esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 3 System Development
### Vision-Based Dis Placement Measurement  
This process demanded four consecutive steps, shown in Figure 1: (i) region of interest (ROI) selection, (ii) feature detection, (iii) detected feature tracking, and (iv) tracking outlier removal. Here, the ROI represents the location of the object that needs to be tracked. Each ROI contains several functions that are typically used for displacement measurement purposes. The ROI is selected manually by drawing a box in the first frame of the video.  
Object tracking is a computer vision technique used to track the motion of an object in an image captured by a general camera through real-time or post-processing [16]. Numerous object tracking algorithms have been introduced to track an object including KLT, CSRT, KCF, MedianFlow, MIL, MOSSE, and TLD. Among these tracking algorithms, a few methods have been applied for structural health monitoring purposes; CSRT has been applied to human motion tracking [17], and KLT has been applied to track the dynamic behavior of various structures [14].  
This study followed the vision-based displacement measurement system proposed by [PERSON] et. al. [14], expanded the system to be applied for FE model updating, instead of system identification. In addition, this study applied six additional tracking algorithms (KLT, CSRT, KCF, MedianFlow, MIL, MOSSE, and TLD) in addition to the KLT tracker, which was used in the original system. To measure the displacement of a structure using tracking algorithms, the first step is to calibrate the camera. There are two main reasons for camera calibration: (1) the removal of the radial distortion and tangential distortion, and (2) to compensate the camera ego motion. Camera lenses have tended to use very wide-angle lenses. However, these lenses intentionally introduce radial distortion to increase the field of view. In addition, the camera can move during the measurement, which can result in significant errors for displacement measurements [16]. The extrinsic camera matrix, which contains the translation and rotation information of the camera, needs to be estimated to compensate for the ego-motion of the camera. Therefore, the camera must be calibrated to minimize these distortions and achieve accurate displacement measurements.  
Figure 1: Overview of the vision-based finite element (FE) model updating.  
Once the camera calibration is conducted, the video is analyzed frame by frame to determine the dynamic response of the structure. This process can be summarized in four steps [14]: (1) selecting a region of interest (ROI), (2) detecting the features, (3) tracking the detected features, and (4) removing tracking outliers, as shown in Figure 2. Here, the region of interest (ROI) indicates the object that needs to be tracked and manually selected in the first frame of the video.
## 4 Performance Test of SOADS
### Simulation Modeling  
To test the performance of the proposed SOADS, the formation process was simulation-modeled to generate event logs. Figure 13 shows the simulation modeling of the battery assembly process and formation process using Arena version 15.1.  
Figure 12: Battery manufacturing process.  
Figure 13: Illustration of the network used to simulate the battery manufacturing process.  
The assembly process consisted of a winding with twenty facilities and an assembly with twenty-four facilities on a dedicated line. In order to derive the high frequency subsequence pattern, the strong path can be shown by setting the probability that the object moves to one facility every quarter to 50% for one dedicated line. The formation process is composed of eight room-temperature aging facilities, four high-temperature aging facilities, six IR test facilities, seven charge/discharge facilities, and seven defect inspection facilities. The basic sequence of the formation process consists of storing all the batteries that have been produced in room temperature and high-temperature aging facilities, IR inspection, charge/discharge, and defect inspection. For IR inspection and charge/discharge, once each process is finished, the battery is inspected for defects and, if found to be defective, it is returned to the previous process. In the last defect inspection, if the battery does not satisfy the criteria, it is returned to the previous process to address the cause of the defect. The anomaly detection process of this study is centered on control flow. Thus, the time required for each process was set to be identical to the travel time between processes. The probability for allocating objects to one of the facilities of each process was varied as 50% to provide a strong path to prevent the attempt to discover the high-frequency subsequence pattern from failing. And the rest of the facilities was set as between 5% and 25% randomly. The defect rate of IR inspection, charge/discharge, and defect inspection was set to 5%. When an object defaults in IR inspection, the probability of returning to the room temperature and high-temperature aging facilities was set 50%, 27% which are the previous process and that before the previous process, and the reprocessing probability was set 23%. When an object defaults in IR inspection, the probability of returning to the room temperature and high-temperature aging facilities was set 50%, 27% and probability of reprocessing and move to IR inspection was set 15%, 8%. In the case of inspection for defects, the probability of returning to the previous process when there are four defective products was set to 32% of room temperature aging, 21% of high temperature aging, 12% of IR inspection, and 35% of charge/discharge.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 7 Conclusions  
We have presented a perspective on the state of the art of the application of HPC methodologies to InSAR processing and shown how, currently, it is becoming more and more relevant as the amount of available data and sensors increases.  
Preliminary, a systematic overview of the current InSAR processing techniques and HPC methodologies was offered. Then the selected parallel InSAR processing solutions were analyzed and discussed at two different levels.  
First, the emphasis was placed on fundamental processing steps, which typically occur in InSAR processing, based on their computational relevance. Existing parallel schemes for the different InSAR stages and their performance, which vary depending on the targeted parallel architecture and implemented parallel strategy, were discussed in detail. The survey shows that all the critical and time-consuming InSAR processing steps can be performed in a timely fashion, benefitting from significant speed-up when appropriate HPC methodologies were adopted.  
Second, selected MT-InSAR processing strategies were analyzed at the whole processing chain level, thus emphasizing their potentialities and limitations. This was achieved by distinguishing high-performance computing approaches for efficient parallel processing and cloud-based services for EO large-scale data exploitation. Remarkably, the convergence of data-centric approaches and the high performance computing (HPC) paradigm remains crucial to the future [224; 225].  
As a matter of fact, the use of innovative computational approaches significantly contributes to the applicative use of InSAR techniques, also favoring multidisciplinary approaches in the various application sectors.  
[PERSON] conceived the paper and prepared a first draft of the manuscript; all authors participated in writing, reviewing, and editing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results
### Morphology of Sella Turcia  
The normal morphology of the sella turcica was observed in 46 of the 90 individuals, which was the majority (51.1%) in the study group (Table 3). Variations in the shape of the sella were present in 48.9% of the subjects: the irregular posterior wall (of the dorsum sella) was found in 11.1% of cases, a pyramidal shape of the posterior wall was present in 10% of cases, an oblique anterior wall was found in 10% of cases, a sella turcica bridge was found in 10% of cases, and a double contour was found in 7% of cases.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & \multicolumn{2}{c}{**95\% Confidence**} & & \\ \cline{5-8} \multirow{-2}{*}{Length} & **Skeletal** & **Number** & **Mean** & **Standard** & **Error of** & **Interval for Mean** & & \\ \cline{5-8}  & **Class** & **Domain** & **Domain** & **Domain** & **Domain** & **Domain** & **Domain** & **Domain** \\ \hline \multirow{4}{*}{Dipolar} & Class I & 30 & 8.53 & 0.990 & 0.180 & 8.18 & 8.88 & 6.84 & 10.5 \\ \cline{2-8}  & Class II & 30 & 8.13 & 1.264 & 0.230 & 7.67 & 8.58 & 6.12 & 11.39 \\ \cline{2-8}  & Class III & 30 & 10.30 & 1.115 & 0.203 & 9.90 & 10.69 & 8.1 & 12.45 \\ \cline{2-8}  & Total & 90 & 8.98 & 1.470 & & 7.13 & 10.84 & 6.12 & 12.45 \\ \hline \multirow{4}{*}{Diameter} & Class I & 30 & 9.96 & 1.008 & 0.184 & 9.60 & 10.32 & 6.86 & 11.68 \\ \cline{2-8}  & Class II & 30 & 9.56 & 0.980 & 0.179 & 9.21 & 9.91 & 6.77 & 11.32 \\ \cline{2-8}  & Class III & 30 & 11.34 & 1.054 & 0.192 & 10.96 & 11.71 & 8.96 & 13.94 \\ \cline{2-8}  & Total & 90 & 10.29 & 1.267 & & 8.165 & 12.41 & 6.77 & 13.94 \\ \hline \multirow{4}{*}{Depth} & Class I & 30 & 7.68 & 0.934 & 0.170 & 7.347 & 8.01 & 6.06 & 9.36 \\ \cline{2-8}  & Class II & 30 & 7.55 & 0.669 & 0.122 & 7.317 & 7.79 & 5.69 & 8.65 \\ \cline{1-1} \cline{2-8}  & Class III & 30 & 8.74 & 1.153 & 0.210 & 8.334 & 9.16 & 6.43 & 10.56 \\ \cline{1-1} \cline{2-8}  & Total & 90 & 7.99 & 1.081 & & 6.343 & 9.64 & 5.69 & 10.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of sella turcica in different skeletal classes.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Frequency** & **Percentage** \\ \hline Normal & 46 & 51.1\% \\ \hline Oblique anterior wall & 9 & 10\% \\ \hline Double contour & 7 & 7.77\% \\ \hline Irregular posterior wall & 10 & 11.11\% \\ \hline Sella turcica bridge & 9 & 10\% \\ \hline Pyramidal shape & 9 & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency of the various sella turcica morphologies.  
Across all skeletal classes, the normal morphology of the sella was the most prevalent. The pyramidal shape of the posterior wall was more frequently encountered in those belonging to class II compared with those belonging to class III (Figure 4). Class III individuals were more likely to have an irregular posterior wall and sella bridging. There was no significant difference between sella turcica shapes and skeletal classes.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### Problem Statement and Mathematical Formulation
 \tag{5}\]  
Suppose the observer location error \(\mathbf{n}_{nav}\) and TDOA measurement error \(\mathbf{n}_{t}\) are zero-mean Gaussian random vectors with covariance matrices \(\mathbf{C}_{nav,1}=diag\{\mathbf{C}_{nav,1},\ldots,\mathbf{C}_{nav,K}\}\) and \(\mathbf{C}_{t}=diag\{\mathbf{C}_{t,1},\ldots,\mathbf{C}_{t,K}\}\). Hence, the covariance matrix of \(\mathbf{n}\) is  
\[\mathbf{C}= \begin{bmatrix}\mathbf{C}_{t}&0\\ 0&\mathbf{C}_{nav}\end{bmatrix}  \tag{6}\]  
The task of this study is to identify the target's position and velocity based on the SAGI network, where the measurement vector is (3) and the constrained conditions are (1).
_Applied_  
_sciences_  
_Article_  
**Durability of Ternary Cements Based on New Supplementary Cementitious Materials from Industrial Waste**  
**[PERSON] 1, [PERSON] 2,3 [PERSON] 4, [PERSON] 3 and [PERSON] 1,3,*(c)**  
1 Department of Construction, School of Engineering, Institute for Sustainable Regional Development (INTERRA), University of Extremadura, 10003 Caceres, Spain; [EMAIL_ADDRESS]  
2 Departamento de Cementos y Recidado de Materiales, Instituto de Ciencias de la Construccion Eduardo Torroja (IEIC-CSIC), c/Serrano Galvache, 28033 Madrid, Spain; [EMAIL_ADDRESS]  
3 Sostenibilidad en Materiales de Construccion, Universidad de Extremadura, Unidad Asociada al CSIC, 10003 Caceres, Spain; [EMAIL_ADDRESS]  
4 Departamento Ingenierias Mecanica, Informatica y Aeroespacial, Escuela de Ingenierias Industrial e Informatica, Universidad de Leon, Campus de Vegazana, s/n, 24071 Leon, Spain; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]  
**Abstract** Cement-based materials decay with exposure to aggressive agents, a development that raises infrastructure operation and maintenance costs substantially. This paper analyses the inclusion of ultrafine construction and demolition (U\({}_{\text{CARDW}}\)) and biomass-fuelled power plant (BA) waste as pozzolanic additions to cement in pursuit of more sustainable and eco-respelfent binders and assesses the durability of the end materials when exposed to seawater, chlorides (0.5 M NaCl) or sulphates (0.3 M Na\({}_{2}\)SO\({}_{4}\)). The effect of adding silica fume (SF) at a replacement ratio of 5% was also analysed. Durability was determined using the methodology proposed by [PERSON] and [PERSON], whilst microstructural changes were monitored with mercury intrusion porosimetry (MIP), X-ray diffraction (XRD) and scanning electron microscopy (SEM) for a fuller understanding of decay processes. According to the findings, the new blended cements containing 20%U\({}_{\text{CARDW}}\) + 10%BA or 20%U\({}_{\text{CARDW}}\) + 20%BA + 5%SF resist the attack by the aggressive media studied, with a 56-d corrosion index of over 0.7. The composition of the reaction products generated with the attack is essentially the same in OPC and the SCM-bearing materials. The results show that the optimal replacement ratio for SCM is 30%.  
biomass ash; construction and demolition waste; supplementary cementitious materials; corrosion index; cement pastes; durability
# Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength
## 5 Conclusions  
The purpose of this study was to evaluate the impact of molasses waste on the concrete compressive strength with a cement replacement approach in concrete. In the present study, a mixing ratio of 1:1.89:2.32 was used with a curing temperature range of 25 \({}^{\circ}\)C to 30 \({}^{\circ}\)C to  
Figure 4: Comparison of compressive strengths at 7, 14, and 28 days, respectively.  
investigate the compressive strength of concrete specimens with different percentages of molasses waste replacement.  
After the experimentation, several considerations can be drawn:  
* A 0.25% replacement ratio (28.85 g) of molasses waste resulted in a significant increase in compressive energy, indicating that incorporating a small amount of molasses waste can enhance the strength development of concrete.
* The use of molasses in concrete production is cost-effective as well. Even a very small dosage of molasses can effectively enhance the setting time of concrete with a very small cost compared to those admixtures available in the market.
* The dosage ratios up to 0.5% (57.7 g) showed a minimal effect on compressive strength, suggesting that a slightly higher substitute level of molasses waste can be applied without compromising the overall performance of the concrete.
* However, replacement ratios above 0.5% (57.7 g) caused a widespread reduction in compressive strength, indicating that relevant replacement levels have a detrimental effect on the strength development of the concrete.
* The decrease in strength at replacement percentages above 0.5% (57.7 g) can be attributed to the elements together with the dilution of cementitious materials, inadequate hydration, and possible interference with the formation of calcium silicate hydrates.
* Up to a 30% cost-benefit ratio can easily be achieved by using molasses in place of other conventional admixtures available in the market to produce conventional concrete.
## 2 EV Power Controlling (EVPC) Scheme
### Coordination of EVs Operation with EVPC  
Different types of charge and discharge of EVs are considered based on electricity cost signal and their \(SOC\) when plugged in: charging with high current (\(I_{H}\)), charging/discharging with medium current (\(I_{M}\)) and low current (\(I_{L}\)). The flowchart of delivered power of EV \(k\) is depicted in Figure 3. The transacted power of each EV would be obtained based on its initial conditions such as \(SOC\) and the calculated EBP and ESP based on SMI index. The relationship between \(SOC\) of EV \(k\) and the charge/discharge current is obtained from (4) [9]:  
\[SOC_{k,t}=1-\frac{I_{k,t}\times\Delta t_{plug}}{3600C_{k}^{a}}, \tag{4}\]  
where \(I_{k,t}\), \(\Delta t_{plug}\) and \(C_{k}^{a}\) are the current, plugged-in time (in seconds) and the available capacity of the battery. Since most EV companies use lead acid batteries for their vehicles, here, this kind of technology is also adopted. The voltage against released capacity at different discharge/charge currents for a lead-acid battery is extracted from [20].  
In addition, in idle or driving mode, there is no power transaction between EV and the MG, but the stored energy might decrease depending on the length of its daily travel (\(L_{k}\)) and its energy consumption per km (\(r_{k}\)). Each EV comes back to the parking lot after driving \(L_{k}\) km and its \(SOC\) at the entrance of parking lot (\(SOC_{k}^{ent}\)) is obtained as following:  
\[SOC_{k}^{ent}=SOC_{k}^{\text{int}}-L_{k}\times r_{k}, \tag{5}\]  
where \(SOC_{k}^{\text{int}}\) is the SOC at the start of a day trip.  
The value of EV SOC at time \(t\) is obtained with respect to the amount of its initial SOC, charge/discharge energy (when it is connected to the grid) and the energy decreased due to its travelling. On the other hand, SMI index at time \(t\) would be calculated from the previously-mentioned equations. With considering both SOC and SMI indexes, the charge/discharge process would be determined. In other words, if EV is connected, the value of current that EV absorbs (injects) from (to) the network would be found. Then, the voltage would be obtained based on the voltage-current characteristic of the battery. Thus, with the obtained voltage and current, the transacted power between EV and the network would be captured. This process repeats at each time \(t\).
# L-Citrulline: A Non-Essential Amino Acid with Important Roles in Human Health
## 2 L-Citrulline Biochemistry  
### Endogenous Synthesis of L-Citrulline  
De novo formation of CIT occurs in enterocytes [3], as described in Figure 1. Hence, CIT concentration is currently considered to be a marker of intestinal function [10]. The substrates for intestinal CIT synthesis are amino acids derived from the diet, such as glutamine, proline and L-arginine (Arg) [11]. In enterocytes, part of the dietary and portal blood glutamine is catabolized to CIT by different enzymes (glutaminase, pyrroline-5-carboxylate synthase, ornithine aminotransferase (OAT), ornithine transcarbamylase (OTC) (also called ornithine carbamoyl transferase) and carbamoyl phosphate synthetase-I [12; 13].  
Glutaminase converts glutamine into glutamate and, therefore, the dietary and portal blood glutamate is catabolized in enterocytes to CIT in a common biosynthetic pathway to glutamine. However, the key regulation enzymes are pyrroline-5-carboxylate synthase and OAT, which are both unique to small intestinal enterocytes [11]. CIT accounts for 27.6% of metabolized glutamine [3]. Glutamine is generally considered the main precursor of intestinal CIT [11]. However, [PERSON] et al. [14] demonstrated that dietary Arg was the principal precursor for CIT synthesis and that the proline contribution was low (3.4%), and glutamine was negligible (0.4%). Approximately 40% of dietary Arg is metabolized by  
Figure 1: De novo formation of citrulline in epithelial absorptive cells of the small intestine (enterocytes). Dashed lines indicate the amino acids flow from oral ingestion through enterocytes (apical transport) and dotted lines indicate the amino acids flow from portal blood through enterocytes (basolateral transport).  
-arginase II into ornithine in enterocytes. Moreover, proline is also a precursor of CIT in the small intestine in enterocytes of postnatal pigs and healthy adults [15,16]. Proline is metabolized into P5C (\(\Delta\)1-l-pyrroline-5-carboxylate) and subsequently into ornithine by proline oxidase and OAT, respectively [11]. Finally, the ornithine produced in enterocytes from these amino acids is metabolized into CIT by OTC. Ornithine may additionally be a direct CIT precursor from portal blood, above all when the supply of Arg is low [14]. OTC, in addition to catalyzing the conversion of ornithine into CIT, is a key enzyme in the urea cycle (Figure 1). Therefore, the CIT circulating in the organism is the sum of CIT that we have absorbed from the diet foods plus the CIT production in the de novo synthesis in the intestine, with CIT being a biomarker of the functional small bowel enterocyte mass [10].
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 5 Results
### Optimization of a Bell Crank Lever  
Figure 6 shows the optimized designs of the bell crank lever for 50% and maximum weight reduction. The general design of the model was not changed. However, holes were created in the structure by removing useless material. The initial weight of the model was 1.965 Kg. It can be observed that the design solutions taken from SolidWorks and ABAQUS are quite similar in both optimization cases, while the optimization in ANSYS Mechanical resulted in different designs. In addition, ABAQUS could create the lightest design with an 88.3% weight reduction compared to 81.9% and 87.5% maximum reductions in SolidWorks and ANSYS Mechanical, respectively. However, SolidWorks optimized the model approximately five to ten times faster than ANSYS Mechanical and ABAQUS.  
The FEA of the bell crank lever was conducted in ANSYS Mechanical with 3 mm tetrahedral elements, resulting in 61,666 elements in total. The maximum identified stress of the model was 11.7 MPa. Figure 7 depicts the maximum Von Mises stresses taken from the validation studies in both optimization cases. The maximum stresses for the optimization case with 50% mass reduction ranged from 17.2 to 18.6 MPa. It seems that the weight of the chosen bell crank lever could be easily reduced by half without compromising its strength. For the optimization case with maximum weight reduction, SolidWorks resulted in the design with the lowest maximum stress, 395.2 MPa, compared to ANSYS Mechanical and ABAQUS with maximum stresses of 526.7 and 487.5, respectively. However, ABAQUS gave the best solution for the combination of weight and strength against yield.
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Dynamic Soaring  
Dynamic or gust soaring is based on the behaviour of albatrosses and prolongs UAV mission duration [43]. The key principle is to gain energy from airflow and wind. Figure 5 presents an illustration of dynamic soaring maneuvering of an albatross. Figure 5 shows that an albatross can gain wind velocity without wasting energy through the alignment of its body and wing position. Dynamic soaring is useful to attain velocity from ambient energy sources without discharging the battery of a UAV. Consequently, it will enhance the durability of UAVs. However, there are several critical concerns related to dynamic soaring. The major drawback is that dynamic soaring is only suitable for fixed-wing UAVs that have sufficient wing geometry to harness the energy from the wind. However, this technology is not applicable to multi-rotor drones due to varying geometries. Another major drawback is the intermittent nature of wind. Thus, the drone will not be able to use this feature in the absence of wind as it is highly dependent on these environmental conditions. Furthermore, to effectively mimic the maneuver and harness maximum energy, fixed-wing UAVs must be equipped with a sophisticated control system.  
Figure 4: Swapping vs. hot swapping technique [42].