esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Data Preprocessing Methods  
The improved Z-score threshold method was used to identify the outliers caused by drilling vibration or instrument noise during directional drilling while drilling. By calculating the statistical distribution characteristics of the parameters while drilling, the method quantifies the degree of deviation of the data points from the normal response range, which is defined as  
\[z=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(z\) is the standardized anomaly score, and \(\mu\) and \(\sigma\) are the mean and standard deviation of the same lithology samples in the current well section, respectively. When the dynamic threshold is set \(|z|>3\), it is determined as an outlier value, and the discrete noise and real geological anomalies are distinguished by combining the drilling condition log. This method eliminates the mechanical interference signal and retains the slight change characteristics of the thin reservoir lithologic interface.  
According to the spatial stability principle of the lithology-log response relationship in the same sedimentary period, the instrument system errors and regional background field differences are eliminated through the standardization of logging curves so as to ensure that the logging values of the same lithology strata at different well locations have the same order of magnitude and distribution characteristics. Standard wells with typical lithology characteristics and excellent data quality are selected, and the mean and standard deviation of logging parameters of the target stratum are extracted as reference values, and the cross-well data standardization is realized by linear transformation, as follows:  
\[V_{norm}=\frac{V_{raw}-\mu_{raw}}{\sigma_{raw}}\cdot\sigma_{std}+\mu_{std} \tag{2}\]  
where represents \(V_{norm}\) the normalized curve value; \(V_{raw}\) is the original curve value; \(\mu_{raw}\) and \(\sigma_{raw}\) represent the local mean and standard deviation of the well to be corrected, respectively; and \(\mu_{std}\) and \(\sigma_{std}\) represent the mean and standard deviation of the corresponding curve of the standard well.  
In this study, we assume a stationary relationship between LWD parameters and engineering drilling parameters within the same geoststratigraphic interval. Within each interval, the LWD tools and drilling mechanics exhibit relatively stable responses due to controlled drilling practices and limited interval lengths. Although dynamic effects such as thermal disturbance, borehole enlargement, and tool-rock interaction may introduce local non-stationarity, their impact is mitigated through the outlier detection and curve normalization methods described above. Therefore, the assumption of local stationarity within stratigraphic units serves as a practical and reasonable approximation for model training and interpretation.
# Bearing Severity Fault Evaluation Using Contour Maps--Case Study
## 1 Introduction
In finance [40], contour maps are used to represent the estimation error and global minimum risk. In manufacturing [41], contour maps are used as a graphical tool to estimate the machining error or kinematic error. In meteorology [42], contour maps are used to represent weather conditions. In navigation [43], contour maps are used to represent risk regions and safe navigation routes. In additive manufacturing [44; 45; 46; 47], contour maps are used to describe the details of the model to be 3D printed. The literature on the application of contour maps to condition monitoring is very scarce. [PERSON] and [PERSON] [48] proposed the use of Iso-Map in wireless sensor networks to reduce energy costs. [PERSON] et al. [49] used contour maps to monitor and diagnose sensor networks to visualise the network in the spatial and temporal domains, as well as to detect sensors that are failing; however, no publication has been found that applies contour maps to bearing failure level classification. On the other hand, the existing fault classification methods can be divided into indirect ones, which require the implementation of artificial intelligence [13; 50], and direct methods [13; 50]. The use of contour maps to graph and predict the level of failure is a direct method that does not require a priori learning. However, unlike other direct methods, it gives a continuous idea of all the failure states of the bearing from which its failure map or footprint has been obtained. Furthermore, for each record to be classified, there will be a multitude of points on each map associated with the frequencies studied (i.e., fundamentals and harmonics). Thus, the proposed method will be a robust tool for fault diagnosis. The present work presents the first steps to assess the possible use of contour maps, based on Envelope Analysis, to generate a bearing degradation map. This map will allow us to classify the failure status of a bearing, based on its footprint or failure map. As it is represented in two dimensions, it offers a broader view of the relationship between the frequency of failure, level of failure and amplitude associated with each fault level. For this, a work methodology was developed and implemented in MATLAB(r), which was applied to real measurements. The results suggest that this methodology can help in the prediction of bearing failures. The document is organised as follows: Section 2 analyses the steps and criteria considered in the construction of contour maps. Section 3 reviews the diagnostic results of different registries. Finally, Section 4 presents the conclusions of this study and analyses directions for future work.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 2 Solar Irradiation Time-Series Analysis
### Data Quality Control
#### 2.3.1 Time-Stamp Correction  
The elimination or minimization of uncertainties in the kt time series must be realized [47; 48; 49], so the results will be relevant only if the clear sky and the measured solar irradiances are correctly time-stamped. It is quite common to observe a time lag (ranging from a few minutes to about 20 min) between the two types of series defining the clear-sky index: GHI and GHICs. The effect is that it is systematically observed in higher measurements than the clear sky at sunset or sunrise. There are several explanations that can be put forward. The first is related to a mislocation (or lack of precision) of the measurement stations (which is unlikely but possible in some cases). Then an error can occur when entering the geographical characteristics of the stations in the McClear interface or a problem of clock between the measurement and the estimate by clear sky. A retiming strategy is proposed to minimize the delay between two time series. For this purpose, the methodology is as follows:  
* Operate a clear-sky shift (\(\Delta t\)) from \(-60\) min to \(+60\) min (by steps of 1 min), based on a simple linear interpolation (121 time series are obtained).
* Compute for the 121 GHICs series generated previously the mean square error (MSE) compared to measurement (GHI).
* Retain \(\Delta t\), the offset that allows us to obtain the minimum MSE.
* Propose the new corrected clear-sky series, which corresponds to the linear interpolation of the series shifted by the offset, \(\Delta t\) (\(GHI_{cs}^{Corrected}(t)=GHI_{cs}(t+\Delta t)\)).  
The offset, \(\Delta t\), is thus the parameter to find and then apply to GHICs in order to find a better concordance in terms of the MSE between GHI and new GHICs.  
As an example, the impact of this shift is seen in Figure 5. Considering the correction and 1 year of acquisition (30 min time step), the average value of kt is decreased by almost 1%, from 0.5814 to 0.5761, and the variance by 4.2% (from 0.1136 to 0.1088).  
Now that the series is correctly recalibrated, we have to check if they are all usable.
## 2 Methodology
### Elastic Properties  
Elastic constants are critical indicators to understand the response of a material to applied macroscopic stress. The tensor of elastic constants \(C_{ijkl}\) is defined by the generalized [PERSON]'s law expressing linear relationships between stress tensor components \(\sigma_{ij}\) and strain tensor components \(\varepsilon_{kl}\) where each of the indices \(i\), \(j\), \(k\), and \(l\) runs over the Cartesian basis vectors \(x\), \(y\), and \(z\). The elastic tensor is commonly represented as a \(6\times 6\) matrix using the following short-hand notations for pairs of indices \(\{ij\}\) and \(\{kl\}\): \(xx\equiv 1\), \(yy\equiv 2\), \(zz\equiv 3\), \(yz\equiv 4\), \(xz\equiv 5\), and \(xy\equiv 6\). Owing to the structural symmetry, the number of independent non-zero elastic constants decrease from 21 to 13 for the monoclinic system, which is expressed as follows,\[C_{ij}=\left[\begin{array}{cccccc}C_{11}&C_{12}&C_{13}&0&C_{15}&0\\ C_{12}&C_{22}&C_{23}&0&C_{25}&0\\ C_{13}&C_{23}&C_{33}&0&C_{35}&0\\ 0&0&0&C_{44}&0&C_{46}\\ C_{15}&C_{25}&C_{35}&0&C_{55}&0\\ 0&0&0&C_{46}&0&C_{66}\\ \end{array}\right], \tag{3}\]  
The strain energy, which is defined as the change in the total energy per unit cell \(E(V,\varepsilon_{i})\) relative to the ground state energy \(E(V_{0},0)\) of the unstrained crystal, may be expanded as a Taylor series in terms the components of the strain tensor \(\varepsilon_{i},i=1,\ldots,6\)  
\[E(V,\varepsilon_{i})=E(V_{0},0)+V_{0}\sum_{i}^{6}\sigma_{i}\varepsilon_{i}+ \frac{V}{2}\sum_{i,j=1}^{6}C_{ij}\varepsilon_{i}\varepsilon_{j}+\text{O}(\{ \varepsilon_{i}^{3}\}). \tag{4}\]  
where \(V_{0}\) is the undistorted unit cell volume. For the monoclinic structure, elastic constants include 13 independent non-zero tensor components, namely \(C_{11}\), \(C_{22}\), \(C_{33}\), \(C_{12}\), \(C_{13}\), \(C_{23}\), \(C_{44}\), \(C_{55}\), \(C_{66}\), \(C_{15}\), \(C_{25}\), \(C_{35}\) and \(C_{46}\)[44]. They may be calculated using the following thirteen different strains,  
\[\begin{array}{llll}\varepsilon^{1}=(\delta,0,0,0,0,0),&\varepsilon^{2}=(0, \delta,0,0,0,0),&\varepsilon^{3}=(0,0,\delta,0,0,0),&\varepsilon^{4}=(0,0,0, \delta,0,0),\\ \varepsilon^{5}=(0,0,0,0,\delta,0),&\varepsilon^{6}=(0,0,0,0,0,\delta),& \varepsilon^{7}=(\delta,\delta,0,0,0,0),&\varepsilon^{8}=(0,\delta,\delta,0,0, 0),\\ \varepsilon^{9}=(\delta,\delta,0,\delta,0,0),&\varepsilon^{10}=(0,0,0,\delta, \delta,0),&\varepsilon^{11}=(\delta,0,0,0,\delta),&\varepsilon^{12}=(0,\delta, 0,0,\delta),\\ \varepsilon^{13}=(0,0,\delta,0,0,\delta).&\end{array} \tag{5}\]  
The strain amplitude \(\delta\) was varied in steps of 0.005 from \(-0.025\) to 0.025. A detailed description of the calculation procedure used here can be found in Refs. [44; 45].
## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.4 Evaluation Metrics for Various Graph Generation Models  
Validity MetricThe validity metric is a crucial evaluation measure in graph generation systems. It assesses the extent to which the generated graphs comply with the rules and constraints of a particular domain. For instance, in the context of generating molecular graphs or designing molecule graphs, validity ensures that the generated structures adhere to the chemical bonding rules and other molecular properties. The junction tree variational autoencoder (JT-VAE) [57] model is an example of a graph generation model that utilizes the validity metric. By leveraging a junction tree representation and variational autoencoder framework, the JT-VAE [57] aims to generate molecular graphs that are structurally valid and consistent within the chemical context it operates in.  
_Reconstruction Accuracy:_ Reconstruction accuracy is a significant evaluation metric that measures how faithfully the generated graphs reproduce the original input or reference graphs. It assesses the ability of the graph generation system to capture the essential features and characteristics of the input data. The JT-VAE [57] model, mentioned earlier, not only focuses on generating valid molecular graphs but also places emphasis on accurate reconstruction of the input molecular structures. By striving to capture the molecular features and properties as accurately as possible, the JT-VAE [57] aims to achieve high reconstruction accuracy.  
_N.U.V (Novel, Unique, and Valid Molecules):_ The N.U.V metric evaluates the novelty, uniqueness, and validity of generated molecules. It measures the degree to which the generated molecular structures are novel (different from existing molecules in a given dataset), unique (not redundant or similar to other generated molecules), and valid (adhering to the chemical rules and constraints). The MoFlow [76] model is an example of a graph generation system that incorporates the N.U.V metric in its evaluation criteria. MoFlow [76] focuses on generating molecular graphs based on chemical properties and aims to produce molecules that are both novel and unique while also maintaining validity in accordance with the underlying chemical knowledge.
# Taxonomy of Scheduling Problems with Learning and Deterioration Effects
## 3 Results
### Solution Methods
#### 3.4.3 Heuristics  
Heuristic procedures are widely used due to the NP-hard nature of the scheduling problem, considering learning or deterioration effects. More than 21.5% of the short-listed papers propose heuristics to solve large instances. The results obtained from the use of heuristics depend on the system configuration: single machine problem (14.3%), parallel machine problem (25.7%), and flowshop problem (40.3%). Dispatching rules are the most frequently employed e.g., earliest due date (EDD), shortest processing time (SPT), weighted shortest processing time (WSPT), as well as the Framinan and Leisten (FL) and Nawaz, Enscore, Ham (NEH)-based heuristics [101; 102]. Note that most of the authors tested the performance of the heuristics against each other. Likewise, heuristic algorithms have regularly been used to obtain the initial solution for the implementation of some metaheuristics. Constructive methods have great relevance to solving problems with variable job processing time. The case of the NEH algorithm is known, which showed good performance with problems with a high number of machines [103] and minimized the makespan as an objective function (among the most common objective functions).
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Satellite Missions for Inland Water Level Monitoring
#### 3.1.1 Radar Altimetry Satellite Missions
In [71] is indicated that HY-2C has the highest temporal resolution among seven altimeters, with a temporal resolution of 7.5 days at one station, although it has the largest RMSE and therefore the lowest accuracy. However, the overall accuracy of HY-2C has a certain improvement compared with HY-2B satellite, while based on its temporal resolution it has great potential for more detailed inland water level measurements. Moreover, it is expected that the follow-up satellites of the HY series will attain greater accuracy. The quantitative assessment of radar altimetry satellite missions is included in Sections 3.2.1 and 3.2.2 together with the relevant waveform retracking algorithms.
## 2 Background on MRAM and Its Variation
### Experimental Setup  
A four-layer PCB is designed to interface the MRAM chip with a logic analyzer and Digilent Basys3 [25]. The PCB is designed with minimum wire resistance, inductance and capacitance to stabilize the supply current for PUF power measurement. A 1 \(\Omega\) thick film current sensing resistor is connected between the ground of the PCB and the MRAM chip to sense the MRAM current during read/write. A heat gun is used to change the ambient temperature. We have analyzed a total of 10 chips.  
First, we interfaced the MRAM chip with logic analyzer and wrote all addresses at faster than 15 ns write time@\(V_{DD}\) = 3.3 V, 25 \({}^{\circ}\)C. We found that the data is written successfully for all chips at 12 ns (Figure 5). Therefore, the write time should be less than 12 ns at nominal operating condition to bias the cells with 50% write probability. We read the data at 30 ns in order to avoid read failures. Next, we interfaced the MRAM chip with the FPGA. The writing is done at much higher frequency to achieve partial write and reading is done at 25 MHz (40 ns). The read/write traces are captured by Keysight DSOS-804A Oscilloscope [26] with sampling frequency of 20 GSa/s and Bandwidth of 8 GHz. The experimental setup is shown in Figure 6. Data is sent to a PC using UART (baud rate 9600 bps) for MATLAB analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Capacity & 16 Mbit \\ Read/Write Cycle & 35 ns \\ Address/Data Bus Length & 21/8 \\ Retention Time & \textgreater{}20 years \\ AC stand by Current & 9–14 mA \\ AC Active Current (Read/Write) & 60–68 mA/152–180 mA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic of the MRAM Chip.  
Figure 4: Read cycle of MRAM chip.  
Figure 3: Write cycle of MRAM chip. Note that the chip requires maximum of 15 ns of write enable (low) signal for successful write.
## 3 Improvements in HY-2B POD
### Performance Analysis of the Refined PEA Model
An overview of SLR validation of the different PEA solutions over a 14-day dataset is shown in Table 3. In general, the 410 orbit solution was the best for SLR validation, and the 460 + C solution closely agreed with the spectral characteristics of acceleration. The  
Figure 4: SLR residuals (red dot) obtained for the six PEA solutions with respect to the azimuth angle for all sites. A trend line (blue) is fitted with polynomials. Figure 3: SLR residuals (red dot) with respect to the elevation angle for all sites, with a trend line (blue). All six PEA solutions are displayed for comparison. SLR validation of this strategy was slightly worse than that of the 410 solution but it still achieved good accuracy. The SLR validation (see Table 3) showed that the 110 solution, which only estimated the acceleration in the along-track and cross-track directions, achieved the expected orbit determination accuracy. The radial direction of LEO satellites is mainly affected by conservative forces such as the gravitational field and tidal perturbation of the Earth; it is also affected by Earth radiation pressure, which results in a slight and constant acceleration. Considering the weak performance of the solutions with estimation of the radial acceleration, the related solutions (i.e., 111, 412, and 462 + C) were not used in the reprocessing of HY-2B POD. Based on the spectral analysis of HY-2B empirical accelerations and orbit validation, the reprocessing arc was extended from 1 November 2018 to 1 January 2020, and three different PEA strategies were used in reprocessing (see Table 3). The SLR validation of the reprocessing of HY-2B POD is shown in Figure 5. The 110 solution participated in reprocessing as a routine strategy, while the remaining two strategies, i.e., the 410 and 460 + C solutions, performed well in the frequency test. Compared with the 110 solution, the 410 solution provided better SLR validation after changing the frequency of the along-track direction. The SLR residuals of the 11 high-performance SLR stations were improved to some extent. According to the SLR validation for the different PEA solutions over 14 months of the HY-2B mission, an optimized PEA strategy can be suggested. The 410 solution appeared to be the best among the three strategies in terms of SLR validation: the RMS of the SLR residuals of the 410 solution was 5.23% better than that of the 110 solution. When the frequency of the along-track direction was kept at 4 CPR, the result of the 460 + C solution with constant and periodic signals in the normal direction was slightly worse than that of the 110 solution. The 460 + C solution was the worst among those analyzed, but the average value of the SLR validation was slightly improved. For the mixed spectrum of the cross-track direction, 1 CPR as a comprehensive solution could deal with this situation well, and the validation using SLR observations proved this point.
## 5 Feature Extraction
### Frequency-Domain Features
Moreover, the FT plays a crucial role in developing time-frequency analysis and is another feature extraction technique used in road surface anomaly detection and classification; these methods are presented in the next section.
## 2 Optical Lattice Clock Based on \({}^{87}\)Sr
### Lifetime of Atoms in the Lattice  
A laser operating at wavelength 813 nm is used to configure a 1-D horizontally oriented optical lattice. The 813-nm ECDL is stabilized to an ULE optical reference cavity, having a finesse of about \(3\times 10^{4}\), using the PDH technique. The 813-nm lattice laser beam was focused to a waist size of 120 \(\upmu\)m; the depth of the trap lattice is about 56 \(\mathrm{Er}\), where \(\mathrm{Er}\) is the lattice photon recoil energy. About \(10^{4}\) atoms are then loaded into the 1-D optical lattice at a magic wavelength of 813.4 nm.  
An image of the 1-D optical lattice with a falling time of 30 ms is shown in Figure 3a. The line above is an in-situ image of the atoms trapped in the lattice; the cloud below is the atomic distribution after time-of-flight imaging. From fitting an exponential decay function (Figure 3b), the 1/e lifetime of the 1-D lattice is about 1.6 s. A long lattice lifetime is useful in resolving the narrow spectroscopy of the clock transition.  
Figure 2: **(a)** Frequency fluctuation of the beat signal with and without the FNC system; **(b)** Optical field spectrum of the beat signal with and without the FNC system; **(c)** In loop frequency stability (Allan deviation) of the FNC system. The data sample has a 1-s gate time.
# Robot Bionic Vision Technologies: A Review
## 2 Literature Review
### Common Bionic Vision Techniques
#### 2.5.3 TOF (Time of Flight)  
This technology is a 3D imaging technology that has been applied to mobile phone cameras since 2018. Its principle is to emit continuous pulsed infrared light of a specific wavelength onto the target and then receive the optical signal returned by the target object via a specific sensor. The round-trip flight time or phase difference of the light is calculated to obtain depth information for the target object. The TOF lens is mainly composed of a light-emitting unit, optical lens, and image sensor. The TOF performs well in terms of close measurements and recognition. Its recognition distance can reach 0.4 m to 5 m. Unlike binocular cameras and structured light, which require algorithmic processing to output 3D data, TOF can directly output the 3D data of the target object. Although structured light technology is best suited for static scenes, TOF is more suitable for dynamic scenes. The main disadvantage is that its accuracy is poor, and it is difficult to achieve millimeter-level accuracy. Because of its requirements for time measurement equipment, it is not suitable for short-range (within 1 m) applications compared to structured light, which performs well for short- and high-precision applications. It cannot operate normally under strong light interference. Representative products that use this technology include TI-Opt and ST-VI53.  
It can be seen that Table 3 presents the following advantages and disadvantages: (1) monocular technology has a low cost and high speed, but its accuracy is limited, and it is unable to obtain depth information; (2) binocular ranging is the closest to human vision, without a passive light source, and the working efficiency is acceptable, but it is difficult to realize a high-precision algorithm, and it is easily disturbed by environmental factors; (3) a structured light scheme is more suitable for short-range measurements but has high power consumption; and (4) TOF and structured light have the same disadvantages. The miniaturization of the sensor has a significant impact on resolution. (5) Although LiDAR has high precision, even for long distances, its resolution is low. The machine vision measurement technologies have both advantages and disadvantages. Therefore, in recent years, many well-known robot manufacturers have adopted technology integration to improve the accuracy and practicality of robotic vision. For example, Atlas [141] of Boston Power adopts a vision combination of binocular and structured light + LiDAR.