esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 3 Analysis of the Research Object Maintenance Process
### Analysis of the Duration of Periodic Maintenance of the M-346 Aircraft  
An analysis of archive data was conducted, with a focus on examining the detailed work cards for each periodic service. Based on the information gathered, a list of periodic services performed at the Polish Air Force (PLAF) and their respective durations was compiled.  
Between 2017 and 2020, a total of 35 periodic Calendar Post-Flight Inspection (CPFI) services were conducted at the PLAF. Each service was analyzed to identify factors that could influence the total service duration.  
In some cases, the CPFI service coincided with a higher-level Post-Inspection (PI) service, resulting in both services being carried out in parallel. The execution of technical tasks included in the CPFI service was dependent on the progress of the PI service [44]. This often led to an extension of the overall maintenance duration. A total of eight CPFI services were carried out alongside PI services.  
Another factor contributing to extended service durations was the unavailability of spare parts or logistic delays in their delivery. The CPFI service itself does not involve any replacements or repairs but focuses primarily on a thorough verification of the aircraft's systems. However, if defects are identified during the inspection, defective components must be replaced. The duration of five CPFI services was extended due to delays in the availability of spare parts.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **2018** & **2019** & **2020** \\ \hline I quarter & 98.76\% & 87.96\% & 75.58\% \\ II quarter & 94.69\% & 68.67\% & 72.57\% \\ III quarter & 100.00\% & 80.18\% & 76.11\% \\ IV quarter & 94.69\% & 53.27\% & 76.11\% \\ Annual & 96.81\% & 64.78\% & 75.75\% \\ \hline \end{tabular}
\end{table}
Table 1: Airworthiness percentage of the M-346 aircraft in 2018–2020 in the Polish Air Force.  
Taking into account the CPFI services performed independently and without logistic delays, we can distinguish 22 services. In the surveyed population of 22 services, the duration of the service (days) can be characterized by the basic statistical parameters listed in the table below (Table 2).  
The presented parameters indicate a significant variation in service durations, as shown in the graph (Figure 1). Such a phenomenon may indicate major organizational problems, problems with planning and scheduling work.  
It is also important to note that the analyses conducted do not encompass other projects performed by technical personnel, which arise from the nature of military service. These include daily musters, shooting training, physical education classes, adherence to regulations, and other routine activities.
## 1 Introduction
It is noteworthy that the state of an electronic motor usually varies with time; the storage and memory of historical information can help to learn the operation mechanism of wind turbines. However, to our knowledge, the above methods do not effectively use the time-varying characteristics of SCADA data. In the literature, we noticed that echo state networks (ESN) [15; 16] can achieve the fast learning of historical law. Therefore, we will use ESN to predict the corresponding parameters of the components in electronic motors. According to the failure frequency of each component of an electronic motor and its importance to the operation system, we propose in this paper a status monitoring model for the pitch motor of the wind turbine from the perspective of normal behavior modeling. The model is based on the SCADA data collected in a wind farm in China. The main goal of the built model is to realize fault warnings. Based on the fact that normal behavior modeling is usually composed of three ingredients, i.e., feature selection, predictive regression analysis and state assessment. Our proposed method is constructed as follows. First, according to the operation mechanism of the pitch motor, the data are preprocessed and some features are selected. Then, the ESN model is used to predict the pitch motor temperature. Finally, the EWMA (exponentially weighted moving average) state assessment is used to build the upper and lower limits of the early warning control, which realizes the effective tracking of the fault components of the pitch motor. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and provide an early warning about the faults of the pitch motor. The rest of the paper is organized as follows. Section 2 introduces the ESN model and its working mechanism. In Section 3, the technique to assess the built model is presented. Section 4 conducts some experiments to validate and compare the behavior of the ESN model with other models. Finally, the paper is concluded in Section 5.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.7 Breakpoint Model  
This model [29] is a modification of the ITU-R model [107] and considers \"breakpoints\" in the rainfall rates in tropical and sub-tropical climates.  
The main steps of determining attenuation with the Breakpoint model are as follows:  
(1) Calculate the \"percentage of exceedance\" at the breakpoint:  
\[p_{R_{B}}\;of\;0.01\%=p_{A_{B}}\;of\;0.021\% \tag{23}\]  
(2) Compute:  
\[A_{p}=\left\{\begin{array}{ll}A_{B}\Big{(}\frac{p-0.011}{0.01}\Big{)}^{P_{1} },&0.021\leq p<1\\ A_{B}\big{(}\frac{p}{0.021}\big{)}^{P_{2}},&p<0.021\end{array}\right. \tag{24}\]  
where \(P_{1}\), and \(P_{2}\) are given by Equations (25) and (26):  
\[P_{1}=-(0.655+0.033\ln(p-0.011) \tag{25}\] \[-0.045\ln(A_{B})-\beta(0.989-p)\sin\theta)\]  
\[P_{2}=-0.5(0.655+0.033\ln(p^{2}) \tag{26}\] \[-0.045\ln(A_{B})-\beta(0.989-p)\sin\theta)\]  
The experimental results agreed with the ITU-R model [107] regarding correction factor-induced attenuation, especially at elevation angles less than 60\({}^{\circ}\). Algorithm 7 shows the procedure for this model.  
Advantages  
This model determined the \"breakpoint\" in the tropical region and introduced a correction factor to the rain attenuation model with an elevation angle range 40\({}^{\circ}\leq\theta<60^{\circ}\).  
Limitations  
The correction factor that is proposed in this model has not been analytically validated.
# Myoepithelioma of the Hand: A Systematic Review
## 4 Discussion
### Prognosis and Predictive Factors  
According to [PERSON] and [PERSON] [38], myoepitheliomas have lower recurrence rates post-complete surgical resection compared to pleomorphic adenomas, with only one recurrence in sixteen cases observed over 1 month to 7 years [4]. [PERSON] et al. found that 93% of myoepithelial tumors followed a benign course, while 7% exhibited malignant behavior [38]. We found only one recurrence described [17], accounting for 13.3%.  
The progression of cutaneous myoepithelioma does not always align with its histological features. While most metastatic myoepitheliomas in the skin and soft tissue exhibit cellular abnormalities, a high rate of cell division, and invasive growth, there are a few cases that behave malignantly despite lacking these features [39]. A high rate of cell division is often linked to the risk of recurrence or spread in tumors with otherwise benign characteristics [40]. Cellular atypia is the primary indicator of malignancy and the best predictor of aggressive behavior. However, even without atypia, factors such as a high mitotic rate, dense chromatin, prominent nucleoli, nuclear pleomorphism, and necrosis can indicate an increased risk of malignancy [41]. The recurrence rate for cutaneous myoepitheliomas is about 20%, but the prognosis is generally favorable even after recurrence [42]. Therefore, the recommended treatment is complete surgical removal of the lesion with clear margins [28].  
Spindle cell myoepithelioma of the hand generally shows a favorable prognosis following complete surgical excision; low recurrence rates are typical with adequate surgical margins, necessitating close follow-up to monitor for any signs of recurrence [16]. Although rare, there is a potential for malignant transformation, emphasizing the importance of long-term follow-up. Patients typically achieve good functional recovery and quality of life post-treatment; most patients regain normal hand function with appropriate surgical and rehabilitative care. This highlights the importance of comprehensive management and ongoing surveillance to optimize outcomes for patients with myoepitheliomas in the hand and wrist.  
Additionally, the rarity of myoepitheliomas in the hand adds complexity to their diagnosis and management, making it crucial for clinicians to be well-versed in their clinical and histological features [43]. Early and accurate diagnosis, coupled with a clear understanding of the tumor's behavior, is essential for ensuring successful treatment outcomes. Moreover, multidisciplinary care involving surgeons, pathologists, and radiologists can significantly improve the management and prognosis of these patients. Regular follow-ups are necessary to detect any early signs of recurrence, and patients should be counseled on the importance of monitoring and reporting new symptoms. Overall, a proactive approach in the diagnosis, treatment, and follow-up care of myoepitheliomas in the hand can greatly enhance patient outcomes and reduce the risk of recurrence or complications.
## 3 Results and Discussion
### Lettuce Growth  
The germination test, recorded within seven days of sowing, gave germination percentages between 80% on the control substrate and 95%, the latter obtained both on the substrate plus APNUT_BSF and plus APV50_BSF. In each pot, four to five lettuce plants were observed (Table 3), indicating that the climatic and soil conditions were adequate and favorable for the selected plant species. No visible symptoms of phytotoxicity were recognizable on the leaves. The plants obtained on the substrate with the two types of PIMs showed better results for all the parameters considered, compared to the plants growing on the control substrate. Figure 6 shows the results of the growth test, recorded after 21 days of cultivation. Plants grown on the substrate with fertilizer PIMs had higher fresh (TFM) and dry mass (TDM). An increase in fresh weight of lettuce of 47.6% with APV50_BSF and 50.8% with APNUT_BSF was observed, compared to lettuce grown on the control substrate. This suggests that the fertilizer PIMs could improve the growth of lettuce. The average dry weight of the plants increased instead to 112.4% and 125.7%, respectively, with the use of vitrified and nonvitrified fertilizer PIMs compared to that in the control test. Furthermore, a greater average leaf area (LA) was measured in lettuce plants grown on substrates with APV50_BSF (45.5%) and APNUT_BSF (50.4%) compared to that of those grown on the control one. These first results suggest that the use of fertilizer granules was relevant for obtaining larger plants with more biomass and larger leaves, using a substrate poor in natural soil. According to these first tests, no significant growth differences were observed when comparing the use of vitrified and nonvitrified fertilizers, although better results were obtained with APNUT_BSF for all the observed parameters. Therefore, the growth data suggest the interchangeability of the two PIMs formulations.  
Figure 5: Bacterial viability (BV (%)) data and antibacterial assay figures. CT = control.
## 3 Case Study  
### Overview of the Project  
The Shenzuo tunnel is a key project of the Maerkang-Jiuzhi Expressway. The Maerkang-Jiuzhi Expressway, one of the important sections of the Dema Expressway, is an important corridor connecting the Chengdu Plain Economic Zone, the Qiang District of the Aba Tibetan Region, and Qinghai Province, as shown in Figure 11. The tunnel under construction in China had a complex geological structure and was subject to strong neo-tectonic movement. The survey area distribution includes accumulated Quaternary and Holocene landslide rock soil, residual silty clay slopes, and breccial and moraine stratum breccial soil. The underlying bedrock is Triassic middle dolomite limestone, lower sandy slate, slate, limestone, lamellar basalt, and fault breccia. According to geological prospecting data, the maximum stress in the tunnel body range was 19-38 MPa, and the lithology in the maximum buried depth of the tunnel was lamellar basalt, sandy slate, and limestone. According to the study's experimental statistics, the compressive strength of the lamellar basalt was R = 23-43 MPa, and that of the limestone was Rc = 23-46 MPa, in which the values were deemed to belong to hard rock with a relatively developed structure, rich water, and local dryness. The strength and stress ratio of the surrounding rock was 1.15:1.65. [PERSON] [26] proposed the concept of deep-engineered soft rock based on the nature of engineered rock bodies. According to the differences in the characteristics of engineered deep soft rock and the mechanism of generating significant plastic deformation, deep soft rock can be classified into four major categories, i.e., expansive deep soft rock, high-stress deep soft rock, jointed deep soft rock, and composite deep soft rock. In this study, significant plastic deformation occurred during the tunnel construction, which belongs to high-stress soft rock.  
An I-22b steel frame with a spacing of 60 cm had been adopted in the original tunnel design. The thickness of sprayed C25 concrete was 28 cm, and a 22-connection steel frame was used longitudinally, 80 cm in length and a ring spacing of 100 cm. Continuous large-scale deformation occurred during the original tunnel construction, which resulted in a series of safety problems such as cracking of the initial support structure, buckling of I-beams, and cracking and falling of concrete blocks in the second lining side wall. A partial collapse  
Figure 11: Study area.  
had occurred during demolition, resulting in many workers sustaining injuries. To ensure construction safety and engineering quality, reinforced support and double- or triple-layer arches were adopted during remedial strengthening works, despite which concrete collapsing and steel frame buckling continued to take place, as shown in Figure 12.
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 5 Experiment Results
### Forecasting Accuracy  
Error measures were calculated to assess the feasibility of both the approaches stated in the Methodology and Implementation section. The MAPE, MSE, MAE, and RMSE were employed to substantiate the performance of the suggested models. Equations (8)-(11) depict their formulas. These three indexes are stated as:  
\[MAPE=\frac{1}{n}\sum_{t=1}^{n}\biggl{(}\frac{\mid\mathit{real}_{t}-\mathit{ predict}_{t}}{\mathit{real}_{t}}\biggr{)}\ x\ 100 \tag{8}\]  
\[MAE=\frac{1}{n}\sum_{t=1}^{n}\biggl{(}\frac{\mid\mathit{real}_{t}-\mathit{ predict}_{t}}{\mathit{real}_{t}}\biggr{)} \tag{9}\]  
\[MSE=\frac{1}{n}\sum_{t=1}^{n}(\mathit{real}_{t}-\mathit{predict}_{t})^{2} \tag{10}\]  
\[RMSE=\sqrt{MSE} \tag{11}\]  
where, \(\mathit{real}_{t}\) is the actual value, \(\mathit{predict}_{t}\) is the predicted value, and \(t=1\dots\ n\) and \(n\) are the number of observations.  
The outcomes for each of the methods are outlined in the Methodology and Implementation section after conducting tests with two different approaches.
## 2 Materials and Methods
### Method
#### 2.3.3 Analysis of the LOS Variation Influence on Annual GPP Changes  
Annual GPP variation is primarily influenced by the length of the growing season (LOS) and maximum photosynthetic capacity (\(GPP_{max}\)). To isolate their individual impacts, we used a statistical model (Equation (6)) on boreal vegetation pixels, simulating observed annual GPP using LOS and \(GPP_{max}\)[42]. By holding LOS at the 1982 value as an initial baseline level and allowing \(GPP_{max}\) to vary, we conducted a control experiment to simulate annual GPP without LOS variations (\(GPP_{\text{XLOS}}\)). The difference between observed GPP and \(GPP_{\text{XLOS}}\) represents the GPP variability induced by LOS variations (\(GPP_{LOS}\)). To assess the contribution of LOS variation on long-term GPP changes, we introduced a new statistical metric, named Relative Incremental Effect (RIE), by calculating the ratio of linear trend in \(GPP_{\text{LOS}}\) to the original GPP time series.  
**Annual GPP simulating with SMIPP model.** Following previous studies [42], the annual GPP was quantitatively simulated as the statistical model of integrating phenology and maximal photosynthetic capacity (SMIPP) (Equation (6)).  
\[GPP=\alpha\cdot GPPmax\cdot LOS \tag{6}\]  
where GPP is the annual cumulated gross primary productivity (g C meter\({}^{-2}\) year\({}^{-1}\)), \(GPP_{max}\) is the maximum daily GPP derived from the fitted GPP curve with Equation (1) at POS, LOS is the length of the growing season, and \(\alpha\) is a coefficient estimated using a linear regression analysis between annual \(GPP\) and \(GPP_{max}\times LOS\).  
**Relative contribution assessment.** After constructing the pixel-level SMIPP model, the simulated \(GPP_{\text{XLOS}}\) could be estimated by holding LOS at the 1982 value (\(LOS_{0}\)) with Equation (7).  
\[GPP_{(XLOS)}(i)=\alpha\cdot LOS_{0}\cdot GPP_{(max)}(i) \tag{7}\]  
where \(GPP_{max}(i)\) is the maximum daily GPP at year \(i\) and \(GPP_{(XLOS)}(i)\) is the simulated GPP excluding the contribution of LOS.  
Then, the GPP variability (\(GPP_{LOS}\)) induced LOS changes could be calculated with Equation (8).  
\[GPP_{(LOS)}(i)=GPP(i)-GPP_{(XLOS)}(i) \tag{8}\]To compare the contributions of LOS changes to annual GPP variations during these two study periods, the pixel-level RIE for each period (P1 and P2) was calculated as the ratio of the linear trend in \(GPP_{LOS}\) to the absolute linear trend of the original GPP time series.  
\[RIE=\frac{\Delta GPP_{LOS-SLOPE}}{|GPP_{SLOPE}|} \tag{9}\]  
where \(\Delta GPP_{LOS-SLOPE}\) is the linear trend of \(GPP_{LOS}\) and \(GPP_{SLOPE}\) is the linear trend of observed annual GPP time series.
## 2 Computational Methods
Because of the way it is constructed, the orbital polarization often enhances spin-orbit coupling, leading to better orbital moments [114]. Both the generalized gradient and the local-density approximations (GGA-PBE and LDA) [101; 115] are applied. For the computation of the magnetic anisotropy energy, i.e., the difference in total energy between spins pointing in plane (100) or perpendicular to the plane along the \(z\) axis (001), one finds small differences. To resolve these small numbers, one has to be very careful calculating the total energy for the two cases as consistent as possible. Here, we decide to remove all crystal symmetry, thus, from a symmetry set-up standpoint, the calculations are identical. This approach is less efficient but in practice more accurate because errors associated with different \(k\) points, Brillouin zones, and other numerical approximations are minimized. Brillouin zone integration over 10,000 \(k\) points is carried out using Fermi-Dirac broadening corresponding to room temperature. The CALPHAD method permits the prediction of phase stability and thermodynamics of multi-component systems based on the extrapolation of the phase diagram data and the thermodynamic properties of its constitutive binary and ternary systems [116; 117; 118]. The CALPHAD formalism consistsof modeling Gibbs energy functions for each phase of the system (potentially including all elements) to reproduce the available thermodynamic (e.g., heat of formation) and phase diagram (e.g., melting point) data. Once the parameters of these functions are assessed for the constitutive systems, extrapolation (or prediction) can be made for multi-component systems. Here, we focus on heat of formation (thermodynamic property), phase diagram data (e.g., melting point, solubility limits), and CALPHAD assessment of the YTM\({}_{5}\) compound with TM = Co, Fe, and Ni as a first step toward the establishment of a complete Y-Co-Fe-Ni database. Model parameters optimization and CALPHAD calculations are performed using the Thermo-Calc software package (2019b version) in combination with its PARROT module [119; 120].
## 2 Materials and Methods
### BRIEF Descriptor  
When the image quality is not high in a light-changing environment, some mismatch point pairs remain after optical flow. To eliminate the mismatch point pairs, the BRIEF descriptors of the remaining feature points are calculated, and the matching point pairs with low similarity are eliminated using global threshold. BRIEF descriptor is a binary local feature descriptor used for image matching and target recognition. The BRIEF descriptor operates by randomly sampling local regions of an image and comparing the grayscale values of the sampled points. Based on the comparison results, a binary string is generated to serve as a descriptor for the region. The descriptor is centered around a feature point and encompasses a neighborhood window of size \(S\times S\). Within this window, \(N\) pairs of random points are selected (usually \(N=256\)), and the grayscale values of each pair are compared. The resulting comparison values are converted into binary codes, which are concatenated to form the BRIEF descriptor for the feature point.