esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Seaborne Passive Radar
## 3 Results of Clutter Structure Analysis
### Clutter Structure Variations  
In moving PCL platforms, the residual clutter structure and intensity change as the platform is moving and changing its orientation [18; 19; 20; 21]. In Figure 11, we can see that the ship is turning over a port, which changes the direction of the antenna array with respect to the illuminating transmitter. This change entailed a drop in the clutter intensity by 15 dB (the same color scale is used on three plots). In all cases, only the direct leakage signal was removed by an adaptive lattice clutter canceler [22] with the length of filtration truncated to the first two range cells.  
The residual clutter structure in marine PCL installation also depends on the sea state as determined by weather conditions. The clutter image observed during calm seas is depicted in Figure 12, with cross-sections for different bistatic ranges (\(R\)). For this experiment, the DAB transmitter (218.64 MHz) was selected for illumination, and the integration time was extended to 5 s to improve the resolution in Doppler frequency and  
Figure 11: Change of clutter intensity caused by turning of platform with antenna array.  
Figure 10: Sudden flash effect caused by the interference from an injected training signal.  
bistatic velocity. The short parts (52 ms) of the signal with interference from the training sequence were excluded from each integrated block's processing to obtain clear images.  
With the increased integration time, we can see artifacts repeating in the bistatic velocity axis with a ca. 15 m/s period. These artifacts result from the periodical structure of the DAB signal observed in the time domain (i.e., the transmission frame duration of 96 ms in mode I [24] with the NULL symbol at the beginning of each frame). A method of mitigating this effect may be developed in further work to improve the image's quality. Moreover, the clutter becomes Doppler-spread (up to 4... 5 m/s) around \(v=0\) line due to the PCL platform's motion.  
A more distorted image of the residual clutter was obtained when PCL signal acquisition was performed during a storm (see Figure 13). The Doppler spread slightly increased for the nearest bistatic distances (e.g., \(R=450\) m). Additional regions of increased noise level (e.g., at \(R=2\) km, 4 km, 6... 9 km) may result from signal reflections from heavy rainfall and rough waves at sea. In these regions, the Doppler spread can reach up to 10... 20 m/s. The detection of departing CASA aircraft is also clearly visible around \(R=9\) km (the red rectangle). The echo is blurry in several range cells because of the long integration period in relation to the target's speed. This aircraft detection proves that the modified version of the algorithm shown in Figure 9 can still detect moving targets.  
Figure 12: Clutter image during calm seas (a) and its cross-sections for selected bistatic ranges (b).  
Figure 13: Clutter image during the storm (a) and its cross-sections for selected bistatic ranges (b).
## 2 Method  
The speech quality evaluation model designed in this paper for air traffic control speech consists of four main components: preprocessing module, shallow feature extraction module, spatiotemporal feature parameter processing module, and score mapping module, as shown in Figure 1. In Module 1, the mel-spectrogram is computed based on the input signal of air traffic control speech, which is then divided into several sub-segments with certain overlapping regions. In Module 2, a feature extractor is designed that computes shallow features suitable for speech quality prediction from mel-spectrogram segments. In Module 3, a parallel network structure of ResNet and BiLSTM is designed to extract spatiotemporal features from shallow features and then splice the features' output by ResNet and BiLSTM, respectively. In Module 4, a fully connected layer with an attention mechanism is used to estimate the quality of air traffic control speech.
## 2 Materials and Methods  
### Materials  
Injection-induced earthquakes mainly occur in sedimentary sandstone and limestone. Classic examples of sedimentary rock were collected as samples to infer how water injection affects fault stability. For comparison, we also performed the injection test in the granite sample in this study; granite is widely distributed in the Earth's crust. The granite chiefly consists of plagioclase, potassium feldspar, quartz, amphibole, and magnetite. The plagioclase and potassium feldspar are in the shape of euhedral and semi-euhedral plate-columns, filled by the fine-grained quartz in allotriomorphic granular and magnetite in irregular shapes (Figure 1a). The sandstone mainly consists of isometric fine-grained quartz and feldspar, filled with a small amount of biotite and clay minerals at the edges and in intergranular spaces (Figure 1b). The limestone is primarily composed of calcite mixed with clay minerals to form the main structure of the rock (Figure 1c). The calcite particles are semi-euhedral, with pressure-dissolved seam lines in local areas. Rock permeability values measured by the pore pressure oscillation method [31] are listed in Table 1.  
Cylindrical samples 20 mm in diameter and 40 mm in length were used. Each sample contained a saw-cut inclined 35\({}^{\circ}\) to the sample axis to simulate a fault, and the saw-cut surface was ground flat and then roughened with 200\({}^{\mathrm{ft}}\) abrasive. A borehole with a diameter of 1 mm was drilled in the upper half of the sample, reaching the position at 1 mm from the fault (blind borehole) (Figure 2a) or the fault plane (open borehole) (Figure 2b), to investigate the influence of rock permeability on injection-induced earthquakes.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Samples** & **Mineral Component** & **Permeability (m\({}^{2}\))** \\ \hline Granite & Plagioclase, potassium feldspar, quartz, & \(2.25\times 10^{-19}\) \\ Sandstone & Quartz, feldspar, biotite, and clay & \(2.66\times 10^{-17}\) \\ Limestone & Calcite and clay & \(3.88\times 10^{-22}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineral components and permeability of the samples.  
Figure 1: Microstructure of the (**a**) granite, (**b**) sandstone, and (**c**) limestone samples.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Reconfigurable Intelligent Surface
Sum rate is affected numerically and conceptuallyby RIS size and the number of discrete phase changes. This contributes to the theoretical investigation of the pure LoS scenario [100]. * **Narrowband and Broadband** An analog beamformer and an IRS reflection matrix are used in narrowband IRS-aided hybrid beamforming via the use of effective channel response. By capitalizing on angular domain frequency-selective mm-wave channels, this approach is also applicable to broadband MIMO-OFDM systems [101]. * **High Array Gain** To transmit several data streams operating concurrently in the spatial domain with the same channel gain, RIS-assisted mm-wave hybrid MIMO systems use a geometric mean decomposition-based beamforming technique. Thus, it is possible to obtain a greater BER without allocating more power or adding more bits. The best multiple beams are obtained from an oversampling 2D-DFT codebook using a simultaneous orthogonal matching pursuit technique [101]. * **Quality of Service** In order to enhance the quality of service (QoS) for customers, mm-wave RIS systems use a hybrid analog and digital beamforming method bounded by symbol detection mean squared error (MSE). An analog transmitter, RIS, and analog receiver are developed using the inner majorization-minimization (iMM) approach, while a digital transmitter is built using an alternating direction method of multipliers (ADMM). When compared to SDR and interior point, the iMM and ADMM approaches are noticeably quicker. The iMM approach employs channel normalization to streamline calculations in response to changes in large-scale route loss. To counteract variations in path loss on a wide scale, the ADMM technique employs adaptive parameterizations [102].
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
Recent rainfall pattern changes (reduction in March-May 'long rains' and wetter October-December'short rains') have partly resulted in flooding of the lakes within the Kenya Great Rift Valley region, causing huge environmental and socio-economic losses [18; 37; 38; 39]. [PERSON] and [PERSON] [40] predicted an increase in the March-May long rains over EA, although [PERSON] et al. [22] noted the 'East African climate paradox', given the opposite trends between the observed and projected rainfall. With such high rainfall uncertainty, it is of significance to quantify future trends of rainfall erosivity and determine their implications on future soil erosion rates within the country. Agriculture contributes the largest share of Kenya's economy and provides employment to approximately 67% of the population [6; 33]. This implies that in order to achieve productivity that matches the demand of an ever-increasing human population (a 60-110% increase in food production [13; 41; 42; 43]), it is imperative to devise eco-sustainable production methods, as well as monitor soil erosion within the country from a climate change perspective. Different studies have applied various approaches to highlight environmental issues related to soil erosion within the country, majorly on short temporal scales [44; 45]. Recent works by [PERSON] et al. [46] and [PERSON] et al. [47] utilized the widely applied Revised Universal Soil Loss Equation (RUSLE) model and noted incremental changes in soil erosion rates by 66% and 14% in the Western and Rift Valley regions, respectively, over the past two decades. Similarly, a study by [PERSON] et al. [48] reported an average annual soil loss rate of up to 549 t ha\({}^{-1}\) yr\({}^{-1}\) in the central highland areas, while [PERSON] et al. [49] presented a rate of 6-10 t ha\({}^{-1}\) yr\({}^{-1}\) in the coastal region. There are very limited studies that have been conducted at the national scale while considering the spatial-temporal risk of soil loss under the current climate conditions, as well as in context of climate change [18]. Numerous studies have incorporated projected precipitation data from General Circulation Models (GCMs) to predict rainfall erosivity climatic factor within RUSLE [25; 41; 50; 51; 52]. In Africa, [PERSON] et al. [53] observed soil loss increases ranging between 12.2-20.6% within the Niger basin, while in Asia, [PERSON] et al. [54] reported a maximum erosion increase of \(>\)135% over Iran. Closer to Kenya, a recent study by [PERSON] et al. [10] showed an increase of 23% in soil loss vulnerability in Ethiopia by the year 2050. Nevertheless, only a small number of researchers have estimated soil loss risk in SSA countries using future GCMs [55]. The aim of this study has been to (i) assess the current soil erosion rates at the national scale by physiographic regions and topography and (ii) evaluate the spatial and temporal variability of future rainfall erosivity and soil loss based on GCMs of CMIP 5 under RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios.
# A Novel Continuous-Discontinuous Multi-Field Numerical Model for Rock Blasting
## 3 Numerical Example
### Blasting of the PMMA Plate
The results of Mises stress are shown in Figure 8. In the beginning of the explosion, the blasting hole area was subjected to strong impacts where the stress peak reached 100 MPa. Before 30 us, the explosive stress wave spread almost symmetrically. It can be found that the blasting wave almost reached the boundary of this region in 30 us. With the propagating of the explosion wave, crushing and cracking zones were formed. When \(t>30\) us, the explosive gas permeated into cracks, promoting the cracking processes. At \(t=36.82\) us, the cracked zone was almost formed where the stress concentrations could be found at the tips of cracks. At \(t=60\) us, the explosive gas began to dissipate. (The blue grid is the fracture grid which supports the explosive gas flow in the crack). When defining the ratio of the number of broken springs to the total number of springs as fracture degree, the history of fracture degree is shown in Figure 9. It can be seen that, in the first 30 us, the fracture degree of the cases with blocked and unblocked blasting holes are almost the same. After this, the fracture degree of the case with a blocked hole continuously increases, which is 30% higher than the case with the unblocked blasting hole in the final stage. This conclusion is consistent with Ref. [40], and it also verifies the proposed model's rationality. However, due to the calculation efficiency, the proposed model is only suitable for the two-dimensional case, and the three-dimensional case will take too much time in calculating the explosive gas flow in the crack. This is also our future work. Figure 8: Mises stress of the PMMA plate.
## 3 Results
### Self-Evaluation and Difficulties Detected (Post-Test Survey)  
* The survey given to the participants after completing the two tests reflected that, in the orthodontist group, the greatest difficulty was the design of the surgical guide  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Digital Knowledge** & & **N** & **Mean** & **Std. Deviation** & \(p\)**Value** \\ \hline T1 (Time in minutes) & No & 13 & 22.2769 & 6.75231 & 0.025 \\  & Yes & 17 & 17.6906 & 3.74830 & 0.025 \\ \hline T2 (Time in minutes) & No & 13 & 14.5315 & 4.42594 & 0.106 \\  & Yes & 17 & 12.2394 & 2.24171 & 0.106 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Planning execution time (T1 and T2) by previous Digital Knowledge.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Study Group** & & **Mean** & **N** & **Std. Deviation** & \begin{tabular}{c} **T1–T2** \\ _p-Value_ \\ \end{tabular} & \
\begin{tabular}{c} **T1** \\ _p-Value_ \\ \end{tabular} &
\begin{tabular}{c} **T2** \\ _p-Value_ \\ \end{tabular} \\ \hline \multirow{2}{*}{Dental Student} & T1 (Time in minutes) & 23.7370 & 10 & 6.72756 & 0.001 & & \\  & T2 (Time in minutes) & 15.6800 & 10 & 4.25727 & 0.001 & & \\ \hline \multirow{2}{*}{Orthodontics Student} & T1 (Time in minutes) & 16.9220 & 10 & 3.77017 & & \\  & T2 (Time in minutes) & 11.5720 & 10 & 2.32213 & 0.001 & 0.012 & 0.016 \\ \hline \multirow{2}{*}{Orthodontist} & T1 (Time in minutes) & 18.3750 & 10 & 3.85744 & & \\  & T2 (Time in minutes) & 12.4460 & 10 & 2.33010 & 0.003 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Planning execution time (T1 and T2) by Study Groups.  
(40%), while for the dental students and orthodontics students groups, the positioning of the miniscrew was more difficult (50% and 40%, respectively).
* An average of 80% of the clinicians considered their own individual software use capabilities to have improved after the second test; this result had no significance between the groups (\(p\) = 0.535).
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 3 Preliminaries
QB computing time depends lineally to the number of samples because (i) it uses MDF with linear complexity and (ii) there is no reassignment of the trajectories clustering. This means that trajectories are evaluated only once when performing the clustering and opposed to K-means, where there is amalgamating phase. Clusters are defined by a triplet \(c=(I,h,n)\), where \(I\) is a list of indexes of the trajectories in the cluster, \(n\) is the number of trajectories in the cluster, and \(h\) is the trajectories sum. \(h\) is a _Kx2_ matrix that can be updated for new trajectories (see Equation (8)). \[h=\sum_{n=1}^{n}s_{i} \tag{8}\]  
This way, the centroid of the cluster is defined by \(v\):  
\[vs.=\frac{h}{n} \tag{9}\]  
Among the strengths of this clustering algorithm, we have its above-mentioned lineal complexity, the easiness to interpret the clusters and the versatility provided by changing the number of points that define each trajectory and the threshold value to consider trajectories being similar. All of these become very useful in the exploration of a dataset of trajectories. Figure 1: Operation of MDF. However, QB was developed to overcome the complexity of segmentation of the brain fibers. In order to apply it to cluster trajectories, the following are required:  
* All trajectories have be defined by the same number of points. * A distance threshold to specify the maximum value to which two trajectories are clustered together has to be defined.
## 7 Spin Hall Effect in Topological Materials  
Because the Berry curvature is odd with respect to the time reversal operation, the AHE can only exist in magnetic systems. However, although the AHE is forbidden, the spin Hall effect (SHE) is allowed in time reversal symmetric systems. In the SHE, a transversal spin current is generated by an applied longitudinal spin current due to the SOC [91; 92; 93; 94] (see Figure 6). Conversely, a transverse charge current can be generated via a longitudinal spin current. Hence, the SHE provides an effective method to generate and manipulate the spin current without a magnetic field.  
The extrinsic SHE was proposed by [PERSON] and [PERSON] in 1971 [91], and the SHE received extensive attention after the theoretical studies of its intrinsic mechanism [95; 96; 97] and its experimental observations [93; 98; 99]. The study of the quantum version of the SHE led to the 2D TI [1; 2; 3], and the spin current from spin-momentum locked topological surface states opens up new approaches to the SHE.  
Figure 5: ANE in magnetic materials. (**a**) Schematic of the ANE. (**b**) Energy dispersion of an effective model for a magnetic WSM. The gray plane represents the energy with the peak value of the ANC. (**c**) Energy dependent ANC. (**d**) Absolute value of the ANC for different magnetic metals, with data collected from Refs. [82; 83; 84; 85; 86; 87; 88; 89].  
Similar to the AHC, the spin Hall conductivity (SHC) can be formulated via the spin Berry curvature in the Kubo formula approach [95; 96; 97], where the observable is the spin current density with \(\hat{A}=\frac{-c}{2\hbar}\frac{\{\overrightarrow{s},\overrightarrow{v}\}}{V}\). The spin current \(\overrightarrow{J}_{s-i}^{k}\) is generated by the applied electric field \(\overrightarrow{E}_{j}\) following the linear response \(\overrightarrow{J}_{s-i}^{k}=\sigma_{ij}^{k}\overrightarrow{E}_{j}\). Here, the SHC tensor \(\sigma_{ij}^{k}\) describes a spin current along \(i\) with spin polarization along \(k\) and an applied field in the \(j\) direction. Because the AHC and SHC share a similar formalism, the linear band crossings and anti-crossings can also generate strong local spin Berry curvature in both magnetic and nonmagnetic systems. Additionally, because the SHE originates from the SOC, a strong intrinsic SHE is expected in both TI and topological metals.  
In SHE experiments, apart from the SHC, there is another important parameter, the spin Hall angle (SHA). It represents the efficiency of conversion from charge current to spin current. Thus far, there are mainly two approaches for SHE materials: one is based on heavy transition metals [94; 106; 107; 108; 109], and the other is based on TIs or topological semimetals [103; 104; 105; 110]. The SHC and SHA for some reported SHE materials are given in Figure 6b. Very recently, it was found that the nodal line band structures play a crucial role for a strong SHE even in transition metals [111].
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results
### The Experimental Settings
#### 5.1.4 Baseline Methods  
We compared our approach (DomainEA) to the following baselines:  
* GCN-Align: GCN-Align [31] employs GCN to effectively encode both the structural and attribute information of the entities and to generate high-quality embedding vectors. The method calculates the similarity of the structural and attribute feature vectors of the entities, and subsequently integrates them via a weighted summation, which provides a criterion for the entity similarity assessment.
* MuGNN: MuGNN [26] employs GNN to embed the structural information of the knowledge graph into multiple channels. Additionally, it utilizes an attention mechanism to assign weights to the relationships between entities, ultimately facilitating the calculation of the similarity of embedding features for achieving alignment.
* HGCN: HGCN [27] is an entity alignment method for employing GCN to capture the implicit features of entities and relationships via jointly learning, and to iteratively learn the embedding representations of entities and relationships.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Description** & **Value** \\ \hline Keep-prob & The keep probability of neuron & 0.9 \\ Learning rate & The step size for model parameter updates & 0.001 \\ Alpha & The slope of LeakyReLu & 0.2 \\ Max epoch & The maximum number of training epochs & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of DomainEA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tools** & **Value** \\ \hline CPU & Intel Core i7-12700H \\ RAM & 32 GB \\ HDD & 2 TB \\ PyTorch & 1.10.2 \\ Python & 3.6.2 \\ Scipy & 1.5.4 \\ Numpy & 1.16.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware and software tools.  
* FGWEA: FGWEA [41] is an unsupervised entity alignment framework with Gromov-Wasserstein distance. The method can make full use of the structural information of the knowledge graph to realize a comprehensive comparison of the corresponding entities in different knowledge graphs through optimizing entity semantics and the knowledge graph structure.
* PEEA: PEEA [42] belongs to a weakly supervised learning framework. In addition to absorbing structural and relational information, PEEA is designed to enhance the connections between distant entities and labeled entities by integrating positional information into the representation learning process through a Position Attention Layer (PAL).
* SDEA: SDEA [43] consists of attribute embedding and relation embedding. SDEA first employs the pre-trained language model transformer to extract the semantic information of attribute values, and then utilizes GRU equipped with an attention mechanism to aggregate the structural information of neighbor entities.
## 3 Results and Discussions
### Condition Migration Strategies
As shown in Figure 11, the NOx emissions are reduced considerably as  
Figure 11: Accumulated real driving emissions of the condition migration strategies. Figure 10: Condition migration strategies to tighten the pollutant emission limits. expected; however, the CO emissions, PN emissions, and fuel consumption are significantly increased, and the overall performance is worse than that of Strategy G in Figure 10c. Comparing the no-start-stop strategy in Figure 8d with the condition migration strategy in Figure 10c, the latter has significantly lower trip pollutant emissions than the former but slightly higher fuel consumption, as shown in Figures 9 and 11. It can be seen that the condition migration strategies can reasonably compromise fuel economy and pollutant emissions concerning the preset emission limits, thus possibly limiting pollutant emissions beyond the start-stop reduction strategy.