esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
In terms of performance comparison between the controllers, the AMPC has the lowest RMSE value. Table 3 shows the mean energy comparison of the different controllers in Figures 4-6. The mean efficiency of the controller is recorded from 60 s to 300 s. The mean efficiency is taken after 60 s to ensure appropriate temperature build up within the heating chamber. Table 3 reveals that the conversion of electrical energy to heat with the CFOH is highest when the AMPC controller is adopted, compared to PID and MPC. This indicates that the rate of conversion of electrical energy to heat is the highest with AMPC. When heating up the tomato sauce (in the first 60 s), low energy efficiencies were observed for all three \begin{table}
\begin{tabular}{c c c} \hline \hline
**Controller** & **Setpoint Temperature (\({}^{\circ}\)C)** & **RMSE Value** \\ \hline PID & 90 & 2.99 \\ MPC & 90 & 1.83 \\ AMPC & 90 & 1.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE values of the PID, MPC, and AMPC controllers. Figure 6: Temperature response of heating tomato basil sauce with the AMPC controller. deployed controllers. This is due to the initial temperature building up within the heating chamber and the viscosity of the tomato sauce. The heating rate and energy efficiency conversion is generally low in the initial build up for product with a higher viscosity [1]. The faster heating rate of the CFOH comes with detrimental effects, such as temperature overshoots, uneven heating, boiling, and large steady-state errors. Hence, a steady rise in temperature is preferred. This steady rise in temperature is seen in Figures 4-6 and ensures the uniform heating of the tomato sauce and that the tomato sauce does not boil within the heating chamber. Therefore, the PID, MPC, and AMPC controllers were tuned to remove temperature overshoots for achieving the desired temperature profiles. The efficiency seen from the results does not take into consideration the energy loss in the positive displacement infeed pump. The energy dissipated in the pump is assumed to be isolated from the heating process. The CFOH process demonstrated in this study shows a comparable results with the various heating technologies discussed in [28]. The study assessed several innovative and conventional food preservation methods, with the aim of evaluating and comparing the energy efficiency of high-pressure processing, microwave volumetric heating, ohmic heating, and conventional thermal treatments. Their findings indicated that energy efficiency for ohmic heating systems tends to improve as the scale of the equipment increases.
## 4 Experimental Results  
### Diagnosis Accuracy for Compound Bearing Faults  
All of the experiments on the datasets were repeated ten times. These first experiments were conducted on Dataset 1 and Dataset 2. Table 3 shows the accuracy of the proposed FD method, where the results are compared to four existing AE-based FD methods for compound faults detection under variations in the rotational speed. Firstly, we compared two CNN-based FD methods [10; 11] using the spectra of AE signals to create two-dimensional (2D) energy distribution maps (EDMs). The created EDMs fed a generic CNN based on Lenet-5 architecture to extract the bearing fault features. After extracting features, in [11], a hybrid ensemble MLP-SVM classifier is used to classify the faults from extracted features. On the other hand, in [10], classification is performed by multilayer perceptron classifiers. Additionally, a stochastic diagonal Levenberg-Marquardt algorithm is used while training to enhance the training process. In the existing FD method proposed in [3], [PERSON] et al. defined fault features by various statistical parameters and complex envelope analysis in the time and frequency domain based on specific fault frequency bands calculated by a specific rotational speed. Then, an outlier-insensitive hybrid feature selection (OIHFS) is applied to determine which features are useful to feed the K-NN classifier. An approach \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Single and Compound Bearing**} & \multicolumn{3}{c}{**Rotational**} & \multicolumn{3}{c}{**Crack Size**} \\ \cline{3-8}  & **Failures** & **Speed (RPM)** & **Length (mm)** & **Width (mm)** & **Depth (mm)** \\ \hline \multirow{2}{*}{**Dataset 1**} & Training subset & 300, 400, 500 & \multirow{2}{*}{3} & \multirow{2}{*}{0.60} & \multirow{2}{*}{0.30} \\ \cline{2-2} \cline{5-8}  & Testing subset & & & & & & \\ (and Validation) & 250, 350, 450 & & & & & \\ \hline \multirow{2}{*}{**Dataset 2**} & Training subset & 300, 400, 500 & \multirow{2}{*}{12} & \multirow{2}{*}{0.60} & \multirow{2}{*}{0.50} \\ \cline{2-2} \cline{5-8}  & Testing subset & & & & & & \\ (and Validation) & 250, 350, 450 & & & & & \\ \hline \multirow{2}{*}{**Dataset 3**} & Training subset & 300, 400, 500 & \multirow{2}{*}{3} & \multirow{2}{*}{0.60} & \multirow{2}{*}{0.30} \\ \cline{2-2} \cline{5-8}  & Testing subset & & & & & & \\ (and Validation) & 250, 350, 450 & & & & & \\ \cline{2-2} \cline{5-8}  & Testing subset & & & & & & \\ (and Validation) & 250, 350, 450 & & & & & & \\ \cline{2-2} \cline{5-8}  & \multirow{2}{*}{250, 350, 450} & \multirow{2}{*}{12} & \multirow{2}{*}{0.60} & \multirow{2}{*}{0.50} \\ \cline{2-2} \cline{5-8}  & & & & & & & & \\ \cline{2-2} \cline{5-8}  & & & & & & & & \\ \cline{2-2} \cline{5-8}  & \multirow{2}{*}{250, 350, 450} & \multirow{2}{*}{12} & \multirow{2}{*}{0.60} & \multirow{2}{*}{0.50} \\ \cline{2-2} \cline{5-8}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluated dataset for bearing faults.
## 3 Results and Discussion
#### 3.1.1 Carbohydrates and Organic Acids from Quinoa Wholeneal Flour (QWF)  
The values of simple carbohydrates (maltose, glucose, and fructose) and organic acids (lactic, acetic, and citric) of QWF are reported in Table 1. The main carbohydrates from QWF was glucose, followed by fructose and maltose.  
The carbohydrate content in quinoa is higher than in the common cereals and might be associated with frost tolerance, as confirmed by [46] who reported that quinoa from mountain region had higher sugar content than those from the valleys region. Likewise, ref. [47] reported that also the method of extraction, the origin of quinoa seeds, cultivation, and environmental stress could also influence the sugars content.  
With respect to fructose content, it is known that its consumption could induce oxidative stress, leading to different type of diseases such as obesity, hypertriglyceridemia, and cardiovascular diseases. [PERSON] et al. [48] showed that although quinoa seeds contain fructose, the seeds are able to reduce the oxidative stress due to its ability to increase MDA (malondialdehydedehyde) level, indicating an intensive lipid peroxidation and protecting plasma against peroxidation. Moreover, reducing the oxidative stress could reduce the free radicals during some pathological states.  
In our previous study, we determined a value of 68.2% total carbohydrates [26], close to the value reported by [1] of 67.9% and [49]. The total carbohydrates content in quinoa could vary between 48.6 and 68.1% of dry matter weight and 45.16-59.78%, respectively, as reported by [50; 51]. Starch, the major carbohydrate of quinoa, ranges between 32 and 69% of total carbohydrates [52] and is followed by total dietary fiber (7-9.7%) [50] and fermentable sugars (2% from the total carbohydrate amount), as reported by [53].  
With respect to organic acids, lactic, and acetic acids could not be found in QWF, but citric acid had a total amount of 8.59 \(\pm\) 0.30 mg/g f.w. (fresh weight). Citric acid was also identified in quinoa flour by other researchers, in the range of 210-317 mg/100 g d.w., and from 0.40 to 0.71 g/100 g f.w., respectively [47; 49].
# Evolution of Water-in-Oil Droplets in T-Junction Microchannel by Micro-PIV
## 1 Introduction
The measured results revealed that the fluid residing inside the droplet intricately observes a circular three-dimensional motion within the constrained volume, as the contacting surfaces, i.e., surrounding walls of the channel, pose drag on the surfaces of the droplet while it moves inside the microchannel. This intricated motion of fluid within the droplet enhances the mixing and, resultantly, the chemical reaction in the device, if any. In a different study, [PERSON] and [PERSON] [30] conducted flow visualization via micro-PIV to investigate water/glycerol-in-oil droplet merging processes in a main Y-microchannel, which is connected downstream to a straight channel or a divergent channel. The results for a straight channel-confined droplet suggest that the rear droplets, at the time coalescent, penetrated the front droplets. While in the divergent channel geometry, as the droplet merges, a strong vortex motion is generated, resulting in the rear droplet enveloping the front droplets. [PERSON], [PERSON] [31] investigated fundamental flow characteristics of a water-ethanol droplet suspended in sunflower oil merging in the T-junction channel and rectangular microgroove, and splitting in two different microstructures, i.e., cylinder obstruction and Y-junction bifurcation via micro-PIV technique. The microgroove generates higher probability for the coalescence of the droplet compared to the T-junction in a microchannel. Moreover, the junction of the Y-shape can result in microdroplets splitting with a higher efficiency (\(\eta>95\%\)) while keeping the microdroplet flow steady during the splitting at the junction. The existed studies focused on the different shape of the microchannels for the droplet merging and splitting process, with less consideration given to the size or diameter of the microchannels [32; 33; 34], which is also one of the properties of channel geometry that can affect the flow characteristics of the droplets. Therefore, in this work, we aim to study experimentally the behaviours of a distilled water droplet suspended in food-grade palm olein at the interfacial surface in different sizes of offset T-junction microchannels, i.e., radiuses of 400 \(\upmu\)m, 500 \(\upmu\)m, 750 \(\upmu\)m and 1000 \(\upmu\)m, using the micro-PIV technique in MATLAB software. The channels' size was described as a main factor affecting the evolution and velocity of the water droplets, and also the movement and velocity of the seeding particles.
## 4 Conclusions and Future Work  
In this paper, to improve the accuracy of PRNU fingerprint extraction and achieve a more lightweight PRNU fingerprint extraction solution in the field of image forensics, we propose an effective PRNU fingerprint extraction algorithm based on an invertible denoising network. The algorithm innovatively combines reversible networks with PRNU fingerprint extraction technology to ensure that the proposed solution achieves a balance between performance and resource efficiency, making it highly relevant to forensic applications in the real world. Experimental results demonstrate that InvDN-based methods exhibit a superior PRNU fingerprint extraction performance compared to other algorithms. However, the algorithm in this paper also has some limitations. The compression operation of the image may cause a certain degree of damage to the PRNU fingerprint, thereby reducing the algorithm performance; the residual enhancement stage of the algorithm may also enhance unnecessary noise. In addition, the noise suppression and noise injection operations may also affect the robustness of the PRNU fingerprint; the anti-forensics operation for image forensics may also affect the extraction quality of the PRNU fingerprint [21,22]. In future work, the author will focus on researching and solving the above problems. Meanwhile, although the algorithm in this paper is easier to deploy on small devices compared to the contrast algorithm, there are still problems of slow speed and low efficiency in actual operation in the experimental environment, which are not sufficient to meet the real-time requirements of some forensic environments. In the future, we will further optimize the proposed algorithm to enhance its PRNU fingerprint extraction performance and efficiency.  
Conceptualization: Z.Y., Y.X. and H.T.; methodology: Z.Y.; software: Z.Y.; validation: Z.Y.; formal analysis: Z.Y.; investigation: [PERSON] and Y.X.; resources: H.T.; data curation: [PERSON] and Y.X.; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON]; visualization: [PERSON]; supervision: [PERSON] and H.T.; project administration: [PERSON] and H.T.; funding acquisition: [PERSON] and H.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Public Security Science and Technology Plan Technical Research Project (grant No. 2022 JSYJC22) and the Fundamental Research Funds for the Central Universities (grant No. 2022 JKF02003).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Design of TLQoS
### Neighbor Manager  
The neighbor manager module in TLQoS performs the following functions: (i) Executes the HELLO protocol; (ii) Builds and manages neighbor table; (iii) Interacts with the delay, reliability, and temperature estimator module to acquire the respective parameter values for the node; (iv) Provides the delay, reliability and temperature module with neighbor table information.  
Periodically, or upon observing significant changes in the parameter values, a node broadcasts a HELLO packet to its neighbors. The HELLO packet includes node ID, hop-count to the BC, average node delay, average packet loss ratio, and average temperature of a node. This module maintains a neighbor table that assigns an entry for each neighboring node. The neighbor table structure is shown Figure 3. The table is updated upon each reception of the HELLO packet from a neighbor. The update includes addition of new entries when a new node enters into the node's neighborhood, deletion of a node entry if the update is not received for a particular time period, and updating information of the existing entries.  
The frequency of HELLO packet exchange is also an important criteria to be considered. Since, most of the parameters (_i.e_., delay, packet loss ratio, temperature) are highly dynamic, higher frequency of HELLO packet exchange would provide up-to-date information but would be resource consuming. However, lower frequency could result in stale information, also cause network connectivity loss. The HELLO packet interval selection mechanism is discussed later in this section.
## 3 Methods
### Loaded Swelling Strain Test  
The loaded swelling strain test was performed in an oedometer (Figure 3). To study the changes in swelling strain during the gradual stabilization process for each MB specimen with a certain initial dry density and under a certain vertical load, load swelling strain tests of MB specimens with a certain water content (19%), different dry densities (1.25, 1.40, 1.55, 1.70, and 1.85 g/cm\({}^{3}\)) and different vertical stresses (25, 50, 100, 150, 200, 300, and 400 kPa) were conducted under saline water conditions. The spacer disk filter paper, specimen, filter paper, spacer disk, and pressurizing upper cover were put into the oedometer in turn. The upper cover was aligned with the center of the pressurization frame, then dial gauge was installed. To ensure full contact between the specimen and the instrument, the pressure was pre-set to 1 kPa and the dial gauge was set to zero before testing. The required load was then applied to the specimen and saline water was added to the sink. We measured, and recorded, the vertical swelling deformation of the specimen using the same method as described for the unload swelling strain test.  
The swelling strain (\(\varepsilon\)) in the experiment under both unloading and loading conditions is given by Equation (1):  
\[\varepsilon=\frac{\Delta H}{H_{0}}=\frac{H_{\text{t}}-H_{0}}{H_{0}}\times 100\% \tag{1}\]  
where \(\varepsilon\) is the swelling strain in the MB specimen; \(H_{0}\) represents the initial height of the MB specimen (mm); and \(H_{\text{t}}\) is the final height of MB specimen after the swelling process was completed (mm).  
Figure 2: Unloaded swelling strain test apparatus.
## 2 Techniques Used for Adaptive Equalization  
Adaptive equalization is a fundamental signal-processing technique utilized in numerous communication systems to improve the quality and reliability of transmitted data [7]. It serves as a crucial step in combating the detrimental effects of channel impairments, such as multipath propagation and frequency response variations, which can introduce inter-symbol interference (ISI) and degrade the received signal quality [8; 9]. The primary goal of adaptive equalization is to dynamically adjust the characteristics of the received signal to closely align with the desired signal [10], effectively mitigating distortions and restoring the fidelity of the transmitted data [11]. To achieve adaptive equalization, a diverse range of techniques has been developed [12], each with its own approach and advantages. These techniques are designed to adaptively modify the parameters or coefficients of the equalizer based on the characteristics of the channel and the received signal. By continuously monitoring and updating the equalizer, it can adapt to the changing conditions of the communication channel and optimize its performance accordingly.  
The field of adaptive equalization has witnessed significant advancements and innovation over the years [13], driven by the increasing demands for high-speed data transmission and reliable communication systems [14; 15]. Researchers and engineers have explored various approaches including algorithmic optimization techniques, machine learning algorithms, and advanced signal-processing methods, to enhance the performance of adaptive equalization [16; 17]. These techniques aim to strike a balance between computational complexity, convergence speed, and adaptability to different channel conditions, providing robust and efficient solutions for adaptive equalization in a wide range of applications [18]. The choice of adaptive equalization technique depends on several factors, such as the specific characteristics of the channel [19], the desired performance metrics [20], the available computational resources, and the trade-off between complexity and effectiveness [21]. As the field continues to evolve, researchers are constantly pushing the boundaries of adaptive equalization techniques, seeking novel approaches to address the challenges posed by emerging communication technologies and ever-changing channel conditions. By harnessing the power of adaptive equalization, communication systems can achieve higher data rates, improved spectral efficiency, and enhanced reliability, paving the way for seamless and efficient transmission of information in diverse environments. Adaptive equalization can be achieved using various techniques [22], each with its unique approach and advantages. Some of the techniques include LMS, RLS, PSO, genetic algorithms (GAs), and deep learning, which are discussed below.
# Mode Profile Shaping in Wire Media: Towards An Experimental Verification
## 3 Modulated Wires in A Supporting Waveguide or Cavity
The simulated field profile \(E_{z}\) itself is normalised and shown with solid black lines, and is compared with the ideal Mathieu function profile shown as a dashed red curve, with a sinusoid shown in blue as a reference. In the simulation, CST gave the maximum longitudinal field as \(3.28\times 10^{7}\) Vm\({}^{-1}\). Figure 4:2 Field profile shaping in a metallic waveguide containing a \(4\times 4\) array of wires with varying radii in a metallic cavity, and with \(L=80.61\) mm. The simulated field profile \(E_{z}\) itself is normalised and shown with solid black lines, and is compared with the ideal Mathieu function profile shown as a dashed red curve. Figure 4:3 Field variation of a longitudinal modes in a \(2\times 2\) array of uniform infinitely long wires in a waveguide. The distances between the wires are given by \(L_{x}=15\) mm and \(L_{y}=13.06\) mm. The single period of the longitudinal electromagnetic wave is given by \(L_{z}=80.6\) mm. The dimensions of the waveguide are given by \(S_{x}=52.5\) mm and \(S_{y}=45.7\) mm. Thus, the distances of the wires closest to the waveguide walls and the walls are \(\frac{1}{4}L_{x}\) and \(\frac{1}{4}L_{y}\). Here the longitudinal component \(E_{z}\) in the \((x,y)\) plane is shown. Figure 4:4 Profile of \(E_{z}\) component for a waveguide with \(2\times 2\) infinitely long wires of varying radii (solid Black) in comparison with Mathieu function (dashed red) and sin function (blue). Here \(L~{}=~{}127.4\) mm. Both \(E_{z}\) and the Mathieu function are normalised. However CST gave the maximum longitudinal field as \(6.03\times 10^{7}\) Vm\({}^{-1}\).
## 3 Numerical Investigation
### The Effect of the Number of Sample Points  
In this section, the effect of the number of sample points is discussed. Similarly, the initial values of the stiffness and damping coefficients \(\alpha\) and \(\beta\) are also assumed to be 3.0 \(\times\) 10\({}^{5}\) N/m and 1.0 and  
Figure 4: The identified structural parameters (10% noise): (**a**) \(k_{1}\); (**b**) \(k_{2}\); (**c**) \(k_{3}\); (**d**) \(k_{4}\); (**e**) \(k_{5}\).  
Figure 5: Identified results of the ground acceleration (10% noise): (**a**) the whole time history; (**b**) the time segment from 4 s to 6 s.  
Figure 6: Comparison of the structural responses on the top floor (10% noise): (**a**) acceleration; (**b**) velocity; (**c**) displacement.  
1.5 \(\times\) 10\({}^{-4}\), respectively. The element-wise coefficient \(q_{i}\) is thus initially equal to 45. For practical consideration, noise of 10% intensity as mentioned before is introduced to the measured signals. Herein, the time durations of 6 s, 12 s, and 18 s with 300, 600, and 900 sample points, respectively, are considered. The identification results for these three cases are given in Table 2. It can be seen in Table 2 that as the number of sample points increases, the identification error decreases. By comparing the identified results using 600 sample points with those using 900 sample points, the results are improved slightly when even more sample points are being used. This means that the effect of the number of sample points on the identification results becomes small when sufficient sample points are used. To further explore the effect on the convergence procedure, the comparison of the identified \(k_{5}\) for the cases of 300, 600, and 900 sample points is shown in Figure 7 as an example. It can be seen that the identified parameters can be stably converged to the actual ones. Moreover, the iterative process is conducted 415, 312, and 291 times for the cases of 300, 600, and 900 sample points, respectively, which means that more iterations are required if fewer sample points are employed.  
Moreover, by using the proposed approach, the ground acceleration can be identified as well. Taking the case of 900 sample points as an example, Figure 8 gives the identified excitation as a dashed line, whereas the actual one is plotted as a solid line. It is clear that the identified ground motion is close to the corresponding actual one.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Structural Parameters**} & \multicolumn{2}{c}{**300 Sample Points**} & \multicolumn{2}{c}{**600 Sample Points**} & \multicolumn{2}{c}{**900 Sample Points**} \\ \cline{2-7}  & **Identified** & **Error (\%)** & **Identified** & **Error (\%)** & **Identified** & **Error (\%)** \\ \hline \(k_{1}\) & 4.715 \(\times\) 10\({}^{5}\) & 1.77 & 4.756 \(\times\) 10\({}^{5}\) & 0.92 & 4.768 \(\times\) 10\({}^{5}\) & 0.67 \\ \(k_{2}\)