esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study  
This section presents practical examples of assessing the technical condition of devices using both the existing and newly developed models. The input data for these assessments are based on actual SB device records from the TSO ELES database. The technical condition estimated by the new model, which integrates both multiplicative and additive criteria, was compared to that obtained from the existing model, which relies solely on additive criteria. To ensure fair comparison conditions, the additive criteria were applied consistently in both models.  
Figure 5: Flow chart for assessing the technical condition of HV SB devices of a specific subtype.
## 5 Experimental Results Analysis
There are a total of seven current sensors built into seven power sources to monitor the pulse current at the time of power discharge. It adopts the Rogowski coil testing principle to output the differential current signal through an active integrator and transmit it to the seven current acquisition channels of the measurement system, with a sampling rate of 100 kHz. The bore pressure collector, located at the end of the transmitter, collects the transient voltage at both ends of the transmitter rail during electromagnetic emission, corresponding to a test channel signal collection rate of 1 MHz.B-point coil acquisition, also known as B-probe. Located on the side of the transmitter guide rail, it is used to measure the rate of change of the magnetic field in the chamber of the  
Figure 16: The block diagram of the system components. Figure 17: The experimental device of the electromagnetic launcher. transmitter during the launch process. This signal corresponds to a test channel acquisition rate of 1 MHz. Figure 18 shows the burning of the cross-section of the rail before and after the launch, with a layer of wear-resistant steel material on the inner side of the inner rail. In Figure 18, the burning of the cross-section of the rail shows that the current of the inner rail is mainly concentrated on the inner side, while the current of the outer rail is mainly concentrated on the outer side. These results are consistent with the theoretical analysis. A comparison of the armature discharge speed and armature movement time obtained from the methods in this paper with experimental data is shown in Table 2.  
As can be seen from Table 2, the theoretical analysis data and the experimental results are in good agreement, with an error of 3.484% in the discharge speed and 0.718% in the discharge movement. The theoretical analysis data obtained by the method presented in this paper demonstrate good consistency with the experimental results. Compared with experimental data, these errors are all within 5%, which is sufficient to prove the correctness of the method proposed in this paper. Therefore, the application of the research method in this article can achieve an accurate prediction of the discharge velocity of the armature.
## 1 Introduction
However, scanning of the real-time crack propagation process with high-resolution imaging is extremely expensive. The scanning still cannot capture the contributions of the bedding strength parameters to the initiation and evolution of micro-cracking morphology. Numerical methods provide a cost-effective alternative to observe the location and failure mode of each crack during the crack propagation process. So far, the crack propagation process of rock has been simulated using numerous approaches, such as the meshless method [20], finite element method [21], extended finite element method [22], finite difference method [23], realistic failure process analysis [24], COMSOL multi-physics [25], MatDEM [26], PB-DEM [27,28] and so on. The effects of mineral composition, the orientation of weak planes, initial fracture characteristics, the elastic modulus, confining pressure, loading rates and many other influencing factors on the mechanical characteristics and fracturing mechanisms of rock materials have been systematically investigated and verified with experimental observations. However, the effects of bedding strength parameters on rock strength and the fracture evolution process still lack comprehensive understanding. Particularly, the influences of beddings strength parameters on the final fracture morphology of shale have been sparingly reported. This study will investigate the impacts of bedding strength parameters on the micro-cracking morphology in laminated shale formations through micro-mechanical simulations. Firstly, a two -dimensional numerical model within the framework of particle flow code (PFC\({}^{\text{2D}}\)) is built to describe the matrix-bedding microstructure of a laminated shale specimen. Particularly, different micro-constitutive laws are used to describe rock matrix and beddings. Then, the micro-mechanical parameters are calibrated using the stress-strain curves obtained from laboratory measurements. Lastly, the impacts of the tensile and shear strength of beddings on the uniaxial compression strength, crack types and crack fractal dimension of shale specimens are investigated under different dip angles. The numerical simulations reveal the role of bedding strength parameters in tensile cracking and micro-cracking morphology in shale formations. These results are helpful in understanding the relationship between bedding strength parameters and the evolution of a fracture network in shale gas formations. It also makes it possible to predict the complexity of the shale fracture network based on the final fracture morphology of shale under uniaxial compression, which can be used to optimize hydro-fracturing design siting.
## 1 Introduction
One of the main challenges in lung cancer detection through microwave imaging is the large number of tissues the electromagnetic wave needs to travel through compared to breast cancer analysis. In fact the ultra-wideband pulse encounters several layers of skin, bones, and muscles before reaching the lung. Therefore we suggest illuminating the lung directly during VATS. This would greatly simplify the problem of multiple scattering given by several layers of different tissues. However, this approach is not free from adversities as lung tissue has high dielectric permittivity and conductivity. The latter strongly attenuates microwaves reducing their penetration depth, but on the other side, it also hinders backscattering and clutter from neighbor organs. Compared to breast cancer, lung cancer has a relatively low difference in dielectric properties compared to the surrounding tissue. The difference in permittivity is between a factor of 1.2 to 3 times higher than the permittivity of the healthy lung tissue and a factor of 1.6 to 2 for the conductivity (values extrapolated from reference [21]). These factors are as high as 5.5 and 2 for the breast cancer permittivity and conductivity, instead [16]. This potentially produces lower contrast in the confocal images. This contribution represents a preliminary analysis, performed by numerical simulations, of microwave imaging of lung cancer. This is the first work on the problem of localization of pulmonary lesions by microwave imaging during video-assisted thoracoscopic surgery. The final goal is to screen several parameters on the confocal imaging which will be used in future works to design a microwave transmitter/receiver for a laparoscopic instrument. The parameters investigated are the frequency of the ultra-wideband (UWB) signal, the depth of the lung nodule to localize, and the distribution of the antennas, as summarized in Table 1. The simulations were performed in such a way to mimic the direct illumination of a collapsed lung, but to keep the results as general as possible, a particular model for the transmitter/receiver antennas was not employed.
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 3 Results
### Anechoic Chamber
#### 3.2.2 EMF Exposure near AG-RFID Reader  
The maximum levels, 15.88 V/m and 10.68 V/m, were registered at the shortest as can be seen. These values decrease notably along with the distance. The lowest levels were registered at 3 m away from AG-RFID reader, with values of 1.49 V/m and 0.71 V/m recorded at 0\({}^{\circ}\) and 45\({}^{\circ}\) angles, respectively, and 0.25 V/m at of from 90\({}^{\circ}\)-270\({}^{\circ}\). Table 7 presents the E-field strength values along the direction of the maximum radiation from the device under test in function of the distance. Figure 9 presents the calculated radiation pattern of the AG-RFID reader, depending upon the distance value [13].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Azimuth** & **Ec** & **Ec \(\pm\) U** & **Em** & **Em \(\pm\) U** \\ \hline
**(o)** & **V/m** & **V/m** & **V/m** & **V/m** \\ \hline
0 & 1.30 & 0.89–1.89 & 1.96 & 1.26–3.04 \\
45 & 1.30 & 0.89–1.89 & 1.79 & 1.16–2.78 \\
90 & 1.08 & 0.74–1.57 & 1.39 & 0.89–2.15 \\
135 & 0.95 & 0.65–1.38 & 0.95 & 0.61–1.48 \\
182 & 0.34 & 0.24–0.50 & 0.46 & 0.30–0.72 \\
225 & 0.33 & 0.23–0.49 & 1.08 & 0.70–1.67 \\
270 & 0.53 & 0.37–0.77 & 1.44 & 0.93–2.24 \\
315 & 1.04 & 0.71–0.71 & 1.79 & 1.16–2.78 \\ \hline \hline \end{tabular}  
* Ec—E-field calculated value; Em—E-field measured value; Ec \(\pm\) U—the range of E-field calculated \(\pm\) uncertainty; Em \(\pm\) U—the range of E-field measured \(\pm\) uncertainty.  
\end{table}
Table 6: The results of measurements and calculations of E-field near MO-RFID reader (at 3 m away).  
Figure 8: Spatial distribution of E-field measured and calculated near MO-RFID reader.  
A concise and precise description of the experimental work is presented here. Additional information is available in [13].
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 5 Results and Discussions
### Evaluation of Mixed Vehicle Applications versus Number of Vehicles
When compared with the CMGM and Adroit schemes, the AQAGS scheme was able to maintain the QoS for both low-density and high-density traffic. Comparing AQAGS's average delay with CMGM's and Adroit's, the difference was 78.53 and 40.12%, respectively. As a result, cluster formation, which involves the CH election process, is eliminated by the AQAGS. As opposed to the CMGM method, which necessitates the clustering of the vehicles and CH election before the gateway discovery process, our proposed method requires no such prerequisites. Additionally, the Adroit scheme does not take cluster formation into account  
Figure 8: Evaluation of mixed application types (FTP vs. VoIP) vs. the number of vehicles: (**a**) the number of vehicles vs. the PDR; (**b**) the number of vehicles vs. the throughput. and does not include application QoS in its solution. Regardless of the number of vehicles, the IV must always choose the MGW. The burden on the IV also rises as a result, which has an impact on the QoS of the applications. The maximum delays observed by CMGM and Adroit were 0.93 and 0.56 s, respectively, and are greater than the minimum delays required by VoIP and FTP, respectively. Figure 9b compares the CMGM and Adroit mechanisms to the CPO measured for the AQAGS method. The overall patterns indicate that whenever the number of vehicles increased, CPO increased as well. In contrast to CMGM and Adroit, AQAGS exhibited a lower CPO. The CPO for AQAGS was lower than those of CMGM and Adroit by 17.8% and 8.5%, respectively. This is because, in contrast to the CMGM scheme, the AQAGS scheme only searches for an MGW when necessary and only within the IV coverage zone. This manages how the network is flooded, which greatly enhances the CPO value. Similar to how the CPO exhibited by CMGM was 15.1% in the sparse network, it was also twice as high as the CPO exhibited by AQAGS. However, when there were 500 vehicles, CMGM's CPO increased to 43.34%, which was 23.23% higher than the CPO reported by AQAGS at the same number of vehicles. As the number of vehicles increased from 50 and 500, Adroit reported a change in CPO from 10.24% to 38.33%, respectively. This represents a difference of 2.47% and 18.23% with respect to the values obtained for AQAGS at the same intervals of vehicle numbers. This is because the Adroit scheme always runs the MGW discovery and selection process without taking into account the LTE-available eNB's load capacity. Regardless, non-cluster-based strategies such as AQAGS and Adroit yield lower CPO than cluster-based strategies (i.e., CMGM).
## 2 Materials and Methods
### Data Sources and Preprocessing  
The MODIS MOD13Q1 data from 2000 to 2018, with a spatial resolution of 250 m and temporal resolution of 16 days, were used to calculate FVC. The original data were projected and the band separated and mosaicked using the MRT (MODIS Reprojection Tools) method. They were then imported into ArcGIS for null filling and cropping to obtain the image dataset of Shaanxi Province. The maximum value composite (MVC) method was used to create monthly, seasonal, and annual NDVI data. Meteorological data, including monthly average temperature (TEM; \({}^{\circ}\)C), monthly total precipitation (PRE;mm), and monthly sunshine duration (SUN; h), were collected from 20 meteorological stations in Shaanxi Province provided by the China Meteorological Science Data Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 10 November 2021). To obtain the spatial variation of the climate factors, we used the Kriging model for temperature and sunshine, while the IDW (inverse distance weighting) model for precipitation was used for the interpolation process; both of these models have high accuracy as indicated in the literature [40; 41; 42].  
Seasonal and annual meteorological data were synthesized based on the monthly data. The DEM data used in this study were derived from the geospatial data cloud (www.gscloud.cn, accessed on 15 October 2021) with a spatial resolution of 90 m and were preprocessed with projection, stitching, and cropping to extract topographic indicators such as altitude (ALT), slope (SLO), aspect (ASP), and relief amplitude (RIF) (Figure 2).
## 3 Classical Rheological Schemes for Consolidation
### Burgers Model for Soil Skeleton  
In this section, we introduce another rheological scheme not widely used in soil mechanics--the Burgers model (see Figure 3). This model is commonly used to model the behaviour of asphalt mixtures [13; 14]. Here, it will be applied for the soil skeleton to simulate the transient soil response during consolidation and to obtain the settlement change in time.  
Figure 2: Terzaghi solution in comparison to test data.  
Assuming the Burgers model for the soil skeleton (see Figure 3), the constitutive relationship can be formulated as follows [10]:  
\[\eta_{2}M_{1}\dot{\epsilon}+M_{1}M_{2}\epsilon=\eta_{2}\dot{\sigma}^{\prime}+M_{1 }M_{2}\epsilon_{v} \tag{4}\]  
where the strain function \(\epsilon_{v}(z,t)\) can be determined by solving the following differential equation  
\[\dot{\epsilon}_{v}=\frac{1}{\eta_{1}}\sigma^{\prime}\qquad\epsilon_{v}(z,0)=0 \tag{5}\]  
and the superimposed dot denotes the derivative with respect to time. In Equations (4) and (5) the rheological structure's parameters (see Figure 3) \(M_{1}\) and \(M_{2}\) can be interpreted as the oedometric (constrained) moduli and \(\eta_{1}\) and \(\eta_{2}\) are the viscosity constants.  
Equations (4) and (5) are an extension of the linear relationship (2). Thus, in order to simulate the process of transient consolidation, the system of coupled differential equations (1), (4) and (5) has to be solved with the boundary conditions (3) and 'zero' initial conditions for \(\epsilon(z,t)\) and \(\epsilon_{v}(z,t)\). After solving the initial boundary value problem, the function of displacement (settlement) can be found by integrating the strain function.  
Advanced elastic-plastic models implemented in various geotechnical finite element method software packages can be extended by taking into account soil's skeleton creep. For example the Drucker-Prager/Cap model available in Abaqus software [27] gives the possibility to implement the constitutive relationship between the creep volumetric strain and effective pressure (first invariant of the effective stress tensor). Thus, the one-dimensional considerations shown in this section can be applied to the existing three-dimensional models. In order to implement such extension, in Equations (4) and (5), 1D strain, \(\epsilon\), should be replaced by volumetric strain, \(\epsilon_{vol}=\text{tr}\boldsymbol{\epsilon}\), and \(\sigma^{\prime}\) by effective pressure \(p^{\prime}=-\frac{1}{3}\text{tr}\boldsymbol{\sigma}^{\prime}\).
## 1 Introduction  
Human activity has modified much of the world's forests, converting large areas of continuous forest into small, fragmented patches, such that 70% of remaining forest is within 1 km of an edge [1].  
Fragmentation degrades ecosystems, reducing biodiversity especially in the smallest and most isolated fragments [2; 3; 4; 5; 6]. Some declines in biodiversity are evident almost immediately after fragmentation, whereas others increase over time, with extinctions occurring decades or more after disturbances [7].  
Managing and conserving trees in fragmented forests and other naturally heterogeneous landscapes such as open woodlands, requires maps that can accurately depict the landscape pattern. High spatial resolution imagery can allow small fragments such as thin corridors of trees, and scattered individual trees, to be mapped. Such detailed maps, however, are usually only produced for small regions [8], and it remains a challenge to create consistent high resolution maps over large areas. Unlike freely available Landsat data, higher resolution satellite data is often prohibitively expensive for mapping large areas. Furthermore, processing large volumes of high resolution data is computationally intensive, especially when multi-temporal imagery is required to correct for data gaps due to cloud, shadow, and other artefacts [9; 10].  
This article describes the production of a large-area tree cover map for a fragmented, heterogeneous landscape using high spatial resolution satellite imagery. The map covers the Australian state of New South Wales (NSW), and was required by the state government for natural resource monitoring at both small and large scales. The following sections outline recent advances in tree cover mapping, before describing the study area, and previous tree cover maps for NSW.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 3 Proposed MAC Protocol: AdHoc-FDMAC
### Data Transmission
#### 3.2.2 Transmitter and Receiver Are within the Data Transmission Range  
Here, IBFD data transmissions are described in the following two sub-sections, when transmitter and receiver are in the data transmission range, namely:  
* BFD communications;
* TNFD communications.  
Source-Based TNFD Communications  
The time sequence for source-based TNFD communication is shown in Figure 6. Here, we have described this communication using five nodes, namely A, C, F, G and M, where node A is considered as PT and SR, node G is PR and node M is ST. When PT wins the channel access, it sends its data to PR. We assume that the node A has data to node G and A obtains the channel access. At first, A transmits its RTS to G. After receiving RTS, the node G waits for a SIFS time and then transmits CTS-AI to A, where the AI value is '0', which means node G has no data for A. After that, the node A broadcasts a '1' bit flag named the Notification Flag (NF) to inform its neighbours that the nodes who want to transmit data to A can transmit now. Hence, the nodes that have data for the PT and hear the NF but cannot hear the CTS-AI, stop their NAV and start their Random Timer (RT). This condition is applied to mitigate the inter-user interference. The node whose RT value is minimum, sends its data packet to the PT after sensing the channel idle. As shown in Figure 6, nodes C, M and F start their RTs and M's random timer value is minimum. Therefore, M sends data to A. When the RTs of other nodes (C and F) stop, the nodes sense the channel and find it busy, as M already started its transmission.  
Therefore, node C and F resume their NAVs. Node A also transmits it data packet to G simultaneously. At the end of data transmission, both receiver nodes reply with ACK to their corresponding transmitters. If the ST does not provide data to PT A, the HD communication will take place.  
The time sequence for the destination-based TNFD communications is shown in Figure 7. As shown in the Figure 7, the PT (node A) starts the transmission by sending RTS to the PR (node C). In this case, the PR has no data to the PT, but it has data for another node B that is the SR now. Thus, after receiving the RTS, the PR waits a SIFS time and transmits a CTS-SRA frame to both the PT and SR. This CTS-SRA frame contains the address of the SR in its SRA field. Therefore, both PT and SR are informed that C has data to B. After hearing CTS-SRA, node B sends CTS, if it cannot hear RTS from A (this condition is to mitigate the inter-user interference). Afterwards, nodes A and C transmit their data to the corresponding receiver nodes C and B, respectively. After receiving the data packets, both PR (node C) and SR (node B) sends the ACK frame to their corresponding transmitters. If the SR does not reply with CTS-SRA, it becomes an HD communication only.  
Figure 5: Time sequence of the proposed MAC for BFD (the MS initiates the transmission).
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
###### Abstract  
In order to address the issue of heart rate susceptibility to motion artifacts (MAs) when extracting it from photoplethysmography (PPG) signals, a heart rate estimation algorithm based on the finite state machine (FSM) is proposed. The algorithm first applies band-pass filtering to the PPG and three-axis acceleration signals. The strength of MA is assessed based on the acceleration data. If a strong MA is detected, recursive least squares (RLS) filtering is applied; otherwise, it is omitted. Then, the signal is subjected to an empirical wavelet transform (EWT). Based on the EWT results, the current state is identified, and the corresponding spectral peak screening method is selected to estimate the heart rate. The mean absolute errors of the algorithm on 12 sets of public data and 8 sets of testing data are 0.93 and 1.76 beats per minute (ppm), respectively. The results of the experiment show that compared with other dominant algorithms, the proposed algorithm estimates heart rate with a smaller mean absolute error and can extract heart rate more effectively.  
photoplethysmography; heart rate estimation; motion artifact; finite state machine Article