esa-sceva/satcom-chunk-collection huggingface dataset:
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 5 Renyi Entropy for 5G Signal Analysis
### Theory
An additional experiment showing a direct dependence between the entropy and a signal filling is depicted in Figure 13. For five noise values \(\text{SNR}=\{0,10,20,30,40\}\) dB, 5G signals were generated with different resource allocation ranging from 0 to 100 %. A one-hundredfold Renyi entropy was calculated for each frame fill value and averaged. The results show that a direct dependence between the entropy and downlink resource allocation exists that can be used to make an adaptive signal integration algorithm in passive radar. Then, regardless of the power or frequency of the signal, one can calibrate the system, e.g., by burdening the 5G network. The base station will then generate a full downlink transmission, allowing the maximum entropy value to be determined. After that, the passive radar can perform adaptive integration only if the amount of data transferred is sufficient to achieve the required resolution. Furthermore, the entropy values for cases presented in Section 4 were obtained. Entropy for a \"no content\" case equals 19.63, for \"some amount of content\" 23.41 and for \"full content\" 24.56. This proves that the entropy-based method outperforms the simple spectrum analysis method (Figure 9), as well as effective bandwidth measurement (Figure 11), in which the difference in signal filling could not be distinguished. The results from the power measurement example (Figure 10 are as follows: 10% of content and low power: 24.56, 10% of content and high power: 23.75 and 70% with low power: 25.72. Furthermore, in this case, the entropy-based approach shows its reliability, which is unobtainable in the power measurement method. Figure 12: A comparison of the Rényi entropy value for different filling and SNR. **(a)** Spectrogram of the simulated 5G transmission with 5% allocated subcarriers and \(\text{SNR}=10\) dB. **(b)** Spectrogram of the simulated 5G transmission with 100% allocated subcarriers and \(\text{SNR}=10\) dB. **(c)** Spectrogram of the simulated 5G transmission with 5% allocated subcarriers and \(\text{SNR}=40\) dB. **(d)** Spectrogram of the simulated 5G transmission with 100% allocated subcarriers and \(\text{SNR}=40\) dB.
## 2 Study Area and Data Preparation  
### Collection and Preparation of Malan Loess Samples  
The sampling site was located in northwestern China, Lanzhou City, Gansu Province, Jiuzhoutai. The loess profile of Lanzhou Jiuzhoutai is covered by the highest terrace river gravel layer of the Yellow River in Lanzhou section, with a measured thickness of 318.2 m. Among them, the wind-formed loess is 297.2 m thick, which is the thickest wind-formed loess profile in the world with the most developed loess sequence [31; 32; 33; 34]. The coordinates of the sampling point are 103.784105\({}^{\circ}\) E, 36.101807\({}^{\circ}\) N, 2023 m above sea level, taking L\({}_{1}\) Malan loess. Malan loess is light yellow in color, with developed vertical pore space and visible soil agglomerates. Figure 1 shows the location of the intact Malan loess sampling site and the intact Malan loess layer in the field.  
A complete square intact Malan loess column with a side length of 10 cm was taken at the sampling point and the orientation of the loess sample was always kept constant during the process of sample making. The made square loess sample was carefully brought to the laboratory and dried in an oven at 105 \({}^{\circ}\)C for 10 h. The weight of the sample was measured to be 1213 g and the dry density of the intact Malan loess sample was 1.213 g/cm\({}^{3}\). When making samples, the excess soil samples were cut out and divided into three parts and some basic physical properties of the loess samples were measured by a Malven Mastersizer 2000 laser analyzer (photoelectric liquid limit and plastic limit combined tester Inmalvern Instruments Co., Ltd., Worcestershire, UK); then, the average value was found. Around the sampling point, three samples were taken by a ring knife with a diameter of 61.8 mm and a height of 40 mm; the permeability coefficient of the soil samples was measured by TST-55 variable-head permeameters (Nanjing Ningxi Soil Instrument Co., Ltd., Nanjing, China) and the average value was obtained. The results are shown in Table 1.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
###### Abstract  
This paper proposes a new system based on the Industrial Internet of Things (IIoT) for the monitoring of Mobile Health (m-Health) of workers in the underground mining industry. The proposed architecture uses a hybrid model in data transmission. Visible Light Communication (VLC) is used for downlink because of its narrow coverage, which aids in worker positioning. Radio frequency (RF) communication technology is used to send data for primary vital signs in the uplink, which is more efficient in transmission and is a viable solution according to the problem raised. The results obtained in terms of coverage and transmission for the downlink and uplink links show the feasibility of implementing the proposed system.  
IIoT; m-health; mining +
Footnote †: journal: Sensors
## 1 Introduction
### Literature Review
It uses a weighted least squares state estimation, neural network, and adaptive Neuro-fuzzy Inference system. Thus, the Decision Maker can determine the optimal ranges for the membership functions used by the fuzzy system. The proposed framework in [26] for the baseline load estimation used the fuzzy C-means features and the fuzzy membership matrix. [PERSON] et al. [27] proposed a machine-learning-based regression model based exclusively on the traditional measurements available before the installation of RTUs. The model predicted traditional measurements when those measurements were unavailable to synchronise on-site and pseudo-measurements. The regression model exhibited improved performance because it was regularly updated as more data were collected from the RTUs. [PERSON] proposed in [28] a different method with three steps for loop distribution networks. He considered aspects regarding network reduction, state estimation, and load forecasting. The analysis results revealed that the average error has been around 3%. A hybrid approach proposed in [29] led to a lower value than 5% for MAPE. [PERSON] et al. built neural network-based models used in the load forecasting process of the MV/LV substations but with a relatively large MAPE (10% approximately) [30]. The benchmark process utilised the multiple linear regression model. All approaches used the SCADA database to estimate the load in the EDSs from the EDNs. A deeper analysis of the methodologies and strategies proposed to ensure the state estimation of the EDNs highlighted two main weaknesses. The first refers to the excessive use of metaheuristic methods to determine the EDSs where the RTUs are integrated into the SCADA system to ensure the on-site measurements. Although metaheuristics are more adaptable than iterative methods and optimisation algorithms, they cannot guarantee a global optimum solution for every problem. For instance, many researchers use stochastic optimisation to determine the optimal solution dependent on the generated random variables. The second weakness has been identified at the level of the pseudo-measurement models. These models have been tested mainly in the theoretical EDNs (for instance, IEEE test EDNs), considering the assumptions and scenarios chosen by the researchers. Thus, in the real operating conditions found in current ENDs with different topologies and supplied load structures, there is a high probability that the estimation errors will increase.
# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Settings
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Architecture** & **Batch Size** & \begin{tabular}{c} **Optimizer** \\ **(Learning Rate)** \\ \end{tabular} \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} CWRU dataset \\ (Case 1) \\ \end{tabular} } & \(8\times\) (\(5\times 1\))-filters + max pooling + Leaky ReLU & \multirow{3}{*}{32} & \\  & \(4\times\) (\(5\times 1\))-filters + max pooling + Leaky ReLU & & \\  & Dense layer of 16 units & & \\  & (i.e., the number of latent features (\(q\)) is 16) & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} PU dataset \\ (Case 2) \\ \end{tabular} } & \(8\times\) (\(5\times 1\))-filters + max pooling + Leaky ReLU & \multirow{3}{*}{128} & Adam optimizer (\(\eta\) = 0.005) \\  & \(4\times\) (\(5\times 1\))-filters + max pooling + Leaky ReLU & & \\  & Dense layer of 16 units & & \\  & (i.e., the number of latent features (\(q\)) is 16) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Details of deep SVDD architecture. signal detection problems. The true positive is the accuracy of classifying target signals as normal signals correctly, whereas the false positive is the error of classifying target signals as fault signals. It should be noted that a high true positive rate with a low false positive rate results under the large threshold values, while a low true positive rate with a high false positive rate results if the thresholds are set as small values. The ROC curve can be obtained by changing the threshold to make a false positive rate from zero to one. The AUROC is the area under the ROC curve, and the larger AUROC value indicates better fault signal detection performance.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 4 QoS and Energy-Saving Routing Based on A3C
### A3C-R Routing Algorithm Process
As training progresses, the algorithm iteratively optimizes the routing policy, ultimately converging toward an optimal solution that enhances overall network performance.
# Exposure Bracketing Techniques for Camera Document Image Enhancement
## 3 HDR Document Image Generation
### Exposure Fusion
#### 3.3.2. Goshtasby's Exposure Fusion Method  
Goshtasby's exposure fusion [15] is another widely employed fusion method. The basic idea behind this method is that we can use Entropy to select the best image patch among all LDR images as Entropy can be used to express the image patch's quality. In order to remove discontinuities across image blocks, a blending function is proposed to assign the maximum weight to the pixel at the center of a block and assigns weights to other pixels that are inversely proportional to their distance to the center of block. The blending function that is employed is the rational Gaussian surface function:  
\[W_{jk}(x,y)=\frac{G_{jk}(x,y)}{\sum_{m=1}^{n_{r}}\sum_{n=1}^{n_{c}}G_{mn}(x,y)} \tag{7}\]  
where \(n_{r}\) and \(n_{c}\) denote the number of image blocks vertically and horizontally and \(G_{mn}(x,y)\) represents the value of a Gaussian function centered at the jkth block. Two parameters must be set in this method: the first parameter is the image patch size and the second parameter is the standard deviation of the Gaussian function in (7). These parameters can be set empirically or can be searched in such a way that the output result reaches the best Entropy among all the parameter candidates. In our experiment, we set the parameters empirically as we found that parameter searching is too time-consuming because we would have to generate multiple intermediate output images. With the empirical setting, we divide the image into 20 by 20 images horizontally and vertically, and the Gaussian standard deviation is set to be equal to the square root of the image patch size.  
As shown in Figure 11, we can see that the fusion method obtained good results. In order to clearly compare the output HDR image with the input LDR images, we zoomed in on some regions to visually see whether the HDR image enhanced the image quality or not, and we found that the texts in HDR are more vivid and the background is more homogeneous.
## 2 Materials and Methods  
### Online Monitoring of PD Using the UHF Method  
#### 2.1.1 Detection of PD Using the UHF Method  
The physical basis of the UHF method is the detection of electromagnetic field disturbances caused by a PD current pulse with a short rise time (<1 ns). Microstrip and printed antennas, monopoles, aperture antennas, and disk couplers are mainly used to detect electromagnetic radio waves propagating inside the transformer tank. The sensitivity of PD detection using the UHF method is assessed as high, but it largely depends on the type and design of the antenna used, the distance between the antenna and the PD source, the method of mounting the antenna in the transformer tank (in a special dielectric window or inside the oil valve), and the presence of metal obstacles between the antenna and the PD source. The main advantage of the UHF method is its greater resistance to electromagnetic interference than the conventional IEC 60270 method, as most of the energy from electromagnetic noise in the power system is transmitted in the range of relatively low frequencies, below several MHz. In addition, some measurement systems allow for the use of an external, broadband noise-gate antenna to reject interference. Another valuable advantage of the UHF method is the possibility of obtaining phase-resolved partial discharge (PRPD) patterns that are used to identify the type of PD, as well as potential electromagnetic interference. It is worth mentioning that in the case of installing the antenna in an oil valve or in special dielectric windows (which are increasingly often transformer factory equipment), there is no need to turn off the monitored device, which, especially from the perspective of the power system operator, is another advantage of the UHF method. The main disadvantage of the UHF method is the lack of a reliable measurement of the apparent charge of PDs and the high price of the measurement system [12; 31; 32].
# Mitigating Global Methane Emissions Using Metal-Organic Framework Adsorbents
###### Abstract  
Global emission of methane reached a record high in 2020. Furthermore, it is expected that methane emissions will continue to rise in the coming years despite the economic slowdown stemming from the coronavirus pandemic. Adsorbents can be used to reduce methane emissions. However, the question remains as to which adsorbents perform best for enhanced methane capture. In this work, it is demonstrated that metal-organic frameworks (MOFs) exhibited the best methane uptakes at 1 bar and 298 K from experiments as compared to tested carbonaceous materials, polymers, and zeolites. In addition, the adsorption entropy, an important thermodynamic property indicating adsorption capacity and kinetics, is determined on well-defined MOFs using a global predictive equation for porous materials. A correlation was used to describe the effect of translation and rotation of methane in the porous material for methane emission abatement. This information and the entropy of adsorption of methane on MOFs has not been reported before. The predicted results were compared to experimental data obtained from adsorption isotherms. Optimum isosteric heats were calculated by the Bhatia and Myers correlation. Finally, the pre-exponential factor of desorption is determined to aid in the design of materials for global methane emissions mitigation.  
metal-organic frameworks; methane; adsorption; entropy; kinetics; universal descriptor Article
For instance, the turbulence effect has been accurately described by many distributions, including lognormal, K, I-K, negative exponential (NE), gamma, gamma-gamma (GG), Malaga (\(M\)) distribution, etc., depending on the prevailing turbulence effect strength regime [4; 5; 6; 7; 9; 10; 12]. Also, TJ influence is statistically estimated based on the selected modulation scheme and the current bit rate. On the other hand, there are several interesting techniques that can be used in order to compensate for the impact of the studied effects and, hence, to improve the overall FSO performance, including the implementation of forward error correction (FEC) on the transmitted data, as well as the use of intermediate relays or/and the single input-multiple output (SIMO) technique [8; 20; 21; 22; 23; 24]. Receiver diversity, applied in SIMO systems, is an alleviating solution, where multiple signal copies originated from a single transmitter are detected by many closely placed receivers and the final output is generated by appropriately combining the signals received. In terms of the most popular combining techniques, optimal combining (OC), equal gaining combining (EGC), and selection combining (SC) have been listed [25; 26]. The benefits of receiver diversity stem from the fact that even if any temporary or permanent obstacles block some of the signal copies, the rest would be finally detected, keeping the link functional and employing time or spatial diversity, respectively [8; 23; 27; 28; 29; 30]. Hence, by selecting a suitable number of detecting apertures--or time slots--at the receiving end, the system's performance is significantly improved, while at the same time, the link length can be further expanded, confronting the bad weather conditions [8]. In addition, either further link expansion or better link performance can be easily achieved, thanks to the multi-hop technique, enriching the system with some decode-and-forward (DF) relays between the original transmitter and the final receiver [21; 31]. In this work, the turbulence effect was treated by \(M\)-distribution, whereas the time jitter effect was modeled by the generalized Gaussian distribution (GGD). Various modulation schemes, including on-off Key (OOK), i.e., both return-to-zero (RZ) and non-return-to-zero (NRZ), \(L\)-ary pulse position modulation (PPM), and \(L\)-ary pulse amplitude modulation (PAM), were studied, along with OC diversity, which is related to the quadratic mean of the incoming signals--analogous to the MRC technique in RF (radio frequency) communications [8]. The remainder of this work is structured as follows. Firstly, the statistical models about turbulence, SJ, and TJ are listed in Section 2 and the thorough, mathematical analysis of average bit error rate (ABER) performance generation is developed in Section 3. Next,the plots obtained by the numerical results and the corresponding discussion are hosted in Sections 4 and 5, respectively, followed by some conclusions of this study in Section 6.
###### Abstract  
This descriptive review summarizes the most recent findings on the induction and distribution of viable non-culturable (VBNC) _Listeria monocytogenes_ in food production conditions and food. The aim was to obtain information on the factors that favor the transition to the VBNC state in _L. monocytogenes_; its resuscitation capacity; and, according to scientific articles published since 2020, how food contamination by the bacterium in a VBNC state can be prevented. The methods used for VBNC _L. monocytogenes_ detection were also reviewed. A few studies reported the presence of VBNC _L. monocytogenes_ in food, in which fresh produce and chicken meat were considered. Different physicochemical stresses such as exposure to disinfectants with diverse actions and essential oils, desiccation, low temperatures, absence of nutrients, high NaCl and iron concentrations, and low pH adjusted with acetic acid were reported to induce the VBNC state in _L. monocytogenes_. The VBNC forms of _L. monocytogenes_ were able to regain growth and virulence. This could pose a safety risk that cannot be revealed by the standard culture-dependent methods recommended for _L. monocytogenes_ detection. Therefore, the presence in food and food production plants of VBNC _L. monocytogenes_ should be prevented by the appropriate use of hurdles and cleaning/disinfection procedures. The opportunity to harmonize VBNC cell detection methods for regular use in food safety evaluation also emerged.  
Listeria monocytogenes; VBNC; detection methods; inducing stress factors; occurrence in food; resuscitation 202515454  
1
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Cuperarbon Simulator  
Cuperarbon is an open-source Java-based network simulator with a focus on Smart Cities, WSN, and IoT [26; 27; 28]. It allows network designers to debug and validate network applications in a 2D/3D graphical environment [29]. Cupcarbon is composed of four main blocks:  
1. The radio channel block: this comprises two Radio-Frequency (RF) propagation models, the first based on a visibility tree and the second one devoted to tracing 3D beans using a Monte Carlo algorithm [26].
2. 2D/3D environment: a graphical environment for implementing 2D/3D maps that can be displayed on Open Street Maps (OSM) or Google Maps [30].
3. Interference block: this can be divided into two layers, the physical layer and interference models layer. It can simulate realistic baseband models for wireless communication technologies, even in the physical layer, including Wi-Fi, Zigbee, and LoRa [26].
* Implementation block: the user-customization block of Cupcarbon is developed in a modular wa with the aim of simplifying the replacement and customization of specific part of the simulator.  
In Cupcarbon, network devices are programmed in a script language called Sensoript, proposed in [29], which allows the generation of code for the Arduino platform. An important feature of Cupcarbon is that the energy consumption of a sensor node can be analysed according to both the classic consumption model and the Heinzelman model.
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 5 Comparison and Verification
### Comparison and Verification for Stellite  
In [10], the authors selected a total of 27 experiments (\(E_{xpNO}\) = 1-27) and the following combinations and ranges of cutting parameters: \(v_{c}\) = 30-80 (m\(\cdot\)min\({}^{-1}\)), feed \(f\) = 0.2-0.3-0.5-0.7-0.8 (mm) and depth of cut \(a_{p}\) = 0.15-0.23-0.31 (mm). The results that are essential for comparison and verification are shown (Figures 12 and 13). Desirability function analysis is applied to predict the optimal cutting conditions when turning Stellite 6 cobalt alloy. The optimal cutting parameters to achieve a minimum value of \(Ra\) are r = 0.605 mm, \(v_{c}\) = 80 m\(\cdot\)min\({}^{-1}\), \(f\) = 0.16 mm\(\cdot\)rev\({}^{-1}\), and \(a_{p}\) = 0.45 mm. For maximum MRR, the optimal cutting conditions are \(r\) = 0.674 mm, \(v_{c}\) = 80 m\(\cdot\)min\({}^{-1}\), \(f\) = 0.083 mm\(\cdot\)rev\({}^{-1}\), \(a_{p}\) = 0.45 mm. Simultaneous compromise between productivity and quality can be obtained by the following optimal cutting conditions: \(r\) = 0.858 mm, \(v_{c}\) = 80 m\(\cdot\)min\({}^{-1}\), \(f\) = 0.160 mm\(\cdot\)rev\({}^{-1}\) and \(a_{p}\) = 0.45 mm. The optimization was conducted according to three approaches, which are \"quality optimization\", \"productivity optimization\" and \"quality-productivity combination\".