esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Signal at Receiver Antenna  
Knowing the frequency domain response of the source signal as \(\mathbf{x}=[x_{1};\;x_{2};\ldots;x_{N_{t}}]\), the received signal is expressed as  
\[y(t) =\Re\left(\sum_{n=1}^{N_{t}}\left(h_{n}w_{n}x_{n}+n_{n}\right)e^{ 2\pi f_{n}t}\right),\] \[=\Re\left(\sum_{n=1}^{N_{t}}\left(h_{n}w_{n}x_{n}+n_{n}\right)e^{ 2\pi\left(f_{c}+m_{n}\Delta f\right)t}\right), \tag{2}\]  
with \(m_{n}=\left(\frac{N_{t}-1}{2}+n-1\right)\) and \(\Delta f=\frac{B}{N_{t}}\) the sub-carrier spacing. Then the signal complex response \(c_{n}\) is \(c_{n}=x_{n}w_{n}h_{n}\). The instantaneous phase per tone \(\varphi_{n}(t)\) is time-varying depending on the carrier and can be written as  
\[\varphi_{n}(t)=2\pi m_{m}\Delta ft+\phi_{n}+\angle h_{n}+\angle w_{n}, \tag{3}\]  
The instantaneous amplitude of each tone remains \(|c_{n}|\), but the vector sum of tones varies over the symbol period. We can simplify \(y(t)\) to  
\[y(t)=\Re\left(\sum_{n=1}^{N_{t}}\left(c_{n}e^{i\Theta_{n}(t)}+n_{n}e^{i\Theta _{n}(t)}\right)\right), \tag{4}\]  
with \(\Theta_{n}(t)=2\pi(f_{c}+m_{n}\Delta f)t\).  
To capture the PAPR of the received signal, we transform (2) into an equation of the envelope multiplied with the carrier wave. By applying [PERSON]'s formula [26], we can rewrite the received signal as a time-varying signal at a carrier frequency \(f_{c}\) as  
\[y\left(t\right)=A\left(t\right)\cos\left(2\pi f_{c}t+\Psi\left(t\right)\right) +\sum_{n=1}^{N_{t}}n_{n}\cos\left(2\pi f_{c}t+m_{n}\Delta f\right), \tag{5}\]  
where \(A\left(t\right)\) is the signal envelope which equals the amplitude of \(\sum_{n=1}^{N_{t}}c_{n}e^{i\Theta_{n}(t)}\) and \(\Psi(t)\) is the phase of \(\sum_{n=1}^{N_{t}}c_{n}e^{i\Theta_{n}(t)}\). The time-varying envelope of the received signal is represented as\[A(t) =\sqrt{\sum_{n=1}^{N_{t}}|c_{n}|^{2}+\sum_{i\
eq j}2|c_{i}||c_{j}| \cos\left[\varphi_{i}\left(t\right)-\varphi_{j}\left(t\right)\right]}\] \[\leq\left\|\mathbf{c}\right\|_{1}, \tag{6}\]  
The bottom equation holds if \(\varphi_{i}\left(t\right)-\varphi_{j}\left(t\right)=0,\ i\
eq j\). Since the noise is very small compared to the transmitted signal and therefore the noise influence on the PAPR of the received signal can be ignored, \(PAPR_{r}\) is bounded by  
\[PAPR_{r}\leq\frac{2\left\|\mathbf{c}\right\|_{1}^{2}}{\left\|\mathbf{c}\right\| ^{2}}. \tag{7}\]  
The RF signal is then split by a power divider (Figure 1). The fraction \(\left(1-\rho\right)\) of the RF signal goes into the information decoder assuming perfect matching and no distortion. Then signal to noise ratio (SNR) for each frequency bin is  
\[\gamma_{n}=\frac{\left(1-\rho\right)\frac{|c_{n}|^{2}}{2}}{\left(1-\rho\right) \sigma^{2}+\sigma_{\text{mixer}}^{2}}. \tag{8}\]  
Hereby, the noise power at each bin \(\sigma^{2}=\frac{kTBN_{F}}{N_{t}}\) is a function determined by thermal temperature \(T\), signal bandwidth \(B\), and noise figure \(N_{F}\). The white Gaussian mixer noise is introduced after the power divider with variance \(\sigma_{\text{mixer}}^{2}\). The other fraction \(\rho\) of the RF signal goes into the energy harvester, producing a DC output.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 2 Materials and Methods  
### Principles of ARC  
The RCS of an ARC is given by Equation (1) [11]:  
\[\sigma=\frac{\lambda^{2}}{4\pi}G_{a}G_{T}G_{R} \tag{1}\]  
where \(G_{T}\) and \(G_{R}\) are the gain of the transmitting and receiving antennas of ARC, respectively; \(G_{a}\) is the gain of the radio frequency (RF) channels inserted between the receiving and transmitting antennas to increase the RCS; and \(\lambda\) is the radar wavelength.  
It can be seen from Equation (1) that the RCS of ARC depends on the gain of the RF channel and the TX and RX antenna gain. Actually, the gains of the receiving antenna and the transmitting antenna are the same. The RCS can be high enough in theory if either the RF channel gain or the antenna gain, or both, is large enough. However, they are restricted by each other. The higher the antenna gain is, the bigger its size is. It is very hard to keep the ARC at a reasonable size, and it is very heavy due to the bigger structure of the antennas, which then have pointing uncertainty and deployment issues. If the RF channel gain is designed to be very high, on the one hand, the gain stability requirements cannot be satisfied, and on the other hand, the isolation between the receiving and transmitting antennas is difficult to satisfy [13; 14]. Thus, many factors should be considered such as the value of RCS, the size and weight of the ARC, the stability of RCS, and so on.  
The level of the receiving signal is also a precondition for the design of an ARC. For a certain RCS, the higher the signal level reaching the ARC is, the higher the output level of the ARC is. Therefore, the demands such as saturated output power, a 1 dB compression point, and linearity for the final amplifier are very high. The received signal level of the ARC can be given according to the radar equation [15; 16]:  
\[P_{r}=\frac{\lambda^{2}}{\left(4\pi R\right)^{2}}\cdot P_{EIRP}\cdot G_{R} \tag{2}\]  
where \(P_{EIRP}\) is effective the isotropic radiation power (EIRP) of SAR, and \(R\) is the distance between SAR and ARC. The distance is decided by the orbit and the incident angle of SAR. The multi-band ARC was designed for several SARs working in the L-, C-, X-, and Ka-bands. The received signal level will be much different even for SARs in the same band, because the EIRP, orbit, and incident angle will be different. Therefore, the ARC is required to have a large dynamic range.  
So, we should calculate the received signal level first and then deduce the output power of ARC according to the RCS. The final amplifier of ARC can be decided based on the output power, and the ARC can be designed, including the antenna size, the gain of RF channels, and so on.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 2 Background and Related Work
### UAV Aerial Base Station
#### 2.3.1 Research on UAV-BS Flight Path Planning  
[PERSON] et al. proposed a low-profile dual-frequency directional antenna for UAV applications [14]. The antenna has an octahedral structure, dual bandwidth, and directional radiation characteristics. By designing a suitable feed structure and adjusting the octahedral structure, the dual-frequency bandwidth of the antenna can be realized, and high gain and directional radiation can be achieved in the horizontal and vertical polarization directions. [PERSON] et al. proposed a linear precoding design scheme to achieve the reliable transmission of data and artificial noise [15]. At the same time, the proposed scheme was applied to fingerprint embedding authentication framework to minimize the probability of eavesdropping detecting authentication marks. [PERSON] et al. proposed a channel model that meets the requirements of MIMO communication and can estimate the direction of arrival [16]. In order to achieve an approximate omnidirectional radiation pattern, a description based on a manifold matrix is used to optimize UAVs. [PERSON] et al. gave an overview of relevant technologies in the field of UAV millimeter wave communication, and predicted the future research direction of millimeter wave beamforming technology in the field of UAVCNs [17].
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 4 Simulation Analysis and Results
### FOP in-Plane Displacements: Rotation and Translation  
The above simulation is based on the premise that the coincidence of the center of a certain fiber and the center of a certain image sensor pixel is taken as the relative initial position. However, in reality, since the fibers arranged in a hexagonal shape, the image sensor pixels arranged in a square lattice, and the diameter of the optical fiber was different from the image sensor spacing; the actual relative initial position was not consistent with the above simulation. The relative initial positions between the FOP and the image sensor  
Figure 5: The impacts of different fiber diameters (3\(\sim\)10 \(\upmu\)m) and different fill factors (0.5 \(\sim\) 0.8) on the CLESD. When the fiber diameter was 4 \(\upmu\)m or 6 \(\upmu\)m, the fill factor had a weak effect on CLESD.  
are irregular. However, it should be noted that once FOP and the image sensor are fixed, FOP will not produce in-plane displacement. The FOP in-plane displacement does not mean that the FOP is actually moving, but refers to simulate the CLA of the imaging spot on the all relative initial positions between the FOP and the image sensor. In this way, the simulation analysis we have done is objective and effective. Considering all the relative initial positions, we can objectively describe the impact of the addition of the FOP on the CLA and find the lowest CLA that the monocentric imager can withstand.  
Since the coupling type between FOP and image sensor is a plane-plane coupling type, the movement of the FOP relative to the image sensor belongs to the in-plane displacement. So we consider two aspects of rotation and translation.
## 6 Influence of Floods on GPS, GLONASS, and BDS CNR
### Influence of Floods on GLONASS with 30 s Data Set  
Comparison results of the CNR on GLONASS satellites (R04, R05, R14, and R23) L1 frequency between normal weather conditions without a flood (DOY:193) and with a flood (DOY:201) is presented in Figure 12. The blue dots denote the CNR collected on DOY:193 without a flood, and the red dots denote the CNR collected on DOY:201 with a flood. It can be seen that the CNR on DOY:193 is larger than that of on DOY:201, especially when the elevation was greater than 45 deg. The maximum decrease can reach 7 dB-Hz for the R14 satellite, and the average of decrease is approximately 3 dB-Hz. This phenomenon is consistent with the GPS satellite, in which the CNR during the flood day is shown to be lower than without the flood day, especially when the elevation is high. It should be noted that this phenomenon not only appeared on these four satellites and the L1 frequency, but could also be found on other satellites and the L2 frequency.  
In addition, compared to these four satellites, it can be found that the starting elevation of CNR reduction performs differently in the two cases in question. For example, the CNR of the R05 satellite shows a downward trend at an elevation of 25 deg. However, the deceasing trend starts at an elevation of nearly 50 deg for the R23 satellite. Moreover, for the R05 and R14 satellites, the CNR on DOY:201 also decreases slightly when the satellite was at a low elevation. By contrast, R04 and R23 did not show this phenomenon. The above phenomena may be caused by the ground track of the satellite, but the specific reasons need to be further researched in the future.
# DAB Signal Preprocessing for Passive Coherent Location
## 3 Results
### Duplication of Five MSC Symbols  
When the Null, PR, and FIC symbols at the beginning of the frame are replaced by the copies of selected five MSC symbols from the previous frame, the periodical artifacts are eliminated from the bistatic \(v/R\) plane as depicted in Figure 18 (their intensity is at least 20 dB lower than in Figure 1). After such a signal modification, the adaptive clutter-removal filter [12] properly removes the direct path interference, which yields a low background noise level visible in the plot. The echo from the moving object is visible on the bistatic \(v/R\) plane at \(R=1.2\) km (marked with a red rectangle). The characteristic spread spectrum of the sea clutter [21] can also be observed.  
Figure 16: PCL detection results after noise insertion in place of Null, PR, and FIC symbols in the DAB frame: (**a**) bistatic velocity/range plane; (**b**) its cross-section at \(R=450\) m.  
Figure 17: PCL detection results after the insertion of noise with reduced power: (**a**) bistatic velocity/range plane; (**b**) its cross-section at \(R=450\) m.  
The detection results of the considered moving object are shown in Figure 19 for the subsequent time stamps. For \(t=1220.56\) s, the echo from this object is at \(R=900\) m, \(v=6.6\) m/s, and it merges with the clutter strip at this scale (not shown here). Due to the long integration time in relation to the moving object's velocity, migration of the detection results is observed. This effect may be compensated using more advanced coherent integration algorithms [19].  
Figure 19: PCL detection results after MSC symbols duplication, subsequent time stamps.  
Figure 18: PCL detection results after MSC symbols duplication to null, PR, and FIC symbols: (**a**) bistatic velocity/range plane; (**b**) its cross-section at \(R=450\) m.
# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 1 Introduction
The study aims to provide insights into the sustainability and performance trade-offs, addressing the lack of longitudinal and group-based analyses in already published research. A summary of the study contributions is presented as follows:  
1. The article compares top-performing supercomputers with top-energy-efficiency supercomputers, revealing a consistent trade-off showing high performance typically means lower energy efficiency in most cases. 2. The paper identifies rare supercomputers such as Frontier, LUMI, MareNostratrum, and Adastra showing the feasibility of achieving both top performance and top energy efficiency. 3. A detailed statistical analysis of power usage, core counts, and EE shows that top-performing supercomputers have core counts in the millions. Energy-efficient supercomputers typically operate with thousands of cores. Frontier achieved an exceptional balance but faced a challenge in maintaining it over the five-year period under study, indicating an unstable trade-off. 4. The research provides empirical evidence that supercomputers with higher performance tend to consume significantly more power, thereby having the potential to contribute more to carbon emissions. 5. The study systematically analyzes five years of data from 2020 to 2024 collected from the TOP500 rankings; such an analysis is rarely found in the literature for such a long period of time. 6. The article highlights the multi-objective challenges of balancing performance with sustainability. 7. The study highlights a shift from CPU-dominated supercomputers to GPU-dominated supercomputers as a result of AI workloads, especially LLMs. Also, it discusses how AI evolution is driving demand for GPUs, increasing power needs, and affecting the sustainability of supercomputers. 8. The research can provide policymakers, researchers, and technologists with foundational evidence for rethinking supercomputing in the era of AI. The rest of this article is organized as follows: Section 2 introduces the fundamental concepts required to understand the study. Section 3 reviews related work, placing the current research within the context of the existing literature. Section 4 describes the methodology used in the study, and Section 5 presents the results along with a detailed discussion. Finally, Section 6 offers concluding remarks.
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 1 Introduction
Current research primarily focuses on modeling continuous traffic flow on road segments, or only a few studies have conducted isolated modeling analyses of intersections. There is a lack of comprehensive research cases that combine cellular automaton models for continuous traffic flow on road segments with those for intermittent traffic flow at intersections. The third issue is the failure to fully take into account the impact of different vehicle types' speeds, deceleration performances, and drivers' reaction times on the safe following distance, thus failing to truly reflect the complex characteristics of mixed traffic flow on urban roads. Therefore, it is particularly urgent to delve deeply into the moving bottleneck phenomenon on urban roads and its traffic impact mechanisms, with the aim of proposing practical and feasible solutions. This paper is based on this core idea, focusing on the in-depth exploration and research of moving bottlenecks on urban roads. Based on the two-lane NaSch model, this paper thoroughly considers the impact of key factors, such as the differences between heavy and small vehicles, driver reaction time, and others, on cellular automaton rules, as well as the formation mechanism of moving bottlenecks. It successfully constructs a heavy vehicle moving bottleneck model applicable to urban road traffic with signal-controlled intersections. This model has significant application value, as it can scientifically and accurately simulate the phenomenon of heavy vehicle moving bottlenecks in urban road traffic, providing strong support for urban traffic management and optimization.
## 4 Design of the Enhanced Fault Current Limiting Circuit  
### Design Objective  
The EFCLC aims to reduce the requirement for the interruption capacity and isolation speed in the DCCB. Several factors are considered to achieve the design objective.  
1. The peak value of the DC line current \(i_{dc}\) must be limited under the maximum breaking current of the DCCB \(I_{B\_max}\) when the main breaker opens.
2. The fault current rising speed (\(di_{dc}/dt\)) must be lower than the largest rate of the current change withstood by the DCCB \(((di/dt)_{B\_max})\).
3. In accordance with the fault current through the converter arms displayed in Figure 10, the freewheeling diodes still withstand overcurrent after blocking IGBTs. Thus, the DCCB is required to operate before the fault current reaches its maximum value. That is, the main breaker must open before the IGBT is blocked, thereby benefiting the system recovery and restart. The time period from fault occurrence to the DCCB operation (\(t_{qp}\)) must be shorter than the time consumed from fault occurrence to blocking IGBTs (\(t_{bl}\)). Specifically, \(t_{qp}\) involves the fault detection time \(\left(t_{fd}\right)\) and DCCB operation time \(\left(t_{B\_op}\right)\) (Figure 15). In accordance with the aforementioned points, the following condition can be obtained: \[\max(i_{dc})<I_{B\_max}\] (38) \[\max(di_{dc}/dt)<\left(di/dt\right)_{B\_max}\] (39) \[t_{bl}>t_{qp}=t_{fd}+t_{B\_op}\] (40)
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 3 Results
### English Channel Test Case
We may notice that for vessels of a size larger than 60-70 m, the detection rate presented high values, that exceeded 80%, for both the \(C_{VV-VH}^{un-norm}\)-based detections as well as for the CFAR \(I_{VV}\) and \(I_{VH}\) results. We can notice that for vessels of sizes ranging from 100 to 200 m, the three probability of detection curves were not completely superposed. The small differences were due to the input parametrization and thresholding definition that are different for each one of the three SAR detectors. The fact that the probability of detection did not equal 100% for large size vessels is due to AIS-SAR matching inaccuracies that cannot be avoided in areas with dense traffic, such as the English Channel. Even though such scenarios rarely occur when considering this case study's maximum matching AIS-SAR distance of two kilometres, the probability of detection can be significantly affected by matching inaccuracies when only a limited number of vessels is available for a given size range. We can notice that for smaller size vessels, the probability of detection was decreasing. Generally, small size vessels were more difficult to be detected as they represent only a few pixels due to the SAR spatial resolution limitations but also to the position of the target with respect to the line of sight of the SAR sensor. For small size vessels, CFAR \(I_{VH}\) gives the best performances, while the coherence-based performance measures are lower and similar to those given by CFAR \(I_{VV}\), although as discussed before, the CFAR detector was more sensitive to false alarms. This first result indicates the limitations of using the joint information of VV and VH with respect to the VH channel for the detection of small size vessels. A more detailed discussion aiming to further investigate the \(C_{VV-VH}^{un-norm}\)-based detection capabilities by addressing some specific examples will be presented in Section 4.
## 3 Results
### Mechanical Properties
#### 3.2.4 Tensile Strength  
Tensile strength is one of the very important mechanical parameters of rocks. It is the average stress on the broken section perpendicular to the direction of tensile stress action in the moment of failure. Because the tensile strength of rocks is much smaller than the compressive strength, it is easy to produce tensile damage. Therefore, the study of the tensile properties of rocks is of great significance to reveal the stability of rocks.  
As shown in Figure 10, the tensile strength of limestone specimens after thermal shock at different cooling temperatures showed a tendency to increase and then decrease with  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Average Value/GPa** & \multicolumn{6}{c}{**Heating Temperature\({}^{\prime}\)C**} \\ \hline
**Cooling Temperature\({}^{\prime}\)C** & **100** & **200** & **300** & **400** & **500** & **600** \\ \hline
20 & 5.84 & 5.56 & 5.35 & 3.23 & 2.76 & 2.37 \\
60 & 6.58 & 6.91 & 6.46 & 3.91 & 3.29 & 2.82 \\
100 & 7.46 & 7.24 & 6.34 & 4.46 & 4.15 & 2.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modulus of elasticity of limestone after thermal shock.  
Figure 9: Trends of elastic modulus of limestone with temperature after thermal shock.  
increasing heating temperature. It is worth mentioning that this is consistent with the evolution of uniaxial compressive strength with heating temperature. The reasons for this result are described in Section 3.2.1. The tensile strength of limestone specimens decreased from 3.39 MPa of the original rock to 2.19 MPa (35.4%), 2.06 MPa (39.2%), and 2.61 MPa (23%) after thermal shock in water at 20 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C, respectively. The percentages in parentheses are the reduction rates. It can be seen that the cooling temperature also has some effect on the tensile strength. A good exponential relationship between the tensile strength and heating temperature of limestone in the range of 200 \({}^{\circ}\)C to 600 \({}^{\circ}\)C was fitted, as shown below:  
\[\sigma_{\text{t20}\,^{\circ}\text{C}}=8.337\text{e}^{\circ}(-0.002\text{T}), (\text{R}^{\circ}2=0.908) \tag{7}\]  
\[\sigma_{\text{t60}\,^{\circ}\text{C}}=7.791\text{e}^{\circ}(-0.002\text{T}), (\text{R}^{\circ}2=0.811) \tag{8}\]  
\[\sigma_{\text{t100}\,^{\circ}\text{C}}=7.148\text{e}^{\circ}(-0.002\text{T}), (\text{R}^{\circ}2=0.930) \tag{9}\]  
where \(\sigma_{\text{t}}\) is the tensile strength at different cooling temperatures (MPa); T is the heating temperature, its range is from 200 \({}^{\circ}\)C to 600 \({}^{\circ}\)C; R^2 is the correlation coefficient.  
The above data indicate that thermal shock has a large effect on the tensile properties of limestone, and the tensile properties of limestone are significantly deteriorated after thermal shock. Therefore, researchers in the engineering field should focus on the weakening characteristics of the tensile strength of rocks after thermal shock.