esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characteristic Analysis of Double Raschel GCCM by Blowing Agent Content  
The blowing agent was considered as a method to relax the cross-section reduction phenomenon caused by concrete shrinkage during the hydration of double raschel GCCMs. Curing was conducted over a 10-day period to assess the volume change with varying blowing agent content ranging from 1 to 11 wt%. While curing the concrete, a problem occurred in which the central portion became elevated due to the effects of the blowing agent treatment. The volume change was quantified by measuring the amount of water needed to match the original volume of the specimens. Although the volume of the concrete increased as the blowing agent content rose, surface cracks were observed when the content reached 7 wt% or higher due to the formation of bubbles. The results of the volume change corresponding to different blowing agent contents are shown in Table 2, and the concrete's surface appearances are shown in Figure 4. Consequently, the optimal blowing agent content chosen was 5 wt%, providing the highest volume change without causing concrete surface cracking. This content was incorporated into the concrete mixture when manufacturing the double raschel GCCM.
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 5 Simulation Results and Analysis
### Effectiveness of Array Antenna Rotation Method
For a fixed jammer number, the optimal anti-jamming ability is the greatest anti-jamming ability among all antenna attitudes in azimuth; it corresponds to the optimal rotation angle. As the jammer number increases, the optimal anti-jamming ability decreases. The decreasing rate gets lower when the jammer number is 12\(\sim\)30, so the lines in the figure are drawn in gray to simplify the figure. It can be seen that the optimal rotation angle changes slightly as the number of jammers increase. The optimal anti-jamming improvement in Figure 10c,d indicates the difference between the greatest and the smallest anti-jamming ability for a fixed jammer number. It is the maximum anti-jamming improvement marked in Figure 10a. As the initial antenna attitude in practical applications is uncertain, the anti-jamming improvement floats in the interval [0, optimal anti-jamming improvement]. Figure 9: Navigation signal \(CNR\) of anti-jamming output. (**a**) Applied in PI algorithm. (**b**) Applied in MVDR algorithm. The performance of the proposed method can be concluded as follows. Under the condition of 4-30 jammers, the optimal anti-jamming ability improvement of the PI algorithm is 21\(\sim\)26 dB and the optimal anti-jamming ability is 44\(\sim\)61 dB (in the \(JSR\) of single interference, similarly hereinafter). The optimal anti-jamming ability improvement of the MVDR algorithm is 5\(\sim\)10 dB and the optimal anti-jamming ability is 47\(\sim\)61 dB after improvement.
# Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports
## 4 The Influence of Support Location on the System Inherent Characteristics
### Calculation of the System Natural Frequencies at Different Spatial Positions of Each Support  
The influence of each support location variation on the system natural frequencies is studied in this section. Considering the actual installation situation of the simplified rigid structure, the variation of the system natural frequencies is analyzed when the corresponding tangential location \(d_{z}\) along the casing changes. The stiffness matrix \(\mathbf{K}\) (Equation (10)) indicates that the variation of the normal stiffness component \(k_{y}\) mainly causes the change of the second systems order natural frequency, and only the variation curve of the second systems order natural frequency with different tangential location \(d_{z}\) of each support is given, as shown in Figure 7. It is observed that the second system order natural frequency is the most sensitive to the top-left support location, followed by the bottom-left support location. It can be known from the dynamic equation that the system natural frequencies are the results of the joint action of the support locations and the support stiffness. Comparing Figure 6a with Figure 7, variation trends of the stiffness and the second system frequency caused by top-left support locations is opposite. The increase of top-left support stiffness is to increase the system's natural frequency; however, the system's natural frequency decreases, which is due to the action of the top-left support location, further indicating that the system's natural frequency is most sensitive to the variation of the top-left support location. The variation of the bottom-left support location  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Location** \\ **(mm)** \\ \end{tabular} } & \multicolumn{6}{c}{**Mount \#**} \\ \cline{2-13}  & \multicolumn{2}{c}{**A**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**D**} \\ \cline{2-13}  & \(\mathbf{d_{\mathrm{Ax}}}\) & \(\mathbf{d_{\mathrm{Ay}}}\) & \(\mathbf{d_{\mathrm{Az}}}\) & \(\mathbf{d_{\mathrm{Bx}}}\) & \(\mathbf{d_{\mathrm{By}}}\) & \(\mathbf{d_{\mathrm{Bz}}}\) & \(\mathbf{d_{\mathrm{Cz}}}\) & \(\mathbf{d_{\mathrm{Cy}}}\) & \(\mathbf{d_{\mathrm{Cz}}}\) & \(\mathbf{d_{\mathrm{Dz}}}\)leads to the increase of the system's natural frequency, which is mainly due to the increase of the stiffness components caused by the change of the bottom-left support location.  
Figure 6: The stiffness components variation of each support with different locations dz along the tangential direction: (**a**) top-left support A; (**b**) bottom-left support B; (**c**) top-right support C; (**d**) bottom-right support D.  
Figure 7: Variation of the second system order natural frequency with different tangential locations \(d_{z}\) of each support.
## 3 Retention Modeling
### Model Development
#### 3.1.2 Mixed-Retention Model  
Next, we develop and study another category of retention models which are based on the following assumption: the solute retention is due to independent adsorption on two types of active sites of the stationary phase, free adsorption sites as well as the active sites covered by adsorbed modifier (Figure 1). We call such models mixed retention models (MRM). Experimentally observed retention is a joint effect of the retentions due to both sites.  
For plain silica, the adsorption sites are silanol groups and methanol molecules adsorbed on the silanols. Since the fraction of the methanol-covered sites is identical to  
Figure 1: A schematic diagram of the mixed retention mechanism in a silica-packed column. The interaction in the green dashed line contributes to \(k_{c}\) in Equation (11) and the interaction in red dashed line contributes to \(k_{0}\).  
the modifier surface coverage, \(\theta\), the retention factor can be expressed by a weighted sum of the retention factor at zero modifier concentration, \(k_{0}\), and the retention factor for completely covered surface, \(k_{c}\) (subscript \(c\) stands for covered):  
\[k=k_{0}(1-\theta)+k_{c}\theta \tag{11}\]  
If we rearrange Equation (11), a linear relation between \(k\) and \(\theta\) is seen:  
\[k=(k_{c}-k_{0})\theta+k_{0} \tag{12}\]  
However, according to the collected experimental data of \(k\) and \(\theta\) in this work, the linear relationship does not hold for the whole range of modifier concentrations. Details will be given later in Section 4.2. Testing various modifications of Equation (12) that better fit the experimental data, we introduced an empirical parameter \(M\) to outweigh the influence of covered active sites; by introducing the term \(Mc_{mod}\) to the denominator, the influence of modifier on retention factor reduces at higher modifier concentration. The extended MRM is given by the following:  
\[k=\frac{k_{0}(1-\theta)+k_{c}(1+M)\theta}{1+Mc_{mod}} \tag{13}\]  
The terms \((1+M)\) in the numerator and \(M\) in the denominator are introduced to make the original model by Equation (11) a special case of the extended model if \(M=0\).  
The MRM given by Equation (13) can also be written in a concentration-explicit form by substitution of a certain adsorption isotherm \(\theta=f(c_{mod})\). For example, for the Langmuir isotherm, we obtain:  
\[k=\frac{k_{0}+K_{eq,L}k_{c}(1+M)c_{mod}}{\big{(}1+K_{eq,L}\epsilon_{mod}\big{)} (1+Mc_{mod})} \tag{14}\]
## 4 Statistical Analysis for Gear System with Uncertainties
### LTCA for Gear System with Uncertainties
Thus, we consider the uncertainties in the gear profiles and elastic modulus when calculating the bending deflection \(\delta^{e}_{p}\), fillet-foundation deflection \(\delta^{e}_{f}\), and contact deflection \(\delta^{e}_{h}\), all of which together determine the elastic deflection \(\delta^{E}\). The gear profile with uncertainties consists of \((n+1)\) points, and the \(i\)th point is expressed as \(r^{i}_{g}\), as shown in Figure 8. A gear tooth is regarded as a nonuniform cantilever beam to calculate the bending deflection. The position of \(r^{i}_{g}\) is used to measure the area of the tooth cross section \(S_{i}\) and the area moment of inertia from the nonuniform cantilever beam. The gear profile changed by the uncertainties affects the contact zone, which can be obtained using Equation (23) of the gear system. The contact zone changes the area where the force is applied and thus determines the bending deflection. The three deflections can be written as  
\[\delta^{e}_{b}= F\cos^{2}\alpha_{m}\sum_{i=1}^{n}e_{i}\left[\frac{d_{i}-e_{i}d_{i} +\frac{1}{3}e_{i}^{2}}{E^{\prime}\overline{T}_{i}^{e}}+\frac{1}{s_{b}G \overline{A}_{i}^{e}}+\frac{\tan^{2}\alpha_{m}}{\overline{A}_{i}^{e}E^{\prime }}\right], \tag{24}\] \[\delta^{e}_{f}= \frac{F\cos^{2}\alpha_{m}}{WE^{e}}\Bigg{[}L^{*}(\frac{u_{f}}{S_{f }})^{2}+M^{*}\frac{u_{f}}{S_{f}}+P^{*}(1+Q^{*}\tan^{2}\alpha_{m})\Bigg{]},\] \[\delta^{e}_{h}= \frac{2F(E_{1}^{e}+E_{2}^{e})(1-\
u^{2})}{\pi WE_{1}^{e}E_{2}^{e}},\] \[E^{\prime}= \frac{E^{e}(1-\
u)}{(1+\
u)(1-2\
u)},\] \[1/\overline{T}_{i}^{e}= (1/I_{i}+1/I_{i+1})/2,\] \[1/\overline{A}_{i}^{e}= (1/A_{i}+1/A_{i+1})/2,\]  
where \(E^{e}\), \(\overline{A}_{i}^{e}\), and \(\overline{T}_{i}^{e}\) are the elastic modulus, area of the tooth cross section \(S_{i}\), and area moment of inertia, respectively, each with uncertainties. The detailed notations are described in Appendix A. The mesh stiffness \(K^{e}_{meshing}\) of the spur gear pair with uncertainties is expressed as  
\[K^{e}_{meshing}= 1/\left(\frac{1}{k^{e}_{b1}}+\frac{1}{k^{e}_{f1}}+\frac{1}{k^{e }_{b2}}+\frac{1}{k^{e}_{f2}}+\frac{1}{k^{e}_{h}}\right), \tag{25}\] \[k^{e}_{b}= \frac{F}{\delta^{e}_{b}},\quad k^{e}_{f}=\frac{F}{\delta^{e}_{f}},\quad k^{e}_{h}=\frac{F}{\delta^{e}_{h}},\]  
where \(k^{e}_{b1}\) and \(k^{e}_{b2}\) are the bending stiffness of the driving and driven gears, respectively, with uncertainties. \(k^{e}_{f1}\) and \(k^{e}_{f2}\) are the fillet-foundation stiffnesses of the driving and driven gears, respectively, with uncertainties.
## 3 Results
### Influence of Empirical Parameters on SIF Retrievals
#### 3.1.2 Influence of Polynomial Order and the Number of Feature Vectors on SIF Retrievals
In addition to the influence of the fitting windows, the scatter points in a certain fitting window also represent the results corresponding to data with different SNR. In some fitting windows, such as 720-758 nm, there was more than one optimal value of \(\mathrm{n_{SV}}\) and \(\mathrm{n_{p}}\) for data with different SNR, which indicates that the SNR also affects the optimization of the empirical parameters. However, in our selected fitting windows (682-697 nm for red SIF retrieval and 735-758 nm for far-red SIF retrieval), there was only one combination of optimal \(\mathrm{n_{p}}\) and \(\mathrm{n_{SV}}\) which was not affected by the SNR.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Deposition Techniques  
Deposition methods were the first methods applied to fabricate micro-supercapacitors. These methods involve chemical vapour deposition, electrolytic, and electrophoretic deposition.
## 4 Limitations and Practical Applications
### Practical Applications  
Despite the limitations above, this study offers valuable insights with significant practical applications. It supports the shift towards more sustainable agricultural practices by demonstrating the effectiveness of FW-derived fertilizers, encouraging recycled organic waste, reducing reliance on chemical fertilizers, and minimizing environmental impact. The findings can help inform policymakers in developing guidelines and regulations that promote organic waste recycling into valuable agricultural inputs, encouraging circular economy practices in the farming sector [74].  
The predicted outcomes for PY and IENU offer practical decision-support tools for farmers and gardeners. By inputting site-specific parameters (e.g., fertilizer dose, plant type, growth period), users can obtain data-driven recommendations to optimize fertilizer strategies--maximizing yield and nitrogen use efficiency while minimizing waste. These predictions support precision agriculture by guiding application rates and timing, tailored to crop and soil conditions. Furthermore, accurate yield estimates enhance resource planning (e.g., labor and equipment) and help manage risks related to yield variability and market fluctuations, ultimately improving operational efficiency and sustainability. The application of ML models in predicting the outcomes of different fertilizer treatments can aid farmers and agricultural practitioners in optimizing the use of resources. This optimization leads to better crop yields and more efficient use of nitrogen, enhancing food security and sustainability.  
To improve accessibility, these models can be integrated into mobile or web-based applications that assist farmers in real time, providing tailored fertilizer plans based on local conditions. ML has recently been gradually used to develop decision support tools for modern agricultural systems, covering nutrient management to improve yields while reducing expenses and environmental impact [66]. It can provide cost-effective and thorough nutrient assessment and decision-making solutions [75; 76].  
Additionally, integrating ML into agricultural practices offers an excellent opportunity for educational programs to teach new technologies in agronomy, fostering a new generation of tech-savvy farmers and researchers. Lastly, there is potential for commercializingthe FW treatment processes and the related ML prediction models developed in this study. These can be offered to agricultural businesses as part of a service or technology package, enhancing their productivity and environmental sustainability.
## 3 Results
### Non-Contact Boxing Training Outcomes  
#### 3.2.1 Heart Rate Responses  
Table 3 summarizes HR responses during boxing sessions one and two. The HRavg, HRmax, and %MA-PHR during each round were significantly lower for PDCI participants than PD non-CI participants and controls during all rounds in both sessions (\(p\leq 0.001\)), but not between PD non-CI participants and controls (\(p\geq 0.05\)). The %HR-CPET was similar between groups across all rounds in both sessions (\(p=0.16\)). Between sessions one and two, the HRavg (\(p=0.51\)), HRmax (\(p=0.72\)), %HR-CPET (\(p=0.76\)), and percentage MA-PHR (\(p=0.72\)) were not significantly different amongst all participants.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **PDCI** & **PD Non-CI** & **Control** \\  & **(_n = 11_)** & **(_n = 13_)** & **(_n = 14_)** \\ \hline HRmax (bpm) & 124 \(\pm\) 3.42 [117, 131] *\({}^{\dagger}\) & 156 \(\pm\) 3.01 [150, 163] & 160 \(\pm\) 3.16 [153, 166] \\
85\% HRmax & 105 \(\pm\) 2.91 [99, 111] *\({}^{\dagger}\) & 133 \(\pm\) 2.56 [128, 138] & 136 \(\pm\) 2.68 [130, 141] \\ HR at VT1 (bpm) & 91 \(\pm\) 3.80 [83, 99] *\({}^{\dagger}\) & 113 \(\pm\) 3.34 [106, 120] & 108 \(\pm\) 3.50 [100, 115] \\ HR at VT2 (bpm) & 114 \(\pm\) 4.07 [106, 123] *\({}^{\dagger}\) & 144 \(\pm\) 3.58 [136, 151] & 143 \(\pm\) 3.76 [135, 151] \\ VO\({}_{2}\)peak (mL/kg/min) & 24.4 \(\pm\) 1.83 [21, 28] & 28.1 \(\pm\) 1.61 [25, 31] & 29 \(\pm\) 1.69 [26, 32] \\ VO\({}_{2}\) at VT1 (mL/kg/min) & 14.6 \(\pm\) 0.86 [13, 16] & 15.7 \(\pm\) 0.75 [14, 17] & 15.7 \(\pm\) 0.80 [14, 17] \\ VO\({}_{2}\) at VT2 (mL/kg/min) & 22.2 \(\pm\) 1.63 [19, 25] & 25 \(\pm\) 1.43 [22, 28] & 25.2 \(\pm\) 1.50 [22, 28] \\ Chronotropic index & 0.65 \(\pm\) 0.03 [0.58, 0.72] *\({}^{\dagger}\) & 0.99 \(\pm\) 0.03 [0.93, 1.06] & 1.02 \(\pm\) 0.03 [0.95, 1.08] \\ \hline \end{tabular} All values are in mean \(\pm\) SE [95% confidence interval]. HRmax: maximum heart rate obtained from the cardiopulmonary-momary exercise test: HR: heart rate; VT1 and VT2: first and second ventilatory thresholds; VO\({}_{2}\)peak: peak oxygen consumption. *\(p\leq 0.05\) vs. control; \({}^{\dagger}\)\(p\leq 0.05\) vs. PD non-CI.  
\end{table}
Table 2: CPET maximal and submaximal physiological markers data.  
\begin{table}
\begin{tabular}{c c c} \hline  & **PDCI** & **PD Non-CI** & **Control** \\  & **(_n = 11_)** & **(_n = 14_)** & **(_n = 14_)** \\ \hline _Average HR (bpm)_ & & & \\  Round 1 S1 & 101 \(\pm\) 5.22 [90, 112] *\({}^{\dagger}\) & 134 \(\pm\) 4.22 [126, 143] & 1  
Figure 2 illustrates HR data from a single PDCI participant who completed four rounds of session one. Using the MA-PHR metric, he did not attain the high-intensity threshold (\(\geq\)85% HRmax) for any of the boxing rounds. However, when the HRmax derived from the CPET was used, the high-intensity threshold was achieved in all four rounds.
## 3 Specim Preparation and Testing Procedures
### Tensile Strength Tests  
Concrete types SCCP6, VCCP6 and VCLP6 were used for tensile strength tests. The halves obtained in the bending tests were adapted to the tensile tests by carefully eliminating concrete portions close to the bending fracture surface and the opposite side. This way, prismatic specimens of \(250\times 150\times 150\) mm\({}^{3}\) and \(150\times 100\times 100\) mm\({}^{3}\) dimensions were obtained from the large and small samples respectively. The sample sides in contact with the testing machine were grinded to create flat and parallel surfaces. With the help of a water-refrigerated diamond disc, a perimetral notch was cut at mid length as shown in Figure 4a being 8 mm and 2 mm deep in large and small specimens, respectively. Consequently, the ligament section was \(60\times 60\) mm\({}^{2}\) for all specimens allowing a direct comparison of the experimental results.  
Then, steel caps, as shown in Figure 4a, were glued to each end of specimens. This was achieved by means of a high-strength two-component epoxy resin with a curing time of 24 h at room temperature. Then the specimens were painted in black with several white spots. The inside of the notch in the rear face of the specimens was painted with a monochrome pattern of random distributed points [29; 30]. These two types of painting generated a speckle pattern that allowed the further study of the tests by video-extensometry.  
Specimens were attached to the lower part of the testing machine by means of a Dywidag rod. The connection to the top frame of the machine included a ball joint to assure the correct alignment of the tensile forces. The setup can be seen in Figure 5a.  
The tests were performed in a servo-controlled machine (Instron 8803, Instron, Norwood, MA, USA) with 500 kN loading capacity. Two extensometers were placed in the notch tips to measure the opening values. The test was recorded with two high definition cameras of 5 MPx. One of them recorded the front side of the specimen in order to detect potential rotations or undesired displacements between cap and specimen. The other one was used to visualize the rear face notch and obtain the nucleation and growth of the cracks. The image record rate was set to one frame per second to enable the synchronization with the testing machine.  
Testing was run under displacement control at the speed rates listed in Table 3. These speeds were set so that in every case the first crack of the ligament section would occur in the first test step. The applied load, actuator displacement and change in the distance between the notch tips were recorded. Also, all relevant images of the test development were obtained.  
Figure 4: (**a**) Notched tensile test specimen and cap. (**b**) External carbon fiber fabric reinforcement glued to the direct shear test specimen face.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 3 Method
### Network Model  
According to [24; 25], as the motion color image is small in size and simple in structure, a shallow CNN framework is enough to extract the deep features of the generated motion images for HAR. Moreover, according to the experimental results, the CNN models with complex structures did not perform ideally, because the size of the motion image proposed in our method is much smaller than the image size predesigned by the classical CNN model. For example, VGG19 specifies that the size of the input is \(224*224\). After the convolution operation of multiple convolutional layers, the feature maps fed into the fully connected layers are quite small. Feature maps that are too small will result in overfitting and reduce the generalization ability of the network. Simply adjusting the image size to meet the input requirement of CNN models will damage the continuity of action; for example, walks may be incorrectly classified as runs. In addition, the authors [26] showed that CNN models could improve performance by balancing network depth, width, and image resolution. However, if magnification increases, the improvement decreases.  
Based on the above analyses, a shallow CNN model is designed to extract the deep features of the generated motion images. Figure 3 shows the detail structures. Our proposed model consists of two convolutional layers with a kernel size of \(3*3\), two max pooling layers with a kernel size of \(2*2\) and three fully connected layers. In particular, rectified linear activation (ReLu) is employed in our network. Compared with saturation nonlinear functions, such as sigmoid, ReLu has a faster convergence rate, which can overcome the vanishing gradient problem and decrease the computing time [27]. In addition, two batch normalization layers have been added between the convolutional layer and max pooling layer, because it can normalize the output of each layer to have a mean value of 0 and variance of 1, which speeds up the training and convergence of the network. Furthermore, the batch normalization layer could replace the Dropout and L2 regularization [28]. For it has a regularization function to prevent overfitting and maintain the generalization ability of the network.  
Figure 3: The CNN model is introduced for human action recognition in our proposed method. Conv represents convolutional layer, BN means the batch normalization layer, and N is the number of action categories.  
Compared with classical CNN models, our model has two advantages. First, it can be easily trained from scratch without any pretraining. Second, it can achieve competitive experimental results with less time complexity and space complexity.