esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussion
### Defect Case Study 2: Defects Placed at 7.12 m for Both Web and Flange  
As in the last case study, the five defect scenarios listed in Table 2 were modelled on both the web and flange of a 7.5 m U-pile. Figures 18 and 19 show the superposed results obtained from defects located at 7.12 m on the web and flange, respectively. These results were normalized to the excitation signal as displayed in Figures 18a and 19a. Figures 18b and 19b show defect reflection, i.e., the percentage of excitation signal reflecting from the defect.  
Figure 16: (**a**) Results obtained from the defects placed at 5.65 m on the web of the U-pile. (**b**) Defect reflection results from the respective defects.  
Figure 17: (**a**) Results obtained from the defects placed at 5.65 m on the flange of the U-pile. (**b**) Defect reflection results from the respective defects.  
In the case of defects located on the web, defect reflection from Defect A was 7.5% of excitation signal, which increased to 47% of excitation signal for the case of Defect E. A similar trend was observed for defects placed on the flange of the pile at the same location. Defect reflection increased from 5% of excitation signal for the case of Defect A to 24% of excitation signal for the case of Defect E.  
The COMSOL models were validated by comparing the theoretical time of arrival (\(\mathcal{T}\circ\mathcal{A}\)) for the expected reflections from the defects at 5.65 m and 7.12 m with the \(\mathcal{T}\circ\mathcal{A}\) obtained from the numerical models. The theoretical \(\mathcal{T}\circ\mathcal{A}\) was calculated using the velocity of the \(\mathcal{SH}0\) mode obtained from the group velocity dispersion curves (Figure 4) for 70 kHz operating frequency. There is approximately 2.5% error in the comparison as presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Defect Location (m)** & **Theoretical \(\mathcal{T}\circ\mathcal{A}\) (s)** & **Numerical \(\mathcal{T}\circ\mathcal{A}\) (s)** & **Error \%** \\ \hline
5.65 & \(2.79\times 10^{-3}\) & \(2.72\times 10^{-3}\) & 2.5 \\
7.12 & \(3.74\times 10^{-3}\) & \(3.65\times 10^{-3}\) & 2.4 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of theoretical and numerical time of arrival.  
Figure 19: (**a**) Results obtained from the defects placed at 7.12 m on the flange of the U-pile. (**b**) Defect reflection results from the respective defects.  
Figure 18: (**a**) Results obtained from the defects placed at 7.12 m on the web of the U-pile. (**b**) Defect reflection results from the respective defects.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 4 Asymptotic Analysis
### FSO Link \((R_{1}\to R_{2})\)  
At high-SNR regime, the CDF of the FSO link, as given in (3), can be written asymptotically using identity ([38], Equation (6.2.2)) [36], as \[\begin{split} F_{\gamma FSO}^{\infty}(\gamma)&=A\sum_{k=1}^{6 }\frac{\Pi_{j=1}^{6}\Gamma(b_{j}-b_{k})\Gamma(b_{k})}{\Pi_{j=2}^{3}\Gamma(a_{j} -b_{k})\Gamma(1+b_{k})}B^{b_{k}/2}\Big{(}\frac{\gamma_{th}}{\gamma}\Big{)}^{ \frac{b_{k}}{2}}\\ &=\left(\frac{X^{\frac{\gamma^{2}}{b_{k}}}}{\gamma_{th}}\gamma \right)^{\frac{-b_{k}}{2}},\end{split} \tag{10}\]  
where \(a_{j}=a_{p}(j)\), for \(j=1\) to \(3\),  
\(a_{1}=1\),  
\(b_{j}=b_{q}(j),j=1\) to \(6\),  
\(b_{7}=0\),  
\(b_{k}=\min\{\xi^{2},\alpha,\beta\}\).  
The constant \(X\) is equal to \(A\sum_{k=1}^{6}\frac{\Pi_{j=1}^{6}\Gamma(b_{j}-b_{k})\Gamma(b_{k})}{\Pi_{j=2}^ {3}\Gamma(a_{j}-b_{k})\Gamma(1+b_{k})}B^{b_{k}/2}\),  
\(A=\left[1-\exp(\frac{-\theta_{2k,U}^{2}}{2\
u_{B}})\times\frac{2^{(}\alpha+ \beta-2)}{2\pi\Gamma(\alpha)\Gamma(\beta)}\right]\),  
\(B=\left(\frac{(\alpha\beta)^{2}}{16}\right)^{\frac{b_{k}}{2}}\).
## 4 Validation and Calibration
### Validation and Intercomparison  
In situ measurements of CO\({}_{2}\) were made using a Picarro CRDS (Cavity Ring-Down Spectroscopy) CO/CO\({}_{2}\)/CH\({}_{4}\)/H\({}_{2}\)O analyzer (model G1301, Picarro Inc., Santa Clara, CA, USA) [23]. After being calibrated, the analyzer pulled air from a wing-mounted inlet continuously at few centimeters from the AMULSE multipass cell for about 6000 s. Measurements from both instruments were averaged for 5 s. Full Picarro measurement (Figure 7a black line on the upper panel) shows a good agreement with the AMULSE measurements over all the concentration range with a minimum residual uncertainty of 0.01% at lower concentrations and 7.39% at higher concentrations. Figure 7b shows the linear correlation between the two instruments involved with an adjusted R\({}^{2}\) of 0.99132.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 5 Conclusions  
The principal structural trends affecting the area are in the N-S, NE-SW, NW-SE, and E-W directions, and these structures control mineralization in the study area, as determined by the application of derivative filters and the processing of aeromagnetic data such as FVD, THG, AS, TDR, and CET techniques. It is shown that the research region's mineral resources are linked to shear zones, which are found in many parts of the area and resulted from the contact of ophiolitic serpentinite rocks with metasediments and other rock units in the study region, based on the integration of geophysical techniques and the geology of the area. The shear zones and favored mineralization zones from the center of exploratory targeting, as well as the other filter maps, match geological research in the area. In our investigation, the CET technique was found to be an excellent tool for constructing the OE maps for the identification of lineaments operating as possible hydrothermal fluid plumbing systems, and this study confirms that aeromagnetic data have a significant impact on alteration zone detection and mineral exploration.  
Conceptualization, A.M. and M.A.; methodology, M.A., A.M. and M.A.M.; software, M.A., A.M. and M.A.M.; validation, M.A. and A.M.; formal analysis, A.M., M.A., F.A., A.A. and M.A.M.; investigation, A.M., M.A. and F.A.; resources, M.A., A.M. and F.A.; data curation, F.A., A.A. and M.A.M.; writing--original draft preparation, M.A. and A.M.; writing--review and editing, A.M. and M.A.; visualization, A.M. and M.A.; supervision, A.M. and M.A.; project administration, A.M. and M.A.; funding acquisition, A.M., F.A., A.A. and M.A.M. All authors have read and agreed to the published version of the manuscript.  
Figure 11: A map depicting the faults, dykes, and shears beneath the surface of the region under study from the AS (**A**), FVD (**B**), HGD (**C**), High Pass (**D**), Low Pass (**E**), and TDR (**F**) maps, the major and minor trends of the subsurface lineaments are depicted in the rose diagrams (**A–F**).  
**Funding:** The authors extend their appreciation to the [PERSON] Chair for Earth Science Remote Sensing Research for funding.  
**Data Availability Statement:** The data is available upon request from the authors.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
###### Abstract
Its applications across an enormous range of engineering problems, such as power systems, job shop scheduling, congestion management, university timetables, clustering, structural design, neural networks, water distribution, renewable energy, data mining, and software have provided excellent results [16; 17; 18; 19; 20; 21; 22; 23; 24]. This article investigates the following research questions:  
* RQ1: How much can HS-based parameter optimization improve SDP performance in maritime domain software? * RQ2: By how much can HS-based parameter optimization method reduce cost? * RQ3: Which parameters are important for improving SDP performance? * RQ4: How does the proposed technique perform in open source software (OSS)? The main contributions of this article are summarized as follows:  
* To the best of our knowledge, we first apply HS-based parameter optimization to JIT-SDP and the maritime domain. * We first apply HS-based parameter optimization with search space of the decision tree and bagging classifier together with balanced fitness function in JIT-SDP. The remainder of this article is organized as follows. Section 2 explains the context of the maritime domain application and the HS algorithm. Section 3 describes the application of the JIT-SDP tailored parameter based on HS in the maritime domain. Section 4 discusses the experimental setup and research questions. The experimental results are analyzed in Section 5. Sections 6 and 7 deal with the discussion and threats to validity, respectively. Section 8 explains the related work of this study. Finally, Section 9 concludes this article and points out some future work.
# An Augmented Warning System for Pedestrians: User Interface Design and Algorithm Development
## 1 Introduction
Handheld devices such as smartphones are smaller, portable, and could support touch screen inputs [14], which are more commonly used for the scenarios when mounted HUD is not available. However, it is infeasible for pedestrians to hold their smartphones while walking in public constantly. Since the warning needs to be carried out promptly, it is infeasible to design a system that requires the pedestrian to check on their phone even when they are not holding it to receive the warning information. Compared with HUD devices and handheld deceives, wearable AR devices are usually lightweight enough for pedestrians to use without interrupting their normal walking. By using wearable AR devices, pedestrians can perceive both warning information and real-world information without taking their eyes off the road for long. As a common wearable AR device, AR glasses may cause the least interruption for pedestrians during walking. Although HoloLens, the platform used in this paper, was still too bulky and heavy to wear daily, the fast evolution of the AR glasses could lead to the development of future wearable AR solutions. For example, a newer version of the AR glasses, Glass Enterprise Edition 2, was only 46 g and was designed to wear all day [18]. Overall, AR glasses are lightweight, can constantly be worn, and there is no need to hold them in hand or pay constant attention as with other AR devices. These potential advantages of AR glasses make them a perfect candidate to carry out the warning information for pedestrians in public with minimal interference. However, the visual field of wearable AR devices is usually superimposed at the center of the user's field of view all the time, which may cause extra distraction or block the user's view. Therefore, it is important to ensure that such AR devices activate the warning interface only when needed. To the authors' knowledge, no research was done to develop an augmented visual-based warning system to deliver warning information about potential risks to pedestrians in real-time. Therefore, the objective of this study was to develop an AR-based wearable interface to deliver warning information to pedestrians after a potential collision is detected. A HoloLens setup was used as an experimental platform to develop the user interface, and a series of projection algorithms were used to carry out the warning information in real-time.
## 3 Results
### Single-Source vs. Multi-Source Training
For every metric, the multi-source model usually exceeds the individual models, while individual models can exceed in one specific metric while generally dropping the performance in the others. That shows using multi-source data helps to mitigate the 'domain shift', where metrics drop significantly in external datasets compared to training-validation datasets due to a lack of generalization, which can be attributed to different factors (equipment, projection, labeling strategies and quality, etc.). We calculated the confusion matrices for all trained models (using multi-source and single-source training datasets), both using a combined test set and also on a per-dataset basis. All results are shown in Figure 8.  
Figure 5: Comparison of evaluation metrics for multi-source model and each single-source trained model, showing that multi-source model outperforms in general metrics to single-source models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensitivity** & **CheXpert** & **MIMIC-CXR** & **NIH** & **PadChest** & **SIM-ACR** & **VinDR-CXR** & **Multi-Source (All)** \\ CRADI & 0.114 & 0.409 & **0.446** & 0.196 & 0.107 & 0.121 & 0.416 \\  & (0.099–0.128) & (0.365–0.451) & (0.398–0.494) & (0.167–0.226) & (0.092–0.122) & (0.074–0.173) & (0.372–0.457) \\ \hline \multirow{2}{*}{Indiana} & 0.105 & **0.176** & 0.154 & 0.076 & 0.038 & 0.138 & 0.141 \\  & (0.071–0.143) & (0.104–0.256) & (0.092–0.218) & (0.034–0.116) & (0.024–0.054) & (0.048–0.235) & (0.088–0.2) \\ \hline \multirow{2}{*}{Micro-Average} & 0.231 & 0.370 & 0.367 & 0.225 & 0.152 & 0.150 & **0.374** \\  & (0.046–0.507) & (0.089–0.650) & (0.123–0.610) & (0.000–0.451) & (0.027–0.332) & (0.013–0.287) & (0.066–0.682) \\ \hline \multirow{2}{*}{**Overall**} & 0.448 & 0.652 & 0.624 & 0.439 & 0.309 & 0.264 & **0.686** \\  & (0.439–0.457) & (0.641–0.664) & (0.612–0.636) & (0.426–0.452) & (0.302–0.316) & (0.251–0.278) & (0.675–0.695) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._Figure 6: Precision–recall curves for each independent single-source dataset. The plot entitled “all” represents the multi-source dataset. Figure 7: ROC curves for each independent single-source dataset. The plot entitled “all” represents the multi-source dataset.
## 5 Illustrative Example
### Scenario 1: Pick and Place  
The human worker is responsible for assembling electronic boards. The cobot assists by picking components from designated storage bins and placing them on the assembly table. This is carried out using the following three steps.  
1. Initial Setup: The workstation map is displayed, showing the locations of various components in storage bins. The cobot's control system stores the coordinates of each bin.
2. Task Execution: - Worker: \"Cobot, use trajectory \"1A\" to pick resistor R1 from Bin A3\". (Figure 2). (a) Cobot: \"Picking resistor R1 from Bin A3\". - Worker: \"Cobot, use trajectory \"1B\" to place it on the spot 1\" (on the table) - The cobot moves and places it on the designated spot on the assembly table. - Cobot: \"Resistor R1 placed on the table\".
3. Follow-up: The worker continues to assemble the board, instructing the cobot to pick and place other components as needed  
Figure 1: The illustrative example workspace.
# Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
## 1 Introduction  
There are two common methods to establish a thermal model in a space: the distributed parameter method and the lumped parameter method. The distributed parameter model is usually established by numerical approximation and discretization using the finite element method. Studies in the literature [1; 2] use the distributed parameter method to establish a heat transfer model, and to achieve the distribution and prediction of object temperature. However, this method has the disadvantages of high computational cost and slow solution process. The lumped parameter model was established for the thermal node network method. The method uses the lumped parameter idea, which equates the object to a thermal node with uniform internal properties. Because of its simple principle, this method has been widely used in the field of heat transfer modeling [3; 4; 5; 6]. One branch of the lumped parameter method is the neural network method. In recent years, with the development of artificial intelligence technology, the Artificial Neural Network (ANN) method has gained more and more of the attention of scholars. The ANN possesses the advantage of strong nonlinear fitting ability. It has become the main research direction when building a heat transfer model. This modeling technology does not require much knowledge about process mechanics and requires only a sufficient amount of experimental data [7; 8; 9; 10; 11]. Recently, the ANN method has been used to establish the nonlinear heat transfer modeland predict temperature trends. This method avoids the difficulty of developing accurate models due to complex thermal processes [12; 13]. The authors proposed the use of cellular neural networks and Tree-Structure Ensemble General Regression Neural Networks (TSE-GRNNs) to establish a heat transfer model. However, the ANN training process with a backpropagation algorithm has the disadvantage of slow convergence and long learning time. In contrast, the Random Vector Functional Link Neural Network (RVFLNN) can overcome these shortcomings. The RVFLNN can fit any nonlinear function by a linear combination of basis functions, randomly assigning input weights and biases [14]. It uses the least-square method to train its output weights [15]. The training process does not require iteration. Hence, it has a great advantage over the traditional ANN in terms of learning speed [16].  
This paper aims to use the RVFLNN to quickly establish a thermal environment model for a metro station. This presented modeling method tries to predict the change trend of the temperature accurately inside the metro station. The RVFL network prediction model is obtained by analyzing the measured data at specified points from inside a metro station. The data for the input layer include temperatures, the passenger flow, and the arrival frequency of metro vehicles. Finally, we show that relatively good prediction results for the metro station can be obtained using the presented thermal prediction method based on an RVFLNN.
## 2 Materials and Methods
### Laboratory Analyses  
All samples were air-dried, homogenised in a porcelain mortar and sieved to <2 mm [37]. Grain size fractions of the reservoir sediments and catchment soils were analysed by a combined sieve and sedimentation analysis. The fine fraction (<63 \(\upmu\)m) was determined using a PARIO analyser (Meter Group AG, Munich, Germany). This system automatically determines the particle distribution by measuring the suspension pressure after the samples are prepared according to DIN ISO 11277 [38]. Before analysis, the organic matter of the air-dried samples was destroyed by the addition of a 30% H\({}_{2}\)O\({}_{2}\) solution. After a 16 h pre-reaction at room temperature, the samples were slightly heated until the release of CO\({}_{2}\) from the samples was complete. The mineral soil particles were subsequently dispersed  
Figure 2: Hillshaded digital elevation models (DEM) of the (**A**) Einsiedel Reservoir and of the (**B**) Berthelsdorf Smelter Pond (BSP) with sampling points (red—reservoir sediments; green—soil samples; yellow—rtiburacy brook sediments). Maps by [PERSON] via QGs; source of DEMs and water bodies: Staatsbetrieb Geobasisinformation und Vermessung Sachsen (GeoSN; dl-de/by-2-0) [35; 36].  
with a Na\({}_{4}\)P\({}_{2}\)O\({}_{7}\) solution overnight before grain size analysis was performed. To analyse the soil organic matter content, aliquots of the sieved (<2 mm) and homogenised samples were dried overnight in an oven at 105 \({}^{\circ}\)C. Soil organic matter was then determined gravimetrically based on loss on ignition at 550 \({}^{\circ}\)C [39]. The pH value was measured in a solution of 0.01 M CaCl\({}_{2}\) (_m/V_ = 1:2.5) [37] with a pH electrode (SenTix 41, WTW, Weilheim, Germany). The electric conductivity was determined using an electrode (Tetra Con 325, WTW, Weilheim, Germany) after the samples were stirred in deionised water (_m/V_ = 1:5) for 2 h [40]. Metal(loid)s were quantified according to DIN EN ISO 17294-2 [41] by inductively coupled plasma mass spectrometry (ICP-MS; XSERIES 2; Thermo Fisher Scientific, Bremen, Germany). For this purpose, 1 g of each sieved (<2 mm) sample was heated (85-90 \({}^{\circ}\)C) in aqua regia for 2 h, filtered and diluted with ultrapure water before analysis [42]. Each sample was measured three times for concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn) nickel (Ni), lead (Pb), vanadium (V) and zinc (Zn). Measuring values with a relative deviation of more than 20% from the average value were excluded.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.5 France  
C-Roads France builds on the results of the European project SCOOP@F [83]. The goal is to test cooperative services within two types of end-user services: services in the urban environment and at the urban/interurban interface as well as traffic information services increasing comfort on transit stretches. Among Day-1 services, the majority of functions are investigated with the exception of some signage applications, namely VSPD, SWD, SigV, and TSP. Moreover, the development of a C-ITS smartphone application is planned, supporting early I2V (Infrastructure-to-Vehicle) services such as logistic services as well as parking and park and ride information. As regards the communication technology implemented, it is represented by a hybrid framework, enabling a seamless switch between ITS-G5 and cellular for non-safety-critical applications.  
The partners involved are public road operators (DIRs EST, Centre-Est, Atlantique, and Ouest), road operator concessionaries (Autoroutes Paris-Rhin-Rhone-APRR, Societe des Autoroutes du Nord et de l'Est de la France-SANEF, and VINCI Autoroutes), car manufacturers (Renault, PSA group), research institutes (CEREMA, IFSTTAR, Telecom Paris Tech, Universite d'Auvergne Clermont-Ferrand, and Universite de Reims Champagne-Ardennes), security experts (IDnomic), mobility labs (Car2 road, Transpolis), and the major urban nodes authorities (Strasbourg Eurometropole, Bordeaux Metropole). According to the timeline stated, the rolling-out phase has just started. Finally, it is worth pointing out that France, in turn, is one of the Member States involved in the European project InterCor.
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 2 Experimental Design of Blasting Vibration
### Layout of the Measurement Point  
The TC-4850 blast vibration test instrument was used for collecting the vibration signals. This vibration test instrument has recording channels in the horizontal and axial directions, X, Y, and the vertical direction, Z. The recording channels effectively collect the particle vibration waveform, peak particle velocity, main frequency, duration, and other parameters in these three directions. This is an essential operation that connects the vibration test instrument to a PC terminal to transfer and analyze the data. The flow is shown in Figure 3. The vibration instrument takes a sampling frequency of 4000 points each second and has a vibration collection range of 125 mm/s, resolution of 0.012 mm/s, recording frequency in the range of 5-500 Hz, and measurement error of \(\pm\)5% \(\pm\)2 digits. During testing, it is necessary to keep the sensor in close contact with the surface of the measuring point, and plaster bonding is required to ensure stability. Because of environmental constraints, the pipeline could not be surveyed directly; thus, ten working points were simultaneously placed directly above the buried pipeline from the blast source of the horizontal distance of the 16-58 m range (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Hole Type** & **Hole Number** & **Blasting Segment Number** & **Hole Depth (cm)** & **Charge per Hole (kg/hole)** & **Total Charge (kg)** \\ \hline Empty hole & 1 & & 210 & & \\ Cut hole & 4 & 1 & 190 & 2.4 & 9.6 \\ Relief hole & 4 & 2 & 190 & 1.8 & 7.2 \\ Auxiliary hole & 12 & 3, 4 & 170 & 1.8 & 21.6 \\ Perimeter hole & 15 & 5, 6 & 170 & 0.9 & 13.5 \\ Bottom hole & 4 & 7 & 170 & 1.8 & 7.2 \\ Sum & 39 & & & 59.1 \\ \hline \end{tabular}
\end{table}
Table 1: Blasting parameters.  
Figure 3: Monitoring system.  
Figure 2: Layout of the blasting hole.