esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Round Robin Test (RRT) and Reliability Analysis
### PAUT Experimental Setup and Perform RRT  
In contrast to the conventional UT method, the PAUT method uses transducers of various arrays, and the results are significantly affected by the focal law. It is practically difficult to evaluate the reliability of each case under all conditions. Therefore, it is necessary to select an inspection system and a scan plan for reliability evaluation. In this paper, inspection devices and software were selected as Olympus(Japan) and ZETEC(USA), equipment mainly used in the field, and the details are shown in Table 3.  
A shear wave transducer with a frequency of 7.5 MHz and 16 elements standardized for tube inspection was used. In addition, a dual linear array (DLA) longitudinal wave transducer with 32 elements of 5 MHz arranged in two rows was used for austenitic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Type** & **Material** & **OD** & **THK** & **Qty.** \\  & & **(ASME Spec.)** & **(mm)** & **(mm)** & **(EA)** \\ \hline T01–T06 & & SA210 Gr.A1 & 50.8 & 5.6 & 6 \\ T07–T12 & & & 42.2 & 6 & 6 \\ T13–T18 & & TP304H & 50.8 & 4 & 6 \\ T19–T24 & & & 63.5 & 4 & 6 \\ T25–T30 & Dissimilar Metal & TP304H + SA213-T91 & 42.2 & 6.6 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the tube weld specimens (weld type, material, size, quantity).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** &
\begin{tabular}{c} **Material** \\ **(ASME Spec.)** \\ \end{tabular} & **Lack of Fusion** & **Crack** & **Porosity** & **Slag** \\ \hline \multirow{2}{*}{Similar Metal} & SA210 Gr.A1 & 8 & 6 & 5 & 8 \\  & TP304H & 8 & 34 & 5 & 8 \\ Dissimilar Metal & TP304H + SA213-T91 & 8 & 6 & 5 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flaw quantities of the tube weld specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} **Ultravision 3.10** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of PA device and software.  
stainless steel and dissimilar metal welds, which are high-attenuation materials. The detailed specifications of the PA probe are shown in Table 4. To ensure the reproducibility of the test results, a scanner mounted with a wedge and an encoder processed to the same curvature as the tube were used. The scan plan used for the testing was prepared so that the welds and heat-affected zone were included in the inspection volume and used for RRT.  
There was a total of six inspection teams participating in the RRT, with two inspectors per team. All inspection teams perform preventative maintenance at actual thermal power plants, and at least one experienced inspector with more than 10 years of experience in ultrasonic inspection (UT and PAUT) participated. All inspectors performed calibration of the equipment in accordance with ASME Section V. Figure 1 is a picture of performing RRT.
## 4 Discussion
Taking the N direction as an example, the coefficients k1, k2 and k3 of the model of the function fitted by the cubic polynomial are \(-8.6\times 10^{-4}\), \(-2.0\times 10^{-6}\) and \(5.4\times 10^{-9}\), respectively. In order to maintain the positive definiteness of the function model, three schemes are used to test the Sichuan region, mainly discussing the range of values of the parameter k. Figure 9 shows the results of the tests. * Set k2 and k3 to the constants that have been calculated and adjust the value of k1;
* Set k1 and k3 to the constants that have been calculated and adjust the value of k2;
* Set k1 and k2 to the constants that have been calculated and adjust the value of k3. The following results can be drawn from Figure 9:  
* Since k1, k2 and k3 are all variables, it is not possible to determine the range of values of all three variables at the same time. Therefore, the control variable method is used to analyze the effect of a particular variable on the positive definiteness of the function;
* In all three schemes, the polynomial fit value decreases as the value of the single variable k decreases. In particular, at values of \(-1.70\times 10^{-3}\) for k1, \(-4.8\times 10^{-6}\) for k2 and \(-4.0\times 10^{-9}\) for k3, the function fit value is close to zero. In order to maintain the positive definiteness of the function, the single variable k may not be smaller than the above values, respectively;  
Figure 9: Effect of parameter k on the fitting of the functional model under different schemes, (**a–c**) only adjusts the values of k1, k2 and k3 respectively. * The polynomial function model does not have positive definiteness, so the k value needs to be carefully chosen to fulfill the requirement. Meanwhile, the method for calculating the range of k-values of the function model in the E, U and L components is also similar to that of the N component, which will not be discussed here.
# Effect of Multiple Reclosing Time Intervals on Axial Vibration of Winding
## 3 Effect of Reclosing Interval on Winding Vibration
### Vibration Response in Different Short Circuit Durations  
To study the effect of the short-circuit interval on winding vibration, different short-circuit cut-off times were analyzed. Three disks of different axial heights, equally spaced in the direction of the low-voltage winding axis, were taken as measurement points. The vibration acceleration is shown in Figure 5.  
The amplitude of the vibration acceleration at the middle disk is much larger than that at other positions before the circuit breaker trips. When the circuit breaker is tripped, the action time is short, which causes the acceleration amplitude of the disk to increase sharply. After the breaker trips, the acceleration amplitude of all disks is quickly reduced and vibration acceleration is also attenuated over time. After the circuit breaker trips, the acceleration of the disk at three positions of the winding decays to less than 10% of the acceleration during the circuit-breaker action within 30 ms. When the duration of the circuit-breaker removal is prolonged, the aperiodic components at disks in different positions in the acceleration are attenuated.  
According to Figure 6, the displacement at 1/4 and 3/4 of the winding axial height is large before the circuit breaker trips and the displacement in the middle is much smaller than that in other positions. Before the action of the circuit breaker, each disk is subjected to damped forced vibration in electromagnetic excitation. After the breaker action, each disk is subjected to damped free vibration and the vibration displacement at different positions is attenuated more slowly than the acceleration amplitude.  
The strength of additive effects varies with the current and the winding structure of the transformer, which is subjected to many short-circuit impacts. When the short-circuit current is small, the superposition of the cardboard is not obvious. With the short-circuit current increasing, the short-circuit electric force on the winding increases and the nonlinear effect of the paperboard is enhanced. The winding loosening caused by the superposition of plastic deformation of the paperboard becomes the main factor influencing the characteristics of the winding vibration.
# Polymer/Silica Hybrid Waveguide Thermo-Optic VOA Covering O-Band
## 4 Discussion  
Table 1 shows a comparison of the VOAs reported in recent years, based on different platforms. Thanks to mature fabrication technology and edge polish technology, the VOA fabricated on a silicon platform shows ultra-low insertion loss. With air trenches introduced, the heat is insulated around cores, resulting in a lower power consumption (PC) and thermal crosstalk. However, the low thermo-optic coefficient of 1.19 \(\times\) 10\({}^{-5}\) K\({}^{-1}\) contributes 155 mW of power consumption, which is about 15 times larger than a polymer-based VOA [12]. Moreover, the power consumption and crosstalk are decreased to 95 mW and 39 dB, since the air trench and etched-free waveguide also release the stress [13]. With suspended narrow ridge structure introduced, an ultralow power consumption of 20 mW is obtained based on a silica-based PLC platform [8]. However, this structure requires a somewhat-complicated fabrication process, including twice-dry etching and once-wet etching. Although the VOA [7] fabricated with silicon on insulator (SOI) shows fast response, the power consumption of 35 mW is still a challenge for the large-scale integration with the compact footprint. In [14], a reflective MZI VOA consists of a 2 \(\times\) 2 MMI coupler and a high reflection efficiency Bragg grating. The power consumption is 10.8 mW at 1580 nm. However, the VOA shows obvious wavelength dependence. The polymer-based devices show low power consumption, owing to a thermo-optic coefficient of 1.86 \(\times\) 10\({}^{-4}\) K\({}^{-1}\). The polymer-based digital optical switch [9] shows a large ER of 45 dB, owing to a quartz substrate with grid patterns, which is also meaningful for our device. Moreover, air trench structure will decrease the power consumption shown in [20]. To improve response speed, a polymer/silica hybrid waveguide is introduced in [10]. Here, in our work, we adopt hybrid waveguides, achieving rise and fall times of lower than 200 \(\upmu\)s. The large bandwidth of 100 nm makes the VOA able to cooperate with the CWDM system. The ER and PC could be further improved with grid pattern substrate and air trench.
## 4 RVE Finite Element Model Based on the Microstructure  
### RVE Geometric Model and Boundary Conditions  
The fracture process of the SiCp/6061 Al composites under the tensile load was simulated by the finite element method. A real and reasonable particle geometry model is an important part to improve the accuracy of the calculation results. In this work, the image processing technology was used to identify the shape of the reinforced phase SiCp particles, and it was further introduced into the finite element model. The interface between the SiCp reinforcement and the matrix was modeled as a zero-thickness interface.  
The metallographic image of the SiCp/6061 Al with a magnification of 100 times was selected as the reference prototype. The geometric features of SiCp/6061 Al composites were obtained by using the image recognition tool in the image toolbox of the COMSOL Multiphysics(r)5.0 (COMSOL, Stockholm, Sweden). The modeling process is shown in Figure 3a. Some research results have shown that when the size of the RVE exceeds 200 \(\upmu\)m, the simulation results begin to converge [21]. Considering the computational cost, the size \(L_{0}\) of the RVE model in this simulation was 300 \(\upmu\)m.  
The RVE model was set to the symmetric boundary condition (SBC). Figure 3b shows the model constraints with the symmetric boundary conditions. The right boundary node of the symmetrical boundary model limited the displacement freedom in the X direction. The left boundary set the displacement difference of the left boundary node in the X direction to be zero with a constraint equation. The lower boundary node only retained the displacement freedom in the X direction. In order to facilitate the postprocessing of the upper boundary of the model, the upper boundary was coupled in the form of RP points, and the displacement load was applied to the RP points with an amplitude of 60 \(\upmu\)m. The mesh seed size of the RVE model was 0.8 \(\upmu\)m, and the two-dimensional finite element model consisted of about 100,000 CPE4R elements and 2000 COH2D4 elements.  
Figure 2: (**a**) The grid division diagram of the nanoindentation finite element model; (**b**) indenter displacement–time curve of the finite element model; (**c**) experiment and FEM calculation of the indentation response of the SiCp/6061 Al matrix; (**d**) equivalent stress nephogram (S) and equivalent plastic strain (PE) nephogram of the SiCp/6061 Al matrix near the tip of the conical indenter.
# Weather Radar in Complex Orography
## 4 Solutions and Results  
There is no perfect solution for the use of radars in complex orography. Many compromises have to be made concerning the choice of the radar site, the design and configuration of the hardware and the algorithms used for data processing and product generation. If an idea is based on a scientific foundation, has been successfully implemented, has proven to work 24/7 and satisfies the needs of the users, in terms of quality, availability and timeliness, we can call it a \"solution\". Ideally, its value is continuously quantified using objective performance indicators, such as statistics of the timeliness, the availability of radar data and accuracy, see Tables 1-3. This section presents certain selected examples of solutions, some major performance indicators, the results of recent innovation projects and new research paths. However, the presentation is not exhaustive. It is a selection of aspects that are relevant in a mountainous region and which have a direct link to the practical needs and requirements listed in Section 2.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 5 Data Mining Techniques Employed in Blockchain Anomaly Detection
### Evaluation Approaches  
The evaluation of the results obtained by unsupervised learning or by combining unsupervised and supervised learning is a very crucial step towards developing effective and robust blockchain anomaly detection methods. Within the subsequent paragraphs, we report the most used measures identified by the current investigation:  
* Within cluster mean value of the sum of squares: It is defined as the average of the square distances between points belonging to a cluster and the respective cluster center. It reveals the compactness degree of the resulting clusters. Thus, it is a measure of the distortion of a cluster. Small values correspond to highly compact clusters.
* Silhouette score: It measures the similarity of a data point belonging to a specific cluster in relation to the rest of the clusters. It employs the criteria of compactness and separation. The compactness is based on estimating the average distance of the point to all other points belonging to the same cluster. On the other hand, the separation is defined as the smallest distance between the point and all points belonging to the rest of the clusters.
* Confusion matrix-based measures: They are the well-known measures coming from the resulting confusion matrices such as true positive rates (TPRs), false positive rates (FPRs), true negative rates (TNRs), precision, recall, and Fowlkes-Mallows index. They can be used when unsupervised learning is combined with supervised or self-supervised learning or there exists a portion of labeled data in the available dataset.
* Rand Index: The Rand index is a measure of similarity between two data clustering partitions of the same dataset. It considers the TPRs and TNRs and compares the agreement between the clustering results and the true class labels, making it suitable for evaluating clustering in the presence of ground truth labels. This measure can also be applied when unsupervised learning is combined with supervised or self-supervised learning or there exists a portion of labeled data in the available dataset.
* Outlier Detection Rate: It is defined as the number of detected anomalies divided by the number of total anomalies that exist in a dataset. In general, high values of this measure imply better performance of the algorithm.
* Optimal clustering: Usually, the clustering algorithms admit a predefined value for the number of clusters. Optimal clustering refers to the process of determining the optimal number of clusters in terms of compactness and separation criteria. This can be performed by iteratively applying the clustering algorithm, where in each iteration, the number of clusters increases by one. For each iteration (i.e., for each number of clusters), a function that includes the compactness and separation criteria is evaluated.  
When the iteration stops, the optimal number of clusters corresponds to the minimum value of the above-mentioned function.
## 2 Single-Mode Fiber (SMF)  
### Theory and Sensing Principle  
SMF is a common type of fiber optic cable in DAS systems. Within an SMF, the core glass typically maintains a diameter of approximately 9 \(\upmu\)m, ensuring the predominance of a solitary, straight signal propagation path along the fiber. Silica, a form of glass, remains the predominant and most widely used material for fiber optics. Silica's suitability for fiber optic cables is attributed to its exceptional characteristics, including extremely low optical attenuation (signifying minimal loss of light intensity) and its ability to be drawn into fragile fibers while maintaining remarkable tensile strength [31]. However, it is noteworthy that SMF facilitates the propagation of two polarization-state modes that are orthogonal and degenerate. In this scenario, the total power is distributed between these two polarization modes [7]. In this context, the relative phase relationships of the two polarization states can undergo alterations due to variations in elastic stress and temperature along the fiber. This phenomenon holds particular significance in interferometric sensing applications, notably in scenarios like DAS.  
Utilizing such fiber optics results in exceedingly feeble Rayleigh backscattered light, causing the optical sensing signal's signal-to-noise ratio (SNR) to be notably diminished, ultimately leading to poor SNR conditions [9]. Moreover, due to the activation of a laser pulse with a slim linewidth and heightened coherence in the fiber, the interference arising from Rayleigh scattering points within a single pulse gives rise to interference cancellation. This phenomenon leads to the coherent attenuation of backscattered light, consequently creating a loss zone along the sensing range [32]. Recently, efforts have been directed toward addressing the limitations of low SNR values and the susceptibility to phase demodulation in SMF DAS systems. Various noise attenuation technologies, including coherent fading, have been developed to counter these issues by performing median filtering in the data  
Figure 2: Schematic diagram of DAS measurement using the OTDR principle to detect the seismic wave incident.  
preprocessing step [7]. However, these advancements have augmented system complexity and elevated costs, entailing trade-offs in terms of the response frequency band and sensing range [33; 34]. Furthermore, it is important to improve the efficiency of the sensing fiber. The development of specialized fibers tailored for DAS technology presents substantial potential for future advancements [9]. When a pulse wave is emitted along the fiber, it induces axial strain, resulting in a change in the phase of the Rayleigh backscattering signal within the fiber. The concept of Ryleigh scattering is a type of elastic scattering that does not involve nonlinear effects, and Rayleigh-scattered light from a distinct location might be differentiated by its reflection time back to the fiber launch point. The optical fiber phase extraction technique, which is specifically reliant on phase-sensitive OTDR, has become widely used within DAS systems [35; 36].
# Machine Learning: Models, Challenges, and Research Directions
## 4 Machine Learning Processes
### Evaluation Metrics
#### 4.3.2 Evaluation Metrics for Unsupervised Learning Models  
Silhouette Score: It computes the similarity of each data point in one cluster to data points of the neighboring clusters. The higher values indicate that the data points are well-clustered and have clear separation.  
Davies-Bouldin Index: It calculates the average similarity among every cluster and its most similar clusters. A lower Davies-Bouldin index indicates better clustering, with smaller values representing tighter and more distinct clusters.  
Calinski-Harabasz Index: This index evaluates the ratio of the between-cluster variance to the within-cluster variance. Higher values indicate a better separation between clusters, implying that the data are well-clustered.  
Dunn Index: This metric assesses the rate of the minimum inter-cluster distance to the maximum intra-cluster distance. A higher Dunn index presents better clustering, as it shows smaller distances within clusters and larger distances between clusters.  
Inertia (Within-Cluster Sum of Squares): Inertia calculates the total distance among data points within the same cluster. Lower inertia values presents denser and more compact clusters.  
Gap Statistic: The gap statistic compares the clustering algorithm performance to the performance of random clustering. A larger gap indicates that the algorithm's clustering is better than random.  
Adjusted Rand Index: It measures the similarity between true class labels and the labels assigned by the clustering algorithm, correcting for chance. The higher values indicate better clustering agreement.  
Normalized Mutual Information: It measures the mutual information between true class labels and cluster assignments, normalized to account for chance. A higher Normalized Mutual Information indicates better clustering quality.  
Homogeneity, Completeness, and V-measure: These three metrics, homogeneity, completeness, and their harmonic mean (V-measure), assess different aspects of clustering quality. Homogeneity computes how pure each cluster is with respect to a single class. Completeness measures how well all instances of a given class are assigned to the same cluster. V-measure combines both homogeneity and completeness into a single metric.  
Spectral Gap: The spectral gap measures the gap between eigenvalues of a similarity matrix or Laplacian graph. A larger spectral gap indicates more distinct clusters.
# Evaluation of Fire Blight Resistance of Eleven Apple Rootstocks Grown in Kazakhstan Fields
## 2 Materials and Methods  
### Disease Monitoring, Sampling, and DNA Isolation  
Eleven types of apple rootstocks, including two local ones, were investigated in the current study (Table 1). These rootstocks are widely used for scion grafting in Kazakhstan commercial gardens. Eight out of the eleven researched apple rootstocks are included in the state register list of tested and approved rootstocks for cultivation in the southern region of Kazakhstan. This register list consists of seven clonal rootstocks: 62-396, M9, MM 106, Arm 18, B.7-35, B.16-20, and Zhetysu 5, as well as a seed rootstock, _Malus sieversii_. All registered rootstocks were included in the present work. Additionally, Geneva 16, Geneva 41, and B.9 fields were evaluated for fire blight occurrences as commonly known disease-resistant rootstocks [14; 22]. For all rootstock types, the average size of growing fields was at least 100 square meters. Most of the studied rootstocks were planted in the pomological garden of Talgar field (Almaty region); only M9 and MM106 types were grown in two different fields (Table 1). Fields typically had an age range of nine to eleven years. The Talgar (Tal), Agricultural Innovations Limited(AIL), Shelek, Kazakhstan, and Sady Zhetysu Trade (SZT), Almaty region, Kazakhstan fields are located in the Almaty region; the Devrosh (Dev) field is located in the Zhambyl region. The apple rootstocks in the Almaty region were grown in the same climate zone; for the fields in the Almaty region, the meteorological station collecting the climate data was located in Esik city. The fourth field was in the city of Merke, in the Zhambyl region, which experiences a dry climate with sharp fluctuations in temperature, according to data from the National Hydrometeorological Service of Kazakhstan (2021 and 2022). The meteorological station in Merke was used to collect the climate data for the Dev field.