esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The ILDACP Algorithm
### Power Consumption Correction  
In this very last phase of our algorithm, the results of the previous step are corrected in order to produce accurate disaggregated end-use trajectories. We correct the results based on two basic facts: an operating appliance is in one state at time \(t\), and the total power value is always greater than or equal to 0. The correction is carried out taking into account the following three-case correction:  
1. False appliance identification correction. Assuming the closest signature of \(A^{j}\) is \(S^{j}\). In one case, if the sub-sequence at the same time in \(A^{j}\) has ever been identified as the appliance \(j\), it is false identification. The other case, when two different appliances have a similar contribution to the total power consumption, if sub-sequence \(A^{j}_{r}\) picked up from \(A^{j}\) is far shorter than the duration of appliance \(j\), it is false identification as well. In these situations, the second highest similarity signature is adopted.
2. False clustering correction. We detect every \(A^{j}_{r}\). If the length of it is less than the minimum duration time of appliance \(j\) in \(S^{j}\), false clustering is considered. It is possible that \(A^{j}_{r}\) or its neighbor is misclassified. Inaccurate power estimation of an appliance generates incorrect new total power, which causes the wrong clustering. Thus, we adjust the false clustered power value to the cluster's center value of its neighbor, and compensate former power estimation.
3. New total power estimation correction. Once any value of new total power estimation is negative, adjust it to the average value of its neighbors and compensate former power estimation of the highest contributing appliance. If there are still positive values in total power after all of the appliances power consumption traces has been retrieved, adjust the negative values to 0, and compensate former power estimation of the highest contributing appliance.
# A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
## 5 Conclusions  
In this manuscript, we convert the QUATRE algorithm to binary version by two approaches in order to enable the QUATRE algorithm to solve the practical application of binary types. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then perform cross operation with other individuals. Mathematical analysis is performed on the proposed algorithm and the improved transfer functions are proposed in order to improve the performance of the proposed algorithm. Furthermore, in order to balance the exploration and exploitation, a new linear increment scale factor is proposed. The proposed algorithm performs well on benchmark functions and then it is applied for practical dimensionality reduction of HSI. It can be seen from the results that the proposed algorithm is superior to state-of-art algorithms and it is helpful for solving the practical problems. The proposed algorithm cannot handle multi-model low-dimensional functions well. This will be the main work in the future.  
Conceptualization, J.-S.P.; data curation, Z.Z.; formal analysis, J.L. investigation, S.-C.C.; methodology, S.-C.C.; software, Z.Z.; supervision, J.-S.P.; writing--original draft, Z.Z.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (61872085).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 14: The classification maps of the different tested algorithms. (**a**) Ground truth (**b**) BQUATE2-S1i-SVM (**c**) BQUATE2-V1i-SVM (**d**) BQUATE1-U1-SVM (**e**) BQUATE1-Z1-SVM (**f**) SVM (**g**) PCA-SVM (**h**) LDA-SVM.
###### Abstract  
In the context of central solar receiver systems, the utilisation of S-CO\({}_{2}\) Brayton cycles as opposed to Rankine cycles confers a number of advantages, including enhanced efficiency, the requirement for less sophisticated turbomachinery, and a reduction in water consumption. A pivotal consideration in the design of such systems pertains to the thermal storage system. This work undertakes a comparative analysis of the performance of an S-CO\({}_{2}\) Brayton cycle utilising two distinct types of molten salts, namely solar salts and chloride salts (MgCl\({}_{2}\)-KCl), as the heat transfer fluid on the thermal energy storage medium. The present study adopts an energetic and exergetic perspective with the objective of identifying areas of high irreversibility and proposing mechanisms to reduce them. The work is concluded with an analysis of the size of the different components. The overall energy efficiency is determined as 22.29 % and 23.76 % for solar and chloride salts, respectively. In the case of chloride salts, this efficiency is penalized by the higher losses in the solar receiver due to the higher operating temperature. The exergy analysis shows that using MgCl\({}_{2}\)-KCl salts increases exergy destruction in the recuperators, lowering irreversibilities in other components. While the sizes of all components decrease when using chloride salts, the volume of the storage system increases. These results demonstrate that the incorporation of MgCl\({}_{2}\)-KCl salts enhances the performance of S-CO\({}_{2}\) recompression cycles operating in conjunction with a central solar receiver.  
supercritical carbon dioxide; Brayton cycle; central solar receiver; heat transfer fluid; exergy analysis Article
## 2 Material and Methods
### Water Samples Analyses  
A total of 76 water samples (Figure 1) were collected during August 2017, distributed across 15 watercourses, including the mouth of both rivers, and 13 drainage channels (azarbes). Samples were collected with a sampling pole to avoid the removal of the bottom and then immediately stored at 4 \({}^{\circ}\)C until their analysis in the laboratory.  
Six water quality parameters were analyzed, namely: pH, electrical conductivity (EC) at 25 \({}^{\circ}\)C, total suspended solids (TSS), nitrates, pesticides (DDT plus the metabolite DDE), and glyphosate. The four first parameters were analyzed according to standard methods [26], while pesticides were determined with an ELISA kit. In this sense, pH was determined with a Crison pH meter GLP 21 and EC (at 25 \({}^{\circ}\)C) with a Crison conductmeter GLP 31 (Crison Instruments, Barcelona, Spain), respectively. Then, water samples were filtered in order to determine TSS and to obtain filtered water for further analyses. Glass microfiber filters with 0.45 \(\upmu\)m of pore diameter were employed (Whatman--Cytiva, Marlborough, MA, USA). Filters with retained particles were dried in an oven (105 \({}^{\circ}\)C) until constant weight and TSS were determined by gravimetry. Nitrates were determined by the second derivative method with a PG Instruments T80 UV/VIS Spectrometer (PG Instruments Limited, Alma Park, UK).  
Both pesticides were analyzed via an enzyme-linked immunosorbent assays (ELISA) technique, employing tests from Eurofins Abraxis (Warmister, PA USA). Firstly, DDT and the metabolite DDE (DDT-DDE) were determined with a microtiter plate ELISA test kit for DDT-DDE in water samples (Eurofins Abraxis product code 540041). In this test, both pesticides (DDT and DDE) were quantified jointly (the resulting value is the sum of DDT plus DDE). Concentrations of the samples were determined using the standard curve run with each test, using seven standards (i.e., 0; 0.625; 1.25; 2.5; 5.0; 10.0; 25.0 ppb). Upper and lower detections limits were 25.0 and 0.625, ppb respectively. Regarding the sensitivity of the test, this DDE/DDT assay has an estimated minimum detectable concentration, based on 90% B/B\({}_{0}\) of 0.4 ng/mL. Secondly, glyphosate was determined with a microtiter plate ELISA test kit for glyphosate in water samples (Eurofins Abraxis product code 500086). Concentrations of the samples were determined using the standard curve run with each test, using six standards (i.e., 0, 0.075, 0.20, 0.5, 1.0, 4.0 ppb). Upper and lower detections limits were 4.0 and 0.075 ppb, respectively. Regarding the sensitivity of the test,this glyphosate assay has an estimated minimum detectable concentration, based on 90% B/B\({}_{0}\) of 0.05 ng/mL. Samples with a value lower than the lower concentration standards were removed; samples with higher glyphosate concentrations (>4.0 ppb) needed a 1:2 dilution. Finally, a HEALES MB-580 (Shenzhen Huisong Technology Development Co. Ltd., Shenzhen, China) microplate reader was used to obtain the results from the ELISA test.
## 3 Results
### Relationship Between Pain Processing and Inflammation  
The correlations between pain sensitivity and inflammatory markers in persons with CNSLBP are presented in Table 3. Significant moderate-to-strong relationships were found between PRE IL-6 and delta CPM (r = 0.724, \(p\) = 0.003), PRE TNF-\(\alpha\) and delta TS (r = 0.613, \(p\) = 0.034), PRE TNF-\(\alpha\) and delta CPM (r = 0.569, \(p\) = 0.043), and delta TNF-\(\alpha\) and PRE cPPT (r = \(-\)0.690, \(p\) = 0.013). The other correlations were non-significant. Two partial  
Figure 3: Overview of the inflammation outcomes (panels **A**–**D**). Abbreviations: IL-6: interleukin-6; TNF-\(\alpha\): cytokine tumor necrosis factor alpha; HCs: healthy controls; CNSLBP: chronic nonspecific low back pain; HIIT: high-intensity interval training; MICT: moderate-intensity continuous training; ns: non-significant.  
correlation analyses were performed. The first, confounding for age and BMI (Table 4), showed very strong significant correlations between PRE IL-6 and delta cPPT (r = 0.756, \(p\) = 0.030), PRE IL-6 and delta CPM (r = 0.853, \(p\) = 0.007), PRE TNF-\(\alpha\) and cPPT (r = 0.721, \(p\) = 0.043), and PRE TNF-\(\alpha\) and delta TS (r = 0.838, \(p\) = 0.009). The second, confounding for depression, stress, anxiety, and sleep (Table 5), showed no significant correlations between pain sensitivity and inflammatory markers.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Inflammatory Markers**} \\ \hline
**QST Variables** & **PRE IL-6** & **Delta IL-6 \(\pm\)** & **PRE TNF-\(\alpha\)** & **Delta TNF-\(\alpha\)\(\pm\)** \\ \hline PRE cPPT & \(-\)0.053 & 0.294 & 0.253 & \(-\)0.575 \\ Delta cPPT & 0.756 \({}^{*}\) & 0.398 & 0.721 \({}^{*}\) & 0.171 \\ PRE TS & \(-\)0.108 & 0.074 & \(-\)0.384 & 0.198 \\ Delta TS & 0.646 & 0.108 & 0.838 \({}^{*}\) & 0.492 \\ PRE CPM & \(-\)0.267 & \(-\)0.306 & 0.193 & \(-\)0.606 \\ Delta CPM & 0.853 \({}^{*}\) & 0.306 & 0.668 & 0.556 \\ \hline \end{tabular} All values are displayed as r values. Abbreviations: QST: quantitative sensory testing; cPPT: cuff pressure pain threshold; TS: temporal summation of pain; CPM: conditioned pain modulation; IL-6: interleukin-6; TNF-\(\alpha\): cytokine tumor necrosis factor alpha. \({}^{*}\)significant \(p\) \(>\) 0.05. \(\downarrow\) nonparametric testing.  
\end{table}
Table 4: Partial correlations between pain sensitivity outcomes and inflammatory markers at PRE and PRE–POST after HIIT corrected for age and BMI in persons with CNSLBP.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Inflammatory Markers**} \\ \hline
**QST Variables** & **PRE IL-6** & **Delta IL-6 \(\pm\)** & **PRE TNF-\(\alpha\)** & **Delta TNF-\(\alpha\)\(\pm\)** \\ \hline PRE cPPT & 0.807 & \(-\)0.182 & 0.326 & \(-\)0.107 \\ Delta cPPT & 0.846 & 0.358 & 0.737 & 0.486 \\ PRE TS & \(-\)0.817 & 0.420 & \(-\
## 3 Results
### Development and Evaluation of Wind Speed Models
In the stationary GEV model, most points cluster near the reference line, indicating general consistency between actual samples and the model. However, at the middle and high quantiles on the horizontal axis, data points begin to deviate from the reference line, indicating less accurate predictions in these areas. In the non-stationary GEV model considering variations in the location parameter over time, overall data points still cluster near the reference line. Although its performance at middle quantiles is better than the stationary GEV model, its fit line begins to deviate from the reference line at high quantiles,  
Figure 11: Quantile–quantile plots of the three generalized extreme value models. (**a**) The stationary GEV model. (**b**) The non-stationary GEV model with variation in the location parameter. (**c**) The non-stationary GEV model with variations in both the location and scale parameters. even more so than the stationary GEV distribution. In the GEV model considering changes in both location and scale parameters, the reference line and fit line become very close, indicating the better capture of extreme value characteristics and closer agreement between predicted and sample quantiles. While visual results allow for intuitive assessment, specific model evaluation requires numerical analysis in addition to visualization. Employing the same evaluation methods as before (excluding K-S due to its inability to reflect dynamism), specific evaluation values are shown in Table 6.  
The results from Table 6 indicate that, in terms of NLL values, the GEVn2 model (NLL = 120.380) demonstrates the best fit, followed by the GEVn1 model (NLL = 127.400), while the stationary GEV model (NLL = 160.038) exhibits the lowest fit. Introducing a function of time variables into the stationary GEV model noticeably enhances the overall model performance. Upon incorporating changes in the scale parameters over time, following the variation in location parameters, the reduction in the model's NLL value is marginal, merely improving some predictive capabilities. Due to the increased number of parameters, both AIC and BIC values are simultaneously employed for comprehensive evaluation to prevent the decrease in NLL values attributed to overfitting. It is evident that the GEVn2 model continues to perform the best (AIC = 258.760, BIC = 280.637), with the GEVn1 model (AIC = 266.799, BIC = 281.384) still outperforming the stationary GEV model (AIC = 326.077, BIC = 333.369). AIC and BIC values strike a balance between model complexity and fit. Integrating visual results, it is concluded that the non-stationary GEV model considering simultaneous variations in location and scale parameters outperforms the other two models. Parameters for the three GEV models are presented in Table 7.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 3 Receivers  
This study considers \(N_{t}\times N_{r}\) MIMO system having \(N_{t}\) transmitting antennas and \(N_{r}\) receiving antennas. The MIMO system equation can be written as  
\[Y=Hx+n \tag{1}\]  
where the transmit signal, \(x\), is involved with time varying flat fading wireless channels, \(H\). Here, \(n\) is the \(N_{r}\times 1\) additive white Gaussian noise (AWGN) vector and \(E[xx^{H}]=\frac{\rho}{N_{t}}I\).
## 5 Results  
The proposed system identification method based on track and response prioritization for the suspension system parameter estimation problem is tested using a hierarchical approach:  
* _Proof-of-Concept Tests:_ These tests are performed in a MATLAB environment. The wheel excitations are generated by the road kinematic model. The true vehicle responses are generated by the _7-dof_ model with known parameters. A system identification procedure based on the track and response prioritization estimates the parameters, reducing the errors between the estimated responses and true responses. In Section 5.1, (1) the evolution of spread of the parameter estimations throughout generations is presented, and (2) the normalized estimation errors for different test tracks are demonstrated and compared against our track prioritization scheme. These two results demonstrate the feasibility of the proposed method.
* _Verification Tests:_ These are performed in a MATLAB-ADAMS co-simulation environment where the high-fidelity vehicle model with the known parameters generates the true responses. A system identification procedure based on track and response prioritization estimates the parameters by reducing the errors between the responses estimated with the _7-dof_ model and the true responses. In Section 5.2,body and axle accelerations and the orientation rates for a selected track are demonstrated for the ADAMS model and the _7-dof_ vehicle model with the estimated parameters. The results verify the capability of the proposed method to execute with the data acquired from a sophisticated model or with real data.
* _Validation Tests:_ These tests are performed on the real test vehicle. The vehicle is driven on a real road, and the true parameters are measured and logged on the system. Similar to the above two models, the system identification procedure based on track and response prioritization estimates the parameters by reducing the errors between the response estimated with the _7-dof_ model and the true responses. In Section 5.3, (1) the error between the true suspension system parameters and the final parameter estimates are tabulated, and (2) the normalized errors between real test data and the _7-dof_ vehicle model with estimated parameters in terms of body and axle accelerations and the orientation rates for the test tracks are demonstrated. These two results validate the proposed method on real test vehicle experiments.
## 3 Results
### Thermal and pH Stability  
In order to use the powders as functional ingredients, it is imperative to investigate the thermal and pH behavior of the bioactive nutrients in a simulated system, due to the fact that many functional foods containing bioactive compounds susceptible to degradation are usually subjected to thermal treatment and pH variation. Therefore, the selected concentration of powders, varying from 0.1 to 0.6%, were dissolved in distillated water and subjected to various temperatures (from 25 \({}^{\circ}\)C to 100 \({}^{\circ}\)C) for 15 min. The flavonoid content was measured after the thermal treatment (Figure 5). Heating caused an increase in flavonoid content, probably due to the thermal degradation of the matrices and the release of the bioactives.  
Figure 4: Light micrographs of L929 cells treated with microencapsulated variants of yellow onion skin extracts for 48 h.  
The flavonoids' release from the powders was found to be temperature and concentration dependent. In variant 1, the highest release of approximately 14% was found at 0.1% and 100 \({}^{\circ}\)C, whereas in variant 2, a significantly higher release of 51% was highlighted under similar conditions. Therefore, it could be understood that variant 2 was less protective when a thermal treatment was applied.  
Regarding the stability at different pH values, the results are shown in Figure 6.  
The pH stability was checked at a constant value for variable periods, ranging from 0 to 120 h. From Figure 6, it can be seen that in general, the flavonoid content increased with time, up to 24 h, and started to slowly decrease at a more prolonged time with every test, regardless of the variant. The highest increase of the flavonoid content was found at pH 2.0, after 2 h, when a two-fold increase of the flavonoid content was found in variant 1.
## 7 Conclusions  
Findings indicated that a discrete time PID with gain scheduling using specifically tuned parameters developed using a non-linear discrete energy usage model for a wildlife behavior monitoring device was capable of sustaining ENO. The battery SOC was maintained at a set-point for longevity and the data collection rate was held constant after a few day cycles to satisfy biologists requests for a stable collection rate. In addition, behavior monitoring devices would be automatically capable of configuring their variable acquisition time sensor data based on available energy. The developed system should be capable of controlling any IoT system that uses varying acquisition time sensors and energy harvesting regardless of data type or environment by following the described tuning method. The type of data required for collection should not impact ENO because the system can adjust the collection interval. Overall, this system can improve functionality, usability, and life expectancy of any embedded system that is desired to maintain ENO, operate power constrained, collect data using sensors with an unpredictable acquisition time, and harvest energy from an unpredictable source.  
The authors would like to thank Cellular Tracking Technologies for funding this work and contributing to the publication fees for open access.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived of an adjustable rate controller together. [PERSON] and [PERSON] developed the energy consumption model and controller algorithm. [PERSON] and [PERSON] analyzed the performance results. All authors contributed to the publication development where [PERSON] is the primary author.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: Increased PVC Energy: (**a**) PVC current; (**b**) SOC (**c**); and GPS fix interval.
# Secure Voting Website Using Ethereum and Smart Contracts
## 4 Proposed System
### Face Recognition  
Integrating AIML face recognition technology with a blockchain-based voting system involves several steps. Firstly, a face recognition model needs to be trained using a large dataset of faces to recognize and verify individuals accurately. This model can be based on machine learning algorithms such as neural networks or support vector machines. Once the model is trained, it must be integrated with the blockchain-based voting system through smart contracts. These smart contracts can be designed to capture and store the facial biometric data of registered voters in a secure and encrypted manner. When a voter logs in to the voting system, the smart contract can use the face recognition model to verify their identity. To ensure the privacy and security of voters, the facial biometric data can be stored on a separate cloud-based service, such as Firebase, integrated with the blockchain-based voting system. This allows for secure and efficient access to necessary data during voting.
## 3 Materials and Methods
### Feature Selection
#### 3.3.4 Recursive Feature Elimination (RFE)  
RFE is an iterative method used for eliminating features until the desired number of features remains. This method recursively removes attributes one by one according to a scoring function. In every iteration, the feature with the lowest score is removed, and the model is reconstructed using the remaining features. This process is repeated until only the most important features are retained. Afterwards, it builds a model for the attributesthat are still present. The features that significantly influence the prediction of the target attribute depend on the model's accuracy [44].  
Embedded methods in ML simultaneously handle feature selection and model training. During the training process, they identify the most relevant features to improve model performance, while excluding those that contribute less or are redundant. In embedded methods, ML models are integral to feature selection, as they account for interdependencies among features and the dynamics of input-output variable interaction [45]. Embedded methods offer several advantages, including reducing overfitting and optimizing both the model and the feature selection process simultaneously.