esa-sceva/satcom-chunk-collection huggingface dataset:
# Supply Chain Data Analytics for Digital Twins:
## 3 Digital Twin Analytics Framework
### Data Models  
The efficiency of the analytics system to deliver actionable insights about every factory process and empower engineers with a tool to make decisions and actions is based on its robust and well-defined data models. These models enable the processes of organizing, storing and analyzing complex data generated throughout the production lifecycle.  
Figure 3 presents the Entity-Relationship Diagram (ERD) showcasing the core data models and their relationships within the Digital Twin Analytics Framework. These models are vital for the framework as they empower it to achieve interoperability across digital twin instances and flexibility. Each entity is defined by a Primary key (PK) that is a unique identifier of the record. Foreign keys (FK) are also included, and their purpose is to link records in one table to records in another.  
The data models that are presented in Figure 3, are designed to handle all types of manufacturing data and to model the processes in the factory. The following are the specific data model types:  
_Components_: This data model captures information about physical components within the factory. Its attributes are the id, which is the unique identifier, the name of the component (for example _Decanter_) and the description of the component (for example _Decanter_ separates solid impurities from liquid molasses_). A component can be related with multiple measurements.  
_Measurements_: This entity is responsible for all measurable parameters and values that are associated with the components. In the attributes are included the id, the component_id, which is the foreign key to components, the name (for example _Molasses pH_), the description and the measurement unit.  
_Measurements_static_values_: This data model stores the stable measurement values that do not change over time. It is linked to the measurement and contains the current value.  
_Measurements_timeseries_values_: This entity stores the dynamic, time series values of measurements. Its attributes contain the id, the measurement_id, which is the related measurement, the value and the timestamp when the value is measured.  
_Measurements_synthetic_timeseries_values_: This table models the generated timeseries values for measurements. These values are synthetic, created by the analytics system for potential evaluation and testing purposes.  
_Anomalies_: This table contains the anomalies that the analytics system detected and their severity. It contains the id of the anomaly and the measurement value id, which is  
Figure 3: Framework Entity–Relationship Diagram.  
classified as an anomaly and the time that is detected. The severity can be low, medium or high.
## 3 Results and Discussion
### Stress Distribution and Progressive Damage  
Taking the center point of the adhesive layer as the coordinate origin, the loading direction as the x-axis, and the width direction of the lap plate as the y-axis, the coordinate system was established, as shown in Figure 11. The normalized shear stress peeling stress of the adhesive layer in the bonded joint and the variations in normalized shear stress and in peel stress of the adhesive layer in the hybrid with \(y\) are also given in Figure 11. When y = 0 mm and y = 3 mm, the existence of the bolt reduced the area of the adhesive layer, which increased the shear stress gradient. The adhesive layer on the edge of the hole (y = 6 mm) indicated an obvious shear stress concentration. The shear stress distribution of the adhesive layer far from the center was similar to that of the bonded joint. The peel stress gradient is lower than that of the bonded joint.  
Figure 11: The schematic of overlapped length (**a**); the normalized shear stress (**b**) and peeling stress (**c**) of the adhesive layer in the two kinds of joints.  
Figure 10: The comparison of the bearing capacity of the two kinds of joints.  
The simulation load-displacement curve of the hybrid joint is shown in Figure 12. Four points A, B, C, and D were taken on the curve at Stage 1 in order to present progressive damage to the adhesive layer. The shear stress of the adhesive layer reached shear strength at Point A, which is the initial point of the damage, and Point D is the adhesive layer failure load point. Similar to [30], the damage evolution process of the adhesive layer was also distributed symmetrically, and the failure region expanded from the edge to the center of the adhesive layer. However, the failure of the adhesive layer near the centerline was slowed down by the bolt. Points E, F, G, and H were taken on the curve at Stage 2. It can be observed that the bolt was slightly damaged when the adhesive layer failed completely, and the bolt damage was aggravated with the further increase of the load. Damage to the element occurred in the middle plane of the bolt and extended from the middle to both sides in the circular section. Due to the eccentric load resulting in the slight deflection of the bolt, the damage is not completely symmetrical. At Point H, shear failure of the bolt rod occurred.
## 2 Modeling Approach
### Model Comparison  
From the list of potential functions in Appendix A, if we choose one of those and substitute it to Equation (9), it is shown that the model complexity varies from two to seven parameters. A first selection of a model that fits better to the measurements of stable isotope ratios analyzed, i.e., with the smallest error residual, would favor the model(s) of larger complexity. Such a model would indeed describe the current samples with the smallest total error, but it would be very likely not a suitable one describing the general population of the bottarga. In machine learning, this is described as model overfitting and must be avoided. On the other extreme, if a very simple model is selected, then it could probably have the capacity to perform well, a situation known as underfitting [29; 32; 33; 39].  
Therefore, the desired models could use limited sample data to fit/estimate their parameters, but, at the same time, should be capable of generalizing beyond the sample (i.e., they describe the population based on the samples) and performing well. In the common machine learning models and algorithms, such as decision trees, random forests, boosting trees, and neural networks [31], the usual practice to avoid overfitting is to perform cross-validation. By splitting the data into training/evaluation parts, we fit the model based on training data only, and we measure the performance on the data the model has never seen before (evaluation part), simulating the model performance on the general population. Unfortunately, when the available data are extremely small, as in this case where the production is very limited and the measurement destructive, cross-validation is not an option.  
This is a very challenging situation, and we need to turn it into theoretical aspects of stochastic modeling. Information theory and information-based criteria help us select the best performing model [33; 34]. The seminal work of [PERSON] [40] provides a model comparison mechanism called Widely Applicable Information Criterion (WAIC) for models that belong to the same family. WAIC provides an approximation of the out-of-sample deviance, and according to its value, we select the best-expected model from Appendix A (i.e., the model of lower WAIC).
###### Abstract  
On 20 May 2012, an M\({}_{\rm w}\) 5.8 earthquake, followed by an M\({}_{\rm w}\) 5.6 event nine days later, struck the Emilia-Romagna region in northern Italy, causing substantial damage and loss of life. Post-mainshock, several water-related phenomena were observed, such as changes in the groundwater levels in wells, the expulsion of sand-water mixtures, and widespread liquefaction evidence such as sand boils and water leaks from cracks. We analyzed the Earth's surface displacement during and after the Emilia 2012 seismic sequence using synthetic aperture radar images from the COSMOSkyMed satellite constellation. This analysis revealed post-seismic ground subsidence between the Sant/Agostino and Mirabello villages. Specifically, the displacement time series showed a slight initial uplift followed by rapid subsidence over approximately four to five months. This widespread ground displacement pattern likely stemmed from the extensive liquefaction of saturated sandy layers at depth. This phenomenon typically induces immediate post-seismic subsidence. However, the observed asymptotic subsidence, reaching about 2.1 cm, suggested a time-dependent process related to post-liquefaction consolidation. To test this hypothesis, we analytically estimated the consolidation subsidence resulting from earthquake-induced excess pore pressure dissipation in the layered soil deposits. The simulated subsidence matched the observed data, further validating the significant role of excess pore pressure dissipation induced by earthquake loading in post-seismic ground subsidence.  
SAR; liquefaction; Emilia earthquake; subsidence; consolidation; excess pore pressure dissipation Article  
InSAR Analysis of Post-Liquefaction Consolidation Subsidence after 2012 Emilia Earthquake Sequence (Italy)
# Development of a Virtual Reality-Based Environment for Telerehabilitation
## 2 Materials and Methods  
Figure 1 shows the general architecture of the proposed LegUp parallel telerehabilitation system. In collaboration with the patient, the therapist remotely develops a rehabilitation plan. These exercises are designed to help the patient improve lower limb flexibility, strength, and mobility while minimizing pain and discomfort. Before sending the parameters of the rehabilitation exercises to the LegUp experimental robotic structure, the therapist tests the rehabilitation exercises on a virtual patient using a VR simulator with a virtual model of the LegUp robot. This preliminary testing ensures the patient's safety. Next, the therapist demonstrates the rehabilitation exercises to the patient using the VR application. To enter the immersive VR environment, the therapist wears a VR headset and uses two controllers to operate the virtual LegUp robotic structure. After this stage, the real patient is placed on the experimental LegUp robotic structure with the help of an assistant while the therapist monitors them via a video camera and an EMG sensor attached to the patient's lower limb to track muscle condition. At this point, the therapist can send the rehabilitation exercise parameters to the LegUp robotic structure to start performing the exercises for the patient. The EMG sensor used is a model EN0240 from DFRobot (DFROBOT, Timisoara, Romania) [22], with an integrated filter that is essential for reducing noise and interference, especially power frequency (50 Hz or 60 Hz), which can affect the accuracy of EMG measurements, helping to obtain a clearer and more precise signal, better reflecting muscle activity. This filter can be used at a sample frequency of 500 Hz and 1000 Hz, with an ADC converter greater than 8 bits. For this application, 1000 Hz was chosen to preserve the original information. To retrieve and transmit data packets from the EMG sensor, an ESP32 microcontroller (Espressif Systems, Targu Jiu, Gorj, Romania) was used, which has the ability to transmit data via the Wi-Fi protocol.  
Figure 1: General architecture of the system.
## 2 Experimental Set Up
### Drainage System  
All previous tests on saturated concrete were done under undrained conditions with a unique measurement of the total stress components. A better understanding of the free water effect on the concrete macroscopic behavior requires a pore pressure measurement under high confinement pressure. The general idea of the drainage system presented in this paper is inspired from these previous works. The proposed drainage system consists in creating a water collecting cap under the tested specimen in which a fabricated pressure sensor is placed. The new concept consists in reducing the length of concrete sample from (L = 140 mm to 80 mm) while conserving the same diameter (D = 70 mm), as shown in Figure 2a,b. Under high confinement, brittle phenomena such as cracking are prevented and thus confined compression tests slightly depend on the specimen slenderness. A L/D ratio equal to 1 is sufficient to limit the influence of boundary conditions and to get a homogeneous stress state into the specimen. This assumption was checked in [22]. A water collecting cap made of steel is then integrated below the shortened specimen. With a cylindrical shape of 60 mm in height and 70 mm in diameter the drainage cap occupies the remaining space (Figure 2b). It serves to collect water coming from concrete during tests and contains a deformable sensor responsible of measuring the concrete interstitial pore pressure while subjected to confining pressure only. The drainage cap is composed of two parts: an upper part, which allows a straight path of the pressurized concrete water to the sensor by means of 40 micro-holes (1 mm of diameter each), and a lower part, which is a movable steel plug allowing an easy access to the cap cavity. The plug is equipped with an O-ring sealing joint, preventing both water running out of the cavity and the DOZ confining fluid filtering inside it. A hole of 1.5 mm in diameter is drilled transversally throughout the cap serving to connect gauge wires to the press data acquisition system (DAS), as shown in Figure 2c.  
Figure 1: Giga press: (**a**) confining cell cross section; and (**b**) sample protection and instrumentation.
# Rapid Synthesis of Silver Nanowires in the Polyol Process with Conventional and Microwave Heating
## 1 Introduction
There are few references dealing with the effect of the viscosity of the reaction environment upon the properties of fabricated nanowires [31]. The viscosity is believed to affect the shape of obtained nanomaterials [32]. A synthesis occurring in a low-viscosity medium guarantees a sufficiently high diffusion rate of monomers supplied to the nanocrystal walls with both high and low surface energy. In the case of high-viscosity mediums, as a priority, monomers are added to high-energy facets to minimise the free surface energy. Another factor influencing nanostructure nucleation and growth is the dielectric constant of the solvent used in the synthesis [33]. On the one hand, applying solvents with high dielectric constant values guarantees the stabilisation of the precursor ions and facilitates the digestion of fine nanoparticles already present in the solution [34]. On the other hand, in a solvent with a low dielectric constant, nanoparticle monomers are stabilised at high concentrations. A large number of monomers favours the formation of fine nanocrystals, which is crucial because of the growth of objects with high-aspect-ratio values [34; 35]. Irrespective of viscosity, the reducing power of the applied polyol also influences the growth of nanoparticles. Any analysis of the latter effect is impeded [30], which is related to the problems inherent in separating the two said effects. Some information regarding the effect of the reducing power on the product size can be found in work on the synthesis of copper nanoparticles [36]. An analysis of the nucleation phenomena and the growth of nanoparticles led to a conclusion that large-sized objects should be expected for both high and low reduction rates of the precursor. It has also been ascertained that small water quantities may influence the output and growth rate of silver one-dimensional nanostructures [15]. A survey of the published reports on the polyol synthesis of AgNWs provides limited data on the impact of the viscosity and dielectric properties of the reaction environment on the final size of the nanostructures under consideration. To the best of our knowledge, there are no reports concerning the simultaneous impact of these parameters in the polyol synthesis of silver nanowires. Detailed studies have been carried out to address this deficiency using five polyols differing in viscosity, dielectric constant and structure. The syntheses were performed in batch mode, under conventional and microwave heating.
# Integration of EMU Overall Design Model Based on Ontology-Knowledge Collaboration
## 3 Integration of EMU Requirement Models
### Requirements Ontology Construction
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**ReqIF**} & **ReqIF Is an XML-Based Request Format, Used as an Exchange Format, XML Data Is Usually Stored in Files with an ReqIF Extension** \\ \hline \multirow{2}{*}{Specifications} & \multirow{2}{*}{Requirement forms, organizing the objects of organizational requirements through a requirement form.} \\ \multirow{2}{*}{Objects} & \multirow{2}{*}{Requirement entry, a data structure used to store requirement information.} \\ \multirow{2}{*}{Relations} & \multirow{2}{*}{Requirement entry association, describes the linking relationship between two requirement entries.} \\ \multirow{2}{*}{Hierarchy} & \multirow{2}{*}{Hierarchy of Requirements, describe the hierarchical relationship between requirement entries.} \\ \multirow{2}{*}{Type} & \multirow{2}{*}{Requirement types, including attribute definitions and data type definitions.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ReqIF terminology. Figure 4: Stakeholders. Figure 5: Partial diagram of requirements. Figure 6: Requirements ontology. The specific transformation process is depicted in Figure 8, with the rules being as follows:  
1. The ReqIF model is converted into a framework comprising top-level classes. 2. Core concepts and relational constructs within the ReqIF model are transformed into subclasses that correspond to classes within the domain ontology framework. 3. Instances are derived from the ReqIF model by leveraging the associated subclasses. 4. These instances are then translated into a knowledge graph representation. 5. Subsequently, through employing the aforementioned steps, the ReqIF model is transmuted into a domain ontology of requirement items articulated in the OWL language.
# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 2 Background
### Fault Signatures from Spectra  
Fault signatures from the stator current can also be obtained in the frequency domain. When a rotor bar breakage occurs, a current is induced, and torque and speed pulsations originate, giving place to two sidebands around the fundamental frequency and other harmonics. These fault-related frequency components are observable in the frequency spectrum [15]. This motivates the use of these sidebands' amplitude to be considered a fault severity indicator of the rotor condition. For their frequency location, Equation (2) is used.  
\[f_{BRB}=(1\pm 2s)f, \tag{2}\]  
where \(f\) is the supply frequency and \(s\) is the rotor slip. The frequency corresponding to the minus sign is known as the left sideband harmonic (LSH); the other one is known as the right sideband harmonic (RSH). Both sidebands are separated from the integer harmonics, a distance that depends on rotor slip and, thus, on the motor load level. Nonetheless, when an IM is fed from an inverter, the stator current contains time harmonics. These harmonics modify the amplitude of the present harmonics or they generate new airgap spatial harmonics. The author in [17] proposed additional faulty signatures for diagnosing rotor cage asymmetries in inverter-fed IM. Some of them are used as candidate features for the diagnosis methodology and they are defined as follows:  
\[\Gamma_{5} =\frac{G7\left(LSH\right)}{G_{5}} \tag{3}\] \[\Gamma_{7} =\frac{G5\left(RSH\right)}{G_{7}}\] (4) \[\Gamma_{11} =\frac{G13\left(LSH\right)}{G_{11}}\] (5) \[\Gamma_{13} =\frac{G11\left(RSH\right)}{G_{13}} \tag{6}\]  
The above expressions' numerators correspond to the amplitudes of the fault-related sidebands around the fifth, seventh, eleventh, and thirteenth harmonics. The nomenclature used is LSH for the left sideband harmonic and RSH for the right sideband harmonic. The denominators are the amplitudes of the above-mentioned integer harmonics. Figures 1 and 2 show the spectrum of one stator phase current to illustrate the presence of these harmonics. Figures 1 and 2 are the spectra for a line-fed and an inverter-fed induction motor respectively. As can be seen, the signal that comes from the inverter supply introduces different harmonics, inter-, and sub-harmonics, into the stator current spectrum, affecting more negatively the extraction of the above-defined indicators and, thus, the diagnosis compared with the line-fed IM case.  
Figure 1: Power spectral density of a measured stator current from a line\(-\)fed induction motor.
# Analysis of Crack Formation and Growth in Tunnel Linings Using Double-K Fracture Criterion
## 5 Conclusions  
Due to the complexity of geological formations, highly indeterminate characteristics of the surrounding soils/rocks, and the complicated interaction between the lining and surrounding soils/rock, the external load acting on the tunnel is usually difficult to determine, which results in great difficulty regarding the fracture analysis of lining. This paper presents a novel analysis method, namely, the modified double-K fracture criterion, for analysis of the fracture mechanism of plain concrete linings. The proposed criterion uses two crack width indices to divide the fracture process into three different stages, i.e., crack initiation, stable propagation, and unstable fracture. The crack width indices including initiation and unstable widths are derived from the equivalent transformation of the fracture toughness. The current fracture stage of the lining can be determined by comparing the field measurement width and two crack width indices. Since this method uses the geometric parameter of the crack width to estimate the mechanical state of cracks, it can avoid complicated mechanical calculations.  
A specific code based on the XFEM-FCM algorithm was developed to simulate the fracture process of concrete lining. The existence of three stages of the lining fracture process was confirmed by numerical experiments and the feasibility of the proposed theory was also verified.  
Conceptualization, C.H. and M.W.; methodology, M.W. and X.L.; software, X.L.; validation, C.H.; formal analysis, C.H.; investigation, C.H.; resources, M.W.; data curation, M.W.; writing--original draft preparation, C.H.; writing--review and editing, X.L.; visualization, C.H.; supervision, M.W.; project administration, M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.
## 6 Frequency Tracking  
As mentioned before, frequency tracking of LTE is conducted with the assistance of RS as auxiliary data. However, because of the narrow bandwidth, more pilot signals should be used by NB-IoT for tracking frequency. The 40 ms UTG demands that NPSS, NSSS and NPBCH be regenerated and applied for residual frequency offset estimation [31]. The received frequency domain signal is defined in Equation (8) as \(R_{p,f_{f}}^{(m)}(k)\) and the stored local data is \(S_{p,f_{f}}^{(m)}(k)\). Firstly, we investigate two existing methods. Then, our solution is presented in detail and the advantages are also discussed.