esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of MCM-41, MCM-41-C\({}_{8}\), and CALB@MCM-41-C\({}_{8}\)  
The surface morphologies and microscopic internal structures of the MCM-41 were observed using SEM (S-4800, Hitachi, Japan) and TEM (TECNAI G2 20S-TWIN, FEI Company, Hillsboro, OR, USA), respectively. The surface area and total pore volumes of MCM-41, MCM-41-C\({}_{8}\), and CALB@MCM-41-C\({}_{8}\) were measured with a computer-controlled nitrogen gas adsorption analyzer (ASAP2010, Micromerities, Norcross, GA, USA) in the relative pressure range of 0.05-1.00 (specifically, the nitrogen partial pressure is nondimensionalized with the saturated vapor pressure of liquid nitrogen at the adsorption temperature). The hydrophobicity of samples was evaluated based on the surface water contact angles via a contact angle goniometer (SDC-200S, Dongguan city sindin PrecisionInstrument Co., Ltd., Dongguan, China). Fourier transform infrared (FT-IR) spectra of MCM-41, MCM-41-C\({}_{8}\), CALB@MCM-41-C\({}_{8}\), and free lipase CALB were determined using an FT-IR spectrometer (Bruker, Karlsruhe, Germany). Data from X-ray photoelectron spectroscopy (XPS) were obtained from the ESCALAB 250 XI (Thermo Fisher Scientific Inc., Waltham, MA, USA) device.  
The protein content of lipase CALB before and after lipase immobilization was measured using the BCA method [42]. The loading amount and immobilization efficiency of the lipase were determined based on the calibration curve, which is prepared using the standard protein solution in the BCA kit with protein concentrations ranging from 0, 0.2, 0.4, 0.6, 0.8, and 1.0 mg/mL protein standard solution (Figure S1). 10 \(\upmu\)L of the test sample was diluted and added to a clean 96-well plate, 10 \(\upmu\)L of PBS buffer, and 200 \(\upmu\)L of BCA working solution (50:1 homogeneous mix of solutions A and B) were placed in a 37 \({}^{\circ}\)C incubator for 15 min, and the absorbance of the enzyme solution was measured at a 562 nm wavelength in a microplate reader. The residual protein concentration in the lipase solution was calculated by:  
\[\text{Loading amount }(\text{mg}/\text{g})=\frac{(C_{0}-C)V}{M} \tag{1}\]  
\[\text{Immobilization efficiency }(\%)=\frac{C_{0}-C}{C_{0}} \tag{2}\]  
where \(C_{0}\) and \(C\) are the protein concentrations (mg/mL) before and after lipase immobilization, respectively; \(V\) is the volume (mL) of the aqueous solution, and \(M\) is the mass (g) of MCM-41-C\({}_{8}\).
# Mechanical Properties of ASTM A572 Grades 50 and 60 Steels at High Temperatures
## 2 Experimental Tests
### Test Methods for Thermal Properties  
The tests were performed in accordance with ASTM E8 [28] and ASTM E21 [29]. The two methods are both steady-state and transient-state tests at elevated temperatures for steel coupons. In this study, the steady-state test method was used to investigate the stress-strain behavior at elevated temperatures.  
In the steady-state test, the samples were heated to a specified target temperature and then loaded until they failed while maintaining the same temperature. To measure the tensile strength of steel at high temperature, the experimental equipment setting consisted of a steel frame, an electric furnace, a high-temperature extensometer, and data logger equipment. The load cell of the universal testing machine (UTM) had a loading capacity of \(\pm\)2000 kN, where the displacement transducer could measure up to 250 mm. An electric heating furnace to surround a steel specimen can be heated up to 1200 \({}^{\circ}\)C and maintain a constant temperature for an extended period. A thermocouple was installed inside the electric heating furnace to verify the internal temperature in real time during heating. The high-temperature extensometer used the Epsilon Model 3549 (Epsilon Technology Co., Ltd., WY, USA), which can measure gauge lengths of up to 50 mm. This high-temperature extensometer, with a sensitivity of 0.00694 mm for axial deformation, was measured by a data logger device, i.e., TDS-150 (Tokyo Sokki Kenkyujo Co., Ltd., Tokyo, Japan). The load displacement data for the UTM, temperature of the electric heating furnace, and displacement measurement data for the high-temperature extensometer were connected to the data logger device and transmitted to the computer. In addition, not only the temperature of the thermocouple inside the heating furnace was measured, but also the surface temperature of the steel was separately measured using SDT25 (Type K Thermocouple) equipment (Figure A1). The samples were heated from ambient temperature to a target temperature, i.e., 25, 100, 200, 300, 400, 500, 600, 700, 800, and 900 \({}^{\circ}\)C. The heating rate of the steel specimen was 10 \({}^{\circ}\)C/min. After reaching the target temperature, a general 30 min period was required for temperature stabilization; after 15 min, the tensile load was applied to the samples. In the steady-state test, strain control was achieved with the UTM. The strain rate was \(\sim\)0.006/min, which is within the range of 0.005 \(\pm\) 0.002/min established by ASTM E21-92 (1997) [30].  
Figure 5: Determination of the yield strength, tensile strength, and elastic modulus according to strain level.
## 2 Analytic Model Development
### The PSJA Aerodynamic Model  
As shown in Figure 1, the PSJA consists of four main parts: Throat, chamber, shell, and electrodes. To simplify the PSJA aerodynamic model, the fluid region including the throat and chamber is treated as two control volumes. The pressure, temperature, and density are averaged over the control volume. Then, the conservation of mass, momentum, and energy equations are manipulated to describe the changes in density, jet velocity, pressure, and temperature in the control volume.  
To simplify these conservation equations, some assumptions are adopted in this model.  
1. The body forces are negligible. The body forces are contributed to by the gravity and electromagnetic force. Compressed with the pressure force, the body forces are so small that they are negligible in almost all simulations of PSJA in the literature [22; 24; 25].
2. The gas velocity in the chamber is negligible, only the velocity of gas in the throat is taken into consideration. The velocity, temperature, density, and pressure across the throat outlet and throat inlet are uniform and represented by scalar values. As heating in the actuator chamber is assumed to be uniform, the error associated with this assumption is small.
3. The gas is seen as inviscid. As the ratio of throat length to throat diameter is not large, the role of gas viscosity plays a less important role. If the throat was long and narrow, viscous forces would need to be considered.
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 3 GIS Defect PD Pattern Recognition Based on YOLOv5 Algorithm
### YOLOv5 Detection Algorithm
#### 3.1.5 Prediction Network  
GIS partial discharge PRPD map can be obtained with three enhanced features after passing through the backbone feature extraction network and the FPN feature pyramid. The shapes of these three enhanced features are (20, 20, 1024), (40, 40, 512), and (80, 80, 256). These three feature layers were passed into YOLO Head to obtain the prediction results. In YOLOv5, a total of nine a priori frames of different sizes are designed. Each feature point of each feature layer corresponds to three prior frames with dimension 9 (containing 4 prediction frame coordinates information, 1 confidence level, and 4 category information). The task of the prediction network is to select the target coordinates, confidence, and category information in the image.  
The target is coarsely located using the a priori frame, and then the center point coordinates of the prediction frame (\(b_{x}\), \(b_{y}\)) and the width and height of the prediction frame (\(b_{w}\), \(b_{h}\)) are calculated by the following equation to determine the prediction frame position.  
\[\left\{\begin{array}{l}b_{x}=\sigma(t_{x})\times 2-0.5+c_{x}\\ b_{y}=\sigma(t_{y})\times 2-0.5+c_{y}\\ b_{w}=P_{w}(\sigma(t_{w})\times 2)^{2}\\ b_{h}=P_{h}(\sigma(t_{h})\times 2)^{2}\end{array}\right. \tag{5}\]  
where \(\sigma\) is the sigmoid activation function, \(t_{x}\), \(t_{y}\), \(t_{w}\), \(t_{h}\) are the offset of the center point coordinates and the width-to-height scaling ratio of the network prediction frame, respectively, \(c_{x}\), \(c_{y}\) are the center point coordinates of the a priori frame, and \(p_{w}\), \(p_{h}\) are the width and height of the a priori frame.  
When performing network inference, a scoring mechanism is applied to filter the most appropriate prediction frames. The class-specific confidence score of each target frame is obtained by multiplying the class information predicted by each grid with the confidence level of the target frame prediction, as shown in Equation (6).  
\[Pr(C_{i}|O)\times Pr(O)\times\text{CIoU}_{\text{pred}}^{\text{truth}}=Pr(C_{i} )\times\text{CIoU}_{\text{pred}}^{\text{truth}} \tag{6}\]where \(Pr(C_{i})\) is the category probability of each grid prediction, \(Pr(O)\) is the target probability of each grid prediction, and \(\text{CIoU}_{\text{pred}}^{\text{truth}}\) is the intersection ratio of the true frame to the predicted frame.  
After getting the category confidence score for each target box, set the threshold value and filter out the target boxes with low scores. The retained target frames are subjected to non-maximal suppression (NMS) [24] to obtain the final detection results.
## Results
### Optimization of Atmospheric and Temperature Conditions for the Activity of Purified MDH\({}^{*}\)  
The reaction conditions for the efficient conversion of pyruvate to malate by MDH\({}^{*}\) require high CO\({}_{2}\) and low O\({}_{2}\) levels. All experiments undertaken were therefore performed under standard MDH\({}^{*}\) reaction conditions (55 \({}^{\circ}\)C, 5% O\({}_{2}\) and 15% CO\({}_{2}\)). This was further explored and optimized by testing the effects of different O\({}_{2}\) and CO\({}_{2}\) ratios at different temperatures on the activity of MDH\({}^{*}\) to obtain optimal reaction conditions. A Box-Behnken design and response surface methodology were used with MATLAB 2019a, to develop a model that identified the conditions leading to maximum MDH\({}^{*}\) activity. The values of the three chosen input variables are shown in Supplementary Table S1. The chosen temperature range of 45-65 \({}^{\circ}\)C was selected on account of instrumental technical specifications and the use of a thermophilic enzyme. Fifteen independent experiments were performed to examine the effect of the experimental variables on the initial reaction rate of MDH\({}^{*}\) activity. The initial reaction rate was calculated via the decrease in NADH concentration at the beginning of the reaction. The full data set for all 15 conditions is shown in Table 2 (and Supplementary Table S2). The highest initial reaction rate, 0.103 mM/min, was detected at 65 \({}^{\circ}\)C, 7.5% O\({}_{2}\), and 10% CO\({}_{2}\) for experiment number eight (Table 2). The maximum initial reaction rate value was calculated by the model to be 0.102 mM/min, with temperature, O\({}_{2}\) and CO\({}_{2}\) values of 65 \({}^{\circ}\)C, 5.625% and 12.5%, respectively. The experimentally obtained value and the value as calculated by the model were in close agreement. These conditions were then used for all further activity assays (65 \({}^{\circ}\)C, 5.6% O\({}_{2}\) and 12.5% CO\({}_{2}\)).
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 5 Simulation
### Comparison of Energy Consumption with EEUC Protocol  
Corresponding to the simulation result in Figure 11, Figure 12a shows the average energy consumption of the surviving nodes in each round \(E_{\text{NODE}}\).  
\[E_{\text{NODE}}=\frac{E_{\text{ROUND}}}{N_{\text{ALIVE}}},\]  
where \(E_{\text{ROUND}}\) is the sum of energy consumption of all nodes in the round, \(N_{\text{ALIVE}}\) is the number of nodes alive in the round. In the whole working process of the system with power line connection, no node exhausts energy in advance and the communication route is unchanged, and the total energy consumption per round is also unchanged. The simulation results show that the average energy consumption of the nodes in each round is always \(9.1\times 10^{-4}\) J. Under the EEUC protocol, most nodes can survive for 100 rounds, and the average energy consumption in each round is about \(4.2\times 10^{-3}\) J. As a result, the power line connection scheme can reduce the node average energy consumption by about 78%.  
According to the total energy consumed by the system and the total number of transmitted packets, the average energy consumed by each packet can be calculated.  
\[E_{\text{PACKET}}=\frac{E_{\text{TOTAL}}}{N_{\text{PACKET}}},\]  
where \(E_{\text{TOTAL}}\) is the sum of the energy consumed by all nodes from the beginning of the work to the current round and \(N_{\text{PACKET}}\) is the total number of data packets transmitted by the system. Figure 10b shows the ratio of the average energy consumed per packet under the application of the power line connection scheme and the application of the EEUC protocol. In the EEUC scheme, when all nodes are dead, the energy consumption ratio is close to 4.3:1, which means that the power line connection scheme can transmit data with less energy consumption.  
Based on the simulation results of Figure 12, the system with the power line connection reduces the average transmission power consumption by 78%, and the overall survival time can be increased by more than four times.  
Figure 12: Node energy consumption and packet energy consumption (**a**) Node average energy consumption for each round, (**b**) Packet average energy consumption ratio.
## 6 Evaluation Metrics and Simulation Results
### Simulation Results
Table 4 compares the authentication performance of different mechanisms, including the authentication approach, the authentication accuracy and SNR, the number of samples per UE in the training dataset, the types of wireless networks, and whether they support simultaneous authentication of multiple devices with different numbers of antennas. FPLA can achieve 96.8% authentication accuracy at 3 dB SNR, while only requiring 300 samples per UE to train an effective model. In addition, the FPLA mechanism can also adapt to the 5G FR1 (3.5 GHz) and support the simultaneous authentication of multiple devices with different numbers of antennas. In contrast, other mechanisms either require higher SNR or more samples, or cannot adapt to 5G networks or support multi-device authentication. Therefore, the FPLA mechanism is an efficient, flexible, and secure authentication mechanism, with strong practical value and application prospects. Figure 6: Train loss, train ACC, and test ACC on datasets with 7 malicious users, \(\text{SNR}=6\) dB, legitimate user \(\text{antennas}=4\). Figure 8: Comparison of ACC under different SNR scenarios. Training on datasets with 7 malicious users, SNR = 6 dB and legitimate user antennas = 4. Figure 7: Performance of DTRN and Res-18 in terms of ACC, MDR, and FAR for different antenna array sizes of legitimate users. The learning rate was set to \(9\times 10^{-5}\), and the performance was assessed at epochs 10 and 20. The UL SNR is 15 dB, and the x-axis represents the antenna array size for legitimate users.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 2 System Model
### Transmit Precoding  
In the downlink of the proposed communication scenario, each AP transmits to a set of users. AP transmission to one user may cause interference to the neighboring users. Every user is affected by the interference, that is, by the data transmission to other users. Using optimal precoding schemes, it is possible that each AP can cancel out each other's interference [42]. By adjusting the AP's transmit powers and phases, the interference can be cancelled out [43]. This is possible in centralized operations where a CPU decides the precoding, whereas in distributed operations, each AP's interference contribution is cancelled out using the appropriate precoding method [44]. Different transmit precodings can be used depending upon the network operation [45].  
Minimum mean square error (MMSE) precoding--The centralized precoding in the downlink is the MMSE precoding, defined as  
\[w_{u}^{MMSE}=\ p_{u}\left(\sum_{i=1}^{U}p_{i}G_{u}(\hat{h}_{i}\hat{h}_{i}^{H}+ C_{i})G_{u}+\sigma^{2}I_{AN}\right)^{-1}G_{u}\hat{h}_{u} \tag{14}\]  
Partial minimum mean square error (PMMSE) precoding--This is an approximation to the MMSE precoding defined below:  
\[w_{u}^{PMMSE}=p_{u}\left(\sum_{i\in A_{u}}p_{i}G_{u}\hat{h}_{i}\hat{h}_{i}^{H} G_{u}+Z_{A_{u}}+\sigma^{2}I_{AN}\right)^{-1}G_{u}\hat{h}_{u} \tag{15}\]  
with \(Z_{A_{u}}=\sum_{i\in A_{u}}p_{i}G_{u}C_{i}G_{u}\)  
Partial regularized zero forcing (PRZF) precoding--Another low-complexity scalable precoding is PRZF precoding, which is an approximation to the MMSE precoding and is obtained as \[w_{u}^{PRZF}=\left[G_{u}\hat{H}_{A_{u}}\left(\hat{H}_{A_{u}}^{H}\;G_{u}\;\hat{H}_{ A_{u}}+\sigma^{2}P_{A_{u}}^{-1}\right)^{-1}\right]_{;1} \tag{16}\]  
where \(P_{A_{u}}\) is a diagonal matrix that contains the transmit powers \(p_{i}\), and \(\hat{H}_{A_{u}}\) is a channel matrix obtained by putting together all the vectors \(\hat{h}_{i}\) for \(i\in A_{u}\), with \(\hat{h}_{u}\) being the first column.  
Local minimum mean square error (LMMSE) precoding--The locally optimal operation in the downlink is the LMMSE precoding, which includes the sum of all the users in the network and is defined as  
\[w_{au}^{LMMSE}=\;p_{u}\left(\sum_{i=1}^{U}p_{i}(\hat{h}_{ia}\hat{h}_{ia}^{H}+C _{ia})+\sigma^{2}I_{A}\right)^{-1}G_{ua}\hat{h}_{ua} \tag{17}\]  
Maximal ratio (MR) precoding--The MR precoding is the first scalable option; it ignores the interference caused by the AP and is obtained as  
\[w_{au}^{MR} = G_{au}\hat{h}_{au} \tag{18}\]  
Local partial minimum mean square error (LPMMSE) precoding--LPMMSE precoding is a scalable approximation of the LMMSE precoding and it includes only the users served by the AP:  
\[w_{au}^{LPMMSE}=\;p_{u}\left(\sum_{i\in U_{a}}p_{i}(\hat{h}_{ia}\hat{h}_{ia}^{ H}+C_{ia})+\sigma^{2}I_{A}\right)^{-1}G_{ua}\hat{h}_{ua} \tag{19}\]
## 3 Network and Energy Models and Assumptions  
### Network Model and Assumptions  
The network model is considered as a graph \(G=(V,\,E)\), where \(V\) is a set of nodes which contains the BS and N sensor nodes distributed in the ROI. The BS node has an unlimited energy source and it can be placed inside or outside of the desired region and collects the receiving data. \(E\subseteq V^{2}\) is the set of links, if two nodes are in communication range of each other, there is a link between them. The links are symmetric and bidirectional. Since nodes do not use GPS positioning equipment, they are not aware of their own geographical coordinates. The nodes equipped with RSSI to measure their distances. The nodes are similar and are distributed evenly or randomly in a square-shaped field. Nodes explore the network topology independently. They send data packets with fixed signal strength and on a fixed frequency. Nodes' position is assumed fixed and the initial energy of nodes is assumed similar.
## 4 Results and Validation
### Study Areas  
For the demonstration and validation of our new approach, we selected 32 globally distributed lakes or reservoirs shown in Figure 12. The major requirement for selecting inland water bodies was that there is a water level time series of a gauging station or at least from satellite altimetry available for validation. Then, the study areas were selected in different climatic regions with varying cloud coverage and rainfall causing data gaps. Also, regions with data gaps caused by ice-coverage are selected. In order to demonstrate the impact of our approach, we selected lakes and reservoirs which show variations in the water level but also in the surface area. Table 4 gives an overview of all study areas and their characteristics. In the first column, the maximum water level variations based on long-term in situ data and satellite altimetry are shown. Furthermore, the maximum surface area and shore length derived from the long-term water probability mask are presented. Finally, the ratio between surface area and shore length, climate zone, annual rainfall and annual clouds are given.  
Figure 12: Map of selected and globally distributed study areas.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
\begin{tabular}{c} **Annual Clouds** \\ **[38] [75]** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of characteristics for 32 selected study areas. For each study area, the water level variations from altimetry or gauge are given. Furthermore, the maximum surface area, length of the shore, and its ratio derived from the long-term water probability mask are shown. Additionally, information about the climate zone, annual rainfall and annual cloud coverage are shown.
## 3 Results and Discussion
### Analysis of Sugars  
The content of sugar and its types depends on the cultivar, weather conditions during harvest time, and the degree of ripeness of the fruit. Therefore, fruits of the same species and cultivar may present differing total sugar values. Depending on their ripeness, fruits may also contain different amounts of sugars.  
The samples were analyzed in terms of the content of three sugars--fructose, sorbitol, and glucose. Additionally, in three juices obtained that were subjected to PEF treatment, we also determined trace amounts of sucrose (Table 3). Other studies indicate the presence of fructose, glucose, and sorbitol in fresh chokeberries and chokeberry juice [29; 30; 33]. [PERSON] et al. [33], apart from fructose, glucose, and sorbitol, also determined sucrose. In our present study, the higher content of sugar in chokeberry juice was glucose (1.87-3.76 g/100 mL in fresh juices and 2.62-3.51 g/100 mL in juices subjected to PEF treatment) with the exception of two samples, PEF 2 and PEF 3, whereby determination indicated more sorbitol. As you can see, the total sugar content increased after PEF processing, but the value varied significantly between results, and at this stage of the study, no reason for this change was found. It seems, however, that the increased dry matter content after PEF treatment resulted in higher sugar content values. The course of the impact of the PEF process on the content of sugars and their passing in juices has not yet been thoroughly investigated.  
[PERSON] et al. [34] showed that the sugar content of chokeberry juices obtained using different enzyme products was slightly higher than in the present study. This may be due to the place of cultivation, a sunnier ripening period, later harvest, and the method by which the juices were made.