esa-sceva/satcom-chunk-collection huggingface dataset:
# Microscopic Experiments to Assess the Macroscopic Sweep Characteristics of Carbon Dioxide Flooding
## 1 Introduction
However, their findings lack the support of field experiments and monitoring data from actual oil fields [8]. [PERSON] et al. (2022) analyzed the diffusion behavior of CO\({}_{2}\) in the subsurface using advanced microscopy and image analysis techniques. It was concluded that the diffusion behavior of carbon dioxide in the subsurface is influenced by subsurface temperature, pressure, pore structure, and other factors [9]. [PERSON] et al. (2020) investigated the effect of carbon dioxide oil displacement on the wave and characteristics of subsurface oil reservoirs and on the recovery rate. Using modern mathematical models and computational methods, they concluded that carbon dioxide oil displacement can significantly improve the wave and sweep efficiency and increase the recovery rate of subsurface oil formations. Furthermore,the effectiveness of carbon dioxide oil displacement on wave and sweep efficiency and recovery rate is influenced by the properties of the subsurface oil formation, temperature, pressure, and other factors [10]. The Lunnan oilfield in the Tarim Basin is in the late stage of dual TI water flooding development, with highly dispersed residual oil and a complex reservoir environment, posing a significant challenge for enhancing the recovery rate. In this study, we focused on the TI reservoir in the No. 2 well area of the Lunnan oilfield in the Tarim Basin. We analyzed the geological characteristics of the Lunnan oilfield and utilized transparent micro-models and high-resolution imaging technology to observe the real-time interaction between carbon dioxide and crude oil. We conducted a visual physical simulation of the \"microscopic wave and characteristics\" of carbon dioxide using a glass etching object model. \"Microscopic wave and characteristics\" specifically refers to the behavior of fluids at the pore scale, including the detailed interaction between carbon dioxide and crude oil within the pores of the reservoir rock. The \"wave\", here, refers to the movement of carbon dioxide at the microscopic level, and the \"characteristics\" include details such as how the carbon dioxide displaces oil, how it interacts with the rock matrix, and how it spreads through the interconnected pore spaces. By analyzing the microscopic wave and characteristics, we aimed to understand the fundamental mechanisms of carbon dioxide displacement and improve the efficiency of oil recovery. This understanding is crucial for optimizing the injection strategy and maximizing the extraction of residual oil. Based on the 2D visual, large physical model, we also conducted simulations of the macroscopic wave and characteristics of carbon dioxide using co-injection and co-precipitation as well as split-injection and co-precipitation models. The goal was to improve the extraction efficiency and economic benefits of the oilfield and provide data support for the physical reality of carbon dioxide oil displacement at both the microscopic and macroscopic levels in the Lunnan oilfield of the Tarim Basin.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### X-Band LNA Design Procedure
#### 5.1.3 X-Band LNA S-Parameter Analysis  
The S-parameters of the LNA are shown in Figure 9. The output and input return losses exceed 10 dB over the full frequency spectrum. The gain (Figure 10) was 40 dB with a 1 dB ripple over the designated range, while the lowest noise (Figure 11) at the resonance frequency of 10 GHz was 0.9 dB.  
Figure 8: A three-stage 8–12 GHz MMIC LNA design schematic ([15]).  
Figure 9: Input and Output reflection coefficient of the X-band MMIC LNA circuit.
# Optimized Combination of Local Beams for Wireless Sensor Networks
## 4 Simulation Results  
This section shows simulation results to evaluate the proposed scheme in terms of the SNR, average INR, and search complexity. Prior to the detailed studies, the system and channel are modeled as follows:  
1. System: Let the 64 sensor nodes be uniformly distributed [22] over a disk having the radius \(R=1\lambda\) and equipped with the \(\frac{\lambda}{2}\) spaced ULAs. We assumed that they have the same transmission power \(P=\frac{\sigma_{2}^{2}\eta}{L_{c}}\), where the power budget \(\eta\) is equal to 20 dB.
2. Channel: Because the mmWave channels are commonly exposed to strong directivity [43], \(\forall_{i,j}h_{i,j}\left(\cdot\right)\) are assumed to be under the line-Of-Sight (LOS) dominant scenario with \(K_{i,j}=13.2\) dB.
3. Optimization: In addition, in all simulations, \(T_{0}\), \(T_{f}\), \(R_{B}\), and \(\rho\) are fixed to \(10^{4}\), \(10^{-5}\), \(10^{2}\), and 0.8, respectively. As studied in [44; 45], these parameters depend on the nature of the problem. Thus, a sensitivity analysis is used to choose the above values which are initial parameters to provide a good balance between exploration and exploitation of the search space. We omit the detailed procedure here due to the limited pages of this paper. Unfortunately, this empirical approach may result in the considerable simulation time consumption. Thus, an analytical and systematic method to determine the control parameters will be researched in our next paper.  
Figures 4 and 5 show the average INRs and SNRs to verify the validation of the proposed algorithm. From the two figures, the appreciable reductions of the average INRs (e.g., 15.8 dB when \(N=3\) and \(\mu=10\) dB) are observed when our algorithm is applied. Especially, compared to the node selection scheme in [28], which is widely used to suppress the sidelobes in the CB, the proposed algorithm provides the better performance under the lower predetermined threshold (i.e., \(\mu\leq 15\) dB) in virtue of its probabilistic greedy exploration. Furthermore, while the former causes the severe loss of the SNRs due to its innate characteristic of node selection, the latter provides the constant SNR regardless of \(N\) and \(\mu\). In addition, Figures 6 and 7 show the complexity of [28] and the proposed scheme. As expected, both of the approaches require the smaller number of iterations when the higher \(\mu\) is set. However, as \(\mu\) is decreased, the proposed algorithm requires more iterations than [28]. It can be explained by the fact that the core of our algorithm (i.e., SA) would spend many iterations to approach \(\mu\) as much as possible.  
Figure 5: Performance of the sidelobe reduction algorithm - \(\Xi\left(\boldsymbol{\theta}_{opt},\boldsymbol{\Gamma}_{1}\right)\).  
Figure 6: Performance of the sidelobe reduction algorithm—Search complexity for the node selection scheme [28].  
Figure 7: Performance of the sidelobe reduction algorithm—Search complexity for the proposed scheme.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 5 Conclusions  
In this paper, an enhanced surrogate ensemble modeling method is proposed for the SCTV prediction. Unlike traditional onboard SCTV prediction by the data table lookup, this paper proposes to distill the data into a surrogate model to be uploaded to a satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to table lookup. The proposed surrogate ensemble modeling method first divides the global earth surface space into multiple sub-domains according to the prior geographical knowledge of the SCTV distribution, and then constructs multiple candidate surrogates in each sub-domain. To fully exploit the candidate surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed surrogate ensemble modeling method could obtain the independent contributing surrogates and weights for each sub-domain so that the ensembles are more suitable for the corresponding sub-domains. Thus, the prediction accuracy and robustness of the ensembles can be improved in the corresponding sub-domains and across the entire domain, which is verified in the test section. For future works, we would like to further study the proposed surrogate ensemble modeling method on a more diverse set of test problems, and more kinds of candidate surrogates will be considered. Moreover, the surrogate update method with the dynamically accumulated SCTV data will be researched to further improve the surrogate prediction accuracy and modeling efficiency.  
[PERSON], [PERSON] and [PERSON] conceived the PWTA method and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper under the supervision of [PERSON] and [PERSON].  
This work was supported in part by National Natural Science Foundation of China under Grant No.51675525 and 11725211.  
The authors declare that the grant, scholarship, and/or funding mentioned in the Acknowledge section do not lead to any conflict of interest. Additionally, the authors declare that there is no conflict of interest regarding the publication of this manuscript.
## Electromagnetic Properties of Graphene-like Films in \(\mathbf{K_{a}}\)-Band  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 3 and [PERSON] 3,***  
Research Institute for Nuclear Problems of Belarusian State University, Bobruiskaya str., 11, Minsk 220030, Belarus; E-Mails: [EMAIL_ADDRESS] (S.V.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (K.B.); [EMAIL_ADDRESS] (P.K.); [EMAIL_ADDRESS] (S.M.)  
National Research Tomsk State University, 36, Lenina Avenue, Tomsk 634050, Russia  
Institute of Photonics, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland; E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +358-50-3216186.  
_Received: 25 February 2014; in revised form: 25 April 2014 / Accepted: 29 April 2014 / Published: 15 May 2014_  
We studied electromagnetic properties of pyrolytic carbon (PyC) films with thicknesses from 9 nm to 110 nm. The PyC films consisted of randomly oriented and intertwined graphene flakes with a typical size of a few nanometers were synthesized by chemical vapor deposition (CVD) at 1100 \({}^{\circ}\)C on a quartz substrate. The reflectance and transmittance of these films in \(\mathrm{K_{a}}\)-band, 26-37 GHz, were studied both experimentally and theoretically. The discovered remarkably high absorption loss of up to 50% of incident power, along with chemical stability, makes PyC films attractive for electromagnetic (EM) interference shielding in space and airspace communication systems, as well as in portable electronic devices occupying this frequency slot. Since, in practical applications, the PyC film should be employed for coating of dielectric surfaces, two important issues to be addressed are: (i) which side (front or back) of the substrate should be covered to ensure maximum absorption losses; and (ii) the frequency dependence of absorbance/transmittance/reflectance of binary PyC/quartz structures in the \(\mathrm{K_{a}}\)-band.  
**Keywords:** microwave frequency range; reflectance; transmittance; absorbance; thin films; pyrolytic carbon; numerical simulation; rectangular waveguide
# Targeted Substituted-Phenol Production by Strategic Hydrogenolysis of Sugar-Cane Lignin
###### Abstract  
In this work, a waste-derived lignin with abundant uncondensed linkages, using accessible solvents (acetone/water mixture) and low-cost catalysts showed successful depolymerization for the production of target molecules 4-ethylphenol, 4-propyl-2,6-dimethoxyophenol and 4-propyl-2-methoxyphenol. Lignin samples were obtained from sugar-cane bagasse residue by an organosolov process. Four alumina-based catalysts (Pt/Al\({}_{2}\)O\({}_{3}\), Rh/Al\({}_{2}\)O\({}_{3}\), Ni/Al\({}_{2}\)O\({}_{3}\) and Fe/Al\({}_{2}\)O\({}_{3}\)) were used to depolymerize the sugar cane lignin (SCL) in an acetone/water mixture 50/50 \(v\)/\(\sigma\) at 573 K and 20 bang hydrogen. This strategic depolymerisation-hydrogenolysis process resulted in the molecular weight of the SCL being reduced by half while the polydispersity also decreased. Catalysts significantly improved product yield compared to thermolysis. Specific metals directed product distribution and yield, Rh/Al\({}_{2}\)O\({}_{3}\) gave the highest overall yield (13%), but Ni/Al\({}_{2}\)O\({}_{3}\) showed the highest selectivity to a given product (~32% to 4-ethylphenol). Mechanistic routes were proposed either from lignin fragments or from the main polymer. Catalysts showed evidence of carbon laydown that was specific to the lignin rather than the catalyst. These results showed that control over selectivity could be achievable by appropriate combination of catalyst, lignin and solvent mixture.  
biomass vaporization; lignin; hydrogenolysis; heterogenous catalysis +
Footnote †: journal: Biomass  
1 Centre for Catalysis Research, School of Chemistry, Joseph Black building, University of Glasgow, Glasgow G12 8 QQ, UK; [EMAIL_ADDRESS]  
2
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 5 Conclusions
The model's accuracy is dependent on the quality and size of the training datasets, and further improvements could be achieved by exploring additional parameters or improved polynomial fitting techniques. Future research could also investigate the model's performance with different types of EDFAs and under varying operating conditions to enhance its generalizability and robustness. In conclusion, the polynomial model developed in this work provides a reliable, efficient, and adaptable solution for estimating the noise figure of EDFAs. Its integration into a deep-learning framework for EDFA performance prediction further validates its practical applicability, including its validation in challenging conditions for prediction, such as in cases with variable spectra. Future research should focus on addressing the identified limitations and exploring additional enhancements to improve the model's accuracy and applicability across a broader range of devices and operating conditions. Conceptualization, methodology, software, validation, formal analysis, and investigation, R.D., A.C., S.S. and V.C. ; resources and data curation, S.S.; writing--original draft preparation, R.D., A.C. and V.C. ; writing--review and editing, R.D. and A.C.; visualization, R.D. ; supervision and project administration: R.D. and V.C. ; funding acquisition, V.C. All authors have read and agreed to the published version of the manuscript. This research has been supported by the EU Horizon Europe research and innovation program, ALLEGRO Project, GA No. 101092766.. The raw data supporting the conclusions of this article will be made available by the authors on request.. The authors declare no conflicts of interest..
## 2 Background
### Overview of Advanced Persistent Threats (APTs)
#### 2.1.1 APT Security Attack Types Penetrating Mobile Phones  
Most mobile APTs depend on social engineering assaults through sensors, including spear phishing, application repackaging, watering holes, and SQL injection [45]. Based on the target, an APT attack may utilize a combination of different attack vectors, as indicated below (see Figure 2):  
* Social engineering: Obtaining a user's assistance in compromising information systems. This approach manipulates individuals with privileged access into disclosing personal information to carry out a harmful attack through control and persuasion [46].
* Spear phishing: This approach usually obtains user credentials, financial details, or private information from a single business [47].
* Watering hole: It is similar to spear phishing in terms of cyber espionage. The attacks are adjusted to the victims' needs. Additionally, it attempts to acquire information regarding victims based on their particular interests [48].
* Application repackaging: Application repackaging creates a new version of an existing software application, often to modify its functionality, compatibility, or distribution method. This process typically involves decompiling the original application, modifying its code and resources, and subsequently recompiling it into a new package [49; 50].
* Malware: It has become one of the biggest threats to corporations and organizations that practice bring your own device (BYOD). Various attacking vectors are available for malware to transmit and release their payload, especially in BYOD environments [45].  
Figure 2: APTs and Their Attacks.
## 3 Results and Discussion
### Consumer Preferences for Yanggaeng  
Figure 2 presents the results of the consumer preference evaluations for Yanggaeng samples containing CF and alternative sweeteners. The Xyl group received the highest scores for several attributes, including color (7.53 \(\pm\) 1.06), flavor (7.33 \(\pm\) 1.18), sweetness (7.40 \(\pm\) 1.76), taste (7.67 \(\pm\) 1.29), overall acceptance (7.20 \(\pm\) 1.26), and purchase intention  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Textural Properties** & **Sug** & **Alu** & **Ste** & **Xyl** \\ \hline Hardness (g) & 1800.21 \(\pm\) 8.01 b & 1631.22 \(\pm\) 80.84 c & 1549.30 \(\pm\) 21.60 c & 1930.75 \(\pm\) 11.67 a \\ Adhesiveness (g.sec) & \(-\)40.36 \(\pm\) 0.35 ns 1 & \(-\)51.64 \(\pm\) 28.47 ns & \(-\)36.87 \(\pm\) 1.88 ns & \(-\)61.55 \(\pm\) 1.42 ns \\ Resilience (\%) & 7.50 \(\pm\) 0.35 ab & 9.30 \(\pm\) 0.85 a & 6.60 \(\pm\) 1.36 b & 7.37 \(\pm\) 0.41 ab \\ Cohesion & 0.28 \(\pm\) 0.03 ns & 0.32 \(\pm\) 0.03 ns & 0.27 \(\pm\) 0.02 ns & 0.33 \(\pm\) 0.02 ns \\ Springiness (\%) & 94.40 \(\pm\) 5.33 ns & 93.72 \(\pm\) 6.82 ns & 90.57 \(\pm\) 2.56 ns & 87.38 \(\pm\) 1.38 ns \\ Gumminess & 509.31 \(\pm\) 24.43 b & 531.17 \(\pm\) 22.29 b & 405.25 \(\pm\) 4.59 c & 639.09 \(\pm\) 0.47 a \\ Chewiness & 489.34 \(\pm\) 15.24 b & 452.22 \(\pm\) 2.46 c & 356.06 \(\pm\) 10.03 d & 540.47 \(\pm\) 21.50 a \\ \hline \end{tabular}
\end{table}
Table 6: Textural properties of Yanggaeng prepared with _Corni fruct_ powder and alternative sweeteners.  
(7.00 \(\pm\) 1.20), all significantly outperforming the Sug, Alu, and Ste groups (_p_ < 0.05). Specifically, the Sug group scored 6.13 \(\pm\) 1.30 in color, 6.27 \(\pm\) 1.22 in flavor, 6.73 \(\pm\) 1.49 in sweetness, 6.73 \(\pm\) 1.71 in taste, 6.40 \(\pm\) 1.55 in overall acceptance, and 5.73 \(\pm\) 1.71 in purchase intention. The Alu and Ste groups generally received lower preference scores across most attributes, with allulose in particular lagging in flavor, sweetness, taste, overall acceptance, and purchase intention. Notably, the Ste group did not differ significantly from the Sug group in attributes such as scent and chewiness, indicating that stevia has weak effects on these sensory aspects. Despite similarities in scent and chewiness across all groups, the Xyl group consistently achieved higher ratings for the most sensory attributes, suggesting that xylose enhances both the visual appeal and overall sensory properties of Yanggaeng. This indicates that xylose may be the most favorable alternative sweetener among those tested for improving the consumer acceptance of CF-treated Yanggaeng.
# Numerical Simulation of Flapping Airfoil Aerodynamic Characteristics
## 2 Control Equations, Numerical Methods, and Model Validation
### Numerical Methods  
The calculation domain and grid division of the flow field are displayed in Figure 2, where the chord length of the airfoil is h, and the calculation area is circular. The watershed radius is 25 h, and the full development of flow is ensured by a sufficiently long watershed radius, so the accuracy of the simulation is guaranteed. The left half circle is the velocity inlet, the right half circle is the pressure outlet, and the wall slip-free boundary condition is adopted by the airfoil surface. The second-order upwind scheme and second-order central difference scheme are, respectively, used for the discretization of the control equation, effectively improving the computational accuracy.  
The flow field mesh is an unstructured hybrid mesh. The mesh around the airfoil is encrypted better to capture the structural changes in the flow field. Additionally, the airfoil boundary layer mesh is set up according to the condition of \(y^{+}<1\) to ensure the accuracy of the simulation results within the airfoil boundary layer. The zoomed-in details are shown in the bottom right corner of Figure 2. The motion of the flapping airfoil is described by the UDF. The airfoil flutter is controlled by establishing the flutter motion function in the UDF. To ensure that the mesh does not produce distortion or negative mesh during the airfoil flutter motion, the mesh is updated using a Diffusion-Based Smoothing approach. In particular, the grid in regions with moving or deforming boundaries is smoothed to maintain the mesh quality near the airfoil, and the internal nodes of the moving grid are adjusted accordingly.  
Figure 2: Flow field grid. (**a**) Grid of overall flow field; (**b**) Dense mesh around the airfoil; (**c**) Airfoil boundary layer.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 4 OS-Based Mitigation Techniques
### Protecting the ELF Binaries
#### 4.3.1 Position Independent Executable (PIE)  
The machine code instructions being kept in the main memory are known as Position Independent Code (PIC), which is able to execute correctly regardless of the address. In general, shared libraries are compiled as PIC files so that they can be shared by several processes independently of one another. This facilitates the implementation of per-process randomization via ASLR. For each process, different PIC libraries are loaded. Unlike absolute code which must be loaded at a specific memory location, the PIC can be loaded at multiple memory locations without any alteration. Instructions belonging to a particular location execute faster than those addressed with relative addresses; however, the difference is insignificant on modern processors. Code that is independent of position is easily randomized.  
The binary generated by the compiler as position independent code is known as Position Independent Executable (PIE) [26]. It provides arbitrary base addresses for the different sections of executable binary. PIE implements the same randomness strategy for executable, similar to the one used for shared libraries and makes exploitation difficult for attackers. If a binary is compiled as Position Independent Executable, the main binary (.text,.plt,.got,.rodata) is also randomized. PIE complements ASLR to prevent attacks. MacOS 10.7, iOS 4.3, and their later versions provide full support for PIE executable. PIE makes it more difficult for adversaries to guess the code address residing in the main executable, just like code reuse attacks using shared library code. The -pie option is used when compiling a program with GCC to make the binary as position independent executable.
## 4 Verification and Analysis
### Analysis of Experimental Data
Given that the RANSAC algorithm carries out the registration first based on the fast point feature histogram, it is superior to the ICP algorithm for the registration of workpieces with obvious surface features, but the speed is slower due to the necessity of extracting the histogram first. For Depth Filtering, the ICP algorithm has a significant increase in error and a significant decrease in alignment speed at higher data volumes. The hereby proposed IWOA-RANSAC-ICP algorithm realizes rapid optimization of the RANSAC algorithm using WOA, accelerates the feature extraction, and provides a more accurate initial point pair for the ICP algorithm. Overall, this very algorithm possesses higher accuracy, but its efficiency still needs further improvement.