esa-sceva/satcom-chunk-collection huggingface dataset:
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 4 Dose Calculation Algorithms for Hadrontherapy
### Pencil Beam Algorithms  
A large variety of pencil beam algorithms (PBAs) exist in commercial TPSs and research software. Their varying degrees of complexity balance the trade-offs between accuracy and calculation times. These analytical calculations are based on the parametrization of each pencil beam, describing the longitudinal depth dose distribution (DDD), as well as the lateral extent perpendicular to the central beam axis. Typically, the DDD is derived from MC calculations and verified against measurements. It is then tabulated for each energy and forms part of the base data for dose calculation in a specific facility. The lateral extent is described by one or more Gaussians, which also describe beam widening through MCS and beam divergence. Double or triple Gaussians offer a better MCS approximation and can describe different beam components, such as large angle scattering from nozzle elements [60] or different beam fragments in ion therapy [61]. The dose at a specific voxel with a position \(x\) is calculated by superimposing all relevant pencil beam contributions with a fluence \(F\), taking into account the respective depth \(d\) and lateral components \(g\):  
\[D(x)=\frac{1}{\rho(r)}\sum_{\text{PB}}F(\text{PB})\cdot g(r,E,z)\cdot d(E,z) \tag{7}\]  
The formulation of the lateral and depth components depends on the actual algorithm, but in general these are dependent on the initial beam energy \(E\), the lateral distance to the beam axis \(r\), and the water-equivalent depth \(z\).  
Heterogeneous density in the beam path poses a significant challenge to pencil beam algorithms. If each beam is calculated along the central axis only, different WET values in the vicinity will not be considered correctly. One possible solution is to split each pencil beam into sub-beams, typically of the same width but of a different weight [60]. The sub-beam weights are fitted to approximate the original pencil beam. Each sub-beam is mapped individually, with its own range estimate at the central sub-beam axis. The accuracy, as well as the computational effort required for this approach, scales with the number of sub-beams [62]. An alternative is to calculate the WET for each dose voxel instead, and to sample each contributing pencil beam at this WET. The drawback of the first method is that each sub-beam still only considers one WET, so that slanted surfaces or other heterogeneities smaller than the sub-beam width still lead to dose errors. The drawback of the second method is that there is only one beam path to each voxel, whereas scattered particles reaching the voxel on curved paths are not considered correctly.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Influence of Terminal Load Branch Impedance on Throughput  
To see the impact of the branch terminal load impedance on the throughput, the following scenario was considered: constant length between transmitter and receiver of 1200 m, constant branch length of 20 m in the middle of transmitter and receiver, and varying branch terminal load impedance (50, 100, 1000, and 10,000 \(\Omega\)).  
Figure 8 shows the influence of terminal load impedance on throughput. When increasing the terminal branch load impedance, the capacity increased evenly if at the same time the increasing impedance leads to deep notches in CTF [31; 51]. At the same time the increasing impedance reduces the attenuation at certain frequencies, thus increasing the transfer capacity. Particularly from 2.65 Mbps at 50 \(\Omega\) impedance to 3.05 Mbps at 10,000 \(\Omega\) impedance.  
Figure 6: Channel capacity for a NAYY150 SE cable with varying length from transmitter to receiver and a varying number of branches.  
Figure 7: Channel capacity for different branch lengths.
## 8 Conclusions  
A selection of studies on copper nanoparticles, their antimicrobial activity and toxicity, and potential uses have been reviewed in the present paper. We have looked at how we can obtain copper nanoparticles using various methods and compared copper nanoparticles with other metallic nanoparticles. Given copper's unique properties--antibacterial, antifungal, anticancer--and high availability, additional studies on its toxicity and stable synthesis would need to be performed in order to better understand its potential applications and effects. As the current paper focuses on copper nanoparticles, we have looked at how copper nanoparticles can be obtained using green, chemical and physical methods. For each of the methods, based on the researched materials, we noted the used materials and the resulted nanoparticles with their corresponding properties.  
As bacteria can be divided in two categories based on cell wall structure: Gram-positive and Gram-negative, Gram-positive bacteria have a higher resistance to the nanoparticle's mechanism of action. On the other hand, Gram-negative bacteria possess an increased risk, considering the majority of human infection and deaths are caused by it. Given their increasing drug resistance, further efforts are needed to develop ways to counter this resistance.  
Copper nanoparticles interact with the bacteria's cell wall, DNA, nucleic acid. Nanoparticles are responsible for ROS generation and oxidative stress. This eventually leads to cell death.  
We then discussed the impact of various metals on two bacteria: Escherichia coli and Staphylococcus aureus. For their antimicrobial activity, we have looked at MIC and MBC. Copper nanoparticles are prone to oxidation, therefore further studies should look into the antibacterial activity or CuNPs and not on CuO particles.  
Copper nanoparticles can be successfully used to combat different fungi, to treat viral diseases and as a delivery system for cancers. The benefit is that the human body is already capable of digesting copper nanoparticles; however, future work is still needed. However, copper nanoparticles also have levels of toxicity which might limit their use. Further studies can look at how varying particles' characteristics affects the toxicity and delivery of copper ions.  
Conceptualization, M.C.C. and M.L. Methodology, M.T. Writing--M.C.C., M.T., M.L. Original draft preparations, M.C.C., M.T., M.L.--Review and editing. Supervision, M.L. All authors have read and agreed to the published version of the manuscript.  
The authors wish to acknowledge financial support from the \"[PERSON]\" University of Medicine and Pharmacy, Cluj-Napoca, Romania, grant contract no. 1529/16/18.01.2019. This work was also supported by the Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI, project number PN-III-P2-2.1-PED-2019-0844, PN-III-P2-2.1-PED-2019-0997, PN-III-P2-2.1-PED-2019-3373.  
Not applicable.  
The authors declare no conflict of interest.
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 1 Introduction  
### Mechanical Fatigue of Metals  
Failure of a part under a repetitive load is called mechanical fatigue break-up. Even today, the most common and straightforward way to investigate mechanical fatigue is to identify a correlation between the number of cycles to failure (\(N_{f}\)) with the average applied stress (\(\sigma_{0}\)) or the strain amplitude (\(\epsilon_{0}\)) for stress or strain controlled tests respectively; i.e., the so-called \(S\)-\(N\) or strain-life curves [1]. In the stress or strain controlled mode, a power-law relation between \(N_{f}\) and the global average stress/strain amplitude is typically observed. In the high cycle fatigue regime, the equation is:  
\[\epsilon_{0}=A_{strain-life}\ N_{f}^{a} \tag{1}\]  
where \(A_{Strain-life}\) and \(a\) are the prefactor and the exponent of the strain-life curve, respectively.  
Strain-life curves report the average value of the number of cycles necessary for part failure under specific loading conditions over a certain amount of fatigue tests for the same testing conditions. Consequently, they only provide information about the most probable number of cycles to failure [2], but no information about the failure process (onset of cracks). In general, reproducibility of the fatigue lifetime under the same measurement conditions is poor, so a wide span of lifetimes is typically measured resulting in high standard deviations [3]. Typically, two orders of magnitude are commonly reported for notched as well as smooth specimens [4]. A more precise prediction is difficult due to the probabilistic nature of mechanical fatigue [5].  
The combination of long testing times and high experimental uncertainties make fatigue testing time and resources intensive. Consequently, the predictive power of a measured fatigue lifetime for a single sample or application is very limited. This is why a deeper understanding of the fatigue process is needed, not only to predict and avoid failure (accidents), but also to save resources by not stopping the test/application too early due to high safety factors.  
For metallic materials, different techniques and concepts have been developed to better describe and understand the fatigue process, as well as to predict failure more precisely. Experimental concepts are based on heat dissipation due to plastic deformation [6; 7], via metal magnetic memory (MMM) [8], or by following the electrical conductivity [9].  
Experimental techniques to quantify nonlinear material behavior have been used, such as ultrasonic techniques to detect nonlinear material behavior. These techniques successfully showed that the nonlinear material properties change due to fatigue [10; 11]. So the objective of this work was to expand these techniques by analyzing the stress response via Fourier transform of metallic specimens in displacement-controlled fatigue tests. From the results obtained, better fatigue life times are predicted, as well as the possibility to easily detect crack initiation and propagation with high sensitivity.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Cell Rover  
In Ref. [110], a sub-micrometer-sized injectable wire antenna is presented for the purpose of smart sensing, modulation, as well as energy harvesting to power in-cell nano-electronic computing. Figure 26 shows the proposed cell rover device, which can help understand the cell biology for different diagnostic and therapeutic applications. The receiver coil antenna is built with an American Wire Gauge (AWG) 47 wire, with a thickness of 0.0355 mm. The diameter and length of the coil are 2 mm and 1 mm, respectively. The  
Figure 24: Miniaturized slot line fed antenna for capsule endoscopy [108]. Reproduced with permission from an open-access article from _IET Microwaves, Antennas & Propagation_ (CC-BY).  
Figure 25: Fabricated MIMO antenna tested inside minced meat [109]. Reproduced with permission from an open-access article from _Scientific Reports_ (CC-BY).  
proposed antenna operates at a 4.5 MHz frequency. It can achieve a PTE of 0.63% and a SAR of 0.0226 mW/kg for 10 g of tissue.
# Further Investigations into the Capacitive Imaging Technique Using a Multi-Electrode Sensor
## 4 Experiments of the Multi-Electrode Sensor
### Defects within the Fiberglass Board and Aluminum Plate  
As shown in Figure 12a, the insulator layer, which was fiberglass board, contained a buried defect. The diameter of this buried defect was 8 mm and the buried depth of the defect was 5 mm. As shown in Figure 12b, the conducting specimen, which was aluminum plate (grounded), contained a surface defect. The diameter of this surface defect was 8 mm and the depth of the defect was 5 mm. As shown in Figure 12c, the complete specimen was the aluminum plate covered by the fiberglass board. The horizontal distance between these two defects was 60 mm and the length of detecting path was  
Figure 11: The signal strength diagrams with the multi-electrode sensor with 0.1 mm lift-off: (**a**) E21, (**b**) E31, (**c**) E41.  
Figure 10: Schematic diagram of a 12 mm thick fiberglass board containing flat bottomed holes of buried depth (from left to right) 3 mm, 2 mm and 1 mm.  
140 mm, leaving 45 mm on each side of the boundary to avoid edge effects. The red line shown in Figure 12c was the inspecting path using the multi-electrode sensor and the lift-off was 2 mm.  
The voltage lines of E21, E31 and E41 obtained by using experimental system were shown in Figure 13a-c respectively. Comparing Figure 13a-c, the offset of any two adjacent electrode pairs (e.g., E21 and E31, E31 and E41), which was discussed in Figure 3b, was 6.5 mm. Through position compensation, the experimental DCRs of E21, E31 and E41 were shown in Figure 13d. The DCRs of the different electrode pairs detecting the center of two different kinds of defects presented different variation patterns, which were consistent with the simulation results. More narrowly, for the defect in the insulator layer, the DCRs of the center of the defect showed a monotonous decreasing trend with the electrode pairs changing from E21 to E31 to E41. While for the defect in the metal surface, the DCRs of the center of the defect showed a monotonous increasing trend with the electrode pairs changing from E21 to E31 to E41. Compared with the conventional CI sensor, the multi-electrode sensor could  
Figure 12: Schematic diagram of insulator layer and conducting specimen: (**a**) insulator layer, (**b**) conducting specimen, (**c**) conducting specimen covered by insulator layer.  
Figure 13: Experimental results: (**a**) the voltages of E21, (**b**) the voltages of E31, (**c**) the voltages of E41, (**d**) the DCRs of E21, E31 and E41.The dynamic change rate of E21, E31 and E41.  
discriminate the defects in the insulation layer and metal surface by these different variation patterns. The two peaks of the DCRs of E31 and E41 detecting the defect in the insulator layer were roughly equal, because the ground aluminum plate weakened the MSDs asymmetry which was induced by the floating potential in actual experiments.
## 2 Methodologies and Dataset
### Arma  
The general auto-regressive moving average (ARMA) model was first described by [PERSON], in 1951, in his thesis titled \"Hypothesis testing in time series analysis\". [PERSON] and [PERSON] [20] popularized the ARMA model and later introduced the auto-regressive integrated moving average (ARIMA) model. ARIMA model transforms a non-stationary series into a stationary series by differencing. ARIMA model has three components:  
1. AR part indicates the autocorrelation between present and past values.
2. MA part indicating the autocorrelation between present and past values of the error term.
3. Integrated part (I), denoting the level of differencing required to make the time series stationary.  
ARIMA \((p,q,d)\) is expressed in terms of lag polynomials as:  
\[\Phi(M)(1-M)^{d}y_{t}=\theta(M)\varepsilon_{t} \tag{1}\]  
\[\Big{[}1-\sum\
olimits_{i=1}^{p}\Phi_{i}M^{i}\Big{]}(1-M)^{d}y_{t}=\Big{(}1+ \sum\
olimits_{j=1}^{q}\Phi_{j}M^{j}\Big{)}\varepsilon_{t} \tag{2}\]  
where \(M\) is the backshift operator; \(\Phi(M)\) is the AR operator and \(p\) is the order of AR; \(\theta(M)\) is the MA operator of error term \(\varepsilon_{t}\) and \(q\) is its order; \(d\) is the differencing level.  
If the value of \(d\) is 0, then the model reduces to an ARMA \((p,q)\) model.  
Auto ARIMA method was used to generate the optimized values of \(p,q\) and \(d\) that were best suited for prediction. The optimum model was identified based on the values of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) [21].  
\[AIC=\frac{-2}{N}(loglikelihood)+2\Big{(}\frac{m}{N}\Big{)} \tag{3}\]  
\[BIC=-2(loglikelihood)+\ln(N)m \tag{4}\]  
The stationarity of the series was tested using the Augmented Dickey-Fuller (ADF) method [22]. The AIC and BIC values were used by the Auto ARIMA model to determine the best \(p,q\) and \(d\) values for forecasting. The Auto ARIMA capability was provided by the Python module 'pmdarima'. Auto ARIMA starts by running differencing tests, with the Augmented Dickey-Fuller (ADF) test being the most popular. The ADF test examines if a series has a unit root and, as a result, whether it is stationary or not. ADF test uses an autoregressive model and optimizes one information criterion. The criterion used in this work was AIC. The null hypothesis was that the time series was not stationary and the alternate hypothesis was that the time series was stationary. The result of the ADF test was interpreted using the \(p\)-value. If the \(p\)-value is less than the threshold value of 5%, then the null hypothesis is rejected otherwise alternative hypothesis is rejected. Algorithm 1 elaborates the algorithm for Auto ARIMA.
## 4 Presumptions and Network Structuring
### State of the Art
#### 4.3.4 Data Aggregation Using the Distance Function (ED)  
A distance function is often used in WSNs for the deployment of sensor nodes in remote areas of a network. For data localization, the distance is estimated for the intra-sensor and inter-sensor nodes. The data measurement along with weights is calculated from the below formula:  
\[D\big{(}X_{i},\,\,X_{j}\big{)}=\sqrt{\sum_{k=1}^{\omega}\Big{(}x_{ik}-x_{jk} \Big{)}^{2}}\]  
where \(\omega\) is the user-defined threshold value.  
When, \(D\leq\omega\), the data set is considered redundant and can be eliminated before transmission [35].
## 2 Capture Probabilities Under Different Fading Channels
### Rician Fading Channel
\begin{split}&\int_{0}^{\infty}ae^{-nK-\frac{\sigma_{t}(z+1)}{z}} \sum_{j=0}^{\infty}\frac{(aK\gamma_{t})^{j}}{(j!)^{2}}\sum_{i=0}^{\infty} \frac{[(n-1)K]^{i}}{i!}\sum_{k=0}^{n-2+i}\frac{(\frac{\sigma_{t}\gamma_{t}}{z} )^{k}}{k!}d\gamma_{t}\\ &=\frac{z}{z+1}e^{-nK}\sum_{i=0}^{\infty}\frac{[(n-1)K]^{i}}{i!} \left[\int_{0}^{\infty}e^{-y}\sum_{j=0}^{\infty}\frac{K^{j}y^{j}}{(j!)^{2}} \left(\frac{z}{z+1}\right)^{j}dy+(z+1)^{-1}\right.\\ &\left.\cdot\int_{0}^{\infty}ye^{-y}\sum_{j=0}^{\infty}\frac{K^{j }y^{j}}{(j!)^{2}}\left(\frac{z}{z+1}\right)^{j}dy+\cdots+\frac{(z+1)^{-(n-2+i) }}{(n-2+i)!}\int_{0}^{\infty}y^{n-2+i}e^{-y}\sum_{j=0}^{\infty}\frac{K^{j}y^{ j}}{(j!)^{2}}\left(\frac{z}{z+1}\right)^{j}dy\right]\\ &=\frac{z}{z+1}e^{-nK}\sum_{i=0}^{\infty}\frac{[(n-1)K]^{i}}{i!} \left[\sum_{j=0}^{\infty}\frac{K^{j}j!}{(j!)^{2}}\left(\frac{z}{z+1}\right)^{j} +(z+1)^{-1}\sum_{j=0}^{\infty}\frac{K^{j}(j+1)!}{(j!)^{2}}\left(\frac{z}{z+1} \right)^{j}+\cdots\\ &\left.+\frac{(z+1)^{-(n-2+i)}}{(n-2+i)!}\sum_{j=0}^{\infty} \frac{K^{j}(j+n-2+i)!}{(j!)^{2}}\left(\frac{z}{z+1}\right)^{j}\right]\\ &=e^{-nK}\sum_{i=0}^{\infty}\frac{[(n-1)K]^{i}}{i!}\sum_{k=0}^{n-2 +i}\frac{\left(\frac{z+1}{z+1}\right)^{k}}{k!}\sum_{j=0}^{\infty}\frac{K^{j}( j+k)!}{(j!)^{2}}\left(\frac{z}{z+1}\right)^{j+1}\end{split}  \tag{21}\]  
Substituting (20) and (21) into (19), we can obtain the capture probability  
\[p_{cap}^{Ric}=n\left\{1-e^{-nK}\sum_{i=0}^{\infty}\frac{[(n-1)K]^{i}}{i!}\sum_{ k=0}^{n-2+i}\frac{\left(\frac{1}{z+1}\right)^{k}}{k!}\sum_{j=0}^{\infty}\frac{K^{j}( j+k)!}{(j!)^{2}}\left(\frac{z}{z+1}\right)^{j+1}\right\} \tag{22}\]The similar expression also can be found in [25; 28]. Although the Rayleigh PDF (Equation (2)) can be obtained by Rician PDF (Equation (12)) with \(K=0\), we cannot let \(K=0\) in Equation (22). The reason is \(K\) cannot be zero in Equation (17). That is to say, we derive the expression of capture probability in Rician fading channel based on the assumption \(K>0\).
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 4 Incorporating RS as Covariates in DSM
### Remote Sensing Data as Proxies of Soil Properties Controlling Factors in the SCORPAN DSM Model
#### 4.2.5 Age  
According to [PERSON] et al. [21], age is the least-used covariate for medium- and large-scale DSM. It is a rather complicated covariate to interpret because it mixes the duration of weathering and pedogenesis (both its duration and the various climatic and vegetation conditions in which soil formation occurred). Overall, age is quite difficult to infer directly from RS data. Where soils have developed from the underlying parent material, the age can be inferred from lithological or geological information (see Section 4.2.4). However, to our knowledge, there are no RS sensors that are capable of capturing age at the pedogenesis time span. For shorter periods, time can be captured through land-cover change history or other types of measurements. Although we did not find any soil studies using gamma-ray spectrometry or other tools to measure the erosion/sedimentation rates in French soils, some works suggest that this technique may have potential for detecting change related to nuclear accidents (e.g., [307; 308]).
## 3 Materials and Methods  
### Remediation of Smoke-Tainted Wine  
Two wines, a 2020 Shiraz Sangiovese and a 2020 Petit Verdot Sangiovese, were sourced from a commercial winery (Cassegrain Wines, Port Macquarie, NSW, Australia) that deemed the wines to be smoke-tainted and in need of remediation. The wines were made from Shiraz and Petit Verdot grapes harvested from vineyards in Mudgee (32\({}^{\circ}\)36\({}^{\prime}\) S 149\({}^{\circ}\)34\({}^{\prime}\) E) and Sangiovese grapes harvested from a vineyard in Hilltops (34\({}^{\circ}\)24\({}^{\prime}\) S 148\({}^{\circ}\)25\({}^{\prime}\) E), that were exposed to smoke from bushfires that burned in New South Wales during the 2019/2020 growing season (albeit, the exact duration of vineyard smoke exposure and density of smoke are not known).  
Wines (\(\sim\)8000 L) were treated by Australian Vintage Limited (at their Burong Hill winery/Austrflavor facility; Buronga, NSW, Australia) using an industrial scale spinning cone column distillation system (SCC10000, Flavourtech, Griffith, NSW, Australia). Briefly, wines were fed into the top of the column (comprising a cone stack \(\sim\)0.88 m wide and \(\sim\)3.64 m high) under vacuum, with stripping steam fed into the base of the column (Figure 1). The vapor that flowed from the top of the column (steam mixed with volatile compoundsstripped from the wine), was passed through a condensing system and collected as 'condensate', while the resulting'stripped wine' was collected from the bottom of the column. Each SCC distillation treatment was conducted under operating conditions (Table 4) that achieved three distinct fractionation end points, \(\sim\)1%, \(\sim\)13-14% and \(\sim\)29% stripping, during which condensate and stripped wine samples were collected. Condensate (\(\sim\)300 mL) was frozen (at \(-\)4 \({}^{\circ}\)C) for chemical analysis, while untreated and stripped wines (\(\sim\)20 L) were bottled (in 750 mL glass wine bottles, under screw-cap closures) and cellated (at 15 \({}^{\circ}\)C) for chemical and sensory analyses (which were performed within 3 months of SCC distillation treatment). Since SCC distillation had to be performed at industrial scale, it was not possible to replicate treatments on individual wines. Instead, SCC treatment of two wines was undertaken.