esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 5 Conclusions  
This study analyzed the variability in Pavement Condition Index (PCI) scores obtained through manual inspection and proposed a validation framework based on repeatability and accuracy. Using the Coefficient of Variation (CV) and Mean Relative Error (MRE) as performance indicators, we demonstrated that both metrics significantly improved after inspector training. Based on the findings, it is recommended that future manual PCI inspection guidelines adopt a CV threshold of \(\leq\)0.1 for repeatability and an MRE threshold of \(\leq\)10% for accuracy. These quantitative criteria can serve as reference standards in field applications, particularly for local government agencies or organizations that rely on manual inspection methods. Implementing these indicators as part of a Quality Control (QC) and Quality Assurance (QA) framework can help improve inspection reliability and data consistency [15, 16].  
In terms of equipment validation, the distance measuring wheel achieved an average relative error of less than 1%, and the PCI scores computed by the developed pavement inspection system showed less than 5% deviation when compared to MicroPAVER software, confirming the system's measurement accuracy [21, 22].  
\begin{table}
\begin{tabular}{c c c} \hline
**Pavement Management System** & **MicroPAVER** & **Relative Error (\%)** \\ \hline
42.8 & 43.3 & 4.7\% \\
36.8 & 37.1 & 0.6\% \\
52.1 & 51.2 & 4.8\% \\   Average Relative Error & & 3.2\% \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of PCI Scores from Pavement Management System and MicroPAVER: Relative Error Analysis.  
This study contributes a practical and measurable method for evaluating manual PCI inspections--an area that has been largely overlooked in recent literature. Future work could focus on extending this validation framework to automated or AI-based pavement inspection systems, enabling performance benchmarking across both manual and intelligent inspection methods [5; 23; 24].  
Conceptualization, S.-H.L. and J.-D.L.; methodology, S.-H.L.; validation, J.-D.L. and Y.-S.C.; formal analysis, S.-H.L.; investigation, S.-H.L. and Y.-S.C.; resources, J.-D.L.; data curation, S.-H.L.; writing--original draft preparation, S.-H.L.; writing--review and editing, J.-D.L. and Y.-S.C.; visualization, S.-H.L.; supervision, J.-D.L.; project administration, J.-D.L.; funding acquisition, not applicable. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
* Image data preprocessing and noise suppression: To enhance the model's performance and generalization ability, this paper conducts efficient preprocessing of the image dataset. This includes the precise removal of the backgrounds and the cropping of fracture regions using DIS-Net in order to minimize the impact of noise interference on model training. * Dynamic cross-feature fusion mechanism: A dynamic cross-feature fusion mechanism is introduced, wherein features from different sources are cross-fused. This enables the model to deeply understand the input feature information from multiple angles and dimensions. Calculating bone sticks matching degree through feature vector differences: The matching relationship between bone sticks is computed using deep feature vector differences. By combining feature vector differences and introducing a binary cross-entropy loss function, the matching degree between bone sticks is quantitatively represented within the range. This approach significantly reduces the computational complexity of the network.
## 6 Conclusions  
Taking a large-span roof with folded plate type as the background, this paper selected the first several eigenmodes of the structure and reconstructed the wind pressure field on the roof surface through the POD method. The statistical characteristics of the reconstructed wind pressure field were compared with the test measurement results, and the following main conclusions were drawn:  
1. With the increase of the selected order of structural eigenmodes, the wind pressure field reconstruction results are closer to the true value. The actual wind field can be reconstructed with basically more than 90% accuracy when 5 eigenmode orders are selected, and the reconstruction can reach more than 95% accuracy when 20 eigenmode orders are selected.
2. The wind pressure field reconstruction effect based on the POD method is related to the spatial position of the predicted point. In general, the wind field reconstruction effect on the windward folded plate is better than that on the leafward folded plate, which is better than that on the roof flat plate and side folded plate. In addition, the effect of wind field reconstruction based on fluctuating wind pressure is better than that based on mean wind pressure.
3. By performing spatial interpolation of eigenmodes on the unknown points, the wind pressure time history of an unknown point of the structure can be effectively predicted based on the POD method. Different interpolation methods all can achieve relatively ideal results. Among them, the bilinear interpolation method has the highest accuracy, and the adjacent point interpolation method and Griddata V4 interpolation method only have certain errors in the low-frequency region.  
In general, for the large-span roof structure with folded plate type, selecting reasonable structural eigenmode orders, the wind pressure field of the structure can be better reconstructed and predicted based on POD technology, which can provide effective technical support for the research and analysis of wind load on this type of large-span roof structures.  
Conceptualization, Y.S. and J.D.; methodology, Y.S. and J.D.; experiments, Y.S. and F.X.; validation, Y.S. and J.D.; investigation, Y.S., J.L. and F.X.; data curation, J.L. and F.X.; writing--original draft, Y.S.; writing--review and editing, Y.S. and J.L.; supervision, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (51978108, 52008357).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Adjacent Point** & **Bilinear Method** &
\begin{tabular}{c} **Bicubic** \\ **Method** \\ \end{tabular} & **Griddata V4 Method** \\ \hline Predicted point C2 & 0.903 & 0.939 & 0.933 & 0.902 \\ Predicted point E2 & 0.799 & 0.846 & 0.841 & 0.814 \\ Predicted point F2 & 0.831 & 0.887 & 0.882 & 0.839 \\ Predicted point K2 & 0.809 & 0.864 & 0.854 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation coefficients between the wind pressure coefficients predicted based on different interpolation methods and the test values at predicted points.
# Thermal and Mechanical Assessment of PLA-SEBS and PLA-SEBS-CNT Biopolymer Blends for 3D Printing
## 2 Materials and Methods  
### Materials and Blending Method  
PLA (NatureWorks Ingeo Biopolymer 4032D), SEBS (MEGOL DP2758/6/47 UVR) and CNT masterpatch (Nanocyl Plasticyl SEBS1001, 10% NC7000 multiwall carbon nanotube) blends and nanocomposites were prepared by Brabender Plastograph and IDMX mixer. A total of 750 g of PLA was used to make a roll of filament.  
The blends contained 1 to 5 weight% SEBS and the nanocomposite samples contained 1% SEBS and 0.06 and 0.1% CNTs. Before extrusion, PLA was dried at 60 \({}^{\circ}\)C and 4 h. The temperatures of the feeding extruder were 190, 200, 210, 220 \({}^{\circ}\)C (from hopper to die) and the rate of the screw was 40 rpm. The temperature of the mixer was 210 \({}^{\circ}\)C, and the rate of the screw was 12.5 rpm. The diameter of the filament was 1.7 mm, and the tolerance was \(+/-0.2\) mm.
## 2 Thermal Error Modeling
### Selection of Temperature-Sensitive Points
#### 2.1.3 Principle of Pearson s Correlation Coefficient  
The Pearson correlation coefficient is a method to assess the strength of linear relevancy between two factors, with values ranging from \(-1\) to \(1\). A value of \(+1\) indicates a perfect positive correlation, signifying a strong positive linear relationship between two variables, and a value of \(-1\) indicates a perfect negative correlation, signifying a strong negative linear relationship. A correlation coefficient close to \(0\) indicates no linear relationship. The Pearson correlation coefficient is calculated as follows.  
\[r(O,U)=\frac{Cov(O,U)}{\sigma_{O}\cdot\sigma_{U}}=\frac{\sum_{i=1}^{n}\big{(} O_{i}-\overline{O}\big{)}\left(U_{i}-\overline{U}\right)}{\sqrt{\sum_{i=1}^{n} \big{(}O_{i}-\overline{O}\big{)}^{2}\cdot\sum_{i=1}^{n}\left(U_{i}-\overline{ U}\right)^{2}}} \tag{11}\]  
where \(r(O,U)\) represents the Pearson correlation coefficient of two variables \(O\) and \(U\), respectively: \(Cov(O,U)\) is their covariance; and \(\sigma_{O}\) and \(\sigma_{U}\) are the standard deviations of variables \(O\) and \(U\), respectively. \(O_{i}\) and \(U_{i}\) are each element of variables \(O\) and \(U\), respectively, and \(\overline{O}\) and \(U\) are the average values of variables \(O\) and \(U\), respectively.
## 5 Rock-Cutting Experiments with Arcing-Blade Cutter  
In order to verify the reliability of the simulation in this paper, linear cutting experiments were conducted with an arcing-blade cutter, and the rock crushing states under different cutter spacing were observed. The size of the arcing-blade cutter ring for the experiment was consistent with that for the simulation, as depicted in Figure 16. In these cutting experiments, the penetration was set at 2 mm and 4 mm, resulting in corresponding cutter spacings of 40 mm, 50 mm, 60 mm, and 70 mm. Subsequently, the crushing state of the rock surface was observed upon the completion of the cutting process.  
Figure 17 illustrates the crushing state of the red sandstone caused by the sequential cutting of the arcing-blade cutter. Figure 17a,b present the crushing state of the rock surface with 2 mm and 4 mm penetration. When the penetration was 2 mm, the cutter left a \"strip\" crushed area on the rock surface, while the rock ridge between the cutters remained uncrushed. On the other hand, with a 4 mm penetration (as depicted in Figure 17b), the rock ridge was effectively crushed at cutter spacings 40 mm and 50 mm, as shown in Figure 17(b1,b2). Further analysis reveals that, at cutter spacings of 60 mm and 70 mm, as illustrated in Figure 17(b3,b4), the rock ridge between two cuts could not be effectively crushed, resulting in only two distinct \"strip\" crushed areas produced by the arcing-blade  
Figure 16: Arcing-blade cutter for experiment.  
cutter. Compared with the rock crushing state from Section 4.2, it can be found that the crushing state observed from this experiment was similar to that observed in the simulation, confirming the reliability of the simulation results.  
The specific energy in the cutting experiments under different cutting parameters is illustrated in Figure 18. At 2 mm penetration, the specific energy of the arcing-blade cutter remained consistently high, regardless of an increase in cutter spacing. This observation suggests a low crushing efficiency of the arcing-blade cutter at such a low penetration. However, at a penetration of 4 mm, the specific energy initially decreased, then rose before stabilizing. Based on the above observations, the variation trend of the specific energy of the arcing-blade cutter in the experiment is consistent with the simulation, further demonstrating the reliability of the simulation results.
## 3 Evaluation and Results
### Evaluation Metrics
#### 3.1.1 Step 1: Optical Wavelength Dither  
First, we will show that the system can be successfully stabilized, i.e., the sideband wavelength can be locked to the MRR's resonant wavelength with a residual wavelength misalignment \(|\Delta\lambda|=|\lambda_{\text{xSB}}-\lambda_{\text{res}}|\leq\varepsilon_{ \lambda}\), constrained by the limits of \(\pm\varepsilon_{\lambda}\). The parameter \(\varepsilon_{\lambda}\) depends on the configuration of the CLC, i.e., the amplitude and the frequency of the triangle-wave excitation, and it cannot be expected to vanish completely. The objective of this first step is to optimize the parameters of the CLC for the minimal dither, while still maintaining the locked state, and to measure its value \(\varepsilon_{\lambda}\). The corresponding experimental setup variant will be referred to as Variant 1.
## 4 Results and Discussion
### Comparison between Simulated and Measured Results of the Pipes with Pre-Existing Cracks
#### 4.3.1 Effect of Crack Length
However, the measured results disclose that the failure load values of the concrete pipes having cracks with lengths of 400, 300, and 200 mm are 11, 12.5, and 14 kN/m respectively. Compared with the measured failure load values of the intact pipes, the ultimate bearing capacities of the concrete pipes are reduced by 52, 49, and 43% respectively. These findings confirm that the length of the crack has a significant impact on the bearing capacity of the concrete pipes. (2) Circumferential strain  
In order to analyze the influence of cracks on the ultimate bearing capacity of the concrete pipes in detail, the strain of the pipes containing cracks with different lengths is extracted in eight circumferential positions, namely 0, 45, 90, 135, 180, 225, 270, and 315\({}^{\circ}\), under a destructive load, as presented in Figure 17.  
As can be inferred from Figure 17, the circumferential strains on the outer wall and inner wall of the pipes are symmetrically distributed in the center. The crown and invert of the outer wall of the pipe are under pressure in the range of 90\({}^{\circ}\) (315-45\({}^{\circ}\) and 135-225\({}^{\circ}\)), while from the shoulder to the haunch is under tension in the range of 90\({}^{\circ}\) (45-135\({}^{\circ}\) and 225-315\({}^{\circ}\)); the springline (90\({}^{\circ}\) and 270\({}^{\circ}\)) is under the maximum tensile stress. The crown and invert of the inner wall of the pipe are under tension in the range of 90\({}^{\circ}\) (315-45\({}^{\circ}\) and 135-225\({}^{\circ}\)), while from the shoulder to the haunch is under pressure in the range of 90\({}^{\circ}\) (45-135\({}^{\circ}\) and 225-315\({}^{\circ}\)); the largest tensile stress is seen at the crown and invert (0\({}^{\circ}\) and 180\({}^{\circ}\)). Therefore, it can be concluded that under the action of an external load, the crown, invert, and springline locations are the vulnerable parts of the concrete pipe. In Figure 17a,b, due to the influence of the pre-existing cracks at the crown, the strains on the inner and outer walls of the crown and invert locations are different. At the crown (0\({}^{\circ}\)), the simulation curve states that the largest and smallest strains are respectively achieved at the crack length of 400 and 200 mm, which indicates that the larger the crack length is, the more easily the pipe is damaged.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 3 Methodology
### Snowmelt Detection Using SSM/I Data  
A thresholding method proposed by [PERSON] and [PERSON] (1994) [6] was used to recognize snowmelt, which was in good agreement with snowmelt derived from air/surface temperature in Antarctica [47]. Snowmelt is identified when horizontally polarized 19.35 GHz \(T_{b}\) exceeds the winter mean plus 30 K, illustrated as follows:  
\[m(t)=\left\{\begin{array}{ll}1,&T_{b}\geq T_{wm}+30\text{ K}\\ 0,&T_{b}<T_{wm}+30\text{ K}\end{array}\right. \tag{2}\]  
where \(t\) is the time, \(T_{wm}\) is the winter (from May to July) mean \(T_{b}\), and \(m(t)\) represents snowmelt, with \(m=1\) indicating melt and \(m=0\) indicating freeze. Figure 2 shows the horizontally polarized 19.35 GHz \(T_{b}\) of a melting pixel (67.81\({}^{\circ}\)S, 64.64\({}^{\circ}\)W) on the Larsen C Ice Shelf and a frozen pixel (72.21\({}^{\circ}\)S, 63.63\({}^{\circ}\)W) on Palmer Land. Melting signals (the red line above 190 K) were clearly observed during summer in the first place, whereas no snowmelt was detected for the other one (the green line).
## 3 Building Blocks of the Overall DFE Architecture
### FIR4 (Carrier Aggregation Filter)  
The role of FIR4 (carrier aggregation filter), for the sake of a given carrier, is to remove the other carrier originating from CA in the LTE. The left half of Figure 13 shows, for the LTE 20 MHz + 20 MHz case, carrier 1 and carrier 2 shifted by the mixer to the center frequency (namely, the origin in the x axis) and farther from the center frequency, respectively. This is the input to FIR4. To the right of carrier 1 exists an inband blocker and in about the middle of the graph exists carrier 2. The right half of Figure 13 displays the output of FIR4 that  
Figure 11: Digital mixer structure and operating principle.  
Figure 12: Digital mixer input and two outputs for case 10.  
runs decimation by 2 such that the carrier 1 signal around the center frequency takes a broader seat while the carrier 2 signal in the middle is attenuated and the inband blocker power is more or less attenuated as well. To this end, low-pass filtering is needed. In other words, in the FIR4 filter, the signal adjacent to the center frequency is sustained while other signals than the desired carrier are attenuated, followed by downsampling through which the sample rate becomes identical to the output frequency.  
The design of the digital FIR lowpass filter is derived from the analog lowpass filter prototype on the basis of impulse invariance. The attenuation at a specified frequency is expressed as a function of the order of the filter and the 3 dB frequency or the corner frequency, \(f_{3 dB}\), as expressed in Equation (9). If the attenuation at a frequency of 2 MHz is required to be, for instance, \(-42\) dB, from the standard specification, then a 3 rd-order filter with a 3 dB frequency of 400 kHz can meet this requirement.  
\[Attenuation=-10\times\log_{10}\left(1+\left(\frac{f}{f_{3 dB}}\right)^{2\times order }\right) \tag{9}\]  
For all the 10 cases, FIR4 (CA filter) filter coefficients (carrier 1 and carrier 2) were obtained. All the coefficients were symmetric and the total numbers of coefficients were 1, 1, 14, 14, 22, and 18 and 14, 9, 30, and 30 for cases 1-6 and 10-13, respectively, in the case of carrier 1. In the case of carrier 2, the total numbers of coefficients were 14, 9, 30, and 30 for cases 10-13, respectively.
###### Abstract  
In this work, we present an innovative approach for damage detection of infrastructures on-edge devices, exploiting a brain-inspired algorithm. The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their theoretical energy efficiency and compactness, to recognise damage conditions by processing data from low-cost accelerometers (MEMS) directly on the sensor node. We focus on designing an efficient coding of MEMS data to optimise SNN execution on a low-power microcontroller. We characterised and profiled LSNN performance and energy consumption on a hardware prototype sensor node equipped with an STM32 embedded microcontroller and a digital MEMS accelerometer. We used a hardware-in-the-loop environment with virtual sensors generating data on an SPI interface connected to the physical microcontroller to evaluate the system with a data stream from a real viaduct. We exploited this environment also to study the impact of different on-sensor encoding techniques, mimicking a bio-inspired sensor able to generate events instead of accelerations. Obtained results show that the proposed optimised embedded LSNN (eLSNN), when using a spike-based input encoding technique, achieves 54% lower execution time with respect to a naive LSNN algorithm implementation present in the state-of-the-art. The optimised eLSNN requires around 47 kCycles, which is comparable with the data transfer cost from the SPI interface. However, the spike-based encoding technique requires considerably larger input vectors to get the same classification accuracy, resulting in a longer pre-processing and sensor access time. Overall the event-based encoding techniques leads to a longer execution time (1.49\(\times\)) but similar energy consumption. Moving this coding on the sensor can remove this limitation leading to an overall more energy-efficient monitoring system.  
spiking NN; SHM; cyber-physical systems; energy efficiency; MEMS +
Footnote †: journal:  
_Article_  
Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring
# Buckling-Restrained Bracing System with Ultra-High-Performance Fiber Concrete
## 2 Implementing UHPFRC Concrete in BRB Device  
The functionality of the BRB mechanism relies on the tensile force exerted by the steel core and the role of the concrete filler in preventing buckling of the steel core under compression. As a result, the concrete component plays a crucial role in the device's ability to withstand compressive forces.  
Due to the recent development of various high-strength types of concrete, the implementation of these new concrete types can significantly impact the performance of the BRB systems. Therefore, the main objective of this study is to focus on the implementation of ultra-high-performance fiber concrete (UHPFRC) in BRB and investigate its behavior under cyclic loads.  
To achieve this objective, a finite element model of the BRB device using UHPFRC material was created using the ABAQUS finite element package. Nonlinear analysis was performed by applying incremental displacement with 1 mm amplitude, as depicted in Figure 1. The purpose of this analysis was to simulate the behavior of the BRB device under cyclic loading conditions. By conducting this analysis, the force-displacement results were obtained, which represent devices' behavior and provide insights into its performance under different loading scenarios.  
In the process of designing a BRB, its core components were divided into two main sections with a 40% ratio of the core length for the plastic section and 60% for the elastic section [29]. Then, the core element was inserted inside the concrete component, considering a gap to allow the core to have local buckling, and in the final step, a steel tube restrainer which is a hollow section steel tube and provides sufficient confinement for the concrete component was designed.  
Initial finite element geometries modeling of the device was completed to provide tentative verification that the device behaves in an expected manner. Table 1 represents the details and specifications of the BRB model using UHPFRC concrete as the filler material. The considered steel core section area was 300 mm\({}^{2}\) with total length of 1500 mm as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Brace** & \begin{tabular}{c} **Steel** \\ **Restrainer** \\ **Tube (mm)** \\ \end{tabular} & \begin{tabular}{c} **Core** \\ **Dimension** \\ **(mm\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Gap** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Concrete** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Steel Tube** \\ **Restrainer** \\ **Length (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Device** \\ **Length** \\ **(mm)** \\ \end{tabular} \\ \hline BRB &
\begin{tabular}{c} 150 \(\times\) 150 \\ \(\times\) 6 \\ \end{tabular} & 300 & 2 & UHPFRC & \(1140\) & \(1500\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: BRB dimension specification.  
Figure 1: Cyclic displacement time history used in experimental and FE models.  
Figure 2: BRB size and dimensions.