esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 5 The Performance Analysis of Dual-Hop Multi-Relay System
### Suboptimal Relay Selection Scheme: S-R Link  
The ORS scheme is the best selection scheme; however, it requires full knowledge of CSI of all hops. In order to reduce the complexity and power consumption, we introduce the first partial relay selection (PRS1) scheme, which maximizes the instantaneous SNR by selecting the best relay based on CSI of \(S\)-\(R_{l}\) links. Mathematically, the best relay, \(k\), is selected by  
\[k=\arg\max_{l}\{\gamma_{s,l}\}, \tag{31}\]  
and the e2e SNR, \(\gamma_{s1}\), under the PRS1 scheme is given by  
\[\gamma_{s1}=\min\{\max_{l}\{\gamma_{s,l}\},\gamma_{k,d}\}, \tag{32}\]where \(\gamma_{k,d}\) is the SNR of the \(R\)-\(D\) link of the selected \(k\) relay. For this scheme, the CDF of \(\gamma_{s1}\) can be expressed by  
\[F_{\gamma_{s1}}(\gamma) =F_{\gamma_{s1}}(\gamma)+F_{\gamma_{s,d}}(\gamma)-F_{\gamma_{s1}}( \gamma)F_{\gamma_{s,d}}(\gamma)\] \[=F_{\gamma_{s,d}}(\gamma)+F_{\gamma_{s1}}(\gamma)\overline{F}_{ \gamma_{s,d}}(\gamma), \tag{33}\]  
where \(F_{\gamma_{s1}}(\gamma)=\prod_{l=1}^{L}F_{\gamma_{s,d}}(\gamma)\), \(F_{\gamma_{s,d}}(\cdot)\) is the CDF of \(\gamma_{k,d}\), and \(\overline{F}_{\gamma_{k,d}}(\cdot)\) is the complementary CDF of \(\gamma_{k,d}\).
## 2 Materials and Methods
### DICM Analysis
To evaluate the damage development, the strain and displacement metrics are used allowing the quantification of accumulated damage apart from visual strain distribution maps which provide only visual representations of strain fields. During the compression test, displacement fields (\(U\), \(V\) and \(W\)) at three directions (\(X\), \(Y\) and \(Z\)), the coordinates of each point of the speckled surface measured in mm and/or pixels, radial displacement (\(dR\)) and the sigma error of the correlation (\(\sigma\)) are derived. Moreover, the displacement fields are differentiated to calculate the normal and shear strains (\(e_{xx}\), \(e_{yy}\) and \(e_{xy}\)) and the principal strains (\(e_{1}\) and \(e_{2}\)). In our research, only circumferential strain (\(e_{xx}\)) and radial displacement (\(dR\)) results of the DICM analysis are presented. To receive behavior of surface strains and displacements during an entire loading and compare it to AE results, a time-series analysis of DIC data was conducted. Time-series analysis can show how variables change over time and how the data adjusts over the course of the data points as well as the final results and set order of dependencies between the data. The images collected in the test were processed using Vic-3D 8 software to calculate the strain development process in the concrete sample with time. For time-series analysis, nine points were selected on the horizontal line in the central part of the cylindrical sample. Calculation points were distributed at equal intervals of 8 mm. It is assumed that the deformation in this part reflects the whole fracture behavior of the concrete sample. The scheme is introduced in Figure 5b. Because DIC data are representative of the deformation process, the time-series data are considered on the strain energy level as well as the AE energy release rate allowing us to see the dependence of both parameters.
## 2 Contribution of This Work
### Closed Loop Congestion Mitigation
#### Detection  
Monitoring and detecting deviations from normal in the immediate environment are the process of detection. In our example, the detection mechanism checks a number of constraints, including queue length and packet delay [17].
###### Abstract  
Herein, we propose the use of the logarithmic values of data transmission rates for multi-armed bandit (MAB) algorithms that adjust the modulation and coding scheme (MCS) levels of data packets in carrier-sensing multiple access/collision avoidance (CSMA/CA) wireless networks. We argue that the utilities of the data transmission rates of the MCS levels may not be proportional to their nominal values and suggest using their logarithmic values instead of directly using their data transmission rates when MAB algorithms compute the expected throughputs of the MCS levels. To demonstrate the effectiveness of the proposal, we introduce two MAB algorithms that adopt the logarithmic rates of the transmission rates. The proposed MAB algorithms also support frame aggregations available in wireless network standards that aim for a high throughput. In addition, the proposed MAB algorithms use a sliding window over time to adapt to rapidly changing wireless channel environments. To evaluate the performance of the proposed MAB algorithms, we used the event-driven network simulator, ns-3. We evaluated their performance using various scenarios of stationary and non-stationary wireless network environments including multiple spatial streams and frame aggregations. The experiment results show that the proposed MAB algorithms outperform the MAB algorithms that do not adopt the logarithmic transmission rates in both the stationary and non-stationary scenarios.  
multi-armed bandit (MAB); CSMA/CA; rate adaptation; MCS; ns-3; machine learning +
Footnote †: journal: Applied  
1
Footnote 1: email: {[[https://doi.org/10.3390/appl3148485](https://doi.org/10.3390/appl3148485)]([https://doi.org/10.3390/appl3148485](https://doi.org/10.3390/appl3148485))}
## 4 Conclusions  
The extraction of _M. oleifera_ leaf polyphenols with a novel, glycerol/nicotinamide DES was performed by deploying an ultrasonication pretreatment step. This process enabled the clarification of the use of ultrasounds in the performance of the extraction, with regard to yield, duration, and energy demands. The optimization through response surface methodology revealed that when samples were ultrasonication-pretreated, the subsequent batch stirred-tank extraction required less amount of DES and much lower stirring speed for maximum polyphenol yield, but also a higher amount of solvent per dry mass unit. However, no significant difference in the maximum polyphenol yield was determined. Kinetics showed that higher extraction rate, and therefore shorter extraction time, of the ultrasound-pretreated samples, could be attained only when stirred-tank extraction was carried out at \(T\geq\) 70 \({}^{\circ}\)C. The higher energy barrier estimated for the ultrasonication-pretreated extractions was representative of the harder-to-extract compounds, since a significant wash out of the most readily extracted polyphenols took place during pretreatment. It is concluded that sample ultrasonication as a pretreatment step might be favourable in reducing extraction time, and solvent and energy requirements, but further studies with various plant tissues are needed to obtain a deeper insight.  
Conceptualization, S.L. and D.P.M.; formal analysis, A.L., S.G., I.K., O.K., G.B., and E.B.; investigation, A.L., S.G., I.K., O.K., G.B., and E.B.; writing--original draft preparation, S.L. and D.P.M.; writing--review and editing, S.L. and D.P.M.; supervision, S.L. and D.P.M. All authors have read and agreed to the published version of the manuscript.  
This research was co-financed by the European Union and the Hellenic Ministry of Economy & Development through the Operational Programme Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH--CREATE--INNOVATE (project code: T1 EDK-05677).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Content (mg g\({}^{-1}\) dm) \(\pm\) \%rsd**} \\ \cline{2-3}  & **No Pretreatment** & **Ultrasonication Pretreatment** \\ \hline Neochlorogenic acid & 6.61 \(\pm\) 0.10 & 6.82 \(\pm\) 0.11 \\ Chlorogenic acid & 0.20 \(\pm\) 0.00 & 0.22 \(\pm\) 0.00 \\ Chlorogenic acid isomer & 5.32 \(\pm\) 0.07 & 5.22 \(\pm\) 0.08 \\ _Total chlorogenates_ & _12.13_ & _12.26_ \\ Multifilm B & 2.91 \(\pm\) 0.03 & 3.11 \(\pm\) 0.03 \\ Quercetin glucoside & 10.58 \(\pm\) 0.11 & 10.65 \(\pm\) 0.12 \\ Quercetin malonylglycoside derivative & 5.97 \(\pm\) 0.06 & 6.99 \(\pm\) 0.07 \\ Kaempfer glucoside & 3.87 \(\pm\) 0.04 & 4.16 \(\pm\) 0.05 \\ Kaempfer malonylglucose & 0.37 \(\pm\) 0.00 & 0.42 \(\pm\) 0.00 \\ _Total flavonoids_ & _23.70_ & _25.33_ \\ _Total polyphenols_ & _35.83_ & 37.59 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantitative data for the major polyphenols detected in _M. oleifera_ extracts.
# Flavor Chemistry of Virgin Olive Oil: An Overview
## 2 VOO Flavor Perception
### Off-Flavors
\begin{table}
\begin{tabular}{c c c c} \hline
**Sensory Defect** & **Chemical Compound** & **Chemical Structure** & **Odor Property** \\ \hline \multirow{4}{*}{Fusty} & butyl acetate & \multirow{2}{*}{\(\circ\)} & green, fruity, pungent \\  & ethyl propanoate & & fruit, strong \\  & ethyl butanoate & & sweet, fruity \\  & propanoic acid & & \\  & butanoic acid & & \\  & butanoic acid & & \\  & butanoic acid & & \\  & pentanoic acid & & \\  & pentanoic acid & & \\  & _trans_–2–heptenal & & \\  & _trans_–2–octenal & & \\  & _trans_–2–decenal & & \\  & pentanal & & \\  & hexanal & & \\  & heptanal & & \\  & octanal & & \\  & nonanal & & \\  & hexanoic acid & & \\  & heptanoic acid & & \\  & 6–methyl–5–hepten–2–one & & \\  & & & \\ \end{tabular}
\end{table} 
Table 2: _Cont._\"Rancidity\" is an off-flavor resulting from the oil oxidation, a phenomenon promoted by long exposure to air, exposure to light and relatively high temperatures. The main compounds present in a rancid oil are the aldehydes (2-heptenal, 2-ocenal, 2-decenal, hexanal, nonanal, octanal, pentanal, heptanal) generated during the fatty acid oxidation. When acids are also found (such as butanoic, hexanoic, heptanoic), it means that the oxidation process is very thorough, as acids are generated by the oxidation of aldehydes [59; 60] (Table 2). The sensory assessment of negative notes, however, is not performed by our nose and tongue in an independent and selective way with respect to positive (\"olive fruity\", \"bitter\", \"pungent\" and \"green\") ones. The intensity of some sensory notes, in fact, can be masked or enhanced by others as recently demonstrated by [PERSON] and coauthors [61], particularly in VOOs rich in phenolic compounds. The authors suggested how these effects can explain the discrepancy among panels sometimes observed during extra virgin olive oil sensory analysis over olive oil storage. In particular, a 'fusty' defect was detected in some one-year old VOOs due to the phenolic compounds natural decrease over time (auto-oxidation), while in the fresher product its higher level contributed to mask this off-flavor.
## 2 Materials and Methods
### Microencapsulation by Spray Drying  
Wall Solutions (WS) containing 20% (\(w\)/\(w\)) solids consisting of 2.5, 5.0, 7.5, or 10.0% (\(w\)/\(w\)) WHPI and 17.5, 15.0, 12.5, or 10.0% (\(w\)/\(w\)) lactose, respectively, were prepared in de-ionized water (Millipore, 18.2 M\(\Omega\).cm). Based on their solids-composition, the WS were designated 2.5/17.5, 5.0/15.0, 7.7/12.5, and 10.0/10.0, respectively. In all cases, an aqueous dispersion of WHPI was prepared at 40 \(\pm\) 1 \({}^{\circ}\)C and 0.02% sodium azide (Fisher Scientific, Pittsburgh, PA, USA) was added in order to prevent microbial deterioration. The WHPI dispersions were slowly stirred for 12 h at 25 \(\pm\) 1 \({}^{\circ}\)C to allow for full hydration and swelling of the protein constituents. Then, the lactose component of the WS was added and the WS was slowly stirred for 2 h at 25 \(\pm\) 1 \({}^{\circ}\)C. CIWEs with a W:C mass ratio of 75:25, 50:50 and 25:75 were prepared as previously reported [30]. In short, a coarse emulsion was prepared using an Ultra-Turrax T25 homogenizer (IKA Works, Cincinnati, OH, USA) operated at 13,000 rpm for 45 s at 25 \(\pm\) 2 \({}^{\circ}\)C. Then, the coarse emulsion was subjected to four successive homogenization steps (50 MPa, ambient temperature), using a model NS1001L2K--Panda high-pressure homogenizer (Niro Soavi S.p.A., Parma, Italy). The CIWEs were designated according to their WHPI and lactose content and their wall-to-core ratio (W:C). As an example, the CIWE 2.5/17.5/25:75 was prepared with WS containing 2.5% WHPI and 17.5% lactose, and it had a wall-to-core mass ratio of 25:75.  
CIWEs were spray dried using an APV Anhydro Laboratory Spray Dryer (APV Anhydro A/S S\(\O\)borg, Denmark). In all cases, the CIWE were atomized using the centrifugal atomizer of the dryer operated at 50,000 rpm. Drying (in the co-current configuration) was carried out at an inlet and outlet air temperature of 160 \(\pm\) 2 \({}^{\circ}\)C, and 80 \(\pm\) 2 \({}^{\circ}\)C, respectively. Microcapsule powders were collected, placed in hermetically closed glass jars, and kept in desiccators pending analyses.
## 2 Materials and Methods
### Data
#### 2.2.2 Meteorological Data  
Gridded daily meteorological data retrieved from the ERA-Interim reanalysis (ERA) dataset and multi-site data of the China Meteorological Administration (CMA) were required to drive the RS crop yield model. Surface net radiation (Rn), vapor pressure  
Figure 1: The study region (North China Plain) and reference region (Rongcheng county and Dingxing county). The reference region is a county, where most of the croplands were cultivated with summer maize, and we obtained a reference LAI time-series curve by averaging the LAI time series of all maize pixels retrieved from MODIS products in the reference region (Section 2.3.4). The reference LAI time series was an important factor for computing the Yb of maize (Section 2.3.4).  
deficit (VPD), and air temperature (T) were directly retrieved from the ERA-Interim dataset ([[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim)), accessed in May and June, 2020). Precipitation (Pr) and global solar radiation (Rg) were retrieved from CMA multi-site data ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed in June 2020). Site-scale CMA data were spatially interpolated to a raster dataset using inverse distance weighted method provided by ArcGIS software (v10.1), and key parameters required were set as follows:  
* Output cell size = 5 km;
* Power = 2;
* Search radius = variable; and
* Search radius settings: Number of points = 12; maximum distances = 100 km.  
Rather than directly retrieving from the site-scale observations, daily solar radiation (\(R\)g) data were calculated in terms of the interpolated site-observed daily air temperature range (\(T_{\text{R}}\)) and daily sun hours (\(H_{\text{Y}}\)s) because sites observing \(R\)g is too sparse to be interpolated. We referred to [PERSON], et al. [39] to calculate daily \(R\)g, such that  
\[R\text{g}/R_{0}=a\times\ln(T_{\text{R}})+b\times\left(Hr_{\text{S}}/Hr_{\text {day}}\right)^{c}+d \tag{2}\]  
where \(R_{0}\) denotes the extra-terrestrial solar radiation; \(Hr_{\text{day}}\) denotes the number of daytime hours; and \(a\), \(b\), \(c\), and \(d\) are empirical coefficients. The average of values for each coefficient across multiple sites over China was used, such that \(a\) = 0.04, \(b\) = 0.48, \(c\) = 0.83, and \(d\) = 0.11. ERA-Interim datasets provide gridded global meteorological variables from 1981 to present in multiple temporal and spatial resolutions. The temporal and spatial resolutions of gridded data retrieved from ERA-Interim were 12 h and 0.125 arc-degree. The daily value of each variable is the sum (for Pr) or average (for variables except for Pr) of two 12-h values in one day.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 4 Experiments and Results
### Principle Verification Experiment on the Attitude Positioning Accuracy of Cross Laser-Pattern  
The attitude of cross laser refers to the angles of X or Y axis of cross laser line in the image coordinates. Since the theoretical angle value of X or Y axis laser line in image coordinates cannot be obtained directly, the attitude positioning accuracy was indirectly verified by a rolling angle measurement experiment.  
The experimental schematic diagram and the experimental process is shown in Figure 17:  
Figure 16: Displacement measurement error after compensation.  
Figure 17: (**a**) Schematic diagram of the experiment. (**b**) Procedure picture of the experiment.  
Figure 15: Displacement measurement error curve.  
Before the experiment, the cross laser is fixed to a high precision turntable with adapting part, the accuracy of the turntable is \(\pm 3\)\", and to ensure the laser projection axis was coaxial with the turntable axis, the laser position is adjusted. That is, the center of cross laser image does not change in the image coordinate when rotating the turntable. The measurement principle is shown in Figure 18.  
In order to express conveniently, the cross laser only draws the X axis laser line. Here, \(\alpha_{0}\) is angle of the laser line at the initial moment in the image coordinate system and \(\alpha_{1}\) is the angle of laser line in the image coordinate system after rotation. \(\theta m\) represents the rotation angle measured by the image acquisition system and \(\theta_{r}\) represents reference value of the rotation angle obtained from the turntable system. Since the projection axis of cross laser cannot be guaranteed to be perpendicular to the receiving screen of the image acquisition system, the projection error will be introduced into the measurement [26], and \(e\) represents the measurement error. The correlation is shown in Table 2:  
The repeatability experiment error curves are shown in Figure 19:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Measurement Value**} & \multicolumn{2}{c}{**Reference Value**} & \multicolumn{2}{c}{**Measurement Error**} \\ \hline Character & Formula & Character & Character & Formula \\ \(\theta_{m1}\) & \(\alpha_{1}-\alpha_{0}\) & \(\theta_{r1}\) & \(e_{1}\) & \(\theta_{m1}-\theta_{r1}\) \\ \(\theta_{m2}\) & \(\alpha_{2}-\alpha_{0}\) & \(\theta_{r2}\) & \(e_{2}\) & \(\theta_{m2}-\theta_{r2}\) \\... &... &... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation of the parameters.  
Figure 19: Repetitive error curves.  
Figure 18: Schematic diagram of the experiment principle.  
Based on the repeatability error curve, the error average value is obtained, and the error model is established by using linear fitting method. The related models is shown in Figure 20 and results is shown in Figure 21:  
According to the results, after compensation, the system angle measurement accuracy is better than \(\pm 10\)\" and the repeatability accuracy is better than \(\pm 10\)\".
# Internet of Things for Sensing: A Case Study in the Healthcare System
## 2 Related Work  
Human activity recognition for various applications has attracted much attention from researchers around the world. Applications include fall detection [8], activity detection for energy saving at homes or offices [9], 24-hour sleep-wake monitoring in narcolepsy [10], a detection system for motion disorders in Autism patients [11], and other uses leveraging IoTs [12; 13; 14; 15; 16; 17; 18]. The methods introduced in [13; 14] leverage body sensor nodes powered by human energy harvesting and wireless sensor networks for remote patient monitoring. [PERSON] et al. [19], [PERSON] et al. [20], and [PERSON] et al. [14] introduced systems for monitoring vital signs in medical problems. Many chronic diseases as in [20; 21] can be forecasted and prevented using activity recognition. However, recording such information in a timely manner is a daunting task. Several methods have been proposed both in academia and in the industry for healthcare informatics and can be categorized as non-invasive sensors [22; 23; 24], and invasive sensors [25; 26; 27]. Currently, invasive sensors are a rich source of information as far as healthcare is concerned. However, some invasive sensors are uncomfortable and not suitable for everyone since patients suffering from [PERSON]'s disease, narcolepsy, skin diseases, and infants are not encouraged for wearing such sensors [22] or to be deployed on their bodies. [PERSON] et al. [27] propose RadioSense, a prototype system of ZigBee radio-based activity sensing leveraging Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 specifications. Thus, the non-invasive sensors might be the only solution for patients with the aforementioned diseases. Various technologies have successfully demonstrated the efficacy of non-invasive sensors. [PERSON] et al. [28] proposed a non-invasive system leveraging a pyroelectric infra-red detector. [PERSON] et al. [29] developed video-based system detecting various activities with good classification accuracy but raised privacy concerns. [PERSON] [23] utilized ultrasound echo to detect different human movements, but the system provided poor coverage range. [PERSON] et al. [30] used Doppler radar for fall detection and gait analysis with adequate performance. [PERSON] et al. [31] exploited an ultra-wide-band (UWB) radar system for vital signs monitoring. However, the aforementioned technique leveraging radar faces limitations such as infrastructure deployment, spectrum licensing, and the fact that it requires specialized hardware. This paper presents a novel method leveraging the S-Band sensing technique using IEEE 802.11 specifications operating at 2.4 GHz exploiting wireless devices such as a card, antennas, etc.