esa-sceva/satcom-chunk-collection huggingface dataset:
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 2 Titanium Dioxide
### Hydrogen Production Exploiting TiO\({}_{2}\) under Visible and Solar Light
#### 2.1.2 Coupling TiO\({}_{2}\) with CdS  
The CdS semiconductor has been extensively studied as a sensitizer for photocatalytic water splitting due to its narrow band gap and strong absorption in the visible-light spectral region [44]. Combining TiO\({}_{2}\) with a CdS semiconductor can further enhance the photocatalytic performance of TiO\({}_{2}\)[45]. [PERSON] et al. [46] synthesized a CdS/TiO\({}_{2}\)(B) type II heterojunction via photo deposition, with CdS dots anchored on TiO\({}_{2}\)(B) nanosheets. Under visible light, a H\({}_{2}\) evolution rate of 1577 umol g\({}^{-1}\) h\({}^{-1}\) was achieved. Additionally, a CdS/TiO\({}_{2}\)(B) type I heterojunction was synthesized via the hydrothermal method; however, it resulted in a H\({}_{2}\) evolution rate of 48 umol g\({}^{-1}\) h\({}^{-1}\) under visible light, significantly lower than that obtained with the type II heterojunction. The proposed mechanism for H\({}_{2}\) production by CdS/TiO\({}_{2}\)(B) reported by [PERSON] et al. [46] is schematically represented in Figure 2.  
[PERSON] et al. [47] utilized TiO\({}_{2}\) nanotube arrays, obtained by anodizing titanium foils, in conjunction with CdS quantum dots to create CdS quantum dots decorated TiO\({}_{2}\) nanotube arrays. Under visible light and in the presence of Na\({}_{2}\)SO\({}_{3}\) and Na\({}_{2}\)S as sacrificial reagents, a H\({}_{2}\) production rate of 1.89 umol h\({}^{-1}\) cm\({}^{-2}\) was achieved [47].  
[PERSON] et al. [44] prepared TiO\({}_{2}\) microspheres with highly exposed (001) facets loaded with CdS. They observed that CdS/TiO\({}_{2}\) microsphere composites with a CdS:TiO\({}_{2}\) molar ratio of 1 indicated the highest activity, resulting in a H\({}_{2}\) production rate of 76.55 umol/h when lactic acid was used as the scavenger for photo-generated holes under visible light [44].  
A porous TiO\({}_{2}\) co-doped with CdS and WO\({}_{3}\) was synthesized by [PERSON] et al. [45]. The ternary porous CdS/WO\({}_{3}\)/TiO\({}_{2}\) photocatalyst, with a molar ratio of CdS:WO\({}_{3}\):TiO\({}_{2}\) equal to 8:8:100, achieved a H\({}_{2}\) evolution rate of 2106 umol g\({}^{-1}\) h\({}^{-1}\) under visible light using Na\({}_{2}\)S and Na\({}_{2}\)SO\({}_{3}\).  
Furthermore, the binary CdS/TiO\({}_{2}\) and WO\({}_{3}\)/TiO\({}_{2}\) porous photocatalysts were tested, resulting a H\({}_{2}\) evolution rate of 1056 umol g\({}^{-1}\) h\({}^{-1}\) and 981 umol g\({}^{-1}\) h\({}^{-1}\) for CdS/TiO  
Figure 2: Schematic representation of a possible mechanism for H\({}_{2}\) production by CdS/TiO\({}_{2}\)(B) photocatalyst under visible light.  
and WO\({}_{3}\)/TiO\({}_{2}\), respectively. The possible mechanism for H\({}_{2}\) production using the CdS/WO\({}_{3}\)/TiO\({}_{2}\) photocatalyst proposed by [PERSON] et al. [45] is schematically represented in Figure 3.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Underwater Wireless Optical Communication
These advancements position UWOC as a transformative technology for constructing scalable, high-speed underwater communication networks capable of supporting critical applications in environmental monitoring, deep-sea exploration, and military operations. Moving forward, future research will continue to address the challenges posed by underwater environments. It will focus on enhancing system efficiency, extending communication distances, and optimizing performance under dynamic conditions.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 4 State-of-the-Art Firmware Emulation and Its Application in Fuzzing
### Solution to CPU Emulation Support  
General binary emulators like _Simics_[17], _QEMU_[12], and _Unicorn_[13] support most common CPU architectures. _QEMU_ and _Unicorn_ also offer memory mapping and peripheral forwarding, making them popular for full-system emulation with peripheral interaction emulation tools. However, these emulators struggle with unknown CPU architectures, requiring significant manual effort to add new ones within their Intermediate Representation (IR) frameworks. The _Ghidra_ Emulator [18], developed by the NSA, supports more instruction set architectures than _QEMU_ and allows users to add new ones using SLEIGH language definitions. For instance, _MetaEmu_[19] addresses the challenge of uncommon processor architectures in automotive firmware through the _Ghidra_ Emulator.  
In addition, _SAFIREFUZZ_[20] pinpoint instruction translation as a key factor slows down emulation. Instead of translating instructions for a general processor, it executes firmware on more powerful hardware within the same instruction set family. By using high-level peripheral function replacement and dynamic binary rewriting, it replaces peripheral interactions and filters ARM Cortex-M specific instructions, allowing ARM Cortex-M firmware to run on ARM Cortex-A servers without CPU emulation.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 4 Wear Rate Characterization
### Wear Rate Variability Among Different Friction Plates  
The run-in effect is reflected in the wear rate being several times higher at the beginning of clutch life than at the end of life (Figures 4 and 7a). There are several physical mechanisms behind this effect. At the beginning of the clutch plate service life, the friction surfaces are not fully conformed, leading to localized areas of increased temperature and, consequently, elevated wear [28]. The rise in temperature accelerates the degradation of the resin matrix in the composite material, contributing to wear. Additionally, the organic material is prone to oxidation, which increases wear in surface layers that remain exposed to air for prolonged periods before first clutch/vehicle use.  
Figure 7: Wear rate versus cumulative dissipated energy obtained over whole clutch life for fixed operating conditions (\(\omega_{\text{0R}}=1700\) rpm, \(M_{\text{zR}}=50\) Nm, \(t_{\text{2R}}=3.4\) s, \(T_{dR}=170\)\({}^{\circ}\)C) (**a**), and normal probability plot of wear rate residuals from stabilized phase (**b**).  
To investigate and model the run-in effect, the run-in and run-out recordings obtained for 13 tested friction plates have been considered. The results shown in Figure 8 indicate rather significant variability among the friction plates (piece-piece variability), which is caused by the heterogeneity and production variations in the friction plates.
## 3 Results and Discussion
### SERS Applications  
We want to mention that both Ag and Au NPs synthesized by using \(\beta\)-CD present SERS activity, being able to significantly enhance the Raman signal of common dyes (e.g., rodhamine 6G, crystal violet, methylene blue). An example of Rodhamine 6G SERS obtained on room temperature-synthesized AgNPs is presented in Figure S11. As a general observation, CD capping reduces enhancement by up to an order of magnitude. In our setup, typical citrate-capped colloids exhibit enhancement factors around \(10^{3}\) to \(10^{4}\), while CD-capped nanoparticles show lower enhancements, ranging from \(10^{2}\) to \(10^{3}\). Interestingly, the highest enhancements are observed in the ultrasound-assisted synthesized colloids, which are more polydisperse and contain larger nanoparticles, contributing to increased enhancement.  
In the following, we like to emphasize the advantages of using \(\beta\)-CD-capped plasmonic NPs for SERS as they behave differently compared with classical citrate-reduced Au or Ag NPs.
# High-Frequency Limits of Graphene Field-Effect Transistors with Velocity Saturation
## 6 Perspectives on High-Frequency Graphene Devices  
In an attempt to assess the intrinsic frequency limits of GFETs, we include here an analysis of Zener-Klein transistors where contact resistance is low enough, mobility is large enough and channel length long enough (\(L=4\)\(\upmu\)m), to access a fully developed inter-band Zener-Klein tunneling regime [11]. A quasi perfect current saturation (\(I_{sat}\gtrsim 20\)\(\mathrm{Acm^{-1}}\)) is achieved thanks to the combined intraband velocity saturation, interband Zener-Klein tunneling and Dirac pinch-off (Figure 1 of Ref. [11]). The small contact resistance \(R_{c}\simeq 150\)\(\Omega\upmu\)m\({}^{-1}\), large DC transconductance \(g_{m}\gtrsim 2.5\)\(\mathrm{Scm^{-1}}\), and saturation resistance \(R_{sat}\) lead to a large voltage gain \(G\simeq 8\). From these numbers, we estimate \(f_{max}\approx f_{T}\sqrt{G/4g_{m}R_{c}}\simeq 7f_{T}\) (taking \(R_{G}\ll R_{c}\) in Equation (10)) translating to \(f_{max}\gtrsim 100\) GHz (taking \(f_{T}=g_{m}/2\pi C_{g}\simeq 16\) GHz with \(L=2\)\(\upmu\)m and \(t_{BN}=20\) nm). Considering the optimization of saturation velocity, contact resistance and channel length, these figures constitute the intrinsic frequency limit of GFETs. It still remains to be demonstrated with edge-contacted, h-BN encapsulated, GFETs at room temperature.  
This intrinsic limitation of GFETs can be circumvented relying on collective modes such as plasmons which benefit from an enhanced velocity \(v_{p}\gg v_{F}\) appropriate for sub-THz operation. This corresponds to plasma resonance transistors (PRTs) [18], which are multi-mode narrow-band detectors. Intrinsic Dirac-fermion plasma modes can be studied in plasma resonance capacitors (PRCs), which behave as quarter-mode resonators (fundamental at \(f_{0}=v_{p}/4L\) and harmonics \(f_{k}=(2k+1)f_{0}\)). In a recent study [17], an h-BN encapsulated PRC working at \(f_{0}=40\)\(\mathrm{GHz}\) (\(v_{p}\simeq 4v_{F}\)) was demonstrated, showing the relevance of long-wavelength plasma resonance devices in fully characterizing high-mobility graphene, offering a full analysis of the temperature and density dependence of the quantum capacitance \(C_{Q}(T,n)\), kinetic inductance \(L_{K}(T,n)\) and damping or mean-free path \(l_{scat}(T,n)\).
## 3 Results
### Modeled Yb  
The Yb of summer maize over the NCP was estimated using the RS-based method presented in Section 2.3.4. Yb estimated for each year in the period 2010 to 2015 was averaged over time (Figure 6a). Results show that Yb estimated in this study has a tight correlation (R = 0.81, RMSD = 0.87 t \(\mathrm{hm}^{-2}\)) with that estimated using a calibrated APSIM-Maize model at 10 agricultural meteorological (AM) sites from existing studies (Figure 6c). The result demonstrates that the performance of the developed approach in estimating Yb is comparable to that of a calibrated CGM. However, Yb values from the two methods were not the same (Figure 6c). Regardless of the differences in formulations between the two methods, Yb in this study represented the period 2010 to 2015, which is different from the data we used for comparisons in Figure 6.  
Our results show that the mean annual Yb in the period 2010 to 2015 ranged between 9 and 16 t \(\mathrm{hm}^{-2}\) over the study region with a regional value of 11.99 t \(\mathrm{hm}^{-2}\). Mean annual Yb over the study region generally increased from southwest to northeast. The northeast of Shandong Province had the highest Yb, whereas the lowest Yb appeared in the southeast of Henan province.  
Figure 4: Maps of modeled Ya of summer maize in 2010 (**a**), 2011 (**b**), 2012 (**c**), 2013 (**g**), 2014 (**h**), and 2015 (**i**); and the pixel-level frequency distribution (FD) of pixel level values in 2010 (**d**), 2011 (**e**), 2012 (**f**), 2013 (**j**), 2014 (**k**), and 2015 (**l**). The scatter plot represents a comparison between modeled yield and reference yield on a prefecture-level, the modeled yield on a prefecture-level is the average of yield values of all pixels within a prefecture-level district, and each scatter plot has 44 samples; the solid line in each scatter plot represents the “1:1 line.” The value ranges in the legend are right-closed and left-open.  
Figure 5: Modeled maize yields vs. reference values on a prefecture-level for pooled data (**a**) and mean annual data (**b**) in the period 2010 to 2015; R, N, and RMSD denote correlation, sample size, and root mean standard deviation, respectively; the error bar represents the standard deviation of multi-year data.  
Figure 6: The map (**a**) and frequency distribution (**b**) of mean annual modeled Yb of summer maize over the period 2010 to 2015, and the modeled Yb of 10 agricultural meteorological (AM) sites vs. the simulations of corresponding sites from [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [11] (**c**). The value ranges in the legend of panel (**a**) are right-closed and left-open. Yb data from [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [11] represent the average of annual Yb in the period 1982 to 2005 for Linyi, 1982 to 2008 for Zibo and Laiyang, and 1982 to 2009 for the remaining sites.
# On Beamforming with the Single-Sideband Transform
## 3 Signal Model and Beamforming
### Conjugate-Frequency Filtering with the SSBT  
It is useful to bring Property A7 to light. From there, we have that RFRs with the SSBT are even functions of the frequency for the same-frequency portion and odd for the conjugate frequency. That is, \(A^{\prime}_{m,k}[I]=A^{\prime}_{m,(K-k)}[I]\) and \(A^{\prime\prime}_{m,k}[I]=-A^{\prime\prime}_{m,(K-k)}[I]\). Using this, from the constraint in Equation (46b), we have  
\[\mathbf{f}^{\mathsf{H}}_{K-k}[I]\mathbf{D}^{\prime\prime}_{K-k} =\mathbf{f}^{\mathsf{H}}_{K-k}[I]\big{(}-\mathbf{D}^{\prime\prime }_{k}\big{)} \tag{50}\] \[=0.\]  
It is also easy to see that \(\mathbf{\Phi}_{\mathbf{y}_{k}}[I]=\mathbf{\Phi}_{\mathbf{y}_{K-k}}[I]\) (for the SSBT), given all the properties from Appendix A. Therefore, we have that \(\mathbf{f}_{k}[I]\) achieves the distortionless constraint for the bin \(K-k\), given that \(A^{\prime}_{m,k}[I]=A^{\prime}_{m,(K-k)}[I]\). It achieves the null of the conjugate-frequency portion, given the results from Equation (50), and also minimizes the power of the output signal, given that \(\mathbf{\Phi}_{\mathbf{y}_{k}}[I]=\mathbf{\Phi}_{\mathbf{y}_{K-k}}[I]\). Consequently, it is unnecessary to calculate \(\mathbf{f}_{K-k}[I]\), given that \(\mathbf{f}_{k}[I]\) fulfills the minimization problem from Equation (48) for the conjugate bin \(K-k\) as well. Although with the STFT, only half the spectrum is needed (given its complex-conjugate properties), with the SSBT, the filter only needs to be calculated for half the spectrum (even though it needs to be applied to the whole spectrum), putting them on a similar footing in this regard.
## 4 Materials and Methods
The optical dataset used in this work is the standard Sentinel-2 Level-1C product that can be downloaded from the ESA Sentinel Hub ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu))). Firstly, two RGB (Red-Green-Blue) colour composites were provided to present visually informative images of the study area: \"True Colours\" composite based on bands 4, 3 and 2; and \"SWIR (short-wavelength infrared) colours\" composite based on bands 12, 8a and 4. The SWIR colour composite allows to easily identify the humid areas, thus water, and highly vegetated area [33]. Secondly, the multi-spectral water index, also called Normalized Difference Water Index (NDWI) proposed by [34], was estimated by means of the following Formula (1):  
\[\text{NDWI}=\frac{\text{band 3 }-\text{ band 8}}{\text{band 3 }+\text{ band 8}} \tag{1}\]  
This index is intended to maximize the reflectance of the water body in the visible green band (band 3 for Sentinel-2) and to minimize the reflectance of water body in the near-infrared (NIR) band (band 8 for Sentinel-2). As a result, a NDWI map of the study area is produced to distinguish water/moisturized soil surfaces from built-up and vegetated areas. Validation step of the maps is finally accomplished. In order to validate our results, the official delineation maps were downloaded from the Copernicus Emergency Management Service and were used to cross-compare products.
## 1 Introduction  
Database Management Systems (DBMSs) often store sensitive information, such as company secrets, financial data, and personal privacy details [1]. However, these systems are vulnerable to both external and internal threats. External threats include social engineering attacks, while internal threats arise from unauthorised access, privilege abuse, and vulnerabilities like SQL injection [2].  
The authors of the research paper by [PERSON] et al. [3] proposed a privacy-enhancing approach involving the deployment of resource-efficient Docker containers within cloud environments. This method effectively isolates sensitive information, enabling flexible implementation of enhanced privacy protections and management strategies as needed. Given the huge increase in the number of network security threats, such as vulnerabilities, attacks, data breaches, and privacy violations [4], this research aims to address these challenges by developing a robust security framework for databases. This framework will mitigate risks associated with external attacks [5], while also safeguarding user data from the destructive potential of SQL injection (SQLi) [6].  
On the other hand, cloud computing has enabled distributed computing, outsourcing infrastructure installation, pricing, maintenance, and server scalability. This has led to a profitable computing industry, where data owners often relinquish control over their data despite significant financial investment [7]. To address this, a comprehensive evaluation of security frameworks is necessary, particularly in scenarios requiring frequent user access to data for real-world applications [8]. While various research papers have explored the use of Docker as a framework, there remains a gap in understanding how to leverage Docker from a user-centric perspective to protect both user data and the underlying database. This research aims to bridge this gap by fostering trust between users and the authorities managing centralised databases.  
This paper is organised with the following structure: Section 1 talks about the introduction of the Database, Docker as a framework, SQL injection (SQLi), research questions, research objectives, Docker containers, and security. Section 2 describes related works, research objectives, suitability of the Docker framework, Docker containers, and the security aims of the research. Section 3 consists of the implementation, the hypotheses, the type of variables, the schematic diagram of the laboratory, ethics approval and GDPR, the environment, databases, network analysis, and data collection. Section 4 presents the results, statistical analysis, tables, and graphs. Section 5 is a summary of findings, while Section 6 elaborates on the conclusions.
## 3 Results
### Error Metrics  
Let us assume that an interpolation or gPC-based polynomial approximation \(\widetilde{g}\approx g\) is available. The following errors are used to estimate the performance of \(\widetilde{g}\) for approximation and UQ purposes.  
First, we want to estimate the performance of \(\widetilde{g}\) in terms of approximation accuracy, i.e., its suitability to act as a surrogate model and reliably replace the input-output map \(g\), equivalently, the original computational model. To that end, we use a validation sample \(\left\{\mathbf{y}_{q}\right\}_{q=1}^{Q}\), which is randomly drawn from the joint input PDF \(\epsilon(\mathbf{y})\) and compute the root-mean-square (RMS) validation error:  
\[\epsilon_{\text{RMS}}=\sqrt{\frac{1}{Q}\sum_{q=1}^{Q}\left(\widetilde{g} \left(\mathbf{y}_{q}\right)-g\left(\mathbf{y}_{q}\right)\right)^{2}}. \tag{14}\]  
From an interpretation point of view, \(\epsilon_{\text{RMS}}\) indicates the average (expected) approximation accuracy of \(\widetilde{g}\), while at the same time being sensitive to outliers by penalizing large errors. In all numerical experiments, the size of the validation sample is set to \(Q=10^{5}\).  
Secondly, we want to estimate the performance of \(\widetilde{g}\) in terms of UQ quality, i.e., estimate the accuracy of the statistics values provided by post-processing the approximation. For that purpose, we use the expected value of the QoI as a representative statistic and compute the relative error:  
\[\epsilon_{\text{rel},\mathbb{E}}=\left|\frac{\mathbb{E}\left[g\right]-\widehat {\mathbb{E}\left[g\right]}}{\mathbb{E}\left[g\right]}\right|, \tag{15}\]  
where \(\mathbb{E}\left[g\right]\) is a reference value and \(\widehat{\mathbb{E}\left[g\right]}\) is an estimate computed by directly post-processing the terms of \(\widetilde{g}\). In all numerical experiments, the reference expected value is computed with a quasi-MC integration scheme based on Sobol sequences, where the size of the quasi-MC sample is equal to \(10^{8}\).
## 4 Discussion
### Effects of Viewing Zenith Angle in OCO-2 Glint Mode
The corresponding SIF values with VZA \(\leq 40^{\circ}\) exhibited slightly higher values compared with those VZA \(>40^{\circ}\) (Figure 1a), and consequently, the GPP-SIF function had certain deviations with the SIF soundings with high VZA values (Figure 1b). The slope of the GPP-SIF is inconsistent between VZA \(\leq 20^{\circ}\) and \(20^{\circ}<\) VZA \(\leq 40^{\circ}\), which had similar SIF values, which may be mainly because of the different cropland types (C3 winter wheat-fix and reduce inorganic CO\({}_{2}\) into organic compounds using only the C3 pathway in photosynthesis and C4 summer maize) in this study site. Therefore, the difference in the slopes of the GPP and SIF functions in this study may partly be attributable to the combining of the SIF Glint and Nadir mode observations and cropland types. There is still much to learn, and further validations with more flux sites are still needed to address the specific effects of VZAs in Glint mode on SIF values and on the relationship between SIF and GPP at different spatial scales and other ecosystems, as well as the mechanism between them.