esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Temperature-Based Routing
#### 3.2.2 A New Energy-Efficient Routing Protocol (ER-ATTEMPT)  
[PERSON] et al. [22] proposed an ER-ATTEMPT protocol, which not only avoids the nodes with high temperature, but also chooses the path with the minimum hops as the best route. This protocol is an optimization of the ATTEMPT protocol, as the protocol of ATTEMPT does not take into account the unavailability of the current path, and ER-ATTEMPT protocol is optimized on this basis. In the initial stage, the node knows all the available paths by exchanging HELLO messages. If the current  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Goal** & **Characteristic** & **Complexity** & **Delay** & **Energy Efficiency** \\ \hline \multirow{4}{*}{MHRP (2017)} & \multirow{4}{*}{\begin{tabular}{c} Dynamic \\ environment \\ practitioner \\ \end{tabular} } & \begin{tabular}{c} A fault-tolerant system \\ consists of two \\ identical and \\ independent sets of \\ nodes \\ \end{tabular} & low & high \\ \hline \multirow{4}{*}{NCMD (2017)} & \multirow{4}{*}{\
\begin{tabular}{c} Dynamic \\ environment \\ Topological fracture \\ treatment \\ \end{tabular} } &
\begin{tabular}{c} Opportunities to \\ establish connections, \\ Minimizing network \\ management \\ \end{tabular} & high & low & high \\ \hline \hline \end{tabular}
\end{table}
Table 2: Posture-based routing protocols comparison.  
Figure 6: Temperature-sensing routing.  
path is not available, then it chooses the sub-optimal path as the transmission path to ensure a reliable transmission. The message format is shown in Figure 7.  
ER-ATTEMPT protocol also pays attention to the deployment of nodes, nodes are placed orderly, according to the level of energy. Initially, the energy is unbalanced, and relay nodes are equipped with more energy, which makes it easier to forward data from other nodes. In the routing stage, when the node generates normal data, relay transmission is used to find the best path, with the least hops, in the optional routing table. When the node produces abnormal data, the emergency transmission mode, which communicates directly with the sink node, is adopted. This routing method can not only ensure the timely transmission of emergency data, but also realize the reliable transmission of general data. However, there may be some problems--direct communication with sink will increase the probability of packet loss, due to long distance, and will also consume a lot of energy. It is not the best choice to ensure low latency at the cost of energy and reliability.
## 2 MIP Application
### Drug Delivery  
Drug-delivery systems (DDS) are a method of delivering drugs to the desired place before drug release and absorption so as to increase the pharmacological activity of the drug and achieve the desired therapeutic effect [46; 47]. DDS must be able to control the amount and speed of drug release [46]. MIP as a DDS agent offers several advantages, namely long shelf life, easy preparation, high chemical, and physical stability, and low cost [39].  
[PERSON] et al. [48] evaluated the ability of drug delivery to cancer cells using an enantioselective receptor for (R)-thalidomide enantiomer designed in nanoparticles using methacrylic acid, a fluorescently active 2,6-bis(acrylamido)pyridine and N,N\({}_{0}\)-methylene-bis-acrylamide, via both a covalent approach and a physical approach. The results of his research show that MIP nanoparticles, through a physical approach, have the potential to make effective drugs to attack multidrug-resistant cells with the right temperature at the target location.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 2 Evolutionary-Based MPPT Algorithm for PV System Power Extraction
### PSO-Based Algorithm Implementation
In the example of Figure 3, a decrease of PV output power triggers a new search of the GMPP and at the end of the third iteration an update of \(D_{best}\), \(P_{PV,Gbest}\) occurs; \(N_{Gbest}\) is 4 and \(N_{Iter}\) is 8. Iterations from 3 to 6 are characterized by the same \(D_{best}\), thus the end of the sixth iteration triggers the check of the solution. The time required for the search of the GMPP, \(T\), is reported in Equation (5), and it depends on the number of particles to be evaluated in each iteration, \(N_{p}\), the number of iterations required to meet the convergence criterion, and the sampling time \(T_{s}\). In order to avoid the measurement of the PV output power during transients, \(T_{s}\) has to be larger than the power converter settling time. It is easy to prove that the search process requires a quite long time interval in which the PV generator works at different power values, even far from the GMPP. \[T=\big{(}N_{p}\cdot CounterITER+1\big{)}\cdot T_{s} \tag{5}\]  
Figure 3: Timing of the proposed PSO-based Maximum Power Point Tracking (MPPT) algorithm. Figure 2: Flowchart of the functions (**a**) “Check for power changes” and (**b**) “Check GMPP”.
## 2 Results and Discussion
#### 2.1.1 Furans and Furanones  
Furans are among the most abundant volatiles that characterize roasted coffee's aroma, exhibiting a sweet, roasted smell, and are produced via thermal degradation of carbohydrates, ascorbic acid or unsaturated fatty acids upon roasting [9; 10].  
A total of nine furans and furanones were detected at high levels in all roasted pit samples. In RS and P2, they amount for 5.9%-26.5%, of their aromatic compounds, including 3-furfurral (1), 2-acetyl-furan (2), dihydro-4-methyl-2(3H)-furanone (3), 5-methyl-2-furaldehyde (4), furfuryl acetate (5), 2-furfurylfuran (6), 5-methyl-2-furfurylfuran (7), diffurfuryl ether (8) and 2-furfuryl-methyl-disulfide  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Peak No.** & \(R_{t}\) **(min)** & **RI** & **Compound** & **RS** & \(\mathbf{P_{2}}\) & **C** \\ \hline Total sulfur compounds & & & & 6.42 & 42.34 & 0.37 \\ Aldehydes/ketones & & & & & \\
42 \({}^{\text{a}}\) & 10.31 & 1101.7 & Nonanal & \(2.20\pm 1.01\) & \(0.01\pm 0.01\) & \(0.37\pm 0.15\) \\
43 \({}^{\text{a}}\) & 11.73 & 1201.9 & \(n\)-Decanal & \(1.13\pm 0.76\) & - & - \\
44 \({}^{\text{a}}\) & 12.83 & 1284.4 & (E)-Cinnamaldehyde & \(1.90\pm 0.40\) & \(7.86\pm 10.28\) & \(5.68\pm 2.07\) \\
45 & 12.87 & 1287.9 & 2-Undecanone & \(1.03\pm 0.47\) & \(1.03\pm 0.75\) & \(1.70\pm 0.46\) \\ Total aldehydes/ketones & & & 6.25 & 8.89 & 7.75 \\ Hydrocarbons & & & & & \\
46 & 11.56 & 1189.5 & 2-Methylundecane & \(0.87\pm 0.04\) & \(0.15\pm 0.05\) & - \\
47 & 14.20 & 1396.3 & 4-Methylridecane & \(1.10\pm 0.04\) & \(0.03\pm 0.05\) & - \\ Total hydrocarbons & & & 1.97 & 0.18 & - \\ Unidentified volatiles & & & & & \\
48 & 12.17 & 1235.4 & Unknown & - & \(0.62\pm 0.61\) & - \\
49 & 20.06 & 1766.7 & Unknown & \(5.96\pm 3.80\) & - & - \\
50 & 20.62 & 1795.7 & Unknown & \(17.04\pm 7.90\) & - & - \\
51 & 20.85 & 1807.9 & Unknown & \(0.33\pm 0.10\) & - & 3.04 \(\pm\) 1.28 \\
52 & 21.25 & 1828.6 & Unknown & \(15.30\pm 11.24\) & - & - \\ Total unidentified volatiles & & & 38.63 & 0.62 & 3.04 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Representative SPME-GCMS chromatograms (\(R_{t}\) 5–16 min) of headspace volatiles derived from roasted pits (**A**), pit product one (**B**), pit product two (**C**) and coffee product (**D**). The corresponding volatile names for each peak follow those listed in Table 1.  
(40) (Figure 1 and Table 1). Furanones are formed mainly via Maillard reaction and subsequent aldol condensation, imparting a sweet, caramelly aroma to roasted seeds in general [9; 11]. All previous furans were also found in C samples with the sulfur containing furan (2-furfuryl-methyl-disulfide (40) though being detected for the first time in roasted coffee (Figure 1 and Table 1). The enrichment of furans in roasted date pit samples being similar to that of coffee justifies their similar characteristic, roasted, sweet aroma.
## 3 Regenerative Braking Torque Distribution and Control Strategy  
### Motor Regenerative Braking Force  
When the electric loader performs regenerative braking, the drive motor is in the generator state, the maximum regenerative braking torque is limited by the motor power and speed, and their relationship is as follows,  
\[T_{r}=\left\{\begin{array}{l}\frac{9550\text{Pr}_{n}}{n_{\text{T}}},\left(n_ {r}<n_{\text{N}}\right)\\ \frac{9550\text{Pr}_{n}}{n_{r}},\left(n_{r}>n_{\text{N}}\right)\end{array} \right., \tag{1}\]  
Figure 1: Dual motor independent drive scheme for powertrain.  
Figure 2: Schematic diagram of proposed brake valve.  
where \(T_{\rm r}\) is the motor regenerative braking torque, N\(\cdot\)m. \(P_{\rm n}\) is the motor rated power, kW. \(n_{\rm N}\) and \(n_{\rm r}\) are the motor rated speed and current speed during regenerative braking process, respectively, rpm.  
It can be seen from Equation (1) that when the regenerative speed is lower than the rated speed, the regenerative torque is constant. While when the regenerative speed is higher than the rated speed, the motor power is constant.  
The motor regenerative braking torque reaches the tire through the gearbox, drive shaft and drive axle assembly, and the torque relationship is  
\[T_{\rm rb}=T_{\rm m}\times i_{\rm t}\times i_{\rm s}\times i_{\rm d}, \tag{2}\]  
where \(T_{\rm rb}\) is the regenerative braking torque of one tire, N\(\cdot\)m. \(i_{\rm t}\), \(i_{\rm s}\) and \(i_{\rm d}\) are the transmission ratio of gearbox, drive shaft, and drive axle assembly, respectively.
## 4 Discussion
Despite these, many authors, to avoid the appearance of the artifacts in the final image, prefer to analyse the apparent resistivity data, instead of applying inversion algorithms. As described in paragraph two, in the last ten years an impressive number of papers concerning novel algorithms for resistivity data processing and inversion have been published. However, there are a limited number of cases in which these algorithms have been applied to analyze the TL-ERT data measured in field-experiments carried out for studyinglandslides. Probably, the use of these mathematical methods is quite complicated when applied to experimental data characterized by large variability in spatial and temporal domains, and affected by a high-level of anthropic noise. The TL-ERT data inversion must also consider the effect of the temperature in the first layers of the subsoil and, in active landslides, the effect of the electrode's movement. Corrective formulas have been introduced for both critical issues [78, 84]. A gap between the quality of the algorithms that are currently available for the TL-ERT data inversion and the methods routinely applied in the interpretation of the experimental resistivity data is quite evident. The use of advanced methods for data processing and inversion will disclose the possibility to apply the TL-ERT method in the monitoring of landslides occurred in urban areas and complex geological slopes. Except for a few studies [80, 85], all the acquisitions were carried out along 2D longitudinal profiles. This still represents a limitation of the method, as the investigated object has a 3D structure. Unfortunately, making 3D acquisitions is not easy and requires the use of manageable instruments. Other reviews [4, 42] have already highlighted this aspect, referring, as a possible solution, to the use of separate current injections and voltage measuring systems, but also to the experimentation of technologies that minimize the number of cables (smart sensors and web-based services for wireless sensor networks). Considering the difficulties that characterize 3D acquisition in landslide areas, it is well understood that these difficulties increase if we want to realize a 3D time-lapse ERT. Other weaknesses identified by the critical analysis of the works presented in the previous paragraph concern the TL-ERT's difficulty in investigating deep subsoil layers in landslide bodies. The ERT method can investigate shallow (0-20 m) and deep layers (>50 m) in landslide bodies, but there are no experimental tests in which the time-lapse monitoring systems have been able to obtain information about the resistivity patterns in deep environments. Generally, the amplitude of the time-dependent changes of resistivity values are higher in shallow layers, and strongly decrease in deep environments; however, the possibility to detect weak perturbations in the resistivity patterns of deep environments (50-100 m) could be very interesting and challenging. In the future, the design of TL-ERT monitoring systems to obtain a time series of 3D resistivity images with an investigation range of 0-100 m could open new research opportunities.
## 2 Construction of Productivity Prediction Method of Reservoir
### Construction of Fracture Permeability Index  
Fracture permeability is a crucial parameter for productivity prediction in metamorphic buried hill fractured reservoirs. The matrix of a buried hill reservoir is tight formation, with undeveloped primary pores. Thus fracture permeability is almost considered as reservoir permeability. However, in metamorphic rock reservoirs buried deep underground, heterogeneous condition is prevalent. Therefore, fracture permeability is not only challenging to determine with accuracy, but also the characterization form is complex.  
Based on previous CT scanning experiments of fractured cores, the influence of fracture parameters on permeability was investigated [6; 29], and we established a fracture permeability index based on the functional relationship between fracture parameters and permeability and combined with the plate flow theory to reflect the differences in permeability between reservoirs (Equation (6))  
\[K_{\text{i}}=\frac{W_{i}^{3}}{D}\cos\alpha \tag{6}\]  
where, \(K_{\text{i}}\) is fracture permeability, mD; \(W_{i}\) is fracture opening, um; \(D\) is fracture spacing, um; \(\alpha\) is fracture inclination, (\({}^{\circ}\)).  
In array logging data, Stoncley waves have the characteristics of low frequency and large amplitude and are sensitive to fractures, pores and holes. There are obvious reflected Stoneley waves at the fractures and layer interfaces. By separating the wave field, the direct Stoneley waves and reflected Stoneley waves are respectively subjected to a fast Fourier transform to obtain the spectrum to calculate the Stoneley wave reflection coefficient, The equation is expressed as [30]:  
\[\text{r}(f)=\frac{X_{R}(f)X_{D}^{*}(f)}{\text{max}\big{[}X_{D}(f)X_{D}^{*}(f),0.01E\big{]}} \tag{7}\]  
where, \(X_{D}(f)\) is the amplitude of the direct Stoneleigh wave, and \(f\) is the frequency; \(X^{*}{}_{D}(f)\) is the conjugation of \(X_{D}(f)\); \(X_{R}(f)\) is the amplitude of the reflected Stoneley wave, and \(E\) is the maximum peak of the direct Stoneley wave amplitude.  
Currently, a majority of scholars estimate the permeability of buried hill reservoirs composed of metamorphic rock through the interpretation and processing of Stoneley wave data. As the permeability of the reservoir section grows, the Stoneley wave amplitude decreases significantly while the coefficients of attenuation and reflection increase. Conversely, as the permeability of the reservoir section diminishes, the Stoneley wave amplitude rises while the coefficients of attenuation and reflection decrease. By using Stoneley wave data to calculate Equation (2) and comparing it with the reservoir permeability index, the accuracy of the verification is confirmed.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### Full-Duplex Jamming Practical Issues
#### 2.5.2 Energy Cost of Jamming  
The energy cost is one of the main drawbacks of employing jamming techniques for physical-layer security (PLS), and one of the reasons why third parties may not be willing to participate as jammers for the benefit of others. This motivates our setup, whereby the receiver [PERSON] acts himself as a jammer for his own security benefit, hence avoiding the need for jamming by third parties. However, this energy cost may still be problematic, particularly for power constrained devices. To reduce this cost, signalling mechanisms such as that proposed in [15] can be employed to coordinate [PERSON]'s jamming with the transmission from [PERSON], thus enabling [PERSON] to jam only when necessary.
## 3 Results
### Selection of the Best Inversion Strategy  
#### 3.3.1 Determination of the Number \(q\) Achieving the Best Inversion Performances  
For each variable, in order to assess the uncertainty associated with determining \(q\) using only validation data from a single date, \(q\) was determined both on a date-to-date basis and on a multidate basis.  
For LAI, ignoring the RMSE INT criterion which performed very poorly, all criteria seemed to have reached a plateau by \(q\) = 100 at most (see Table 7 and Figure 8a). As there was no obvious optimal \(q\) which could minimize RMSE, it was decided to consider the 100 best performing solutions for LAI estimation. While for summers 2014 and 2016, SAM INT LAI, NDVI, and MSAVI2 performed similarly, for fall 2013, only the VI-based criteria achieved acceptable performances (around 0.6 m\({}^{2}\)/m\({}^{2}\) for RMSE and SAM INT LAI, around 0.2 m\({}^{2}\)/m\({}^{2}\) for NDVI and MSAVI2).  
C\({}_{ab}\) results showed a similar plateauing behavior (see Table 7 and Figure 8b). Therefore, it was decided to proceed the same way as the LAI and to consider the 300 best performing solutions for C\({}_{ab}\) estimations, since the RMSE plateau was always reached at this point. The lowest RMSE for summer 2013 was obtained with TCARI/OSAVI (5.86 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\)). The lowest RMSEs for fall 2013 and summer 2014 were obtained with gNDVI (3.86 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\)) and GM_94b (2.89 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\)), respectively. MCARI2 performed very poorly for all dates, even more so than the RMSE and SAM criteria that at least showed acceptable performances for summer 2014.  
Figure 7: Computed Vegetation Index (VI) for each LUT entry over (**a**) LAI, (**b**) C\({}_{ab}\), and (**c**) Car. In color, distinction by CC. Only one example is given for each variable, as other VIs behaved the same.  
For Car, an optimal \(q\) value was found when singling out the summer 2013 data and using the RMSE INT CAR criterion. However, at the other dates, the RMSE INT CAR criterion barely reached its plateauing value by \(q=400\) (see Table 7 and Figure 8c), so the 400 best performing solutions were considered for the multidate Car estimations. RMSE INT CAR presented the lowest RMSE for summer and fall 2013, while only presenting acceptable performances for summer 2014. Only RMSE INT CAR and CRI consistently showed acceptable performances, with a slight advantage for RMSE INT CAR. Both SAM INT CAR and R515/R570 had important RMSE variability over the three available dates.
## 6 Establishing the Simulation Environment
### Experimental Setup
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network size (km\({}^{2}\)) & 10 \\ \hline Mobility model & Peak District \\ \hline Vehicle communication standard & (DSRC) IEEE 802.11 P \\ \hline Road Type & Multiple ways \\ \hline Transmission Range: R (m) & 250 \\ \hline Simulation Time (s) & 90,464 \\ \hline Total Number of vehicles & 21,650 \\ \hline Number of vehicles per kilometer & 10, 15, 20, 25, 30, 50 \\ \hline Vehicle Length & 2.5 \\ \hline Roadside Units (RSU) & 0, 1, 2, 3, 4, 5, 7, 10, 15 \\ \hline Overnight Connectivity Percentage & 0\% to 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. This scheme enables us to calculate and analyze communication patterns based on data collected from a 24 h simulation conducted under various parameter settings. The aggregated data allow us to answer various questions, including how effective our approach is in the scenario. Furthermore, this approach efficiently processed large datasets, allowing for a rapid assessment of dynamic communication patterns over time. This capability was crucial for understanding how different parameters, such as vehicle density and RSU placement, impact overall network connectivity and performance.
# Induction Coil Design Considerations for High-Frequency Domestic Cooktops
## 6 Discussion  
In this study, the effect of using WBG switches on the induction coil in domestic induction cookers is discussed in detail. This study is based on GaN HEMT power switches. Switching and conduction losses are minimized thanks to the WBG switch, making it  
Figure 20: Gate Driver Section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Induction** & & & & & & \\
**Cooker** & **Switches** & **Input Power** & **Switching** & **Conduction Loss** & **Switching Loss** & **Reverse** \\
**Operating Mode** & & **(W)** & **Frequency (kHz)** & **(W)** & **(W)** & **(W)** & **Losses (W)** \\ \hline \multirow{3}{*}{Conventional} & Si IGBT & \multirow{3}{*}{3700} & \multirow{2}{*}{20} & \multirow{2}{*}{24} & \multirow{2}{*}{1.27} & \multirow{2}{*}{0.52} \\  & IHW50N65R6 & & & & & \\ \cline{1-1} \cline{5-6}  
possible to work at higher frequency levels. In an induction cooker that operates at high frequencies, the induction coil has some advantages and disadvantages compared to its conventional counterparts. High-frequency operation of induction coils can lead to potential long-term reliability issues. These include increased thermal stress, magnetic core losses, dielectric breakdown, mechanical vibrations and acoustic noise, skin and proximity effects, component aging, electromagnetic interference (EMI), and material fatigue. Due to their high frequency and thermal efficiency, wide bandgap (WBG) switches can improve Full-Flexi, All-Metal, and Standard induction cooktop performance. Switching frequency, thermal control, and EMI reduction matter. Adaptive coil design and zoning improve Full-Flexi cooktop flexibility and energy economy. To accommodate different cookware, all-metal cooktops in this study included broad frequency ranges and material detection. Standard cooktops maximize power transmission efficiency and remain affordable. Optimizations speed up cooking, save energy, and improve the user experience. In this study, the advantages and disadvantages of operating at high-frequency levels on the induction coil were assessed. The changes occurring in the induction coils used in Full-Flexi, All-Metal, and Standard induction cooktops were discussed in detail. The coils for which FEM analyses were carried out were implemented and tested.