esa-sceva/satcom-chunk-collection huggingface dataset:
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Link Budget Analysis  
The quality of the proposed communication link is estimated with the formulation of conservative parameters, regarding all detrimental influences.  
The mission requires the communication system to perform at an elevation angle of 5\({}^{\circ}\) with an orbit altitude of 600 km. The system uses UHF frequencies of the amateur-radio band with a data rate of 9600 bps. The quality of the signal is parameterized with a bit error rate of less than 10\({}^{-5}\).  
The ground station consists of a high gain antenna and a 120 W transmitter. Similar to most CubeSats, the satellite transmits with 1 W of power. The antenna of the satellite is a ground-plane antenna consisting of four monopoles, resulting in an omnidirectional radiation pattern with right-handed circular polarization.  
A strong signal can be achieved in the uplink path due to high transmitting power of the ground station. The calculated link margin is 26 dB.  
The downlink path is a lot more fragile, considering a noisy environment at the ground station and far less transmitting power at the satellite. Without polarization loss and the proposed data rate, a link margin of 5 dB is expected. The resulting margin in operation is expected to be higher, as the noise level of the ground station and the discrimination of the signal on the satellite need to be determined.
## 4 Results of Experiments and Discussion
### Experimental Setup  
The control system for the automatic adjustment of preload force is shown in Figure 7. To verify the effectiveness of the proposed control model, the experiments are conducted using three commercial PEAs (Physik Instrumente, Lederhose, Germany, P-888.91). In addition, the range of the input voltage is set between 0 V and 100 V. The press force sensors (Kistler, Winterthur, Switzerland, Type 9313 AA1) are respectively connected to the PEAs. Two I/O boards (Advantech, Taiwan, China, PCI-1716, PCI-1723) are used to transmit the data. The control model and data saving are programmed in Labview. The piezo driver is a low-cost amplifier for low-voltage PEAs. In addition, the system schematic connection is shown in Figure 8.  
Figure 6: The inverted P-I model.  
Figure 7: The experimental test rig: (**a**) overall configuration of the rig; (**b**) stability control system.
## 4 Experiments and Results
### Discussion  
For fair comparison, all works are pre-trained on the EuroSAT dataset. And the hyperparameter settings are as shown in Table 9.This article shows the performance comparison results of each model on the test set in Table 10. Our proposed WaveNet achieves the highest accuracy, which further illustrates the effectiveness of the WaveNet model. Other models have a high number of layers and a large number of parameters, but their performance is poor. This may be because the larger the model, the larger the dataset required for learning to ensure that the model is effective.  
Figure 9 shows the t-SNE 2D visualization of semantic features extracted by WaveNet on datasets enhanced by traditional data augmentation and data augmentation based on the modified DCGAN, respectively. The feature distribution of the same type of data has a large overlap, while the data of different types are far apart. The feature distribution of each type of image shows an obvious balloon-like distribution, which shows that for WaveNet, these subtle features are distinguishable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Batch Size** & **Learning Rate** & **Training Epochs** & **Optimizer** \\ \hline
128 & 0.001 & 500 & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 9: Hyperparameters for training.  
Figure 9: Classification results of WaveNet using different data augmentation methods. (**a**) Classification results of WaveNet using the DCGAN-augmented dataset. (**b**) Classification results of WaveNet using the traditionally augmented dataset.
## 3 Performance Evaluation of the Proposed SAR Processor  
Performance evaluation of this proposed SAR processor was conducted by evaluating the precision comparison of the processed SAR image using both MATLAB and the proposed SAR processor. Total hardware resources utilised and total time taken in order to form a SAR image were evaluated in the same manner. The evaluation and simulations are based on the parameters listed in Table 1.
## 3 Experimental Results and Analysis
### Deformation Characteristic Analysis  
Water undergoes a complex physical, chemical, and mechanical interaction with the minerals in rock. The seepage stress of water reduces the effective stress of rock mass and causes the shear strength to decrease. The coupling effect of water and rock affects the stress distribution of rock mass, thus affecting its strength and deformation. The elastic modulus increases with the increase in confining pressure, and the elastic modulus and water content of shale rocks show an exponentially decreasing distribution under the same confining pressure (see Figure 5). When the water content is 0%, the original micro-fissure intergranular cement is packed tightly, and the mechanical integrity is strong. However, with the increase in water content, the slope of the decline of the elastic modulus curve gradually decreases; that is, with the increase in water content, the rate of decrease in the elastic modulus of shale slows down. From Figure 5, it can be seen that as the water content increases, the gradient of decrease of the elastic modulus curve gradually decreases, which means that the rate of decrease in the elastic modulus of shale slows down. This is due to the increase in water content, which causes more of the clay particles in the shale to expand. Under hydrolysis, the cementation of rock particles in shale is gradually undone, leading to a looser structure in the entire rock sample.  
Figure 5: The influence of different water contents on the elastic modulus of shale. (**a**) Combined curve of elastic modulus and water content for confining pressure 60 MPa. (**b**) Combined curve of elastic modulus and water content for confining pressure 70 MPa. (**c**) Combined curve of elastic modulus and water content for confining pressure 80 MPa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Confining Pressure/MPa**} & \multicolumn{1}{c}{**The Ratio of Residual Strength to Peak Strength Under Different Water Contents \%**} & \multicolumn{1}{c}{**Softening**} \\  & **0** & **2** & **4** & **6** & **8** & **Coefficient \(\eta\)** \\ \hline
60 & 65.12 & 55.57 & 52.45 & 49.71 & 41.66 & 24.89 \\
70 & 66.85 & 59.74 & 59.23 & 58.54 & 58.31 & 26.84 \\
80 & 67.48 & 65.89 & 64.58 & 63.89 & 62.76 & 27.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Residual strength/peak strength percentage and softening coefficient (%) of triaxial shale.  
When saturated with water, due to the dissolution effect of water, the degree of cementation between particles is quite poor, the particle fullness is significant, the particles are arranged in a turbulent manner, the initial micro-cracks are fully developed, and many small holes emerge to be used as channels by which water can form micro-cracks, giving rise to a rather loose mesostructure. Thus, the elastic modulus of the saturated shale rock is reduced. However, the elastic modulus of shale increases with the increase in confining pressure because the confining pressure causes the cracks inside the shale rock to close, thus increasing the stiffness of the shale rock.
# Seismic Response of Loess-Mudstone Slope with High Anti-Dip Angle Fault Zone
## 2 Shaking Table Test Programme
### Sensors in the Test  
The seismic response of acceleration and soil pressure in the model slope were monitored as shown in Figure 4. A YD-31D acceleration sensor model (denoted \"A\") was used, and the X-direction of the sensor sensitivity spindle was aligned with the horizontal vibration direction. A ZFCY380 soil pressure sensor model (denoted \"P\") was used, and its force surface was perpendicular to the horizontal vibration direction to measure horizontal soil pressure. Measuring point A0 was placed at the bottom of the shaking table, A1-A5 were placed on the slope surface, A6-A8 and P1-P3 in the loess stratum, and A9-A13 and P4-P8 along the loess-weathered mudstone (L-W) interface. A14, A15, A17, and A18 were arranged along the weathered mudstone-mudstone (W-M) interface and A16 was in the fault zone. A dynamic signal acquisition system (YQ2014002031, LMS Company, Leuven, Belgium) was used to collect data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil** & **Type** & **Unit Weight (\(\gamma\))/(kN\(\cdot\)m\({}^{-3}\))** & **Cohesion (c)/kPa** & **Internal Friction Angle (\(\varphi\))/(\({}^{\circ}\))** & **Elastic Modulus (E)/MPa** & **Poisson’s Ratio (\(\mu\))** \\ \hline \multirow{2}{*}{Loess} & Prototype & 18 & 5.0–10.0 & 18–22 & 6.00–9.00 & 0.33–0.38 \\ \cline{2-6}  & Model & 18 & 0.4 & 20 & 0.41 & 0.36 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Weatherned \\ mudstone \\ \end{tabular} } & Prototype & 18.5 & 15.0–20.0 & 22–28 & 70–100 & 0.28–0.31 \\ \cline{2-6}  & Model & 18.5 & 0.9 & 26 & 4 & 0.30 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Mudstone \\ \end{tabular} } & Prototype & 21 & 35–43 & 26–32 & 196.00–220.00 & 0.27–0.33 \\ \cline{2-6}  & Model & 21 & 2 & 30 & 10.59 & 0.32 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Fault zone \\ Model \\ \end{tabular} } & Prototype & 20 & 15–22 & 18–23 & 98.00–109.00 & 0.29–0.34 \\ \cline{2-6}  & Model & 20 & 1 & 20 & 5.29 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The values of primary parameters of prototype and model strata [6].  
Figure 3: (a) Simple mould, (b) test sample, (c) uniaxial compression test, and (d) direct shear test in mixing ratio test.
###### Abstract  
The purpose of the present study was to investigate the relationship between re-injury worry, confidence, and attention and athletes' functional status upon returning to sport after an injury. The sample consisted of 28 amateur-level male football players, aged 18 to 35 years, with a previous lower-limb injury. The athletes followed a physiotherapy rehabilitation program and completed three valid questionnaires examining re-injury worry, sport confidence, and attention returning to sport. The sample also performed three functional tests: (a) single-leg hop for distance, (b) side hop, and (c) the vertical jump. The results showed high correlations between the psychological factors between the functional tests. Physiotherapy duration was positively highly correlated with the time of absence from sport and severity of injury. The severity of the injury was also positively highly correlated with the time of absence from sport. The factors \"Functional Attention\" and \"Distraction Attention\" showed a positive and negative correlation with the single-leg hop for distance and the vertical jump, respectively. Athletes with a grade II severity injury showed greater attention compared to grade III severity injury. The increased level of re-injury worry was positively related to \"Distraction Attention\" and negatively related to the functional ability of the injured limb. The psychological readiness was partially related to the athletes' functional status. The present study reports the importance of psychological readiness and its relationship with athletes' functional status of returning to sport following a musculoskeletal sport injury.  
re-injury worry; attention; confidence; functional tests; sport +
Footnote †: journal: Applied Sciences
# Study on Stress-Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan-Chang Model
###### Abstract  
Compared with other natural fibers, coir fiber has good strength characteristics and long-term anti-biodegradation ability. At present, most studies on randomly distributed coir fiber-reinforced soil have focused on cohesionless soil or granular soil. In this paper, the influence of randomly distributed coir fiber on the deviatoric stress and shear strength index of red clay with different fiber content was assessed by a consolidated undrained (CU) triaxial compression test. Since the hyperbolic variational character of the stress-strain relation of the samples conformed to the hyperbolic hypothesis of the Duncan-Chang model of nonlinear elastic model, the Duncan-Chang model was used to fit it, and the influences of fiber content and confining pressure on the parameters of the Duncan-Chang model were studied. The fiber content was determined by testing to be 0%, 0.2%, 0.25%, 0.3%, 0.35% and 0.4% of the dry soil mass. It has been found that coir fiber distributed in a random radial manner can significantly increase the deviatoric stress of red clay, and thus can be effectively used in the case of soil and fiber mixing. The cohesion of the red clay first increases and then decreases with the increase in fiber content, with an optimum content of 0.3%. The internal friction angle changes little with increasing fiber content.  
triaxial test; coir fiber; stress-strain relationship; shear strength; Duncan-Chang model
## 5 Summarizing Energy Saving Results  
### Reactive Electricity Saving  
The active electricity consumption saving imposes corresponding reduction on the reactive electricity consumption. The new calculated annual active electricity consumption, after the realization of the proposed energy performance upgrade measures, is summarized in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Intervention** & **Annual Electricity Saving (kWh)** \\ \hline Lighting & 356,630 \\ Hot water production & 139,436 \\ Swimming pools heating & 52,198 \\ Passive and active measures for indoor spaces conditioning & 107,052 \\ Building energy management system (BEMS) & 181,696 \\ Total annual energy saving: & 837,012 \\ Existing annual electricity consumption (kWh) & 1,889,198 \\ New annual electricity consumption (kWh) & 1,052,186 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total annual electricity consumption savings with the introduced passive and active systems.  
Given the current electricity consumption analysis, the annual average power coefficient for the current operation state is calculated at 0.978. Although the existing infrastructure will be upgraded, leading to improved inductive performance of the Stadium's electrical installation and, consequently, to higher power coefficient, for the calculation of the expecting annual reactive consumption saving in this study the same power coefficient with the existing operation state will be adopted. Given the total new annual electricity consumption presented in Table 6 and keeping the same annual average power coefficient, the new annual reactive consumption is calculated at 224,429 kWh. The corresponding annual reactive electricity saving percentage is calculated at 42%.
# Enhancement of the Neuroprotective Effect of
###### Abstract  
This study is the first demonstration of the neuroprotective activity of the marine alga, _Spirulina maxima_, fermented by _Lactobacillus plantarum_ HY-08, which was freshly isolated from fermented vegetables. The combined process of the fermentation with ultrasonic extraction at 40 kHz for 4 hr greatly increased the amounts of \(\beta\)-carotene in the extract up to 1.62 mg/g, along with a high extraction yield of 18.26%, compared to 1.03 mg/g and 12.8% from ultrasonic extraction itself, and even much higher than 0.81 mg/g and 10.6% from conventional water extraction. It was also proved that the neuroprotective activities of the extract were enhanced possibly by higher contents of \(\beta\)-carotene in the extract, compared to those from water extraction or ultrasonic extraction itself. This extract was found to have strong antioxidant properties, with 49.01% 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity, which resulted in 90.30% neuroprotective activity. Specifically, the fermented extract markedly enhanced the brain-derived neurotrophic factor (BDNF)/p-CREB signaling pathways, which can effectively prevent memory impairment in mice caused by oxidative stress. \(\beta\)-Carotene also increased the expression of p-CREB and BDNF, and a mechanism of their upregulation by \(\beta\)-carotene is first proposed in this study. It can be concluded that the strong neuroprotective effect of the extract was markedly influenced by the antioxidant activities, due to the synergistic effects of high amounts of \(\beta\)-carotene and other biologically active substances in the extract that can be obtained by lactic acid fermentation, combined with ultrasonic processing.  
Spirulina maxima; fermentation; ultrasound extraction; neuroprotective effects +
Footnote †: journal: Applied Sciences
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 4 System Design and Simulation Configuration
### Simulation Tool, Models and Parameters  
All of the propagation and other models are implemented in MATLAB platform for simulations. First, a full three-dimensional (3D) ray tracing tool based on image theory was developed by the authors for finding the propagation paths between the transmitter and  
\begin{table}
\begin{tabular}{l c c} \hline  & **Number** & **Percentage** \\ \hline Total BDs & 750 & 100 \\ Outdoor & 113 & 15.06 \\ Indoor & 667 & 84.94 \\ Ground floor & 243 & 32.40 \\ Second floor & 232 & 30.93 \\ Fifth floor & 138 & 18.4 \\ Seventh floor & 24 & 3.2 \\ \hline \end{tabular}
\end{table}
Table 1: Distribution of backscatter devices.  
Figure 5: Distribution of backscatter devices.  
the BD. In our simulations, we utilize directional antennas at the Tx i.e., cellular antenna; therefore, the information about the directions of the propagation path is required for the forward link. The RT tool not only provides the path loss, but it also gives the delay and angular information of the propagation paths. We have considered the propagation paths with two reflections and one diffraction at maximum. Similarly, MATLAB is used for implementing ITU outdoor D2D model and 3 GPP InH model for computing the path loss in the backscatter link. Details of the other models considered in the simulation are given in forthcoming subsections.
## 2 Preliminaries and Problem Formulation  
### Preliminaries  
**Notation 1**.: _In this paper, \(|\cdot|\) stands for the absolute value of a scalar; \(\overset{\wedge}{(\cdot)}\) stands for the estimate of \((\cdot)\); \(\overset{\sim}{(\cdot)}=\overset{\wedge}{(\cdot)}-(\cdot)\) defines the estimation error._  
**Definition 1**.: _([23]) A barrier Lyapunov function is a scalar function \(V(x)\) defined with respect to the system \(\dot{x}=f(x)\) on an open region \(D\) containing the origin. The \(V(x)\) is continuous, is positive definite, has continuous first-order partial derivatives at every point of \(D\), has the property \(V(x)\to\infty\) as \(x\) approaches the boundary of \(D\), and satisfies \(V(x)\leq b,\forall t\geq 0\) along the solution of \(\dot{x}=f(x)\) for \(x(0)\in D\) and some positive constant \(b\)._  
**Definition 2**.: _([24]) Consider the following system:_  
\[\dot{x}(t)=f(x(t)) \tag{1}\]  
_where \(f:D\to\mathbb{R}^{n}\) is continuous on an open neighborhood \(D\subseteq\mathbb{R}^{n}\) of the origin and \(f(0)=0\). The origin is said to be a finite-time-stable equilibrium of (1) if there exists an open neighborhood \(N\subseteq D\) of the origin and a function \(T:N\backslash\{0\}\to(0,\infty)\), called the settling-time function, such that the following statements hold:_  
1. _Finite-time convergence: For every_ \(x(0)\in N\backslash\{0\}\)_,_ \(x(t)\) _is defined on_ \([0,T(x))\)_,_ \(x(t)\in N\backslash\{0\}\) _for all_ \(t\in[0,T(x))\)_, and_ \(\lim\limits_{t\to T(x)}x(t)=0\)_._
2. _Lyapunov stability: For every open neighborhood_ \(U_{\varepsilon}\) _of_ \(0\) _there exists an open subset_ \(U_{\delta}\) _of_ \(N\) _containing 0 such that, for every_ \(x(0)\in U_{\delta}\backslash 0\)_,_ \(x(t)\in U_{\varepsilon}\) _for all_ \(t\in[0,T(x))\)_._  
_The origin is said to be a globally finite-time-stable equilibrium if it is a finite-time-stable equilibrium with \(D=N=\mathbb{R}^{n}\)._  
**Lemma 1**.: _([25]) Suppose that there is a positive definite continuous Lyapunov function \(V(x,t)\) defined on \(U_{1}\subset\mathbb{R}^{n}\) of the origin, and  
**Lemma 2**.: _((26)) For any \(x_{i}\in\mathbb{R}\), \(i=1,2,...,n\), and a real number \(0<p\leq 1\), then_  
\[\left(\sum_{i=1}^{n}|x_{i}|\right)^{p}\leq\sum_{i=1}^{n}|x_{i}|^{p} \tag{3}\]
## 6 Results and Comparison
### Performance Metrics
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**References\#** & **Platform** & **Frequency** & **Slices** & **LUTS** & **Time** & **T/Slices** \\  & & **(in MHz)** & & & **(in \(\mu\)s)** & \\ \hline \hline \multicolumn{6}{l}{Virtex 4 Results} \\ \hline BEC [25] & Virtex-4 & 48 & 21,816 & 35,003 & \(-\) & \(-\) \\ BEC halving [25] & Virtex-4 & 48 & 22,373 & 42,596 & \(-\) & \\ GBEC: \(d=59\ -3M\)[26] & Virtex-4 & 255.570 & 29,255 & \(-\) & 14.83 & 2.38 \\ GBEC: \(d=59\ -1M\)[26] & Virtex-4 & 257.535 & 12,403 & \(-\) & 32.81 & 2.45 \\ BEC: \(d=59\)[32] & Virtex-4 & 277.681 & 31,702 & \(-\) & 13.39 & 2.35 \\ GBEC: \(d=59\)[15] & Virtex-4 & 127.261 & 17,158 & 2663 & 25.5 & 2.28 \\ \hline \multicolumn{6}{l}{Virtex 5 Results} \\ \hline GBEC: \(d=59\ -3M\)[33] & Virtex-5 & 337.603 & 9233 & \(-\) & 11.22 & 9.67 \\ GBEC: \(d=59\ -1M\)[33] & Virtex-5 & 333.603 & 4019 & \(-\) & 25.03 & 9.94 \\ GBEC: \(d_{1}=d_{2}=59-3M\)[24] & Virtex-5 & \(-\) & 4581 & \(-\) & 51.46 & 4.24 \\ BEC: \(d_{1}=d_{2}=1\)[26] & Virtex-5 & 205.1 & 1397 & 4340 & 4560 & 0.1569 \\ \hline \multicolumn{6}{l}{Virtex 6 Results} \\ \hline BEC: \(d_{1}=d_{2}=1\)[26] & Virtex-6 & 107 & 1245 & 3878 & 6720 & 0.119 \\ GBEC: \(d=59\)[15] & Virtex-6 & 186.506 & 2664 & 22,256 & 17.39 & 21.5 \\ \hline \multicolumn{6}{l}{Virtex 7 Results} \\ \hline GBEC: \(d=26\)[15] & Virtex-7 & 179.81 & 2662 & 24,533 & 18.04 & 20.82 \\ \hline \multicolumn{6}{l}{BEC architectures over _GF(p)_ field} \\ \hline ED25519 [29] & Virtex-6 & 93 & 6600 & \(-\) & 2130 & 0.071 \\ CURVE25519 [34] & Zynq7000 & 137 & \(-\) & 7380 & 511.78 & 0.26 \\ \hline \multicolumn{6}{l}{Without Pipeline} \\ \hline GBEC: \(d=59\) & Virtex-4 & 89.2 & 3192 & 2525 & 48.18 & 6.5 \\ GBEC: \(d=59\) & Virtex-5 & 130.119 & 2532 & 2432 & 33.03 & 11.95 \\ GBEC: \(d=59\) & Virtex-6 & 178.223 & 1660 & 3087 & 24.11 & 24.98 \\ GBEC: \(d=59\) & Virtex-7 & 198.244 & 1662 & 3470 & 21.6 & 27.85 \\ \hline \multicolumn{6}{l}{Two Stage Pipelined Architecture} \\ \hline GBEC: \(d=59\) & Virtex-4 & 195.508 & 3302 & 2723 & 32.1 & 9.43 \\ GBEC: \(d=59\) & Virtex-5 & 245.669 & 2714 & 2502 & 25.59 & 14.39 \\ GBEC: \(d=59\) & Virtex-6 & 290.92 & 1770 & 3597 & 21.61 & 26.14 \\ GBEC: \(d=59\) & Virtex-7 & 320.584 & 1771 & 4470 & 19.61 & 28.79 \\ \hline \hline \end{tabular} The amount of multipliers that are effectively used in the architecture is determined by \(M\). \(T/slices\)—is throughput/slices.  
\end{table} 
Table 4: Comparison of implemented results with state-of-the-art implementations.