esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 2 Methods and Materials
### Denoising Autoencoder
As explained earlier, the original A-scan signal used in this study is a mixture of the flaw signal and other signals, such as those from the geometrical interfaces. The additional signals are manually zero-padded, referring to the pre-marked gates indicating the location of the flaw during the acquisition of A-scan data. A sample validation signal is shown in (b) of Figure 6. The features of the input are encoded in the bottleneck hidden layer, as with the ordinary DAE. The decoder then uses these features to reconstruct the input and produce the output. The network then compares the output to the validation signal to reduce the mean squared error (MSE) loss. The comparison between the validation signal and output is represented by the arrows across (b) and (c) in Figure 6. As the MSE loss decreases, the output of the proposed DAE becomes more similar to the validation signal, and the DAE learns to extract the flaw signal. Figure 4: Architecture of the conventional denoising autoencoder. Figure 5: Difference between ordinary denoising autoencoder and one used in this study: (**a**) the inputs \((\widetilde{x})\) and outputs \((x^{\prime})\) of the ordinary denoising autoencoder that the noisy input is denoised throughout the autoencoder; (**b**) the inputs \((\widetilde{x})\) and outputs \((x^{\prime})\) of the DAE used in this study that the autoencoder extracts the flaw signal out of the mixed signal and denoises the other extra signals beside the flaw signal. Figure 6: The architecture of proposed denoising autoencoder in this study: (**a**) The input or \(\widetilde{x}\) used in this study where it goes through the denoising autoencoder and its features are saved in the bottleneck hidden layer; (**b**) The flaw signal or \(x\) that is used as validation where all of the extra signals beside the flaw signal are zero-padded. The validation is used for the MSE calculation with the output; (**c**) The output or \(x\prime\) that is reconstructed through the denoising autoencoder where it is compared to the validation signal and the autoencoder learns to extract the flaw signal.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 3 Materials and Methods
### Research Questions and Databases Searched  
A review protocol was established before the bibliographic analysis to systematically identify, assess, and interpret findings relevant to the research focus. First, this review investigates the current applications of DTs in agricultural water management, aiming to understand the existing scope and practical implementations of DTs. Then, the focus shifts to exploring the types of DT models utilized in this domain, providing insights into their functionalities and their suitability and adaptability for different water scenarios. Further, the integration of AI with DTs is examined, along with the outcomes achieved through this combination. This helps evaluate the potential of AI to enhance the predictive and analytical capabilities of DT systems. Finally, this review extends to assessing the extent to which DTs have been applied, including the tools, techniques, the maturity levels of the systems, and identifying the key benefits associated with implementing DT technologies in precision agriculture water management. Highlighting these benefits emphasizes DTs' potential to improve resource efficiency and decision-making. These interconnected focal areas collectively form a comprehensive framework for reviewing the existing literature and advancing the understanding of DTs in water management practices.  
Next, a search strategy was devised to pinpoint the appropriate keywords for the search string, with the goal of identifying relevant information sources, such as academic databases and search engines that provide extensive digital documentation. Figure 3 depicts the step-by-step process for extracting studies from prominent scientific research databases, including Scopus, Web of Science (WoS), ScienceDirect, and IEEE Xplore. Specific search strings were employed by the authors in these databases to locate pertinent research on DTs. The following string was used to search the literature after modifications relevant to each database: (\"Water\") AND (\"digital twin\") AND (Agri\" OR \"smart farming\" OR \"crop\" OR \"Precision\").  
Figure 3: PRISMA flow diagram for literature review.
## 2 System Model
This is chosen to balance throughput and latency efficiently while maintaining simplicity in optimization. The weight represents the hyper-parameter for adjusting the contribution between two objectives. The range of weight value is [0,1]. The larger the \(\omega_{1}\) value, the more emphasis is  
to system limitations, such as delay thresholds and valid task partitioning among MEC servers. Equation (8a) means that the time taken to process a task occurring in HST \(n\) at time \(t\) must be within the delay threshold (\(L_{max}\)). Equation (8b) represents the proportion of a task assigned to serving MECSs \(\mathcal{K}\) for processing. In other words, \(x_{k}^{n}(t)\) represents the proportion of task \(W_{n}(t)\) assigned to MECS \(k\). Therefore, they must sum to 1, as in Equation (8c). And they must have values between 0 and 1, as in Equation (8d).
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Integrated Topologies
#### 3.3.2 Non-Isolated PWM Three-Port Converter  
In TPCs, the duty cycle during each pulse cycle is shared among many input ports. This effectively reduces the duty cycle of individual ports, leading to increased RMS currents and reduced efficiency [112; 113]. Moreover, problems due to unshared grounds and the complexity of control circuits also exist [37]. To overcome such challenges, [PERSON]. [114] proposed a novel non-isolated PWM TPC using a uni- and bidirectional PWM converter, as shown in Figure 18a. The converter is simple, avoids unshared ground issues, has higher effective duty cycles, and reduces inductor sizes. The size of passive elements in the converter decreased by about 19%, compared to the conventional non-isolated TPC.
## 2 Methods
### Grid Generation and Boundary Conditions  
Herein, the unstructured hybrid grids are generated by the commercial software ICEM 18.0, as shown in Figure 3. The computational domain is divided into three sub-zones: the far-field zone, the zone near the inlet, and the zone inside the inlet. The mesh of the far-field zone is sparse, while the other zones are relatively dense. In addition, the prism grids near the aircraft surface and inlet walls are refined enough (the first grid height is 2 \(\times\) 10\({}^{-5}\) m, the prism layer is 25, and the growth rate is 1.2).  
As can be seen from Figure 3, the boundary conditions are mainly divided into INLET, OUTLET, SYM, EXIT, and FARFIELD. The INLET boundary is assumed to be a uniform velocity inlet with different Mach number and attack angle. The OUTLET and FARFIELD boundary is assumed to be \"Opening\" with a reference pressure of 17,900 Pa and temperature of 242 K. The SYM boundary is assumed to be \"Symmetry\". The EXIT boundary is assumed to be \"Outlet\" with a given mass flow rate. In addition, the no-slip wall boundary conditions are set at the aircraft fuselage and the inlet walls.  
Figure 1: Aerodynamic scheme diagram of variable geometry auxiliary inlet: (**a**) 2D diagram; (**b**) Configuration I (K = 100%); (**c**) Configuration II (K = 65%); and (**d**) Configuration III (K = 0%).  
Figure 2: The integrated model of configuration I and aircraft fuselage.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 3 System Description
### Hardware of the Transmitter
#### 3.1.3. Wattmeter  
The antenna tuning procedure became critical for some frequencies and some antenna tuners were damaged during the experimental preliminary tests. Once the adaptation network has been tuned at a certain frequency with a low-power tone, \"hold\" position was activated to keep the configuration settings. However, when a spread spectrum signal was being transmitted, the device began to re-tune at full power sometimes, ignoring the \"hold\" signal and, as a result, some relays, inductors and capacitors were burned out.  
To overcome the above incidences, a remote controlled VSWR meter was designed. It measured during the whole transmission and warned the control system in case of mismatch to switch off the power amplifier. The block diagram of this VSWR meter is shown in Figure 6. The logarithmic amplifier AD8310, followed by the AD8362 true power detector, detects the forward and reverse  
Figure 5: Mesured radiation diagram of the monopole (**a**) without radials and (**b**) with radials.  
power signals from the directional coupler. Concerning the amplifier, it presents a rise time smaller than 15 ns, allowing a quick reaction when a mismatch occurs, and a high dynamic range (up to 95 dB), which is necessary for the detection of a weak reflected signal in the presence of a high power transmitted signal. When the VSWR meter notices the increase in the reverse transmitted power, the control system switches off the amplifier, protecting both the amplifier and the antenna tuner. Since the Wattmeter was put into operation, no other antenna tuner has been damaged, overcoming the aforementioned problems.  
Two temperature sensors were installed for further protection: inside (A) and outside (B) the power amplifier box (see Figure 6). When the value from the A or B exceeds a certain threshold, the fans of the power amplifier or the fans of the whole system are activated, respectively. Yet, if the temperature is too high, the transmitter is switched off.
## 3 Methodology
### Satellite Selection (SS) Techniques  
In this subsection, various methods are explored for eliminating noisy measurement satellites or, in other words, determining the set of satellites used for positioning.  
1. Based on El: This model is employed by selecting the satellite with the highest (EL) angle and disregarding the rest. While this approach ensures good traceability, it may not provide the best availability [42; 43].
2. Based on \(C/N_{0}\): In [44], a fuzzy SS algorithm was utilized to select a set of satellites with the highest \(C/N_{0}\) and minimum geometrical dilution of precision (GDOP).  
3. Brute force (optimal) method: Through the process of evaluating all possible combinations and selecting the optimal performance set, this approach ensures the best performance. However, it comes with the drawback of high computational cost [43]. Given \(n\) satellites on the horizon and a receiver with \(k\) channels, the set of available options for the optimal method, denoted as \(AS_{\text{opt}}\), is determined by \[\text{AS}_{opt}=\frac{n!}{(n-k)!k!}\] (9) For example, if n = 30 and k = 25, 142,506 geometries should be evaluated.
4. Greedy method: Similar to the optimal method for evaluating subset performance, this approach removes only one satellite at a time and utilizes the determined geometry to calculate the next iteration [43; 45]. The numbers of AS\({}_{greedy}\) are given by \[\text{AS}_{greedy}=\frac{1}{2}[n(n+1)-k(k+1)]\] (10) For example, if n = 30 and k = 25, then the number of geometries to evaluate is reduced to 140.
5. Downdate method: Given our objective to optimize elements of the covariance matrix, we revert to employing the greedy algorithm approach. The position covariance matrix (CM) in the north-east-down (NED) frame is used: \[\text{CM}=\left(G^{T}RG\right)^{-1}\] (11) where G is the geometry matrix in the NED frame and R is the weighting matrix. To find the best subset, the CM must be calculated in Equation (12): \[\text{CM}_{i}=\text{CM}+\frac{S_{i}.S_{i}^{T}}{P_{i,i}}\] (12) where \(CM_{i}\) is the position CM with the \(i^{th}\) removed satellite and \(S_{i}\) is the \(i^{th}\)\(S\) matrix column. \[S=\left(G^{T}RG\right)^{-1}G^{T}R\] (13) \[P=R-RG\left(G^{T}RG\right)^{-1}G^{T}R\] (14)  
Although the downdate method suggests a more efficient way to implement the greedy algorithm, it also provides insights into an even more efficient algorithm. By examining Equation (12), we can observe this potential improvement.  
\[(\text{CM}_{ij})_{i}=\text{CM}_{ij}+\frac{S_{ji}^{2}}{P_{i,i}} \tag{15}\]  
The rise in CM will manifest in the final term of Equation (15). The smaller this term, the less impact it will have on increasing the corresponding covariation term.  
To obtain the best subset, one can sort the values in Equation (16) from all calculations and then retain the largest values of this cost function [43; 46].  
\[\text{cost function}=\frac{0.25(S_{1,i}^{2}+S_{2,j}^{2})+S_{3,i}^{2}}{P_{i,i}} \tag{16}\]This method outperforms the greedy approach. Similar to the EL-based method, it involves calculating a set of all in-view solutions initially and then selecting the optimal subset.
## 1 Introduction
proposed a double-loop compensation design approach based on the generalized notch filter (GNF), but in which the sign of the convergence coefficient needed to be changed according to the rotational speed [20]. [PERSON] et al. formulated a plural phase-shift notch filter (PSNF) that can identify synchronous components by tuning the amplitude of the controller and phase of the plural notch filter [21]. A parallel notch filter scheme was explored to enhance notch performance at high speeds in [22]. Although the synchronous current elimination was achieved by using an adaptive notch filter (ANF) in [23], a trade-off between steady-state accuracy and transient convergence speed must be established by adjusting notch parameters. Importantly, [PERSON] et al. proposed that further research should focus on the criterion for selecting optimal values of the notch parameters. Therefore, this research continues to carry out the structural improvement and multi-parameter optimization of notch filters. To the best of our knowledge, there is limited research on the suppression of self-excited oscillations in multi-stage-blade modes of MSTMPs. To sum up, an active vibration control strategy based on notch filters demonstrates strong engineering practicability, making it highly suitable for the micro-vibration suppression of compressors, turbines, and pumps. Addressing rotor mass imbalance vibration and multi-stage-blade modal vibration are critical for the engineering applications of MSTMPs. The innovative contributions of this research are primarily as follows:  
1. A novel integrated AMB controller consisting of parallel co-frequency ANF and cascaded multi-frequency improved double-T notch filters (DTNFs) is proposed. The structural improvement of the AMB controller and the multi-parameter optimization of the algorithm are carried out. 2. To address the issue that the phase-lag angle introduced at open-loop cutoff frequency by the ANF with an inappropriate bandwidth factor can easily affect the phase stability margin of the MSR system, an ANF bandwidth factor rectification method based on displacement stiffness perturbation is designed. 3. To mitigate the problems of an excessive notch (resulting in too large a phase-lag angle) and an incomplete notch (resulting in insufficient notch depth), taking the notch depth at the notch frequency, the notch width corresponding to \(-3\) dB, and the phase-lag angle introduced at the open-loop cutoff frequency by the DTNFs as constraints, a multi-objective constrained optimization method of cascaded improved DTNFs based on linear normalization is designed. The rest of this paper is organized as follows. In Section 2, the AMB-rotor system is modeled, which considers multi-stage-blade modal vibration obtained through sinusoidal sweep excitation. In Section 3, the structural improvement and multi-parameter optimization algorithm of parallel co-frequency ANF and cascaded multi-frequency improved DTNFs are proposed, respectively. In Section 4, a comprehensive simulation analysis is conducted, followed by experimental validation on an MSTMP. Concluding remarks are provided in Section 5.
## 4 Results and Experimental Evaluation
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
We evaluated the performance of the models using the following metrics:  
* Mean Squared Error (MSE): Measures the average squared distance between predicted and actual trajectory points.
* Training Loss: The value of the loss function on the training dataset.
* Validation Loss: The value of the loss function on the validation dataset.
* Uncertainty Calibration: For the StochasticTrajectory model, we assessed how well the predicted uncertainty matched the actual error distribution.
###### Abstract  
Porous polymer membranes with highly interconnected open-cellular structure and high toughness are crucial for various application fields. Polymerized high internal phase emulsions (polyHIPEs), which usually exist as monoliths, possess the advantages of high porosity and good connectivity. However, it is difficult to prepare membranes due to brittleness and easy pulverization. Copolymerizing acrylate soft monomers can effectively improve the toughness of polyHIPEs, but it is easy to cause emulsion instability and pore collapse. In this paper, stable HIPEs with a high content of butyl acrylate (41.7 mol% to 75 mol% based on monomers) can be obtained by using a composite emulsifier (30 wt.% based on monomers) consisting of Span80/DDBSS (9/2 in molar ratio) and adding 0.12 mol\(\cdot\)L\({}^{-1}\) CaCl\({}_{2}\) according to aqueous phase concentration. On this basis, polyHIPE membranes with high open-cellular extent and high toughness are firstly prepared via reversible addition-fragmentation chain transfer (RAFT) polymerization. The addition of the RAFT agent significantly improves the mechanical properties of polyHIPE membranes without affecting open-cellular structure. The toughness of polyHIPE membranes prepared by RAFT polymerization is significantly enhanced compared with conventional free radical polymerization. When the molar ratio of butyl acrylate/styrene/divinylbenzene is 7/4/1, the polyHIPE membrane prepared by RAFT polymerization presents plastic deformation during the tensile test. The toughness modulus reaches 93.04 \(\pm\) 12.28 kJ\(\cdot\)m\({}^{-3}\) while the open-cellular extent reaches 92.35%, and it also has excellent thermal stability.  
polyHIPE membranes; RAFT polymerization; open-cellular extent; toughness
# JSON Schemas with Semantic Annotations Supporting Data Translation
## 4 Proposed Annotation Mechanisms for JSON Schemas
### JSON Schema with Semantic Annotations  
In this paper, we propose semantic annotation mechanisms to add semantic annotations to JSON Schemas. SAWSDL is a W3C recommendation for adding semantic annotations to WSDL and XML Schemas [6]; however, for JSON Schemas, there is no recommendation (or standard) for adding such annotations. There are open topics [49] on this subject, where the use of an attribute named \"modelReference\" is informally proposed, in analogy to SAWSDL, to introduce annotations pointing to semantic concepts. In this paper, we follow this route but we work out the required details for successfully annotating the schemas.  
The semantic annotation can be inserted into the JSON schema by putting the semantic annotation in the property itself, with the keyword \"modelReference\". Since we aim to annotate data-carrying leaf-nodes, we support the annotation of the following JSON-Schema properties:  
* \"string\";
* \"integer\";
* \"number\";
* \"boolean\";
* \"array\".  
It was not considered the \"object\" property, since the annotation method is used for annotating data carrying nodes, similar to the previous works for XML Schema [4; 5; 33]. It was also not considered upper-level elements because this may lead to problems if the structure of the ontology and the schema differs. Therefore, the complete semantics is defined on the leaf level, using expressive annotation paths and group identifiers. This extension of the JSON Schema is backward compatible since JSON validators should ignore unknown keywords, giving the freedom to define additional keywords for custom implementations.  
In general, the property values of the \"modelReference\" keyword are not limited to a specific format. However, we propose the use of an extension of annotation paths as applied in the Arrowhead Framework [5]. An example of a JSON schema with the attribute \"modelReference\" is shown in Listing 3.  
```
1{-Sschema^: \"[[http://json-schema.org/draft-07/schema^](http://json-schema.org/draft-07/schema^)]([http://json-schema.org/draft-07/schema^](http://json-schema.org/draft-07/schema^)),
3-type^: _object\".
4'properties^: {
5'databases^: {
6'type^:object^,
7'properties: {
8'units^: {
9'type^: \"string\",
10'modelReference^: ^/TemperatureSensor/hasTempUnis/TemperatureUnits^
11},
12'sensorttemp^: {
13'type^: \"number\",
14'modelReference^: ^/TemperatureSensor/hasDecValue^
15},
16'sensor2 temp^: {
17'type^: \"number\",
18'modelReference^: ^/TemperatureSensor/hasDecValue^
19}
20}
21}
22}
23}
```  
Listing 3: JSON Schema with semantic annotations.
# Reflective Inverse Diffusion
## 4 Results
### Non-Mechanical Beam Steering  
Non-mechanical beam steering, in the context of reflective inverse diffusion, refers to the ability to change the location of the focus spot in the observation plane without any physical modification of the experiment setup; only phase modulation from the SLM was used. The algorithm, with pre-optimization for \(N=2\) through 16, sequentially targeted the four corners of the CCD, which represents a deflection angle of 1.8\({}^{\circ}\). The enhancement of the four target corners was comparable with less than a 15% difference between maximum and minimum enhancement.  
The size of a playing card with dimensions of 88 mm \(\times\) 63 mm represents a maximum deflection angle of 9.0\({}^{\circ}\) horizontally and 12.6\({}^{\circ}\) vertically and was chosen to demonstrate beam steering over a larger angle. The brushed aluminum reflector lost almost 60% of peak spot enhancement when the target location moved the maximum diagonal dimension. The reduction in enhancement corresponds to the focus spot moving from the specular region of the bidirectional reflectance distribution function (BRDF) into the diffuse region.  
The spot enhancement from the diffuse region of reflection was measured for each of the reflector materials and compared with its specular reflection results. The specular enhancement was measured using the same setup as described in Section 3.1, and the algorithm was allowed to run with pre-optimization from \(N=2\) through 16. The CCD was then moved to the diagonal displacement of 108.2 mm (15.5\({}^{\circ}\)) to approximate the diffuse region of reflection while maintaining a distance of 40 \(\pm\) 0.5 cm from the reflector. The algorithm was run with pre-optimization from \(N=2\) through 16. The results are plotted in Figure 13. Brushed aluminum achieved a specular enhancement of approximately 60, but only achieved an enhancement of approximately four in the diffuse region. The white paint and graphite reflectors both achieved a specular enhancement of approximately 30 and diffuse enhancements of approximately six and two, respectively. Sandblasted aluminum and Infragold(r) both achieved specular enhancements of approximately 40 and diffuse enhancements of 20. Spectralon(r) achieved a specular enhancement of 16 and a diffuse enhancement of 12, retaining 75% of its specular enhancement in the diffuse region due to its highly Lambertian reflective properties.  
Figure 12: Autocorrelation for each reflector from \(N=8\) through \(N=32\). Red vertical lines mark the predicted spot size. The upper and lower bounds of the measured spot sizes are marked with vertical lines in cyan and magenta, respectively.