esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The equivalent source method (ESM) based on compressive sensing (CS) requires that the source has a sparse or approximately sparse representation in a suitable basis or dictionary. However, in practical applications, it is not easy to find the appropriate basis or dictionary due to the indeterminate characteristics of the source. To solve this problem, an equivalent redundant dictionary is constructed, which contains two core parts: one is the equivalent dictionary used in the CS-based ESMs under the sparse assumption, and the other one is the orthogonal basis obtained by the singular value decomposition (SVD). On this foundation, a method named compressed ESM based on the equivalent redundant dictionary (ERDCESM) is proposed to enhance the performances of source field reconstruction for different types of sources. Moreover, inspired by the idea of functional beamforming (FB), ERDCESM with order \(v\) (ERDCESM-\(v\)) can possess a high dynamic range when detecting the source location. The numerical simulations are carried out at different frequencies to evaluate the performance of the proposed method, and the results suggest that the proposed method performs well both for sparse and even spatially extended sources. The validity and practicality of the proposed method are also verified by the experimental results.  
Keywords:equivalent source method; functional beamforming; compressive sensing; equivalent redundant dictionary; sound field reconstruction +
Footnote †: journal: Applied
## 4 Performance Evaluation
Moreover, the delay of the proposed circuits compared to that of the design in [26] is more than one clock phase (0.25); however, the consumption area and the number of cells in this design are much higher than those of our proposed designs, and most importantly, in this design, not only is the crossover used, but also 45-degree cells are used to implement it, and this reduces the delay of this design, which instead has less cost function and strength and stability. Regarding the parameters of the number of crossing intersections, the number of inverter gates, and the number of 45-degree cells, it can be said that none of these circuit parameters were used in the proposed design, which made the cost function of the proposed designs superior to that of previous works. It should be noted that the three-input majority gate used in the proposed FA design-2 is realized by four inverter gates; however, this majority gate is considered as one gate. In this paper, the coefficients of L, K, and P are considered equal to one (L, K, P = 2); to evaluate the performance of the proposed XOR gate and compare the performance of the previous work, an evaluation was carried out. The diagram in Figure 16 shows the evaluation and comparison of the cost function criterion for L, K, P = 2. As can be seen in this diagram, the evaluation criterion of cost function for our proposed XOR gate design and that of the design in [24] is significantly superior to that of all previous designs. The reasons for this superiority are the lack of use of NOT gates and crossovers and the reduced number of majority gates used in the implementation of the proposed designs. These results and this comparison were carried out in the size range of 1\(\leq\) n \(\leq\) 128. Figure 17 also shows a comparison diagram of the complexity parameter. In this evaluation, the number of majority gates, the number of inverter gates, and the number of crossovers were compared. To measure the complexity of the proposed XOR gate (nbit-input) with previous designs, this evaluation was performed in the range of 1\(\leq\) n \(\leq\) 128. As can be seen in the review of this evaluation criterion, our proposed XOR gate design and the design in [24] are significantly superior to all previous designs. The diagram in Figure 18 also shows the delay parameter. This parameter is also used to measure the speed of the XOR gate. This comparison is also carried out in the range of 1\(\leq\) n \(\leq\) 128, and as can be seen, the fastest XOR gate is related to our proposed design, [19; 20; 24; 28; 29; 30; 31; 32; 33; 34; 35]; our proposed design is coplanar, and crossovers are not used in any of the designs. Figure 16: Comparison of QCA XOR with cost function. Figure 17: Comparison of QCA XOR with complexity.
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
###### Abstract  
Within this paper, a PLC system that takes advantage of the loop resonance of an entire DC-PV string configured as a circular signal path is developed and implemented. Low cost and extremely simple transceivers intended to be installed within each PV module of a string have been designed and successfully tested. In addition, an anti-saturation coil has been conceived to avoid saturation of the core when the entire DC current of the string flows through it. Bi-directional half-duplex communication was successfully executed with up to a 1 MHz carrier frequency (150 kbps bitrate), using a simple ASK modulation scheme. The transmission and reception performance are presented, along with the overall system cost in comparison to the previous literature.  
low cost; monitoring; power-line communication (PLC); resonance +
Footnote †: journal: Sensors
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 3 The Software-Defined Media Ontology
### Description of Class Definitions
#### 3.3.3 **Recorder** Class  
The **Recorder** class expresses the devices and functions that perform media recordings. The **Recorder** object performs a recording of the **Target** object referenced by the **records** property and generates a **Media** object as a areult of the recording. The **Recorder** object links the **Media** object to the **outputTo** property. As mentioned above, a composite of **Recorder** objects can be defined using the **CompositeRecorder** subclass. The **Recorder** class defines three subclasses: **AudioRecorder**, **VideoRecorder**, and **DataRecorder**, depending on the type of media that will be recorded. These subclasses were defined for convenience. There is a degree of freedom in how the **Recorder** object is expressed, and the data creator can decide which subclass to use and whether or not to describe it as a composite device using **CompositeRecorder**. For example, a **VideoRecorder** generally combines stereo audio recording and video recording functions. In the SDM Ontology, this  
Figure 4: **Recorder, Processor, Player class structure.**  
can be represented as a single **VideoRecorder** or as a **CompositeRecorder** that combines a stereo audio **AudioRecorder** and a video-only **VideoRecorder**. Furthermore, a stereo **AudioRecorder** can represent two monaural **AudioRecorder**. It is up to the data creator to decide which form of representation should be adopted.
## 3 Experiment and Analysis
### Evaluation Metrics  
To assess the performance of the model on the dataset, this study selected three commonly used evaluation metrics in classification models: accuracy, recall, and \(F1\) score. Their calculation formulas are shown in Equations (14)-(16):  
\[accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{14}\]\[recall=\frac{TP}{TP+FN} \tag{15}\]  
\[F1=\frac{2 TP}{2 TP+FP+FN} \tag{16}\]  
In the formulas, _TP_ represents true positives, _TN_ represents true negatives, _FN_ represents false negatives, and _FP_ represents false positives. _TP_ is the number of positive samples correctly predicted by the model, _TN_ is the number of negative samples correctly predicted, _FN_ is the number of positive samples incorrectly predicted as negative, and _FP_ is the number of negative samples incorrectly predicted as positive.
# Acoustic Pressure Amplification through In-Duct Sonic Black Holes
## 2 Materials and Methods  
Enhanced sensing of acoustic signals has been observed through metamaterials with axially graded cavity depths [14] eventually coiled [15] but set in a free-field environment. The amplification of the incident pulse signal is now achieved in a waveguide using an outer acoustic metamaterial with graded cavity depths, as shown in Figure 1b, the so-called Opened Sonic Black Hole (O-SBH) sensor.  
The outer cavities are eventually shielded by a micro-perforated panel (MPP) from an air flow conveyed in the main duct to avoid substantial drag, coined MPP-SBH. Its slow sound, dissipative, and sensing performance will be compared against those obtained in the no-flow case from an inner graded metamaterial, shown in Figure 1a, denoted closed SBH (C-SBH), which is geometrically similar to the anisotropic acoustic sensor configuration proposed in [14; 15] but now with cavities backed by sound-hard walls.  
Figure 1: **(a)** Sketch of an inner SBH-type acoustic sensor with open cavity rings; **(b)** sketch of an outer SBH-type acoustic sensor with cavity rings eventually coated by a micro-perforated panel; the red dots show the microphone locations at the back of the cavities.  
The graded metamaterials of total length \(L\) are inserted in a cylindrical duct of radius \(R\). They are composed of a finite number \(N\) of cavities of width \(w\) and whose depths follow a power-law \(D_{n}(x)=R\,x^{m}/L^{m}\), \(m\geq 2\), \(0\leq x\leq L\). The cavities are separated by rigid annular rings of thickness \(w_{p}\). The MPP covering the cavity apertures of the MPP-SBH sensor is described by an overall transfer impedance, \(Z_{\text{MPP}}/\sigma\)[22], approximated by  
\[\frac{Z_{\text{MPP}}}{\sigma}\approx\left(\frac{A}{d_{h}^{2}}+\mathrm{j}\rho_ {0}\omega\right)\frac{t_{h}}{\sigma}, \tag{1}\]  
with \(A=2.2\times 32\eta\), \(\eta\) the air dynamic viscosity, \(t_{h}\) the MPP thickness, \(d_{h}\) the circular hole apertures, \(\sigma=\pi\,d_{h}^{2}/\left(4\Lambda^{2}\right)\) the perforation ratio and \(\Lambda\) the holes pitch. The real part of \(Z_{\text{MPP}}/\sigma\) in Equation (1) relates to resistive dissipation effects within and at the inlet/outlet of the MPP holes, whereas the imaginary part describes inertial reactive effects. Equation (1) is valid in linear acoustic regime, provided that the hole Shear number, \(\mathrm{Sh}_{h}=(d_{h}/2)/r_{\text{visc}}(\omega)\), e.g., the ratio of the hole radius to the viscous boundary layer thickness \(r_{\text{visc}}(\omega)=\sqrt{\eta/\rho_{0}\omega}\), is smaller than one, with \(\rho_{0}\) the air density and \(\omega\) the angular frequency. The convention \(\mathrm{e}^{\mathrm{j}\omega t}\) is assumed throughout the study.
## 4 Network Functionality of Emergency Response
### Network Performance Metric  
In transportation networks, the connectivity between origin and destination (OD) is usually measured by their shortest path distance [76; 77; 78] or number of redundant paths [62; 79]. The former is commonly obtained by the Dijkstra [80] or Floyd [81] algorithm, and the latter can be measured by OD-based independent paths, which is considered to be a good measure of network redundancy in recent studies [23; 82]. In the following, an improved connectivity-based network performance metric is introduced to take into account both emergency transportation redundancy and path distances.  
Considering that there are usually several parallel emergency facilities (e.g., hospitals) in an urban transportation network, the post-earthquake network performance should fully account for the contribution of these emergency resources, rather than considering only the nearest facility. Therefore, a novel path search algorithm that captures the dual redundancy of emergency facilities and network paths is presented as shown in Figure 1. This algorithm could obtain all the independent paths between a rescue demand node \(O\) and the emergency destination set \(\mathbf{D}=\{D_{1},\ldots,D_{n_{e}}\}\), without reused network links. Furthermore, based on the idea of network efficiency, we measure the contribution of each independent path to the emergency connectivity performance by the reciprocal of its path distance. Then the connectivity performance between origin \(O\) and destination set \(\mathbf{D}=\{D_{1},\ldots,D_{n_{e}}\}\) is defined as:  
\[IPE_{O,\mathbf{D}}=\sum_{k=1}^{K_{O,D}}\frac{1}{d_{O,D}^{k}} \tag{12}\]  
where \(IPE_{O,\mathbf{D}}\) is the independent path-based efficiency, \(d_{O,D}^{k}\) is the distance of \(k\)th independent path in kilometers, and \(K_{O,\mathbf{D}}\) is the total number of searched independent paths between \(O\) and \(\mathbf{D}\). The integral network performance of emergency response is quantified as the average emergency connectivity of all the normal nodes that need emergency rescue in the network:
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### Models for \(QoE_{i}\) Classification
SVM maps data into a multidimensional space of variables so that the input data can be categorized, even when the data are not linearly separable. Thus, it is necessary to find a separator between the categories, after which the data are transformed in such  
Figure 11: Results of testing the \(QuEi\) classification model in four iterations of increasing the dataset using the DA method. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Created** & **Total Classification Accuracy [\%]** \\ \hline
1. k-NN & 50.00 \\
2. C\&R tree & 46.15 \\
3. Neural network & 42.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ranking list of the best tested models for \(QuE_{i}\) classification. a way that the separator can be constructed as a hyperplane, which has the maximum margin between the categories. After that, the characteristics of new data can be used to predict the group to which the new data should belong [66]. The mathematical function used for this transformation is known as the _kernel_ function. SVM in IBM SPSS Modeler supports linear, polynomial, radial basis function (RBF) and sigmoid kernel function, and in this paper, the RBF kernel was used for the observed SVM model. The kernel has a regularization parameter C, which controls the relationship between the maximum margin and the minimum number of misclassified data. In this research, by default, its value is set to 10. The stopping criterion defines the moment when the optimization algorithm should be stopped. Its value ranges from \(10^{-1}\) to \(10^{-6}\), and according to default settings in the software, it is C = \(10^{-3}\). A smaller criterion value results in a more accurate model, but it takes more time to train [67]. The RBF _gamma_ parameter is 0.1 and can be considered as the \"expansion\" of the kernel and thus the decision region. When gamma is low, the decision area is very wide. In the case of a high value of _gamma_, the decision boundaries are very distorted and so-called decision boundary islands are created around the points belonging to the same category.
## 5 Results and Discussion
### Hydrocarbon Delineation in Well Log Scale  
After the features are selected, hydrocarbon delineation can be conducted. The final attributes selected are gradient, sign product (sign A * B), product (A * B), scaled Poisson ratio change, fluid factor, SQp, SQs, and far minus near times far (see Figure 7).  
Figure 5: Heatmap correlation of SOM features map.  
Figure 6: Mean–median score analysis on oil & gas condition.  
The selected attributes were used as an input for hydrocarbon delineation. The detected hydrocarbons were treated as anomalies. Therefore, hydrocarbon delineation in this research comes down to the anomaly detection problem.  
Using this method, the quantization error is used as a metric for anomaly detection. The higher the quantization error is, the farther its data sample from the dataset's overall distribution. The error threshold needs to be defined to identify the anomaly of the datasets. Error threshold is determined by the local minima detected from the distribution of quantization error (see Figure 8). Figures 9 and 10 below show the result of the hydrocarbon detection using self-organizing maps on the oil and gas conditions, respectively.  
From the figures above, the model can delineate hydrocarbons at three reservoir areas in the well log data, especially, in gas condition. The model can also delineate hydrocarbons in the oil condition, although there is a misdelineation. However, overall, the model has done an effective analysis in the well log area with different fluid scenarios. To validate the overall performance of the model, water saturation was used as a validation parameter. For the validation purpose, labelling needs to be conducted where any data sample with less than 0.5 water saturation was be assigned as a hydrocarbon; meanwhile, any data samples with greater than or equal to 0.5 water saturation values were assigned as not a hydrocarbon. The model resulted in an accuracy of 82% in the oil condition and 85% in the gas condition.  
The model was tested in A well area where A-1 and A-2 are used for model testing. To validate the overall performance of the model, water saturation was also used as a validation parameter where any data sample with less than 0.5 water saturation was assigned as a hydrocarbon; meanwhile, any data samples with greater than or equal to 0.5 water saturation values were assigned as not a hydrocarbon. The model resulted in an accuracy of 92% in A-1 and 93% in A-2 (see Figures 11 and 12). Qualitatively, the delineation  
Figure 7: Selected AVO attributes for anomaly detection.  
result matches well with the indication of hydrocarbons from the water saturation log, although there are some misdelineations at the shallow part on well A-1.  
Figure 8: Error threshold assignment from quantization error distribution using local minima.  
Figure 9: Hydrocarbon delineation result on oil condition.
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 3 Protein Families
### Pectin Methylesterase Inhibitor  
The pectin methylesterase inhibitor (PMEI) Act d 6 is an 18 kDa minor allergen from kiwifruit. PMEI is a natural inhibitor of pectin methylesterase and has wide application in fruit and vegetable processing and preservation [60]. Act d 6 has a low isoelectric point, and an increase in pH can cause structural changes, resulting in unfolding via disulfide bond breakage and the consecutive loss of inhibition activity [60]. PMEIs share structural similarities with invertase inhibitors, and both are grouped in the same protein family as major pollen allergen Pla a 1 from plane trees [42; 61]. A study among 49 kiwifruit-allergic subjects showed 73% Act d 6-positive sera in an immunoblot assay; however, only one serum of those tested showed monospecificity to this allergen [42]. Furthermore, skin prick testing and immune solid-phase allergen chip (ISAC89 version) testing showed only 1% (of 36) and 0.05% (of 9107) of kiwifruit-allergic subjects, respectively, to be positive to Act d 6, indicating the weaker allergenic prevalence of this protein [42].
## 2 Experiment Details
### Data Acquisition with Smartphone
#### 2.1.1 Acquisition with iPhone  
In the iOS category, an iPhone 7 Plus (Made by Apple Inc, San Francisco, CA, USA) phone was used for noninvasive data collection. The imaging unit in the device consists of a WLED (White LED) as the illumination source next to a 12-megapixel camera at a center-to-center separation of around 5 mm. The phone supports color video recording at about 30 fps up to a resolution of 3840 \(\times\) 2160 pixels. For this study, the fingertip was recorded at 30 fps at a resolution of 720 pixels. This did not reduce signal quality as compared to that of 1080 pixels or 4K but took up significantly less phone memory and data transfer time.
# Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space
## 3 Results and Discussion
### Examples  
In this section we provide three illustrative examples. In the first example we compute focusing of Gaussian beams for different values of f-number and compare the focused beam field with that obtained analytically within the paraxial approximation. This example both provides independent validation based on the known analytical result and indicates the importance of using the complete computation for small values of f-number. In the second example we again consider the described case of a short pulse with a circular flat-top transverse profile and compute the peak field strength as a function of f-number. The obtained data provides an opportunity to assess the potential benefit of using tight focusing for increasing peak field strength at current and future laser systems. Finally, in the third example, we apply the method to illustrate the degradation of the intensity profile at focus when using real laser pulses. This shows the necessity to describe accurately the laser pulse for obtaining realistic computational predictions and for interpreting the experimental data.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 5 Use Case
### Global Operation  
As the broaching process slice and the IIoT network slice reside in different VLANs, layer-3 routing is necessary to enable the connectivity between them. OpenStack supports the creation of virtual routers to make this possible without additional VMs being necessary for their implementation. Furthermore, a virtual firewall defines the security policies that make sure each component of the broaching process slice can only access the components of the IIoT network slice that it is authorized to (such as the Hidra client establishing a connection with the Hidra server and the sensors of the WSN). The virtual firewall can also be created in OpenStack on top of the virtual router.  
Thanks to the expressiveness provided by the Hidra access control mechanism, IIoT applications, such as the client gathering data from the broaching process slice, can be granted or revoked access to any of the resources provided by the CDSs in a very granular, dynamic, and flexible manner. In this way, requesting clients within the different slices of SN4I will be granted or revoked access to the data and management services provided by the different CDSs deployed in the WSN according to a very expressive access control policy that allows also the evaluation of local context conditions. As an example, a client preliminary authorized by the ACS to perform a certain operation on a specific CDS could then be denied the requested operation by the CDS itself according to the evaluation of the \"battery level\" local variable, if, for instance, performing the requested operation would imply draining the battery of the CDS before its finalization.  
The overall topology combining both slices is shown in Figure 10.  
Figure 9: Industrial Internet of Things (IIoT) network slice.
## 4 Case Study
### Data Acquisition and Result  
The data used in this study were obtained from the Sichuan Statistical Yearbook (2011-2020), the China City Statistical Yearbook (2011-2020), the China Urban Construction Statistical Yearbook (2011-2020), and statistical yearbooks of every city. The missing data were filled by interpolation and linear regression.  
By inviting 12 experts from the Center for Rural Construction Integrated Management to score the AHP judgment matrix, the final weight allocation result was obtained.