esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Algorithm Design
#### 2.4.4 Threshold Analysis of Combined Acceleration  
The combined acceleration threshold analysis method attempts to combine the advantages of the Stage 1, Stage 2, and Stage 3 methods to enhance the overall accuracy and robustness of activity levels classification.  
* Equation (9) represents the combined acceleration set with a data collection window size of \(n\). \[A=[a_{1},a_{2},a_{3},\ldots,a_{n}]\] (9)
* Equation (10) is used to calculate the data volume for low-activity behavior; \(I(\text{condition})\) is the indicator function. \[count\_stand(A)=\sum_{i=1}^{n}I(9.0\leq A_{i}\leq 10.5)\] (10)
* Equation (11) is used to calculate the variance within the interval. \[\sigma^{2}=\frac{1}{n}\sum_{i=1}^{n}(A_{i}-\mu)^{2},\,\mu=\frac{1}{n}\sum_{i=1 }^{n}A_{i}\] (11)
* Equation (12) returns the final activity level, using Stage 1 methods to determine low activity, Stage 2 methods to determine medium activity, and Stage 3 methods to determine high activity. \[f(A)=\left\{\begin{array}{ccc}\text{``}low\ activity\text{''}&if\ count\_stand(A)\geq\frac{2N}{3}\\ \text{``}medium\ activity\text{''}&if\ count_{stand(A)}<\frac{2N}{3}and\ 1.2< \sigma^{2}\leq 29.0\\ \text{``}high\ activity\text{''}&\text{most\_common}\big{(}\big{\{}y_{i_{1}},y_{i_ {2}},\ldots,y_{i_{k}}\big{\}}\big{)}\end{array}\right.\] (12)  
Algorithm A1 presents the pseudocode for the threshold analysis of combined acceleration.
## 4 Results
### Experimental Results
#### 4.3.2 Real Log
The indirect chaotic activity filtering method proposed in this paper is significantly superior to the indirect chaotic activity filtering method based on activity entropy in terms of operational efficiency. Therefore, through comparative experiments, it can be concluded that although the difference in time performance between different chaotic activity filtering methods for different event logs is related to the characteristics of event logs, the chaotic activity filtering method proposed in this paper is compared with the traditional chaotic activity filtering method. In terms of time performance, the chaotic activity filtering method proposed in this paper can effectively improve the efficiency of chaotic activity filtering because of the traditional chaotic activity filtering algorithm. The results of experiment show that the effect of the filtering approach is closely related to the event log itself, so different results can be obtained for different event logs. However, comparing the approaches proposed in this paper with other approaches, the approaches proposed in this paper can reduce the running time without losing too much accuracy and ensure a certain integrity of the log.
# Overview of Clean Automotive Thermal Propulsion Options for India to 2030
## 2 The Current and Future Technological State-of-the-Art
### Advanced Internal Combustion Engine Solutions for Near Zero Emissions
#### Advanced Combustion
However, to attain homogenous mixture, several methods such as high fuel injection pressure with small nozzle holes, high boost pressure, and higher swirl ratios are used to increase the homogeneous mixture properties of the air-fuel mixture injected in the combustion chamber. Premixed Charge Combustion Ignition (PCCI): PCCI was developed from HCCI due to the latter's limiting operating range, high rise in pressure and inability of controlling the combustion parameters such as SOC [36]. This depends on several factors such as fuel auto-ignition qualities, fuel mixture homogenous characteristics, in-cylinder temperature and pressures etc. These challenges motivated the researchers to develop PCCI. This method is capable of reducing NOx and soot emissions simultaneously with improved engine efficiency. It was observed by the authors that there was 31% reduction in CO2 emissions with an increase in 14% of BSFC with the aid of diesel and ethanol-blended composition of the fuel. As per the authors in a review paper based on LTC for Bharat Stage VI standards [41], fuel mixing time is an important building block in low emission policy. As the mixing is increased there are lower possibilities of the formation of PM, HC, CO and NOx due to the increase in engine efficiency. The PCCI method of combustion has a requirement to obtain premixed charge before the start of combustion, achieved by ignition delay before SOC of high-temperature reactions. Some of the ignition delay methods are as discussed in HCCI, however, use of low cetane fuel with low compression ratio can also aid towards delaying in the ignition. The key in achieving successful implementation to PCCI lies in harmonizing the mixing and chemistry times, where prolonging the mixing time improves particulate matter emissions, for example. The time of mixing must be extended so that the ignition cannot occur before the desired level of mixing. Therefore, if the mixing rates can be increased by utilizing mixing related design constraints such as spray targeting techniques, swirl geometry, the necessary mixing timing and ignition delay can be reduced as a consequence of gaining control over the phase of combustion. In the case of PCCI, there are typically two types of injection strategies, that are adopted depending on when the fuel injection occurs relative to TDC [42].
[32] introduced an enhanced ambiguity resolution algorithm aimed at achieving centimeter-level RTK positioning. [PERSON] et al. [33] introduced a multi-GNSS RTK positioning method tailored for smarthones, specifically suitable for real kinematic conditions. Their experimental results showed that DF RTK could attain meter-level accuracy. They concluded that smartphones hold significant potential for achieving high-precision navigation and positioning in real urban environments. In the previously mentioned studies, both PPP and RTK, which rely on carrier phase observations, were utilized to achieve high-precision GNSS positioning with smartphones. However, it is worth noting that only a small number of smartphones can output the _AccumulatedDeltaRangeMeters_ data, which is essential for obtaining carrier phase measurements [34]. This results in the limited applicability of PPP and RTK techniques for widespread use in smartphones among the general public. Additionally, the vast majority of smartphones can now access dual- or even triple-frequency pseudo-range observation data. However, traditional single-point positioning (SPP) cannot fully harness the raw smartphone GNSS observation data. Therefore, multi-frequency ionosphere-free precise code pseudo-range positioning suitable for smartphones holds substantial research significance and practical value in enhancing smartphone GNSS positioning. The main contributions of this study can be summarized as follows. Firstly, an analysis was conducted on BDS-3 B1C/B2a/B1I, GPS L1/L5, and Galileo E1/E5a measurements collected in various scenarios using Xiaomi 11 Lite (Mi11) and Huawei P40 (HP40) smartphones. Second, various PCP models were derived. Third, the performance of these positioning methods was assessed using real-world datasets obtained from various scenarios. Finally, conclusions based on the findings in this study are provided.
## 3 Vehicular Communications: Core Concepts and Challenges  
This section highlights the critical role of Pseudonym Certificates PCs in the Security Credential Management System SCMS and delves into the intricacies of Dedicated Short-Range Communications DSRC. Additionally, it offers a concise overview of the challenges associated with rural areas and the sparse presence of Roadside Units RSUs, setting the stage for further exploration in subsequent sections.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 3 Materials and Methods
### PDIV Measurement Procedure  
To define the PDIV, the 50 Hz sinusoidal voltage was increased by 10 V/s from 0 until the first PD was detected. The minimum PD charge to be considered as PD was defined at 0.05 nC. As specified in UNE-EN 60270:2002 [81], this value should be defined as at least twice the background noise in pC before the measurements; 0.05 nC showed good agreement between surrounding noise avoidance and PD detection for all the developed experiments. Therefore, the first voltage level at which a PD with a charge equal to or higher than 0.05 nC occurs was defined as the PDIV. For each condition, the average of five freshly manufactured sample data points were used to define the PDIV value [24; 36; 83]. Each sample was measured three times with an interval of 60 s between measurements, in order to avoid the memory effect [41; 43], and the average value is used. The results are represented by the average, maximum, and minimum measurement values.
# Improvement of Recycled Aggregates Properties by Means of CO\({}_{2}\) [PERSON]
## 2 Research Significance  
This work is part of the FastCarb National project, which investigates the optimization of CO2 uptake of RCAs from laboratory to industrial scales. The main objective of this research is to study all the parameters which impact accelerated carbonation and CO2 uptake and to study the effect of accelerated RCA carbonation to improve their properties. The main contributions of this paper are as follows:  
* The new calculation of CO2 uptake, taking into account the water generated by the carbonation of portlandite;
* The identification of a relationship between the RCA's optimum water content and their WAC for a maximum CO2 uptake. This study shows the range of optimum water content related to the WAC of RCAs, with many of them being from laboratory concretes or from old demolition concretes. This data are important for industrial applications. This interval is known in the literature in concretes (around 65% and 80% in relative humidity), but these values are not transposable to RCAs. This study proposes a process of imposing the optimum water content by impregnation and drying. This process generates the saturated surface dry state of RCAs and good filling of the pores by water for an optimal diffusion of CO2;
* The contribution of accelerated carbonation compared to natural carbonation for RCAs from recycling platforms, which has not been shown in the literature;
* The range of pores clogged by CaCO3 and the range of pores formed after carbonation, derived to be essential data for the durability of carbonated RCAs. These tests with mercury intrusion porosimetry are related to the decrease of the WAC on the same RCA (RB 1-4 C).
## 4 Conclusions  
Herein, we evaluated three different methods of Raman imaging data analysis, showing that all of them were effective for adulterant screening in food powder and eventually developing a quantitative model. Notably, although the univariate method is the simplest and most easy to implement, is suffers from an increased risk of generating false positive pixels and does not perform well when the peaks of the target adulterant overlap with those of another sample constituent. On the other hand, SAM considers the complete dataset but did not perform very well in this study, possibly for reasons set out above. Conversely, bivariate analysis relies on the multiplication of two band intensities, enhancing adulterant signals and suppressing background noise, and can thus be considered a simple but effective Raman imaging method, with the produced Raman maps providing valuable information on the spatial distribution of adulterant particles and thus allowing the further development of accurate quantitative models.  
However, in this study, the comparison of the three different data analysis methods was carried out with a single data set (adulterated paprika powder), therefore the obtained results are practically valid only for that particular data set. However, the findings of this study show that the univariate method is sensitive to background noise, which can appear in Raman band images of any kind of powdered food sample, and the SAM-based method considers the spectral pattern regardless of the particular adulterant (used in this study). Therefore, we believe that the findings of this study can be used to select appropriate methods for Raman imaging data analysis of any kind of (adulterated) powdered food sample.  
Figure 7: Combined images of adulterants produced using the three different analysis methods.  
**Acknowledgments:** This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Research Center Support Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA), Republic of Korea (No. 717001071 WT111).  
**Author Contributions:** [PERSON] and [PERSON]-K.C. conceived the structure of the paper; [PERSON] performed the experiment; [PERSON], [PERSON] and [PERSON] helped in the development of system and software; [PERSON] and [PERSON]-K.C. collected the references and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment
## 1 Introduction
CORT3D, which is based on RT3D, enables the modelling of numerous subsurface processes such as dense NAPL (DNAAPL) dissolution, equilibrium or rate-limited sorption, second-order kinetic contaminant oxidation, and the kinetic oxidation of NOD (both as a fast and a slow kinetic part). The output of the model allows the tracking of aqueous pollutants, aqueous chloride, and aqueous oxidants, as well as DNAAPL, sorbed pollutant, manganese oxide, fast NOD, and slow NOD. The ISCO3D code was developed to simulate the coupled processes of NAPL dissolution, chemical reactions, and the mass transfer in the ISCO scheme. The model includes a kinetic reaction between the permanganate and the chlorinated ethylenes, as well as the dissolution of the NAPL. It is also able to simulate reactions between aquifer material and permanganate, as well as the kinetic sorption of chemicals. ISCO modelling can be performed with user-modified open-source codes such as PHREEQC [68] or PHT3D [69]. This article describes the integrated modelling tool for the design and optimisation of ISCO performance. This tool is integrated into the user-friendly Excel spreadsheets, thus making it easy and intuitive to enter data and retrieve results. The tool is based on the well-known PHREEQC code and also uses Python code for data visualisation. The strongest features of the model are the solutions for feedback control, including calculations of the actual oxidant arrival rate, the delineation of zones susceptible to oxidation, the estimation of oxidation efficiency, and alarms for anomalous events. By using the well-known geochemical code and simple visualisation methods, the authors were able to create useful solutions for both practitioners and researchers. The feedback solutions and other features of the model can be customised for other contamination sites and reagent types using expert knowledge.
## 3 Proposed Approaches
### Proposed Method
#### 3.3.1 Data Pre-Processing  
Data collected on a monthly and yearly basis are divided into training (80%) and test (20%) data. To prevent divergence in the training set, the training data were standardized as shown in Equation (10) so that the average is 0 and the variance is 1. The test data were standardized as shown in Equation (11) at the time of prediction using the same parameters as the training data:  
\[Train_{std}=\frac{Train_{i}-Train_{mean}}{Train_{sig}}, \tag{10}\]  
\[Test_{std}=\frac{Test_{i}-Test_{mean}}{Test_{sig}}. \tag{11}\]  
Figure 6: Solar power forecasting using a traditional long short-term memory (LSTM).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Month**} & & & & & **Year** \\ \hline  & & & \multicolumn{6}{c}{**2018**} & & & & & **2019** & & \\ \hline
4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 1 & 2 & 3 \\ \hline
720 & 744 & 696 & 744 & 744 & 720 & 672 & 720 & 743 & 744 & 672 & 720 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The amount of data per month and year.  
In Equation (10), \(Train_{t}\) is the training data, and \(Train_{mean},Train_{sig}\), and \(Train_{std}\) are the mean, standard deviation, and standardization of the training data, respectively. In Equation (11), \(Test_{i}\) is the test data, and \(Test_{mean}\), \(Test_{sig}\), and \(Test_{std}\) are the mean, standard deviation, and standardization of the test data, respectively.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 1 Introduction
The main contributions of this paper include:  
1. A cluster of virtual nodes is designed around a group of IoMT devices, ensuring untrusted cloud service providers are unaware of computation tasks performed on data. 2. IoT device data are shared using unsecured channels, resulting in delay and replay attacks. Timestamps and nonce values included in packets detect any eavesdroppers on the communication channel. A time synchronization protocol of the IoMT device clock ensures that no malicious node prevents the time reset process. 3. Data transmission from IoMT devices is secured using homomorphic encryption, thus avoiding data manipulation by cyber attackers. Data intercepted by cyber attackers are in an encrypted ciphertext state, preventing any intruder from learning or manipulating the data. 4. Computation of data is performed on the network's edge layer using virtual nodes powered by resources from untrusted cloud services. Offloading operations from the Cloud to the edge prevents untrusted cloud servers from learning the computation process. 5. Using a share-based virtual node selection process, homomorphic secret sharing prevents attackers from learning which selected virtual nodes are used for the computation process. 6. A comparative analysis with existing research shows the proposed scheme provides data confidentiality, integrity, and a privacy-preserving secure computation process. The remainder of this paper is organized as follows. In Section 2, we discuss technologies for secure encryption of data and the computation process. In Section 3, we present an overview of the proposed system and its workflow process. In Section 4, we perform a comparative analysis of the proposed scheme with recent research studies; weaknesses of other schemes and future research area are also discussed and finally, in Section 5, we conclude our paper.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 5 Potential Research for ISAC towards 6G ISTNs
### Standardization  
The deployment of the ISAC system towards 6G ISTNs will face multiple demands, requiring corresponding policies and regulations. Internationally, standardization organizations typically establish standards to provide overall regulations. There are already several standards for existing ISTN and ISAC systems [14; 66; 67]. However, no specific standards have been proposed for ISAC towards 6G ISTNs. We suggest that standardization can be approached from the following directions.  
Firstly, the division of spectrum resources needs to be addressed. The World Radio-communication Conference of the ITU regularly conducts functional allocation of frequency bands based on the existing network layout [132]. Since the ISAC system combines communication and radar sensing in the same frequency band, using the same spectrum for a dual-functional system will inevitably impact the original spectrum planning.  
The ISAC system has open links and environmental sensing capabilities, which allows it to fully sense the surrounding environment, especially in confidential areas. Therefore, the usage of the ISAC system needs to be properly registered to prevent unauthorized information gathering.  
Furthermore, future ISAC system devices within the ISTN architecture require standardized parameters to ensure their production follows standards, enabling lawful usage and reasonable planning. This will also support multi-party technical management and prevent monopolies.
## 3 Probabilistic Seismic Demand Model  
Performance-Based Earthquake Engineering (PBEE) delineates the quantitative ways to achieve specific predefined performance level earthquake intensity. Pacific Earthquake Engineering Research (PEER) Center has evolved a probabilistic framework for performance-oriented design and evaluation. One of the fundamental components of the PBEE framework is a probabilistic seismic demand model (PSDM).  
A PSDM represents the statistical relationship between responses on a structure or its components and the ground motion IM. The pioneering work by [PERSON] et al. [53] demonstrated that conditional PSDMs could be modeled using a lognormal distribution.  
The relationship between the median of seismic demands, \(S_{D}\), and seismic IM can be expressed in a power function.  
\[S_{D}=a\text{IM}^{b} \tag{1}\]  
where \(a\) and \(b\) are the regression coefficients. This expression can be rearranged in forms of linear regression as follows:  
\[\text{ln}(S_{D})=\text{ln}(a)+b^{a}\text{ln}(\text{IM}) \tag{2}\]  
where constant \(\text{ln}(a)\) is the vertical intercept and \(b\) is the slope. Output data for the regression analysis are originated from performing nonlinear time history analyses. Peak demands (\(d_{i}\)) are plotted against the IM to estimate the regression parameters and the dispersion, \(\beta_{D\,|\,IM}\).  
The following expression is used to estimate the dispersion, where \(d_{i}\) is the \(i\)th structural demand and \(N\) is the total number of ground motions.  
\[\beta_{D|\text{IM}}\ =\sqrt{\sum_{i=1}^{N}\frac{\left[\text{ln}(d_{i})-\text{ ln}(S_{D})\right]^{2}}{N-2}} \tag{3}\]