esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 2 Methodology
### Determination of Weighting Factor (WF)
Meanwhile, high and low tracer concentrations were dominant at both sides of the mixing interval within the mixing zone region. Figure 2: Numerical simulation results of the 1D model of water displacement illustrating (**A**) the profile of tracer concentration and (**B**) water saturation profile. The waterfront and the mixed zone were highlighted in both profiles. A significant width of the tracers' mixing zone was indicated by the simulation result, as illustrated in Figure 2. The width of the mixing zone depends on the petrophysical properties of the medium, as well as on the flow properties of the existing fluids. Moreover, physical phenomena can also influence the width of the mixing region, such as the dispersion effect that tends to spread the injected waterfront out. Therefore, a normalised distance was used to represent the change in the tracer concentration, as illustrated in Figure 3.  
In Figure 3, the tracer concentration curve contained two dominant intervals (Region 1 and Region 3) of approximately 23% of the normalised distance for each region of low and high tracer concentration. The change in the tracer concentration in these regions is minimal. However, a significant change occurs between 23% and 77% in the dimensionless distance, as the tracer concentration exponentially raises in Region 2. Therefore, regardless of the interval width of the mixing zone, the dilution of the solute tracer concentration has the illustrated shape in Figure 3. Likewise, the mixing zone during low salinity brine injection is expected to have the same pattern. Therefore, the obtained mixing curve pattern of the tracers can be utilised to conclude a weighting factor formula. The estimated weighting factor formula, which was based on the tracer simulation results, has employed the normalised salinity (NS) to obtain a general form, as shown in Equation (4). Meanwhile, the normalised brine salinity can be calculated using Equation (5). A comparison between the linear weighting factor normally used and the current work of the exponential weighting factor has been illustrated in Figure 4. The linear approach uses Equation (3), while the proposed weighting factor formula is given in Equation (4). The outcome of the simulated tracer model was validated using the analytical solution of the fractional flow, as explained in the Appendix A.  
\[WF=a-b\times e^{cNS^{d}}+g\times e^{h\times S^{d}}-k\times e^{NS^{\frac{n}{2}}} \tag{4}\]  
The values of the parameters are listed below;  
a = 0.59; b = 0.4276; c = 0.9807; d = 0.2351  
g = 1.046; h = 0.08132; k = 0.2126; n = 3.887  
\[Normalised Salinity\ (NS)=\frac{SC_{mix}-SC_{LoSal}}{SC_{Maxeff}-SC_{LoSal}} \tag{5}\]  
Figure 3: The obtained change by the simulation results in the tracer concentration in the mixing zone. Region 1 and Region 3 represent the dominant regions by low and high tracer concentrations, respectively. Meanwhile, Region 2 represents the beginning of the interaction between the two tracers.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 6 Conclusions  
1. Compared to traditional disc spring bearings, the proposed VVSDSI bearing in this study demonstrates two distinct vertical stiffness stages. During the isolation phase, the VVSDSI isolation bearing exhibits lower stiffness with a bilinear hysteretic curve. As the displacement load increases and the bearing enters the protection stage, its vertical stiffness shows significant enhancement.
2. The load-displacement hysteresis curve of the VVSDSI bearing exhibits typical asymmetry. Studies have shown that increasing the preloading displacement results in a fuller hysteresis curve, enhancing the energy dissipation capacity of the bearing. In contrast, increasing the load amplitude reduces the fullness of the hysteresis curve and exacerbates its asymmetry. During the seismic isolation phase, the preloading displacement is positively correlated with the equivalent stiffness and damping ratio, while the load amplitude shows a negative correlation with these parameters.
3. A comparison between the experimental and theoretical results shows that the maximum errors for \(F_{\mathrm{max}}\) and \(F_{\mathrm{min}}\) are 7.13% and 3.45%, respectively, with average errors of 2.20% and 1.23%. The average error in the equivalent stiffness (\(K_{\mathrm{e}}\)) is 6.53%. These findings demonstrate that the theoretical model is accurate enough to predict the actual mechanical behavior and can be applied for design purposes.
4. Under the action of ultimate load and cyclic load, the load-displacement hysteresis curve characteristics of the VVSDSI bearing simulation and test are similar, and the simulation results are in good agreement with the experimental results. Among them, the peak load error is 10.07%, and the average error of the equivalent stiffness value is 5.32%.  
Conceptualization, J.-C.X. and K.-J.M.; Methodology, Y.L. and J.-C.X.; Investigation, C.L.; Writing--original draft, Y.L.; Writing--review and editing, Y.L. and H.-M.Z.; Funding acquisition, J.-C.X.; Resources, K.-J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 50978064); the Guiyang Municipal Civil Air Defense Engineering Quality Supervision Station Project (No. 2017-068 CG); and the Guiyang Science and Technology Plan Project (No. [2022]44-1).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Preloading Dis-Placement with** & **Simulated Values** & **Experimental Value** & **Relative Error** \\
**Loading Amplitude (mm)** & **(kN/mm)** & **(kN/mm)** & **(\%)** \\ \hline
16 \(\pm\) 1 & 24.49 & 24.26 & 0.94 \\
16 \(\pm\) 2 & 17.62 & 18.16 & 2.97 \\
16 \(\pm\) 4 & 13.51 & 14.02 & 3.63 \\
16 \(\pm\) 6 & 11.93 & 12.63 & 5.54 \\
23 \(\pm\) 1 & 27.86 & 30.07 & 7.34 \\Data Availability Statement: The raw data are available on request via email from the corresponding author.
## 3 Experiment Results and Discussion  
### Parametric Study for the Effect of Flapping Frequency Variation  
As described in Sections 2.2 and 2.4, it is possible to vary the flapping frequency in up-stroke and down-stroke separately by employing the USM. The frequency and corresponding rotational speed of the FWR were measured by using laser velocimeter, as described in Section 2.2. In this study, the flapping frequency was altered from 1 Hz to 3 Hz to set a combination of flapping motion cases including the constant flapping frequency (CFF) mode in case 1, 2, and 3 and variant flapping frequency (VFF) mode in case 4 and 5, as listed in Table 2. According to Table 2, it can be found that higher average flapping frequency usually lead to higher rotational speed \(\dot{\psi}\).
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Other Plant Metabolites Produced by Endophytic Fungi
#### 4.6.4 Skyrin  
Genome sequencing of _Cyanodermella asteris_, a non-lichenized representative of the fungal class Lecanoromycetes isolated as an endophyte from the Chinese medicinal plant _Aster tataricus_, provided an interesting insight into the biosynthesis of skyrmion (Figure 1). A gene cluster of five genes was identified through genetic and in silico analyses to possibly encode enzymes catalyzing biosynthesis of skyrmion [81]. The proposed pathway of skyrmion biosynthesis in _C. asteris_ would go via atrochrysone carboxylic acid (ACA) and emodin. A putative gene encoding ACA-synthase has been identified, as well as genes encoding two dehydrogenases and a dehydatase potentially involved in the multistep conversion of ACA to emodin and a gene encoding for a monooxygenase that putatively produces skyrmion from two emodin molecules.  
Genome analysis of _C. asteris_ also revealed an intriguing aspect, that is a very high gene density (363 genes/Mb). Higher gene density was observed in the genome of another endophytic fungus, the previously mentioned _S. indica_[21]. Generally, higher gene density is more specific to endophytic species which may lead to the conclusion that a more \"compressed\" genome somehow supports the endophytic lifestyle.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Segmentation with 2D Architectures
#### 3.3.3 Attention UNet  
Attention UNet [11] introduces an innovative approach to improve the effectiveness of the UNet architecture. With a core objective of enhancing the model's ability to capture relevant information, Attention UNet aims to refine the segmentation process by leveraging an attention gate mechanism. This mechanism facilitates the network's capacity to focus on the most pertinent features within the input or output for a given segmentation task. Particularly, Attention UNet employs this attention mechanism between the skip connections, an essential component of the UNet architecture.  
The attention mechanism in Attention UNet holds a fundamental role in reshaping how the model processes and utilizes information. This mechanism operates by filtering the features extracted from the encoder before these features are concatenated with those from the decoder. As the decoder provides the contextual information, the attention mechanism dynamically assigns varying weights to different regions of the feature map. This weight assignment is contextually informed by the decoder, enabling the network to accentuate significant features while attenuating noise or irrelevant information.  
A prominent contribution of Attention UNet lies in its capacity to eliminate the need for an external organ localization module. Unlike many segmentation frameworks that often incorporate such modules to simplify tasks, Attention UNet achieves this without additional supervision or computational complexity. By harnessing attention gates, Attention UNet autonomously learns to localize and segment the target structures. Illustrated in Figure 3, adapted from [11], is the overall structure of Attention UNet.  
Figure 3: Attention UNet architecture [11].  
In our implementation of Attention UNet, the model consists of a total of 75 layers, with 38 of these layers containing parameters. The model has a total of 7.9 million parameters. For a more detailed architectural overview of Attention UNet, please refer to [11].
## 2 Measuring System, Materials and Methods
### Methodology
#### 2.1.2 Simulations  
Simulation studies constitute an essential component of the initial stage in the modified CRISP-DM model. This stage is crucial for understanding physical phenomena and interactions in the process.  
The experiments were conducted utilizing the finite element method through the commercial software COMSOL v6.0. Initially, the tests were conducted to evaluate theoretically developed concepts and design preliminary versions of the laboratory system. Simulations facilitated the prediction of measurement outcomes and the efficacy of specific solutions. After the simulation phase, laboratory systems were constructed, and empirical measurements were executed. Another essential function of the simulations is to verify and validate the obtained measurement results by comparing them with simulation results. The results of the simulations are presented in [8].
## 1 Introduction
With the rapid development of digital signal processing, various ANC algorithms [22; 23; 24; 25; 26; 27] specially designed for narrowband disturbances have been proposed in the past two decades. Compared with the original FxLMS algorithm in which the reference signal is picked up by a reference microphone, [PERSON] et al. [22] proposed to use internally generated tonal signals as the references. However, the noise reduction performance is rather sensitive to the frequency mismatch (FM) [23; 24], which is the frequency error between the references and the disturbances. Consequently, different adaptive control methods with online frequency estimation algorithms [25; 26; 27] have been proposed. Since the cavity resonance noise is narrowband and the resonance frequency is fixed, it is interesting to test the control performance with narrowband ANC systems. This is the basic motivation of this work. Compared with traditional methods of active flow control, this method should not introduce noise enhancement at other frequencies, since the secondary noise generated by the loudspeaker only contains tonal components corresponding to the resonance frequency. In this paper, experimental researches are carried out, in which the effectiveness of narrowband ANC systems on the control of flow-induced cavity resonance noise is tested. Instead of the wind tunnel, a duct system is built up with rectangular side-wall cavities. Numerical simulations are conducted to predict the frequency of flow-induced resonance noise. The hybrid method combining Large Eddy Simulations (LES) and the Ffowcs Williams-Hawkings (FW-H) equation is used, which has been widely used in practical applications [28; 29; 30]. Then, a narrowband active noise control system is established to reduce the flow-induced resonance noise. It has been shown by experimental results that the flow-induced resonance could be globally suppressed, even with relatively large FMs. This paper is organized as follows. Section 2 gives the numerical and control methods. The experimental duct system is presented in Section 3, based on which simulations and experiments are carried out, and the results are shown in Section 4. Some conclusions are drawn in Section 5.
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 1 Introduction  
### Background and Motivation  
Blockchains are a transparent permanent public ledger of transaction records, independently verified and recorded, and available for inspection at any moment [1]. A great benefit of blockchains is that they are a form of decentralized information technology that could apply to many situations beyond its current most common use of cryptocurrency and financial asset management [2, 3]. The growth and popularity of blockchain have sparked interest, especially in the financial sector. Most traditional blockchain applications focus on asset transfers and knowledge sharing through networks using smart contracts, suitable for commercial processes and business sectors, such as secure data sharing [4]. Initially, blockchain technology was positioned as an enabling mechanism for cryptocurrencies [5]. Later, its decentralized, distributed, immutable ledger capability attracted many e-Governance initiatives [6], resulting in its utilization in various public notary services, voting systems, citizen identity services, passport registration, and migration services [7]. Recently, also new applications have emerged, such as for internet-of-things (IoT) [8], smart home [9], industrial manufacturing [10], intelligent transportation systems [11], and vehicular networks [12]. Blockchain networks are decentralized, immutable, and provide high security, making them an attractive option for businesses and organizations. However, the security and privacy concerns related to sharing sensitive data on blockchain networks and the potential threat of attacks against blockchains have hindered their widespread adoption.
## 3 Results and Discussion
### Amount of Oil Sample  
The basic operation of MHS-SPME involves repeating extraction several times from the same sample, while the peak area decreases exponentially with the number of extractions. Therefore, the amounts of the sample should not only meet the LOQs but also ensure the linearity between ln A\({}_{\text{i}}\) versus the extraction numbers (i \(-\) 1), where ln A\({}_{\text{i}}\) shows linear decay with the number of extractions (Equation (2)). In order to satisfy that requirement, 0.4 \(<\beta<\) 0.95 should be fulfilled, which indicates the slope of the linear plot ln A\({}_{\text{i}}\) versus i\(-\)1 must less than \(-\)0.0513 and more than \(-\)0.9163 [37]. Compared with the traditional SPME fiber, the SPME-arrow device has a larger sorbent phase and showed higher sensitivities and extraction amount [20]. In this study, the effects of seven sample amounts and two fiber types on \(\beta\) and coefficient of determination (R\({}^{2}\)) were studied on flavor-enhanced rapeseed oils using the 20 mL HS vial.  
For MHS-SPME, two conditions should be met: (1) R\({}^{2}>\) 0.75, (2) 0.4 \(<\beta<\) 0.95, indicating that the ln A\({}_{\text{i}}\) was linearly decayed with the number of extractions. Figure 2A shows the sample amount meeting R\({}^{2}>\) 0.75 while Figure 2B meeting 0.4 \(<\beta<\) 0.95. As shown in Figure 2, traditional SPME fiber exhibited the most pyrazines that met requirements when the sample amount was 20.0 mg, while the SPME-arrow fiber showed the highest number at 20.0 mg and 50.0 mg samples. This was in line with the advantage of the SPME-arrow fiber that larger sorbent phase volume gives higher extraction capacity. When the sample amount was greater than 200 mg, almost no \(\beta\) satisfied the MHS-SPME applicable requirement, regardless if the traditional SPME fiber or SPME-arrow fiber was used. The reason might be that the headspace was saturated in the multiple headspace extractions. However, the low sample amount may lead to poor repeatability due to the mass of analytes closed to LOQs, which means that the SPME-arrow fiber was more suitable for the quantitation of pyrazines in flavor oils by MHS-SPME. Therefore, 50.0 mg was selected as the sample mass in the following study.
## 5 Construction of Sample Adversarial Attacks and Defense Model
By injecting \(\delta\) into the original data, the model is compelled to maintain robustness against adversarial samples \(x+\delta\). Consequently, the adversarial loss function is formulated as follows:  
\[L_{\mathrm{Adv}}(x+\delta,y)=-\sum_{i=1}^{2}y_{i}\log(p_{i}(x+\delta)) \tag{20}\]  
The final adversarial training objective function is formulated by jointly optimizing the original data loss and adversarial sample loss, as mathematically expressed below:  
\[L_{\mathrm{Total}}=L_{\mathrm{CE}}(x,y)+\lambda L_{\mathrm{Adv}}(x+\delta,y) \tag{21}\]  
As indicated in Equation (21), the adversarial loss weighting coefficient \(\lambda\) is empirically set to 0.5. This hyperparameter serves as a critical trade-off parameter to balance the model's learning focus between clean sample feature extraction and robustness enhancement against adversarial perturbations, thereby preventing either loss term from dominating the optimization process.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 4 Results and Discussion  
### High-Temperature Performance  
The results of the wheel tracking test and uniaxial penetration test are presented in Figures 4 and 5 respectively.  
It can be seen from Figures 4 and 5 that the overall change pattern of the dynamic stability and the uniaxial penetration strength is the same. The high-temperature performance of the BFSMA-13 is better than that of the LFSMA-13 at different aging degrees. The dynamic stability and the uniaxial penetration strength of the unaged BFSMA-13 are 25% and 30% higher than those of the unaged LFSMA-13. After being short-term aged, the dynamic stability and the uniaxial penetration strength of BFSMA-13 are 52.4% and 64.7% higher than those of the LFSMA-13. The corresponding indexes of the long-term aged BFSMA-13 are 68.4% and 92.4% higher than the LFSMA-13. When the aging degree increases, the difference of the high-temperature performances between BFSMA-13 and LFSMA-13 increase.  
The high temperature performance of BFSMA-13 increase with the increase of the aging degree, while the aging process decrease the high temperature property of LFSMA-13. After being short term aged and long term aged, the dynamic stability of BFSMA-13 increase by about 14.29% and 22.46% than the unaged BFSMA-13. The decrease extent of the uniaxial penetration strength of the short term aged and long term aged LFSMA-13 are 5.56% and 12.22% respectively.  
This is because there are two phenomena in the aging process of asphalt mixtures [36; 37; 38]. One is the degradation of the SBS modifier, making the properties of the SBS modified asphalt approach to that of pure asphalt. The other is the aging of the asphalt binder, making it stiffer. In LFSMA-13, the deeper the aging of the asphalt mixture, the more severe the SBS degradation, resulting in a continuous decline in the high-temperature performance of the asphalt mixtures. In BFSMA-13, although SBS degradation will also lead to the decline of high-temperature performance of asphalt mixture, the basalt fibers can synergize better with the stiffer binder, which eventually leads to an increase in the high-temperature performance of the asphalt mixtures. We found that lignin fiber and basalt fiber play different roles in the asphalt mixture. The flocculent lignin fiber can only absorb extra asphalt, while short-cut basalt fiber can cooperate with asphalt mixture to resist the load.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 2 Sensing Methods
### Chaotic Compressed Sensing  
Since chaotic sequences generated by chaotic systems are pseudorandom, they are well-suited to be used as measurement matrices. In chaotic compressed sensing (CCS), chaotic systems can generate pseudorandom sequences by certain methods, which simplifies the construction of sensing matrices compared to a random-sensing matrix. We take the Chebyshev chaotic system as an example [15]:  
\[z_{k+1}=\cos(w\arccos z_{k}), \tag{13}\]  
where \(w\geq 2\), \(z_{k}\in[-1,1]\). If parameter \(w\) and initiation value \(z_{0}\) are given in advance, sequence \(z_{k}\), \(k=1,2,3...\), can then be generated on the basis of Equation (13). After obtaining this sequence, we use sampling distance \(d\) and sampling initial position \(n_{0}\) to obtain the following sampled sequence:  
\[x_{n}=z_{n_{0}+dn}. \tag{14}\]  
In chaotic systems, the starting value and chaotic parameter have high sensitivity. A completely different sequence is obtained by slightly disturbing the starting value or system parameter, which proves that chaotic systems have high security.  
[PERSON] et al. proposed CCS by using the chaotic system of the T-way Bernoulli shift [16], and applied it to data transmission to achieve security. The CCS-based secure-data-transmission scheme has inherent encryption attributes with no additional cost. In this scheme, they used the Bernoulli chaotic system to generate the Bernoulli shift chaotic sequence, which constructs the Bernoulli chaotic-sensing matrix (BCsM).  
To guarantee transmission security, [PERSON] et al. improved the generation of chaotic measurement matrix, including chaotic parameters, sampling rate, matrix mapping functions, etc. [17]. We only need to save the matrix seeds such as the initial value, chaos parameters, sampling start position and sampling step, instead of saving the entire sensing matrix. The chaotic sensing matrix can be given as  
\[\Phi=T(S(n_{0},d,C(z_{0},\epsilon))) \tag{15}\]  
where \(z_{0}\), initial value; \(\epsilon\), chaotic parameter; \(C\), chaotic system; \(S\), sampler; and \(T\), mapping function. According to sampling initial position \(n_{0}\) and sampling step \(d\), the chaotic sequence was obtained after sampling. The required sensing matrix for chaotic compressed sensing could be generated with the mapping function. Compared with traditional CCS, the improved CCS simultaneously solves the problems of energy efficiency and security, and performs very well in image encryption.  
[PERSON] et al. presented the incoherence-rotated-chaotic (IRC) matrix as a measurement matrix [18]. They used the pseudorandomness character of chaotic sequences, the concepts of incoherent factors, and rotation to obtain the IRC sensing matrix. The obtained IRC sensing matrix was suitable for sparse reconstruction, satisfying the RIP criterion during the sparse-reconstruction phase and performing well in RIP with a smaller RIP ratio. Simulation results showed that the IRC matrix performed better than classical random-sensing matrices did.