esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this article, a novel upsetting method named cone-end billet upsetting (CEBU) was proposed with the aim of restraining upsetting bulging in LHDR billet. Finite element analysis and experiments were conducted to compare the deformation behavior between TU and CEBU. Meanwhile, the influence factors of CEBU were examined by simulations. Some conclusions can be drawn from the present study:  
1. In CEBU, the deformation is more concentrated at the billet cone end, and two fan-shaped high-strain zones are correspondingly generated at the billet ends. With increasing reduction, the two fan-shaped strain zones gradually approach each other, resulting in a large compression effect at the billet center;
2. In CEBU, the frictional effect of upsetting is largely dispersed by metal flows around the conical ends of the billet. Compared with TU, CEBU can produce a much smaller rigid deformation zone at the same reduction ratio, resulting in a much smaller bulging;
3. Upsetting bulging can be eliminated when the reduction ratio reaches about 50% in CEBU. If the reduction ratio is less than 50%, bulging will occur at the bottom of the billet cone end. However, when the reduction ratio is above 50%, bulging may occur at the billet middle;
4. Benefiting from the cone end, the deformation character is not sensitive to the friction effect at the billet end in CEBU. It is much better to use a small taper angle to control bulging and improve the compaction effect at the billet center. In addition, the billet HDR should be less than 3.0 to avoid instability and deformation. Regardless of the different friction coefficients, taper angles, and HDR, bulging can only be eliminated with a 50% reduction ratio.  
Figure 12: Influence of height-to-diameter ratio. (**a**) Equivalent strain (50% reduction ratio); (**b**) bulging index.  
In summary, CEBU has a major advantage over TU in reducing bulging and improving the compaction effect of upsetting. In addition, CEBU can eliminate bulging with a reduction ratio of 50%. Despite the above advantages, CEBU also has shortcomings compared to TU. CEBU requires the billet end to be tapered prior to upsetting, which is an additional operation compared to TU. In addition, the studies on CEBU in this article were carried out under laboratory conditions. How to use CEBU effectively in industrial production requires further research, which will be the focus of our future work.  
Conceptualization, J.F. and C.W.; Methodology, J.F., W.L. and Z.L.; Validation, J.F. and C.W.; Formal analysis, J.F. and Z.L.; Investigation, J.F., Z.L., W.L. and C.W.; Writing--original draft preparation, J.F. and Z.L.; Supervision, J.F.; Project administration, J.F.; Funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No.51405136), the Young Backbone Teachers Training Program of Henan Polytechnic University (No.2020 XQG-01) and Henan Polytechnic University Innovation Team Project (No.T2019-5).  
Not applicable.  
The authors declare no conflict of interest.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 6 Summary and Discussion
Let us turn our attention to the issue of computational cost. In the case of white noise, the SVD and MDL methods have the same cost as SSA itself, which is \(O(N\log N)\). The TRMAT method requires an additional computation that can be implemented at the same asymptotic cost as \(N\to\infty\). Consequently, TRMAT is slightly more expensive. The MGN method is significantly more expensive, as demonstrated in [21], with a computational cost of one iteration being \(O(N\log N)\). However, the number of iterations required for convergence can be considerable. In order to apply the given criteria to the case of red noise, the well-known technique of noise whitening was employed. Due to the linearity of the model, the approach can be reduced to multiplying the estimated variance by \(1-\phi^{2}\), where \(\phi\) is the AR(1) coefficient. In the case of an incorrect model, \(\phi\) can be estimated by an irrelevant value; this may lead to an incorrect estimation of the signal rank. The proposed approach is as follows. If the series has a trend, one can first test the residual of the extracted signal of rank \(r\) for stationarity and if the hypothesis is rejected, not consider this value of rank as a candidate for the rank estimation. Numerical experiments have shown that the stationary check improved the accuracy in the considered examples. However, in the absence of a trend, such a check leads to a worsening of the rank estimation. For the TRMAT method, the estimate of \(\phi\) can be improved using the same weights caused by the Hankel structure of trajectory matrices as it is in the TRMAT method itself. We do not discuss the costs of the methods for the red noise case because estimating \(\phi\) and checking the stationarity of the residuals can be conducted in different ways and it is currently difficult to choose the best one. A general recommendation on the choice of a criterion that is based on numerical experiments is as follows: If one does not take into account the considerable time consumption associated with MGN, TRMAT can be recommended for both white and red noise cases. In addition, the paper presented an approach to comparing methods based on the relative MSE as a function of the noise variance. The range of noise levels is divided into plateaus and transitions, providing a more structured framework for interpreting the comparison results. Without this approach, the comparison results were less organized and difficult to interpret. The application of the methods to real-world data sets demonstrated satisfactory outcomes, with the suggestion that a variance-stabilizing transformation should be performed.
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 2 Methodologies
### PVA-ECC Specim Mix Design  
The ECC mixes integrate general purpose cement (Type-GP cement conforms to AS3972-1997), fine silica sand (supplied by Sibelco Australia), ASTM class-F fly ash, polyvinyl alcohol (PVA) fibers, water, and admixture. The silica sand has a maximum size and a mean grain size of 250 \(\upmu\)m and 122 \(\upmu\)m, respectively. The PVA fibers are the essential admixture that allows the ECCs to exhibit excellent ductility and strain hardening behavior. A standard PVA fiber at a volume fraction of 2% was used in this study. This fiber fraction has been proven to offer an interfacial bond and was tailored to satisfy the strain-hardening criteria [24]. The mechanical and physical properties of the PVA fibers are listed in Table 1. Throughout the study, the standard mix design of PVA-ECC (ECC-M45) with a water-to-binder (\(w/b\)) ratio of 0.255 as initiated by [PERSON] et al. [24,25] was used as the reference ECC mix, which is designated as PE2. The binder system was defined as the total amount of raw cementitious materials, such as cement and fly-ash. The ingredients and mix proportions of ECC mixes are given in Table 2, where a sand-to-binder ratio (\(s/b\)) of 0.364 is used for all mixes. The use of fine sand, fiber volume fraction, ratios of \(s/b\) and \(w/b\) was optimized to satisfy the multiple cracking criteria [25]. The chemical compositions of the raw materials, Portland cement (PC), fine sand (FS), and Class-F fly ash (FA-F), were analyzed by using the scanning electron microscope (SEM) of JEOL JSM6510 LV. The corresponding chemical compositions are shown in Table 3. From the analysis one can see that the ECC mixtures have a higher amount and finer particle size of chemical constituent of silica (SiO\({}_{2}\)) than common concrete mixes.  
In order to investigate the effect of slight variations in \(w/b\) ratio, reference PVA-ECC materials with decreases (\(w/b\) = 0.20) and increased (\(w/b\) = 0.30) initial water content were included in the experimental program. The corresponding ECC materials are denoted as PE1 and PE3, respectively, as displayed in Table 2. Except the use of high range water reducer (HRWR) admixture, all other proportions with respect to cement remained constant for all ECC specimens. Owing to the water demand from PVA fibers, which affected the rheological properties to a different degree from batch to batch, appropriate amounts of HRWR admixture were empirically added into the mixes. Moreover, with the aim of improving fiber distribution and, hence, obtaining better mechanical properties of ECC, a new mixing sequence [29] was used in this study. In order to obtain good plastic viscosity for the ECC mixture, a 40 L Hobart planar mixer was used throughout the experiment program.
# Rifampicin-Loaded PLGA/Alginate-Grafted pNVCL-Based Nanoparticles for Wound Healing
## 3 Results and Discussions  
### Matrix Characterization  
#### 3.1.1 FTIR Spectroscopy  
Spectroscopy tests were performed to detect either the appearance of new chemical bonds or the modification of existing ones, which can be attributed to possible interactions between grafted alginate (AgA-g-pNVCL) and polylactide-glycolide (PLGA) to form NPs compared with their pure constituents (AgA; PLGA and AgA-g-pNVCL). The obtained spectra are represented in Figure 1.  
The purpose of the spectra analysis was to confirm the structural identity of the grafted copolymer and the nanoparticle formation of PLGA/AgA-g-pNVCL. The grafted copolymer's structural identity has been reported in detail in our publication [4]. By combining the grafted copolymer of alginate with PLGA as a coating shell, it was expected that the spectra of the final nanoparticle would consist of both the specific vibration bands from the alginate-based copolymer and PLGA. The formation of new vibration bands and/or shifts to lower or higher peaks within the spectrum range were expected to occur. The spectra of PLGA contained a strong peak at 1094 cm\({}^{-1}\) specific for C-O-C stretching, as well as a peak at 1740 cm\({}^{-1}\) for C=O stretching. By analyzing the spectrum of the NP, the specific peak of C-O-C from PLGA was found at 1086 cm\({}^{-1}\) even though alginate-based matrix also contained a peak at 1020 cm\({}^{-1}\) thanks to the presence of -C-O- of the glucoside moieties indicating the presence of PLGA moieties. The peaks of CH, CH\({}_{2}\) stretching vibrations from 2900 to 2800 cm\({}^{-1}\), were observed to be more intense signaling than the conjugate formed between the PLGA and alginate matrix. Moreover, the peak at 3282 cm\({}^{-1}\) of OH groups increased in intensity and was wider, indicating the formation of the conjugate and the interactions between the two polymers via H-bonding.
## 2 HT Distribution and Simulation Results
### Elements and Linear Array
Even though the gain currently declines by roughly 2-3 dB, we can enhance it by adding more elements. According to our simulations, the proposed HT method fits well for arbitrary structures, frequency, or feeding ways in N element arrays, and the gain reduction caused by HT can easily be improved by increasing N.  
\[|S_{11}|=20\lg|\Gamma| \tag{2}\]3 Linear array when N = 12: (**a**) gain; (**b**) power distribution. Adjusting the sidelobe suppression (dp) degree can also improve the gain performance, as shown in Figure 8d: the maximum ONF gain of the 8-element linear array operating at 3.5 GHz is only reduced by 0.5 dB compared with the uniformly excited array when \(\mathrm{dp}=-30\) dB; the maximum gain is reduced by 2 dB when \(\mathrm{dp}=-60\) dB; and the maximum gain is reduced by 3 dB when \(\mathrm{dp}=-100\) dB and \(-120\) dB. Thus, we can deduce that the gain difference no longer changes when \(\mathrm{dp}\) is lower than \(-100\) dB. Furthermore, Figure 8d indicates that when the absolute value of \(\mathrm{dp}\) increases, the gain at the null point increases. This implies that the filling effect improves and the beam widens, but the maximum gain decreases, necessitating a balance between the filling effect and the gain when required. In this paper, we choose \(\mathrm{dp}=-60\) dB because, incidentally, the feed network is more complex at that value than it is at \(-30\) dB. We can find that the gain pattern is not symmetrical since the HT excitations are asymmetric, and this causes the upper sidelobe (\(-90\)-\(0\) deg arrange) to converge faster, which plays a significant role in upper-sidelobe suppression. The beam's scanning ability is one of the important indexes of a BSA. Since \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**N** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
6 & 0.02 & 0.081 & 0.25 & 0.517 & 0.808 & 1 & & \\
8 & 0.02 & 0.053 & 0.144 & 0.293 & 0.492 & 0.707 & 0.892 & 1 \\
12 & 0.02 & 0.034 & 0.074 & 0.138 & 0.226 & 0.338 & 0.469 & 0.609 \\
16 & 0.02 & 0.028 & 0.05 & 0.086 & 0.135 & 0.198 & 0.273 & 0.362 \\ \hline
**N** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline
12 & 0.746 & 0.856 & 0.953 & 1 & & & & \\
16 & 0.459 & 0.562 & 0.666 & 0.764 & 0.852 & 0.923 & 0.974 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Linear array excitation amplitude (W). Figure 8: Gain when N and \(\mathrm{dp}\) are different: (**a**) N = 6; (**b**) N = 8; (**c**) N = 16; (**d**) \(\mathrm{dp}\) is different. the HT does not change the excitations' phase, Figure 9 illustrates that we can change the beam direction by varying the phase difference, which can be as much as \(\pm 200^{\circ}\), \(\pm 150^{\circ}\), \(\pm 100^{\circ}\), or 0, and it also can realize a beam navigation ability of \(\pm 42^{\circ}\) since the HT approach does not change the phase of the excitations.
# A New BAT and PageRank Algorithm for Propagation Probability in Social Networks
## 3 Research Methods
### Propagation Status
#### 3.2.2 State Probability and Propagation Probability  
In Section 2, it is mentioned that the social network is a scale-free network; that is, it conforms to the Barabasi-Albert model with a growth mode and a preferential connection mode. In preferential connection mode, the probability that a new node can be found to connect to node \(i\) is proportional to the node's connectivity \(k_{i}\), so the connection probability of a web page is particularly important. Using the PageRank value introduced in Section 2.3 as probability, the \(PR(S_{k}\left(i\right))\) is defined as the probability of \(S_{k}\left(i\right)\), called state probability. When this node occurs in state \(S_{k}\left(i\right)\), define \(Pr(S_{k}\left(i\right))\) as the probability of occurrence of state \(S_{k}\left(i\right)\). In other words, \(Pr(S_{k}\left(i\right))\) can be regarded as the probability of node propagation. \(PR(S_{k}\left(i\right))\) is to calculate the connection probability obtained by a node according to the PageRank values of all nodes in the state corresponding to node \(i\)[82], as shown in Equation (5).  
\[PR(S_{k}(i))=\sum_{\left|S_{j}\right\rangle\left|=1 amdS_{j}\right\rangle\subseteq S _{k}\left(i\right)}PR(S_{j}(i)) \tag{5}\]  
Because all possible state probabilities must sum to 1, the PageRank value is normalized as in Equation (6).  
\[Pr(S_{k}(i))=\frac{PR(S_{k}(i))}{\sum_{k=0}^{2|Dgg(i)|-1}PR(S_{k}(i))} \tag{6}\]  
In all nodes, the probability of state 0 is non-transitive. From the preferential connection mode in the Barabasi-Albert model, a newly added node can freely connect to any node in the network, and the probability of connecting to two nodes is twice as high as connecting to one node [82]. Hence, the probability of state 0 is defined as Equation (7).  
\[PR(S_{0}(u))=0.5\times\text{Min}\{PR(i),i\in V(u)\} \tag{7}\]  
Taking node 1 of the network in Figure 6 as an example, the state probability and propagation probability are shown in Table 6.  
According to the above method, the state probability and the propagation probability of the network in Figure 6 are obtained as shown in Tables 7 and 8.
## 6 Performance Evaluation on Simulator
### Performance of Path Planning
#### 6.3.3 Performance Evaluation on Campus Driving Situation Related to Hazard Scenarios
For (g), this is a scenario in which the micro-EV meets static obstacles on the road while the obstacle vehicles are moving in the side lane in which the micro-EV should drive when the obstacle avoidance system is activated. The obstacle vehicles are set to move at 40 km/h, and the number of the obstacle vehicles is five. During the time when the obstacle vehicles pass, the obstacle avoidance system is not be activated. After this, the micro-EV activates the obstacle avoidance system, offering collision-free operation. In these hazardous environments, the performance of path planning in the multiple-obstacle environment is evaluated. The results are shown in Figure 14. In this figure, each sub-item, (a)-(f), corresponds to the sub-item of Figure 13. The red-colored lines denote the speed of the micro-EV. The blue-colored lines denote a distance between the dynamic obstacles and the micro-EV. The black-colored lines denote a distance between the static obstacles and the micro-EV. For all situations, there was no collision, which was confirmed from blue and black lines. After the braking period, the micro-EV accelerated toward the generated path as confirmed by the red lines. During this acceleration, there were also no collisions related to obstacles. Based on these facts, we confirm that our proposed urban-based path planning algorithm is applicable for a complex campus environment. Figure 13: The hazardous scenarios: (**a–c**) these are regarded as moving pedestrians; (**d–g**) these are regarded as moving vehicles.
## 3 RS Applications in Ocean
### Ship Detection (SD)
#### 3.6.4 Summary and Future Direction  
In summary, optical and SAR images have been extensively used for SD. However, they have usually been employed separately, and multisensor approaches have rarely been explored. Each of these data has its own advantages and disadvantages, and combining them for SD overcomes their limitations and provides better results. The focus of the future SD studies using SAR images should be concentrated on incorporating more precise spatial feature extraction and speeding up the algorithms of the spatial information extraction. This can improve the detection accuracy and reduce the detection time consumption [336]. Furthermore, in case of the existence of cooperative positioning data, more accurate and better results can be achieved.  
DL methods have shown promising results for a variety of SAR applications. However, some further investigations are suggested for SD. First, more annotated SAR data are needed for accurate detection because it can improve the learning network. Thus, it is expected that more high-quality SAR datasets for SD will be provided for research in the future [338]. Additionally, it is recommended to concentrate on improving the segmentation algorithms in terms of both accuracy and computation time. In terms of optical images, it might be valuable to carry out more research on more complex CNN and RNN methods [325]. Moreover, regarding the spaceborne RS technology, it is worthwhile to exploit the hyperspectral data to for SD.
# Graph Theory-Based Characterization and Classification of Household Photovoltaics
## 3 Methodology
### Expected Value of Power (EVP)  
In the previous section, a reliability analysis of all valid array-digraphs of \(p\) panels has been carried out. However, it is not enough to choose a panel structure depending on its reliability since it is possible that a very reliable structure performs very poorly in terms of power production. This is why we should combine the probability of a structure working properly with the power that it can provide. To do so, the EVP given by every array-digraph \(D\) will be computed in terms of \(x\) (again \(x\in[0,1]\) represents the probability of every single panel working properly).  
Let \(D\) be a size \(p\) array-digraph, and let \(S_{D}(i)\) be the set of all subdigraphs of \(D\) with order \(i\) that hold the array-digraph conditions. Let \(pow(d)\) be the maximum power extracted from \(d\in S_{D}(i)\). Then, the EVP obtained from \(D\) is:  
\[E_{D}(x)=\sum_{i=0}^{p}\left(\sum_{d\in S_{D}(i)}pow(d)\right)x^{i}(1-x)^{p-i}. \tag{3}\]  
The probability of any array-digraph \(d\in S_{D}(i)\) is \(x^{i}(1-x)^{p-i}\). Thus, \(E_{D}(x)\) is the summation of the product of \(pow(d)\) times the probability of having \(d\in S_{D}(i)\), which is precisely the definition of an expected value.
## 3 Materials and Methods
### Gridded Precipitation Products (GPPs)
#### 3.2.1 Gauge-Based GPPs  
The Global Precipitation Climatology Centre (GPCC) obtained the primary data from meteorological agencies at National level (NMAs), Food and Agriculture Organization (FAO), Climate Research Unit, University of East Angelia (CRU), and Global historical climatology network (GHCN) at the National Centers for Environmental Information [48]. The product covers the period from 1891 to 2018. In this research, the monthly product of 0.5\({}^{\circ}\) spatial resolution is used from the period 1979-2017. The product from University of Delaware, USA (UDel) acquired the primary data from GHCN2, daily GHCN from National Centers for Environmental Information, National Center Atmospheric Research (NCAR), Project Greenland of automatic meteorological stations, Data archives of Nicholson for African continent, records of daily summary at the global level [49]. The product covers the period from 1900 to 2017. The monthly precipitation data of 0.5\({}^{\circ}\) spatial resolution from the period 1979-2017 is utilized in the study. The monthly product Climate Research Unit (CRU) acquired the primary data from the World Meteorological Organization (WMO), Food and Agriculture Organization (FAO), and National meteorological departments. The CRU product covers the period from 1901 to 2019. The monthly precipitation data of 0.5\({}^{\circ}\) spatial resolution from the period 1979-2017 is used in the present study [50].  
The daily Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) product that was obtained the primary source data from multiple Asian agencies including Global Telecommunication System (GTS), National weather agencies, International Centre for Integrated Mountain Development (ICIMOD), International Water Management Institute (IWMI) and other national and international projects on climate [51]. The product covers the period from 1961 to 2015. The daily precipitation product of 0.5\({}^{\circ}\) spatial resolution was accumulated into monthly time scale from the period 1979-2015 in the present study. The CPC is the first product of Unified Precipitation Project at the National Oceanic and Atmospheric Administration (NOAA). The product acquired the primary data from National weather agencies, quality controlled station data from GTS, and Cooperative Observer Network [52]. The CPC product covers the period from 1948 to 2018. The daily precipitation product of 0.5\({}^{\circ}\) spatial resolution was accumulated into monthly time scale from the period 1979-2017 in the present study.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### SPAR SR1010/SR1020 UWB Transceiver  
The Spark SR1010/SR1020 (Spark Microsystems International Inc., Montreal, QC, Canada) is a low-power UWB transceiver suitable for energy-efficient communications with ultra-short latency and supports a reconfigurable frequency range of 3.1-6 GHz for SR1010 and 6-9.3 GHz for SR1020. The transceiver's data rate can be contentiously scaled from 1 Kbps to 10 Mbps to achieve ultra-low power consumption over a wide range of data rates, which makes it a flexible solution for many applications. These chips can measure the distance by measuring the time of flight of the generated UWB signal between two chips, with a range approaching 100 m in LoS conditions, and with a measurement accuracy of about \(\pm\)30 cm [40].