esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Characterisation--Aged Cell
#### 3.2.2 Thermal Response--Room and Cycling Temperatures  
The thermal response in terms of temperature increase of the cell 2 measured during the discharge process under various current rates 6 A, 20 A, 40 A (corresponding to 0.3C, 1C, 2C) at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C at the top, middle and bottom regions are presented in Figure 12. The measurements were performed in a climate chamber (Clima Temperatur Systeme GmbH (CTS), Hechingen, Germany) in which the cell is subjected to forced convection. Similarly to the fresh cell, the difference observed in the temperature increases at the three top, middle and bottom regions on the surface of the aged battery cell indicate for a non-uniform temperature distribution. The highest temperature rises are detected in the middle and bottom regions of the cell, with up to more than 30% increase under room temperature. The evolution of the temperature in the three regions is similar at low current rates, although more significant temperature rises are observed at high current rates under room in comparison to the the cycling temperature. These differences in temperature increases initially observed in Figure 10, are seen now in Figure 12 to be even more prominent due to the ageing of the battery cell caused by long-term cycling.  
These results stress the importance of a good thermal monitoring of cycled battery cells, especially in real applications when embedded in battery modules and packs. Thermal runaway phenomenon at the scale of a battery pack is in practice often initiated by the thermal failure of a single cell, which in turns causes other cells to fail. Within a pack composed of aged cells, this phenomenon is even more likely to occur and hence critical. The strong difference up to 15% (Figure 12) in the temperature increase of a cycled battery cell under room compared to the cycling temperature, emphasises for the issues of possible mismatch in the thermal behaviour of battery cells at the level of a battery pack not being initially appropriately conditioned.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 8 Discussion  
It can be inferred that the smart card security schemes mentioned in [PERSON] et al. [11], [PERSON] et al. [22], [PERSON] et al. [29], [PERSON] et al. [47], and [PERSON] et al. [49] either fail to satisfy one or more security attributes listed in Table 5 or fail to resist one or more security attacks mentioned in Table 6. Only the schemes of [PERSON] et al. [7], [PERSON] et al. [27], [PERSON] et al. [55], and [PERSON] et al. [61], and the proposed ECSSP-SC, successfully satisfy each security attribute listed in Table 5 and can counter every attack enumerated in Table 6. However, it is revealed that the proposed ECSSP-SC consumes the least computational time in comparison to the schemes of [PERSON] et al. [7], [PERSON] [11], [PERSON] et al. [22], [PERSON] et al. [27], [PERSON] et al. [29], [PERSON] et al. [55], and [PERSON] et al. [61] Though the scheme of [PERSON] et al. [61] satisfies all the required security features, it consumes three ECPM operations for both the server and the smart card, whereas the ECSSP-SC consumes only two ECPM operations for both the smart card and the server. Thus, ECSSP-SC saves 33.3% computational time compared to the schemes of [PERSON] et al. [27], [PERSON] et al. [55], and [PERSON] at al. [61] and 6% computational time compared to the scheme of [PERSON] et al. [7] Additionally, the scheme of [PERSON] et al. [61] consumes 896 bits and 640 bits of bandwidth for the smart card and the server, respectively, whereas the proposed ECSSP-SC consumes only 736 bits and 416 bits of bandwidth for the smart card and the server respectively, i.e., saving 17.8% bandwidth for the smart card. Thus, the proposed mutual authentication mechanism based on elliptic curve signcryption is more computational time-efficient than the related schemes and protocols for smart cards. Furthermore, from Figure 10, it is clear that the communication cost of the ECSSP-SC \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{8}{c}{**Resistance to Attacks**} \\ \cline{2-10}  & **RP\({}_{\mathbf{A}}\)** & **KK\({}_{\mathbf{A}}\)** & **US\({}_{\mathbf{I}}\)** & **SV\({}_{\mathbf{I}}\)** & **IN\({}_{\mathbf{S}}\)** & **PG\({}_{\mathbf{A}}\)** & **DE\({}_{\mathbf{A}}\)** & **MI\({}_{\mathbf{A}}\)** \\ \hline [PERSON] et al. [7] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] [11] & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ \\ [PERSON] et al. [22] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ [PERSON] et al. [27] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [29] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [47] & ✓ & \(\times\) & ✓ & ✓ & \(\times\) & ✓ & ✓ & ✓ \\ [PERSON] et al. [49] & ✓ & ✓ & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & ✓ \\ [PERSON] et al. [55] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [61] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
**ECSSP-SC** & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Resistance capability of different smart card security protocols against attacks.
# A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems
## 4 Results and Discussion  
### Case Studies  
We selected an industrial park in Xi'an as a typical IES for analysis, collected the relevant data of the park from January to July 2020, processed the original data and combined the processed data with the graph drawn. The overall energy supply and use characteristics of the park from January to July were analyzed from five aspects: power supply, power consumption, heating, energy consumption of heating water and cooling. as shown in Figure 5.  
The operation characteristics of the system can be summarized as follows.  
1. The power supply of the park mainly depends on the municipal power supply, the external power supply of the park accounts for 96.21% of the total power supply of the park, the total power supply of the photovoltaic system and the energy storage microgrid system accounts for 3.79%, and the energy storage micro-grid power generation base is small and can be ignored.
2. In the subsystem, the regenerative electric boiler and ground-source heat pump system occupy the first and second places in the power consumption, respectively, accounting for 16.02% and 12.19% of the total power consumption of the park, respectively. The power consumption of ground-source heat pump system decreases from January to April and increases from May to July.  
Figure 4: IES evaluation process based on combined methods.  
3. The heating in the park is mainly supplied by the ground-source heat pump system, supplemented by the regenerative electric boiler. The heat supply of the ground-source heat pump accounts for 62.24% of the total heat supply in the park.
4. The park is mainly cooled by the ground-source heat pump, supplemented by a base load chiller, ice storage system and air conditioning. The cooling supply of ground-source heat pump system accounts for 73.36% of the total cooling supply in the park, and the cooling supply increases from May to July.  
For the relevant evaluation indicators proposed in the literature, combined with the indicator classification method proposed in [50], the system evaluation indicators are divided into two levels. There are five first level indicators, which include reliability, network loss, response efficiency, equipment economy and environmental friendliness. There are 12 secondary indicators, which include average time of disability, electricity network loss rate and so on, as shown in Figure 6.
# Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
## 6 Conclusions  
This paper combines the wavelet transform and TL to diagnose industrial faults using limited target samples. The wavelet transform converts a one-dimensional time-series sample into a two-dimensional time-frequency image by extracting the hidden time and frequency domain characteristics of various faults as the input data to enhance the feature learning ability. To deal with limited target samples and the domain discrepancy between the source and target domains, we adopt the TL framework where we partially retrain the pre-trained model of the source domain using the intermediate samples that are conceptually similar to the target domain but less expensive to collect. We use a Simulink model of a triplex reciprocating pump with various fault combinations and severity levels to evaluate the performance of the proposed method. The proposed fault diagnosis method precisely classifies different single fault types and achieves the highest classification accuracy among SVM and the CNN. In particular, the classification accuracy improves as the fault condition becomes severe due to its significant impacts on the time and frequency domains. On the other hand, the results show that the combined faults are challenging to detect due to the complex interactions between the different faults and severity levels. Furthermore, the required number of training samples required to meet the accuracy demands of the proposed CNN-TL is lower than those required for SVM and the CNN.  
We plan to develop the optimization technique to decide the retraining portion of the TL model based on the similarity between the intermediate and target domains and the number of available intermediate samples to improve the classification accuracy and computational efficiency.  
Conceptualization, J.B. and P.P.; Formal analysis, P.P.; Investigation, H.S. and P.P.; Methodology, J.B. and P.D.M.; Software, P.P.; Validation, P.D.M.; Writing--original draft, P.P.; Writing--review & editing, J.B., P.D.M. and H.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the research fund of Chungnam National University.  
Not applicable.  
The authors declare no conflict of interest.
# A Redactable Blockchain Scheme Supporting Quantum-Resistance and Trapdoor Updates
## 5 Experiments and Results Analysis
### Performance of HIBCH-RS
#### 5.1.3 Scalability  
In this paper, the scalability of HIBCH-RS refers to the fact that the system can still maintain its existing efficiency and correctness in the face of large-scale modification requests, large-scale node users, and complex problems. First, the hierarchical identity structure of HIBCH-RS has controllable time complexity and space complexity in delegating keys, and the system performance will not be dramatically degraded with the growth of data or users. Secondly, in terms of privilege control and access efficiency, the design of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Size (Public Key)** & **Size (Trapdoor)** & **Parameter** \\ \hline HIBCH-RS & \(n\times(\overline{m}+2w)\) & \((\overline{m}+w)\times w\) & \(\overline{m}=nlogq\) \\ HIBPE & \(n\times\ell^{*}m\) & \((\ell+\eta^{*}+1)m\times(\ell+\eta+1)m\) & \(m=nlogq\) \\ AHIBET & \(n\times(m+\underline{1}w)\) & \((m+\underline{1}w)\times(m+\underline{1}w)\) & \(w=nlogq,m\geq w+\omega(logn)\) \\ \hline \hline \end{tabular}  
* \(\ell\) is level of identity, \(\ell<d\), where \(d\) is the maximum hierarchical depth. \(\eta\) is the number of punctured times.  
\end{table}
Table 4: Comparison of delegation key sizes.  
Figure 5: Comparison of hash running times. (**a**) The run time of the chameleon hash algorithm in HIBCH-RS scheme; (**b**) the run time of the forge algorithm in the HIBCH-RS scheme [15].  
HIBCH-RS model can effectively realize the control of privileges, in that only users with trapdoors can perform editing operations. Meanwhile, the algorithms can be efficiently converted between different layers when performing encryption and collision-finding operations. This scalable performance can be demonstrated in the experiments.
# Research on Speech Emotion Recognition Method Based A-CapsNet
## 3 Preprocessing and Feature Extraction
### Feature Extraction  
Feature extraction is a key component of an emotion recognition system. Whether the extracted features can correctly reflect emotions exactly or not will directly affect the recognition effect. The input of the proposed model A-CapsNet consists of 21D LLD features [32]. Each speech is segmented into frames with a 25 ms window and a 10 ms shifting step. For each frame, 21D LLD features, including 1D ZCR and 20D MFCCs, are extracted. Some features of the samples are quite different, so when the samples are directly inputted into the training, the contour of the loss function will be flat and long [33]. Before finding the optimal solution, the gradient descent process is not only complex, but also very time-consuming. Therefore, it is necessary to quantify each feature into a unified interval.  
After data standardization, all indicators are in the same order of magnitude, and they are suitable for comprehensive evaluation. After feature normalization, the contour of the loss function will be a partial circle, the gradient descent process will be flatter [28], and the convergence will be faster; therefore, the performance will be improved. The StandardScaler [34] is used to normalize the data so that the new dataset has a mean and standard deviation of zero. It should be noted that the normalization procedure should be applied to each feature rather than each sample.
## 3 Aqueous Film Forming Foam (AFFF)  
Developed in the mid-1960s by the United States Navy and 3M (St. Paul, MN, USA), aqueous film forming foams (AFFFs) were developed to specifically combat liquid fuel fires [100]. The foams are water-based hydrocarbon surfactants that frequently contain sodium alkyl sulfate, and fluorosurfactants, such as fluorotelomers, perfluorooctanoic acid (PFOA), or perfluorooctanesulfonic acid (PFOS). Able to spread rapidly across the surface of hydrocarbon fuels and form a thin film, AFFF can preventing fuel vapor and oxygen interaction to extinguish the fire. The efficacy of AFFF led to its widespread use as a fire suppressant for airports, naval vessels, and fire departments. However, when it is discharged, the components have a propensity to runoff and contaminate surrounding environments [61; 101; 102; 103]. Specifically, the PFOS within AFFF was later recognized under Annex B of the Stockholm Convention  
Figure 2: Proposed degradation pathway for PFOS and PFOA in plasma treatment. Plasma electrons, aqueous electrons, and argon ions are the primary species responsible for degradation of PFOS and PFOA to form perfluoroalkyl radicals. Chain propagation reactions involving the reductive and oxidative species along with subsequent hydrolysis yield short chain PFACs. Reproduced from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Environmental Science Technology 2019, 53, 5, 2731-2738 (reference 48). Reproduced with permission from [PERSON], [PERSON], Environmental Science Technology; published by American Chemical Society 2019.  
as a toxic and bio-accumulative pollutant [76; 82; 104; 105]. After years of firefighters training and using AFFF, runoff from the airports contaminated their local fresh water source as identified by the EPA in a study of emerging contaminants [106]. The recognition of AFFF as a toxic component has fostered a field of science focused on the detection and remediation of PFOS and many other perfluoroalkyl acid (PFAA) contaminates across the globe [62; 64; 67; 68; 70].
## 3 Results and Discussion
### Adsorption Kinetics of PTSA  
Adsorption kinetics is significant in the study of the performance of adsorbents for practical application. The kinetic curves of PTSA adsorption on five resins are displayed in Figure 5. To further evaluate adsorption behavior, the data were analyzed using the pseudo-first-order and pseudo-second-order kinetic models (Equations (3) and (4) respectively).  
\[Q_{t}=Q_{e}(1-e^{-k1t}) \tag{3}\]  
\[Q_{t}=\frac{k_{2}Q_{e}^{2}t}{1+k_{2}Q_{e}t} \tag{4}\]  
where \(Q_{e}\) and \(Q_{t}\) (mmol/g) are the adsorption capacities of PTSA at equilibrium and at time \(t\) (min), respectively; and \(k_{1}\) (1/min) and \(k_{2}\) (g/(mmol-min)) are the rate constants of the pseudo-first-order and pseudo-second-order kinetic models, respectively. The calculated correlation parameters are presented in Table 3.  
As can be seen from Table 3, the values of the calculated adsorption capacities \(Q_{e,cal}\) were much closer to the experimental uptake \(Q_{e,exp}\), and the higher values of \(R^{2}\) suggested that the pseudo-second-order model could better explain the adsorption process. The rate constants \(k_{2}\) for PTSA adsorption onto SD1, SD2, and SD3 resins were not significantly different, but significantly larger than SD4 and SD5, which might be related to the surface area and pore structure of the resins. According to Table 1, the BET surface area and pore volume of the five resins are in the following order: SD1 \(>\) SD2 \(>\) SD3 \(>\) SD4 \(>\) SD5. Hence, the resins with larger pore volume and specific surface area can accelerate the continuous reaction between adsorbent and PTSA molecules. Additionally, in water, the amino groups on resins would stretch due to their hydrophilic property, which could result in the obstruction of the effective pore canal for the adsorbate diffusion [39]. Thus, a slower PTSA adsorption rate reasonably occurred in the resins with higher functional group density, such as SD4 and SD5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multicolumn{2}{c}{**Initial Concentration of PTSA (mg/L)/SO\({}_{4}\)\({}^{2-}\) (mg/L)**} \\ \cline{2-4}  & **100/100** & **500/500** & **1000/1000** \\ \hline SD1 & 3.2010 & 0.8078 & 0.5074 \\ SD2 & 2.9223 & 0.7786 & 0.4628 \\ SD3 & 2.3752 & 0.6774 & 0.4014 \\ SD4 & 2.1478 & 0.6490 & 0.3455 \\ SD5 & 1.3690 & 0.5812 & 0.3003 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selectivity coefficient \(\alpha\) of PTSA and SO\({}_{4}\)\({}^{2-}\) adsorption on resins in a binary system.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements
### Requirements of Low Frequency Accelerometers
#### 2.2.1 Inertial Navigation  
Navigation techniques are divided into two categories: positioning and trajectory extrapolation [9]. One representative of positioning technology is a global navigation satellite system. The trajectory extrapolation method recursively measures the amount of variation in the real-time motion state of a moving object relative to the initial motion state. Then, the real-time position of the moving object based on that amount of change is determined. Inertial navigation is an example of the trajectory extrapolation method used as a navigation technique. An accelerometer in an inertial navigation system can measure the acceleration and angular acceleration of the carrier. In accordance with [PERSON]'s second law of motion, time integrates acceleration and angular acceleration to obtain the velocity, angular velocity, and position information of a moving object. The operation of time integration will amplify the error of the accelerometer, and thus, an accelerometer used in an inertial navigation system must meet high accuracy, stability, and anti-interference capability requirements. Optical accelerometers exhibit advantages in terms of accuracy, stability, and immunity to electromagnetic interference by utilizing optical sensing. They demonstrate good prospects in inertial navigation.
## 3 Measurement of State Data Based on Non-Contact Image Analysis
### Optical Blood Flow (Heart Rate) Signal Acquisition
In order to estimate the heart rate that changes according to the physiological state of the body in real time and to estimate the stable heart rate from the power spectral density, a sliding window is applied at 1 s intervals while using a window of about 4 s. The physiological parameters obtained in this way operate well when the user is not in motion, but stable estimation may be difficult due to noise when facial movement occurs. This is because, while the face is close to an ellipse, it is detected in a rectangular shape due to the characteristics of the existing face detector, increasing the probability of including background areas other than the face. When the face is rotated, the light reflection from the surface of the face changes, causing unstable detection of areas such as skin color, background, and hair. In order to alleviate the instability caused by noise, pulse rate filtering was performed using the characteristic that the pulse rate continuously beating follows a Gaussian distribution. Outliers were removed by applying Gaussian filtering to the power spectral density for pulse rate estimation by deriving the mean value and standard deviation of recent pulse rate estimates. The input data can be largely divided into pre-recorded video files and image sequences, or real-time camera input. In the case of pre-recorded video files and image sequences, the input data must be assumed to be a fixed frame rate or include frame-by-frame timestamp information. In the case of real-time camera input, processing time per frame may vary depending on the state of the processor, leading to difficulty to assume a fixed frame rate. Assuming a real-time camera input with a frame rate of 30, it is theoretically possible to read 30 frames per second, but in reality, there may be cases where only one or two frames are missing and only less than 30 frames are read. For example, assuming that a time window having a length of 4 s is used, an error of a physiological parameter estimated later may increase due to the accumulation of such missing frames. To solve this problem, in the case of real-time camera input, the real-time frame rate was calculated by storing the timestamp at the point of processing each frame internally in a separate array. If the signal length corresponding to the time window is k, the real-time frame rate is calculated by (2). \[\text{frame rate}=\frac{k}{\times\text{\it tamps}[k-1]-\times\text{\it tamps }[0]} \tag{2}\]  
Figure 6: Peak detection results in the time series domain for PPI calculation. More accurate physiological parameter estimation is possible by calculating the frame rate at the time of calculating the filtering unit and the physiological parameter estimating unit as a value approximating the actual frame rate.