esa-sceva/satcom-chunk-collection huggingface dataset:
Salt caverns can have varying depths, heights, and sizes, reaching up to 1,000,000 m\({}^{3}\)[58]. Usually, leaching procedures are utilized to build these caverns, which are then filled with cushion gas, such as nitrogen or natural gas, to maintain their integrity. Tightness tests are performed before gas storage activities to assure cavern integrity [55]. Salt caverns use less cushion gas than other storage solutions, such as drained reservoirs. Studies have demonstrated that multiple injection and production cycles in a year make salt caverns feasible, safe, flexible, and cost-effective for storing H\({}_{2}\)[16; 47; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66]. Thus, salt caverns can be used to store H\({}_{2}\) for long-term periods, and further stored H\({}_{2}\) can be utilized to meet short to medium-term fluctuations during energy demands. Furthermore, currently, four H\({}_{2}\) storage (composed of 95% H\({}_{2}\)) projects are operating around the globe: one in the United Kingdom (UK) and three in the United States of America (USA) [16; 26; 45]. In Teesside, UK, H\({}_{2}\) storage began in 1972, with a total of 210,000 m\({}^{3}\) storage volume and an energy storage capacity of approximately 29.9 GWh, requiring no cushion gas. In Clemens, USA, H\({}_{2}\) storage began in the year 1983, with a total storage volume of 580,000 m\({}^{3}\) and an energy storage capacity of around 157.4 GWh, requiring 2.21 \(\times\) 10\({}^{6}\) Kg H\({}_{2}\) of cushion gas. In Moss Bluff, USA, H\({}_{2}\) storage began in the year 2007, comprising a total storage volume of 566,000 m\({}^{3}\) and storing around 146.7 GWh of energy, requiring 2.30 \(\times\) 10\({}^{6}\) Kg H\({}_{2}\) of cushion gas. In Spindletop, USA, H\({}_{2}\) storage began in the year 2017, with a total storage volume of 906,000 m\({}^{3}\) and storing around 274 GWh of energy [45]. Some previous storage sites have been repurposed for natural gas storage, such as in Kiel Germany, due to the presence of insoluble content when creating the caverns. From this study, we aim to identify the UHS potential in the Baltic basin particularly in Lithuania. To the best of the authors' knowledge, neither a review nor a research study has been conducted before in Lithuania on UHS prospects. Hence, the objectives of this review article include the following points (a) to compile and synthesize existing research and data on UHS in the Baltic basin and Lithuania; (b) to evaluate various geological formations in Lithuania to determine their suitability for UHS; and (c) to identify research gaps and areas for further research through small scale feasibility study on UHS by simulation using existing data.
## 4 Test and Evaluation Methods  
For ASTM D638 and ASTM D4402, the mechanical properties and construction allowable time of the ESA binder were evaluated [31; 32]. According to \"Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering.\" (JTG E20-2011) and \"Field Test Methods of Highway Subgrade and Pavement\" (JTG 3450-2019) [33; 34], the rutting test, beam bending test, water immersion Marshall test, freeze-thaw splitting test, and pendulum friction test were conducted. The high-temperature rutting resistance, low-temperature cracking resistance, moisture damage resistance, and skid resistance of ESAC and HSAC were analyzed. The detailed concrete test information, specification requirement values, and evaluation index calculation methods are shown in Tables 3 and 4. Additionally, with reference to \"Technical Specification for Maintenance Technology of Long Span Bridge Steel Deck Epoxy Asphalt Concrete Pavement\" (DB32/T 3292-2017) and \"Highway Performance Assessment Standards\" (JTG 5210-2018) [35; 36], the pavement performance of the trial road after one year of operation was tested and evaluated.
## 4 Comparison with Other Similar Solutions  
Similar software have been used to analyze the movements of small animals, such as ZebraLab (ViewPoint Behavior Technology), EthoVision [13], idTracker [14], JAABA [16], CTRAX [11], wrMTrck (www.phage.dk/plugins/wrmtrck.html, accessed on 23 December 2022) [17], and AnimalTracker [18]. ZebraLab or EthoVision (Noldus Information  
Figure 5: A case study of the effect of household cleaning agents on the swimming speed of zebrafish. (**A**) After image analysis with AniWellTracker, the average speed of individual animals over time was displayed as a heatmap chart in the time series heatmap viewer window. Individual rows represent different animals. (**B**) From the raw data of A, the average speed and 95% confidence interval of several animals per hour were calculated and plotted as a chart. (**C**) The average speed over 2 h was finally plotted as a bar graph. n.s.: not significant, ***:**\(p\)-value < 0.01.  
Technology) can quantitatively analyze the movements of various animals, but they are commercial solutions and thus have limitations in terms of cost. In the case of idTracker and JAABA developed with the commercial software MATLAB, there are limitations not only in terms of cost but also in terms of the installation and use period of the software. The AnimalTracker and wrMTrck were developed as ImageJ plugins depending on ImageJ, and BonZeb [12] utilizes the OpenCV library. Conversely, AniWellTracker is a free software in which all image processing algorithms are written natively using VB.NET and do not use external libraries, such as ImageJ or OpenCV. AniWellTracker specializes in image analysis using multiwell plates compared to other software. In addition to the ability to track animals, it is also an advantage that the results can be displayed immediately after analysis without using other software. In addition, regarding the difficulty of using the software, AniWellTracker has a user-friendly interface that allows users to intuitively press a button to proceed with the analysis. Unlike other software, such as MATLAB, LABVIEW, ImageJ, and OpenCV, even beginners without programming experience can easily use the software. The software installation process is also simple, and because AniWellTracker does not require any other software or plugins, an executable file can be executed immediately without a complicated setting process. Because it is not a commercial software, there is no limit on the usage period, and it can be freely installed on multiple PCs.
# Reconstructing the 26 June 1917 Samoa Tsunami Disaster
## 3 Methods and Data
### Tsunami Exposure and Damage Analysis  
#### 3.2.1 Building and Population Exposure Data  
Buildings on Savaii and Upolu that were located within the maximum tsunami inundation extent were remotely digitized from aerial and Google satellite imagery captured between 2016 and 2020. Buildings were manually digitized in GIS software, using roof outlines to create a vector polygon layer. Physical and non-physical attributes including use category and construction frame were assigned to each building object (Table 1). Samoan building construction frame typologies defined by [9] were attributed to features based on their size (i.e., outline area), roof shape, and use category. In the absence of resources such as Google street view to visually validate use category and construction frame, these attributes were confirmed by local engineers and disaster risk management experts. The outline area (m\({}^{2}\)) for confirmed buildings was calculated in GIS software (Esri, Redlands, CA, USA).  
Samoa's usually resident population was obtained from the 2016 national census [13]. Descriptive statistics for 'usually-resident population' at their residence on census day are aggregated and publicly available at national, district, and village levels. Here, we apply usually resident population at village levels (\(V_{Pup}\)) to determine a residential building-object population rate (\(BP_{Rate}\)) as follows:  
\[VB_{area}=\sum_{j=1}^{n_{i}}BA_{i} \tag{1}\]  
\[VBP_{Rate}=VP_{j}\ /VB_{area} \tag{2}\]  
\[BP_{Rate}=VBP_{Rate}\ /\ BA_{i} \tag{3}\]  
where \(BA_{i}\) is the outline area (m\({}^{2}\)) for a residential building located in village \(j\), with \(n_{i}\), the number of residential buildings within village \(j\). The variable \(VB_{area}\) is the total residential building outline area within village \(j\). Residential building-object \(VBP_{Rate}\) is the per m\({}^{2}\) residential building population based on the usually resident population (\(VP\)) of village \(j\).
# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 2 Related Work
Insights into how hyperparameters were chosen and their impact on model performance would be valuable for replication and further research. [PERSON] et al. [19] presented a comprehensive overview of methodologies aimed at detecting and classifying PQ events within smart grid systems. Their study delves into the intricate realm of power quality analysis, shedding light on key approaches utilized in the identification and categorization of PQDs. In [20], the authors explore the realm of power quality event classification through the integration of complex wavelets phasor models and a tailored convolutional neural network. Their study delves into the utilization of advanced signal processing techniques and machine learning algorithms for accurate classification of PQ events. The study may lack a thorough discussion on the generalizability of the proposed methodology, potentially leaving questions about its applicability to diverse power quality event scenarios. PQDs can significantly impact electrical equipment lifespan, performance, and overall power system reliability. The limitations of traditional methods in accurately identifying these disturbances present substantial challenges in maintaining reliable and efficient electrical systems. Undetected or misclassified power quality issues can lead to operational problems, equipment failures, increased downtime, and potential safety hazards. Traditional approaches often rely on simplistic measurements and standardized thresholds, failing to capture complex disturbances or provide a detailed analysis of their root causes. This inadequacy in accurate identification hinders the implementation of targeted mitigation strategies and effective addressing of underlying issues, resulting in compromised power quality and potential economic losses. Consequently, this paper proposes the integration of multi-scale CNNs with attention mechanisms for PQD classification. The proposed model inherently learns features through its convolutional layers, utilizing a streamlined architecture (as detailed in Section 3). This design significantly reduces the need for extensive feature extraction, allowing the model to operate efficiently with fewer layers. As a result, the proposed model requires less computational power, facilitating faster forward and backward passes during training. Moreover, data augmentation is utilized to reduce overfitting problems, thus enhancing the efficiency and reliability of PQDs classification.
## 5 Conclusions  
This study demonstrated the effectiveness of a novel algorithm-based system for analyzing three critical phases of canoe performance in real time. Our analysis revealed significant performance patterns across multiple trials: rapid initial acceleration in Series 1 (5 s to stabilization) compared to longer durations in subsequent trials (9-10 s), consistent stabilized speeds (14.62-14.98 km/h) but increasing space requirements for stabilization (13.49 to 31.70 m), and clear performance deterioration evidenced by decreasing maximum speeds (18.58 to 17.30 km/h) and minimum speeds (11.17 to 10.17 km/h). The marked differences in acceleration efficiency, quantified through slope analysis (2.58% to 0.74%), provided novel insights into technique deterioration across trials.  
These findings have several practical applications for coaches and athletes. The real-time analysis of acceleration profiles enables immediate feedback on technique efficiency, while the quantification of the space required for stabilization offers a new metric for assessing training adaptations. The comprehensive analysis of velocity maintenance through maximum and minimum speed tracking provides objective measures for evaluating technical consistency and fatigue resistance. This technological approach has the potential to enhance canoe training methodology by providing coaches and athletes with objective data to support performance optimization and training periodization. It can be implemented post-training and, in the future, could allow real-time monitoring through on-device algorithm integration.  
Conceptualization, S.A. and S.B.; methodology, S.A., S.B. and C.D.G.-C.; software, S.A. and S.B.; validation, C.D.G.-C., M.G.-L. and J.P.-O.; formal analysis, S.A., S.B. and C.D.G.-C.; investigation, C.D.G.-C. and M.G.-L; resources, J.P.-O. and M.G.-L.; data curation, C.D.G.-C.; writing--original draft preparation, S.A. and S.B.; writing--review and editing, C.D.G.-C., M.G.-L. and J.P.-O.; visualization, S.A. and C.D.G.-C.; supervision, M.G.-L. and J.P.-O.; project administration, J.P.-O.; funding acquisition, J.P.-O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University Chair in Physical Activity, Sport, and Health, supported by the Cartagena City Council, and derives from the doctoral thesis of [PERSON] entitled \"Development and applica-tion of mathematical algorithms applied to the monitoring of physical exercise and sports training\", completed in the framework of the PhD program in Physical Activity and Sport Sciences of the University of Murcia.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 4 Case Study
### Ablation Experiment  
To investigate the effect of the bidirectional long short-term memory network, attention mechanism, and Pearson correlation analysis on the present prediction model, an ablation experiment is conducted, where the model parameters and dataset are the same. As shown in Table 3, compared to CNN-LSTM, BiLSTM, attention mechanism, and Pearson correlation analysis are helpful for improving the accuracy of predicting coal mine water inflow.BiLSTM provides bidirectional information exchange, the attention mechanism can enhance the level of attention to key parts of time series, and Pearson correlation analysis weights multiple factors related to water inflow; therefore, the CNN-BiLSTM-Attention model based on Pearson correlation analysis can extract accurate features from time-series data, improving the accuracy of predicting coal mine water inflow.
## 4 Simulation and Test Verification
### Simulated Test  
For the simulation experiments, the signal used a down-converted Iridium intermediate frequency (IF) simulated signal with a center frequency of 270,833 Hz. The jamming signal was modeled as Gaussian band-limited noise with a mean of 0 and a variance of 1.
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
## 5 Evolution of ScienceLoRa towards ScienceIoT
### Accommodating High Performance Access Network
The research for accommodating high-performance wireless technologies includes designing an integrated network architecture based on license-free communication protocols, devising network slicing techniques for high-performance data transmission, and interconnecting a high-speed wired backbone (i.e., ScienceDMZ) [25] and wireless networks. In addition, flow management and communication mechanisms should be developed according to the application scenarios. For example, the 5G use cases consist of enhanced mobile broadband for high-speed data transmission, massive machine-type communication for accommodating a large number of end devices, and ultra-reliable and low latency communication to support mission-critical applications. Furthermore, the special requirements of each research field such as privacy, QoS, and multi-level security would be supported by the integrated wireless network.
## 3 Derivations of the Solutions  
### Semi-Analytical Solutions  
According to Equations (3), (4), (7), and (10), the governing equations can be expressed in the following matric expression:  
\[\frac{\partial\widetilde{\mathbf{X}}}{\partial z}=\mathbf{A}\widetilde{ \mathbf{X}}+\mathbf{B} \tag{11}\]  
where  
\[\widetilde{\mathbf{X}}(\text{z},s)=\left[\widetilde{u}_{\text{a}}(\text{z},s), \ \widetilde{u}_{\text{w}}(\text{z},s),\ \widetilde{J}_{\text{a}}(\text{z},s),\ \widetilde{v}_{\text{w}}(\text{z},s)\right]^{\text{T}}\!,\]\[\mathbf{A}= \begin{pmatrix}0&0&-\frac{g}{k_{\mathrm{a}}}&0\\ 0&0&0&-\frac{\gamma_{\mathrm{w}}}{k_{\mathrm{w}}}\\ s\frac{k_{\mathrm{a}}}{C_{\mathrm{a}}g}&s\frac{k_{\mathrm{a}}\widetilde{C}_{ \mathrm{a}}}{C_{\mathrm{a}}g}&0&0\\ s\frac{k_{\mathrm{w}}C_{\mathrm{w}}}{C_{\mathrm{w}}^{\prime}\gamma_{\mathrm{w} }}&s\frac{k_{\mathrm{w}}}{C_{\mathrm{w}}^{\prime}\gamma_{\mathrm{w}}}&0&0\\ \end{pmatrix} ,\ \mathbf{B}= \begin{bmatrix}0\\ 0\\ -\frac{k_{\mathrm{a}}}{C_{\mathrm{a}}^{2}g}\left(u_{\mathrm{w}}^{0}+ \widetilde{C}_{\mathrm{w}}u_{\mathrm{a}}^{0}+\frac{\widetilde{D}_{\mathrm{w}}} {s+C}\right)\\ -\frac{k_{\mathrm{w}}}{C_{\mathrm{w}}^{\prime}g}\left(u_{\mathrm{w}}^{0}+ \widetilde{C}_{\mathrm{w}}u_{\mathrm{a}}^{0}+\frac{\widetilde{D}_{\mathrm{w}}} {s+C}\right)\\ \end{bmatrix} .\]  
It is supposed that the boundary conditions are permeable on the top surface and impermeable on the bottom surface to the air and water phases. The specifics are outlined as follows:  
Boundary at \[z=0\] (12a) Boundary at \[z=H\] : \[\frac{\partial u_{\mathrm{a}}(H,t)}{\partial z}=\frac{\partial u_{\mathrm{w}}(H,t)}{\partial z}=0\] (12b)  
The solutions in the Laplace domain of EPAP, EPWP, and normalized settlement can be obtained by using the same method described after bringing in the boundary conditions and initial conditions:  
\[ \begin{array}{l}\widetilde{u}_{\mathrm{a}}(z,s)=\frac{1}{C_{\mathrm{a}}^{2}( \xi^{2}-\eta^{2})}\Big{\{}-\frac{\mathrm{ch}[\xi(H-z)]}{\xi^{2}\mathrm{ch}( \xi H)}\Big{[}\Big{(}\eta^{2}+\frac{s}{C_{\mathrm{a}}^{2}}\Big{)}\Big{(}u_{ \mathrm{a}}^{0}+\widetilde{C}_{\mathrm{a}}u_{\mathrm{w}}^{0}+\frac{\widetilde{ D}_{\mathrm{a}}}{s+b}\Big{)}+\frac{s\widetilde{C}_{\mathrm{a}}}{C_{\mathrm{w}}^{ \prime}}\Big{(}\widetilde{C}_{\mathrm{w}}u_{\mathrm{a}}^{0}+u_{\mathrm{w}}^{0} +\frac{\widetilde{D}_{\mathrm{w}}}{s+b}\Big{)}\Big{]}\\ +\frac{\mathrm{ch}[\eta(H-z)]}{\eta^{2}\mathrm{ch}(\eta H)}\Big{[}\Big{(}u_{ \mathrm{a}}^{0}+\widetilde{C}_{\mathrm{a}}u_{\mathrm{w}}^{0}+\frac{\widetilde{ D}_{\mathrm{a}}}{s+b}\Big{)}\Big{]}\Big{(}\xi^{2}+\frac{s}{C_{\mathrm{w}}^{ \prime}}\Big{)}+\frac{s\widetilde{C}_{\mathrm{a}}}{C_{\mathrm{w}}^{\prime}} \Big{(}\widetilde{C}_{\mathrm{w}}u_{\mathrm{a}}^{0}+u_{\mathrm{w}}^{0}+\frac{ \widetilde{D}_{\mathrm{w}}}{s+b}\Big{)}\Big{\}}+\frac{u_{\mathrm{a}}^{0}}{s}+ \frac{\widetilde{D}_{\mathrm{a}}-\widetilde{C}_{\mathrm{a}}\widetilde{D}_{ \mathrm{w}}}{s(1-\widetilde{C}_{\mathrm{a}}\widetilde{C}_{\mathrm{w}})(s+b)} \end{array}  \tag{13}\]  
\[
# An Improved YOLO Model for Traffic Signs Small Target Image Detection
## 4 Experiment and Discussion
### Ablation Experiment  
In order to validate the efficacy of the improvements proposed in this study on traffic sign detection performance, we designed and executed a series of ablation experiments for comparative analysis as follows: (1) the original YOLOv5 model; (2) the YOLOv5 model augmented with a Context Augmentation Module (CAM); (3) the YOLOv5 model enhanced with SPD-Convolution modules; (4) the YOLOv5 model with the incorporation of a decoupled head; (5) the simultaneous integration of the SPD-Convolution modules and decoupled head to the YOLOv5 model; (6) the YOLOv5 model with the combined addition of CAM, SPD-Convolution modules, and a decoupled head. We conducted the experiment under the same conditions on the TT100K dataset, with an input size of 640 \(\times\) 640. Please note that any mention of YOLOv5 in this context, and throughout the paper, specifically refers to the YOLOv5s model. The specific performance results of these experiments are detailed in Table 8.  
Table 8 provides a comprehensive analysis of our experimentation results. With the integration of the enhanced Context Augmentation Module (CAM), the model's capacity for assimilating contextual information from varied receptive fields has been significantly  
Figure 12: Comparative analysis of training and validation losses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Parameters/M** & **P\%** & **R\%** & **mAP\%** \\ \hline YOLOv5 & 7.13 & 92.9 & 86.8 & 91.8 \\ CAM & 9.1 & 92.5 & 86.6 & 92.0 \\ SPD & 8.67 & 93.2 & 90.3 & 94.0 \\ Head & 14.4 & 93.3 & 89.9 & 93.8 \\ SPD + Head & 15.9 & 95.3 & 90.0 & 95.2 \\ SPD + Head + CAM & 24.3 & 95.0 & 91.6 & 95.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation experiments.  
bolstered. As a direct result, the mean Average Precision (mAP) experienced an uptick of 0.2%. Incorporating the SPD-Convolution modules into the network architecture necessitated a minor augmentation of the model parameters, yet these alterations proved beneficial. They were instrumental in attaining an amplified precision, recall, and mAP of 0.3%, 3.5%, and 2.2%, respectively. The introduction of the advanced coupled head provided a notable boost to the detection accuracy and kindled a more rapid network convergence. These enhancements were quantified through a 0.4% boost in precision, a 3.1% rise in recall, and a 2% increase in mAP.  
On implementing all proposed improvements in the YOLOv5 model, our proposed algorithm outperformed the original YOLOv5 on all metrics with significant margins. Specifically, we observed lifts of 2.1%, 4.8%, and 3.6% in precision, recall, and mAP, respectively. From these experimental results, it is evident that the proposed modifications have succeeded in significantly enhancing the effectiveness of the YOLOv5 model.