esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Climate Data and Future Scenarios  
The climate database of the Climatic Research Unit (CRU; [61]) was used as reference for historical climate conditions. For future climate, the outputs of 33 general circulation models (GCMs) were acquired from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database [52] of the Intergovernmental Panel on Climate Change (IPCC). We analyzed the capacity of those models to reproduce the annual and seasonal climate over Africa in terms of mean, interannual variability and trend of temperature and precipitation over the reference 1980-1999 period by comparing them to the CRU data [50]. We selected the five most performing models, here presented by decreasing rank: CNRM-CM5 [62], MPI-ESM-LR [63], CESM1-BGC [64], CanESM2 [65] and MPI-ESM-MR [63] (see Supplementary S2). This list of five models was completed by four GCMs producing extreme results in terms of future minimum and maximum precipitation and temperature changes in order to illustrate the range of potential climate change (see Supplementary S3). As such, we obtained under the RCP 8.5 scenario: CSIRO-Mk3.6.0 [66] for the largest reduction in precipitation (ranked 22 nd); CESM1-BGC for the largest increase in precipitation (already among the five best GCMs); CanESM2 for the largest temperature rise (already among the five best GCMs) and INMCM4 [67] for the lowest temperature rise (as all increase; ranked 25 th). For each GCM, climatic anomalies between any given year and the reference 1980-1999 period were statistically downscaled to a 0.5\({}^{\circ}\) spatial resolution with a 2D linear interpolation and then combined to 0.5\({}^{\circ}\) CRU data for the reference period, using a procedure similar to that proposed by [PERSON] et al. [46].  
To account for the range of possibilities the future may hold, these climate models operated under four different scenarios of greenhouse gas forcing [53] established by the IPCC. Here, we excluded the unlikely strong mitigation scenario (RCP2.6) and concentrated on more extreme scenarios: RCP4.5 (538 ppmv of CO\({}_{2}\) in 2100) and RCP8.5 (936 ppmv of CO\({}_{2}\) in 2100), respectively representing stabilization without overshoot and rise of external forcing.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Concentrating Systems  
Concentrating solar collector systems are those which focus incident sunlight through a reflective surface--generally a polished metal, metalized glass or plastic. Solar concentrators can also present tracking systems with one or more axis to follow the sun's position during the day [27; 28; 29].  
The advantages of concentrating systems are: (1) potentially small reactor tube area, thus allowing for easier handling of the wastewater; (2) a limited reactor area is also more compatible with supported catalysts and turbulent flows, thus avoiding the issue of catalyst sedimentation; (3) the evaporation of volatile compounds can be controlled.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Feature** \\ \hline Mirror design & Parabolic/Dish/Plane \\ Tracking system & Automatic/Manual/Fixed \\ Catalyst & Dissolved/Suspended/Supported \\ Reactor configuration & Single/Parallel/Series \\ Wastewater loading & Once-through/Batch \\ Flow rate & Volume per time/On-Off \\ System pressure & Pumping employed \\ Pretreatment & Present/Absent \\ Oxidant loading method & Once/Periodically dosed \\ PH control & Acidic/Neutral/Basic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design factors for a solar wastewater remediation AOP.  
Furthermore, it has been shown that degradation rates are generally improved by an increase of radiation intensity within given limits. This was clearly established for both photo-Fenton reactions and direct photocatalysis for SODIS applications [30; 31; 32], where a range of linear dependency between rates and irradiance was found and a minimum solar dose identified.  
On the other hand, they present disadvantages such as the use of only (or primarily) direct solar radiation, possible high cost, and water overheating [19].  
Among the concentrating systems, 5 are of particular interest in the context of wastewater remediation: (1) the parabolic through collectors (PTCs), which concentrate sunlight in a line; (2) compound parabolic collectors (CPCs), a design variant of PTCs; (3) the parabolic dish, designed to focus on a specific spot; (4) Fresnel concentrators, that can be designed to both single spot or line systems; and (5) optical fiber photoreceptors, which are presented as a possible solution for reaching remote and difficult-access contaminated water sources.
## 3 Materials and Methods
### Validation and Reliability of the Results (Confusion Matrix)  
A confusion matrix is a tool employed to assess the errors of a classification system [31; 32].  
A statistical methodology of multivariate data was used to validate the study method, which compares the information obtained in situ against data calculated by software and the classification method. In the case study presented herein, data on the estimated or observed stability of slopes were compared with the information calculated by the empirical Q-slope method and factor of safety (FoS) obtained with software through a confusion or error matrix [33].  
The first step in developing a confusion matrix is to classify obtained data as true positives and true negatives or false positives and false negatives. This was accomplished for the sub-classes stable, quasi-stable, and failed. The later calculation of the truth overall and classification overall of each class required the horizontal and vertical sums of the data classified in step one.  
The percentage value of user accuracy--recall (UA) and producer accuracy--precision (PA) is the ratio between the true positive values of each analyzed class and the values obtained in step one for truth overall and classification overall, respectively (Equations (4) and (5)).  
\[\text{UA (recall)}=\frac{\text{True positive}}{\text{Truth overall}} \tag{4}\]  
\[\text{PA (precision)}=\frac{\text{True positive}}{\text{Classification overall}} \tag{5}\]  
Finally, the overall accuracy (OA) is calculated from the sum of all true positive values of all variables, divided by the amount (n) of samples analyzed (Equation (6)). The overall error of the method is obtained from the difference between the maximum OA value possible and the value calculated by the confusion matrix for the same parameter.  
\[\text{OA}=\frac{\Sigma\text{True positive (class Stable, Quasi -- stable and Failed)}}{\text{n}} \tag{6}\]
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 3 Shearer Cutting Unit Tracking in Infrared Thermal Images
### Spatio-Temporal Context (STC) Target Tracking Algorithm  
The core of the STC algorithm is to make full use of the STC information in the image sequence to track the target. By using the coherent information of time and space in a video image, any tracking problem can be described as a probability matter, in that the prediction target may appear at all locations in the scene. Thus, a confidence map can be calculated to represent the likelihood ratio of the predicted target location, as follows:  
\[\begin{array}{c}c(x)=P(x|o)=\sum_{c(z)\in X^{c}}P(x|c(z),o)P(c(z)|o)\\ X^{c}=\left\{c(z)=(I(z),z)|z\in\Omega_{c}(x^{*})\right\}\end{array} \tag{3}\]  
where \(x\) represents the predicted target location, and \(o\) denotes the STC information. Therefore, the location \(x^{*}\) that makes the maximum value of confidence map \(c(x)\) is the target location predicted in the next frame. In addition, \(I(z)\) is the grayscale of point \(z\) and \(\Omega_{c}(x^{*})\) represents a local region of target \(x^{*}\).  
Seen from Formula (3), the confidence map \(c(x)\) can be divided into two parts. First, the conditional probability \(P(x|c(z),o)\) establishes the spatial relationship between the target and the context information, which is called the spatial context model. The second part is the context priori probability \(P(c(x)|o)\) of each point x in the modeling region. Conditional probability needs to be learned in advance. The schematic diagram of the STC tracking algorithm can be shown in Figure 6.
## 4 Evaluation of Quality and Efficiency
### Relationship between the Load-Notch-Strain Curve for the Initial Load and the Hysteresis Branch  
Due to the relationship between the load-notch-strain curve for the initial load and the hysteresis branch, it is not necessary to determine the load-notch-strain curve for the initial load; see Section 3.1. This does not result in any loss of quality, but the efficiency can be increased because the number of load steps that need to be calculated is reduced by one-third. In the following sections, this insight is used, and only the determination of the load-notch-strain curve for the hysteresis branch is discussed.
## 2 Materials and Methods
### Co-Precipitation Assay, MSPL Protease Digestion Assay, and DC-SIGN Lectin Blot  
HEK293 cells stably expressing full-length MSPL or TMPRSS13 were lysed with lysis buffer [150 mM NaCl, 50 mM HEPES (pH 7.4), and 5 mM CaCl\({}_{2}\) containing 1% NP-40]. The whole-cell lysate was incubated with DC-SIGN-Fc or IgG-Fc recombinant proteins, which were precipitated using Protein G sepharose beads (Thermo Fisher Scientific, Waltham, MA, USA) in the presence of Ca\({}^{2+}\). After washing with wash buffer (TBS containing 5 mM CaCl\({}_{2}\) and 0.05% Tween 20), the beads were suspended in elution buffer (TBS containing 10 mM EDTA) to chelate Ca\({}^{2+}\). The eluted solution was subjected to SDS-PAGE and western blotting using anti-FLAG M2 antibody. For the MSPL protease digestion assay, purified DC-SIGN-ECD (3 \(\mu\)g) and MSPL-ECD (0-1.0 \(\mu\)g) were mixed and co-incubated in the presence of Ca\({}^{2+}\) or EDTA for 0-6 h at 37 \({}^{\circ}\)C. To confirm the effect of N-glycosylation, MSPL-ECD was pre-treated with recombinant N-glycosidase F (PNGase F) (Roche, Basel, Switzerland). For this treatment, MSPL-ECD (1 \(\mu\)g) was dissolved in reaction buffer (TBS containing 0.5% SDS, 40 mM EDTA, and 1% 2-mercaptoethanolhanol) and heated to 105 \({}^{\circ}\)C for 5 min. Then, PNGase F (3.2 units) was added to the MSPL-ECD solution, which was incubated for 24 h at 37 \({}^{\circ}\)C. The solution was treated with five times SDS sample buffer (2% SDS, 10% glycerol, 0.001% bromophenol blue, and 65 mM Tris-HCl, pH 6.8) and heated to 98 \({}^{\circ}\)C for 3 min under reducing conditions. The samples were separated via SDS-PAGE using a 5%-20% gradient gel, followed by Coomassieie Brilliant Blue (CBB) staining. DC-SIGN lectin blotting was performed using DC-SIGN-ECD as the primary reaction solution. Reacted DC-SIGN-ECD on a nitrocellulose membrane was incubated with anti-DC-SIGN monoclonal antibody (R&D Systems), followed by detection with HRP-conjugated anti-mouse IgG antibody.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 4 Data-Cleaning Methods
### Cleaning Methods for Redundant, Conflicting, and Erroneous Data  
There are many participants in a distributed data collection system. These include the terminal, the message middleware, and the backend storage system. Redundant data are caused by network instability and limitations of the data transmission protocol among  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Table Name** & **\# of Attributes** & **Description** \\ \hline
1 & QR\_code & 15 & QR code table \\
2 & Swipe & 15 & Swipe card table \\
3 & IAO\_station & 15 & Inbound and outbound information table \\
4 & Bluetooth & 38 & Bluetooth table \\
5 & GPS & 13 & GPS table \\
6 & Wi-Fi & 38 & Wi-Fi table \\
7 & Bus\_first & 28 & Vehicle Table 1 \\
8 & Bus\_second & 9 & Vehicle Table 2 \\
9 & Route & 12 & Route table \\
10 & Station & 12 & Station table \\
11 & Route\_station & 17 & Route station table \\
12 & Install\_register & 25 & Vehicle registration table \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data table information for a domestic city bus dataset.  
different participants. For example, a terminal may submit a record to the backend multiple times as the connection is lost and the storage system does not have the ability to detect duplicate data. This ultimately results in the creation of duplicate records within the data-storage system. Several cleaning strategies have been proposed to address this problem. These representative solutions include buffering methods [14], entity recognition methods [16], sorting methods [37], redundant data models/frameworks [38], machine learning [39], and other methods. In this paper, we implement redundant data cleaning on Apache Spark for \"QR_code\", \"Swipe\", \"IAO_station\", \"Bluetooth\", \"GPS\", and \"Wi-Fi\" tables.  
Conflicting data include DQ issues such as value-field mismatches, a single field containing multiple value types, structural errors, and inconsistent naming of data from multiple data sources. This paper addresses two conflicting data issues: naming conflicts in the \"Bus_first\", \"Bus_second\", and \"Install_register\" tables for multi-source data, and inconsistent data in the \"Route\" and \"Install_register\" tables, and uses data standardization methods to resolve data conflicts.  
Erroneous data include spelling errors in attribute values and other related formatting errors such as misspellings, typos, inconsistent units, inconsistent date formats, and inconsistent value formats. In this paper, we use a customized date conversion function to convert inconsistent date formats in dynamic and static data tables to a standard format, thus completing the cleaning process for erroneous data.
## 7 Results and Discussion
Figure 12a-e show how sensitive the input design variables are to the structure based on the different design criteria. The bar heights indicate sensitivity in these charts. In Figure 12a, for the probability distribution of the maximum equivalent von Mises stress, it can be observed that the thrust magnitude and the torsional moment are positively correlated with direct impact. The RNA weight and the hydrodynamic load are negatively correlated and have little influence but inversely. The Young's modulus of the soil system and the tilting moment are neutral and have limited or no impact. In Figure 12b, for the probability distribution of the deformation at the jacket top, it can be observed that four variables are positively correlated with direct impact. These are the thrust load, hydrodynamic load, and tilting and torsional moments, which have significant influence. The Young's modulus of the soil system has little or no impact. The RNA weight is negatively correlated and has a significant impact. In Figure 12c, for the probability distribution of the minimum output parameter safety factor, it can be observed that Young's modulus of the soil system is neutral and has little or no impact. The RNA weight and hydrodynamic load are positively correlated with direct impact; the RNA weight has significant influence while the hydrodynamic load has quite some impact. The thrust and the torsional moments are negatively correlated and have significant influence but inversely. The tilting moment has very little or no impact. Figure 11: Fatigue reliability index with different coefficients of variation (COVs). Figure 12: _Cont._  
As can be seen from Figure 12d, the RNA weight and the thrust load have the highest impact and are positively and negatively correlated, respectively, with the other variables having little or no impact on the load multiplier probability distribution. It can also be observed that, in Figure 12e, the structure self-weight has the highest direct impact, with the thrust load and the second soil strata having very little inverse impact on the distribution of the output parameter, the first eigen-frequency, and, hence, the reliability of the structure.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 1 Introduction
Following this, the concept of using a batch of trains is introduced to find the profile, without prior knowledge of the train masses and velocities. This is effectively a train self-calibration process. Moving reference influence lines are calculated from APs of the bridge which are shown to be a good indication of the bridge behaviour and condition. A blind test is used to validate these methods: bridge damage is calculated for simulated data provided by an independent laboratory, without prior knowledge of the damage levels used in the simulations.
# Assessment of the Durability of Threaded Joints
## 3 Experimental Results and Their Analysis
### Analysis of the Crack Propagation  
The crack initiation and propagation patterns in the tightened threaded joint subjected to cyclic bending had specific attributes compared to the joints subjected to cyclic tension. Distribution of the stresses was non-uniform along the turn. The turn load increased closer to the tensioned layers and reduced when positioned further away from them. In the stud-nut force distribution system, the investigation showed the position subjected to the highest load. The maximum stresses resulting from the total stress and bending action developed only on the strain plane. This determines the stress-strain state and crack initiation conditions in the root of the turn. The crack initiation and propagation analysis showed that the crack may be either one- or two-sided (Figure 7).  
When the crack propagates in the stud cross-section, as in Figure 7a, it is either one-sided or asymmetric. When the crack reaches the critical value, the remaining part fails largely due to the strain energy accumulated during stud tension.  
If the cross-section of the crack occurs in the cross-section of the stud, as shown in Figure 7b, it is symmetrically two-sided. When the crack reaches the critical value \(h_{cr}\) or \(h_{a}=2h_{cr}\), the rest of the crack disintegrates, mainly due to the strain energy that has been accumulated by stretching the stud. In contrast, cyclic crack propagation is due to bending deformation.  
The study of the distribution of the load in the stud-nut system showed the position of the most loaded position, so that when the threaded joint was assembled, one of the nuts was placed in such a way that the stud received a uniform load. The other nut can be placed in any position depending on the amount of tension (Figure 2a). The combined stress and bending action of the stud results in maximum indentations only in the plane of deformation. This determines the stress-strain state and the conditions for the formation of a crack in the cavity of the turns.  
Investigating the stress behavior and experimental studies of the indentation distribution show that, depending on the position of the nut in the bending plane, the angle \(\alpha\) can vary from \(0^{\circ}\) to \(60^{\circ}\) depending on the position of the nut in the maximum stresses in the thread recess. Patterns of crack initiation and propagation also vary. Fracture analysis showed that the angle of initiation of the crack in the recess varies from \(0^{\circ}\) to \(45^{\circ}\). The  
Figure 6: Comparison of the experimental results with the fatigue curves according to the PNAE (straight line) and ASME (dotted line) norms: \(\bullet\)—experimental data; a—maximum nominal stresses \(0.95\sigma_{y}\); b—maximum nominal stresses \(0.6\sigma_{y}\).  
initiation of the fracture determines its subsequent propagation according to one of the patterns shown in Figure 7c.
## 4 Results and Discussion
### Three-Dimensional Property Model
#### 4.2.2 Compression Modulus (Es)  
Figure 15 shows the Es-attributed 3D geological model. It reveals that Es values increased gradually as depth increased. To determine the depth-related distribution features of the compression modulus in the vertical direction, diverse depth intervals of 0-10 m, 10-17 m, 17-35 m, and >35 m based on local cyclic depositional features described above are further discussed separately.  
Figure 14: Three-dimensional property model of (**a**) water content (w%); and (**b**) national void ratio (_e_).  
Figure 13: The initial void ratio of subsoils **(a,b**) in west–east (WE18) and north–south (NS12) orientations, respectively; (**c**) all measured results vs. the depth.  
The Es values varied from 2 MPa to 27 MPa with an average value of 6.4 MPa for silty clay at depths up to 10 m, from 4 to 33 MPa with a mean value of 9.6 MPa for the unit at 10-17 m depths, and from 5 to 47 MPa (average of 11.8 MPa) for silty clay at depths between 17 m and 35 m and 6 to 54 MPa (16.8 MPa on average) for the unit that lies at 35-50 m. As shown in Figure 16, silty clay at shallow depths (0-10 m), with a compression modulus generally lower than 7.5 Ma, was identified as a medium-to-high compressibility unit (Es \(\leq\) 7.5, Beijing Geotechnical Institute and Beijing Institute of Architectural Design, 2017, DBJ 11-501-2009). For layers at depth intervals of 10-17 m and 17-35 m, with Es values normally concentrated in the range of 4-7.5 MPa and 7.5-11 MPa (Figure 16), respectively, they were thus considered as having medium-to-high compressibility and low to medium-low compressibility, while low compressibility silty clay dominated across 35-50 m depth intervals. Moreover, as seen from Figure 17 (i.e., compression modulus at varying depths derived from 3D property model), Es of silty clay units was heterogeneous, for example, at the depth of 10 m, and a small area in the central part of the region showed slightly higher Es values than did the surroundings at the same depth, with relatively lower compressibility.  
Figure 15: Three-dimensional geological model of shallow superficial deposits in Tongzhou District. Upper: Es-attributed 3D geological model; below: fence diagrams of the model.