esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Memristive Analog Design
### Digital to Analog Converter
This wide range of programmable resistance allows for the fine-tuning of the filter response, enabling precise control over the modulator's performance characteristics. Furthermore, the use of memristors in the FIR DAC provides enhanced stability and reliability, as they have inherent non-volatile memory properties that can retain their resistance values even when power is removed. The modulator was designed and simulated using 180 nm standard CMOS technology and a memristor model. The simulation results show that the FIR DAC improves the modulator signal-to-noise and distortion ratio (SNDR) from 44.36 dB to 62.29 dB, even with 5 ns of RMS jitter at the sampling clock. The presented studies highlight the potential of using memristors to implement digital-to-analog converters, yet they lack a comprehensive analysis of the possible challenges and limitations that could emerge during the implementation process. One issue with using memristors to design a digital-to-analog converter is their inherent variability, which can lead to inaccuracies in the conversion process. Additionally, the non-linear behavior of memristors can make it challenging to achieve consistent and reliable analog output. To address these challenges, researchers should explore techniques such as calibration and error correction algorithms to improve the accuracy and stability of memristor-based digital-to-analog converters. Additionally, advancements in material science and fabrication processes may help mitigate the variability and non-linearity issues associated with memristors. Despite these challenges, memristors still hold promise for advancing DAC technology due to their unique properties and potential for high-density integration.
## 2 Biogas Production from POME  
About 53 million tons of palm oil production and 13 million tons of empty fruit bunches were recorded annually in Malaysia [52]. This phenomenon has pulled in researchers and investigators to deal with energy production from POME [53]. To date, most oil palm processes in Malaysia have applied POME as a feedstock for biogas generation [54]. Production of biohydrogen using digested POME as inoculum was examined by [PERSON] et al. using the anaerobic sequencing batch reactor (ASBR) [55]. The impacts of hydraulic retention time (HRT), temperature and organic loading rate (OLR) were explored for process stability in ASBR in a continuous process. In their study, they used a thermophilic condition (60 \({}^{\circ}\)C) to enhance biohydrogen production. At the end of the experiment, they found out that there was a significant increase in biohydrogen production under thermophilic condition, as compared to mesophilic temperature (37 \({}^{\circ}\)C). This is because thermophilic bacteria were present in POME sludge (inoculum) due to a long adaptation time, thus making it more favorable for biohydrogen production.  
Different studies on the effects of volatile fatty acids (VFAs) [56], pH [57] and organic loading rate (OLR) [58] using POME were also done, either using single stage or integrated reactors. Studies using a single-stage reactor revealed that the biogas production rate could be accomplished at a HRT of 1.5 days and the system was capable to effectively treat POME [40]. On the other hand, several researchers also reported higher efficiency in energy recovery using an integrated system as compared to a single-stage process, as well as increased process stability [59]. These findings showed that an integrated system using a two-stage bioreactor is better in terms of COD removal efficiency, stability and gives a significant impact on biogas production and yield, in comparison to a single-stage reactor.
## 3 Results
### Validation
#### 3.2.1 Remote Sensing Reflectance
The errors for the Red-edge bands were lower for Polymer, with 0.00206 sr\({}^{-1}\) for \(R704\) and 0.00058 sr\({}^{-1}\) for \(R783\), except for the R740 band, whose lowest RMSE of 0.00080 sr\({}^{-1}\) was obtained with C2X. For the NIR band, the lowest error of 0.00048 sr\({}^{-1}\) was obtained with C2X. The most interesting results were obtained with the RRMSE, the percentage error in relation to the in situ reflectances mean value. Coinciding with the other calculated errors, Polymer was the AC with the lowest percentage error for the blue and green bands, with 30% for \(R443\), 25% for \(R492\), and 20% for \(R560\), while for red band, the lowest RRMSE of 30% was obtained with C2X. For the NIR band, the lowest RRMSE of 41% was obtained with C2X. The Red-edge bands presented differences with respect to the other statistics, obtaining a lower RRMSE of 33% for \(R704\) and 28% for \(R783\) with C2 XC, except for the R740 band, of which the lowest error of 34% was obtained with C2X. RRMSE indicated that, for the visible region (blue, green, red), the lowest errors were obtained with Polymer and C2 XC, and for the Red-edge and NIR region, the errors increase more for Polymer than for C2 XC, while with C2X, fewer errors were obtained than in the visible region. Calculating the mean RRMSE of the eight bands studied, gives an error rate of 67% for C2 RCC, 41% for C2X, 34% for C2 XC, and 38% for Polymer.
# Correlation Plenoptic Imaging: An Overview
## 4 A Different Architecture of CPI with Chaotic Light
Similar to the previous scheme, the incoherent image of the object is recovered by integrating the correlation function over the whole angular detector D\({}_{a}\)  
\[\Sigma^{\text{foc}}(\mathbf{\rho}_{b})=\int d\mathbf{\rho}_{a}\Gamma_{(S_{1},S_{2})}(\bm {\rho}_{a},\mathbf{\rho}_{b})\propto\int d\mathbf{\rho}_{o}\left|A(\mathbf{\rho}_{o})\right|^ {2}\left|\int d^{2}\mathbf{\rho}_{\ell}P(\mathbf{\rho}_{\ell})e^{-i\frac{\omega_{0}}{c }\left(\frac{\partial\Sigma}{S_{1}}+\frac{\rho_{b}}{S_{2}}\right)\rho_{\ell}} \right|^{2}, \tag{21}\]  
where the focusing condition is related with the disappearance of the quadratic phase in Equation (19). As expected, the width of the PSF is determined by the lens diameter (\(D_{\ell}\)), which is, \(\Delta\rho_{b}\sim\lambda_{0}\,S_{2}^{f}/D_{\ell}\); hence, the object resolution is \(\Delta\rho_{o}\sim\lambda_{0}\,S_{1}/D_{\ell}\). On the other hand, the incoherent ghost image of the lens is retrieved by integrating the correlation function over \(\rho_{a}\), to get  
\[\int d\mathbf{\rho}_{b}\Gamma_{(S_{1},S_{2})}(\mathbf{\rho}_{a},\mathbf{\rho}_{b})\propto \int d\mathbf{\rho}_{\ell}\left|P(\mathbf{\rho}_{\ell})\right|^{2}\left|\tilde{A} \left(\frac{\omega_{0}}{c\,S_{1}}(\mathbf{\rho}_{\ell}-\mathbf{\rho}_{a})\right) \right|^{2}. \tag{22}\]  
The PSF of this image is represented by the Fourier transform of the object transmission function \(\tilde{A}\); the resolution \(\Delta\rho_{a}\sim\lambda_{0}\,S_{1}/a\) is thus fixed by the typical size \(a\) of the smallest details of the object. In addition, in this case, the angular resolution is thus only hindered by diffraction at the object, provided if the source is large enough not to play the role of a limiting pupil. The plenoptic properties of the correlation function can be unveiled by imposing the stationary phase condition in the integral of Equation (19). The most relevant term in the geometrical optics regime reads  
\[\Gamma_{(S_{1},S_{2})}(\mathbf{\rho}_{a},\mathbf{\rho}_{b})\sim\left|P(\mathbf{\rho}_{a}) \right|^{2}\left|A\left[\frac{S_{1}}{S_{2}}\left(-\mathbf{\rho}_{b}+\left(1-\frac {S_{2}}{S_{2}^{f}}\right)\mathbf{\rho}_{a}\right)\right]\right|^{2}. \tag{23}\]  
As in the previous setup, the dependence of the retrieved correlation function on the coordinates (\(\mathbf{\rho}_{a}\simeq\mathbf{\rho}_{\ell}\)) of the focusing element provides different viewpoints on the object, as required for 3D imaging.
The main contributions of this paper are:  
* mathematical formulation and explanation of the SS models which take into account the impact of NU variations, DT adaptations, and both, on the probability of ED,
* presentation of the algorithm developed for simulating ED performance on differently modulated OFDM signals detected in MA, RA, and combined RA and MA based implementations,
* a systematic investigation based on extensive simulations on how the different OFDM modulations and corresponding Tx powers, the SNRs in the position of SU, the levels of NUs, the sample quantity, and the false alarm probability impact on the probability of signal detection in the ED process with DT adaptation,
* explanation of the limitations of ED as one of the local SS methods, with discussion on future research challenges and opportunities related to the improvement of the ED technique. The rest of the paper is structured as follows. In Section 2, an overview of the topic related to the SS of OFDM signals is presented. Section 3 describes communication technologies and versatile communication systems, which use OFDM as a transmission technique. ED of OFDM signals as the SS model is introduced in Section 4. The mathematical formulation of SS models which take into account the impact of NU variations, DT adaptations and both on the probability of ED is presented in Section 5. The algorithm used for simulation of the ED process based on DT adaptations is presented in Section 6. The extensive simulation results to determine the ED of signals transmitted using distinct OFDM-based systems are presented and surveyed in Section 7. Based on the analyses presented in Section 7, the main ED drawbacks and future research challenges and opportunities for future research relating to the improvement of ED performance are thoroughly discussed in Section 8. Finally, some concluding remarks are given in Section 9.
## 3 Load-Shedding Optimization Model
### Constraints  
(1) Active power balance constraint considering the droop characteristic of DGs.  
The active power shortage is supposed to be smaller than the result of DGs' generated active power minus \(P_{Los0}\).  
\[P_{Lto\_re}=\sum_{i=1}^{m}{(1-x_{ik})P_{Li}}\leq\sum_{j=1}^{n_{k}}{P_{DGj\_ }}-P_{Los0} \tag{2}\]  
where \(P_{Lto\_re}\) represents the total active power capacity of the recovered loads, \(P_{Los\_0}\) is the initial active power loss of the initial island to be studied, and \(P_{DGj\_}\) is the output active power of \(j\)th DG under frequency \(f\) if the DG can participate frequency modulation, which can be calculated according to the droop characteristic as follows [18].  
\[P_{DGj\_}=P_{DGj\_}}\times[1+K_{pfj}(f_{r}-f)/f_{r}] \tag{3}\]  
where \(P_{DGj\_}\) is the rated output active power of \(j\)th DG under rated frequency \(f_{r}\) and \(K_{pfj}\) is its active power-frequency adjustment coefficient. According to Reference [19], \(f_{r}\) and the minimum value of \(f\) are set as 50 Hz and 49.5 Hz, respectively.  
Thus, to maximize the total recovery capacity, \(P_{DGj\_max}\), which is the maximum value of \(P_{DCj\_}\), \(f_{r}\) can be calculated by setting the value of \(f\) as 49.5 Hz as follows.  
\[P_{DGj\_}\simeq P_{DGj\_}}\times[1+K_{pfj}(50-49.5)/50]\]  
Correspondingly, Equation (2) can be modified as the equation below.  
\[P_{Lto\_re}=\sum_{i=1}^{m}{(1-x_{ik})P_{Li}}\leq\sum_{j=1}^{n}{P_{DGj\_}}-P_{ Los0}\]  
(2) Reactive power balance constraint  
The reactive power shortage is supposed to be smaller than the result of DGs' generated reactive power plus \(Q_{cmax}\).  
\[\sum_{i=1}^{m}{(1-x_{ik})Q_{Li}}\leq\sum_{j=1}^{n}{Q_{DGj\_}}+Q_{c}\max \tag{4}\]  
where \(x_{ik}\) is defined in Section 3.1, \(Q_{Li}\) is the reactive power of the \(i\)th load, and \(Q_{c\max}\) represents the maximum total reactive compensation capacity of the initial island. \(Q_{DCj\_max}\) is the maximum allowable reactive power of the \(j\)th DG, which is decided by the apparent power constraint as follow:  
\[Q_{DCj\_}\max=\sqrt{{(S_{DGj\_}\max)}^{2}-{(P_{DGj\_}\max)}^{2}}\]  
where \(S_{DGj\_max}\) and \(P_{DGj\_max}\) are the maximum apparent power and active power of the \(j\)th DG.
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 3 Evaluation Method of Heat Transfer Performance  
To enhance heat transfer, resistance loss should be considered at the same time, so the comprehensive heat transfer factor _PEC_ is used to evaluate the heat transfer performance [13].  
\[PEC=\frac{(Nu_{h}/Nu_{s})}{\left(f_{h}/f_{s}\right)^{\frac{1}{3}}} \tag{7}\]  
where \(Nu_{h}\), \(f_{h}\) are Nusselt number and resistance coefficient of dimpled tube, \(Nu_{s}\), \(f_{s}\) are Nusselt number and resistance coefficient of smooth tube.  
The \(Nu\) and \(f\) can be written as [14; 15]:  
\[Nu=\frac{h_{i}D_{H}}{\lambda} \tag{8}\]  
\[f=\frac{2\Delta PD_{H}}{L\rho u^{2}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Boundary** & **Type** & **Parameter Setting** \\ \hline Inlet of inner tube & velocity inlet & T = 546 K \\ Inlet of outer tube & velocity inlet & T = 288 K \\ Outlet of inner tube & pressure outlet & Pi,out = 20 MPa \\ Outlet of outer tube & pressure outlet & Po,out = 20 MPa \\ Inner wall of inner tube side & coupled wall & Fixed wall \\ Inner wall of inner tube side & coupled wall & No slip \\ Outer wall of inner tube side & coupled wall & Fixed wall \\ Inner wall of shell side & coupled wall & No slip \\ Outer wall of shell side & Fixed heat flow wall & heat insulation \\ \hline \end{tabular}
\end{table}
Table 4: Boundary conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Simulation Method** & **RANS** \\ \hline The turbulence model & RNG \(k\)-\(\epsilon\) \\ Pressure velocity coupling method & SIMPLE \\ Gradient interpolation & least square method \\ Pressure interpolation & Standard \\ Convection interpolation & Second Order Upwind \\ Wall function & Standard Wall Function \\ \hline \end{tabular}
\end{table}
Table 5: Turbulence model, discrete format, and algorithm.  
where \(h_{i}\) is convective heat transfer coefficient, \(D_{H}\) is hydraulic diameter, \(\lambda\) is heat conductivity coefficient, \(\Delta P\) is the pressure drop on the flow channel length \(L;\rho\) is the density of fluid, \(u\) is the average velocity of the fluid on the cross section of the flow channel.  
When \(PEC>1\), the comprehensive heat transfer performance of dimpled tube is higher than that of smooth tube. When \(PEC<1\), the comprehensive heat transfer performance of dimpled tube is lower than that of smooth tube.
## 2 Sensor
### Exemplary Sensor  
To illustrate the expressions derived in this article, the properties of an exemplary cantilever-type sensor are referred to. The micromechanical cantilever is designed for the detection of low-frequency magnetic fields (\(f<100\,\text{Hz}\)) in the picotesla regime. It  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Static capacitance & \(C_{0}=44\,\text{pF}\) \\ \hline Loss factor & \(\tan\delta_{0}=5\cdot 10^{-3}\) \\ \hline Loss resistance & \(R_{0}(f_{\text{res}})=97.5\,\text{M}\Omega\) \\ \hline Resonance frequency & \(f_{\text{res}}=7421.1\,\text{Hz}\) \\ \hline Quality factor & \(Q=887.1\) \\ \hline Motional resistance & \(R_{\text{m}}=476.67\,\text{k}\Omega\) \\ \hline Motional capacitance & \(C_{\text{m}}=50.71\,\text{fF}\) \\ \hline Motional inductance & \(L_{\text{m}}=9.07\,\text{k}\text{H}\) \\ \hline Magnetic operating point & \(B_{\text{bias}}=0.65\,\text{mT}\) \\ \hline Magnetic sensitivity & \(S_{\text{mag}}=80\,\text{Hz}/\text{mT}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electromechanical and magnetic properties of the exemplary magnetic field sensor referred to in this article.  
is based on a polysilicon cantilever of 3 mm length, 1 mm width, and 50 \(\upmu\)m thickness. The bottom side is coated with 2 \(\upmu\)m of magnetostrictive metal ((Fe90C010)7 sSi12B10) that affects the cantilever's resonance frequency by inducing an additional strain depending on an ambient magnetic field. On the top side, a film of 2 \(\upmu\)m aluminum-nitride (AlN) piezoelectric material, surrounded by two electrodes, is deposited.  
Further details about this sensor [10; 11] with a focus on the MEMS fabrication process [34], the magnetic properties [35; 36], and the readout and noise behavior [12; 37; 38] can be found in the literature. All electromechanical and magnetic properties required here are summarized in Table 1, where \(B_{\text{bias}}\) defines the constant magnetic operating point, and thus, the resonator's initial state. This bias is used to maximize the magnetically induced resonance shift, i.e., the magnetic sensitivity \(S_{\text{mag}}\).
# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 3 Models and Algorithms for Route Planning
### A Heuristic Algorithm for Path Planning (A-Star Algorithm)  
In this study, an A-star algorithm was used to identify a path that avoids obstacles on any path segment. The algorithm can be described as follows:  
During the path search of an A-star algorithm, an \"OPENED\" list and a \"CLOSED\" list are used to keep track information during the search. The \"OPENED\" list stores the paths that will be explored. The \"CLOSED\" list contains all explored paths. Let \(h(n)\) represent the cost from an origin to the current node (\(n\)) and let \(g(n)\) represent the cost to get from the current node (\(n\)) to a destination node. The total cost, \(f(n)\), is calculated for each successor node that includes both \(h(n)\) and \(g(n)\). The procedure consist of seven steps [24]:  
1. The start node is removed from the \"OPENED\" list. The cost function \(f(n)\) is calculated; note that \(h(n)=0\) and \(g(n)\) are calculated on the basis of the distance between the start position and the destination (\(f(n)=g(n)\)).
2. A node with the smallest cost function is removed from the \"OPENED\" list and inserted into the \"CLOSED\" list. The node is set as node \(n\) (break ties arbitrarily, if two or more nodes have the same cost function). If one of the nodes is the destination node, then the destination node is selected.
3. If \(n\) is the destination node, the algorithm is terminated; otherwise, it continues.
4. The cost function for each successor of \(n\) that is not on the \"CLOSED\" list is computed.
5. Each successor not on the \"OPENED\" list or CLOSED list is associated with the calculated cost and put on the \"OPENED\" list.
6. Any successor already on the \"OPENED\" list is associated with the minimum cost (\(min(\text{new}(f(n))\), \(\text{old}(f(n)))\)).
7. Return to step 2.
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 1 Introduction
The artificial bee colony (ABC) algorithm adopts bee swarm activities and therefore it belongs to the category of swarm intelligence algorithms. Honey bees have several behaviors, such as the waggle dance, to search for better food sources in a natural environment. The ABC algorithm was proposed by [PERSON] first in 2005 [17]. This algorithm provides a widely used optimal technology for solving multivariable function's approach to various applications. For example, a better solution based on an ABC alternative was provided for vehicle routing problems [18]. Setting the parameters of the digital filter is important to the output signal, and the authors designed an infinite impulse response (IIR) to adjust the parameters by the ABC algorithm until the error between the output of the filter and the unknown system was minimized [19]. The literature [20] has attempted to enhance the performance by changing the selection mode of the original structure. In this paper, a novel ABC-based positioning algorithm is proposed to determine the MS location in wireless communication systems. The migration operator of the biogeography-based optimization (BBO) approach with the ABC algorithm is proposed to solve poly-phase code design [21]. In most areas, users may face the restriction of poor hearability, and MS may not detect more than three BSs for location purposes in wireless communication systems. This paper applied an approach in which the MS's coordinate needs to be improved when three BSs are available. Three TOA circles can be generated from the distance measurements. The distance's relationship between the intersections of three circles with the estimated location can be defined as an objective function. The approach was accomplished by minimizing the nonlinear objective function under nonlinear geometrical constraints. ABC was employed here to search for the optimal MS location in the range of feasible intersections. The proposed algorithm can be applied not only to time-based technologies but also the circles generated from signal strength measurement. In the simulation results, the Taylor series algorithm (TSA) [22], linear lines of position algorithm (LLOP) [23], and range-scaling algorithm (RSA) [24] were used for wireless location estimation. The results of the proposed algorithm and other different algorithms were compared to each other. Lastly, the performance of the proposed algorithm based on ABC was compared to GA, including the positioning accuracy, efficiency, and convergence speed. The simulation results show that the proposed location algorithm can mitigate the NLOS ranging errors and improve the positioning performance. This paper is organized as follows. In Section 2, we will introduce some related localization methods, including TSA, LLOP, RSA, and GA. Section 3 describes the detailed procedure of the ABC algorithm. Section 4 derives the MS location from the proposed algorithm based on ABC. Then, Section 5 discusses the performance of the mentioned methods according to numerical simulations. Eventually, the conclusion is presented in Section 6.
# Survey for Soil Sensing with IOT and Traditional Systems
## 3 RF-Based Soil Sensing Systems in Internet of Things (IoT)
### RFID-Based Soil Sensing Systems  
In their research, Ref. [14] presents GreenTag, an economical RFID-based system for soil moisture detection. They utilize two RFID tags attached to a plant container to convert soil moisture content variations into their Differential Minimum Response Threshold (DMRT) metric at the reader. Commercial RFID readers offer three signal metrics: Minimum Response Threshold (_MRT_), Received Signal Strength (\(RSS\)), and phase. These can be broken down into components influenced by the soil moisture level, as detailed in Equations (7)-(9) [14]. The constants \(C_{m}\), \(C_{r}\), and \(C_{p}\) play specific roles; \(C_{m}\) represents the tag's receiving sensitivity. \(C_{r}\) and \(C_{p}\) pertain to the amplitude and phase of both the reader's transmitted signal and the reflection coefficient of the tag's chip. Parameters \(h_{a}\) and \(h_{T}\) characterize the channels over the air and the tag's antenna, respectively, while theta encapsulates phase information over the air and tag via \(\theta_{a}\) and \(\theta_{t}\).  
\[MRT=C_{m}-20\log|h_{a}|-20\log|h_{T}| \tag{7}\]  
\[RSS=C_{r}+40\log|h_{a}|+40\log|h_{T}| \tag{8}\]  
\[Phase=C_{p}+2\theta_{a}+2\theta_{T} \tag{9}\]  
The system design of GreenTag is shown in Figure 5.  
With the incorporation of a low-pass filter to the DMRT metric, their approach effectively compensates for fluctuations in the external RF environment due to factors like human movements and changes in pot positioning or pot orientation. Impressively, GreenTag boasts a 90 th percentile moisture estimation error of just 5%, aligning closely with the 4% errors of high-end soil moisture sensors. The system's efficacy was tested in an actual greenhouse setting. Given its affordability and precision comparable to premium soil moisture sensors, GreenTag holds significant promise in enhancing greenhouse productivity and revolutionizing smart greenhouse irrigation techniques.