esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Forming Performance  
In practical manufacturing, thin aluminum alloys are typically subjected to integrated welding, drawing, and bulging to achieve formed parts that meet specific requirements. For the TWBs to be suitable, they must exhibit high ductility within a two-dimensional plane, ensuring a relatively uniform material flow [25]. However, traditional tensile tests only assess material ductility in a one-dimensional direction. The Erichsen cupping test is employed to evaluate the forming performance of the TWBs. Figure 13 shows the force-displacement curves during the cupping test for the BM, the AW TWBs, the PWCR TWBs, the PWSHT TWBs, and the PWCR-SHT TWBs. Additionally, it includes data on cupping depth and forming ratios. Figure 14 shows the surface condition of the TWBs post-cupping test. The peak punch load for the BM measures 20.56 kN, resulting in a cupping depth of 8.1 mm, signifying successful forming. Figure 14e shows the cupping surface of the BM. During the initial stages of the cupping test, substantial friction between the punch's top and the plate concentrates deformation near the 45\({}^{\circ}\) region relative to the punch's top. The structure and mechanical properties across the BM's two-dimensional plane are consistent, leading to circular cup surface fractures. The AW TWBs exhibit a significantly lower peak punch load than the BM, along with a forming rate of 91% of the BM; due to the structural and performance discrepancies between the FZ, the HAZ, and the BM, the AW TWBs experience reduced forming performance. Figure 14a displays the cupping surface of AW TWBs, with cracks perpendicular to the FZ surface near the top of the punch at 45\({}^{\circ}\). The PWCR TWBs exhibit the lowest peak punch load among all the TWBs, with a forming rate of 73% for the AW TWBs and only 66% for the BM. The CR induces strain-hardening behavior in the FZ and parts of the HAZ, limiting further deformation; strain-hardening predominantly occurs in the centerline area of the TWBs. Consequently, the deformation degree in this central area differs from other regions, ultimately reducing the TWBs' formability. Figure 14b shows the cupping surface of the PWCR TWBs, with fracture originating at the weld toe and rapidly extending to the opposite side, consistent with brittle fracture characteristics. The PWSHT TWBs yield a peak punch load of 14.99 kN, comparable to the AW TWBs. The forming rate is 115% for the AW TWBs and 105% for the BM. The PWSHT TWBs exhibit a microstructure similar to the AW TWBs but with more uniform mechanical properties across all areas, improving formability. Figure 14c shows the cupping surface of the PWSHT TWBs, with fracture perpendicular to the welding direction near the punch's top at 45\({}^{\circ}\). Finally, the PWCR-SHT TWBs attain a peak punch load of 20.47 kN, closely mirroring the BM. The forming rate reaches 119% of the BM, attributed to the recrystallization phenomenon in the FZ, discussed in the subsequent Section 4.1.  
Figure 12: The fracture surface of joints: (**a**) the AW joint; (**b**) the PWCR joint; (**c**) the PWSHT joint; and (**d**) the PWCR-SHT joint.
BFSK demonstrates stronger resistance to multipath interference and performs better in theoretical BER performance. The UV communication system based on 4 FSK modulation achieved a communication rate of 1.2 kbps. Under LOS conditions, communication was established up to 300 m, while under NLOS conditions, it reached 80 m. The optimal BER was less than 0.4 and 0.6 when the SNR exceeded 7, and less than \(10^{-5}\) when the SNR exceeded 20 [29]. Existing UV communication systems employing frequency modulation often rely on detector waveform detection or pulse laser measurement for signal recovery. This imposes stringent requirements on the detection of light, limiting its performance in practical applications. Therefore, seeking a more efficient modulation and demodulation scheme, as well as one that can adapt to NLOS scenarios, has become an important direction in current research on UV  
Figure 1: Ultraviolet communication system flow chart. communication systems. To address this, we propose a novel UV weak light communication scheme using fourth-order frequency-shift keying (4 FSK) modulation and photon pulse decoding algorithms. This approach utilizes a low-pressure mercury lamp emitting light at 254 nm for transmission, modulated via 4 FSK. A photomultiplier tube (PMT) acts as the detector, with signal processing algorithms based on discrete-time Poisson distribution channel models and signal correlation detection. These algorithms enable the demodulation and recovery of weak signals, reducing the required detection light energy. The adoption of 4 FSK modulation enhances transmission rates, overcoming the low-rate issue associated with mercury lamps. Computational simulations evaluate system performance, followed by hardware system development using FPGA for implementing the frequency modulation photon decoding algorithm. Real-time outdoor communication experiments validate the approach's effectiveness. In the second section, we analyze the single-scattering model of ultraviolet NLOS communication, examine the probability density function of discrete Poisson channels, and present the structural diagram of the ultraviolet communication system. The third section delves into the principles governing signal transmission and processing within the transmitter-receiver setup. Computational simulations and experimental results of the hardware system are showcased in the fourth section. Finally, a comprehensive summary of this article is provided in the fifth section.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 2 Related Work
Differently, for each packet, we directly examine a specific sequence of bytes without requiring to compute and store any packet-related metadata. In addition, we use artificial neural networks, which are simpler deep learning models and, hence, require less computing resources to be trained.
## 4 Discussion
### Polarimetry  
The results presented previously comprise the VV polarization only, which is a channel present both in SAOCOM-1 and Sentinel-1. In order to understand if the polarization state for transmission and reception is a determinant factor on the coherence behavior, we considered also the coherence for SAOCOM-1 in a similar way to Figure 11(b), but for all the polarimetric channels, always referring to the Corrientes site, where the full polarimetric images are available.  
Figures 14 and 15 confirm that the influence of \(B_{\perp}\) on \(|\gamma|\) is very strong and it is an important factor to be considered in this analysis. However, polarization has an effect on the coherence, as it can be seen in the same figure, where co-polarized channels have higher coherence values than the cross-polarized ones.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 4 Experiment
### Comparison of Original Coordinates and Relative Coordinates  
To validate the superiority of the relative coordinates proposed in this paper, the original coordinate is used to form a new feature combination with an immediate displacement and immediate motion orientation. After that the new feature combination are encoded into motion images similarly. It can be seen that Table 3 shows that the first feature combination achieves test accuracies of 93.64% and 93.13% on the two datasets separately. Compared with the former, the feature combination adopted in our method is improved by 2.95% and 4.35%, respectively. It shows that the relative coordinate proposed in this paper is more general applicability, because the relative coordinate is only related to the starting position of action but is not affected by the position of the camera. In addition, compared with the original coordinate, the relative coordinate could provide more accurate spatial relations of human skeleton joints and long-term dynamical information.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Score Item** & **LeNet** & **AlexNet** & **VGG19** & **ResNet101** & **DenseNet121** & **Our Model** \\ \hline \multirow{3}{*}{Florence-3D} & Original samples & 77.27\% & 69.7\% & 54.56\% & 47.73\% & 40.91\% & 72.73\% \\  & Generated samples & 82.33\% & 92.4\% & 93.18\% & 95.66\% & 96.74\% & 96.59\% \\  & Improved accuracy & 5.06\% & 22.7\% & 38.62\% & 47.93\% & 55.83\% & 23.86\% \\ \hline \multirow{3}{*}{UTkinect-3D} & Original samples & 75\% & 72.5\% & 65\% & 52.5\% & 50\% & 75\% \\  & Generated samples & 86.77\% & 94.56\% & 96.64\% & 95.14\% & 97.32\% & 97.48\% \\ \cline{1-1}  & Improved accuracy & 11.77\% & 22.06\% & 31.64\% & 42.64\% & 47.32\% & 22.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the test accuracy of different CNN models on original images and generated images of Florence-3D dataset and UTkinect-3D dataset.  
Figure 5: Training process of multiple CNN models based on original samples of Florence-3D (**a**) and UTkinect-3D (**b**).
## 6 Conclusions  
The proposed scheme calculates the NEDs with respect to the number of iterations for optimal error performance in the downlink massive MIMO systems. For the uniqueness of the proposed scheme, this paper considers the massive MIMO systems where \(\alpha\) is larger or equal to two which  
Figure 11: The BER performances for GS pre-coding based on the number of optimal iterations, GS pre-coding based on the proposed estimation scheme, and ZF pre-coding in \(100\times 20\), \(100\times 30\), \(100\times 40\), and \(100\times 50\) systems when an initial solution is \(\mathbf{s}_{\text{NS,Stair}}^{(0)}\) and 16-QAM is used in Rician fading (The LoS is applied to the first path and \(K\) factor is set as 5 dB).  
Figure 12: The number of required multiplications for GS pre-coding based on the proposed estimation scheme when an initial solution is \(\mathbf{s}_{\text{NS,Stair}}^{(0)}\).  
was not studied in the GS pre-coding. The proposed scheme uses the two term NS based on the stair matrix as an initial solution for the first time in the GS pre-coding for improving an accuracy of estimation and accelerating the convergence rate of the GS pre-coding compared to the existing initial solutions. The proposed scheme estimates the number of optimal iterations with high accuracy, without presentations of the closed-form solution for the GS pre-coding symbols. Additionally, the added complexity for the GS pre-coding based on the proposed estimation scheme is very low. However, an accuracy of the proposed estimation scheme decreases as \(\alpha\) decreases. In low \(\alpha\) systems such as \(\alpha=2\), an initial solution which is more robust than the two term NS based on the stair matrix, and an optimal adaptive threshold are required. Thus, our future works will focus on the design of an adaptive optimal initial solution and threshold according to a given \(\alpha\).  
[PERSON] proposed an algorithms for estimating the number of optimal iterations in the downlink massive MIMO system; [PERSON]-[PERSON], [PERSON], [PERSON], and [PERSON] advised an algorithm for higher error performance and low complexity, and gave feedbacks about the modified algorithm and all simulation results. Finally, [PERSON] provided the experimental materials for better computational simulations and revised critical errors of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2018-0-01423) supervised by the IITP (Institute for Information and communications Technology Promotion) and was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2020R1A6A1A03038540).  
The authors declare no conflict of interest.
## 2 Description of the Experiment
### Mathematical Models of the Packed Column Used for Tar Absorption Processes  
Modelling packed column aims to predict the overall performance of the packed column. The gas with a temperature \(T_{V,in}\), flow rate \(\dot{N}_{V}\) and mole fraction \(y_{i,in}\) for the component \(i\) enters the bottom of the packed column, and it exit with temperature \(T_{V,out}\), mass flow rate \(\dot{N}_{V}\), and mole fraction \(y_{i,\ out}\) for the component \(i\). The liquid with temperature \(T_{L,in}\), flow rate \(\dot{N}_{L}\) mole fraction \(x_{i,in}\) for the component \(i\) enters the top of the packed column, (countercurrent flow), and it exit with temperature \(T_{L,out}\), flow rate \(\dot{N}_{L}\), and mole fraction \(x_{i,\ out}\) for the component \(i\). Basically, two common models are used for calculating the absorption process parameters: the equilibrium model and the rate-based model [26].
## 4 Results and Discussion
### Water Use Efficiency  
The change in the soil water content \(\Delta S\) was not significantly different, while the WUE was 59.7% and 54.9% significantly (\(p<0.05\)) higher under the MRDR system than the TR system for the eggplant and capsicum, respectively, in the field experiment (Table 5). The slight difference in \(\Delta S\) might generally be due to the rainfall from May to September in 2021, which was 179.7% higher than that in 2020. The \(\Delta S\) results are similar to those found by [PERSON] et al. [41], who reported that, when soil in an uncovered plot reached moisture levels of more than 35% (typically following rainfall), the mulch-covered plots were drier by 5% to 10% on average.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Solanaceous Vegetable**} & \multirow{2}{*}{**Treatments**} & \multicolumn{2}{c}{**Plant Spacing**} & \multicolumn{2}{c}{**Planting Depth**} \\ \cline{3-6}  & & **Mean Value/cm** & **Coefficient of Variation \(\boldsymbol{CV_{X}}\)/\%** & **Mean Value/cm Variation \(\boldsymbol{CV_{H}}\)/\%** \\ \hline \multirow{2}{*}{Eggplant} & MRDR & 40.30 a & 5.60 a & 9.66 a & 15.13 a \\  & TR & 42.77 a & 8.83 b & 9.95 a & 28.80 b \\ \hline \multirow{2}{*}{Capsicum} & MRDR & 30.60 a & 6.19 a & 9.90 a & 17.83 a \\  & TR & 32.47 a & 15.63 b & 10.38 a & 29.60 a \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transplanting quantities of eggplant and capsicum under the MRDR and TR cultivation systems. Note: means of the same crop within a column followed by the same letters are not significantly different at \(p=0.05\).  
The results reported here also demonstrate that the WUE of the MRDR system was associated with the trending yield improvement Since plastic film mulch has been reported to be effective in improving soil water storage during early crop growth [42; 43], the MRDR system has the potential to improve the WUE further, which is extremely important in the Huang-Huai-Hai Plain. [PERSON] et al. [44] also reported that there is a potential to save water by reducing current irrigation rates without negatively impacting fruit yields. [PERSON] et al. [45] believed that this planting pattern helps maintain soil temperature and improve soil temperature conditions.
## 2 Materials and Methods
### Vegetative Growth, Yield and Grape Composition  
Agronomic traits and grape composition were analyzed on 24 vines for each treatment (6 repetitions of 4 vines). The grape harvest was performed on the same day for all the fertilized treatments (K15, K30, K60) and for the control (K0). Harvest dates for treatments and control in the four vintages were: 12 September 2013, 4 September 2014, 9 September 2015, 12 September 2016. Grapes were harvested at technological maturity, defined as total soluble solids (TSS) \(\geq\) 15 \({}^{\circ}\)Brix and tittatable acidity (TA) \(\leq\) 10 g/L of tartaric acid. Yield per vine and average cluster weight were recorded for each vine using a hanging scale (CH, Kern, Germany). Cluster weight was calculated by dividing fruit yield per vine by the number of clusters harvested. Berry weight was measured on a subsample of 30 berries randomly collected from all measuring vines in each plot using a precision scale (PJ3600 DeltaRange; Mettler Instruments AG, Greifensee, Zurich, Switzerland). Winter pruning wood from the same vines was weighed, as an indicator of vine vigor. Fruit composition at harvest was measured on a sample of 1 kg of grapes randomly collected from all vines of each replicate. Soluble solids were measured by refractometer (Atago PR32; Norfolk, VA, USA) at 20 \({}^{\circ}\)C; pH and tittatable acidity (expressed as g/L of tartaric acid) were determined using an automatic titrator (Crison Micro TT 2022; Crison Instruments, Barcelona, Spain) by titration with 0.1 n NaOH. Malic and tartaric acid content (g/L) were determined using RP-HPLC method (Agilent 1220 Infinity LC; Agilent Technologies, Santa Clara, CA, USA). Samples were diluted 50 times and then filtered through 0.2 mm nylon filters and 20 uL and then directly injected onto GRACE Alltima HP C18 5 micron 150 mm \(\times\) 4.6 mm. Separations were carried out under isocratic conditions at 36 \({}^{\circ}\)C using a 6 \(\times\) 10\({}^{-3}\) mol/L H\({}_{3}\)PO\({}_{4}\) solution mobile phase at a 0.6 mL/min flow rate. Organic acids were detected at 210 nm as described by [PERSON] et al. [33]. K concentration was analyzed on the must obtained from 30 berries randomly collected from all the vines of each replicate. After removing the skins and seeds, the musts were diluted 10 times and analyzed by atomic absorption spectroscopy (Perkin-Elmer, model 2380, Norwalk, CT, USA).
## 2 Related Work
### Wi-Fi Power Savings  
[PERSON] _et al_. [5] designed a WuR mainly to save the power of idle Wi-Fi access points. However, they do not take power savings of the station side into account. [PERSON] _et al_. [20] proposed an IEEE 802.11-enabled device as the WuR transmitter and the low-power WuR receiver. However, the WuR receiver supports the limited sensitivity and communication range. For energy-constrainedstation-side and long-range communication, we propose our Wi-Fi MAC protocol and strategies to save power on energy-constrained devices, such as mobile phones, tablets, laptops, _etc_.  
This work is an extended work based on our previous design [21] for long-range communication scenario in wireless mobile networks. Previous work adopts an omnidirectional antenna that supports less antenna gain than a directional antenna. Using an omnidirectional antenna, one can receive interferences from all directions. However, previous works cannot support a longer range and less interference for wireless mobile networks.  
The main contributions of this paper are three-fold. First, we enhance the communication range of our WuR by directional antennas or beamforming. Second, WuRs collaboratively detect the transmitter's location and receive the wake-up signal sending from the transmitter by static or dynamic strategies without receiving interferences from other directions. Third, we propose the MAC protocol for Wi-Fi power savings. To our knowledge, no research exists addressing WuRs and beamforming for wireless mobile networks.
###### Abstract  
A comprehensive multiparameter model is proposed for underwater wireless optical communication (UWOC) channels to integrate the effects of absorption, scattering, and dynamic turbulence. The simulation accuracy is further improved by the combined use of the subharmonic method and the strict sampling constraint method by comparing the phase structure function with the theoretical value. The average light intensity and scintillation index are analyzed using the channel parameters of absorption, scattering, turbulence, flow velocity, and transmission distance. Under weak or medium turbulence, the bit error rate (BER) performance can be effectively improved by increasing the transmitting light power. The power penalty of a 50 m UWOC channel is 5.8 dBm from pure seawater to ocean water and 1.0 dBm from weak turbulence to medium turbulence, with the BER threshold of \(10^{-6}\).  
underwater wireless optical communications; turbulence; channel model; phase screen; bit error rate performance
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 2 Study Area and Data
### Data and Processing
#### 2.2.2 Meteorological Data  
The average daily temperature, hours of sunshine, and daily precipitation used herein were provided by the National Meteorological Information Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))) [48]. The processing consisted of the following sections:  
1. The meteorological data from five meteorological stations around the study area was selected, and then it was arranged with 5-day intervals to obtain the 5-day-average temperature, total hours of sunshine, and total precipitation.
2. The spatial coordinate information based on the longitude and latitude of each station was assigned to meteorological data.
3. The meteorological data with a spatial resolution of 30 m was produced by spatial interpolation of the date from the five meteorological stations.
## 5 Experiment Setup and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Length of Text, Symbols** & **2 Authors** & **5 Authors** & **10 Authors** & **50 Authors** \\ \hline
1000 & 0.92 & 0.74 & 0.68 & 0.53 \\
5000 & 0.93 & 0.81 & 0.79 & 0.58 \\
10,000 & 0.95 & 0.87 & 0.85 & 0.62 \\
20,000 & 0.99 & 0.98 & 0.96 & 0.81 \\
40,000 & 0.99 & 0.99 & 0.98 & 0.84 \\
60,000 & 0.99 & 0.99 & 0.98 & 0.91 \\
80,000 & 1 & 0.99 & 0.99 & 0.95 \\
100,000 & 1 & 0.99 & 0.99 & 0.97 \\ Average accuracy & 0.97 & 0.92 & 0.90 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average accuracy of author identification using SVM with extracted aspects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Length of Text, Symbols** & **2 Authors** & **5 Authors** & **10 Authors** & **50 Authors** \\ \hline
1000 & 0.68 & 0.51 & 0.4 & 0.23 \\
5000 & 0.75 & 0.53 & 0.45 & 0.3 \\
10,000 & 0.82 & 0.59 & 0.49 & 0.37 \\
20,000 & 0.88 & 0.64 & 0.55 & 0.41 \\
40,000 & 0.91 & 0
Obtained results allow one to form a conclusion about the special effectiveness of SVM trained on accurately selected parameters and features. The approach based on SVM demonstrates superior accuracy to modern deep NNs architectures, regardless of the number of the samples and their volume. It should also be noted that the SVM classifier is able to learn on large volumes of data 10 times faster than deep NNs architectures. The average training time for SVM was 0.25 machine-hours, while deep models were trained for an average of 50 machine-hours.