esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Information Management Processes  
Realigning business processes to fit in with those recommended by the information management standards and the tools that may have to be adopted to comply with them can be challenging [26]. Several studies in the infrastructure domain have shown that there is still a need to align processes, as there are still gaps related to the creation of information and the governance of the production and use of information [17]. A review of transport infrastructure further reinforced these observations and showed that there is a lack of alignment of standards with transport industry processes, hindering adoption even further [18].  
The analysis of standards shows that ambiguities in industry standards and terminology can also lead to a lack of adoption or a deviation from the standards [27]. Therefore,there is a need to ensure that project participants are aware of these challenges and to ensure that their current processes are aligned in order to adopt information management. As a result, several organisations have produced more specific guidelines to help organizations follow a standard procedure on their projects [28]. The inclusion of concepts related to the standards, guidance for the transition from the previous standards, and processes for project delivery would be beneficial for producing good-quality asset information.  
The studies carried out both in academia and industry have highlighted the need to realign existing processes to ensure that the standards are being adhered to while bringing value to the organisations implementing them. Further, the current guidelines have been developed to leave them open for interpretation by users. For example, ISO 19650-1 [29] describes the Project Information Model (PIM), which is expected to contain all the details of a construction project within a short paragraph. It was essential to ensure that all stakeholders interpreted the guidelines and standards in the same way. Then, their information requirements were enforced while complying with the ISO standards. Therefore, it is still necessary for clients to ensure that, on a project or organization level, information requirements are specified clearly, and the processes that are going to be used are correctly enforced.
# A Creep Model of Steel Slag-Asphalt Mixture Based on Neural Networks
## 5 Analysis of Prediction Results
### Insights from the Test Set Predictions  
Based on the abovementioned hyperparameters, the model underwent comprehensive training with the entire dataset from the training set. The strain in the test set was subsequently predicted. Figure 13 demonstrates the juxtaposition of experimental values with the predictions made by two distinct creep models, with Table 5 enumerating the evaluative metrics across different temperatures. Notably, the LSTM creep model surpasses the BP model in predictive efficacy, registering RMSE and MAE that are merely 45.26% and 45.09%, respectively, of the BP model's figures. Optimal predictive performance is noted at 25 \({}^{\circ}\)C, beyond which, as temperatures escalate, prediction errors incrementally rise. However, the LSTM model consistently correlates strongly with the actual strain trajectory, evidenced by an almost perfect average correlation coefficient of 0.991.  
Furthermore, it is noted that prediction errors increase as temperatures rise, a consequence of the relatively poor thermal stability of asphalt materials. The softened point of asphalt in this paper is about 46 \({}^{\circ}\)C. Beyond this temperature, the mechanical characteristics of the steel slag-asphal mixture underwent significant transformations.  
Figure 12: Comparison of the sliding window prediction results ((**a**) LSTM, (**b**) BP).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **RMSE/10\({}^{-4}\)** & **MAE/10\({}^{-4}\)** & **R** \\ \hline \multirow{5}{*}{BP} & 25 \({}^{\circ}\)C & 5.59 & 3.63 & 0.956 \\  & 35 \({}^{\circ}\)C & 9.86 & 9.00 & 0.962 \\  & 45 \({}^{\circ}\)C & 17.54 & 16.96 & 0.959 \\  & 55 \({}^{\circ}\)C & 20.19 & 19.64 & 0.964 \\  & 65 \({}^{\circ}\)C & 27.47 & 26.60 & 0.960 \\  & Average value & 16.13 & 15.17 & 0.960 \\ \hline \multirow{5}{*}{LSTM} & 25 \({}^{\circ}\)C & 2.07 & 1.15 & 0.991 \\  & 35 \({}^{\circ}\)C & 5.29 & 4.87 & 0.994 \\ \cline{1-1}  & 45 \({}^{\circ}\)C & 5.95 & 5.79 & 0.993 \\ \cline{1-1}  & 55 \({}^{\circ}\)C & 7.44 & 7.07 & 0.992 \\ \cline{1-1}  & 65 \({}^{\circ}\)C & 15.75 & 15.31 & 0.987 \\ \cline{1-1}  & Average value & 7.30 & 6.84 & 0.991 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation indicators for the test set.  
Figure 13: The BP and LSTM prediction values for different temperatures in the test set: ((**a**) 25 \({}^{\circ}\)C (**b**) 35 \({}^{\circ}\)C (**c**) 45 \({}^{\circ}\)C (**d**) 55 \({}^{\circ}\)C (**e**) 65 \({}^{\circ}\)C).
## 2 Problem Formulation and Preliminaries
### Problem Formulation  
In general, the multi-objective and multi-parameter antenna designs can be mathematically described as  
\[\left\{\begin{array}{l}\min F(\mathbf{x})=\left(f_{1}(\mathbf{x}),f_{2}(\mathbf{x}), \ldots,f_{m}(\mathbf{x})\right)^{T}\\ s.t.\,\mathbf{x}\in\mathbf{X}\end{array}\right. \tag{1}\]  
where \(\mathbf{X}\) is a design space; \(\mathbf{x}=(x_{1},\ldots,x_{n})\) is an \(n\)-vector design variable, representing a defined multi-parameter antenna structure; \(f_{k}(\mathbf{x}),k=1,2,\ldots,m\) is the \(k\)th design objective, such as reflection coefficient, gain, efficiency, antenna size and so on; The goal of the multi-objective antenna optimization problem is to find a Pareto front (PF) [5; 27], i.e., multiple designs showing the trade-off among various antenna characteristics under consideration.  
For a multi-objective problem (MOP), any two designs \(\mathbf{x}^{(1)}\) and \(\mathbf{x}^{(2)}\) for which \(f_{k}\) (\(\mathbf{x}^{(1)}\)) \(<\)\(f_{k}\) (\(\mathbf{x}^{(2)}\)) and \(f_{l}\) (\(\mathbf{x}^{(1)}\)) \(<\)\(f_{l}\) (\(\mathbf{x}^{(2)}\)) for at least one pair \(k\
eq l\), are not commensurable, that is to say, none is better than the other in the multi-objective sense [27]. Therefore, we define the Pareto dominance relation \(<\) as: for the two designs \(\mathbf{x}^{(1)}\) and \(\mathbf{x}^{(2)}\), we have \(\mathbf{x}^{(1)}\)\(<\)\(\mathbf{x}^{(2)}\) (\(\mathbf{x}^{(1)}\) dominates \(\mathbf{x}^{(2)}\)) if \(f_{k}(\mathbf{x}^{(1)})\)\(\leq\)\(f_{k}(\mathbf{x}^{(2)})\) for all \(k=1,2,\ldots,m\) and \(f_{k}(\mathbf{x}^{(1)})\)\(<\)\(f_{k}(\mathbf{x}^{(2)})\) for at least one \(k\). In a MOP, we want to find a representation of a so-called PF \(\mathbf{X}_{p}\) (viz. Pareto-optimal set) of the design space \(\mathbf{X}\), such that for any \(\mathbf{x}\in\mathbf{X}_{p}\), there is no \(\mathbf{x}^{\prime}\in\mathbf{X}\) for which \(\mathbf{x}^{\prime}<\mathbf{x}\)[27]. For clarity, Figure 1 indicates the PF of two goals \(F_{1}\), \(F_{2}\). It can be seen that points A, B, and C are located at the PF, and these three solutions are the Pareto-optimal ones at the same time. That is to say, none of these three solutions is dominant over the other two. Relatively, points D, E, F, and G are feasible solutions but not Pareto-optimal ones.
## 3 Test Results and Analysis
### Failure Mode Analysis  
The evolution of the crack propagation and strain field can be visually observed by the 3D-DIC system. Identifying the final failure mode of the sample under biaxial test was critical for understanding the crack propagation characteristics and general failure mechanism inside the rock mass. The failure modes of schist and sandstone under various lateral stresses and sample types are displayed in Figure 7. The failure modes of different types of rocks were classified as follows:  
Figure 6: Strain–time curves for the step-loading process.  
Figure 7: Strain contours and failure photos of rock samples under biaxial step loading: (**a**) schist; (**b**) sandstone.  
1. Intact schist sample: The failure mode of sample SC-BI-5 is single-slope shear failure. The macroscopic cracks consist of coalescence cracks with a single slope and arc-like cracks close to the inverted arch. The major crack was indicated by the greatest strain value in the strain concentration zone, which was seen from the strain contour to be in the main diagonal direction (second and fourth quadrants). The failure mode of sample SC-BI-10 is tensile failure along the weak bedding plane and shear failure across the weak bedding plane. The failure mode of sample SC-BI-15 is tensile-shear composite failure along the weak bedding plane. The failure process was similar to that of sample SC-BI-10, but the crack propagation range was wider, and the sample was separated by the crack into slab-shaped rock blocks with a wider width;
2. Schist sample with a prefabricated inverted arch: The failure mode of sample SC-BP-5 involved splitting tensile failure along the weak bedding plane and shear failure across the weak bedding plane. First, several early cracks that formed near the free surface expanded out and coincided with the weak bedding plane. Then, parallel platy structures formed in the central layers at the bottom of the inverted arch of the samples, followed by the outward bulging of the crushed fractures. Subsequently, the tensile cracks interconnected with each other at the distal ends to form new cracks, showing an inverted trapezoidal failure surface, and eventually an overall destabilization fracture. The failure modes of samples SC-BP-10 and SC-BP-15 are similar to that of sample SC-BP-5;
3. Intact sandstone sample: The failure modes of samples SA-BI-5, SA-BI-10 and SA-BI-15 are composite failures of tension and shear. The sandstone failure process was relatively simple. There were arc-shaped rock blocks peeling off near the free surface in the same direction as the concave mold, and an oblique dislocated shear plane was formed on the plane perpendicular to the _Y0Z_ plane, accompanied by massive fragments peeling off;
4. Sandstone sample with a prefabricated inverted arch: The failure mode of samples SA-BP-5, SA-BP-10, and SA-BP-15 involved compressive shear failure, which formed an oblique shear plane perpendicular to the _X0Y_ plane, resulting in overall failure of the sample. The sample remained relatively intact after failure, as shown in Figure 8.
# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
###### Abstract  
This study aims to predict leaks in water-carrying pipelines by monitoring pressure drops. Timely detection of leaks is crucial for prompt intervention and repair efforts. In this research, we represent the network structure of pipelines using graph representations. Consequently, we propose a machine learning model called Graph Convolutional Neural Network (GCN) that leverages graph-type data structures for leak prediction. Conventional machine learning models often overlook the dependencies between nodes and edges in graph structures, which are critical in complex systems like pipelines. GCN offers an advantage in capturing the intricate relationships among connections in pipelines. To assess the predictive performance of our proposed GCN model, we compare it against the Support Vector Machine (SVM) model, a widely used traditional machine learning approach. In this study, we conducted experimental studies to collect the required pressure and flow data to train the GCN and SVM models. The obtained results were visualized and analyzed to evaluate their respective performances. The GCN model achieved a performance rate of 94%, while the SVM model achieved 87%. These results demonstrated the potential of the GCN model in accurately detecting water leaks in pipeline systems. The findings hold significant implications for water resource management and environmental protection. The knowledge acquired from this study can serve as a foundation for predicting leaks in pipelines that transport gas and oil.  
graph convolutional network; graph machine learning; leakage detection 2023
## 1 Introduction
The sensitivity of the signal to flaw detection was also studied using the response signal from the artificially made defects in composite parts. Next, guided wave modes were generated using the PAUT system for defect detection in sample plates. The guided waves generated using PAUT were used to show the feasibility of flaw detection on composite plates.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 6 Simulation and Analysis
### Simulation Results and Analysis
DC-NOMA uses the dislocation folding method to pair group users, which can ensure the diversity of users in the group, so its performance is better than CC-NOMA algorithm and CR-NOMA algorithm. The average throughput of the proposed DD-NOMA algorithm is 2.6%, 8.1% and 12.5% higher, respectively, than that of the DC-NOMA, CC-NOMA and CR-OMA algorithms when \(\eta=0.5\). This is because the proposed user pairing algorithm can avoid users with similar channels from being divided into the same group when the channel gain distribution of  
Figure 6: Comparison of BS capacity and satellite capacity with different \(\gamma_{S}\). users is unbalanced. Meanwhile, the optimal power control factor is given to guarantee the throughput of edge users, which can effectively improve the throughput of far users. Figure 8: Average throughput of BS far users under different \(\eta\). Figure 7: Total user throughput under different SINRs. Figure 9: Average throughput of satellite far users under different \(\eta\). Figures 10 and 11 compare the outage probabilities of the four algorithms; it can be seen that the outage probabilities of the BS far user and the satellite far user increase with the increase of the distance between the far user and the BS or the beam center. This is because with the increase of the distance, the more serious the channel fading of the far user, the lower the transmission rate, and the higher the outage probability. DD-NOMA algorithm has the best outage probability performance. When \(\eta=0.5\), the outage probability of satellite far users is only 0.01%. The reason is that in order to meet the outage performance requirements of users with poor link quality, the optimal power control factor designed can ensure the minimum transmission rate of far users and reduce the outage probability. The far user in CR-OMA systems only achieves a diversity gain of primary user, which is worse than DC-NOMA and CC-NOMA. This is because in CR-OMA systems, only after primary user's QoS requirement is strictly satisfied, far user is allowed to be admitted into the system. Note that increasing \(\eta\) from 0.4 to 0.5 can increase the outage probability. This is because when \(\eta=0.5\), the path loss of far users become larger, the transmission rate is reduced, the CR method designed cannot ensure the minimum transmission rate of far users, which in turn affects the outage probability. In contrast, DC-NOMA can ensure that both users' outage probability are lower
## 1 Introduction  
Competitive swimmers compete in various swimming distances that last from 20 s (50 m) to 15 min (1500 m), and energy demands are covered by both aerobic and anaerobic metabolic systems with varying percentages of contribution [1]. Consequently, swimmers apply endurance training sets within the moderate, heavy, and very heavy exercise intensity domains [2; 3; 4] or sprint interval training (SIT) sets with maximum effort called \"all out\" to improve both metabolic systems [5; 6; 7].  
Applying SIT may consist of four to twelve bouts of 10 to 30 s duration with various recovery intervals [8; 9]. It is suggested that SIT is effective in improving swimmers' performance in competitive distances of 50 to 400 m [10; 11; 12]. Then, coaches may include SIT during the specific or competitive preparation periods aiming to improve performance in short-duration races [10; 11; 12; 13; 14]. However, SIT is a strong stimulus, and the physiological response of its systematic application should be examined to evaluate training adaptations and performance. There is no information concerning the progression in performance and physiological variables (i.e., blood lactate, heart rate) during and after a period of training in a specifically designed SIT set. Such data will help coaches to decide on appropriate periodization within a mesocycle.  
Moreover, swimmers' performance optimization may occur as they increase anaerobic power, buffering capacity [10; 11], maximum oxygen uptake, and blood lactate concentration [14; 15] alongside aerobic indices such as speed corresponding to 4, 5, and 10 mmol/L after a period of two to eight weeks of training [12]. The various physiological effects of SIT are underlined by central and peripheral adaptations that enable increases in both aerobic [16; 17] and anaerobic performance [18]. SIT may increase the activity of both aerobic and anaerobic enzymes and improve performance [18; 19]. However, there is limited information concerning the progression of adaptations occurring within a 6-week SIT that may rely on aerobic and anaerobic adjustments. Such changes may be detected using critical speed as an index of aerobic endurance and blood lactate concentration as an index of glycolytic metabolism, respectively.  
To our knowledge, there is no study including swimmers that examines the effect of SIT on performance and the progression of physiological parameters during a 6-week training period. The aim of the current study was to examine the effect of SIT on performance and physiological responses (a) during and (b) after a 6-week SIT period. We hypothesized that SIT will improve performance and will progressively increase physiological responses during the 6-week training period.
## 2 Methods
### Data Preparation  
#### 2.2.1 Data Compression  
For the CNN-based hotspot-detection method, model training and model verification are based on layout pattern data. The design resolution of the IC layout is as high as 1 nm. For a layout pattern with an area of 1 \(\upmu\)m\({}^{2}\), the number of pixels is as high as 1000 \(\times\) 1000. Model training based on high-resolution pattern data requires a high-performance hardware system. Therefore, it is necessary to perform data compression on the original high-resolution patterns. The density-based feature-extraction method [20,21] is a conventional feature-extraction method for hotspot detection, which can be used for data compression. The proposed method takes the strategy of calculating the local density to down-sample the original pattern, thereby achieving data compression. The proposed method is different from the other methods [19,26]. On the one hand, the resolution of the down-sampled patterns is higher, which is 240 \(\times\) 240 pixels. On the other hand, the resolution of the down-sampled patterns is similar to the resolution of the ImageNet dataset, but not completely the same.  
The schematic diagram of pattern down-sampling is shown in Figure 3. After the high-resolution pattern is obtained, the pattern is gridded, and each grid window corresponds to a pixel of the down-sampled pattern. The pattern down-sampling can be expressed by Equation (5), and the pixel value of the down-sampled pattern is the average pixel value of the corresponding window. In Equation (5), \(\text{p}_{\text{k}}\) represents the \(\text{k}^{\text{th}}\) pixel value of the down-sampled pattern, \(\text{win}(\text{k})\) refers to the \(\text{k}^{\text{th}}\) window in the grid, \(\text{I}_{\text{i}}\) refers to the \(\text{i}^{\text{th}}\) pixel value of the original pattern in \(\text{win}(\text{k})\), and \(\text{N}\) refers to the total number of pixels in the window.  
\[\text{p}_{\text{k}}=\frac{1}{\text{N}}\sum_{\text{i}\in\text{win}(\text{k})} \text{I}_{\text{i}}, \tag{5}\]
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 2 System Modeling
### Line-to-Line Fault Characteristic  
This study aims to investigate a method for locating line-to-line short-circuit faults in the PV array on the direct current (DC) side of a PV system. As mentioned in past studies, line-to-line short-circuit faults can be categorized into two types, intra-string and cross-string, as shown in Figure 3. An intra-string fault occurs within the same string, where one or more modules experience a short-circuit fault. In contrast, a cross-string fault spans across strings, with short-circuit faults occurring at the endpoints of different strings.  
For the two different types of line-to-line short-circuit faults, P-V characteristic curves of the module array under fault conditions were plotted and compared with those under normal conditions. This is illustrated in Figure 4. It is observed that, whether in intra-string or cross-string fault conditions, the P-V characteristic curves exhibit a smooth arc shape. Unlike partial shading faults, there are no dips in the curve that create multiple local maximum power points [26]. However, the maximum output power generated is lower than under normal conditions, which is consistent with the results found in past studies [27].  
Figure 3: Schematic diagram showing line-to-line fault.  
Figure 2: Array size of the PV module used.  
In solar power plants, converters equipped with maximum power point trackers are generally installed to achieve maximum power output. Based on the characteristics of line-to-line short-circuit faults in PV arrays mentioned above, such faults do not cause dips in the P-V curve, as there remains only a single maximum power point. Therefore, in this study, line-to-line faults were analyzed by extracting the P-V characteristic curve of the array through a simulation system. This approach avoids the lengthy maximum power point tracking (MPPT) process, thus significantly reducing the time required to obtain simulation data. For practical applications, a similar effect can be achieved by simply averaging the measured electrical signals.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 1 Introduction
In contrast, we propose to use fiducials regions in non-GPS denied environments to improve GPS accuracy by reducing errors associated with time-variant variables for a short period of time. Our technique, FAGPS (Fiducial Augmented GPS), which augments traditional GPS data with position data acquired from a stationary geo-referenced fiducial marker. Hence, for a time interval in the order of 20-30 min a more accurate position determination is achieved. We validate FAGPS through empirical studies and simulation based on corrections of GPS data for stationary and moving GPS receivers. This paper is structured as follows: Section 2 covers some of the related research in the fields of GPS augmentation using DGPS techniques and an overview of fiducial markers. Following this Section 3 describes the proposed FAGPS scheme. Section 4 describes the experiments used to validate FAGPS followed by the experimental results and a discussion in Section 5. Finally, Section 6 provides some concluding remarks.