esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Testing and Evaluation
### Functionality Testing  
1. Web Application  
The black box method was used to test the proposed Web application \"Iraq Red Wings (IRW)\" to check whether it could run correctly based on the defined functional system requirements [55].  
Table 3 shows the functionalities of the IRW Web app that needed to be tested following the system implementation stage by the black box method. The tester was only aware of the input and was unaware of the output. If the application met the functional system requirement, the column was marked Yes (). However, if it was the other way around, the column was marked No ().  
Figure 12: The monitoring process: (**a**) shows the temperature degree within an acceptable range; (**b**) shows the warning message if the temperature is outside of the acceptable range.  
2. Temperature Sensor Testing Figure 14 below shows the temperature sensor testing results (DHT-11). It was tested in two stages: first, a test of one unit sensor; and, second, a test of eight sensor units together. The test was performed in refrigerators with different temperature settings. The test process was conducted by connecting the temperature and humidity sensor (DHT-11), ESP8266 module, breadboard, and colored wires. Then, the temperature data were read in the Excel sheet via Arduino IDE 1.8.19 software, and the screen results were as shown.  
Figure 13: Testing and evaluation step.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Functional System Requirement** & **Yes** & **No** \\ \hline
1. & The system can process blood donation data. & ✓ & - \\ \hline
2. & The system provides the ability to manipulate master data. & ✓ & - \\ \hline
3. & The system may display contact information, donor history, and blood type. & ✓ & - \\ \hline
4. & The system includes a feature for verifying blood donor orders. & ✓ & - \\ \hline
5. & The system provides a facility for searching blood inventory & ✓ & - \\ \hline
6. & The system is capable of automatically updating the blood inventory. & ✓ & - \\ \hline
7. & The system has the ability to monitor the temperature of the blood & ✓ & - \\ \hline
8. & The system has the ability to send notifications in the case of a shortage of a certain blood group. & ✓ & - \\ \hline
9. & The system can be used to document blood examination results. & ✓ & - \\ \hline
10. & The system includes the ability to generate reports for blood & ✓ & - \\ \hline  & & & \\ \end{tabular}
\end{table}
Table 3: Functional system requirement checked by black box testing.  
This test helped to ensure that the sensors were correctly connected and that all components were not damaged. Both functioned properly and successfully, integrating with the proposed Web application, as indicated in Table 4.
# Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
## 2 Modeling Network Flows
### Representing Memory and Multilayer Networks with Sparse Memory Network
Specifically, we can lump together state nodes \(\alpha_{i}\) and \(\beta_{i}\) in the same physical node \(i\) if they have identical outlinks, \(w_{\alpha_{i}\gamma_{j}}=w_{\beta_{i}\gamma_{j}}\) for all \(\gamma_{j}\). The lumped state node \(\tilde{\alpha}_{i}\) replaces \(\alpha_{i}\) and \(\beta_{i}\) and assembles all their inlinks and outlinks such that the transition probabilities remain the same. For example, for describing where flows move from physical node \(i\), state nodes \(\varepsilon_{i}\) and \(\delta_{i}\) have identical outlinks--reflecting that it does not matter which friend was previously active in the conversation--as well as state nodes \(\beta_{i}\) and \(\alpha_{i}\)--reflecting that it does not matter which colleague was previously active in the conversation. Lumping together all such redundant state nodes, such as \(\varepsilon_{i},\delta_{i}\rightarrow\tilde{\alpha}_{i}\) or \(\beta_{i},\alpha_{i}\rightarrow\tilde{\delta}_{i}\), gives the sparse memory network in Figure 2e, where state nodes no longer are bound to capture the exact previous steps but still capture the same dynamics as the redundant full second-order model. These unbound state nodes are free to represent abstract states, such as lumped state nodes in a second-order memory network, but can also represent state nodes in a full second-order memory network or state nodes in a multilayer network such as \(\eta_{i}\rightarrow\tilde{\alpha}_{i}\) in Figure 2d,e. For example, lumping state nodes with minimal information loss can balance under- and overfitting for efficient sparse memory networks that represent variable-order network flow models [18]. Consequently, rather than an explosion of application and data dependent representations, sparse memory networks with physical nodes and state nodes that can represent abstract states provides an efficient solution for modeling higher-order network flows.
## 4 Feature Selection Results
However, compared to k-NN, the Haar Wavelet algorithm performs \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**File**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{
\begin{tabular}{c} **k-NN** \\ **Accuracy \%** \\ \end{tabular} } & \multicolumn{2}{c}{**Haar Wavelet**} & \multicolumn{1}{c}{**GLCM**} \\  & & & **Accuracy \%** & **Accuracy \%** \\ \hline \multirow{3}{*}{Beef} & Fresh Beef & 99 & 89.96 & 122.89 \\  & Frozen Beef & 98 & 88.25 & 96.15 \\  & Rotten Beef & 96 & 87.97 & 115.79 \\ \hline \multirow{3}{*}{Buffalo} & Fresh Buffalo Meat & 97 & 89.75 & 116.93 \\  & Frozen Buffalo Meat & 97 & 87.96 & 95.80 \\  & Rotten Buffalo Meat & 94 & 86.88 & 102.33 \\ \hline \multirow{3}{*}{Goat} & Fresh Goat Meat & 99 & 89.47 & 183.21 \\  & Frozen Goat Meat & 98 & 86.73 & 185.75 \\  & Rotten Goat Meat & 96 & 86.79 & 94.84 \\ \hline \multirow{3}{*}{Horse} & Fresh Horse Meat & 99 & 89.85 & 128.81 \\  & Frozen Horse Meat & 98 & 87.56 & 110.28 \\  & Rotten Horse Meat & 95 & 86.26 & 77.32 \\ \hline \multirow{3}{*}{Pork} & Fresh Pork & 98 & 89.25 & 51.52 \\  & Frozen Park & 99 & 87.67 & 78.25 \\ \cline{1-1}  & Rotten Pork & 96 & 86.36 & 34.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of performance measurements of the k-nearest neighbor algorithm, Haar Wavelet algorithm, and gray level co-occurrence matrix. worst (87.56%) on the frozen beef texture images. However, the algorithm continues to produce good results. In total, 88.25 percent is the highest performance in the frozen beef texture image. Based on the GLCM algorithm, the frozen pork texture image (C) has the lowest value, 78.25%. However, GLCM results can still be used to classify frozen meat images. After testing three rounds, k-NN maintains its top ranking in frozen meat texture image classification, with Wavelet Haar and GLCM ranking second and third, respectively. This shows that the k-NN technique is more suitable for applications that require frozen meat texture image classification. GLCM is the best model in performance testing, with an accuracy of 185.75%. Rotten Meat: (A) has the lowest percentage (94%), although the texture images of rotten beef, goat meat, and pork have the highest k-NN value (96%). This shows how effective the k-NN algorithm is while classifying images of rotten meat. Meanwhile, (B) k-NN outperforms the Haar Wavelet algorithm (86.26%); there is an image of rotten buffalo meat. However, the algorithm continues to produce good results. In contrast, (C) shows that the GLCM approach produces uncertain results; the rotten pork texture image has the lowest performance percentage (34.62). However, GLCM data can still be used to classify frozen meat images. The rotten beef texture image has the highest performance percentage (115.79%). After testing three different algorithms, k-NN, GLCM, and Wavelet, Haar continues to produce the best classification results for images containing rotten meat textures. This shows that the k-NN approach is more suitable for classifying rotten meat texture images. Classification accuracy based on meat texture analysis is presented in (Table 8) below:
# Legumes as Functional Food for Cardiovascular Disease
## 3 Legumes as Functional Food for Cardiovascular Diseases
### Pea (Pisum sativum L.)  
Peas (_Pisum sativum_ L.) contain an average of 22% protein (ranging between 13-33% protein) and such proteins were found to improve against CVD and its risks [306; 307; 308; 309]. [PERSON] et al., (1996) found that a daily serving of peas contributed to the reduced risk of heart attack in human male professionals [307]. Using rat models, [PERSON] et al., (1995) found that 20% of dietary pea proteins significantly reduced plasma TC by 61%, TAGs by 47% and hepatic cholesterol levels by 94% (\(p<0.05\)) and increased HDL-C concentrations versus the casein-fed group [164]. [PERSON] et al., (2010) [165] found that rats fed with pea proteins for 14 and 28 days had significantly lower plasma TC and TAGs, lower expression of genes involved in fatty acid synthesis including FASN and stearoyl-CoA desaturase, and increased hepatic LDL-receptor mRNA than those fed with casein (\(p<0.05\)). Rats fed with 200 g pea protein per kg body weight for 16 days showed significantly (\(p<0.05\)) reduced hepatic TC and VLDL proteins and increased levels of cholesterol-7-alpha-hydroxylase levels, hepatic SREBP-2 and its target genes such as HMG-CoA and LDL-receptor, and fecal bile acid excretion [166]. Employing obese Dutch people with untreated elevated BP, [PERSON] et al., (2014) found that a pea protein meal significantly sustained blood pressure levels (\(p\leq 0.01\)) and induced the production of nitrite, nitro, and NOx compounds [310].  
Hydrolysis of pea proteins may yield peptides with various bioactivities such as those for ACE1 inhibition and improvement against CVD [258, 306]. For example, Alcalase-digestion of pea proteins liberated three dipeptides (IR, KF, and EF) that exerted ACE-inhibitory effects in vitro [163]. Recent publications reported that the LRW tripeptide and its similar isomer IWR exhibited their antihypertensive activity by activating angiotensin II [167, 311, 312, 313]. Incubation of vascular smooth muscle cells (VSMCs) with this LRW pea tripeptide reduced angiotensin-triggered superoxide production, inflammation and proliferation by inducing the ACE2-Ang-(1-7)-MasR axis and modulating the nuclear factor-kappa B (NF-\(\kappa\)B) pathway [167].
###### Abstract  
A rolling element signal has a long transmission path in the acquisition process. The fault feature of the rolling element signal is more difficult to be extracted. Therefore, a novel weak fault feature extraction method using optimized variational mode decomposition with kurtosis mean (KMVMD) and maximum correlated kurtosis deconvolution based on power spectrum entropy and grid search (PGMCKD), namely KMVMD-PGMCKD, is proposed. In the proposed KMVMD-PGMCKD method, a VMD with kurtosis mean (KMVMD) is proposed. Then an adaptive parameter selection method based on power spectrum entropy and grid search for MKKD, namely PGMCKD, is proposed to determine the deconvolution period T and filter order L. The complementary advantages of the KMVMD and PGMCKD are integrated to construct a novel weak fault feature extraction model (KMVMD-PGMCKD). Finally, the power spectrum is employed to deal with the obtained signal by KMVMD-PGMCKD to effectively implement feature extraction. Bearing rolling element signals of Case Western Reserve University and actual rolling element data are selected to prove the validity of the KMVMD-PGMCKD. The experiment results show that the KMVMD-PGMCKD can effectively extract the fault features of bearing rolling elements and accurately diagnose weak faults under variable working conditions.  
rolling element; feature extraction; variational mode decomposition; maximum correlation kurtosis deconvolution; optimization method; kurtosis mean; variable conditions
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation
### Evaluation Results and Analysis
#### 5.2.2 Experiments in the Different Slice Link Connected Probability Scenario  
We experimented on the different slice link connected probability scenario, in which the slice link connected probability is 0.2, 0.5, and 0.8, respectively, to investigate its impact on the performance of the algorithms. Figures 4 and 5 present the results of the slice acceptance ratio and slice provisioning revenue performance.  
Figure 4 shows that the slice acceptance ratio decreases as the slice link connection probability increases. This is because slice requests with more slice links demand more bandwidth resources, which makes the physical network difficult to satisfy bandwidth demands, resulting in more rejected slice requests. On the other hand, RT-CSP+ always has the highest slice acceptance ratio because of its efficiency. Figure 5a shows that, as the slice link connection probability increases, the long-term average revenue of all algorithms increases except for that of CC. For algorithms except CC, although the slice acceptance ratio is smaller at larger slice link connection probability, more slice links are provisioned in this case, which brings more provisioning revenue. For CC, when the slice link connection probability is 0.2, it can obtain much better slice acceptance ratio compared with 0.5 and 0.8, which contributes a lot to provisioning revenue. The long-term average revenue of CC has a similar trend as other algorithms when the slice link connection probability gets larger. With regard to the long-term average  
Figure 4: Slice acceptance ratio in the different slice link connected probability scenario.  
Figure 5: (a) Long-term average slice provisioning revenue in the different slice link connected probability scenario; and (b) slice provisioning revenue-to-cost ratio in the different slice link connected probability scenario.  
provisioning revenue, RT-CSP+ still outperforms others. Figure 5b shows that the revenue-to-cost ratio decreases as the slice link connection probability increases. The reason more revenue cannot result in larger revenue-to-cost ratio is that more slice links should be provisioned when the slice link connection probability is larger, in which case slice links are easier to be provisioned to a longer physical path, resulting in more provisioning bandwidth cost.
## 3 Results
### Comparison of Different Controllers  
To show the advantages of the control technique used in this investigation, comparisons among PID, conventional SMC, and SMC with a washout filter are performed. The design of the PID controller and SMC, with the parameters of the buck converter, are explained in [25]. The results shown below are simulated in continuous time and some parameters have been changed with values such as \(C=46\) uF, \(L=2.47\) mH, and the switching frequency \(Fc=20\) kHz. As the parameters of the \(LC\) filter are smaller, they let signals of higher frequency pass because the settling time in continuous time is much less than the settling time in discrete time (Figures 5-7), given in the previous results of this document.  
Figure 10 shows the voltage \(v_{c}\) and error for the three controllers: PID, SMC, and SMC with the washout filter. Besides, this figure shows the response of the controllers when the loads are only CILs. The voltage and error are shown in Figure 10a,b when CIL = 10 \(\Omega\) and CPL = 0 W; in Figure 10c,d when CIL = 25 \(\Omega\) and CPL = 0 W; and in Figure 10e,f when CIL = 50 \(\Omega\) and CPL = 0 W.  
Figure 9: Bifurcation diagram of the _error_ vs. \(k\).  
during the steady-state operation for the three loads when SMC with the washout filter is used, which makes it more suitable for regulating voltage signals in this type of system; however, the three controllers have steady-state errors less than 0.5% for the different load values.  
Figure 11 shows the voltage behavior \(v_{c}\) and error for the three controllers: PID, SMC, and SMC with washout filter. Besides, Figure 11 shows the response of the controllers when the loads are CIL and CPL. The voltage and error are shown in Figure 11a,b when CIL = 25 \(\Omega\) and CPL = 40 W; in Figure 11c,d when CIL = 25 \(\Omega\) and CPL = 16 W; and in Figure 11e,f when CIL = 25 \(\Omega\) and CPL = 8 W, respectively.  
The settling time for the SMC with the washout filter remains similar to that obtained previously and is close to 2.5 ms. For both the PID and conventional SMC, the value of the settling time obtained is around four times that obtained with the SMC with the washout filter. Regarding the settling time, it can be concluded that the SMC with the washout filter controller is capable of regulating the voltage faster when there are CPLs. The steady-state error for the three load variation tests shows that the SMC  
Figure 11: Voltage \(v_{c}\) and error for the CIL-CPL loads and the three controllers: PID, SMC, and SMC with washout filter.  
with the washout filter behaves better than the other two controllers as the maximum error for the PID is close to 12%; for the conventional SMC, it is 10%; and for the SMC with the washout filter, it is less than 2% when CIL = 25 \(\Omega\) and CPL = 40 W. For the other load variations tested, the error for the SMC with the washout filter remains less than 1%, and for the other controllers, it is still greater than 2%.
## 3 Global Horizontal Irradiance Models
### Clear-Sky Irradiance Formula
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Equation** & (3) \\ \hline \multirow{2}{*}{Beyer} & \(CC_{B}=\frac{\text{Cat}-\text{Cat}}{(0.7)\varepsilon\cos\theta_{z}\cos^{9+10} \theta_{z}}\) & (4) \\  & \(C_{0B}=4.3+\left[4.5(1+\cos^{2}\psi)\frac{\cos^{18}\theta_{z}}{\cos^{18}\theta _{z}}\right]\) & (4) \\ \hline \multirow{2}{*}{[PERSON]} & \(CC_{R}=\frac{\text{Cat}-\text{Cat}}{T(\theta_{r}T_{L})T_{int}(\omega_{r}T_{L})}\) & (5) \\  & \(C_{0R}=\left(\frac{\text{DH}_{R}}{\pi}\right)\left(\frac{\text{hf}_{0}}{\pi} \right)\left(\frac{0.5}{\cos\psi}\right)0.8\) & (6) \\ \hline \multirow{4}{*}{[PERSON]} & \(CC_{H}=\frac{Cat-\text{Cat}}{T_{0}\varepsilon\cos\theta_{z}}\) & (7) \\  & \(C_{0H}=4.3+\left[(1+\cos^{2}\psi)\frac{F_{H}}{\cos^{18}\psi}\right]\) & (8) \\  & \(F_{H}=-0.55-25.2\cos\theta_{z}-38.3\cos^{2}\theta_{z}+17.7\cos^{3}\theta_{z}\) & (9) \\ \hline \multirow{2}{*}{Perez} & \(CC_{P}=\frac{C_{0H}AM\varepsilon}{2.2837\cos^{-18}\exp(0.004\gamma)}\) & (10) \\ \hline \multirow{2}{*}{Hybrid} & \(CC_{M}=\frac{C_{M}}{C_{M}}\) & (11) \\  & \(C_{0M}=\left[0.85+0.15\left(\frac{\cos\theta_{z}\sin\theta_{z}\exp(0.04-0.044M)}{ \cos\theta_{z}(\cos\theta_{z}+\sin\theta_{z})}\right)\right]\left[1+0.7\left( \frac{50-\psi}{50}\right)^{2}\right]\) & (12) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pixel normalization formulas for obtaining an independent cloud index (CI) (step 2). Each “raw” pixel (\(C_{sat}\)) is subjected to this normalization before CI extraction.
# A Wideband Hybrid Envelope Tracking Supply Modulator with Slew-Rate-Enhanced Linear Amplifier
###### Abstract  
A wideband hybrid envelope tracking supply modulator (HETSM) with a slew-rate-enhanced linear amplifier (LA) is described in this work. The proposed slew-rate enhancement (SRE) structure introduces a parallel auxiliary current path directly to the gate of the class-AB output stage, significantly accelerating the charging and discharging processes of the Miller capacitor without modifying the operating point of the remaining LA. The current delivered via this supplementary path shows a rapid increase, with changes in input voltage, culminating in diminished quiescent current levels. The supply modulator is fabricated in a 180 nm CMOS process. The measurement results show that HETSM is able to track a 100 MHz, 16 QAM signal accurately, achieving the maximum efficiency of 87.4% at a 5.12 W output power, with a load that consists of a 5 \(\Omega\) resistor and a paralleled 100 pF capacitor. The proposed LA realizes a slew rate of \(-\)1857/+1239 V / us and a bandwidth of 226.6 MHz under a 24 mA quiescent current.  
envelope tracking; wideband hybrid envelope tracking supply modulator; slew-rate enhancement +
Footnote †: journal: Electronics Electronics
## 3 Experimental Results  
This technique was applied to 2.4 GHz FWCW SAR data taken with a low-cost radar and platform constructed by the authors in the same way as the previously published coffee-can radar [23]. The radar Voltage Controlled Oscillator (VCO) was purposefully driven over a bandwidth of 1.1 GHz, well outside its linear region. The radar was mounted on a cart and pushed along a straight line at a constant speed. Data were collected by a laptop and saved as a wave file.  
In the first experiment presented here, three corner reflectors were placed approximately 12 m apart, 5.5 m from the broadside track of the radar, as shown in Figure 4. SAR images were formed by taking the sampled data of one pass along a 50-m-long track marked 'Radar Path' in Figure 4. Data were stored and post-processed to form the SAR images shown here.  
The sampled 'IF' voltage from one modulation ramp every 3.5 cm along the synthetic aperture was windowed with a Hanning window. Next a cross track Fast Fourier Transform (FFT) was calculated that resulted in the S(\(k_{x}\),\(k_{r}\)) space. The RMA algorithm was used on that data without attempting to correct for modulation error. Figure 5 below shows the resultant image. The three corner reflectors stand out as the three bright returns but show considerable broadening in range due to the nonlinearity of the modulation.  
Figure 4: Diagram of the test setup of three corner reflectors and the radar synthetic aperture superimposed on a satellite photo of the location.  
Figure 5: Radar image before correction.  
To use the technique described here, using Equation (11) in the RMA, the phase error \(k_{e}\) can be found by autofocusing on one of the corner reflectors in the image by the same technique as described by phase resampling [21].  
The corner reflector labeled as number 1 in Figure 4 was used as the calibration target to find the frequency and phase error shown in Figure 6. This phase error \(\varphi_{n}\) is used in Equation (12) to find \(k_{e}\). This was then used as Equation (11) was substituted for Equation (10) in the Range Migration algorithm and Stolt Transform.  
Figure 7a below shows a close up of the calibration reflector before error correction and Figure 7b shows two SAR images of one corner reflector before and after phase error correction using the technique described here. Figure 5 shows the image power of a range slice through the center of the peak.  
Figure 8 below shows the range profiles as a slice of the images through the peak at approximately 12 m from the center of the image. The uncorrected range profile is wide and contains multiple sidelobes. After the correction, the theoretical range resolution is achieved. Figure 9 shows the whole corrected image of all three corner reflectors showing that all have benefited from phase correction.  
Figure 6: (**a**) Phase error vs. sample number (time) and (**b**) frequency error.  
Figure 7: SAR image of first corner reflector (**a**) before phase correction and (**b**) after phase correction.