esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation Results and Comparisons
### Comparison of Our Dedicated Multipliers with Scalable Architecture

Table 1: Implementation results of our proposed dedicated and scalable least-significant digit-parallel multiplier(s) over \(GF(2^{m})\) with \(m=163,233,283,409\) and 571. Comparison over slices \(\times\) latencyThe calculated values for the product of slices \(\times\) latency of the dedicated architectures are 2.364, 3.047, 3.416, 4.625 and 7.764. Similarly, the value for the flexible design is 49.201. As we mentioned earlier in this article, the lower the value for slices \(\times\) latency, the higher will be the performance. Thus, the performance (in terms of _slices \(\times\) latency_) of our scalable architecture can be improved by either reducing the hardware resources or shortening the critical path by improving the latency. The hardware resources of our flexible architecture can be reduced by employing a digit-serial multiplication approach instead of the digit-parallel, but with a performance overhead. Another technique to reduce the hardware resources is the use of a unified reduction block for Algorithms A1-A5. On the other hand, the latency of our flexible design can be improved by reducing the clock cycles and shortening the critical path. In this context, pipelining could be beneficial.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 3 Proposed Method  
Our proposed model \"GCBAM-UNet\" aims to identify and separate the areas of an image affected by sun glare from the rest. Sun glare can obscure important details and reduce the overall quality of an image, making this segmentation useful in various applications such as autonomous driving. For this purpose, we employed the U-Net [18] architecture, the popular segmentation method in deep learning. Using a pre-trained U-Net model with weights initialized from an ImageNet [40] pre-trained encoder can significantly improve the performance and reduce the training time for sun glare segmentation. The common approach is to use a backbone (i.e., encoder) from a popular pre-trained model such as VGG19 and then add the U-Net's decoder on top of it. Finally, we integrated CBAM [41] with VGG19-UNet to enhance the segmentation performance by allowing the model to focus on important features in both spatial and channel dimensions. CBAM adds attention mechanisms to convolutional neural networks, which can be particularly beneficial in highlighting regions affected by sun glare. The whole architecture of GCBAM-UNet is depicted in Figure 2.
## 2 Materials and Methods
### Remote Sensing Data
#### 2.4.3 Time Series Data Smoothing  
We applied a smoothing method to the NDVI time-series using TIMESAT software (v3.1.1) to model the rubber phenological metrics. This software is a free software package, developed by [PERSON] and [PERSON], and offers seasonality computation of satellite time-series data while relating the relationship to phenological properties of vegetation [56]. [PERSON] and [PERSON] [57], after evaluating several freely available software packages offering similar functionality to TIMESAT, concluded that the models used in TIMESAT are highly competitive and able to preserve signal integrity.  
Several recent studies employed this approach on various MODIS data products to study the phenology of croplands from several regions including the Yangtze River Delta [58], Sri Lanka [59], California [60], and North America [61]. They have applied one or more of the three smoothing methods in TIMESAT: asymmetric Gaussian (AG), Savitzky-Golay (SG), and Double Logistic (DL) [56]. Smoothing needs to be applied because raw time series data may contain residual noise or spikes caused by disturbance from atmospheric conditions [21], while retaining the seasonal curve [34].  
In this study, the AG smoothing method was applied to the NDVI time series. This method was chosen because it is robust and tends to eliminate noise in peak and valley time series. Additionally, it helps reconstruct seasonal patterns when the data are noisy [62]. [PERSON] et al. [63] found AG function to be less sensitive to noise and missing data. The formula for the AG smoothing method used is presented in Equation (2):  
\[\mathrm{g(x_{1},x_{2},\ldots,x_{5})=\left\{\exp\exp\left[-((t-x_{1})/x_{2})x_{3 }\right]\text{ if }t>x_{1}\exp\exp\left[-((x_{1}-t)/x_{4})x_{5}\right]\right.} \tag{2}\]  
where \(t\) is time, \(x_{1}\) determines the maximum or minimum position of the independent time variable, \(x_{2}\), \(x_{3}\) determine the width and flatness of the right function half, and \(x_{4}\) and \(x_{5}\) determine the width and flatness of left half.  
The seasonal parameter and adaptation strength were set to 1 and 2, respectively. The spike method of median filter was used to reduce noise in satellite time series data. The seasonal parameters were set to 1 because the rubber sheds its leaves once a year.
## 2 Research Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Country/Region**} & \multirow{2}{*}{**GDP (us5 mm)**} & **GDP per** & **Population** & **Urbanization** & \multirow{2}{*}{**HDI**} & \multirow{2}{*}{**Land Area (km2)**} \\  & & **Capita (USD)** & **(Million)** & **Rate (\%)** & & \\ \hline Algeria & 156,080 & 3715 & 42.0 & 72 & 0.758 & 2,381,740 \\ Argentina & 545,866 & 12,215 & 44.7 & 88 & 0.832 & 2,736,690 \\ Australia & 1,204,616 & 48,628 & 24.8 & 89 & 0.937 & 7,682,300 \\ Austria & 386,428 & 44,154 & 8.8 & 65 & 0.912 & 82,409 \\ Belarus & 47,433 & 5018 & 9.5 & 75 & 0.815 & 202,910 \\ Belgium & 466,366 & 40,559 & 11.5 & 96 & 0.917 & 30,280 \\ Brazil & 1,796,186 & 8518 & 210.9 & 84 & 0.76 & 8,358,140 \\ Bulgaria & 52,395 & 7446 & 7.0 & 74 & 0.813 & 108,560 \\ Canada & 1,529,760 & 41,397 & 37.0 & 81 & 0.921 & 909,510 \\ Chile & 247,028 & 13,575 & 18.2 & 90 & 0.845 & 743,532 \\ China & 11,199,145 & 7914 & 1415.0 & 58 & 0.753 & 9,388,211 \\ Colombia & 282,463 & 5710 & 49.5 & 79 & 0.76 & 1,109,500 \\ Czech Republic & 192,925 & 18,157 & 10.6 & 74 & 0.888 & 77,240 \\ Denmark & 306,143 & 53,202 & 5.8 & 87 & 0.929 & 42,430 \\ Finland & 236,785 & 42,722 & 5.5 & 84 & 0.924 & 303,890 \\ France & 2,465,453 & 37,794 & 65.2 & 80 & 0.89 & 547,557 \\ Germany & 3,477,796 & 42,261 & 82.3 & 76 & 0.938 & 348,560 \\ Greece & 194,559 & 174,622 & 11.1 & 78 & 0.871 & 128,900 \\ Hungary & 124,343 & 12,834 & 9.7 & 73 & 0.841 & 90,530 \\ India & 2,263,792 & 1672 & 1354.1 & 32 & 0.643 & 2,973,190 \\ Indonesia & 932,259 & 3494 & 266.8 & 54 & 0.704 & 1,811,570 \\ Iran & 425,326 & 5186 & 82.0 & 74 & 0.799 & 1,628,550 \\ Italy & 1,858,913 & 31,352 & 59.3 & 72 & 0.881 & 294,140 \\ Japan & 4,940,158 & 38,842 & 127.2 & 94 & 0.913 & 364,555 \\ Libya & 29,153 & 4505 & 6.5 & 79 & 0.704 & 1,759,540 \\ Malaysia & 296,359 & 9249 & 32.0 & 75 & 0.802 & 328,550 \\ Mexico & 1,046,922 & 8006 & 130.8 & 78 & 0.765 & 1,943,950 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major countries selected in this study [27; 28; 29]. The key issues of this article are concluded as follows: (1) Evolution of planning research investment; (2) Characteristics of investment in planning research; (3) Relationship between investment in planning research and urbanization; and (4) Relationship between planning investment intensity and level of development.
## 4 Experimental Simulation and Results Analysis
### Performance Comparison Between MSEChoA and Other Algorithms
#### 4.3.2 Convergence Curve Analysis  
Convergence curves are commonly used to illustrate the overall performance of optimization algorithms during the iterative process. They depict the changes in the objective function value over successive iterations. By analyzing the convergence curves, one can intuitively assess the convergence speed and stability of the algorithm. As shown in the convergence curves in Figure 5, the MSECHOA algorithm demonstrates outstanding performance across multiple test functions, showing significant advantages compared to other algorithms.  
Figure 5: \(Cont.\)  
Figure 5: Convergence curves of various algorithms on benchmark functions. (**A–E**) Convergence results on fixed-dimension multimodal functions (F1–F5); (**F–I**) Convergence results on fixed-dimension multimodal functions (F6–F9); (**J.K**) Convergence results on fixed-dimension multimodal functions (F10, F11).  
In the tests on unimodal functions (F1 to F5), the convergence curve of the MSECHOA algorithm exhibits an extremely rapid convergence trend. Particularly on the F1, F2, F3, and F4 functions, MSECHOA quickly approaches the theoretical optimal value of 0 almost in the initial iteration stages.  
In the tests on multimodal functions (F6 to F9), although the EO algorithm slightly outperforms in some iterations on the F6 function, the convergence curve of MSECHOA shows a more stable downward trend, particularly as it approaches the optimal solution, where the curve's volatility significantly decreases, indicating good convergence stability of the algorithm. The F7, F8, and F9 functions, as typical representatives of complex multimodal functions, show MSECHOA's outstanding performance, with the convergence curve not only quickly approaching the optimal value but also displaying high stability in later iterations, with a standard deviation close to zero.  
In the tests on fixed-dimension multimodal functions (F10, F11), MSECHOA also performs excellently. Its convergence curve shows that when dealing with high-dimensional complex search spaces, although other algorithms such as MSGWO and AWChOA perform well, they still exhibit some fluctuations in later iterations, whereas MSECHOA's curve tends to stabilize, with a low standard deviation.
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 2 Materials and Methods
### Materials and Architecture of the Devices
#### 2.1.2 VCSEL-Driver Architecture and Transmitter Assembly  
The single-channel block diagram of the PAM-4 VCSEL driver is shown Figure 2.a and is thoroughly described in [31]. It comprised a four-tap symbol-spaced feed-forward equalizer (FFE) and a selective falling-edge pre-emphasis (SFEP). The equalizer's taps were independently controlled, which allowed the driver to be optimally configured according to the specific transmission scenario, whereas the SFEP module was used to reduce the effect of pulse width distortion on the generated PAM-4 signal. The VCSEL driver chip was fabricated in 130 nm SiGe BiCMOS technology and exhibited a footprint of 1 \(\times\) 2.8 mm. The transmitter board comprised a two-channel VCSEL driver and a respective array of single-mode VCSELs described in Section 2.1.1. Initially, the development of a four-channel VCSEL driver was targeted but the mismatch between the channel width of the VCSEL driver (>1 mm due to the on-chip PAM-4 generation and equalization) and the cathode pitch of the VCSELs (350 \(\upmu\)m) prohibited such a design. The limitation in the channel pitch of the VCSEL driver mainly derived from the bond pad spacing of its data inputs (MSB (most significant Bit) and LSB (least  
Figure 1: (a) The structure of a double-mesa vertical-cavity surface-emitting laser (VCSEL), (b) room-temperature LIV (Light-Current-Voltage) characteristics and optical spectrum (inset), (c) s21 graph of a 16 \(\upmu\)m double-mesa VCSEL.  
significant Bit)). The assembly rested on a PCB (Printed Circuit Board), designed to simultaneously drive two VCSEL driver chips. To reduce the parasitic inductance between the VCSEL and the driver, the VCSEL anode voltage was supplied by the driver chip. A second technique used to reduce the parasitic inductance was to place the decoupling capacitors directly underneath the VCSELs, omitting additional bond wires, while using the thickness of the capacitors to put the VCSEL and driver surface at the same height.
# A Framework for Symmetric-Quality S3D Video Streaming Services
###### Abstract  
This paper proposes an efficient encoding framework based on Scalable High Efficiency Video Coding (SHVC) technology, which supports both low- and high-resolution 2D videos as well as stereo 3D (S3D) video simultaneously. Previous studies have introduced Cross-View SHVC, which encodes two videos with different viewpoints and resolutions using a Cross-View SHVC encoder, where the low-resolution video is encoded as the base layer and the other video as the enhancement layer. This encoder provides resolution diversity and allows the decoder to combine the two videos, enabling 3D video services. Even when 3D videos are composed of left and right videos with different resolutions, the viewer tends to perceive the quality based on the higher-resolution video due to the binocular suppression effect, where the brain prioritizes the high-quality image and suppresses the lower-quality one. However, recent experiments have shown that when the disparity between resolutions exceeds a certain threshold, it can lead to a subjective degradation of the perceived 3D video quality. To address this issue, a conditional replenishment algorithm has been studied, which replaces some blocks of the video using a disparity-compensated left-view image based on rate-distortion cost. This conditional replenishment algorithm (also known as VEI technology) effectively reduces the quality difference between the base layer and enhancement layer videos. However, the algorithm alone cannot fully compensate for the quality difference between the left and right videos. In this paper, we propose a novel encoding framework to solve the asymmetry issue between the left and right videos in 3D video services and achieve symmetrical video quality. The proposed framework focuses on improving the quality of the right-view video by combining the conditional replenishment algorithm with Cross-View SHVC. Specifically, the framework leverages the non-HEVC option of the SHVC encoder, using a VEI (Video Enhancement Information) restored image as the base layer to provide higher-quality prediction signals and reduce encoding complexity. Experimental results using animation and live-action UHD sequences show that the proposed method achieves BD-RATE reductions of 57.78% and 45.10% compared with HEVC and SHVC codecs, respectively.  
hybrid 3 DTV; stereoscopic 3D; SHVC; cross-view SHVC; conditional replenishment algorithm; VEI +
Footnote †: journal: applied sciences
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 3 Validation Methodology
### Validation of the Phenological Metrics  
We compared the annual phenological metrics extracted from the hectometric and kilometric LAI products with the phenology from the reference Sentinel-2 LAI time series. Before the extraction of phenological metrics, we applied the Savitzky-Golay (SG) [63] method to smooth time series of reference LAI, hectometric and kilometric LAI products. The SG filter fits local polynomial functions in a temporal moving window. In this paper, a quadratic polynomial SG filter with a time window of 7 days was used. This way, the SG filter captures subtle and rapid changes in the data while being little sensitive to outliers.  
The dynamic threshold method was applied to the smoothed time series for the extraction of phenological metrics [64; 65]. The start of season (SoS) was defined as the date for which LAI value rises to a given percentage (50% in this study) of its seasonal amplitude. The end of season (EoS) was computed as the date for which LAI value decreases to the same 50% percentage of the amplitude. The peak of season (PoS) was defined as the date for which LAI value rises to the maximum value. The SG filter and the dynamic threshold method are executed in TIMESAT [66] software to extract the SoS, PoS and EoS metrics.  
The validation of phenological metrics consists in the comparison of the timing of the SoS, PoS and EoS for the year 2019 as extracted from hectometric and kilometric LAI products vs. the phenological metrics derived from the reference Sentinel-2 LAI time series after smoothing.
# Rapid Parametric Modeling and Robust Analysis for the Hypersonic Ascent Based on Gap Metrics
## 5 Simulation Analysis  
### Static and Dynamic Analysis  
The given flight envelope consists of flight Mach range from 6 to 10 and flight altitude range from 25 km to 29 km. Correspondingly, the trim states and control inputs regarding the different heights and speeds are listed in Table 2.  
The trim angle of attack, elevon deflection angle, and stoichiometric ratio increase with the increase in the flight height, whereas they will decrease with the increase in the flight velocity. In addition, the poles and zeros of the right half plane are plotted in Figure 5.  
According to Figure 5, poles and zeros of the right half plane show that the model is unstable and difficult to control with the fixed structure. Furthermore, there are three motion modes: short-period modes, long-period modes, and height mode. Among them, the eigenvalues corresponding to the short period mode are positive and negative roots,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mach** & **Altitude (km)** & **AOA (deg)** & **Elevon Deflection Angle (deg)** & **FER** \\ \hline
6 & 25 & 2.1645 & 14.4488 & 0.391 \\
7 & 26 & 1.9073 & 13.6513 & 0.4044 \\
8 & 27 & 1.7253 & 13.1218 & 0.4393 \\
9 & 28 & 1.6016 & 12.7670 & 0.4811 \\
10 & 29 & 1.5224 & 12.5351 & 0.5309 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trim values of hypersonic vehicles.  
and this indicates that the short-period mode is unstable. Additionally, the long-period mode is stable in neutrality as the characteristic root is close to zero.
## 5 Calculation of the Loss Contributions
### Heat Entering the Cable Cryostat from the Outside  
The heat load entering the cryostat from the outside in a cable segment is calculated as shown in [59] for a flexible cryostat. Users should consider whether to opt for rigid or flexible cryostats in their design, as the latter may result in thermal inputs 70% higher than those of the former [41], albeit while providing significantly more mechanical flexibility to the cable:  
\[Q_{cryo}=d_{stat}\frac{2\pi\ \lambda\ [T_{amb}-(T_{max_{m}}+T_{0})/2]}{\ln \bigl{(}R_{cryo_{cat}}/R_{cryo_{int}}\bigr{)}} \tag{37}\]  
where \(\lambda\) is the \"effective\" thermal conductivity of the cryostat, equivalent to that for Multi Layer Insulation (MLI). A value of \(\lambda\) equal to 0.4 mW/m\(\cdot\)K is set in this work based on the reported ranges [60; 61; 62; 63]. \(T_{amb}\) is the average ambient temperature at the cable installation site over a year. \(R_{cryo_{ext}}\) and \(R_{cryo_{int}}\) are the outer and inner radii of the cryostat, respectively. \(R_{cryo_{int}}\) is equal to \(R_{ext_{an}}\), whose calculation has been described in Section 4.4, while \(R_{cryo_{ext}}\) depends on \(R_{cryo_{int}}\) and the thickness of the cryostat walls (\(t_{cryo}\)), imposed by the user.
## 3 Reinforcement Learning
### State Value and State-Action Value Function  
The state value function, denoted as \(V^{\pi}(s)\), is used to specify the long-term desirability of being in a specific state. On the other hand, the state-action value function, referred to as the Q-function (\(Q^{\pi}(s,a)\)), specifies how good it is for an agent to take certain action \(a\) in state \(s\) under a given policy \(\pi\). In Q-learning, the Q-values of state \(s\) and action \(a\), i.e., \(Q(s,a)\), is determined as follows [59]:  
\[Q^{\pi}(s_{t},a_{t})=E_{\pi}\bigg{[}r_{t+1}+\beta\max_{a^{\prime}}Q^{\pi}(s^{ \prime},a^{\prime})\mid s_{t}=s,a_{t}=a\bigg{]} \tag{2}\]  
Equation (2) is referred to as Bellman's equation, in which \(Q^{\pi}(s,a)\) is the Q-value for state \(s\) and action \(a\) under policy \(\pi\), \(E_{\pi}\) is the expected value under policy \(\pi\), \(r_{t+1}\) is the immediate reward received, \(\beta\) is the discount factor, \(s^{\prime}\) and \(a^{\prime}\) are the next state and action, respectively, and \(s_{t}=s\) and \(a_{t}=a\) are the current state and action.  
In addition, we obtain the expected discounted returns for the next potential state-action pair. The update rule for the Q-value function is described as follows:  
\[Q(s_{t},a_{t})=(1-\alpha)\cdot Q(s_{t},a_{t})+\alpha\bigg{(}R(s_{t},a_{t})+ \beta\cdot\sum_{a^{\prime}}\pi(a^{\prime}|s_{t+1})\cdot Q(s_{t+1},a^{\prime}) -Q(s_{t},a_{t})\bigg{)} \tag{3}\]  
The values of \(\alpha\) and \(\beta\) are between 0 and 1. The learning rate \(\alpha\) indicates to what extent new information overrides the previous information. If \(\alpha\) is 0, the agent learns nothing and relies on previous knowledge only, whereas if \(\alpha\) is 1, the agent only considers new information irrespective of previous knowledge. Similarly, the discount factor \(\beta\) indicates the importance of future rewards. If \(\beta\) is 0, that means only the current reward is considered, and if \(\beta\) is 1, the agent considers long-term future rewards. The estimated Q-values are stored in a look-up table for each \(s\) and \(a\) pair. The update rule adjusts the current Q-value estimate based on the observed reward and the maximum expected cumulative reward from the next state. This allows the Q-value estimate to converge towards the true Q-value as more and more experience is gathered.
## 4 Discussion
### Practical Applications  
The findings of this study have several practical applications that can benefit both athletes and sports professionals. Firstly, the development of sport-specific visual training programs can directly enhance key skills such as dynamic visual acuity, stereopsis, and hand-eye coordination, which are critical for athletic performance. Coaches and trainers can use these insights to design individualized training regimens that target the specific visual demands of different sports. Additionally, integrating advanced technologies like the COI-Sport Vision system into regular training routines can provide precise and real-time feedback, enabling athletes to monitor and improve their visual performance systematically.  
Psychological assessments could also be incorporated into training to address the motivational and psychological factors that influence performance. These combined approaches would help athletes not only refine their visual skills but also enhance their overall readiness and adaptability in competitive environments. Lastly, sports organizations and universities could adopt these training programs to foster a more scientific and comprehensive approach to athlete development, maximizing both performance and injury prevention.