esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Information Transmission with Self Jamming SWIPT
## 2 System Model
The SINR of \(\mathbf{Y}_{\xi(c)}\) for the condition \(\mathcal{L}(\mathcal{V}^{(k)})=\epsilon^{(k)}_{\xi(c)}\) is given as:  
\[\Gamma_{\xi(c)}=\frac{\mathbb{E}[|\mathbf{X}_{I}^{T}\mathbf{H}_{T\xi}|^{2}]}{ \mathbb{E}[(\sum_{k=0}^{K-1}|\epsilon^{(k)}_{\xi(c),E1}|^{2})]+\mathbb{E}[( \sum_{k=0}^{K-1}|\epsilon^{(k)}_{\xi(c),E2}|^{2})]+\mathbb{E}[||\mathbf{J}||^ {2}]}=\frac{\sigma^{2}_{X,I}\sigma^{2}_{H,T\xi}}{\sigma^{2}_{\epsilon,\xi(c),E1 }+\sigma^{2}_{\epsilon,\xi(c),E2}+\sigma^{2}_{\gamma}}. \tag{14}\]  
For all the scenarios of \(\xi\) and \(R(b)\), the channel precoding error is denoted as \(\epsilon_{A}\), and its variance is denoted as \(\sigma^{2}_{A}\). In this system model, both the channel estimate errors and the channel correlation errors are considered to be the sole contributors of \(\epsilon_{A}\), and \(\epsilon_{A}\) proportionally increases with the increase in \(\beta\). To form the generalized SINR equation, \(\sigma^{2}_{H,TR}\) and \(\sigma^{2}_{H,T\xi}\) are assumed as one; then, the SINR of the received signal at \(R\) and \(\xi\) can be written as:  
\[\Gamma=\frac{\sigma^{2}_{X,I}}{\sigma^{2}_{N}+\beta\sigma^{2}_{A}}, \tag{15}\]  
At a very high SINR of the information signal, \(\sigma^{2}_{N}\) can be neglected. Then, Equation (15) can be written as:  
\[\Gamma\approx\frac{\sigma^{2}_{X,I}}{\beta\sigma^{2}_{A}}. \tag{16}\]  
Therefore, even at a very high SINR condition, \(\beta\sigma^{2}_{A}\) can be used to degrade the performance of \(\xi\). The secrecy of \(\mathbf{X}_{I}\) can be improved at \(R(a)\) and \(R(b)\) by increasing \(\beta\) under the condition that the \(\sigma^{2}_{A}\) value in \(\mathbf{Y}_{R(a)}\) and \(\mathbf{Y}_{R(b)}\) is lesser than the \(\sigma^{2}_{A}\) value in \(\mathbf{Y}_{\xi(a)}\), \(\mathbf{Y}_{\xi(b)}\), and \(\mathbf{Y}_{\xi(c)}\). Another observation from Equation (15) indicates that, if \(\beta\geq 1\), then \(\xi(a)\) will always be in the negative SINR region.
## 1 Introduction
The smart meter continuously provides information related to hourly tariffs that are declared by the electricity provider to the consumer. Due to this, the electricity users schedule their hourly load according to the declared utility tariffs. This hourly electricity load management is useful for both electricity consumers and electricity providers in terms of reducing electricity consumption costs and improving the stability of the power system. In the proposed HEMS scheme, an ESS and photovoltaic (PV) generation system were further integrated to improve the performance of the primitive HEMS. Three optimization algorithms, i.e., a genetic algorithm (GA); grey wolf optimization (GWO); and the proposed hybrid technique based on GWO and GA, named a hybrid grey wolf genetic algorithm (HGWGA), that considers real-time pricing (RTP) and critical peak pricing (CPP) tariff schemes, were used to solve the scheduling problem. Simulation results demonstrate that the proposed hybrid optimization technique performed well at reducing the PAR and consumption costs of electricity. However, there is always a trade-off between consumer electricity consumption cost and consumer appliance waiting time (AWT). Whenever the consumer electricity consumption cost was at a minimum, the consumer AWT was at a maximum and vice versa.
# Intelligent Monitoring and Compensation between EDM and ECM
###### Abstract  
Electric discharge machining (EDM) is a type of high-precision machining usually applied to hard-material machining for mold manufacturing and in the aerospace industry. Longer process times typically reduce facility efficiency. The use of electrochemistry machining (ECM) can overcome this challenge to efficiently machine large workpieces. Some industries have adopted and combined these two processes for Inconel 718 material machining. However, the use of coordinate-measuring machine times to determine the machining accuracy of these two processes is difficult. This study matched process features by analyzing the electric driving pulses of ECM and EDM. Fitting intelligent sensing signals that respond to dimensional measurements can be used to analyze electrical pulse signals. For analyzing a cross-process model using extracted key features of the process, our feedback-based system determines lower machining measurement errors and improves geometric size. Finally, the processing time of experiments can be reduced by 80%, and our proposed model has a prediction accuracy of approximately 0.01 mm\({}^{2}\).  
electrical discharge machining (EDM); electrochemical machining (ECM); intelligent monitoring; accuracy prediction 2023
## 3 Air Pressure Compensation Algorithm
### Air Pressure Compensation of Elevator Car Based on PD-ILC  
The air pressure compensation system has the characteristics of high coupling and non-linearity, and the operating conditions of the high-speed elevator in the hoistway are repetitive and controllable, so the ILC algorithm can be used to compensate the air pressure in the elevator car.  
According to the above-mentioned basic principles of ILC, a commonly used open-loop iterative learning law can be expressed as:  
\[u_{k}(t)=Q[u_{k-1}(t)+Le_{k-1}(t)], \tag{26}\]  
where \(e_{k-1}\) is the tracking error at the \((k-1)\)-th iteration; the matrix \(Q\) is the filter coefficient matrix; and the matrix \(L\) is the gain matrix.  
The selection of the learning law in ILC is particularly important. The reasonable selection of the gain matrix is also a process of rational selection of the learning law. Several typical iterative learning laws are as follows:  
Figure 6: Iterative learning principle of ILC.  
1. Proportion-Iterative Learning Control (P-ILC) controller: \[u_{k}(t)=u_{k-1}(t)+k_{p}\cdot e_{k-1}(t)\] (27) 2. Differential-Iterative Learning Control (D-ILC) controller: \[u_{k}(t)=u_{k-1}(t)+k_{d}\cdot\dot{e}_{k-1}(t)\] (28) 3. Proportion Differential-Iterative Learning Control (PD-ILC) controller: \[u_{k}(t)=u_{k-1}(t)+k_{p}\cdot e_{k-1}(t)+k_{d}\cdot\dot{e}_{k-1}(t)\] (29) 4. Proportion Integral Differential-Iterative Learning Control (PID-ILC) controller: \[u_{k}(t)=u_{k-1}(t)+k_{p}\cdot e_{k-1}(t)+k_{d}\cdot\dot{e}_{k-1}(t)+k_{l}\int e _{k-1}(\tau)d\tau\] (30) where \(k_{p}\), \(k_{d}\) and \(k_{i}\) are, respectively, the constant gain of the proportional, derivative, and integral parts of the learning law. From the iterative solution form of Equation (26), it can be seen that it belongs to a typical open-loop feedforward control form. In order to ensure the control accuracy, Equation (26) is often improved to the following closed-loop learning law form: \[u_{k}(t)=u_{k-1}(t)+k_{p}\cdot e_{k}(t)\] (31)  
It can be seen from Equation (31) that the control system selects the error information of this iteration for feedback, so it can better suppress real-time environmental interference and converge faster. Combined with the operating environment of high-speed elevators, there are many factors affecting the air pressure change in the elevator car, and various uncertain disturbances may exist. Therefore, the closed-loop learning law is selected for the air pressure compensation system. As the ILC algorithm itself has a certain integral effect, the PID-ILC law can be simplified to the PD-ILC for the simulation solution. For the air pressure compensation system, the basic block diagram corresponding to the designed PD-ILC controller is shown in Figure 7. The input of the controller is the deviation between the IAPC and the actual air pressure curve inside the car, and the output is the operating frequency of each fan in the ventilation system of the elevator car.
# Extending Battery Lifetime by Avoiding High SOC
## 3 Model Development
### Cycling Ageing Model  
To translate the ageing function of the cell model into a battery pack ageing model, the capacity of the cells were normalized and from here on all the capacity degradation will therefore be presented in %.  
In vehicles, the full capacity of a battery pack is normally not utilised, in order to extend the lifetime of the battery. When 100% SOC is displayed in the vehicle, i.e., fully charged, this could typically correspond to the single cells being charged to about 90% of the upper SOC limit given by the manufacturer. In the same way, the 0% SOC indication in the vehicle are normally not the 0% rated SOC of the battery cells. Typically, this will be around 10-15% of the battery cell SOC.  
For this model, we will disregard the lower limit and allow the use until 0% SOC for the battery cell. The model validity is limited to 90% SOC as an upper limit. With these limits, the model can be run in intervals up to 90% DOD.  
To calculate the capacity degradation in intervals larger than 10% DOD, the contributions from each 10% DOD were added together. For example, a 30% DOD in 0-30% SOC adds the contribution for 0-10%, 10-20% and 20-30% SOC. Figure 6 shows experimental data and the model results for this interval using a 2C rate in 35 \({}^{\circ}\)C. As can be noted, the ageing function predicts the result well also for the 0-30% SOC case.
## 3 Charrelation Matrix Based Blind Detector for DS-CDMA System
### Performance Analysis
The ISR performance of JADE can be shown [28] to given by:  
\[ISR_{{}_{ij}}^{\text{JADE}}\approx\frac{1}{M}\frac{\kappa_{{}_{j}}^{{}^{4}}+ \kappa_{{}_{i}}^{{}^{2}}+\kappa_{{}_{j}}^{{}^{2}}}{\left(\kappa_{{}_{j}}^{2}+ \kappa_{{}_{i}}^{{}^{2}}\right)} \tag{33}\]  
Likewise, taking into account the diagonal structure (at \(\tilde{\mathbf{G}}=\mathbf{I}\)), we can obtain approximatively the ISR performance of the proposed method as follows:  
\[ISR_{{}_{ij}}^{\text{Proposed}}\approx\frac{1}{M}\frac{\kappa_{{}_{j}}^{{}^{2} }}{\kappa_{{}_{j}}^{{}^{2}}+\kappa_{{}_{i}}^{{}^{2}}+\kappa_{{}_{i}}^{{}^{2} }\kappa_{{}_{j}}^{{}^{2}}} \tag{34}\]  
where, the \(\kappa_{{}_{i}}\) and \(\kappa_{{}_{j}}\) are statistical moment parameters about unknown \(i\mathrm{th}\) and \(j\mathrm{th}\) source. For convenience, without loss of generality, the sources come from the uniform distribution, assuming \(\kappa_{{}_{i}}=\kappa_{{}_{j}}=\kappa\). We define the ratio of two ISR to evaluate the performance. We can obtain:  
\[\eta=ISR_{{}_{ij}}^{\text{Proposed}}\Big{/}ISR_{{}_{ij}}^{\text{JADE}}=\frac{ 4\kappa^{6}}{4\kappa^{{}^{4}}+4\kappa^{{}^{6}}+\kappa^{{}^{8}}}=\frac{1}{1+ \kappa^{{}^{-2}}+\kappa^{{}^{2}}\Big{/}4}\leq\frac{1}{2} \tag{35}\]  
Based on the above analysis, we find that \(ISR_{{}_{ij}}^{\text{Proposed}}<ISR_{{}_{ij}}^{\text{JADE}}\) and the ISR performance is inversely proportional to the number of samples \(M\). ISR denotes that the ISR is smaller when the \(\mathbf{V}\) matrix is closer to the generalized permutation matrix, and the demixing performance of the algorithm is better. We can know that the proposed method outperforms JADE, and the ISR improvement becomes more pronounced as the number of sample increases. In the next section we will give the simulation results and demonstrate the performance of the DS-CDMA system aided by the proposed method to verify the analyzed case.
###### Abstract  
Extensive use of organophosphorus pesticides in agriculture leads to adverse effects to the environment and human health. Sample preparation is compulsory to enrich target analytes prior to detection as they often exist at trace levels and this step is critical as it determines the concentration of pollutants present in samples. The selection of a suitable extraction method is of great importance. The analytical performance of the extraction methods is influenced by the selection of sorbents as sorbents play a vital role in the sensitivity and selectivity of an analytical method. To date, numerous sorbent materials have been developed to cater to the needs of selective and sensitive pesticides' detection. Comprehensive details pertaining to extraction methods, developed sorbents, and analytical performance are provided. This review intended to provide a general overview on different extraction techniques and sorbents that have been developed in the last 10 years for organophosphorus pesticides' determinations in food and water samples.  
extraction; pesticides; sample preparation +
Footnote †: journal: 10 September 2021  
have been adopted for OPPs' analysis, which has been widely reported in the literature [8; 9; 10]. These techniques have their own inherent advantages and drawbacks, which prompted the development of new analytical methods, particularly the microextraction method, which offers notable merits in terms of facile extraction procedure, being less time consuming, minimizing organic solvents' consumption, and enhancing extraction efficiency [11]. In addition, the possibility of reusing the extraction device is an important advantage of microextraction techniques.  
Recently, development of selective sorbents has been emerging and received substantial interest due to its crucial roles in enhancing selectivity and sensitivity of analytical methods. Therefore, numerous adsorbents have been developed and reported in the literature. Numerous review articles have been published in recent years on the advances of extraction techniques and sorbents' development for pesticides analysis. Over 150 articles were found when a search in the Science Direct was carried out using the keywords \"sorbents\", \"extraction techniques\", \"food and water samples\", and \"organophosphorus pesticides\" within a period of 2011-2021. In 2018, a review published by [PERSON] et al. [12] in Trends in Food Science & Technology covered a review of extraction and analytical and advanced methods for determination of pesticides in environment and foodstuff. However, the review did not provide an extensive discussion on the sorbents used in the extraction methods.  
This present paper briefly discusses the method performance and strengths and weaknesses of sample preparation techniques that are commonly used for determination of OPPs. In addition, a short review on the development of extraction sorbents is also highlighted. This review presents an overview on how the selection of the extraction method and the choice or sorbents influences the performance of a developed method.
## 3 Experiment I: Minimal SBR for Full Sentence-Level Intelligibility
### Procedure and Listeners  
The determination of SBR\({}_{min}\) was a 1-up/1-down adaptive process in progression by following the procedure described in [39]. The experiment was conducted using a MATLAB (v9.1 by MathWorks, Natick, MA, USA, 2016) graphical user interface (GUI). Each background condition consisted of 10 non-repetitive stimuli which were presented in a random order. Starting from a very low SBR (\(-21\) dB), the listener was instructed to manually increase the background level until they could only just understand everything in the first sentence. Within the first trial, the listener could hear the sentence as many times as s/he needed to find the appropriate SBR. Once the initial SBR was set, the listener was required to type what was heard using a physical computer keyboard. Upon finishing the transcription of the sentence, the original correct answer was displayed with all the keywords capitalised. The listener self-checked the correctness of all the keywords; s/he was instructed to ignore any errors due to typos, homophones, contractions, punctuation or differences in tense and plurality. The programme then decreased or increased the SBR by 2 dB for the next sentence according to whether or not the listener obtained a score of 100% correct in the current sentence.  
In the subsequent nine trials, the SBR could no longer be adjusted by the listener; instead it was adaptively controlled by the programme using the same 1-up/1-down criteria. Each sentence was played only once prior to the listener's response. The final SBR\({}_{min}\) for this condition was the average SBR across the final eight trials. The same procedure was repeated for all the 20 conditions, the order of which was also randomised. Through all the conditions, the listener did not hear any utterance twice.  
The experiment took place in an semi-anechoic room with a background noise level of 3.8 dB(A). The stimuli were presented to listeners monaurally over a pair of Sennheiser (Wedemark, Germany) HD650 headphones after being pre-amplified by a Focusrite (High Wycombe, UK) Scarlett 2i4 USB audio interface. The SBR for each speech-masker pair was calculated across the entire signals. The presentation level of speech only over the headphones was calibrated using Bruel & Kjaer (B&K, Narum, Denmark) 4153 artificial ear with a B&K 2610 measuring amplifier, and fixed at 69 dB(A). Thus, in the experiment the SBR was altered only by varying the level of the background sound.  
A total of 14 native British English speakers (ages: 18-30 year, mean age: 24 year) participated in this experiment. All participants reported normal hearing, and were paid for their participation.  
The Research Ethics Panel at the College of Science and Technology, University of Salford, granted ethical approval for all the experiments conducted in this work.
## 2 Materials and Methods  
The composition of the ECC is shown in Table 1. The mass ratio of cement, silica fume, and fly ash in the binder system is 7:2:1, the mass ratio of standard sand to cementitious materials is 0.3, the water-to-binder ratio is 0.22, the polycarboxylate superplasticizer is added at 0.02 by mass of cementitious materials, and the polyethylene (PE) fiber content is 2% by volume of the sample. CN-ECC samples were prepared by incorporating CNs into the mix at mass ratios of 0.05%, 0.10%, 0.15%, and 0.20% of the cementitious materials.  
The sample preparation procedure is as follows: first, the dry materials, including cement, silica fume, fly ash, and sand, were mixed in a mortar mixer at low speed for 3 min. Water, a water-reducing agent, and CNs were then added to the dry mix and stirred at high speed for 1 min. Subsequently, PE fibers were introduced into the slurry, followed by further mixing for 3 min.  
The binder material was sourced from Jiuqi Building Materials Co., Ltd., (Yantai, China) with the cement being ordinary Portland cement (P-O 42.5). The CN was obtained from Zhejiang Yuewei New Material Technology Co., Ltd., (Hangzhou, China) and is a white liquid (Figure 1a) with a TEM and AFM morphology shown in Figure 1b,c. The CN has a diameter ranging from 15 to 20 nm and a length between 200 and 500 nm. The PE fiber, sourced from Guangdong Teveron New Materials Application Co., Ltd., (Shenzhen, China) has a length of 9 mm and a diameter of 12 \(\upmu\)m.  
A uniaxial tensile test was carried out by a MTS-universal mechanical (Eden Prairie, MN, USA) testing machine to analyze the ECC's strain rate and tensile strength. The sample and instrument for the uniaxial tensile test are shown in Figure 2. The testing machine is adopted by displacement control, and the loading speed is 0.5 mm/min [31]. As for the compressive strength test, the sample with the size of 100 mm \(\times\) 100 mm \(\times\) 100 mm was prepared. This study refers to the GB/T50082-2009 standard [34] for experimentation with the sulfate corrosion resistance of the ECC. The sample should be soaked in a 5% Na\({}_{2}\)SO\({}_{4}\) solution until it reaches corrosion age. Then, one should take the sample out, dry the surface of the sample, and test its strength and strain rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Binder (100\%)** & & & & & \\ \hline
**Cement** & **Silica Fume** & **Fly Ash** & & & & \\ \hline
70 & 20 & 10 & 2\% & 22 & 2 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The proportion of ECCs.  
Figure 1: The morphology of a CN. (**a**) CN gel, (**b**) TEM, (**c**) AFM.
## 2 Materials and Methods
### Human Electrocardiogram (ECG) Data Collection and Processing
#### 2.3.3 ECG Data Analysis  
Two bipolar ECG signals selected to emulate LIIRD TCRE (\(U_{2}-U_{1}\) and \(U_{4}-U_{1}\)) were digitally high pass filtered (0.3 Hz, fifth-order zero-phase Butterworth filter) to reduce remaining baseline drifts. Subsequently, two surface Laplacian estimates were obtained, one for the NDM and one for the FDM, according to equations derived in Section 3.4 below.  
ECG fiducial points were identified by detecting the R-wave in both Laplacian estimate signals using Hamilton & Tompkins algorithm [29]. The average beat (\(\overline{ECG}\)) for each subject was computed in a 60 s window, covering from 250 ms prior to 375 ms after the R-wave. To compare the two Laplacian estimates the average values of the following parameters were computed for each recording session:  
* The amplitude of the QRS complex of \(\overline{ECG}\), computed as the peak-to-peak amplitude of \(\overline{ECG}\) in the interval [\(-\)90 ms, \(+\)90 ms] of the detected R-wave.
* Normalized amplitude of the P- and T-waves with respect to the peak to peak amplitude of QRS [30]. P- and T-wave amplitude computed as the peak-to-peak amplitude of \(\overline{ECG}\) in the interval [\(-\)250 ms, \(-\)90 ms] and [\(+\)90 ms, 375 ms] of the detected R-wave. The higher the normalized amplitude of the wave, the easier it is to identify and to assess its morphology, which is helpful for diagnosis of cardiac pathologies,
* Signal-to-noise ratio: the ratio of the peak-to-peak amplitude value of \(\overline{ECG}\) and the root mean square value of the noise during the isoelectric interval between beats, the latter being computed for all the isoelectric intervals in the 60 s window. \[SNR\ (dB)=20\text{-}log_{10}\Bigg{(}\frac{v_{PP}\overline{\big{(}\overline{ECG} \big{)}}}{v_{RMS}\text{(}noise\big{)}}\Bigg{)}\] (5)
# Design and Application of a New Visible Light-Curing 3D Printing Platform
## 3 Results
### Study on the Formulation and Combustion Performance of Visible Light-Cured Slurry  
The core ingredients of the visible light-curing paste comprised the prepolymer, active diluent monomer, photoinitiator, and various additives. The prepolymer was EA/PUA, the active diluted monomer was PETA/HDDA/BA, and the photoinitiator selected was  
Figure 8: Time relay test.  
Figure 7: Single cantilever swing test.  
Figure 9: Working test of light-curing lamp.  
camphorone (CQ), augmented with dimethyl terephthalate (DMT) as the active agent. The additives encompassed the leveling agent BYK-333, the defoamer BYK-A530, and the adhesion accelerator HEMAP [14; 15]. The curing time \(t\), curing hardness \(H\), and curing depth \(h\) are the important indexes used to evaluate the performance of the photocuring paste: EA/PUA ratio (A), the active diluent-monomer blend of PETA/HDDA/BA (B), the photoinitiator CQ's concentration (C), and the quality of the additive (D). Orthogonal experiments helped determine the optimal formula. Table 1 shows the quality represented by the four levels, while Table 2 represents the result of orthogonal test.  
As shown in Tables 1 and 2, the curing time, curing hardness and curing depth were analyzed using the range analysis method. The curing time of visible light-curing pastes is less than 12.1 s, and the main factor affecting the curing time is the content of CQ (C). The factor that had the greatest influence on the curing hardness was the active diluent monomer (B). CQ (C) was the most important factor affecting the curing depth. Each factor affected the main order from the largest to the smallest for curing depth, curing time, curing hardness. Therefore, \(\text{A}_{4}\text{${}^{\#}$B}_{3}\text{${}^{\#}$C}_{1}\text{${}^{\#}$D}_{2} \text{${}^{\#}$}\) was selected as the best formula, and its mass fraction is shown in Table 3. Its viscosity, curing hardness, curing time, and other performance parameters are shown in Table 4.  
\begin{table}
\begin{tabular}{  
The properties of visible light-cured paste are shown in Table 4. The curing time is 7.5 s, the viscosity is 226 mPa-s, the curing hardness is 2 H, the gel rate is 96.67%, the shrinkage rate is 98.35%, and the curing depth is 9.41 mm.