esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Embedded Device Registration Procedure Based on the OGC SensorThings API
###### Abstract  
Sensor Web and Internet of Things (IoT) (SW-IoT) have been attracting attention from various fields. Both of them deploy networks of embedded devices to monitor physical properties (i.e., sensing capability) or to be controlled (i.e., tasking capability). One of the most important tasks to realize the SW-IoT vision is to establish an open and interoperable architecture, across the device layer, gateway layer, service layer, and application layer. To achieve this objective, many organizations and alliances propose standards for different layers. Among the standards, Open Geospatial Consortium (OGC) SensorThings API is arguably one of the most complete and flexible service standards. However, the SensorThings API only address heterogeneity issues in the service layer. Embedded devices following proprietary protocols need to join closed ecosystems and then link to the SensorThings API ecosystem via customized connectors. To address this issue, one could first follow another device layer and gateway layer open standards and then perform data model mapping with the SensorThings API. However, the data model mapping is not always straightforward as the standards were designed independently. Therefore, this research tries to propose a more direct solution to unify the entire SW-IoT architecture by extending the SensorThings API ecosystem to the gateway layer and the device layer. To be specific, this research proposes SW-IoT Plug and Play (IoT-PNP) to achieve an automatic registration procedure for embedded devices. The IoT-PNP contains three main components: (1) A description file describing device metadata and capabilities, (2) a communication protocol between the gateway layer and the device layer for establishing connections, and (3) an automatic registration procedure for both sensing and tasking capabilities. Overall, we believe the proposed solution could help achieve an open and interoperable SW-IoT end-to-end architecture based on the OGC SensorThings API.  
Internet of Things; sensor web; plug and play; interoperability; OGC SensorThings API +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Identification of Volatile Organic Compounds (VOCs)
#### 2.3.2 Identification of Untargeted VOCs  
For the analysis of untargeted VOCs, Gas Chromatography and Mass Spectrometry (GC-MS) was performed. The chromatographer was equipped with a column DB-5M (length, 30 m; internal diameter, 0.25 mm; layer thickness, 1 \(\upmu\)m; Agilent Technologies, Glostrup, Denmark). It was coupled to a MS-detector equipped with Solid Phase Micro Extraction (SPME, Multi-Purpose Sampler, Gerstel, Skovlunde, Denmark) for identification and relative quantification of volatiles. A 2 cm fiber (DVB/Carboxen/PDMS, Supelco) was used for the analysis. Helium was selected as the carrier gas. Prior to analysis, a KI solution containing linear alkanes from C\({}_{5}\) to C\({}_{20}\) was prepared according to an internal protocol. For the internal standard solutions (IS), 10 g/L stock solutions of ethanol-d6, dimethyl-d6 disulfide, 2-methyl-3-metpanone, and pyrazine-d4 were prepared by dissolving in 1-Methyl-2-pyrrolidone. All four IS stock solutions were stored at \(-80\)\({}^{\circ}\)C. In a 200 mL measuring flask, approximately 150 mL water was added and the individual IS stock solutions were added according to previously defined ratios. The measuring flask was filled to 200 mL with water as diluent. At this concentration level, the expected signal-to-noise values for all compounds was of around 50. IS-solutions were stored at \(-18\)\({}^{\circ}\)C.  
For the sample preparation, 1 mL or 1 g of fermented sample was dispensed into a 20 mL vial with 100 \(\upmu\)L IS solution and stored at \(-18\)\({}^{\circ}\)C until analysis. Fermented samples were analyzed after a blank sample and the retention index sample (KI). GC was performed at an initial temperature of 32 \({}^{\circ}\)C that gradually increased to 325 \({}^{\circ}\)C. The incubation temperature and time for the SPME were 10 min and 60 \({}^{\circ}\)C, respectively. Penetration distance of the fiber in the vial was 30 mm and extraction time was 20 min. Volatiles were adsorbed or absorbed onto the SPME fiber. The fiber was retracted and injected into the GC inlet at 60 mm distance, liberating volatiles to be separated in the column and detected by MS. Fiber was desorbed for 2 min. The results were analyzed with MatLab and reported as relative signal-to-noise values for each sample (Equation (3)).  
\[\text{S/N }=\ \frac{\text{Height}_{\text{analyte}}}{\text{Height}_{\text{IS }}\times\text{Noise}_{\text{analyte}}} \tag{3}\]  
Chemicals for targeted and untargeted VOCs identification were obtained from Sigma-Aldrich (Munich, Germany), except from 1-Methyl-2-pyrrolidone, which was purchased from Merck (Merck KGaA, Darmstadt, Germany).
## 4 Numerical Examples and Validation of the Software
### Comparison to Other Methods
#### 4.3.4 Comparison of the Different Mehtods  
This section is devoted to the explicit comparison of the different numerical schemes. However, before the actual comparison is undertaken the general setup of the experiments have to be stated.  
First, note that the parameters of the mathematical problems to approximate in this section are given in the Tables 2 and 3. The corresponding RHS are the same as in the respective examples above. Furthermore, in the 2d experiments, the geometry considered is the L-Shaped domain.  
Furthermore, for every method, the initial mesh \(\mathcal{T}_{0}\) for the refinement scheme was always the same in the respective dimension. Furthermore, note that refinement loops are broken if the mesh has 10,000 nodes in one space dimension and 1000 nodes in two space dimensions.  
The loops of the respective augmented Lagrangian algorithms are broken if the relative error  
\[\mathrm{err}=\left|\sum_{S\in\left\{A,B,C\right\}}\left(\mu_{S},c_{S,h}\right) _{L^{2}(\Omega)}\right|\]  
is bounded by the tolerance \(\varepsilon=1e-4\).  
First, note that there is no visual remarkable difference between the outputs of the algorithms. Hence, in this algorithm only the CPU times of the algorithms will be compared. Table 6 assembles the actual data for the comparison.  
In the context of the described algorithms, one would expect that the Primal-Dual Active-Set Strategy is the most inefficient in terms of needed RAM space during the computation due to the fact that the system of Equation (36a)-(36c) contain more variables than the other optimization-problems, since the augmented Lagrangian methods treat the multiplier \(\lambda\) as constant and not as a variable.  
In one space dimension and for the parameters given in Table 2, the Primal-Dual Active-Set Strategy is the most efficient, while the two augmented Lagrangian schemes need approximately the same CPU time, as seen in Table 6. In contrast to that the augmented Lagrangian algorithm that is introduced in this paper is more efficient for non-convex domains and nonlinear problems than the other algorithms.  
Modelling the Stationary Species Transport in the Diffusion-Boundary Layer during the Metal Deposition from a Cu\({}^{2+}\) Electrolyte  
This section is devoted to the derivation a model, see Section 5.1, for the metal deposition from a Cu-electrolyte, taking the complexation with \(\beta\)-alanine (\(\beta\)-ala) in the diffusion-boundary layer into account. In Section 5.2, the numerical strategy, which is discussed in this article, will be applied to the model.
# Retinex-Based Relighting for Night Photography
## Appendix A Symbols Table  
\begin{tabular}{l l} \(a,b,c\) & parameters of quadratic function \\ \(\mathbb{A}\) & set of light source samples \\ \(B\) & value of maximum intensity (brightness) \\ \(d\) & number of dimensions for color signals \\ \(\mathbf{C}\) & vector of user-determined light color \\ \(D\) & number of dimensions \\ \(\mathbb{D}\) & set of dithering points \\ \(\mathbf{f}\) & input image (\(\mathbf{f_{p}}\), \(\mathbf{f_{q}}\): intensity vectors of pixel positions \(\mathbf{p}\) and \(\mathbf{q}\)) \\ \(\mathbf{f_{o}^{\prime}}\) & derived function of \(\mathbf{f}\) at \(o\) \\ \(\mathbf{F}\) & illuminating color map \\ \(g\) & Gaussian convolution operator \\ \(h\) & variable of a histogram index in clipping linear function \\ \(i,j\) & coordinates of an arbitrary point in the quadratic function \(Q\) \\ \(t\) & number of JBF iterations \\ \(\mathbf{I}\) & output image \\ \(k\) & constant scalar factor that enhances luminance \(\mathbf{F}\) \\ \(l_{l_{l}}\), \(l_{a_{l}}\),\(l_{b}\) & linearly darkening factor in Lab color space \\ \(\mathbf{L}\) & illumination map \\ \(M()\) & luminance remapping function \\ \(n\) & subscript representing the index (\(\mathbf{P^{n}}\) and \(\mathbf{C^{n}}\)) \\ \(N\) & number of samples (\(N_{d}\): desired number of samples, \\  & \(N_{l}\): number of target samples), \(N\): number of image pixels \\ \(\mathbf{p}\), \(\mathbf{q}\) & pixel position \\ \(P^{n}\), \(P^{n}\) & PSF for \(n\)-th sampling point and its cropping version \\ \(Q\) & set of clipped quadratic function \\ \(r_{l}\) & minimum distance between light source points \\ \(r\) & subscript of a smoothing parameter \(\sigma_{r}\) \\ \(\mathbf{R}\) & reflectance map (\(\mathbf{R_{p}}\): intensity vectors of pixel positions \(\mathbf{p}\)) \\ \(\mathbb{R}\) & set of real numbers \\ \(\mathcal{R}\) & range domain \\ \(s\) & subscript of a smoothing parameter \(\sigma_{s}\) \\ \(\mathcal{S}\) & spatial domain \\ \(\mathcal{S}\) & saliency map (\(\mathcal{S}^{t}\)) \\ \(t\) & iterating subscript (e.g., \(\mathcal{S}^{t}\)) \\ \(T\) & threshold value used in our experiment \\ \(U()\) & clipping linear function (\(R(x,s)\)) \\ \(v\) & variable of saturation-clipping linear function (\(R(x,s)\)) \\ \(w_{s}\), \(w_{r}\) & weight function for edge-preserving smoothing filtering \\ \end{tabular}
## 4 Simulation
### Discussion of Results
#### 4.2.2 Method Performance Discussion
However, it can be seen that the RMSE of VG-SBL is consistently maintained within 0.1 m for sources at frequencies of 200 Hz and above. This shows that VG-SBL effectively avoids basis mismatch errors while weakening the influence of noise and improving the accuracy of sound source location identification. As shown in Figure 5b, the amplitude error of FISTA is the largest, followed by OMP and off-grid-\(GL_{1}\), then SBL, and again RPCA-SBL, while VG-SBL has the slightest error. The root causes of the above phenomena are analyzed as follows. The main flaps obtained by the FISTA method are wide, with many false ghost points and very serious energy dissipation, which results in low amplitude energy at the source point. In addition, FISTA also incorrectly estimates two low-frequency sources that are closely spaced from each other, and it cannot successfully separate the two sources, leading to a large deviation between the estimated amplitude and the actual amplitude. The first step of OMP can be understood as a conventional beamforming process [59]. The amplitude error is large for low-frequency sound sources that are closely spaced because the main lobes are fused together. In the solution vector of off-grid-\(GL_{1}\), there is also the problem of underestimated source strength due to multiple interference sources. For the SBL method, the error mainly comes from the basis mismatch problem. The sound source energy is dispersed to several adjacent grid points, which leads to a certain deviation in the source strength quantification. RPCA-SBL can effectively remove part of the noise and make the energy more concentrated, but it still cannot avoid the problem of basis mismatch. VG-SBL effectively avoids the above issues, and the RMSE of the method is consistently maintained within 5 dB for sources at 200 Hz and above. In summary, the VG-SBL method obtains the most accurate source identification results in terms of localization and strength estimation in the entire range of frequency bands analyzed.
## 3 Results and Discussion
### PPF Model's Results
#### 3.1.4 SVM Model  
With regard to SVM models, a couple of parameters, denoted as solvers and kernel functions, must be selected. Because there are different types of solvers and kernel functions, different combinations were analyzed in order to select the optimal configuration. These solvers serve the same function in SVM models as LM algorithms do in FFNN and RNN  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Structure**} & **Delay** & **Hidden Layer** & **Training** & **Validation** \\  & **(z\({}^{-\mathbf{t}}\))** & **Neurons** & **RMSE (MW)** & **RMSE (MW)** \\ \hline
1 & **1:2** & **10** & **0.0883** & **0.0766** \\
2 & 1:3 & 10 & 0.0946 & 0.0813 \\
3 & 1:4 & 10 & 0.0906 & 0.0779 \\
4 & 1:5 & 10 & 0.0900 & 0.0778 \\
5 & 1:6 & 10 & 0.0962 & 0.0852 \\
6 & 1:2 & 5 & 0.0939 & 0.1067 \\
7 & 1:2 & 15 & 0.0904 & 0.0783 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RNNs analyzed structures’ averaged results for fixing hidden layer’s number of neurons.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**RMSE Train. (MW)**} & **RMSE Val. (MW)** & **R\({}^{2}\) Train. (-)** & **R\({}^{2}\) Val. (-)** \\ \hline Value & 0.0883 & 0.0766 & 0.9949 & 0.9959 \\ Improve (\%) & 29.58\% & 39.15\% & 0.53\% & 0.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computed RMSE and R\({}^{2}\) index values for optimal RNN model.  
Figure 2: RNN forecaster’s layout.  
structures, so to avoid the no-convergence issue, five tests of each configuration were run and their results were averaged. Table 6 show the computed averaged RMSE index values.  
The results presented in Table 6 demonstrate that there is not a single structure which minimizes both databases' RMSE index results. Based on the fact that the PPF model needs to have higher accuracy with previously unseen data, the 'SMO' solver and the 'linear' kernel function were chosen to compare this model against the other computed models. Table 7 show the best configuration's error metrics as well as its improvement in percentage against the benchmark model.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 2 Proposed Method 1: Robust Models with Gabor Dictionary Analysis and Machine Learning
### Gabor Dictionary  
For the conceptualization of basis functions, the analysis dictionary is commonly used. An overcomplete dictionary is generally used so that a better sparsity can be obtained by means of the versatile localization of atoms. The analytic Gabor dictionary is used here, where a Gaussian window function is used and can be translated, scaled and modulated as follows [31]:  
\[d_{[\alpha,\beta,w,\beta]}[n]=\frac{\gamma_{[\alpha,\beta,w,\beta]}}{\sqrt{ \alpha}}g\left[\frac{n-\beta}{\alpha}\right]\cos[w[n-\beta]+\theta] \tag{5}\]  
where the scaling constant is expressed by \(\gamma_{[\alpha,\beta,w,\beta]}\) and the Gaussian factor is expressed by \(g\left[\frac{n-\beta}{\alpha}\right]=2^{\frac{1}{4}}e^{-\pi\left[\frac{n-\beta }{\alpha}\right]^{2}}\), with \(n=0,1,2,\ldots,2^{n-1}\). The Gaussian window length is considered as \(L=2^{n}\), and it should correspond to the audio signal length which is supposed to be decomposed. Initially, the scale parameter \(\alpha\) is chosen, and the translator parameter \(\beta\) and frequency \(w\) are expressed as follows:  
\[[\alpha,\beta,w,\theta]=\left[2^{h},2^{h-1},q,2^{-h-1}\pi f,0\right] \tag{6}\]  
where \(0<h<N,q=0:1:2^{N-h+1}-1\) and \(f=0:1:2^{h+1}-1\).  
The shift parameter is presumed to be zero here, and the total number of atoms are modelled accordingly. The \(h^{th}\) Gabor dictionary \(C_{h}\) of size \(2^{N}\times 2^{N+2}\) is developed for a particular value of \(h\) in a matrix format. All the audio cough signals have a similar length, and the variables of the \(h^{th}\) dictionary are assigned approximately. By means of altering \(h\), the tradeoff between the spectral resolution and temporal resolution of the dictionary is solved. There is a slow decay in the Gaussian function \(g\left[\frac{n-\beta}{\alpha}\right]\) as the value of \(h\) increases. Therefore, the time duration of the atom is enhanced, and the temporal resolution is severely degraded. So, there is an increase in the Gaussian function duration, and the overall bandwidth of atoms present around the frequency reduces, leading to the atoms attaining a higher spectral resolution.  
Figure 1: Proposed work of robust models with Gabor dictionary analysis and machine learning.
# The Effect of Pre-Treatment on the Rehydration of Dried Apple Cube
## 4 Conclusions  
In the context of various drying techniques, microwave drying demonstrated better re-hydration capacity compared to traditional methods due to faster heating and the potential preservation of cellular structure. The results obtained suggest that the rehydration properties of dried apples are influenced by a combination of factors, including pre-treatment, the geometric properties of the biomaterial and the drying techniques employed.  
Blanching, microwave heating and osmotic drying reduce water adsorption in materials dried in dryers without air circulation compared to those dried in dryers with forced air circulation. The process of prior osmotic drying significantly affects water absorption, irrespective of the drying method used in conventional dryers, reducing the rehydration capacity of dried apple cubes by up to 32%.  
Blanching the raw material before drying decreases the speed and effectiveness of rehydration, indicating that this process may help preserve the internal structure of dried apple cubes prepared for long-term storage.  
The conclusions from this research may be valuable for both the food industry and nutrition science, contributing to the development of higher-quality storage products that retain the taste and nutritional value of the raw material. Furthermore, the findings can be used to optimize the drying and rehydration processes of other types of fruit.  
Conceptualization, H.T. and B.B.; methodology, G.J. and C.K.; formal analysis, H.T. and B.B.; resources, H.T.; data curation, B.B and G.J.; writing--original draft preparation, B.B.; writing--review and editing, H.T. and C.K.; visualization, G.J. All authors have read and agreed to the published version of the manuscript.  
This paper was financed from the subsidy of the Ministry of Education and Science for the Agricultural University of Hugo Koldataj in Krakow for the year 2025.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.  
Figure 10: The rate of rehydration process (from absolute moisture content); (**a**) air-circulated drying, (**b**) non-air-circulated drying.
## 2 Materials and Methods
### Biodiesel Production and Its Characterization
#### 2.2.2 Design and Optimization of a Base-Catalyzed Transesterification Method  
Figure 2 explains the framework of the proposed research work used for the conversion of NS oil to high-yield biodiesel. RSM is a collection of statistical and mathematical techniques that could determine the perspectives of the process (terms: linear, square, and interaction variables) according to the CCD and BBD experimental matrices [71]. The points (BBD focuses on middle-level experiments and CCD focuses on corner-level experiments) considered in design space were often different for the two models (BBD and CCD) and thus resulted in a difference in polynomials (e.g., coefficients, fits, and significance) [64; 72]. BBD was employed in the present work for experimentation; an analysis of biodiesel yield is presented in Table 2.  
Figure 2: Framework for modeling and optimization for higher biodiesel yield.  
The experimental matrix corresponding to the Box-Behnken design (BBD) is defined according to Equation (1).  
No. of Experiments = 2 \(\times\) No. of independent variables (No. of independent variables \(-\) 1) + Center points (1)  
In the present work, four independent variables and five center point experiments were considered in our experimentation. Table 3 displays the results of 29 experimental trials. Each experiment was repeated thrice, and the average values corresponding to yield were recorded. The Design-Expert software, version 11, was used to define the experimental matrix and perform statistical analysis. After each experimental run, the biodiesel samples were permitted to settle in a separate sieve for approximately twelve hours. Note that the samples were separated, with methyl esters appearing on the apex and glycerol settling at the bottom of the funnel. To eliminate the water content in the biodiesel, the resulting methyl esters were water-washed and warmed to a high temperature. The steps involved in converting NS oil-to-biodiesel yield using the transesterification process are explained in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Factors** & \multicolumn{2}{c}{**Levels**} \\ \cline{3-4}  & **Low** & **Medium** & **High** \\ \hline A & Methanol-to-oil molar ratio & 3 & 7.5 & 12 \\ B & Catalyst concentration (wt.\%) & 0.5 & 1.0 & 1.5 \\ C & Reaction temperature (\({}^{\circ}\)C) & 50 & 57.5 & 65 \\ D & Reaction time (min) & 60 & 90 & 120 \\ \hline \end{tabular}
\end{table}
Table 2: Transesterification parameters and operating levels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Run** & **A:** & **B: Catalyst** & \multicolumn{2}{c}{**C: Reaction**} & **D: Reaction** & \multicolumn{2}{c}{**Experimental**} & **Predicted** \\
**Run** & **Methanol-to-Oil** & **Concentration** & **Temperature (\({}^{\circ}\)C)** & **Time (min)** & **Biodiesel** & **Biodiesel** \\
**7** & **12** & 1.5 & 57.5 & 90 & 91.0 \(\pm\) 0.5 & 90.9 \\
8 & **7.5** & 1.0 & 57.5 & 90 & 92.0 \(\pm\) 0.4 &
## 3 Results
### Characterization of PS/OMMT Nanocomposite
#### 3.2.2 XRD Analysis  
Figure 6 shows the XRD patterns of the PS, PS/P-MMT, PS/P-SLS-MMT, and PS/P-KH570-MMT nanocomposites. As shown in Figure 6, no characteristic diffraction peak of OMMT was observed for the PS/P-MMT, PS/P-SLS-MMT, or PS/P-KH570-MMT in the range of 1.5\({}^{\circ}\)-10.0\({}^{\circ}\) of 20. This result suggested that the PS chains entered into the interlayer space of OMMT and destroyed the intercalated clay structure, leading to exfoliated structure in the resulting PS/OMMT nanocomposite during the soap-free emulsion polymerization. The clay platelets could be exfoliated in the PS matrix owing to the role of the intercalated phosphonium surfactants, SLS molecules, and/or adsorbed KH570 silanes that obviously enlarged the interlayer space of the MMT, which could provide a bigger space for the ease of PS macromolecular chains penetration. In addition, the complete exfoliation of individual clay platelets in the polymer matrix was also because of the favorable miscibility between the organic clay platelets and polymer matrix [35]. Furthermore, the KH570 molecules that grafted on the broken edges of the MMT could improve the hydrophobicity of the layered silicates, thus increasing their compatibility with the PS matrix [36]. However, the disappearance of XRD peaks does not necessarily suggest complete delamination of the clay platelets. Thus, other analytical tools, such as TEM, were needed to prove the dispersion and degree of exfoliation of the clay platelets. In order to determine the  
Figure 5: FTIR spectra of (**A**) PS, (**B**) PS/P-MMT, (**C**) PS/P-SLS-MMT, and (**D**) PS/P-KH570-MMT.  
deductions from XRD results, the morphology of the nanocomposites was further analyzed by the use of TEM. Figure 7 shows the typical TEM images of the PS and PS/OMMT nanocomposites.