esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Decomposition Results of the Lithospheric Magnetic Field  
We obtain the decomposition results of the SD profile lithospheric magnetic field F\({}_{1}\) using the upward continuation method described in Section 2.3. The characteristics of the results are shown in Figure 3.  
Figure 3 shows the decomposition results of lithospheric magnetic field F\({}_{1}\). The basement part f\({}_{\rm b}\), the middle layer part f\({}_{\rm m}\), and the superficial part f\({}_{\rm s}\) obtained after the decomposition of the lithospheric magnetic field F\({}_{1}\) show significant zoning characteristics, which are basically identical to those of the above lithospheric magnetic field F\({}_{1}\).  
The zonal characteristics of f\({}_{\rm b}\) in the basement part shown in Figure 3 are as follows: In zone A, f\({}_{\rm b}\) presents a complete upward trend and reaches its maximum value (f\({}_{\rm b}\) = 246.3 nT) at measuring point M022, with a variation range of 269.5 nT. In zone B, f\({}_{\rm b}\) shows a slow downward trend and two small peaks with a variation range of 83.5 nT at both ends. In region C, f\({}_{\rm b}\) shows an upward trend, with a large peak and a small peak and a variation range of 107.6 nT. In zone D, f\({}_{\rm b}\) shows a gentle decreasing trend, with a decrease of approximately 15.1 nT. The regional characteristics of f\({}_{\rm m}\) in the middle layer are as follows:  
Figure 3: Decomposition results of the lithospheric magnetic field: basement part f\({}_{\rm b}\), middle part f\({}_{\rm m}\), and surficial part f\({}_{\rm s}\).  
In zone A, \(\rm f_{m}\) exhibits the most dramatic change, with three peaks, with increases followed by decreases. The maximum value of \(\rm f_{m}\) is at M024 (\(\rm f_{m}=425.0\) nT), and the variation range is approximately 603.4 nT. In zone B, \(\rm f_{m}\) exhibits gentle changes, except for two small peaks with a variation range of 153.5 nT at both ends. In zone C, \(\rm f_{m}\) exhibits sharp changes, with four peaks and a variation range of 151.3 nT. In the D region, the changes in \(\rm f_{m}\) are relatively gentle, with a variation range of 39.8 nT.  
As shown in Figure 3, the zoning characteristics of \(\rm f_{s}\) in the superficial parts are described as follows: In zone A, \(\rm f_{s}\) exhibits the most dramatic changes, with six large, sharp peaks, and reaches the maximum value at measuring point M006, \(\rm f_{s}=1623.7\) nT, with a variation range of 2201.0 nT. In region B, \(\rm f_{s}\) exhibit gentle changes, with a range of 441.3 nT, except for small changes at both ends. In zone C, \(\rm f_{s}\) exhibits sharp changes, with 8 peaks and a variation range of 1134.5 nT. In region D, \(\rm f_{s}\) has a small change of 215.1 nT.  
Based on the above results, Table 1 shows the maximum and minimum values of \(\rm F_{I}\) and \(\rm f_{b}\) in the basement, \(\rm f_{m}\) in the middle layer, and \(\rm f_{s}\) on the superficial surface of the SD profile in each partition. The distribution characteristics of each region in the SD profile are shown intuitively in this table.
## 4 Conclusions  
A SPME-GC-MS method was adapted based on previous studies for the simultaneous analysis of 10 carbonyls from ozone-based food colorant decomposition. On-fiber derivatization SPME for aldehydes extraction was improved compared to previous studies using the same method. PFBHA dopant concentration, doping and derivatization temperature, and derivatization time were optimized. Inaccuracy of 2-furfural analysis resulting from competitive derivatization was minimized with a shortened derivatization time of 5 s. The short derivatization time also shortened the on-fiber derivatization SPME-GC-MS cycle. The LOD values of the developed method were in the range of 0.016 to 0.030 ppb. A much lower LOD of 0.016 ppb for 2-furfural was achieved compared with 6 and 35 ppb in other studies [26, 27].  
This enhanced method was applied for the first time in the analysis of carbonyls as ozonolysis byproducts, certainly in a complex matrix of ozonated FL solution/suspension. Among the ozonolysis byproducts, benzaldehyde and 2-furfural in waste fruit leather were generated below their LODs, both 0.016 \(\mathrm{\SIUnitSymbolMicro g}\)/L of 100% fruit leather solution/suspension, while hexanal was the most abundantly generated, and the maximum generation was 80.0 \(\pm\) 22.0 mg/L. The test results on the new possible ozone application demonstrated that through adapted methodology, byproduct toxicity concern was addressed regarding the use of ozone in colorant removal from food material.  
Figure 7: Ethanal and hexanal generation-ozone dose profile (ozone was introduced into 200 mL of 100% fruit leather solution/suspension at 26.6 mg/(L\(\cdot\)min) ozone dosage rate).
###### Abstract  
Research on nanotechnology application in the oil and gas industry has been growing rapidly in the past decade, as evidenced by the number of scientific articles published in the field. With oil and gas reserves harder to find, access, and produce, the pursuit of more game-changing technologies that can address the challenges of the industry has stimulated this growth. Nanotechnology has the potential to revolutionize the petroleum industry both upstream and downstream, including exploration, drilling, production, and enhanced oil recovery (EOR), as well as refinery processes. It provides a wide range of alternatives for technologies and materials to be utilized in the petroleum industry. Nanoscale materials in various forms such as solid composites, complex fluids, and functional nanoparticle-fluid combinations are key to the new technological advances. This paper aims to provide a state-of-the-art review on the application of nanoparticles and technology in the petroleum industry, and focuses on enhanced oil recovery. We briefly summarize nanotechnology application in exploration and reservoir characterization, drilling and completion, production and stimulation, and refinery. Thereafter, this paper focuses on the application of nanoparticles in EOR. The different types of nanomaterials, e.g., silica, aluminum oxides, iron oxide, nickel oxide, titanium oxide, zinc oxide, zirconium oxide, polymers, and carbon nanotubes that have been studied in EOR are discussed with respect to their properties, their performance, advantages, and disadvantages. We then elaborate upon the parameters that will affect the performance of nanoparticles in EOR, and guidelines for promising recovery factors are emphasized. The mechanisms of the nanoparticles in the EOR processes are then underlined, such as wettability alteration, interfacial tension reduction, disjoining pressure, and viscosity control. The objective of this review is to present a wide range of knowledge and expertise related to the nanotechnology application in the petroleum industry in general, and the EOR process in particular. The challenges and future research directions for nano-EOR are pinpointed.  
nanotechnology; nanoparticle; petroleum; enhanced oil recovery; recovery mechanism; wettability alteration; interfacial tension reduction
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 2 Target Detection in Passive Forward Scattering Radar
### Target Doppler Signature in Passive FSR
The target Doppler frequency can be calculated as below. The target Doppler frequency component due to motion relative to the transmitter and receiver is given in Equations (7) and (8) respectively:  
\[f_{tx}=\left(v/\lambda\right)cos\left(\beta/2+\delta\right) \tag{7}\]  
\[f_{rx}=\left(v/\lambda\right)cos\left(\beta/2-\delta\right) \tag{8}\]  
whereby, \(\delta=180^{\circ}-\beta/2\), and the total target Doppler frequency:  
\[S_{sc}\left(t\right)=A_{sc}\text{sin}\left(\left(\omega_{o}+\omega_{sc}\right)t\right) \tag{2}\]0  
\[S_{sc}\left(t\right)=A_{sc}\text{sin}\left(\left(\omega_{o}+\omega_{sc}\right)t\right) \tag{2}\]1  
\[S_{sc}\left(t\right)=A_{sc}\text{sin}\left(\left(\omega_{o}+\omega_{sc}\right)t\right) \tag{2}\]2  
Based on Equation (9), the maximum Doppler shift occurred when the target trajectory perpendicular to the baseline (\(\delta=0^{\circ}\)) and therefore, the target frequency Doppler is determined by target's speed. The higher the target's effective speed, the higher the Doppler shift at the received signal.
# Experimental Study on the Shear Strength of Silt Treated by Xanthan Gum during the Wetting Process
## 2 Materials and Methods  
### Materials  
Figure 1 shows that Henan Province is located in the North China Plain of the middle and lower reaches of the Yellow River, where silt is widely distributed. The silt sample location is at an engineering project in the Xing-yang area, west of Zhengzhou city in northern Henan Province, which adjoins the south bank of the Yellow River. The experimental alluvial silt has poor early strength and water stability [40; 41]. Xanthan gum is therefore used to strengthen the silt in the Yellow River flooding area due to the complex engineering properties of silt.  
Xanthan gum is a high molecular anionic polysaccharide polymer produced by the aerobic fermentation of Xanthomonas and carbohydrates. It is carbon neutral, sustainable and reproducible, stable to acid, alkali, and heat, and has excellent compatibility with a variety of salts. It is made from non-food crops at a low cost and can be prepared in large quantities [26]. The xanthan gum used to treat the silt was purchased from Fu Feng Biotechnology Co., Ltd. in Inner Mongolia. The reagent was of food grade and analytical grade. The storage environment conditions were 25 \({}^{\circ}\)C and 35% relative humidity. Pictures of the dry silt and xanthan gum are shown in Figure 2.  
Figure 1: Salt sample location.  
The basic physical properties of natural silt are listed in Table 1. The grading curve determined by hydrometer analyses is shown in Figure 3. The gradation parameters of the soil sample are coefficient of curvature \(C_{\text{c}}=0.75\), nonuniform coefficient \(C_{\text{u}}=4.39\), and liquid limit \(w_{\text{l}}=21.4\%\), and the silt shows poor gradation with a low liquid limit.  
The basic parameters of xanthan gum (XG) are shown in Table 2, and its molecular structure is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Parameters** & **Values** \\ \hline Plastic limit, \(w_{\text{p}}\) (\%) & 12.6 \\ Liquid limit, \(w_{\text{l}}\) (\%) & 21.4 \\ Plasticity index, I & 8.8 \\ Specific gravity of the solid, \(\text{G}_{\text{s}}\) & 2.70 \\ Natural water content, \(w_{\text{0}}\) (\%) & 5.10 \\ Natural dry weight, \(\rho_{\text{d}}\) (g/cm\({}^{3}\)) & 1.62 \\ Maximum dry density, \(\rho_{\text{dmax}}\) (g/cm\({}^{3}\)) & 1.77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of silt.  
Figure 3: Grading curve of the silt.  
Figure 2: Samples of natural soil and xanthan gum: (**a**) silt; (**b**) xanthan gum.
## 5 Conclusions  
This paper proposes a correction method based on the background-oriented schlieren technique. The method can correct the distortion caused by heat waves to digital image correlation measurements. Through theoretical analysis and experiments, the characteristics of the distortion due to heat waves on the images were researched. The effectiveness of the proposed method was verified by experiments. Through the research of this paper, the following conclusions are drawn:  
1. The distortion on the images caused by heat waves can be divided into the main distortion and a random distortion. In the experiments performed in this paper, the main distortion reached 0.05 pixels, and the most significant swing amplitude of the random distortion reached 0.2 pixels. The effect of this distortion on the measurement results of digital image correlation is not negligible.
2. Spot patterns used in digital image correlation measurements can also be used in the background-oriented schlieren technique. The background schlieren method can be used to obtain the vector displacement fields of the main distortion caused by heat waves.
3. The main distortion vector obtained by the background-oriented schlieren technique remap the deformed images to eliminate the main distortion. Then, the time-average method should be used to eliminate the random distortion. The experimental results showed that the proposed correction method can effectively remove the disturbance of heat waves and obtain high precision DIC measurement results.  
Figure 21: Plots of displacement in U vs. Y and in V vs. X: (**a**) Displacement component in U direction vs. Y (X = 226); (**b**) displacement component in V direction vs. X (Y = 226).  
Although the proposed method can effectively improve the measurement accuracy of DIC, it also has a certain limitation. This method is especially suitable for cases where the temperature of the heat source is stable. When the temperature changes drastically, the correction is less than ideal.  
Conceptualization, C.M.; Data curation, Z.Z.; Formal analysis, X.R.; Funding acquisition, H.Z.; Investigation, C.M.; Methodology, C.M.; Project administration, Z.Z.; Resources, Z.Z.; Software, X.R.; Supervision, Z.Z.; Validation, H.Z.; Visualization, C.M.; Writing--original draft, C.M.; Writing--review & editing, H.Z. and X.R.; All authors reviewed the manuscript.  
This research was funded by the National Key R&D Program of China (No. 2016 YFF0101802) and Natural Science Foundation of Tianjin (No. 17 CYBJC19300).  
The authors declare no conflict of interest.
## 4 Overview: Signal Processing Methods
### Average Referencing  
Average referencing is commonly used in signal processing for EEG and EMG to improve the accuracy of the recorded signals. It is a technique used to remove common noise present across multiple channels in a signal. Averaging is done by calculating the average of all channels at each point of time and subtracting this average from each individual channel. With the common noise shared among the channels effectively removed, the signal-to-noise ratio and quality of recorded data are improved and the data are ready for further processing. The GRABMyo dataset consists of 28 electrodes capturing signals from the forearm. The captured signals often contain the desired signal data along with background or random noises, either from the environment or interference from nearby electrodes. When average referencing is applied among multiple electrodes, such as the 16 channels from the forearm and 12 channels [12; 21; 22; 23; 24] from the wrist in the GRABMyo dataset, it becomes particularly useful for improving the quality of the recorded signals. Average referencing helps in reducing common noise that may be present across the 28 electrodes. By subtracting the average signal from each channel, it cancels out common noise sources among the electrodes, enhancing the signal-to-noise ratio. By aligning the signals from different electrodes around a common reference point, zero, average referencing contributes to signal consistency. This is beneficial when analyzing patterns or trends that involve the interaction of signals from the 28 electrodes. Averaging referencing is particularly beneficial when dealing with uncorrelated or random noise present in signal data. It helps to enhance the clarity and accuracy of the desired signal representation, as demonstrated in Figures 11 and 12 compared to Figures 9 and 10.  
Figure 11: PSD analysis of EMG signals captured from the wrist after average referencing.
## 4 Proposed Methodology
### Sign-Character Detection
Consequently, this enhances the capacity to extract features. The YOLO Head is the final feature-output module in the design. In the framework of a single-stage detector, the YOLO Head is responsible for performing dense prediction.The final output is a dense prediction, which consists of a vector containing the coordinates (\(U_{x}\), \(U_{y}\), \(U_{w}\), \(U_{h}\)) of the predicted bounding boxes as shown in Equation (2), where \(Q_{w}\) and \(Q_{h}\) denote the width and height of the bounding boxes, respectively, \(\sigma\) represents the sigmoid function, and \(e\) represents the exponential. The values (\(V_{x}\), \(V_{y}\)) denote the top-left corner of the image's coordination, \(P_{x}\) is the predicted x-coordinate offset, and \(P_{y}\) is the predicted y-coordinate offset; \(t_{w}\) and \(t_{h}\) are the predicted bounding box width and height offset, respectively. \[ \begin{array}{l}U_{x}=\sigma(P_{x})+V_{x},\\ U_{y}=\sigma(P_{y})+V_{y},\\ U_{w}=Q_{w}\cdot e^{t_{w}},\\ U_{h}=Q_{h}\cdot e^{t_{h}}\end{array}  \tag{2}\]  
The YOLOv4-Tiny model optimizes the loss function through parameter iterative regression to achieve the model optimization objective. The total loss function has three components: Classification (CLF) loss, Confidence (CNF) loss, and Complete Intersection Over Union (CIOU) loss. The total loss is represented in Equation (3). \[TotalLoss=CLFLoss+CNFLoss+CIOULoss \tag{3}\]  
In order to figure out whether the target is included, confidence loss is utilized. Classification loss is utilized to fit the positioning information, which consists of aspect ratio, overlap area, and center point distance. CIOU loss is the combination of the overlap area, the distance, and the aspect ratio.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 4 The Proposed Data Caching and Sharing Security (DCSS) Protocol
### Security Assumptions and Requirements  
We assume that the UE would have achieved primary authentication to HN and service authorization to the SP services. The UE will be in possession of the pre-shared key \(K_{UIESA}\), \(K_{UIESS}\) and the access token \(AcT\) as the results of those procedures. The expected security properties to be achieved by the DCSS protocol are data confidentiality, integrity, authenticity, and entity authentication. Even though the DCSS is intended to provide data caching and share authorization, to harden the security in 5G, this protocol will re-authenticate the participating entities. These security properties are defined informally before being formalized, two taxonomies are adopted for precise and meticulous security analysis [4]. First, the security properties of the protocol are specified from an agent \(A\)'s point of view, with defined between two agents \(A\) and \(B\) as set 1 [48];  
* aliveness,
* weak agreement,
* non-injective agreement
* injective agreement  
Secondly, the security protocol should meet the following security properties as set 2 [49];
* mutual entity authentication
* mutual key authentication
* mutual key confirmation
* key freshness
* unknown-key share
* key compromise impersonation resilience.
## 3 Results  
### Flask Scale Experiments  
The preliminary experiment has been designed to evaluate the effect of culture time as well as limiting components such as phosphorus and nitrogen source on the growth of _Y. lipolytica_ yeast biomass, cellular lipid yield, and the utilization of hydrophobic substrate as a carbon source. The cultures have been conducted in 16 medium variants, in which the concentrations of KH\({}_{2}\)PO\({}_{4}\) (3.0, 7.0, 9.0, 11.0 g/dm\({}^{3}\)) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (4.0, 6.0, 8.0, 10.0 g/dm\({}^{3}\)) have been modified according to the experimental scheme (Table 1). The incubations were terminated after 3, 4, 5 and 6 days. As can be seen in Figure 1A, the value of biomass yield was closely related only to the time of culture (\(p\)-value 0.01). The effect of the duration of yeast cultivations was also significant for intracellular lipid content. The yeast cells accumulated higher amounts of lipids on the 5 th and 6 th days of culture. The level of KH\({}_{2}\)PO\({}_{4}\) supplementation appeared to be a significant factor influencing lipid accumulation efficiency (\(p\)-value 0.01) (Figure 1B). Higher lipid biosynthesis yield was correlated with lower doses of inorganic phosphorus source in the medium. In the case of the level of the nitrogen source additive in the form of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), no significantrelationship was found in the studied concentration range. It is noteworthy that none of the three analyzed factors was significantly associated with changes in the content of waste post-frying oil (carbon source) in the medium. Nevertheless, it decreased with time of culture (Figure 1C).  
Figure 1: Statistical analysis of correlation between (**A**) biomass yield (g\({}_{\rm d.m.}\)/dm\({}^{3}\)), (**B**) cellular lipid content (g/g\({}_{\rm d.m.}\)), (**C**) waste post-frying oil content in medium (g/dm\({}^{3}\)) and concentration of KH\({}_{2}\)PO\({}_{4}\) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), and culture time of _Y. lipolytica_ KKP 379 yeast in medium with 5% addition of post-frying rapeseed oil.
# Structure-Property Relation of Trimethyl Ammonium Ionic Liquids for Battery Applications
###### Abstract  
Ionic liquids are attractive and safe electrolytes for diverse electrochemical applications such as advanced rechargeable batteries with high energy densities. Their properties that are beneficial for energy storage and conversion include negligible vapor-pressure, intrinsic conductivity as well as high stability. To explore the suitability of a series of ionic liquids with small ammonium cations for potential battery applications, we investigated their thermal and transport properties. We studied the influence of the symmetrical amide-type anions bis(trifluoromethanesulfonyl)imide ([TIS]I-) and bis(fluorosulfonyl)imide ([FSI]I-), side chain length and functionalization, as well as lithium salt content on the properties of the electrolytes. Many of the samples are liquid at ambient temperature, but their solidification temperatures show disparate behavior. The transport properties showed clear trends: the dynamics are accelerated for samples with the [FSI]I- anion, shorter side chains, ether functionalization and lower amounts of lithium salts. Detailed insight was obtained from the diffusion coefficients of the different ions in the electrolytes, which revealed the formation of aggregates of lithium cations coordinated by anions. The ionic liquid electrolytes exhibit sufficient stability in NMC/Li half-cells at elevated temperatures with small current rates without the need of additional liquid electrolytes, although Li-plating was observed. Electrolytes containing [TFS]I- anions showed superior stability compared to those with [FSI]I- anions in battery tests.  
ionic liquids; ammonium; batteries; electrolytes- +
Footnote †: journal: Applied Sciences