esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB
## 3 NoSQL Document Databases  
NoSQL databases were created to scale easily as data grow. Their main advantage is dealing with unstructured data such as text files, email, and multimedia files. NoSQL databases are known to have good horizontal scalability [12]. On the contrary, relational databases are known for having a predefined structure for their tables and for using SQL (Structured Query Language) as their standard query language. NoSQL databases do not have a predefined structure or a standard query language since each one usually provides its own query language [13].  
NoSQL document databases store and organize their data in the form of document collections. Collections are equivalent to tables in relational databases, and documents can be seen as records in relational tables. The difference is that the documents do not have a defined structure, and each document may have different fields and attributes. Documents from the same collection must be similar, although the database system does not require it. Documents do not have a predefined structure and do not depend on each other, and when they present some structure, they are called semi-structured documents [1; 2].  
We selected the top three document databases according to the DB-Engines Ranking 2022: MongoDB, Couchbase, and CouchDB [14]. In the following subsections, we describe each one of these NoSQL document databases.
###### Abstract  
Data provenance is information about where data come from (provenance data) and how they transform (provenance transformation). Data provenance is widely used to evaluate data quality, trace errors, audit data, and understand references among data. Current studies on data provenance in relational database management systems (RDBMS) still have limitations in supporting full-featured SQL or procedural languages. With these challenges in mind, we present a formal definition of provenance data and provenance transformation for relational data. Then, we propose a solution to support data provenance in relational databases, including provenance graphs and provenance routes. Our method not only solves the complicated problem of modeling provenance in DBMS but also is capable of extending procedural languages in SQL. We also present ProvPg, a PostgreSQL-based prototype database system supporting data provenance in multiple granularities. ProvPg implements extraction, calculation, query, and visualization of provenance. We perform TPC-H tests for ProvPg and PostgreSQL, respectively. Experimental results show that ProvPg addresses the vision of supporting data provenance with little extra computation overhead for the execution engine, which indicates that our model is applicable to lineage tracing applications.  
data provenance; relational database; SQL; stored procedure; transformation Article Modeling the Data Provenance of Relational Databases Supporting Full-Featured SQL and Procedural Languages.
## 5 Vulnerability Detection
### Detailed Comparative Analysis on Vulnerability Detection Methods
#### 5.2.2 Static and Program Analysis-Based Methods  
Static analysis and program analysis-based methods play a crucial role in reducing false positives and reliably detecting vulnerabilities. Tools such as SmartCheck [75] and  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Categories** & **References** & **Strengths** & **Weaknesses** \\ \hline \hline \multirow{8}{*}{Comprehensive Surveys and Reviews} & \multirow{8}{*}{[63; 65; 68; 73; 77; 98; 95; 96; 97; 98; 93; 95; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 103; 105; 106; 107;  
SafeCheck [70] apply rule-based analysis to Solidity code, effectively detecting known anti-patterns such as re-entrancy and integer overflow. Other approaches, including RA [74] and SmartFast [78], introduce specialized symbolic execution engines tailored to uncover logic bugs in Ethereum bytecode. Formal verification methods [73; 76] use rigorous mathematical proofs, such as model checking, to guarantee the absence of certain classes of bugs.  
Issues in the Existing Approaches  
While these methods are cost-effective and efficient in detecting vulnerabilities, their implementation complexity, resource requirements, and need for expert configuration can limit their accessibility. Balancing cost and performance, simplifying the implementation process, and reducing dependency on specific tools can improve the overall usability of these methods.
## 7 Impact of Processing Methods
### Extraction Methods  
Different methods of essential oil extraction have been shown to significantly influence the composition of thyme essential oil. A comparison of distillation methods, specifically, water-steam distillation and hydrodistillation, as detailed by [PERSON] et al. [134], demonstrated significant differences in the chemical composition of garden thyme oil, resulting in variations in the dominant components such as thymol, carvacrol, p-cymene, and \(\gamma\)-terpinene. [PERSON] et al. [135] revealed that using the steam distillation method for producing essential oil in both thymol and linalool chemotypes of _Thymus vulgaris_ resulted in a decrease in their main compounds (linalool and thymol, respectively) compared to the hydrodistillation method using Clevenger apparatus. This alteration also affected the composition of other components. Conversely, [PERSON] et al. [136] found that steam distillation of _Thymus pallescens_ yielded slightly higher contents of monoterenes, sesquiterpenes, and oxygenated sesquiterpenes compared to steam diffusion and hydrodistillation methods.  
Supercritical fluid extraction (SFE) is an efficient and selective alternative to conventional extraction methods that uses supercritical fluids, such as CO\({}_{2}\), as the extracting solvent [137]. [PERSON] et al. [138] revealed that by using SFE, they obtained a higher amount of carvacrol but a lower amount of thymol compared to steam distillation. After comparison with different extraction conditions, [PERSON] et al. [139] proposed the optimal parameter profile for SFE-CO\({}_{2}\) to obtain a rich extract with volatile compounds for _T. vulgaris_ (17 MPa, 45 \({}^{\circ}\)C, 30 min) and for _T. pannonicus_ (24 MPa, 55 \({}^{\circ}\)C, 20 min). In contrast, a higher amount of monoterenes in _T. vulgaris_ was detected using simultaneous distillation extraction (SDE) compared to SFE [140]. Additionally, hydrodistilled essential oil of _T. vulgaris_ showed a higher thymol ratio (48-50%) than the SFE samples (10-15%) [138]. Another modern method for extracting organic compounds is solid-phase microextraction (SPME), which is also a solvent-free method. It uses stationary phases on a fiber to extract analytes for GC analysis, classified as direct or headspace methods [141]. [PERSON] and [PERSON] [142] advised optimizing the SPME method according to each plant species and revealed higher content of active compounds, particularly thymol, in the hydrodistilled oil than when using the SPME method.  
Interestingly, contrasting studies have demonstrated that the extraction method, whether classical hydro-distillation, microwave hydro-distillation, or supercritical fluid extraction, did not influence the composition of thyme essential oil, as indicated by [PERSON] et al. [143]. Similarly, [PERSON] et al. [144] observed a similar composition of thyme essential oil using both extraction methods: hydrodistillation and microwave-assisted extraction.
## 3 Results  
### Tibial Metallic Component  
The micro-Raman spectra and corresponding images recorded on the surface of new (unused) and retrieved tibial component are presented in Figure 3, showing that the spectral features of metallic component (titanium) are well preserved during the implantation interval. Both spectra revealed an intense, broaden band at 144 cm\({}^{-1}\), which is typical for titanium oxide polymorphs [19; 20; 21; 22]. Raman scattering is sensitive to material phase and polymorphism. Thus, even though two materials may have identical chemical formulae, any different crystal structure or phase will often result in distinct spectra. According to literature [23] the strongest vibrational mode of anatase polymorph is Eg mode at about 144 cm\({}^{-1}\). The titanium anodization spectra reported by [PERSON] et al. [22] using acid media, indicating the formation of titanium oxide layer (crystalline anatase) with typical bands at 144,396,515 and 638 cm\({}^{-1}\). Rutile, other TiO\({}_{2}\) polymorph, exhibits characteristic stretching peaks at 144 (very week), 430 and 590 cm\({}^{-1}\) (very strong) that correspond to the symmetries of \(B_{1\,g}\), Eg and \(A_{1\,g}\), respectively. In our case, even if the intense band at 144 cm\({}^{-1}\) may indicate a polymorphism, we noted the absence of all the other Raman active modes of the TiO\({}_{2}\) polymorphs.  
On the other hand, the passive film formation on pure titanium on different anodic potential treatment conditions has been investigated by [PERSON] et al. [21]. In this case, the overall Raman feature of the oxide layer showed decreased intensity of the Raman modes of anatase and main band (144 cm\({}^{-1}\)) broadening with decreasing the applied potential. Comparing our result with these reports, we may conclude that our spectra collected from metallic component resembled poor crystalline anatase film on pure titanium, either newly open or after prosthetic retrieval.  
Thus, the overall vibrational features (Figure 3) of titanium surface remained unchanged.
# Numerical Simulation of Fluid-Structure Coupling for a Multi-Blade Vertical-Axis Wind Turbine
## 3 Condition Setting
### Coupled Meshing  
For the meshing of the coupling model, the solid region and fluid region need to be distinguished, and grids should be divided respectively for different regions. Meanwhile, an interface is used to connect the interface between fluid and solid [36; 37].
## Study Areas and Data
### MCD43A3 BRDF/Albedo Product  
MCD43A3 (Version 6) (V006) is the latest version of the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product. It includes bi-hemispherical reflectance (white-sky albedo) and directional-hemispherical reflectance (black-sky albedo) with a 500 m spatial resolution. The daily albedo is composed of 16-day multi-angle observations, where the Julian date of each specific file represents the 9 th day of the 16-day retrieval period. MCD43A3 was produced based on the Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (AMBRALS), which uses all atmosprically corrected, high-quality, cloudless surface reflectance over the course of 16 days to achieve the best-fit surface bidirectional reflectivity via Ross-Li kernel models [29; 30]. The Ross-Li kernel model is expressed as follows:  
\[R(\theta,\theta,\phi,\Lambda)=f_{\text{iso}}(\Lambda)+f_{\text{pol}}(\Lambda) K_{\text{pol}}(\theta,\theta,\phi)+f_{\text{geo}}(\Lambda)K_{\text{geo}}( \theta,\theta,\phi) \tag{1}\]  
Figure 1: Distribution of available Thematic Mapper (TM) data corresponding to Flux sites (one year).  
where \(R(\theta,\vartheta,\phi,\Lambda)\) is the frontal reflectivity with a solar zenith angle \(\theta\), observed zenith angle \(\theta\), relative azimuth \(\phi\), and wavelength band \(\Lambda\). \(f_{iso}(\Lambda)\) is the proportion of uniform scattering in all directions, \(f_{vol}(\Lambda)\) is the proportion of body scattering, \(f_{geo}(\Lambda)\) is the proportion of geometric optical scattering, \(K_{vol}(\theta,\vartheta,\phi)\) is the RossThick kernel, and \(K_{geo}(\theta,\vartheta,\phi)\) is the LiSparse kernel.  
MODIS V006 products provide shortwave black-sky albedo and white-sky albedo [31], which must be converted into blue-sky shortwave albedo according to the proportion of sky scattered light [32].  
\[\alpha(\theta_{\rm i},\lambda)=(1-\mathrm{s}(\theta_{\rm i}\tau(\lambda))) \alpha_{\rm bs}(\theta_{\rm i},\lambda)+\mathrm{s}(\theta_{\rm i}\tau(\lambda) )\alpha_{\rm ws}(\theta_{\rm i},\lambda) \tag{2}\]  
where \(\alpha(\theta_{\rm i},\lambda)\) is the blue-sky albedo of the band \(\lambda\) at a solar zenith angle of \(\theta\), \(\alpha_{\rm bs}(\theta_{\rm i},\lambda)\) is the black-sky albedo, \(\alpha_{\rm ws}(\theta_{\rm i},\lambda)\) is the white-sky albedo, and \(\mathrm{s}(\theta_{\rm i},\tau(\lambda))\) is the fraction of diffuse skylight when the solar zenith angle is \(\theta\), which is a function of aerosol optical depth and can be calculated using a predetermined look-up table (LUT) based on the 6S atmospheric radiative transfer code [33].  
There was some data missing in MCD43A3 due to retrieval failure, so we used a gap filling algorithm to construct a continuous albedo data set. Missing data was filled with the average of all pixel values of the same land type in a given window (\(10\times 10\) pixels) centered on the target pixel.
# Analysis of Video Transmission Capabilities in a Simulated OFDM-Based Supplementary BPL-PLC System
## 3 Materials and Methods  
Considering the importance as well as practical aspect of the analyzed problem, we intended to determine the impact of various factors on the transmission of video signals. In our case, we utilized the OFDM technique along with quadrature amplitude modulation (QAM).  
The resultant resistance of the cable plays a fundamental role, limiting both the quality and distance of the transmitted signal. It depends both on the structure of the utilized cable and the physical quantity of its materials. Due to this fact, the value of the signal-to-noise ratio (SNR) factor is significantly reduced because of the relatively high value of the cable's attenuation. It should be noted that the SNR is also related to other physical cable quantities,such as its capacitance and/or inductance. Additionally, it does change with frequency as well. The methods of approaching this problem and respective results are presented in [11]. In this paper, we focus on investigating the influence of numerous factors, which may affect the quality of video data transmission.
## 2 Materials and Methods
### Adsorption Tests of GA-FK Gel to Fe(III) in Aqueous Solution  
The adsorption experiment was carried out by batch process, the operation was as follows: 25 mL FeCl\({}_{3}\) aqueous solution was added into a 100 mL conical flask, the conical flask was put into the constant-temperature water bath oscillator. When the system temperature reached the set value, a certain amount of GA-FK gel was added to it. Adsorption at a set temperature was conducted for a certain time, where the rotational speed of oscillator was 120 r\(\cdot\)min\({}^{-1}\). Then, the absorbance of the supernatant was measured (A) with an atomic absorption spectrophotometer. The removal rate and adsorption capacity were calculated according to the following formulae [24; 25]:  
\[E(\%)=\frac{C_{0}-C}{C_{0}}\times 100\% \tag{1}\]\[Q(\text{mg}\cdot\text{g}^{-1})=\frac{(C_{0}-C)V}{m} \tag{2}\]  
where \(C_{0}\) (mg\(\cdot\)L\({}^{-1}\)) is the concentration of Fe(III) before adsorption; \(C\) (mg\(\cdot\)L\({}^{-1}\)) is the concentration of Fe(III) after adsorption; \(V\) (L) is the volume of the Fe(III) solution; and \(m\) (g) is the mass of adsorbent.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 5 Simulation Results
### Network Performance Simulation  
Our study simulates the saturation bandwidth utilization and access delay performance at a PHY transmission rate of 1 Mbps.
## 2 Novel Technologies of 6G for Smart Grid Applications
### Beamforming  
Beamforming is a technique used in wireless communication systems to focus the radiated beam of radio frequency (RF) signal in a certain direction. A narrow and concentrated beam of energy can be directed in a certain direction by utilizing the beamforming technique. This directed beam enhances the signal strength and quality between a transmitter and receiver, thereby improving the overall communication performance. The omnidirectional radiation pattern of the cellular devices or BS can create severe interference, which can degrade the network performance. Thus, the beamforming technique is introduced in modern cellular technology as it can radiate a beam to a particular device in a directional manner [81]. AI can also be integrated with beamforming techniques to improve efficiency [97].  
Therefore, it can provide faster, more reliable, and interference-free communication. Moreover, the spectral efficiency is improved due to interference minimization [1].
## 2 Experimental Investigations
### Experimental Setup
#### 2.2.2 Fourier-Transform Infrared Spectroscopy of the Powder Coatings  
Fourier-transform infrared (FT-IR) spectroscopy (Thermo-Nicolet, Waltham, MA, USA) was employed to investigate the chemical structure of the powder coatings. The potassium bromide tablet method was used for testing, and the test range was set to 500-4000 cm\({}^{-1}\).
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 6 Numerical Studies  
### Parameter Setting  
In this section, we present numerical studies to evaluate the performance of the proposed JBG optimization. For this study, we use the parameters from ([5], Section V) unless stated otherwise. Specifically, the system consists of \(N=30\) sensors, and, thus, the group size \(K\) is chosen from the set \(\mathcal{K}=\{1,2,3,5,6,10,15,30\}\). The system bandwidth is normalized to \(B=1\). Each status packet sent by a sensor contains \(S=100\) information bits. The average SNR at the sink node and the edge server is set to \(\rho_{1}=\rho_{2}=\rho\), where \(\rho\in\{1,3,5\}\) dB. Table 1 summarizes the parameter settings used in the numerical studies.
## 4 Results
### Model comparison  
Table 2 presents a comparison of the models in terms of speed and inference time, while Table 3 compares them based on accuracy. The YOLO model is distinguished as the most efficient in terms of speed, achieving a performance of 134 FPS and an inference time of 7.5 ms, rendering it the preferred option for real-time applications. It is 3.5 times faster than the transformers and 1.4 times faster than GELAN. In contrast, GELAN, while slower, demonstrates acceptable performance, being 2.5 times faster than DETR and ViTDet. The latter two transformer-based models are considerably slower, which may restrict their applicability in scenarios where speed is critical.  
Regarding accuracy, GELAN excels with a mAP of 50:95, achieving a score of 77%, closely followed by DETR at 75.7%. Both methods significantly outperform YOLO, which, while exhibiting good accuracy, is more oriented toward applications where speed is a determining factor. Although ViTDet demonstrates lower accuracy, it may be beneficial in contexts where detection under complex conditions is paramount. However, its low speed and accuracy render it less suitable for real-time scenarios.
## 4 Analysis
### Complexity  
This section analyzes the complexity of the proposed methods. The worst-case complexity of solving an SDP, as mentioned in [54], can be expressed as:  
\[\sqrt{\mu}\left(m\sum_{i=1}^{N_{\text{sec}}}(n_{i}^{\text{sec}})^{2}+m^{2}\sum _{i=1}^{N_{\text{sd}}}\left(n_{i}^{sd}\right)^{2}+m\sum_{i=1}^{N_{\text{sd}}} \left(n_{i}^{sd}\right)^{3}+m^{3}\right)\times\ln(1/\epsilon) \tag{51}\]  
where \(m\) is the number of variables, \(N_{\text{sec}}\)(resp. \(N_{\text{sd}}\)) is the number of second-order cone (resp. semidefinite cone) constraints, and \(n_{i}^{\text{sec}}\) (resp. \(n_{i}^{sd}\)) is the dimension of the \(i\) th second-order cone (resp. semidefinite cone),  
\[\mu=\sum_{i=1}^{N_{\text{sd}}}n_{i}^{sd}+2N_{\text{sec}} \tag{52}\]  
is the so-called barrier parameter that measures the geometric complexities of the cones involved, and \(\epsilon>0\) is the solution precision. The proposed SDP algorithm (37) has \((2 MK)^{2}/2+(MK)+2N+3\) variables, \(4.5 KM+K^{2}M^{2}/2\) semidefinite cone constraints of size 1 (\(2 MK\) linear equality constraints in (28) and (31); \(M^{2}K^{2}/2+KM/2\) linear inequality constraints in (29) and (33)); and one semidefinite cone constraint of size \(2 MK+1\) in (34), one semidefinite cone constraint of size \(N+2\) in (35), and \(MK\) second-order cone constraints of size \(N\) in (36). Since \(MK\) is significantly larger than \(N\) in practice, the worst-case complexity is in the order of \(O((\sqrt{4.5K^{2}M^{2}+N^{2}})(M^{6}K^{6})(\ln(1/\epsilon)))\).  
Regarding the computational complexity of the GN iteration, updating \(\mathbf{\alpha}^{(g)}\) in (17) requires \(K(2M+4N+5)\) additions, \(K(2N+9)\) multiplications, and \(K\) square root operations. Computing \(\mathbf{G}^{(g)}\) needs \(K(M-1)(12N+5)\) additions and \(K(M-1)(15N+13)\) multiplications, putting them into the (17) iteration and obtaining a new \(\mathbf{\beta}^{(k+1)}\) requires \(16 NK^{2}M^{2}-32 NK^{2}M+16 NK^{2}-8 NKM+8 NK+16N^{2}KM-8 KN^{2}-4N^{2}-8N^{2}K+2 KM-2K\) additions and \(16 NK^{2}M^{2}-32 NK^{2}M+16N^{2}KM-16N^{2}K+4 NKM-4 NK\) multiplications. The computational load of inverting \(\mathbf{G}^{(g)T}\mathbf{WG}^{(g)}\) is \(O(N^{3})\). As a result, the amount of computation in each GN iteration is \(O(NK^{2}M^{2})\).  
Following the similar analysis above, the computational loads of the QN and LM methods are also \(O(NK^{2}M^{2})\) and are dominated by updating the Jacobian and the correction amount. The QN needs to approximate Hessian by an additional iterative optimization, e.g., BFGS [55], while the LM method introduces a term with a damping factor that will be updated in each iteration. They both require more computational load. The processing times tabulated in Table 5 validate the above analysis.