esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antimicrobial Susceptibility Profiling  
Antibiogram testing evaluates the susceptibility profiling of the bacterial strains to various antimicrobial agents, which is one of the most routinely performed investigations in clinical bacteriology laboratories, and was performed using both manual and automated methods. Antibiotic susceptibility testing was determined by using the Kirby-Bauer disk diffusion susceptibility method following CLSI and EUCAST guidelines. It consists of seeding the strain of interest to be scrutinized on the surface of solidified Muller-Hinton medium and applying antibiotic discs using an automatic disc dispenser to its surface, following standard guidelines provided by CLSI 2025 [24] and EUCAST 2024 [25]. The antibiotics will diffuse into the surrounding agar medium. As the antibiotics move far away from the disc, their concentration becomes low. Bacteria do not grow in the places on the plate where the concentration of the antibiotics is powerful enough to inhibit their growth. The clear zone formed around each disc is known as the zone of inhibition. The size of the zone is measured and compared according to standard guidelines provided by CLSI. Based on the size of the inhibition zone, the bacteria are classified as sensitive, intermediate, and resistant to a particular antibiotic.  
Automated identification and antibiotic resistance were analyzed by the Vitek2 Compact System for the identification and antibiotic susceptibility testing of bacteria from clinical samples. Identification and antibiotic resistance profiles were determined by the Vitek2 system using identification cards (ID-GN) and susceptibility cards (AST). For performing AST, the bacterial isolates were first suspended in a 0.45% saline solution to achieve a standard turbidity of 0.5 McFarland that ensures uniform preparation of inoculum. The ID-GN panel consists of 46 fluorometric tests that measure changes in pH and enzymatic reactions through kinetic fluorescence. The system has an integrated platform known as an Advanced Expert System (AES) Version 9.02, a sophisticated software that comprises test results with an extensive database to validate and interpret both identification and susceptibility findings for better accuracy and reliable results [1,26].
# A Dual-Adaptive Approach Based on Discrete Cosine Transform for Removal of ECG Baseline Wander
## 2 Methods  
### Theoretical Backgrounds of Discrete Cosine Transform  
DCT was first defined and implemented by using fast Fourier transform (FFT) in 1974 [21]. It was often used in feature extraction [22] to generate better classification. Equations (1) and (2) reveal DCT and inverse discrete cosine transform (IDCT), respectively.  
\[y(k)=w(k)\sum_{n=1}^{N}x(n)\cos(\pi(2n-1)(k-1)/2N),\ k=1,2,\ldots,N \tag{1}\]  
\[x(n)=\sum_{k=1}^{N}w(k)y(k)\cos(\pi(2n-1)(k-1)/2N),\ n=1,2,\ldots,N \tag{2}\]  
\[\textit{where}\ w(k)=\left\{\begin{array}{c}1/\sqrt{N}\ \textit{if}\ k=1\\ \sqrt{2/N}\ \textit{if}\ 2\leq k\leq n\end{array}\right. \tag{3}\]  
\(N\) is the length of both time-domain data \(x\) and DCT-domain data \(y\). Given the sampling frequency \(F_{s}\), the frequency difference between two adjacent DCT coefficients is \((F_{s}/2N)\) Hz. Equation (4) describes the correlation between the frequency and the DCT-index.  
\[k=(N\times f)/(F_{s}/2) \tag{4}\]  
where \(f\) is the corresponding frequency of DCT-index \(k\).  
The properties of DCT are exploited to suppress electromyogram interference on ECGs [23]. A DCT algorithm can also be useful to extract the morphological characteristics from ECG signals and generate the coefficients in the preprocessing stage [24].
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 5 Sub-Optimal Number of Sensor Nodes  
In this section, we give a closed-form sub-optimal number of sensor nodes to achieve the best system timeliness and fidelity. By applying proper approximations on two metrics, a closed-form sub-optimal number of sensor nodes can be derived.  
Theorem 5.1: _The sub-optimal number of sensor nodes can be approximately given by:_  
\[N^{*}=\arg\min\{f(\lfloor\tilde{N}\rfloor),f(\lceil\tilde{N}\rceil)\}, \tag{21}\]  
_where \(\tilde{N}=\exp\left(-\frac{1}{\gamma_{0}}+\sqrt{\frac{2v(1-a)}{3\alpha}}\right)\)._  
Proof of Theorem 5.1.: Firstly, recall that \(\overline{\Delta}(N)=\frac{3H_{N}}{2v}+\frac{G_{N}}{2 vH_{N}}\), in fact, the latter part \(\frac{G_{N}}{2 vH_{N}}\) is small enough to omit, then we have \(\overline{\Delta}(N)\approx\frac{3H_{N}}{2v}\). For MMSE, we try to use \(1/\mathbb{E}[g+1/\gamma_{0}]\) to approach \(\overline{D}(N)\). That is  
\[\overline{\mathbb{E}[g+\frac{1}{\gamma_{0}}]} =\frac{1}{\int_{0}^{\infty}\big{(}g+\frac{1}{\gamma_{0}}\big{)}p (g)\mathrm{d}g}\] \[=\frac{1}{\sum_{i=1}^{N}(-1)^{i-1}(\binom{i}{N}\big{[}\int_{0}^{ \infty}ige^{-ig}\mathrm{d}g+\int_{0}^{\infty}\frac{i}{\gamma_{0}}e^{-ig} \mathrm{d}g\big{]}}\] \[\overset{\mathrm{(b)}}{=}\frac{1}{H_{N}+\sum_{i=1}^{N}(-1)^{i-1} (\binom{i}{N}\frac{1}{\gamma_{0}}}\] \[=\frac{1}{H_{N}+\frac{1}{\gamma_{0}}} \tag{22}\]  
where \(\mathrm{(b)}\) follows from: \(\sum_{i=1}^{N}(-1)^{i-1}(\binom{i}{N}\int_{0}^{\infty}ige^{-ig}\mathrm{d}g=H_ {N}\).  
Then, the objective problem can be further reformulated as:  
\[f(N)=\frac{3\alpha H_{N}}{2v}+\frac{1-\alpha}{H_{N}+\frac{1}{\gamma_{0}}}. \tag{23}\]  
By searching for the zero-point of its first-order derivative and omit the unnecessary root, the sub-optimal number of sensor nodes can be figured out through  
\[\frac{3\alpha}{2 vN}-\frac{1-\alpha}{N(H_{N}^{2}+\frac{2H_{N}}{\gamma_{0}}+ \frac{1}{\gamma_{0}^{2}})}=0. \tag{24}\]  
Noticing that \(H_{N}\approx\ln N\), it yields  
\[3\alpha\Big{(}\ln^{2}N+\frac{2\ln N}{\gamma_{0}}+\frac{1}{\gamma_{0}^{2}} \Big{)}-(1-\alpha)2v=0. \tag{25}\]  
After some manipulations, \(\tilde{N}=\exp\left(-\frac{1}{\gamma_{0}}+\sqrt{\frac{2v(1-\alpha)}{3\alpha}}\right)\) can be calculated where another unnecessary root is omitted.  
At last, by comparing \(f(\lfloor\tilde{N}\rfloor)\) with \(f(\lceil\tilde{N}\rceil)\), the solution \(N^{*}\) can be obtained by choosing the integer resulting in smaller data freshness and fidelity weighted-sum tradeoff.  
One can conclude from Theorem 1 that the optimal number of sensor nodes \(N^{*}\) is strongly affected by SNR \(\gamma_{0}\), the weighting factor \(\alpha\) and the arrival rate \(v\).
## 3 Results and Discussion
### Question 6 Distribution of Respondents  
Strong agreement with the use of a computer for trend identification was also evidenced with 93% of respondents indicating some form of agreement (between agree-no preference and strongly agree). No preference was indicated by 5% of respondents and some form of disagreement (between disagree-no preference and strongly disagree) was indicated by 1% of respondents. Note that the percentages do not add up to 100% due to rounding and one survey where this question was not responded to. This data is presented in Table 5 and Figure 6.
## 3 Parameter Setting  
Combining the fluid-structure interaction and electromechanical coupling mentioned above, we constructed the geometrical model of a fluid-conveying pipe with PVDF piezo electric thin films attached to the outer wall, as shown in Figure 2. The figure shows a pipe that is 4 mm in thickness, 300 mm in length, and 60 mm in its inner diameter. The length of the PVDF piezoelectric thin film is 20 mm. The film width is 10 mm, and the film thickness is 0.5 mm.  
The relationship among internal fluid pressure signal, stress and strain signals of pipe wall, and piezoelectric sensor response signal without the influences of flow velocity pulsation and mechanical vibration were first investigated. We constructed the fluid model by ANSYS Fluent in ANSYS Workbench. Because the geometry of the fluid domain is a cylinder, we used structured meshing to mesh it. The finite element model of the fluid domain is shown in Figure 3.  
Transient analysis was selected in Fluent. Considering the influence of gravity, we applied a gravity load in the \(Y\) direction of the fluid domain, and the gravitational acceleration was set to \(-\)9.81 m/s\({}^{2}\). The fluid was water with a density of 998.2 kg/m\({}^{3}\). The viscous model was set to LES, and wall-adapting local eddy-viscosity (WALE) subgrid-scale model was chosen [21]. From the previous turbulence simulations, we know that WALE subgrid-scale model can produce satisfactory fluid simulation results in a wide range of flow velocities. The velocity-inlet boundary condition (BC) was selected for the pipe inlet. The velocity magnitude was set to 1.6 m/s, and gauge pressure was set to 100 kPa, considering the fluid pressure measured in experiments. The pressure-outlet BC was selected for the pipe outlet, and gauge pressure was also set to 100 kPa. The outer surface of the fluid was set to fluid-structure interface.  
Then, the finite element model of pipe and PVDF piezoelectric thin films was constructed by using the structured meshing in Transient Structural, as shown in Figure 4. Since polyvinyl chloride (PVC) has small rigidity and it is highly deformable, it is convenient to carry out experiments with this pipe material. The pipe material was specified to be PVC, of which density was 1380 kg/m\({}^{3}\), [PERSON]'s modulus was 3.4 GPa, and Poisson's ratio was 0.319. The density of PVDF piezoelectric thin films was 1780 kg/m\({}^{3}\). The gravitational acceleration was also set to \(-\)9.81 m/s\({}^{2}\) in the \(Y\) direction of the pipe. The pipe support type was specified to be fixed supports as both inlet and outlet boundary conditions. The internal wall of the pipe was set to fluid-structure interface. The PVDF piezoelectric thin films were set to piezoelectric body.  
Figure 3: The finite element model of fluid domain.  
Figure 2: The geometrical model.  
The interactive transmission of data between the fluid and the structure was realized by System Coupling in ANSYS Workbench. The time step size was set to 0.001 s, and the simulation time was set to 0.15 s.
## 1 Introduction
The SIF retrieval represents electromagnetic energy emitted in the 650-800 nm spectral range during plant photosynthesis [25], which has been shown to be proportional to GPP estimated from tower carbon flux measurements representing major global biomes [26; 27; 28; 29; 30]. Approximately 1-2% of total photosynthetically active radiation (PAR) absorbed by the canopy is emitted as SIF [31], which has been found to have stronger correspondence with GPP than satellite vegetation greenness indices [26; 32]. SIF is also positively related to LUE [29], and is expected to be sensitive to bioclimatic constraints influencing plant photosynthetic activity. However, a large sensor footprint and coarse temporal compositing of the satellite SIF observations is generally needed to enhance sensor signal-to-noise, which constrains capabilities for finer landscape assessments. Despite this limitation, the satelliteSIF record provides a close observational analog for GPP. SIF may also provide an effective global observational benchmark for evaluating bioclimatic factors influencing vegetation growth and for verifying LUE model parameterizations of these processes for improving GPP estimation accuracy and performance [33]. To achieve the goal of documenting the bioclimatic factors and response patterns influencing ecosystem productivity from the perspective of the global SIF observation record, we examined moisture and temperature related controls influencing ecosystem productivity using global satellite based SIF observations as a surrogate for GPP. A multi-scale analysis was used to investigate bioclimatic factors influencing spatial and seasonal patterns in vegetation growth. The analysis included comparisons between satellite SIF and stand level GPP observations from tower sites representing major global biomes; seasonal assessments of SIF and selected bioclimatic factors for individual sub-regions, and a global analysis of predominant bioclimatic control patterns influencing productivity. We also compared global productivity response relationships indicated from the satellite SIF record against prescribed LUE model parameterized relationships developed from sparse tower observations. Regression analysis was used to assess relationships between SIF and GPP, and selected meteorological variables frequently used in LUE models to define moisture and temperature related controls to GPP, including VPD, Tmin, and surface to root zone (1m depth) soil moisture (SM). A generalized additive model was used to define the best-fit curvilinear relationships between SIF and underlying environmental constraints at the global scale.
###### Abstract  
Interleaved power factor correction (PFC) is widely used circuit topology due to good efficiency and power density for single-switch boost PFC. As the differential mode (DM) electromagnetic interference (EMI) noise magnitude depends upon the input current ripple, this research details a comprehensive study of DM EMI filter design for interleaved boost PFC with the aim of minimizing the component size. It is also demonstrated that the different numbers of interleaved stages and switching frequency influence the filter attenuation requirement and, thus, the EMI filter size. First, an analytical model is derived on the basis of the Norton equivalent circuit model for the differential mode noises of interleaved boost PFC within the frequency range of 9-500 kHz. The derived model can help identify the proper phase shifting among the interleaved boost converters in order to minimize the considered differential mode noises at the filter design frequency. So, a novel phase-shift method is developed to get a minimized attenuation required by a filter in Band B. Further, a volume optimization of the required DM filter was introduced based on the calculated filter attenuation and volumetric component parameters. Based on the obtained results, unconventional and conventional phase shifts have demonstrated a good performance in decreasing the EMI filter volume in Band B and Band A, respectively. A 2-kW interleaved PFC case study is presented to verify the theoretical analyses and the impact of phase-shifting on EMI filter size.  
DM noise estimation; interleaved PFC converters; phase-shifting; EMI filter design Article
## 7 Simulation Results and Discussion  
In this section, the proposed nonlinear MPPT control scheme, namely FDTSMC, is tested and evaluated in MATLAB/Simulink through simulations. The FTSMC [26], CSMC, FBLC [27] and PID have been used as benchmarks to manifest the superior tracking performance of the proposed MPPT paradigm. For performance evaluation, the wind speed is assumed to have a stochastic profile.  
Figures 4 and 5 demonstrate the application of the proposed nonlinear MPPT control paradigm to the standalone, variable speed, fixed-pitch, 3 kW PMSG-WECS. The MPPT strategy ensures the maximum power extraction from the VSWT while keeping the tip speed ratio, \(\lambda\), at its optimal value, \(\lambda_{opt}\). Table 1 describes different significant parameters of the proposed FDTSMC.
## 5 Performance Evaluation  
This section focuses on evaluating the performance of the SDMob. We have conducted a set of simulations based on Contiki's native emulator, Cooja [47]. The SDN controller has been implemented using a Linux machine with a Python-based filter, which connects to a C-based Contiki border router. For the IoT RPL/6 LoWPAN network, we rely on the Contiki-NG/COOJA simulation environment [48]. Contiki-NG is an open-source embedded operating system, which is easily portable to commodity hardware. We have chosen the well-known Sky platform for the nodes as it is very constrained (in terms of memory), however, the results have also been verified using a more recent Zolertia platform. Sky motes are using an MSP430 F1611 micro-controller featuring 10 kB of RAM and 48 kB of flash memory. Zolertia motes run on an MSP430 F2617 MCU with 8 KB of RAM and 92 KB of flash memory. Both motes communicate using Chipcon's IEEE 802.15.4 compliant CC2420 Radio working in the 2.4 GHz ISM band. The radio model incorporated in Cooja is the Distance Loss mode of the Unit Disk Graph Medium (UDGM). This model considers  
Figure 8: Simulation results for increasing (**a**) path loss variance and (**b**) velocity.  
Figure 7: As illustrated in (**a**) with a higher variance of RSSI, it is expected to experience more packet loss in the system as the links are less stable. In (**b**), the packet loss probability with respect to the expected time for receiving the route-injection packet or \(T_{TX}\) (or Mean handoff time) is illustrated. The minimum packet loss happens when the packet is expected to be received in the middle of the trajectory (t = 5 for the scenario with 10 m distance).  
two circles around each note that define the distance in which the packets can be received or can interfere with other transmissions.  
Table 3 highlights the selected configuration for the simulations. The following metrics have been extracted for SDMob.  
* _Position estimation accuracy:_ is the difference between the estimated position and the ground truth and is expressed by Root Mean Squared Error (RMSE) which is a measure that represents the quadratic mean of the error and penalizes the larger errors.
* _Handoff delay:_ Measuring handoff delay for SDMob is non-trivial as the start time of handoff is not defined and SDMob may perform a handoff in a proactive way when the previous link is still active. To define an upper bound for handoff delay, we assume the time that a link's quality drops below a lower threshold to be the start time of handoff.
* _End-to-end delay:_ The time for data packets to reach the destination (sink node) from MN.
* _Packet Delivery Ratio:_ The ratio of received packets (in the sink) to the transmitted packets.
* _Communication Overhead:_ The accumulated number of bytes for control packets.
## 3 Graphene/Graphene Oxide-Based Bio and Gas sensors  
### Performance of Graphene and GO-Based Biosensors  
Table 1 represents the performance of various graphene and its derivatives-based enzymatic biosensors. Here, graphene can detect diverse analytes by enhancing the electron transfer rate [48]. Moreover, rGO and GO play a role in a suitable matrix for immobilization, as shown in Table 1. Here, rGO was used as a biosensing matrix for detecting important biomarkers, such as H\({}_{2}\)O\({}_{2}\), glucose, nicotinamide adenine dinucleotide (NADH), DNA, and cholesterol. This is because rGO shows biosensing performance better than GO due to the higher conductivity of rGO [49]. rGO shows less charge transfer resistance than GO due to the conjugated network revival after reduction. In general, graphene derivatives conjugated with nanoparticles indicate the lower limit of detection (LOD) in biosensors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensing** & **Analytes** & **Detection** & **1 LOD** & **Ref.** \\
**Materials/Electrodes** & & **Range** & & \\ \hline rGO & H\({}_{2}\)O\({}_{2}\) & 1.5–28.5 \(\mu\)M & 0.5 \(\mu\)M & [50] \\ rGO-PANI-modified SnO\({}_{2}\) & Glucose & 0.1 nM–5 mM & 0.26 nM & [51] \\ rGO & \({}^{2}\) NADH & 0–500 \(\mu\)M & 0.6 \(\mu\)M & [52] \\ rGO-AuNPs & DNA & 0.1 \(\mu\)M–5.1 \(\mu\)M & 35 AM & [53] \\ GO-Chitosan & DNA & 10 \(\mu\)M–50 nM & 10 fM & [54] \\ Graphene-Chitosan & Cholesterol & 0.005–1 mM & 17.39 \(\mu\)M & [55] \\ Graphene-PtNPs & Cholesterol & 0.035–12 mM & 0.2 \(\mu\)M & [56] \\ \hline \hline \end{tabular} \({}^{\dagger}\) LOD and \({}^{2}\) NADH represent the limit of detection and nicotinamide adenine dinucleotide, respectively.  
\end{table}
Table 1: Performance between graphene and graphene derivatives in biosensors.  
Figure 4: Optical transparency. (**a**) Light transmittance of monolayer and bilayer graphene and (**b**) optical image of graphene flakes with one, two, three, and four layers on a 285-nm thick SiO\({}_{2}\)-on-Si substrate [47].
## 2 Theory and Analysis
### Lamb Excitation  
The lamb wave has a dispersion characteristic and is classified into a symmetric lamb mode or an anti-symmetric lamb mode. With the difference of frequency, the lamb wave mode changes and has different phase velocities. The dispersion curve of the 2 mm aluminum plate can be obtained by the matlab numerical method [33]. The longitudinal wave velocity of the aluminum plate is 6300 m/s, and the transverse wave velocity is 3080 m/s. The dispersion curve is shown in Figure 2.  
It can be seen from the above figure that when the excitation frequency is lower than 0.9 MHz, only two modes of A0 and S0 exist. The relationship between the excitation angle \(\alpha\), the phase velocity cp, and the longitudinal wave velocity c in the probe wedge is:  
\[sin\alpha=c/cp, \tag{2}\]  
A specific mode lamb wave can be excited by adjusting the angle. However, when the excitation frequency is 0.9 MHz, the A0 mode phase velocity is 2610 m/s. The material of the oblique probe's wedge used in the experiment is plexiglass, and the longitudinal wave velocity of the plexiglass is 2730 m/s. It can be seen that c/cp is always greater than 1 when the excitation frequency is lower than 0.9 MHz, in other words, the A0 mode below 0.9 MHz is difficult to excite. So a single-mode S0 lamb wave can be obtained. As shown in Figure 3, the received pulse signal has a single mode, and the reflected signal from the edge of the sheet is weak, which will not interfere with the result. That is, only the waveform of received pulse signal is obvious.  
Figure 1: Probability distribution: (**a**) Probability scan path generated by the excitation and reception probe; (**b**) three-dimensional probability distribution.  
When the excitation frequency is 0.5 MHz, the best excitation angle is about 31\({}^{\circ}\), according to Formula (2), the receiving efficiency of lamb wave is the best, and the velocity is 5278 m/s. The excitation and receiving probes are placed at different distances, \(L\), and the time difference, _time_dif_, between the excitation signal and the received signal is multiplied by the phase velocity to obtain a theoretical distance, _L_cal_. Compare _L_cal_ with \(L\) to verify whether the excited mode is S0 mode or not; Figure 4 is a comparison chart.  
It can be seen from the above figure that the calculated distance is almost the same as the actual distance, which proves that the mode excited is the S0 single mode lamb wave.  
Figure 2: Dispersion curve in 2 mm aluminum plate: (**a**) Phase velocity dispersion curve; (**b**) group velocity dispersion curve.