esa-sceva/satcom-chunk-collection huggingface dataset:
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 5 Conclusions  
In this paper, a cognitive and dynamic channel algorithm named RL-CAA has been presented. An optimized channel can give better throughput and QoS as shown by our simulation results. In static environments, where the sensor nodes are not much mobile, computationally expensive algorithms are not much required as can be seen in our simulation results for Case A. In highly demanding environments, which can be due to the change of the throughput requirements on the run or due to the sensor nodes' frequent handovers, the RL-CAA is a better choice for C-BANs. The static channel assignment algorithm cannot keep up with the requirements of volatile network conditions. However,  
Figure 16: (**a**) Comparison of individual dissatisfaction probabilities of 5–50% throughput margin values; (**b**) Comparison of total averaged dissatisfaction probabilities for 5–50% throughput margin values.  
the RL-CAA has high computational overheads, i.e., complexity, memory requirements, execution time, etc. Hence, it is recommended to use it in an application where the throughput and QoS requirements must not be compromised.  
Our proposed RL-CAA presented here is evaluated for a specific health related application, but it is largely application-independent. It can be used in other types of sensor networks applications with little or no modification to the internal structure of the algorithm. Due to its unsupervised learning approach, the RL-CAA is well suited for distributed WSNs where there is no need for a central controller or coordinator to supervise spectrum sharing.  
The power optimization algorithm gives better performance by reducing the transmission power and better performance in terms of interference. The radio module is one key component which drains the batteries of the sensor nodes. Our proposed power optimization algorithm saves up to 4.4973 dBm in transmission power. Reducing the transmission power can increase the battery life of the sensor nodes. The algorithm is most suited for base stations or gateway nodes, which are not mobile or not changing their positions over longer periods. It is not computationally intensive so it is quite suitable for resource-limited platforms. The downside of the algorithm is that it requires the path loss matrix to be calculated and stored in memory before executing the power optimization algorithm.
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 5 Results and Discussions  
Figure 6a-e show the measured reflection coefficients of five kinds of materials in the D-band range, compared with the theoretical values using modified Fresnel reflection Formula (7). These symbols represent the measured reflection coefficients, which are calculated by the reflection loss [\(P_{ref}\) (dBm) \(-\)\(P_{LoS}\) (dBm)] in Table 6 using (3), whereas the group of broken lines denotes the theoretical results. What's more, the black solid lines are plotted on the basis of (7) and previous research about relative permittivity shown in Table 3. They act as reference lines for more intuitive comparison and analysis. \(|\Gamma|_{T}\) in legends represents the theoretical reflection coefficient and \(|\Gamma|_{M}\) denotes the measured value. The optimal estimated relative permittivity \(\epsilon_{r_{E}}\) and surface roughness \(h_{rms}\) of the materials in the full D-band range are summarized in Table 7. \(\epsilon_{r_{R}}\) represents the results of previous research. MMSE\({}_{ave}\) is the average minimum mean square error between the measured \(|\Gamma|_{M}\) and theoretical \(|\Gamma|_{T}\) from 110 GHz to 170 GHz.  
Figure 5: (**a**) The indoor reflection measurement campaigns in D-band for five materials, (**b**) Wood, (**c**) Plexiglass, (**d**) Drywall (plaster), (**e**) Concrete board, (**f**) Red brick.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Size (cm \(\times\) cm)** & **Radius of First Fresnel Zone in D-Band (cm)** \\ \hline Wood & 49.2 \(\times\) 35.8 & \\ Plexiglass & 88.2 \(\times\) 42.9 & \\ Drywall (plaster) & 56.1 \(\times\) 37.5 & 2.10–2.61 \\ Concrete board & 59.7 \(\times\) 39.3 & \\ Red brick & 45.6 \(\times\) 31.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Size of Materials and First Fresnel Zone.
## 4 Results: Path Loss
### Calculated PL versus Simulated PL in Layered Tissues Equivalent to the Adult Frontal Thorax  
The results obtained from the configuration mentioned in Section 2.2 are discussed here. Figure 7 shows the PL in the lossy layered medium obtained by calculations and MoM (FEKO) and FDTD (SEMCAD-X) simulations. The simulations, as well as the calculations from the simplified PL model display a standing wave pattern in the layered medium. The standing wave behavior is caused by the reflections of the propagating waves at different tissue layer interfaces [16]. It is noted that for this configuration, also the interface of the insulation of the receiving antenna is responsible for the wave pattern seen in Figure 7. Since the dielectric properties of the antenna insulation (\(\epsilon_{r}=2.07\) and \(\sigma=0\) S/m) are very different from that of the lossy human tissue layers, it causes a pronounced standing wave pattern. In spite of developing a 1D simplified PL model and comparing it with a 3D-model, the PL model shows very good agreement with the simulations and shows a maximum deviation of 3.3 dB and 2.8 dB and an average deviation of 2.9 dB and 2.5 dB with that of the SEMCAD-X and the FEKO simulations, respectively.  
Figure 6: Simulated and modeled PL of the insulated dipole in an air-muscle configuration _versus_ the separation distance between antennas.  
Figure 7: Simulated (FEKO and SEMCAD-X) and PL model of the insulated dipole in multilayer lossy medium _versus_ the separation distance between antennas.  
For simulations carried out in the layered medium, the time required for a sample size of 30 antenna separations is 22,320-times more than that required for calculation using the PL model, thus saving valuable time (for a system with a Windows 7, 64-bit operating system, with an Intel CPU at 2.50 GHz and 64 GB of RAM). There is a deviation of 2 dB between the two simulations in Figure 7, and this can be attributed to the way the antennas and the layered medium are modeled. In FEKO, the length of the layers are modeled as infinite medium, while that in SEMCAD-X is finite and equal to 40 cm.  
Next, the applicability of the PL model in the heterogeneous human body is verified.
## 3 Model Formulation
### Covariate Selection Process
#### 3.4.3 Akaike Information Criterion and Bayesian Information Criterion  
Two other statistical measures for model selection are the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which balance goodness of fit with model complexity. For both criteria, lower values indicate a better model fit, with AIC applying a moderate complexity penalty and BIC imposing a stronger penalty. These criteria assist in identifying the most appropriate model.  
\[\text{AIC}=-2\ln(L)+2k, \tag{5}\]  
\[\text{BIC}=-2\ln(L)+k\ln(n), \tag{6}\]  
where \(L\) is the maximum value of the likelihood function of the model, \(k\) is the number of parameters in the model, and \(n\) is the number of observations.
## 6 Conclusions  
The spherical scattering electrical field of the SSEP formed during probing was modeled by a 3D seven-point finite difference method, and the characteristics and law of surface charge distribution on the probing ball of the SSEP were analyzed and verified with FEM simulation. Analysis results indicate that the surface charge on the probing ball of SSEP has the characteristic of concentrating towards the probing point. When a probing ball of \(\phi\)1 mm in diameter probes a grounded plane with a microprobing gap of 0.3 \(\upmu\)m, more than 31% of surface charge on the probing ball concentrates in the aiming area, which covers 1% of the total probing ball surface. The surface charge concentration remains the same when surfaces of different geometrical shapes are probed with different probing gaps in the sensing range, demonstrating the approximate point sensing capacity of the SSEP. Together with its non-contact and 3D isotropy sensing capability as well as nanometer resolution, the SSEP has all the necessary characteristics required for precision measurement of miniature internal structures with high aspect ratios, showing great potential to remedy the lack of measurement methods for internal structures with aspect ratios over 10:1. Our future work will focus on miniaturization of the SSEP.  
Conceptualization, J.C. and X.B.; Funding acquisition, J.C.; Methodology, X.B. and J.C.; Project administration, J.C. and J.T.; Software, X.B. and Y.L.; Validation, Y.Z. and Z.C.; Writing--original draft, X.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 51675139, L1924059 and the Fundamental Research Funds for The Central Universities. The authors declare no conflict of interest.
# Multi-Label Classification for Predicting Antimicrobial Resistance on _E. coli_
## 2 Predicting Antimicrobial Resistance
### Data Collection  
The data were collected from research carried out by researchers from the University of Giessen, and a collection of data from environmental sources [20; 21]. Four antibiotics were used in this research. Ciprofloxacinacin (CIP) is a fluoroquinolone antibiotic used for the treatment of several bacterial infections including skin infections, urinary tract infections, and respiratory tract infections. CIP is effective against gram-negative bacteria, including _E coli_. Both cefotaxime (CTX) and ceftazidine (CTZ) are cephalospororin antibiotics used for the treatment of urinary, abdominal, and respiratory infections, as well as the majority of gram-positive and gram-negative bacteria such as meningitis. Gentamicin (GEN) is an aminoglycoside antibiotic used to treat several infections caused by gram-negative bacteria and severe bacterial infections.  
The data include human and animal samples of _E coli_. The data were collected from a total of 987 whole-genome sequences of _E. coli_ containing the resistance information of CIP, CTX, CTZ and GEN. The additional data from environmental sources include samples curated from the environment and ecological sources, notably the diagnostic laboratory at Bhuddhasothom University Hospital and canals surrounding the hospital, the Cambridge University Hospitals NHS Foundation Trust, and the Bacteria Resistance Surveillance Programme (BSAC collection) sewage plant. The samples collected from human isolates included urine and blood samples from infected patients.
## 2 SPME-GC/MS Methods
### _\"Foxy Smelling Compounds\" and 3-Alkyl-2-Methoxyprazines in Grape Juice_
SPME-GC/MS of 3-alkyl-2-methoxypyrazines in grape juice and wine was performed by using DVB/CAR/PDMS, PDMS/DVB and CAR/PDMS fibers [69, 70, 71, 72]. Also, a SPME-GC/MS and multiple mass spectrometry (MS/MS) method for simultaneous determination of _o_-AAP, MA, and the main four 3-alkyl-2-methoxypyrazines [ethylmethoxypyrazine (ETMP), IPMP, SBMP, and IBMP] in grape juice was proposed [73]. Quantitation of methoxypyrazines was performed on the signal area of MS/MS ions at _m/z_ 119 (for ETMP), _m/z_ 109 (IPMP), _m/z_ 81 (IBMP), _m/z_ 81 (SBMP) using 2-ethoxy-3-isopropylpyrazine as internal standard. For quantification of _o_-AAP and MA, as internal standard 2,4-dichloroeniline was used. The optimized HS-SPME experimental conditions are described in Table 5 and GC/MS conditions in Table 6[73]. \begin{table}
\begin{tabular}{l l c c} \hline \hline GC column & HP-5 ms: (5\%-phenyl) methylpolysiloxane (30 m \(\times\) 0.25 mm i.d; 0.25-um film thickness) \\ Carrier gas & Helium at constant flow 1.2 mL/min \\ Injector & 250 \({}^{\circ}\)C \\ Oven program & 40 \({}^{\circ}\)C for 5 min, 5 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, held 3 min \\ MSD conditions & Ionization energy 70 eV, transfer line temperature 280 \({}^{\circ}\)C, ion source 250 \({}^{\circ}\)C, ion trap in MS/MS mode \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & & IT-MS/MS \\  & & Precursor ion & MS/MS signal \\ \cline{2-4} Analyte & MW & GC retention time (min) & _m/z_ \\ \hline
3-ethyl-2-methoxypyrazine & 138.17 & 15.10 & 138 & 119 \\
3-isopropyl-2-methoxypyrazine & 152.20 & 16.46 & 137 & 109 \\
3-isobutyl-2-methoxypyrazine & 166.22 & 18.90 & 124 & 81 \\
3-sec-butyl-2-methoxypyrazine & 166.22 & 19.14 & 138 & 81 \\
2-ethoxy-3-isopropylpyrazine (IS) & 166.22 & 18.45 & 166 & 123 \\ methyl anthralate & 151.16 & 23.71 & 151 & TIC \\
2’-aminoacetophenone & 135.16 & 22.59 & 135 & TIC \\
2,4-dichloroeniline (IS) & 162.02 & 23.35 & 161 & TIC \\ \hline \hline \end{tabular}
\end{table} 
Table 6: GC/MS conditions used for simultaneous analysis of _o_-AAP, MA and 3-alkyl-2-methoxypyrazines in grape juice [73]. MW: molecular weight. \begin{table}
\begin{tabular}{l l} \hline \hline SPME fiber & 50/30 \(\upmu\) divinylbenzene/CarboxenTM/polydimethylsiloxane (DVB/CAR/PDMS) \\ Sample volume & 10 mL \\ Vial volume & 20 mL \\ Addition to the sample & 3 g NaCl \\ Extraction temperature and time & 50 \({}^{\circ}\)C for 30 min \\ Desorption temperature and time & 250 \({}^{\circ}\)C fo 5 min \\ Fiber cleaning & 10 min \\ \hline \hline \end{tabular}
\end{table} 
Table 5: HS-SPME conditions used for simultaneous analysis of “foxy smelling compounds” (_o_-AAP and MA) and 3-alkyl-2-methoxypyrazines (ETMP, IPMP, IBMP, and SBMP) in grape juice [73].
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 1 Introduction  
Since its development in 1989 by [PERSON] and [PERSON] [1], solid-phase microextraction (SPME) has attracted increasing attention, being nowadays considered one of the most widely applied sample pretreatment techniques [2; 3; 4]. Its unique features, integrating sampling, extraction, preconcentration, and desorption into one single step, have provided noticeable advantages in terms of extraction efficiency, reduced sample handling, time consumption, and ease of use [5], thus enabling the development of fast, efficient, and high-throughput analytical methods [6; 7]. Both the principles and the different geometries of SPME technologies and devices have been discussed in already-published reviews [2; 3; 8]. Environmental friendliness is another key feature of SPME, being that the reduced consumption of organic solvents, the prevention of waste generation, the automation, miniaturization, and possibility of in situ measurements are some of the criteria that perfectly comply with the principles of green analytical chemistry [9; 10]. Therefore, SPME has been widely applied in different research fields, including environmental monitoring [6; 11; 12], food [13; 14], forensics [15; 16], pharmaceutical [15; 17], and bio-analysis [4; 18; 19; 20; 15].  
The performance of SPME in terms of selectivity and extraction efficiency is mainly related to the absorption/adsorption capacity of the coating [5; 21]. Commercial coatings such as polydimethylsiloxane (PDMS), polydivinylbenzene (DVB), polyacrylamide (PA), carboxen (CAR), and their combinations are usually characterized by low selectivity and extraction efficiency. To overcome these limitations, molecular hosts as well as nanostructured and porous materials able to promote an enhanced selectivity via size-exclusion mechanisms and oriented intramolecular interactions [6; 21; 22] have been proposed as novel SPME coatings, providing more selective and efficient solutions [5; 7; 23; 24; 25]. In this context, the functionalization of the material can both strengthen the interactions between the materials and the target compounds or improve analyte segregation.  
Owing to the designed 3D architecture and adsorption capacity driven by chemical and size synergistic complementarity, supramolecular receptors have emerged as alternative coatings cable of strongly affecting both the selectivity and extraction capabilities of SPME. The aim of the present review is to cover the state of the art of the last 5 years, summarizing the main advances in the development of SPME coatings based on metal organic frameworks (MOFs), covalent organic frameworks (COFs), and supramolecular macrocycles, including cyclodext (CDs), calixarenes, and cavitands, for environmental monitoring. In vivo SPME applications are not discussed since reviews focused on the matter have been very recently published [26; 27; 28].
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.4 Sensium  
Sensium [68] is an ultra-low power wireless platform mainly designed to provide customized health services for chronic disease management applications [69]. Sensium is capable of providing an ultra-low power monitoring of vital health signs such as PH levels, blood glucose and ECG signals.  
The main aim of Sensium is to be embedded in a digital plaster to be prescribed by physicians. Sensium operates in the 900 MHz frequency band and supports a data rate of 160 kb/s. Sensium is considered as one of the leading ultra-low power wireless technologies for low data-rate on-body applications. Sensium utilizes a master/slave communication structure, in which an on-body slave node transmits multiple vital signs to a personal server such as smart phone, PDA or a personal computer from time to time. Since sensitium utilizes a star topology, joining a network is managed centrally. Energy consumption of nodes is also managed centrally; nodes are programmed to keep their radios in sleep mode until they are given time slots by central server.
# Provision of Energy- and Wavelength-Efficient Traffic
## 4 Simulation and Performance Evaluation
### Analysis of Convergence Property  
Figure 8 is the comparison of convergence property between the TPTG_MA and DLG_GA. As can be seen, the convergence of the DLG_GA appears at the 50 th iteration, while the proposed TPTG_MA begins to converge at about the 65 th iteration. This is due to the massive compare-and-swap operations in the process of flow matching for the TPTG_MA.  
Figure 8: Comparison of convergence property between the TPTG_MA and DLG_GA.  
Figure 7: Comparison of the blocking probability between the DLG, TPTG, DLG_GA, and TPTG_MA.
###### Abstract  
Recent volcanic gas compilations have urged the need to expand in-situ plume measurements to poorly studied, remote volcanic regions. Despite being recognized as one of the main volcanic epicenters on the planet, the Vanuatu arc remains poorly characterized for its subaerial emissions and their chemical imprints. Here, we report on the first plume chemistry data for Mount Garet, on the island of Gaia, one of the few persistent volatile emitters along the Vanuatu arc. Data were collected with a multi-component gas analyzer system (multi-GAS) during a field campaign in December 2018. The average volcanic gas chemistry is characterized by mean molar CO\({}_{2}\)/SO\({}_{2}\), H\({}_{2}\)O/SO\({}_{2}\), H\({}_{2}\)S/SO\({}_{2}\) and H\({}_{2}\)/SO\({}_{2}\) ratios of 0.87, 47.2, 0.13 and 0.01, respectively. Molar proportions in the gas plume are estimated at 95.9 \(\pm\) 11.6, 1.8 \(\pm\) 0.5, 2.0 \(\pm\) 0.01, 0.26 \(\pm\) 0.02 and 0.06 \(\pm\) 0.01, for H\({}_{2}\)O, CO\({}_{2}\), SO\({}_{2}\), H\({}_{2}\)S and H\({}_{2}\). Using the satellite-based 10-year (2005-2015) averaged SO\({}_{2}\) flux of \(\sim\)434 t d\({}^{-1}\) for Mt. Garet, we estimate a total volatile output of about 6482 t d\({}^{-1}\) (CO\({}_{2}\)\(\sim\)259 t d\({}^{-1}\); H\({}_{2}\)O \(\sim\)5758 t d\({}^{-1}\); H\({}_{2}\)S \(\sim\)30 t d\({}^{-1}\); H\({}_{2}\)\(\sim\)0.5 t d\({}^{-1}\)). This may be representative of a quiescent, yet persistent degassing period at Mt. Garet; whilst, as indicated by SO\({}_{2}\) flux reports for the 2009-2010 unrest, emissions can be much higher during eruptive episodes. Our estimated emission rates and gas composition for Mount Garet provide insightful information on volcanic gas signatures in the northernmost part of the Vanuatu Arc Segment. The apparent CO\({}_{2}\)-poor signature of high-temperature plume degassing at Mount Garet raises questions on the nature of sediments being subducted in this region of the arc and the possible role of the slab as the source of subaerial CO\({}_{2}\). In order to better address the dynamics of along-arc volatile recycling, more volcanic gas surveys are needed focusing on northern Vanuatu volcanoes.  
Vanuatu; Gaia; Mount Garet; Multi-GAS; volcanic gas compositions; volatile fluxes 29 September 2020; Accepted: 15 October 2020; Published: 19 October 2020
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 2 Wi-Fi Basics
### MAC in Wi-Fi
#### 2.2.3 Power Management in Legacy Wi-Fi  
The use of wireless devices in smart homes, industry, healthcare, etc., has considerably increased in recent years and many wireless devices use a battery as a power supply [1]. Therefore, power efficiency is a critical issue in Wi-Fi. To save energy, legacy Wi-Fi introduced the Power-Saving Mode (PSM) [17]. PSM-enabled stations alternate between active and doze modes. Stations in active mode are ready for transmission or reception. Stations in doze mode save energy by turning off their radio chipsets and are thus unable to transmit or receive packets.  
PSM-enabled stations inform the access point that they have started power-saving mode. The access point then starts buffering the packets destined for this sleeping station.  
Figure 6: Channel access using the Distributed Coordination Function (DCF) in Wi-Fi.  
The access point piggybacks the list of pending destinations (Traffic Indication Map (TIM)) in its beacons. Thus, stations wake up regularly to receive beacons to check whether the access point has buffered packets for them. Since the beacon interval is indicated in the beacons, a station knows when the next beacons will be transmitted and wakes up slightly before their transmission to cope with clock drifts. More precisely, a station must wake up to receive at least one beacon at every Delivery Traffic Indication Map (DTIM) interval.  
When a station decodes the beacon, the station knows if some packets are pending. In the absence of pending packets, the station may switch immediately to doze mode again. Otherwise, it stays active and notifies the access point that it is ready to receive packets. If the station has packets to send, it stays active and uses DCF for transmission.
# Rheological Properties of Lunar Mortars
## 1 Introduction
It has been studied indirectly because, for obvious reasons, it was not possible to use an original lunar regolith, and therefore, the lunar regolith simulant (LRS) that mapped the grain size distribution of the real regolith was used instead. The used lunar regolith simulant was created for the purpose of studying the erosive effect of dusty regolith fractions on the moving parts of lunar landers and other mechanical equipment; therefore, it simulated well the behavior of regolith particles in relation to cement paste. Additionally, the research itself was aimed at the rheological characterization of concrete mixes with the lunar regolith simulant, important from the point of view of the technology of making elements on the Moon. The obtained results made it possible to develop preliminary compositions for \"lunar mortars\" with w/c ratio possible to apply in e.g., 3D concrete printing and to prepare, test, and evaluate mortar properties in comparison to traditional quartz mortars (under the conditions of the Earth laboratory).