esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To do this, it was necessary to define values to normalize the different parameters in order to make them comparable in numerical terms and congruent from a physical and mathematical point of view. Moreover, since the different environmental parameters do not have the same impact in relation to the specific application context under study, it was decided to assign different weights to the different parameters. The definition of these weights has been described in Section 5.1. The index obtained through the normalization and weighting processes of the different parameters then provides a dimensionless number summarising the different environmental impacts of a given pavement maintenance process. However, the opinion of the authors is that an environmental impact index defined only from the parameters derived from EPD certifications cannot be considered exhaustive if it does not also take into account the mechanical performance and the service life of the mixtures evaluated through the index itself. Since the index must be able to quantify any proposals for improvement over the entire life cycle, the assessment must necessarily also take into account the service life of the asphalt mixtures considered in order to compare them to reference mixtures (i.e., the current state-of-the-art). For this reason, alongside the environmental parameters, it was decided to create performance parameters to be applied to the overall definition of the EAR index. These parameters are used as multiplier coefficients for the EAR index obtained from environmental data only, as will be explained in the next Section Section 5.2. Finally, the EAR index was set in order to have variability between zero and one, hypothetical values representing the minimum and maximum environmental impact, respectively. On the contrary, the value of the index does not have a maximum threshold value but increases as the environmental impact increases. The decision not to have a maximum threshold value was also supported by the fact that it is not possible to identify absolute reference values against which to relate the value of the index.
## 2 Methodology
### CT Scanning and 3D Reconstruction  
In this research, a high-resolution micro-CT scanner (ZEISS Xradia520 Versa, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China) was employed to scan the specimens. This instrument offers a maximum resolution (volume pixel) of 0.35 \(\upmu\)m. In this study, a scanning resolution of 1 \(\upmu\)m was utilized and each specimen yielded 800 grayscale images, each containing 1,002,820 pixels (1015 \(\times\) 988) with gray values ranging from 0 to 255. Following the combination of these grayscale images, 3D cylindrical micro-samples were reconstructed using the software Avizo 2020 (Figure 4a). Afterward, the 3D cylinder model was preprocessed using a Gaussian filter to enhance particle boundaries (Figure 4b), aiding in the subsequent threshold selection. To enable the following quantitative analysis of the 3D microstructure, a region-of-interest (ROI) was selected from the central portion of the 3D cylindrical micro-sample, consisting of a 600 \(\upmu\)m \(\times\) 600 \(\upmu\)m \(\times\) 800 \(\upmu\)m cube (Figure 4c), which is the largest cubic region that could be extracted from the original 3D cylinder model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{3}{*}{**Specimens**} & **Dry** & **In-Situ** & \multicolumn{4}{c}{**Afterberg Limits**} \\  & **Density** & **Water** & **Void** & **Plastic** & **Liquid** & **Plastic** \\  & **(g/cm\({}^{3}\))** & **Content (\%)** & **Ratio** & **Limit** & **Limit** & **Index** \\ \hline Q\({}_{3}\) & 1.37 & 9.7\% & 0.97 & 17.8 & 27.6 & 9.8 \\ Q\({}_{2}\) & 1.54 & 13.9\% & 0.75 & 18.5 & 28.5 & 10.0 \\ F1 & 1.61 & 16.5\% & 0.68 & 17.4 & 26.8 & 9.4 \\ C1 & 1.66 & 13.9\% & 0.63 & 18.5 & 28.5 & 10.0 \\ \hline \end{tabular} Note: “Q\({}_{3}\)” represents the intact Q\({}_{2}\) loess, “Q\({}_{2}\)” represents the intact Q\({}_{2}\) loess, “F1” represents the remodled loess obtained from the GMRL project, and “C1” represents the remodled loess obtained from the MBCC project.  
\end{table}
Table 1: Basic physical properties of the specimens.  
Figure 3: Cumulative grain size distribution curves of the tested specimens.
## 2 Collection of Arc Fault Data in Series
### Analysis of Arc Current Characteristics of Failure  
Periodic sampling of normal working and fault arc current signals is performed. Waveforms of normal and fault load currents are shown in Figures 2-5.  
Under normal conditions (subfigure (a) in each figure), the time-domain waveforms of currents under different loads show significant differences. Some are sinusoidal waves, such as Figure 5a; some approach triangular waves, like Figures 2a and 3a; and some exhibit zero-crossing lags, as seen in Figure 4a. These variations reflect the impedance characteristics of different loads and demonstrate how load diversity affects the series current. At the same time, the current waveforms under a specific load are stable over cycles, with consistent repetition before and after each cycle.  
\begin{table}
\begin{tabular}{c c c} \hline
**Load Category** & **Load** & **Rated Electrical Parameters** \\ \hline \multirow{4}{*}{Linear load} & Electric kettle & 220 V/1600 W \\  & Halogen lamp & 220 V/600 W \\  & Hair dryer & 220 V/1000 W \\  & Vacuum cleaner & 220 V/1300 W \\  & Voltage regulating circuit & 220 V/800 W \\ Nonlinear load & Computer & 220 V/100 W \\  & Electric fan & 220 V/60 W \\  & Hair dryer + electric kettle & 220 V/1000 W + 220 V/1600 W \\ Mixed load & Hair dryer + computer & 220 V/1000 W + 220 V/100 W \\  & Electromagnetic oven + & 220 V/1300 W + 220 V/800 W \\  & voltage regulating circuit & \\ \hline \end{tabular}
\end{table}
Table 1: Representative test loads and their main parameters.  
Figure 1: Series arc fault test platform.  
Figure 4: Normal and arc current waveform of vacuum cleaner load. (**a**) Normal current. (**b**) Arcing current.  
Figure 3: Normal and arc current waveform of electric fan load. (**a**) Normal current. (**b**) Arcing current.  
Figure 2: Normal and arc current waveform of halogen lamp load. (**a**) Normal current. (**b**) Arcing current.  
In the arc state (subfigure (b) in Figures 2-5), the current amplitude under most loads does not significantly increase compared to its normal state; under resistive loads, the current amplitude even slightly decreases. The differences in the time-domain waveform of currents under different loads remain significant. Besides the impact of impedance differences, random spikes, waveform gaps, and zero-crossings caused by the arc introduce strong diversity and randomness to the arc current waveform. The waveform varies between cycles under a certain load, and the waveform is not fixed before and after each cycle.
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 6 Discussion and Conclusions
### Future Work  
There are many possibilities to explore in order to improve the performance of graphical page object detection in document images. In general, recently proposed novel neural network architectures for object detection [56; 59; 60; 61] can improve performance of graphical page object detection systems. The second promising direction is the multimodal processing of the graphical objects. In the case of graphical page object detection, multimodal processing, in the simplest form, is the processing of image information and text information together [62; 63]. An example of such a case is when a figure is categorized as a table and vice versa; the text information can be beneficial. The table is the most complicated graphical page object among all the graphical page objects [48]. To improve the performance further, another promising path to explore is the localization of individual columns and rows of the specified tables. Furthermore, identifying headers of the table can significantly help to understand the table's inner structure. Furthermore, the following directions can be explored in the future:  
* **Weak/Unsupervised learning:** At present, all the reliable graphical page object detection systems depend on large-scale labeled datasets. The processing of annotating the document images with graphical page objects is laborious and inefficient. Hence, there is a dire need to build weak/unsupervised graphical page object detection systems that produce impressive results after training limited samples.
* **Light weight systems:** Modern state-of-the-art graphical page object detection methods are not efficient at all. However, there is a growing need to build intelligent information extraction systems that can work effortlessly on mobile devices [64; 65].  
* **Domain adaptation:** There is still a significant gap in developing clever page object detection methods that can adapt to different domains. An example of such a scenario is building a system that works equally well on the historical and modern document images.
* **Neural architecture search:** Deep learning enables us to eliminate custom features engineering, which demands domain knowledge. However, the current employed deep neural network also requires setting precise hyperparameters. Another exciting direction could be leveraging neural architectural search to automate the design of a number of layers and anchor settings as accomplished in the field of computer vision [66; 67; 68; 69].  
Writing--original draft preparation, J.B., K.A.H., M.Z.A.; writing--review and editing, K.A.H., M.Z.A.; supervision and project administration, D.S. All authors have read and agreed to the published version of the manuscript.  
The work leading to this publication has been partially funded by the European project INFINITY under Grant Agreement ID 883293.  
Not applicable.  
The authors declare no conflict of interest.
# Industrial-Grade Edge Computing Device for Smart Furniture Products
## 4 Solution/Candidate Product
### Security Aspects
#### 4.1.3 Platform Software  
Platform software (or cloud software) is a software application or a set of applications that is run at a separate datacenter location and is available and accessible via the Internet regardless of the availability of the local (edge) device. This platform is highly exposed to public access, and will be specially considered regarding communication and cyber security. Platform software is often proofed by penetration tests to assure the ruggedness and safety of the system, data, and communication channels. The system must be resilient to outside attacks and must always provide functionality. Data and especially user data must be kept safe from any leakage. User data that are associated with the edge device are completely sensitive.
## 2 Analysis of Modulation Process
### Coordinate Transformations in Roll Angle Measurement
\begin{array}{ccc}x_{r}^{i}&y_{r}^{i}&z_ {r}^{i}\end{array} \right]^{T}\] (11) where \(R_{r/n}\) is the rotation matrix from the vehicle-carried NED frame to the reference coordinate system, which is given by: \[R_{r/n}=\left[ \begin{array}{ccc}\cos\psi_{ref}&\sin\psi_{ref}&0\\ -\sin\psi_{ref}&\cos\psi_{ref}&0\\ 0&0&1\end{array} \right]\left[ \begin{array}{ccc}\cos\theta_{ref}&0&-\sin \theta_{ref}\\ 0&1&0\\ \sin\theta_{ref}&0&\cos\theta_{ref}\end{array} \right]\] (12) where \(\Psi_{ref}\) and \(\theta_{ref}\) are the yaw and pitch angle of the vehicle respectively. 4. Lastly, \(\Psi_{i}\) is obtained by the arctangent value of the LOS vector in the reference coordinate system. \[\Psi_{i}=\arctan\left(y_{r}^{i}/z_{r}^{i}\right)\] (13)
## 4 Results
### Fitness Evaluation
Figure 8d,h show a complex waveform type with multiple peaks resulting from underwater objects such as fish and corals or rugged seafloor topography. The underwater peaks and bottom returns were not properly decomposed in the CGD results (Figure 8d), but PGD effectively decomposed multiple \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method** & **Normalized RMSE** & **R\({}^{2}\)** & **SSIM** \\ \hline \multirow{3}{*}{Site 1} & CGD & 0.0635 \(\pm\) 0.0302 & 0.709 \(\pm\) 0.140 & 0.535 \(\pm\) 0.184 \\  & PGD & 0.0179 \(\pm\) 0.0114 & 0.978 \(\pm\) 0.019 & 0.907 \(\pm\) 0.055 \\  & CGD & 0.0759 \(\pm\) 0.0363 & 0.808 \(\pm\) 0.142 & 0.695 \(\pm\) 0.183 \\  & PGD & 0.0260 \(\pm\) 0.0183 & 0.980 \(\pm\) 0.023 & 0.901 \(\pm\) 0.072 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitness evaluation results of conventional Gaussian decomposition (CGD) and progressive Gaussian decomposition (PGD). peaks by estimating three additional Gaussian components (Figure 8h). These examples confirm that the proposed PGD successfully decomposes different types of waveforms.
## 3 Results and Discussion
### Impacts of Filtering
#### 3.1.2 Signal-to Noise Ratio (SNR)  
Filtering caused average SNR to stabilize around 29-32 dB for all trucks. Overall, filtering decreased SNR data spread. The minimum SNR was only affected by filter 3 (SNR < 15 dB). As intended, filter 3 subjected the data to a step-change, starting at 6.7-6.9 dB and ending up for all three trucks at 15.1 dB. The effects of each filter gradually diminish. Interestingly, all trucks measured different maximum SNR values, which were reduced in filter 5 (downsampling). As shown in Figure 4(b), SNR filtering worked as intended by removing measurements at short distances.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 3 Interference Evaluation Model
### Generic Airborne Civil Aviation BDS Receiver  
To support the evaluation model, this paper described a generic structure of the airborne civil aviation BDS receiver (as shown in Figure 3) and analyzed the impact of its key components on the received signal.  
The composite received signals were captured at the antenna port (Point A) and processed by the radio frequency front-end (RFFE) block (Point B). Within the RFFE, the signal underwent low-noise amplification, and this was followed by frequency downconversion to an intermediate frequency (IF) and multi-stage filtering for image rejection and spurious signal suppression. A digital pulse blanker (PB) was introduced after the RFFE block (Point C), which is key for the pulse interference suppression in the receiver [23]. The ideal PB operates as a zero-latency comparator that suppresses the signal to zero when the input power exceeds a predefined threshold. The blanking operat  
Figure 2: The time domain waveform and spectral characteristics of the DME pulse pairs.  
components, including useful signals, interference, and noise. While effectively reducing noise power, it also diminishes the peak amplitudes of useful signals. Consequently, the overall \(C/N_{0}\) experiences degradation [24]. The digitized and blanked signals are then fed to the correlator. At this stage, the residual RFI components present at the correlator output (Point D) will degrade the navigation signal reception performance.
## 2 Materials and Methods
### Determination of Cell Death by Flow Cytometry  
Following the manufacturer's instructions, a commercial kit (Bioscience, CA, USA) was used to determine the proportional amount of cell death caused by apoptosis or necrosis. Briefly, MCF-7 were treated for 24 h with CV, SeNPs-CV or CP, then the cells were then stained with Annexin V conjugated with fluorescein isothicarate (FITC) and propidium iodide (PI) and subsequently analyzed by flow cytometry (Cytomics FC500-MPL). Cells positive for Annexin V-FITC but negative for PI were considered as early apoptotic cells,while double positive cells were considered late apoptotic cells. Cells that were positive only for PI staining were identified as necrotic cells.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 5 Experiment Validations
### Tracking Effect Analysis of a Cutting Unit  
In order to verify the superiority of the presented tracking algorithm, the single STC algorithm was also applied to the cutting unit tracking in an infrared thermal imaging video. The tracking results are shown in Figures 15 and 16. Due to the limited space, only some tracking results in the key frames are listed in the paper.  
In this infrared thermal-imaging video, the shearer cutting unit was completely blocked from the 12 th frame, and did not reappear until the 17 th frame. When the cutting section was tracked by the original STC algorithm, the target-tracking locations of the 13 th to 16 th frames were completely wrong and could not be re-tracked after the cutting unit reappeared. The reason for this is that the original STC was used to locate the target position in the next frame based on the relationship between the target and the surrounding space. When the cutting unit was completely obscured, the surrounding space information was insufficient, resulting in a failure to track the subsequent target location. However, the algorithm proposed in this paper can determine whether the target has disappeared by setting the maximum likelihood probability threshold, and then locating it by morphology. Even if the cutting unit is completely blocked in a few frames, it can still be re-tracked (from the 17 th frame).  
Figure 14: Maximum likelihood probability value of confidence map in the tracking process.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level 2 SVMs (L-2): Features and Optimal Weight Calculation  
The SVM model at L-2 is built and utilised after PU modes are evaluated for the current CU depth level. In terms of the overall encoding algorithm (Figure 5), the CU split decisions that are not classified by the L-1 SVMs are subjected to the L-2 SVM model. In this case, a single SVM is used at each CU depth level.
## 4 Simulation Analysis
### Simulation Results
#### 4.2.1 Network Lifetime  
We use two definitions for the network lifetime: FND (First Node Dies) and HNA (Half of the Nodes Alive). We consider network lifetime in terms of rounds. Network starts operating at round 1. We measure at which round the first node dies (FND) and up to which round at least half of the nodes are still alive.  
Figure 3 shows that the protocols we developed have a better performance when compared to LEACH, because death of the first node in our protocols is more delayed when compared to the LEACH. This lifetime improvement results from two factors. It is firstly due to the use of critical and base threshold and secondly due to the fact that the sending of the duplicated data to the sink is prevented by performing a compression (eliminating redundancies) operation. Thus, the volume of traffic, which directly affects the energy consumed by the cluster heads and ordinary sensor nodes while sending their packets, is reduced. In this way, the lifetime of the network is increased significantly.
# In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI
## 2 Materials and Methods
### Registration Algorithm  
With the employed scanner, DMRI results in a series of subsequent planar acquisitions taken while the subject is moving. More than one plane can be scanned for the same joint pose: for instance, as clarified below, we chose to use two synchronized planes in this case, although the number could be higher. Joint kinematics can, thus, be reconstructed by registering a 3D model of the moving objects on the DMRI planes for each measured joint pose.  
The 2D/3D registration algorithm developed for this study is based on voxel intensity, exploiting the low signal associated with the bone tissue within the MR scans to register 3D bone models on the DMRI images through rigid rototranslations. To this aim, a model of the inner bone, namely the internal surface of the cortical and subchondral bone, was also segmented from the non-weight-bearing MRI with an offset of 0.5 mm. Thanks to this offset, when the model is correctly registered on MRI data, for each image, the intersection between the inner bone model and each DMRI plane will take place inside the cortical bone region, which should correspond to the minimum signal intensity on the DMRI images.  
Optimal registration is performed within a proprietary C++ code. Bones are moved within the MR reference system, and the intersection between the DMRI planes and stl model is computed for each pose. Since DMRI has a non-zero thickness, all the stl points whose distance to the scanning plane is less than half the slice thickness are considered as belonging to the intersection and projected on the scanning plane. For each intersection point, the corresponding intensity is computed by bilinear interpolation of the DMRI voxel values. For each frame, optimal bone position is obtained by minimizing the mean of this intensity value extended on the overall intersection between DMRI planes and bone stl model.  
Figure 1: The employed MR scanner in traditional (**a**) and weight-bearing (**b**) configuration.  
Figure 2: The MR-compatible rig used to guide the knee flexion.  
The registration process just presented is fully automatic; however, it is affected by the initial registration, i.e., the first guess of the first joint pose. To minimize the impact of the operator, initialization of the registration is partially automatized in a separate step. The first DMRI frame of each plane is processed by means of a Channy edge detection algorithm to identify the bone contours, resulting in a cloud of points. The operator is then requested to refine the detected edges, manually eliminating points that do not correspond to the cortical bone. The operator manually registers the bone stl models to this cloud of points. This initial registration is then refined by means of an ICP algorithm and passed, as a starting point, to the 2D/3D intensity-based registration algorithm. A schematic representation of the overall code workflow is given in Figure 3.
###### Abstract  
Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.  
**Keywords:** generator; partial discharge; signal denoising; wavelet shrinkage; mother wavelet