esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### XUV Optimization Studies
#### 2.2.2 Gases  
Another crucial parameter in the HHG optimization study is the generating gas in use. Since noble gases are mostly used in such studies, comparative HHG measurements using Ar, Kr, and Xe gases were performed. The HHG spectra were obtained using the setup with the scanning mode MCP detector as in the laser diameter and focusing studies. In Figure 3, the comparative HHG spectra for Ar, Kr, and Xe obtained are presented. An overall higher HHG efficiency was evident for Kr and Xe gases compared to Ar. This is due to their lower  
Figure 2: Laser beam diameter and laser focus position study. (**a**) HHG spectra obtained for various laser beam diameters and corresponding energies. (**b–e**) Indicative laser focus position study with respect to the exit pinhole of the semi-infinite gas cell: (**b**) \(-\)1 mm, (**c**) 0 mm, (**d**) 1 mm, and (**e**) 2 mm.  
ionization potential (15.76 eV, 13.99 eV, and 12.13 eV for Ar, Kr, and Xe, respectively), which, however, affects their spectral cut-off. Specifically, Xe is known to have a maximum cut-off at laser peak intensities close to \(8\times 10^{13}\) W/cm\({}^{2}\), which corresponds to a cut-off of 43 nm, in accordance with our measurements. For the laser pulse energy of 1 mJ in use, the Ar and Kr cut-offs resulted in 30 and 32 nm, respectively, in accordance with our measurements. From this comparative study, it is clear that for the wavelengths of 47.6 nm and 32.2 nm, the use of Kr and Ar gases, respectively, is preferable.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Unsupervised ML Solutions
#### 4.2.1 Clustering  
Clustering is a method used to categorize data into groups with maximal similarity, such as categorizing clients based on their purchase behaviors. Common clustering techniques include centroid-based and hierarchical clustering, which focus on the internal structures of the input data [84]. The quality of clustering results is evaluated based on the specific application employing a clustering technique [49; 78]. For instance, Lloyd's clustering technique is used to group activity instances, and the evidence-theoretic K-Nearest Neighbors learning approach is used to identify and predict user activities in intelligent environments [85]. Unsupervised learning approaches have been employed to address challenges in daily living activities in smart homes, such as assisting individuals with dementia, detecting atypical visits by older individuals, and identifying social interactions and daily activities within a smart environment. An unsupervised algorithm is also used to monitor habitual acts in an individual's routine using the basic k-means algorithm. Finally, an unsupervised ML approach is proposed to identify behavioral patterns of occupants, using unannotated data from low-level sensors in a smart building [82; 83; 86].
# Recent Developments in Low-Power AI Accelerators: A Survey
## 2 Background and Related Work
### Core Operation in Artificial Neural Networks  
The core operation found in all types of neural networks (CNN, DNN, LSTM, RNN, Transformer, etc.) is the matrix multiplication and accumulation (MAC) operation, excluding Spiking Neural Networks (SNN).  
For example, there are two common layers in all CNN models; convolution (CONV) and fully connected (FC). In both layers, the scalar product (MAC) is the core in their computations. Both layers use the scalar product to multiply inputs with weights (filter elements for the convolution layer), and then accumulate the results into one value. In the CONV layer, a sliding window (filter) is moved over the input data, calculating the scalar product between the selected inputs and the filter. Similarly, one FC layer is constructed with multiple neurons, each of which calculates the scalar product between the inputs and the weights, adding a bias and then using the result in the activation function to produce an output. As a demonstration, the calculation of one neuron in the FC layer of an artificial neural network (ANN) is presented in Equation (1) and illustrated in Figure 1.  
\[y_{j}=f\left(\sum_{i=1}^{N}w_{ji}\times x_{i}+b_{j}\right), \tag{1}\]  
where \(w\) is a matrix of weights, \(x\) is a vector of inputs, \(b\) correspond to the bias, \(f\) refer to the activation function, \(N\) is the number of inputs to the neuron, and \(y\) is the neuron output. Because each neuron is computed with Equation (1) and independent of other neurons in the same layer, it enables parallelization of the computation between neurons in a layer. This is achieved by computing both the neurons and the individual input-weight pairs in parallel (accumulating the results in the process).  
The parallelization of the CONV layer is done in a similar way; computing each frame of the sliding window with the corresponding inputs and the individual input-filter weight pairs in parallel (also accumulating the results in the process). A similar process, using the MAC operation, is used for other types of ANNs.
# Characterization of the Migration of Soil Particles in Lateritic Soils under the Effect of Rainfall
###### Abstract  
Rainfall is the main cause of erosion damage in loose slope deposits. During rainfall infiltration, fine particles in the soil mass will move with water infiltration, thus changing the localized particle distribution of the soil mass, which, in turn, causes changes in the pore water pressure and volumetric water content within the slope and ultimately affects slope stability. In order to develop advanced soil and water conservation programs to prevent slope damage, it is crucial to understand and accurately reproduce the particle migration and aggregation characteristics of soils under different rainfall conditions. Therefore, this paper systematically investigates the soil particle migration characteristics of the soil body under rainfall conditions by simulating the internal erosion of the lateritic soil slope body under rainfall conditions via slope internal erosion simulation experiments and experimentally analyzing the migration and aggregation of fine particles in the slope body, as well as the changed rules regarding pore water pressure and volumetric water content at different locations of the slope body with rainfall. The results of this study show that (1) with the infiltration of rainfall, the fine particles in the slope body mainly infiltrate in the vertical direction in an early stage of rainfall; in a later stage, there is vertical downward and down-slope seepage. Therefore, fine particles always gather at the toe of the slope, which leads to relatively high water content and pore water pressure at the toe of the slope, and thus, the slope is always damaged from the toe of the slope. (2) Inside the slope, the fine particles always gather at the smallest pore diameter. With the enhancement of hydrodynamic force, they will be lost again, which leads to a sudden decrease in the local volumetric water content of the slope, and the pore space increases. Then, it is filled with seepage water, which makes the pore water pressure fluctuate or increase. (3) Based on the particle distribution parameter, the present study produced a distribution map of the fine particle content of the slope body under different rainfall intensities and established a model of the dynamic change of fine particles, which improves the understanding of the effect of the change in the fine particle composition of the slope body on the water content and the pore water pressure and may be helpful for the assessment of the initiation of the mudsides.  
soil particles; fine particle content; internal erosion characteristics; pore water pressure; volumetric water content +
Footnote †: journal: Applied Sciences
## 4 Discussion
The mechanism displayed in Figure 8 may provide a way to completely remedy the structural memory of the preexisting GLPC defect at certain network sites, so that further irradiation treatments would not regenerate it, leading to a partial regeneration of GLPC in O\({}_{2}\)-loaded samples. Furthermore, the latest experimental studies indicate that the oxygen is most probably incorporated into the network in the form of peroxy bridges [49]. Even if the CL experiment implies that the data of the first image is due to an irradiated sample, and even if a quantitative comparison of the final intensities detected in the O\({}_{2}\)-loaded and unloaded fiber should be performed (however difficult), it is interesting to note that in both samples, a limit value is reached at a high dose (Figure 5). Consequently,although a quantitative comparison of these two values needs further investigation, a stable amount of GLPC exists. Furthermore, we note that in one case (unloaded sample) we bleached and destroyed the GLPC centers, whereas in the other case, we generated them after the O\({}_{2}\) loading had significantly reduced their concentration before irradiation. It is also worth noting that both the normalized profiles reported in Figure 5 strongly suggest a role of the Ge content in determining such stable content, at least for the investigated values of 5 wt% and 9 wt%. The latter remark implies that suitable doping strategies may be envisaged to control the final amount of GLPC centers. In summary, our investigation provides a strong support in favor of the formation and stability of DIOG bulk point defects in O\({}_{2}\)-loaded Ge-doped fibers. Next, under electron irradiation, the DIOG can release molecular oxygen and convert back to GLPC, thus providing a reasonable explanation for the observed CL increase in GLPC intensity in irradiated samples (Figures 3 and 4). Furthermore, our calculations suggest that an important fraction of GLPC sites could be passivated permanently by O\({}_{2}\), resulting in the formation of a low-energy configuration which exhibits \"healed\" Ge tetrahedra linked to peroxy bridges (Figures 8 and 9). The latter configuration might not convert back to GLPC under irradiation, resulting in a lower final intensity of GLPC in O\({}_{2}\)-loaded samples compared to the unloaded and nonirradiated one.
## 3 Methods
### Threshold-Based Rule for Pace Artifact Detection  
Considering the negligible influence of the EMG and ECG components in the frequency range for which ShE is calculated (i.e., 1000-2000 Hz) a threshold-based rule for detection of the pace pulses positions in the ShE signal should be applicable on the one hand and easy to adjust on the other hand. In order to prevent the influence of steep artifacts with unknown origin on the detection accuracy, we decided to design a threshold-based rule (presented via Equation (6)) involving the mean value of the rectified ShE signal.  
\[abs(ShE(j))>K*mean(abs(ShE)), \tag{6}\]  
The value of the coefficient \(K\) is empirically adjusted as follows:  
* The pace pulses detection accuracy (defined in the next Section 3.4) is estimated for \(K\) values in the range (2, 4, 6,... 16, 18) over the training dataset (312 ECG+EMG recordings), in order to highlight \(K\) values that provide high accuracy on ECG+EMG signals.
* For the highlighted \(K\) values, the pace pulses detection accuracy is also estimated over the validation dataset (390 clean ECGs), in order to select a single \(K\) value that simultaneously provides high accuracy for ECG+EMG and clean ECG signals.
## 4 Evaluation of Results
### Public Transport Priority
#### 4.2.1 Unconditional Priority  
The unconditional priority of trans, after only 30 min of simulation runs, leads to a blockade and halting of traffic flow in the analyzed corridor of the central part of the City of Zagreb. Due to the high frequency of tram vehicles along the corridor, frequent minimal green times on opposite approaches create large queues that lead to unsustainable traffic conditions and oversaturation of vehicles at neighboring intersections. In the morning peak period, as many as 44.34% of vehicles failed to enter the network. It can be noted that due to the blockade by individual motor vehicles of the corridor through which the largest number of tram lines pass, the average travel time of trans would increase by 13.03% compared to the existing fixed control of signal controllers (Figure 9).  
Figure 8: Comparison of maximum queue length and sum of vehicle delays of existing state and scenario “do-nothing” in the morning peak period.  
Based on the conducted analysis using a simulation tool, it can be concluded that the introduction of unconditional priority of tram vehicles in the analyzed central part of the City of Zagreb would lead to complete traffic congestion in individual and tram traffic; thus, the analyzed scenario is not realistic.
## 3 Numerical Modeling
For laser fluence of 2 J/cm\({}^{2}\), which was computed to be above the melting threshold of the sample, two SAWs could be depicted having an amplitude of 3 nm and 6 nm and 550 \(\upmu\)m and 350 \(\upmu\)m, far away the epicenter, respectively. However, this non-uniform grid generation is not efficient to properly model the microscale characteristics of the surface roughness. Any geometrical modification of the non-uniform element distribution is affecting the entire solution domain and disturbs the smooth nodal distribution. Therefore, a new FEM model with a uniform mesh was here developed within a small solution domain, able to simulate the laser heated area of interest, around the laser spot and the characteristic part of the roughness periodic profile included therein. The model was based on the models that have been validated by surface acoustic waves' generation and propagation experiments with ns and fs laser sources in the works [6; 7; 8; 9]. The increase of the discretization level, due to the downscaling, aimed at in this work, was further increasing the approximation accuracy of the simulation. The dimensions of the solid target were 400 \(\upmu\)m \(\times\) 400 \(\upmu\)m \(\times\) 12 \(\upmu\)m, while a total of approximately 1.9 million elements were used. The element size of the uniform mesh was 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m \(\times\) 0.04 \(\upmu\)m. To model the surface roughness, a linear approximation on the depth (vertical \(z\)-axis) of the roughness profile was performed. The flat surface was sequentially downscaled from 0 to 3 \(\upmu\)m, to 1.5 \(\upmu\)m, to  
Figure 2: The FEM model. (**a**) Geometry and mesh. (**b**) Representative result of simulated ultrasound propagation 60 ns after the beginning of laser irradiation. 1 \(\upmu\)m, and finally to 0.5 \(\upmu\)m, reaching with increasing accuracy the spatial roughness profile. Thus, a step-like linear profile was developed according to the experimentally measured real geometry characteristics. A range of approximately 1.7-1.8 million elements was used for the models where surface roughness was considered. The beam spot had the same previously mentioned geometry. A hexahedral, 3D-solid, eight-node element was adopted for the transient analysis for all models. The temperature-dependent thermomechanical properties, the J-C material model, and failure parameters as well as the Gruneisen EOS coefficient properties of the sample were taken from the literature [23; 24; 25].
## 6 Simulation Result
### Evaluation of Driving Stability, Efficiency, and Comfort  
Table 10 compares the performance of various DRL algorithms in terms of Centerline Deviation Mean and Speed Mean, which represent their lane-keeping stability and driving efficiency, respectively.  
It is important to note that these two metrics often exhibit an inverse relationship. A higher Speed Mean may lead to an increased Centerline Deviation Mean due to the influence of vehicular inertia, particularly during curved driving. However, excessively reducing the speed to maintain center alignment can degrade driving efficiency. Therefore, an optimal trade-off between stability and efficiency must be maintained, as encouraged through reward function's design.  
TQC achieved the lowest Centerline Deviation Mean of 0.11, indicating its superior ability to maintain alignment with the lane center throughout the driving episodes. SAC followed closely with a deviation of 0.16, also demonstrating effective and stable lane-keeping behavior.  
CrossQ, TD3, and PPO recorded deviations of 0.18, 0.19, and 0.20, respectively, reflecting their moderate performance in terms of lateral control. Notably, CrossQ exhibited relatively favorable lane-keeping stability despite its overall low Route Completion performance, which is a noteworthy observation.  
In contrast, DDPG recorded the highest Centerline Deviation Mean of 0.41, suggesting significant deviation from the center of the lane. This outcome highlights a severe limitation in DDPG's ability to maintain stable vehicle alignment.  
Driving speed, represented by Speed Mean, encapsulates aspects of both driving efficiency and passenger comfort [78]. In terms of efficiency--and excluding DDPG--TD3, TQC, and CrossQ exhibited the highest performance. The fact that TQC simultaneously maintained excellent lane stability and a high average speed underscores its overall effectiveness in balancing safety and efficiency.  
On the other hand, DDPG showed an abnormally high average speed combined with poor lateral control, implying a potentially uncomfortable and erratic driving experience for passengers. This suggests that DDPG may not be suitable for deployment in scenarios requiring both safety and ride comfort.
## 1 Introduction
To achieve pixel-level interpretation, [PERSON] [37] integrated features from three polarization target decomposition methods during the analysis of target structures. Furthermore, [38] introduced a target structure interpretation method based on null-polarization features. However, the aforementioned methods interpret strong scattering points in ISAR images as canonical structures (i.e., dihedral corner reflector, trihedral corner reflector, and plate), as shown in Figure 1.  
Figure 1: Traditional method recognition results of the “MQ\(-\)1” and “Frontier” UAVs [37]. These canonical scatterers differ from the actual structure of the target, and have limited significance for target recognition. To solve this problem, we propose a novel algorithm to identify the physical structure of space targets on the basis of the component decomposition and polarimetric decomposition methods. The advantages of the proposed methodology are as follows:  
* A novel component decomposition method for electromagnetic simulation of space target components. * Direct pixel-by-pixel identification of the components of space targets. The rest of this paper is organized as follows: Section 1 presents the state of current research on ISAR image target interpretation and outlines the structure of the article; Section 2 presents the methods for component decomposition, then introduces relevant work on polarization target decomposition and feature selection; Section 3 describes the series of experiments we conducted on simulated and real data to validate the performance of the proposed methodology; Section 4 presents the results, demonstrates the limitations of the study, and suggests possible solutions to these limitations; finally, conclusions and an outline for future work are provided in Section 5.
## 2 Derivation of Boundary Conditions of 4D C/C Composite
### The Temperature Boundary Conditions of UC2  
In Figure 6, UC2 is obtained by two \(180^{\circ}\) rotational transformations of UC1. The sequence of the two rotations has no effect on the results. As shown in Figures 6 and 7, the first \(180^{\circ}\) rotational conversion to construct the UC2 is around the X1 axis (line \((x,w/2,h/2)\)), and the second \(180^{\circ}\) rotational conversion is around the Y1 axis (line \((l/2,y,h/2)\)). Finally, UC2 is constructed, which is only a quarter of the size of UC1. As seen in the Figure 6, any node \(M=(x_{1},y_{1},z_{1})\) can be transformed from the corresponding nodes in the other three regions, namely \(M1=(x_{1},w-y_{1},h-z_{1})\), \(M2=(l-x_{1},y_{1},h-z_{1})\), and \(M3=(l-x_{1},w-y_{1},z_{1})\). Two sets of new boundary planes are formed by the two rotational transformations. The first set of boundary planes are \(((0,l/2),0,z)\) and \(((0,l/2),w/2,z)\), which are generated after the first rotational transformation. The second rotational transformation constructs UC2 with another set of boundary planes, which are \((0,(0,w/2),z)\) and \((l/2,(0,w/2),z)\). Both sets of new boundary planes need to derive the appropriate temperature boundary conditions.  
Since the set of boundary planes formed by the first rotational transformation and the second rotational transformation are perpendicular to each other, the temperature gradient imposed on UC1 may become the different thermal stimulus for the two new sets. For instance, if the temperature gradient act on the \(x\)-axis, the heat flux may be perpendicular to plane \((0,(0,w/2),z)\) and plane \((l/2,(0,w/2),z)\), but parallel to plane \(((0,l/2),0,z)\) and plane \(((0,l/2),w/2,z)\). Thus, the temperature boundary conditions applicable to the two groups of boundary planes are different.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Feature Selection and Preprocessing
#### 2.2.2 Outlier Removal  
Outliers can distort model training by skewing important statistical measurements and introducing noise. Hence, they can diverge model predictions from the actual underlying patterns resulting in misleading conclusions. To this end, outlier detection and removal was accomplished via the IQR method. Specifically, data points that substantially deviate from the center distribution are identified as outliers by determining the range between the 25 th and 75 th percentiles and establishing thresholds which are 1.5 times above the upper quartile and 1.5 times below the lower one [56; 57].