esa-sceva/satcom-chunk-collection huggingface dataset:
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 5 Selected HPC Approaches in InSAR Fundamental Functional Stages  
Typical algorithms that realize fundamental functional stages in InSAR processing are data- and computational-intensive. The fundamental processing stages referred to here have been suitably introduced in Section 2, thus emphasizing their inherent functional description.  
Although parallel computing methods can be used to adapt these algorithms to leverage parallelism offered by current HPC platforms, hardware is often under-utilized because developing efficient parallel algorithms might be costly and challenging. Nonetheless, adequately designed parallel algorithms might result in significant speed-up and energy reductions. Moreover, specifically, since serial algorithms running on modern computational platforms are time-consuming and \"waste\" potential computing power, in the last two decades, many research efforts have specifically been dedicated to exposing the abundance of parallelism in the relevant algorithms, thus accelerating computationally expensive modules commonly occurring in InSAR processing. In particular, highly innovative and sophisticated parallel schemes have been proposed for the different stages, which vary according to the targeted parallel architecture and utilized application frameworks.  
We are concerned explicitly with some recently developed parallel schemes for implementing InSAR fundamental stages, which can be identified and categorized according to their computational relevance. Afterwards, they constitute the building blocks on which the HPC based InSAR processing chain are constructed (Section 6). These HPC approaches relevant to accelerate the performance of diverse interferometric tasks have been developed by adopting different parallel patterns, diverse computing architecture, and various programming models, as discussed in the following.
## 4 Results and Discussion
The electrical resistivity results of these saturated calcareous sands obtained through testing are shown in Figure 8. Due to significant differences in the resistivity values of saturated calcareous sand with different solutions, the relationship between some resistivity and solution concentration is highlighted in Figure 8. The resistivity of saturated calcareous sand prepared with these six solutions showed a decreasing trend. The higher the ion concentration in pore water was, the smaller the influence of particle size on resistivity was. When the NaCl concentration reached 2%, the influence of particle size on the resistivity of saturated calcareous sand did not exceed 1 \(\Omega\cdot\)m, and the influence of particle size on the resistivity of the sample could be ignored. The resistivity of the calcareous sand samples of the three size fractions changed with the pore water salt concentration in a power function relationship (Figure 9). As the concentration of pore water salt increased, the decrease in resistivity of calcareous sand gradually decreased. This was because as the concentration of pore water salt increased, the number of conductive ions gradually met the conductive needs of the pore water solution. When the conductive ions reached a certain amount, increasing the salt concentration no longer affected the resistivity of calcareous sand samples, and this result was the same as the research result of [PERSON] et al. [24]. Figure 8: Electrical resistivity test values of saturated calcareous sand with different pore water contents. Figure 7: Resistivity of different pore solutions.
## 3 Numerical Results  
In the numerical analysis section of this study, several problems are addressed to evaluate the nonlinear buckling analysis behaviour of beams with CUF theory: (1) thin-walled single-cell isotropic box beam, (2) thin-walled single-cell composite box beam, (3) thin-walled single-cell FG box beam, (4) thin-walled two-cell composite box beam, and (5) thin-walled two-cell FG box beam. Here, particular focus is on the displacements and stress behaviour of these structures under clamped-free boundary conditions. The properties of orthotropic composite materials with different fiber angles are taken as \(E1=155\) GPa, \(E2=E3=15.5\) GPa, [PERSON]'s ratio \(\
u=0.25\) and shear modulus \(G12=G13=9.3\) GPa, \(G23=7.75\) GPa. The material properties of FG for ceramic (c) and metal (m) components are \(E_{c}=380\) GPa, \(E_{m}=70\) GPa, \(\rho_{c}=3950\) kg/m\({}^{3}\), \(\rho_{m}=2700\) kg/m\({}^{3}\), and \(\
u=0.3\). In this study, the beam with a cross section of the cartesian reference system in the \(xz\) plane is located along the \(y\)-axis and has a length of L = 8 m. Furthermore, 12-B4 elements along the beam axis with CUF formulation and L4 and L9 point extensions are used in LE model. Studies have been validated with the literature.
## 2 Materials and Methods
### Data Processing  
Data collection and preliminary data processing were performed using ChromaTOF software, version 4.72 (LECO Corp). For peak finding, a signal-to-noise (S/N) cutoff of 50:1 (with a minimum of three apexing masses) was used in at least one chromatogram and a minimum of 20:1 S/N in all others. The NIST 11 library was used for the initial putative identification of the analytes with a match threshold \(>\) 700, as referred to in a previous study [49]. The identified compounds were also given certain chemical classes [50]. Moreover, the chemical identities of a subset of molecules were confirmed by comparing retention indexes with the library reference, using a criterion of difference of less than 30.
## 1 Introduction
The flexible polymer waveguide exhibited a loss increase of less than 0.5 dB following 2000 h of a reliability test under conditions of 85% relative humidity and a temperature of 85 \({}^{\circ}\)C. The experimental results show that the meter-scale long paper-like polymer waveguide film has both excellent optical properties and large bandwidth and is suitable for high-speed board-level optical interconnects application with a single-lane data rate of 100 Gbps and beyond, especially those that have a strict requirement on the length of connection and compactness.
# An Observation Scheduling System for Radio Telescope Array
## 4 Main Function Testing
### Calibrator Prediction Module  
For calibrating target sources positioned at various locations, we utilize a calibration source library consisting of radio sources with stable flux densities spread across different right ascensions and declinations. The selection of calibration sources is tailored to each observation. Figure 9 depicts the 24-h elevation tracking for these calibration sources. It presents the tracking curves for four calibration sources with strong flux: 3C48, 3C196, 3C286, and Cygnus A. This visual representation facilitates the identification of calibration sources that correspond to the sky region of the target source at any given time.  
For instance, when tracking the fast radio burst FRB190523 (represented by the blue line at the top of Figure 8), 3C286 (the blue line at the lower left corner of Figure 9) can be observed both before and after to serve as a flux calibration source for FRB190523. Similarly, when tracking the target source J1846-0258 (the orange line at the top of Figure 8), Cygnus A (the red line at the lower left corner of Figure 9) can be observed before and after to act as a flux calibration source for J1846-0258. The calibration source signal is weak. At present, the number of our array antennas is small, so we use the Sun as the calibration source, and the receiving time is 4 s.
## Materials and Methods
### Setup for Tracking of Pressure Waves in the Medium  
Propagation of pressure waves within the medium inside a pipe was caused by gas explosions in a laboratory-scale gas-filled pipe. The setup for these experiments was placed in an underground bunker dedicated to pipe tests involving explosive gas mixtures. The pipe equipped with sensors and the ignition device was set up in a separate room whereas control of gas filling and ignition as well as data acquisition was carried out remotely. The pipe used for explosion tests was a stainless-steel pipe of 5.54 m length, inner diameter of 30 mm and outer diameter of 38 mm, which was sealed on both ends and contained two sensor penetrations.  
Figure 3a gives a schematic overview of the instrumented pipe. A continuous configuration of optical fiber was chosen similar to configuration 1 as described above to ensure fully distributed measurements, which are a prerequisite to track accelerated pressure waves. Fiber pitch was 1 cm yielding a total applied fiber length of 54 m on a stretch of 4.55 m of the pipe (detail photo shown in Figure 3b). As a reference, two piezoelectric pressure transducers were placed in the sensor penetrations. Their positions were one meter apart from each other at the end of the region monitored by the optical fiber (Figure 3a).  
After evacuation of the pipe different gas mixtures were introduced into the pipe to the desired pressure level (0 to 10 bar overpressure). Consequently, ignition was carried out at one end of the pipe employing a melting wire. Gas mixtures used were 4.2% vol. propane in air and 28.5% vol. hydrogen in air.  
Overall guide values of pipe temperature were monitored using a conventional thermocouple placed close to the sensor penetrations on the surface of the pipe. Maximum temperatures throughout the course of the test series amounted to around 50 \({}^{\circ}\)C.  
Figure 2: Schematic illustration of the two optical fiber configurations used to track propagating acoustic waves in the pipe wall after excitation by a short mechanical impact. A and B indicate the positions in which mechanical impacts were caused on the pipe. (**a**) Fully distributed sensor setup. (**b**) Quasi-distributed sensor setup with two localized sensor ranges applied to the pipe.
# Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT
## 4 Scenario I: Automatic Baggage Mistake Prevention
### Solution  
DLT is suitable for helping achieve this improvement as some DLT frameworks can now provide information sharing only among related parties. We depict a general flow of the improved process in Figure 3. As the figure shows, a generated data package containing biological information from self-check-in can be used as a unique identifier for the journey of a passenger. The data package is generated based on the token that is generated through the SHA-256 encryption of flight number, passenger ID, passenger name, timestamp and face ID. SHA-256 can guarantee the confidentiality of data on the basis of being efficiently computed [34]. This can avoid leaking passengers' personal information from unauthorized inquiries. The face ID can then be used for self-bag-dropping service. It is used as a process initiator for a bag-dropping process, the baggage tags with RFID are printed for passengers to stick on their baggage and are automatically combined with the token. This combined information that indicates the baggage ownership is then shared with related airlines and arrival airports through DLT. At the arrival airport, as a simple structure, controlled gates with a facial recognition machine and RFID readers should be set up at exits. After the face is scanned, a baggage-checking process is invoked. The cloud server will check whether the scanned facial information corresponds to the tags that the RFID reader sensed. The returned checking results from the cloud server will decide whether the gate is open or not.  
The structure of the CorDapp that helps with the sharing of tokens and RFID combinations is shown in Figure 4. At the time, the passenger doing the self-bag-drop, the flow is invoked by the node of the departure airport to issue a BaggageState, which contains the token of the passenger information, and the RFID tags are stuck on the baggage. The issued BaggageState is verified by the corresponding BaggageContract to ensure its validity. Different from conventional CorDapp, the issue process here does not require to be signed by all three parties but only the departure airport to finalize. The flow includes checks to ensure that the parties specified as participants in the state are the intended parties. In this way, the baggage mistake issue should be alleviated. This structure is flexible for extending to more services. For example, during the pandemic period, the baggage checking process at the gate can also be combined with the vaccine check to ensure public health control in the airport.
## 3 Statistical Methods
### Goodness-of-Fit Test
#### 3.2.2 Weibull Distribution  
The Weibull distribution is useful for determining the probability of breakdown occurring at different rates, as well as estimating the lifespan of insulating materials and power equipment [35]. The Weibull distribution can be described by three parameters: the location parameter \(x_{0}=\gamma\) (initial value, lower extreme value), the 63 % quantile \(x_{63}\), and the Weibull index \(\beta\) (shape parameter or slope). Sometimes, the scale parameter is referred to as the characteristic life [24]. Sometimes the difference \(\alpha=x_{63}-x_{0}\) is called the scale parameter. According to [24], the three-parameter Weibull distribution, defined by the location, shape, and scale parameters, typically provides a good approximation for the cumulative frequency polygon of a series of measurements. Moreover, the Weibull approximation is generally superior to the approximation of the infinite Gaussian normal distribution with two parameters. However, the two-parameter Weibull distribution without the location parameter is widely applied. Therefore, this study used the two-parameter Weibull distribution without the location parameter. This implies that the starting point of the data distribution in the Weibull distribution is 0, meaning that the location parameter is omitted (i.e., \(\gamma=0\)). The pdf and cdf of the Weibull distribution are expressed as follows [24]:  
\[D_{W}(x)=\frac{\beta}{\alpha}\Big{(}\frac{x}{\alpha}\Big{)}^{\beta-1}\text{ exp}\bigg{(}-\Big{(}\frac{x}{\alpha}\Big{)}^{\beta}\bigg{)} \tag{11}\]  
\[F_{W}(x)=1-\text{exp}\bigg{(}-\Big{(}\frac{x}{\alpha}\Big{)}^{\beta}\bigg{)} \tag{12}\]where \(\beta\) represents the shape parameter and \(\alpha\) represents the scale parameter; these are the parameters considered in the two-parameter Weibull distribution. For a more detailed understanding of Weibull distribution, readers are encouraged to refer to the respective literature [24; 38].
## 3 WLTC-Based Analysis of Study Results
### Ecological Parameters
When fuelled with E10 and E70, cumulative WLTC CO\({}_{2}\) emissions were 3223 g and 3183 g, respectively, with specific emissions of 138.8 g/km and 137.1 g/km--representing reductions of approximately 4.4% and 5.6% compared to E0. Figure 8: NO\({}_{\rm x}\) concentration and emissions during the WLTC, using different fuels: (**a**) before the catalytic converter; (**b**) after the catalytic converter. The catalytic converter uses the small amount of oxygen present in the exhaust gas and free oxygen generated from NO\({}_{x}\) reduction to further oxidise CO and HC, leading to an additional increase in the CO\({}_{2}\) concentration downstream of the catalytic converter (ii). The data show that the CO\({}_{2}\) concentration upstream of the catalytic converter ranged between 14.2% and 14.5% (Figure 9b), while downstream of the catalytic converter, the CO\({}_{2}\) concentration increased by approximately 5.5%, reaching between 15.0% and 15.3% (Figure 9b) during engine operation. The use of E0 fuel resulted in a further increase of around 5.9% in total CO\({}_{2}\) emissions downstream of the catalytic converter, reaching approximately 3566 g (Figure 9b), with a corresponding benchmark emission of 153.8 g/km. Increasing the bioethanol concentration to 10% and 70% resulted in reductions of approximately 3.5% and 5.0% in specific CO\({}_{2}\) emissions downstream of the catalytic converter, to 147.6 g/km and 145.5 g/km, respectively. The CO\({}_{2}\) emissions for the tested Toyota Prius II were initially determined according to the voluntary agreements made between the European Commission and representatives of the automotive industry. These agreements aimed to reduce the average CO\({}_{2}\) emissions of new vehicles sold in the European Union to 140 g/km by 2009 [52]. This target was based on testing according to the New European Driving Cycle (NEDC) standards [53], which were used until 2017-2019. Currently, the WLTC test cycle is used, which includes longer driving times, greater distances, and higher acceleration, speed, and engine load. These factors are likely why the Toyota Prius II CO\({}_{2}\) emissions measured during this study exceeded the 140 g/km threshold. It is important to emphasise, when analysing CO\({}_{2}\) emissions, that bioethanol is a renewable fuel. On a lifecycle basis, pure bioethanol has approximately 60% lower CO\({}_{2}\) emissions than fossil-based gasoline.
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 2 Methodology of the Multi-Classification Multi-Leak Identification (MC-MLI) Scheme
### The Development of Multi-Classification for MLI  
Inspired by biometric authentication, each pipe section is assigned with a unique classifier to identify the pipe status. Figure 2 shows the parallel detection structure of the proposed MC-MLL. The input of MC-MLL is the feature vector composed of the measured parameters from practical WDI and the estimated parameters from the hydraulic model. For a classifier \(C_{h}\) of the \(h\)-th pipe section, the output \"1\" means the pipe section operates normally, where the output \"0\" means leakage exists in the pipe section. Each pipe status is identified by a unique classifier. It is worthwhile to point out that the parallel structure facilitates the expansion of WDI. For example, when a new pipe section (e.g., \((G+E)\)th pipe section) is connected to the WDI, a new classifier \(C_{(G+E)}\) will be trained for the newly added pipe section. Other existing classifiers do not need to be re-trained unless the new pipe section influences the hydraulic behaviors of other pipe sections. The advantages of the proposed multi-classification structure are summarized as:  
\[S_{a,b}=\frac{V_{a,b}}{\sum_{l=1}^{NS}V_{a,l}}\text{ {with }}\sum_{l=1}^{NS}S_{a,l}=1 \tag{7}\]  
where \(V\) is the value of Acc, Se, Sp, and T. \(NS\) is the number of classifier scenarios.  
The overall scoring \(OS_{b}\) of classifier \(C_{h}\) in the b-th scenario is computed as:  
\[OS_{b}=\sum_{a=1}^{4}S_{a,b}w_{a} \tag{8}\]  
where \(w_{a}\) is the weighting of the \(a\)-th criterion obtained from AHP (with pairwise comparison [22]).  
The overall scoring obtained from MCDM is a good indicator to evaluate classifier performance. The values of the four criteria are obtained from 10-fold cross-validation. Ten-fold cross-validation is a commonly adopted method for training and validating classifiers.  
The proposed MC-MLI was implemented experimentally and evaluated practically. Around 500,000 pieces of data were collected and analyzed. The huge amount of data was sufficient to develop and validate the proposed MC-MLI.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 4 Covariance Analysis
### Extended Kalman Filter
 \tag{4}\]  
It is assumed that measurements are available at epoch \(k\) in the form described by Equation (5), where \(H_{k}\) denotes the design (or measurement) matrix (which is defined in Section 4.3), and \(\epsilon_{k}\) denotes the measurement noise, assumed to be zero-mean and with covariance matrix \(\Sigma_{k}\) (which is defined in Section 4.5). \[\mathbf{y}_{k}=H_{k}\mathbf{x}_{k}+\mathbf{\epsilon}_{k} \tag{5}\]  
Equation (6) shows the definition of the Kalman gain matrix. \[K_{k}=\overline{P}_{k}H_{k}^{T}\Big{(}H_{k}\overline{P}_{k}H_{k}^{T}+\Sigma_{ k}\Big{)}^{-1} \tag{6}\]  
Equation (7) shows the definition of the state covariance update step using the Bucy-Jopseh formulation (note that \(I\) denotes the 8 \(\times\) 8 identity matrix). This formulation has been selected as it ensures \(P_{k|k}\) is symmetric. \[P_{k}=(I-K_{k}H_{k})\overline{P}_{k}(I-K_{k}H_{k})^{T}+K_{k}\Sigma_{k}K_{k}^{T} \tag{7}\]
## 2 Materials and Methods
### Volatile Compounds  
The determination of the profile in volatile compounds was carried out by HS-SPME (headspace solid-phase microextraction) and GC/MS (gas chromatography coupled to a mass spectrometer) [22]. In short, 3 g of olive oil were measured in a 50 mL glass vial. Five \(\upmu\)L of internal standard (4-methyl-2-pentanol) (Sigma-Aldrich, St. Louis, MO, USA) at 0.125 mg/mL was added. The vial was placed in a water bath at 40 \({}^{\circ}\)C and shaken at 300 rpm for 5 min to release the volatile compounds. Then, under the same temperature and agitation conditions, the SPME fibre (divinylbenzene/carbonex/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m) (Supelco, Bellefonte, PA, USA) was exposed for 30 min for adsorption of the volatile compounds present in the headspace. Volatile compounds were removed from the fibre by thermal desorption (220 \({}^{\circ}\)C) for 1 min in the chromatograph injection port. Nevertheless, the fibre was kept in the injector for 10 min for cleaning and conditioning.  
The gas chromatograph used was a Shimadzu GC-2010 Plus, equipped with a Shimadzu GC/MS-QP2010 SE mass spectrometer. A TRB-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) (Teknokroma, Barcelona, Spain) was used. The injector was at a temperature of 220 \({}^{\circ}\)C, and manual injection was performed in splitless mode. The mobile phase consisted of helium 5.0 (Linde, Lisboa, Portugal) at a linear velocity of 30 cm/s and a 24.4 mL/min flow rate. The oven temperature was 40 \({}^{\circ}\)C for 1 min, followed by an increase of 2 \({}^{\circ}\)C/min until reaching 220 \({}^{\circ}\)C. The ionisation source was maintained at 250 \({}^{\circ}\)C with an energy of 70 eV and a current of 0.1 kV. All mass spectra were obtained by electronic ionisation in the m/z range 35-500. Compounds were identified by comparing mass spectra and Kovat's Indexes, using the NIST 69, PubChem, and ChemSpider databases. The areas of the compounds were determined by integration of TIC (total ion chromatogram). The semi-quantitation of volatile compounds was performed from the relative area of each peak. Then, each relative area was converted into a mass equivalent to the mass of the added internal standard. Each sample was evaluated in triplicate.