esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electrophysical Measurements  
The examination of the structures of both pristine ferrites and ferrites deposited onto carbon nanotube surfaces was conducted through transmission electron microscopy (TEM) on a Tecnai F20 TWIN microscope (FEI Company, Hillsboro, OR, USA) equipped with a field emission gun, operating at an accelerating voltage of 200 kV. For this analysis, a droplet (6.0 \(\upmu\)L) of the dispersion of powdered samples in acetone was applied to a grid (Lacey Carbon Film, 200 mesh copper) and air-dried for 24 h. TEM microphotographs were captured using a Gatan Rio 16 CMOS 4k camera (Gatan Inc., Pleasanton, CA, USA) and processed with Gatan Microscopy Suite (GMS) software (Version 3.31.2360.0, Gatan Inc.).  
The crystalline structures of synthesized samples, i.e., pristine ferrites and CNTs covered with ferrites, were determined by the D8 Advance diffractometer (Bruker AXS, Karlsruhe, Germany) with a Cu-K\({}_{\alpha}\) cathode (\(\uplambda\) = 1.54 A). The diffractometer operated at a voltage of 40 kV and a current of 40 mA, employing a Bragg-Brentano geometry. X-ray scattering curves were recorded over a diffraction angle (20) range of 2-50\({}^{\circ}\) with a step size of 0.02\({}^{\circ}\), utilizing a LYNXEVE XE-T detector (Bruker AXS, Karlsruhe, Germany). Crystallite size calculations were performed through Rietveld refinement in the TOPAS 6 program, based on Williamson-Hall theory [26; 27]. The pseudo-Voigt function was utilized in describing diffraction line profiles during the Rietveld refinement. Numerical criteria such as R\({}_{\mathrm{wp}}\) (weighted-pattern factor) and GOF (goodness-of-fit) parameters were employed to assess the quality of the fit relative to experimental diffraction data [28]. The dislocation density was computed using Equation (1), where \(\delta\) represents dislocation density and \(D\) is crystallite size. The chemical composition of the as-synthesized ferrites and ferrite/CNT hybrids was determined using S2 Puma X-ray fluorescence spectroscopy (Bruker AXS, Karlsruhe, Germany).  
\[\delta=\frac{1}{D^{2}} \tag{1}\]  
The complex dielectric permittivity, with its real (\(\epsilon^{\prime}\)) and imaginary (\(\epsilon^{\prime\prime}\)) components, and the complex magnetic permeability, with its real (\(\upmu\)') and imaginary (\(\upmu\)'') components, for composites at microwave frequencies (9 GHz) were determined using an interferometer (RFK 2-18, measuring phase differences) and a standing wave meter (R2-60) through an electrodeless method [29]. The testing involved polymer-based samples with dimensions of 10 \(\times\) 23 mm and a thickness of 2 mm. An immittance meter (E7-14) was employed to measure electrical conductivity (\(\sigma\)) at low frequencies (100 Hz) using a two-contacts method [30]. For samples with a square shape (10 \(\times\) 10 mm) and a thickness of 2 mm, graphite electrodes were utilized in the electrical conductivity measurements, with the experimental error not exceeding 5%.
## 3 UWOC Channel Modeling
Additionally, salinity, pressure, and temperature are factors that affect the refractive index of sea water, creating an optical boundary that alters the original propagation path of the optical wave. In this scenario, Mie scattering accurately describes the scattering behavior. The scattering coefficients for small and large particles in sea water can be expressed as follows [31]:  
\[b_{l}(\lambda)=1.51302\,\left(\frac{400}{\lambda}\right)^{1.17} \tag{4}\]  
\[b_{s}(\lambda)=0.341074\,\left(\frac{400}{\lambda}\right)^{0.3} \tag{5}\]  
In the given context, \(b_{l}\) represents the scattering coefficient for large particles, while \(b_{s}\) represents the scattering coefficient for small particles. Apart from absorption and scattering, turbulence plays a crucial role in influencing underwater optical wireless communication (UWOC). The underwater environment experiences variations in temperature, density, and salinity, which lead to fluctuations in the refractive index of the underwater channel. Consequently, these fluctuations cause variations in the intensity of the received signal, commonly known as turbulence [34; 35]. Furthermore, air bubbles present in the water channel contribute to random fluctuations in the refractive index. Turbulence has a detrimental effect on the performance of UWOC, and therefore its impact needs to be thoroughly analyzed. The salinity of water, which is determined by the concentration of dissolved salts, is typically expressed in parts per thousand (ppt) or kilograms of salt in 1000 kg of water. Another commonly used unit for salinity is the practical salinity unit (psu), which is almost equivalent to ppt [36]. In ocean water, the average salinity ranges from 31 to 37 ppt. However, polar regions tend to have salinity levels below 30 ppt, while Antarctic regions typically have salinity levels around 34 ppt. Salinity can be measured using two main methods. The first method involves measuring electrical conductivity (EC), expressed in micro-siemens per centimeter. The second method measures the quantity of salt particles present in the solution, known as total dissolved salts. Sea water typically contains dissolved salts such as chlorides, sulphates, sodium carbonate, potassium carbonate, calcium carbonate, and magnesium carbonate. The average salinity of ocean water is approximately 35 ppt [37], indicating that 35 g of material is dissolved in every 1000 g of ocean water. In CGS units, where the density of water is 1, this can be equivalently expressed as 35 g per liter (gm/L).
# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 1 Introduction
Through the analysis of simulation experiment results, the LISA-RBF-BP model can predict the gold tungsten powder stock more accurately than the RBF-BP model and the SSA-RBF-BP model. [PERSON] et al. [11] used BP and RBF neural networks to make a short-term prediction of SST, and the results showed that both the BP and RBF neural networks could effectively predict the seasonal changes of SST data, but the overall prediction effect of the RBF neural network was better than that of the BP neural network for different prediction times. In addition to the study described above, neural networks can be applied to other types of research [12; 13; 14; 15; 16]. This paper suggested the RBF-BP neural network model based on a chaotic particle swarm optimization algorithm to forecast the temperature of the reactor, based on the theoretical foundation of the above scholars' research. By correcting the initial weight and threshold of the RBF-BP neural network with a chaotic particle swarm optimization algorithm, the neural network can effectively avoid falling into the local optimal solution and improve the convergence rate, thereby improving the prediction accuracy of reactor temperature. The model was tested against the BP, RBF-BP, and PSO-RBF-BP algorithms. The simulation results indicate that the CPSO-RBF-BP model predicts the reactor temperature more accurately and with less error. The remainder of this document will be organized as follows: Section 2 describes the RBF-BP neural network model's structure and content, as well as the temperature prediction algorithm of the CPSO-RBF-BP reactor. The contrast of four neural networks for temperature prediction is summarised and analyzed in Section 3. Section 4 summarises the paper's work substance.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
###### Abstract  
In recent trends, wireless sensor networks (WSNs) have become popular because of their cost, simple structure, reliability, and developments in the communication field. The Internet of Things (IoT) refers to the interconnection of everyday objects and sharing of information through the Internet. Congestion in networks leads to transmission delays and packet loss and causes wasstage of time and energy on recovery. The routing protocols are adaptive to the congestion status of the network, which can greatly improve the network performance. In this research, collision-aware routing using the multi-objective seagull optimization algorithm (CAR-MOSOA) is designed to meet the efficiency of a scalable WSN. The proposed protocol exploits the clustering process to choose cluster heads to transfer the data from source to endpoint, thus forming a scalable network, and improves the performance of the CAR-MOSOA protocol. The proposed CAR-MOSOA is simulated and examined using the NS-2.34 simulator due to its modularity and inexpensiveness. The results of the CAR-MOSOA are comprehensively investigated with existing algorithms such as fully distributed energy-aware multi-level (FDEAM) routing, energy-efficient optimal multi-path routing protocol (EOMR), truncate swarm grey wolf optimization (TSGWO), and CoAP simple congestion control/advanced (CoCoA). The simulation results of the proposed CAR-MOSOA for 400 nodes are as follows energy consumption, 33 J; end-to-end delay, 29 s; packet delivery ratio, 95%; and network lifetime, 973 s, which are improved compared to the FDEAM, EOMR, TSGWO, and CoCoA.  
congestion; Internet of Things; scalability; seagull optimization algorithm; wireless sensor network +
Footnote †: journal: Physics Letters B  
1
# Energy Efficiency of D2D Multi-User Cooperation
## 2 Cooperation of D2D Multiple Users
**\(a>1,0\leq b<x\), the size of \(Y_{1}\big{(}y_{1}^{1},y_{2}^{1}\cdot\cdot\cdot y_{x}^{1}\big{)}\) is smaller than that of \(N_{1}\Big{(}n_{1}^{1},n_{2}^{1},\cdot\cdot\cdot,n_{(a-1)x+b}^{1}\Big{)}\). The \(x\) D2D users chosen by \(Y_{1}\) from \(N_{1}\) will be the relaying D2D users of the rest of \((a-2)x+b\) D2D users of \(N_{1}\). \(Y_{1}\) and \(N_{1}\) are supposed to form bipartite graph. \(G_{2}(Y_{1},N_{1})\). Hence, before matching \(Y_{1}\) and \(N_{1}\) based on SNRs, it is necessary to consider the energy of the relaying candidates. \(v^{\prime}\) D2D users whose maintaining energy \(B_{i}\) is bigger than energy threshold \(l^{\prime}\)are chosen from \(N_{1}\) to form set \(V^{\prime}\):  
\[V^{\prime}=\left\{U_{i}\big{|}B_{i}\geq l^{\prime},1\leq i\leq v^{\prime},x \leq v^{\prime}\leq K-x\right\} \tag{6}\]  
Then, \(Y_{1}\) is matched with \(V^{\prime}\) according to the SNRs as in Situation A. The corresponding expanding label matrix \(\hat{L}\) can be expressed as:  
\[\hat{L}=\left[ \begin{array}{cccc}\hat{l}_{1,1}&\cdots&\hat{l}_{1,v^{\prime} }\\ \cdots&\cdots&\cdots\\ \hat{l}_{x,1}&\cdots&\hat{l}_{x,v^{\prime}}\\ 0&\cdots&0\\ \cdots&\cdots&\cdots\\ 0&\cdots&0\end{array} \right]_{(x^{\prime}\cdot v^{\prime})}\qquad,x<v^{\prime} \leq K-x \tag{7}\]  
where \(\hat{l}_{i,j}\), \(1\leq i\leq v^{\prime}\), \(1\leq j\leq v^{\prime}\), \(\hat{l}_{i,j}=0\)\(x<i\leq v^{\prime}\). The adjacency matrix of the paired D2D users can be expressed as:  
\[A=\left[ \begin{array}{cccc}a_{1,1}&\cdots&a_{1,m}\\ \cdots&\cdots&\cdots\\ a_{m,1}&\cdots&a_{m,m}\end{array} \right]_{(m\cdot m)},m=min(x,K-x) \tag{8}\]There is only one 1 in each row and each column, which means that \(a_{i,j}=1\) and \(a_{h,c}=1\) when \(i\
eq h,j\
eq c\). The rest of the elements are all zero. Hence users \(U_{i},U_{h}\) of \(Y_{1}\) pair with \(U_{j},U_{c}\) of \(N_{1}\), respectively.
# 3D Reconstruction of a Single Bubble in Transparent Media Using Three Orthographic Digital Images
## 4 Benchmark with Synthetic Bubbles
### Effects of Deformation  
The above analysis revealed that the degree of bubble deformation has a significant effect on the reconstruction accuracy. Therefore, the following ellipsoids (samples No. 1-11) were used as examples to analyze the influences of the horizontal deformation of the bubble, in terms of the ratio of the vertical axis to the horizontal axis, i.e., (_b/a_), and the sphericity of the bubble on the accuracy of the reconstruction algorithms.  
Figure 11 shows that the MRE values of the three methods are similar when the ratio of the vertical axis to the horizontal axis, (_b/a_), is greater than 0.5 and confirms that the method in this paper performs better than the two other methods with a reconstruction error smaller than 6%. Reducing the ratio of the vertical axis to the horizontal axis in the range of 0 to 0.5 increases the mean relative error obtained by the three methods. Moreover, the mean relative error in reconstructing the bubble by the method in this paper is not more than 24%, which denotes a satisfying degree of accuracy. Nonetheless, the accuracy of the method in [PERSON] et al. [21] is closely similar to that of the method in [PERSON] et al. [20] since all the analyzed samples are standard ellipsoids, the vertical projection of which is oval. Therefore, the capability of the method in [PERSON] et al. [21] to resist vertical deformation does not give it an advantage over the method in [PERSON] et al. [20] in this case.  
Sphericity (s), as a parameter measuring the degree of deviation of an object from a sphere, is defined by:  
\[s=S_{s}/S_{o} \tag{17}\]  
where, \(S_{S}\) denotes the surface area of a sphere having the same volume as the object, and \(S_{O}\) stands for the surface area of the object.  
Figure 11: The accuracy of the reconstruction methods as a function of the ratio of the vertical axis to the horizontal axis.  
Figure 10: The accuracy of the reconstruction methods in reconstructing different shapes.  
Figure 12 demonstrates that the accuracy of the three methods in calculating the bubble volume, or rather reconstructing the bubble, is not much different when the sphericity is greater than 0.90; however, the method in this paper is slightly more accurate than the two other methods.  
Therefore, based on the above discussion, the method in this paper is more accurate and has better applicability when the bubble is horizontally or vertically deformed.
## 2 Proposed Multimodal Reconfigurable Sensor Readout Circuit
### Detailed Description of the Amplifiers  
#### 2.3.1 Chopper-Stabilized, Multi-Path Operational Amplifier with an AC-Coupled RRL  
The block diagram of the chopper-stabilized, multi-path operational amplifier with an AC-coupled RRL is shown in Figure 5. This circuit has two main signal paths: a low-frequency path and a high-frequency path. The low-frequency path determines the gain and noise at low frequencies, while the high-frequency path determines the gain at high frequencies. This multi-path structure can compensate for the bandwidth limiting caused by chopper stabilization [17].  
In the high-frequency path, the folded cascade stage is used to provide a high gain. A nested Miller compensation through C5 is applied for compensation in the high frequency path. To obtain a high power efficiency and slew rate, a Monticelli class-AB output stage was applied.  
In the low-frequency path, the chopper stabilization technique was implemented to reduce the low-frequency noise. This technique modulates the low-frequency noise into the high-frequency band with low noise and then demodulates the input signal. It can reduce the low-frequency noise, but a ripple is generated, which can restrict the dynamic range of the amplifier. For a low supply voltage, the effects are more severe and should be reduced. To reduce the ripple, an AC-coupled RRL was applied. When the offset is at the input of the low-frequency path, the offset is up-modulated from DC to AC and amplified by Gmd. After entering the chopper circuit, the offset is demodulated from AC to DC and integrated in Gmd, causing a ripple approximated by the following equation:  
\[\mathrm{V_{ripple}}\approx\frac{\mathrm{V_{os}G_{m4}}}{2\mathrm{f_{chop}C_{2}}} \tag{9}\]  
The ripple enters the AC-coupled RRL through the capacitor C3 and the RRL reduces the ripple at Gmd according to the following equation:  
\[\mathrm{V_{rip\_RRL}}\approx\frac{\mathrm{V_{os}G_{m4}}}{2\mathrm{G_{m}} \mathrm{G_{m8}}} \tag{10}\]  
By implementing the AC-coupled RRL, the ripple is reduced, but it causes a notch in the transfer function of the amplifier at the chopper frequency. This notch restricts the bandwidth of the amplifier. By combining the low-frequency path and high-frequency path signals in the current  
Figure 5: Block diagram of the chopper-stabilized, multi-path operational amplifier with an AC-coupled RRL.  
adder, the chopper-stabilized, multi-path structure can compensate for the notch caused by the AC-coupled RRL.
## 2 Theoretical Background
### Machine Learning  
ECD-AS is an adaptive data-driven anomaly detection framework presented in [4]. ECD-AS learns and adapts from the real-time SG data to distinguish any anomalous behavior from the normal behavior of the system. The ECD-AS detector learns a series of statistics (\(\mu_{m}\), \(\mathbf{\Sigma}_{m}\) and \(\tau_{m}\)), one for each bus \(m\) and then updates them with new incoming data samples to adapt them. ECD-AS uses mean (\(\mu_{m}\)) and covariance matrix (\(\mathbf{\Sigma}_{m}\)) of each bus established using normal samples data to calculate squared Mahalanobis distance (\(\delta_{m}^{ECD-AS}\)) and uses it as a decision score for the bus \(m\) and detects any anomalies by comparing it to the adaptive threshold of bus \(m\) (\(\tau_{m}\)). The squared Mahalanobis distance is calculated as  
\[\delta_{m}^{ECD-AS}(\mathbf{z}_{m})=(\mathbf{z}_{m}-\mu_{m})^{\mathsf{T}}\Sigma_{m}^{-1 }(\mathbf{z}_{m}-\mu_{m}) \tag{4}\]  
where \(\mathbf{z}_{m}\) is the measurement vector of bus \(m\), \(\mu_{m}\) is the mean and \(\Sigma_{m}^{-1}\) is the inverse covariance matrix of normal samples related to \(m^{th}\) bus. The mean, \(\mu_{m}\), and inverse covariance matrix, \(\Sigma_{m}^{-1}\), for each bus are updated using the Woodbury Matrix Identity equations provided in [18]. The adaptive threshold (\(\tau_{m}\)) of bus \(m\) is updated with a sliding window of size \(\beta\) over recent normal samples using the equation  
\[\tau_{m}=\mu_{thr,m,-\beta}+\eta*\sigma_{thr,m,-\beta} \tag{5}\]  
where \(\mu_{thr,m,-\beta}\) and \(\sigma_{thr,m,-\beta}\) are the mean and standard deviation of the squared Mahalanobis distance values of normal samples in \(\beta\) most recent samples of selected measurements associated with \(m^{th}\) bus, and \(\eta\) is a hyper-parameter that decides how many standard deviations the threshold should be from the mean.
## 1 Introduction
This enables actors to make a more informed choice about which technology could be valuable for specific supply chains, contributing to optimized product qualities and reduction of losses. For bibliographic retrieval, scientific databases (Web of Science, Google Scholar, Wiley) were used to collect scientific literature. The research was conducted on the main topics of spoilage characteristics, quality-relevant ambient parameters, IoT sensor and communication technology, and application studies. Examples of keywords used for the search are \"fruit and vegetables\" and \"shelf life\", \"wireless sensing\" and \"postharvest quality\", \"IoT\" and \"food supply chain\", \"IoT\" and \"cold chain management\". For examples of sensor technology and related technical information, sources directly retrieved from the web were used additionally, since scientific literature is not exhaustive enough for this kind of information. After reviewing the search results, 105 bibliographic references from scientific databases and 22 web sources were included in this review. Firstly, spoilage mechanisms of fresh fruit and vegetables and their implications for the quality and shelf life are described in Section 2. This is followed by Section 3, in which relevant ambient parameters and the corresponding IoT-enabled sensors are presented. In Section 4, IoT communication technologies enabling wireless data exchange are described. In the discussion in Section 5, the findings from the previous sections are integrated. The potential, but also the challenges of ambient parameter monitoring in fresh fruit and vegetable supply chains using IoT-based sensor and communication technology are pointed out.
## 4 Evaluation Results and Discussion
### Performance across Session Durations
#### 4.4.1 First Model Group
In addition, although the bitrate average and the average of switching amplitudes are generally better than the QP average, they are still not effective enough to model the impact of quality variations.
# Numerical Study of Geotextile-Reinforced Flexible Pavement Overlying Low-Strength Subgrade
## 1 Introduction
[22] focused on the impact of geosynthetic reinforcement in asphalt pavements on critical strains under different levels of axle loads. Three kinds of geogrid materials were used. It was noted that the placement of a geogrid beneath the asphalt layer reduced longitudinal tensile strain, which is in agreement with the findings from [PERSON] et al. [23]. Moreover, it was also observed that, with higher axle loads, a geogrid placed at the subgrade was more effective in minimizing compressive strain on the subgrade. The strength of the subgrade has an impact on the effectiveness of geosynthetic reinforcement. In the previous work of [PERSON] and [PERSON] [24], it was reported that geogrid placement on a weak subgrade was more effective than placing it on stronger subgrades. In the same study, a more significant reduction in surface deflection was achieved when a thinner asphalt layer was used. Finite Element modeling of low-volume flexible pavement reinforced with a geogrid was conducted by [PERSON] et al. [25] using a 2-D FE model. The geogrid was placed at the base-subgrade interface in their study, and the pavement model was exposed to a cyclic load. The study reported a significant decrease in vertical deformation for the reinforced section. [PERSON] et al. [26] performed 3-D FEA and an experimental study of unpaved roads reinforced with a geogrid under cyclic loading. The study observed minimum surface deformation when the geogrid was embedded at a depth equivalent to half of the radius of the loaded area. [PERSON] et al. [1] carried out 3-D FEA of unpaved pavement reinforced with a geogrid under cyclic loading and concluded that placing a glass fiber grid at the base-subgrade interface was more effective. While past studies have generally acknowledged the potential for geosynthetics as reinforcement material, they mainly focused on a single criterion. Therefore, this paper presents a numerical analysis of low-volume flexible pavement reinforced with geotextile material under static loading to determine the improvement due to reinforcement based on three criteria: rutting performance, geosynthetic placement location, and base course thickness reduction. Various pavement material characteristics were used to define material properties and behavior. The study is based on 3-D Finite Element Analysis using ABAQUS software.