esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SAT-Hadoop-Processor
### RS Data Integration and Storage: Hadoop Framework
#### 3.5.3 The Storage of the RS Data in Hive  
Hive is an open-source data warehousing tool made on top of Hadoop. It was open-sourced in August 2008, and since then, it has been explored by many Hadoop users for their data processing requests. Hive executes queries in the SQL declarative language, HiveQL, which are performed in MR jobs using Hadoop [39]. Furthermore, HiveQL allows users to plug custom MR code into queries. The language contains a type system supporting tables containing native types, such as arrays and maps. The HiveQL includes a subgroup of SQL and some extensions that we have found useful in our environment. Standard SQL features are similar to clause subqueries, various types of joins: joins, cartesian products, grouping, aggregations, union all, create table as select, and several useful functions on primitive and sophisticated types make the language very analogous to SQL. Hive also  
Figure 5: The general architecture of data integration in Hadoop.  
takes in a system catalog, the Metastore, containing schemas and statistics useful for data exploration, query optimization, and compilation [40].  
Hadoop is not easy for end-users who are not familiar with MR. End-users must write MR scripts for simple tasks, such as calculating raw counts or averages. Hadoop requires popular query languages' expressiveness, particularly SQL, and thus users spend a long-time coding program for even simple algorithms. We frequently run thousands of Hadoop/Hive cluster jobs for various applications, from simple summarization to machine learning algorithms. Hive serializes and deserializes using a java interface offered by the user. Thus, custom data formats can be taken and queried (see Supplementary Material File).
# Combining NMR Spectroscopy and Chemometrics to Monitor Structural Features of Crude Hep-arin
## 1 Introduction
Many companies buy crude heparins from third parties, which have collected mucosa at multiple small slaughterhouses. These multiple sites increase the risk of un-intended or intentional contamination. Although it is impossible to eliminate all possible risks, reinforcing of appropriate requirements, controls, and best practices at the level of crude heparin as a key intermediate might help ensure the quality of the final pharmaceutical grade product. A first step to define crude heparin as an intermediate in the production process of the API requires the specification of a normal range of composition found in heparin currently on the market. Different intermediates may exist and be qualified for use in the manufacture of heparin, such as resin bound heparin (early intermediates), a brown powder containing a large amount of RNA/DNA and galactosaminoglycans (mainly dermatan sulfate (DS) and chondro sulfate A (CSA)), or partly purified crude heparin, a light yellow powder containing only a small amount of dermatan sulfate [7]. Although different techniques have been used to identify and quantify OSCS in crude heparin, to our knowledge, these studies did not fully characterize the average composition of crudes produced in the global marketplace [8; 9]. In the present study, 88 crude heparins collected from 2010 to 2015, representing 13 different manufacturers, have been characterized by NMR spectroscopy and strong anion exchange HPLC (SAX-HPLC). Particularly, a multivariate analysis (principal component analysis (PCA)) of one-dimensional (1D) proton NMR spectra was used to differentiate the crudes on the basis of their structural features and animal/organ of origin. Moreover, the quantitative composition of crude samples was performed using an adapted version of the HSQC method recently described in [10].
# Conditional Dependencies and Position Fixing
## 2 Understanding Position Fixing
In this paper, I present a revised approach to the exploitation of unprocessed data. The measures that represent the true location of any point in the neighbourhood of the observation are the conditional dependency values, which are written as \(P(X|O)\) and are considered as a measure of support for the hypothesis (\(x_{i}\)) that is embedded within the observation (\(o_{j}\)). Random data processing contains three steps: (1) the learning phase, in which the raw experimental data are gathered and recorded; (2) the upgrading of the x- and y-axis histograms; (3) the evaluation of the empirical data structures to extract the embedded uncertainty, which is the main factor to consider when obtaining the converted forms of stepwise density functions. To achieve unique solutions, the density functions should be injective ones, and to guarantee this, I propose the injective function transformation of histograms. The fuzzy-set concept is helpful for the conversion from the empirical form of the probability density. In the traditional approach, the modelling and processing of imprecise data are impaired. Moreover, the results of the observations and their quality evaluation can be achieved prior to the measurements being taken. The a posteriori analysis is compromised and is not embedded into the scheme of the traditional manner of inaccurate data handling. In the new solutions, one starts with an alternative approach to doubtfulness modelling. Fuzzy density sets are established around each of the nautical indications. The concept should be taken into consideration once the most probable measurement location has been detected, the position fixing has been performed, and the systematic error has been identified. The presented rational refers to the evidence and hypothesis scheme of reasoning that are encountered in nautical practice and science. No measurement or indication read from a nautical aid is exact. When a quantity is measured or indicated, the outcome depends on the aid or the involved system precision. The measurement environment conditions, as well as the skill of the observer, are also important. Thus, the available pieces of evidence support hypothesis items with different factors. The higher-quality data should more decisively contribute to the hypothesis truth. In the case of position fixing, the nautical quantity magnitude of the uncertainty should affect the final solution. Less uncertain measurements and/or indications have a greater effect on the selection of the true location. Therefore, the evaluation of the random variable instances is important, and it starts from an analysis of the experiments that involve the observations of the instances given an indication.
## 3 Results and Discussion
### Material Characterization
#### 3.1.4 FTIR Analysis  
The FTIR spectra of SiO\({}_{2}\), SiPMA, and SiPMANs are shown in Figure 5. The peak at 3452 cm\({}^{-1}\) corresponded to the stretching vibration of the adsorbed aqueous hydroxyl groups. The peaks at 1639 cm\({}^{-1}\) and 2366 cm\({}^{-1}\) corresponded to the bending vibrations of these hydroxyl groups [36; 37]. The peaks at 1738 cm\({}^{-1}\) corresponded to the vibration of the carbonyl group in the intermediate methyl acrylate [30]. The peaks at 472 cm\({}^{-1}\), 800 cm\({}^{-1}\), and 1110 cm\({}^{-1}\) corresponded to SiO\({}_{2}\)[30; 37]. The spectra of all of the polyamine resins exhibited these characteristic peaks, which indicated that the structure of the synthesized adsorbent was stabilized. The spectrum of SiPMANs featured a unique peak at 1566 cm\({}^{-1}\), which corresponded to the bending vibration of N-H [30]. These results demonstrated that the surface of the SiPMA material was successfully amidated, and SiPMANs were successfully prepared.
## 2 Proposed Circuit Topology and Operation Analysis
### Starter Circuit  
In the self-excited circuit, a circuit starter is indispensable to initialize the operation. Figure 7 shows the starter circuit used in the developed LED drive. The starter circuit is connected in parallel with the dc-bus capacitor \(C_{bus}\) with a simple resistor-capacitor (RC) circuit. Prior to the action of the starter, the dc-bus voltage \(V_{Chus}\) charges toward \(C_{start}\) via \(R_{start}\). With the time constant being represented by Equation (18), when the exponentially increasing voltage on the starting capacitor \(C_{start}\) reaches the breakover voltage of DIAC, the DIAC conducts and the power switch is triggered. As soon as \(S_{2}\) commences to conduct, the resonant process begins. However, to avoid \(V_{Chus}\) continually charging \(C_{start}\), a bypass diode, \(D_{bp}\), is connected across the power switch, \(S_{2}\). Of course, the bypass current through \(D_{bp}\) is drastically quantitatively smaller than the resonant current. To prevent the upper and lower switches from shooting through, the switching frequency needs to be far greater than the reciprocal of the time constant, as shown in Equation (19).  
\[\tau=R_{start}C_{start} \tag{18}\]  
\[f_{s}>>\frac{1}{\tau} \tag{19}\]
## 2 Experiment Details
Figure 1: (**a**) Experimental set-up schematic diagram; (**b**) Experimental set-up site photo. The experiments were conducted in a horizontal oil-fired boiler, with a size of 500 mm \(\times\) 800 mm, in Shandong University. The boiler site map and the flame shape are shown in Figures 3 and 4. For detection, two observation windows fitted with quartz glass were opened on the side and top of the boiler, with a size of 120 mm \(\times\) 600 mm and 60 mm \(\times\) 140 mm, respectively. The burner model was RIELLO40-G10 LC (RIELLO, Legnago, Italy), whose the maximum output is 2.5 GPH (10 kg/h). The jet was a solid nozzle (Steinen S) with an output of 1.0 GPH (3.73 kg/h), and the atomization angle was 60\({}^{\circ}\). The oil pump pressure was 12 bar. Heavy oil possesses a high molecular weight, viscosity, and asphaltene contents, as well as containing ash. The combustion process will be accompanied by a large amount of pollution. Meanwhile, heavy oil is difficult to burn. So, the heavy oil for the experiment, strictly speaking, was nonstandard fuel oil with a mixture of heavy and light oils [17]. Figure 3: The boiler site map. Figure 2: Flame evolution following spark ignition, field of view = 110 mm \(\times\) 110 mm. Frame rate = 400 Hz. A and B mean flame surface and wrinkled structure, respectively. The oxygen concentration is 33%. The excess air coefficient \(\alpha\) = 1.652, which is the ratio of the practical oxygen supply to the theoretical oxygen consumption. The oxygen consumption is 103 L/min, and the total gas flow is 676 L/min.
## 3 Results  
### Characteristic of Briquette Cyanobacteria during Densification Process  
#### 3.1.1 Mechanical Strength Analysis Based on Single Briquetting Unit  
In this section, how the densification experiments of cyanobacteria were conducted in the single briquetting unit is explained. Two factors, i.e., moisture content and compressive load, were investigated. At first, the moisture content varied from 5, 10, 15 to 20% by heating cyanobacteria samples on a received basis in an oven at 60 \({}^{\circ}\)C for different drying times. The received cyanobacteria samples were densified under the compressive load of 3 tons. The forming rate and relax density for the briquette cyanobacteria samples of different moisture contents are shown in Figure 7.  
Based on Figure 7, it is found that the influence of moisture content on forming rate is very small, which maintains at about 99%. The maximum is 99.6% when moisture content is 10%. The high forming rate is due to the fine particle size range of raw cyanobacteria. The briquette cyanobacteria are compact and surface smooth with no delamination. The high forming rate reflects a good molding effect of the single briquetting unit. On the other hand, although relax density shows the same changing trend with forming rate, the change range of relax density with moisture is relatively large, which is varied from 1.057 to 1.348 g/cm\({}^{3}\) and reaches the peak under the moisture content of 10%. Therefore, the moisture content of 10% is the optimal moisture content for the cyanobacteria densification process of a single briquetting unit.  
The effect of compressive load on the forming rate and relax density is shown in Figure 8. The briquette cyanobacteria were produced by the compressive load of 1, 3, 5 and 7 tons with a moisture content of 10%. The variations in the forming rate and relax density with compressive load show a similar changing trend, which increases with the increase in compressive load. The increasing extent from 1 to 3 tons is greater than other compressive load ranges. Therefore, the densification process exhibits an evident change when the compressive load increases from 1 to 3 tons, while the molding effect is not that obvious when the compressive load is over 3 tons.  
Figure 7: Single briquetting unit: the effects of moisture content on the forming rate and relax density.
## 3 Method
### Data Matching  
The CYGNSS spaceborne data are continuous time-varying data, while the DTU18 mean sea surface model is a 1\({}^{\prime}\) grid data with latitude and longitude. Therefore, we space-matched the CYGNSS measurements to the DTU18 mean sea surface model (within 0.5\({}^{\prime}\) difference in latitude and longitude). The tide modification is then estimated and applied to the DTU18 MSS. The CYGNSS measurements and the DTU18 validation model comprise the original matchups.  
The original matchups are split into training, validation, and test sets. The three datasets did not overlap to confirm the universality of the model throughout time. It can be seen in Figure 4 that the training set is selected for the first 247 days of data in 2020, with about 6.8 \(\times\) 10\({}^{6}\) samples, for model training and development. The validation set is selected for the next 92 days of data, with about 1.9 \(\times\) 10\({}^{6}\) samples, for the hyperparameter tuning and preliminary model performance evaluation. The test set is selected for a total of 91 days of data from January to March 2021, with about 1.9 \(\times\) 10\({}^{6}\) samples. The test data are not involved in model building; the data are only utilized to examine the final accuracy and generalization performance of the model.  
In this paper, mean absolute difference (_MAD_), root mean square error (_RMSE_), and Pearson Correlation Coefficient (_PCC_) are adopted to test the reliability of the method [43]. Lesser _MAD_ and _RMSE_ values indicated a favorable fit between the predicted and real values. The closer the _PCC_ is to 1, the better the correlation between the retrieval results and the DTU18 validation model. The corresponding definitions are as follows [43]:  
\[MAD=\frac{1}{n}{\sum_{i=1}^{n}}|Ti-Ai| \tag{7}\]  
Figure 4: Daily sample counts in training, validation and test sets.  
\[RMSE=\sqrt{\frac{1}{n}\times\sum_{i=1}^{n}(Ti-Ai)^{2}} \tag{8}\]  
\[PCC=\frac{\text{Cov}(T,A)}{\sigma_{T}\sigma_{A}} \tag{9}\]  
where \(T\) denotes the forecast sequence of the model; \(A\) denotes the SSH value of the validation model; and \(n\) denotes the predicted number of values. \(\text{Cov}(T,A)\) denotes the covariance between the predicted and the validated values; \(\sigma_{T}\) and \(\sigma_{A}\) denote the variance of the estimated and validated values, respectively.
## 3 Kinematic Model of Continuum Robotics
### Kinematic Equations of Continuum Manipulator  
As shown in Figure 4, three identical single-section robotic arms are connected in series to form an overall number: \(i-1,\ i,\ i+1\). Then, the central axis is the \(z\)-axis direction on the front-end disk of this multi-section robotic arm. Next, establish a coordinate system and record it as \(\{\text{F}\}\), and set up a coordinate system on the end disc with the central axis as the direction of the \(z\)-axis and record it as \(\{\text{E}\}\). Assume that the center point at the top of the segment \((\text{i}+1)\) is O, which is expressed as \(\mathbf{O}_{E(i+1)}=(0,0)\) in the coordinate system \(\left\{\text{E}_{(i+1)}\right\}\).  
This point is denoted \(\mathbf{O}_{F(i+1)}=\left(\mathbf{O}_{F(i+1)},\mathbf{m}_{F(i+1)}\right)\) in the coordinate system \(\left\{\text{F}_{(i+1)}\right\}\), because \(\left\{\text{F}_{(i+1)}\right\}\) and \(\left\{\text{E}_{(i)}\right\}\) are the same in space. Therefore, the end position of the robot arm in the section \(i+1\) can be expressed as \(\hat{\mathbf{O}}_{E(i)}\) in the end coordinate system of the section \(i\).  
\[\hat{\mathbf{O}}_{E(i+1)}=\hat{\mathbf{q}}_{(i+1)i}^{*}\otimes\hat{\mathbf{O}}_{E(i)} \otimes\hat{\mathbf{q}}_{(i+1)i} \tag{13}\]  
Figure 4: The coordinate system of a multi-section continuous robot in a driven state. \(\left\{\text{F}_{(i-1)}\right\}\) represents the coordinate system at the front end of the continuous robot in the first section, while \(F_{(i)}\) represents it in the second section. Due to physical model limitations, the \(i\)-th manipulator’s front-end coordinate system differs from the 1-section robot arm, but \(F_{(i)}\) and \(E_{(i-1)}\) remain the same. Thus, a total of \(i+1\) coordinate systems are needed for the \(i\)-section robotic arm.  
Similarly, the position of the central axis point of the front end of the multi-segment continuous arm in the coordinate system of the end of the first continuous arm is:  
\[\hat{O}_{E(i-1)}=\hat{\boldsymbol{q}}_{(i+1)i}^{*}\odot\hat{\boldsymbol{q}}_{(i- 1)i}^{*}\otimes\hat{\boldsymbol{O}}_{E(i+1)}\otimes\hat{\boldsymbol{q}}_{(i-1) i}\otimes\hat{\boldsymbol{q}}_{(i+1)i} \tag{14}\]  
Then the \(z\) and \(y\) axis directions of the frontmost position point are:  
\[\hat{\boldsymbol{L}}_{E(i-1)}=\hat{\boldsymbol{q}}_{(i+1)i}^{*}\odot\hat{ \boldsymbol{q}}_{(i-1)i}^{*}\otimes\hat{\boldsymbol{L}}_{E(i+1)}\otimes\hat{ \boldsymbol{q}}_{(i-1)i}\otimes\hat{\boldsymbol{q}}_{(i+1)i} \tag{15}\]  
\[\hat{\boldsymbol{P}}_{E(i-1)}=\hat{\boldsymbol{q}}_{(i+1)i}^{*}\odot\hat{ \boldsymbol{q}}_{(i-1)i}^{*}\otimes\hat{\boldsymbol{P}}_{E(i+1)}\otimes\hat{ \boldsymbol{q}}_{(i-1)i}\otimes\hat{\boldsymbol{q}}_{(i+1)i} \tag{16}\]  
Through (15) and (16), the expression of the end position of the overall mechanical arm can be obtained in the first section of the robotic arm, and the complete forward kinematic equation can be obtained.
## 3 Iterative Learning Observer-Based Thruster Fault Reconstruction Approach
### ILO-Based Thruster Fault Reconstruction  
In the simulation, both the simulation step and the learning interval \(\tau\) were set as 0.001 s. Choosing parameters \(\mu=0.001\), \(\gamma_{1}=0.0193\), and \(\beta=1\), one obtained \(Q_{2}=0.999\) and gain matrix \(\mathbf{Q}_{1}=diag\{0.99,0.99,0.99\}\). Choosing parameter \(\gamma=3.231\times 10^{-3}\) according to space perturbation \(d(t)\) such that the condition in (11) was satisfied. To guarantee good transient performance, all poles of matrix \(\mathbf{A}-\mathbf{L}_{1}\mathbf{C}\) could be assigned to stability region \(S(-5,2)\)[29]. Solving (8) and (27) with \(\lambda=10^{-8}\) using the Matlab/LMI toolbox yielded  
\[\mathbf{L}_{1}=\left[\begin{array}{cccccc}4.5471&0&0&1.0000&0&0\\ 0&4.5471&0&0&1.0000&0\\ 0&0&4.5471&0&0&1.0000\\ 0&0&0&4.5470&0.0030&0\\ 0&0&0&-0.0030&4.5471&0\\ 0&0&0&0&0&4.5471\end{array}\right]\]  
\[\mathbf{L}_{2}=\left[\begin{array}{cccccc}0&0&0&456.2710&0&0\\ 0&0&0&0&456.2530&0\\ 0&0&0&0&0&456.2642\end{array}\right]\]  
Simulation results on ILO-based thruster fault reconstruction are shown in Figure 3. It was observed that the proposed ILO not only accurately reconstructed the abrupt constant fault in the \(x\)-axis thruster, but also had an accurate reconstruction of time-varying faults in the \(z\)-axis thruster. In addition, it could be noticed that the reconstruction signal from the proposed ILO converged to the zero region on the \(y\)-axis. The estimation errors of relative position and relative velocity are illustrated in Figures 4 and 5. It was clearly demonstrated that the errors could still converge quickly to zero in different fault scenarios. Therefore, the designed ILO could achieve accurate estimations of relative position and relative velocity of the satellite formation flying system.  
Figure 4: Estimation error of relative position using the designed ILO.  
Figure 5: Estimation error of relative velocity using the designed ILO.  
Figure 3: Reconstruction signals from the different fault scenarios.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## Appendix A Queries
### GSQL  
Listing A9: A GSQL query that returns all the review text and ratings for businesses within 50 km of the Phoenix area during 2018.
```
CREATEQUERYgetReviewsFromPhoenix2018()FORGRAPHMyGraph{  TYPEDEFtuple<STRINGtext,INTstars,INTcool,INTfunny,INTuseful>review;  DOUBLElat=33.45;  DOUBLElon=-112.56;  INTdistKm=50;  ListAccum<review>@reviewList;  Grids=to_vertex_set(getNearbyGridId(distKm,lat,lon),\"geo_Grid\");  NearbyBusinesses=  SELECTb  FROMGrids:s-(Business_Geo:e)-Business:b  WHHEREgeoDistance(lat,lon,e.LATITUDE,e.LONGITUDE)<=distKm;  ReviewsForBusinesses=  SELECTb  FROMNearbyBusinesses:b-(reverse_Reviews:r)-User  WHHEREYEAR(r.REVIEW_DATE)==2018 ACCOMM @reviewList += review(r.TEXT, r.STARS, r.COOL, r.FUNNY, r.USEFUL); PRINT @reviewList; }
```
# The Symmetry of GPS Orbit Ascending Nodes
## 3 Results
An approximately 1\({}^{\circ}\) higher value occurred in OP C at the end of the analysis in 2020. A major decrease in the OP C d\(\Omega\) standard deviation was observed in 2015 when the SVN33 and SVN36 satellites ended their mission. The satellites' d\(\Omega\) values at the end of their activity equaled \(-\)10.37\({}^{\circ}\) and \(-\)5.09\({}^{\circ}\), respectively. Significantly larger separations of their ANs resulted in high values of the standard deviation of OPs. The step changes in the standard deviations were caused by the modernization of the GPS space segment and the change in the number of active satellites. Confirmation of this relationship is indicated by the last chart's parameter presenting the number of active OP D satellites. Comparing the epochs of the change in the number of satellites (black) with the corresponding d\(\Omega\) standard deviation (green), a coincidence is visible. In general, the step changes did not change the upward trend of the standard deviations of the GPS satellites' AN separations from the OP. The last point of the study was the analysis of the adjusted OP \(\Omega\) values. Figure 9 shows the derived estimators of the OP ANs' symmetry. In the left part of the figure, the red vertical arrows indicate the hexagonal symmetry between the six OPs of the GPS space segment. The orbital planes A, B, C, D, E, and F are presented from bottom to top in the figure. The horizontal red lines refer to the reference values of the research, i.e., hex-LS adjustment results providing the initial GPS geometry with the hexagonal division of the equatorial plane by OPs. Using the hex-LS estimators, the robust adjustment \(\Omega\) deviations (green color) were calculated. The last elements of the figure are marked in black: deviations of the OPs arithmetic mean \(\Omega\) values from the hex-LS reference. The colors of the parameters correspond to Figures 3 and 4. Figure 8: Standard deviations of the OPs’ d\(\Omega\) parameter. The d\(\Omega\) OP D deviation values took the smallest and the largest values in the dataset from \(-7^{\circ}\) to \(3.5^{\circ}\). In the case of the remaining OPs, the deviations mostly range from \(-2^{\circ}\) to \(2^{\circ}\). The differences in the course of the polylines representing the arithmetic mean (black) and the robust estimation (green) method indicate minimization of the impact of outliers on the determined OP AN position robust estimator. The results are visible, e.g., in the case of OP D, where almost from the beginning of the SVN46 mission in 2000, the weights of its anomalous orbit were systematically minimized. In the research timeframe, for all OPs except for OP D, similar values of approximately \(2^{\circ}\) of the AN position error with respect to the hex-LS estimators were obtained. This OP AN deviation doubled in OP D in 2012 and has tripled since 2018. In the last two years, the OP AN deviations remained relatively constant in five out of six OPs. The exception is OP F, where a reverse trend of the OP AN closing to the initial symmetry was noted.