esa-sceva/satcom-chunk-collection huggingface dataset:
# Activation Analyses of Disposal Options for Irradiated Be\({}_{12}\)Ti
## 2 Be\({}_{12}\)Ti Neutron Multiplier
### Be\({}_{12}\)Ti Blocks  
The manufacturing method for the Be\({}_{12}\)Ti blocks is based on the vacuum hot pressing of Be\({}_{12}\)Ti powder [8]. Because beryllides are very hard materials, electrical discharge machining was employed to cut out flat surfaces and holes to form hexagonal blocks with a material density of 2.25 g/cm\({}^{3}\)[8]. Shown in Figure 1 are the Be\({}_{12}\)Ti blocks assembled in a unit according to the latest consolidated design of the HCPB blanket [3].  
\begin{table}
\begin{tabular}{l c c c} \hline \multicolumn{1}{c}{**Element**} & **Be\({}_{12}\)Ti Min** & **Be\({}_{12}\)Ti Max** & **“Average”** \\ \hline Be & 68.983 + 0.134 & 68.807 + 0.260 & 68.612 + 0.206 \\ \hline BeO & 0.371 & 0.721 & 0.573 \\ \hline C & 0.038 & 0.247 & 0.098 \\ \hline Fe & 0.095 & 0.123 & 0.114 \\ \hline Al & 0.005 & 0.035 & 0.019 \\ \hline Si & 0.041 & 0.062 & 0.052 \\ \hline Mg & 0.000 & 0.011 & 0.007 \\ \hline Zn & 0.001 & 0.004 & 0.002 \\ \hline Cr & 0.001 & 0.011 & 0.007 \\ \hline Ni & 0.065 & 0.078 & 0.072 \\ \hline Cu & 0.001 & 0.018 & 0.008 \\ \hline Ti & 29.671 & 29.688 & 29.680 \\ \hline Zr & 0.001 & 0.005 & 0.003 \\ \hline U & **0.00014** & **0.0105** & **0.0021** \\ \hline Mn & 0.000 & 0.005 & 0.003 \\ \hline Ag & 0.000 & 0.004 & 0.002 \\ \hline Co & 0.000 & 0.004 & 0.002 \\ \hline Pb & 0.001 & 0.019 & 0.010 \\ \hline Ca & 0.001 & 0.004 & 0.002 \\ \hline Mo & 0.001 & 0.004 & 0.002 \\ \hline O & 0.560 + 0.237 & 0.560 + 0.461 & 0.560 + 0.367 \\ \hline F & 0.000 & 0.001 & 0.000 \\ \hline N & 0.031 & 0.046 & 0.038 \\ \hline H & 0.120 & 0.120 & 0.120 \\ \hline W & 0.007 & 0.007 & 0.007 \\ \hline Na & 0.001 & 0.001 & 0.001 \\ \hline Ta & 0.001 & 0.001 & 0.001 \\ \hline \end{tabular}
\end{table}
Table 4: Chemical compositions of Be\({}_{12}\)Ti with minimum, maximum and averaged impurities content, wt%.  
Radial cuts of the Be\({}_{12}\)Ti unit shown in Figure 1 were proposed to implement in the HCPB blanket design to reduce thermal stresses that take place during the warming up and cooling down cycles. Such radial segmentation of the Be\({}_{12}\)Ti unit can also be used for a mapping of the blocks in the blanket space to apply a practical classification of their properties after the thermal stresses and the neutron irradiation. This feature could be used in the future for the separate processing of the irradiated blocks to elaborate on a smart cycle with activated materials.
## 3 Results
### The Mean Spatial Distribution of LSP  
Spatial distributions of LSP parameters, i.e., SOS, LOS and EOS, in the SNP from 2001 to 2015 are illustrated in Figure 9. The SOS of the natural ecosystem mainly occurred at day of year (DOY) between 100 and 150. The earliest SOS was found in the eastern parts of the SNP region, while the southwestern region had the latest SOS (Figure 9a). The growing season of DBF and MF started from mid-March, and GRA and WET had later start of the growing season (early April). SOS began in March and April, the vegetation began to accumulate GPP, but the CUE value was in a very small range, almost neglected, so we began to record CUE from May.  
The distribution of EOS dates showed similar pattern to that of SOS, gradually increasing from west to east, mainly in late October and November (290-330 DOY) (Figure 9b). The end dates of the growing season of DBF and MF occurred in early November. GRA and WET ended their growing seasons about ten days earlier than the forestland. During 2001-2015, the average LOS of natural ecosystems in the SNP was about 192 days, showing similar spatial distribution to SOS and EOS (Figure 9c). The average LOS of MF and GRA was 213 days and 176 days, respectively. LOS dates of DBF were about 5 days shorter than those of MF, and the growing season of WET was about 4 days longer than that of GRA.  
Figure 8: CUE of different ecosystems and seasons from 2001 to 2015. (**a**) Spring; (**b**) summer; (**c**) autumn.
## 2 Materials and Methods
### Method for Spectrum Assignment Using Multi-Level Formats and Optical Frequency Combs--RMLSA
The arrays (\(bw_{mod}\) and \(VL\)) are searched in decreasing order of modulation level, since higher levels require less bandwidth and enable better resource utilization. If sufficient consecutive lines are available, they are assigned to the request and marked as unavailable for other requests. Otherwise, the request is marked as rejected. We use several auxiliary functions to perform these tasks, including a function to calculate the required bandwidth for each modulation format, a function to determine the required carrier count according to the bandwidth and modulation format, a function to compute the OSNR required for a given BER, and a function to verify the available and contiguous wavelengths on the optical comb.
## 1 Introduction  
The Design and Digital Manufacturing Laboratory of the University of La Laguna (Fab Lab ULL) has worked in the digital fabrication of a full-scale high-detail model (HDM) of the spatial instrument of the European Space Agency, named the Solar Orbiter mission Polarimetric and Helioseismic Imager (SO/PHI) [1]. This sort of physical replica entails deep information, which is essential for the preservation and transfer of aerospace heritage since projects launched into space are normally designed in order to not return to Earth.  
The manufacturing of the HDM of the SO/PHI instrument was carried out under an approach that combined digital manufacturing techniques (low cost 3D printers, a CNC milling machine, and a laser cutting machine) and the use of a 3D computer-aided collaborative modeling software. The aim of this paper is to provide stages in the graphic engineering procedure for the digital fabrication of challenging projects, which require high detail through the resolution of a case study: the manufacture of a full-scale HDM of the spatial instrument PHI for the Solar Orbiter mission of the European Space Agency.  
Rapid prototyping has made it possible to print 3D physical prototypes from CAD files [2]. However, this process is not always fast (since the model is not a single piece, and the printing process is not instantaneous). This lack of immediacy is plausible in architecture environments, where reduced scale models are indispensable and geometric simplification processes are required since buildings cannot be uniformly scaled [3].  
3D digital printing precision and other fabrication variabilities have been a focus of interest in the scientific literature, including studies that compare the difference between the CAD model and the physical model [4]. In addition, other issues have dealt with the use of different digital manufacturing techniques with materials such as concrete [5] or the study of numerical models to analyze the behavior of timber folded surface structures using semi-rigid multiple tab and slot joints [6].  
Nevertheless, there are many other challenges [7] when we face manufacturing a full-scale HDM of an engineering project. Although details are easily achieved in digital modeling, physical models provide important additional information for the conservation of engineering heritage, such as SO/PHI. Therefore, this article proposes a procedure to recreate engineering projects without the need for geometric simplifications through the use of low-cost digital fabrication methods.
## 2 Study Area and Dataset
### Dataset
#### 2.2.2. GIMMS NDVI Dataset and Preprocessing  
Normalized difference vegetation index (NDVI), defined as the difference between the near-infrared and red reflections divided by the sum of the two, is a dimensionless index that can be used to estimate the density of green on an area of land [40]. The Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g.v1 dataset, with a spatial resolution of \(0.083^{\circ}\times 0.083^{\circ}\) and a temporal resolution of 15-day intervals, has been the most widely used remote sensing product for monitoring vegetation phenology over large areas [41]. It was generated from the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard on several NOAA satellites. At the same time, it was also the latest version of the GIMMS NDVI dataset, spanning the period from January 1982 to December 2015. This dataset has improved data quality at high latitudes, especially in regions where the growing season is shorter than 2 months [42].  
Before using the GIMMS NDVI dataset, some preprocessing procedures were needed to reduce the effects of clouds and the atmosphere. Specifically, the maximum value composition (MVC) was first applied to the NDVI dataset to eliminate noises caused by cloud and snow contamination [43]. Then, the Savitzky-Golay filter was used to remove outliers and smooth the NDVI time series [44]. Finally, regions with a multiyear average NDVI less than 0.1 were excluded from the analysis to further minimize the impacts of barren land and non-vegetation [24].
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### The Influence of Cluster Setup
#### 4.5.4 The Influence of Mixed Applications  
Since there are other types of jobs in HPC platforms, we discuss the influence of mixed applications in this section. VASP jobs account for about 50% of the Supercomputing Center of USTC [27]; therefore, the prediction model in RLSchert was trained after the feature engineering of VASP jobs. For other types of iterative jobs, a different feature engineering needs to be performed (this is beyond of the scope of this work). For this part of the jobs whose input parameters cannot be extracted or feature engineering has not been completed, we used the mature general predictor based on user behavior [28]. The predictor does not need to know the input parameters and running features. The features it uses include: user ID, job submission time, previously completed jobs from the same user, request cores, queue ID, group ID, and so on. We implemented the user behavior predictor to predict all applications. The prediction results are shown in Table 10.  
Especially, to run predictable jobs (VASP), RLSchert predicts them. For the rest of the random applications, the general predictor predicts them, which basically addresses the random applications issue. Furthermore, we experimented with our RLSchert based on all jobs (VASP and other applications) of the USTC Supercomputing Center. The experimental results are shown in Table 11. Table 11 shows the average slowdown of each scheduler when all VASP jobs use RLScher to predict the runtime and others use the user behavior predictor. The experimental results showed that RLSchert has the lowest average slowdown, which illustrates the effectiveness of RLSchert in practical HPC with a mix of applications.
## 3 Threat Model and Security Requirements
### Security Requirements  
Based on threat analysis, we establish five core security requirements driving SMART DShot design and validation.  
Real-Time Attack Detection requires detection within 10 ms of occurrence to ensure sufficient time for corrective action before stability loss, with the target metric of mean detection latency below 10 ms for 95% of attack instances. The low false positive rate maintains the false positive rate below 5% to avoid unnecessary flight disruptions and loss of confidence in the pilot. Multi-Attack Coverage detects the six defined attack scenarios with a precision greater than 80% per attack and an aggregate rate greater than 85% for complete protection against known threat vectors.  
Resource Efficiency operates within existing hardware constraints, consuming less than 5% additional FPGA resources and under 20% power increase to enable deployment on current UAV platforms without redesign. Graceful degradation maintains basic control functionality under attack, preserving greater than 50% control authority during active attacks to ensure pilot can maintain control during security incidents.
## 3 Results and Discussion
### Shear Strength Parameters
For the O- model, the calculated unstable stacking energies \(\gamma_{us}\) along the <1\(\overline{1}\)01> and <11\(\overline{2}\)0> slip directions are 399 and 612 mJ/m\({}^{2}\), respectively. For the 2O- model, the calculated unstable stacking energies \(\gamma_{us}\) along the <1\(\overline{1}\)01> and <11\(\overline{2}\)0> slip directions are 349 and 494 mJ/m\({}^{2}\), respectively. Furthermore, the calculated unstable stacking energies \(\gamma_{us}\) for the Zr- model along the <1\(\overline{1}\)01> and <11\(\overline{2}\)0> slip directions are 245 and 437 mJ/m\({}^{2}\), respectively. Figure 2: Shear stress–displacement curves of the c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interface models: (**a**) O-c1\(\overline{1}\)01>, (**b**) O-c1\(\overline{1}\)0>, (**c**) 2O-c1\(\overline{1}\)01>, (**d**) 2O-c1\(\overline{1}\)0>, (**e**) Zr-c1\(\overline{1}\)01>, and (**f**) Zr-c1\(\overline{1}\)20>. The horizontal lines are the boundaries between region I and II. The shear stresses in region I are positive values, instead those in region II are negative. Specific Zr-O and Zr-Al bonds are also depicted at certain stages. The mechanical properties of the three interface models can be analyzed by combining the calculated ideal shear strength with the unstable stacking energy. The present results indicate that the O- model has the strongest shear resistance, and the Zr- model is the most prone to slip among the three models. It can also be concluded from the results that the shear resistance in the \(<\)11\(\overline{2}\)0\(>\) slip direction is higher than that in the \(<\)11\(\overline{1}\)01\(>\) direction in the same model. The O-\(<\)11\(\overline{2}\)0\(>\) model \(\text{c-ZrO}_{2}(001)/\alpha\text{-Al}_{2}\text{O}_{3}(1\overline{1}02)\) interface may be useful for optimizing TBC structures to meet harsh requirements.
## 4 Results
### Evaluation of DAM of Winter Wheat at Field Scale  
The DAM in Hengshui City was evaluated based on the three GPP estimation models at the mature period of the winter wheat in 2017 (Figures 9 and 10). The difference in the winter wheat DAM was only due to the different GPP estimation models. The results showed that the evaluated DAM based on GPP1 and GPP2 estimation was \(<\)6 t/ha (Figure 9a,b). The evaluated DAM, based on the GPP1 was the lowest and the evaluated DAM, based on GPP2 was also much lower than the actual DAM of the winter wheat (Figure 10a-d). The main reason was that the serious underestimation of the GPP2 and GPP1 models resulted in the decreased DAM accumulation. By contrast, the ACPM model obtained improved estimation accuracy with an error of \(<\)10% (Figure 10e,f). Based on the results of the ACPM model in Hengshui City, the RMSE of the measured DAM versus the estimated DAM was 0.98 t/ha, which was equivalent to a 7% error in the pure winter wheat pixels (Figure 10e). Setting the intercept to zero, the linear regression slope was 1.0141, and the correlation coefficient was 0.47 in the pure pixels. Furthermore, the RMSE of the measured DAM versus the estimated DAM, by the ACPM model, was 1.33 t/ha, which was equivalent to a 9% error in the pure and mixed winter wheat pixels (Figure 10f). The linear regression slope was 1.0205, and the correlation coefficient was 0.59 in the pure and mixed pixels. The mixed-pixel increased the estimation error, but the ACPM clearly obtained a higher estimation accuracy of the DAM than the GPP1 and GPP2 models.  
Figure 9: Distribution of the simulated DAM of the winter wheat with a 1 km resolution at the senescence stage, in Hengshui City in 2017. Simulated DAM based on the (**a**) GPP1 model, (**b**) GPP2 model, and (**c**) Agriculture Crop Photosynthesis Model (ACPM) model.
## 4 Discussion
Additionally, integrating advanced GPU optimization techniques, such as tensor cores for cryptographic computations, presents an exciting avenue for future exploration. Investigating the impact of varying encryption key sizes (AES-192 and AES-256) on hybrid workflows could also provide valuable insights, as would applying similar hybrid strategies to other cryptographic algorithms like RSA or ECC.
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
## 4 Method
### Auxiliary Information Constraint  
In addition, the model integrates the question embeddings \(Q^{\prime}\) obtained from graph convolution with auxiliary information to estimate the question difficulty \(\hat{D}\) and calculates the loss against the difficulty \(D\) derived from dataset statistics. By combining the reconstruction graph loss \(L_{G}\) and the difficulty loss \(L_{D}\), the model optimizes the embeddings. This approach not only refines the question embeddings but also enhances their accuracy based on difficulty analysis.  
We treat each question individually, collecting continuous variables from the dataset as inputs for the question difficulty analysis module. For the Assistant-2009 dataset, we use the following data as inputs: total number of hints, average number of hints, first response time, average response time, and number of attempts. We compute the average for each variable across the dataset and normalize these averages across all questions, feeding these normalized values into a multi-layer perceptron (MLP) to obtain \(D_{A}\). Simultaneously, the question embeddings are fed into another MLP to obtain \(D_{Q}\), and both results are combined through a fully connected (FC) layer to produce the final difficulty representation \(\hat{D}\):  
\[\hat{D}=\text{FC}\bigg{(}\bigg{[}\text{MLP}_{A}\bigg{(}\frac{\mathbf{a}-\mu}{ \sigma}\bigg{)},\text{MLP}_{Q}(\mathbf{q})\bigg{]}\bigg{)} \tag{6}\]  
where \(\mathbf{a}\) is the vector of auxiliary information for the questions, consisting of continuous variables. \(\mu\) and \(\sigma\) are the mean and standard deviation of the auxiliary information acrossall questions in the dataset. MLP\({}_{A}\) and MLP\({}_{\mathbb{Q}}\) are two different multi-layer perceptrons. FC is a fully connected layer. \(\hat{D}\) is the final difficulty representation.  
For optimizing difficulty with auxiliary information constraints, we use the error rate, statistically derived from the dataset, as the label:  
\[\mathcal{L}_{D}(\mathbf{Q})=\sum_{i=1}^{|\mathbb{Q}|}-(D_{i}\log\hat{D}_{i}+(1-D_{i })\log(1-\hat{D}_{i})) \tag{7}\]  
This loss function helps align the model's difficulty prediction with observed error rates, thereby refining the embeddings through both structural and difficulty constraints.
## 4 Flight Load Prediction Case Study  
Taking the flight recorder data of an aircraft loop maneuver as the studying dataset, 14 loop maneuvers with a flight parameter size of \(5645\times 13\) are used as the training dataset, and 2 loop maneuvers with a flight parameter size of \(851\times 13\) are used as the testing dataset. To remove the outlier data points and reduce the effects of measurement noise, the original dataset is first processed by smooth filtering, and 13 flight parameters are initially adopted as input parameters, namely the altitude, Mach number, angle of attack, vertical load factor, roll rate, pitch rate, roll angle, angle of right outer aileron, angle of left front flap, angle of left outer aileron, angle of right inner aileron, angle of left inner aileron, and pitch angle. The wing bending moment is adopted as the output for the prediction. Based on a loop maneuver, the altitude, Mach number, vertical load factor, and wing bending moment are shown in Figure 6.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 2 Methodology
### Fdfd  
The 2D Finite Difference Frequency Domain (FDFD) algorithm is well suited to nearfield analysis of dielectric or metal targets from about 0.1 to 30 wavelengths in size placed in lossy, rough dielectric backgrounds [12]. This algorithm simulates nearfield scattering from objects that are of electrical sizes that are particularly difficult to model (less than about 30 wavelengths), filling a desirable niche between geometric optics methods (high frequency or electrically large scatterers) [13] and Born approximation methods (low frequency or electrically small scatterers) [14]. The scattering objects and backgrounds can both be lossy dielectrics of any contrast and any loss tangent. The 2 DFDFD algorithm subdivides space into uniform Yee cells and applies simple finite differences to describe the 2D partial differential Helmholtz wave equation for which one dimension, typically z, is invariant. Termination of the space is done by using a perfectly matched layer (PML) to minimize scattering from the computational boundaries [15]. Compared with 3 DFDFD methods which require iterative (and slow) GMRES (Generalized Minimal Residual Method) or LGMRES (\"loose\" GMRES) solvers, the simpler 2 DFDFD algorithm uses direct matrix inversion for both the TM and TE subclasses of problems. Computational time is relatively fast and complex geometries are easy to model.
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 4 Simulation and Performance Analysis
### Analysis of Simulation Results
#### 4.2.1 Algorithm Performance Comparison
Finally, to illustrate the performance of the DRL-SRA, we set the weight factor to \(\left(\alpha_{1}=\frac{1}{3},\alpha_{2}=\frac{1}{3},\alpha_{3}=\frac{1}{3}\right)\) and analyzed the optimization of \(L_{total}\), \(SI\), and \(LI\) using the four algorithms. As shown in Figure 13, according to the deployment requirements of the short-message terminals described above, as the number of short-message terminals increased from 1100 to 2400, the \(L_{total}\) obtained by different algorithms showed a trend of rising at first and then decreasing. \(SI\) basically remained stable, and \(LI\) showed a trend of growing at first and then becoming steady. This is in line with the expected effects. With an increase in the number of short-message terminals, the number of short-message tasks received by the short-message  
Figure 11: Convergence comparison of the DRL-SRA and DQN. Figure 12: Comparison of the running times of different algorithms. satellite also increased, and L also increased. However, the number of tasks increased to a certain extent because the choice of tasks that the satellite can respond to will also increase when both \(SI\) and \(LI\) remain stable, and \(L_{total}\) will slowly decrease. At the same time, when the short-message satellite resources are fixed and when the number of short-message tasks reaches a certain threshold, the task processing capacity of the short-message satellite reaches saturation, leading to the inability to respond to more short-message tasks in time. Figure 13: Performance analysis and comparison of different algorithms. Figure 13a shows that the DRL-SRA performed the best and the TS-IHA performed the worst. The \(L_{total}\) of the DRL-SRA was lower than that of the other three algorithms in the simulation process. Figure 13b shows that DRL-SRA performed the best and GA performed the worst. When the number of terminals was 1100 or 2250, the \(LI\) obtained by the DRL-SRA algorithm was about 89.14%, which is higher than the values obtained by the other algorithms: 83.58% by the DQN algorithm, 76.37% by the DQN, and 73.83% by the GA. Figure 13c shows that the DRL-SRA performed the best and the GA performed the worst. When the number of short-message terminals was 2050, the \(SI\) obtained by the DRL-SRA was about 2000, which is higher than the value of 1800 obtained by the DQN algorithm when the number of short-message terminals was 2100 and the value of 1700 obtained by the TS-IHA algorithm when the number of short-message terminals was 2250.