esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Vibration Imaging Using Discrete Orthonormal Stockwell Transform (DOST)
If a 1D function of time \(t\) for a constant frequency \(f\) is represented as \(V_{s}(\tau,f)\), then the continuous [30; 35] ST of a function \(h(t)\) is:  
\[V_{s}(\tau,f)\ =\ \int\limits_{-\infty}^{\infty}h(t)\frac{|f|}{\sqrt{2\pi}}e^{-( \tau-t)^{2}\frac{f^{2}}{2}}e^{-i2\pi ft}dt \tag{2}\]  
Equation (2) demonstrates the change of the amplitude and phase for this frequency over time. In the discrete case, there are computational advantages to using the equivalent frequency domain definition of the ST. However, as [PERSON] pointed out, standard ST is computationally expensive (\(O\left(N^{2}\right)\)) [27]. In [36], [PERSON] et al. demonstrated that the DOST basis is not suited to a standard Gaussian window. Later, in [37], [PERSON] et al. proposed a fast algorithm for the ST with a time complexity of \(O(N\log N)\). [PERSON] et al., in [36], extended that work and provided an adapted basis to decompose the ST with a general admissible window. Let us consider a signal \(f\) with finite energy, where \(f\in L^{2}(R)\). If \(w\) is the window in \(L^{2}(R)\), then the S-transform \(S_{w}f\) is:  
\[(S_{w}f)(\mathrm{b},\xi)\ =\ (2\pi)^{-\frac{1}{2}}\int\limits_{R}e^{-2\pi t \xi}f(t)|\xi|\overline{w(\xi(t-b))}dt \tag{3}\]  
Figure 3: Discrete orthonormal Stockwell transform (DOST) basis construction process, where FFT stands for fast Fourier transform. where \(b,\zeta\in R\). In [32], [PERSON] et al. assumed that one could find such a basis \(E_{p}^{w}\) of \(L^{2}\)([0,1]), where \(w\) will be dependent on choice. Then, \(S_{w}f\) can be expressed as:  
\[(S_{w}f)(\mathbf{b},\xi)\ =\ \sum_{p}c_{p}^{w}(S_{w}E_{p}^{w})(\mathbf{b},\xi) \tag{4}\]  
where \(f\ =\ \sum\limits_{j}c_{p}^{w}E_{p}^{w}\). They proved that an orthonormal basis of \(L^{2}([0,1])\) can satisfy the condition that ST is local in time and frequency and has a fast computation algorithm of \(O(N\log N)\) time complexity to compute the coefficients. To form the identical patterns, the considered DOST basis output of the segmented signals of all the types are stacked together. These stacks of the preprocessed signals form identical patterns for each health type. When stacking a huge number of segmented signals, these images are large. To address this issue, heights of the stacked signal are fixed for generating identical images in the experiment. If the total number of segmented signals is \(M\) and each segment has length \(Q\), then the size of the image is \(M\ \times\ Q\) (_height_\(\ \times\ width\)). This height is very large as well as challenging to feed to the proposed network. To generate small images, \(m\) samples from the \(M\) segmented samples (where \(m\ \in\ M\), \(m\ <\ M\) and \(M>8\)) are considered. Then, all the resized images are bunched together and fed into the network. In this study, \(m\ =\ 8\) can generate good resolution. This \(m\) can be larger but note that increasing \(m\) reduces the total number of samples.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 4 Discussion  
### Generalization to Large Scale  
The proposed optimization method is highly generalizable to the regional or even global scale, provided that local input data are available, such as VHR aerial imagery, LiDAR data, and meteorological measurements. First, the detection of PV modules in VHR imagery does not differ fundamentally between different geographical regions, as most standard PV modules share highly similar visual characteristics, such as rectangular shape, homogeneous color, and regular grid patterns. Hence, a well-trained semantic segmentation model can be widely applicable, even in geographically diverse settings, as long as the underlying resolution and VHR image quality are sufficient. Second, although the proposed method was validated locally, due to the availability of detailed validation data, the solar irradiance and shadowing methodology considered within the optimization has already been validated by previous works by the authors on the installation of hypothetical new PV modules' placement [8] or for large-scale applications [48]. In addition to the previous work of the authors, the approach considered for the estimation of direct irradiance and the effects  
Figure 11: (**a**) Simulated cumulative electrical energy production from individual arrays, where the colors correspond to colors in Figure 8b,c; (**b**) comparison between cumulative measured and simulated electrical energy production from the four PV systems; and (**c**) comparison between cumulative simulated and optimal electrical energy production from the four PV systems.  
Figure 10: Estimated total annual solar irradiance per detected array by changing the tilt angle with an exhaustive method. The stars denote the optimum tilt angles for a given PV array, where the received solar irradiance is maximized.  
of shading can be applicable to any geographical location with diverse climate conditions, as was shown by [PERSON] et al. [49]. Moreover, the Perez anisotropic irradiance model [52; 56] used is a well-known approach to estimate diffuse irradiance for any geographical location.  
Finally, the expected energy gains on regional or global scales could be significant even when the tilt angle is improved by 5 % for existing photovoltaic systems, especially when considering several years of future operation. Such a large-scale detailed analysis is beyond the scope of this paper and can be considered as an extension for future work, where HPC or cloud-based computing would be required.
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 2 Related Work
### Ensemble Extreme Learning Machine  
Ensemble Extreme Learning Machine [15] is a single hidden layer feedforward neural network whose goal is to classify time series. To detect concept drift, the method calculates and records the accuracy and the standard deviation of each data block, where a data block represents the data updated from the data stream each time. All data blocks are required to be the same size. Suppose that \(p_{i}\) is the accuracy and \(s_{i}\) is the standard deviation of the newest block \(i\), \(p_{best}\) and \(s_{best}\) are the highest accuracy and the corresponding standard deviation recorded up to some point \(i\)-1 in the stream. Given that \(\epsilon\) is a hyperparameter representing the accuracy threshold, when \(p_{i}+s_{i}<p_{best}+2*s_{best}\) and \(\epsilon<p_{i}\), the system will not change the model at all (for lack of evidence of concept drift). When \(p_{i}+s_{i}\geq p_{best}+2*s_{best}\) but \(\epsilon<p_{i}\), the system will update the model using the new data (for evidence of mild concept drift). When \(\epsilon\geq p_{i}\), the system will forget all retained data and all previous accuracy and standard deviations recorded and the system will retrain the model with new data (for evidence of abrupt concept drift). Our problem and data structure are different because we are concerned with prediction on tree structures, but we appreciate the intuition of the authors' detection and treatment of concept drift.  
In summary, our forgetful data structures take much from the related work with two primary innovations: (i) We discard data in a continuous fashion with respect to the gain or loss in accuracy. (ii) We have designed our tree structures to support fast incremental updates for streaming data.
## 2 Average Annual Collapse Probability and Collapse Safety Margin Ratio
### Average Annual Collapse Probability-Based Collapse Safety Margin Ratio  
Equations (5) and (6) show that the average annual collapse probability and CMR are determined by the median seismic collapse intensity \(\hat{S}a,c\), which is a representative of the average collapse resistance capacity of structures. Substituting the median earthquake intensity of collapse \(\hat{S}a,c=CMR\cdot S_{a,MCE}\) into Equation (5) leads to the following relation between average annual collapse probability and \(CMR\):  
\[\lambda_{collapse} =k_{0}\cdot(CMR\cdot S_{a,MCE})^{-k}\cdot\exp\left(\frac{1}{2}k^{ 2}\cdot\sigma_{S_{a,c}}^{2}\right). \tag{7}\] \[=k_{0}\cdot S_{a,MCE}^{-k}\cdot\exp\left(\frac{1}{2}k^{2}\cdot \sigma_{S_{a,c}}^{2}\right)\cdot CMR^{-k}\]  
Let \(a=k_{0}\cdot S_{a,MCE}^{-k}\cdot\exp\left(\frac{1}{2}k^{2}\cdot\sigma_{S_{a,c }}^{2}\right)\). Then, Equation (7) can be transformed into Equation (8).  
\[\lambda_{collapse}=a\cdot CMR^{-k} \tag{8}\]  
Equation (8) shows that the average annual collapse probability varies reciprocally with \(CMR\) in a form of power function. Figure 2 shows the relationship between \(\lambda_{collapse}\)-\(CMR\), taking \(k\) = 2.31 and a = 0.000803. This relationship coincides with the inference that the higher the \(CMR\) of the structure, the lower the collapse risk.
## 3 Architectures in the 5G and LPWAN-IoT and Smart Cities
### Ubiquitous Connectivity Challenges and Solutions in 5G and LPWAN-IoT  
Over 80% of the earth is not covered by broadband services. Thus, greater proportions of the earth should be covered by broadband services for the ubiquitous Internet of Things (IoT) to be actualized. One of the key technologies that will support ubiquitous IoT connectivity is the Low Earth Orbit (LEO) satellites. The fifth generation (5G) network will also support the ubiquitous IoT. This is because 5G has been envisioned to enable massive IoT. In the past, broadband services and IoT applications such as smart metering, smart home, smart building, smart cities, e-health, factory automation, asset tracking, and so on have been supported by communication systems such as cellular (2G, 3G, GSM, and 4G), wireless fidelity (WI-FI), Bluetooth, and ZigBee. Presently, IoT applications are being supported by low power wide area networks (LPWAN) such as LoRa, Sigfox, LTE-M, and NB-IoT for long-range connectivity solutions. Yet, historically, the connectivity solutions have not addressed the challenge of ubiquitous IoT. More recently, the LEO satellite has emerged as a potential technology for ubiquitous IoT connectivity to serve the underserved area including ocean, forest, and remote areas, especially in developing countries region. Due to the closeness of the LEO satellite to the earth, it has an appreciable data rate and low latency better than the geosynchronous equatorial orbit (GEO) satellite. To integrate non-terrestrial networks (NTNs) such as LEO satellite constellations with terrestrial networks (TNs), 5G NB-IoT standards for Non-Terrestrial Networks (NTNs) have been standardized in the 3 GPP release-17's 5G NB-IoT NTN specifications [33]. This will help to facilitate connectivity between IoT devices via TNs and NTNs globally. Hence, LPWAN-IoT can leverage 5G NB-IoT NTN for ubiquitous connectivity in smart city applications.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 2 Materials and Methods
### Sample Testing and Characterization  
The flexural and compressive strength were tested at the age of 28 days according to ASTM C348 and C349 [24; 25]. The debris was chosen from broken specimens and used for scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analysis. Hitachi S-4200 Scanning Electron Microscopes (Hitachi, Tokyo, Japan) were used for this analysis. For sorption and XRD analysis, blast furnace slag was uniformly mixed with alkaline activator with the same Na\({}_{2}\)O concentration as mortar mixture. After 28 d of curing, the hardened paste specimen was ground into powder with a particle size less than 0.63 mm to be used for X-ray powder diffraction (XRD) analysis. XRD patterns were collected on a 10 to 70\({}^{\circ}\) 20 scale by using a Siemens D-500 Diffractometer (Siemens, Munich, Germany).  
The concentrations of PHE, PYR, NAP, DiBP, and DBP were measured using gas chromatography-mass spectrometry (GC-MS) on an Agilent 7890A gas chromatograph. The GC-MS data and chromatograph of determination of target compounds are shown in Figure S1 and Table S1. The MS library from National Institute of Standards and Technology (NIST) was used to identify the target components. An Agilent HP-5 column with an internal diameter of 0.25 mm, a film thickness of 0.25 \(\upmu\)m, and a stationary phase composed of 5% phenyl and 95% dimethylpolysiloxane was used. The temperature program for the oven commenced at 50 \({}^{\circ}\)C (held for 3 min), followed by an increase at a rate of 10 \({}^{\circ}\)C/min until reaching 300 \({}^{\circ}\)C, which was maintained for 5 min. The total GC running time was 33 min. The interface temperature was maintained at 285 \({}^{\circ}\)C. Helium gas flow rates were set at 1 mL/min. The injection and ion source temperatures were maintained at 250 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. The scan mass ranged from 80 to 400 amu. The injection volume was 1 \(\upmu\)L in splitless mode. The detection limit of phenanthrene was determined to be 0.387 \(\upmu\)g/L, following the detection limit method [26].
## 3 Proposed Network Model and the Problem Formulation
### Intra Cluster Communication  
The lifespan of the network is typically shortened by long-distance transmission, which uses a lot of energy. Long-distance communication should therefore be prohibited during intra cluster communication. Our proposed structure enables the member nodes to choose the best relay node or to send data directly by calculating the energy consumption cost of the various routing paths. Transmission uses around 1000 times as much energy as the calculation, as mentioned by [PERSON] et al. [33]. Hence, it is desirable to select a better routing path using the calculations. The energy consumption in the routing path using direct communication \(E_{1}(S_{i},CH_{Si})\) can be calculated as  
\[E_{1}(S_{i},CH_{Si})=\begin{cases}l.E_{elec}+l.e_{fs}d_{Si,CH_{Si}}^{2}\;if\;d_ {S_{i},CH_{Si}}<d_{0}\\ l.E_{elec}+l.e_{mp}d_{Si,CH_{Si}}^{4}\;if\;d_{S_{i},CH_{Si}}\geq d_{0}\end{cases} \tag{6}\]  
where \(d_{Si,CH_{Si}}\) represents the communication distance between the corresponding _CH_ and the node with the distance threshold \(d_{0}\). If the source node and its CH are far away from each other, a relay node \(j\) will be chosen to forward the data package. The energy, from the direct path, depends on the energy from the transmitter electronics and receiver electronics \(E_{elec}\) for \(l\) bits with \(e_{fs}\) that are the free space path loss factor and \(e_{mp}\) the multipath loss factor. The total energy consumed during the entire routing is given in Equation (7), as follows:  
\[E_{2}(S_{i},S_{j},CH_{Si})=E_{TX}(l,d_{Si,S_{j}})+E_{RX}(l)+E_{TX}(l,d_{Sj,CH_{ Si}}) \tag{7}\] \[=3l.E_{elec}+\epsilon_{fs}d_{Si,S_{j}}^{2}+\epsilon_{fs}d_{Sj,CH_{ Si}}^{2}\]  
where \(E_{TX}\) is the transmission energy consumed, \(E_{RX}\) is the reception energy consumed for \(l\) bits of information over the distance.  
The nodes compare \(E_{1}(S_{i},CH_{Si})\) and \(E_{2}\big{(}S_{i},S_{j},CH_{Si}\big{)}\) and select the smallest one as the routing path. The intra cluster communication can be determined using  
\[E(S_{i})=\min(E_{1}(S_{i},CH_{Si}),E_{2}\big{(}S_{i},S_{j},CH_{Si}\big{)}) \tag{8}\]
Above a certain high voltage, the membrane can no longer withstand this pressure and a pore is formed (cf. Figure 2). Through this pore, internal components can penetrate through the previously protective layer and result in different effects, such as an inactivation of microorganisms or an increased extraction yield of valuable ingredients from fruits [23; 24]. Important process factors that describe the intensity of the PEF treatment are electric field strength, specific energy input, pulse width and the so-called input temperature, which is the temperature of the product prior to PEF treatment [24]. As just described, the place of action of the PEF treatment is the permeable membrane, which is an electric double layer. Casein micelles also have a similar structure in the broadest sense (cf. Figure 1). Here, too, there is a charged cell that consists of a double layer that acts like an insulator. Therefore, there is a possibility that PEF can also affect the structure of casein micelles. Figure 1: Schematic diagram of casein submicelles (**a**) forming a whole casein micelle (**b**). When suspended in water, an electric bilayer is formed at the CM surface (**c**) (for detailed description see text). Up to now, only little is known about the influence of PEF application on the properties of native CM. Although treatment of milk with PEF is the subject of some scientific investigations, these aim primarily at improving the microbiological condition, as well as enzyme inactivation, and thus extension of shelf life, with focus on the preservation of vitamins and heat-sensitive proteins [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. However, changes in physico-chemical and/or techno-functional aspects are also reported, and these changes are not sufficiently traced back to changes in the material state of milk ingredients. Furthermore, detected effects are not consistent. While [PERSON] et al. [29] found a decrease in viscosity and an increase in protein coagulation, [PERSON] and [PERSON], 2001, reported that the influence of PEF on rennet coagulation of milk resulted in a decrease in gel strength and an increase in coagulation time [34]. This suggests that CM were partially disintegrated by PEF, which is comparable to the effects of cooling milk [17; 18]. However, [PERSON] found that PEF treatment did not induce significant changes in rennet coagulation properties [35]. Although there is not yet any scientific evidence that the use of PEF can specifically change the structural properties of CM, there are several indications that this is possible and likely. Hence, the aim of this work was to use PEF technology in order to disintegrate CM for loading with FSBS and the subsequent restructuring of micellar structure. In this study, we used hydrophobic \(\beta\)-carotene, which is provitamin A, as an example for FSBS. For this, necessary PEF parameters affecting the CM have to be identified, and the respective effect on \(\beta\)-carotene load of casein micelles has to be monitored.
# A 4.1 GHz-9.2 GHz Programmable Frequency Divider for Ka Band PLL Frequency Synthesizer
## 2 Materials and Methods
### Circuit Design of MMD
#### 2.2.2 Program Counter and Swallow Counter
\begin{table}
\begin{tabular}{c c c} \hline  & **Time Delay** & **Time Delay** \\  & **(Rising Edge)** & **(Falling Edge)** \\ \hline \(-\)40 \({}^{\circ}\)C TSPC & 19 ps & 69.5 ps \\ \(-\)40 \({}^{\circ}\)C Conventional & 93.5 ps & 76 ps \\ \(50\) \({}^{\circ}\)C TSPC & 17.5 ps & 62 ps \\ \(50\) \({}^{\circ}\)C Conventional & 85.5 ps & 68 ps \\ \(125\) \({}^{\circ}\)C TSPC & 20.5 ps & 76 ps \\ \(125\) \({}^{\circ}\)C Conventional & 102.5 ps & 82 ps \\ \hline \end{tabular}
\end{table} 
Table 1: Time delay of the two structure of the DFF. Figure 8: Simulation result and comparison of time delay between the conventional structure (transmission-gate based) and TSPC DFF. Figure 7: **(a)** Circuit of the conventional DFF; **(b)** Circuit of TSPC DFF. Time delay of the counter is different as the code state change. Take counter P as an example. The largest time delay occurs when the code turns from 100000 to 011111. The time delay is \(5_{\text{rise}}+t_{\text{fall}}\), which is much smaller by using the designed TSPC-DFF chain than using the conventional DFF chain. The calculated theoretically maximum working frequency in 50 \({}^{\circ}\)C of counter P based on TSPC DFF is 6.67 GHz, 3.3 times larger than using conventional DFF. The power consumption of the TSPC is also smaller than the conventional transmission-based DFF, which is important for the low-power design. The input clock of the counter P and counter S is the output signal of prescaler, which is nearly a pulse signal. The operating frequency range of the DFF when the input signal is a pulse signal is simulated. It can work at a frequency range of 25 MHz to 12 GHz, with a pulse signal input. As counter P provides the reset signal of the counter S, the counter S has a same period of the counter P's reset signal. It counts P + 3 before the reset signal is active. The period of the clock signal is not a constant, it varies due to the N/N + 1 prescaler. Assume the MC in Figure 3 is active, the prescaler counts P-S + 2 before the prescaler works at div-5 modes. The state will maintain until it counts S + 1. So the division ratio can be expressed as in (3). The duty cycle of the output signal should be considered in the design. Duty cycle will influence the drive capability, and it is important for the high speed circuit [11]. The duty cycle of MMD can be adjusted by adjusting P and S. It can be expressed as in (4):  
\[\text{mmd\_ratio} =\text{NP}+3\text{N}+\text{S}+1 \tag{3}\] \[\text{duty\_cycle} =\frac{\text{NP}-\text{NS}+2\text{N}}{\text{NP}+3\text{N}+\text{ S}+1}. \tag{4}\]  
For a certain division ratio, there are a lot of schemes by choosing different P and S. Duty cycle should be considered as a factor when choosing P and S.
# Dynamic Responses of Liquid Storage Tanks Caused by Wind and Earthquake in Special Environment
## 6 Numerical Example
### Comparison of Dynamic Responses under Different Actions  
In view of the possibility that the liquid storage tank may be damaged under the action of wind and earthquake, and the combined action of wind and earthquake will have more adverse effects on the structure, a comparative study on the dynamic response of the liquid storage tank under the action of wind, earthquake, and wind and earthquake is carried out, and the specific results are shown in Figures 6-8 and Table 2.  
Figure 5: Wind field calculation model. (**a**) Single tank. (**b**) Double tank.  
Figure 6: Comparison of tank effective stress (unit: Pa). (**a**) Wind. (**b**) Earthquake. (**c**) Wind and earthquake.  
Figure 7: Comparison of tank displacement (unit: m). (**a**) Wind. (**b**) Earthquake. (**c**) Wind and earthquake.  
As shown in Figures 6-8, the maximum effective stress and displacement of tank under wind load is located in the unfilled area of the upper part of the liquid storage tank, while the maximum effective stress and displacement of the structure under earthquake is located in the filled area of the lower part of the liquid storage tank, and the maximum of base shear force appears near the contact position between the tank wall and foundation.  
As shown in Table 2, it can be seen that when the wind speed is larger, the effective stress and displacement under wind load is greater than that under earthquake. However, a large number of researches on liquid storage tanks have been carried out on the basis of considering only the earthquake action, so there are some defects. Besides, the dynamic responses of tank under the combined action of wind and earthquake are obviously greater than those under the separate action of wind and earthquake. The effective stress, displacement, and base shear force obtained by SRSS are 130.04 Mpa, 7.65 mm and 257.73, respectively, which are very different from considering the interaction of wind and earthquake at the same time.  
Therefore, when the wind speed is large, the influence of wind load on the liquid storage tank cannot be ignored. The location of maximum dynamic responses of liquid storage tank under wind and earthquake is different, and the combined effect of wind and earthquake will have a more adverse impact on the liquid storage tank. Therefore, for the liquid storage tank in special areas (such as coastal areas), the combined effect of wind and earthquake should be reasonably considered in its design.