the fact that there is no dust pretreatment device, liquid substances can also be theoretically measured as fugitive dust. On rainy days, rainwater enters the sensor and meets the laser signal, which increases the laser-reflection signal. Therefore, the dust indicator value is too high, which results in increasing the PM10 monitoring value to exceed the standard. Under this circumstance, if \(r_{PM10}(t_{i})\) is less than the limited value, with the rain and snow sensors revealing the precipitation at this time, it is judged as abnormal data. Figure 7 presents the identification of abnormal data in the perception layer, according to the previously mentioned abnormal detection.
## 4 Discussion
Furthermore, FAPROTAX was also used to predicted bacterial function of the same dataset following the FAPROTAX's instruction ([[http://www.loucalab.com/archive/FAPROTAX/](http://www.loucalab.com/archive/FAPROTAX/)]([http://www.loucalab.com/archive/FAPROTAX/](http://www.loucalab.com/archive/FAPROTAX/))) [9]. A total of 36 functions were derived from FAPROTAX (Figure A1). FAPROTAX showed ecologically relevant functions which concern biogeochemical cycles, such as nitrification, cellulolysis and hydrocarbon degradation, and the interaction of microbe to plants/animals, such as plant pathogen or invertebrate parasites (Figure A1). On the other hand, Tax4 Fun provided a total of 6338 functions which included functions that involved in soil biogeochemical cycles, such as chitinase, beta-glucosidase or acid phosphatase, and not involved in soil system, such as sn-glycerol 3-phosphate transport system ATP-binding protein, queuing tRNA-ribosyltransferase, and DNA-directed RNA polymerase. In order to use Tax4 Fun, the researcher might have an overview of functions required for their work, then sort only interesting functions to be investigated. For example, in this case, we gathered 30 enzymes involved in soil system (Table A1) and show the functional profile presented in Figure A2. However, we believe that both functional annotation tool contributes most benefit, but different aspect for each work. The selection of these tools should depend on the purpose of each study. If a study focuses on the biogeochemical cycle or the interaction of microbe to plants/animals, FAPROTAX would be the best alternatives. Moreover, FAPROTAX is quite easy to follow as it provides plain functional phenotypes. On the other hand, if a study pays more attention to enzyme activity, Tax4 Fun should be a great choice. Figure 10: Heatmap shows functions of bacteria predicted by FAPROTAX. The data based on OTUs abundance derived from rhizosphere soil samples (TRh) of red clover (_Trifolium pratense_).
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 5 Conclusions  
This paper has proposed SetGSO to handle the stochastic many-objective optimal power flow of power systems penetrated with renewable energy sources. The following conclusions have been drawn:  
1. A modified IEEE-30 bus system is used to conduct numerical case studies to illustrate that the proposed solution yields more significant time savings compared to the sampling methods. The obtained Pareto fronts and multiple performance metrics have shown that the SetGSO obtains better convergence solutions. Compared with the basic GSO-MC, the SetGSO could solve the problem with much less running time. 2. Numerical case studies are conducted using enhanced IEEE 57 and IEEE 118 node systems. The results indicate that, in terms of performance metrics, the algorithm introduced in this study yields superior Pareto optimal solutions compared to the existing SetGA algorithm. Moreover, it produces solutions that are more stable in terms of average imprecision. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**HV**} & \multicolumn{3}{c}{\(\mathbf{I_{\text{ave}}(X)}\)} & \multicolumn{3}{c}{**MED**} \\ \cline{3-8}  & & **SetGA** & **SetGSO** & **SetGA** & **SetGSO** & **SetGA** & **SetGSO** \\ \hline \multirow{8}{*}{30\% REs} & Average & 165,510 & **214,100** & 0.51 & 0.51 & 2651.6 & **2515.2** \\  & Median & 168,910 & **213,470** & 0.51 & **0.50** & 2648.9 & **2497.8** \\  & STD.Dev & 9307.4 & **7088.3** & 0.034 & **0.016** & 63.9 & **26.8** \\  & Worst & 148,230 & **205,930** & 0.57 & **0.53** & 2745.6 & **2553.9** \\  & Best & 175,780 & **227,130** & **0.47** & 0.49 & 2578.3 & **2480.7** \\ \hline \multirow{8}{*}{40\% REs} & Average & 162,080 & **225,960** & 1.78 & **1.65** & 2515.6 & **2347.9** \\  & Median & 168,400 & **226,420** & 1.81 & **1.66** & 2489.0 & **2351.2** \\  & STD.Dev & 19,609 & **2395.6** & 0.14 & **0.016** & 116.7 & **30.54** \\  & Worst & 129,010 & **223,210** & 1.96 & **1.67** & 2717.4 & **2397.6** \\  & Best & 183,500 & **229,050** & 1.55 & **1.62** & 2357.2 & **2304.6** \\ \hline \multirow{8}{*}{50\% REs} & Average & 184,660 & **267,880** & 1.80 & 1.72 & 2351.4 & **2129.1** \\  & Median & 178,290 & **266,690** & 1.82 & **1.73** & 2427.4 & **2137.2** \\ \cline{1-1}  & STD.Dev & 28,001 & **4327.9** & 0.13 & **0.018** & 107.3 & **21.5** \\ \cline{1-1}  & Worst & 155,310 & **263,470** & 2.00 & **1.73** & 2435.1 & **2154.5** \\ \cline{1-1}  & Best & 234,970 & **274,890** & **1.64** & 1.68 & 2160.6 & **2091.6** \\ \hline \hline \end{tabular} Bold text represents superior values.  
\end{table} 
Table 7: Statistical results for the IEEE-57 bus system with three different scenarios.
## 1 Introduction
### Literature Survey and Research Motivation
in their paper [23], proposed optical wireless communication system integrated with the mm-wave system for 5G network. They used the OFDM technique for high-speed indoor communication. They have transmitted 40 Gbps radio signals over the 100 GHz band. However, the OFDM technique needs a transmitter and receiver to be coherent, which makes its architecture complex. Similarly, [PERSON] et al., in their research work [24], investigated and put forward an idea of microwave photonic link, which integrates optical fiber, FSO and radio link to deliver all-time link connectivity. The idea in [24] is about using a modulation format that can ensure seamless connectivity among different communication networks. Thus, for the same reasons as discussed here, we investigated the performance of the FSO link using different modulation formats, benchmarked with OOK, which are simple, cost-effective and user-friendly. The modulation formats were chosen with the intent to develop an FSO framework that can readily converge with different communication networks. Furthermore, spatial diversity with two transmitters and two receiver configurations (\(2\times 2\)) was also used to strengthen the system performance over the turbulence channel. The rest of the paper is categorized into the following different sections. Section 2 elaborates on the turbulence theory. Section 3 provides the mathematical analysis of different modulation techniques, while the investigation results are reported in Section 4. Finally, the concluding remarks can be found in Section 5.
## 4 Signal Processing
### Distance Signal Reconstruction  
According to theory [39], the time-dependent relative target displacement \(d(t)\) is linearly related to the change in phase \(\varphi(t)\) between sent and received RF signal of known wavelength \(\lambda\):  
\[d(t)=\frac{\lambda}{2}\cdot\frac{\varphi(t)}{2\pi}. \tag{1}\]  
The required measurement of the phase is done by the six-port receiver and the subsequent detectors. It can be shown that the output signals form the real and imaginary part of a complex signal \(\underline{Z}(t)\), which corresponds to the complex baseband of a quadrature down-conversion:  
\[\underline{Z}(t)=[B_{5}(t)-B_{6}(t)]+j[B_{3}(t)-B_{4}(t)]. \tag{2}\]  
Figure 6: Photographs of prototype and validation systems. (**a**) Photograph of the radar system prototype; (**b**) Photograph of the study setup.  
Ideally, the argument of this number equals the desired phase angle:  
\[\varphi(t)=\arg\left\{\underline{Z}(t)\right\}=\arctan\left(\frac{B_{3}(t)-B_{4} (t)}{B_{5}(t)-B_{6}(t)}\right). \tag{3}\]  
If the radar target moves radially with a constant velocity, the phase will change linearly and hence \(Z(t)\) represents a circle centered around the origin. However, cross-talk between transmitting and receiving antennas and offset errors will move the circle center. Moreover, gain errors will change the shape of the circle to an ellipse. In order to reconstruct the true phase from the measured raw data, an ellipse is fitted on measured samples in I/Q domain. Using the extracted parameters of the fitted ellipse, the data can be normalized to a unit circle around the coordinate origin [40], of which the phase angle can then be translated into a displacement.
## 4 Simulation Analysis  
### Data Acquisition  
The vibration signal of a bearing with incipient faults can be represented as an amplitude modulation model. Considering the random sliding process, the modeled vibration signal can be expressed in Equation (11):  
\[\left\{\begin{array}{c}x_{1}(t)=\sum\limits_{m=1}^{M}Al_{1}\bigg{(}t-\frac{ j}{f_{i}}-\tau_{i}\bigg{)}\\ h_{1}(t)=\exp(-C_{1}t)\cos(2\times 3000\pi t)\end{array}\right. \tag{11}\]  
where A = 1 is the amplitude of the modeled signal, \(C_{i}\) = 900 is attenuation coefficient, \(f_{i}\) = 100 Hz is the fault characteristic frequency, \(\tau_{i}\) is the random fluctuation that occurs during the \(i\)-th impact. The sampling frequency of the data is 20 kHz, and the sampling points is 8192.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 4 Performance Evaluation
### Results Analysis and Comparison  
Figure 7 shows the experimental correlation between the average total throughput and packet sizes. After that, we also evaluate the average forwarding latency of single-queue transmitting packets of various sizes (64 B, 128 B, 256 B, 512 B, 1024 B, 1536 B), and Figure 8 shows experimental results.  
The ability to run the sample program (pkggen) to send and receive packets properly indicates that the accelerator can correctly adapt to the zero-copy driver. Figure 7 show that the higher the number of queues enabled, the higher the throughput when transmitting packets of the same size. As the packet size increases, the total throughput growth rate slows. At a packet size of 1536 B, the throughput of an eight-queue approaches the line rate (100 G). It means that an eight-core host may meet the line-rate forwarding requirement by using our accelerator. With VxLAN enabled for all three nodes, the same packet can be sent and received by programming the flow table. Compared to the equivalent case without a vxLAN-disabled accelerator, there is a decrease in throughput, but the impact is less, verifying the multilayer forwarding capability of the accelerator. Combined with FPGA resources required and throughput gain, every time the number of instanced queues doubles, the FPGA hardware resources consumed need to be more than doubled, but the throughput can only be increased by less than half.  
Figure 8 shows the correlation between the average latency, which is Timestamp.recv minus Timestamp.sent, with various packet sizes enabled single-queue on both the sender and receiver ends. As the packet size increases, the packet sending and receiving latencies also gradually increase. Since the intermediate node forwards the data packet, the latency of sending and receiving at one time should be half of the calculated average latency. For example, the average latency for 64 B (packet size) is around 6.37 compared to 7.05 in 1536 B. After verifying the forwarding performance, we referred to the experimental parameters and design of the ovs-DPDK [31] forwarding latency test and compared it with the state-of-the-art forwarding software. As a result, when the forwarding latency is the lowest, it can approach the performance of ovs-DPDK.  
The above two experiments verified and analyzed the zero-copy driver adaptation of the accelerator, the high throughput of packet sending and receiving, and the programmability and low latency of packet forwarding and generally achieved the above experimental goals. Our future work will continue to focus on the accelerator, mainly extending and implementing deterministic forwarding for multiple queues and further  
Figure 8: Forwarding Latency With Various Packet Sizes.  
Figure 7: Throughput with Various Queues..  
exploring the enhancement of the high performance of programming between Agent agents and flow tables.
# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
## 2 Materials and Methods
### Algorithm Proposed in This Work
In the second stage, all wavelet coefficients of the detail sub bands are subjected to a soft truncation operation, determining the coefficients:  
\[w_{\mathrm{s}}(i,j)\ =\ \eta_{\mathrm{s}}\Big{(}w(i,j),\hat{\lambda}_{i,j} \Big{)} \tag{19}\]  
\(\eta_{\mathrm{s}}\) being the threshold soft truncation operator. In the third stage, a correction is made on the \(w_{\mathrm{s}}(i,j)\) coefficients obtained in the second stage, depending on the contour map. Thus, the coefficients that were established as belonging to a contour are estimated by the arithmetic mean between the initial value and the value obtained in the second processing stage, the rest of the coefficients remaining unchanged:  
\[\hat{w}(i,j)\ =\ \left\{ \begin{array}{cc}\frac{(w_{\mathrm{s}}(i,j)+w(i,j))}{ 2},&\text{if }(i,j)\in\text{ to a contour}\\ w_{\mathrm{s}}(i,j),&\text{otherwise}\end{array} \right. \tag{20}\]  
We call this correction switched correction based on the hierarchical correlation map. Another proposed possibility of correction on the \(w_{\mathrm{s}}(i,j)\) coefficients according to the hierarchical correlation map, this time a continuous one, is given by:  
\[\hat{w}(i,j)\ =\ \rho(i/2,i/2)w(i,j)+(1-\rho(i/2,i/2))w_{\mathrm{s}}(i,j) \tag{21}\]  
where \(\rho(k,l)\) represents the normed value of the hierarchical correlation corresponding to the coordinate coefficient \((k,l)\).
## 2 Material and Methods
### PV Technology Description and Modeling
#### 2.3.6 Description of the Examined Designs  
Figure 6 depicts the four examined PV designs of the present study. Figure 6a shows the conventional or simple PV, Figure 6b shows the radiative PV which has a radiative coating in the upper part of the PV cell, Figure 6c depicts the PCM PV which has a PCM layer at the back of the PV and Figure 6d shows the finned PV which has rectangular fins in the back part of the PV. In the present work, the examined PV is the module SHARP, Sakai, Osaka, Japan, NUSC360!360 W [34] and its characteristics are summarized in Table 5. Also, this table shows the input parameters of the extra-examined PV cases. Moreover, ONYX, Avila, Spain, BIPV was used in this work [35], and, more specifically, the module 034_N-1245030 with zero transparency.  
Figure 6: The examined PV designs.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \multicolumn{2}{c}{Simple PV} \\ \hline Module length [m] & 1.9556 \\ \hline Module width [m] & 0.992 \\ \hline Module area, \(\text{A}_{\text{PV}}\) [m\({}^{2}\)] & 1.94 \\ \hline Reference efficiency, \(\text{n}_{\text{ref}}\) & 0.185 \\ \hline Thermal loss coefficient, \(\beta\) [K\({}^{-1}\)] & 0.0039 \\ \hline Upper emittance, \(\text{\varepsilon}_{\text{u}}\) & 0.7 \\ \hline Down emittance, \(\text{\varepsilon}_{\text{d}}\) & 0.7 \\ \hline Glass transmittance, \(\tau\) & 0.94 \\ \hline Cell absorptance, \(\alpha\) & 0.92 \\ \hline Back material thermal conductivity, \(\text{k}_{\text{b}}\) [W/mK] & 0.15 \\ \hline Back material thickness, \(\text{t}_{\text{b}}\) [m] & 0.01 \\ \hline \multicolumn{2}{c}{Radiative PV} \\ \hline Upper emittance, \(\text{\varepsilon}_{\text{u}}\) & 0.95 \\ \hline \multicolumn{2}{c}{PCM PV} \\ \hline Melting temperature, \(\text{T}_{\text{melt}}\) [\({}^{\circ}\)C] & 25 \\ \hline \multicolumn{2}{c}{Finned PV} \\ \hline Number of the fins, \(\text{N}_{\text{fin}}\) & 200 \\ \hline Length of the fin, \(\text{L}_{\text{fin}}\) [m] & 0.12 \\ \hline Width of the fin, \(\text{W}_{\text{fin}}\) [m] & 0.1 \\ \hline Thickness of the fin, \(\text{t}_{\text{fin}}\) [m] & 0.005 \\ \hline Thermal conductivity of the fin, \(\text{k}_{\text{fin}}\) [W/mK] & 237 \\ \hline BIPV \\ \hline Module length [m] & 1.245 \\ \hline Module width [m] & 0.3 \\ \hline Module area, \(\text{A}_{\text{BIPV}}\) [m\({}^{2}\)] & 1.375 \\ \hline Reference efficiency, \(\text{n}_{\text{ref}}\) & 0.056 \\ \hline Thermal loss coefficient, \(\beta\) [K\({}^{-1}\)] & 0.0019 \\ \hline PV parameter, \(\text{f}\) & 0.0538 \\ \hline \end{tabular}
\end{table}
Table 5: Input parameters of the studied photovoltaics.