esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Various faults and disturbances often make the grid unbalanced and distorted. To make the grid-connected converters resilient to unbalanced and distorted grid, real-time estimation of grid voltage sequences are essential. Adaptive-observer based grid-synchronization techniques are very suitable for this purpose. In this paper, a comparative analysis of three recently proposed adaptive observer-based grid-synchronization technique are presented for frequency estimation and sequence extraction. The considered techniques are: global adaptive observer, gain normalized adaptive observer, and second-order generalized integrator type adaptive observer. Following a fair and systematic gain tuning process, the performances are verified through extensive numerical simulation and experiments. Experimental results demonstrate the merits and demerits of the individual techniques. Academic researchers and industrial practitioners will find the results useful for the selection of adaptive observer-based grid-synchronization and sequence extraction techniques.  
grid-synchronization; frequency-locked loop; sequence extraction; grid-connected renewable energy systems +
Footnote †: This paper is an extended version of our paper published in the 46 th Annual Conference of the IEEE Industrial Electronics Society (IECON-2020), Singapore.
###### Abstract  
Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces.  
electroencephalography (EEG); autoregressive (AR) model; least mean square (LMS); alpha rhythm; time-series prediction; neural oscillations; instantaneous phase +
Footnote †: journal: Applied Sciences
# Towards DC Energy Efficient Homes
## 3 Factors for DC Installations
### Distributed Generation and Renewables Available at Home  
Distributed generation is an option that guarantees sustainable and secure energy in homes. One of the main sources of distributed energy is solar PV, produced on the rooftops of houses. The potential for solar PVs is wide, and it can be estimated that it is possible to generate 25% of all current energy consumption [15]. Nowadays, the percentage of PV rooftop panels covering a surface is lower than a 10% of the available space, but, as can be seen in Figure 6, sales are increasing and will continue to do so in the long term. Thus, in 2019, the installed power in Europe has doubled that of the previous year and the forecast for 2021 is to become the highest ever in terms of solar installations in a single year [68].  
The prices of PV panels range from 0.05 \(\upcels\)/kWp to 0.6 \(\upcels\)/kWp depending on the efficiency, the material and the structure. These prices are expected to be lower in the coming years because the increase in demand, since it has been shown that a growth in production reduces costs [70]. Typically, PV systems in the residential sector generate between 1 kWp and 10 kWp with the grouping of modules in series or parallel arrays. The power of these modules is between 160 W and 300 W at 36 V [71]. Despite the great potential and the adjustment that it presents to homes, the production of PV energy is in DC, as such, nowadays, converting it to power household devices is necessary [72; 73].  
As the objective is the reduction of domestic electricity consumption, the efficiency of a PV system depends on the amount of power transformed rectifications using this type of production. Therefore, the more DC appliances there are, the greater the PV system efficiency.
## 2 Modeling and Theory
### Periodic Modulation of Irradiation Architecture  
To achieve the effective operation of NDIR measurement, the modulated heating of the light source and the corresponding signal acquisition are arranged. Based on the periodic modulation of heating and the average working temperature of the source, it is necessary to derive and build a new theoretical model and transient response for NDIR gas detection. The characteristic behaviors of the system are described and illustrated in Figure 2.  
The heating of IR light source produces IR passing the optical detection tube and part of radiation impinges on the thermopile through filter. At the heating phase, the temperature rise for the light source reaches a stable working temperature, and at the subsequent cooling phase, the temperature fall will drop to the ambient temperature. For each cycle of the modulation, the time for the cooling and heating is denoted as \(t_{c}\) and \(t_{h}\) are the durations for the cooling and heating phase, respectively, as shown in Figure 2. The modulation of heating is conducted by an ARM-based microprocessor which controls a N-type Power MOS as a switch with on and off settings, as shown in Figure 3. An NDIR CO\({}_{2}\) measurement system was developed and set up in a CO\({}_{2}\) gas chamber to evaluate the feasibility of our proposed NDIR electrothermal modeling. The measurement module consists of a light source at one end of a 5 cm long optical tube and an IR sensor at the other end of the optical tube, as shown in Figure 3. An NDIR CO\({}_{2}\) measurement system was developed and set up in a CO\({}_{2}\) gas chamber to evaluate the feasibility of our proposed NDIR electrothermal modeling. The measurement module consists of a light source at one end of a 5 cm long optical tube and an IR sensor at the other end of the optical tube, as shown  
Figure 1: Schematic diagram of NDIR system.  
Figure 2: Output voltage and the periodic modulation of IR light source.  
in Figure 3. At the other end of the optical tube, there is a thermopile infrared detector. The sensed voltage is input to the electrical module for calculation and comparison. The result is input into the microcontroller for feedback control of the heating of the infrared light source.
## 4 Results and Discussion
### Degradation Effect Changes with the Length of Degradation Time  
According to the properties of nano-TiO\({}_{2}\) photocatalytic materials, nano-TiO\({}_{2}\) will not decrease in the chemical reactions. In this section, as the degradation reaction time is prolonged, the main study target is on the changes of degradation performance on exhaust gas. The exhaust degradation test sustained 4 h. The xenon lamp configuration is Scheme B, and the particle size is 5 nm. The test results are shown in Figure 8.  
From the above experimental results, the overall trend is that the degradation rate of the exhaust gas increases with the extension of the reaction time, and the degradation rate curves of each gas generally agrees with the different amounts of variation of the nano-TiO\({}_{2}\). The degradation rate of CO and HC gas did not change much, but for the overall upward trend. From the trend line it can be seen that the three curves will eventually stay at a steady degradation rate, that is, CO\({}_{2}\) and CO will be close to 20%, HC close to 30%; the NO concentration will have a significant effect, more sustained and eventually will reach 85%. There is a more obvious linear correlation on the NO curve, accordingly, in the next study, the NO degradation rate is used as the recommended index.  
The fact is that when the traffic flows in and out, greenhouse gases or harmful gases such as vehicle exhaust will be maintained at a specific concentration dynamically in a relatively confined space such as underground roads. The degradation effect of photocatalytic pavement will tend toward the best reduction rate as time goes by.
## 2 System Model
### Channel Model
In addition, when the signal bandwidth \(\mathrm{B}\) is much lower than the carrier frequency \(f_{c}\), the channel will be a frequency flat-fading channel. At this point, the channel model can be further simplified as:  
\[\mathbf{H}=\sqrt{\frac{\kappa}{\kappa+1}}\mathbf{H}_{LOS}+\sqrt{\frac{1}{ \kappa+1}}\mathbf{H}_{NLOS} \tag{11}\]
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 3 Control Design
### Robust Pole Placement--Pole Region Approach  
Robust control will be designed for a linearized model, considering three balls of different sizes and their corresponding masss.  
The first step is to linearize the nonlinear model (5) around the determined working point using standard Jacobian method. In the following we will use the closed-loop response comparison for the position control of the ball at _pos_ = 0.01 m. The resulting continuous-time linear state space model is:  
\[\frac{dx}{dt}=A_{c}x(t)+B_{c}u(t)=\begin{bmatrix}0&1&0\\ a_{21}&0&a_{23}\\ 0&0&a_{33}\end{bmatrix}x(t)+\begin{bmatrix}0\\ 0\\ b_{3}\end{bmatrix}u(t)\]  
\[y(t)=C_{c}x(t)=\begin{bmatrix}1&0&0\end{bmatrix} \tag{6}\]  
where  
\[a_{21}=\frac{1}{2m}\frac{F_{\text{exp}1}}{F_{\text{exp}2}^{2}} \Big{(}x_{30}^{2}exp\Big{(}-\frac{x_{10}}{F_{\text{exp}2}}\Big{)}\Big{)}\] \[a_{23}=-\frac{x_{30}}{m}\frac{F_{\text{exp}1}}{F_{\text{exp}2}} \Big{(}x_{30}^{2}exp\Big{(}-\frac{x_{10}}{F_{\text{exp}2}}\Big{)}\Big{)}\] \[a_{33}=-\frac{1}{f_{i}(x_{10})}\] \[b_{3}=k_{1}\frac{1}{f_{i}(x_{10})}\] \[x_{10}=pos,\quad x_{20}=0,\quad x_{30}=\sqrt{\Big{(}\frac{2 mgF_{\text{exp}2}}{F_{\text{exp}1}}\Big{)}exp\Big{(}-\frac{x_{10}}{F_{\text{exp}2}} \Big{)}}.\]  
The control will be designed in discrete time domain, therefore, in the next step continuous time model (6) is discretized for appropriate sampling period \(T_{s}=0.001\) s. Since we consider three different balls, the uncertain parameter corresponds to their mass \(m\). The resulting discrete-time linearized model is then represented by a polytopic system (\(\mathcal{T}\)) and (8), where the polytope vertices are obtained for different ball masses.
## 5 Discussions
### Shape Effect of Pores  
To facilitate the afterward investigation, only the porous tantalum is taken as an illustration and the pore distribution mode is specified as the CRS mode defined in the former section. Equation (11) is adopted to generate pores of different shapes by varying the exponent \(n\). Here, a cubic domain of side length 4000 \(\upmu\)m is employed and meshed by \(89\times 89\times 89\times 5\) tetrahedral elements to perform the investigation. The model parameters in Equation (11) are specified as \(a=400\)\(\upmu\)m, \(b=200\)\(\upmu\)m, \(c=200\)\(\upmu\)m and \(n\in[0.5,8]\). The material parameters of the porous tantalum are the same as those in Ref. [46]. The predicted overall isotropic elastic moduli of porous tantalum containing differently-shaped pores are shown in Figure 14. Note that all numerical predictions presented have been normalized with respect to the relevant elastic moduli of the solid material, and only the mean value of the effective moduli are given in the figure for conciseness. Likewise the former subsection, the convergence analysis of the number of simulations is carried out again to ascertain a reasonable value to generate converged evaluations, and 15 repeated simulations are performed for each porosity of the model under consideration here.  
It is seen that the effective elastic modulus of porous tantalum decreases rapidly with the increment of the porosity \(f\), and shows obvious dependence on the pore shapes particularly when \(f\) is larger than 0.2. Among all shapes of pores under investigation, the model consisting of the pores with \(n=0.5\) have the maximum elastic modulus as \(f\) is a constant. This is attributed to the fact that the shapes of pores greatly affect the connectivity of porous microstructures whose distribution mode of pores conforms to the CRS mode. As \(n\) takes a smaller value, more pores have to be generated by the computational method developed so as to guarantee that the porosity of all models are identical. Hence, the pores are distributed more uniformly in these microstructure and the materials of this kind are less prone to generating deformation.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 3 Methodology
### Matrix-Based Missing Data Description  
From [PERSON] et al. [47], three kinds of missing data are classified according to the missing data characteristics.  
(1) Missing completely at random (MCR): missing values are independent of any other values;  
(2) Missing partially at random (MPR): missing values have a relationship with other existing values, and the missing values could be estimated by other existing values;  
(3) Missing not at random (MNR) or missing due to systematical errors [12]: missing data have a relationship not only with other missing values, but with other existing values, and cannot be estimated just by using other existing values.  
Figure 2: Workflow of the research framework. MPR means missing partially at random; FCM-GA means fuzzy C-means with genetic algorithm.  
Thanks to the GPS technique, firstly, we calculated the number of pick-ups by taxi in the region of Manhattan, New York City, to represent the taxi demand volume. Secondly, due to the limitation of the taxi GPS datasets, we assumed that the type of missing data belonged to the MPR. Moreover, the observed taxi demand volume data from the taxi GPS datasets and missing data at a 25% missing ratio are shown in Table 2 and Figure 3.  
The \"question marks\" in Table 2 represent the missing values. Specifically, the data filled in each column denote the same days of one week. We can see that if the collection interval was set as 5 min, the length of each column would be 288.  
Schematically, as shown in Figure 3, the taxi demand volume data collected on each weekday usually had two peaks. Meanwhile, the height and the position of each peak shared similarities to some extent. These patterns fell into the category of data similarity. Theoretically, one of the mathematical methods to include the data similarity is the matrix-based structure. The advantage of the matrix-based method is that explicit topological information around the missing data is utilized; therefore, the data imputation accuracy is improved. The general form is represented by Equation (1).
## 4 Protocol Description
### Opportunistic Data Transmission  
Data transmission takes place in an opportunistic fashion within the network. A node \(v_{j}\) with data to send to the gateway will include its current metric \(m_{j}\) in the DATA packet, which is broadcast to the local one-hop neighborhood. An arbitrary neighbor \(v_{i}\) that receives the data packet will send an acknowledgment packet ACK to \(v_{j}\), if it has a better metric to the gateway, _i.e._, \(m_{i}<m_{j}\). As wireless links may fluctuate, the transmitting node \(v_{j}\) can retransmit DATA packets up to a maximum of five times before the packet is discarded from its transmit buffer. The process is repeated along each hop until the gateway receives the DATA packet. Due to the opportunistic nature of the routing protocol, it is possible for duplicate copies of the same packet to arrive at the gateway by using sequence numbers. The duplication of packets is resolved at the back-end through the use of sequence numbers.
## 4 Conclusions  
In this study, variable-angle shear tests were performed on coarse- and fine-grained granite samples after high-temperature heating, followed by liquid nitrogen cold shock and water-cooling shock. Combined with nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM) tests, the following main conclusions were reached:  
1. As the shear angle is increased, a continuous decrease in peak load and peak displacement is exhibited by both coarse- and fine-grained granite samples. Furthermore, liquid nitrogen cold shock exhibited a more pronounced effect on the peak displacement of fine-grained granite when the heat treatment temperature and shear angle were constant, with the overall effect being less than that of water-cooling shock.
2. Granite shear strength and cohesion generally exhibit a trend of initial increase followed by a decrease with rising thermal treatment temperatures, whereas the internal friction angle shows an inverse trend. Notably, the thermal hardening effect is most pronounced at 200 \({}^{\circ}\)C. Furthermore, at the same thermal treatment temperature, granite subjected to liquid nitrogen thermal shock has lower shear strength and cohesion compared to granite treated with water thermal shock.
3. This study established that the application of liquid nitrogen cold shock resulted in a pronounced alteration in the internal pore distribution within granite, thereby enhancing the connectivity between pores. In comparison to the impact of water-cooling shock, liquid nitrogen cold shock exhibited a significant augmentation in the proportion of medium pores and large pores within the granite.
4. At ambient temperature, intragranular cracking is the predominant form of internal cracking in granite; however, as the heat treatment temperature is increased, the cracks gradually transition to transgranular cracks and the integrity of the fracture surface is reduced, with the surface changing from smooth to rough. Furthermore, the application of liquid nitrogen cold shock has been demonstrated to result in the emergence of numerous micro- and small-sized pores on the fracture surface of granite. This effect underscores its superior efficacy in fostering the development of intricate fracture networks within dry hot rock reservoirs.  
Conceptualization, F.Z. and S.L.; Data curation, S.L.; Methodology, F.Z., S.L. and S.Z.; Software, Y.Z. and S.Q.; Supervision, M.L.; Writing--original draft, F.Z.; Writing--review and editing, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the National Natural Science Foundation of China (Grant No. 51979100).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Adsorption and Photocatalytic Experiments  
The photocatalytic degradation of MO was conducted in a 100 mL quartz tube. The quartz tube was connected to a 300 W Xe short arc lamp (solar simulator equivalent to 1 sun radiation) as a source of light. In the reactions, 100 mg of the nanocomposite catalyst was introduced into 100 mL of 10 mg/L of MO solution at pH 7. Aliquots were withdrawn from the solution at appropriate intervals for analysis.  
The experiment was first conducted in the dark for 30 min to determine the effect of any background illumination effects on the adsorption-desorption reaction. Then, the lamp was switched on, and 2 mL aliquots were withdrawn from the reaction mixture at 5, 10, 15, 20, 30, 60, and 120 min intervals. The final concentration of MO was determined by ultraviolet-visible (UV-Vis) spectroscopy, using a Cary 100 spectrophotometer at a wavelength of 465 nm.  
Equation (1) was used to calculate the %degradation of methyl orange and Equation (2) (Langmuir-Hinshelwood kinetic model) was used to estimate the kinetic rate.  
\[\%\text{Degradation}=\frac{\text{C}_{\text{O}}-\text{C}_{\text{e}}}{\text{C}_{ \text{O}}}\times 100 \tag{1}\]  
\[\text{Ln}\frac{\text{C}_{\text{O}}}{\text{C}_{\text{t}}}=\text{K}_{\text{ app}}\times\text{t} \tag{2}\]  
where C\({}_{\text{O}}\) and C\({}_{\text{e}}\) are the initial and equilibrium concentration of methyl orange, t is time (min), C\({}_{\text{t}}\) is the concentration at any time, t and K\({}_{\text{app}}\) is the rate constant.
## 4 Discussion
There is ongoing concern regarding adverse bio-effects, and there is no reason to believe these will be fewer with 5G added to the mix. Impacts on skin have been found in previous research. A recent critique of the 99 mmW studies on skin and skin cells that have been published reports that none examined possible delayed or long-term exposure effects, and none considered co-exposures. The author concludes that the research status is insufficient to \"develop science-based human health policies\" [32]. The only human 5G study published reports skin problems after nearby 5G installation [21]. Many other bio-effects have been found repeatedly; the range and frequency of these is reported by [PERSON] et al. [33], who report that biochemical changes and signs of oxidative stress are those most commonly observed. The added frequency range that comes with broadband 5G increases the likelihood of there being frequency-specific responses. It is possible some may be advantageous, but this overlooks the fact that some may trigger adverse effects or exacerbate existing conditions. Again, research is needed. A report commissioned by the European Parliament's Global Committee on Industry, Research and Energy emphasizes the complexity of 5G and concludes that its \"unpredictable propagation patterns that could result in unacceptable levels of human exposure to electromagnetic radiation\" (p. 6) [34]. This is challenging for those in standards organisations who need to prepare and incorporate exposure specifications suitable for 5G. This also affects standard-setting of how to undertake laboratory and environmental measurements. Hence, the report's foremost recommendation is to increase long-term research and development to allow better understanding, measurement and control of exposures at mmW frequencies (pp. 27-28). Early approaches have been published on methods to evaluate these highly complex exposures in the environment [35; 36] and in the laboratory [37].