esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 1 Introduction
Accordingly, the fog server can handle urgent requests (i.e., requests that need an emergent response, such as delay sensitive and emergency services) and meet the requirements of latency-sensitive services. Urgent requests will be pre-processed by the fog server and returned to the clients directly, while other requests will be transferred to the cloud for further processing and storage. A child abduction alert fog-based service is implemented to evaluate the proposed framework. Users of the service have to configure and set the threshold of the fog server first. The locations of parents and children will be uploaded to the fog server continuously. The fog server computes the distance between children and their parents to check whether it becomes over the configured threshold or not. An alert with the current location of the child will be sent to the parent by the fog server whenever the computed distance is over the user-defined threshold. Data from multiple clients will be aggregated and processed at the nearest fog node before being transferred to the cloud. Data aggregation will be performed on IoT clients that are located in the same geographic region before being transferred to the cloud, which is potentially located on a distant geographic region. We evaluate the performance and the feasibility of the proposed framework. The response time and the delay time of the proposed CFC architecture and the traditional cloud were compared. Additionally, the aggregation rate was evaluated by simulating the speed of bike riding as well as the walking speed of young adults and elderly. Results show that comparing with the traditional cloud, our proposal noticeably reduces the average response time and the delay time (i.e., whether the newest data or the historical data being queried). Results indicate the capability of the proposed framework to reduce the response time by 32% and the data transferred to the cloud by 30%. This paper is organized in five main sections. Section 2 discusses the related work. Section 3 illustrates the design and the implementation of the proposed framework. The detailed functions provided by the proposed fog server is presented in Section 4. Section 5 demonstrates the conducted experiments and their results. Finally, Section 6 concludes this paper and identifies the future work.
# An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern
## 3 Proposed Method
### Camera Calibration  
To convert from a 3D world point to a 2D pixel coordinate, camera calibration is used to calculate the camera matrix (image points) [40; 41; 42; 43]. Firstly, we must understand the camera-calibration process, which depends on two components: the intrinsic and extrinsic parameters of a camera. Extrinsic parameters are used to convert the document image into 3D camera coordinates (3D world point \(P_{w}\)) unlike the intrinsic parameters, which are used to convert the 3D camera coordinate into a 2D pixel coordinate. Figure 9 explains the difference between the intrinsic and extrinsic parameters.  
Mathematically, the 3 \(\times\) 4 camera matrix \(P\) is defined as the following:  
\[P=K\ [R\ |\ t] \tag{5}\]  
Figure 8: Detecting the corners of checkerboard calibrating pattern in (**a**) and its corresponding warped document in (**b**).  
Figure 7: Local corner selection.  
Figure 9: Intrinsic and extrinsic parameters.  
Such that \(K\) is the intrinsic camera matrix which describes the camera parameters. \([R\mid t]\) are the extrinsic parameters which consist of rotation \(R\) and translation \(t\).  
The 3 \(\times\) 3 intrinsic camera matrix \(K\) is defined as  
\[K=\begin{bmatrix}f&0&x_{c}\\ 0&f&y_{c}\\ 0&0&1\end{bmatrix} \tag{6}\]  
where \(f\) is the focal length and (\(x_{c}\), \(y_{c}\)) is the camera center. Unfortunately, the intrinsic camera parameters are unknown in the case of a document image, so we compute the camera parameters based on the following equations.  
From the corresponding checkerboard pattern, the camera center (\(x_{c}\), \(y_{c}\)) is defined as  
\[x_{c},y_{c}=\left(\
icefrac{{W}}{{2}},\;\
icefrac{{H}}{{2}}\right) \tag{7}\]  
The focal length \(f\) is defined as  
\[f=\frac{W}{2}\;\left[\tan\frac{\theta}{2}\right]^{-1} \tag{8}\]  
such that \(H\) is the height and \(W\) is the width of the checkerboard pattern. \(\theta\) = 180\({}^{\circ}\) is the field of view.  
The intrinsic camera \(K\) can be used directly to map from a 3D world coordinate \(D_{w}\) of the document image to a 2D pixel coordinate \(x_{p}\) (\(x_{s}\), \(y_{s}\)), as the following,  
\[x_{p}(x_{s},y_{s})=K\,D_{w} \tag{9}\]  
where \(k\) is the intrinsic camera matrix defined in Equation (5) and \(D_{w}\) is a 3D world point of the document image as described in Section 3.3.
# Indoor Air Pollution Increases the Risk of Lung Cancer
## 1 Introduction  
Indoor air pollution is considered to be an important environmental risk factor for lung cancer in nonsmoking Chinese women. Although the prevalence of cigarette smoking and secondhand smoking among women in Taiwan is low [1], the incidence of lung adenocarcinoma among women in Taiwan is still rising [2]. Incense burning and cooking were suspected to be important indoor air pollutants for lung cancer in Chinese women [3]. Adenocarcinoma is the primary histological type of lung cancer associated with air pollution [4], especially among women who never smoke [5]. Although the International Agency for Research on Cancer (IARC) classified outdoor air pollution and particulate matter (PM) from outdoor air pollution as carcinogenic to humans (IARC Group 1) in 2013, the evidence between indoor air pollution and lung cancer is still insufficient. Currently, no research has provided biological evidence of air pollutants in the lungs to support the association between indoor air pollution and lung cancer.  
Exposure assessment of air pollution remains a significant challenge for environmental epidemiology. Polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, ethylbenzene,and xylene (BTEX) are common pollutants of cooking and incent burning [6]. Although many methods have been developed to model air pollution [7], air pollutants are produced by many sources and have substantial time and spatial variations. Since the lung is the main target organ for the health effects of air pollutants, it is suitable for measuring the concentrations of air pollutants in the lung. The pleural space is located between the lung and the thoracic cavity. Inhaled air pollutants dissolve and accumulate in the pleural fluid through pulmonary microcirculation [8]. Pleural fluid enters the pleural cavity through the lung interstitial fluid and pleural capillaries [9]. Analysis of pleural fluid may be used to measure the internal exposure dose of air pollutants in the lungs.  
The objective of this study was to assess the association between indoor air pollution and lung cancer (Figure 1). This study used a traditional multivariate regression model to assess the risk of indoor air pollutants and applied machine learning statistical methods to calculate the probability of lung cancer caused by environmental pollutants.
# Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter
## 3 Methods and Experimental Design  
### Finite-Element Single-Factor Simulation Experiment  
#### 3.1.1 Indicators and Factors  
Cutting force [22] and cutting power [23] are crucial parameters in evaluating the cutting performance of a tool, reflecting energy consumption during the cutting process, the force exerted on the tool, and the efficiency of shrub material removal. These two parameters were selected as the evaluation criteria for the single-factor experiment.  
The primary movements of the tool include rotational motion and feed motion, with tool rotational speed and feed rate being the most critical influencing factors in the cutting process. The intrinsic properties of the shrub also significantly influence cutting performance, particularly variations in diameter [24] and moisture content [25,26], which greatly affect the ease or difficulty of cutting. The distribution of moisture within the shrub is complex, making it difficult to establish a finite-element model related to its moisture content. Therefore, only the diameter of the shrub is considered in the simulation of the single-factor experiment, while moisture content will be investigated in subsequent cutting tests on the test bench.  
The key cutting parameters considered as influencing factors are the quasi-planetary tool rotational speed (\(n\)), the feed speed (\(v\)), and the diameter \(D_{\text{a}}\) at the actual cutting position of the shrub. These factors substantially impact the cutting force and cutting power of the tool.  
Before the experiment, a pre-cutting test was conducted on the tool. The test indicated that when the tool's rotational speed (\(n\)) is below 1000 r/min, shrub-cutting efficiency is low, leading to incomplete cuts, excessive noise, and tool vibration. These negative effects are further exacerbated by an excessively high feed speed at low rotational speeds. Given the poor road conditions in forested areas, which result in slower forward speeds for brush cutters, and considering the maximum speed limitations of the test bench equipment's motor, the quasi-planetary tool rotational speed (\(n\)) was set between 1000 and 2000 r/min, with a feed speed (\(v\)) of 30 to 90 mm/s. The shrub diameter, \(D_{\text{a}}\), was set between 10 and 40 mm, with a length of 2000 mm. The levels of the experimental factors are listed in Table 3.
## 2 Materials and Methods
### Data Representation  
Application of a blunt notch in the specimens creates a situation where plastic deformation is not uniform over the gauge length, nor entirely localised to the notch zone. This complicates interpretation of test data since no straightforward stress-strain relationship is present around a notch. Instead, the tensile curves in this paper are represented as stress versus elongation over the gauge length.  
A digital extensometer was used in each test to monitor the elongation over a 20 mm section of the specimen \(\Delta I_{\text{Ext}}\) during the initial test stage. The extensometer was removed after the yield point, after which elongation over the entire gauge length was monitored through the cross-head displacement. Stress-strain relationships for smooth specimens can be easily calculated since deformation in these samples is uniform over the gauge length. The extensometer was placed over the notch area for notched specimens. Elongation of the remaining section of the gauge length that was not covered by the extensometer was obtained from stress-strain data of a representative smooth specimen \(\Delta I_{\text{Smooth}}\). Since all specimens consist of BM outside the notch, extensometer data for each test yields the total gauge length elongation \(\Delta I_{\text{Gauge}}\).  
\[\Delta I_{\text{Gauge}}=\Delta I_{\text{Ext}}+\Delta I_{\text{Smooth}} \tag{1}\]  
Fractured specimens were analysed using optical and electron microscopy. A Keyence VHX-5000 digital microscope was used to automatically determine the area of each fracture surface \(A_{\text{f}}\). This was compared to the original cross-sectional area of the fracture location \(A_{0}\) to calculate the reduction in cross-sectional area %RA for each specimen.  
\[\%\text{RA}=(A_{0}-A_{\text{f}})/A_{0}*100\% \tag{2}\]  
A reduction in cross-sectional area is the result of necking, which is limited by the reduced ductility caused by HE. Therefore the %RA value can be used as an HE indicator, where a lower %RA signifies more HE. The %RA values of specimens tested in an H\({}_{2}\) environment were compared to those tested in either N\({}_{2}\) or air and represented in an embrittlement index \(I\).  
\[I=(\%\text{RA}_{\text{N${}_{2}$,Air}}-\%\text{RA}_{\text{H${}_{2}$}})/\% \text{RA}_{\text{N${}_{2}$,Air}}*100\% \tag{3}\]  
Figure 6: An overview of the stress triaxiality distribution in the deformation model around the onset of necking.  
\(I\) therefore represents the percentage of ductility that was lost as a result of the hydrogen environment. The automatic determinations of \(A_{\mathrm{f}}\) allow for manual correction based on the image of the fracture surface. To check the accuracy and repeatability of the area measurements, one surface was observed 5 times, with separate manual corrections. The largest deviation in \(A_{\mathrm{f}}\) was found to be 0.38% of the mean value; therefore, this method is within the tolerance necessary for assessing the extent of HE. Scanning electron microscopy (SEM) tractography, performed on a JEOL JSM IT-100 machine, was utilised to identify fracture morphologies characteristic of HE.
## 3 Meshing and Computational Method Validation
### Method Validation  
Figure 9 presents a comparative analysis between the numerical simulation results and experimental data [19] under the designed rotational speed. It is evident that the variation trend of the numerical results closely aligns with that of the experimental findings. Upon comparison with the experimental data, it is noted that the numerical computation exhibited a discrepancy of 3.68% for the near-design point total pressure ratio and 3.67% for the isentropic efficiency. At the designed rotational speed, the flow rate at the rotor choke point was measured at 20.93 kg/s, while the numerical calculation yielded 20.90 kg/s, resulting in a marginal difference of 0.14%. Similarly, at the rotor near-stall point, the flow rate was recorded at 19.36 kg/s in contrast to the numerical prediction of 19.24 kg/s, indicating a slight deviation of 0.62%. Overall, the numerical results tended to underestimate the experimental values, which may be attributed to the limitations in experimental measurements, where the highest measurement position can only reach 95%  
Figure 6: Schematic of blade single passage meshing.  
of the blade height, while numerical simulations can capture flow losses across the entire blade height.  
Figure 8: Mesh independence study.  
Figure 7: Mesh diagrams near the blade for different numbers of nodes.  
The analysis indicates that the predicted characteristics and trends of the numerical simulation closely aligned with the experimental results. Moreover, the discrepancies in the flow choke point and stall point between numerical predictions and experimental values were minimal and fell within acceptable margins. Therefore, it can be concluded that the numerical computational method employed in this study is reliable.
###### Abstract  
Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.  
landslide and collapse identification; SBAS-InSAR; visibility analysis; kernel density analysis; field investigation 2021 13 1017 10.3390/su13031017 [[https://www.mdpi.com/journal/sustainability](https://www.mdpi.com/journal/sustainability)]([https://www.mdpi.com/journal/sustainability](https://www.mdpi.com/journal/sustainability))
# On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
## 3 Enhancing MMM Safety
### Safety Architectural Patterns  
Considering the safety measures proposed by current functional safety standards (ISO 26262, IEC 61508) and the architectural patterns described in reference [18] for HPEC platforms, we define two examples of safe architectural patterns that support the integration of the previously defined'safe MMM' (see Figure 4) with different HFT levels (e.g., HFT = 0,1,2).  
* _Periodic diagnosis with design-time fixed data pattern(s)_: The'safe MMM' is executed at least once every diagnostic time interval (DTI) (Figure 4b) with predefined design-time reference input data vectors that should lead to known reference outputs and an array of ESs values (see Figure 4a). This approach can be used in a single-channel architecture (HFT = 0) or in redundant architectures (e.g., triplicated architecture with HFT = 2). The error detection can be used to detect the erroneous channel prior to the voting process and application-specific measures can be implemented (e.g., restart erroneous channel, activate safe state);
* _Redundancy (with or without diversity)_: This pattern redundantly executes the'safe matrix-matrix multiplication' with \(n\) replicas, each of which uses the same real-time input data and generates both an output and ESs array (Figure 4c) for each computation cycle (Figure 4d). A voting mechanism continuously compares those ESs arrays (and even the outputs), discarding the replicas with discrepancies and/or implementing application-specific measures (e.g., restart erroneous channel).  
Figure 4: Safety architectural patterns. (**a**) Periodic diagnosis pattern; (**b**) Periodic diagnosis pattern scheduling; (**c**) Redundancy pattern; (**d**) Redundancy pattern scheduling.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 2 OAI-Based 5G mmWave Network  
OAI is an open-source software that implements a full 5G protocol and is widely used in research and experimental testbeds [19]. During recent years, OAI has also introduced the support of positioning capabilities, with key developments originated from the HOP-5G project. The physical layer includes the sounding reference signal (SRS) as in 3 GPP Release 15 and the PRS as in 3 GPP Release 16, both necessary to perform UL- and DL-based positioning methods, respectively. Moreover, OAI has implemented the NRPPa protocols and procedures to perform standard-compliant UL-TDoA positioning [20], where a development of the Location Management Function (LMF) also exists.  
There are several examples on the use of OAI for positioning, in addition to the HOP-5G testbed. In [21], OAI is combined with a multi-channel SDR and a custom 2 \(\times\) 2 patch antenna array for enhanced cell-ID (E-CID) positioning in indoor scenarios, by using RTT and AoA estimates based on the demodulation reference signal (DM-RS). Thanks to the OAI PRS developments in the HOP-5G project, a testbed with four OAI base stations or gNBs, and the OAI user equipment was deployed in [22] to exploit the PRS transmissions for DL-TDoA positioning. More recently, a novel framework was presented in [23] to estimate the RTT between a UE and a gNB with existing 5G NR transmissions.  
In this paper, the OAI-based 5G mmWave network is based on downlink PRS transmissions between a single gNB and a single UE to estimate the time-of-arrival (ToA) and the received signal reference power (RSRP) at the OAI UE. The following subsections further describe the ToA and RSRP estimators.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Feature Extraction  
This is one of the most important steps in classification problems. Table 1 lists the 39 features considered in this work for each segment of the signal dataset. All these have been used succesfully as features in previous EEG seizure detection works [14; 30; 31; 32; 33; 34; 35; 36; 37].  
The features considered are from different domains, such as time, frequency, information theory and entropy. But note that all of them are univariate and imply low or medium computational load to extract them.  
Statistical parameters such as mean, variance, skewness and kurtosis have been used to extract information on changes in the distribution and amplitude of the EEG data. Those parameters have been considered on the first and second derivative too. Frequency parameters have been calculated by means of the DFT transform, as spectral power or relative energy between different bands. In addition, some nonlinear features have been calculated, such as fractal dimension, used to compare rhythms in the self-similarity present in the signals; entropy of the signal; and spectral entropy, to depict randomness of the EEG in the frequency domain.  
However, note than other features having greater computational complexity were not considered, even if these had been used successfully in other works. The reason is obvious; this work aimed to select the set of features having the lowest computational load while providing similar classification accuracy to other works in the bibliography. Thus, the more computationally complex features were discarded from the beginning, such as the calculation of certain entropy measures from IMF signals calculated using EMD, wavelet transform or other time-frequency domain features.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Localization Measurements
#### 4.4.1 Localization with Various Amounts of Reflections  
In our first experiment, six beacons were utilized. At each reference point, two independent measurement runs were made: a \"normal\" measurement with no artificial reflections, and a measurement with white paper placed next to the PCPA (50 cm to the right) as a reflective surface. The placement of the beacons and the measurement results can be seen in Figure 14, utilizing the LS and the FD methods for bearing estimation.  
The statistical results of the experiments are presented in Table 2, where the localization accuracies are also calculated for a case where only four beacons are present. In these scenarios the measurements of the top and bottom beacons are excluded from the localization.  
As can be seen in Figure 14 and Table 2, the localization accuracy based on the LS estimator outperforms the FD estimator in similar cases. This is probably due to the bias caused by the reflections of the environment, to which the FD estimator is more sensitive than the LS estimator (see Figure 12).  
It is noteworthy that the white paper placed right to the reference positions are clearly biased the position estimates to the left. Without artificial reflections, which can be considered \"normal\" operation condition, the accuracy of the systems is around 7-8 cm for the LS, and 14-16 cm for the FD estimator. The effect of artificial reflections is clearly higher when the number of beacons is only four (since the redundancy is small), resulting in 15-28 cm of mean error; while for the six-beacon case the effect is smaller (due to higher redundancy), increasing the error to 10-18 cm.