esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Work
### Lightweight Cryptography: ASCON  
In the proposed protocol, the lightweight cryptography algorithm ASCON-128 plays a central role in securing the content IDs (CIDs) generated by IPFS. These CIDs, which are unique identifiers used to locate data on the IPFS network, are encrypted using ASCON-128 before being stored on the blockchain.  
Encrypting the CIDs with ASCON-128 ensures that only authorized parties can access these identifiers. Indeed, the encrypted CIDs are stored on the blockchain, and their decryption can only be performed with the appropriate shared key. This guarantees restricted access to the data, even though the blockchain is public.  
The CIDs generated by IPFS have a standardized size of approximately 34 bytes (or 46 characters in base58), which is a significant advantage for the use of ASCON-128, an algorithm designed for efficient encryption management. During the encryption of the CIDs, the process unfolds as follows:  
1. Padding the data: The 34-byte CID is padded with 2 additional bytes, bringing the total size to 36 bytes. This size is a multiple of 16 bytes, meeting the requirements of ASCON-128, which encrypts blocks of 128 bits (or 16 bytes).
2. Encryption with ASCON-128: This 36-byte block is then encrypted in a single operation using ASCON-128. The result of the encryption is also a 36-byte block, containing the encrypted data as well as an authentication tag. This tag ensures the integrity of the information and detects any attempts to modify the CIDs.  
Figure 7 illustrates the process, detailing each step from padding a CID to encrypting it with ASCON-128 and producing the final encrypted output.  
As shown in Table 2, the data inserted into the system can take different forms, such as images, files, or text. Regardless of their nature, they are first stored on IPFS, which generates a unique 34-byte CID (content identifier). Before encryption with ASCON-128, this CID is converted to a hexadecimal format and then padded to reach the required size of 36 bytes. The table below illustrates this process for different types of data.  
Figure 7: CID padding in encryption phase with ASCON.
## 3 Methodology
### Outlier Detection  
If the data contain outliers, then any test used above can fail due to their presence. The absence of outliers is an implicit (seventh) assumption for any MLR model. That assumption can hardly be relaxed. The only rational solution is to identify the outliers and remove them from the data. We apply an elaborate algorithm proposed in [45] to solve the stated problem. The algorithm works in cycles containing two phases. A LOO procedure testing for outliers in the training data is performed in the first phase of any cycle. Each training record is classified as outlier using single comparison significance level \(\alpha\) with test statistics with the predicted residual normalized by its standard deviation. Finally, we construct high-quality regression model using the purged training dataset, because every possible outlier record for this cycle is probably purged easily. In the second phase, all previously declared outliers (in the first phase and in the previous cycles) are subjected to confirmative Benjamini-Hochberg step-up multiple testing procedure controlling the false discovery rate (FDR). Only those records which are confirmed by the multiple hypothesis testing are declared outliers from this cycle whereas the rest return to the training dataset. The latter is used to construct a regression model which can be used in the next cycle. The procedure stops when the predetermined count of cycles is reached or when the current cycle does not change the training dataset from the end of the previous sample. The advised procedure is computationally expensive but allows a lot of flexibility when dealing with data deviation of different order of magnitude and at the same time provides a satisfactory balance between high quality of the models and conservative results. Let us denote the above algorithm as CODPA (cycled outlier detection phase algorithm). The problem we face is when to apply CODPA in the sequence of diagnostic actions described in Section 3.3. We have four different options:  
Op1: Do not apply CODPA at all.  
Op2: Apply CODPA at the end and continue the stepwise regression procedure with the purged training data.  
Op3: Apply CODPA at the beginning and perform the stepwise regression procedure with the purged training dataset.  
Op4: Apply CODPA at the beginning of every step of the stepwise regression procedure for the current set of regressors and perform each step with unique purged training dataset.  
Option Op1 makes sense because our hydrodynamic efficiency prediction problem uses a controlled experiment, and an observed measurement with a severe deviation from its expected values should be repeated. Obviously, option Op4 should be the most computationally expensive one and may create problems with the stability of the advised procedure.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 3 Synthesis of Molecularly Imprinted Polymers
### Precipitation Polymerization  
Precipitation polymerization is a relatively simple one-step method for the formation of micro- or sub-micrometer polymeric beads under appropriate conditions. The main difference between this method and bulk polymerization is the large volume of polymerization solvent in case of precipitation method. Starting with a dilute homogeneous monomer solution, the growing polymeric beads precipitate out from the solution, owing to their low solubility in the solvent. Remarkably, precipitation polymerization has been mainly used for the preparation of high-quality MIP beads with no need for stabilizer or surfactant [13; 14].  
The main disadvantages involve the excessive use of porogen in the polymerization process and the high amounts of template required for the synthesis. Yet, it is one of the most commonly used approaches to overcome the irregularity in shape of resulting particles when using bulk polymerization drawbacks.  
Figure 1: The most common polymerization methods used for MIP synthesis.
# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 4 Discussion on Synthetic Data-Based Privacy Methods
As shown in Figure 8, the real data are mostly limited to the data owners' environments, and therefore, generative models of a suitable type can be moved closed to that data to produce synthetic data. The structure of each generative model is different, and therefore, a suitable model is chosen based on the data type. For instance, to generate tabular data, Table-GAN can be used. In contrast, DCGAN is suitable for generating synthetic images. Similarly, for the time-series data, a different GAN model can be used. After training GAN models by connecting with real data, the SD can be obtained in diverse formats depending upon the application. Later, the SD can be used for different purposes, as shown in Figure 8(4). In recent years, SD has been widely used for data augmentation purposes while building classifiers. With the advent of the DC-AI concept, SD has become an enabling technology for AI applications. In the coming years, SD will be used as an integral component for medical applications, where access to real data is often prohibited owing to privacy concerns. Figure 8: Overview of synthetic-data-based developments along with experimental details. In step 1, relevant data sources are identified and properties of real data are analyzed. In step 2, the appropriate GAN model is chosen and brought close to real data. In step 3, synthetic data is curated in the relevant modality. In step 4, the synthetic data is utilized either to train ML models or to share it for data mining.
# Portable X-ray Fluorescence Analysis of Organic Amendments: A Review
## 3 Results and Discussion
### Effect of Moisture Content
Each particle can be mostly organic (e.g., plant debris) or mostly mineral (soil particles or small rock fragments). The concentration of chemical elements in these different types of particles can be very different [28; 53]; plant nutrients are concentrated in organic materials, while Si, Al and trace elements are usually enriched in the fine fractions; and the water contained in a wet sample can also preferentially be associated with particles of an organic type. The result is that geometrically the distribution of water and chemical elements in the particles of an organic amendment sample is not homogeneous. Therefore, the effects of incident radiation absorption and secondary scattering could vary depending on the specific characteristics of each organic amendment. This could be one of the reasons for the disparity in results that has been mentioned, both for organic amendments and also for soils. The effects of organic matter content and moisture content are likely to overlap when fresh samples are analyzed. In addition, its separation for statistical purposes or for modeling and correcting results could be complicated if a heterogeneous set of samples is studied. The moisture content of an organic sample can also have another effect that has not normally been considered in previous studies. The increase in humidity in organic amendments usually leads to a decrease in apparent density; this is a compaction of the sample [54]. This is a well-known physical property of organic growing media referred to as shrinkage [55]. In the case of highly compactable materials, this could lead to a high interaction between the effects of organic matter and moisture content, possibly accentuated in conditions close to saturation of the sample with water. In most of the studies discussed, comparisons of the pXRF technique with other laboratory procedures such as digestion followed by measurement by ICP-OES have been made. In these cases, pXRF has generally been used in the laboratory, after drying, grinding, and sieving the samples, which reduces problems of heterogeneity and facilitates the comparability of procedures under the same conditions. However, the influence of sample moisture is especially relevant if we want to use the technique in situ, under field conditions, preventing the previous steps of sample preparation.
## 5 Examples of Forward-Inverse Modeling  
### Introduction  
A primary goal in _remote sensing_ of the Earth from space is to retrieve information about atmospheric and surface properties from measurements of the radiation emerging at the top-of-the-atmosphere (TOA) at several wavelengths [24; 81]. These _retrieval parameters_ (RPs), including cloud phase and optical depth, aerosol type and loading, and concentrations of aquatic constituents in an open ocean or coastal water area, depend on the _inherent optical properties_ (IOPs) of the atmosphere and the water. If there is a model providing a link between the RPs and the IOPs, a forward radiative transfer (RT) model can be used to compute how the measured TOA radiation field should respond to changes in the RPs, and an inverse RT problem can be formulated and solved to derive information about the RPs [3; 82]. A _forward RT model_, employing IOPs that describe how atmospheric and aquatic constituents absorb and scatter light, can be used to compute the _multiply scattered light field_ in any particular direction (with specified polar and azimuth angles) at any particular depth level (including the TOA) in a vertically _stratified medium_, such as a coupled atmosphere-water system [83]. In order to solve the _inverse RT problem_ it is important to have an accurate and efficient forward RT model. Accuracy is important in order to obtain reliable and robust retrievals, and efficiency is an issue because standard iterative solutions of the _nonlinear inverse RT problem_ require executing the forward RT model repeatedly to compute the radiation field and its partial derivatives with respect to the RPs (the _Jacobians_) [82].  
In addition to scalar forward RT models, vector RT models that consider polarization are important (see Section 4). Numerous RT models that include polarization effects are available (see [PERSON] et al. [84] and references therein for a list of papers), and the interest in applications based on vector RT models that apply to coupled atmosphere-water systems is growing. Examples of vector RT modeling pertinent to a coupled atmosphere-water system include applications based on the _doubling-adding method_[85; 86; 87], the _successive order of scattering method_[84; 88; 89], the _matrix operator method_[90; 91], and _Monte Carlo methods_[92; 93].  
The purpose of this section is not provide a comprehensive review of forward-inverse methodology, but rather to provide a few examples of how RT modeling involving coupled atmosphere water systems described in the previous sections can be used to solve the inverse problem with an emphasis of how machine learning techniques (neural networks) can be used to our advantage.
# Review of Automatic Estimation of Emotions in Speech
## 7 Neural (Machine Learning) Techniques for Emotion Classification of Speech
### Common Architectures for ANNs  
The simplest fully connected feedforward (FFNN) MLP structure has all nodes in each layer feed into all nodes in the succeeding layer. For most applications, such an approach is often overly general and too costly in terms of size and computation. As useful information in speech data is spread very non-uniformly across both frequency and time, fully connected approaches are inefficient [97]. Most ANN applications instead use different versions of the more efficient components noted below (Table 2).
## 3 An Improved DPC Algorithm Based on Chebyshev Inequality and Differential Privacy
### Privacy Analysis of DP-CDPC Algorithm  
The DP-CDPC algorithm implements privacy protection by introducing appropriate noise to the core statistic of the CDPC algorithm, this section aims to analyze and establish this claim, and the proof process is presented as follows:  
Let \(D\) and \(D^{\prime}\) denote a group of adjacent datasets of local density, \(M(D)\) and \(M(D^{\prime})\) respectively represent the set of all output results of adjacent datasets through DP-CDPC algorithm, and \(S\) represent all output results of algorithm. Since the inequality \(|a-c|-|b-c|\leq|a-b|\) holds, and exponential function \(e^{x}\) is an increasing function, then:\[\begin{array}{l}\frac{P(M(D)\in S)}{P(M(D^{\prime})\in S)}=\frac{\frac{1}{2} \exp(-\frac{|D-\mu|}{b})}{\frac{1}{2}\exp(-\frac{|D^{\prime}-\mu|}{b})}\\ =\exp(\frac{|D^{\prime}-\mu|}{b}-\frac{|D-\mu|}{b})\\ \leq\exp(\frac{|D-D^{\prime}|}{b})\\ \leq\exp(\frac{\max(|D-D^{\prime}|}{b})\\ =\exp(\frac{\Delta f}{\Delta f/\varepsilon})\\ =e^{\varepsilon}\end{array} \tag{14}\]  
In the DP-CDPC algorithm, each generated cluster family is independent of each other, and the clustering process does not involve iterative steps. Hence, there is no need to allocate the privacy budget. Therefore, the DP-CDPC algorithm guarantees \(\varepsilon\) differential privacy protection.
# Evaluation of the Coupling of a Hybrid Power Plant with a Water Generation System
## 2 Methodology  
### Exergetic Analysis  
In contrast to energy, exergy can be destroyed or lost. An exergetic analysis is a helpful tool in the evaluation of energy conversion systems because it reveals the main sources of inefficiencies in a component or a process. Basic exergy definitions and other relevant aspects of this analysis are presented in [44].  
The exergetic efficiency (%) of a component or a process, \(\varepsilon\), is defined as:  
\[\varepsilon=\frac{\dot{E}_{P}}{\dot{E}_{F}} \tag{1}\]  
The exergy of the product (\(\dot{E}_{P}\)) is the desired outcome from the operation of the specific component or system. The exergy of the fuel (\(\dot{E}_{F}\)) is the input required to generate the final product.  
The exergy of solar power in the case of thermosolar plants is calculated applying the following equation [45]:  
\[\dot{E}_{S}=\dot{Q}_{S}\cdot\Psi_{S}=\ A_{Refl}\cdot DNI\cdot\Psi_{S} \tag{2}\]  
where, \(\dot{Q}_{S}\) is the heat flow rate (W) that reaches the solar field, defined by the DNI \(\left(\frac{W}{m^{2}}\right)\) and the heliostats reflective surface \(A_{Refl}\left(m^{2}\right)\). \(\Psi_{S}\) is the ratio between exergy and energy calculated as:  
\[\Psi_{S}=\left[1-\frac{4}{3}\left(\frac{T_{a}}{T_{s}}\right)+\frac{1}{3}\left( \frac{T_{a}}{T_{s}}\right)^{4}\right] \tag{3}\]  
where, \(T_{a}\) is the ambient temperature and \(T_{s}\) is the apparent black body temperature of the sun (5600 K).
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 3 Results and Discussion
### Apparent Density  
To assess the impact of modified aggregate on the apparent density of the UHPC, the apparent density of samples cured for 28 days was measured, as depicted in Figure 9.  
Figure 8: Flow chart.  
Figure 9: Apparent density.  
When no modified aggregate was incorporated, the apparent density of the UHPC after 28 days of curing was 2189 kg/m\({}^{3}\). Upon adding modified aggregate, within a mass substitution rate of 40%, the apparent density increased, and it peaked at a 20% dosage, reaching 2210 kg/m\({}^{3}\), which is 1% higher than M0. With the mass substitution rate of modified aggregate surpassing 40%, the apparent density started to decline. At a 100% substitution rate, the apparent density dropped to only 2090 kg/m\({}^{3}\), 4.5% lower than that of M0.  
The measurement of the UHPC's apparent density is related to its internal pore volume. When the mass substitution rate of modified aggregate is below 40%, it is speculated that the internal pore structure is refined, with a reduction in pore quantity, so the apparent volume is reduced, which leads to an increase in apparent density. Conversely, when the mass substitution rate exceeds 40%, there is an uptick in the number of internal pores. As a result, the apparent volume of the UHPC expands, causing its apparent density to decline.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of the Lithium-Ion Battery under a Low-Pressure Environment  
In particular, an alarming increase in the number of incidents on airlines caused by malfunctioning LIBs has raised more concerns among safety and aviation experts. The Federal Aviation Administration (FAA) has reported 46 incidents involving LIBs on aircraft in 2017, which is up from 31 in 2016 [145]. It is known that the low-pressure environment not only exists on the plane, but also in some high altitude areas. Moreover, it is important to note that there are many risks associated with both transportation and utilization in low-pressure environments. Fire behaviors under low pressure is different from that under normal conditions. Current research mainly focuses on the influence of low-pressure environment on the combustion characteristics of conventional liquid and solid fuels. Experimental results showed that pressure was the main factor in affecting the ignition [146; 147; 148]. More studies concerning the kinetics and mechanisms of fires at low pressures were also conducted to reveal the pressure effects on the burning process [149; 150; 151; 152]. However, the research on the combustion characteristics of LIBs under a low-pressure environment is currently insufficient. [PERSON] et al. took the pioneering studies on the fire behaviors of LIBs under low-pressure environment. The experiments were conducted in a sea-level city, Hefei (100.8 kPa/24 m), and a high-altitude city, Lhasa (64.3 kPa/3650 m) to offer guidance to facilitate the safe handling of battery under normal and high-altitude conditions. The results showed that the ejection behavior of LIBs in Lhasa appeared earlier than that in Hefei as shown in Figure 8, due to the lower pressure required to rupture, meanwhile the mass loss, heat release rate and combustion heat of the fire reduced with decreasing pressure [121]. They also studied the fire behaviors of primary lithium batteries at two different pressures. The mass loss, heat release rate, combustion heat, combustion efficiency and heat flux in Lhasa were smaller than those in  
Figure 7: Two typical tab configurations of packs: (**a**) branched style of tabbing (M-type); (**b**) serpentine style of tabbing (S-type). Adopted from Ref. [126].  
Hefei which mean that the primary batteries at lower pressure were less dangerous than those at higher pressure [163]. [PERSON] et al. studied the ignition and combustion characteristics of LIBs under low atmospheric pressure using a low-pressure tank from 30 to 101 kPa. Results indicated that the low atmospheric pressure could largely extend the ignition and weakened the combustion intensity of LIBs [110]. In summary, the study of the combustion characteristics of LIBs under low-pressure conditions has scarcely been examined, while it is of great importance to the safety management of LIBs in air transport and more work on this issue are necessary to be conducted in the future.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 4 Time-Frequency Driving Algorithm Principle
### Using GA to Improve Control System Parameter Selection  
Use Equation (17) to draw the SSB phase noise spectral density curves \(L(f)_{TA(Cs)}\) and \(He(f)\), use the frequency value of the intersection point of the two curves as a fixed reference value \(f(L(f))\). Then, substituting \(z=e^{2\pi fT}\) into Equations (24) and (25), the  
Figure 3: The functional block diagram of the equivalent DPLL steering control system.  
amplitude-frequency response curves \(H(f)\) and \(He(f)\) of the system transfer function \(H(z)\) and system error transfer function \(He(z)\) in the frequency domain can be obtained [22].  
\(H(f)\) and \(He(f)\) respectively have the characteristics of low-pass and high-pass filters in the frequency domain, and the frequency intersection value of the two frequency response curves is used as the dynamic debugging value \(f(H)\). Based on the principle of optimal overall frequency stability, when the output signal of the driving system fully takes into account the frequency stability advantages of \(TA(Cs)\) and \(TA(Rb)\), it is necessary to make the dynamic tuning value \(f(H)\) consistent with the fixed reference value \(f(L(f))\) as much as possible. However, the existing parameter adjustment of the equivalent DPLL steering control system requires multiple discrete debugging and approximate selection, so there are precision limitations and adaptive reliability problems, thus further improvement is necessary. In this section, a genetic algorithm is introduced to improve the selection process of noise variance parameter pairs. The driving system expects the overall frequency stability of the output signal to be better, so the more consistent the dynamic debugging value \(f(H)\) is with the fixed reference value \(f(L(f))\), the better. The fitness function to establish the minimization of the deviation of the control intersection frequency value is as follows:  
\[\begin{array}{l}F_{\cos t}=\left|f(H)-f(L(f))\right|_{\alpha}\\ =\left|\int_{\textit{down}}^{\textit{f}_{up}}\textit{find}(\min\left|H(f)- He(f)\right|)_{\alpha}-\int_{\textit{down}}^{\textit{f}_{up}}\textit{find} \left(\min\left|L(f)_{TA(Cs)}-L(f)_{TA(Rb)}\right|\right)_{\alpha}\right|\end{array} \tag{39}\]  
where \(\left|\right|\) means to obtain the absolute value of the deviation, \(\int_{\textit{down}}^{\textit{f}_{up}}\textit{find}(\min\left|\right|)_{\alpha}\) means to search for the corresponding frequency value at the intersection of two frequency domain curves with the minimum resolution \(a\) within the frequency search threshold given in \(\left[\textit{f}_{down},\textit{f}_{up}\right]\).  
The genetic algorithm in this section adopts the decimal coding method. The length \(L\) of the coding string of each variable is 10, the population size \(M\) is 100, and the termination algebra \(T\) is 100. Genetic operators that use crossover and mutation, \(P_{c}\) and \(P_{m}\), are 0.9 and 0.2, respectively.