esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Edge Computing Overview
#### 2.3.1 Edge Computing Incentives in Manufacturing  
Edge computing [6; 7] provides an ecosystem where the execution environment (e.g., compute and storage resources) is closer to the location where it is needed in contrast with a remote cloud. The proximity of the edge premises results in reduced latency between a client and the server application, so edge computing can support use cases where ultra-low latency and high-reliability characteristics are crucial. The typical usage of edge computing in smart manufacturing could be to perform tasks on the edge infrastructure that are intensive in complexity, computation, memory, and storage. For example, various analytics and monitoring tasks can be executed in the edge domain, utilizing the cloud capabilities (e.g., resource scalability, robustness, flexible application software management) and processing huge amounts of data locally. However, edge computing with (near-) real-time cloud execution capabilities, integrated with 5G NPNs and Ethernet-based industrial networks will be able to provide ultra-low end-to-end latency to support a wide range of the time-critical Industry 4.0 use cases. Moreover, such an edge computing solution enables offloading of the time-critical industrial device (closed-loop) control functions, such as mobile robot control intelligence from the device to the edge domain.  
Consequently, edge computing is not limited only to improve the effectiveness of existing use cases, but it can support new or evolved use cases as well:  
* Edge can host such resource-consuming (industrial control) tasks, which cannot be deployed on a device (due to limited compute resource or battery power). For example, edge computing can enable more complex AI/ML supported control mechanisms for mobile robots (e.g., AGVs), which can improve the efficiency of the service;
* The edge-enabled, centralized device control makes it possible to support extensive coordination between different devices (e.g., AGVs, robotic arms), resulting in improvements in productiveness;
* Edge computing enables the balance between local (close to the premises where the data is generated) and central cloud-based data processing.
## 4 Discussion
Total nitrogen was higher under integrated practice in 'Bebecou' while the exact opposite stood for 'Diamantopoulou'. This may rely on the differences in fertilization practice among farmers employing integrated practices between the two cultivars. Furthermore, the organic matter concentration in 'Diamantopoulou' orchards may also explain the observed higher total nitrogen in the soil. High organic matter has been linked to high total nitrogen content since proteins and amino acids found in organic matter contribute to the increase in total soil nitrogen through organic matter mineralization [65]. Available P concentration was higher under integrated practice in 'Bebecou', while in 'Diamantopoulou', the solubilization of native phosphates due to the release of organic acids by the microbial decomposition of organic matter counterbalance the P content and, therefore, no significant differences were observed between the two cultivation practices. Significantly higher exchangeable K was observed in the organic than in the integrated system (in 'Diamantopoulou'), supporting the hypothesis that in soils under organic management, the organic amendments, besides acting as a significant source of K, also release organic colloids that attract K from the nonexchangeable fractions increasing the available K. According to the critical limits of DTPA-extractable Fe (4.5 mg kg\({}^{-1}\)), Mn (3.5 mg kg\({}^{-1}\)), Zn (0.6 mg kg\({}^{-1}\)), and Cu (0.2 mg kg\({}^{-1}\)) [66], Cu and Zn concentration were above the critical limit for the two management practices. The differences observed in soil nutrient concentrations seem to be relevant, at least to some extent, with the nutrient concentration found in the leaves. The leaf nitrogen concentration was higher in 'Bebecou' orchards under integrated practice, whereas potassium was found in a slightly higher concentration in the leaves of organically grown 'Diamantopoulou' trees. The latter reveals the close relationship between soil and leaf nutrient concentration. According to [PERSON] et al. [67], the application of manure for a long period of time could result in reduced or even zero addition of potassium fertilizers in rice and wheat culture, while at the same time, manure enhances nutrient availability and thus nutrient use efficiency [68]. In general, contrasting effects of management practice on soil properties have been reported in the literature [61], ascribed probably to the differences in pedoclimatic conditions, water management, cultivars, etc.
## 2 Passive Wake-Up Radio
### Passive Wake-Up Radio Hardware Architecture
#### 2.2.3 Voltage Multiplier  
To charge the capacitor of the passive wake-up radio, the RF signal from the matching network need to be converted to DC by a rectifier circuit. A voltage multiplier is a specially designed rectifier circuit that changes the alternating input RF signal to a higher output DC-voltage, which is theoretically an integer multiple of the peak AC input. In a two times multiplier (voltage-doubler) for example, a 100 V peak voltage input can produce 200 V DC and 400 V DC for a quadrupler.  
The voltage multiplier is a network of capacitors and diodes, which only permit the flow of electron in one direction, placed between the matching network and the energy storage. Based on the Friis transmission equation [65], the antenna gain, RF source power, transmission frequency, and the distance between the transmitter and receiver determines the signal strength, which may result in low conversion efficiency and as such low output DC voltage.  
The voltage multiplier circuit helps to boost up the output DC voltage [59]. The most commonly used diode for rectification in passive wake-up radio is the Schottky diode [66] due to its high conversion efficiency at low input power attained from its low built-in voltage. The rectifier can be classified as half-wave or full-wave rectifier based on their conducting characteristics and as single ended topology [67] and differential topology [68; 69; 70; 71] based on their symmetry properties.
## 3 Results and Discussion
### Active and Titractable Acidity  
According to Table 1, there is an increase in the titratable acidity as a function of the VRR, from 0.180% lactic acid for initial buttermilk to 0.405% lactic acid for VRR 3 and up to 0.675% lactic acid for VRR 5. This increase can be explained by the fact that the acidity is due to the presence of proteins, phosphate salts, and citric acid, a small amount of dissolved carbon dioxide and organic acids present in buttermilk, and concentrating these components leads to an increase in lactic acid content [37]. However, the main cause of this increase is the ultrafiltration concentration of the proteins presented in buttermilk. The main objective of this process is to concentrate caseins which are acidic proteins with an isoelectric pH of 4.6, thus increasing the protein concentration from 1.69 at VRR 2 to 2.99 at VRR 5. Our results showed that a big part of the proteins in buttermilk was concentrated by ultrafiltration because the protein content in the permeate was 0.36%. This low protein content may be explained by the fact that the permeate contains a part of the soluble proteins of buttermilk which could not be retained by the ultrafiltration membranes because of their solubility and molecular weight. Our results showed a slight decrease in the pH from 6.29 (initial buttermilk) to 5.98 at VRR 5. There is no significant difference between the values of pH of initial buttermilk and VRR 2, as well as at VRR 3 and 4. This small decrease can be explained by the total ash content designating the mineral substances of the product. Depending on the ionic composition of the minerals, they are influenced directly by the pH. Thus, by concentrating the minerals in buttermilk from 1.6 times at VRR 2 to 2.57 times at VRR 5, the pH decreases.  
Figure 2: Effect of volume reduction ratio on the energy demand during ultrafiltration of sweet buttermilk. Different lowercase letters (a, b, c) show a significant difference between the energy demand at 0.2, 0.35, and 0.5 MPa (\(p<0.05\)).
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### System Evaluation  
#### 4.7.1 Comparison of Small-Scale Feature-Level Sinusoidal Scanning Tests  
After collating and comparing the results of three small-scale feature-level scanning tests, it is possible to obtain the resonance frequency of each measurement point of the mirror component, so that the intrinsic frequency of the component can be obtained. The final results show that the intrinsic frequency of the mirror component in the X direction is 173.8 Hz, the intrinsic frequency of the mirror component in the Y direction is 176.4 Hz, and the intrinsic frequency of the mirror component in the Z direction is 271.5 Hz as listed in Table 9. The simulation result is 179.6 Hz, and the error with the real test result is 5.8 Hz, which meets the requirement of analyzing accuracy, and verifies the validity of the finite element model.  
After conducting small-scale feature-level scanning tests three times, the intrinsic frequency of the mirror component varies by 4.8 Hz, 1.5 Hz, and 1.6 Hz in X, Y, and Z directions, and the frequency deviation is less than 5%, which shows that the component has good stiffness and little variation before and after the test [21].
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 3 Proposed Techniques of Demodulation and Modulation  
### General PPM Demodulation  
PPM demodulation generally involves converting PPM back into PWM, which requires a reference clock pulse signal and the PPM pulse as inputs to an RS Flip-Flop. Once the PWM signal is recovered, it is passed through a Low Pass Filter (LPF) to obtain the recovered information signal, \(\widetilde{m}\)\((t)\). The block diagram of the demodulation process is shown in Figure 4.
## 3 Results and Discussion
### Comparison between IFOA-LS-SVM Model and MR Model  
In order to further compare the prediction ability of the proposed model, a classic multiple regression (MR) analysis was employed for the determination of the empirical relationships for estimation of target extract yield. Due to similar extraction characteristics between quercetin and rutin, to simplify the modeling process, the extraction process of quercetin was selected as the only study object in this study.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 3 The Inverse Model of Radiation Thermometry  
### Derivation of the Inverse Model of Radiation Thermometry  
The direct model is based on a real physical model of conversion of object temperature to radiant power as a sensor value, including the inevitable influences of the environment. The inverse model, i.e., the inverse of the physical model, is used for the calculation from the radiant power on the sensor to the temperature of the object.  
In all inverse models, inevitable influences of the measured object, environment and air, must be calculated and compensated for from the measured radiance. After compensating for these influences, only the black body radiation of the measured object should be present in the resulting signal as raw surface emission \(M_{\text{BB,obj}}(T,\lambda)\), used in Equation (6) in the direct model (of the real system of measurement). This pure black body emission from Equation (1) can be converted to the object temperature using a black body radiance characteristic function, which is theoretically the inverse of the spectrally integrated [PERSON]'s law.
# Parametric Analysis of Cylinder Drying Process in Association with Various Materials
## 3 Results and Discussion
### Effects of the Cylinder Diameter  
For investigating the effects of cylinder diameter on the drying process of the cylinder dryer, five different cylinder diameters consisting of 0.2 m, 0.4 m, 0.6 m, 0.8 m, and 1 m are examined in detail with a thickness of 1 mm. The aforementioned boundary conditions are also employed. Figure 7a presents the temperature of the 50% layer versus the drying time of the five different cylinder diameters.  
Figure 7b presents the temperature at the four phases of three different examining layers of 25%, 50%, and 75%. Figure 7c,d illustrate the velocity-and-temperature distributions for three computational models with cylinder diameters of 0.2 m, 0.4 m, and 0.6 m, respectively. Figure 7a shows that the peak temperature is slightly increased with the increase in the diameter from 0.2 m to 0.4 m; however, the maximum temperature is almost unchanged by increasing the diameter from 0.4 m to 1 m. Note that with cylinder diameters of 0.2 m and 0.4 m, the heat transfer area between the cylinder and the drying material is comparatively smaller as compared to those with larger diameters; therefore, the heat transfer rate is not able to raise the temperature of the drying material to an equilibrium  
Figure 7: Temperature vs. time subjected to diameter length and layer thickness. (**a**) Temperature vs. time at a layer of 50%, 1 mm thickness, and velocity of 1.27 m/s for different cylinder diameters. (**b**) Temperature vs. time of 1 mm thickness, and velocity of 1.27 m/s for different layers. (**c**) Velocity profiles of different cylinder diameters. (**d**) Temperature profiles of different cylinder diameters.  
temperature. This explains why the peak temperature is slightly increased with the increase in the cylinder diameter from 0.2 m to 0.4 m.  
When the cylinder diameter increases from 0.4 m to 1 m, the contacting area between the cylinder and the drying material is so large that the temperature reaches the state where the heat to be transferred from the cylinder to the drying material equals the heat loss from the drying material to ambient environment (equilibrium state). This explains the unchanged peak temperature when increasing the diameter from 0.4 m to 1 m as presented in Figure 7a and illustrated in Figure 7d. It is interesting to find that although the distances between the 50% layer and 25% layer or 75% layer are the same, the temperature difference between the 75% layer and 50% layer is greater than that between the 25% layer and 50% layer as presented in Figure 7b.  
The heat is transferred from the cylinder to the drying material through the basic layer; therefore, the temperature of the lower percentage layer is higher than those of the greater percentage layers in phases I, II, and III. The thermal diffusivity of a material with a higher temperature is higher than that of a lower temperature; therefore, the heat diffusion from the 25% layer to the 50% layer is faster than that from the 50% layer to the 75% layer as seen in Figure 7b.
# Estimating Optical Flow with Streaming Perception and Changing Trend Aiming to Complex Scenarios
## 3 Method  
### Parameters Definition  
In the first section, we would like to explain some symbols that will appear in the figures and equations of this paper.  
Image frame: the time node of a certain image in the time-ordered image sequence of the input structure, which corresponds to \(I_{n}\) in the diagram of network structure, where n is in the range \([0,\ N)\), and \(N\) is the total number of images in an image sequence.  
Optical flow frame: a new term is proposed specifically to explain the network structure designed in this paper. It represents the time node where a certain feature map is in the optical flow sequence (a new sequence generated by two consecutive image frames, which must also be ordered by time), and corresponds to \(F_{i}\) in the diagram of the network structure. The functional mapping between the optical flow frame and the image frame is \(F_{i}=\Theta(I_{i},I_{i-1})\), where \(i\) is in the range \([1,\ N)\) because the first frame of the optical flow can only be generated after the second image frame is input into the network. \(\Theta\) indicates the backbone of one certain optical flow estimation method. The input of the backbone is two consecutive image frames, and the output is one feature map. In this paper, we choose the backbone from the strong baseline, RAFT, as \(\Theta\) to extract the optical flow feature.  
Optical flow value: we set the optical flow value as \(f_{i}\) in the formula of this paper. Actually, \(f_{i}\) is a matrix with the dimension of \(H\times W\times 2\), and each element of the matrix represents the optical flow value of the pixel point in the image. Thus, \(f_{i}^{gt}\) and \(f_{i}^{pred}\) represent the ground truth optical flow value and the estimated optical flow value of the ith optical flow frame, respectively.  
Optical flow trend: to make our proposed method more sensitive to the optical flow motion, especially at the position of the object contour, we introduce the parameter OFT (Optical Flow Trend) to describe the change of optical flow trend, whose value is obtained from the pixel-by-pixel difference in the optical flow values \((f_{i},f_{i-1})\) between two consecutive optical flow frames, and is represented by the symbol \(\Gamma\). Therefore, the computation for the OFT of the ith optical flow frame is \(\Gamma_{i}=f_{i}-f_{i-1}\).
## 3 Results and Discussion
### Antioxidant Responses Triggered by Hydroxyytrosol  
The presence of HXT elicited antioxidant mechanisms mediated by genes coding for enzymes known to counter oxidative stress (Scheme 1).  
Some of these enzymes are encompassed within the ROS resistance of lactic acid bacteria [23], including _kat_ or _lp_3578_ (catalase), _lp_3587_ or _pox4_ (pyruvate oxidase) and _lp_2544_ or _npr2_ (NADH-peroxidase), which are dedicated to inactivate or prevent the formation of toxic oxygen radicals. This profile was accompanied by downregulation of _lp_2992_ or _mnthH3_, a membrane  
Figure 2: DAVID analysis of functional gene ontology (GO) categories and KEGG pathways related to the _Lactobacillus plantarum_ genes differentially expressed in 10 mM hydroxytrosol. Fold enrichment (bars) parameter measures the magnitude of enrichment, both for up and down-regulated gene groups.  
protein-encoding gene previously linked in _L. plantarum_ WCFS1 to the transport of iron [24], which is a metal precursor to ROS-induced damage via Fenton chemistry.  
In addition, the response of _L. plantarum_ WCFS1 to HXT revealed the induction of genes coding for enzymes involved in counteracting thiol-specific oxidative stress in this microorganism [25], including _msrA3_ (methionine sulfoxide reductase), _gshR4_ (glutathione reductase) or _lp_0858_ (regulator of disulfide bond formation). Furthermore, the cystathionine-\(\beta\)-synthase (CBS) (_lp_0255_) and cystathionine-\(\gamma\)-lyase (CSE) (_lp_0256_) encoding genes, which have been recently shown as the most important H\({}_{2}\)S-generating enzymes in many bacteria [26], were also up-regulated. While the role of H\({}_{2}\)S is disputed [27] it has been proposed that this gas, when produced endogenously by CBS and CSE, partially neutralize the effectiveness of the oxidative stress that, regardless of the diversity of their action, antibiotics induce [4; 28]. Notably, the glutathione antioxidant network [29] and the expression of antioxidant enzymes such as catalase [29; 30] have been also elicited by HXT in other biological systems, which support the expression profiles found in this study.  
Overall, the observed gene expression patterns, including the induction of genes encompassed within the ROS resistance of lactic acid bacteria, the down-regulation of an iron transporter, the induction of genes coding for enzymes counteracting thiol-specific oxidative stress and the up-regulation of H\({}_{2}\)S-generating enzymes suggest that HXT acts as an oxidative stressor to _L. plantarum_ WCFS1. These effects are in line with the responses to counteract the oxidative stress generated by other phenolic compounds with antimicrobial activities in _L. plantarum_ WCFS1, such as tannic acid [5] or p-coumaric acid [7].
## 5 Generation of Malicious IoT/IIoT Datasets
### Malicious \"Powertrace\" Dataset Generation  
#### 5.1.1 Malicious \"Powertrace\" Dataset Generation  
The approach followed for the \"powertrace\" dataset generation from the UDP flooding attack scenario was similar to the approach followed for the \"powertrace\" dataset generation from the benign IIoT network scenario in Section 4.1.1. In addition, the \"powertrace\" plugin was similarly enabled for collecting \"powertrace\" related features, summarised in Table 1, from the motes of the attack scenario every two seconds. In Figure 26, the depicted mote output window displays the captured \"powertrace\" information every two seconds and also the messages sent and received by each mote during the simulation time (60 min).  
When the timeout occurred, the simulation stopped, and all the captured information and prints were stored in the \"COOJA.testlog\" file. Afterwards, the \"IoT_Simul.sh\" file, described in Section 4.1.1, created (a) a new root folder named as \"2020-12-09-14-59-59\", and (b) the \"log\" folder, inside the \"2020-12-09-14-59-59\" folder, where the \"COOJA.testlog\" file was copied from the \"... /cooja/build\" folder located in the Cooja Simulator. Then, the \"IoT_Simul.sh\" file following the same process, as described in Section 4.1.1, extracted the required \"powertrace\" information from the \"COOJA.testlog\" file and saved it in the \"pwrtrace.csv\" file in the \"dataset\" folder that was created by the batch file inside the \"2020-12-09-14-59-59\" folder, as shown below in the left part of Figure 27. In the \"dataset\" folder, apart from the \"pwrtrace.csv\" file, the \"IoT_Simul.sh\" file generated two more files (i.e., the \"recv.csv\" file and the \"send.csv\"), following the same process as in Section 4.1.1. The \"recv.csv\" file and the \"send.csv\" file include the \"received\" and \"sent\" messages printed by the motes, respectively.  
Finally, similar to the benign \"powertrace\" dataset generation approach in Section 4.1.1, the \"IoT_Simul.sh\" file extracted the information related to each mote from the \"pwrtrace.csv\" file and generated one csv file for each mote with the corresponding information from the \"pwrtrace.csv\" file. The generated six csv files (i.e., mote1.csv, mote2.csv, mote3.csv, mote4.csv, mote5.csv, and mote6.csv) were stored in the \"motedata\" folder, created also by the \"IoT_Simul.sh\" file, as shown in the left part of Figure 27.  
Figure 26: Cooja Simulator—Mote output window.  
Figure 27: Location of the generated “pwrtrace.csv”, “recv.csv”, and “send.csv” files by the “IoT_Simul.sh” bash file.  
Malicious \"recv.csv\": The generated malicious \"recv.csv\" file consists of 21,573 records and its first 27 records (i.e., 1-27) are depicted below in Figure 30.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 3 Simulation of Ship's Wake Bubble Laser Dual-Mode Detection Based on Monte Carlo
### Ship's Wake Laser Dual-Mode Fusion Detection Signal Processing Method
#### 4.2.2 Image Processing Algorithm Based on Time Difference Accumulation  
Aiming at the problems of poor image contrast, blurred target edge information, and small target pixel points, and combined with the characteristics of the system's high frame rate and short exposure time imaging conditions, an image processing method based on time difference accumulation is proposed. The principle of multi-frame image accumulation is used to effectively improve the underwater image quality under low-light conditions. The identification and judgment of the ship's wake bubbles can effectively resist the ship's maneuvering evasion in a certain form. The image processing flow chart is shown in Figure 14.