esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 8 Conclusions  
In this study, we proposed a novel strategy of clustering and self-healing for WSNs under jamming attacks, called FCPA. On average, cooperation strategies with power control transmit two times more information than those with fixed power. However, power control strategies without cooperation exhaust the energy rapidly. But, despite the benefit of integrating both techniques, FCPA outperforms the other self-healing and clustering strategies in terms of information transmitted and residual energy, thanks to the load balancing that it implements.  
Integrating cooperation and power control, named the PAC strategy, reduces the disadvantage of each approach. However, PAC does not balance the cooperation routes. Therefore, the network relay nodes spend their energy quickly, but FCPA effectively addresses this problem. The FCPA strategy surpasses the information transmitted by approximately 54.09% on average from the power control strategies in all scenarios. All the experiments confirmed that FCPA performs exceptionally in all the analyzed jamming scenarios.  
In future work, we will evaluate different jamming strategies and propose strategies adaptable to them. We also require studying strategies that allow us to face the presence of intelligent and mobile jammers. Additionally, we intend to address artificial intelligence techniques to predict the effects of non-constant jamming and generate alternative paths.  
Figure 17: Bar plot for residual energy in J and information transmitted in kb for scenarios with the jammer moving diagonally across the cluster. The tradeoff is worse than the previous scenarios.  
**Author Contributions:** Conceptualization, S.M.-S., N.L.-V., R.D.S. and C.V.-C.; methodology, S.M.-S. and N.L.-V.; formal analysis, S.M.-S., R.D.S. and C.V.-C.; software, N.L.-V.; validation, S.M.-S., R.D.S. and C.V.-C.; investigation, N.L.-V. and S.M.-S.; resources, N.L.-V. and S.M.-S.; writing--original draft preparation, N.L.-V.; writing--review and editing, S.M.-S., R.D.S. and C.V.-C.; Visualization, N.L.-V. and S.M.-S.; supervision, S.M.-S.; funding acquisition, S.M.-S., N.L.-V., R.D.S. and C.V.-C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors are grateful for the financial support given by ANID FONDECYT Iniciacion No. 11200659 \"Toward High Performance Wireless Connectivity for IoT and Beyond-5G Networks\" and in Brazil by CNPq.  
**Acknowledgments:** This research was partially supported in Chile by FONDECYT Iniciacion No. 11200659 and in Brazil by CNPq.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Rapid In-Field Volatile Sampling for Detection of _Botrytis cinerea_ Infection in Wine Grapes
## 3 Materials and Methods
### VOCs Detection Using GC-MS
#### 3.4.2 SPME Extraction Parameter  
Individual grape berries were carefully removed from bunches after whole-bunch volatile extraction and homogenised using a high-speed grinder (UltraTurrax T25 IKA Dispersers, Staufen, Germany). The grape homogenates were stored at \(-20\)\({}^{\circ}\)C until immediately prior to analysis. During SPME analysis, 2.5 mL of grape homogenate was mixed with 7.5 mL. Milli-Q water, 3 g sodium chloride, and 10 uL of the IS solution, as previously reported [5] in a 20 mL headspace glass vial (Agilent Technologies Ltd., Mulgrave, VIC, Australia). All samples were analysed in randomised order, and the vials were vortexed for 10 s before analysis.  
During SPME extraction, a DVB/CAR/PDMS fibre (Stableflex 23 Ga fibre; Sigma-Aldrich, Sydney, NSW, Australia) was used with an MPS2 XL Multipurpose Sampler (Gersett Inc., Linthicum, MD, USA), a Peltier sample cooler tray, and a heated agitator stirrer. Sample vials were placed into the Peltier sample cooler (4 \({}^{\circ}\)C) before analysis. Vials were subsequently transferred into the heated oven (65 \({}^{\circ}\)C), where they were incubated for 2 min before insertion of the SPME fibre for 64 min under agitation at 250 rpm. Injection of the SPME fibre into an ultra-inert straight SPME liner (0.75 mm; Agilent Technologies Ltd., Mulgrave, VIC, Australia) was performed at an inlet temperature of 260 \({}^{\circ}\)C in split-less mode for 1 min. The fibre was then baked for 10 min at 270 \({}^{\circ}\)C in a secondary injector.  
The calibration curves for SPME GC-MS were established using mixed standard solutions prepared in synthetic grape juice at six concentrations, in triplicate. The calibration curves and specific GC parameters were previously reported [5]. A mix of internal standards was added to each sample/standard vial to achieve the final concentration of hexanal-d12 (6.25 ug/L), 2-octanol (0.22 ug/L), naphehalene-d8 (0.10 ug/L), 2,4,6-trichlorovanisole-d5 (0.15 ug/L), and phenol-d6 (3.00 ug/L).
# GPS Multipath Analysis Using Fresnel Zones
## 4 Multipath Analysis from a Spatially-Limited Reflector Using Fresnel Zones
### Multipath Analysis
Figure 21: Observed (blue) and simulated (ocher) \(\delta\)-SNR time series of G19 for antenna A (**top** panel) and antenna B (**bottom** panel). The black line shows the elevation angle of G19 during the observation period. Except for the black curve during the rising phase of G14 at antenna A (cf. top panel of Figure 22), the correlation of the simulated and observed \(\delta\)-SNR time series corresponds to the visual analysis of Figures 20 and 21. The highest correlations can be found when the Fresnel zone is completely located on the roof. From the time from where percentage of overlap decreases (solid red line), the correlation also decreases. Especially in the setting phases of both satellites, this becomes visible. Furthermore, the high variation of \(C_{t}\) in the middle of the time series is reasonable, since in these periods, the Rayleigh criterion is not fulfilled. This leads to a higher probability of diffuse reflections, which are not modeled by the simulation. Nevertheless, since the correlation does not immediately drop at the borders between the grey and white areas, it is confirmed that the Rayleigh criterion cannot be interpreted as a sharp border between diffuse and specular reflection. The numerical analysis of the simulated and observed \(\delta\)-SNR time series substantiates the findings from the visual analysis. The percentage of overlap between the Fresnel zone and the reflecting surface is the crucial factor for multipath occurrence. Although the correlations show that the proposed approach is well suited for the analysis of the relationship between Fresnel zones and multipath occurrence, at the same time, limitations of this method are revealed. The simulation of the \(\delta\)-SNR time series heavily relies on the assumption of a horizontal and smooth reflecting surface. If these assumptions are violated, the observed \(\delta\)-SNR time series will differ from the simulations. For example a slight phase shift between both time series, as appears for antenna A during the rising phase of G14 (22:25 to 22:55), will directly lead to high differences and a decreasing correlation. Furthermore, the observed SNR-values can be influenced by effects, such as polarization loss or mismatch [36]. Although these effects are included in the attenuation factor \(\alpha\) used for the simulation of the \(\delta\)-SNR time series, they can also affect the correlation between observation and simulation.
## 8 Performance Comparison of Proposed Antenna  
The performance comparison of the proposed four-element mobile MIMO antenna with other mobile MIMO antennas is presented in Table 2.  
From the above table, it is observed that the proposed antenna outperforms the other smartphone MIMO antennas in terms of the following criteria:  
1. Four-port structure unlike other antennas [9; 10; 11; 12; 13; 14; 15; 16] that have two-port configurations.
2. In comparison to four-port antennas [17; 18; 19; 20; 21; 22; 23; 24; 25], the proposed antenna covers the entire sub-6 GHz 5G band along with the WLAN band (2.37-5.85 GHz) with SAR analysis carried out at two resonant peaks (3.5 and 5.1 GHz).
3. The antennas in [20; 21; 23; 25; 18] also cover the sub-6 GHz band; however, in [22; 23; 24; 25; 18], no SAR analysis is carried out. In [22] as well, the analysis of gain and SAR is missing.
4. The antenna in [21] has a lower bandwidth (3.4-3.6 GHz). Additionally, no bending analysis is carried out for any of the antennas.
5. Flexible structure, high gain, high inter-elemental isolation, and reasonable efficiency with all the diversity parameters such as ECC, DG, TARC, MEG, and channel capacity meeting the requirements of the MIMO antenna make the proposed antenna a good contender for future mobile devices. Scattering, transmission, and MIMO parameters matched well under normal as well as bending conditions.  
Figure 21: SAR investigation at (**a**) 3.5 GHz, (**b**) 5.1 GHz.
# The Influence of Sebum on Directional Reflectance of the Skin
###### Abstract  
The sebaceous glands are responsible for the secretion of sebum. Its function is to maintain a proper epidermal barrier and participate in metabolic processes within the epidermis. Excessive sebum secretion leads to the development of various seborrheic diseases. The aim of this study was to determine the in vivo correlation between the amount of sebum and the directional reflectance of the skin. Measurements were performed using a Sebumeter (Courage + Khazaka, Germany) and a directional hemispherical reflectometer (Solar 410, SOC, USA). It has been shown that the amount of sebum does not affect the directional reflectance of the skin at a wavelength of 335-380 nm. With an increase in the amount of sebum, the directional reflectance of the skin decreases at wavelengths of 400-540 nm and 480-600 nm. However, with an increase in the amount of sebum, the directional reflectance of the skin increases at wavelengths of 590-720 nm, 700-1100 nm, 1000-1700 nm, and 1700-2500 nm. The closest relationship between amount of sebum and directional reflectance of the skin was observed at a wavelength of 700-1100 nm. Reflecting/scattering radiation from the skin surface, depending on the sebum content, may be clinically significant not only in the context of exposure to solar radiation but also in the context of numerous therapeutic methods based on artificial sources of radiation. In this area, it is desirable for the radiation to penetrate the skin as effectively as possible. The obtained preliminary results confirm that the used method is an interesting alternative to spectroscopic methods.  
sebaceous glands; sebum; directional reflectance of the skin; visible light (VL) +
Footnote †: journal: Physics Letters B
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MIMO & Multiple-input multiple-output \\ ZF & Zero-forcing \\ MMSE & Minimum mean squared error \\ ML & Maximum likelihood \\ DetNet & Detection network \\ CSI & Channel state information \\ OAMP & Orthogonal approximate message passing \\ DNN & Deep neural network \\ SNR & Signal-to-noise ratio \\ SVD & Singular value decomposition \\ SER & Symbol error rate \\ ReLU & Rectified linear unit \\ QAM & Quadrature amplitude modulation \\ \end{tabular}
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Noise Levels** & \multicolumn{3}{c}{**PWV Mean \(\pm\) SD (m/s)**} & \multicolumn{3}{c}{\(p\)**Pairwise Comparisons (DSCP) \(p\)**} \\  & **G** (**N = 10**) & **R** (**N = 10**) & **V** (**N = 10**) & **(Corrected)** & **G-R** & **G-V** & **R-V** \\ \hline \multirow{2}{*}{10\%} & Lt & 2.74 \(\pm\) 0.27 & 2.79 \(\pm\) 0.35 & 1.13 \(\pm\) 2.56 & 1.000 & 0.993 & 0.102 & 0.087 \\  & Rt & 2.76 \(\pm\) 0.50 & 2.82 \(\pm\) 0.54 & 0.26 \(\pm\) 1.33 & 0.016 \({}^{*}\) & 0.938 & 0.004 \({}^{*}\) & 0.003 \({}^{*}\) \\ \hline \multirow{2}{*}{20\%} & Lt & 2.65 \(\pm\) 0.25 & 2.76 \(\pm\) 0.48 & 0.75 \(\pm\) 2.28 & 0.586 & 0.893 & 0.060 & 0.060 \\  & Rt & 2.80 \(\pm\) 0.50 & 2.92 \(\pm\) 0.43 & 0.19 \(\pm\) 0.99 & 0.001 \({}^{*}\) & 0.493 & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{2}{*}{30\%} & Lt & 2.67 \(\pm\) 0.19 & 3.12 \(\pm\) 0.77 & 0.24 \(\pm\) 1.78 & 0.109 & 0.684 & 0.037 \({}^{*}\) & 0.009 \({}^{*}\) \\  & Rt & 2.74 \(\pm\) 0.43 & 2.99 \(\pm\) 0.88 & 0.09 \(\pm\) 1.01 & 0.001 \({}^{*}\) & 0.951 & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{2}{*}{40\%} & Lt & 2.78 \(\pm\) 0.28 & 3.28 \(\pm\) 0.63 & 0.40 \(\pm\) 1.53 & 0.012 \({}^{*}\) & 0.219 & 0.007 \({}^{*}\) & 0.003 \({}^{*}\) \\  & Rt & 2.83 \(\pm\) 0.43 & 3.01 \(\pm\) 1.26 & -0.22 \(\pm\) 1.27 & 0.002 \({}^{*}\) & 0.493 & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{2}{*}{50\%} & Lt & 2.63 \(\pm\) 0.22 & 3.17 \(\pm\) 1.32 & -0.46 \(\pm\) 1.16 & 0.001 \({}^{*}\) & 0.493 & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\  & Rt & 2.82 \(\pm\) 0.54 & 2.95 \(\pm\) 1.29 & -0.22 \(\pm\) 0.71 & 0.001 \({}^{*}\) & 0.893 & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{2}{*}{60\%} & Lt & 2.74 \(\pm\) 0.30 & 2.76 \(\pm\) 0.72 & \(-0.19\) \(\pm\) 1.98 & 0.047\({}^{*}\) & 0.988 & 0.007 \({}^{*}\) & 0.009 \({}^{*}\) \\  & Rt & 2.86 \(\pm\) 0.48 & 3.15 \(\pm\) 1.54 & -0.67 \(\pm\) 0.83 & 0.001 \({}^{*}\) & 0.588 & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \hline \multirow{2}{*}{70\%} & Lt & 2.68 \(\pm\) 0.25 & 2.93 \(\pm\) 1.07 & 0.50 \(\pm\) 1.62 & 0.038 \({}^{*}\) & 0.540 & 0.007 \({}^{*}\) & 0.009 \({}^{*}\) \\  & Rt & 2.80 \(\pm\) 0.43 & 2.25 \(\pm\) 1.28 & 0.14 \(\pm\) 0.84 & 0.004 \({}^{*}\) & 0.951 & \textless{}0.001 \({}^{*}\) & 0.003 \({}^{*}\) \\ \hline \multirow{2}{*}{80\%} & Lt & 2.87 \(\pm\) 0.59 & 2.69 \(\pm\) 1.18 & 0.44 \(\pm\) 1.93 & 0.074 & 0.997 & 0.007 \({}^{*}\) & 0.018 \({}^{*}\) \\  & Rt & 2.89 \(\pm\) 0.56 & 2.11 \(\pm\) 0.97 & -0.14 \(\pm\) 0.88 & 0.002 \({}^{*}\) & 0.493 & \textless{}0.001 \({}^{*}\) & 0.002 \({}^{*}\) \\ \hline \multirow{2}{*}{90\%} & Lt & 2.71 \(\pm\) 0.56 & 1.00 \(\pm\) 1.94 & 0.22 \(\pm\) 1.13 & 0.003 \({}^{*}\) & 0.006 \({}^{*}\) & \textless{}0.001 \({}^{*}\) & 0.220 \\  & Rt & 2.77 \(\pm\) 0.53 & 2.33 \(\pm\) 2.10 & -0.43 \(\pm\) 0.98 & 0.0
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 5 Hybrid Cooperative Scheme for Energy Efficient WBAN
If the length between source and destination node is 26 cm, a direct link performs better up to a packet size of 600 bits after which two-way relay transmission gives maximum efficiency. For a link length between 26 and 50 cm, two-way relay transmission gives maximum EE for any number of bits in the packet. After 50 cm, the two-way relay link performs better for 1300 bits packet, which decreases to 800 bits for 51 cm and so on, packets with a greater number of bits move more efficiently using a one-way relay link after that. Figure 11: Energy efficiency vs. packet size for the proposed scheme (on-body communication). (**a**) energy efficiency vs. packet size for proposed scheme (on-body LOS communication); (**b**) energy efficiency vs. packet size for proposed scheme (on-body NLOS communication). Figure 10: Energy efficiency vs. packet size for proposed scheme (in-body communication).
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS)  
GC-MS analyses were performed using an Agilent 7890B series gas chromatograph (Agilent Technologies, Milan, Italy) coupled with an Agilent 5977A mass selective detector (MSD) equipped with an HP-5 MS capillary column (30 m \(\times\) 0.25 mm ID, 0.5 \(\upmu\)m film thickness, J&W Scientific Inc., Folsom, CA, USA). The desorption step was carried out in the splitless mode (4 min) with a programmed temperature from 60 \({}^{\circ}\)C to 250 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, with a final holding time of 15 min. Spectra were recorded in the electron impact mode (ionization energy, 70 eV) in a range of 15-550 amu at 2.9 scans/s. The identification of volatile compounds was achieved by comparing mass spectra with those of the data system library (NIST08, \(p\) > 90%), and, wherever possible, by comparing retention times (R.T.) and mass spectra with those of commercially available standards. Moreover, a mixture of a continuous series of straight-chain hydrocarbons, C5-C40 (Alkane Standard Solution C6-C40, Sigma Aldrich, Milan, Italy), was injected into an HP-5 MS column under the same conditions previously described for the pasta samples to obtain the linear retention indices (RIs) [36]. Component relative percentages were calculated based on GC peak areas. Each extract was analysed in triplicate.
## 2 Results and Discussion
### Removal of Uremic Toxins  
The investigation of the capacity of the bulk materials to trap uremic toxins from human blood sera was performed using the system shown in Figure 7. Serum samples (s) were pumped by using an in/out 2-channel peristaltic pump (p) with a 0.5-15 mL/min flux speed. A lab-made (5 cm \(\times\) 15 cm) cartridge-like glassy cylinder, containing different amounts of sorbing materials, was positioned after the p element, and a collection (c) processing in vials was used for HPLC analyses, coupled with mass spectrometry as explained in the experimental section.  
Sorbent adsorption capacity for IS and PCS was expressed as removal percentages (%RR) (Figures 8-10), exploiting a quantification approach as reported in the experimental section. The investigated variables of the samples were: use of single and double poly-dopamine coatings, mass of the sorbing bulk materials, kinetics definition using different  
Figure 7: Scheme of the samples processing from the pumping to the HPLC investigation and detection.  
times of adsorption of the toxic molecules. Considering the same quantity of sorbent (20 mg), after 30 min at room temperature, the single PDA layer increased the sorbent capacity of both DE and PVP-co-S compared to that of bare controls, while the double-coated PDA layer inhibited the adsorption of both IS and PCS only in the DE case (Figure 8). This phenomenon could be ascribed to a general clogging of the silica structures, which had been previously predicted.  
Figure 8: Percentage of removal (%RR) of IS and PCS in relation with coatings (+: 1 layer; ++: 2 layers). Control used: both uncoated materials (DE; PVP). Amount of each sorbent used: 20 mg; time of incubation at room temperature: 30 min. Mann–Whitney U test validation (left graph for PVP and DE): ctrl vs. (+) with \(p<0.05\); vs. (++) with \(p<0.05\); (right graph for DE): with \(p<0.05\). Significance for PVP only between ctrl and (++).  
Figure 9: Percentage of removal (%RR) of IS and PCS in relation with different quantities of sorbent materials (20, 50, 100 mg). Time of incubation at room temperature: 30 min. Mann–Whitney U test validation (left graph for DE): (20) vs. (50) vs. (100) with \(p<0.05\); significance for PVP only between (20) and (100); (right graph for both PVP and DE): (20) vs. (50) vs. (100) with \(p<0.05\).  
When we look at the amount of sorbent material, taking into account the best performing materials (DE+ and PVP+, with one layer of PDA), an increase in quantity seemed to affect the adsorption capacity of both materials for IS and PCS, maybe due to a more pronounced packing effect of the matrix, which reduced the analyte diffusion into and out of the structures (Figure 9). Finally, the time of incubation mostly influenced the removal of both uremic toxins by PDA-DE rather than PVP-co-S (both with one coating of PDA, Figure 10). Both sorbents seemed to exercise their maximum function in the first 30 min, after which a gradual release of toxins was observed, more evident for DE.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications
### Wind Direction  
Although they were initially studied experimentally using aircraft-based instrumentation, Refs. [88; 89] demonstrated that GNSS-R measurements were sensitive to not only wind speed, but wind direction as well. Although this initial and subsequent airborne analysis (e.g., [41; 90]) showed promise, spaceborne wind direction applications have not developed at the same pace as wind speed applications. Wind direction is a more challenging retrieval problem than spaceborne platforms because of signal-to-noise requirements and solution ambiguities. Recently, several studies have demonstrated the Doppler difference in the DDM to be sensitive to wind direction [91; 92].
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Ins-MODY (mody 10)  
The insulin (_INS_) gene encodes for pro-insulin, and its mutation can result in primary defects in nuclear factor Kappa-light-chain-enhancer of activated B cells (NF-kB) [40; 87]. Heterozygous gene mutations in the _INS_ gene results in MODY 10, which is characterized by decreased beta cell mass, gradual loss of insulin secretion, and variable-onset diabetes mellitus. Although dominant misfolding mutations in the _INS_ gene are a frequent cause of isolated permanent neonatal diabetes, the onset age of the disease varies [88]. These mutations result in a severe folding defect, an abnormal response to unfolded proteins, and \(\beta\)-cell apoptosis [89]. During the time of diagnosis, diet or OADs may be used as treatment for patients with MODY, but they eventually become insulin dependent [88; 90].
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
## 2 Synthetic Strategies of Mn-Zn Ferrites
### Sol-Gel Method  
In the sol-gel method, a colloidal suspension of solids undergoes a process towards hydrolysis and condensation polymerization reactions to form a gel phase. In a typical reaction, the precursor is dispersed in a host liquid either by shaking, stirring or sonication, and the resultant system contains a liquid and a solid phase. Phase separation is carried out to recover the nanoparticles by various methods, such as sedimentation, filtration and centrifugation and the moisture is further removed by drying. Usually, the sol-gel method can be used for the preparation of a variety of ferrite nanoparticles in the stated temperature range. Typically, a citrate-gel is used as a template for the synthesis. The control of some parameters is crucial to obtain monophase Mn-Zn ferrite nanoparticles, as the pH of the precursor solution, the temperature of the process and the stoichiometry of the ferrite. Additionally, in this case, the best nanoparticles were observed with a Mn/Zn ratio between 1 and 1.5 [39; 40].  
Scheme 1 summarizes all the reported synthetic methods, with some representative TEM images of obtained nanoparticles.