esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Machinery Readiness Using Semi-Markov Processes
## 1 Introduction
### The Aim of the Study  
The need to check [PERSON]'s property is discussed in more detail in the literature [10; 11], while less attention is paid to the distribution of variables studied. Therefore, this publication compares the results of process examination according to the semi-Markov model for variables of non-parametric distribution with the analysis according to the Markov model, in which their exponential form was (falsely) assumed. The differences in the results obtained clearly indicate that it is necessary to carry out a preliminary test before choosing the right model. Failure to meet the assumptions leads to an inaccurate analysis of the process.  
The aim of the article was also to evaluate the readiness of a production machine, which is an important element of the analyzed production process. The results obtained made it possible to determine the probabilities of transitions between the individual states distinguished in the production process, as well as to define limit probabilities and the technical readiness coefficient. This allows to assess the compliance of the functioning of the analyzed process with the schedule adopted in the company, or to evaluate the results of production abilities. The proposed models can also be used to simulate the production process, e.g., at the design phase.  
The article consists of five sections. The first one presents an analysis of the literature on the application of Markov models for studying the technical readiness of machine tools in a production line. Section 2 presents a mathematical formulation of the research problem. In Section 3, a description of the studied company was given and data analysis was carried out in terms of studying the Markov property and the form of distribution of variables. Section 4 presents a case study containing the estimation of Markov and semi-Markov models parameters, as well as a numerical example and accurate calculations according to the developed model. The article ends with conclusions describing the goals achieved and indicating the added value of the study.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 3 Results
### SBAS Co-Seismic Displacement at the 2008 Earthquake Epicenter  
Figure 8A shows the Line of Sight (LOS) velocity ranging from \(-\)10 mm/year to 40 mm/year averaged for four years (24 December 2006-19 February 2011). On an ascending path, the ALOS-1 satellite moves north with its antenna pointed to the right. Therefore, a positive value can be regarded  
Figure 7: Schematic cross-section AA’ (parallel with seismic profile 3) modified from a previous study [20] to explain subsidence in the Quetta Valley and surrounding areas. The Bouguer anomaly profile of AA’ shows highs and lows forming a trend likely related to mountain ranges and basins. This model shows the activity of blind thrust faults 1 and 2 mapped in subsurface using 2D seismic profiles, which are part of the Chiltan Thrust System. High-risk locations for potential fissures are shown near the Murdar Range based on differential compaction theory in Section 2.5.  
as a westward shift, uplift, or both. We attributed this movement primarily towards westward due to the strike-slip movement of Harnai and Karahi Faults.  
The DInSAR for co-seismic event was also calculated (11 November 2008-29 December 2008), which shows the effect of EQ 5, 6, 7 and 8 (Figure 1) with a half fringe (a bright yellow dot in Supplementary file Figure S1) on the west side of the Harnai Fault suggesting \(\sim\)55 mm of co-seismic displacement. Another fringe on the north side of the Harnai Fault can also be observed, but the time and location do not correspond to any of the eight earthquakes. This fringe could be caused by slow uplift of the Takatu Mountain Range.
## 3 Datasets  
### Ground Points Collection  
The three-dimensional coordinates (WGS84 UTM coordinates) of the 102 GPs shown in Figure 1 were obtained by means of a total GPS Topcon HiPer PRO station working in real time kinematic mode and using the satellite's carrier. The goal was to obtain a reliable measurement of the GPs surveyed providing a position accuracy of better than 10 cm. All these GPs were well-defined and evenly distributed across the whole study area (Figure 1). The entire set of GPs was divided into three sets of six GCPs each to compute the sensor orientation model, leaving the remaining 84 GPs as independent check points (ICPs) to carry out the accuracy assessment (Figure 1).
# Pipeline Damage Identification Based on Additional Virtual Masses
## 5 Conclusions  
This paper proposes an effective method for pipeline damage identification by constructing virtual structures with additional virtual masses. The virtual structures are constructed to increase their sensitivity with respect to the considered damage parameters. Through numerical simulation and test verification, the damage identification of the pipeline can be realized. We summarize the most important features below:  
1. Based on the concept of VDM, we propose a method of adding virtual masses to the structure. The great advantage is that it evades practical difficulties of adding real mass to the structure, thus reducing the negative effect of real masses on the structure itself and making the actual operation flexible and convenient.
2. By constructing the virtual structure on the pipeline with the additional virtual masses, the frequency of high relative sensitivity can be selected and combined with a diagonally dominant sensitivity matrix as well as with frequency changes before and after damage. Hence, the degree of damage on the substructure can be accurately identified.
3. Damage identification of global structures requires only two sensors: excitation and acceleration, which are sequentially used for constructing the considered virtual structures one by one. Therefore, the experimental costs can be significantly reduced.  
**Acknowledgments:** The authors are grateful for the financial support from the National Key Research and Development Program of China (Project No. 2017 YFC0703410), and National Natural Science Foundation of China (NSFC) under Grant Nos. 51478079, the Fundamental Research Funds for the Central Universities (DUT16 LK10).  
**Author Contributions:** To complete this article, the whole team worked together and each of them made outstanding contributions. [PERSON] provided the theoretical support. [PERSON] and [PERSON] conceived and designed the experiments.Dang [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] contributed acquisition program and analysis tools. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
## 2 Related Work
### Impact of Analyst Recommendations  
In a nutshell, analyst recommendations [34] are typically buy-hold-sell ratings predicting an equity's future performance and are issued in the public by industry experts. [PERSON] [35] studied the effect of analyst ratings on investor sentiment and observed that stock prices adjusted up to five per cent for changes to buy recommendations or up to 11 per cent for changes to sell recommendations. Taking it a step forward, [PERSON] et al. [36] in 2001 investigated if the investors could profit from these publicly available recommendations and they reported that just by adopting the strategy of purchasing'strong buy' stocks and shorting'strong sell' stocks they were able to generate excess returns of up to 4% but stated due to a substantial transaction costs the abnormal net returns for these strategies was not reliably greater than zero. Yet, about more than a decade later in 2018, [PERSON] et al. [37] used the same strategy to produce abnormal returns of 4.7-5.8% per year post accounting for transactions costs during the period of 2001-2016. They attributed this result to decreasing transaction costs and also to their more precise consideration of these costs rather than using a fixed number. Another recent study [38], suggested that it was possible to build profitable strategies based on analysts' recommendations as they identified that the top-rated stocks generally deliver higher returns than the bottom rated stocks.  
While these ratings seem to possess the ability to sway the markets, they are known [39; 40; 41; 42] to be biased because of these analysts' affiliations with the investment banking realm that generates pressure on them to produce relatively optimistic recommendations regarding affiliated stocks. Moreover, [PERSON] and [PERSON] [43] established that there exists a level of herding in the recommendations issued by analysts and this phenomenon is more profound for hard-to-value firms, large firms or firms with high institutional ownership.  
Finally, analyst ratings haven't always proven to be correct [44], as in the study by [PERSON] et al. [45], they discovered that stocks with average analyst recommendation level of hold or worse on the S&P500 outperformed other stocks over the period 2009 to 2016. However, according to [PERSON], this result was driven by two factors: the impact of the period of 2009 and 2010, and low-priced stocks. Yet when the same strategy was applied since the beginning of 2011 or when the low-priced stocks were removed, no significant outperformance of these stocks was observed. This presented an interesting result as these supposedly poor performing stocks as described the analysts didn't truly perform as poorly as predicted but rather managed to stay afloat and provided their investors just as many returns as the stocks with a positive rating if not more.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 3 Quantum Computing/Quantum Gate  
### Quantum Computing and Quantum Simulator  
Quantum computing processes information in qubits to use the principles of quantum mechanics. However, the qubit indicating the status of a quantum particle is easily transformed into a decoherence state by the influence of the external environment; hence, it should be kept at a cryogenic state to avoid external influences. Equipment used to maintain cryogenic conditions is also expensive. Therefore, global IT companies developing quantum systems provide quantum simulators.  
A quantum simulator is a software program that can run and test quantum algorithms on a classical computer like running quantum algorithms on a quantum computer. In the future, when quantum computers are commercialized, users will be able to run applications on them.
## 2 Materials and Methods
### Gene Expression by TaqMan Apoptosis Array  
RNA isolation was done from both control (untreated cells) and treated cells (30 uM and 50 uM) according to Gen Elute Mammalian Genomic Total RNA Kit's procedure (Sigma, St. Louis, MO, USA). After a qualitative check, quantitation of RNA was done by Nanodrop (Nanodrop 2000c; Thermo Scientific(tm), MO, USA). Then, cDNA was synthesized with the help of a reverse transcription kit from Applied Biosystems(tm), USA. A pre-designed, ready-to-use TaqMan(r) quantitative gene Expression Array (Apoptosis Array 4414072 & oncogene array 4369514) was employed to evaluate cell cycle regulatory and apoptotic pathways' gene expression in both treated and control cells. A mix of 10 uL of complementary DNA (100 ng) with 10 uL of master mix was added/well and the qPCR plate was run on a thermocycler (QuantiStudio3, Applied Biosystems, USA), and data was analyzed by DataAssistTM software through 2 -\(\Delta\)ACT method. The data was normalized using housekeeping gene 185 rRNA. Differential gene expression of the treated sample compared to control is shown through the RQ value displaying the fold change.
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 3 Case Analysis
### Ways to Improve Accuracy of ADRC and LQR  
The traditional ADRC method is used for a SISO problem, rather than a MISO problem. Because the proposed method is an improved ADRC method, the way to improve accuracy is to determine major and minor controlled state, select the appropriate feedback control coefficient and improve the observer bandwidth.  
In the LQR method, the way to improve accuracy is to carefully increase the number of discrete points and set weighting matrix, which influences the results significantly.
# A Graduated Non-Convexity Technique for Dealing Large Point Spread Functions
## 2 Regularization of the Problem
Given a vertical clique  
\[c=\{(i,j),(i+1,j),(i+2,j)\},\quad i=3,\dots,n-2,\ j=1,\dots,n,\]  
we define its preceding clique \(c-1\) as follows:  
\[c-1=\{(i-2,j),(i-1,j),(i,j)\}.\]  
If \(c\) is a horizontal clique,  
\[c=\{(i,j),(i,j+1),(i,j+2)\},\quad i=1,\dots,n,\ j=3,\dots,n-2,\]  
then its preceding clique \(c-1\) is defined by setting  
\[c-1=\{(i,j-2),(i,j-1),(i,j)\}.\]  
A _regularized solution_\(\widetilde{\mathbf{x}}\) is defined as a minimizer of the following energy function (cf. [5]). \[E(\mathbf{x})=\|\mathbf{y}-\widehat{A}\mathbf{x}\|^{2}+\sum_{c\in\mathcal{C} }\psi(D_{c}(\mathbf{x}),D_{c-1}(\mathbf{x})), \tag{1}\]  
where  
\[\psi(t_{1},t_{2})=\left\{ \begin{array}{ll}\overline{g}(t_{1},0),&\text{if} \,|t_{2}|<s=\frac{\sqrt{\delta}}{\lambda},\\ \\ \overline{g}(t_{1},\overline{\epsilon}),&\text{if}\,|t_{2}|\geq s,\end{array} \right. \tag{2}\]  
Figure 2: The blurred image is in (**a**); the image reconstructed without (respectively, with) non-parallelism constraint is given in (**b**) (respectively, (**d**)), with line elements drawn in (**c**) (resp., (**e**)). and  
\[\overline{g}(t,k)=\left\{ \begin{array}{ll}\widehat{\lambda}^{2}t^{2},&\text{ if }|t|<\frac{\sqrt{\widehat{\alpha}+\widehat{k}}}{\widehat{\lambda}},\\ &\\ \widehat{\alpha}+k,&\text{ if }|t|\geq\frac{\sqrt{\widehat{\alpha}+\widehat{k}}}{ \widehat{\lambda}}.\end{array} \right.\]  
The first term of the energy function \(E\) in (1) is the so-called _data consistency term_, while the second additive term in (1) is the _smoothness term_. Note that the free parameters in the energy function in (1) are \(\widehat{\lambda}\), \(\widehat{\alpha}\), and \(\widehat{\varepsilon}\). The parameter \(\widehat{\lambda}\) plays the role of adjusting the degree of smoothness of the solution; \(\widehat{\alpha}\) represents the cost to add a discontinuity to the estimated solution, while \(\widehat{\varepsilon}\) is an extra cost for an adjacent parallel discontinuity. A correct value of these parameters allows for more accurate reconstructions (cf. [40]). Figure 3c presents the function \(\psi\) considering \(\widehat{\lambda}=1\), \(\widehat{\alpha}=80\), and \(\widehat{\varepsilon}=80\).
# Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline
## 4 Conclusions  
In this paper, we studied the effect of design parameters of square mesh and diamond mesh pattern on the performance of transmission line and antenna. In addition, as each design variable changes, the interrelationship between the transparency and performance was investigated. The definition of the transparency of the square mesh is newly applied to the diamond mesh. The diamond mesh showed better performance than the square mesh for transmission lines. The diamond mesh also showed an advantage in terms of process by enabling it to have higher transparency than a square mesh even with a wider line width. The measured results showed that the proposed transparent transmission line and antenna using the diamond and square mesh had similar performance to that of the non-transparent transmission line and antenna. As a result, the possibility of substituting each of the square mesh and diamond mesh in order to avoid haze and moire in specific parameters was presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value** & **Antenna w/o Meshed Feedline** & **Antenna w/Meshed Feedline** \\ \hline \multirow{2}{*}{OT (\%)} & Square & Diamond & Square & Diamond \\  & 82 & 82 & 85 & 85 \\ \multirow{2}{*}{Peak gain (dBi)} & 3.65 & 3.64 & 3.56 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of measured antenna performance.  
Figure 14: Measured 2-d radiation pattern of antennas with meshed feedline: (**a**) E-plane; (**b**) H-plane.  
**Author Contributions:** Conceptualization, S.Y.; methodology, S.Y.; software, S.Y. and S.L.; validation, S.Y.; formal analysis, S.Y.; investigation, S.Y.; writing--original draft preparation, S.Y., S.L., H.L. and Y.B.P.; writing--review and editing, S.Y., S.L., H.L. and Y.B.P.; visualization, S.Y.; supervision, Y.B.P.; project administration, Y.B.P.; funding acquisition, Y.B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Electric Power Corporation, Grant Number R21X001-12.  
The authors declare no conflict of interest.
## 4 Antenna Farm Based on Two Dual-Band Dual Reflectarray Configurations
The main cuts of the radiation patterns have revealed similar C/I levels as those of the previous 1.8 m reflectarray. Figure 14 shows the cut of the radiation patterns in the plane u = 0 for the beams produced by the central row of feeds at both frequencies. The simulated cross-polar radiation provides an XPD lower than 20 dB for some beams. The analysis of the results has shown that the increase of the cross-polar component is mainly produced in the subreflectarray, so an optimization procedure should be applied to the dual-band dual-LP cells to reduce the cross-polarization. Moreover, the use of a dual configuration could be used to implement a bifocal technique to improve the shaping of the beams generated in the edge of the coverage [24]. In conclusion, the simulated results of the dual reflectarray configuration prove the capability of the proposed multibeam antenna to generate half the required four-color coverage simultaneously in Tx and Rx. The dual-band operation involving separate frequencies simplifies the design of the reflectarray elements, while the dual configuration simplifies the process of generating two spaced beams in orthogonal CP per feed, splitting it into two straightforward stages (deviation of the LP beams in opposite directions and dual-LP to dual-CP conversion), the principles of which have been experimentally validated previously. The simulated results show that a further optimization is required for the subreflectarray to improve the XPD, although the flexibility provided by the dual configuration could be also used to improve the overall performance of the antenna. Figure 13: Simulated pattern contours for the 30 beams generated by 15 feeds illuminating the proposed dual reflectarray at 19.7 GHz and 29.5 GHz.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs  
In the electrochemical system for detecting HMIs, nanomaterials such as graphene oxide, carbon nanotubes and carbon nanofibers are considered to play an important role in improving the sensitivity of electrode sensors due to their good electrical conductivity, large effective activity and specific surface area [74]. This section discusses the development of different types of nanomaterials from the perspective of materials, focusing on the application research of nanomaterials in the field of HMIs imprinting electrochemical analysis, including carbon-based nanomaterials (such as carbon nanotubes, graphene, graphene oxide, reduced graphene oxide) and carbon-nitrogen nanomaterials), noble-metal  
Figure 3: Basic mechanism of Th electropolymerization.  
nanomaterials (such as gold and silver nanomaterials), metal oxide nanomaterials (such as zinc oxide and magnetic iron oxide nanomaterials), silica nanomaterials, and so on.
## 5 Discussion
### Oil-Source Correlation
#### 5.2.2 Potential Oil-Source Changing  
Individual traps containing fluids from multiple sources are a difficult problem in oil-source correlation analysis [14]. Multivariate statistical analysis is widely used to reduce the complexity associated with variations in the deposition and maturity of samples and the degree of oil mixing in oil-source correlation [4; 5]. The differences in geochemical characteristics between reservoir oils (present) and inclusion oils (paleo) show a possible change in source types over geologic time. This possibility is supported by the HCA, PCA, and basin modeling results, which demonstrate a trend of increasing hydrocarbon generation in the Jurassic coaly sources through time (Figures 10, 11 and 16). In HCA and PCA analysis, the reservoir oils exhibit a higher input of higher terrestrial plants than the inclusion oils (Figures 10 and 11). The differences between the reservoir oils and inclusion oils are probably influenced by the rapid maturation of Jurassic source rocks during the late Himalayan orogen (Figure 13). Significant amounts of hydrocarbons have been generated in the Jurassic coal measures since -5 Ma (Figures 13 and 16C). The levels of maturity and transformation ratios (TR) indicate that Jurassic coaly source rocks can have significant source rock potential in the area, especially the shallow buried source rocks located in the Slope Belt and Anticline Belt (Figure 15).
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### The Floodwater/Permanent Water Mapping Algorithm
#### 3.2.1 Preliminary Operations  
The preliminary operations carried out by the water mapping algorithm are shown in Figure 6, where \(t_{2}\) indicates current time, while \(t_{1}\) indicates the time of the previous S1 acquisition from the same orbit (typically \(t_{2}-t_{1}\) = 6 days). The spatial subsetting divides the S1 mosaicked data in frames (i.e., smaller rasters having a maximum number of lines equal to 17,000) in order to avoid dealing with big rasters, which may require an excessive use of memory. The operations described hereafter are referred to a single frame and are sequentially performed until all the frames are processed.  
The ancillary data and the map produced using the data acquired at \(t_{1}\) are resampled and reprojected onto the spatial grid of the current frame. Then, the exclusion mask is derived from the ancillary data to exclude from the classification areas where radar backscatter is insensitive to the presence of surface water, such as urban areas (identified through the CLC data), dense vegetation (pixels where \(NDVI\) is larger than a threshold equal to 0.7), and areas with complex topography (pixels having a slope larger than 7\({}^{\circ}\)). The last preliminary operation is the application of a difference operator (corresponding to the log-ratio operator because the data are in dB units), which allows AUTOWADE to compare pixel by pixel the S1 images acquired at \(t_{1}\) and at \(t_{2}\) on the geographical area corresponding to the current frame.  
Figure 5: Example of output of the S1 data preprocessing block of AUTOWADE (relative orbit 95 and VV polarization).