esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 2 Dynamic Performance Evaluation of New Type of PSC I-girder Bridge  
### Structure Considered in Analysis  
The new type of PSC I-girder considered in this study applies the half-slab to take full advantage of its characteristics in term of construction stability. The so-called half-slab girder of which upper flange plays the role of slab presents a reduced height thanks to the efficient arrangement of the prestressing tendon through the dualization of the anchors of the tendon by disposing intermediate anchors in addition to the end anchors as shown in Figure 3. Four different span lengths (30, 35, 40, 45 m) of the girder were adopted considering recent trends in the span lengths for the PSC I-girder. Figure 3 depicts the longitudinal plan of the new type of PSC I-girder. The sectional dimensions were determined by applying the Korean standard train load KRL-2012 [10] for passenger car mixed line in the design. Figure 4 shows the section of PSC I-girder designed to sustain two railway lanes. Four girders are applied for the span lengths of 30 m and 35 m, and five girders for the span lengths of 40 m and 45 m. The height of the girder is set to 2.5 m for the span lengths of 30 m and 35 m, 2.7 m for the span length of 40 m, and 3.2 m for the span length of 45 m. The thickness of the slab is 280 mm including the 140 mm-thick half-slab. Concrete track is adopted in the model. Table 2 arranges the material properties of the considered bridge. Concrete with compressive strength of 50 MPa is adopted for the girder and concrete with compressive strength of 30 MPa is adopted for the slab.  
Figure 3: Longitudinal plan of railway bridge applying the new type of PSC I-girder.  
Figure 2: Yearly evolution of prestressed concrete (PSC) I-girder bridge inventory in Korea: (a) market share; (b) distribution per span length.
## 3 Experimental Procedure and Analysis Process
### Preprocessing of the Data into Performance Index
#### 3.2.1 Analysis of Contour Error and MT  
The contour error is defined as the short distance between the desired contour and machining contour, as shown in Figure 6, where the desired contour is obtained from the G-code and the machining contour is obtained from the position feedback. The rectangular trajectory, as displayed in Figure 7, can be tested by tuning various interpolator parameters. The maximum acceleration, ADAI, time constant of the S-shaped Acc/Dec corner velocity, and maximum velocity all may have dramatic effects on corner errors, vibration, and MT. Thus, the testing trajectory in Figure 7 was applied as the machining trajectory under various interpolator parameters. For the trajectory consisting of a straight-line segment, the contour error could be computed by simply applying the point-to-line formulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline \(A_{max}\) & mm/s\({}^{2}\) & 900 & 1500 & 2100 & 2700 \\ \hline \(T_{a}\) & ms & 35 & 42 & 49 & 56 \\ \hline \(T2\) & ms & 0 & 8 & 16 & 24 \\ \hline \(V_{c}\) & mm/min & 220 & 340 & 460 & 580 \\ \hline \(f_{max}\) & mm/min & 300 & 500 & 1000 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Levels of each interpolation parameter.  
Figure 6: Definition of contour error.  
Figure 8 reveals the contour errors of the experiment, with the \(A_{max}\), \(T_{a}\), \(T2\), \(V_{c}\), and \(f_{max}\) being given as 2700 mm/s\({}^{2}\), 35 ms, 0 ms, 220 mm/min, and 1000 mm/min, respectively. The maximum contour in corners 1, 2, and 3 was approximately 60 \(\upmu\)m. The other contour errors away from the corner were much less than that of the maximum contour. On the basis of this observation, the MCoE was selected as the first performance index. The MT is also a critical factor and thus was selected as the second performance index. In this case, the MT equaled 7.361 s.
## 2 Materials and Methods
### Variables  
From these data, four SAR-derived vegetation indices were calculated from the backscatter coefficients: DPSVI, RFDI, Pol, and RVI. Additionally, EVI was calculated using optical data (Table 2). The EVI was used based on studies by [6; 35], which demonstrated that using this index in machine learning algorithms can assist in soybean yield prediction.  
Therefore, for this study, eight variables were generated from SAR data (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, _VH_, and _VV_), in addition to the EVI. For the SAR data, each of these variables was evaluated using the four different speckle noise filters. All SAR variables and EVI were obtained for the three development stages of the crop and for the three tested growing harvests. After obtaining all the images, a 15 m buffer was applied along the field boundaries to crop the images, with the aim of eliminating the influence of uncultivated areas in the pixels.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 3 Evolution of pavement Image Acquisition Systems
### Image Acquisition During Non-Destructive Automated Detection Stage  
Modern non-destructive automated testing systems have reached maturity in the 21 st century through key technological integrations. The adoption of high-speed line-scan digital cameras and infrared laser illumination has significantly improved both the accuracy and efficiency of road surface imaging.  
Canada's INO Corporation developed the LRIS system [5] (Figure 2) as an advanced pavement imaging solution. The system combines two high-speed line-scan cameras with dual linear laser sources in a bilateral configuration. This setup provides uniform illumination for each camera, ensuring consistent image quality across varying light conditions. The synchronized dual-camera arrangement captures complete 4 m road sections while overcoming the low-light limitations of traditional systems, particularly during night and tunnel inspections.  
Recent advances in 3D imaging [6] and UAV-based systems [7; 8; 9; 10; 11; 12; 13; 14; 15] have expanded pavement assessment capabilities. Laser profilometer-equipped vehicles [16; 17] now produce high-resolution 3D point clouds for precise elevation measurements [18; 19], proving particularly effective for identifying three-dimensional distresses, including potholes and rutting. A notable advancement is [PERSON]'s PaveVision3D system [20], which achieves millimeter-level 3D data accuracy, significantly improving detection reliability for various pavement distresses.  
UAV systems provide flexible and cost-effective solutions for infrastructure inspections. In 2022, [PERSON] et al. [21] developed a deep learning framework for UAV images that automatically addresses vehicle occlusions and shadows to improve detection accuracy. He et al. (2025) further contributed HighRPD [22], a comprehensive UAV dataset containing 11,696 pavement distress samples for advanced image analysis. However, UAV applications still face two major constraints: sensitivity to weather conditions affecting flight stability and a restricted operational range due to limited battery life. These limitations currently position UAV technology as more suitable for detailed local inspections than large-scale network surveys.  
Modern non-destructive evaluation systems have significantly advanced image acquisition capabilities, evolving from 2D to 3D imaging and from ground-based to aerial platforms. These multidimensional developments provide a comprehensive data foundation for accurate pavement distress identification and quantification.
# Widely-Tunable Ring-Resonator Semiconductor Lasers
## 4 Advanced Designs for Linewidth Reduction
### Control of Widely-Tunable Laser with High-\(Q\) Resonator
Figure 16a shows the output spectra as we tune one of the rings, while optimizing the phase and high-\(Q\) heaters for maximum current at the output monitor photodetector. The side-mode suppression-ratio (SMSR) across 39 nm of tuning range in steps corresponding to the FSR of one of the Vernier rings is greater than 40 dB, with best values exceeding 48 dB. By tuning both rings, it is possible to hit any wavelength inside the tuning range of the laser. The same optimization, and maximization of the output monitor photodetector current, results in optimal laser relative intensity noise (RIN) performance, as shown in Figure 16b. Here we plot laser RIN as we sweep the high-\(Q\) ring heater and the measurement clearly shows direct correlation between laser RIN and output power current. When latter is maximized, RIN is optimized. This once again shows the benefits of using on-chip detectors for laser control. For this reason, we expect that as the complexity of photonic integrated chips increases, they will include a larger number of monitor photodetectors for control. Figure 16: (**a**) Optical spectra as one of the Vernier rings is tuned and high-\(Q\) ring and phase are adjusted for maximum current at the output monitor photodetector; (**b**) Correlation between laser relative intensity noise (RIN) measured at 0.55, 2.25 and 11.25 GHz, and monitor photodetector currents, as high-\(Q\) ring is swept. RIN is optimized when output power is maximized. Figure 15: Monitor photodetector current for the (**a**) output MPD; and (**b**) high-\(Q\) ring MPD as a function of heater power delivered to phase section and high-\(Q\) ring. The blue rings show the region of maximum output power in (**a**) and identical position for the high-\(Q\) monitor that is in the local minimum (**b**). For proper linewidth measurements, laser packaging is required to suppress sources of technical noise, such as air drift over the laser (typically wavelength changes in 10 MHz/mK) as well as electrical noise sources coming from long unshielded wires of the DC probe card. The packaging is currently in development, and linewidth measurements will be presented in a future publication.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 2 Data  
### Data Samples and Image Reconstruction  
Monte Carlo (MC) simulations are a widely used computational technique for modeling the behavior of IACTs. In this context, MC simulations are used to model the interactions between very high-energy cosmic rays and gamma rays with the Earth's atmosphere.  
This technique consists of generating a large number of random samples, or \"trials\", based on a set of probability distributions that describe the properties of the incoming particles, such as their energy, direction, and type. These samples are then used to simulate the shower of secondary particles that is produced when the incoming particles interact with the atmosphere. The resulting shower of particles is used to estimate the properties of the Cherenkov radiation that would be detected by the IACTs.  
By simulating a statistically significant number of events, we can accurately assess and compare the performance of different Machine Learning methods for gamma/hadron segregation and energy estimation on the ASTRI Mini-Array Cherenkov telescopes. This allows us to optimize the performance of our Machine Learning techniques for the specific properties of the data generated by the IACTs.  
The data used in this work (dubbed _ASTRI MA Prod2-Teide_) [7] are a large set consisting of \(4\times 10^{7}\) on-axis photon events and \(2\times 10^{9}\) proton simulations, assumed to fall in an area that includes the nine telescopes of the ASTRI Mini-Array on the Teide site. Heavy hadrons constitute a relatively small fraction of the overall hadronic events and have been excluded from the analysis. The simulation parameters have been defined according to the site settings (coordinates, height, atmosphere features, etc.) and to the telescopes' positions on the ground. The simulation consists of two subsets, according to the direction of the primary particles: \(20^{\circ}\) (zenith angle) South and \(20^{\circ}\) North. The energy range of the simulated primary particles is 0.1-330 TeV for photons and 0.1-600 TeV for protons, with a spectral slope of \(-1.5\). These spectral slopes differ from the actual measured spectra (which are steeper [7]) in order to allow us to reach sufficient statistics at the highest energies. This choice does not impact the analysis, which is performed with a differential approach.  
The first step of the simulation tracks the development of each simulated shower along the atmosphere to the ground. To this end, we use the Corsika software package [8], whose output is the distribution of the Cherenkov light pool (photon position and direction) on the ground. In the second step, we perform the ray-tracing of each Cherenkov photon through the telescope optics to the focal plane camera and the camera response to the signal, producing an image of the shower as seen by the telescope. This is carried out using the sim_telarray package, a highly configurable software for the simulation of arrays of imaging atmospheric Cherenkov telescopes [9].
## 5 Simulation Results
### Lessons Learnt and Guidelines for Future Research  
The \(2^{k}\) factorial analysis has shown that the key factors to take into account when simulating VANETs are: (i) the radio propagation model; (ii) the density of vehicles; and (iii) the roadmap used. By evaluating the impact of each factor one by one, we confirmed the outcome of the \(2^{k}\) factorial analysis. We observed that the results obtained are highly affected by the selected radio propagation model, the roadmap and the density of vehicles. The propagation of warning messages works better with simpler layouts and higher vehicle densities.  
Figure 8: Evolution of the warning message dissemination process after 20 s, when simulating (**a**) New York; (**b**) San Francisco; and (**c**) Rome scenarios.  
Results also showed that other important factors, such as the broadcast scheme used, the channel bandwidth, and the priority and the periodicity of messages, have little impact in the warning message delivery process. Nevertheless, we believe that these parameters could be important factors in other VANET scenarios and applications, such as live video streaming services to vehicles.  
Although the selected roadmap is a key factor in VANETs, the majority of VANET proposals tend to use very simplistic scenarios. We consider that the use of more realistic topologies is required in order to obtain meaningful results. However, the very large number of possible scenarios and the differences among them become a drawback when attempting to follow our strategy. Thus, in the next section we present a roadmap profile classification that will be very useful for future VANET research works by aggregating cities into a same group depending on their characteristics.
## 4 Discussion
### Implications and Opportunities  
A site's GPP ultimately reflects the combination of LAI and A\({}_{\text{leaf}}\) (Figure 3). The ideal VI should show sensitivity to LAI and a similarly strong, multiplicative sensitivity to some aspect of the land surface that is closely related to A\({}_{\text{leaf}}\) (Figure 4, [50]). EVI and NIR\({}_{\text{v}}\) do a better job of mimicking this sensitivity than NDVI does (Figure 6c,d vs Figure 3b), and as a result provide tighter relationships with GPP (Figure 2). Nonetheless, there is no reason to believe that EVI or NIR\({}_{\text{v}}\) represent the optimal solution to this problem and alternative VIs may do even better jobs of predicting GPP.  
Recent advances in remote sensing and plant physiology may further improve predictions of GPP. Studies are exploring the relationships between plant traits and GPP across ecosystem types [14; 15; 28; 62]. Plant trait complexes have relatively strong spectral signals over visible and near infrared broad-bands [67], and advances in simulating canopy radiation transfer should allow further explorations of the links between GPP and surface reflectance [68]. Alternative VIs that better mimic the relative importance of LAI and A\({}_{\text{leaf}}\) in controlling GPP, or that more correctly account for the rate that the LAI and A\({}_{\text{leaf}}\) controls on GPP saturate (Figure 3, Figure 6), may provide even more general predictions of GPP. Likewise, alternative VIs or retrieval approaches that combine sensitivity to LAI with a proxy that is better correlated with A\({}_{\text{leaf}}\) than is LMA (Figure 4; [21; 31; 32]) may provide improved predictions of GPP.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
###### Abstract  
Digital Twins (DTs) are becoming essential tools for real-time decision-making in transportation systems. This paper presents a macroscopic traffic digital twin developed for a 50 km segment of the C-32 interurban highway in Spain. The digital twin replicates highway conditions using real-time data from roadside sensors and connected vehicles via Vehicle-to-Everything (V2X) communications. It supports intelligent decision-making for traffic management, particularly during incident situations, by recommending macroscopic strategies such as variable speed limits and re-routing. Unlike many existing DTs focused on microscopic modeling or urban contexts, our approach emphasizes a macroscopic scale suitable for interurban highways, enabling faster computation and system-wide insights. The decision-making module evaluates candidate strategies using real-time simulations and selects the most effective option based on key performance indicators (KPIs), including congestion, travel time, and emissions. The system has been validated under realistic traffic scenarios using historical data, considering both congestion and pollution use cases. Strategies are communicated back to the physical infrastructure via V2I messages (IVIM) and a mobile application using the cellular communication network, enabling a closed-loop architecture. This paper contributes a scalable, real-time, and field-integrated macroscopic DT framework for highway traffic management.  
digital twin; Intelligent Transportation Systems (ITS); V2X communications; variable speed limits (VSL) 2025157454  
1  
[PERSON], [PERSON]; 1 Fundacio i2 CAT, 08034 Barcelona, Spain; [EMAIL_ADDRESS] (E.C.-M.); [EMAIL_ADDRESS] (C.V.-V.); francisco.vazquez@i2 cat.net (F.V.-G.); jesusa.alonso@i2 cat.net (J.-A.-Z.) Department of Network Engineering, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain; [EMAIL_ADDRESS]
## 2 Statistical Analysis for the Determination of Operating Parameter Requirements
### Correlation of Operational Actions and Mechanical Responses  
The mechanical responses to operational actions can vary in complex ways, and the correlations of the actions and responses can be found by multiple regression analysis. The analysis used data obtained from one specific project in Korea: the northbound lane (comprising 143,514 data points) [15]. The data were collected from the real-time monitoring system during TBM excavation. Ground conditions of the northbound lane comprise sand and clay, with a maximum vertical earth pressure of \(\sim\)0.5 MPa. Data for the southbound lane were excluded. All variables for the regression analysis, operational actions, and mechanical responses, were normalized by the maximum capacity of the TBM used for this project (Table 3). Table 4 summarizes the regression equations for estimating the mechanical responses to operational actions. The three operational actions associated with speed parameters (cutterhead rotation speed (\(A_{1}\)), shield jack speed (\(A_{2}\)), and screw conveyor rotation speed (\(A_{3}\))) are the key drivers controlling the mechanical responses. Therefore, their coefficients should have greater effect than those of the other three input variables (opening ratio of the screw conveyor gate (\(A_{4}\)), number of shield jacks (\(A_{5}\)), and number of cutterhead motors (\(A_{6}\))). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Estimated Operating** & **Equation Number** & **Equation** & **R\({}^{2}\)** \\ \hline Shield jack pressure (\(Y_{1}\)) & Equation (1) & \(Y_{1}=0.59.A_{1}+0.40A_{2}-0.34A_{3}+0.12A_{4}-0.33A_{5}+0.06A_{6}+20.91\) & 0.41 \\ \hline Thrust (\(Y_{2}\)) & Equation (2) & \(Y_{2}=0.50A_{1}+0.36A_{2}-0.30A_{3}+0.10A_{4}+0.23A_{5}-0.01A_{6}-18.38\) & 0.38 \\ \hline Cutterhead torque (\(Y_{3}\)) & Equation (3) & \(Y_{3}=0.18A_{1}-0.02A_{2}+0.04A_{3}+0.03A_{4}+0.04A_{5}+0.27A_{6}+11.80\) & 0.19 \\ \hline Screw conveyor jack pressure (\(Y_{4}\)) & Equation (4) & \(Y_{4}=0.14A_{1}-0.19A_{2}+0.68A_{3}-0.03A_{4}-0.07A_{5}+0.15A_{6}-16.21\) & 0.60 \\ \hline Left earth pressure (\(Y_{5}\)) & Equation (5) & \(Y_{5}=0.38A_{2}-0.24A_{3}+36.71\) & 0.36 \\ \hline Right earth pressure (\(Y_{6}\)) & Equation (6) & \(Y_{6}=0.38A_{2}-0.24A_{3}+36.71\) & 0.36 \\ \hline Inner cutter seal temperature (\(Y_{7}\)) & Equation (7) & \(Y_{7}=0.30A_{1}-0.05A_{2}-0.01A_{3}+0.01A_{4}-0.04A_{5}-0.03A_{6}+38.99\) & 0.62 \\ \hline Outer cutter seal temperature (\(Y_{8}\)) & Equation (8) & \(Y_{8}=0.25A_{1}-0.05A_{2}+0.02A_{4}-0.09A_{5}-0.09A_{6}+50.79\) & 0.52 \\ \hline Oil tank temperature (\(Y_{9}\)) & Equation (9) & \(Y_{9}=0.26A_{1}-0.09A_{2}+0.03A_{3}+0.04A_{4}-0.11A_{5}-0.11A_{6}+61.38\) & 0.64 \\ \hline Cooling water temperature (\(Y_{10}\)) & Equation (10) & \(Y_{10}=0.32A_{1}-0.16A_{2}+0.18A_{3}+0.03A_{4}-0.10A_{5}-0.21A_{6}+81.13\) & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of regression equations estimating mechanical responses to operational actions.
## Materials and Methods
### SIMCA and Statistics of PCA Models  
PCA is often the first step of the exploratory analysis to detect groups in high-dimensional datasets. The SIMCA method based on disjoint PCA modeling consists of a collection of PCA models, one for each class in the calibration set [31]. In the test phase, the unseen instance will be recognized as a member of a class if it is similar enough to the other members; else, it will be rejected. Specifically, measures of \(\mathbf{Q}\) residuals and Hotelling's \(\mathbf{T}^{2}\) of a sample are compared to a PCA submodel in SIMCA. It is often useful to determine whether a sample is a member of a particular group based on the lack-of-fit statistic of \(\mathbf{Q}\), which accounts for the residual part of the variation that is not explained by the PCA model.  
For a training matrix \(\mathbf{X}\) consisting of m rows (samples) and n columns (variables), the following equation describes the relationship between the data and the PCA model:  
\[\mathbf{X}\ =\ \mathbf{T}\mathbf{P}^{\mathrm{T}}+\mathbf{E} \tag{1}\]  
where \(\mathbf{T}\) is the scores matrix of all the training samples, \(\mathbf{P}\) is the loadings matrix, and \(\mathbf{E}\) is the residual matrix.  
\(\mathbf{Q}\) residuals are calculated as the sum of squares of each row (sample) of \(\mathbf{E}\), i.e., for the \(i\)th sample in \(\mathbf{X}\left(\mathsf{x}_{i}\right)\),  
\[\mathbf{Q}_{i}=e_{i}e_{i}^{T}=x_{i}\left(\mathbf{I}-\mathbf{P}_{k}\mathbf{P}_{k}^{T}\right)x_{ i}^{T} \tag{2}\]  
where \(e_{i}\) is the \(i\)th row of \(\mathbf{E}\), \(\mathbf{P}_{k}\) is the matrix of the k loadings vectors retained in the model (where each vector is a column of \(\mathbf{P}_{k}\)), and \(\mathbf{I}\) is an identity matrix of the appropriate size (\(n\) by \(n\)).  
[PERSON]'s \(\mathbf{T}^{2}\) is the sum of the normalized squared scores and is defined as  
\[\mathbf{T}_{i}^{2}=t_{i}\lambda^{-1}t_{i}^{T}=x_{i}\mathbf{P}_{k}\lambda^{-1}\mathbf{P}_{ k}^{T}x_{i}^{T} \tag{3}\]  
where \(t_{i}\) refers to the \(i\)th row of \(\mathbf{T}_{k}\) in the m by k matrix of scores from the model, and \(\lambda\) is a diagonal matrix containing the eigenvalues (\(\lambda_{1}\) through \(\lambda_{k}\)) corresponding to the \(k\) principal components retained in the model.  
\(\mathbf{Q}\) is a measure of the distance from a sample point to its projection onto the k factors retained in the model; a large \(\mathbf{Q}\) value indicates that unusual variation exists outside the model. \(\mathbf{T}^{2}\) represents a measure of the variation in each sample within the model; a large \(\mathbf{T}^{2}\) value indicates that unusual variation exists within the model.