esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CytoSolve In Silico Modeling Protocol
#### 2.2.1 Control Conditions
Determining the second endpoint of this range involves reversing the sequential addition of the compounds at theirspecific dosages. In this study, given that there are two compounds, apigenin was added first, followed by hesperidin to determine the first endpoint for a particular biomarker. Then, the sequence is reversed by adding hesperidin followed by apigenin to determine the second endpoint for the same biomarker. These two endpoints provide a lower and upper limit for the additive effect range of that biomarker for the specific dosages of these two compounds. Once the additive effect range is determined, the two compounds at the specific dosages are added simultaneously to estimate the levels of the same biomarker. If the value of the biomarker obtained by the simultaneous addition of both compounds lies within the additive effect range, then the combination of these two compounds are deemed to have an additive effect on that particular biomarker. If the value of biomarker obtained by the simultaneous addition of both compounds is not in the additive effect range, the two compounds may have a synergistic or an antagonistic effect. A combination is deemed to be synergistic if the value of a biomarker is lower or higher than the additive effect range, provided that the biomarker must be lower or higher, respectively, than the control in order to have the desired beneficial effect. In this study, lowering each biomarker relative to control is considered to have beneficial effects. The following simulations are conducted to determine the additive effect range across the five biomarkers of joint pain:  
1. Sequential addition of apigenin first and then hesperidin to estimate COX-2 production;
2. Sequential addition of hesperidin first and then apigenin to estimate COX-2 production;
3. Sequential addition of apigenin first and then hesperidin to estimate PGE2 production;
4. Sequential addition of hesperidin first and then apigenin to PGE2 production;
5. Sequential addition of apigenin first and then hesperidin to estimate TRPV1 production;
6. Sequential addition of hesperidin first and then apigenin to estimate TRPV1 production;
7. Sequential addition of apigenin first and then hesperidin to estimate CGRP production;
8. Sequential addition of hesperidin first and then apigenin to estimate CGRP production;
9. Sequential addition of apigenin first and then hesperidin to estimate ROS production;
10. Sequential addition of hesperidin first and then apigenin to estimate ROS production. The additive effect ranges for all five biomarkers were determined. The values of all five biomarkers obtained from combination simulations from Section 2.2.1 were compared to their respective additive effect ranges to determine the synergistic or additive effects of apigenin and hesperidin combination on all five biomarkers of joint pain.
## 3 Verification and Comparison of Proposed MPFE
### Comparison of Theoretical and Experimental Results  
Figure 5 illustrates a comparison between the calculated failure envelopes and the corresponding experimental data obtained at different strain rates. The comparison demonstrates a significant level of agreement between the calculated results and the experimental data, indicating the reliability and accuracy of the proposed mathematic method.  
Figure 6 displays a comparison between the experimental data and the calculated results obtained using the NU-Daniel and Hashin criteria [7] at various strain rates. Obviously, the partially interactive Hashin criterion shows more inaccurate results than the MPFE proposed in this paper. In order to compare the accuracy of the NU-Daniel criterion and the MPFE, we use the coefficient of determination (R-square). Table 4 provides the coefficients of determination for the NU-Daniel criterion and the MPFE at different strain rates. This suggests that the MPFE offers improved accuracy in predicting the mechanical behavior of the composite material under different strain rates.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Loadings** & \(\sigma_{2}^{\star}\) & \(\sigma_{2}^{-}\) & \(\tau_{6}^{0}\) **(Unit: MPa)** \\ \hline Quasi-static & 65 & \(-285\) & \(125\) \\ Static & 80 & \(-345\) & \(160\) \\ Dynamic & 90 & \(-390\) & \(175\) \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics for the UD composite AS4/3501-6 at different strain rates.  
Figure 5: Comparison of failure envelopes predicted by MPFE and experimental data at three different strain rates.  
In [PERSON]'s work [7], it was indicated that the NU-Daniel criterion more accurately describes the failure envelope at three different strain rates than the classical failure criteria do, including the criteria of maximum stress, maximum strain, Tsai-Hill, Tsai-Wu, Sun, etc. (shown in Figure 2). Notably, the failure envelopes generated by the MPFE demonstrate a superior level of agreement with the experimental data compared to other criteria at various strain rates.
## 4 Implementation
### Implementation System Monitoring Using MIMO-OOK  
The proposed system consists of multiple sensor modules, with light transmitters for data sensing at multiple locations, and a camera. Figure 11 illustrates the proposed system architecture. Temperature and humidity sensors are selected as the key parameters to be monitored in the environment monitoring system to ensure a healthy and comfortable environment for humans. The sensing data from the sensor are collected by the MCU. From the MCU, sensing data is modulated by OOK modulation. LEDs are used to transmit data from Tx to Rx through the ON/OFF status of the LEDs. On the receiver side, the camera captures the signal from the transmitter. Figures 12 and 13 illustrate the scenario and the OOK waveform of the receiving camera.  
Figure 14 shows the results of the experiment, which was conducted several times with different distances and exposure times. The use of a preamble not only helps detect the start frame of the OOK modulation but also distinguishes the signal and noise light sources from other lights. These results reveal that exposure time affects the performance of OOK modulation as well as OCC modulations. Subsequently, we can suitably adjust the exposure time to achieve optimal performance. Detecting objects using RoI signaling allows us to apply dozens of signals from many users. However, the trade-off between the number of users and work distance mu.  
Figure 11: Transmitter module with environment sensors and modulation circuit.  
\(10^{-4}\) which meets the performance requirement of Single-Carrier Modulation in the Light of Sight Communication system. These implement results illustrate that the performance of the proposed scheme has many advantages of the VLC system using color light which proposed in [11], in terms of both bit error rate and work distance. To mitigate the Bit Error Rate in the far distance, we can use Forward Error Correction to improve the performance. The parameters of MIMO-OOK scheme is shown in Table 2. We implement this scheme with temperature and humidity sensors at the distance of 20m, which can be visually assessed by Video S1 (See the Supplementary Materials.
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 3 System Model and Mathematical Background
### Node Parameters  
It is a well-known fact that the quality of each node in a network can be determined by examining the node information at the local and network levels. When determining the quality of a node, parameters such as its capacity, degree, available bandwidth, and proximity to the center of the network are the most important to consider. The mathematical expressions relating to the parameters are illustrated in the expressions below.  
* Capacity of a Node: Equation (1) defines the capacity of a node '\(i\)' in the given network. Each node of a network is specified with the CPU capacity value. \[CN(i)=CPU_{i}\quad;\quad\forall i\in N^{PI},N^{NR}\] (1)
* Degree of a Node: It is determined by the number of adjacent links connected to any node '\(i\)' in the given network. Equation (2) shows the expression for degree of a node. \[DN(i)=\sum_{j}E_{ij}^{N}\quad;\quad i\
eq j\quad\&\quad\forall j\in N^{PI},N^ {NR}\] (2) where \(E_{ij}^{N}\) is a binary variable; it returns 1 if there exists a connection between nodes '\(i\)' and '\(j\)' in the network, and 0 otherwise.
* Available bandwidth of a node: It is determined by the sum of the bandwidth of each adjacent link connected to any node '\(i\)' in the given network. Equation (3) shows the expression for determining the bandwidth of a node. \[BN(i)=\sum_{j}B_{ij}^{N}\quad;\quad i\
eq j\quad\&\quad\forall j\in N^{PI},N^ {NR}\] (3)
* Closeness Centrality of a Node: Determined by the shortest route between any two nodes, which is used to determine the global importance of any node. As a result, a node's centrality is increased in direct proportion to its distance from other nodes. Equation (4) shows the expression for determining the closeness centrality of any node in the network. \[LN(i)=\{\sum_{j}L_{i,j}\}^{-1}\quad;\quad i\
eq j\quad\&\quad\forall j\in N^{ PI},N^{NR}\] (4)
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Factors Influencing Fungal Growth and spoilage in Different Food Matrices
#### 2.4.3 Interactions with Microorganisms  
Fungal spoilage of food products often occurs in conjunction with the activities of other microorganisms, including bacteria, yeasts, and molds. Interactions between fungi and bacteria can result in synergistic or antagonistic effects on food spoilage, with certain bacterial species producing metabolites that inhibit fungal growth or vice versa [97]. Understanding these complex interactions is essential for predicting and controlling fungal spoilage in food products.
## 7 Comparison of Methods for Instability Analysis of Liners
### Instability of Stiffened Steel Liners  
The parameters that define the slenderness of a stiffened liner are \(D/t\) and \(L/D\) (spacing of stiffeners). Since the most commonly used method is that of [PERSON] [19], its results were compared with those from three-dimensional finite element models [17]. [PERSON] [21; 27] compared the values obtained for unstiffened liners with the [PERSON] formulation, showing that the latter is conservative for usual \(D/t\) values; however this would not be so for thin-walled liners. Regarding stiffened liners, the results of a three-dimensional finite element model were compared with those of the [PERSON] formulation, with the aforementioned conclusion being reached. Hence, it is recommended that results obtained through the [PERSON] formulation be verified with those from a finite element model. Otherwise the liner could become oversized in the case of high thicknesses whereas the design could fall out of the safety side for thin-walled liners.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Calibration Parameters**} \\ \hline  & \multicolumn{2}{c}{\(g=3\) mm} & \multicolumn{2}{c}{\(g=8\) mm} \\ Design method & \(g/R=0.00075\) & & \(g/R=0.002\) \\  & \(\mathbf{b}\) & \(\mathbf{m}\) & \(\mathbf{b}\) & \(\mathbf{m}\) \\ Thepot & 3.85 & \(-2.51\) & 9.77 & \(-2.74\) \\ El Sawy & 3.14 & \(-2.46\) & 5.89 & \(-2.63\) \\ Boot & 1.39 & \(-2.27\) & 2.22 & \(-2.38\) \\ [PERSON] & 1.00 & \(-2.20\) & 1.00 & \(-2.20\) \\ [PERSON] & 0.12 & \(-2.03\) & 0.10 & \(-2.07\) \\ Montel & 0.05 & \(-1.79\) & 0.11 & \(-2.02\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Calibration parameters for different formulations.  
Figure 13 compares the results of critical pressure depending on slenderness \((D/t)\).  
Figure 14 compares the results obtained for the critical pressure depending on the ratio \(L/D\).  
Figure 12: Factor \(K\) obtained through analytical approaches, compared with FEM results, for a linear with radial constraint and \(g/R=0.0015\) and \(g=0.6\) mm.  
Figure 13: Comparison between Jacobsen and FEM results for critical pressure \(Pcr\).  
Figure 15 compares the results in the case of a very slender liner for different values of the gap (\(g/D\)). It is notable that the Jacobsen formula offers non-conservative values.  
As can be seen in Figure 15 for very slender tubes (\(g/D\in(0.0005;0.003)\)), it is more conservative to use the FEM formulation, whilst when the liner is not so slim, Figure 14 proves that Jacobsen formulation can be more conservative.  
Figure 14: Comparison of Jacobsen values with FEM results versus \(L/D\).  
Figure 15: Comparison between Jacobsen and FEM results for a range of ratios \(g/D\).
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
## 4 Performance Analysis
### Energy Consumption  
The overall energy consumption by all IoT devices during packet transmission is known as system energy consumption. As the SDS establishes the communication link by considering the predefined values available in the LSA corpus table, only the prudent links are chosen, and the rest of the links are ignored, consequently, it prevents energy wastage and energy is only utilized for the prudent link. In Figure 6, it can be seen that the SDS has only utilized a confined energy level when the link was selected by the IoT device 10 whereas HSPA, CCN, and DSR utilized exorbitant energy. Similarly, at IoT device 50, the SDS still has a substantial energy level while the rest of the protocols' energy is almost empty. Considering the above discussion, it can be concluded that the proposed SDS mechanism has extraordinary performance as compared to the rest the protocols (HSPA, CCN, and DSR). This only became possible due to adopting a prudent LSA technique.
## 2 Chip Architecture  
### Image-Sensor-Based Optical Communication Systems  
A conceptual figure of the proposed image-sensor-based optical communication systems is depicted in Figure 1[7]. In this system, a CMOS sensor which has both an imaging pixel array and LPR cell array is used. The lens of the ISC camera focuses the light sources at the focal plane of the CMOS sensor. This greatly enhances the optical energy density at the focal plane and by receiving the optical energy with a small-size photodiode, relatively high-speed optical communication is realized if the position of the signal spot lights irradiation on the focal plane is known. Using an image signal output, an external image processor finds the position of the signal spot light and the vertical address (V-address) and horizontal address (H-address) are sent back to the CMOS sensor chip. Then the communication signals from the selected address and its peripherals are read out.
# Germanium Single Crystals for Photonics
## 10 Plasma Antennas Formed by Laser Radiation in Germanium  
As we have already noted, germanium is the most studied semiconductor material. Therefore, studies of some relatively new phenomena are usually initially conducted on germanium samples. After identifying the main physical processes and obtaining the first positive results, the search for more effective or cheaper semiconductors usually begins. Very often, it quickly becomes clear that this search has been successful, and further research is carried out using another, more suitable material. This was repeated in the study of plasma antennas. Further research was carried out on more accessible materials [98].  
An interesting direction of modern laser technology is the creation of Ge-based devices for modulating IR and microwave radiation: modulation using controlled introduction of moderate amounts of nonequilibrium excess carriers into the surface layer of a germanium plate by excitation with diode laser radiation [54]; modulation in Ge waveguide structures on silicon substrates by controlling the absorption of free carriers [55; 56]. This idea was further developed in the creation of plasma antennas for various radio and telecommunication devices. The work in [99] reports on experimental studies of the efficiency of transmission of high-frequency signals by a semiconductor vibrator-transmitting antenna based on Ge and Si single crystals, on the surface of which, due to the internal photoelectric effect, a nonequilibrium electron-hole plasma is formed by radiation from a laser diode. It turned out that this is a very convenient tool for controlling the electromagnetic properties of objects due to the insignificant energy costs for the formation of nonequilibrium free-charge carriers with a high concentration of charge carriers (10\({}^{16}\) cm\({}^{-3}\) and higher). In these experiments, a nonequilibrium electron-hole plasma was created using continuous radiation from a laser diode with a fiber output (\(\lambda\) = 975 nm). The nonequilibrium plasma stimulates the growth of the transfer characteristics of a semiconductor antenna (Figure 11).  
Figure 11 shows a typical oscillogram of signals on the receiving microwave detector, from an antenna made of a Ge single crystal (a rectangular plate measuring 5 \(\times\) 45 mm and 1 mm thick, \(\rho\) = 47 Ohm\(\cdot\)cm). It is clearly seen that the nonequilibrium electron-hole plasma created by laser action has proven itself as a vibrator plasma antenna.
# Influence of Right Triangular Prism Rough Beds on Hydraulic Jumps
## 3 Results and Analysis
### Effects of Right Triangular Prism Rough Beds on Energy Loss  
A stable and free hydraulic jump is characterized by significant energy dissipation. Therefore, we need to evaluate the variation in the energy loss through hydraulic jumps. The energy loss values were calculated as the difference in flow energy before and after the jump from data from the numerical simulation results (see Table 7). Using the MLR method, combined with Equation (3), this research found the energy loss ratio \(E_{L}/E_{1}\) relative to the parameters of inflow conditions and roughness configuration can be written as:  
\[\frac{E_{L}}{E_{1}}=0.045 Fr_{1}+0.007(\frac{t}{y_{1}}+\frac{L_{s}}{y_{1}})+0.35 4,R^{2}=0.965 \tag{14}\]  
Equation (14) shows that the parameter \(E_{L}/E_{1}\) had an extreme variation versus \(Fr_{1}\). When the \(Fr_{1}\) value increases, then the energy loss value also increases (see Figure 15a). At the same time, two geometries of the rough bed parameters (\(t/y_{1}\), \(L_{s}/y_{1}\)) had minor effects on increasing the energy loss ratio (see Figure 16). In addition, Figure 15a also, once again,  
Figure 14: (**a**) \(L_{j}/y_{2^{*}}\) versus \(Fr_{1}\) for rough models; (**b**) Variation of jump length between rough and smooth beds versus \(Fr_{1}\).  
Figure 13: \(L_{j}/y_{1}\) versus \(t/y_{1}\) and \(L_{s}/y_{1}\) for all rough bed models.  
shows that the \(E_{L}/E_{1}\) values predicted by Equation (14) agreed with the \(E_{L}/E_{1}\) values from numerical simulation results (error \(<\) 5%).  
This research uses the \(\eta\) parameter to evaluate the energy loss of the flow through the hydraulic jump zone. It is determined by the formula as follows: \(\eta=(E_{L}-E_{L^{*}})/E_{L^{*}}\) where \(E_{L^{*}}\) is the energy loss for smooth beds. Based on the numerical simulation results for different rough bed models, this study found a relationship between energy loss and the \(Fr_{1}\) parameter as follows:  
\[\eta(\%)=188.6 Fr_{1}^{-1.425},R^{2}=0.861 \tag{15}\]  
Figure 17 shows the \(\eta\) values versus \(Fr_{1}\) parameters. This figure indicated that the right triangular prism rough beds used in this study had increased the energy loss in flow. In more detail, the increase in energy loss varies from 4% to 24% compared with smooth beds at the same initial Froude number condition. Therefore, these rough beds improve the flow's energy dissipation in the hydraulic jump region.  
Figure 16: \(E_{L}/E_{1}\) versus \(t/y_{1}\) and \(L_{s}/y_{1}\) for all rough bed models.  
Figure 15: **(a)** Predicted versus numerical results of \(E_{L}/E_{1}\), **(b)**\(E_{L}/E_{1}\) parameter against \(Fr_{1}\).
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 3 Results and Discussion
### Results Comparison on Recurrent Units  
GRU and LSTM were further compared. The performance comparison on GRU and LSTM with testing data are shown in Figure 17. The results were based on five iterations of cross-validation.  
Figure 16: GRU model performance comparison on different architectures: (**a**) the accuracy comparison on different architectures; (**b**) the computation cost comparison on different architectures.  
Figure 17: GRU and LSTM performance comparison, described by prediction accuracy indicators (boxplots) and computation cost indicator (bar plot).  
As indicated by the boxplots of Figure 17, it is clear to observe that the spread of LSTM in the results of different evaluation metrics is larger than GRU model, although the median and mean values of each evaluation metric, \(R^{2}\), adjusted \(R^{2}\), RMSE, MAE and MAPE, are similar to each other. With the same configuration of the network, 48,201 parameters need to be trained in LSTM, while only 38,701 parameters need to be trained in GRU. As mentioned at the beginning, the GRU model is the simplified version of LSTM. It is also proven here that there is no additional cost of model performance, when using GRU instead of LSTM for Blansko traffic noise data modelling. Hence, the GRU model, with Architecture 2 (many-to-many), is the finalized traffic noise model for Blansko dataset.
## 2 Materials and Methods
### Step 4: Scenario Modeling
#### 2.3.1. Scheduled and Corrective Maintenance Scenarios (1, 2, 3, and 4)  
Scenario 1 includes failure rates and associated MTTR values that are extracted from the notification system of the case company. The data is extracted exactly how it is presented in the notification system.  
Scenario 2 includes the same data as in the previous scenario. However, the difference in Scenario 2 is that all the values considered as \"unreasonably extreme\" are replaced with values the authors anticipate to be more reasonable when taking the connection between the specific failure and associated MTTR value into consideration.  
Scenario 3 addresses input data involving both failure rates and MTTR values extracted from the OREDA database [17]. The OREDA database is in fact well-known and highly adopted by O&G companies in connection with analyses concerning risk and technical integrity. In practice, the OREDA database categorizes failure rates in terms of \"lower\", \"mean\", and \"upper\" failure rates, and MTTR values in terms of \"mean\" and \"max\". This research adopts the \"upper failure rates\" and the \"max MTTR values\", which experts claim to represent industrial experience the best.  
The interesting context of this use case scenario is to highlight whether the case company experiences either higher or lower failure rates and MTTR values in comparison to the OREDA database. This will underpin whether the industry shall be recommended to support integrity assessments based upon their own empiric case study data or the OREDA database, dependent on the associated risk profile (\"risk-averse\", \"risk-seeking\", etc.).  
The estimation of MTTR values originating from the empiric case study data is associated with the highest uncertainty as it depends on two different variables the maintenance personnel need to report (start of maintenance and end of maintenance). Therefore, Scenario 4 replaces the MTTR values from the empiric case study data with the ones presented in the OREDA database.