esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Plugging Agent Optimization Results
#### 3.2.3 High-Temperature Plugging Performance  
Using the high-temperature and high-pressure coreflooding apparatus, the plugging effect of the high-temperature plugging agent, HTP, on sandpacks with different permeability was analyzed through displacement experiments. Considering that the formation permeability had been greatly increased because of steam channeling during steam injection, the core of high permeability was made by sandpacking. The sandpack was vacuumized first, the water-phase permeability K\({}_{\text{w1}}\) was measured, and then HTP was injected until it reached the pore volume of the sandpack. Then the inlet and outlet at both ends of the sandpack were closed, and the water phase permeability K\({}_{\text{w2}}\) of the sandpack was measured at the specified temperature 273 \({}^{\circ}\)C for 8 h. The pressure resistance intensity was recorded, and the plugging ratio for water phase and breakthrough pressure gradient was calculated. The results are shown in Table 3.  
Figure 9: Compressive strength of HTP at different time and temperature levels.  
As can be seen from Table 3, HTP has a good plugging effect on the high permeability core. The plugging ratio of the core with high permeability was higher than 99.5% and the pressure gradient was greater than 28 MPa/m after being pressed into the core at 220-300 \({}^{\circ}\)C for 8 h. The steam injection pressure of Fengcheng oilfield in Xinjiang was mostly less than 15.0 MPa. HTD can therefore meet the requirements of high-temperature plugging of heavy oil and inhibiting steam channeling.
## 2 Materials and Methods
### Measurement Setup: Requirements and Procedures
#### 2.2.2 White Standards  
In general, experimental determination of precise, absolute values for diffuse reflectance of a sample is challenging. It inevitably requires the thorough knowledge of the applied spectrometer, in particular of its optical transfer function. The use of reference materials with well documented diffuse reflectance is a common approach to study the spectrometers' pecularitites and for calibration purposes. Such _white standards_ typically possess a characteristic diffuse reflectance over a wide spectral range. The diffuse reflectance \(R_{\infty}=I_{\mathrm{s}}/I_{\mathrm{ws}}\) then is determined from both, the diffuse intensity signal of sample \(I_{\mathrm{s}}\) and of the white standard \(I_{\mathrm{ws}}\).  
Historically, micro- and nano-scaled powders of magnesium oxide (MgO) and barium sulfate (BaSO\({}_{4}\)) are chosen as white standard materials since they feature a pronounced and spectrally homogeneous diffuse reflectance in the visible [1; 3; 15; 18; 61; 62; 63]. Within the past decades, powders of aluminium oxides (Al\({}_{2}\)O\({}_{3}\)), titanium dioxide (TiO\({}_{2}\)) or sintered powder polytetrafluoroethylene (PTFE) [64; 65; 66; 67; 68; 69; 70; 71; 3] became available with high optical quality as well. Table 2 summarizes the most prominent white standards and their individual optical characteristics.  
The choice of white standards therefore is dependent on the material under investigation and must accord with the particular features of the spectrometer. It should be noted that the given features refer to materials of highest chemical purity, i.e., the sample features may vary dependent on the materials' manufacturer. For the investigations described below, we have chosen alpha-aluminum oxide, \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) and prepared a powder pellet according to the procedure described in Section 2.1.
## 6 Conclusions  
Conventional COG and ZMP are variables for judging stability in the walking system. The concept of CP was added to these conventional indices to supplement them with additional information regarding walking symmetry, walking states (balance), and reaction in the particular environment. In general terms, CP was similar to the COG in the \(x\)-axis and \(y\)-axis; furthermore, left and right symmetry was estimated by comparing the \(y_{COG}\) with \(y_{CP}\). Moreover, in the middle of a single stance, the inverted point of the incline of CP was related to the transition period of the leading limb's change from the \(\text{HP}_{\text{L(R)}}\) to the \(\text{TP}_{\text{L(R)}}\). Its period helps to carry out a specific walking analysis. In the numerical comparison using SD and the RMSE, the results showed meaningful relationships between the indices and the movement of limb joints in environment when walking over hurdles of various heights.  
In conclusion, it is possible to use CP to enhance the role of COG and ZMP as integral indices, including the features of the upper and lower body. Although the studying CP is not an obvious step, because CP is estimated based on a geometrical concept, while the COG and the ZMP are calculated based on dynamics, it is nevertheless expected that the indices will compensate each other, since the CP has many similarities with the COG and the ZMP in balanced symmetrical walking. In the future, the determination of the value of CP using stability indices should be verified through lots of experiments in various environments.  
Conceptualization, S.K. and T.M.; methodology, S.K. and T.M.; software, S.K.; validation, S.K.; formal analysis, S.K.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.K. and T.M.; supervision, T.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
The purpose of this study was to investigate the differences between weightlifters and track and field throwers in terms of the rate of force development (RFD), lean mass, muscle architecture and one-repetition maximum (1-RM) muscle strength. Sixteen elite male athletes (eight weightlifters and eight track and field throwers) participated in the study. Measurements were performed one week after the annual national championships and included anthropometry, body composition analysis (dual X-ray absorptiometry), vastus lateralis (VL) muscle architecture (ultrasonography), countermovement jump (CMJ), leg press isometric RFD and 1-RM strength in snatch and back squat. RFD was similar between groups (\(p>0.05\)), although RFD relative to lean mass was higher for weightlifters (\(p<0.05\)). Throwers had a higher trunk lean mass compared to weightlifters (\(p=0.007\)). Weightlifters had a longer VL fascicle length compared to throwers (\(p=0.037\)). Performances in CMJ height, CMJ power relative to body mass and 1-RM strength were higher for the weightlifters (\(p<0.05\)). lean body mass index was correlated with back squat (\(r=0.667\)) and snatch (\(r=0.498\)). VL fascicle length was significantly correlated with snatch (\(r=0.631\)) and back squat (\(r=0.718\)). These results suggest that weightlifters may produce greater power outputs and 1-RM strength than track and field throwers, which may be associated with longer VL fascicle length.  
Keywords:lean body mass; power; fascicle length; 1-RM strength; athletic performance +
Footnote †: journal: Applied Sciences
## 2 Methods
### CqSL Model Overview
#### 2.1.4 Image Reconstruction  
To better capture the anatomical shape and the intensity information in the synthetic image, we propose a two-branched reconstruction architecture featuring two separate decoders: one is conditioned with FiLM [38], and the other with SPADE [39] (Figure 6a) and both are then concatenated to produce a realistic image. The FiLM decoder consists of multiple FiLM layers, a gamma-beta predictor, and convolutional layers with 3 \(\times\) 3 kernel and (8, 8, 8, 8, 1) channels in the stride of 1. Each convolution layer is followed by batch normalization layer along with a Leaky-ReLU layer.  
To better retain the non-spatial information in the MR image, we integrate the shape knowledge into the idea of SPADE [39] and form a shape-aware normalization layer (see Figure 6). SPADE first normalizes the input feature \(F_{in}\) with a scale \(\alpha\) and a shift \(\mu\) learned from sampled \(z\) using an instance-normalization (InstanceNorm) layer, inspired by [38] and then denormalizes it based on a spatial representation \(f_{SK}\) through learnable parameters \(\gamma\) and \(\beta\). \(f_{SK}\) is then interpolated to match the texture dimension of the sampled \(z\) from the sentiency encoder and used as a semantic mask for SPADE:  
\[F_{out}=\frac{F_{in}-\mu}{\alpha}\times\gamma(f_{SK})+\beta(f_{SK}) \tag{5}\]  
where \(F_{in}\) and \(F_{out}\) denote the output feature maps. \(\gamma\) and \(\beta\) are learned from \(f_{SK}\) by three Conv layers. Thus, the learned shape information precludes washing away the anatomical information, which encourages the image synthesis to be more accurate. The first convolution layer inside the SPADE block (Figure 6) encodes the interpolated \(f_{SK}\), and the other two convolution layers learn the spatial tensors \(\gamma\) and \(\beta\). Simultaneously,  
Figure 6: Detailed architecture of SPADE block: (**a**) shape-aware normalization block where the spatial tensors, \(\gamma\) and \(\beta\) are multiplied and added to the input features; (**b**) decoder block \(f_{SES}\) with shape-aware normalization.  
an instance normalization layer is applied to the intermediate feature map, which is then modulated by the scale and shift parameters \(\gamma\) and \(\beta\) learned from sampled \(z\) to produce the output. Finally, the output of the two decoders is re-entangled in order to reconstruct an image.
# The Effect of Varying Out Beverage Ratios on the Characteristics of Fermented Dairy-Oat Beverages
## 3 Results
### Changes in the Color Spectrum of Beverages Subjected to Fermentation  
Color is a key sensory parameter that influences consumer acceptance of fermented milk beverages. The L* (lightness), a* (red/green hue), and b* (yellow/blue hue) values allow for the assessment of the impact of oat beverage addition on the color of the final product. The lightest beverage is 0% oats (L = 89.4), which is consistent with the natural whiteness of fermented milk products. Lightness (L*) decreases with increasing oat beverage content, indicating a gradual darkening of the beverage. Statistically significant differences (superscripts a, b in Table 3) suggest that even at 50% oat addition, there are significant changes in the lightness of the product. The a* value indicates the balance between red (+a) and green (\(-\)a) color. Milk samples (0%) have the lowest a value (4.1), meaning a more neutral hue*. The values increase with increasing oat beverage proportion, indicating a more reddish hue as the amount of oats increases. Statistically significant differences appear from 66.6% oat addition (Table 3), meaning that at lower proportions there are no significant visual changes in terms of red/green hue. The b* value indicates the balance between yellow (+b) and blue (\(-\)b) color. Milk samples have the lowest b value = 4.0*, meaning the most neutral color. Increasing the proportion of oat beverage significantly increases the b* value, resulting in a more yellowish hue of the beverage. Statistically significant differences (Table 3) appear from 33.3% oat addition, meaning that the change in hue is more noticeable than in the case of a*.  
Figure 2: The pH value (means and SD) during the fermentation.
## 2 Materials and Methods
### Artificial Neural Network (ANN) Model
The neurons in the output layer use a linear activation function suitable for producing continuous output values. Each output neuron calculates a weighted sum of the outputs from the hidden layer neurons, including a bias term. The linear function is then applied to this weighted sum to produce the final output, which in this case is a prediction of delamination. 4. Loss calculation. The network's predicted output is compared with the actual measurements of delamination. The difference between these values is quantified using the mean squared error (MSE) loss function, which measures how well the network's predictions match the real data. This loss value indicates the network's performance and guides further adjustments. 5. Backward propagation. To improve the network's performance, backpropagation is used; this calculates how much each weight and bias contributes to the total error. The error at the output layer is determined by comparing the predicted and actual values. It is then used to calculate the error for each neuron in the hidden layer, considering how the output of each hidden neuron influences the final prediction. 6. Parameter update. Using the errors calculated during backpropagation, the network updates its weights and biases to reduce the loss. This is accomplished using a technique called gradient descent, which adjusts each parameter slightly in the direction that will decrease the error. The learning rate determines the size of these adjustments. In this study, the Levenberg-Marquardt algorithm is used for training, which is known for its efficiency in handling non-linear least squares problems. 7. Iteration. The entire process of forward propagation, loss calculation, backward propagation, and parameter update is repeated for many iterations, known as epochs. This iterative process continues until the network's performance stabilizes and the loss converges to a minimum value, indicating that the network has been effectively trained. The MATLAB program developed for ANN modeling of AWJ piercing is presented in Appendix A.
## 4 Conclusions  
The influence of component distribution, including the distribution mode and the minor component position pattern, on the radiometric properties of particle aggregates has been studied in this work. The main conclusions are as follows.  
With the considered radii and wavelengths, the particle aggregates of Mode A have greater extinction efficiency, lower absorption efficiency, greater scattering efficiency, and smaller asymmetric parameter values than the aggregates of Mode B when \(m_{2}=0.15+3.15\)i and \(m_{2}=1.46+2.75\)i, while both modes have similar values in all radiometric parameters when \(m_{2}=3.102+0.0925\)i. This indicates that, when the minor component has an imaginary part of the refractive index larger than the major component, Mode A has greater extinction efficiency than Mode B, mainly due to the greater scattering of radiation caused by concentrating the minor component in a few particles. These particles contribute significantly to scattering of radiation. Since Mode B has a relatively even distribution of the minor component compared to Mode A, the difference in radiometric properties between these two modes suggests that evenly spreading the minor component, which is more absorptive than the major component, in the particle aggregate helps to increase the absorption efficiency, decrease the scattering efficiency, and ultimately decrease the extinction efficiency.  
In Mode A, the positions of the minor component particles can also influence the radiometric properties of the particle aggregate. When the minor component particles are relatively evenly positioned in the aggregate or has an even position pattern (like a random position pattern), the particle aggregate has relatively high absorption efficiency, low scattering efficiency, and low extinction efficiency. This phenomenon is similar to that observed by the comparison of Modes A and B: as the minor component has a relatively large imaginary part of the refractive index and is more absorptive, evenly spreading the minor component in the particle aggregate helps to raise absorption and decrease extinction and the scattering of radiation in the aggregate.  
**Author Contributions:** Conceptualization, Y.C.; Validation, Y.C.; Writing--original draft preparation, Y.C.; Writing--review and editing, Z.Y., Y.D.; Funding acquisition, Z.Y., Y.D.  
**Funding:** This research was funded by National Basic Research Program of China (no. 2015 CB251502) and the National Natural Science Foundation of China (nos. 51621062).  
**Acknowledgments:** [PERSON] and [PERSON] are gratefully acknowledged for making the MSTM and Mie code publicly available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension
The proposed scheme first transforms the received PU's signal into a frequency signal by discrete Fourier transform (DFT) and then calculates the SFD of the frequency signal. The judgment is conducted by comparing the above SFD value with the threshold, where the noise SFD is selected as the decision threshold. Since the noise SFD changes randomly in a certain range, the single threshold method cannot meet the requirement of detection precision when the SNR is low or the noise is uncertain. To address this, we resort to the double-threshold method in local sensing. Furthermore, multi-user CSS method is adopted to promote the reliability of sensing results and the detection accuracy. The simulation results demonstrate that the proposed method achieves better detection performance at low SNR. Simulation results also show that the scheme presented in this paper has the advantage of insensitivity to noise uncertainty and is less affected by signal modulation parameters. The rest of this paper is organized as follows. Section 2 introduces the double-threshold cooperative spectrum sensing algorithm based on Sevcik fractal dimension. Simulation results are given in Section 3. The paper concludes in Section 4.
## 3 Materials and Methods
### Fluid Contacts Determination  
Identifying fluids within rocks and determining possible contacts yield crucial insights for petrophysical analysis. The point at which oil or gas fluids are found in the rock pores is called the water contact. The water gradients and intersection with hydrocarbons are utilized to ascertain the free water level and identify the fluid contact [18; 21]. In conditions of very low permeability, mud filtrate invasion can transform formation layers into \"supercharges,\" creating pressure points that veer off and diverge from the normal pressure gradient line. Supercharged points are often excluded from the pressure versus depth plot due to their lack of reliability [18; 21].  
In this study, fluid contacts were first determined using a qualitative method by observing the behavior of resistivity and density/neutron porosity logs within the studied reservoirs. Density logs are more reliable for identifying fluid contacts because they are sensitive to the fluids' densities in the formation and are less impacted by the formation matrix [58; 59]. Neutron logs are effective for detecting changes in fluid saturation because they are sensitive to the hydrogen index, which is associated with the presence of fluids in the formation, and they are highly sensitive to porosity [60; 61]. Therefore, the density and neutron log combination provides a potent tool for fluid contact identification, providing higher accuracy, reduced uncertainty, and improved interpretation of formation properties.  
Secondly, the quantitative method utilized the Repeat Formation Test (RFT) data by constructing pressure versus depth cross-plots. RFT data were used to construct the pressure profiles. Analyzing the gradient provides more information about the type of fluids and the contact between them. Hence, this tool is useful in identifying fluid contacts. The pressure versus depth plots were utilized to determine the fluid densities, fluid contacts, and gradients with respect to the anticipated pore pressures at different depth zones of the selected reservoirs.  
The following relationship was used to calculate the fluid densities based on the pressure gradient [21]:  
\[\text{Fluid density}\;(\text{g/cc})\;=\;\text{Pressure gradient}\;(\text{psi/ft})\; \times\;2.3072 \tag{16}\]  
Nevertheless, the psi/m units for pore pressure gradients were expressed as shown below using the comparable connection by modifying Equation (16):  
\[1\;\text{psi/m}\;=\;0.7034\;\text{g/cc} \tag{17}\]
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 6 Sensitivity and Results Analysis
### Influence of the Major Maintenance Cost  
In discussing the sensitivity of the major maintenance cost, we examine four scenarios for the cost values \(c_{m}=20,\ 30,\ 40\) and \(60\). The obtained results are presented in Figure 9. When we set the major maintenance cost to a low value such as \(c_{m}=20\) and \(30\), the maintenance policy indicates that the major maintenance zone covers a greater surface on the plane \((x,a)\). Nevertheless, if we increase the major maintenance cost to \(c_{m}=40\) and \(60\), minimal repairs are more recommended, reducing considerably the conduction of major maintenance. The observed pattern in the maintenance policy implies the fact that as beneficial and productive as a major maintenance can be, at increasing the \(c_{m}\) cost, the machine must reach higher levels of deterioration to justify the expensive cost of such type of maintenance. Regarding the production policy, we observe that the major maintenance cost has no influence on this policy. Furthermore, that the minimal repair cost \(c_{r}\) has the inverse effects of the major maintenance cost.
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 2 Materials and Methods  
In our prior study [2], a wireless sensor system (WSS) was developed to collect real-time, three-axis acceleration data of daily activities and fall events. The WSS consists of a three-axis digital accelerometer sensor (ADXL345), a 32-bit ARM M3 microprocessor (MCU LPC17680 M3), and Wi-Fi module (RN131). The WSS is used to sense body biomechanics, control the flow of data, and transmit/receive data to a remote server for analysis. The WSS is placed at the center of chest during data collection for optimal activity detection [16]. As shown in Figure 1, the orientation of data collected include superior-inferior (\(x\)-axis), lateral (\(y\)-axis), and anterior-posterior (\(z\)-axis) directions. The data was collected with a sampling rate of 100 Hz.  
Once received, daily collected activity data is analyzed using Matlab (Mathworks, Inc., Natick, MA, USA). The program receives and displays real-time data from the WSS. It continuously plots the acceleration and angular velocity values of each activity.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 3 Comprehensive Cluster Division Index Considering Frequency Regulation Response Speed
### Functional Index Considering Frequency Regulation Speed  
#### 3.2.1 Frequency Regulation Flexibility Response Speed Index  
Based on the analysis in Section 1, this paper adopts the cluster response speed index as one of the bases for cluster division, and the value of the index reflects the speed of response of the cluster. The index is used for functional division and is normalized to propose the cluster frequency regulation flexibility response speed index with the expression shown below:  
\[\varphi_{t}=\frac{1}{N_{c}}\sum\limits_{c\in N_{c}}\left(\sum\limits_{t\in T} \left(\frac{k_{c}(t)}{\max\{k_{c}(t)\}}\right)\right)+\sum\limits_{t\in T} \left(\frac{\sum\limits_{i\in c}E_{left,i}(t)}{\max\{\sum\limits_{i\in c}E_{left, i}(t)\}}\right)\right), \tag{10}\]  
where \(\varphi_{t}\) is the cluster frequency regulation flexibility response speed index, \(N_{c}\) is the total number of clusters, and \(T\) is the study period. \(\sum\limits_{i\in c}E_{left,i}(t)\) is the sum of the remaining capacity of all energy storage devices in the \(c\)th cluster.  
The frequency regulation response speed index assesses the net power magnitude of a cluster on a short time scale, and the exact calculation procedure can be found in Section 2. When a cluster has more surplus power only by consuming more generated power from renewable energy units, it has more potential and speed for frequency regulation.