esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The use of bioactive compounds is gaining increasing attention in scientific communities and among food manufacturers as well. The volatile terpen, terpenoids and phenylpropanoids have been widely shown to have preservative effects: CARV, 1,8-cineole, eugenol, carvone, p-cymene, \(\gamma\)-terpinene, thymol, cinnamaldehyde, geraniol, menthol, chavicol, citral, estragole, terpineol, eugenyl acetate, geranyl acetate, citronello1 and vanillin can be used effectively in a wide range of foods [21; 22; 23; 24; 25]. Their effects on increasing shelf-life, inhibiting lipid and protein oxidation and discoloration and inhibiting microbial growth in fresh meat, minced meat and fish have been observed. These are on the Council of Europe's positive list, classified as Generally Recognized as Safe (GRAS) by the US Food and Drug Administration (FDA) and registered as a flavoring and food additive by the European Commission [26; 27]. Some bioactive compounds may react with meat constituents (thiols, sulfhydryl groups or proteins), or the fat content of the meat may form a protective layer around the contaminant microorganism or bind the lipophilic part of the bioactive compound [28; 29]. As with essential oils, bioactive compounds can be used alone or in combination with other bioactive compounds and/or a preservation method in meat, although it is important to carefully select the right type of bioactive compound and its concentration [30]. There is 2-7.4% of piperine in black pepper, which is one of the most widely used spices all over the world. Piperine has numerous pharmacological effects; it has been found to have antiproliferative, antitumor, antiangiogenesis, antioxidant, antidiabetic,antiobesity, cardioprotective, antimicrobial, antiaging and immunomodulatory effects in various in vitro and in vivo experimental trials [31]. Piperine has a proven antioxidant and antimicrobial effect [32]. A summary is given in Table 1 about the effects of investigated treatments. HHP treatment and enrichment with bioactive components are innovative, minimal processing preservation methods that have not yet been investigated in combination with storage experiments on marinated meat. As meat preservation is especially important and meat is considered to be a relatively valuable raw material, it was appropriate to investigate the effect of HHP treatment and bioactive component addition on the properties of this material during storing. Thus, the aim of the research was to investigate the combination of a natural bioactive compound and different levels of HHP treatment (0 MPa, 300 MPa, 450 MPa or 600 MPa) on pork meat with different marinades (paprika or herbs) to increase shelf-life. Different marinades have been studied because the changes to the raw meat are masked by the marinades, so more beneficial properties can be achieved besides those caused by the treatments. To examine texture, color, pH and microbiological characteristics, measurements of the meats were carried out during storage on days 0, 4, 8, 12 and 14.
## 3 Results
### Results Without Synchronisation  
A simulation test was performed to examine the power waveform when the heaters were activated without synchronisation. Unsynchronised switching of 389 heaters with a power output of 1.4 kW each can generate a power peak of up to 545 kW. However, due to the disturbances affecting the object, switching of the heaters is distributed in time, so the total power of the system has a much smaller deviation from the average value. Nevertheless, it is still unacceptable. Figure 7 shows the simulated power trend in the absence of synchronisation between heater activations and the standard deviation \(\sigma\), which is a measure of the occurring power disturbances. In all simulation tests presented below, the standard deviation was calculated for a time period of 50,000 s.  
Figure 6: Flowchart of the conducted research.  
Figure 7: Simulated power waveform in the creep test laboratory without synchronisation between heater activations.  
As a comparison, the real power peaks measured in the Creep Test Laboratory using a network quality meter Fluke 1738 are shown in Figure 8. The graph is shown as a waveform of minimum and maximum values measured within a one-second period. The power peaks in the real system are smaller. This is caused by appropriate software systems whose task is to reduce them. They also contain an additional DC component due to the load on the air conditioning system.  
The temperature fluctuations occurring for a representative PJ17 creep test machine for the real (a) and simulated (b) systems are shown in Figure 9.
## 4 Results  
ANNs directly transform coded and normalized input data into output data by extracting non-linear rules combining outputs with inputs and quickly estimating the results of the study. As part of a model, it can be applied to predict the output state with intentional or independent changes of one or more input parameters. The aforementioned framing of the problem provides the basis for extending the model to a digital twin, fed automatically with data via the IIoT, where you can check subsequent modifications without having to rebuild part or even the entire production line.  
In the model, each ANN layer contained neurons with the same activation function (sigmoid) due to its high flexibility (Table 1). ANN results after the application of other activation functions were significantly worse.  
In assessing and comparing the precision of the ANNs, we considered the MSE, Accuracy (learning) and Accuracy (training) values. The learning process for the selected ANNs involved repeating patterns and modifying the weights of the network until it reached the target MSE (after no more than 1000 epochs)(Figure 2).  
The best results in the study were obtained for the following ANN structure: 6 neurons in the input layer, 12 neurons in the hidden layer and 3 neurons in the output layer(i.e., MLP 6-12-3). The ANN minimized the MSE for the training set data to the lowest values (best 0.001) (Tables 2 and 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ANN Structure** & \
\begin{tabular}{c} **Activation Function** \\ **in the Hidden Layer** \\ \end{tabular} &
\begin{tabular}{c} **Activation Function** \\ **in the Output Layer** \\ \end{tabular} \\ \hline MLP 6-10-3 & Sigmoid & Sigmoid \\ \hline MLP 6-11-3 & Sigmoid & Sigmoid \\ \hline MLP 6-12-3 & Sigmoid & Sigmoid \\ \hline MLP 6-14-3 & Sigmoid & Sigmoid \\ \hline MLP 6-16-3 & Sigmoid & Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 1: The best MLP network models for MdP assessment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Name** & \
\begin{tabular}{c} **Accuracy** \\ **(Learning)** \\ **[\%]** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(Testing)** \\ **[\%]** \\ \end{tabular} \\ \hline MLP 6-10-3 & 85.02 & 86.72 \\ MLP 6-11-3 & 86.23 & 88.01 \\
**MLP 6-12-3** & **86.89** & **87.83** \\ MLP 6-14-3 & 86.13 & 87.32 \\ MLP 6-16-3 & 85.11 & 86.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected ANNs accuracy assessment.  
Figure 2: Values of mean square error (MSE) during learning.  
ANN-based optimization provides relatively simple and fast solutions to the classification problem in the absence of full knowledge of the rules and mechanisms, solely on the basis of appropriately selected input and output data (the so-called data-driven approach). This allows both the selection of training data and the inclusion of new data or new equipment or production lines with untested data sets.
## 2 System Model
### UHF MIMO RFID System Architecture  
#### 2.2.1 UHF MIMO RFID Tag Architecture  
Front-End and Tuning  
The schematic of a UHF MIMO RFID tag analog circuit is shown in Figure 2 and was built with reference to WISP tag design [7]. The circuit structure is composed of two front-end antennas, matching circuit, charge pump circuit, demodulation circuit, voltage regulator/rectifier, and back-end signal processing module. The UHF MIMO RFID tag has relatively high power consumption as compared with the single antenna one, therefore, the rectifier is designed to deliver more current than ordinary tags. The circuit is excited by a commercial EPC Class 1 Generation 2 compliant reader, operating at 902-928 MHz with allowable transmission power of 4 W\({}_{\text{EIRP}}\) (effective isotropic radiated power) [7].  
The front-end part of the tag is composed of two antennas shifted 90\({}^{\circ}\) (with polarization directions orthogonal to each other) as for vertical and horizontal antenna polarization. This design was made in order to achieve independent channel features. Each antenna has two load impedances, \(Z_{i,k}\) representing the impedance k responsible for the \(i\)-th antenna as in Figure 2. The five-volt doubling circuit is used for collecting enough energy to activate the integrated circuit (IC). Low threshold RF Schottky diodes are used to maximize the output voltage of the rectifier. Finally, a large capacitor stores the rectified direct current (DC) voltage and supplied it to a 1.8 V regulator. Then, this voltage is used to power the MSP430 (manufactured by TEXAS INSTRUMENTS family). The matching circuit of the passive UHF MIMO RFID tag, composed of a series inductor and parallel trimmable capacitor, is tuned until the output voltage of the tag is maximized. The power harvester is a nonlinear device with a load dependent efficiency.  
The front end must be tuned to provide maximum output voltage in the presence of the desired load.  
In the design, Stage 1 of the charge pump is replaced by the transistor HSMS-285C to achieve high performance and an optimized size. The matching part is fine tuned in the last step after collecting the latest tag impedance. Finally, the impedance matching is implemented to obtain 50 Ohm with the optimized transmission line. When operating in the forward link (reader-to-tag), the matching impedances maximize the power received by the tag and in the backward link (tag-to-reader), the tag impedance (controlled by the tag) generates the desired backscatter signal.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 2 Materials and Methods  
### Study Area  
The Caatinga is a semiarid region of northeast Brazil with 862,640 km\({}^{2}\) (larger than the sum of the areas of continental France and Italy) harbouring over 28 million inhabitants [36]. The region is bounded by the humid Atlantic forests to the east and by the Cerrado savannas to the west and south. In this extensive region the vegetation is frequently armed with thorns and also commonly deciduous, it includes a range of woody and succulent species and a few ephemeral herbs and grasses distributed in a complex mosaic of physiognomies [36, 37, 38, 39]. Most of its area is not subject to frequent fires, common to savanna vegetation and, the local physiognomies include mostly sparse and dense shrublands (scrublands), savannas, Seasonally Dry Tropical Forests (STDF), some areas suffering desertification and also enclaves of humid tropical forests usually isolated in landscape features favoured by orographic precipitation [36; 40]. The region has been scantly studied [40; 41] and its relatively high levels of endemism [36; 39] point to an urgent need for further study and protection.  
The Caatinga has also been identified as a region of extreme fluctuations in its productivity dynamics and as an area of uncertainty for GPP observations and modeling efforts [42; 43]. Despite its diversity and considerable levels of endemism, this region has often been neglected by scientific research [40; 41].  
In this study, we subset the region into smaller ecoregions, as defined by [35], and use these as our experimental units (Figure 1). These ecoregions were defined on the basis of geomorphologic, climatologic and ecologic characteristics [35] which will likely influence local seasonality and abiotic stress occurrence [38; 44]. Apart from translating the names of these ecoregions from the Portuguese originals, we have subdivided the large Southern Sertaneja Depression into Major and Minor since we have observed marked differences on climate between these sub-regions _a posteriori_. The region we call the Minor Southern Sertaneja Depression (Figure 1h), is situated near the coast and locked from the rest of the Sertaneja Depression (a geomorphological feature) by a relatively drier ecoregion called Raso da Catarina (Figure 1g) and an elevated ecoregion called the Borborema Plateau (Figure 1d).
## 2 System Overview
### Hardware  
The drone shown in Figure 1 was assembled using various custom components. The drone comprises four propellers, four motors, four electronic speed controllers (ESCs), the crossarms and baseplate (forming the body of the drone), a telemetry link (transceiver), a remote control radio receiver (not visible in Figure 1), a fuel cell holder, a fuel stick and battery (FSB) pouch which contains the metal hydride fuel stick and a Li-ion battery, supercapacitors, a global positioning system (GPS) module, and the flight computer (or autopilot). As seen in Figure 1, the fuel cell holder box is held by wire ties (black thin wires running vertically). This arrangement was used so that flight tests could be carried out easily, with or without including the fuel cell assembly. Details related to the electrical system configuration when the drone was used with/without the fuel cell and supercapacitor are provided below.
## 3 Results and Discussion  
### Orthogonal Experimental Analysis of Scaffold Materials  
Optimizing the composition of biomaterials stands as a pivotal strategy for attaining superior mechanical performance. HA and \(\beta\)-TCP exhibit remarkable resemblance to the inorganic constituents of bone, offering a synergistic approach to circumvent the limitations associated with individual biomaterials in terms of their biological response and degradation kinetics. Furthermore, the incorporation of PCL and PVA solution serves to augment the mechanical robustness of composite bone scaffolds, ensuring sustained mechanical integrity within the physiological milieu and fostering an optimal environment for osteogenic cells. The outcomes of the orthogonal experimental analysis, delineated in Table 2, have validated the optimal formulation of composite scaffold materials through meticulous assessment of compressive strength. The minimal range (R) values observed in the empty columns signify negligible cross-factor interactions, underscoring the thorough consideration of influential parameters pivotal to experimental outcomes. Deconstruction of the R values has unveiled the primary and ancillary determinants governing the mechanical efficacy of composite scaffold materials: PVA solution concentration, HA to PCL mass ratio, and HA to \(\beta\)-TCP mass ratio. The identified optimal material parameters encompass a 13 wt% PVA solution concentration, a 9:1 ratio of HA to \(\beta\)-TCP, and a 4:1 ratio of HA to PCL.
# The Impacts of Random Distributed Vacancy Defects in Steady-State Thermal Conduction of Graphene
## 2 Materials and Methods
### Finite Element Model for Graphene  
FEM is applied for spatial discretization. In the weighted residual method, trial functions serve as weighting functions. In the finite element model, the conducting bars in graphene lattice is defined by two nodes, a cross-sectional area, and the material properties, as in Figure 2. Specific heat and density are ignored for steady-state solutions. The thermal conductivity is in the element longitudinal direction.  
The conductivity matrix of element can be expressed as:  
\[[K^{e}]=\frac{AK}{L}\bigg{[}\begin{array}{cc}1&-1\\ -1&1\end{array}\bigg{]} \tag{13}\]  
where \(A\), \(K\), and \(L\) is the area, thermal conductivity, and distance between nodes, respectively.  
The output is computed as:  
\[q=K\frac{(T_{i}-T_{j})}{L} \tag{14}\]  
\[Q=qA \tag{15}\]  
where \(q\), \(T_{i}\), \(T_{i}\), and \(Q\) is the thermal flux, temperature at node \(i\) and \(j\), and \(Q\) is the heat rate.  
Energies are available in the solution for each element.  
\[E_{e}^{po}=\left\{\begin{array}{c}\frac{1}{2}\sum\limits_{i=1}^{N}\{\sigma \}^{T}\Big{\{}\varepsilon^{ei}\Big{\}}vol_{i}+E_{e}^{pi}+E_{s}\\ \frac{1}{2}\{u_{e}\}^{T}([K_{e}]+[S_{e}])\{u_{e}\}\end{array}\right. \tag{16}\]  
where \(N\) is the number of integration points, \(\{\sigma\}\) is the stress vector, \(\Big{\{}\varepsilon^{ei}\Big{\}}\) is the elastic strain vector, \(vol_{i}\) of integration point \(i\), \(E_{e}^{pi}\) is the plastic strain energy, \(E_{s}\) is the stress stiffening energy.  
\[E_{s}=\left\{\begin{array}{c}\frac{1}{2}\{u_{e}\}^{T}[S_{e}]\{u_{e}\}\\ 0\end{array}\right. \tag{17}\]  
where \([K_{e}]\) is the element stiffness or conductivity matrix. \([S_{e}]\) is the element stress stiffness matrix. \(\{u_{e}\}\) is element degree of freedom vector.  
Figure 2: Finite element model (FEM) for graphene lattice.  
Figure 3 shows the finite element model based on honeycomb lattice structure of graphene. Its heat source is discussed in armchair and zigzag edges, respectively, as shown in Figure 3a,b. The colorful contour depicts the thermal conduction in the steady-state.  
Vacancy defects are an unavoidable existence in graphene. However, the shape, size, and location in the entire graphene are unpredictable and uncertain. In order to discuss the impacts of vacancy defects in the steady-state thermal conduction of graphene, three different vacancy defects are compared and discussed in the following results. As shown in Figure 4, center concentrated vacancy defects, periodic vacancy defects, and random distributed vacancy defects are included as three typical vacancy defects in graphene.
# Model Test on Effect of Ground Fissure on the Behavior of Oblique Two-Section Subway Tunnel
## 1 Introduction  
The dynamic interaction between the ground fissure and subway tunnel attracted much attention after the operation of the Xi'an subway in China, which is of great significance to the long-term sustainability and vibration effect prevention of the subway. Recently, much effort has been paid to the dynamic impacts of subway-travelling-induced vibration on the tunnel structure and the propagation process of vibration in the soil stratum [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Nevertheless, Xi'an subway has special features in terms of geological settings and tunnel structure: (1) a total of 14 active ground fissures were identified in Xi'an, and the operated subway was in the special geological condition of active ground fissures. (2) Segmented tunnel structures were selected for the Xi'an subway to ensure its safe operation. The comprehensive interaction between the ground fissures and segmented tunnel structure eventually caused a relatively complicated propagation for the subway-induced vibration.  
[PERSON] et al. [14; 15] performed a dynamic model test for the scenario that the ground fissure was orthogonal to the horseshoe-shaped tunnel, and analyzed the mutual influence of subway tunnels, stratum, and ground fissures under the action of traffic load. The model results helped reveal the dynamic response of monolithic and segmented tunnels under the complied action of active ground fissures and traffic loads. [PERSON] et al. [16; 17] established a tunnel-ground fissure-stratum interaction model with different lining types to investigate the basic characteristics of soil vibration adjacent to the ground fissure, and the influence of the size and shape of the tunnel section on the vibration response. [PERSON] et al. [18] analyzed the dynamic response among the ground fissure, subway tunnel, and surrounding rockunder subway vibration load through the physical model test under the condition that the ground fissure and the shield tunnel were orthogonal. Although these studies focused on the interaction between ground fissures and the dynamic response of subway tunnel via model test, very few works have been conducted on the dynamic effect of traffic loading on the subway tunnel with the impact of ground fissures, especially for the scenario that the ground fissure is obliquely intersected with a subway tunnel.  
This study performed a model test to investigate the dynamic interaction characteristics of ground fissures and an oblique two-section horseshoe-shaped subway tunnel under a dynamic subway load.
## 6 Performance Analysis
### LoRa Sensitivity  
As discussed earlier in Section 3, the spreading factor, noise figure, and bandwidth are the input parameters for calculating the LoRa sensitivity of the receiver. Here, we utilized Equation (4) for calculating the sensitivity of the receiver. Noise fig. (SNR) value is different for distinct SF. SNR of the SF 7 is \(-\)7.5, SF 8 is \(-\)10, SF9 is \(-\)12.5, SF 10 is \(-\)15, SF 11 is 17.5, and SF 12 is \(-\)20. The above SNR value is considered for calculating the sensitivity of the receiver. As discussed in the data rate section, the same type of BW is utilized for sensitivity calculation. Generally, the sensitivity of power is in terms of a negative value; for example, \(-\)127 dBm, where the value going above this value indicates that the sensitivity is in decline. Figure 9 presents the sensitivity from the SF 7 to SF 12; the sensitivity power is highest for the SF 7 at the BW 10 (500 kHz), and the lowest sensitivity is observed for the SF 12 at the BW 1 (7.5 kHz). The sensitivity of every SF is gradually increasing after the BW 4 (20.8 kHz).
###### Abstract
However, a patient's continuous exercise treatment is difficult, and due to the nature of IGD, the problem of concentration deterioration occurs. To address this problem, exercise content, such as games that arouse interest, is required [3]. In particular, a necessity for augmented reality (AR)-based exercise content for combating IGD has arisen [11; 12]. In order to stimulate interest and require game-like concentration, a computer-based game, rather than a typical exercise game, is needed. However, PC-based games cannot realize brain changes through exercise even if a joystick is used. Therefore, AR exercise games that combine reality and virtuality comprise suitable content for people who are familiar with games. However, as a preliminary experiment to check whether the corresponding movements are suitable for the AR exercise game, movements suitable for virtual reality (VR) exercise games must be extracted. As a result of producing a pilot exercise game based on a VR environment and conducting preliminary experiments, offline exercise performed with the body and without equipment and VR exercise games showed the same exercise effect [13]. Exercise games using VR are interesting, but because there is no view of real space when more movement and exercise are carried out, the risk of injury is higher. Therefore, the need for AR-based exercise content to combat IGD has emerged [12]. AR technology refers to a situation where virtual objects are superimposed onto reality, and this reality is viewed while wearing a HoloLens2 headset equipped with holographic technology. AR exercise content is designed to allow users to exercise while wearing AR technology equipment; users directly interact with virtual objects overlaid on reality. It is still greatly affected by lighting, and there are technical limitations that prevent virtual objects from being 100% completely fixed. In addition, since it is necessary to check whether the AR exercise content is effective before diversifying the movements, the movements must be simplified to extract movements suitable for low- to medium-intensity aerobic exercise that causes users to slightly sweat. The medical community, especially psychiatric clinics, has attempted to use gaming in physical activity as a way to treat excessive gaming problems. A recent research trend is to use VR content as a treatment tool, but there is not much research on treating gaming addiction using AR. In this paper, considering the above matters, we conducted a preliminary experiment to plan, design, and treat AR exercise content for patients with Internet Gaming Disorder.