esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results  
Figure 6 shows the transient response of a CFIA with a 20 mV amplitude input sine wave of 1 kHz. The gain of the CFIA was set to have 11 V/V through a 5-bit programmable resistor. The graph shows the input/output in the form of a differential signal. The output signal is simulated to have a peak value of 220.07 mV at 0.25 ms. Figure 7 shows the simulated transfer function in differential mode, VDD, GND, and common-mode. The simulated CMRR achieves 162 dB. The power supply rejection ratio (PSRR+ and PSRR-) are simulated at 112 and 110 dB, respectively. Figure 8 shows the frequency response of the CFIA. The open-loop gain is simulated at 132.5 dB at 1 mHz. The simulated unit gain bandwidth (UGBW) is 59.2 kHz, and the phase margin is 75.1\({}^{\circ}\) at UGBW. Figure 9 shows the simulated input-referred noise. The simulated input-referred noise is 30 nV/\(\sqrt{\text{Hz}}\) at 1 Hz and 28.3 nV/\(\sqrt{\text{Hz}}\) at 1 kHz.  
Figure 4: Schematic of the high-frequency path.  
Figure 5: Schematic of the high-frequency path common-mode feedback (CMFB) circuit.  
Figure 8: (**a**) Gain of the CFIA and (**b**) phase of the CFIA.  
Figure 6: Transient response of CFIA with 20 mV amplitude input sine wave.  
Figure 7: Simulated transfer function with differential mode, VDD, ground (GND), and common-mode.  
Figure 10 shows the simulated CFIA output DC operating point under R-2R DAC digital input variation. It shows that the offset of the CFIA output can be linearly calibrated within a \(\pm\)3.3 V range. The performance summary and comparison with other papers are shown in Table 1. The efficiency of the instrumentation amplifier can be evaluated using a noise efficiency factor (NEF), as shown in (3), where \(V_{rms,ni}\) is the input-referred noise RMS value, \(I_{tot}\) is the total current consumption, U\({}_{\text{T}}\) is thermal voltage, and BW is bandwidth of the amplifier.  
\[NEF=V_{rms,ni}\times\sqrt{\frac{2\times I_{tot}}{\pi\times U_{T}\times 4 kT \times BW}} \tag{3}\]  
Figure 10: Simulated CFIA output DC operating point under R-2R DAC digital input variation.  
Figure 9: Simulated input-referred noise of CFIA.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Image Acquisition  
We used a total of 9 (PAN images 4 and MS images 5) scenes from different satellite images. We downloaded the Landsat-8 OLI MS, PAN and Moderate Resolution Imaging Spectroradiometer (MODIS) MS images from the United States Geological Survey's (USGS) web portal ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))). The GF1, GF2 and GF4 MS and PAN images were downloaded from Earth Observation Portal (EOP) ([[https://directory.eoportal.org/web/eoportal/](https://directory.eoportal.org/web/eoportal/)]([https://directory.eoportal.org/web/eoportal/](https://directory.eoportal.org/web/eoportal/))). As long as the time-lag is small with respect to the time scale of the variations in small-size features, its influence upon the quality of the transformation of the spectral content when enhancing the spatial resolution is low or negligible [36]. Thus, GF1, GF2, GF4 and MODIS images were acquired on the same date, 13 July 2017, but Landsat-8 OLI images were not available on the same date so they were acquired on 17 July 2017 (after four days). All the images were selected carefully (cloud and snow-free) to ensure better image quality. The details of different resolutions, imager types, selected MS and PAN bands with their corresponding spectral ranges are listed in Table 1.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 5 Conclusions and Future Work  
Our proposal represents an important step and a major challenge in terms of usability and acceptance by users. Mobile remote technologies used as part of health monitoring and maintenance strategies will grow in importance in the future. The PlaIMoS platform performed well in a hospital setting, securely monitoring and reporting patient information in real-time circumstances. As PlaIMoS can also be used as an at-home cost effective alternative or expansion of traditional clinical healthcare services, it can function alone or potentially in conjunction with health information systems (SIS), hospital information systems (HIS) and electronic medical records, through integration and management of health databases.  
PlaIMoS platform was validated to currently monitor and report six parameters (temperature, electrocardiogram, heart rate, blood oxygen, respiration rate and fall data), as well as the indoor location of the patient. Our next is to increase the number of parameters PlaIMoS can handle (i.e., adding blood pressure, blood sugar etc.) and develop software to cross reference these parameters and provide a more in-depth diagnostic tool for medical practitioners. Future work will also include validation of the galvanic skin response recorded by the PlaIMoS platform using hospital equipment.  
The authors wish to thank to the partners (ACESyD and Everest Software Solutions) involved with the development of the PlaIMoS platform and CONACYT for its financial support.  
[PERSON] designed the PlaIMoS platform, developed the PlaIMoS App and designed the tests. [PERSON] deployed the WSN Infrastructure and provided suggestions for the article. [PERSON] developed the PlaIMoS fixed measurement station software and the Web service. [PERSON] developed the PlaIMoS remote node software. [PERSON] designed and manufactured the electronic of the PlaIMoS devices. [PERSON] designed and manufactured the case of the PlaIMoS devices. [PERSON] assisted with the documentation, structure and organization of the study reports.  
The authors declare no conflict of interest.
## 3 Results
### Subcellular Localization of DcTPSb1  
Initially, the subcellular localization of DcTPSb1 was predicted using prediction tools like WoLF PSORT and pLoc-mPlant, which suggested that DcTPSb1 is located in chloroplasts and is likely a mono-TPS in the MEP-pathway rather than in the cytosolic MVA pathway. To confirm this localization, a vector containing a GFP fragment linked with the _DcTPSb1_ sequence was transformed into _N. benthamiana_ leaves. Subsequently, confocal laser scanning microscopy was used to visualize yellow fluorescence signals. The resulting images clearly show that DcTPSb1 was indeed localized in chloroplasts, providing experimental validation of the initial prediction (Figure 3).  
Figure 2: The evolutionary tree was formed based on the neighbor-joining method in the MEGA 11.0 tool. All sequences belonging to several different plants were obtained from NCBI. Seven TPS subfamilies were shown in six colors, in which DcTPSb1 protein from _D. chrysotaxum_ was distributed in the largest blue area of the TPS-b family (marked with red star). All sequence information can be found in Supplementary Table S3.
## 4 Conclusions  
The analyses of volatile organic compounds proved to be a useful analytical tool to assess mouth-related bacterial conditions. Our work focused on the development of a new methodology to differentiate oral malodor from the presence of a submandibular abscess, a condition not always easily recognized by conventional medical examination. Volatiles from halitosis cohort were abundant in sulfur-containing compounds originated from bacterial metabolism and also esters. Submandibular abscess group was characterized by the occurrence of inflammation indicators, like aldehydes. Statistical approaches used in our research showed that the combination of saliva incubation and GC/MS analysis is a promising method for discrimination of patients with halitosis, abscess, and those presenting healthy oral microbiota. In a future perspective, the developed method can be employed in investigations regarding which bacterial strains contribute the most to disease conditions and whether it is possible to find differences in VOC profiles from other types of skin abscess. Moreover, the present study could represent a valuable material for companies interested to build or calibrate dedicated sensors, which have the ability for real-time detection of these kinds of diseases based on some targeted components.  
extraction temperature = 37 \({}^{\circ}\)C), Figure S2: Average total area obtained for different times of extraction (sample volume = 1 mL, extraction temperature = 37 \({}^{\circ}\)C), Figure S3: Average total area obtained for different volumes of samples, number of peaks is presented above the bars (extraction time = 45 min, extraction temperature = 37 \({}^{\circ}\)C), Figure S4: Comparison between fresh and incubated saliva, in terms of (A) average total area and (B) number of detected peaks, Table S1: Model analyses, limit of detection, limit of quantification, defined quality controls and linearity data, Table S2: Average imprecian calculated between triplicates of real samples (RSD% = relative standard deviation), Table S3: Average imprecian and inaccuracy calculated for spiked samples (RSD% = relative standard deviation, RSE% = relative standard error), Table S4: Stability results obtained from saliva pool samples, Table S5: Stability results obtained from spiked samples, Table S6: Matrix effect (NMF = normalized matrix factor, RSD% = relative standard error).  
Conceptualization, F.M. and M.M.; Methodology, F.M. and M.M.; Software, F.M. and I.-A.R.; Validation, F.M., M.M., and I.-A.R.; Formal analysis, F.M., M.M., and H.Z.; Investigation, F.M. and H.Z.; Resources, F.M. and H.Z.; Data curation, F.M.; Writing--original draft preparation, F.M., M.M., and I.-A.R.; Writing--review and editing, T.L. and B.B.; Visualization, F.M. and I.-A.R.; Supervision, T.L. and B.B.; Project administration, B.B.; Funding acquisition, M.M.  
We are grateful to the National Science Center, Poland (Preludium grant No. 2014/15/N/ST4/03702), for their financial support.  
The authors declare no conflict of interest.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
###### Abstract  
The rapid development of nanosatellite technologies, their low development cost, and their economical launching due to their small size have made them an excellent option for Earth Observation (EO) and remote sensing. Nanosatellites are widely used in generic applications, such as education, vegetation monitoring, natural disasters, oceanography, and specialized applications, such as disaster response, and they serve as an Internet of Things (IoT) communications platform. This paper presents a review of the latest public nanosatellite EO missions, their applications, and their propulsion systems. Furthermore, we discuss specialized applications of the nanosatellites and their use in remote sensing for EO. Likewise, we aim to present the limitations of the nanosatellites in remote sensing, a comprehensive taxonomy according to propulsion systems, and directions for future research.  
s +
Footnote †: journal: Future Internet  
1  
e1 Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece; [EMAIL_ADDRESS]
# Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
## 2 Unmanned Vehicle Trajectory-Tracking Model
### Simulation Verification of Lateral Force Estimation  
The designed SCKF estimator was tested by using Carsim and the Matlab/Simulink joint simulation platform, and the two working conditions of a high-adhesion road surface and low-adhesion road surface were set for validation, respectively, in which the high-adhesion condition was set with a road adhesion coefficient of 0.9 and a vehicle speed of 90 km/h, and the low-adhesion condition was set with a road adhesion coefficient of 0.4 and a vehicle speed of 50 km/h. The initial state \(\hat{x}_{0/0}=\left[0,0,0,0,0\right]^{T}\), the square root of the error covariance was taken as \(S_{0/0}=10^{-3}diag[1
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 5 Performance Evaluation and Results
### Results and Discussion
In case of distributed concurrent applications, such as block chain systems or multi-parallel processing queue service streaming, heuristic solutions have been proposed in [49; 50; 51] which provide scalability-related solutions to reduce the gap between the service provider and the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CPU Utilization** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline Bouncer & 2.93 & 3.45 & 3.90 & 4.22 & 4.53 \\ Load Balancing and Control Policy (LBCP) & 2.68 & 3.12 & 3.87 & 4.14 & 4.36 \\ Smart Job Admission Policy (SJAP) & 3.16 & 3.58 & 3.98 & 4.29 & 4.83 \\ Maximal Admittance Policy (MAP) & 3.46 & 3.83 & 4.32 & 4.65 & 5.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monitoring overhead cost (in ms) for evaluated service requests. Figure 4: CPU resource utilization and system throughput estimation. (**a**) CPU utilization; (**b**) throughput. clients by using a utility factor. A utility factor in case of cloud services admission may be referred to time (scheduling, response time) and budget (pay as you go service usage policy, etc.). Resource allocation challenges are often addressed on basis of an optimization related constraint; therefore, in order to ensure the service quality assurance on a multi-platform scale such as block-chain technology, we measure the services output success on basis of the SLA principles of success. These service levels are ensured by server-client coordination constraints and are not included in the local optimization problem on an individual server in the decentralized method. We performed resource consumption for core switches under the pretext of traffic utilization. In this concern, two scenarios namely low traffic level and high traffic level were implemented. The purpose of the evaluation was to evaluate the effect of cloud services admission control scheme on network switches. The switching load functions were evaluated by using an SDN-enabled evolved packet core (EPC) gateway which can extract switching statistics from the 8 slot SDN enabled HP 12508E SDN-enabled switch. From the results in Figure 5, we can see that Bouncer's core switch utilization has relatively little impact on overall data usage whereas the other three benchmark criterions are relatively consuming more switching resources for performing the same task. We believe that the Poisson arrival scheme and associated parameters reduced the overall switch utilization which is absent for the other 3 benchmark services and can also handle scalability related challenges. In summary, the Bouncer algorithm provides a simple, easy and horizontal scaling solution for admitted service requests through a standardized and logical resource allocation method.
## 5 Stability Enhancement Using FACTS Devices
### Voltage Stability
While providing voltage support, TCSCs require bulky capacitors and reactors which is a demerit of TCSCs. The SSSC, on the other hand, does not require bulky capacitors and reactors. But the SSSC has a higher cost and complexity compared to the TCSC. Therefore, selecting a FACTS device is a trade-off among cost, maintenance, and complexity. Similarly, an advantage of the SVC is its lower cost and lower losses as compared to STATCOM. But its slower response due to time delay associated with its thyristor switching is a demerit. Finally, the UPFC is superior to other FACTS devices discussed above, but its complexity and higher cost are considered as disadvantages of its usage [177]. 8. Regarding optimization techniques, meta-heuristics and hybrid optimization are the most preferable techniques for optimally placed FACTS devices (to provide voltage stability). 9. Conventional optimization techniques have difficulties managing constrained optimization problems but have effective convergence characteristics. 10. Analytical approaches are unable to provide solutions for optimal placement of FACTS devices but play an important role when combining meta-heuristics optimization techniques. 11. The authors of [19] state the conclusion of [90] that, despite providing better performance in reduction of voltage deviation and line loading, the UPFC is less likely to be installed in real-world scenarios due to its higher cost than that of the SVC and TCSC put together. Therefore, more studies will be conducted in future which focus more on price optimization to make its use economically pragmatic.
## 2 Materials and Methods
### Western Blot Analysis  
Samples were prepared as previously reported [26]. Briefly, cells were lysed in RIPA buffer (0.1% sodium dodecyl sulfate, 1% NP-40, 0.5% sodium deoxycholate in PBS, pH 7.4) supplemented with a protease and phosphatase inhibitor cocktail, using sonication. The lysates were centrifuged at 13.000 rpm for 10 min at 4 \({}^{\circ}\)C and protein concentration was measured using the Bradford solution. Proteins (40 \(\upmu\)g) were mixed with Laemmli sample buffer (60 mM Tris-HCl, pH 6.8, 10% glycerol, 2% SDS, 1% \(\upbeta\)-mercaptoethanolhanol and 0.002% bromophenol blue) and separated by SDS-PAGE. After electrophoresis, proteins were transferred onto nitrocellulose membranes. Membranes were blocked in 5% non-fat milk prepared in PBS with 0.05% Tween-20 (PBST) for 2 h at room temperature and incubated overnight at 4 \({}^{\circ}\)C with specific primary antibodies: 1:400 anti-\(\upbeta\)-actin monoclonal antibody (A5441; Sigma-Aldrich, Merck KGaA, Darmstadt, Germany); 1:1000 monoclonal anti-\(\upalpha\) -tubulin (T9026; Sigma-Aldrich, Merck KGaA); 1:500 MRP1 Polyclonal Antibody (PA5-30594, Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA); 1:20 Anti-BCRP/ ABCG antibody (ab24115, Abcam, Cambridge, UK); 1:1000 Anti-MDR1/ABCB1 Antibody (E1Y7B, Cell Signaling, Danvers, MA, USA); 1:400 GPX-1/2 antibody (sc-133160, Santa Cruz, Dallas, TX, USA); 1:1000 purified anti-ATF4 antibody (W16016A, BioLegend, San Diego, CA, USA); 1:1000 purified anti-ATF6 antibody (W17028A, BioLegend). The membrane was washed with PBST and incubated with an HRP-conjugated secondary antibody at room temperature for 1 h. Signal was visualized using ECL(tm) Western blotting Detection Reagents or the SuperSignal(tm) West Pico PLUS Chemiluminescent Substrate on a Chemidoc(tm) XRS detection system (Bio-Rad Laboratories, Hercules, CA, USA) equipped with Image Lab 5.1 software. Densitometric analysis was performed using GelAnalizer 19.1 ([PERSON], www.gelanalyzer.com (accessed on 1 January 2023). Protein expression levels were normalized to the control sample, set at 100. Each experiment was performed in triplicate.
# Optimization of Electricity Generation Technologies to Reduce Carbon Dioxide Emissions in Egypt
## 3 Results and Discussion
### Environmental Effect during Normal Operation  
Figure 13 shows the total damage cost due to the electricity-generation technologies from the Burullus fossil fuel, Aswan High Dam hydro, and El Dabaa nuclear power plants during normal operation. Furthermore, the total damage cost of the fossil fuel is represented by the sum of the damage costs of health and agriculture effects. The total damage cost of the nuclear power plant is equal to the sum of fatal cancer, non-fatal cancer, and specific hereditary disease costs, whereas that of the hydropower plant is equal to the sum of the losses in farmland and agricultural/livestock production as well as the emission damage cost. Additionally, the damage cost is directly related to the environmental effect with respect to the exposure risk factor where higher or severe exposure results in higher risk or effects. Figure 13 illustrates that the hydropower plant yields the highest damage cost because of the large area inundated by water. The damage cost considers the losses in the agricultural and livestock production in this large area. The agriculture loss incurs the highest value, which explains the reason why hydropower plants have the highest total damage cost. The nuclear power plant exhibits the lowest damage cost during normal operation; however, the cost of a power-plant accident is not considered in our calculation. Therefore, the nuclear power plant can be considered to be the best alternative choice for electricity-generation technology from the perspective of environmental effect.  
Figure 12: Summarizes the average contribution of each technology in every scenario.
## 2 The System Model of LDPC  
In satellite communications, signals may be affected by atmospheric fading, interference, and noise, resulting in transmission errors. LDPC codes have excellent error-correcting performance, which can effectively detect and correct errors during transmission, thus improving the reliability of communication systems. The constraint relationship between the check nodes and variable nodes of LDPC codes can be represented by the factor graph model in Figure 1. The factor graph model of LDPC mainly includes the variable nodes (VNs), the check nodes (CNs), the parity-check matrix, and the side information connecting the variable nodes and the check nodes.  
Consider the regular NB-LDPC code \(F_{q}[n,k]\) over \(q=2^{r}\). The code \(F_{q}[n,k]\) is defined by a sparse parity-check matrix \(\mathbf{H}=\left[h_{ij}\right]_{\mathbf{y}_{i}\times n}(h_{ij}\in F_{q})\), which has a constant row weight \(\rho\) and column weight \(\gamma\). We design the set \(N_{i}\) of non-zero positions for the \(i\)-th row of \(\mathbf{H}\) by:  
\[N_{i}=\left\{j:0\leq j\leq n-1,h_{i,j}\
eq 0\right\} \tag{1}\]  
In Equation (1), for the \(i\)-th row, \(N_{i}\) is the set of \(h_{ij}\
eq 0\) and \(j\) from \(0\) to \(n-1\) in \(\mathbf{H}\).  
And we design the set \(M_{j}\) of non-zero positions for the \(j\)-th column of \(\mathbf{H}\) by:  
\[M_{j}=\left\{i:0\leq i\leq m-1,h_{i,j}\
eq 0\right\} \tag{2}\]  
In Equation (2), for the \(j\)-th column, \(M_{j}\) is the set of \(h_{i,j}\
eq 0\) and \(i\) from \(0\) to \(m-1\) in \(\mathbf{H}\).  
Assume that \(\mathbf{c}=(c_{0},c_{1},\cdots,c_{j},\cdots,c_{n-1})\in F_{q}^{n}\) is a codeword to be transmitted. Each symbol \(c_{j}\) is converted to its binary representation by \(\mathbf{c}_{j}=(c_{j,0},c_{j,1},\cdots,c_{j,t},\cdots,c_{j,t-1})\in F_{2}^{r}\), where \(0\leq j\leq n-1\) and \(0\leq t\leq r-1\). For simplicity, we only consider the additive white Gaussian noise (AWGN) channel with the binary phase-shift keying (BPSK) modulation. Each bit \(c_{j,t}\) is modulated to a real sequence \(\mathbf{x}_{j}=(x_{j,0},x_{j,1},\cdots,x_{j,r-1})\) with constellation mapping rules \(\phi(\bullet)\), where \(x_{j,t}=\phi(c_{j,t})=1-2c_{j,t}\). The received signal \(\mathbf{y}_{j}=(y_{j,0},\cdots,y_{j,t},\cdots,y_{j,r-1})\) is obtained from the modulated signal through the AWGN channel, where \(y_{j,t}=x_{j,t}+n_{j,t}\) and \(n_{j,t}\sim N(0,\sigma^{2})\).  
Design of LDPC Decoding Algorithm Based on Multi-Dimensional Reliable Information Addition Iteration  
The wtd-AlgB decoding algorithm in reference [16] uses the advantage of the majority logic rule as a symbol decision, but it involves frequent finite-field multiplication and ignores the channel received information. To achieve a low-complexity and high-reliability decoding scheme, this paper presents a new LDPC decoding algorithm based on multi-dimensional reliable information addition iteration (MRAI-LDPC), which is described below.