esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Principal Component Analysis (PCA)
#### 2.3.1 Volatile Compounds in Proso Millet Wine using SPME with Different Fibers as Indicated by PCA  
In Figure 2, the scatter plot shows scores for the two first principal components (PC1 and PC2), indicating the volatile compounds distribution differences of in proso millet wine using SPME with four fibers. PC1 and PC2 explained approximately 80.89% and 14.89% of the total variation, respectively (95.78% collectively). PA lay in the positive region of PC1 and the negative region of PC2. Alcohols (A2, A4, A5, and A7), ketones (K3, K4, and K5), acids (AC1 and AC2), phenols (P1 and P2), and esters (E1) are all volatile compounds and were here found to be closely related to PA fibers. Polyacrylate-coated PA showed good absorption for strongly polar compounds [35]. PDMS lay in the positive region of PC1 and PC2. Hydrocarbons (H1, H1, H3, H4, H5, H7, H8, H10, and H12), an ester (E3), an alcohol (A6), and a ketone (K1) were closely correlated with PDMS. PDMS fiber, which is composed of polydimethylsiloxane, is effective for the extraction of nonpolar compounds [35]. CAR/PDMS lay in the negative region of PC1 and PC2, and it is related to esters (E10, E12, and E13), aldehydes (AL1 and AL3), and benzene derivatives (B1 and B8). DVB/CAR/PDMS was located near the original point, where benzene derivatives (B1, B4, B5, B6, B7, B8, and B9), esters (E6, E7, and E9), aldehydes (AL2 and AL3), an alcohol (A1), and a hydrocarbon (H4) were clustered. DVB/CAR/PDMS had a divinylbenzene-Carboxen carbon molecular sieve and a PDMS-fitted coating fibrous layer, which were together more sensitive and selective than PDMS and CAR/PDMS [35].
## 5 Discussion
### FAIRification  
As we have mentioned above, one of our main objectives has been to contribute to the FAIRification process of DA infrastructures. For this, we have proposed a central station registry, which initiates the actual on-boarding on a technical level. Since the definition of Findable includes the introduction of a searchable resource, we argue that the station registry, which captures all participating institutions in one central platform, is a suitable component to make stations findable. Additionally, this design decision has another effect on the on-boarding. We have designed the station registry as an essential component of the on-boarding workflow. This implies that the integration of a new institution is not feasible without prior registration of the station. This guarantees that the recently generated digital asset (station) is always linked to a searchable resource in the registry using a globally unique identifier for example. Furthermore, our station registry provides a core set of metadata to describe each institution, which is the first step towards transparency of the DA ecosystems. In particular, this holds for new researchers who want to participate in the network. They can explore the station list(s) or create a new consortium and invite other institutions to share their data. Having the station identifier, the researcher is able to define a route in order to analyse the provided data. This contributes to the accessibility of each station--or more precisely: the data provided by the station. Regarding accessibility, the FAIR definition also requires that metadata is retrievable, even when the data collections are no longer available. In other words, an archiving functionality is needed. Partially, we enable archiving by using online/offline indicators for stations: If a station is currently unavailable, it is not accessible for analyses but its metadata is still persistent and searchable in the station registry.  
In our study, we have not investigated the interoperability and reusability part of our on-boarding service. However, we argue that the station registry is also compatible with other ecosystems as far as there are corresponding interfaces for an on-boarding workflow. Since our design decisions are based on REST/HTTP(S) and JWT, we involve well-established communication standards. Additionally, we have not focused on a usage licensing and detailed provenance yet although information about the station administration has to be provided, which gives base information about the data provenance. Nevertheless, the used data model and the workflow are extendable to meet the I&R requirements.
## 5 Results
### Time and Space Complexity
#### 5.3.2 Q-Gram Filter  
We consider the building of a Q-gram inverted index, which depends on the used algorithm. Furthermore, we take into consideration the T-similarity join problem as a merging of posting lists. Furthermore, each query record, \(\mathcal{R}_{1}\), needs the construction of its Q-gram set, \(|\mathcal{Q}_{\mathcal{R}_{1}}|\), incurring \(\mathcal{O}(n)\). Such an architecture is quite common in the majority of the production systems of real-world search engines, so it is not our intention to discuss it in detail. We concentrate only on the pre-calculation of the filter within the time of the building of the inverted index, which is simply an operation added to the existing algorithms for constructing the inverted index. To speed up the computation of \(\widehat{t}_{\mathcal{M}}\) to \(\mathcal{O}(1)\), we must keep within the index all the pre-calculated discrete distribution functions of ascending sorted lengths \(F_{\mathcal{R}_{2}}\) for each record, \(\mathcal{R}_{2}\). Hence, the required space complexity is an array of integers of the average record size \(\Theta(|\mathcal{R}_{2 arg}|)\) for each record, \(\mathcal{R}_{2}\). Note that an efficient encoding of unsigned integers with a bounded maximum size of the records in the production system could take only a few bytes per record. Access to the two-dimensional array, \(F_{\mathcal{R}_{2}}[r](|\mathcal{M}|)\), is clearly in \(\mathcal{O}(1)\), where the index, \(r\), means the record position. Finally, the calculation of \(\widehat{t}_{\mathcal{M}}\) consists of a few multiplication and summation  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Similarity** & **Elapsed Time** & **Similarity** & **Elapsed Time** \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{13 s:426 ms} & L2 Monge- [PERSON] & \multirow{2}{*}{14 s:209 ms} \\  & & ([PERSON]) & \\ \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{22 s:824 ms} & [PERSON] 2-gram & 10 s:542 ms \\  & & & \\ \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{3 s:902 ms} & Jaccard 3-gram & 9 s:829 ms \\  & & & \\ \multirow{2}{*}{Needleman-Wunsch} & \multirow{2}{*}{28 s:170 ms} & Dice 3-gram & 10 s:717 ms \\  & & & \\ \multirow{2}{*}{Smith–Waterman} & \multirow{2}{*}{28 s:600 ms} & Overlap 3-gram & 10 s:251 ms \\  & & & \\ \multirow{2}{*}{FRMS} & \multirow{2}{*}{13 s:474 ms} & Overlap 2-gram & 11 s:549 ms \\  & & & Fuzzy Jaccard \\ \multirow{2}{*}{Q-Gram Filter+FRMS} & \multirow{2}{*}{0 s:220 ms} & \multirow{2}{*}{(\(\delta=0.8\))} & 12 s:824 ms \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relative time complexity.  
operations; therefore, we obtain a total of \(\mathcal{O}(1)\) for the calculation of the Q-gram filter threshold for each record, \(\mathcal{R}_{2}\). The Q-gram filter and FRMS together have a time complexity of \((1-\gamma)\cdot O(1)+\gamma\cdot O(n^{4})\), where \(\gamma\) is a small fraction of potential candidates, as shown in Figure 6 and Table 6.
## 1 Introduction
Metrological traceability of results (i.e., traceability of the results to the SI) and uncertainty assessments according to the GUM [34] of the NO\({}_{2}\) results derived using spectroscopic measurement methods are critical for quality control measurements [2] and type approval purposes [35]. Direct tunable diode laser absorption spectroscopy (dTDLAS) combined with the TILSAM (traceable infrared laser-spectrometric amount fraction measurement) method [36], has been demonstrated in a variety of applications addressing metrology data quality (SI-traceability and GUM compliant uncertainty assessments). dTDLAS is a variant of TDLAS that combines TDLAS with a special data evaluation procedure to derive concentration results that are directly traceable to the SI [7, 37]. As reported on our previous work, a dTDLAS instrument is referred to as an optical gas standard (OGS) [7, 8, 9, 10, 37]. An OGS is similar to the ozone standard reference photometer (SRP) [38]. Employing dTDLAS, fast measurement frequencies (from sub-Hz to Hz) and response times (e.g., milliseconds to seconds) required in field measurements can be reached [39, 40, 41, 42]. Focusing here on a new development for direct NO\({}_{2}\) (reactive gas) measurements in car exhaust gases, our previous works in [7, 8, 9, 10, 37] focused on dTDLAS instrument developments for the measurement of H\({}_{2}\)O, CO and CO\({}_{2}\) and H\({}_{2}\)O as well as reaching fast measurement frequencies [39, 40, 41, 42]. In this paper, we report the details of a new dTDLAS spectrometer [43] specifically designed and developed for accurate direct NO\({}_{2}\) measurements. The focus of this work was to report the development stages of the NO\({}_{2}\) dTDLAS instrument towards an OGS (an instrument standard for direct NO\({}_{2}\) measurements), the testing and validation steps performed, the potentials and the current limitations of the instrument. The capabilities for direct NO\({}_{2}\) measurements (looking at all measured quantities desired for the results) are demonstrated via NO\({}_{2}\) concentration measurements. A comparison of calibration-free dTDLAS NO\({}_{2}\) concentration results (range: 100-1000 \(\mu\)mol/mol, within the car exhaust NO\({}_{2}\) concentration range) versus certified reference gas mixtures is done as a validation step. We further show that the instrument is capable of direct NO\({}_{2}\) measurements in car exhaust gas matrix. In order to compete with, and compliment CRMs and supplement the SRM for NO\({}_{2}\) measurements, with other gas matrices such as the car exhaust gas matrix in focus, the traceability of the spectroscopic results to the SI is addressed and an uncertainty assessment following GUM principles is made for the NO\({}_{2}\) results.
## 3 Geolocalization of Historical Image Collections
### Location Information Extraction  
Geographical information is sometimes provided in the captions, which aids in geolocating the historical images. The captions of an illustration or article text usually contain information about the event or location. In this step, natural language techniques (NER methods [72]) were applied to extract the location information from the article caption or text. The location information could be a place name, a city name, or a country name. An example of location information extraction from the text is shown below.  
Caption tag: Le siege central de la\" t Prevoyance Soc aie, square de l'Aviation, -Bruxelles. Location information: Prevoyance Sociale [LOC], square de l'Aviation, -Bruxelles [LOC]. When extracting location information, we encountered difficulties with poor OCR quality and multiple location detection. The location cannot be determined if the OCR quality is not high enough to provide enough information about the location. When we had more than one location in the caption or article text, we matched historical image features with Google images to determine the exact location.
## 2 Performance of a Correct Thermography
### Action to Be Carried out in a Hot Spot  
The purpose of maintenance techniques is to keep the equipment in operating condition, avoid possible failures, and maintain safety conditions for people and facilities. In this way, its useful life is increased. The results obtained will allow one to decide what actions to be taken. Basically, they can be reduced to three: the equipment parameters are adequate, and therefore there is no action to take; there is some variation in the parameters that indicate that more continuous monitoring of the equipment is necessary; or the results of the parameters lead to perform an intervention on the equipment to return it to its normal service condition. Something similar happens with thermography.  
The goal of thermography as a maintenance technique is not to acquire knowledge of the temperature of an identified hot spot. The goal is to discern whether to take any action on it. Table 1 shows the actions suggested by the American National Standards Institute [35]. It is based on the temperature difference between the hot spot and a reference. The reference can be the temperature of the ambient air or that of another point with similar characteristics, such as that corresponding to another electrical phase. In each case, the \(\Delta\)T ranges are different, being lower when the difference is between the hot spot and another element. The recommendations vary between a possible defect to observe, a follow-up of the hot spot through thermographic inspections with different time frequencies, or immediate repair to avoid a major defect.  
Another important issue to remember is that the precision of the measurements takes second place in terms of priority because it involves field inspections, which would not occur in laboratory tests.
## 2 Materials and Methods
### Structural Construction
#### 2.1.2 Unit Cell Structure Design  
(1) Pore Size  
This part uses a holder composed of several unit cells with a side length of 1 mm. Every unit cell has square holes on six sides, with a pore size of 0.7 mm, and its porosity is calculated to be 79.4%. Through pre-simulation experiments, the simplified model consists of 5 \(\times\) 5 \(\times\) 10 unit cells, and this scaffold is named S-0.7. On the basis of the S-0.7-type scaffold, the pore sizes of the cells are adjusted to be 0.4 mm (named S-0.4) and 0.62 mm (named S-0.62), respectively. The cell geometries of S-0.4 and S-0.62 are shown in Figure 5, and the porosities are, respectively, 35.2% and 67.7%.  
(2) Unit Cell Size  
In order to investigate the modulation of unit cell size on bone tissue repair scaffolds, scaffolds with cell lengths of 0.8 mm and 1.2 mm were designed for comparative studies with scaffolds with cell lengths of 1 mm. To ensure the same porosity and pore shape, the scaffold with a cell length of 0.8 mm had a square pore diameter of 0.56 mm (78.3% porosity) and was named S-0.8-0.56; the scaffold with a cell length of 1.2 mm had a square pore diameter of 0.84 mm (78.4% porosity) and was named S-1.2-0.84.  
(3) Unit Cell Shape  
Three different cell structures were designed in this experiment, namely circular pore cell, rhombic pore cell, and columnar cell, which have the same cell size and are all square cells with 1 mm side length. Two pore sizes were considered for the circular pore cell: the pore size of 0.7 mm with 66.9% porosity, with the scaffold of this cell structure named C-0.7; and the pore size of 0.8 mm with 79.4% porosity, with the scaffold of this cell structure named C-0.8. The rhombic pore cell had a pore size of 0.62 mm with 64.8% porosity, named D-0.62. The cylindrical cell has a pore diameter of 0.6 mm and a porosity of 71.38% and is named Z-0.6. The CAD models of several cell types are shown in Figure 6.
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 3 Invasive Corrosion Monitoring Techniques
### Electrochemical Corrosion Potential
However, there are still the following issues in the current ECP monitoring of nuclear power plants: (1) there is no mature high-temperature reference electrode that fully meets the long-term monitoring requirements; (2) due to the complex structure of nuclear power plants, the corrosion reactions occurring at different structural sites are also complex. The cost is high once the online ECP monitoring is performed on many sites. Figure 8: Typical dependence of ECP (OCP) on (**a**) pressure at 25 \({}^{\circ}\)C and (**b**) temperature at 10 MPa for Alloy 690 and Alloy 800 Reprinted from Ref. [91]. 2013, Elsevier. The theoretical calculation of ECP can somewhat solve the complex, costly online monitoring problem. [PERSON] et al. [95], [PERSON] et al. [96], and [PERSON] et al. [97] proposed a water radiolysis model that can calculate the concentrations of oxidation-reduction substances at selected locations inside nuclear power plants. [PERSON] et al. [98] and [PERSON] et al. [99; 100] combined the water radiolysis model with the mixed potential model and then proposed a semi-empirical formula based on laboratory test results to calculate the ECP value of 304 SS at a selected location inside the BWR reactor. [PERSON] et al. [101] also extended the ECP calculation to PWR. However, due to the lack of primary thermodynamic data, there are currently no reports on ECP calculations for other materials, and the reliability of the calculated ECP values also needs to be verified.
## 4 Proposed Hybrid Model of NLMPC and Data-Driven Approach
### Solution Approach  
The NMPC controller now utilizes both the physical model and the data-driven model to predict the future states of the system. The controller adapts to the nonlinear and time-varying nature of the power system by continuously updating the data-driven model using real-time data.  
* Learning Phase: Collect operational data to train the data-driven model \(\hat{\epsilon}_{i}(\boldsymbol{x}_{i},\boldsymbol{u}_{i})\)
* Prediction Phase: Use the trained model to predict the error term and correct the physical model's predictions.
* Control Phase: Solve the NMPC optimization problem considering both the physical and data-driven models.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 1 Introduction
This study takes into account the conditions of the city of Madinah, especially at peak times of Haji and Umrah. To the best of our knowledge, very few works are conducted on autonomous vehicles in the Kingdom of Saudi Arabia. The contribution of our work is as follows:  
* We study the effect of different routing protocols in VANET communication systems. Two types of routing protocols (proactive and reactive) are considered, and a performance evaluation is conducted of the VANET system under varying traffic scenarios. * Furthermore, we demonstrated the effect of various populations of autonomous to human-driven vehicles in the smart city by analyzing the impact of the introduction of autonomous vehicles on the trip times of the vehicles in the VANET scenario. * Finally, we evaluated the main factors that influence the performance of the VANET system through the analysis of variance (ANOVA) test, and our study illustrated the contrasting influence of the factors captured on multiple metrics in a smart city simulation. The organization of this paper is as follows: Section 2 provides a brief summary of recent studies, while Section 3 provides a detailed methodology of our work. Section 4 explains the simulation details and various test scenarios we considered in our study. Section 5 elaborates and analyzes the results and findings of our simulation. Lastly, the conclusion is presented in Section 6.
## 3 Results
### Patient Population
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Pre** & **Post** \\  & **(N = 23)** & **(N = 23)** \\ \hline
**Ultrasound of the liver** & & \\ \hline
**Vertical diameter—pre-aortic measurement** & & \\  Mean (SD) & 9.55 (2.53) & 10.3 (2.94) \\  Median [Min, Max] & 9.65 [5.30, 15.5] & 10.2 [4.10, 15.0] \\  Missing & 1 (4.3\%) & 1 (4.3\%) \\
**Vertical diameter—medio-clavicular line** & & \\  Mean (SD) & 15.1 (3.68) & 16.5 (2.99) \\  Median [Min, Max] & 15.1 [7.60, 25.0] & 16.9 [10.6, 22.0] \\  Missing & 1 (4.3\%) & 0 (0\%) \\
**Vertical diameter—pre-renal measurement** & & \\  Mean (SD) & 15.1 (3.13) & 16.3 (2.78) \\  Median [Min, Max] & 15.1 [8.10, 21.0] & 17.1 [10.5, 21.7] \\  Missing & 1 (4.3\%) & 1 (4.3\%) \\
**Maximal thickness of gallbladder** & & \\  Mean (SD) & 0.256 (0.0934) & 0.314 (0.0954) \\  Median [Min, Max] & 0.250 [0, 0.400] & 0.310 [0.100, 0.430] \\ Missing & 3 (13.0\%) & 5 (21.7\%) \\
**Ultrasound of hepatic vessels** & & \\ \hline
**Maximal diameter of liver veins** & & \\  Mean (SD) & 0.752 (0.267) & 0.868 (0.312) \\  Median [Min, Max] & 0.700 [0.370, 1.20] & 0.850 [0.200, 1.90] \\ Missing & 0 (0\%) & 0 (0\%) \\
**Maximal velocity of liver veins** & & \\  Mean (SD) & 17.9 (7.77) & 17.9 (9.99) \\  Median [Min, Max] & 17.8 [5.00, 37.1] & 16.3 [0, 30.9] \\ Missing & 4 (17.4\%) & 14 (60.9\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sonography Measurements. We observed a significant decrease in AST (34.3 U/L pre, 26.7 U/L post; \(p=0.048\)) and ALT (53.3 U/L pre, 28.3 U/L post; \(p=0.0031\)) during the course of allogeneic stem cell transplantation. Thromocyte counts (140 G/L pre, 45.8 G/L post; \(p=0.0051\)) were also considerably reduced after alloHST. Moreover, a significant increase in bilirubin (0.6 mg/dL pre, 2.96 mg/dL post, \(p=0.0001\)) and CRP (22.1 mg/L pre, 61.4 mg/L post; \(p=0.0091\)) levels was noted after alloHST. Levels of \(\gamma\)GT (168 U/L pre, 114 U/L post; \(p=0.12\)) and AP (154 U/L pre, 110 U/L post; \(p=0.36\)) remained statistically unchanged. Presence of fever throughout the course of alloHST (defined as measurable body temperature \(\geq\)38.5 degrees Celsius within the timespan of the alloHST inpatient stay) occurred in 19 out of 23 patients. In most cases, this was temporally correlated with the administration of anti-thymocyte-globulin (\(n=10\)) or intravenous immunoglobulins (\(n=2\)). In fewer cases fever was interpreted as a sign of infection. In seven out of 23 patients, abdominal pain was recorded during the peritransplantperi-transplant period. Four cases were interpreted as painful hepatomegaly, one case showed CT-proven mild appendicitis, in one case computer tomography revealed an incomplete
## 2 Methodology
### Determination of Displacement Monitoring Index for Rockfill Dams
#### 2.2.2 Displacement Monitoring Index  
Utilizing the aforementioned stochastic finite element analysis, the results of the displacement calculation are then subjected to statistical organization, thereby yielding the probability density distribution of the dam's displacement under each water level. Choosing the significance level \(\alpha=5\%\)[12; 42], the bounding values of the displacement confidence intervals at different water levels are calculated via Equation (11):  
\[y=\mu\pm Z_{\alpha/2}\sigma \tag{11}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of the probability density distribution of the dam displacement under each water level, respectively; \(Z_{\alpha/2}\) is the standardized fraction, which is 1.96 at the 5% significance level. Through the polynomial fitting of the boundary displacements \(y\) of the displacement confidence intervals at different water levels according to Equation (7), the interval boundary expressions for the water pressure component at a 5% significance level, i.e., the upper limit of the water pressure component and the lower limit of the water pressure component, are obtained. Subsequently, the upper and lower bound expressions of the water pressure component are substituted into the trained SFEMM model to obtain the displacement monitoring index of the rockfill dam.  
In summary, a method for determining monitoring indexes for rockfill dams based on SFEM is proposed in this paper, taking into account material uncertainty. The proposed method is a refinement of the conventional confidence interval methods, utilizing the SFEM to simulate the water pressure component and thereby incorporating the uncertainty of the material parameters of the dam. This is in contrast to traditional structural analysis methods, which are unable to reflect the deformation mechanism of the dam body with the requisite realism and accuracy. The method's ability to determine the relevant monitoring indices directly based on the monitoring model of the rockfill dam, even with limited data, is noteworthy. This approach, which integrates statistical characteristics of displacements under different water levels, demonstrates enhanced theoretical soundness and higher rationality through dynamic adjustments to monitoring indices in response to variations in the engineering environment.