esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Packet Length Control for Sparse Signal Transmission
### Performance Improvement with Data Interleaving
#### 5.3.2 Interleaving Effect on Sparse Signal Transmission  
In this part, we present the interleaving effect on sparse signal transmission by analyzing mutual coherence, the relationship between mutual coherence and recovery error and the relationship between mutual coherence and BER.  
Figure 10 shows the relationship between mutual coherence and packet length using RCS projection with interleaving. We can see that data interleaving actually lower the mutual coherence of equivalent matrix \(A\). This is because data interleaving makes data loss more random, which helps to make projection matrix and basis matrix more incoherent. It is also noteworthy that the performance of using RCS with interleaving as the projection matrix is even better than using the random Gaussian projection matrix.  
Next, Figure 11 shows the packet length effect on the relationship between mutual coherence and reconstruction error using RCS projection with interleaving. We can see that packet length has little effect on the relationship between mutual coherence and reconstruction error. This is because interleaving makes data loss random whichever packet length it is, therefore eliminating the packet length effect. Since when signal sparsity is five, one packet with the payload length being 128 bytes can recover the original signal, the curve corresponding to a 128-byte payload is a point.  
Figure 12 shows the packet length effect on the relationship between \(BER\) and mutual coherence. The dotted line is drawn using RCS projection with interleaving, while the solid line is obtained by using only RCS projection. This shows that mutual coherence increases when the channel condition becomes worse and the packet length becomes longer. However, as long as there is a received packet, interleaving can lower the mutual coherence under the same channel condition and packet length, therefore promoting the sparse signal transmission performance. Considering the specific application (specific signal sparsity) and relationship between mutual coherence and recovery error, as shown in Figure 11, appropriate parameter selection can be made to meet the application's requirements.  
Figure 11: Relationship between mutual coherence and reconstruction error using RCS projection with interleaving.  
Figure 10: Relationship between mutual coherence and packet length using RCS projection with interleaving.
###### Abstract  
The accurate prediction of the amplitudes of vortex-induced vibrations (VIV) is important in wind-resistant design. Wind tunnel tests of scaled section models have been commonly used. However, the amplitude prediction processes were usually inaccurate because of insufficient evaluations of three-dimensional (3D) effects. This study presents experimental measurements of VIV responses in a prototype rectangular prism and its 1:1 two-dimensional section model in smooth flow. The results show that the section model vibrates with the same Reynolds number, equivalent mass, frequency, and damping ratio as those of the prototype prism without scale effects. The VIV amplitudes can be qualitatively and quantitatively measured and analyzed. The measured VIV lock-ins of these two models agree with each other. However, the prototype prism produces a 20% higher maximum amplitude than the section model. Several classical VIV mathematical models are used to validate the wind tunnel test results. This confirms that the 3D coupling effects of the modal shape and the imperfect correlations of excitation forces positively contribute to the maximum amplitude. Based on the section model outcomes, the amplified factor of 1.2 is found to be appropriate for the amplitude prediction of VIV for the present prism, and it can also provide a reference for other structures.  
amplitude prediction; mode shape; spanwise correlation; mathematical model; wind tunnel tests +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Low Power Consumption Test  
With a power source and power meter for power consumption tests, the results are shown in Figure 8. When the buoy is underwater, the buoy main control board controls the BTTT to power off the relay, which is the stopping state W0. The Beidou communication interval is 60 s, during which communication cannot be performed, representing the standby state W1. The microcontroller controls the 12 V and 5 V power supplies of the BDM, retaining only the 3.3 V power supply that maintains basic functions while entering STOP mode to reduce power consumption further, defining this low-power standby state as W2. When the buoy floats to the surface and communicates with the shore base, the terminal receives and sends data, consuming the most power, representing communication state W3. In addition, the single low power consumption state W4 was also tested, which only enters STOP mode without taking measures to disconnect the BDM power.  
Figure 6: Huffman data compression algorithm implementation flowchart.  
Figure 7: Field testing picture.  
When the USER initiates the awakening of the system, the corresponding transmission or reception durations are clocked at 0.3 s, interspersed by a 55 s period of low power functionality between communications, followed by a 5 s latency post-activation. The process duration for the terminal to handle incoming data and orchestrate its dispatch is pegged at 1 s. The associated power consumption metrics are as follows:  
\[0.3\times W3+1\times W1+0.3\times W3+55\times W2+5\times W1=34.276] \tag{1}\]  
Upon activation by the RDSS, a power supply to the BDM is necessary to apprehend the arousal signal from the shore base. Post-signal dispatch by the shore base, a 60 s interlude is mandated prior to enabling remote communication. During this interval, the power consumption, sustained in a non-energy-conserving standby state, is tabulated as follows:  
\[0.3\times W3+60\times W1+0.3\times W3+1\times W1+0.3\times W3+60\times W4=287.6 34] \tag{2}\]  
Absent the implementation of any power-saving protocols, the power draw of the terminal for a single communication session is calculated accordingly:  
\[0.3\times W3+1\times W1+0.3\times W3+60\times W1=150.876] \tag{3}\]  
Consequently, tapping into the USER port through the buoy's control board to signal awakening within the low power mode of the terminal is proven to be energy-efficient both in consumption and duration. Embracing power-conservation strategies culminates in a reduction of 116.600 J per communication, slashing power usage by 77.282% relative to scenarios devoid of such measures. Additionally, the duration of each communication session is maintained at 61.6 s.
## 3 Methodology
### Cnn  
CNNs are models inspired by the human visual system, making them highly effective for image-processing tasks. These deep-learning models are particularly adept at extracting complex features from HSI because they use spatial kernels. The weight-sharing technique of CNNs is a significant benefit as it drastically lowers the number of parameters and increases the computing efficiency. Convolutional layers, activation functions, poolinglayers, and fully connected layers are the common layer types seen in a CNN design (Figure 2) [36].  
Convolutional layers work by moving a small filter across the input image, calculating the dot product between the filter and each image patch to detect specific features such as edges or textures. This process produces feature maps, in which the image content features are highlighted and enable the network to learn complex patterns. Multiple convolutional layers are usually used to extract deeper features. Early layers consider basic features such as edges and gradient information, while deeper layers detect more abstract features. By adding non-linearity, activation functions enable the network to simulate intricate connections found in the data. Tanh, sigmoid, and ReLU (rectified linear unit) are examples of standard activation functions. Pooling layers downsample the feature maps, reducing their size and complexity, which improves the network's efficiency and robustness to minor input variations. Standard pooling methods include max pooling, which selects the maximum value within a region, and average pooling, which computes the average value. These layers are exceptionally well suited for HSI classification since they generate position- and rotation-invariant characteristics. Typically, convolutional layers are followed by activation and pooling layers. Following a number of these layers, the obtained spectral-spatial information is combined into a 1-D vector by a fully connected layer, which is subsequently input into a softmax function.  
The unknown parameters of a CNN, represented by the convolutional kernel values, are determined through training [43]. During the forward propagation phase, random weights are assigned to the kernels, which are convolved with the inputs and passed through the activation functions before reaching the pooling layer. By employing average or maximum operators, the pooling layer shrinks the feature maps' size. The final layer transforms the output into a deep feature vector, which is then processed by a fully connected layer to determine the pixel labels. The weight of the convolution kernel is modified in the backpropagation stage to minimize the loss performance. Batch normalization (BN) is also used to increase the accuracy and speed up network learning [44].
# Modeling the Impact of Atmospheric Warming on Staple Crop Growth in China in the 1960s and 2000s
## 1 Introduction
Climate warming has been perceived to have negative impacts on crop production in China, considering that climate warming has the effects of shortening the duration of crop growing period [27; 28; 29]. However, observations showed no decline of staple food crop production in China during the period from the 1960s to 2000s. This can be attributed to the improvements of agronomics in recent decades, e.g., enhanced fertilizer application, more efficient field irrigation and variety renewals, etc. In addition, the different responses of crop photosynthesis and respiration to the increasing temperatures might also be an important reason for it. In this paper, the calibrated Agro-C model was used to compare simulation results for the 1960s (1960-1969) and 2000s (2000-2009) on the five sites in four typical rotation areas in Northeast, North, East and Central China. The main reason that we compared temperature and made model simulation during the 1960s and 2000s is that the field observational data we used to validate the model are to the year 2010 [30] and back to the year 1980 [31]. We primarily confined our study to the time duration of model validation. The extending from 1980 to 1960 is on the assumption that from 1960 to 1980, the agronomy of China crop production changed much less than after 1980 so that the model is applicable when we need also a long time period for the study of climate warming effects on crops. The main objectives of this research were to: (1) analyze the regional crop photosynthetic productivity responses to temperature increase in different crop rotation areas in China; (2) investigate the main causes of such responses; (3) quantify the contribution of climate warming to crop photosynthetic productivity before and after heading (silking).
# Full Three-Dimensional Inverse Design Method for S-Ducts Using a New Dimensionless Flow Parameter
## 2 Numerical Procedure
### Filtering Operation  
Since the wall is considered as a set of separate virtual balls, the wall curvature may be discontinuous between adjacent nodes during the geometry correction process. To smooth  
Figure 2: Inverse design flowchart.  
the wall curvature, a Savitzky-Golay filter [43; 44] was applied to the wall nodes after each geometry correction step. The Savitzky-Golay filter method performs a local polynomial regression in order to determine the smoothed values for each data point. The filtration was performed using a second-order polynomial with 5 data points. The equation for this particular Savitzky-Golay smoothing is defined as follows:  
\[g_{i}=1/35\Bigg{(}\sum_{n=n_{L}}^{n_{R}}C_{n}f_{n+i}\Bigg{)} \tag{2}\]  
where \(g_{i}\) is the filtered value at the position \(i\), \(f_{j}\) is the unfiltered value at the position \(j\), \(n_{L}\) and \(n_{R}\) respectively, specify the data points on the left and right of the point that is supposed to be smoothed, and \(C_{n}\) is the weighting function according to Table 1.  
Table 1 shows the weighting function for the Savitzky-Golay smoothing scheme using a second-order polynomial and five data points. The filtering operation was carried out along the longitudinal direction of the duct and each cross-section Figure 4 because the geometry was a three-dimensional shell. The filtering for all points of the longitudinal sections (red points) was performed based on Equation (2) and Figure 4. The end point of each longitudinal section was fixed due to the fixed outlet section. The filtering was also performed at each cross-section (blue points) in which the upper and lower points of each cross-section and the duct centerline were kept constant on the symmetry plane.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \(n_{L}\) & \(n_{R}\) & \(n=-4\) & \(n=-3\) & \(n=-2\) & \(n=-1\) & \(n=0\) & \(n=1\) & \(n=2\) & \(n=3\) & \(n=4\) \\ \hline \multirow{2}{*}{For the beginning points} & 0 & 4 & \(-\) & \(-\) & \(-\) & \(-\) & \(31\) & \(9\) & \(-3\) & \(-5\) & \(3\) \\ \cline{2-11}  & 1 & 3 & \(-\) & \(-\) & \(-\) & \(9\) & \(13\) & \(12\) & \(6\) & \(-5\) & \(-\) \\ \hline \multirow{2}{*}{For the middle points} & 2 & 2 & \(-\) & \(-\) & \(-3\) & \(12\) & \(17\) & \(12\) & \(-3\) & \(-\) & \(-\) \\ \hline \multirow{2}{*}{For the end points} & 3 & 1 & \(-\) & \(-5\) & \(6\) & \(12\) & \(13\) & \(9\) & \(-\) & \(-\) & \(-\) \\ \cline{2-11}  & 4 & 0 & 3 & \(-5\) & \(-3\) & \(9\) & \(31\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weighting function for SG (Savitzky-Golay) using second-order polynomial and five data points.  
Figure 3: Position and direction of control points (spines) on 2-D planar ducts.
## 2 Materials and Methods
### Soil Analysis and Rice-Seedling Determination  
The pH and conductivity of the soil were measured using the electrode method at a soil-to-water (_w/v_) ratio of 1:5. Soil exchangeable sodium was determined through ammonium acetate and ammonium hydroxide exchange, followed by flame photometry.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & **Gypsum** & **Straw** & **Sulfuric Acid** & **Chemical Fertilizer** \\ \hline JCK & - & - & - \\ JCW & 2\% & 6\% & - & - \\ JCWH & 2\% & 6\% & 40 mL of a (1 + 1) sulfuric acid & - \\  & & & solution per kilogram of soil & 4 g of urea, 8 g of diammonium \\ JCWF & 2\% & 6\% & - & hydrogen phosphate, and 4 g of \\  & & & potassium sulfate per kilogram of soil \\ \hline \hline \end{tabular}
\end{table}
Table 1: Addition status of amendments for each treatment.  
The total alkalinity of the soil was calculated as the sum of the soluble carbonates and bicarbonates, which were determined by extracting the soil with a solution at a soil-to-water (\(w/v\)) ratio of 1:5 and titrating with a 0.1 M hydrochloric acid solution.  
The separation of soil humus components was conducted following the modified method of soil humus composition as described by [PERSON] et al. [21]. Soil samples were extracted using a mixed solution of sodium hydroxide (0.1 M) and sodium pyrophosphate (0.1 M), followed by centrifugation and filtration. The remaining soil residue was identified as humin (HU), while the filtrate constituted the extracted humus extract (HE). Subsequently, the filtrate was acidified to a pH of 1 to facilitate the separation of humic acid (HA) and fulvic acid (FA) from HE.  
The contents of soil organic carbon (SOC), humic acid carbon (HAC), and fulvic acid carbon (FAC) were determined using the K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) oxidation method followed by FeSO\({}_{4}\) titration. The PQ value, calculated as PQ = HAC/(HAC + FAC), serves as an indicator of the degree of soil humification. Both HA and FA solutions were appropriately diluted, and their absorbances at 400 nm and 600 nm (A\({}_{400}\) and A\({}_{600}\)) were measured. The value of \(\Delta\)log K was computed as \(\Delta\)log K = log A\({}_{400}\) - log A\({}_{600}\). This \(\Delta\)log K value reflects the complexity of the soil humus molecular structure [22]. A larger \(\Delta\)log K indicates a lower degree of aromaticity and condensation in the molecular structure, suggesting a simpler structure of humic substances.  
The determination of leaf age, plant height, root length, and dry weight of rice seedlings was conducted using the following methods: The roots of the seedlings were washed with water, and surface moisture was removed using absorbent paper. Subsequently, the leaf age, plant height, and root length of the seedlings were measured. The seedlings were then placed in an oven at 105 \({}^{\circ}\)C for 30 min for blanching, followed by drying in an oven at 70 \({}^{\circ}\)C to 80 \({}^{\circ}\)C until a constant weight was achieved, at which point they were weighed.
###### Abstract  
The main objective of this paper was to demonstrate the capability of dedicated small satellite infrared sensors with cooled quantum detectors, such as those successfully utilized three times in Germany's pioneering BIRD and FireBIRD small satellite infrared missions, in the quantitative characterization of high-temperature events such as wildfires. The Bi-spectral Infrared Detection (BIRD) mission was launched in October 2001. The space segment of FireBIRD consists of the small satellites Technologie Errprobungs-Trager (TET-1), launched in July 2012, and Bi-spectral InfraRed Optical System (BIROS), launched in June 2016. These missions also significantly improved the scientific understanding of space-borne fine monitoring with regard to climate change. The selected examples compare the evaluation of quantitative characteristics using data from BIRD or FireBIRD and from the operational polar orbiting IR sensor systems MODIS, SLSTR and VIIIS. Data from the geostationary satellite \"Himawari-8\" were compared with FireBIRD data, obtained simultaneously. The geostationary Meteosat Third Generation-Imager (MTG-I) is foreseen to be launched at the end of 2022. In its application to fire, the MTG-I's Flexible Combined Imager (FCI) will provide related spectral bands at ground sampling distances (GSD) of 3.8 \(\upmu\)m and 10.5 \(\upmu\)m at the sub-satellite point (SSP) of 1 km or 2 km, depending on the used FCI imaging mode. BIRD wildfire data, obtained over Africa and Portugal, were used to simulate the fire detection and monitoring capability of MTG-I/FCI. A new quality of fire monitoring is predicted, if the 1 km resolution wildfire data from MTG-1/FCI are used together with the co-located fire data acquired by the polar orbiting Visible Infrared Imaging Radiometer Suite (VIIRS), and possibly prospective FireBIRD-type compact IR sensors flying on several small satellites in various low Earth orbits (LEOs).  
fire detection; fire monitoring; fire attributes; infrared sensors; small satellites; fire radiative power (FRP) +
Footnote †: journal: Imaging  
0000-0001-9999-2291][PERSON]  
1,2489 Berlin, Germany; [EMAIL_ADDRESS]
# Seismic Performance of Curved Bridges with Longitudinal Slope
## 3 Seismic Wave Selection and Test Case Setting
### Seismic Wave Selection
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**NGA No.**} & \multirow{2}{*}{**Earthquake**} & \multirow{2}{*}{**Station Name**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Mw**} & \multicolumn{2}{c}{**V\({}_{\text{30}}\)**} & \multicolumn{2}{c}{**PGA (g)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**SF**} \\ \cline{3-3} \cline{5-10}  & & & & & & & & & \\ \hline \multirow{4}{*}{Site A} & 804 & \multirow{2}{*}{Loma} & So. San Francisco\_ Sierra & 1989 & 6.93 & 1021 & 0.054/0.105 & 0.151 & 1.108 \\  & 3920 & & Tottori & OKYH02 & 2000 & 6.61 & 1047 & 0.032/0.028 & 0.073 & 2.712 \\  & 6287 & & Tottori & KYTH04 & 2000 & 6.61 & 1069 & 0.010/0.011 & 0.049 & 8.743 \\ \hline \multirow{4}{*}{Site B} & 5013 & Chuetsu-oki & FKSH15 & 2007 & 6.8 & 804 & 0.013/0.016 & 0.052 & 11.063 \\  & 6255 & & Tottori & KOC008 & 2000 & 6.61 & 822 & 0.017/0.022 & 0.059 & 7.966 \\  & 6041 & El Mayor-Cucapah & San Diego Road Dept & 2010 & 7.2 & 827 & 0.024/0.033 & 0.073 & 4.540 \\ \hline \multirow{4}{*}{Site C} & 15 & \multirow{2}{*}{Kern County} & Taft Lincoln School & 1952 & 7.36 & 385 & 0.159/0.180 & 0.033 & 1.091 \\  & 40 & Borrego & San Onofre-So Cal & 1968 & 6.63 & 443 & 0.041/0.047 & 0.036 & 4.366 \\  & 164 & Imperial & & Cerro Prieto & 1979 & 6.53 & 472 & 0.168/0.157 & 0.046 & 1.005 \\ \hline \multirow{4}{*}{Site D} & 138 & \multirow{2}{*}{Takas} & Boshrooyeh & 1978 & 7.35 & 325 & 0.106/0.085 & 0.027 & 1.914 \\  & 12 & & Kern County & LA–Hollywood Stor FF & 1952 & 7.36 & 316 & 0.042/0.059 & 0.037 & 3.561 \\  & 169 & Imperial & Delta & 1979 & 6.53 & 242 & 0.236/0.350 & 0.042 & 0.692 \\ \hline \multirow{4}{*}{Site E} & 2510 & Chi-Chi & CHY107 & 1999 & 6.2 & 176 & 0.047/0.037 & 0.015 & 4.404 \\  & 1212 & Chi-Chi & CHY054 & 1999 & 7.62 & 172 & 0.095/0.096 & 0.022 & 2.004 \\  & 2476 & Chi-Chi & CHY054 & 1999 & 6.2 & 172 & 0.048/0.060 & 0.037 & 4.124 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the selected PEER ground motion records. Figure 5: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Site Types**} \\ \cline{2-6}  & **A** & **B** & **C** & **D** & **E** \\ \hline NEHRP (m/s) & \textgreater{}1500 & 760–1500 & 360–760 & 180–360 & \textless{}180 \\ Vs30 (m/s) & 1500 & 800 & 400 & 200 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the Pacific Earthquake Engineering Center (PEER) NGA 2 spectra model under different site conditions.