esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Materials  
Isophorone diisocyanate (IPDI, resin, M\({}_{\mathrm{w}}\): 222.28 g/mol, CAS No.: 4098-7-19), polyether diols (PED, resin, M\({}_{\mathrm{w}}\): 2000 g/mol, CAS No.: 25322-69-4), hydroxyethyl methacrylate (HM, resin, M\({}_{\mathrm{w}}\): 130.14, CAS No.: 868-77-9), 2,2-dihydroxypropionic acid (DMPA, Chain extender, M\({}_{\mathrm{w}}\): 134.13 g/mol, CAS No.: 4767-03-7), tripropylene glycol diacrylate (TGD, Active diluent, M\({}_{\mathrm{w}}\): 300.35 g/mol, CAS No.: 42978-66-5) and 2,4,6-trimethylbenzoyl-diphenylphosphine oxide (TBDPO, Photoinitiator, M\({}_{\mathrm{w}}\): 348.4 g/mol, CAS No.: 75980-60-8) were offered by Linyi Dongyi Chemical Co., Ltd., Shandong, China. Filler: Al powder 4017 (piece diameter: 10 \(\upmu\)m, thickness: 220 nm, solid concentration: 65.0%, 65.0% Al powder mixed with 35.0% oleic acid) was offered by Xuhui New Material Technology Co., Ltd., Shandong, China. Dispersant: KH560 was offered by Zhongjie Chemical Technology Co., Ltd., Guangzhou, China. Other auxiliary materials: nano silica slurry (particle size: 150 nm, solid concentration: 15.0%) was offered by Nanjing Tianxing New Material Co., Ltd., Jiangsu, China. Anhydrous ethanol was offered by Zhongji Chemical Co., Ltd., Shandong, China. Al substrate (100 mm \(\times\) 50 mm \(\times\) 1 mm) was provided by Wuxi Dongyue metal material Co., Ltd. All reagents were pure without further treatment.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 4 Numerical Simulations
### Single-Factor Numerical Simulation Analysis
#### 4.4.3 Depth of Buried Pipeline  
Pipeline burial depth is one of the primary factors influencing pipeline deformation. In this project, the pipeline's burial depth was set at 5 m. Numerical simulations were conducted for six scenarios with burial depths of 1, 3, 5, 7, 9, and 12 m. Figure 10, based on these simulation results, compares the vertical and horizontal displacements of the pipeline at different burial depths.  
From Figure 10a, it is evident that uneven vertical displacement of the pipeline primarily occurs within 0-60 m of the pit ends. Furthermore, as the burial depth of the pipeline increases, the uneven vertical displacement gradually decreases. At a burial depth of 5 m, the pipeline's maximum settlement reaches the warning level. Figure 10b shows that the pattern of the pipeline's horizontal displacement is generally consistent with its vertical displacement. The greatest uneven horizontal displacement occurs at a burial depth of 1 m, particularly within 0-60 m of the pit ends. Therefore, to ensure the integrity of the pipeline and prevent damage due to uneven displacement, the burial depth of the pipeline should not be less than 5 m.
## 4 System Model
### Network Model
Each SN\({}_{i}\) extracts the required fields based on its role from this message, which has two forms: Firstly, the DFGMs table message, including fields specific to SN\({}_{i}\) itself such as the SN's ID, RE, type, status, DFG number, DFG ID, distance to the sink node, distance to its DFH, and message arrival time in sec. Secondly, the DFH table message, in the case of the proposed protocol without routing, detailing a DFH and its DFGMs, contains the SN's ID, RE, type, status, DFG number, distance to the sink node, and priority of TDMA TS, while the proposed protocol with routing has thesame message fields with additional fields for PDFR information such as the next PDFR ID, the distance between it and its PDFH, RE, status, DFG number, distance to the sink node, and priority of TDMA TS. (4) Disjoin message (to sink node): If a DFH's energy drops below the threshold, it sends this message to the sink node, which is a unicast message containing the DFGMs' IDs, their RE, and DFH information, to inform the sink node that regrouping is necessary. (5) Disjoin message (to SNs): A DFH broadcasts this message to all SNs, instructing them to cease data transmission to their DFH due to imminent regrouping, thereby ensuring synchronization across the network, regardless of whether a DFH's energy is below the threshold. Figure 1: DFG model of an IoT network area for the proposed protocol. (a) DFG model of a IoT network area for the proposed protocol without routing. (b) DFG model of a IoT network area for the proposed protocol with multi-hop routing. Figure 2: Breaking down the operational lifetime of the IoT network in the proposed protocol without routing.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 5 Discussion
### Considering Different Spatial Locations  
This section applies the LSTM model to the entire southern section of the North-South Seismic Belt (Region B) to validate the model's robustness. Region B covers a broader area, potentially including different geological features and seismic activity patterns compared to the original study area. Such testing helps us to understand the model's performance under different conditions and assess its potential in practical applications.  
After applying the model to Region B, the results are as expected: 63 seismic events were accurately predicted in 94 time windows, achieving an accuracy rate of 67%; 14 false alarms with a 15% false alarm rate; and 17 missed alarms with an 18% missed alarm rate (Figure 9). Among these, the results of the LSTM model for predicting medium-strong earthquakes of magnitude five or higher within the time window are shown in Table 7. Specifically, 49 forecasts fall within the interval of magnitude (\(5<M_{\rm L}\leq 6\)), with 35 accurate predictions, yielding an accuracy rate of 71%, and 14 false alarms, with a false alarm rate of 29%. Additionally, there were 45 predictions within the interval of magnitude (\(M_{\rm L}>6\)), of which 28 were accurate, with an accuracy rate of 62%, 0 were false predictions, and 17 were missed, with a missed alarm rate of 38%. It can also be observed that, with the increase in the number of strong earthquake cases (Table 1), there is a significant improvement in predicting strong earthquakes. Overall, the model is shown to have good generalizability for predicting medium-strong earthquakes through the extended test in Region B. Despite different challenges and constraints, the model still demonstrates better prediction results in the new region.  
Figure 9: The comparison results of the predicted and true values of LSTM in the test set.
## 2 Dataset
### Sparse-Layer Inversion  
[PERSON] and [PERSON] [5] presented an inversion approach using the fact that any local earth-impedance structure can be represented by the superposition of a limited number of layers; thus, the seismogram can also be represented as the superposition of a limited number of layer responses. This inversion assumes a sparse number of layers and can be parameterized to resolve thinner intervals than can generally be obtained with sparse-spike inversion methods [5].  
The sparse-layer inversion is accomplished using basis pursuit [9] by minimizing the objective function:  
\[\min[\parallel\mathbf{d}-\mathbf{G}\mathbf{m}\parallel_{2}+\lambda\parallel\mathbf{m} \parallel_{1}], \tag{1}\]  
where \(\mathbf{m}\) are the model parameters (analogous to a reflectivity series) that indicate the magnitudes of impulse pairs that sum to form the reflectivity series; \(\mathbf{d}\) is the seismic trace; and \(\mathbf{G}\) corresponds to a dictionary of pre-defined seismic layer responses. This objective function minimizes the mean-squared error of the fit to the data and introduces sparsity by minimizing the L1 norm of the solution [9]. The seismic responses are obtained by convolving the seismic wavelet with reflectivity patterns that are designed using even and odd impulse pairs with a range of allowable thicknesses defined by geological a priori information. This removes the bias of the sparsity constraint against seismically thin layers [6]. \(\lambda\) is the regularization parameter that acts to balance the inverted reflectivity resolution and noise. Increasing this parameter decreases the resolution of the inverted reflectivity and decreasing it too much could cause noise amplification. The optimum value of \(\lambda\) is determined empirically and depends on the data and earth structure.  
Well logs are not used as an initial model for sparse-layer inversion, which operates on a trace-by-trace basis. The output of this technique is an inverted reflectivity series that is filtered to a frequency band which subjectively best balances the resolution, stability, and accuracy of the synthetic ties to wells. The output filtered volume is referred to as spectrally broadened seismic data [2].  
Figure 1: (**a**) Location of the seismic survey. Black dots represent the relative location of 24 wells. (**b**) Inline of spectrally broadened volume. The yellow horizon represents the top of the reservoir interval.  
[PERSON] and [PERSON] [6] shows that spectral broadening accomplished by using sparse-layer inversion can be valid if the data quality permits, and the subsurface can be represented as a sparse number of layers. The study concluded that in a worst-case scenario with a highly stratified random earth reflectivity, no practical spectral broadening could be achieved outside the band of the original data. However, the researchers reasoned that this extrapolation could be useful in boosting the high-frequency signal within the original seismic bandwidth more than the high-frequency noise in spectral bluing, hence improving the seismic resolution within the original band of the data.
## 3 Model and Algorithm Flow
### Mimu/mc/ddo Integration Model Odometer  
Curved tracks are set in tunnels only in extreme cases. The length of such curves should be as short as possible and the radius should be as large as possible [22]. Even if there is a short turn, the navigation can be completed by P-INS.  
When the GNSS signal is completely lost in a tunnel, the installation angle errors are no longer estimated since there is no position or other speed reference. Motion constraints provide virtual lateral and upward speed observations of the train body. The state equation is the same as Equation (16), and the state vector is 15-dimensional. Regarding the estimated MIMU installation angles as priori constant values, the third part of Equation (24) is zero. Equation (24) can be rewritten as  
\[\left[\begin{array}{c}\delta v_{x}^{m}\\ \delta v_{z}^{m}\end{array}\right] =\left[\begin{array}{ccc}1&0&0\\ 0&0&1\end{array}\right]\mathbf{C}_{p}^{m}(\mathbf{C}_{n}^{b}\mathbf{\phi}^{n}\times\mathbf{v }^{n}+\mathbf{C}_{n}^{b}\delta\mathbf{v}^{n}) \tag{28}\] \[=\left[\begin{array}{ccc}1&0&0\\ 0&0&1\end{array}\right](-\mathbf{C}_{n}^{m}(\mathbf{v}^{n}\times)\mathbf{\phi}^{n}+\mathbf{C} _{n}^{m}\delta\mathbf{v}^{n})\] \[=\left[\begin{array}{ccc}1&0&0\\ 0&0&1\end{array}\right](\mathbf{M}_{1}\mathbf{\phi}^{n}+\mathbf{M}_{2}\delta\mathbf{v}^{n})\]  
The forward speed error of the train can be obtained from the difference between the forward speed obtained by the MIMU and the odometer:  
\[\delta v_{y}^{m} =v_{y}^{m}-v_{wheel,y}^{m} \tag{29}\] \[=\left[\begin{array}{cc}0&1&0\end{array}\right](\mathbf{M}_{1}\mathbf{ \phi}^{n}+\mathbf{M}_{2}\delta\mathbf{v}^{n})\]  
The observation equation composed of Equations (28) and (29) is  
\[\mathbf{Z}_{3}=\left[\begin{array}{c}\delta v_{x}^{m}\\ \delta v_{y}^{m}\\ \delta v_{z}^{m}\end{array}\right]=\left[\begin{array}{c}v_{x}^{m}-0\\ v_{y}^{m}-v_{alloy}^{m}\\ v_{z}^{m}-0\end{array}\right]=\mathbf{H}_{3}\mathbf{X}_{1}+\mathbf{V}_{v} \tag{30}\]  
where the observation matrix is rewritten as  
\[\mathbf{H}_{3}=\left[\begin{array}{cc}\mathbf{M}_{1}&\mathbf{M}_{2}&0_{3\times 9}\end{array}\right] \tag{31}\]
## 4 Experiments Results
### Discussion  
Figures 8-10 show the best performing models of ML, DL and the proposed models for each of the three datasets. The best performing model from ML algorithms is the LR for the three datasets. The hybrid CNN-LSTM model achieved the best results from the optimized DL models. These models are compared with the proposed stacking ensemble model. We can see that the proposed models with CBOW for all datasets produced the highest results compared to ML and DL models. For the Main-AHS dataset, the proposed model with CBOW has obtained the highest terms of performance at accuracy = 92.12%, Precision = 91.31%, Recall = 91.38%, and f1-score = 91.27%. While LR with Unigram has the worst terms of performance at accuracy = 85.96%, Precision = 86.41%,Recall = 85.96%, and f1-score = 85.13%. For the Sub-AHS dataset, the proposed model with CBOW has obtained the highest terms of performance accuracy = 95.81%, Precision = 96.06%, Recall = 95.81%, and f1-score = 95.67%. LR with Unigram has the worst terms of performance accuracy = 87.9%, Precision = 88.29% of, Recall = 87.9% of, and f1-score = 87.19%. For the ASTD dataset, the proposed model with CBOW has obtained the highest terms of performance at accuracy = 81.4%, Precision = 80.69%, Recall = 81.4%, and f1-score = 80%. LR with Unigram has the worst terms of performance at accuracy = 74.79%, Precision = 72.65%, Recall = 74.79%, and f1-score = 70.35%.  
The proposed model is compared with the existing literature for the three datasets in Table 7.  
Comparing the proposed model with the existing models proved that our model improved the performance of other approaches. For comparison with the authors who used the ASTD dataset in [9], the accuracy of CNN-LSTM was registered as 65.05%, and the f1-score of CNN-LSTM was registered as 64.46%. Performance in [17] for CNN-LSTM was registered as 66% for accuracy, 62% for f1-score, and 66% for recall. The accuracy of CNN-LSTM was 79.18% in [7]. In [18], the accuracy of CNN was registered as 79.07%. In [9], the authors used an ensemble model using voting based on CNN-LSTM, which was registered as 64.46% for f1-score. In [21], the Bi-LSTM was registered as 79.25% for accuracy and 76.83% for f1-score.  
Figure 8: The best models for the Main-AHS dataset.  
For Main-AHS, an accuracy of 88% was obtained in [7]. In [21], Bi-LSTM was registered with 92.61% for accuracy and 86.03% for f1-score. In [63], an accuracy of 92% was obtained. For Sub-AHS, in [63], the accuracy was registered as 95%. Overall, the proposed model achieved the highest performance compared to approaches that were used in the existing literature.  
Figure 10: The best models for the ASTD dataset.  
Figure 9: The best models for the Sub-AHS dataset.
# The Status of Space Weather Infrastructure and Research in Africa
## 3 Human Capital Development
### Initiatives by Professional Societies/International Organizations  
Some selected initiatives in support of space weather research in Africa are mentioned here.  
(a) African Geophysical Society  
The African Geophysical Society (AGS) is a dynamic, innovative, and interdisciplinary scientific association committed to the pursuit of understanding Earth and space for the benefit of mankind. The objectives of the AGS are to expand and strengthen the study in the African continent of the Earth and other planets, including their environments. The main goals are to facilitate cooperation among scientists; create national, regional, and international scientific organizations involved in geophysical and other related research and initiating new research and training programs; and popularize various geophysical research and training programs via scientific conferences, publications, and training in both the short and long terms.  
The AGS has brought together African researchers and created platforms for collaborative research, co-supervision of students, and external examination of theses/dissertations. This has greatly improved the space weather research landscape in Africa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **SchoolWorkshop** & **PND** & **Universities/Institutions** \\ \hline \multirow{4}{*}{ Morocco} & ISIT School of Space Weather and GNSS, 16-21 February 2015 & African Workshop on GNSS and Space Weather, 5-4 October 2020 & African Regional Centre for Space Science and Technology Education in French Language \\  & ICTP activity or copyrighted by CRASE-F UNNONOS-ICC and Boston College & Technology Education in French Language (CASTE-L) \\  & scheduled in Radar and held online because of COVID-19 & (CASTE-L) \\ \hline \multirow{4}{*}{Marco} & Third United Nations/European Space Agency Workshop on Basic Space Science, 18-22 October 1993, Lagos & \\  & First Annual National Workshop of the International Telephoxyl Year (HF), Nigeria, 27 October 2005, Federal & \\  & African Regional International Telephoxyl Year (HF), Nigeria & 27 October 2005, Federal & \\  & African Regional International Telephoxyl Year (AFN), Nigeria & 29-2 November  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **SchoolWorkshop** & **PJD** & **Universities/Institutions** \\ \hline \multirow{3}{*}{Rewands} & \multirow{3}{*}{School for describing and analyzing solar data for a better prediction of space weather: postponed in July-August} & \multirow{3}{*}{PhDs} & University of Brandrand–College of Science and Technology \\  & & & in progress: 2 \\  & & & University of Brandrand–College of Education \\ \hline \multirow{3}{*}{Republic of Congo} & \multirow{3}{*}{School HY in RC, 2,-9 December 2009} & \multirow{3}{*}{PhD defended: 1; in progress: 2} & University Mainten Ngoonuki \\ \cline{2-3} \cline{5-5}
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 1 Introduction
### High-Performance Computing in Water Demand Forecastingy  
HPC has emerged as a critical tool for handling the substantial computational demands associated with large-scale data processing and deep learning model training. Apache Spark is a widely adopted distributed computing framework that facilitates efficient data handling and analytics at scale [8; 9]. In contrast, the Message Passing Interface (MPI) enables fine-grained parallel execution across multiple processors, allowing for optimized performance in computationally intensive tasks.  
Research in water system forecasting has leveraged distributed computing frameworks for various applications. [PERSON] et al. employed clustering and support vector regression (SVR) for anomaly detection in water demand data using distributed computing platforms [10]. [PERSON] and [PERSON] [11] explored GPU-based concurrency for shallow-water equation solvers using CUDA-aware OpenMPI, demonstrating substantial computational speedups. While such studies highlight the benefits of parallel computing for environmental modeling, they primarily focus on physics-based simulations rather than data-driven machine learning approaches for forecasting.  
Existing studies have also explored the integration of Spark and MPI for specific computational tasks. Projects like MPI4 Spark [12] have demonstrated how combining Spark's ease of use with MPI's high-performance capabilities can enhance the efficiency of data-intensive applications. However, the application of such hybrid architectures in urban water demand forecasting remains underexplored. Furthermore, most studies focus on CPU-based clusters, whereas integrating GPU acceleration with Spark and MPI offers further performance improvements that have yet to be fully realized in water demand forecasting.  
An additional area of interest is the comparison between HPC-based architectures and cloud-based serverless solutions for large-scale forecasting [5]. While cloud-based auto-scaling provides an alternative to Spark/MPI-based HPC, it introduces higher operational costs and often lacks low-level optimizations, such as GPU parallelization, which are crucial for real-time water demand forecasting [13]. The MPI-based approach used in this study ensures near-optimal GPU utilization, providing a cost-effective, flexible alternative for utilities handling sensitive infrastructure data [14; 15].
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator
### Channel Models  
The proposed NOMA link-level simulator supports the block-fading channel. In the block-fading channel, the channel response is constant for a specific duration of time. This duration is usually equal to the transmission duration for one sub-frame. Therefore, for one sub-frame, the channel conditions are assumed to remain constant. Such channels are  
Figure 2: NOMA downlink link-level simulator (**a**) transmitter side; (**b**) receiver side.  
relatively easy to handle compared to fast fading channels, where the channel changes its response even for each symbol in one sub-frame. At this stage, the different channel models considered for the simulator are listed below:  
* Additive white Gaussian channel (AWGN);
* Flat Rayleigh fading;
* Flat Rician fading.  
AWGN is the simplest channel model that does not consider any type of fading and only deals with the linear addition of background white noise. The amplitude of AWGN follows a Gaussian distribution. To study the fading effects on the NOMA signal, Rayleigh, and Rician fading models were considered. The Rayleigh model is reasonable for scattered or reflected paths. However, it does not consider the line of sight (LOS), which is not the case with the Rician fading model where LOS is also taken along with non-LOS. As the presented simulator is the first version of the NOMA simulator, the channel models are limited to these models only. However, for the next upgraded version, ITU Pedestrian B, ITU Vehicular A and Winner models were incorporated.
## 4 Results and Discussion
### Generation of OCT Images
#### 4.1.2 Evaluation Criteria for Generative Models  
To evaluate the quality of the generated OCT images, we adopt the Peak Signal-to-Noise Ratio (PSNR) [29], Structural Similarity Index (SSIM) [30], Multi-Scale Structural Similarity Index (MS-SSIM), and Cosine Similarity (COSIN) [31] as the evaluation criteria. PSNR is the ratio between the maximum value of the measured signal and the number of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Quantity** & **Training** & **Testing** \\ \hline
**Fundus** & 441 & 220 & 221 \\
**OCT** & 441 & 220 & 221 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantity of the experimental images.  
Figure 16: Schematic diagram of the incremental method for glaucoma classification model training.  
noises which affect the image. The Peak Signal-to-Noise Ratio between the real OCT image \(x\) and generated OCT image \(y\) is defined as  
\[PSNR(x,y)=10\cdot log_{10}\Big{(}255^{2}/MSE(x,y)\Big{)}, \tag{10}\]  
where \(MSE(x\),\(y)\) denotes the mean square error of the real OCT image \(x\) and generated OCT image \(y\), i.e.,  
\[MSE(x,y)=\frac{1}{MN}{\sum_{i=1}^{N}{\sum_{j=1}^{M}{\left({x^{ij}-y^{ij}} \right)}^{2}}}, \tag{11}\]  
where \(M\) and \(N\) represent the width and height of the image, respectively. Here, the contrast enhancement method with a smaller MSE value and larger PSNR value significantly outperforms other contrast enhancement methods.  
The Structural Similarity Index between the real OCT image \(x\) and generated OCT image \(y\) is defined as  
\[SSIM=l(x,y)\cdot c(x,y)\cdot s(x,y), \tag{12}\]  
where \(l(x,y)\), \(c(x,y)\), and \(s(x,y)\) represent the image brightness, contrast, and structure defined in Equations (2)-(4).  
The Multi-Scale Structural Similarity Index between the real OCT image \(x\) and generated OCT image \(y\) is defined in Equation (1).  
The Cosine Similarity between the real OCT image \(x\) and generated OCT image \(y\) is defined as  
\[COSIN(x,y)=\frac{x\cdot y}{\|x\|\times\|y\|}=\frac{\sum_{i=1}^{n}{x_{i}\times y _{i}}}{\sqrt{\sum_{i=1}^{n}{x_{i}^{2}\times\sqrt{\sum_{i=1}^{n}{y_{i}^{2}}}}}}, \tag{13}\]  
where the real and generated OCT images are transposed into the vector space. The similarity between real and generated OCT image pixels is obtained from the corresponding feature vectors. The cosine, i.e., the angle between the two vectors, is closer to 1. The smaller the angle, the more similar the generated OCT image \(y\) is to the real OCT image \(x\).