esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Technique Validation and Discussion
### Differential Code Biases and Absolute Slant TEC  
Figure 13 presents the modeled results of the DCB (red circles) recovery. The data are in TECU. A random number generator assigned the initial biases (black triangles). Then, there was an IRI-2012 simulation and determining DCBs by a set of integral TEC data through the above algorithm. For simulation, we used the geometry of a real station, IRKJ, as of 10 April 2012.  
One can see a good performance of the algorithm both for the GPS data and for the GLOMASS data. The maximal error is 0.6 TECU, RMS being 0.3 TECU. The obtained estimates are sufficiently robust both for quiet and for disturbed conditions.  
Figure 14 presents the DCB experimental estimates (in TECU, red circles) obtained from real GPS and GLOMASS measurements through the algorithm operation for all the GPS (a) and GLOMASS (b) satellites. The estimates were obtained from the IRKJ 1 March 2015 measurements. For comparison, we show the CODE estimates (black diamonds). For the GPS DCBs, the divergence of estimates is  
Figure 12: GNSS-based TEC time derivative for IRKJ. (**a**) 5 March 2015 (\(Kp_{max}=2.3\)); (**b**) 17 March 2015 (\(Kp_{max}=7.7\)). The blue dot-and-dash line presents the JPL data, the black dotted line denotes the CODE data, and the red solid line displays the TuRBOTEC-obtained values.  
Figure 13: Simulating the DCB recovery from GPS (**a**) and GLOMASS (**b**). The black triangles are the initial values; the red circles present the recovered values; the blue line shows the difference between the results of the TuRBOTEC operation and the initial values. The data are in TECU.  
sufficiently small. The maximal error for the GPS satellites is 5 TECU, RMS being 2.4 TECU. For the GLONASS satellites, this error is higher. The GLONASS maximal error is 17 TECU, RMS being 8.8 TECU. The results for GPS quite correlate with the estimates by [PERSON] et al. [16]. At the same time, we observe a significant deviation of the CODE and TuRBOTEC estimates for GLONASS. There is no such difference between GPS and GLONASS in simulation. Slant TEC demonstrates non-negative values for GLONASS, when we use TuRBOTEC DCB instead of CODE DCB for GLONASS.  
Figure 15 shows absolute slant TEC for both techniques with constraint (TuRBOTEC, blue dots) and without (black dots). The data are for 5 January 2014. We show the data for all satellites in view. After least squares without constraint, we note negative TEC values after 12 UT for several satellites. Constraint provides non-negative values for all the satellites.
## 4 Performance Evaluation
### Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multirow{2}{*}{**SNMF**} & \multirow{2}{*}{**DRNN**} & \multirow{2}{*}{**SEGAN**} & \multirow{2}{*}{**U-Net**} & \multirow{2}{*}{**MTU-Net**} \\ \cline{5-6} \multicolumn{1}{c}{**Hyper-Parameters**} & & & & & \\ \hline \multirow{8}{*}{Network structure} & \multirow{8}{*}{- Speech Basis} & \multirow{8}{*}{- BNN} & \multirow{8}{*}{\(\times 1000\)} & \multirow{8}{*}{\(\times 1000\)} \\  & & & & (16,384 \(\times\) 1) & & - Input \\  & & & - 1 DConv\_Enc & & (256 \(\times\) 32) \\  & & & \(\{8192\times 16,4096\times 32,\) & - Input & - 2 DConv\_Enc \\  & & - Input & 32, 2048 \(\times\) 32, & (256 \(\times\) 32) & \(\{128\times 16\times 64,64\) \\  & & (513 \(\times\) 1000) & 1024 \(\times\) 64, 512 \(\times\) & - 2 DConv\_Enc & \(\times\) 8 \(\times\) 128, 32 \(\times\) 4 \\  & & - Dense & 64, 256 \(\times\) 128, 128 & \(\{128\times 16\times 64,64\) \\  & & (1000 \(\times\) 1000) & \(\times\) 128, 64 \(\times\) 256, & \(\{128\times 16\times 64,64\) \\  & - Speech Basis & (1000 \(\times\) 1000) & 32 \(\times\) 256, 16 \(\times\) & \(\times\) 8 \(\times\) 128, 32 \(\times\) 4 & 512) \\  & - RNN & 32 \(\times\) 256, 16 \(\times\) & \(\times\) 256, 16 \(\times\) 2 \(\times\) & - 2 DConv\_Dec \\  & (513 \(\times\) 64) & (1000 \(\times\) 1000) & 512, 8 \(\times\) 1024) & 512) & \(\{32\(\times\) 4 \(\times\) 256, 64 \\ structure & - Noise Basis & - Dense & - 1 DConv\_Dec & - 2 DConv\_Dec & \(\times\) 8 \(\times\) 128, 128 \(\times\) \\  & (513 \(\times\) 64) & (1000 \(\times\) 1000) & \(\{8\times\) 1024, 16 \(\times\) 64, & \(\{32\times\) 4 \(\times\) 256, 64 \\  & & - Output & 512, 32 \(\times\) 256, 64 & \(\times\) 8 \(\times\) 128, 128 \(\times\) \\  & & (1000 \(\times\) 513) & \(\times\) 256, 128 \(\times\) 128, & \(\times\) 16 \(\times\) 64] & \(\times\) 8 \(\times\) 128, 128 \(\times\) \\  & & (1000 \(\times\) 513) & 64, 1024 \(\times\) 64, & - Output & \(\{16\times\) 64\(\}\) \\  & & & \(2048\times\) 32, 4096 \(\times\) & (256 \(\times\) 32) & - Output \\  & & & 32, 8192 \(\times\) 16] & & (256 \(\times\) 32) \\  & & & - Output & & (256 \(\times\) 32) \\  & & & (16,384 \(\times\) 1) & & \\ Feature type & \begin{tabular}{c} Spectral \\ magnitude \\ (513) \\ \end{tabular} & \begin{tabular}{c} Log spectral \\ magnitude \\ (513) \\ \end{tabular} & \begin{tabular}{c} Time sample \\ (16384) \\ \end{tabular} & \
\begin{tabular}{c} Spectral \\ magnitude (257) \\ \end{tabular} &
\begin{tabular}{c} Spectral \\ magnitude (257) \\ \end{tabular} \\ Model footprint & 1.0 MB & 40.2 MB & 1.0 GB & 27.5 MB & 42.3 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the model footprints of each speech enhancement method used in the experiment.
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 5 Effect of a Solvent on a Porous Support  
It has been reported that the amount of adsorbed lipase on a pretreated support by ethanol is 2-fold higher than an untreated support [56,82]. With pretreatment by ethanol, the porous carrier  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Method** & **Chemical Method** & **Support Material** \\ \hline Adsorption & Covalent attachment & Magnetic nanoparticles \\ \hline Entrapment (gel/fiber entrapping, & Crosslinking (crosslinking to a support & \\ microencapsulation, and metal-organic & matrix, CLEAs) & Membranes \\ frameworks (MOFS) embedding) & Ionic bonding & Capillary columns \\  & Conjugation by affinity ligands & Polymer \\ \hline \hline \end{tabular}
\end{table}
Table 2: Immobilization methods and supports materials.  
Figure 1: Scheme of various enzyme immobilization techniques.  
increases hydrophobicity and immobilization rates [82]. Besides, the pretreated immobilized enzyme has better stability in broad temperature and pH ranges [82; 32]. A similar pattern is exhibited by a native lipase, which has an optimum temperature at 37 \({}^{\circ}\)C but increases to 50 \({}^{\circ}\)C after immobilization on hydrophobic microspheres [5]. Several publications agree on the enhancement of the support affinity to lipase aqueous solutions due to the ethanol pretreatment of the support [82]. The ethanol pretreatment of the support leads to a more uniform catalyst with a higher adsorption efficiency and increases solid recovery [83]. The initial size of supports becomes smaller due to the stirring during the immobilization process [66; 83]. The size of the immobilized _Candida antarctica_ on a polypropylene support (CA/PP) at the beginning and the final stage of the immobilization procedure deserves great attention. An additional size reduction of CA/PP particles during the reaction complicates their recovery and prevents their reuse [82; 83].  
Pretreatment of Accurel MP is needed to improve the penetration of an aqueous lipase solution into hydrophobic Accurel MP pores [84; 85]. In the literature, two protocols are described: protocol A consists in wetting a support in a sequence of ethanol, an aqueous ethanol solution, and finally with water, with intermediary filtration [86; 87]; protocol B consists in a single wetting with ethanol and then directly contact with an enzyme solution without removing ethanol [86]. With CALB being well-known for its strong tolerance to high solvent quantities, a third method (protocol C) was proposed by replacing ethanol in protocol B with a more polar solvent, acetone [88]. It was checked that no loss of enzyme activity occurred when acetone or ethanol was added to the lipase solution under the same conditions during the immobilization process over 48 h at room temperature [48]. Ethanol improves the immobilization process by inducing better penetration of the enzyme solution inside the highly hydrophobic Accurel MP and by reducing the enzyme thermodynamic activity, thus forcing the adsorption process [77; 89].
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 5 Performance Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device Name** & **Precision** & **Recall** & **F1 Score** \\ \hline Smart Things & 0.9091 & 0.9565 & 0.9322 \\ Amazon Echo & 0.9864 & 0.9933 & 0.9899 \\ Nettamo Welcome & 0.9910 & 0.9987 & 0.9948 \\ TP-link Cloud Cam & 0.9916 & 0.9834 & 0.9875 \\ Samsung SmartCam & 0.9963 & 0.9963 & 0.9963 \\ Dropcam & 0.7778 & 0.8400 & 0.8077 \\ Insteon Camera & 0.9983 & 0.9963 & 0.9973 \\ Withings Baby Mon & 1.0000 & 0.9988 & 0.9994 \\ Belkin Wemo switch & 0.9963 & 0.9970 & 0.9967 \\ TP-Link Smart plug & 0.9892 & 0.9892 & 0.9892 \\ iHome & 0.9821 & 0.8594 & 0.9167 \\ Belkin wemo sensor & 0.9973 & 0.9957 & 0.9965 \\ NEST-P smoke alarm & 0.9565 & 1.0000 & 0.9778 \\ Nettamo weather station & 0.9959 & 0.9986 & 0.9972 \\ Withings Smart scale & 1.0000 & 0.9545 & 0.9767 \\ Blipcare BPMeter & 1.0000 & 1.0000 & 1.0000 \\ Withings slp sensor & 0.9954 & 0.9973 & 0.9963 \\ Light LiFX Smart Bulb & 0.9933 & 1.0000 & 0.9966 \\ Triby Speaker & 0.9722 & 0.9333 & 0.9524 \\ PIX-STAR Photo-frame & 1.0000 & 0.9831 & 0.9915 \\ HP Printer & 0.9852 & 0.9568 & 0.9708 \\ Samsung Galaxy Tab & 0.9731 & 0.9393 & 0.9559 \\ Nest Dropcam & 0.7500 & 0.5455 & 0.6316 \\ non-IoT & 0.9785 & 0.9881 & 0.9833 \\ Accuracy & & & 0.9876 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation results for the experiment with non-IoT device.
## 3 Materials and Methods
### Stereo Vision and Image Correction  
Stereo vision is a technique used to calculate the distance and position of a point to the camera plane viewed by two cameras whose relative positions and projections are known. A single camera is a mapping between a 3D world and a 2D image [26; 27; 28]. The geometry of a stereo setup consisting of two identical cameras is shown in Figure 4.  
Here, \(O_{l}\) and \(O_{r}\) are the focal points of both cameras, \(f\) is the focal length of both cameras, \(P\) is any point in space, \(Z\) is the distance of this point in space to the camera plane, \(T\) is the translation value between the two cameras. \(x^{l}\) and \(x^{r}\) are reflections of the \(P\) point on both viewing planes. This geometry creates similar triangles between the \(P-x^{l}-x^{r}\) and \(P-O^{l}-O^{r}\) points. The \(Z\) value can be easily calculated using Equation (1) and the similarity theorem.  
\[\frac{T-(x^{l}-x^{r})}{Z-f}=\frac{T}{Z}\to Z=\frac{fT}{x^{l}-x^{r}}\to Z= \frac{fT}{d} \tag{1}\]  
In Figure 4, the \(x_{l}-x_{r}\) value is expressed with the variable \(d\). In stereo vision, the \(d\) value is also expressed as disparity. In order to increase the accuracy of the stereo vision calculation, stereo calibration is required. Stereo calibration is related to the rotation matrix \(R\), which defines the relative rotation between the coordinate systems of the two cameras, and the transformation vector \(T\), which defines the translation of the two camera centers. After a correctly performed calibration, \(R\) and \(T\) matrices are obtained. By using the  
Figure 3: Architectural components of the stereo setup.  
Figure 2: The stereo vision mechanism used in the study.  
calibration matrices obtained as a result of stereo calibration, corrections or rectification processes can be made on stereo images. Stereo rectification ensures that objects are positioned correctly in pairs of images to match the stereo arrangement. Thus, the stereo distance calculation is performed with less cost and higher accuracy. Stereo rectification aligns the image pair for more reliable stereo distance results [28]. Example images obtained with the help of stereo setup are shown in Figure 5.
## 5 Power Optimization
### Field Test Validation  
The experiment was conducted in Wugong Village, Shihezi City, Xinjiang in May 2022. The average soil firmness was 2.16 MPa and the moisture content was 10.64%. The supporting power was a TN654 tractor, and the test equipment included: a rotary tillage device, a mechanical tachometer (measuring range 0-400 r\(\cdot\)min\({}^{-1}\)), and NJTY3 general dynamic telemetry system of agricultural machinery. The power consumption measurement system and field rotary tillage operation are shown in Figure 12.  
The power consumption measurement used wireless telemetry technology [26; 27] and the technical scheme of the integrated torque sensor of the power output shaft and frameless three-point suspension traction sensor. The measurements from the initial simulation parameters showed a forward speed \(v=1100\) m/h, speed \(n=120\) r/min, and actual power consumption of 1.71 kW. At the same time, field tests consistent with the parameters of the above 17 simulation tests were conducted, and data J was obtained for comparison, as  
Figure 11: X\({}_{2}\)X\({}_{3}\) interaction response surface.  
Figure 12: Field test. (**a**) general dynamic telemetry system for agricultural machinery; (**b**) field operation device.  
shown in Figure 13. The actual power consumption was higher than the simulation value because the actual operation was caused by the extra power consumption caused by the root system, debris, and other friction and wear in the soil. However, the simulation test error was less than 12%, which verifies the accuracy of the simulation test and has reference value to establish the power model.  
The soil disturbance during cutting and the soil appearance after cutting were similar to the results of discrete element simulation, which further verified the accuracy of discrete element simulation.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 4 Results and Discussions  
For validation and to demonstrate the performance of the mixed AC/DC PF algorithm, a five-bus AC grid with a three-bus MT-HVDC system is simulated in MATLAB software, as shown in Figure 1. The test system is composed of both AC and DC grids. The data of the system is provided in Tables 1-5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bus** & **Type** & \(V\) **(p.u.)** & \(\theta\) (\({}^{\circ}\)) & \(P_{G}\) **(MW)** & \(Q_{G}\) **(MVAR)** & \(P_{D}\) **(MW)** & \(Q_{D}\) **(MVAR)** \\ \hline
1 & Slack & 1.060 & 0.00 & – & – & 0.00 & 0.00 \\
2 & PV & 1.000 & – & 40.00 & – & 20.00 & 10.00 \\
3 & PQ & – & – & – & – & 45.00 & 15.00 \\
4 & PQ & – & – & – & – & 40.00 & 5.00 \\
5 & PQ & – & – & – & – & 60.00 & 10.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the AC system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**From** & **To** & \(R\) **(p.u.)** & \(X\) **(p.u.)** & \(B\) **(p.u.)** & \(S_{\text{N}}\) **(MVA)** \\ \hline
1 & 2 & 0.02 & 0.06 & 0.06 & 100 \\
1 & 3 & 0.08 & 0.24 & 0.05 & 100 \\
2 & 3 & 0.06 & 0.18 & 0.04 & 100 \\
2 & 4 & 0.06 & 0.18 & 0.04 & 100 \\
2 & 5 & 0.04 & 0.12 & 0.03 & 100 \\
3 & 4 & 0.01 & 0.03 & 0.02 & 100 \\
4 & 5 & 0.08 & 0.24 & 0.05 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the AC lines.  
Figure 1: Single-line diagram of a three-bus MT-HVDC system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Converter Station** & \(S_{\text{N}}\) **(MVA)** & \(R_{\text{TR}}\) & \(X_{\text{TR}}\) & \(B_{F}\) & \(R_{\text{TR}}\) & \(X_{\text{TR}}\) \\ \hline
1 & 100 & 0.0015 & 0.1121 & 0.0887 & 0.0001 & 0.1643 \\
2 & 100 & 0.0015 & 0.1121 & 0.0887 & 0.0001 & 0.1643 \\
3 & 100 & 0.0015 & 0.1121 & 0.0887 & 0.0001 & 0.1643 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the converter stations in per-unit.  
The parameters of the converter stations for the PF calculations are as follows:  
* Converter Station #1: \(P-Q\) control mode, \(P_{g}=-60\) MW and \(Q_{g}=-40\) MVAR
* Converter Station #2: \(V_{DC}-V\) control mode, \(V_{DC}=1\) p.u. and \(V=1\) p.u.
* Converter Station #3: \(P-Q\) control mode, \(P_{g}=35\) MW and \(Q_{g}=5\) MVAR  
It should be noted that \(S_{B}=100\) MVA, \(V_{DC_{B}}=345\) kV, and \(V_{B}=345\) kV are the base values for the hybrid AC/DC grids per-unit system. Also, the links between the two nodes on the DC grids are bipolar.  
The simulations are accomplished using a laptop with the Intel Core i7-8550U processor at 1.80 GHz clock speed and 12-GB of RAM.
## 2 Materials and Methods
### Framework Architecture  
Our final network has four inputs and two outputs. The main input are the image data and the remaining inputs are the associated text labels of the paintings (genre, style, and media). The image input is fed to a CNN in order to learn and extract the visual features. We used the ResNet50 network with fine-tuning of all its layers. This was decided after performing several tests with other CNN-based models such as the VGG16 and the VGG19 for the visual extraction task, from which ResNet50 was found to yield the best performance. ResNet50 is a convolutional neural network of 50 layers based on blocks called residual blocks. These blocks were introduced to deal with the problem of the vanishing gradient in the training stage of the network. Due to GPU memory limitations, we could not use the ResNet152 network as a base classifier. The textual inputs are encoded with One Hot arrays regarding the number of samples per class for each of the five textual labels. The textual inputs (three) are then connected to an aggregation layer of 256 units in order to aggregate textual features and reduce their dimension. The CNN output is connected to an average pooling layer of 2048 units. We then merged the 2048 units and the 256 units in a concatenation layer. This concatenation layer was then regularized with 50% dropout and connected to a final aggregation layer of 1024 units which represents the global features (text + image). The output SoftMax layers are then connected to the global features layer. For each of the outputs, the error used is the categorical cross-entropy (CCE) and the global network error is the sum of the errors of each output. Figure 9 outlines the global architecture of our multimodal classification network. The final architecture of the network was established after several tests and assessment of the settings for the network global shape, such as kernel types, layer count, the shared representation layer length, etc. This architecture was validated through many tests to maximize its performance and to reduce its complexity.  
Figure 8: Single input network.  
The training of the networks requires setting several hyperparameters, particularly when using the Stochastic Gradient Descent (SGD) optimization. Several strategies can be followed to establish these hyperparameters [44]. In this study, we initially set their values according to those used in previous works. These hyperparameters were then tuned through several tests leading to the values outlined in Table 3.  
To train and validate the model, we used five-fold cross validation. At each fold, we used 80% of the data for training and 20% of the data for validation. The process was performed five times while changing the training and validation sets. We validated our approach using this protocol as a large training set was not available.
## 5 Conclusions  
In this paper, the optimal \(\mathrm{g_{m}}\) cell position for the performance of the DCO and an Al metal layer for linear frequency control were implemented and compared using the 55 nm CMOS process. The fabricated DCOs presented different oscillation frequency ranges, phase noises, and power consumptions depending on their layout. According to the results, the DCO's layout for BLE seems to be suitable for NDCOs, with the \(\mathrm{g_{m}}\) cell located at the neck of the inductor and a frequency tuning range of 17.8%, power consumption of 0.5 mW, and excellent noise performance of 116 dBc/Hz. For the TDCO, power consumption and phase noise of 0.58 mW and 114 dBc/Hz were recorded, respectively. The AIDCO for linear frequency control showed relatively poor performance, with 0.6 mW power consumption and 113.5 dBc/Hz phase noise, but showed a linear frequency change compared to the other DCOs.  
Conceptualization and writing--review and editing, M.-S.K.; writing--review and editing, S.-S.Y. All authors have read and agreed to the published version of the manuscript  
This research was funded by the Basic Science Research Program under Grant NRF-2018R1D1A1B07045432.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **NDCO** & **TDCO** & **AIDCO** & **JSSC 2014 [12]** & **JSSC 2015 [13]** \\ \hline Frequency (MHz) & 4750–5673 & 4746–5650 & 4551–5326 & 4450–7100 & 4400–5400 \\ \hline Tuning range (\%) & 17.8\% & 17.4\% & 15.6\% & 45.8\% & 20.4\% \\ \hline Current (mA) with 0.8 V & \multirow{2}{*}{0.66} & \multirow{2}{*}{0.73} & \multirow{2}{*}{0.75} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} \\ supply voltage & & & & & \\ \hline PD \({}^{\star}\) (mW) with 0.8 V & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.6} & \multirow{2}{*}{1.2} & \multirow{2}{*}{1.65} \\ supply voltage & & & & & \\ \hline Phase noise (dBc/Hz) & \multirow{2}{*}{\(-116\) at 1 MHz} & \multirow{2}{*}{\(-114\) at 1 MHz} & \multirow{2}{*}{\(-113.5\) at 1 MHz} & \multirow{2}{*}{\(-104\) at 1 MHz} & \multirow{2}{*}{\(-120\) at 1 MHz} \\ at 4.8 GHz & & & & & \\ \hline FoM (dBc/Hz) & 192.6 & 190 & 189 & 180 & 193 \\ \hline CMOS process & \multirow{2}{*}{55 nm} & \multirow{2}{*}{0.06} & \multirow{2}{*}{0.19} \\ \hline Area (mm\({}^{2}\)) & & & & & \\ \hline \hline \end{tabular} \({}^{\star}\) PD: power dissipation.  
\end{table}
Table 1: Summary of performances of designed DCOs and comparison with previous works. FoM, figure-of-merit.  
Figure 9: Measurement result of phase noise in an NDCO.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Acknowledgments:** The electronic design automation (EDA) tool was supported by the IC Design Education Center (IDEC), Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.