esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Hardware Accelerators for Large Language Models
## 6 In-Memory Hardware Accelerators
### FlashAttention-3  
FlashAttention elaborates on an approach to speed up attention on GPUs by minimizing memory reads/writes. A paper presented in 2024 proposed three main techniques to speed up attention on Hopper GPUs. These include exploiting the asynchrony of Tensor Cores and TMA to (1) overlap overall computation with data movement through warp-specialization, (2) interleave block-wise matrix multiplication and softmax operations, and (3) implement block quantization and incoherent processing, which takes advantage of hardware support for FP8 low-precision. Based on these novel techniques, the performance evaluation demonstrated that FlashAttention-3, achieves speedups on H100 GPUs by 1.5-2.0\(\times\) with FP16 reaching up to 740 TFLOPs/s, and with FP8 reaching close to 1.2 PFLOPs/s. At the same time, it was validated that FP8 FlashAttention-3 achieved a 2.6\(\times\) lower numerical error than baseline FP8 attention.
## 2 Methodology
### Methodology Algorithm  
The following Algorithm 1 outlines the proposed methodology for classifying electrical disturbances in distribution systems using a hybrid approach that combines the Hilbert-Huang transform (HHT) with artificial neural networks (ANNs). The procedure begins with the controlled injection of sag, swell, and interruption events into an IEEE 33-bus test system. This is followed by decomposing the resulting voltage signals into intrinsic mode functions (IMFs) through empirical mode decomposition (EMD). Subsequently, the Hilbert transform is applied to each IMF to extract time-frequency features such as instantaneous amplitude and frequency. These features are used to construct descriptive input vectors for training a supervised ANN model, which accurately identifies the type of disturbance present in each scenario. The algorithm includes all stages from simulation and preprocessing to model training and validation, providing a robust framework for detecting and classifying power quality events under dynamic operating conditions. Figure 2: IEEE 33-bus radial distribution system used for simulating power quality disturbance events, consisting of 33 buses and 32 branches arranged in a single-feeder layout. Bus 0 represents the substation or source bus, while representative disturbance cases were injected at buses 4, 12, 18, and 30 to simulate voltage anomalies at various network depths. ``` Step 1:Initialize Test System Load IEEE 33-bus topology, line impedances, and load conditions: \(System\gets LoadIEEE33 Bus()\) Step 2:Inject Disturbances Apply \(d\in\{\text{Sag,Swell,Interruption}\}\) at selected nodes \(i\in\{1,\dots,33\}\) with durations \(T_{d}=\{0.2,0.4,0.8\}\) seconds. Record voltage signal \(x_{i,d}(t)\) for each scenario.
## 2 Chang'E-5T Onboard GPS Data Analysis
### Number of Visual GPS Satellites and PDOP  
The C/N0, an important indicator of GNSS receiver design, would affect the number of visible satellite. Figure 4 shows the number of visible GPS satellites with respect to spacecraft altitude and different C/N0 thresholds, and only the GPS observations with C/N0 values larger than the threshold are utilized in calculations. The C/N0 thresholds are set as 24, 28, 31 and 34 dB-Hz, respectively. During the returning experiment arc, the altitude fell from 50,000 km to 20,000 km. The average number of tracked GPS satellites is 7.8 when the threshold of 24 dB-Hz is taken into account. Increasing the signal tracking threshold to 31 dB-Hz, the average number decreased to 6.1. Finally, an increase of the threshold from 31 dB-Hz to 34 dB-Hz witnessed a significant decrease of the average number of visible GPS satellites to 2.2.  
PDOP reveals the geometry strength between the receiver and GNSS transmitters. Usually, small PDOP value indicates good geometry condition. The PDOP variations against different C/N0 thresholds are also investigated and shown in Figure 5. Since the PDOP could only be derived with simultaneously tracked signals from at least four GPS satellites, the threshold of 34 dB-Hz led to quite few results, most of which were near the earth. When the C/N0 thresholds are set as 31 dB-Hz and 34 dB-Hz, the PDOP values can even reach over 50 in some epochs and thus are not displayed in the plot. For the case of Chang'E-5T, the PDOP values are greatly affected by the altitudes. The main reason is that GPS satellites can be observed with better spatial distribution from the receiver antenna when the spacecraft is in the lower altitudes, indicating better geometry condition.  
Figure 4: The number of observed GPS satellites with respect to C/N0. The orange line represents the corresponding altitudes of the flight arc, which are the distances from Chang’E-5T to the Earth center.  
Figure 5: PDOP values time series according to the different C/N0 thresholds. The orange line represents the altitudes of the Chang’E-5T with respect to the Earth center.
# Study on the Ablation Behavior of High-Intensity Lasers in Vacuum
## 3 Results and Discussion
### Ablation Volume and Efficiency
The discrepancy can be attributed to the experimental setup in Figure 6, where the lens is positioned near the focal point, ensuring a higher energy density in the laser-irradiated area. At this focal position, the increased laser energy density likely leads to a more efficient material removal process. Furthermore, the vacuum environment in which these experiments were conducted may contribute to this trend by facilitating localized heat accumulation and reducing plasma shielding effects due to the absence of convective cooling. These conditions promote sustained high temperatures and greater energy absorption over successive pulses. Previous studies have shown that repeated laser irradiation can induce surface microstructural changes, reducing reflectivity and thereby enhancing laser energy absorption efficiency [30]. This phenomenon is particularly significant at high pulse energies and may explain why the ablation efficiency observed in Figure 6 does not decrease as rapidly with increasing pulse numbers, as would be expected under atmospheric conditions. Taken together, these factors--the high energy density near the focal point, the vacuum environment's effect on heat accumulation and plasma dissipation, and the reduced reflectivity due to surface changes--contribute to the differences observed in Figure 6.
## 2 The Power Zeghdoudi Distribution  
In this section, based on the same power transformation considered by [2], \(X=Y^{\frac{1}{\beta}}\), a random variable \(X\) is said to have a power Zeghdoudi distribution with parameters \(\theta\) and \(\beta\) if its survival function is given by  
\[\begin{array}{l}S_{PZD}(x;\theta,\beta)=P(X>x)=1-F_{PZD}(x;\theta,\beta)\\ =\frac{\mathrm{e}^{-\beta\theta}\left(2+\theta+x^{\beta}\theta\left(2+\theta+ x^{\beta}\theta\right)\right)}{2+\theta},x>0,\theta>0,\beta>0\end{array} \tag{2}\]  
Figure 1 involves some plots of the survival function of the PZD for some distribution parameters. We can note that the survival function of the PZD has many shapes, such as decreasing-constant (\(\theta=10,\ \beta=2\)) and decreasing (\(\theta=2.5,\ \beta=0.9\)), which are heavy-tailed to the right.  
Hence, from (3) the corresponding cdf of PZD is given by  
\[F_{PZD}(x;\theta,\beta)=1-\left(1+\frac{\theta x^{\beta}\left(\theta+2+ \theta x^{\beta}\right)}{\theta+2}\right)e^{-\theta x^{\beta}},x>0,\ \theta>0,\beta>0. \tag{3}\]  
It is clear that for \(\beta=1\), the PZD reduces to the ZD. Plots of the PZD cdf for some selections of \(\beta\) and \(\theta\) are presented in Figure 2. It is noted that the cdf shapes of the PZD vary with the distribution parameter values.  
Figure 1: Plots of the PZD survival function for different parameter values.  
Figure 2: Plots of the cdf of PZD for selected values of \(\beta\) and \(\theta\).  
The pdf of the PZD can be obtained by taking the derivative of \(F_{PZD}(x;\theta,\beta)\) as  
\[f_{PZD}(x;\beta,\theta)=\frac{\beta\theta^{3}}{2+\theta}x^{2\beta-1}\Big{(}1+x^ {\beta}\Big{)}\text{e}^{-\theta x^{\beta}},x>0,\,\theta>0,\beta>0. \tag{4}\]  
Different PZD pdf plots are given in Figure 3 with various parameters. The suggested distribution pdf is asymmetric, unimodal, semi-symmetric (\(\theta=8\), \(\beta=5\)), increasing-decreasing (\(\theta=16\), \(\beta=1.5\)), skewed to the left (\(\theta=0.2\), \(\beta=5\)), and skewed to the right (\(\theta=5\), \(\beta=0.8\)), according to the parameter's values.  
The Mode PZD  
The logarithm of the PZD pdf given in Equation (4) is  
\[log[f_{PZD}(x;\beta,\theta)]=log\bigg{(}\frac{\beta\theta^{3}}{2+\theta}\bigg{)} +(2\beta-1)\log(x)+\log\Big{(}1+x^{\beta}\Big{)}-\theta x^{\beta}.\]  
The first derivative of this equation with respect to \(x\) is  
\[\frac{\partial log[f_{PZD}(x;\beta,\theta)]}{\partial x}=\frac{2\beta+x^{ \beta}\big{[}-\big{(}\beta\big{(}\theta+\theta x^{\beta}-3\big{)}+1\big{)} \big{]}-1}{x\big{(}x^{\beta}+1\big{)}}\]  
Let \(\frac{\partial log[f_{PZD}(x;\beta,\theta)]}{\partial x}=0\) to obtain the mode of the distribution as  
\[x_{0}=\Bigg{(}\frac{-1+3\beta-\beta\theta+\sqrt{-4(1-2\beta)\beta\theta+(1-3 \beta+\beta\theta)^{2}}}{2\beta\theta}\Bigg{)}^{\frac{1}{\beta}}.\]  
Figure 3: Pdf plots of the PZD with various values of parameters.
# Study on Correction Method of Internal Joint Operation Curve Based on Unsteady Flow
## 3 Results
### Simultaneous Unsteady Correction at Compressor and Turbine
Compared with the steady flow curve, the curves are corrected by the unsteady coefficients at both ends shifting both to the left and up, with a larger shift to the left because the shift to the left corresponds to the turbine unsteady flow effect and the shift up corresponds to the compressor unsteady flow effect. The turbine unsteady flow effect is stronger than the compressor unsteady flow effect, especially in the medium-low-speed range; therefore, the correction of the internal joint operation curve shifts more to the left. The curve of the optimized turbocharger is corrected by unsteady coefficients at both ends shifting more compared to the original turbocharger due to the stronger unsteady flow effect at both ends of the optimized turbocharger. Figure 11: Joint point correction of the original turbocharger at 150,000 rpm. In order to compare the performance of the two turbochargers, Figures 12 and 13 are combined into a comparative figure of turbocharger performance in the same coordinate system, as shown in Figure 14. The main difference in the corrected curves of the two turbochargers is in the medium-low-speed range. The corrected curve of the optimized turbocharger is to the left of that of the original turbocharger, and the corrected curve of the optimized turbocharger is in the stable region to the right of the optimized compressor surge line. It shows that after unsteady correction at the compressor and turbine, the internal joint operation curves shift toward the surge line. The original turbocharger's surge margin is reduced by 4.6% to 11.8%, and the optimized turbocharger's surge margin is reduced by 15.2% to 21.9% in the medium-low-speed range, which makes the turbocharger work earlier and improve the response characteristics. It can improve the admission performance of the engine at medium-low speeds. Figure 12: The original turbocharger corrected internal joint operation curves. Figure 13: The optimized turbocharger corrected internal joint operation curves. There is no significant difference between the corrected internal joint operation curves of the two turbochargers at 190,000 rpm, except near the compressor blocking point, as shown in Figure 14. In the operating conditions near the blocking point, the corrected curve of the optimized turbocharger is lower than that of the original turbocharger because the inlet diameter is reduced by 1 mm. The pressure ratio and power consumption of the optimized compressor in this range are lower than that of the original compressor. The steady flow curves of the two turbochargers basically overlap in the medium-low-speed range, which differs significantly from the assessment results of the unsteady flow. In contrast, both ends of the turbocharger are operating under unsteady flow conditions of the engine, so the unsteady flow method should be used to evaluate and correct the turbocharger performance, and the assessment of the turbocharger performance using the steady flow method shows significant deviations.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Final Decision  
Once every income for each behavior has been calculated, a final decision process takes place. For each surrounding cell \(g\), a final decision income is calculated as follows:  
\[I_{f_{g}}=I_{\theta_{g}}+\alpha_{E}C_{E_{g}}+\alpha_{DM}I_{DM_{g}}+C_{C_{g}}+ \alpha_{D}I_{D_{g}}+\alpha_{V}I_{V_{g}} \tag{48}\]  
where \(I_{f_{g}}\) is the total income of cell \(g\), \(I_{\theta_{g}}\) is the income due to the pheromone concentration, \(C_{E_{g}}\) is the energy cost, \(I_{DM_{g}}\) is the income due to the diagonal movement behavior, \(C_{C_{g}}\) is the collision cost, \(I_{D_{g}}\) is the income due to the keep distance behavior and \(I_{V_{g}}\) is finally the income due to the keep velocity behavior. Note that the above Equation (48) is a weighted linear sum of all costs and incomes, where \(\alpha_{E}\), \(\alpha_{DM}\), \(\alpha_{D}\) and \(\alpha_{V}\) are the weights to be set. The next cell to visit is then chosen so that the total income is maximum.
###### Abstract  
Muscle stiffness provides a key insight toward clinical assessment for rehabilitation. Regarding the high-cost and technical skill requirement of ultrasound shear wave elastography (SWE) restricting extensive clinical use, MyotonPRO has been proposed as a complementary tool for muscle stiffness measurement. There is a deficiency of studies revealing the use of this tool for measuring muscle stiffness contributing to hand control. The purpose of this study was to assess the capability and effectiveness of MyotonPRO and SWE for hand muscle stiffness characterization. The stiffness of the dominant flexor digitorum superficialis (FDS) muscle of 25 healthy participants (12 males and 13 females) aged 29.60 \(\pm\) 9.81 years was evaluated while they performed grip tasks. The muscle stiffness of males and females was compared. The correlation between dynamic muscle stiffness given by MyotonPRO and [PERSON]'s modulus obtained from SWE was investigated. Statistical analysis indicated a significant difference in the dynamic muscle stiffness between genders in all conditions (\(p<0.05\)), whereas a significant difference in [PERSON]'s modulus was found only at the resting state. A moderate correlation was found between dynamic muscle stiffness and [PERSON]'s modulus (\(r\) ranged from 0.243 to 0.489). Therefore, MyotonPRO can be used to assess the muscle stiffness of the FDS muscle at rest and during muscle contraction.  
Keywords:ultrasonography; elastography; diagnostic imaging; elastic modulus; hand muscle; MyotonPRO +
Footnote †: journal: Applied Sciences
## 5 Description of Experiment and Analysis of Experimental Result
### Comparisons of Two-Stage Distribution Location Routing and Single-Stage Vehicle Routing  
A comparison between Tables 3 and 4 shows that, for single-stage vehicle paths, when the distribution vehicle capacity is 25 t, the vehicle path cost is 8001.18, when the distribution vehicle capacity is 8 t, the vehicle path cost is 8685.68, and for two-stage distribution location-routing, vehicle routing cost is 5757.35, transfer cost is 2212.5, total cost is 7969.85. Thus, the single-stage vehicle path increases the vehicle path cost. Despite the cost savings representing a small percentage of the total cost, the large activity of two-stage distribution in a big city may represent a substantial benefit per year. Beside the significant economic advantages yielded by this kind of distribution approach, two-stage distribution systems may result in being more efficient with respect to standard approaches and also from an environmental point of view. In addition, with the gradual implementation of vehicle restriction policy in China, the two-stage distribution scheme established can avoid large vehicles in the city center in this paper, thus saving distribution cost and improving operating profits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle Capacity/t**} & **Transportation Cost/yuan** & **Refrigeration Cost/yuan** & **Damage Cost/yuan** & **Total Cost/yuan** \\ \hline
25 t & 4035.03 & 2871.21 & 1094.74 & 8001.18 \\ \hline
8 t & 6051.54 & 1666.79 & 967.35 & 8685.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The single-stage vehicle routing cost details.  
Figure 6: Shortest distance two-stage location-routing diagram.
## 2 Materials and Methods
### Proposed Framework for AQI Forecasting  
The proposed framework consists of two distinct modeling approaches for air quality forecasting. The first is a neural network (NN) model composed of multiple sub-model strands as a unified structure, each strand of a varying input size [44; 45]. This design enables adaptability to different input lengths while preserving structural coherence. The input for the NN model is a time series of particulate and meteorological measurements formatted as a one-dimensional (1D) matrix with all data points arranged sequentially. Its output consists of predicted AQI values for specific future hours, depending on the selected sub-model, ensuring scalability across various temporal resolutions. To this extent, a second model based on Gated Recurrent Units (GRUs) was developed to enhance predictive performance. The GRU model utilizes temporal dependencies within the time series more effectively, improving forecasting accuracy over extended periods. These models provide a robust framework for flexible real-time deployment on edge devices and high-accuracy air quality prediction. The proposed models were constructed using the Tensorflow Keras framework [46; 47].
# Comparative Study of the Seismic Response Characteristics of Three Special Soils
## 2 Materials and Methods
### Establishment of the Soil Profile at the Actual Sites  
To comparatively study the dynamic responses of the three special soil sites, this paper selects measured borehole data from each site in the typical special soil areas and establishes a calculation model of the special soil layer. Tables 3-8 represent the sections of the soil layer seismic response analysis model, including schematic diagrams and the parameters of each soil layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Soft Soil** & **Loess** & **Laterite** \\ \hline H (m) & 10 & 10 & 10 \\ V\({}_{\text{s}}\) (m/s) & 125 & 270 & 280 \\ \(\rho\) (g/cm\({}^{3}\)) & 1.77 & 1.83 & 1.74 \\ \hline \hline \end{tabular} In Table 1, H is the burial depth, V\({}_{\text{s}}\) is the shear wave velocity, and \(\rho\) is the density.  
\end{table} 
Table 1: List of physical and mechanical parameters of three special soils. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Parameter** & \multicolumn{6}{c}{**Shear Strain (\(\times\)10\({}^{-4}\))**} \\ \cline{2-9}  & **0.05** & **0.1** & **0.5** & **1** & **5** & **10** & **50** & **100** \\ \hline \multirow{3}{*}{Soft Soil} & G/G\({}_{\text{max}}\) & 0.9915 & 0.9995 & 0.9491 & 0.8967 & 0.7037 & 0.5899 & 0.2543 & 0.0791 \\  & \(\lambda\) & 0.0324 & 0.0468 & 0.0810 & 0.0961 & 0.1320 & 0.1478 & 0.1854 & 0.2019 \\ \multirow{3}{*}{Loess} & G/G\({}_{\text{max}}\) & 0.9982 & 0.9781 & 0.8813 & 0.8180 & 0.6208 & 0.5143 & 0.2168 & 0.0670 \\  & \(\lambda\) & 0.0071 & 0.0255 & 0.0677 & 0.0857 & 0.1268 & 0.1443 & 0.1843 & 0.2013 \\ \multirow{3}{*}{Laterite} & G/G\({}_{\text{max}}\) & 0.9986 & 0.9855 & 0.8989 & 0.8374 & 0.6383 & 0.5283 & 0.2167 & 0.0582 \\  & \(\lambda\) & 0.0053 & 0.0088 & 0.0249 & 0.0352 & 0.0671 & 0.0842 & 0.1319 & 0.1558 \\ \multirow{3}{*}{Mudstone} & G/G\({}_{\text{max}}\) & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\  & \(\lambda\) & 0.0040 & 0.0080 & 0.0100 & 0.0510 & 0.0210 & 0.0300 & 0.0360 & 0.0460 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of three kinds of special soil dynamic shear modulus ratio and damping ratio. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Soil** & **Buried Depth** & **Shear Wave Velocity** & **Density** \\  & **(m)** & **(m/s)** & **(g/cm\({}^{3}\))** \\ \hline T1 & Silty Clay & 3.9 & 134.0 & 1.81 \\ T2 & Clay & 16.8 & 139.0 & 1.74 \\ T3 & Clay & 22.3 & 204.0 & 1.99 \\ T4 & Silt & 32.4 & 293.0 & 2.08 \\ T5 & Clay & 36.7 & 285.0 & 1.99 \\ T6 & Silt & 49.0 & 373.0 & 2.09 \\ T7 & Clay & 53.4 & 365.0 & 1.98 \\ T8 & Sand-Silt & 63.3 & 492.0 & 2.07 \\ T9 & Clay & 69.5 & 442.0 & 1.98 \\ Bedrock & Sand-Silt & 72.1 & 526.0 & 2.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of soft soil layers.
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios  
[PERSON]  
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complex processing schemes should be applied in other cases, such as Global Navigation Satellite System (GNSS) based PRs, where the platform movement and the medium access technique require the isolation and tracking of the reference signal.  
GNSS satellites are promising illumination sources for PRs because of the global coverage they provide, and the simultaneous availability of multiple transmitters anywhere in the world. GNSS constellations have been exploited for maritime target detection and imaging [9; 10; 11; 12] and for aerial targets detection [13; 14]. A GPS based PR for ground target detection was presented in [15]. In all cases, long integration times are used to address the low GPS power at ground level, and multistatic configurations are considered for target localization by means of multilateration. These localization techniques require the detection of a target from at least three transmitter-receiver pairs. Nevertheless, in the case of weak echo targets as the ground vehicles, the detection from multiple transmitter-receiver pairs can fail.  
In this paper, a digital processing scheme is proposed for ground target passive localization by means of a single GPS transmitter. This work is a continuation of [16], where a study of the potentials of array surveillance antennas for ground target localization exploiting a single GPS satellite was presented. The civilian navigation signal at L1 band is selected as the illumination signal. A processing scheme based on the reconstruction of the reference signal is designed to tackle the limitations related to the low power of the received GPS signal. Adaptive filtering techniques based on the Extensive Cancellation Algorithm (ECA) are considered to reject the Direct Path Interference (DPI) and digital array processing schemes have been implemented to include the spatial integration gain in the detection stage. The ground target location is estimated applying high resolution Direction of Arrival (DoA) techniques and considering the bistatic reception configuration. The proposed localization scheme is validated with synthetic and real radar data acquired by PR demonstrator IDEPAR developed in the University of Alcala [17].  
The rest of the paper is organised as follows: in Section 2, the operation principle of a GPS based PR is detailed. The proposed digital processing architecture for ground target detection and localization is presented in Section 3, which is the Cartesian tracker stage detailed in Section 4. System localization performances are analysed in Section 5 with synthetic data. Section 6 presents the system validation with real GPS data acquisition, including a description of the selected radar scenario and the experimental results for cooperative and non-cooperative ground targets. Finally, the conclusions are remarked in Section 7.