esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Validation Procedure  
The validation of all geometrical and numerical parameters used in the FE model involved the numerical and experimental comparison of the load passing the artificial callus. Nevertheless, because the cell load is composed by two composite plates connected by two aluminum tubes, the final stiffness of the tension/compression cell load was verified using a calibration procedure as defined in the ASTM E74 Standard, leading to a stiffness of 220 N/mm. This value was obtained by performing a linear regression of the experimental force-displacement results with the coefficient of determination of 0.9587. Moreover, the load applied over the nylon bars was correlated with the strain gage measurements, by means of tensile and compression tests, and the calibrated equation was defined as,  
\[\mathrm{P}=\frac{\varepsilon-1.3718}{8.1263}, \tag{1}\]  
where \(\varepsilon\) is the deformation value, in micro strains (\(\mu\varepsilon\)), and \(\mathrm{P}\) is the value of load. Equation (1) was used to evaluate the value of load in the artificial fracture focus.  
The experimental results show that the free clamp does not have totally free movement in the rail of the LRS external fixator. In fact, there is a bending effect in the external fixator plane that results from the deformation of the nylon bars, pins, and external fixator, which promotes the rotational movement of the free clamp actuating as a blocking force. This force appears to be dependent on the friction coefficient and on the clearance between surfaces of the clamp and rail of the LRS external fixator. Hence, the spring model, shown in Figure 4a, was proposed to obtain the locking stiffness of the fixator, where each spring represents the stiffness of the elements shown in the Figure 4b.  
The stiffness of the pins was computed assuming that each pin was a cantilever beam which were mounted in parallel. The stiffness of the nylon was evaluated using the axial stiffness of a bar in tension/compression. The locking stiffness is computed as,  
\[K_{s}=\frac{K_{sp}K_{p}}{K_{p}-K_{sp}};\;K_{sp}=\frac{K_{eq}K_{n}}{K_{n}-2K_{eq }}-K_{c}, \tag{2}\]  
Figure 4: External fixator assembly: (**a**) spring model, where \(Kn\) is the nylon stiffness, Ks is the locking spring, and \(Kp\) is the pin stiffness; (**b**) experimental setup.  
where _Keq_ is used to represent the experimental equivalent stiffness of fixator. Equation (2) was used to evaluate the locking stiffness of each configuration for a distance between axes of 70 mm and the results were compared with those obtained numerically. The numerical locking stiffness was evaluated using the results related to the free clamp body, the force in the cell load, as well as the total applied load. Actually, this is similar to creating the free body diagram of the free clamp, pins and nylon bar. Hence, it was computed from the ratio of the average value of the displacement of the free clamp in the compression direction to the resultant force acting on the same clamp.
# Evaluation of Malolactic Starters in White and Rose Winemaking of Moschofilero Wines
###### Abstract  
The aim of the present study was to induce malolactic fermentation (MLF) after alcoholic fermentation (AF) of must of the Moschofilero cultivar, the only 'gris' native grape variety that is cultivated in Greece. For this purpose, _Oranococcus euni_ strains Vinflora(r) CH16, Vinflora(r) Oenos and Vinflora(r) CNe were inoculated after the completion of AF driven by the _Saccharomyces cerevisiae_ strain UCLM S325. Growth of the aforementioned starter cultures was assessed during fermentation by classical microbiological techniques, and verification of their dominance was performed by (GTG)\({}_{5}\) fingerprinting. Assessment of standard enological parameters and colorimetric analysis were performed by established approaches. Identification and quantification of organic acids, ethanol and glycerol was performed by high performance liquid chromatography (HPLC), while the solid-phase microextraction method (SPME), coupled with gas chromatography/mass spectrometry (GC/MS), was employed for the identification and quantification of volatile compounds. Finally, sensory analysis took place according to ISO 13299-2016. The suitability of the starter cultures employed to drive AF and MLF was exhibited; AF and MLF of the white and rose wines were completed after 15 days. Upon completion of AF, substantial differences were observed in the chemical characteristics of the white and rose wines, which were also reflected in the balance descriptor. MLF also resulted in significant changes. In all cases total acidity decreased and volatile acidity and pH value increased, while the vanilla and butter descriptors increased. Interestingly, the color intensity of the rose wines also increased. A series of strain-dependent changes in the chemical composition and sensory analysis of both white and rose wines was also observed.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 3 Practical Application of RF-CNN-LSTM Model  
The door electronic control unit is referred to as the Emergency Drive Control Unit (EDCU) [20], which is the core of the motorized passenger compartment door. The EDCU ensures that the door is accurately and timely closed and locked, realizing its basic on/off door function. In addition, the EDCU can also monitor the position of the door in real time, to ensure that any abnormal situation can be quickly responded to. Each subway door is equipped with an EDCU that is installed in the top box on the upper part of the door body. The principle of the common EDCU is shown in Figure 2.  
The fault distribution of the key components of the door system is shown in Figure 3. According to the actual records of vehicle failure of a certain city in 2023, the fault for the door system failure mainly lied on the EDCU, with the EDCU failures accounting for 65.74% of door system failures. The EDCU directly affects the operation safety and service quality of vehicles, so research of the EDCU failure rate is significant for train safety.  
Taking the subway vehicle EDCUs as the research object, the EDCU failure rate data is used as an example to use the RF-CNN-LSTM model for failure prediction. Since the data are confidential, the train models applied in this paper are replaced by vehicle type A. The EDCU of vehicle type A is selected for failure rate prediction. Vehicle type A was put into service in 2009 and underwent a frame overhaul in 2017-2020. This urban subway vehicle adopts the maintenance mode of planned repair, which carries out a frame overhaul every five or ten years. The basic information of vehicle type A is shown in Table 1.  
The overall distribution of the EDCU faults conforms to the Weibull distribution [21]. And the EDCU fault rate data is obtained by using a two-parameter Weibull function to calculate the gate controller fault rate from the fault count data. The fault number data is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **A** \\ \hline Service (year) & 2009 \\ Overhaul Time (years) & 2017–2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of vehicle type A.  
Figure 3: Fault distribution of the door system.  
Figure 2: Door controller schematic diagram.
## 2 Experimental Setup
### CT Sample Description  
A cylindrical aluminum container with 20 mm in height and 25 mm in diameter was used as a specimen holder for the CT sample in both experiments. Three circular holes with identical diameters of 6.1 mm were drilled into the container body at a 120\({}^{\circ}\) pitch angle. A set of various cylindrical rods with diameters of 6 mm and heights of 6 mm were used to fill the holes as shown in Figure 1a with the following arrangements. (i) An iron (Fe) rod was inserted in the bottom hollow of the first hole (1), followed by an enriched lead isotope (\({}^{208}\)Pb) rod, leaving the third hollow unfilled. (ii) The bottom of the second hole (2) was filled with an enriched lead isotope (\({}^{206}\)Pb) rod, followed by an aluminum (Al) rod and a \({}^{208}\)Pb rod. (iii) A \({}^{208}\)Pb rod was placed in the bottom of the third hole (3), followed by an Fe rod and a \({}^{206}\)Pb rod. Figure 1b displays a picture of the CT sample. An automated scanning system implemented with LabVIEW software was built to scan the CT sample in three dimensions. The scanning system consisted of three components: (i) a three-axis moving stage that can move in three dimensions (vertical (\(y\)), horizontal (\(x\)), and rotational (\(\theta\))), (ii) a data analysis system, and (iii) a detector spectrum recording system which includes a multichannel analyzer (APG7400, Techno AP Co., Ltd., Mawatari, Hitachinaka-shi, Ibaraki, Japan).
## 6 Conclusions  
In conclusion, the implementation of a smart grid demand scheme utilizing the power line communication (PLC) technique marks a significant stride towards a more efficient, responsive, and consumer-driven energy landscape. The integration of PLC technology into the smart grid framework has yielded transformative outcomes that bridge the gap between energy generation, distribution, and consumption. The results underscore the potential of PLC-enabled communication to enhance grid efficiency, minimize disruptions, and empower consumers to actively manage their electricity usage. The combination of real-time communication and dynamic load management has proven instrumental in achieving grid stability and reliability. A swift response to demand fluctuations, enabled by PLC, contributes to a more balanced distribution of load across the grid, reducing the risk of blackouts during peak usage periods. Moreover, the consumer engagement aspect of the scheme has fostered a heightened awareness of energy consumption patterns among users. By providing them with access to their consumption data and incentivizing energy-efficient practices through dynamic pricing, the demand scheme promotes a culture of conscious energy consumption. Furthermore, the interactive nature of PLC-driven demand response initiatives has redefined the relationship between utilities and consumers. The ability of consumers to actively participate in load management signifies a collaborative approach towards a sustainable energy future. This not only benefits individual consumers through reduced utility bills but also contributes to the overall optimization of the grid's performance, achieving an accuracy of 98.87%. In essence, the smart grid demand scheme utilizing the power line communication technique stands as a testament to the potential of technological innovation in revolutionizing the energy sector. Its outcomes reflect an alignment with the goals of increased grid efficiency, consumer empowerment, and environmental sustainability. As the energy landscape continues to evolve, the integration of PLC technology into demand schemes is poised to play a pivotal role in shaping a resilient and consumer-centric energy ecosystem.  
Conceptualization, S.W.T.A.-M. and S.K.; methodology, S.W.T.A.-M. and S.K.; software, S.W.T.A.-M.; validation, S.W.T.A.-M. and S.K.; formal analysis, S.W.T.A.-M.; investigation, S.W.T.A.-M.; resources, S.W.T.A.-M.; data curation, S.W.T.A.-M.; writing--original draft preparation, S.W.T  
Data Availability Statement: [[https://www.kaggle.com/datasets/pcbreviglieri/smart-grid-stability](https://www.kaggle.com/datasets/pcbreviglieri/smart-grid-stability)]([https://www.kaggle.com/datasets/pcbreviglieri/smart-grid-stability](https://www.kaggle.com/datasets/pcbreviglieri/smart-grid-stability)) (accessed on 22 January 2024).
## 6 Test Results
### Jacket Strains  
The circumferential strains were measured for the RR columns on the concrete jacket surface using the strain gauges placed at the positions given in Figure 10d. There were five sections at different heights along the jacket height with strain gauges placed along the section circumference (Figure 10d). The strain gauges labelled as W1 and W5 on the circumferences R1, R2, and R3 and the strain gauges W1 and W3 on the circumference R4 and R5 measured the maximum strains during the tests, as they were located along the column load direction.  
For each measurement section, the mean value of the strains measured by W1 and W5 on R1, R2 and R3 and W1 and W3 on R4 and R5 during the Tol and Tol \(\times\) 2 tests are shown in Figure 16a,b for the RR columns. For each RR column, there was great variation in the jacket strains along the column region at a height of about 150 mm from the column base, aligned with the turned steel segments. This is an experimental measure of the plastic hinge length, which can result in up to half of the theoretical length of 250 mm. For that reason, the length of the turned segment may be shortened, reducing time, cost, and impacts of the repair strategy. The concrete jacket with polymer fibers showed larger circumferential strain compared with the jacket with steel fibers, due to the different deformation capacities of the fibers and the different fiber volume fractions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Column** & **R16-UHS** & **R16-UHP** \\ \hline V\({}_{\text{Rd,cc}}\) & 29.2 & 29.2 \\ V\({}_{\text{Rd,cj}}\) & 254.5 & 230.2 \\ (V\({}_{\text{Rd,cjmin}}\)) & (234.5) & (206.1) \\ V\({}_{\text{Rd}}\) & 283.7 & 259.4 \\ (V\({}_{\text{Rd,min}}\)) & (263.7) & (235.3) \\ BS\({}_{\text{max}}\) & 186.4 & 172.0 \\ \hline \end{tabular}
\end{table}
Table 6: Repaired column shear strength: concrete core contribution (V\({}_{\text{Rd,cc}}\)), concrete jacket contribution (V\({}_{\text{Rd,cj}}\)), column shear strength (V\({}_{\text{Rd}}\)), minimum values of the jacket contribution (V\({}_{\text{Rd,cjmin}}\)), and of the shear strength (V\({}_{\text{Rd,min}}\)).  
Figure 16: Experimental circumferential strain on the concrete jacket of the RR columns with: (**a**) steel fibers (R16-UHS) or (**b**) polymer fibers (R16-UHP).
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 4 Discussion and Recommendation
### Insufficient Coverage of Environment Types  
Above, we assumed that all measurements (or all measurement locations) could be used to form an empirical model. However, if we have to create separate models for each environment (see Figure 1 and Section 3.2), the data set will be split over the different environments. From Table 4 it becomes clear that not every country equally distributed their measurements over locations in the different environments.  
Therefore, when subsequently the empirical data would be used to generate a man-made noise model for each environment type, each model would have a strong bias towards a single country. The remote rural model would represent the Netherlands, the office area model would represent the UK, and the other models would represent Japan.  
The uneven spread of the measurements over different environments is explainable. The preference for measurements in certain environments is driven by the practical need for man-made noise data in these countries. Also, it is not easy to design a system that is sensitive enough to accurately measure Quiet Rural noise levels, as the system must be highly linear to withstand the very strong radio signals in the same or adjacent frequency bands. The German measurement system, for example, did not have the ability to measure Quiet Rural levels.  
Of course, if the noise levels would be the same in these environments regardless of the country, the result would be consistent and usable. But this is not very likely, ans such an assumption would have to be proven first. Our expectation is that differences between these 6 countries, and even more so the significant differences with LMIC's, may require an extra parameter (or several) to account for differences between individual countries and regions. However, despite the number of measurements, the data set is too scarce to determine these parameters empirically.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 2 Related Work of MFSR
This method can be formulated as a convex optimization problem. [PERSON] et al. [19] proposed a blind MFSR algorithm based on combining ANN learning and non-subsampled Contourlet directional image representation. [PERSON] et al. [20] proposed a joint deblurring, denoising and super-resolution method for multi-frame RGB/NIR imaging. Such methods are suitable for complex environments. However, they are not easy to converge, and the quality of the reconstructed HR image is not ideal because there are many uncertain factors to be considered at the same time. In order to accelerate the convergence speed of the algorithm and obtain better image quality, we propose the confidence weight of LR images obtained in image reconstruction, and the parameter value of the motion model are added to the modified motion estimation and blur kernel estimation.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 1 Introduction
LASIS data are usually processed directly as traditional hyperspectral data or rearranged into an interferogram cube called LAMIS [19; 20] in these approaches. In addition, most of the approaches exploit spectral redundancy as well as the spatial dimension and have been lossy compression algorithms [22]. Although these algorithms lead to a much higher compression ratio, information is discarded in the process. In most of the corresponding applications of LASIS, the information loss resulting from the use of lossy compression algorithms is unacceptable. Hence, lossless compression algorithms are needed for this new type of data to avoid useful information retrieval degradation. In this paper, we focus on the lossless compression of LASIS data. Taking into consideration the unique characteristics of LASIS, we have divided the lossless compression process of LASIS images into three components: data rearrangement, prediction, and coding. The novel stage, data rearrange, refers to selecting a permutation of the data cube in which the interference and spatial information along the columns can be separated. In this way, we can make better use of the correlation in the spatial and OPD dimensions. The Multispectral and Hyperspectral Data Compression (MHDC) working group of the Consultative Committee for Space Data Systems (CCSDS) has designed the standardized CCSDS-123, primarily for the onboard lossless compression of multispectral and hyperspectral images [23]. This standard is then employed in the two later stages because it has a combination of low computational complexity and compression effectiveness. The standard includes several user-specified configuration parameters, and previous work has allowed users to determine suitable values for these parameters for a variety of sensors [12; 24; 25]. However, relevant studies on LASIS have not been performed. Finally, a series of experiments are conducted to evaluate the efficiency of the proposed compression scheme on real LASIS data and optimize the compression settings involved. These experimental results can offer instructive guidance regarding lossless compression in other hyperspectral interferometers based on LASIS-like principles. The subsequent sections of this paper are organized as follows: Section 2 presents an overview of the imaging principle and the unique data characteristics of the LASIS sensor. Section 3 details the proposed compression scheme. Section 4 presents the experimental results of the proposed techniques conducted on real LASIS data. Section 5 provides some conclusions and summarizes the paper.
## 4 Analysis of Results  
### Analysis of Subsidence Monitoring along the Great Wall Area  
The primary focus of this study was the 250 m buffer zone along the Ming Great Wall in Shanxi Province, with a reference point coordinates of (112.5\({}^{\circ}\) E, 39.8\({}^{\circ}\) N). The average annual rates of vertical ground deformation along this buffer zone were obtained and presented in Figure 4, while Figure 5 illustrates the corresponding components of vertical ground deformation. The analysis reveals that the majority of deformation rates for the Great Wall range from \(-10\) mm/year to \(10\) mm/year, with deformation components concentrated between \(-60\) mm and \(40\) mm. Notably, significant subsidence is observed in several segments, namely the Yulong Bay section (D1), the Shuocheng section (D2), the Youyu section (D3), and the Lierkou section (D4). Among these four key subsidence segments, the maximum rate of subsidence reaches \(-30.11\) mm/year, with the maximum subsidence measured at \(-148.63\) mm.
## 3 Data and Methodology  
### Data  
Our experimental dataset included 950 companies in the MENA region. All of the selected companies are from different sectors, including manufacturing, technology, energy, telecommunications, real estate, and insurance. Data were collected from the Osiris global company database (source: [[https://www.bvdinfo.com/en-gb/our-products/data/international/Osiris](https://www.bvdinfo.com/en-gb/our-products/data/international/Osiris)]([https://www.bvdinfo.com/en-gb/our-products/data/international/Osiris](https://www.bvdinfo.com/en-gb/our-products/data/international/Osiris)) (accessed on 20 December 2022)). Based on the availability of the data, we selected two consecutive years from 2012 to 2019 for each company, for a final tally of 102 fraudulent years and 1798 non-fraudulent years. The financial indicators in the database are taken from the respective companies' financial statements and balance sheets. The details of the 26 financial attributes, including financial attributes from the Beneish model [13], are provided in Table 2. All attributes are quantitative, with the target value being discrete and the others being continuous. Professor [PERSON], in June 1999, published his study \"The Detection of Earnings Manipulation\" in which he argued that high sales growth, declining gross margins, soaring operating expenses, and increasing leverage encourage companies to manipulate profits. Such companies are most likely to alter profits by speeding up sales recognition, increasing accruals and cost deferrals, and minimizing depreciation. Therefore, we have taken these attributes into account in our study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Description** \\ \hline a1 & Accounts receivable \\ a2 & Sales \\ a3 & Cost of Goods Sold (COGS) \\ a4 & Current asset \\ a5 & Fixed assets \\ a6 & Total assets \\ a7 & Depreciation \\ a8 & General and administrative expenses \\ a9 & Long term debt \\ a10 & Total current liabilities \\ a11 & Change in current assets \\ a12 & Change in cash \\ a13 & Change in current liabilities \\ a14 & Change in income tax payable \\ a15 & Current maturity of long term debt \\ a16 & Current maturity of LTD \\ a17 & Change in current maturity of long term debt \\ a18 & Amortization \\ a19 & Day’s Sales in Receivables Index (DSRI) \\ a20 & Gross Margin Index (GMI) \\ a21 & Asset Quality Index (AQI) \\ a22 & Sales Growth Index (SGI) \\ a23 & Depreciation (DEPI) \\ a24 & Sales, General and Administrative Expenses \\ a25 & Leverage Index (LVGI) \\ a26 & Total Accruals to Total Assets (TATA) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Financial attributes.