esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Anti-Icing Performance of NS-DBD Plasma Actuator
###### Abstract  
An experimental study was conducted to evaluate the anti-icing performance of NS-DBD plasma actuator under the conditions of airflow speed U = 65 m/s, ambient temperature T = \(-\)10 \({}^{\circ}\)C, liquid water content LWC = 0.5 g/m\({}^{3}\), mean-volume diameter MVD = 25 \(\upmu\)m, mainly to clarify the effect of pulse frequency and voltage amplitude of actuation on anti-icing performance. A NACA0012 airfoil model with a chord length of c = 280 mm was used in the tests. The NS-DBD plasma actuator was mounted at the front part of the airfoil. A FLIR infrared (IR) imager and CCD camera were used to record the anti-icing process of the NS-DBD plasma actuator. Two typical discharge conditions were selected for the anti-icing experiments. The first was HV-LF discharge, corresponding to discharge under higher voltage amplitude with lower pulse frequency; the second was LV-HF discharge, corresponding to discharge under lower voltage amplitude with higher pulse frequency. Results reveal that NS-DBD is a very promising method for anti-icing. With the same power consumption, the LV-HF discharge shows a better anti-icing performance compared to HV-LF discharge under the same icing conditions. In view of pulse duration and duty circle, combined with heat dissipation, it is suggested that there is a threshold frequency, corresponding to the voltage amplitude of electric actuation signal and the incoming flow condition, to achieve effective anti-icing performance.  
Keywords:anti-icing; plasma actuator; power consumption; nanosecond surface dielectric barrier discharge (NS-DBD) +
Footnote †: journal: Applied Sciences
## 3 Modeling and Performance Analysis
### Registration and Paging Cost  
The following symbols are used to compute the registration and paging cost:  
\(U\): Unit cost required to process one location registration procedure  
\(C_{u}\): Registration cost incurred during unit time for each UE  
\(V\): Unit cost of handling one paging per zone  
\(C_{p}\): Paging cost incurred during unit time for each UE  
\(C_{t}\): Total cost for registration and paging per unit time for each UE (\(C_{t}=C_{u}+C_{p}\))  
First, the registration cost per UE during unit time \(C_{U}\) can be given as follows:  
\[C_{u}=U\mu\sum_{j\in A}\Bigl{[}\widetilde{\pi_{j}}\times p_{R}(j)\Bigr{]} \tag{14}\]  
where \(A\) is the set of states for boundary zones in each RA, and \(p_{R}(j)\) is the probability that the UE moves from state j to a new zone to request registration. For instance, in state \(E_{21}^{I}\), if UE moves to a new zone in a manner that requires registration, as shown in Figure 5, and its state changes to \(E_{21}^{I}\) or \(E_{21}^{II}\), it is then registered with a probability of (\(\widetilde{\pi_{E_{21}^{I}}}\times p_{R}(E_{21}^{I})\)), where \(p_{R}(E_{21}^{I})=\frac{3(1-\theta)}{5}m+\frac{2(1-\theta)}{5}m=(1-\theta)m\). It should be noted that if the UE requests registration while moving, then that situation corresponds to the case where Network_DB stores the most recently registered zone as  
new\(|\)previously registered zone\(|\)oldest registered zone\(|\)as shown in Figures 5-9.  
In the 3Z system, three selective paging procedures are assumed. The paging cost incurred for each UE during unit time is obtained as follows:  
\[C_{p}=V\lambda_{in}n\{P[1 st\text{ \emph{paging success}}]+2\times P[2 nd\text{ \emph{paging success}}]+3\times P[3 rd\text{ \emph{paging success}}]\}\] \[=V\lambda_{in}n\Sigma_{j\in B}\Bigl{[}\widetilde{\pi_{j}}\times N _{paging}(j)\Bigr{]}=V\lambda_{in}n\times\{1\times(E_{21}^{I}+I_{21}^{I}+I_{2 1}^{I}+C_{12}^{I}+C_{22}^{I}+E_{21}^{II}+ \tag{15}\] \[E_{31}^{II}+I_{21}^{II})+2\times\bigl{(}E_{12}^{I}+E_{32}^{I}+C_{ 21}^{I}+E_{12}^{II}+E_{32}^{II}\bigr{)}+3\times\bigl{(}E_{23}^{I}+C_{23}^{I}+E _{23}^{II}+E_{13}^{II}\bigr{)}\}\]  
where \(B\) is a total set of 17 states, and \(N_{paging}(j)\) is the average number of paging procedures in state \(j\) for an incoming call. That is, the average number of paging procedures until the next call occurrence for each state can be calculated by multiplying the steady-state probability and number of paging processes, as depicted in Figures 5-9.  
Finally, the total cost under the 3Z system can be obtained as follows:  
\[C_{t}=C_{u}+C_{p}. \tag{16}\]
## 1 Introduction  
Proton conductivity in solid state materials is a required property for the electrolytic components of electrochemical devices such as proton conducting fuel cells (PCFCs) and proton conducting electrolyzers (PCECs) [1; 2; 3; 4; 5; 6; 7; 8]. These devices belong to the hydrogen energy field and serve the goals of clean energy and sustainable environmental development [9; 10; 11; 12; 13; 14; 15; 16]. The efficiency of PCFCs and PCECs depends on many factors, including parameters such as the magnitude of the proton conductivity of the electrolyte material. There are several strategies to improve conductivity, one of which is doping, including cationic co-doping. The co-doping (double doping) method worked well for the most studied proton conductors such as barium create [2; 17; 18; 19; 20; 21] and lanthanum scandate [22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. However, data on the application of this method to improve the transport properties of proton conducting layered perovskites are limited.  
The layered perovskites \(\mathrm{AA^{\prime}_{n}B_{n}O_{3n+1}}\) are a structural class derived from the classical perovskite ABO\({}_{3}\), where the perovskite blocks (\(\mathrm{A^{\prime}BO_{3}}\))\({}_{n}\) are separated by the rock salt layers AO [32]. The ionic (oxygen-ionic) conductivity of this type of material was first opened up about 10 years ago by [PERSON] et al. and by [PERSON] et al. for compositions based on BaNdInO\({}_{4}\)[33; 34; 35; 36; 37] and SrLaInO\({}_{4}\)[38; 39; 40], respectively. The possibility of proton conductivity in layered perovskites was opened in 2019 for the compositions based on BaLaInO\({}_{4}\)[41]. A few years later it was demonstrated for many materials based on Ba(Sr)La(Nd)In(Sc)O\({}_{4}\)[42; 43; 44; 45; 46]. These compositions can be described by the general formula \(\mathrm{AA^{\prime}BO_{4}}\) and are called monolayed perovskites (\(n=1\)). Last year (2022), bilayered perovskites \(\mathrm{AA^{\prime}_{2}B_{2}O_{7}}\) (\(n=2\)) based on BaLa\({}_{2}\)In\({}_{2}\)O\({}_{7}\)[47; 48], BaNd\({}_{2}\)In\({}_{2}\)O\({}_{7}\)[49] and SrLa\({}_{2}\)Sc\({}_{2}\)O\({}_{7}\)[50] were investigated in terms of oxygen ionic and proton conductivity. Cationic doping was found to increase the proton conductivity values by up to 1.5 orders of magnitude [12]. However, the possibility of a co-doping strategy applied to bilayeredperovskites has not yet been investigated. In this work we have carried out double isovalent Gd\({}^{3+}\)\(\rightarrow\)La\({}^{3+}\) and acceptor Sr\({}^{2+}\)\(\rightarrow\)La\({}^{3+}\) doping in the cationic sublattice of the bilayerd perovskite BaLa\({}_{2}\)In\({}_{2}\)O\({}_{7}\). The influence of doping on the crystal lattice, the possibility of water uptake and the proton conductivity were revealed.
## 3 Results
### Sensitivity and Specificity Maps  
From these maps (Figure 3), it is possible to maximize either sensitivity or specificity by selecting specific values of both R/NR PLS score and percentage of pixels. For example, from Figure 3B, we see that specificity to identify non-responders patients can be maximized when 10% of the pixels present an R/NR score below 4 or 80% of the pixels with a score below 1 as indicated by the black crosses positioned on the map. Interestingly, Figure 3 showed that the sensitivity and specificity values were represented as sigmoidal curves. This might reflect that the most important variable in our model is the R/NR PLS scoring, the percentage of pixels having less influence.
## 3 Description of the Modified Method
### Root-Locus Method  
The block diagram of a closed-loop control system with an ideal PI regulator (18) controlling the plant \(G_{p}\)(s) is shown in Figure 1.  
A root-locus plot will be used to analyze this system. Thanks to it we can find the values of the design parameter \(K_{p}\) for specific pole locations along the root-loci. These are  
Figure 1: The block diagram of a closed-loop control system.  
used to show movement of the roots of the characteristic equation for all the values of a system parameter. We plot the roots of the closed-loop characteristic equation:  
\[1+G_{o}(s)=0 \tag{31}\]  
where \(G_{o}(s)\)--open-loop transfer function.  
The characteristic polynomial of the closed-loop system after extracting the proportional gain \(K_{p}\) from \(G_{o}(s)\) will take the following form:  
\[1+K_{p}\cdot G_{o1}(s)\text{=0} \tag{32}\]  
where \(G_{o1}(s)=\frac{G_{o}(s)}{K_{p}}\).  
This transfer function can be represented as the quotient of the polynomials of a numerator \(N\) and a denominator \(D\):  
\[G_{o1}(s)=N(s)/D(s) \tag{33}\]  
The root-locus plot depicts the trajectories of closed-loop poles when the feedback gain \(K_{p}\) varies from 0 to infinity. It can be treated as a set of a functions:  
\[\left\{[0,\infty)\
i K_{p}\to s_{j}\in\mathbb{C}\right\}_{j=1}^{n} \tag{34}\]  
where \(s_{j}(K_{p}),j=1,\ldots,n\) is the \(j\)-th root of Equation (32), and after taking into account (33), we can transform it to the following form:  
\[D(s)+K_{p}\cdot N(s)\text{=0} \tag{35}\]  
One of the rules of the root-locus method says that the common points correspond to multiple elements of the characteristic equation of a closed-loop system and belong to the set of solutions of the equation  
\[N(s)D^{\prime}(s)-N^{\prime}(s)D(s)=0 \tag{36}\]  
where \(D^{\prime}(s)\) and \(N^{\prime}(s)\) denote the derivatives of the polynomial denominator and numerator.  
This condition is a necessary condition for a given complex number \(s\in C\) to be a multiple element of the characteristic polynomial of the closed-loop system under consideration. Break-away (and break-in) points exist only on the real axis root-locus branches. If there exists a real axis root-locus branch between two open-loop poles, then there will be a break-away point in between these two open-loop poles. The branching point of the elemental lines corresponds to a critically damped system, i.e., one that meets the stated requirements.
## 6 Security Solutions
### Communication Security
#### 6.2.3 End-to-End Transport Security
For rest of the communication, the device only performs encryption or decryption with the session keys. In theory, the proposed method should reduce communication overhead for the constrained IoT devices. However, the research work does not provide any proof-of-concept implementation and performance analysis. Moreover, an adversary can learn the communications if it compromises the Gateway to get access to the session key. The research in [132] provides a solution that enables IoT devices to process certificate chains and revocation lists. In the proposed scheme, an IoT device delegates the certificate validation task to a Delegation Server (DS), a rich-resource device co-located with IoT nodes in the same IoT network. The DS checks the validity of the certificates, ensures that certificates are not listed in the revocation list, and verifies the certificate chain if necessary. The proposed solution also supports the session resumption [133] feature of the DTLS protocol. Security contexts of a connection are preserved in the DS, which allows resuming the connection after it is terminated. In the session resumption, the DS sends the stored security contexts to the constrained device after certificate validation. Although both of the schemes mentioned above increase resource efficiency of the DTLS protocol, they are vulnerable to a single point of failure because they have to trust the Gateway and DS. An adversary can perform resource exhaustion-type attacks on trusted devices to prevent them from participating in the handshake phase. As a result, IoT devices cannot establish a secure connection with their peers and provide services. **Analysis and Comparison:** We analyze and compare the outcomes of DTLS-based methods in terms of the metrics listed in Table 7 to determine the extent to which we can apply these existing security schemes on the IoT systems. The findings are briefly summarized in Table 11. Because of their low memory, computing, and communication overheads, the delegation-based DTLS methods [131; 132] are advantageous for an IoT environment. However, these schemes severely suffer from a single point of failure and DoS attacks. These solutions are also not appropriate from a scalability perspective, since the delegation server is required to manage a significant number of requests resulting from the exponential increase in the deployment of smart devices. Conversely, the certificate-based DTLS solutions [128] are beneficial in terms of interoperability, scalability, and resilience. Nevertheless, because of their resource-hungry characteristics, these methods are inefficient: they consume substantial memory, computation, and communication overheads for storing, exchanging, and validating certificates.
## 2 System Model
### Downlink Transmission and Minimum Mean Square Error (MMSE) Estimation  
For the \(n\)th subchannel that contains several subcarriers and the received signal, \(y_{k,n}\) of the \(k\)th terminal can be expressed as  
\[y_{k,n}=g_{k,n}^{T}x_{n}+w_{k,n}, \tag{9}\]  
where \(g_{k,n}\in\mathbb{C}^{M\times 1}\), and \(x_{n}\in\mathbb{C}^{M\times 1}\) is the transmitted precoded signal or pilot signal. For convenience, we only consider the channel estimation on a subchannel. For example, a block as shown in Figure 3.  
In the CSI acquisition phase, the \(i\)th AP sends pilot sequences \(\mathbf{\varphi}_{i}\in\mathbb{C}^{\tau_{p}}\), and \(\mathbf{\varphi}_{i}^{H}\mathbf{\varphi}_{j}=\delta_{ij}\). In FDD cell-free massive MIMO, downlink CSI is estimated at terminals and is fed back to the CPU. Pilots \(\mathbf{\varphi}_{1},\ldots,\mathbf{\varphi}_{M}\) are assigned for each AP, \(\tau_{p}=M\), and the pilot transmit power limit is \(\rho=\rho_{ap}\). The MMSE estimate of \(g_{mk}\) is  
\[\hat{g}_{mk}=c_{ml}\mathbf{\varphi}_{m}^{H}\Bigg{(}\sqrt{\rho^{\tau_{p}}}\sum_{j= 1}^{M}g_{jk}\mathbf{\varphi}_{j}+\mathbf{w}_{k}\Bigg{)}, \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\mathbf{M=10^{1}}\) & \(\mathbf{M=10^{2}}\) & \(\mathbf{M=10^{3}}\) & \(\mathbf{M=10^{4}}\) & \(\mathbf{M=10^{5}}\) \\ \hline Massive MIMO & 0.1005 & 0.0099 & 0.001 & \(9.9\times 10^{-5}\) & \(9.98\times 10^{-6}\) \\ Cell-Free & 0.6173 & 0.5152 & 0.3745 & 0.1839 & 0.048 \\ \hline \end{tabular}
\end{table}
Table 1: Channel Hardening.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\mathbf{M=10^{1}}\) & \(\mathbf{M=10^{2}}\) & \(\mathbf{M=10^{3}}\) & \(\mathbf{M=10^{4}}\) & \(\mathbf{M=10^{5}}\) \\ \hline Massive MIMO & 0.1001 & 0.0101 & \(9.99\times 10^{-4}\) & \(9.92\times 10^{-5}\) & \(1.009\times 10^{-5}\) \\ Cell-Free & 0.1016 & 0.0107 & 0.0012 & \(3.385\times 10^{-5}\) & \(6.371\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 2: Favorable Propagation.  
where \(c_{mk}=\frac{\sqrt{\rho\tau_{P}}\rho_{mk}}{1+\rho\tau_{P}\rho_{mk}}\). \(\mathbf{w}_{k}\sim\mathcal{CN}(0,\mathbf{I}\sigma_{w})\). \(\hat{g}_{mk}=g_{mk}-\hat{g}_{mk}\) is the channel estimation error. \(\hat{g}_{mk}\) and \(\hat{g}_{mk}\) are uncorrelated, with  
\[\hat{g}_{mk}\sim\mathcal{CN}(0,\gamma_{mk}) \tag{11}\]  
and  
\[\hat{g}_{mk}\sim\mathcal{CN}(0,\beta_{mk}-\gamma_{mk}), \tag{12}\]  
where \(\gamma_{mk}=\frac{\rho\tau_{P}\beta_{mk}^{2}}{\sigma_{w}^{2}+\rho\tau_{P}\beta _{mk}}\).  
The estimated CSI is fed back to the APs via the uplink and is used during precoding and power control. It is assumed that there is sufficient time for downlink channel estimation and CSI feedback in a slowly varying channel environment.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 3 Spectral Analysis System  
Spectral analysis refers to the method of identifying a substance and determining its chemical composition and relative content based on its spectrum. Since PSS-1.0 is a spectrometer for non-professionals, appropriate mathematical models should be built in advance for their use. The labeled spectral data can be pre-collected for building various spectral analysis models.  
Spectrometers may be affected by environmental noise and instrument errors when measuring spectral data. In order to reduce the measurement error, the spectral data should be pre-processed. Pre-processing methods include normalization, smoothing, Standard Normal Variate, Multiplicative Scatter Correction and so on [20]. The spectral analysis model can then be built using deep learning techniques such as convolutional neural network (CNN) and autoencoder [21; 22]. For example, CNN can be used to build models for the classification of spectral data [23], such as distinguishing whether a food contains a certain composition.  
Since online spectral analysis requires spectral data to be uploaded to the cloud and calculation in the cloud, when there are many requests for spectral analysis, the pressure on cloud computing can be large. Moreover, these spectral data may involve privacy and users do not necessarily want to upload them to the network. Therefore, the spectral data should be offline analyzed. Nowadays, most smart devices are sufficient for a small amount of spectral analysis. Figure 7 shows the spectral analysis system. The spectral analysis system is used to train models and provide model downloads. Spectral analysis models can be refined with the help of big data. The models should be versatile and can be applied to spectral data collected by different specifications of spectrometers. The smart device can download an appropriate spectral analysis model, according to the default or selected item type, to analyze the measured data and obtain the result.
# Fatigue Performance of Different Rib-To-Deck Connections Using Traction Structural Stress Method
###### Abstract  
The fatigue performance of an orthotropic steel bridge deck is significantly influenced by the type of the rib-to-deck connection considering the crossbeam. Fatigue fracture of the weld seam at the rib-to-deck connection has been a serious problem in such decks. In this paper, numerical models are developed for the orthotropic steel bridge decks and are analyzed for the fatigue behavior. The traction structural stress method is proven to be more accurate and effective in predicting the fatigue life. Fatigue behavior of three typical rib-to-deck connections are obtained by using traction structural stress method and by considering the effect of crossbeams. Compared to the bridge deck with weld seam of a large root, the fatigue performance of the deck with single-sided weld seam is much better with lower equivalent structural stress. The results indicate that the weld seam size should be strictly controlled for better fatigue resistance. The fatigue performance of the bridge deck with double-sided seam is significantly better than that of the bridge deck with single-sided weld seam. An increase in the thickness of the inner weld seam in the rib-to-deck connection optimizes the distribution of the equivalent structural stress and shifts the fatigue failure location from the weld root of the outer weld seam to the weld toe of the inner weld seam thus demonstrating the effect of the crossbeam. The trends of equivalent structural stress with geometric parameters of the weld seam in the rib-to-deck connection are obtained in this study. The fatigue behavior of the components and the equivalent structural stress are significantly influenced by the bridge deck thickness.  
Keywords:orthotropic steel bridge; traction structural stress; equivalent structural stress; fatigue failure mode; geometry parameters analysis +
Footnote †: journal: Applied Sciences
While the modulation of nutrients is the most acknowledged trigger for metabolic switches, light availability also plays an essential role in the shift to starch accumulation within the cell. The biomass specific photon supply rate (BSPSR) is a parameter that allows the quantifying and standardisation of the light received from cultures in different setups, based on the surface irradiance, the depth and the density of the culture. Though it is not frequently reported in the literature, BSPSR has shown a strong effect on the accumulation of storage compounds for several species [23; 24]. Thus, it can be inferred from the literature that high starch accumulation was frequently achieved in _Chloella_ species at low irradiance and low inoculum concentration [18; 25; 26], whereas the lowest reported starch contents have been reported either at a lower light irradiance or at high inoculum density or in the presence of wider light paths. Considering the variety of parameters that may influence the accumulation of starch in _Chloella vulgaris_, our investigations began with a high-throughput screening performed in a traditional shake flask system combined with a high-throughput Fourier Transform Infrared (FTIR) spectroscopy, to test different nutrient and light conditions. FTIR spectroscopy is a biophysical technique that allows the profiling of the main cellular components--lipids, proteins, polysaccharides, polyphosphates, pigments--in a rapid and high-throughput manner. The analysis of cell chemistry by FTIR is done on the intact cells and does not require tedious extraction of cell components, therefore, it has been positioned as a next-generation phenotyping technique suitable for extensive screenings in biotechnology [27; 28; 29] Recently, the technique was utilised for screening oleaginous yeast [30; 31; 32; 33; 34; 35; 36; 37; 38], filamentous fungi [39; 40; 41] and microalgae [42; 43; 44; 45]. In this study, FTIR spectroscopy was used to map the chemical changes in the biomass of _Chloella vulgaris_ under multiple nutrient and light stress conditions using a factorial design on 12 different treatment combinations. Based on this, optimal parameters for starch production were selected and scaled to 1.5 L flat panel photobioreactors, to assess the effect of light distribution and BSPSR. Finally, the best conditions were translated to a 25 L tubular reactor, to evaluate the scalability and productivity of starch.