esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Studies
### Hydraulic Conditions  
Precipitation and snow melt affect the groundwater level; therefore, analyses without and with seepage were carried out. As the snowmelt takes time and the corresponding rise in the groundwater level does not change dramatically in a short period, a steady-state analysis for the seepage condition was adopted. The height of the groundwater level was unavailable when the landslide occurred, so different groundwater tables were chosen in the calculations to see the failure mechanism for different cases. The location of the groundwater table at the leftmost boundary was defined as the initial groundwater table in this contribution.  
Three initial groundwater tables were applied in the calculation, which were 1400 m (GWT1), 1430 m (GWT2), and 1458 m (GWT3), respectively (Table 8). Compared to GWT1, the average height of GWT2 was increased by roughly 21 m, and the average height of GWT3 was increased by about 36 m. The largest part of the assumed basal shear zone was above the groundwater table for the lowest one (GWT1), and it was below the groundwater table for the middle one (GWT2). The highest groundwater table (GWT3) was almost approaching the slope surface. The stability analyses for the no-seepage condition and seepage condition from the lowest to the highest level could basically reflect the effects of seepage and changes in pore water pressure on the slope stability and the range of instability due to snow melting and possible precipitation. The changes in the pore pressure distribution at the location of the basal shear zone is explored in Section 5.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Unit** & **Upper Layer** & **Lower Layer** \\ \hline \(\gamma\) & (\(\mathrm{kN}/\mathrm{m}^{3}\)) & 20 & 23 \\ \(\varepsilon^{\prime}\) & (MPa) & 20 & 100 \\ \(\
u^{\prime}\) & (\(\cdot\)) & 0.4 & 0.4 \\ \(c^{\prime}\) & (kPa) & 31.4 & 96 \\ \(\varphi^{\prime}\) & (\({}^{\circ}\)) & 19.1 & 27.6 \\ \(\psi\) & (\({}^{\circ}\)) & 0 & 0 \\ \(c^{*}\) & (kPa) & 29.7 & 85.1 \\ \(\varphi^{*}\) & (\({}^{\circ}\)) & 18.1 & 24.9 \\ \(k\) & (m/s) & \(9\times 10^{-11}\) & \(5\times 10^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 7: Material parameters for both soil layers.
## 3 Results and Analysis
### Analysis of Bearing Performance  
#### Loading-Displacement Curve  
Please see Figure 9 for the axial loading-axial deformation curves (\(F\)-\(U\) curves), of all specimens measured on site.  
From Figure 9, it can be observed that (1) the loading-displacement curve of the specimens with recycled concrete bricks bears resemblance to that with clay bricks. In contrast, the curves for specimens with no stem embedded show no sign of reduction, which indicates that the impediment is a brittle one. (2) The compressive resistance and ductility of the structure can be largely elevated via using the square-shaped wood stem, square-shaped concrete-filled steel pipe, and circ  
Figure 9: Axial loading-axial displacement curve: (**a**) Group One; (**b**) Group Two; (**c**) Group three; and (**d**) Group four.  
pipe, the last one among which stands as the most preferable choice. (3) With the support of fly ash blocks, the integrated structure grows in compressive strength, yield limit and ductility.
## 7 Simulation Results and Performance Analyses
### The Network Lifetime  
This section compares the six protocols and analyzes the network lifetime of them by the number of surviving nodes in deferent rounds when 300 nodes are considered in the network. As illustrated in Figure 9, regardless of which protocol we use, the number of surviving nodes decreases as the number of rounds increases. Nevertheless, our proposed EECRP outperforms its competitors in the number of nodes alive. For better evaluation of the EECRP, we bring in indicators namely FND (first node dead), HND (half of the nodes dead), and LND (last node dead). As shown in Figure 10, the first node of the LEACH, LEACH-ERE, EGRC, FBCPSO, FCMMFO, and EECRP dies in about the 353 rd, 451 st, 505 th, 548 th, 574 th, and 623 rd round, respectively, which means that in terms of the FND indicator, the efficiency of the EECRP is 8.5%, 13.7%, 23.4%, 38.1%, and 76.5% higher than that of the FCMMFO, FBCPSO, EGRC, LEACH-ERE, and LEACH, respectively. In terms of the HND and the LND, the EECRP outperforms the LEACH protocol by 57.3% and 46.5%, respectively. To conclude, the proposed EECRP is the most effective in extending the network lifetime as it uses the enhanced CHN selecting scheme, which distributes the network load equally. In addition, the EECRP uses the BPNN to fuse data and adopts the GA to identify the optimal multi-hop transmission paths, reducing and balancing the energy consumption. The LEACH performs the worst among these protocols because it does not take the residual energy of nodes into account when selecting CHNs, which makes some selected nodes with low energy die too early. Additionally, the multi-hop transmission paths between the CHNs and the SN have not been considered in the LEACH. The FCMMFO and the FBCPSO outperform the LEACH, LEACH-ERE and EGRC. Nevertheless, they are both inferior to the EECRP, which is because they do not optimize the multi-hop routing paths between the CHNs and the SN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Network size & \(5\) km \(\times\) \(5\) km \(\times\) \(1\) km \\ The number of nodes & 200, 250, 300 \\ The percent of CHNs & 10\% \\ SN coordinate & (2500, 2500, 0) \\ Data packet size & 1024 bits \\ Other packets size & 64 bits \\ Sound velocity & 1500 m/s \\ Transmission rate & 2048 bps \\ Receiving power & 50 uW \\ Energy initialization of nodes & 100 J \\ Energy consumption for data fusion & 50 nJ/bit \\ Frequency (\(f_{c}\)) & 10 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters.  
Figure 9: The number of nodes alive versus the number of rounds for different protocols.
# MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-to-Text Challenge: Extension
## 3 MLLP-VRAIN Systems
### Experiments and Results
On the one hand, surprisingly, the offline system is surpassed by the three streaming ones on _test-2020_, by up to 1.1 absolute WER points (6% relative). We believe that this is caused, first, by the Gaussian mixture HMM-based VAD module producing false negatives (speech segments labelled as non-speech). As the non-speech model was trained with music and noise audio segments, and given the inherent limitations of Gaussian Mixture models, it is likely to misclassify speech passages with loud background music and noise (often present in TV programmes) as non-speech. Second, the Full Sequence Normalization technique might not be appropriate for some types of TV shows, as local acoustic condition changes become diluted in the full-sequence normalization, leading to somewhat inaccurate acoustic scores that can degrade system performance at that point. On the other hand, it is remarkable that our primary 2020 system significantly outperforms the 2018 winning system by 28% relative WER points on both _dev2_ and _test-2018_ (25% in the case of our latency-focused system _c2-streaming_600 ms_t_), and also works under strict streaming conditions. All these streaming ASR systems can be easily put into production environments using our custom gRPC-based server-client infrastructure ( [[https://mllp.upv.es/git-pub/jorge/MLLP_Streaming_API](https://mllp.upv.es/git-pub/jorge/MLLP_Streaming_API)]([https://mllp.upv.es/git-pub/jorge/MLLP_Streaming_API](https://mllp.upv.es/git-pub/jorge/MLLP_Streaming_API)), accessed on 7 January 2022). Indeed, ASR systems comparable to _c2-streaming_600 ms_t_ and _c1-streaming_1500 ms_nt_ are already in production at our MLLP \begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & _dev2_ & _test-2018_ & _test-2020_ \\ \hline _p-streaming_1500 ms\_nlt_ & 11.2 & 11.6 & **16.0** \\ _c1-streaming_1500 ms\_nt_ & - & 11.6 & 16.1 \\ _c2-streaming_600 ms\_t_ & 12.0 & 12.3 & 16.9 \\ _c3-offline_ & - & 12.0 & 17.1 \\ \hline
2018 open-cond. winner [28] & 15.6 & 16.5 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: WER of the participant systems, including our open-condition system submitted to the 2018 challenge, computed over the _dev2_, _test-2018_ and _test-2020_ sets. In bold, the result from our primary submission in the contest main test set _test-2020_. Figure 2: WER versus mean empirical latency (in seconds) on _dev1-dev_, measured with different pruning parameters for the search, and considering only interpolation schemes including TLM. An acoustic window size of 0.6s was used in all cases (plotted points). Transcription and Translation Platform ([[https://ttp.mllp.upv.es/](https://ttp.mllp.upv.es/)]([https://ttp.mllp.upv.es/](https://ttp.mllp.upv.es/)), accessed on 7 January 2022) for streaming and off-line processing, respectively. Both can be freely tested using our public APIs, accessible via the MLLP Platform.
## 8 Summary and Future Prospects
This method has been validated using CYGNSS raw IF data, and the results show that compared with the L1C/A waveform, the signal-to-noise ratio of the combined reflection waveform can be improved by about 2 dB. In experiments on height measurement and wind speed measurement, the combined reflection measurement results showed a significant improvement in measurement accuracy compared to L1C/A. This study provides us with new research ideas. Therefore, the new GNSS-R spaceborne signal processing method can provide a reference for the design of future GNSS-R instruments. In the future, we should also explore and process various types of spaceborne GNSS-R reflection signals, improve the design of GNSS-R instruments, and continuously improve the quality of spaceborne GNSS-R signals to achieve higher-accuracy measurement results. All authors have made significant contributions to this manuscript. Conceptualization, J.B.; validation, J.B. and Q.W. ; formal analysis, Q.W., Y.W., D.H. and H.Y. ; writing--originaldraft preparation, Q.W., Y.W. and D.H.; writing--review and editing, J.B., Q.W., Y.W. and X.Z. ; supervision, J.B.; funding acquisition, J.B. and X.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grants 42404037 and 42471483, in part by the Yunnan Fundamental Research Projects under Grant 202401 CF070151, in part by the Platform Construction Project of High-level Talent in Kunming University of Science and Technology under Grant 20230041, in part by the National College Students' Innovation and Entrepreneurship Training Program, under Grant 202410674088; in part by the Student Extracurricular Academic and Technological Innovation Fund of Kunming University of Science and Technology, under Grant 2024 ZX091; and in part by the Innovative Training Plan Program for College Students of Yunnan Province, under Grant S202310674221. The GNSS-R data of FY-3E can be obtained from the Fengyun Data Service Network, the URL is [[https://satellite.nsmc.org.cn/portalsite/default.aspx](https://satellite.nsmc.org.cn/portalsite/default.aspx)]([https://satellite.nsmc.org.cn/portalsite/default.aspx](https://satellite.nsmc.org.cn/portalsite/default.aspx)), accessed on 12 March 2025; The CYGNSS data presented in this study are openly available in PO.DAAC at [[https://cygnss.engin.umich.edu/data-products/](https://cygnss.engin.umich.edu/data-products/)]([https://cygnss.engin.umich.edu/data-products/](https://cygnss.engin.umich.edu/data-products/)), accessed on 12 March 2025. The spaceborne GNSS-R data from TDS-1 mission is available at www.merbys.co.uk, accessed on 12 March 2025. We would like to thank NASA for providing CYGNSS data and the TDS-1 and CYGNSS scientific teams for their hard work in providing the data used in this study. The authors would like to thank the anonymous reviewers for their in-depth reviews and helpful suggestions that have largely contributed to improving this article. The authors declare no conflicts of interest.
## 2 Review of the Standard PSO and Its Variants  
### Standard PSO  
As a kind of population intelligent optimization algorithm, PSO is generally used because of its excellent performance in solving continuous optimization problems. It is assumed that the population contains \(M\) particles in the \(D\)-dimensional search space, and each particle is composed of two kinds of information, the particle position, and velocity, in which the particle position vector, \(\mathbf{x}_{i}=(x_{i,1},x_{i,2},\ldots,x_{i,D})^{T}\), represents a solution in the search space, and the particle velocity vector, \(\mathbf{v}_{i}=(v_{i,1},v_{i,2},\ldots,v_{i,D})^{T}\), indicates the search direction of the particle. Particle \(\mathbf{x}_{i}\) moves at speed \(\mathbf{v}_{i}\) in the search space. Due to the influence of inertia, it is more inclined to maintain its own speed and can learn from its personal best experience \(\mathit{Pbest}_{i}\) and population best experience \(\mathit{Gbest}\) to improve the \(\mathbf{v}_{i}\). In the initialization phase, each particle is given a random initial position and an initial velocity. After that, the update equations of \(\mathbf{v}_{i}\) and \(\mathbf{x}_{i}\) are as follows:  
\[v_{i,j}^{(t+1)}=wv_{i,j}^{(t)}+c_{1}\tau_{1,j}(\mathit{Pbest}_{i,j}^{(t)}-x_{ i,j}^{(t)})+c_{2}\tau_{2,j}(\mathit{Gbest}_{j}^{(t)}-x_{i,j}^{(t)}) \tag{1}\]  
\[x_{i,j}^{(t+1)}=x_{i,j}^{(t)}+v_{i,j}^{(t+1)} \tag{2}\]  
where \(i=1,2,\cdots,M,j=1,2,\cdots,D\). \(w\) is an inertia weight, \(c_{1}\) and \(c_{2}\) are the acceleration coefficients, and \(\tau_{1,j}\) and \(\tau_{2,j}\) are two uniformly distributed random numbers independently generated within [0, 1] for the jth dimension. \(t\) is the number of iterations. The velocity and position of \(\mathbf{x}_{i}^{(t)}\) are updated with Equations (1) and (2) and achieve the new position \(\mathbf{x}_{i}^{(t+1)}\) of particle \(i\). If the fitness of \(\mathbf{x}_{i}^{(t+1)}\) (\(\mathbf{f}(\mathbf{x}_{i}^{(t+1)})\)) is better than \(\mathit{Pbest}_{i}^{(t)}\), \(\mathit{Pbest}_{i}^{(t)}\) is replaced by \(\mathbf{f}(\mathbf{x}_{i}^{(t+1)})\). Otherwise, \(\mathit{Pbest}_{i}^{(t)}\) will remain unchanged. Similarly, \(\mathit{Gbest}\) can be updated until the termination conditions are met.
## 2 Materials and Methods
### Regression Models and Genetic Algorithm
#### 2.1.3 Tree Models: Decision Tree, Random Forest, and Extreme Gradient Boosting  
Decision trees (DTs) are hierarchical models introduced by [PERSON] and [PERSON] [76], applicable to both classification and regression tasks. A DT consists of a root node, internal decision nodes, and terminal leaf nodes. It follows a divide-and-conquer strategy, recursively splitting data until achieving homogeneous subsets. While effective, DTs tend to overfit, necessitating pruning techniques to eliminate redundant branches. [PERSON] [77] proposed the random forest (RF) model as an ensemble of DTs that aggregates predictions from multiple subtrees trained on different data subsets. RF improves robustness and generalization but increases computational cost and reduces interpretability.  
Tree-boosting techniques combine multiple weak learners sequentially, refining predictions iteratively. Gradient boosted decision trees leverage gradient descent to minimize residual errors. [PERSON] and [PERSON] [78] introduced extreme gradient boosting (XGB), a highly scalable tree-boosting algorithm that employs parallel computation, regularization, and sparsity-aware learning to handle missing data. Despite its strong predictive capabilities, XGB is computationally expensive and prone to overfitting with excessive trees or small datasets. Further details on these models can be found in the work of [PERSON] et al. [66].
# MIMO Self-Heterodyne OFDM Using Band Selection Technique
###### Abstract  
The 5G technology is a promising technology to cope with the increasing demand for higher data rate and quality of service. In this paper, two proposed techniques are implemented for multiple input multiple output (MIMO) self-heterodyne OFDM system to enhance data rate and minimize the bit error rate (BER). In both of the two proposed techniques, Band Selection (BS) approach is used, once with Space Time Block Coded (STBC) for the first proposed technique (BS-STBC), and once again with Frequency Space Time Block Coded (FSTBC) for the second proposed technique (BS-FSTBC). The use of the BS in the proposed techniques helps to choose the sub-band with better subchannels gains for sending the information and consequently, minimize the BER. Moreover, the use of the FSTBC instead of STBC helps to use the spectral efficiently and hence increase data rate. The simulation results show that the proposed techniques BS-STBC and BS-FSTBC, for the MIMO self-heterodyne OFDM system, provide a great enhancement in the BER performance when compared to the conventional techniques. Moreover, the simulation results show that the first proposed technique BS-FSTBC outperform the second propose technique BS-STBC in term of the BER performance.  
self-heterodyne; OFDM; space time coded block; band selection frequency space time block coded (BS-FSTBC); space time block coded approach (STBC); band selection space time block coded (BS-STBC) +
Footnote †: journal:  
Article  
MIMO Self-Heterodyne OFDM Using Band Selection Technique
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Principal Component Analysis (PCA) for the Crime Rate Index  
To enhance the analytical rigor of the study while simplifying the crime data representation, principal component analysis (PCA) was judiciously applied post-train-test split to ensure that no data leakage occurred. The process began with a meticulous standardization of all crime rate variables using Z-score normalization, a crucial step to ensure equitable contributions to the PCA without undue influence from variables with greater magnitude.  
Subsequently, PCA was implemented solely on the training dataset, reducing dimensionality while capturing the most explanatory variance. This analysis unveiled a primary principal component that encapsulated dominant crime trends across various types. The  
Figure 7: Contextual analysis of crime rates across Maryland Counties: (**a**) AGG assault rates by County, (**b**) robbery rates by county, (**c**) rape rates by county, (**d**) murder rates by county, (**e**) motor vehicle theft rates by county, (**f**) lareeny theft rates by county.  
loadings from this component suggested that high-impact crimes such as robbery and aggravated assault were most strongly predictive of regional crime disparities, particularly in urban settings.  
To aid in the practical application of these findings, the resultant crime rate index was normalized between 0 and 1. This normalization not only facilitated comparisons across diverse Maryland counties but also enhanced the interpretability and utility of the index for policy-making and strategic law enforcement deployment.  
The index has proven to be an efficacious predictor of crime trends, effectively reducing the complexity of the model while maintaining high predictive accuracy. By integrating PCA in this manner, the study offers a robust, streamlined view of crime dynamics, supporting the development of targeted, data-driven public safety initiatives across the state. The careful application of PCA in a scenario constrained to training data, corroborated by methodological transparency, establishes a solid foundation for the predictive model, ensuring its relevance and reliability in real-world applications.
## 4 Test Results and Discussion
### Surface Roughness Degradation Anisotropy
#### 4.2.1 Analysis of the Roughness Degradation along the Shear Direction  
Figure 13 shows the change process of the roughness parameter \(A_{0}\theta_{max}^{*}/(C+1)\) along the shear direction (the \(90^{\circ}\) and \(270^{\circ}\) analyzed directions) during 20 cycles of shear testing under normal stresses of 0.4 MPa, 1.0 MPa, and 2.5 MPa. A roughness degradation trend the same as that shown in Figure 12 was observed, and the residual roughness parameters decreased as the normal stress increased.  
To reveal the relationship between the number of shear cycles, normal stress, and surface roughness degradation, considering that roughness deterioration is a nonlinear process, a roughness degradation fitting formula (RDF) under cyclic shearing is proposed. The fitting formula can be rewritten as follows:  
\[r=\frac{a}{n+b}+c \tag{4}\]  
where \(n\) is the number of shear cycles, \(r\) is the joint roughness parameter \(A_{0}\theta_{max}^{*}/(C+1)\), parameters \(a\) and \(b\) are factors related to the roughness degradation rates, and \(c\) is the residual roughness when \(n\) is large enough.  
Fitting the test data plotted in Figure 13 shows that the RDF fit the relationship between the number of cyclic shears and the variation in the joint surface roughness. Furthermore, due to the anisotropy of the joint surface roughness, when an analyzed direction of the  
Figure 12: Box-and-whisker plot of the statistical roughness parameter for cyclic sheared joints under 2.5 MPa.  
Figure 13: Roughness parameter change process along the cyclic shear directions under different normal stresses: (**a**) \(90^{\circ}\) and (**b**) \(270^{\circ}\) analyzed directions.  
joint roughness was determined, parameters \(a\), \(b\), and \(c\) had the relationship \(a/b+c=r_{0}\) with the initial roughness \(r_{0}\) (constant) of this analyzed direction.  
Figure 14 shows that parameter \(c\) decreased as the normal stress increased. These two factors followed a linear correlation, which is valid for predicting the residual roughness \(c\) of joint surfaces after cyclic shears under different normal stresses.
# Assessing the Carbon Intensity of e-fuels Production in European Countries: A Temporal Analysis
## 3 Results & Discussion
### Statistical Analysis of Potential Load Factor of Low-Carbon \(H_{2}\)  
As presented in the introduction, the European legislative framework sets a threshold for the use of grid electricity for hydrogen production through water electrolysis, achieving a GHG emission reduction of at least 70%. Assuming an electrolysis efficiency of 65%, the corresponding limit for the carbon intensity of electricity used is 18.3 gCO\({}_{2}\)eq/MJ of electricity. Relying on the previously presented carbon intensity time-series, we retrospectively calculated the potential load factor that could be achieved while respecting this limit for the year 2023 (e.g., how many hours can the electrolysis operate in a year to produce low-carbon hydrogen), which is shown in Figure 10. To reveal the impact of the produced versus consumed electricity, this figure shows the impacts of the type of electricity considered in the calculations on the load factor. We first see that many countries are never able to reach the limit. For the countries able to reach this limit, many are reaching it for a limited number of hours per year. There are, finally, a low number of countries that have a carbon intensity of the production mix below this limit for an important number of hours a year. However, when looking at the consumption mix and not the production mix, the potential load factor is reduced for most of them; Luxembourg is an extreme case because the carbon intensity of the production mix, mainly relying on hydropower, is often below the limit, whereas the consumption mix, involving the significant import of fossil-based electricity, is rarely lower. Denmark and Finland are the only countries performing better with their consumption mix due to imports of low-carbon electricity from Sweden and Norway.  
Figure 10: Percentage of time where the carbon intensity of hydrogen is below the threshold.