esa-sceva/satcom-chunk-collection huggingface dataset:
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 4 Development Details
### FPGA Design and Integration with PYNQ  
To target the Zynq platform, we use the Vivado IP Integrator tool to create a block design, consisting of our generated core, the PS, a DMA, and any AXI interconnect blocks necessary to establish communication. The resulting diagram is shown in Figure 6. The Python code running in the PS is in charge of instructing the DMA to perform streaming transfers of contiguously allocated chunks of main memory to the PL. The AXI interconnects handle transforming these transfers to ensure their compatibility with, e.g., the data widths of the PS and PL interfaces. Specifically, the Hard SyDR design occupies three of the PS's high-performance AXI interfaces: one master interface (\" M_AXI_HPMQ_FPD\"), for data and control transfers via the DMA to the PL, and two subordinate interfaces (\" S_AXI_HPO_FPD\" and \" S_AXI_HPO_FPD\") for return data transfers, also via the DMA. Both data interfaces are manually specified to be 64 bits wide, while the control channel is automatically sized by Vitis HLS and Vivado.  
Once assembled, the block diagram can be synthesized and implemented to target the desired device. The results include a bitstream and a so-called hardware header/handoff file, which includes information about the IP blocks and addresses of the memory-mapped registers included in the design. The PYNQ flow needs both these files to generate a driver to interface the design automatically. While we aim to incorporate this driver into SyDR, we use the Jupyter Notebook environment for initial testing as it lets us interact with the FPGA through a browser.  
Figure 6: Block diagram of Hard SyDR for implementation in Vivado IP Integrator.  
Figure 7 shows a simplified flow chart of the Jupyter Notebook. The main elements of its code are included in Appendix A. We first program the FPGA with our bitstream and allocate arrays for inputs and outputs. Then, we loop over the processing steps for several milliseconds of RF data, writing the tracking results to a file.
## 3 Results and Discussion
### Demulsification Performance of Ti-Mt-APTES  
Demulsification performance is evaluated by using the absorbance of emulsion before and after treating acidic oil-in-water emulsions, and observed intuitively by color change of emulsions. Photographs of the acidic oil-in-water emulsion and after demulsification by Ti-Mt-APTES-0.10 are shown in Figure 6. This clearly shows that the acidic oil-in-water emulsion is a uniform milky liquid, while the mixture shows three parts after demulsification by Ti-Mt-APTES-0.1: the uppermost part is oil phase, the middle part is water phase, and the bottom part is modified montmorillonite. The results demonstrate that Ti-Mt-APTES-0.10 composite is an effective demulsifier to separate oil from acidic oil-in-water emulsions.  
Figure 5: SEM image of Na-Mt (**a**), circle enlarged image (**b**), SEM image of Ti-Mt-APTES (**c**) and EDS results of Ti-Mt-APTES.  
Figure 7a clearly indicates that pure Mt and Ti-Mt \(\mathrm{E_{D}}\) are 2.67% and 3.17%, respectively, at the dosage of 2.5 g/L, and show almost no demulsification performance while, upon anchoring APTES, the demulsification efficiency of Ti-Mt and Na-Mt can be seen to significantly improve in acidic conditions. As shown in Figure 7b, the Ti-Mt-APTES demulsification performance increases with the increase of APTES-anchoring densities, but when it exceeded 0.10g/1g, the demulsification ability decreased, which is likely to decrease the active hydroxyl on the materials. Furthermore, the dosage of Ti-Mt-APTES also has an important effect on the \(\mathrm{E_{D}}\) of acidic oil-in-water emulsions and, as the dosage of Ti-Mt-APTES increases, the performance of demulsification increases, which is attributed to an increase in the addition of Ti-Mt-APTES, which will provide more active sites for adsorbing oil droplets. As for Ti-Mt-APTES-0.10, the \(\mathrm{E_{D}}\) was 94.8% at a dosage of 2.5 g/L. In addition, it can be concluded that 0.10 g/1 g of \(\mathrm{R_{A/M}}\) is the optimal condition for separating oil from an acidic emulsion by Ti-Mt-APTES, and the optimal dosage was 2.5 g/L.  
As shown in Figure 8, the stirring time plays a significant role in achieving \(\mathrm{E_{D}}\) during the demulsification process. Initially, Ti-Mt-APTES-0.10 shows low \(\mathrm{E_{D}}\) in a relatively short period of time, and approximately 6.8% of the \(\mathrm{E_{D}}\) at a dosage of 2.5 g/L after 2 h. Subsequently, the \(\mathrm{E_{D}}\) increased and reached 94.8% at a dosage of 2.5 g/L after 5 h. The \(\mathrm{E_{D}}\) is about 94.9% for Ti-Mt-APTES-0.10 at the same dosage after 6 h. Additionally, it can be concluded that 5 h for the continuous separation of oil from an acidic emulsion is optimal for breaking the emulsion using Ti-Mt-APTES-0.10.  
Figure 6: Photographs of the acidic oil-in-water emulsion and after demulsification by Ti-Mt-APTES-0.10.  
Figure 7: (**a**) Demulsification performance of Na-Mt, Ti-Mt, Na-Mt-APTES-0.10, and Ti-Mt-APTES-0.10.
## 3 Distributed Target Tracking Systems
### Phase III: Target Positioning
#### Angle-of-Arrival Neural Networking with CFBP  
The feed-forward backpropagation (FFBP) and cascade-forward backpropagation (CFBP) are supervised learning algorithms for artificial neural networks which most commonly used for prediction, pattern recognition, and nonlinear function fitting [25]. Since the CFBP provides a better performance in terms of convergence time, optimum of network structure and recognition performance [26], the neural network with CFBP is applied for analyzing the performance of the TCAT.  
Assume that the network under consideration has a general architecture with three layers of neurons. In our case, input and output layer neurons are linear, whereas neurons in the hidden layer are tan-sigmoidal. Let the vector pairs in \(\mathcal{T}\) be sample representation of the unknown function \(f:\mathcal{R}^{n}\rightarrow\mathcal{R}^{p}\): \(T=\{(X_{q},D_{q})\}_{q=1}^{Q}\), where \(n\) is the neuron index range in the input layer, \(p\) is the neuron index range in the output layer, \(X_{q}\in\mathcal{R}^{n}\), \(D_{q}\in\mathcal{R}^{p}\), \(Q\) is the number of training vector pairs, and \(q\) is the iteration index. Note that \(D_{q}\) is the desired vector response for the network input \(X_{q}\). Thus, the mean square error of the entire training set is: \(\mathcal{E}=\frac{1}{Q}\sum_{q=1}^{Q}\mathcal{E}_{q}\), where \(\mathcal{E}_{q}=\frac{1}{2}E_{q}^{T}E_{q}\), and \(E_{q}\) is the instantaneous error of the training pair \((X_{q},D_{q})\).  
\begin{table}
\begin{tabular}{l} \hline
3. _Measurement Update_: The weights of the likelihood function \\ \(p(z_{k+1}|\hat{x}_{k+1}(i))\) are updated for each sample in the random \\ set \(i=1,2,\ldots,N_{PF}\) and the normalized weights are \\ \(\xi_{k+1}(i)=\frac{p(z_{k+1}|\hat{x}_{k+1}(i))}{\sum_{j=1}^{N_{PF}}p(z_{k+1}| \hat{x}_{k+1}(j))}\) \\ for each sample. \\
4. _Re-sampling_: Take \(N_{PF}\) samples with replacement from the \\ random sample set \(\hat{x}_{k+1}(i)\), \(i=1,2,\ldots,N_{PF}\), to generate the \\ new sample set \(x_{k+1}(i)\). \\
5. _Position_: The best single estimate of the position is the mean \\ of \(x_{k+1}(i)\), \(\overline{x_{k}}\). \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 5 Hybrid SC Converters
### The Traditional Boost Converters with an Output SC Multiplier  
Instead of connecting an SC circuit as the input of a boost converter, an SC circuit can be connected as the output of a boost converter. This brings the topology of the multilevel boost converter [51], a converter that drives a voltage multiplier such as the ones in Figure 3 with a boost converter. Any of the voltage multipliers in Figure 3 can be used for this purpose. Figure 26 shows three possible options.  
The multilevel boost converter has been studied in several articles [51; 52; 53; 54; 55; 56; 57; 58; 59], with contributions that include their modeling and control [56; 57; 58; 59]. Some works include the negative extension (instead of the positive) or both [51; 55; 57].  
The multilevel boost converter has several advantages which make it very attractive. It requires only one transistor rated to a relatively low voltage, all the diodes and the transistors are rated to the same voltage, depending on the voltage multiplier, i.e., in Figure 26a, and all capacitors are rated to the same voltage, which is relatively low compared to the output voltage. The input current is continuous, and the transistor is grounded, simplifying the gate drive design. As shown in [41], only one tiny capacitor is required to achieve the resonant charge interchange in all capacitors.  
Figure 25: Transistors-based series parallel SC converters in the literature, (a) circuit studied in [47; 48], (b) circuit studied in [49].
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 4 Results
### Multi-Temporal Characteristics and Patterns of Backscatter Intensities
The blue bars mark the analysed date at the flood event.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### FSS in Wireless Communications
#### 2.3.4 Bandwidth Expansion  
Bandwidth refers to the range of frequencies over which the antenna maintains efficient performance. FSS elements can expand the bandwidth of antenna arrays by allowing for multiple frequency bands to pass through or be reflected, depending on their design. The use of multi-layer-FSSs, incorporating various unit cell geometries such as fractal, hexagonal, and Jerusalem cross, has been shown to increase the bandwidth by 20-40% compared to traditional antenna designs [104]. The bandwidth \(\Delta f\) of an FSS is defined as follows:  
\[\Delta f=f_{H}-f_{L} \tag{1}\]  
The fractional bandwidth can be expressed as follows:  
\[\text{Fractional Bandwidth}=\frac{\Delta f}{f_{0}}=\frac{f_{H}-f_{L}}{f_{0}} \tag{2}\]  
where  
* \(f_{H}\) is the upper cutoff frequency.
* \(f_{L}\) is the lower cutoff frequency.  
* \(f_{0}\) is the center frequency.
Extracellular Lipases of _Yarrowia lipolytica_ Yeast in Media Containing Plant Oils--Studies Supported by the Design of Experiment Methodology  
[PERSON]  
1 Department of Chemistry, Institute of Food Sciences, Warsaw University of Life Sciences--SGGW, 159c Nowouvsynowska Street, 02-776 Warsaw, Poland; [PERSON]@sggwd.ucl (B.Z.); [PERSON] (K.J.); [PERSON]@sggwd.ucl (B.X.); [PERSON]@sggwd.ucl (B.X.); [PERSON]@sg  
specific temperature and pH. Moreover, lipases are enzymes characterized by high region- and stereospecificity [4].  
Microbial lipases have many applications, including organic synthesis, in the food and detergent, paper and oleochemical, cosmetic, medicine, and waste treatment industries [5]. Whole-cell lipases and esterases have become increasingly important in recent years, because the organic solvent-tolerant cells allow the production of optically active alcohols, acids, esters, and lactones. The majority of commercially significant lipases are produced by _Candida rugosa_, _C. antarctica_, _Aspergillus niger_, _Thermomyces languinosus_, _Rhizomucor miehei_, and _Rhizopus arrhizus_[6].  
_Yarrowia lipolytica_ is a commonly occurring yeast species in nature, considered to be non-pathogenic with GRAS status. The high secretion activity of _Y. lipolytica_ is well known, and the most desirable yeast metabolites are citric acid, 2-phenylethanol, \(\gamma\)-decalactone, erythritol, and enzymes: RNase, phosphatases, esterases, and lipases. The genes of the _LIP_ family encoding for _Y. lipolytica_ lipase proteins were discovered in recent decades [7; 8]. The main extracellular lipase of _Y. lipolytica_ is the Lip2p protein encoded by the _LIP2_ gene. It is a \(sn\)-1,3-regiosolective enzyme, and the products of the catalyzed reaction are mainly 2-monoglycerides. Other _Y. lipolytica_ lipases have been cloned and characterized, although knowledge about them is still small [9; 10].  
It is widely known that the microbial production of lipases is strongly influenced by culture conditions such as carbon and nitrogen sources, the presence of lipid sources, minerals, pH, temperature, inoculum age, the presence of activating and inhibitory compounds, and other factors. There is still intrigue as to whether strain differences exist in lipase enzyme profiles. This study aimed to investigate the biodiversity of extracellular enzyme properties produced by four strains of _Y. lipolytica_, and to determine the optimal conditions of catalysis for the enzymes, according to temperature and pH, in a model hydrolysis reaction. Moreover, extracellular lipase from wild-type strain KKP 379 was characterized, followed by exploring the activity of whole-cell biocatalyst and lyophilized enzyme solutions.
## 2 Materials and Methods
### Experimental Procedure  
The crystallographic analysis of the bottom surface (i.e., side facing the supports, as shown in Figure 3) of the as-built samples was determined using Bruker D8 Advance X-ray diffractometer (Bruker, Germany). The X-ray source was Copper (Cu) k-alpha, and the measurements were conducted at room temperature where the wavelength of X-rays was 1.5406 A, step intervals of 0.02 and in 20 between 35\({}^{\circ}\) and 100\({}^{\circ}\). Vickers hardness analysis was performed with the help of a LECO LM 300 Vickers Hardness tester (LECO co., St. Joseph, MI, USA) on the bottom surface (i.e., side facing the supports, as shown in Figure 3) of the cut specimen. The test was conducted under 500 g loads applied for 10 s based on the ATSM E92-82 standard [61]. Five indentations were done for each sample, including the areas near and far from the edges at the interface plane (See Figure 3). It should be noted that the interface plane was selected for taking the measurements as this plane is affected directly by the support structures, without getting influence from the epitaxial growth occurring along the building direction. Moreover, consistency between XRD and hardness requires generating results at the same plane. In this case, the average  
Figure 3: The schematic of (**a**) the fabricated main part on top of the allocated support structure, (**b**) the main part separated from the support structure, (**c**) the sectioning of the main part at 1 mm above the support/part interface through a plane normal to the building direction (0.5 mm reduction equal to the thickness of the cutter blade has been considered), and (**d**) the different views of the sectioned portion demonstrating the employed surfaces for the SEM and XRD characterization.  
hardness value for each sample was reported for the interface plane. To evaluate the effect of support structures on the microstructure of the fabricated parts, a Hitachi S-3000N Variable Pressure SEM was used. To be able to investigate the effect of support structure on the depth of the melt pools, the side surface closer to the support zone (i.e., polished side surface, as shown in Figure 3) was selected for the SEM analysis and the dimensional analysis was performed according to the NASA MSFC-SPEC-3717 Standard [62]. It should be mentioned that, as the goal of study is evaluating the effect of support structure on the microstructure, the melt pool analysis was performed on the first few layers manufactured on top of the support structures (See Figure 3). Using the same equipment, energy dispersive X-ray spectroscopy (EDS) was performed on the samples to evaluate the compositional analysis. The microstructure features (i.e., melt pool), hardness, and composition of all the samples were evaluated and compared.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### UAV Networks and Communications
#### 2.3.4 Software Architecture  
Previously, it was stated that LAUs were connected via LANs. A LAN is a network that connects processing systems, in this case, UAVs, in a localized area. In a LAN, a system can run multiple applications at once, meaning a UAV and a group of interconnected UAVs can do multiple tasks at once [28]. In a singular, autonomous UAV, the LAN helps the software programs communicate with each other and the flight hardware/external systems.  
The general layout of the software systems includes the autopilot software, the navigation system, and the communication system [24]. This autopilot software can be broken into three levels: the execution level, the coordination level, and the organization level. These three levels all communicate with each other throughout the UAV's flight. The least intelligent but most precise level is the execution level. The execution level is responsible for sensing the UAV's surroundings as well as detecting failures. The middle level is the coordination level. The coordination level is responsible for mainly navigation, processing data, and trajectory planning. The least precise but most intelligent level is the organization level. This level is responsible for communication with other UAVs and the ground in A2G and A2A networks and decision-making [26].  
In order to perform various tasks, different UAVs have varying speeds and ranges. Hence, they require different communication methods. The selection of the most compatible wireless communication methods and communication protocols greatly boosts the efficiency of UAVs [30]. The autopilot software platforms for UAVs, like ArduPilot and PX4, use certain communication protocols for UAV monitoring, control, and integration into the internet. These protocols comprise a set of messages exchanged between the UAV and ground stations. Micro Aerial Vehicle Link (MAVLink) is the most popular protocol for UAVs, which ensures bidirectional communication between the UAVs and the ground stations. MAVLink is a lightweight protocol that is apt for transmitting small amounts of data over wireless mediums [31].
###### Abstract  
Conventional personal identification methods (ID, password, authorization certificate, etc.) entail various issues, including forgery or loss. Technological advances and the diffusion across industries have enhanced convenience; however, privacy risks due to security attacks are increasing. Hence, personal identification based on biometrics such as the face, iris, fingerprints, and veins has been used widely. However, biometric information including faces and fingerprints is difficult to apply in industries requiring high-level security, owing to tampering or forgery risks and recognition errors. This paper proposes a personal identification technique based on an ensemble of long short-term memory (LSTM) and convolutional neural network (CNN) that uses electrocardiograms (ECGs). An ECG uses internal biometric information, representing the heart rate in signals using microcurrents and thereby including noises during measurements. This noise is removed using filters in a preprocessing step, and the signals are divided into cycles with respect to R-peaks for extracting features. LSTM is used to perform personal identification using ECG signals; 1D ECG signals are transformed into the time-frequency domain using STFT, scalogram, FSST, and WSST; and a 2D-CNN is used to perform personal identification. This ensemble of two models is used to attain higher performances than LSTM or 2D-CNN. Results reveal a performance improvement of 1.06-3.75%.  
Personal identification; electrocardiogram signals; deep learning; long short-term memory; convolutional neural network; ensemble approach
## 2 Principles  
### S Transform (ST)  
Before presenting with the details of the synchrosqueezed S transform, the S transform is first introduced. The ST is a signal processing method proposed by [PERSON] [20]. For \(x(t)\), the ST is defined as follows:  
\[ST_{x}(f,b)=\frac{|f|}{\sqrt{2\pi}}\int_{-\infty}^{\infty}x(t)exp(\frac{-(t-b) ^{2}f^{2}}{2})exp(-i2\pi ft)\mathrm{d}t. \tag{1}\]  
The basic wavelet function in the ST is defined as follows:  
\[w_{f}(t)=\frac{|f|}{\sqrt{2\pi}}exp(\frac{-(t-b)^{2}f^{2}}{2}) \tag{2}\]  
where \(x(t)\) represents a signal, \(ST_{x}\) represents the ST of \(x(t)\), \(f\) denotes frequency, \(t\) denotes time, \(b\) denotes the time shift, and \(i\) is the imaginary unit.  
The ST is invertible. The inverse transform corresponding to the ST is as follows:  
\[x(t)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}ST(f,b)exp(i2\pi ft) \mathrm{d}b\mathrm{d}f \tag{3}\]  
The ST is similar to the STFT but with a Gaussian window whose width scales inversely and whose height scales linearly with frequency. The ST can obtain a varying TF resolution of the signals and inherits the advantages of both the STFT and the WT.
## 4 The Proposed Method for Predicting the Reactive Power Adjustment
### Reactive Power Optimization Method Based on Big Data
#### 4.3.3 Fine Adjustment Method Based on Sensitivity Analysis  
When the load similarity between the forecasting load augmented matrix \(A_{F}\) and the historical load augmented matrix \(A_{H}\) is smaller than the minimum load similarity margin \(s_{\text{min}}\), the reactive power control devices dispatching scheme cannot be achieved by the load similarity matching directly. Then, a fine adjustment method based on sensitivity analysis is required because the forecasting load and the historical load share little similarity. Based on Equation (31), the total daily active power loss sensitivity to the control variable can be achieved. To reduce the total daily active power loss, the increment should satisfy the constraint \(\Delta P_{\text{loss}}<0\) during the fine adjustment processes. According to Equation (30), the sensitivity and control variable increment should satisfy the following inequalities constraints:  
\[\left\{\begin{array}{l}S_{u}<0,\Delta u>0\\ S_{u}>0,\Delta u<0\end{array}\right. \tag{34}\]  
Based on Equation (34), in order to adjust the action moment of the control devices only without increasing the actions of the control devices, the control variable increment can be calculate according to Algorithm 1.  
```
if the sensitivity of active loss to control variable \(S_{u}<0\) if\(u^{h}>u^{h+1}\) or \(u^{h}>u^{h-1}\)  control variable increment \(\Delta u^{h}=\max(u^{h+1},u^{h-1})-u^{h}\) endif elseif the sensitivity of active loss to control variable \(S_{u}>0\) if\(u^{h}<u^{h+1}\) or \(u^{h}<u^{h-1}\)  control variable increment \(\Delta u^{h}=\min(u^{h+1},u^{h-1})-u^{h}\) endif Else control variable increment \(\Delta u^{h}=0\) endif
```  
**Algorithm 1**Control variable increment calculation rules  
With the control variable increment calculation rules, the control variable fine adjustment method can be presented as follow:  
_Step 1_: According to reactive power control devices dispatching scheme achieved by load similarity matching, initialize the control variable \(u^{h}_{k}\), where \(h=1,2,3,...,24\). Let the iteration number \(k=1\).  
_Step 2_: Calculate the reactive power loss \(P_{loss,k}\) when the control variable is \(u^{h}_{k}\).  
_Step 3_: Calculate the active power loss sensitivity \(S_{u,k}\) to the control variable.  
_Step 4_: According to the control variable increment calculation rules, calculate the control variable increment \(\Lambda u^{h}_{k}\).  
_Step 5_: Update the control variable by \(u^{h}_{k+1}=u^{h}_{k}+\Delta u^{h}_{k}\) and calculate the new active power loss \(P_{loss,k+1}\).  
_Step 6_: If \(P_{\text{loss},k+1}<P_{\text{loss},k}\), let \(k=k+1\) and continue the iteration process to _Step 3_. Otherwise, output the final control variable \(u^{h}_{k}\).  
The computing flow chart of the he control variable \(u^{h}_{k}\) is shown in Figure 5.