esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 5 Methodology
### Measures for Collision Prevention
#### 5.3.1 Adaptive Cruise Control for Inter-Ship Collision Prevention  
ACC technology is simulated as an additional safety system to prevent the ship from shipping collision for multiple ship crossing. It also creates a distance gap among ships when they are detected by buoys to prevent any possible conflict or miscalculation in sensing ships. In our simulation environment, a ship is manually controlled, and a secondary ship is automated to maintain the ACC without crashing with the primary ship. The velocity, acceleration, and distance between two ships are continuously monitored and recorded in the simulation environment and visualized in real-time by the graphs of time vs. distance, speed, and acceleration. The change in these aforementioned parameters is demonstrated in Figures 10 and 11.  
Figure 10: Adaptive Cruise Control among ships at phase one.  
Figure 10 demonstrates two ships starting from the left portion of the GUI, going towards the right. The simulation environment resembles the real-world scenario and the change in speed among ships. Two ships are also depicted at a specific distance using a GUI. For maintaining ACC, a radar system and a camera are required to be installed in ships. Any obstacle or moving object in front is comprehensively detected by the radar using frequency modulated continuous wave (FMCW). Camera detection eventually helps in path prediction [21]. For phase one, the curves for velocity, acceleration, and distance between ships are found consistent with the principle of ACC. Initially, the rear ship was approaching from afar at a slow speed. The ship then begins to decelerate and reduces its velocity. After some time, both ships start maintaining reasonable speed, acceleration, and a level of distance.  
Figure 11 illustrates two ships in a more forward position than those in Figure 10. The GUI shows the rear ship attempting to maintain a safe distance from the forward ship; however, the forward ship accelerates greatly, and the rear ship attempts to match this. Lastly, ships maintain ACC in movable bridge zone successfully, follow a specific distance among them, and help buoys preventing any conflict in sensing or miscalculation. As depicted, ACC acts as a collision prevention technology among ships in the bridge zone, when any sudden speed variation occurs in a leading ship.  
Figure 11: Adaptive Cruise Control among ships in phase two. The forward ship (green) accelerates greatly, and the rearward ship attempts to match this.
## 3 Energy Efficient Joint Power Control and User Association Optimization Algorithm
### Joint Optimization Algorithm  
To solve Problem P2, we reformulate the problem by separating the individual variables \(\left(\mathbf{x},\mathbf{k}\right)\) and \(\mathbf{p}\) to decompose the joint optimization problem into two dependent Sub-Problems P3 and P4: the Lower level problem (Power control) and Master problem (User association optimization).  
\[P3:\quad\underset{p}{\max}f\left(p\right)=\sum\limits_{j} \sum\limits_{k}x_{jk}\ln\left(a_{jk}\right)-\omega P_{tot} \tag{15}\] \[s.t.C1 \tag{16}\]  
\[P4:\quad\underset{x,k}{\max}g\left(x,k\right)=\sum\limits_{j} \sum\limits_{k}x_{jk}\ln\left(a_{jk}\right)-\sum\limits_{j}k_{j}\ln\left(k_{j}\right) \tag{17}\] \[s.t.C2-C5 \tag{18}\]  
Here, \(a_{jk}=k_{j}r_{jk}\). We can obtain the joint power control and user association optimization algorithm as follows. In addition, the convergence of the algorithm can be seen in the simulation.  
```
0: the weight coefficient \(\omega\), iteration number \(t=0\), the maximum tolerance error \(\sigma_{1}\), the actual error \(\Delta x^{t}\), each BS's transmit power \(p_{j}^{0}=p_{j,\max},\forall j\) while\(\Delta x^{t}>\sigma_{3}\)do Update\(t=t+1\);  According to \(\mathbf{p}^{t-1}\), calculate the optimal user association vector \(\mathbf{x}^{t}\) by solving Problem P4;  According to \(\mathbf{x}^{t-1}\), calculate the optimal user association vector \(\mathbf{p}^{t}\) by solving the problem P3; Calculate\(\Delta x^{t}=\underset{j,k}{\max}\left|x_{jk}^{t}-x_{jk}^{t-1}\right|\); endwhile
```  
**Algorithm 2** Joint optimization algorithm
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 1 Introduction
Previous studies demonstrated that IMERG is a suitable product for monitoring the diurnal cycle of precipitation: IMERG precipitation estimates agree with ground-based radar measurements over the contiguous US for temporal resolutions less than 24 h, though there are regional differences [17], and IMERG captures the diurnal cycle of precipitation better than TRMM TMPA over Africa (a region where in situ observations are sparse) [18]. There are several key diurnal cycle features determined by past analyses: (1) diurnal variations in precipitation tend to be better represented by a 24 h harmonic than a 12 h harmonic [3; 14]; (2) occurrence is the dominant contributor over intensity to the diurnal cycle of accumulation [14]; (3) the diurnal cycle over land is much stronger than that over ocean, with summertime mean-to-peak amplitudes over land (ocean) at 30%-100% (10%-30%) of the daily mean accumulation and peaking from afternoon to evening (midnight to early morning) [2; 4; 7; 14]; (4) the diurnal cycle tends to be stronger in summer than in winter over land and comparable between the seasons over the ocean [4; 19]. This study aims to use four years of the version-5 (V05) level-3 IMERG product to improve understanding of the summertime diurnal cycles of precipitation accumulation, intensity and occurrence. Focussing on the summertime diurnal cycle is motivated by the stronger diurnal signals during this season over land [4; 19]. IMERG offers a novel opportunity to determine the diurnal cycle of precipitation on the global scale thanks to the finest spatio-temporal resolution precipitation estimates for a global product currently available. So far, no global analysis of the diurnal cycle has been conducted with IMERG, and no assessment of the impact of the precipitation product uncertainties onto the diurnal cycle characterisation has been performed. Ultimately, this study provides quantitative analysis of the diurnal cycle across the globe that will be useful for evaluating and constraining model representation of precipitation and its characteristics. Section 2 outlines the IMERG data product used for analysis and the methodology for deducing the diurnal cycle of precipitation. Section 3 presents case studies of the summertime diurnal cycle for land and ocean regions, and details the results of the analysis including the amplitude and phase of the global summertime diurnal cycle and their uncertainties at 2\({}^{\circ}\times\) 2\({}^{\circ}\) resolution using 24 h and 12 h harmonics. Furthermore, Section 3 provides an analysis of the variation in the diurnal cycle across the meteorological seasons. Section 4 discusses the results, and finally Section 5 covers the conclusions of the study.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 2 Related Work
### Scenario-Based Validation of AVs
Variance reduction techniques, such as importance sampling and subset simulation, rely heavily on the availability and validity of prior knowledge to shape the probability distribution used for sampling. Such data can be sourced from accident databases or large scale naturalistic driving trials [14], but this carries the risk of excluding scenarios that emerge from new automation technology. This new technology is often not included in historic accident databases and naturalistic driving trials and thus questions the validity of the used prior probability distributions. A relatively unexplored field concerns the selection of the correct parameters to describe a logical scenario (and by extension, the concrete scenarios derived from it). During the process of defining logical scenarios from the functional scenarios, parameters that formally describe the functional scenario need to be chosen. This is frequently done based on expert knowledge on which parameters might have a higher impact on the scenario outcome. Furthermore, determining the effect of the scenario parameters on the outcome is important for understanding the behaviour of AVs, and can be done via sensitivity analysis of scenarios. In the context of testing AVs, SA are commonly applied to find sensitivities of model parameters in AV sub-modules. It has been applied to assess which parameters of a radar simulation have the greatest impact on the performance of a spatial clustering algorithm which is applied to the simulated output of the radar models [16]. Vehicle dynamics models have also been analysed towards their sensitivities, as the quality of the used vehicle dynamics models has an effect on AV simulations [17]. In order to find the optimal calibration of the trajectory planning module of an AV for several different functional scenarios, sensitivity analyses can be used to reduce the complexity of a genetic algorithm based optimisation as shown in Reference [18]. The sensitivity analysis is herein used to restrict the search space of possible calibration parameters by excluding parameters with a sensitivity below a certain threshold from further simulation. The presented approach includes the simulation of the AV on a set of functional scenarios, it is however not specified how these scenarios are defined, nor does the sensitivity analysis and optimisation include a variation of these functional scenarios.
## 1 Introduction  
The widening of embankments and pavements has been increasingly undertaken to improve traffic volume. During the process of adding a new embankment to the existing embankment, additional stresses and deformations can develop under both the new and existing embankments [1; 2]. Differential settlement often occurs between and within the widened portion and the existing embankments [3; 4]. After construction for widening the embankment, pavement damage may occur due to differential settlement, such as longitudinal cracks or drop-off of pavement sections [5; 6; 7; 8], especially during the operational period of the road. Therefore, the deformation mechanics should be studied in order to avoid the emergence of these problems if possible.  
Recently, field tests and numerical analysis have been used to study the deformation mechanics for embankment projects [9; 10; 11; 12; 13; 14]. [PERSON] et al. [15] determined the three-dimensional strain localization of water saturated clay, using a numerical simulation with an elasto-viscoplastic model. [PERSON] et al. [16] studied the numerical analysis of foundation columns to support the widening of embankments. [PERSON] et al. [17] analyzed the damage patterns of river embankments due to the 2011 earthquake off the pacific coast of Tohoku, and established numerical modeling of the deformation of river embankments with a clayey subsoil layer. [PERSON] and [PERSON] [18] studied the displacements of column-supported embankments over soft clay after widening, considering soil consolidation and column layout, by using numerical analysis. These research results have indicated that the selection of appropriate calculation models combined with field tests is important to the successful design and construction of widened embankment projects.  
In this paper, the widened embankments of Binjiang Avenue in Nanjing city, China, were studied. Field tests and a numerical simulation were conducted to analyze the coordinated deformations of the subgrade combined with the old embankment, under conditions of drainage consolidation. Dynamic deformation of the subgrade was monitored during the construction of the subgrade, and the horizontal and vertical displacements occurring with the process of construction were analyzed. The post-settlement was forecasted based on the monitoring data using the three-point method and finite element method. During the process of numerical simulation, the vertical drainage system in the compressible soft ground layers was simplified as the drainage band model and equivalent sand-wall model. Meanwhile, a soft ground creep model was adopted to calculate the deformation of the subgrade with the construction process. The comparison in results between field tests and the numerical simulation were also carried out.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Control Plane
#### 3.1.1 Link Management Protocol
LinkSummaryNack message: used to reply to the LinkSummary message when the node disagrees on the link-related parameters, including the parameters that could not be accepted. Based on the design discussed above, the link management protocol can build and maintain control channels to support the transmission of management and control messages. It can also collect local resources to support resource collection and route path calculation in the routing protocol.
## 3 Materials and Methods
### Evaluation of Created Models  
#### 3.4.1 Classification Accuracy  
The confusion matrix is used to calculate the accuracy of the classifier as a whole. This matrix classifies the images into two classes: one with calcification and one without calcification. In addition, it determines the accuracy of each mammographic image. The sensitivity, specificity, F1 score, positive predictive value (PPV), and negative predictive value (NPV) of the model can be calculated using true positive (TP), false positive (FP), true negative (TN), and false negative (FN). Their definitions and calculation formulas are shown below.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Without Calcification**} & \multicolumn{2}{c}{**With Calcification**} \\ \cline{2-5}  & **Original** & **Augmented** & **Original** & **Augmented** \\ \hline Fold 1 & 10,668 & 21,336 & 835 & 20,040 \\ Fold 2 & 11,019 & 22,038 & 898 & 21,552 \\ Fold 3 & 11,691 & 23,382 & 791 & 18,984 \\ Fold 4 & 11,031 & 22,062 & 833 & 19,992 \\ Fold 5 & 11,671 & 23,342 & 759 & 18,216 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of images after data augmentation in training data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**No. of Images Used for Training**} & \multicolumn{2}{c}{**No. of Images Used for Testing**} \\ \cline{2-5}  & **Without** & **With** & **Without** & **With** \\  & **Calcification** & **Calcification** & **Calcification** & **Calcification** \\ \hline Fold 1 & 10,668 & 835 & 3352 & 194 \\ Fold 2 & 11,019 & 898 & 3001 & 131 \\ Fold 3 & 11,691 & 791 & 2329 & 238 \\ Fold 4 & 11,031 & 833 & 2989 & 196 \\ Fold 5 & 11,671 & 759 & 2349 & 270 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of images in each fold.  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]  
\[\text{F1 score}=\frac{\text{TP}}{\text{TP}+\frac{1}{2}(\text{FP}+\text{FN})}\]  
\[\text{PPV}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
\[\text{NPV}=\frac{\text{TN}}{\text{TN}+\text{FN}}\]  
In this study, we performed fivefold cross-validation and then averaged the results of the five models to calculate the accuracy, sensitivity, specificity, PPV, and NPV of the classifiers created by each CNN. In addition, receiver-operating characteristic (ROC) analysis was performed, and the area under the curve (AUC), which is the area under the ROC curve, was calculated by averaging the results of the five models and the AUC of the classifier created by each CNN.
## 1 Introduction
To address this issue, we used clustering or pooling algorithms to aggregate similar substructures and retain only one representative substructure. Consequently, we introduce an innovative approach for identifying DDIs based on substructure signature learning (DDI-SSL), which operates directly on the interaction representation between the original subgraphs of drugs to achieve more topologically preserved and physically interpretable feature extraction. Thus, substructure markers can be effectively used to aid in predicting drug-drug interactions. DDI-SSL offers these technical advancements:  
1. Enhancing the model's clarity. By employing an attention mechanism, we derive signal intensity ratings for subgraphs. Each substructure is labeled with related drug attributes, influenced by adjustable weights through this attention process, resulting in malleable dimensions and atypically contoured substructures. Additionally, because the receptive field of message passing is variable, the model has high scalability. Both the freedom of the model and the interpretability of the model's predictions are improved. 2. Improving the model's generalization ability. Compared to the global molecular structure, DDI-SSL extracts useful information from local subgraphs centered on drug atoms, proposing an interaction modeling network to accurately mine the corresponding modality of drug composition individuals, thereby achieving a modular representation of complex topological structures. The subgraph formula may project local structural details of the attribute graph into the subgraph signature set, reducing noise by anchoring relevant information. Meanwhile, the final representation of drugs is no longer a single vector but an expression of the connections between substructures, effectively improving the model's generalization ability. 3. Integrating a cooperative attention mechanism. The forecast of DDI between two pharmaceuticals hinges on the interaction score matrix learned among their substructures. However, the structural and property differences between molecules themselves can cause many irrelevant pairings, leading to the production of many unmergeable pieces of information. A collaborative attention mechanism can be used to model the mutual influence between drugs. The addition of collaborative attention effectively avoids this type of noise. This method provides an explanation for driving the DDI process and can provide domain experts with more detailed information, including which substructures may cause DDIs.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 4 Methodology
### Transmission Power Allocation  
In the previous subsection we have addressed the possible pairing of C-UE and V-UE. Once we have finished the pairing process and obtained the possible pairs, we have to assign the transmission power for each user using the formula defined by Equation (15)  
\[P_{tx}=\min(P_{RBmax},\frac{P_{o}}{PathGain*\alpha}) \tag{15}\]  
where \(P_{RBmax}\) is the maximum transmission power each C-UE or V-UE is allowed to use per RB, \(P_{o}\) is the minimum threshold for RSRP (Reference Signal Received Power), \(PathGain\) is attenuation of the signal as it propagates through space from source to destination, and \(\alpha\) is the pathloss compensation factor that can vary from 0.1 to 1.
## Materials and Methods
### Characterization Techniques  
All scanning electron microscope (SEM) images were obtained by JSM-6700F scanning electron microscope (JEOL, Tokyo, Japan), and the elements analysis of surface micro-area composition was carried out by energy dispersive spectrometer (EDS). Transmission electron microscope (TEM) images were got by JEM-2100 transmission electron microscope with a voltage of 200 kV (JEOL, Tokyo, Japan). Atomic force microscope (AFM) images were obtained by using a Bruker MultiMode 8 atomic force microscope (Bruker, Germany). These methods were used to characterize the surface and internal morphologies of the prepared composites. An Ultima IV X-ray diffractometer (Japan science. Co., Ltd., Japan) has been used to identify phase and lattice structure. For the X-ray photoelectron spectroscopy (XPS) we used an ESCALAB 250 X-ray photoelectron spectroscopy (Thermo Scientific, Massachusetts, M.A., USA) to detect the elements and determine the valence states of elements. Fourier transform infrared spectroscopy (FT-IR, Nicolet NEXUS) was used to detect the types and contents of functional groups in substances. UV-vis absorption spectroscopy combined with fluorescence spectroscopy was used to detect the composition and purity of substances
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 3 Results
### Oil Spill Classification Based on Dimension Reduction of Features  
Based on the new feature sets, classification was conducted by using SVM. The classification results obtained by employing the three feature dimension reduction methods are shown in Figure 8. Tables 12-14 demonstrate the performance of classification. The feature set derived from LLE achieved the highest overall accuracy of 92.1696%. The feature set derived from PCA obtained an OA of 91.1322%, with the lowest false alarm rate. The feature set derived from ISOMAP had an OA of 90.8705%, which is the lowest among these three algorithms. Generally, feature reduction algorithms have acceptable performance in keeping the key information for distinguishing mineral oil and biogenic films. However, in this experiment, the performance achieved by dimension reduced feature sets is constantly lower than the original feature sets, which may be related to the issue of sample selection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Ground Truth (Pixels)**} \\ \cline{2-4}  & **Oil** & **Sea** & **Total** \\ \hline
**Oil** & 5316 & 595 & 5911 \\
**Sea** & 234 & 4940 & 5174 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 92.5214% (10.256/11,085), kappa coefficient = 0.8504.  
\end{table}
Table 10: Confusion matrix achieved by SVM based on 9\(\pi\)/4 mode features.  
Figure 7: Classification result using SVM based on the features of: (**a**) DP mode; (**b**) \(\pi\)/4 mode; (**c**) \(\pi\)/2 mode; (**d**) \(S^{2}_{VV}\).
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 3 Case Study
### Data Collection
#### 3.1.3 Pumped-Storage Hydropower  
The site for the PSH plant has a gross head of 80 m. The average pump and turbine efficiencies (mechanic + electric) are set to 70% and 80%, respectively. To ascertain the optimal average pump and turbine heads, the model was initially run with the gross head value to retrieve the typical flow range of the hydraulic circuit with various PSH installed capacities. Given a flow rate range of 30 to 65 l/s, the optimal diameter for the length of a 350 m single pipeline, constructed from cast iron, is 200 mm (flow velocity does not exceed 3 m/s in the penstock). Based on this, it is feasible to calibrate the average pump and turbine heads, defined as 82 and 77 m, respectively. The PSH system was modeled  
Figure 8: Microgrid’s yearly load profile.  
with two water levels, the bottom--the river--and the upper water storage tanks, with a maximum and minimum volume of 10,000 and 1000 m\({}^{3}\), respectively. The designed optimization model applies to both water levels at the starting and ending nodes of the hydraulic circuit. This is because the total volume of water, which is equal to the capacity of the upper reservoir, remains constant. Accordingly, the mathematical algorithm developed remains applicable for this setup. The case study could also use a smaller hydropneumatic tank, or a combination of several, to produce hydropower with the storage of potential energy and compressed air.  
Concerning the grid-connected setup solutions for the small energy community system, the import and export tariffs must be defined. The grid cash flow highly depends on the type of energy contract and the price market fluctuation. Nevertheless, a fixed (simple tariff) purchase and sale price of 0.22 and 0.08 \(\upxi\)/kWh were defined, respectively, for this analysis.