esa-sceva/satcom-chunk-collection huggingface dataset:
# Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
Moreover, the technique itself does not require any atmospheric compensation, that is instead mandatory for multitemporal target displacement analysis, since a single image has to be considered in the proposed approach. The complete vibration profile of the infrastructure under analysis is extracted from the satellite SAR data highlighting the presence of any lesions, cracks or other damage (e.g., inaccuracies, singularities, or anomalous vibrations) (Each extended material body is a continuous of material, having the same physical characteristics for each part of it. When there is a crack, which is synonymous with lesion or fissure, or fracture, we mean an interruption in the continuity of the material as a result of tensional or deformation states that are not compatible with its mechanical characteristics.). As a consequence, we highlight the possibility of estimating early-warning cracks by evaluating any anomalies on the vibration maps, resulting in an efficient structural health monitoring and predictive maintenance. Vibration estimation is a fundamental activity conducted through the modal analysis usually performed using in-situ sensors. Instead of measuring the displacement vector through a conventional measurement tool, the estimated phase shift from a single spaceborne SAR observation can be used. This is done after an appropriate signal processing procedure. Particularly, the vibrations of moving targets are detected by the radar and their positions in a SAR-image are shifted in azimuth and range-azimuth and a defocusing may occur (it is worth to notice that due to the layover all the oscillations in height are spread in the range and azimuth dimensions). This defocusing component can be estimated through a single pass SAR observation and it represent a precious information to be used in a modal analysis. For this reason, the proposed method would have a great impact in infrastructure monitoring and could potentially either replace or work together with the in-situ sensors network, providing additive/complementary infrastructure health data. Summarizing, in this work, we investigate the main question: can spaceborne single pass SAR data be used to assess critical structural health conditions of Bridges? The purpose of the experimental analysis is to present an initial assessment, answering this question through the observation of various bridges. A complete survey of the \"Morandi\" bridge is also reported, in order to understand possible causes that led to its collapse. The results have been obtained by processing data observed by the COSMO-SkyMed (CSK) satellite constellation, in the context of the \"Mapitaly\" mission. It is important to highlight that the proposed technique can be also applied to the freely distributed Sentinel-1 data ensuring a worldwide coverage.
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 4 Results and Discussion
### Simulation Dataset Test Results
The compensation performance of the T-L model is better than that of BPNN and WilliamsNN, with an STD and IR of 0.037 nT and 427.641, respectively, which shows that the neural network compensation method that has not fully converged performs worse than the traditional T-L model compensation method. ResNet and MagTCN achieve full convergence in the training phase due to the convolutional structure and residual connection. Therefore, they are better than the T-L model in compensation performance. Among them, the STD and IR of ResNet are 0.033 nT and 480.082, respectively, and those of MagTCN are 0.024 nT and 651.61, respectively. The above results show that, based on the separable convolutional neural network structure and loss function proposed in this article, MagTCN is better than ResNet in compensation performance and has achieved the best compensation performance and generalization ability among the above compensation methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epoch** & **WilliamsNN** & **BPNN** & **ResNet** & **MagTCN** \\  & **Loss** & **Loss** & **Loss** & **Loss** \\ \hline
0 & 93,392.16 & 47,583.50 & 969.29 & 826.76 \\
50 & 72,419.97 & 17,197.35 & 154.81 & 39.44 \\
100 & 36,749.54 & 9321.64 & 47.71 & 16.32 \\
150 & 11,337.10 & 1408.63 & 48.01 & 9.84 \\
200 & 1792.37 & 303.06 & 42.36 & 13.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of neural network losses using simulation datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **WilliamsNN** & **BPNN** & **ResNet** & **MagTCN** & **T-L Model** \\ \hline STD (nT) & 1.320 & 0.551 & 0.033 & 0.024 & 0.037 \\ IR & 12.276 & 29.401 & 480.082 & 651.61 & 427.641 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The statistics of compensation results within the bandpass from simulation datasets.
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
## 2 Materials and Methods
### Modeling Methodology
#### 2.2.3 Performance Metrics of the Predictive Tool  
RMSE and MAE are commonly applied in model performance evaluation for hydrodynamic and water quality models and statistical models [50; 51]. These two performance measures are in the same unit as the model output, and hence, making them easily interpretable for the reader, and they also work well for continuous long-term simulations [50]. In this study, the root mean square error (RMSE) and mean absolute error based on the previous study were used for evaluating model predictive performance [52]:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P_{i}-O_{i})^{2}} \tag{2}\]  
\[MAE=\ \frac{1}{n}\sum_{i=1}^{n}|P_{i}-O_{i}| \tag{3}\]  
where \(P_{i}\) and \(O_{i}\) represent the prediction and observations at the step of \(i\), respectively, and \(n\) represents the number of observations.
###### Abstract  
The low-frequency vibration and radiation performance of a locally resonant (LR) plate with periodic multiple resonators is studied in this paper, with both infinite and finite structure properties examined. For the finite cases, taking the LR plate attached with two periodic arrays of resonators as an example, the forced vibration response and the radiation efficiency are theoretically derived by adopting a general model with elastic boundary conditions. Through a comparison with the band structures calculated by the plane-wave-expansion method, it shows that the band gaps in the infinite LR plate are in good agreement with the vibration-attenuation bands in the finite LR plate, no matter what boundary conditions are applied to the latter. In contrast to the vibration reduction in the band gaps, the radiation efficiency of the finite LR plate is sharply increased in the band-gap frequency ranges. Furthermore, the acoustic power radiated from the finite LR plate can be seriously affected by its boundary conditions. For the LR plate with greater constraints, the acoustic power is reduced in the band-gap frequency ranges, while that from the one with fully free boundary conditions is increased. When further considering the damping loss factors of the resonators, the attenuation performance can be improved for both the vibration and radiation of the LR plate.  
Keywords:locally resonant plate; periodic multiple resonators; boundary condition; vibration attenuation; radiation efficiency +
Footnote †: journal: Applied Physics Letters
## 5 Conclusions and Limitations
### Limitations  
Based on MGWR, this paper has developed a multiscale flow-focused geographically weighted regression model and used it to analyse transport flow data from the Jiangsu province in eastern China. However, this study also raises new questions for future research. Firstly, the transaction data used in this study have very a high temporal resolution. Temporal heterogeneity [40], together with spatial heterogeneity, are two sides of a coin. Both need to be integrated into an analysis of geographic mobility. The current version of MFGWR lacks the consideration of temporal dimension. A new development of multi-scale geographically and temporally weighted flow modelling will be recommended for future work. Secondly, MFGWR is powerful for mapping parameter estimation but it is hard to visualize and interpret these geographical patterns, particularly when dealing with large-scale flows. It presents a new challenge of how to intelligently represent the flow space. Lastly, the back-fitting algorithm in MFGWR has to perform a large number of iterative operations. Further work is required to improve its operating efficiency. Parallel computing may be an efficient solution.  
Conceptualization, J.C., H.Z. and C.J.; methodology, L.Z.; software, L.Z.; validation, L.Z., J.C., H.Z. and C.J.; formal analysis, J.C. and H.Z.; investigation, C.J.; resources, C.J.; data curation, C.J.; writing-original draft preparation, L.Z. and C.J.; writing-review and editing, J.C.; visualization, L.Z.; supervision, J.C. and H.Z.; project administration, J.C. and H.Z.  
This research was funded by National Natural Science Foundation of China, grant number 41871137, 41571134, & 41571124 and the Natural Science Foundation of the Jiangsu Higher Education Institutions, grant number 16 KJA170002.  
The authors thank the reviewers for their helpful comments and suggestions, which led to a significant improvement on the manuscript.  
The authors declare no conflict of interest.
## 1 Introduction
### Methodology Proposed to Calculate the Maximum Power Point (MPP)
It is also fair to mention that the key factor in large photovoltaic power stations (solar parks) is different. In these industrial power plants, the power estimations should always provide the most accurate results, as the impact of this accuracy on the economic revenue can be huge [25], and the calculation resources and time consumptions being proportionally large as well. This work is organized as follows. The methodology developed is explained in Section 2, whereas the results, firstly applied to both the photovoltaic cell and panel mentioned in this section (R.T.C. France and Photowatt PWP201, respectively), and then applied to a SELEX 5-cell space photovoltaic module (the solar panels of the UPMSat-2 satellite integrate several of these modules, both series and parallel connected), are included in Section 3. Finally, conclusions are summarized in Section 4. Equations Proposed to Calculate the 1-Diode/2-Resistor Equivalent Circuit and the Maximum Power Point (MPP)  
Particularizing the 1-diode/2-resistor circuit equation in the characteristic points of the \(I\)-\(V\) curve, the following equations can be obtained [3]:  
\[I_{sc}=I_{pv}-I_{0}\bigg{[}\exp\bigg{(}\frac{I_{sc}R_{s}}{NaV_{T}}\bigg{)}-1 \bigg{]}-\frac{I_{sc}R_{s}}{R_{sh}}, \tag{6}\]  
\[\frac{1}{R_{sh}}-\frac{1}{R_{sh0}-R_{s}}+\frac{I_{0}}{NaV_{T}}\exp\bigg{(} \frac{R_{s}I_{sc}}{NaV_{T}}\bigg{)}=0, \tag{7}\]  
\[0=I_{pv}-I_{0}\bigg{[}\exp\bigg{(}\frac{V_{oc}}{NaV_{T}}\bigg{)}-1\bigg{]}- \frac{V_{oc}}{R_{sh}}, \tag{8}\]  
\[I_{mp}=I_{pv}-I_{0}\bigg{[}\exp\bigg{(}\frac{V_{mp}+I_{mp}R_{s}}{NaV_{T}} \bigg{)}-1\bigg{]}-\frac{V_{mp}+I_{mp}R_{s}}{R_{sh}}, \tag{9}\]  
\[-\frac{I_{mp}}{V_{mp}}=-\frac{I_{0}}{NaV_{T}}\bigg{(}1-\frac{I_{mp}}{V_{mp}} R_{s}\bigg{)}\bigg{[}\exp\bigg{(}\frac{V_{mp}+I_{mp}R_{s}}{NaV_{T}}\bigg{)} \bigg{]}-\frac{1}{R_{sh}}\bigg{(}1-\frac{I_{mp}}{V_{mp}}R_{s}\bigg{)}, \tag{10}\]  
where Equation (6) corresponds to the short-circuit point, Equation (7) to the slope at the short circuit point, Equation (8) to the open-circuit point, Equation (9) to the maximum power point, and finally, Equation (10) is derived from the condition \(\mathrm{d}P/\mathrm{d}V=0\) at that point.
# Rock Indentation Behavior: Effects of Penetration Rates and Indenter Types
## 2 Methodology  
### Experiment Apparatus  
Rock indentation experiments are conducted using a rock indentation testing apparatus, as shown in Figure 1. This apparatus includes a Geo-con indentation testing machine, an instrument control system, data acquisition instruments, data processing software, and a high-speed camera. During the rock indentation process, this apparatus can adjust the type of indenter and loading rate according to the experimental requirements. It can also monitor the deformation and displacement of the rock in real time during the indentation process, while the high-speed camera records the rock-indenter interaction phenomena and the degree of rock fracturing under different loading conditions.  
The indentation testing machine consists of an axial loading device, a pressure sensor, and a displacement sensor. The axial loading device can move vertically to adjust the position of the indenter. A pressure sensor is located beneath the rock sample and is connected to the data acquisition instruments to monitor loading pressure changes in real time. The displacement sensor (LVDT) is positioned beneath the rock sample's support layer, perpendicular to the horizontal rod under the support layer, and is also connected to the data acquisition instruments to record displacement changes of the indenter. The instrument control system, installed on a computer, allows graphical adjustments of the indenter's position and loading rate. The data acquisition instrument records sensor information and transmits the recorded data in a fixed format to the data processing software.  
Figure 1: Schematic diagram of rock indentation apparatus.  
The high-speed camera (i-speed 220) is used to capture the entire rock indentation process, with its control unit installed on a computer. The data processing software, also installed on the computer, connects directly to the data acquisition instruments and the high-speed camera, enabling the analysis and output of both numerical and visual data. During the rock indentation testing, the type and angle of the indenter can be replaced as needed. In this study, experiments were conducted using conical and spherical indenters/picks fabricated from medium carbon steel (200-300 HB). The 60\({}^{\circ}\) cutting-edge angle was selected based on stress field optimization criteria derived from [PERSON] fracture mechanics.
# Design and Application of Daylight-Based Lighting Controller on LED Luminaire
## 3 The Control Circuit and Test Setup
### Lighting Control Circuit  
The proposed daylight control circuit for LED luminaire was based on a concept that illuminance on a working plane must always be maintained at the desired value. The proposed algorithm must control the light output from LED luminaire according to the illuminance value obtained from photosensors. A diagram of the proposed daylight control circuit and LED luminaire is shown in Figure 6. The system is composed of the following components: a power supply unit that supplied +5 V, +12 V, and +18 V for the microcontroller, gate drive and LED luminaire, respectively. The microcontroller received illuminance data from the photosensors installed on the working plane and converted the analog data to digital data with an analog to digital converter (ADC). The microcontroller then set a duty cycle of Pulse Width Modulation (PWM) signal which was sent to a MOSFET to control the power and light output from the LED luminaire. Since the microcontroller only supplied the PWM signal at a voltage of 5 V, it needed a gate drive to boost the voltage to 12 V in order to successfully operate the MOSFET. The flowchart of the daylight control circuit operation is illustrated in Figure 7. The operation sequence  
Figure 5: Layout of experimental unit: (a) LED luminaire layout; (b) Photo sensor layout.  
can be described as followed. First, the microcontroller set the duty cycle of the PWM signal at 90% and then received illuminance values from photosensors through ADC. These values were then compared with the set value in the microcontroller, which, in this case, was 300 lux. If the illuminance values were higher than the set value, the microcontroller reduced the duty cycle value by 5% before checking the illuminance values at the working plane again. If the illuminance values were lower than the set value, the microcontroller increased the duty cycle value by 5% then rechecked the illuminance values. The duty cycle directly affected the power supplied to the LED luminaire through MOSFET operation.
# Highly Efficient Blue Host Compound Based on an Anthracene Moiety for OLEDs
## 3 Results and Discussion
### Electroluminescence Properties
In Figure 6d, the peak EL\({}_{\text{max}}\) is observed at 460 nm, closely matching the shape of the PL spectrum and the PL\({}_{\text{max}}\) value of the blue dopant material 3 Me-1 Bu-TPPDA in solution, as shown in Figure S5. Furthermore, when comparing the shape of the EL spectrum of the doped device with the PL spectrum of 3 Me-1 Bu-TPPDA in the solution state and comparing their FWHM values, which were both 41 nm, it indicates that the EL spectrum originates from emission by the dopant. Efficient energy transfer from the host to the dopant was confirmed, as described in Figure 3. Furthermore, as depicted in Figure 6c, the device showed minimal roll-off. The doped device exhibited a 1.9% decrease in EQE at 4000 nits compared to the maximum value. When 2-NaAn-1-PNa was used as the host, it exhibited very stable roll-off characteristics. [PERSON] et al. reported on four host materials based on anthracene [21]. Among them, NA-AN-NA, which has a molecular structure most similar to 2-NaAn-1-PNa, was used as a host, and when doped with 5 wt% DITBDAP, achieved a CE\({}_{\text{max}}\) of 8.97 cd/A and EQE\({}_{\text{max}}\) of 8.10%, demonstrating excellent EL characteristics. Comparing these results, it can be inferred that 2-NaAn-1-PNa can also effectively enhance EL performance due to balanced charge transport and efficient exciton recombination. The stability of the fabricated doped OLED devices was assessed by measuring their lifetime at 1000 cd/m\({}^{2}\). The LT\({}_{95}\) values were 440 h, indicating excellent device longevity surpassing 400 h (Figure 7). LT\({}_{95}\) denotes the device lifetime where the final luminance reaches 95% of the initial luminance. Extrapolated to LT\({}_{50}\), the doped device is estimated to have a lifetime exceeding 28,000 h [22]. Asymmetric anthracene-based devices may offer extended lifetimes, possibly attributed to improved thermal stability. Due to their high luminous efficiency, low operating voltages, and prolonged lifetimes, these devices are suitable not only for mobile phones but also for OLED TVs, which require low power consumption and long-lasting performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Device** & **O.V.a** & **Luminance** & **CE** & **EQE** & **EL\({}_{\text{max}}\)** & **FWHM (nm)** & **CIE (x, y)** \\ \hline Non-doped & 8.1 & 335 & 3.4 & 3.9 & 457 & 65 & (0.150, 0.113) \\ Doped & 3.9 & 934 & 9.3 & 8.3 & 460 & 53 & (0.134, 0.139) \\ \hline \hline \end{tabular}  
* Operating voltage at 10 mA/cm\({}^{2}\).  
\end{table} 
Table 2: EL performances of the fabricated OLED non-device and doped device at 10 mA/cm\({}_{2}\).
## 4 Strategies for Separation of AE Impulses from the Background Signals of Various Types
### Algorithm for Isolating the Useful Signal from the Continuous Noise-Like Background
Five percent of the total volume of AE pulses is recorded in the time interval not exceeding 40% of the sample fracture time, and ten and twenty percent in the interval not exceeding 50% and 60%, respectively. Despite the fact that the total number of the reliably detected AE pulses for three out of four modes does not exceed a few percent, the time required to register 5, 10, and 20% of these pulses significantly differs from the time of the \"possible\" fracture of the test object. The strategy for isolating a useful signal from a continuous noise-like background requires sequential implementation of the following procedures:  
* Frequency filtering of the original signal using the high-frequency filter with a cutoff frequency of 200 kHz;
* Isolation of single AE impulses with a maximum amplitude that exceeds the maximum amplitude of the background signal by 5 dB.
## 1 Introduction
Laboratory testing involving a four-story shear-frame structure validated the system's ability to extract modal parameters using Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI), which were further corroborated through finite element modeling. Field deployment on the Lamberti Bridge in Parma, Italy--with 30 installed nodes--demonstrated the system's potential for long-term, remote SHM through continuous OMA and displacement estimation via Kalman filtering. Despite these achievements, further efforts are needed to verify the system's performance on large-scale structures and to develop efficient algorithms for processing the large volumes of acquired data for actionable performance evaluation. In recent years, the demand for structural monitoring systems tailored to the increasing complexity and diversity of civil infrastructure has led to a paradigm shift from proprietary, closed platforms to open-source-based wireless sensing technologies. In particular, there is growing interest in the development of integrated wireless monitoring systems that support autonomous modular configurations, enable rapid high-volume data transmission via 5G communication, and facilitate the fusion of multiple sensor modalities for comprehensive structural assessment. In this context, the present study introduces the development of the MANTiS-32 wireless sensing system, which leverages an open-source platform built on the ESP32 microcontroller in conjunction with MPU-6050 sensors. The system is designed to provide scalable, real-time structural response monitoring with an emphasis on modularity, cost efficiency, and field applicability. To verify the fundamental performance of the MANTiS-32 system, a series of evaluations were conducted focusing on its capabilities in acceleration and frequency response measurement, wireless communication stability, and real-time data acquisition between the MANTiS-32 hub and eight connected MPU-6050 units via RS485 communication. Furthermore, to assess the applicability of the system in a realistic structural setting, four MANTiS-32 units were deployed on a lab-scale cable-stayed bridge model designed to reflect the dynamic characteristics of small- to medium-span long-span bridges. The system's ability to capture and transmit dynamic response data in real time was successfully demonstrated, thereby validating its effectiveness for structural performance monitoring. Table 1 summarizes key technical distinctions between recent SHM platforms and the proposed MANTiS-32 system, with a focus on synchronization methodology, timing accuracy, and sensing capabilities. As shown in the comparison, MANTiS-32 exhibits a less than 1 ms synchronization error and sampling rates up to 200 Hz while maintaining a per-node of USD 70-80, offering high performance at a low cost. The subsequent sections provide a detailed account of the system architecture, implementation methodology, and performance validation experiments.