esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description and Assumptions
### Working Process of the sCO2/AHP System  
As can be seen in Figure 1a, the sCO2/AHP system consists of four parts. The working process for each part is introduced below,  
1. sCO2 topping cycle 1. The CO2 working fluid (stream 4) absorbs heat from the reactor (stream 5) and expands in a turbine to drive an electric generator; 2. After expansion, the vapor exhaust (stream 6) flows into the HTR to heat stream 3, and then the outlet working fluid (stream 7) flows into the LTR to heat stream 2; 3. The working fluid after releasing heat (stream 8) is split into 8a and 8b; 4. The stream 8a releases a part of heat to drive the AHP bottoming cycle, and then (stream 9) flows into the DHE (stream 10) and the cooler before compression (stream 1) in the main compressor and afterwards (stream 2) flows into the LTR to be heated (stream 3a); 5. The stream 8b is compressed directly to stream 3b; 6. The compressed stream 3a and stream 3b are mixed to stream 3 and then heated to stream 4 before moving into the reactor.  
Figure 1: Schematic diagrams of combined heat and power systems: (**a**) sCO2/AHP system; (**b**) sCO2/DH system.  
2. AHP bottoming cycle 1. The diluted solution A3 (or ammonia-rich solution) absorbs heat in the SHE to state A4, and then is heated by stream 8a in the generator and separated into a strong solution A7 (or ammonia-poor solution) and a vapor stream A8; 2. The stream A8 is cooled to the saturated liquid (stream A9) in the condenser, throttled by the valve 1, and then heated to saturated vapor (stream A1) in the evaporator. It is noted that the heat release in the condenser is applied to reheat the heating water; 3. The strong solution A7 (or ammonia-poor solution) releases heat in the SHE and decompresses through the valve 2 to a low-pressure solution A5; 4. The strong solution A5 (or ammonia-poor solution) absorbs the vapor A1 in the absorber. The merged diluted solution (or ammonia-rich solution) is cooled by the heating water, and pressured by the pump to the high-pressure solution A3.
3. Heating water cycle 1. The low-temperature heating water from heat users is divided into stream H1 and stream H4; 2. The stream H1 obtains heat from the absorber and the condenser of AHP to state H3; 3. The stream H4 is heated by the CO2 working fluid (stream 9) to state H5, then mixed with stream H3 to the high-temperature heating water H6, and finally supplied to heat users.
4. Cooling water cycle  
The cooling water (stream 11) absorbs heat in the cooler (stream 12), and then a part of it (stream 13) flows into the evaporator to heat the working fluid of AHP and finally back to the cooling tower, while another part of cooling water flows back directly to the cooling tower.
###### Abstract  
Moisture is one of the critical failures affecting asphalt pavement. It has been recently found that moisture created by rainwater and undrained water deteriorate the bonding interface between asphalt mastic and aggregate. Using different mineral fillers can lead to different moisture resistance in the same mix design. Nowadays, waste natural materials allow agriculturalists to receive more income by recycling in many industries. In this study, the researchers adopted bagasse and coconut peat grinding into very fine particle passing through the sieve number 200 (0.075 mm) to replace the mineral fillers. Although the indirect tensile strength (ITS) test is commonly used for evaluating the moisture damage resistance of hot-mix asphalt (HMA) in a laboratory, there are some shortcomings in using this test, such as costly, heavy and indirect equipment. Another potential test that is likely to be more advantageous than the ITS test for evaluating the moisture damage resistance is the pull-off tensile strength (POTS) test. However, it is typically measured on a concrete surface, and no results using an asphalt mixture have been reported. The objectives in this study were to investigate the effects of waste natural fillers in asphalt mixture on adhesive bonding caused by moisture, and to determine whether the POTS test is a potential method in measuring moisture damage resistance in a laboratory. Results showed that the tensile strength ratios (TSR) of asphalt mixture with bagasse and coconut peat fillers are approximately equivalent to those with mineral fillers. Results also showed that asphalt mixtures with bagasse and coconut peat fillers can effectively prevent the moisture damage resistance. In addition, with the preliminary result, it was found that the POTS test showed a very good R square (R\({}^{2}\)) for a relation of TSR with the ITS test. As a result, the POTS can be a valid tool of quantifying moisture damage resistance with better simulating to field behavior, lower cost of equipment, and light weight.  
moisture damage; fillers; bagasse; coconut peat; pull-off tensile strength; indirect tensile strength +
Footnote †: journal: Applied Soft. 2020, 10, 4318; doi:10.3390/app10124318
## 5 Hardware Detection Circuit Debugging and Result Analysis
### Phase Sensitive Detector Module Testing  
The correlator channel serves as the central component in the overall design. This section emulates a multiplier's functionality, where the input signal is derived from the target signal after being filtered through a bandpass filter. The reference signals utilized by the two multipliers are harmonic signals with a phase angle difference of 90 degrees. Figure 12a shows the test circuit for the phase-sensitive detector. The circuit uses a 1 MHz signal with an output amplitude of 100 mV as the signal source. The input signal is used as a reference signal, and the signal to be tested is accessed to the X and Y ends of the AD835 chip for multiplication. Oscilloscope 4's left channel (Channel-A) detects the input signal waveform, while the right channel (Channel-B) detects the output signal waveform. The multimeter (5) measures the output DC signal magnitude, and the distortion analyzer (4) measures the harmonic distortion rate at 2 MHz after the correlator.  
Figure 12b displays the results from oscilloscope 4. The blue waveform represents the input signal, while the red waveform represents the output signal after the multiplier. The delay response of the output signal is 2.5 us, and the period of the output signal is 498.2 ns. The voltage amplitude is 4.979 mV, and the DC bias voltage is 5.098 mV. The correlator meets design requirements, as it can multiply the input signal to be measured with the reference signal. The input signal was brought into Equation (6) and calculated to obtain an ideal output AC signal amplitude of 5 mV, a frequency of 2 MHz, and a DC signal output of 5 mV. The total harmonic distortion is less than \(-100\) dB.  
Figure 11: Modeling of phase shifter module. (**a**) Circuit of phase sensitive detector, (**b**) output waveform of phase sensitive detectors.  
Figure 12: Modeling of phase sensitive detector module. (**a**) Circuit of phase sensitive detector module testing. (**b**) Input and output waveform of phase sensitive detectors.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
###### Abstract  
The perceptual hash algorithm is a technique to authenticate the integrity of images. While a few scholars have worked on mono-spectral image perceptual hashing, there is limited research on multispectral image perceptual hashing. In this paper, we propose a perceptual hash algorithm for the content authentication of a multispectral remote sensing image based on the synthetic characteristics of each band: firstly, the multispectral remote sensing image is preprocessed with band clustering and grid partition; secondly, the edge feature of the band subsets is extracted by band fusion-based edge feature extraction; thirdly, the perceptual feature of the same region of the band subsets is compressed and normalized to generate the perceptual hash value. The authentication procedure is achieved via the normalized Hamming distance between the perceptual hash value of the recomputed perceptual hash value and the original hash value. The experiments indicated that our proposed algorithm is robust compared to content-preserved operations and it efficiently authenticates the integrity of multispectral remote sensing images.  
multispectral remote sensing image; perceptual hash; integrity authentication; affinity propagation; feature fusion +
Footnote †: journal:  
_Article_  
A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
# A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices
## 4 Fairness-Aware Decentralized Learning
### Problem Formulation  
We denote the local learner set as \(N=\{1,2,\ldots,i,\ldots\}\) and denote the local datasets as \(D_{i}\). Generally speaking, the central parameter server, in the federated learning framework, executes two major tasks: model updating and model aggregation. To generate a global learning model, federated learning adopts the weight averaging-based aggregation scheme [6; 20]. The global optimization problem is formulated as:  
\[\mathbf{w}_{G}^{*}\leftarrow\mathit{arg~{}min~{}F(\mathbf{w}_{G})}=\sum_{i=1}^ {|N|}P_{i}F_{i}(\mathbf{w}_{G},D_{i}). \tag{1}\]  
Here, denote \(F(\cdot)\) as the loss function, \(\mathbf{w}_{G}\) as the global learning model, and \(P_{i}\) as the weight of local learner in the model aggregation. The federated learning framework provides a distributed way of exploiting the on-device data samples at the edge of network; however, it is challenging for distributed learning to be implemented in real-world scenarios. The central parameter server of federated learning causes the potential security risk, such as the single-point failure issue. The learning efficiency, in federated learning, would degrade  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbb{W}^{*}\) & Group weight vector \\ \(\mathbf{w}\) & Weight vector \\ \(\mathbf{w}^{*}\) & Optimized parameters \\ \(\mathbf{w}_{i}\) & Local learning parameters \\ \(F(\cdot)\) & Loss function \\ \(D\) & Local datasets \\ \(\delta\) & Local update \\ \(U\) & Update set \\ \(t\) & Iteration index \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Key Notations.  
due to the diversity of local end-devices. To be specific, data transmissions (networking) and the efficiency of local model updates (with respect to computing) might bring the bottleneck of the global learning performance.  
In decentralized learning, all local learners intend to find a learning pair to exchange its local model iteratively, known as peer-to-peer manner. Both model update and model aggregation are deployed on local learners. The objectives of decentralized learning differ from federated learning, aiming to train a number of local learning models with users' preferences. Mathematically, the optimization goal can formulated as:  
\[\mathbb{W}^{*}\langle\mathbf{w}_{1}\cdots\mathbf{w}_{|N|}\rangle\leftarrow \mathit{arg~{}min}_{\mathbf{w}_{i}\in\mathbb{R}^{d}}\sum_{i=1}^{|N|}F_{i}( \mathbf{w}_{i}D_{i}), \tag{2}\]  
where \(\mathbf{w}_{i}\) is the \(i^{\prime}s\) local learning model. The nature of decentralized learning brings challenges to solve Equation (2) directly.
# Effects of Bilberry Extract on Hepatic Cholesterol Metabolism in HepGG2 Cells
## 3 Results
### BE Altered the Expression of Genes for Biosynthesis and Flux of Cholesterol  
To examine the effects of BE on hepatic cholesterol metabolism, we first assessed the mRNA abundance of genes for cholesterol biosynthesis and uptake in HepG2 cells treated with BE. In response to intracellular cholesterol deprivation, SREBP2 upregulates the transcription of HMGR and LDLR. Conversion of HMG-CoA to mevalonic acid by HMGR is the rate-limiting reaction in cholesterol de novo synthesis. The expression of SRBP2, the transcription factor for cholesterol metabolism, was significantly decreased in BE-treated HepG2 cells. There was a dose-dependent and significant reduction in HMGR expression by BE treatment. Hepatic lipoprotein receptors, i.e., LDLR, LDL-related protein 1 (LRP1) and SR-B1, play prominent roles in cellular cholesterol homeostasis by removing lipoprotein-derived cholesterol in the circulation. There were no changes in the mRNA abundance of LDLR, the receptor for LDL, in BE-treated cells. The mRNA levels of PCSK9, an enzyme responsible for LDLR degradation, were significantly decreased by BE treatment. The mRNA abundance of LRP1 was significantly upregulated, whereas the expression of SR-B1 was downregulated in 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of BE-treated cells. Hepatic very low-density lipoprotein (VLDL) assembly and secretion are essential for maintaining plasma cholesterol and TG levels. The expression of microsomal triglyceride transfer protein (MTTP), essential for the hepatic assembly of VLDL particles, was dose-dependently and significantly decreased by BE treatment. There was a significant reduction in ACAT2, a significant cholesterol esterification enzyme, by 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of BE treatment (Figure 2). The  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Total Antioxidant Capacity**} \\ \hline
**DPPH (mM VCE/g)** & **ABTS (mM VCE/g)** & **FRAP (mM FeSO/g)** \\ \hline
282.6 \(\pm\) 13.5 & 560.8 \(\pm\) 62.6 & 4.73 \(\pm\) 0.33 \\ \hline \end{tabular} Data represent mean \(\pm\) SEM. VCE, vitamin C equivalents.  
\end{table}
Table 1: Total antioxidant capacity of BE.  
Figure 1: Cell viability of BE-treated HepG2 cells. Cells were treated with increasing concentrations from 0 to 200 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of BE for 24 h. Different letters on the data points represent significant differences (\(p<0.05\)). Values are means \(\pm\) S.E.M. \(n\) = 6.  
protein expressions of mature SREBPP2 and HMGR were noticeably reduced in BE-treated HepG2 cells. BE markedly increased the protein levels of LDLR without alteration of mRNA levels. Moreover, PCSK9 protein levels were noticeably reduced by BE treatment. There was no induction of SR-B1 protein level in BE-treated HepG2 cells (Figure 3).
## 6 Results and Discussion
### Discussion  
In this case study, the HybVOR approach is implemented in order to find the optimal placement of camera network in a region of interest within the Jeddah Seaport. The proposed location of cameras is based on Voronoi diagram generated from buildings' footprint. The use of the Voronoi diagram to place the camera network allows determining an optimal number of cameras for an almost complete coverage. The results of our implementation show that the optimal number of cameras is 48 cameras over an area of 0.1336 km\({}^{2}\). A surface coverage of almost 100% is obtained based on the raster based viewshed calculation (surface of visible zones = 0.13 km\({}^{2}\), surface of non-visible zones = 0.036 km\({}^{2}\), where the non-visible zones mainly correspond to some buildings' surfaces). Then, the 3D Vector-based analysis of visibility is used to ensure that the main buildings' entrances are fully covered by the camera network.  
It is important to mention that the number of cameras strongly depends on the characteristics of cameras used for monitoring the environment. The HybVOR approach takes into consideration this aspect through the database which is used to store the relationship among cameras and buildings' entrances (Figure 14). In addition, this database supports the use of cameras with different properties within the same network.  
At the moment, the implemented HybVOR approach requires some manual intervention in order to ensure a complete coverage for the main buildings' entrances when performing the 3D Vector-based  
Figure 21: 3D Vector-based analysis of visibility based on HybVOR approach.  
analysis of visibility. However, it is possible to automate this process through an Agent Based Modeling (ABM) process. In this case, each camera will be considered as an agent, where the initial location can be deduced from the HybVOR methodology. Then, these agents (cameras) can change their position, with some minor displacements, in order to resolve any issue related to a lack of coverage. The rules that constrain these ABM process have to respect the HybVOR approach. Such an improvement of the HybVOR approach will be the subject of future research work.
## 3 Results
We obtain three L1-band signal scintillation patches recorded by the Chungli software-defined GPS receiver at different data segments, which are from 13:29 to 13:44 UT, 14:29 to 14:49 UT, and 13:44 to 14:19 UT on 26 October 2021, for GPS satellites #8, #21, and #27 signal observations, respectively. The derived \(VS_{4}\) values are consistent with CWB GPS observation results and are from 0.19 to 0.25, 0.16 to 0.22, and 0.14 to 0.19 for GPS satellites #8, #21, and #27 signal measurements, respectively. Figure 6 shows the corresponding signal spectrums obtained via a Lomb periodogram algorithm [21]. We note that above approximately 1 Hz, the power spectral densities (PSD) are near the noise level where the minimum frequency of the noise level is defined as the deviation frequency of the signal intensity spectrum [26, 27]. The 50-Hz sampling rate is approximately one order higher than the derived deviation frequency. Below 1 Hz, the PSDs are more or less a flat portion at low frequencies and decay from their maxima at a break frequency of \(f_{B}\) toward the noise level in an approximately linear fashion on the log-log scale shown. This indicates and confirms a power-law variation \(f^{1-p}\) of the plasma irregularity PSD with the frequency as discussed. We note that the spectrum break frequencies of \(f_{B}\) can be treated as the experimental Fresnel frequencies and are approximately 0.07, 0.15, and 0.12 Hz, and the derived spectral index \(p\) values are 3.57, 4.36, and 3.59 for the L1-band signal scintillation patches from GPS satellites #8, #21, and #27, respectively. Comparing with the corresponding IPP positions shown in Figure 4 and the derived \(VS_{4}\) maps shown in Figure 5, we note that the area with a higher spectral index \(p\) has a stronger scintillation intensity. The spectrum analysis results of the L1-band signal scintillation patches recorded by the Hualien software-defined GPS receiver are similar to those from the Chungli system and are not shown in this study.
## 3 Results and Discussion
### Cooking Loss, Water Holding Capacity, and pH of Patty  
Cooking loss in meat products is closely related to the water holding capacity of meat [23]. A high percentage of water holding capacity indicates a high percentage of cooking yield. Whereas meat with low water holding capacity results in more water and cooking losses. In this study, adding seaweed powder significantly (\(p<0.05\)) reduced the cooking loss (Table 3). F4 had the lowest (10.02%) cooking loss, while the control patty showed the highest (16.85%) cooking loss. Seaweed is a rich source of dietary fibre [33], affecting meat properties, including water and fat binding ability. As the dietary fibre was hydrated, the water molecules were retained and occupied in the pore space of fibre, which eventually minimised the cooking loss [34]. The results were in agreement with [23], which reported that adding seaweed (_Kappaphycus alvarezii_) powder in mechanically deboned chicken meat (MDCM) sausages reduced the percentage of cooking loss compared to those without seaweed (\(p<0.05\)).  
The water holding capacity (WHC) is expressed as the percentage of extracted water, as presented in Table 3. The results showed that the F2, F3, and F4 samples retained more water than the control patty (\(p<0.05\)). At similar seaweed content, treatments containing higher levels of salt (F2 and F4) showed lower percentages of extracted water, which were 10.03% and 8.25%, respectively. A low salt concentration affected the heat-induced gelation in meat products due to low myofibrillar protein dissolution. On the contrary, a high amount of salt minimized the extracted water by increasing the solubility and strength of myofibrillar proteins network [3]. However, F1 (11.26%) and F3 (8.50%) showed lower extracted water than the control (12.60%, \(p<0.05\)), despite having lower salt content, indicating that _K. alvarezii_ could retain water in meat products.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Formulation** & **Hardness (N)** & **Chewiness (N)** & **Cohesiveness** & **Elasticity (mJ)** \\ \hline Control (C) & 1.32 \(\pm\) 0.06 d  & 0.76 \(\pm\) 0.02 c  \\ \end{tabular}
\end{table}
Table 2: Textural properties of chicken patties with and without the addition of seaweed at different salt levels.  
pH is an important factor associated with the meat's water-holding capacity. Except for F1, the addition of _Kappaphycus alvarezii_ significantly increased (\(p<0.05\)) the pH of the patty, with F4 showing the highest pH value of 6.06. The pH of meat products affects their texture, as low pH indicates a decreased water holding capacity [35]. The pH value shown in Table 3 agreed with the texture profile analysis (Table 2), as the addition of seaweed increased the hardness, chewiness, cohesiveness, and elasticity of the parties.
_Article_  
**State-Constrained Sub-Optimal Tracking Controller**  
**for Continuous-Time Linear Time-Invariant (CT-LTI)**  
**Systems and Its Application for DC Motor**  
**Servo Systems**  
**[PERSON] 1,*(), [PERSON] 2 and [PERSON] 1,* 1 Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (H.L.); Tel.: +82-2-2220-1685 (H.L.)**  
Received: 22 July 2020; Accepted: 13 August 2020; Published: 18 August 2020  
**Abstract: In this paper, we propose an analytic solution of state-constrained optimal tracking control problems for continuous-time linear time-invariant (CT-LTI) systems that are based on model-based prediction, the quadratic penalty function, and the variational approach. Model-based prediction is a concept taken from model-predictive control (MPC) and this is essential to change the direction of calculation for the solution from backward to forward. The quadratic penalty function plays an important role in deriving the analytic solution since it can transform the problem into a form that does not have inequality constraints. For computational convenience, we also propose a sub-optimal controller derived from the steady-state approximation of the analytic solution and show that the proposed controller satisfies the Lyapunov stability. The main advantage of the proposed controller is that it can be implemented in real time with a lower computational load compared to the implicit MPC. Finally, the simulation results for a DC motor servo system are shown and compared with the results of the direct multi-shooting method and the implicit MPC to verify the effectiveness of the proposed controller.**  
**Keywords: continuous-time linear time-invariant (CT-LTI); DC motor servo system; linear-quadratic tracking (LQT); model-based prediction; quadratic penalty function; state-constrained optimal control; variational approach**