esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Figure 13 shows the results of experimental application of the proposed algorithm for the case of determining **Al** in steels for the experimental conditions in Figure 2. From the data in Figure 2, it can be seen that under the selected operating parameters of the SPAS-05 spectrometer ([[https://spas05.com/en/products/emission-spectrometers/optical-emission-spectrometer-oes-spas-05-metal-analysis](https://spas05.com/en/products/emission-spectrometers/optical-emission-spectrometer-oes-spas-05-metal-analysis)]([https://spas05.com/en/products/emission-spectrometers/optical-emission-spectrometer-oes-spas-05-metal-analysis](https://spas05.com/en/products/emission-spectrometers/optical-emission-spectrometer-oes-spas-05-metal-analysis)) (accessed on 27 February 2022)), concentrations of several thousandths of a percent are reliably determined. At concentrations less than 1%, the blooming effect is not pronounced. For the relative intensity measured by the traditional method, when the concentration exceeds 1%, the derivative of the dependence \(I_{Al}/I_{Fe}(C)\) begins to drop sharply and, at a concentration above 2.2%, the intensity does not depend on the concentration. Thus, the technique for determining the concentration of **Al** that is described by the calibration curve (Equation (2)) is applicable over the **Al** concentration range \(C=10^{-3}\div 1\%\). At the same time, the use of the proposed algorithm makes it possible to expand the upper range of its applicability to 2.6%. Figure 5:0 Application of the proposed technique on the example of determining the concentration of **Al** in steels. The area of high concentrations is shown; the conditions are the same as those in Figure 2; 1—data processing according to the proposed algorithm; 2—data processing according to the traditional algorithm without adjustments for the blooming effect; 3—calibration curve (Formula (2)); 4—limiting relative intensity of the analytical line **Al** 394.4 nm (reference line **Fe** 389.57 nm) for the used CCD (TCD1304 DG).
## 5 FPGA Implementation
### FPGA Implementation of Transmitter  
The top-level FPGA design of the DSSS transmitter is shown in Figure 33. It mainly contains four modules, which are PLL 280, kuopin, NCO BPSK and DAC out. PLL 280 is the SPSP module and is implemented by the SPSP function of Quartus II. The main function of PLL 280 is to generate a systematic clock according to the real system's requirements. kuopin is the BPSK SPSP module. The main function of kuopin is to make the baseband signal correlate with the local PN code, and then obtain the necessary SPSP code. NCO BPSK is the modulation module, the main function of which is to complete BPSK demodulation. DAC out is the driver module of D/A. The main function of DAC out is to send the modulated digital signal to the D/A converter.  
Figure 31: Timing simulation result of the code tracking module.  
Figure 32: The waveform of the code tracking module.  
Figure 33: The top-level design of the Direct Sequence Spread Spectrum (DSSS) transmitter.
# GTP-Bound N-Ras Conformational States and Substrates Are Modulated by Membrane and Point Mutation
## 2 Results
### Influence of G12V Mutation on the N-Ras Conformational (Sub)States  
The presence of state 1 increases to a majority of approximately 95% of the time (see Figure 2A(d)). This indicates a significant decrease in Thr35 binding stability to Mg\({}^{2+}\) once the mutation occurs in the absence of the membrane (i.e., in solution only). When membrane-bound, on the other hand, both WT and the G12V mutation are sampled at about the same time in state 1.  
Despite the oncogenic nature of the G12V mutation (and, therefore, its high stability), none of the substates of the \"active\" state 2 is present in the absence of the membrane (i.e., in solution only). This underscores the crucial importance of the protein-membrane interaction in the oncogenic mutation [22; 59; 60].  
Due to the much larger size of Val compared to Gly in membrane-bound systems, the G12V mutation of N-Ras (see Figure 4) significantly reduces the presence of the protein in  
Figure 6: **Individual residue mobility in the principal component analysis for all (sub)states.** PCA from the MD trajectory was performed with the NMWiz plugin [57] in VMD [58] for WT in solution (i.e., without membrane). Of the first ten, three major modes are displayed (with magenta, brown, and cyan lines, respectively), separately for substate 2\({}_{OX}\) (top), substate 2\({}_{OY}\) (center), and substate 2\({}_{OX}\) (bottom), respectively. The normal modes of the first eigenvector are represented on the three protein insets for each substate.  
substate \(2_{OX}\) from \(\sim\)55% in WT to only\(\sim\)10% in G12V. Furthermore, the sampling time of substate \(2_{OY}\) is increased five-fold, from \(\sim\)6% for WT to \(\sim\)30% for G12V (see Figure 2B(d)).  
Perhaps the most important effect of the G12V mutation is on substate \(2_{OX}\), which is not found in either membrane-free (i.e., solution-only) or membrane-bound systems. This is extremely important since the substate \(2_{OX}\) conformation corresponds to the GAP-bound conformation. The lack of sampling of this substate suggests the appearance of a significant energy barrier that most likely eliminates (or at least significantly reduces) enhanced GTP hydrolysis, rendering G12V into an oncogenic mutation.
## 3 Results and Discussion
### Dissolution Studies  
The dissolution profile of the FRG--15 sample and GFD are presented in Figure 2. Release assay was performed only for the sample coded FRG--15 (the size of the film was 2 \(\times\) 2 cm and the mass of one film was 0.95 g), since it showed higher elongation and tensile strength, and faster disintegration time, which are important physicochemical properties for production and marketing of this dosage form (Figure 2).  
Three selected flavonoids--quercetin, kaempfer and isorhamnetin--were identified in freeze-dried _Ginkgo bilob_ extract. Flavonoids were identified by comparing the retention  
Figure 1: Scanning electron micrographs of films loaded with GFD (FRG—15 sample containing 0.4 g of GFD, 0.3 g of glycerol and 2 g of 2% CMC and without GFD CMC—0—the film sample containing 0.3 g of glycerol and 2 g of 2% CMC.  
Figure 2: In Vitro release profile of flavonoid glycosides from film based on CMC with incorporated _G. bilob_ ethanol extract of FRG—15 formulation and _G. bilob_ freeze-dried extract (GFD).  
times and spectral characteristics (\(\lambda\) = 370 nm) of eluting peaks with those of reference compounds. The percentage content of flavonoid glycosides was calculated using following formula, which is described in HPLC conditions for determination of flavonoids. An example of a chromatogram is presented in Figure 3. The obtained results showed that flavonoid glycosides, presented in FRG--15, demonstrated 1.3-2.0-fold higher dispersion in solvent than GFD. Moreover, the film exhibited maximal release of flavonoid glycosides in 10 min--it was more than 87 % in sample coded FRG--15. Consequently, by comparing two different preparation methods, the freeze-dried and film casting techniques, it was observed that the FRG--15 sample had a significantly better in vitro dissolution profile than the freeze-dried extract. Moreover, high dissolution data might influence the choice of polymer as a drug carrier, which plays an important role in drug delivery systems [27]. CMC has the ability to increase viscosity, high pigment binding capacity, bioadhesion and biocompatibility, which enhance the bioavailability of active compounds, in particular those with low aqueous solubility [26; 38; 39; 40]. Moreover, the fast dissolution rate of the films has been attributed to a number of properties, which include a hydrophilic nature of the polymer and flavonoid glycosides, a high surface area of the film structure. [PERSON] [41] and coauthors reported that according to the in vitro release profile, 80% of phenolic compounds were released in 5 min from hydroxypropyl methylcellulose films with incorporated peanut skin extract.
## 3 Power System Reliability Assessment Considering Natural Gas Supply Fluctuations
### Power System Reliability Assessment Considering Gas Losses
Therefore, after the gas supply fluctuation event \(w_{\text{g}}\) occurs, the output power of the coal-fired unit at node \(i\), which is expressed as \(P_{i,c}^{\text{mg}}\), and the power load \(D_{i,L}^{w_{\text{g}}}\) can be respectively expressed as  
\[P_{i,c}^{\text{mg}}=P_{i,c}^{\max} \tag{12}\]  
\[D_{i,L}^{w_{\text{g}}}=D_{i,L}^{0}\cdot\left[1-\frac{\sum\limits_{i=1}^{N_{ \text{s}}}{(D_{i,L}^{0}-P_{i,c}^{\max}-P_{i,g}^{w_{\text{g}}})}}{\sum\limits_ {i=1}^{N_{\text{s}}}{D_{i,L}^{0}}}\right] \tag{13}\]  
To evaluate the impact of the natural gas supply on the power system, the following reliability-assessment indicators of the power system are selected, including Expected Demand Not Supplied (_EDNS_), Severity Index (_SI_), and Service Availability (_SA_) [40,41]. Expected Demand Not Supplied _EDNS_: Indicating the amount of power load lost from the system in MW in the event of a gas supply fluctuation event \(w_{\text{g}}\), which can be expressed as  
\[EDNS=\sum\limits_{i=1}^{N_{\text{s}}}{(D_{i,L}^{0}-D_{i,L}^{w_{\text{g}}})} \tag{14}\]  
where \((D_{i,L}^{0}-D_{i,L}^{w_{\text{g}}})\) indicates the amount of electrical load lost at node \(i\) when the gas supply fluctuation event \(w_{\text{g}}\) occurs; \(N_{s}\) is the number of loads. The larger the _EDNS_, the larger the electrical load removed due to the fault and the lower the system reliability. Severity Index _SI_: Based on the _EDNS_ obtained from (14), calculating the ratio of the electric load removed from the system at the load point due to a gas supply fluctuation event \(w_{g}\) (e.g., due to policy regulation or economic fluctuations) to the total electrical load that would have occurred if no gas supply loss event had occurred, which can be expressed as  
\[SI=\frac{EDNS}{L_{\text{total}}}\times T \tag{15}\]where \(L_{\text{total}}\) is the total system electrical load and \(T\) is the time from the gas supply loss event to repair. \(SI\) represents the system severity, and the larger this indicator is, the greater the severity of the system failure and the lower the system reliability. Average Service Availability Index \(ASAI\): Indicating the ratio of the normal electrical load of the system in this scenario to the total electrical load when no gas loss event occurs [42], which can be expressed as  
\[\Delta W_{i,\rm g}^{w_{\rm g}}=F_{l}^{aft} \tag{7}\]0  
where \(ASAI\) is the power supply availability of the system. The larger the value, the higher the power supply margin of the system and the higher the system reliability. Average Energy Not Supplied \(AENS\): Indicating the average amount of power shortage at each node when a load-shedding event occurs, which can be expressed as  
\[\Delta W_{i,\rm g}^{w_{\rm g}}=F_{l}^{aft} \tag{7}\]1  
where \(AENS\) is the average amount of load loss per node in a power system. The more severe the power system failure, the lower the system reliability.
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 4 Result and Discussion
### Experimental Setup  
In the experiment, the Ubuntu 18.04 operating system with 128 G of running memory and an RTX3090 graphics card with 24 G of video memory are implemented. The deep learning framework is Pytorch1.7.1, and the CUDA version is 11.1.  
Data augmentation on the cloud images were performed to increase the noise of the dataset and the robustness of the model. The operations included (1) random horizontal flip, (2) vertical flip, (3) grayscale with a probability of 50%, (4) random rotation of 45\({}^{\circ}\), and (5) random change of brightness, contrast, saturation, and hue. The input cloud images' size was adjusted to \(224\times 224\) pixels, and transfer learning was used to train these cloud images. The learning rate of two sub-networks is fixed to 0.000001, and the Adam optimizer is used to optimize the gradient operation. The batch size of training and testing are both set to 50. The epoch of the NRELCD dataset was set to 200, and the epoch of the MGCD dataset was set to 100.
## 7 Results and Discussions
### Optimal Configuration from the HPRALC, MPRAMC, and LPRAHC-Based Configurations
#### 7.5.1 The HPRALC-Based Scenario (without DSM)  
Without DSM, the Ni-Fe-battery-based IRES @80% DOD (base case) provides the lowest life-cycle cost (LCC) of $951,257 for the HPRALC-based scenario. Compared to this, the LA battery-based IRES provided an LCC of $1,595,267 at 80% DOD, which is approximately 68% higher. If the IRES were powered by a lithium-ion battery at 50% DOD, its optimal life-cycle cost would be $2,712,794, which is approximately 185% higher than the base case LCC. It is, thus, evident that nickel-iron batteries offer the most economical solution in this study area, followed by lead-acid and lithium-ion batteries. Therefore, they are omitted from additional analysis in Section 7.5.2 (MPRAMC-based scenario) and Section 7.5.3 (LPRAHC-based scenario).
# Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation
## 4 Results and Discussion  
### Adsorption Capacity and Adsorption Energy  
To verify the reliability of simulation results, the simulated adsorption isotherms of CO\({}_{2}\) on MIL-101 (Cr) at 298 K were compared to experimental data [33; 34]. As shown in Figure 3, the simulated adsorption isotherms were slightly higher than the experimental values, and the deviation from the experimental value increased with the increase of pressure. These phenomena provide reasonable explanations: (1) the model of MIL-101 (Cr) that was used in the calculations contained no crystal defects and no impurities. However, actual MIL-101 (Cr) may have defects, and its pores may contain residual terephthalatic acid, which can result in a relatively low CO\({}_{2}\) adsorption capacity; (2) when the pressure was low, the CO\({}_{2}\) adsorption capacity on MIL-101 (Cr) was also low, CO\({}_{2}\) molecules only occupied a small amount of space, and the residual impurities in the pores had little effect on the adsorption capacity. With the increase of pressure, the adsorption capacity also increased, and CO\({}_{2}\) molecules occupied more and more space in the pores. The existence of impurities hindered the adsorption of CO\({}_{2}\), resulting in the increase of deviation between the experimental and simulated values. In addition, Table 5 shows the standard deviations of adsorption capacity. The value of the standard deviations of adsorption capacity are all less than 0.2, which indicated that the results could be repeated and the MC method could be applied to this system. Therefore, this simulation method, which is based on an MC simulation in a grand canonical ensemble, was utilized for further study, and the simulation results were considered to be accurate and reliable.  
Figure 3: A comparison of the simulated and experimental adsorption isotherms of CO\({}_{2}\) on MIL-101 (Cr) at 298 K.  
Figure 5 shows the fitting correlation curve of the average adsorption energy data points under different pressures using Equation (3). It can be seen that Equation (3) can well describe the relationship between the adsorption energy and pressure. It also can be seen from Table 6 that the determination coefficient (R\({}^{2}\)) for linear regression was greater than 0.98 and the residual sum of squared estimate of errors (SSE) was only 0.12. In conclusion, there was a linear relationship between the adsorption energy and the adsorption pressure, which can be written as Equation (3).
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 5 Results and Analysis
### Prediction Model Validation  
To validate the established model, five sets of data were randomly selected within the parameter ranges. Table 6 shows a comparison of the energy consumption predicted by the regression model and the numerical simulation results. The maximum error was 1.2%, indicating the accuracy of the prediction model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{X_{1}}\) (\(\mathbf{kW}\)) & \(\mathbf{X_{2}}\) (\(\mathbf{{}^{\circ}C}\)) & \(\mathbf{X_{3}}\) (\(\mathbf{rpm}\)) & \(\mathbf{X_{4}}\) (\%) & \(\mathbf{X_{5}}\) (\(\mathbf{m^{3}/s}\)) & **Predicted** (\(\mathbf{kW}\)) & **Simulation** (\(\mathbf{kW}\)) & **Relative Error (\%)** \\ \hline
4740 & 20 & 470 & 20 & 0.12 & 61.75 & 61.02 & 1.2 \\
9480 & 32 & 1500 & 80 & 0.18 & 139.27 & 140.52 & 0.9 \\
7110 & 26 & 985 & 50 & 0.15 & 92.08 & 91.17 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of predicted and simulated calculation results.  
Figure 4: Residual normal probability plot.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factor** & **Result** \\ \hline \(R^{2}\) & 0.9688 \\ \(R^{2}_{\text{adj}}\) & 0.9438 \\ \(R^{2}_{\text{pred}}\) & 0.8752 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model evaluation results.
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 3 Resource Profiling and Performance Modeling
### Resource Profiling and Prediction Models
#### 3.2.3 Adaptive Filter-Based Online Linear Regression (AFOLR)  
A difference equation relates the current and past predicted values to current and past input values [36]. Most commonly expressed difference equation is as follows:  
\[Y(k)=Y(k-1)+u(k) \tag{13}\]  
Here, \(Y(k)\) means the last estimated output value which is calculated using past \(Y(k-1)\) estimated values and the latest actual input value \(u(k)\). Filter technique is widely applied in the field of prediction modeling because it helps to eliminate the noise from the output and smooth the estimated value if the system is extremely dynamic (e.g., available free CPU cores, average response time in a distributed scientific computing environment). Applying simple filter technique to the prediction model of the Equation (13) is as following:  
\[Y(k)=(1-\alpha)*Y(k-1)+\alpha*u(k) \tag{14}\]  
For \(\alpha\) value close to 1 means more heavily relying on the current measurement \(u(k)\), and if the value of \(\alpha\) becomes close to 0, we make decisions more based on previous estimation \(Y(k-1)\) (i.e., \(0\leqslant\alpha\leqslant 1\)).  
Our proposed AFOLR prediction model forecasts depending on the last predicted values \(Y(k-1)\) as well as recent profiled information. AFOLR model employs the Online Linear Regression model (discussed in Section 3.2.1) to estimate the last predicted value \(Y(k-1)\) within the most recent \(w\) intervals, together with the latest profiled data \(u_{scout}(k)\). Therefore, our proposed AFOLR model can be represented using the following equation:  
\[Y(k)=(1-\alpha)*Y_{OLR}^{w}(k-1)+\alpha*u_{scout}(k) \tag{15}\]  
The proposed AFOLR model basically integrates online linear regression (OLR) to make it computationally efficient, interpretable, and scalable as illustrated in the Algorithm 2. It uses the OLR determined in the \(w\)-window size as well as the latest profiled information \(u_{scout}\).  
```
Result: Return AFOLR Input: OLR, \(\alpha\), window length \(w\), \(u_{scout}\) for Each CE in \(L\)do  Calculate: AFOLR using the Equation (15)  end for
```  
**Algorithm 2**Adaptive Filter-based Online Linear Regression (AFOLR).
# Hybrid Power System for the Range Extension of Security Robots: Prototyping Phase
## 4 Vehicle Testing  
### Integration Test Bench  
For unit testing, the electronic load was employed to emulate the loads. As previously assumed, the majority of the loads placed on the security robot were split in two, for surveillance and for patrolling. The load required for patrolling comes from the four electric motors on the security robot, whereas the load required for surveillance comes from the sensors and computers. In the plug-in HPS prototype integrated into the security robot, the load necessary for patrolling was directly provided by a chassis dynamometer through the tires and wheels of the security robot, instead of the electronic load. The security robot was placed on a rolling road, but not on an actual road crowded with objects to be perceived using the sensors and computers. As before, the load required for surveillance was substituted with the electronic load.  
Chassis dynamometers are one of the widely used equipment for vehicle testing purposes. One or more fixed rollers are used to simulate different road loads within a controlled environment. However, to the best of our knowledge, there is no commercially available dynamometer that caters to our security robot. Dynamometers for automobiles are too lengthy to be installed in our security robot, in which the wheelbase is only 0.8 m long. Dynamometers for motorcycles are sufficiently short, but a four-wheel drive is required for our security robot. A dynamometer dedicated to our security robot was therefore developed in-house. Unlike conventional dynamometers, the set of two rollers per wheel is not fixed. The rollers can move up to 0.5 m back and forth, accommodating vehicles with different wheelbases.  
Typical dynamometers are capable of auto-regulating dynamic loads that vary as a function of the vehicle speed, which is usually represented by three relevant parameters. Unfortunately, this capability is still under development, and hence a static load which can be set manually was exerted instead. However, we discovered that the settable range of loads was shifted upwards with respect to what was initially assumed, which means that the security robot was subjected to a much higher load demand while it was spinning its rollers. Despite these limitations in accurate load regulation, the integration tests under conditions similar to real-world environments were still worth performing, especially at the late prototyping phase.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 4 Results
### Experimental Results
#### 4.2.3 Pixel-Level Anomaly Location
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **SPADE** & **RIAD** & **P-SVDD** & **P-SVDD-C** & **PaDiM** & **CutPaste** & **Ours** \\  & [5] & [15] & [31] & [32] & [6] & [10] & **Ours** \\ \hline \multirow{4}{*}{Texture} & Carpet & 97.5 & 96.3 & 92.6 & 92.9 & 98.8 & 98.3 & 98.9 \\  & Grid & 93.7 & 98.8 & 96.2 & 97.2 & 93.6 & 97.5 & 94.0 \\  & Leather & 97.6 & 99.4 & 97.4 & 98.2 & 99.0 & 99.5 & 99.2 \\  & Tile & 87.4 & 89.1 & 91.4 & 91.9 & 91.7 & 90.5 & 94.0 \\  & Wood & 85.5 & 85.8 & 90.8 & 92.1 & 94.0 & 95.5 & 91.6 \\  & Average & 92.3 & 93.9 & 93.7 & 94.5 & 95.3 & 96.3 & 95.5 \\ \hline \multirow{4}{*}{Object} & Bottle & 98.4 & 98.4 & 98.1 & 98.6 & 98.1 & 97.6 & 97.3 \\  & Cable & 97.2 & 84.2 & 96.8 & 97.6 & 94.9 & 90.0 & 96.0 \\  & Capsule & 99.0 & 92.8 & 95.8 & 96.3 & 98.2 & 97.4 & 97.5 \\  & Hazelnut & 99.1 & 96.1 & 97.5 & 98.2 & 97.9 & 97.3 & 96.6 \\  & Metal nut & 98.1 & 92.5 & 98.0 & 98.1 & 96.7 & 93.1 & 94.1 \\  & Pill & 96.5 & 95.7 & 95.1 & 92.4 & 94.6 & 95.7 & 95.7 \\  & Screw & 98.9 & 98.8 & 95.7 & 95.3 & 97.2 & 96.7 & 96.7 \\  & Toothbrush & 97.9 & 98.9 & 98.1 & 96.0 & 98.6 & 98.1 & 97.2 \\  & Transistor & 94.1 & 87.7 & 97.0 & 93.5 & 96.8 & 93.0 & 96.4 \\  & Zipper & 96.5 & 97.8 & 95.1 & 96.0 & 97.6 & 99.3 & 97.9 \\  & Average & 97.6 & 94.3 & 96.7 & 96.2 & 97.1 & 95.8 & 96.5 \\ \hline Average & & 96.0 & 94.2 & 95.7 & 95.6 & 96.5 & 96.0 & 96.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of pixel-level AUC% with different methods on MVTec.