esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimal Transmission Policy Based on POMDP
In order to calculate the expected reward of the optimal action when the belief is \(\lambda_{1}\) or \(\lambda_{0}\), we start by comparing these value functions established in Theorem 2 to the threshold \(\rho\) in Theorem 1. Then, all that remains is solving a system of two linear equations with two unknowns, i.e., \(V_{\beta}(\lambda_{1})\) and \(V_{\beta}(\lambda_{0})\), in three cases: \(\rho<\lambda_{0}\), \(\lambda_{0}\leq\rho\leq\lambda_{1}\) and \(\lambda_{1}<\rho\). To illustrate the procedure of determining \(V_{\beta}(\lambda_{1})\) and \(V_{\beta}(\lambda_{0})\), we consider the example where \(\rho<\lambda_{0}\), and the optimal transmission action is \(a_{i}=A\) regardless of the delayed feedback CSI in this case, as we have proved in Theorem 2; we have then  
\[V_{\beta}(\lambda_{0})=V_{\beta,A}(X_{i}=\lambda_{0})=\lambda_{0}R_{g}+\beta( \lambda_{0}V_{\beta}(\lambda_{1})+(1-\lambda_{0})V_{\beta}(\lambda_{0})), \tag{13}\]  
\[V_{\beta}(\lambda_{1})=V_{\beta,A}(X_{i}=\lambda_{1})=\lambda_{1}R_{g}+\beta( \lambda_{1}V_{\beta}(\lambda_{1})+(1-\lambda_{1})V_{\beta}(\lambda_{0})). \tag{14}\]  
Recall that we have \(\alpha=\lambda_{1}-\lambda_{0}\), and solving for \(V_{\beta}(\lambda_{1})\) and \(V_{\beta}(\lambda_{0})\) leads to  
\[V_{\beta}(\lambda_{0})=\lambda_{0}R_{g}/((1-\beta)(1-\alpha\beta)), \tag{15}\]  
\[V_{\beta}(\lambda_{1})=(\lambda_{1}-\alpha\beta)R_{g}/((1-\beta)(1-\alpha\beta )). \tag{16}\]  
All other cases can be solved similarly, and the closed form computation expressions of the one-threshold policy are given in Table 2.  
Next, assume that in the two-thresholds policy region, the optimal policy has two thresholds \(0<\rho_{1}<\rho_{2}<1\), and the transition probability matrix is \(\mathbf{G}= \begin{bmatrix}\lambda_{1}&1-\lambda_{1}\\ \lambda_{0}&1-\lambda_{0}\end{bmatrix} \). Then, the optimal transmission action \(a_{i}\) is given in the following Theorem 3. Theorem 3.: _Let \(a_{i}=\{A,C,O\}\) denote the action space in the two-thresholds policy region, and \(R_{g}\) and \(R_{b}\) denote the transmitted numbers of data bits that correspond to the actions \(A\) and \(C\), respectively. Recall that \(\tau\) is the sensing cost that is the ratio of the round trip time \(d_{\mathrm{RTT}}\) to the time slot duration \(D\) and satisfies \(\tau<(1-R_{b}/R_{g})/(2-R_{b}/R_{g})\) as in Theorem 1, and \(X_{i}\) is the channel belief, then \(a_{i}\) is determined as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Delayed Feedback CSI \(s_{i-1}\)** & **Optimal Transmission Action \(a_{i}\)** \\ \hline \(R_{b}/R_{g}<\lambda_{0}\) & \(s_{i-1}=1/0\) & \(a_{i}=A\) \\ \hline \(\lambda_{0}\leq R_{b}/R_{g}\leq\lambda_{1}\) & \(s_{i-1}=1\) & \(a_{i}=A\) \\ \hline \(R_{b}/R_{g}>\lambda_{1}\) & \(s_{i-1}=1/0\) & \(a_{i}=C\) \\ \hline \end{tabular}
\end{table} 
Table 1: Optimal transmission action in the one-threshold policy region.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 1 Conv Block
#### 2.2.3 Comparison Methods  
Nine representative deep learning models were selected for comparison. Among them, four models are the most popular solutions for semantic segmentation tasks in the computer vision domain, including UNet [45], Deeplab v3+ [52], SegFormer [53], and Swin-UNet [54]. The remaining five are the most advanced models in the current HSI classification tasks, including UNet-m-se(prelu)-gan [44] (hereinafter referred to as UNet-m), SS3 FCN [42], ENLFCN [26], SSDGL [55], and FSSANet [31]. Among them, UNet and SS3 FCN are methods based on two-dimensional and three-dimensional convolution respectively. Deeplab v3+ belongs to the method of directly expanding the receptive field of the convolution kernel, UNet-m, ENLFCN, and SSDGL belong to the methods that introduce the attention mechanism, and SegFormer, Swin-UNet, and FSSANet are methods based on Transformer. Please refer to the corresponding paper for details and parameter settings of the model.
## 1 Introduction
While the U.S. Environmental Protection Agency (EPA) has not established NH\({}_{4}\)\({}^{+}\) concentration limits for drinking water or freshwater aquifers, their recommendations for aquaticlife state that NH4+ concentrations should not exceed \(\approx\)1 mM and \(\approx\)0.1 mM for acute (1 h) and chronic (30 day) exposure, respectively [21]. Other recommended limits for total NH4+ in surface water vary throughout the U.S. depending on location and governance; however, these limits generally range between \(\approx\)0.01 mM and \(\approx\)1.8 mM [22]. The European Union has also set a maximum total NH4+ concentration for drinking water at \(\approx\)0.03 mM [23]. While acceptable NH4+ concentrations following MICP will likely be site-specific, concentrations produced during bioementation will likely exceed such recommendations by several orders of magnitude, therefore necessitating removal and/or in situ remediation in order to comply with water quality requirements [24]. In past laboratory experiments and field studies, rinse solutions have been applied to treated soils to remove NH4+ byproducts following MICP [19; 24; 25; 26]. Although rinsing may require large quantities of materials and energy to remove NH4+ byproducts at a field scale, it can be effective for NH4+ byproduct removal in the absence of other proven remediation technologies. With the appropriate management of NH4+ byproducts, the sustainability benefits afforded by MICP may be maximized and the process may become increasingly competitive relative to conventional geotechnical ground improvement technologies, which primarily use high mechanical energy and energy- and emissions-intensive materials, like Portland cement. To date, the impacts associated with stimulated MICP treatment techniques and post-treatment rinsing, including environmental, social, and economic impacts, have not been quantified, although recent LCSAs have been performed for other biomedical soil improvement technologies such as enzymatically induced carbonate precipitation [27; 28; 29]. In an effort to bridge this gap in knowledge, this study conducts a life cycle sustainability assessment (LCSA) that compares the environmental (i.e., resource depletion, acidification, eutrophication, ecotoxicity, human health impacts, ozone depletion, and smog formation), social, and economic impacts of different ureolytic MICP treatment approaches applied at a meter-scale and evaluates the potential benefits of post-treatment NH4+ byproduct removal using rinsing. The employed MICP treatment techniques included the augmentation of soils with _Sporosarcina pasteurii_ (_S. pasteurii_), a well-studied ureolytic bacterium, and the stimulation of indigenous ureolytic microorganisms. The methods, models, and data used in the study were informed by the results of meter-scale bioementation experiments on poorly graded natural sands [19; 24]. Using the obtained LCSA results, the objective of this paper is to discuss the implications for field-scale applications of MICP and present sustainability-oriented guidance for future research and development.
## 2 Existing IFTAs  
GS algorithm [16] is the most widely used algorithm for the computer generation of phase-only holograms. GS algorithm involves iterative calculation of the light field distributions on the hologram plane and the image plane via a known propagating function of the optical system. In most cases, the propagating function is the Fourier transform. One loop of GS can be mathematically described as:  
\[h_{n}^{\prime}=\ FT^{-1}(g_{n-1}), \tag{1}\]  
\[h_{n}=\ \frac{h_{n}^{\prime}}{\left|h_{n}^{\prime}\right|}, \tag{2}\]  
\[g_{n}^{\prime}=\ FT(h_{n}), \tag{3}\]  
\[g_{n}=\ G_{0}\frac{g_{n}^{\prime}}{\left|g_{n}^{\prime}\right|}, \tag{4}\]  
where \(h_{n}^{\prime}\) and \(g_{n}^{\prime}\) are the light field distributions at the hologram plane and image plane, respectively; \(FT\) and \(FT^{-1}\) represent the Fourier transform and the inverse Fourier transform, respectively; and \(G_{0}\) is the intensity of the target image. The initial field distribution at the image plane, \(g_{0}\), is assigned \(G_{0}\) multiplied by a random phase. Then the loop will be repeated until the reconstructed image quality meets the requirement.  
The GS algorithm is very stable and always leads to convergence. However, it usually needs a large number of iterations to achieve a reasonably good image quality. To improve the calculation speed, various derivatives of the GS algorithm have been developed, most notably among them Fienup [17] and FiDOC [18; 19]. In the Fienup algorithm, Equation (5) is used to replace the standard Equation (4) in the standard GS algorithm. This introduces the feedback mechanism into the iterations.  
\[g_{n}=\ G_{n}\frac{g_{n}^{\prime}}{\left|g_{n}^{\prime}\right|} \tag{5}\]  
The \(G_{n}\) in this equation is expressed as:  
\[G_{n}=\ G_{0}+\ \Big{(}G_{0}-\left|g_{n}^{\prime}\right|\Big{)}k, \tag{6}\]where \(k\) is the feedback coefficient.  
FiDOC algorithm further improved Fienup algorithm by assigned areas around the target images as the don't-care area. The don't-care area on the image plane allows noise on the hologram plane in the form of spatial frequency components. As a result, the reconstruction quality in the don't-care area is sacrificed while the image quality in the target area is enhanced. Correspondingly, the feedback function for \(G_{n}\) is changed into [18; 19]:  
\[G_{n}=\ M\Big{[}G_{0}+\ \Big{(}G_{0}-\big{|}g_{n}^{\prime}\big{|}k\Big{]}+ \gamma(1-M)\big{|}g_{n}^{\prime}\Big{|}, \tag{7}\]  
where \(\gamma\) is the noise suppression parameter for the image plane; \(M\) equals \(1\) within and the target area and \(0\) in the don't-care area.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)
### Signal Decomposition Methods
#### 3.4.3 Wavelet Packet Decomposition (WPD)  
WPD corresponds to a generalization of the DWT technique that offers a more efficient breakdown of the high-frequency constituents of the input signal [50]. It contrasts with the DWT technique in that both the approximation and the detail constants participate in disintegration at each WPD decomposition level. This is in contrast to DWT decomposition, where just the approximation coefficient takes part in decomposition. WPD causes the segregation of the time-frequency space into boxes with a fixed characteristic proportion as the decomposition process continues [51]. To put it another way, as the level of disintegration increases, the side of the box that represents the time axis expands, while the side that represents the frequency axis contracts along with it. The WPD method has been gaining more popularity for HRV analyses in recent years than other decomposition methods mentioned above [52]. Notable applications of a WPD-based HRV analysis include automatic sleep apnea detection [53], depression [54], etc.
## 2 Methodology
### Poisson-Inverse-Gaussian Regression Model (PIGM)
The MMSE and SMSE are defined as follows:  
\[MMSE\Big{(}\widetilde{\tau}\Big{)}=\text{cov}\Big{(}\widetilde {\tau}\Big{)}+\text{bias}\Big{(}\widetilde{\tau}\Big{)}^{\prime}\text{bias} \Big{(}\widetilde{\tau}\Big{)}\] \[SMSE\Big{(}\widetilde{\tau}\Big{)}=\text{tr}\Big{[}\text{cov} \Big{(}\widetilde{\tau}\Big{)}\Big{]}+\text{tr}\Big{[}\text{bias}\Big{(} \widetilde{\tau}\Big{)}^{\prime}\text{bias}\Big{(}\widetilde{\tau}\Big{)} \Big{]},\]  
where \(\text{tr}(\cdot)\) is the trace operator of a matrix. The MMSE and the SMSE for the estimators are obtained using spectral decomposition of the estimated weighted information matrix \((X^{\prime}\hat{W}X)\). Assuming the existence of an orthogonal matrix \(\theta\) such that \(\theta(X^{\prime}\hat{W}X)\theta^{\prime}=\Lambda\), so \(X^{\prime}\hat{W}X=\theta^{\prime}\Lambda\theta\). Here, \(\Lambda=\text{diag}\big{(}\lambda_{j}\big{)}\) contains the ordered eigenvalues \(\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{q}\), we define \(Z=X^{\prime}\theta\) and \(\alpha=\theta^{\prime}\beta\), then \(\theta\big{(}X^{\prime}\hat{W}X)\theta^{\prime}=Z^{\prime}Z=\Lambda\). The MMSE and the SMSE for \(\hat{\beta}^{PIG-MLE}\) are:  
\[MMSE\Big{(}\hat{\alpha}^{PIG-MLE}\Big{)}=\hat{\omega}\Lambda^{-1} \tag{7}\]  
\[\mu_{i}=\exp\Big{(}x_{i}^{\prime}\beta\Big{)}\hskip 14.226378 pti=1,2,\ldots,n, \tag{2}\]0  
PIG-MLE exhibits high variance and instability in multicollinearity, highlighting the need for alternative estimators. We discuss the alternative methods in the next Section.
###### Abstract  
The corrosion of metals and alloys presents a significant challenge in many industries, demanding constant maintenance, and thereby increasing costs. In response to this problem, the smart corrosion protection coating has emerged as a promising solution. By enabling the immediate detection of, and response to, environmental changes, such as in the temperature and pH, these smart coatings contribute significantly to extending a material's lifespan, and reducing maintenance expenses. In this study, nanomagnetic [poly(aniline-co-o-toluidine)/Fe3O4] systems were prepared and used as a self-healing corrosion inhibitor, mixed with alkyd paint at different weight percentages (5-25%). The composites were used as a coating on carbon steel (C1010), and their corrosion protection performance was tested in 0.1 mol/L HCI, using electrochemical impedance spectroscopy (EIS), scanning electron microscope (SEM), and FTIR analyses. The results showed an adequate corrosion inhibition performance for the developed composites, compared to the alkyd paint alone, reaching an inhibition efficiency of 80% at 20 wt.% of composite. Adding increasing weight percentages of the developed composites to the paints led to a significant increase in the corrosion resistance, accompanied by a remarkable decrease in the double-layer capacitance. Thus, these developed composites show excellent potential as a corrosion protection formulation in paints.  
corrosion protection; smart coating; alkyd; paint; carbon steel; polyaniline; nanoparticle Article
## 3 Results and Discussions  
### Characterization of SSO-MIPs and NIPs  
The polymers were characterized by field emission gun scanning electron microscopy. As depicted in Figure 3, spherical polymers can be synthesized by the emulsion polymer method. Moreover, MIPs surface are rougher than NIPs, which is due to the template molecules in the MIPs being removed by elution to leave stereo holes. The particle size of the SSO-MIPs was analyzed by a laser particle size analyzer, SSO-MIPs were synthesized under the optimal synthesis conditions, and the results are shown in Figure 4. The figure shows that SSO-MIPs are distributed in 50-500 \(\upmu\)m, and SSO-MIPs in 125-300 \(\upmu\)m accounts for nearly 76%.  
Figure 3: SEM images of MIPs (**a**) and non-molecularly imprinted polymers (NIPs) (**b**).  
Figure 5 reveals the Fourier transform infrared (FT-IR ) of MIPs and NIPs, the absorption around 1728 cm\({}^{-1}\) and 1639 cm\({}^{-1}\) corresponded to the stretch vibration of C=O and C=C double bond, respectively [26; 27], which indicates the presence of MMA in the MIPs and NIPs. In the NIPs, there was a -CH3 stretch vibration at 2958 cm\({}^{-1}\), while in the MIPs, the position of this stretch vibration was shifted to 2924 cm\({}^{-1}\). In addition, by comparing the stretch vibration of the MIPs and NIPs, it can be determined that the stretch vibration at 1255 cm\({}^{-1}\), 1161 cm\({}^{-1}\), and 1053 cm\({}^{-1}\) in the MIPs respectively moved to 1265 cm\({}^{-1}\), 1153 cm\({}^{-1}\), and 1060 cm\({}^{-1}\), which indicates that the structures of the MIPs and NIPs were different; this was also reported by [PERSON] [21].
# Broadband Passively Mode-Locked Fiber Laser with DNA Aqueous Solution as Saturable Absorber
###### Abstract  
We demonstrate a passively mode-locked fiber laser using aqueous DNA solution as a saturable absorber (SA), with broadband pulse laser emission from 1 to 1.5 \(\upmu\)m. The mode-locked laser with erbium-doped fiber as the gain material has a center wavelength of 1563 nm, a 3 dB bandwidth of 3.9 nm, and a pulse width of 822 fs, whereas the laser with ytterbium-doped fiber as the gain material and an identical DNA aqueous SA has a center wavelength of 1037 nm, a 3 dB bandwidth of 5.04 nm, and a pulse width of 250 ps. The proposed laser, which is simple and cost effective to fabricate, exhibits excellent long-term stability as well as thermal stability during high-power operation. This mode-locked laser scheme with a liquid-phase DNA component has the potential to provide in-depth understanding of the optical nonlinearity and usefulness of DNA.  
DNA; ultrafast laser; mode-locking; saturable absorber; liquid phase +
Footnote †: journal: Applied Sciences  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Broadband Passively Mode-Locked Fiber Laser with DNA Aqueous Solution as Saturable Absorber. _Appl. Sci._**2021**, _11_, 9871. [[https://doi.org/10.3390/app1219871](https://doi.org/10.3390/app1219871)]([https://doi.org/10.3390/app1219871](https://doi.org/10.3390/app1219871))
## 4 Simulation and Result Analysis  
### Simulation Parameter Setting  
In this section, we make use of a MATLAB simulation to test the performance of the convergence speed, ICR, tracking speed, and so on. The conditions of the computer simulation parameters are:  
1. Simulating modulation using quadrature phase shift keying (QPSK) with a transmission rate of 10 Mbps and a carrier frequency of 2.1 GHz, regardless of the nonlinear and ADC quantization noise effects;
2. Setting the noise limit of the received channel to \(-95\) dBm by referring to reference [25]; the power of desired signal is \(-85\) dBm, the SI signal is \(-10\) dBm (i.e., the modulated channel ISR = 75 dB), and the SNR = 10 dB;3. The step interval is \(\Delta\mathrm{t}=\Delta 0.03\)ms. Let \(\mathrm{t}=\mathrm{nt}\), and change the phase of the SI signal at the 500 th (i.e., \(\mathrm{t}=15\) ms) iteration;
4. Extract 1000 sampling points and perform 200 statistically independent simulation experiments.  
The improved LMS algorithm proposed in this paper is compared with the existing RF cancellation algorithms of SVSLMS [20], VSSLMS [19], and MVSSLMS [21]. Refer to the parameter setting principle of reference in [19; 20; 21; 29], as the parameters of these algorithms are selected to produce a comparable level of mis-adjustments, assuming that the delay estimation error is 0, and the power value of the reference signal is 0 dBm [30], and give the specific parameters of the four algorithms as shown in Table 1.
## 2 Basic Equations and Parameter Settings
### Control Equations and Turbulence Models
\begin{array}{llll}u_{t}\ =\ C_{u}\rho\frac{k^{2}}{\varepsilon}&G_{k}\ =\ (-\frac{2}{3}\rho k \delta_{ij}-\frac{2}{3}u_{t}\overline{S}_{kk}+2u_{t}\overline{S}_{ij})\frac{ \partial\overline{u}_{ij}}{\partial x_{j}}\\ \overline{S}_{ij}\ =\ \frac{1}{2}(\frac{\partial\overline{u}}{\partial x_{j}}+\frac{ \partial\overline{u}_{ij}}{\partial x_{i}})&G_{b}\ =\ \beta\tau\rho\delta_{ii}\frac{ \partial\overline{u}}{\partial x_{i}}\frac{\partial\overline{u}}{\partial x_ {i}}\\ Y_{\rm M}\ =\ 2\rho\varepsilon Ma_{t}^{2}&\text{Ma}_{t}\ =\ \sqrt{\frac{2\kappa}{k}}\\ c\ =\ \sqrt{\gamma k\overline{T}}&\beta_{T}\ =\ -\frac{1}{\rho}\frac{\partial \rho}{\partial\overline{t}}\end{array} \]  
\(G_{k}\) represents the turbulent energy caused by the average velocity gradient, \(G_{b}\) represents the turbulent energy generated by the buoyancy effect, \(Y_{M}\) represents the influence of the compressible turbulent pulsation expansion on the total dissipation, \(\text{Ma}_{t}\) is Mach number, \(c\) is the speed of sound, and \(\beta_{T}\) is the coefficient of thermal expansion.
## 5 Online Task Offloading Decision Algorithm
### Mobility-Aware BS Learning Algorithm  
In this algorithm, the UEs only have the UE-side state information. UEs are required to learn the BS state information to make offloading decisions. Unlike the UE-BS algorithm where all UEs stick to a single and optimum BS for all sub-tasks of \(m\), the learning process causes sub-optimal BS selection. Choosing sub-optimal BS will cause additional cost as well as HOs during the learning process.  
One solution for learning optimal BS is to offload all sub-tasks of \(m\) to every candidate BS \(\eta_{m}\in\mathcal{A}(m)\) and to observe the total energy and time costs. Let \(\tilde{t}_{m,n}\) and \(\tilde{e}_{m,n}\) represent the observed time cost and energy cost, respectively. Based on Equation (18), the observed optimisation function is  
\[\tilde{z}_{m,n}=\alpha\tilde{t}_{m,n}+\beta_{m}\tilde{e}_{m,n} \tag{20}\]  
where \(\tilde{z}_{m,n}\) is a noisy version of \(z_{m,n}\) with a specified variance. The main challenge in the BS learning scenario is that UEs may offload their sub-tasks into sub-optimal BSs due to the variance of \(\tilde{z}\). The UE will try to offload their tasks into all BSs to learn the best optimum BS. However, offloading many tasks is not practical and may cause frequent HOs. A possible solution is to assign a stop parameter for the learning process. Therefore, the algorithm will only apply to the first \(k_{s}\) sub-task, and the remaining sub-tasks will be offloaded to pre-determined optimal BS. The second challenge is choosing the stop learning parameter. The large \(k_{s}\) may cause frequent HOs and may increase costs, whereas the small \(k_{s}\) will lead to the selection of sub-optimal BS. The proposed Algorithm 2 presents the online UE-BS algorithm for minimising the cost \(\tilde{z}_{m,n}\) in Equation (20) for each task \(m\).  
In Algorithm 2, the task is divided into \(k_{m}\) number of sub-tasks. Therefore Algorithm 2 has service continuity despite handover because of dividing the task into small-sized sub-tasks. However, these division introduces additional complexity, as seen in Algorithm 2. As noted above, the algorithm only applies into \(k_{s}\) number of tasks, which results in computation complexity of \(\mathcal{O}(k_{s}|\mathcal{A}(m)|)\). This means the small learning step \(k_{s}\) leads to lower complexity. However, as noted above, the lower the \(k_{s}\) means sub-optimal BSs may be selected. Therefore, there is a trade-off between having optimum BS and lower computation complexity in the algorithm.
## 2 Principle of the Conventional and Improved FOC Strategy
### Principle of the Improved FOC Strategy
#### 2.2.1 Principle of 3L_NPC Inverter  
As presented in Figure 5, six clamped diodes and four vertical IGBTs form each leg of the 3L_NPC inverter. As given in Table 1, switching states gives switches that generate five voltage levels. Figure 6 presents the SPWM control strategy using a NPC inverter [21; 22].  
The following Figure 7 shows an experimental result of the output voltages of a 3L_NPC inverter implemented using dSPACE 1104 where three voltage levels are observed.