esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fault Experiment Signal Analysis
### Case Western Reserve University (CWRU) Dataset
#### 4.1.2 Experimental Validation  
Firstly, the AOA optimizes the VME parameters \([\alpha,\omega]\) to extract the desired mode components from the original vibration signals. For example, considering inner fault 1, the optimal parameter combination is [203; 2524]. The results of the parameter-optimized VME are shown in Figure 11. The signal undergoes decomposition during VME by optimizing the parameters \([\alpha,\omega]\), based on the optimal combination identified in Table 4.  
The neural network parameters were set as follows: the number of Softmax classification layers corresponded to the total number of fault types, which was 10. For the training set, the parameters were configured with an Adam optimizer learning rate of 0.002, a batch size of 32, and a maximum of 100 training iterations, and the chosen loss function was categorical cross-entropy. To demonstrate the need to remove the residual structure of the BN layer, we compared a model with the BN layer removed, a model retaining the BN layer, and an MSCNN without the residual structure; the results are displayed in Figure 12. The training and validation sets achieve a steady state after four iterations in the model without the BN layer's residual structure. While all three models achieve 100% accuracy, the model lacking the BN layer's residual structure exhibits the best convergence. The confusion matrix is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & \([\alpha,\omega]\) & **Fault Type** & \([\alpha,\omega]\) \\ \hline Normal & [726; 1497] & ORF2 & [877; 4296] \\ IRF1 & [203; 2524] & ORF3 & [201; 2370] \\ IRF2 & [378; 1892] & BF1 & [2271; 3459] \\ IRF3 & [200; 4865] & BF2 & [2778; 3088] \\ ORF1 & [2847; 765] & BF3 & [386; 2333] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal parameter combination for VME.  
Figure 11: Inner fault 1 after parameter-optimized VME: (**a**) time-domain signal; (**b**) signal spectrum; (**c**) envelope spectrum.
# Shear Properties of the Interface Between Polyurethane Concrete and Normal Concrete
## 1 Introduction
In large infrastructure projects such as bridges and highways, the reliability of the PUC-NC interface ensures the smooth operation of transportation systems, safeguards public safety, and contributes to the sustainable development of the construction industry. Novelty of This Research  
1. A comprehensive evaluation of the influence of various factors, including interface treatment protocols, adhesive formulations, and steel fiber incorporation, on the interfacial bond strength has been carried out. 2. A generalized predictive model for the shear strength of the PUC-NC interface, applicable to a variety of concrete substrate surface treatment techniques, has been established. 3. The failure mechanisms of the PUC-NC interface under different surface treatment conditions have been systematically investigated through room-temperature oblique shear testing. While much research has focused on the mechanical and durability properties of polyurethane additives in mortars, studies on PUC used for concrete repair are still limited. This study focuses on investigating the impact of various factors on the bond strength between PUC and NC at their interface. Inclined shear tests are conducted on cast-in-place PUC-NC specimens at ambient temperature, followed by an examination of the shear performance and failure modes of the PUC-NC interface with different surface treatments, including smooth, roughened, and grooved surfaces. The effects of interface adhesives and steel fiber content on the shear strength of the PUC-NC interface are also discussed. Using the shear strength of the PUC-NC interface, the existing models are evaluated, and an improved predictive model for the shear strength of the PUC-NC interface is proposed and validated.
## 3 Results and Discussion
### Effect of Cut-Off Temperature
According to Figure 3, increasing the cut-off temperature reduces the difference between the cut-off and bivalent temperatures, resulting in the heat pump having a smaller role in meeting the demand. There is no significant difference in the number of on-off cycles across the various configurations, as they are nearly identical; however, configuration 1 shows fewer on-off cycles than the others do. Looking at Figure 9b, the number of on-off cycles performed by the boiler decreases as the cut-off temperature increases. This means that at higher cut-off temperatures, the biomass boiler operates for more hours and has a larger share in meeting the building's demand. Additionally, in configuration 4, the biomass boiler cycles on and off more frequently than in the other configurations, with configuration 3 experiencing the fewest cycles, since the inertia of the buffer tank and the temperature drop to start up the boiler reduces its activation. Furthermore, in this case, the location of the inlets and outlets forboth the heat pump and the biomass boiler as well as the demand to use the buffer tank are key values that are not so crucial for the other configurations. Figure 8: The effect of the cut-off temperature on the seasonal energy performance indicators: (**a**) \(\eta_{\text{S}}\), (**b**) \(\text{SCOP}_{\text{net}}\), (**c**) heat portion provided by the HP. For considering the effect of cut-off temperature on the environmental and economical KPIs, Figure 10 illustrates the EWI and OC as functions of both the heat pump size and the cut-off temperature. As expected, all configurations exhibit a decline in the Equivalent Warming Impact as the cut-off temperature increases because the biomass boiler plays a larger role in the HHP system compared to the heat pump (Figure 10a). Moreover, the EWI improves with a smaller heat pump size because less electricity is consumed. The effect of cut-off temperature on the operational cost is depicted in Figure 10b. It can be seen that the operational cost increases with rising cut-off temperature due to increased biofuel consumption [42]. The most important point is that configuration 2 is the cheapest among all configurations, and the HHP system with an 8 kW heat pump has the lowest operational cost. Figure 9: The effect of cut-off temperature and heat pump size on number of cycles: (**a**) heat pump on–off cycles, (**b**) biomass boiler on–off cycles.
## 4 Discussion
### Language-Specific Effectiveness Patterns  
Our analysis reveals that the effectiveness of MuRXLS varies significantly across different languages, suggesting that intrinsic linguistic properties influence the success of retrieval-based example selection. Several key patterns emerge from our cross-linguistic comparison:  
**Morphological Complexity and Performance Gains**: Languages with different morphological structures show varying degrees of improvement from retrieval-based selection. Burmese, which employs a relatively isolating morphology, demonstrates the largest performance gains (+4.20 ROUGE-L points with Llama-3-70B), while languages with more complex morphological systems show more modest improvements. This pattern suggests that languages with simpler morphological structures may benefit more from semantic similarity-based retrieval, as surface-level variations are less likely to obscure underlying semantic relationships.  
**Script Systems and Embedding Quality**: The writing systems employed by different languages appear to influence the effectiveness of multilingual embeddings used in our retrieval mechanism. Thai and Burmese, which use unique scripts, show substantial but variable improvements, while Devanagari-script languages (Marathi) and Arabic-script languages (Pashto) exhibit different response patterns. This variation suggests that the quality of multilingual embeddings varies across script systems, potentially affecting the accuracy of semantic similarity computations.  
**Language Resource Availability**: The baseline performance levels across languages correlate with their relative resource availability, which in turn affects the magnitude of improvements achievable through retrieval-based methods. Languages with extremely limited baseline performance (such as Pashto in some configurations) show larger absolute improvements, suggesting that retrieval-based methods are particularly valuable for the most resource-constrained scenarios.  
**Syntactic Distance from English**: Languages that exhibit greater syntactic divergence from English tend to benefit more from retrieval-based example selection in the X\(\rightarrow\)English direction. This pattern aligns with our directional asymmetry findings and suggests that when translating from syntactically distant languages, providing contextually relevant examples helps bridge the structural gaps more effectively.
## 5 Analysis Results
### Geo-Sensor Framework Performance  
In Figure 13, we evaluate the connection request response time from the geo-sensor framework for selected geo-sensor service visualization. The service visualization will load the geo-sensor network with a loading site map along with the sensors list, details, and available controls. In order to test the geo-sensor service access and visualization response time, multiple access attempts are made to the service. It takes around 231.74 ms to access the service visualization, which seems to be a reasonable amount of time for the proposed scenario.  
Figure 12: Geo-Sensor composite toolbox execution screen: (**a**) building information management; (**b**) floor information management; (**c**) sensor binding.  
Figure 13: Execution time for geo-sensor service visualization and access request.  
However, the tested services are for loading the mini-map visualization, sensors involved along with the details of each sensor, sensor images if available, and sensors' states. For viewing the total map, a separate request is made further.  
In Figure 14, we compare the execution time results for the total map size loading process in comparison to the mini-map size loading process.  
Now, we test the response time for multiple services access by multiple users at the same time to evaluate the system performance under-load scenario. Figure 15 presents the comparison of the connection request response time made from multiple users to access multiple services at the same time. The service access and visualization have to be loaded for multiple requests simultaneously in this scenario. We have evaluated multiple geo-sensor service access with a varying number of requests and varying number of active services. In the graph below, we can clearly observe that as the load of access requests and active sessions increases, the delay in responses the system witnesses, and, hence, the response time also increases. In our performed evaluation scenarios, the maximum execution time under 51 requests and 10 active users turn out to be around 3880.63 ms, and the average response time for the system under varying load scenarios turn out to be 1558.16 ms. Hence, the system response time under the worst load scenarios have delays up to a maximum of around a little less than 4 s, and on average, with varying load scenarios, remains around 1.5 s. We can safely say that the overall system performance under varying loads is tolerable, considering system service load requirements.  
Figure 14: Execution time comparisons for loading of total map vs. mini-map.  
Figure 15: Multiple geo-sensor service access analysis with varying number of requests and varying number of active services.
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
###### Abstract  
The study presented in this paper analyses the results of measurements and data processing for documentation and quantification of material in heaps in large areas, where UAVs may no longer be effective due to a large range. Two test heaps were selected from a whole area, where the aim was to confirm the suitability of using the method of digital aerial photogrammetry by manned (crewed) aerial vehicle. For comparison, a commonly used GNSS RTK method was also used. Terrestrial laser scanning was chosen as the control reference method. TLS measurement is a trusted method with high accuracy. The methods were compared with each other through the quality of the mesh, analysis of the cross-sections, and comparison of the volumes of heaps. As a result, the determination of heap volumes and documentation using digital aerial photogrammetry can be confirmed as an appropriate, efficient, fast, and accurate method. The difference in the detected volume was less than 0.1%, the mean difference of the meshes was less than 0.01 m, and the standard deviation was less than 0.05 m.  
aerial photogrammetry; SfM; TLS; point cloud; TIN model; mesh; volume analysis; cross-section analysis +
Footnote †: journal: Applied Sciences
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring  
In this section, we present our proposed architecture for the real-time vision-based flood monitoring system. The architecture utilised the combination of a Y16 stream, which is a 16-bit greyscale format, and a depth stream from the RealSense D455 RGB-D camera [42]. The process began with the acquisition of images from the Y16 pipeline to determine the region of interest (ROI) of the target water surface. The ROI information was subsequently used for extracting the depth information from the depth stream. The RealSense depth camera is capable of activating multiple pipelines concurrently. Leveraging this feature, greyscale and depth pipelines were activated followed by different procedures to extract the depth readings. In this study, the stereoscopic capability of the RealSense D455 camera played an important role in measuring the water level without requiring external references such as a staff gauge or mapping pixel coordinates to physical dimensions.  
The RealSense D455 is the latest addition to the RS400 line of stereo vision cameras manufactured by Intel. The camera package combines image sensors with hardware-accelerated on-board chips for calculating and perceiving depth information with metrical accuracy. The two infrared (IR) imagers are placed at a known fixed distance apart to measure the disparity between the depth of pixels in order to calculate and provide the distance of the said pixel from the camera. The matching algorithms of the RealSense 400 series are aggregated based on the values of the neighbouring pixels, which are then used to estimate the total depth measurement [43]. Since the pixel depth value is provided with a confidence score, if the confidence score does not meet the minimum expected value, the value is discarded, and the pixel depth is rendered invalid.  
The on-board processing capabilities of the camera, such as filtering and averaging, can be externally configured through the camera utility software or the RealSense Software Development Kit (SDK). The RealSense SDK is a cross-platform software development kit which allows the camera to be interfaced and configured through different popular programming languages such as C, C++, MATLAB and Python. In this study, the software system was developed using Python 3.6.9 running on an Ubuntu 18.04 operating system. Apart from the depth processing which happened on the camera's ASIC chip itself, the rest of the algorithm was executed on an edge device. Figure 1 shows the system block diagram of the implementation. The detailed operation of each phase is further explained in the subsequent subsections.
# Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging
## Materials and Methods
### Dip Frequency Filtering  
Filtering is a well-known method used to separate seismic events that are available in the seismic data, using the suitable apparent velocity as the selection criterion. The gradient of apparent velocity, \(\mathrm{V}_{\mathrm{app}}=\mathrm{d}\mathrm{x}/\mathrm{d}t\), will give the arrivals for the seismic data section.  
The one-dimensional Fourier transform \(F(u)\) of a single variable, namely a continuous function \(f(x)\), is defined by the following equation [21]:  
\[F(u)=\int_{-\infty}^{\infty}f(x)\ \mathrm{exp}\ (-j2\pi ux)dx \tag{3}\]  
where \(=\sqrt{-1}\). Conversely, given \(F(u)\), we can obtain \(f(x)\) by employing the inverse Fourier transform, as given below:  
\[f(x)=\int_{-\infty}^{\infty}F(u)\ \ \mathrm{exp}\ (j2\pi ux) \tag{4}\]  
These two equations contain the pair of Fourier transforms and were used to convert our data from the time domain to the frequency domain. They were easily extended to the two variables \(u\) and \(v\).  
For the illumination of the coherent wavefront of the constant amplitude, the distribution of the amplitude in the spatial frequency spectrum \(b(x,y)\) and the object plane \(B(u,v)\) can be defined by using the inverse Fourier transform [22]:  
\[b(x,y)=\int_{-\infty}^{\infty}\cdot\int_{-\infty}^{\infty}B(u,\ v)\ \mathrm{exp}\ (-i2\pi(ux+vy))\ du\ dv \tag{5}\]  
Figure 3: Diffraction hyperboloids: (**a**) increasing velocity with depth and (**b**) decreasing velocity with depth.  
Alternatively, it can be explained using a simplified notation, where \(\rightleftharpoons\) shows the pair of Fourier transforms.  
\[b(x,y)\rightleftharpoons B(u,v) \tag{6}\]  
A spatial frequency spectrum is defined as the description of the image forming process.  
\[B^{\prime}(u^{\prime}v^{\prime})\rightleftharpoons b(x,y).\;a(x,y) \tag{7}\]  
\[=B^{\prime}(u^{\prime},\;v^{\prime})\;\;^{*}\;A\;(u^{\prime}v^{\prime}) \tag{8}\]  
where \(A\;(u^{\prime}v^{\prime})\rightleftharpoons a(x,y)\) is called the spread function, the * symbol shows the convolution, and \((u^{\prime},\;v^{\prime})\) are the coordinates of the spectrum window for magnification of the filter amplitude.  
Filtering is applied to the frequency-wavenumber (F-K) spectrum to separate the diffracted events from the reflection data by defining a filter, in which slopes monotonically increase and amplitudes of the data correspond to the slopes of dipping events, for example in an inclined structure:  
\[Slope=\frac{dt}{dx}=\frac{1}{V_{app}} \tag{9}\]
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 1 Introduction
A further paper [14] applies the two-switch active clamp to a forward converter for high input voltage applications. The work of [15] applies the phase-shift control to the dual active clamp forward converter to reduce the conduction loss. The work of [16] utilizes the combination of active clamp and passive clamp to improve the light-load efficiency. A further paper [17] applies the secondary-side resonance scheme to the active clamp flyback converter to shape the primary-side current waveform, and hence to reduce the primary-side root-mean-square (RMS) value. Another paper [18] employs the primary-side digital control to regulate the active lamp flyback converter. The work of [19] applies the bidirectional concept to the ZVT active clamp boost converter. The work of [20] applies a coupled inductor to the ZVS active clamp boost converter. In the work of [21], the improved ZVS topology is applied to the active clamp buck converter. Although the converters shown in [19; 20; 21] have ZVS or ZVT turn-on of the main switches, the number of components used is large, leading to an increase in cost as well as difficulty in circuit analysis. In addition, in CCM, there is no ZCS turn-off of the output diode, leading to a problem in reverse recovery current created from the output diode, which operates during the turn-off period. Based on the aforementioned, in this paper, one traditional boost converter along with one auxiliary circuit is presented. This auxiliary circuit is composed of one auxiliary switch, one resonant inductor, and one active clamp capacitor, so that the main switch and the auxiliary switch are turned on with ZVS and the output diode is turned off with ZCS, thereby resulting in upgrading the overall efficiency. Furthermore, the proposed active clamp has a function of voltage clamp, thereby leading to reducing the voltage spike on the main switch to some extent, whereas this structure can also cause the output diode to have ZCS turn-off, thereby removing the reverse recovery current. Above all, the proposed auto-tuning technique of the second blanking time is applied to the proposed circuit to upgrade the overall efficiency further. By the way, the potential application of the proposed soft switching converter is for high-power light-emitting diode (LED) lighting fed by the direct current (DC) voltage to improve the overall efficiency [22].
## 2 System Model  
### System Architecture  
We consider the system architecture depicted in Figure 1, consisting of a set of \(K\) backscattering passive nodes, an illuminator, and a set of \(M\) receivers. The illuminator triggers the passive nodes by sending a _beacon_ signal that instructs the nodes to acquire and communicate data (e.g., sensory data, identification codes, etc.). The illuminator transmit power is fixed and we denote it by \(P_{\text{tx}}\). The data acquired at the \(k\)-th tag is communicated by modulating the reflected signal by a simple impedance switching logic. Given their passive nature, the nodes extract the power required to activate the switching modulator from the incident signal and this can only occur if the incident power at the \(k\)-th node's antenna, which we denote as \(P_{f,k}\), is sufficiently high. The modulated backscattered signalsare detected by the receivers at different spatial locations. The power reflected by the \(k\)-the node and received at the \(m\)-th receiver's antenna is denoted as \(\mathsf{P}_{b,k}^{(m)}\). Communication occurs over a shared channel affected by fading. As a consequence, both the received power at the backscattering nodes and the received power at the backscattering receivers undergo random fluctuations that affect the communication quality. Moreover, access to the shared medium is coordinated by the illuminator that implements a FSA protocol to mitigate multiple access interference. The remainder of the proposed analysis is based on the following assumptions:  
* information about the successful detection of a node's data is available immediately at the illuminator. This can be achieved by a high speed network between the receivers and the illuminator. Nonetheless, the characterization of this high speed network is beyond the scope of this paper and is not explicitly included here;
* communication occurs over a slow varying channel, where fading is time invariant within a sufficiently long time interval;
* fading components are log-normally distributed. Although it may appear limiting, this assumption is well suited to describe different propagation phenomena, such as shadow fading and, in several indoor scenarios, even multipath fading [44].
* fading components do not exhibit spatial correlation;
* the network operates in saturation conditions, where each node has always data to send.
* buffering and/or re-transmission policies are not included in our framework, so as data packets are dropped if not successfully delivered in a slot.  
The propagation channel abstraction and the medium access scheme model, including the effect of the channel impairments on the dynamic of the multiple access coordination are described in the reminder of this section.