esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Problem Solving
### Optimizing BS Transmit Beamforming for a Given IRS Phase Shift  
For a given IRS phase shift \(\mathbf{\Theta}\), problem P1 can be rewritten as:  
\[\begin{split}\text{P2}:&\max_{f}\frac{R}{P_{all}}\\ &\text{s.t.}\ \text{C1}:\lVert\mathbf{f}\rVert^{2}\leq P_{\text{max}} \end{split} \tag{13}\]  
Since the objective function of P2 is a fractional function, it is first transformed into an easily handled phase-reduction form [35].  
**Theorem 1**.: _Let \(R(\mathbf{f}^{*})>0,P_{all}(\mathbf{f}^{*})>0\) be the optimal achievable rate and the minimum total power consumption, respectively, for problem P2. Let \(\mathcal{F}\) be the feasible solution set of problem P2. Assume that the energy efficiency of the system is maximized when the BS transmit beamforming is \(\mathbf{f}^{*}\), such that:_  
\[\lambda^{*}=\frac{R(\mathbf{f}^{*})}{P_{all}(\mathbf{f}^{*})}=\max_{f\in\mathcal{F}} \left\{\frac{R(\mathbf{f})}{P_{all}(\mathbf{f})}\right\} \tag{14}\]  
_If and only if \(R(\mathbf{f}^{*}),P_{all}(\mathbf{f}^{*}),\lambda^{*}\) satisfy the following condition:_  
\[\max_{f\in\mathcal{F}}\{R(\mathbf{f})-\lambda^{*}P_{all}(\mathbf{f})\}=R(\mathbf{f}^{*})- \lambda^{*}P_{all}(\mathbf{f}^{*})=0 \tag{15}\]  
_Thus, the parametric programming with parameter \(\lambda\) is defined as [35]:_  
\[\max_{f,\lambda}\{R(\mathbf{f})-\lambda P_{all}(\mathbf{f})\} \tag{16}\]  
**Proof of Theorem 1.** See Appendix A. \(\square\)  
According to Theorem 1, by introducing the energy efficiency factor \(\lambda\), P2 is equivalently expressed as:  
\[\begin{split}\text{P2}-1:&\max_{f}\ \left[R(\mathbf{f})- \lambda\ P_{all}(\mathbf{f})\right]\\ &=\max_{f}\log_{2}\left(1+\frac{\left|\left(\mathbf{h}_{i}^{H}\mathbf{ \Theta}\mathbf{G}+\mathbf{h}_{d}^{H}\right)\right|^{2}}{\sigma^{2}}\right)\\ &-\lambda\left(\frac{1}{\varepsilon}\lVert\mathbf{f}\rVert^{2}+P_{ BS}+NP_{R}+P_{MR}\right)\\ \text{s.t.}\ \text{C1}:&\lVert\mathbf{f}\rVert^{2}\leq P _{\text{max}}\end{split} \tag{17}\]  
\(\lambda\) is updated with \(\lambda_{\text{i}}=R(\mathbf{f}_{\text{i}})/P_{all}(\mathbf{f}_{i})\) from \(\lambda_{0}\), until \(|\lambda_{i}-\lambda_{i-1}|\leq\varepsilon\) is satisfied. \(\varepsilon\) is the convergence threshold.
## 2 Methods: Experiment and Numerical Simulation
### Theory of Numerical Simulation Method  
#### 2.2.1 Description of Submarine Motion  
The earth-fixed inertial coordinate system \(\mathbf{O}_{E}\)--\(\xi\eta\zeta\) and the body-fixed coordinate system \(\mathbf{O}_{B}\)--\(xyz\) are defined respectively to describe the motion of submarine. As shown in Figure 2, each coordinate axis is determined according to the right-handed system. The state variable \(\mathbf{y}\) is used to describe the speed and position of the submarine:  
\[\mathbf{y}\ =\ \mathbf{y}(u,v,w,p,q,r,\xi_{0},\eta_{0},\zeta_{0},\phi,\theta,\psi) \tag{1}\]  
Among them, \((u,v,w)\) and \((p,q,r)\) are the translational and rotational velocity of the submarine with respect to the x, y, z body-axes direction respectively, while \((\xi_{0},\eta_{0},\zeta_{0})\) is the position of the submarine with respect to the \(\xi,\eta,\zeta\) direction of the inertial coordinate system. \((\phi,\theta,\psi)\) is the Euler angles orientating the body with respect to the inertial coordinate system, and are respectively the roll, pitch and yaw angles. In order to determine the state variable \(\mathbf{y}\), it is necessary to solve the equation of motion for both the submarine and fluid regions simultaneously, which will be discussed in detail in Sections 2.2.2 and 2.2.3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Number** & \(\mathbf{d_{0}}\) **(m)** & \(\mathbf{u_{0}}\) **(m/s)** & \(\mathbf{x_{blown}}\) **(mm)** & \(\mathbf{\delta_{s}}\) **(\({}^{\circ}\))** \\ \hline
1 & 8 & \(1.15\) & 362 & 0 \\
2 & 8 & \(1.15\) & 362 & 3 \\
3 & 8 & 0.95 & 362 & 9 \\
4 & 8 & 0.75 & 568 & 3 \\
5 & 8 & 0.75 & 568 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The working conditions list of 5 representative model tests.  
Figure 2: Definition of the coordinate system, 6 degrees of freedom (DOF) motion parameters, and hydrodynamic angle.  
Furthermore, referring to [PERSON]'s research ideas, the hydrodynamic angles used in the stability analysis is defined as the Equation (2):  
\[\left\{\begin{array}{l}\beta\ =\ \tan^{-1}(-v/u)\\ \Theta\ =\ \tan^{-1}\left(\sqrt{(-v)^{2}+(-w)^{2}}/u\right)\end{array}\right. \tag{2}\]
# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## 4 Discussion  
The bolt loosening angle detection algorithm proposed in this paper successfully achieves the correct loosening angle with high precision and stability demonstrated. However, in the process of conducting the research and analyzing the results, we discovered several issues that could be solved with attention in subsequent studies.  
1. Although the addition of synthetic datasets improves the performance of the model in all aspects of both training and detection, the actual improvement was not as significant as expected. There is still a lot of work ahead before the use of synthetic datasets can fully replace real datasets. This requires greater proficiency in modeling and game engines, as well as the development of deep learning algorithms with better generalization capabilities.
2. In order to avoid the use of manual markers, the method in this paper must be used with the neighboring bolt as a reference. However, in some engineering scenarios,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **True Angle** & **Detected Angle** & **Error** \\ \hline
1 & 0 & 0.475 & \(-\)0.475 \\
2 & 15 & 13.554 & 1.446 \\
3 & 15 & 16.204 & \(-\)1.204 \\
4 & 30 & 30.598 & \(-\)0.598 \\
5 & 30 & 31.818 & \(-\)1.818 \\
6 & 90 & 88.641 & 1.359 \\
7 & 90 & 91.208 & \(-\)1.208 \\
8 & 180 & 182.875 & \(-\)2.875 \\
9 & 300 & 299.298 & 0.702 \\
10 & 330 & 328.697 & 1.303 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Bolt loosening angle detection results.  
Figure 21: Bolt loosening angle detection visualization (**a**) Bolt in the tight state; (**b**) Bolt of the current state.  
there will be situations where only one bolt can be seen. At this time, the proposed method is no longer applicable.
3. In actual engineering conditions, when used in buildings or mechanical structures, the bolts are often in the form of a matrix of connection plates. Therefore, the obtained images often have more than two bolts in them. Although the present method is still applicable, the issue of how to efficiently identify and separate neighboring bolts still needs to be addressed in future studies.
# Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging
## 5 Conclusions  
In conclusion, this work presents a novel method of motion deblurring for single-pixel SFDI. The method adopts the FSI technique to measure the Fourier coefficients of the reflected images and uses the model-based phase-shift compensation to eliminate to effects of target motion on the measurements. Experimental results prove that the method can effectively deblur the moving target in the measured reflected image, and accordingly improves the accuracy in the subsequently extracted optical properties. The scenes imaged in the experiments are with uniform backgrounds, but the method also works with non-uniform backgrounds. Because the Fourier spectrum of the scene is the superposition between the Fourier spectrum of the target image and the background image, background subtraction can be adopted before deblurring the target image.  
Methodology, M.D.; validation, M.D. and M.L.; writing review and editing, M.D.; supervision, F.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (81871393, 62075156).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to laboratory guidelines.  
The authors declare no conflict of interest.
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 3 System Model  
In this section, we present the system model used in this article. Firstly, we assume the NDN network used in this article is a fully random network. In this network, we assume that there are \(N\) nodes, indexed by \(N=\{1,2,\ldots,n\}\). These nodes may be servers, gateways, or mobile terminals. Since the hardware configuration of each node is different, we assume that each node has different transmission capabilities. In the same network environment, we believe that there is a positive correlation between node's transmitting capacity and communication efficiency, we use \(T(i)\) to represent the transmission capacity of node \(i\).  
In a fully random network, there may be multiple paths between nodes \(i\) and \(j\), and the priority of data transmission on different paths is also different. Assuming that data packets sent from node \(i\) to node j are always transmitted via node \(k\), we think that the communication weight of the data link between node \(i\) and node \(k\) is higher, we use \(W(i,j)\) to represent the communication weight of the data link between node \(i\) and node \(j\).  
Figure 1: The packet forwarding model of the NDN.  
Secondly, we assume that the blockchain system used in this article is the Bitcoin system. Well known, there are three different nodes in the Bitcoin system, namely miner nodes, wallet nodes and forwarding nodes. Miner nodes are responsible for calculating the transaction results and broadcasting to the whole network, wallet nodes are responsible for generating transactions and sending information to the miners, forwarding nodes are responsible for the distribution of data in the network.  
In a bitcoin network, there are three steps in a transaction from start to finish:  
(1) The wallet node generates a transaction and sends the transaction information to the miner node in the network.  
(2) After receiving the information, the miner node calculates the hash value corresponding to the transaction information, and the miner node that first calculates the result broadcasts the result to the whole network.  
(3) All nodes receive information and change local records.  
Since the time difference between the information received by each miner node and the information sent by the wallet node is much smaller than the time required for the miner node to calculate the corresponding hash value, we can consider that each node receives the information of the wallet node at the same time and starts the calculation at the same time. Therefore, when comparing network delays, we can think of the delay in completing the entire transaction as the delay announced by the miner to the entire network.
# Integration of Functional Link Neural Networks into a Parameter Estimation Methodology
## 3 Proposed Dual-Response Model Based on Functional Link NNs
### Proposed Functional Link NN
The output performance of the layers in the hidden neuron can be represented in mathematical formulas as:  
\[y_{j}^{hid}(\mathbf{x})=\left(\sum_{i=1}^{k}w_{ji}x_{i}+b_{j}^{hid}\right)+ \left(\sum_{i=1}^{k}w_{ji}x_{i}+b_{j}^{hid}\right)^{2} \tag{5}\]  
The outcome of the functional-link-NN-based RD estimation model can be written as:  
\[\hat{g}_{out}(\mathbf{x})=\sum_{j=1}^{h}w_{j}\Bigg{[}\left(\sum_{i=1}^{k}w_{ji }x_{i}+b_{j}^{hid}\right)+\left(\sum_{i=1}^{k}w_{ji}x_{i}+b_{j}^{hid}\right)^{ 2}\Bigg{]}+b^{out} \tag{6}\]  
Hence, the regressed formulas for the estimated mean and standard deviation are given as:  
\[\hat{g}_{NN}(\mathbf{x})=\sum_{j=1}^{h\_mean}w_{j}\Bigg{[}\left(\sum_{i=1}^{ k}w_{ji}x_{i}+b_{j}^{hid\_mean}\right)+\left(\sum_{i=1}^{k}w_{ji}x_{i}+b_{j}^{hid\_ mean}\right)^{2}\Bigg{]}+b_{mean}^{out} \tag{7}\]  
\[\hat{g}_{NN}(\mathbf{x})=\sum_{j=1}^{h\_std}w_{j}\Bigg{[}\left(\sum_{i=1}^{k} w_{ji}x_{i}+b_{j}^{hid\_std}\right)+\left(\sum_{i=1}^{k}w_{ji}x_{i}+b_{j}^{hid\_std} \right)^{2}\Bigg{]}+b_{std}^{out} \tag{8}\]  
where \(h\_mean\) and \(h\_std\) denote the quantity of the hidden neurons of the \(h\)-hidden-node NN for the mean and standard deviation functions, respectively. Figure 3: Functional-link-NN-based RD estimation method.
## 3 Moving Virtual Sensing Algorithms
### The adaptive LMS moving virtual microphone technique  
The adaptive LMS moving virtual microphone technique [11] uses the adaptive LMS virtual microphone technique [7] to obtain estimates of the virtual error signals at the moving virtual locations. The adaptive LMS moving virtual microphone technique is used to compute estimates of the virtual error  
Figure 10: Block diagram of the remote moving microphone technique.  
signals, \(\tilde{\mathbf{e}}_{v}(n)\), at the moving virtual locations, \(\mathbf{x}_{v}(n)\). A block diagram of the adaptive LMS moving virtual microphone technique is shown in Fig. 11.  
In this moving virtual sensing algorithm, the adaptive LMS virtual microphone technique, as described in Section 2.4, is first used to obtain estimates of the virtual error signals, \(\tilde{\bar{\mathbf{e}}}_{v}(n)\) at the spatially fixed virtual locations, \(\bar{\mathbf{x}}_{v}\). As shown in Fig. 11, the primary component of the physical error signals is first calculated using the matrix of physical secondary transfer functions \(\tilde{\mathbf{G}}_{pu}\) and is given as [12]  
\[\tilde{\mathbf{d}}_{p}(n)=\mathbf{e}_{p}(n)-\tilde{\mathbf{y}}_{p}(n)=\mathbf{ e}_{p}(n)-\tilde{\mathbf{G}}_{pu}\mathbf{u}(n). \tag{67}\]  
Matrices of the primary and secondary weights, \(\bar{\mathbf{w}}_{p}\) and \(\bar{\mathbf{w}}_{u}\), of size \(M_{p}\times\bar{M}_{v}\), at the \(\bar{M}_{v}\) spatially fixed virtual locations, \(\bar{\mathbf{x}}_{v}\), are then estimated separately using Eq. (18). Estimates, \(\tilde{\bar{\mathbf{e}}}_{v}(n)\), of the total virtual error signals at the spatially fixed virtual locations, \(\bar{\mathbf{x}}_{v}\), can then be calculated as  
\[\tilde{\bar{\mathbf{e}}}_{v}(n)=\tilde{\bar{\mathbf{d}}}_{v}(n)+\tilde{\bar{ \mathbf{y}}}_{v}(n)=\bar{\mathbf{w}}_{p}^{\mathrm{T}}\tilde{\mathbf{d}}_{p}(n )+\bar{\mathbf{w}}_{u}^{\mathrm{T}}\tilde{\mathbf{y}}_{p}(n). \tag{68}\]  
As shown in Fig. 11, estimates, \(\tilde{\mathbf{e}}_{v}(n)\), of the virtual error signals at the moving virtual locations, \(\mathbf{x}_{v}(n)\), are now obtained by spatially interpolating the virtual error signals, \(\tilde{\bar{\mathbf{e}}}_{v}(n)\), at the spatially fixed virtual locations, \(\bar{\mathbf{x}}_{v}\).  
The performance of the adaptive LMS moving virtual microphone technique has also been experimentally investigated in an acoustic duct at an acoustic resonance [11; 12]. Again, the virtual microphone was moved sinusoidally between a virtual distance of \(v=0.02\) m and 0.12 m with a period of 10 s. Experimental results demonstrated that minimising the moving virtual error signal using a feedforward control approach achieves an additional 18 dB of attenuation at the moving virtual location compared to minimising the error signal at a fixed physical microphone at \(v=0\) m or a fixed virtual microphone at \(v=0.02\) m.
###### Abstract  
Microplastics (MPs), chlorinated phenols (CPs), polycyclic aromatic hydrocarbons (PAHs) and halogenated benzenes (HBs) are pollutants that are widely present in freshwater systems. As alternatives to conventional plastics, bioplastics are receiving a lot of attention, but there are limited data on their impact on pollutant behavior. This work therefore investigated the impact of pH on the sorption of CPs, PAHs and HBs, as some of the toxic and highly persistent pollutants, on seven different plastics using kinetic and isotherm studies. The pH of the water matrix impacted the adsorption behavior of CPs on all selected MPs, with the highest degree of adsorption occurring at pH 7 for the majority of the selected CPs. The highest adsorption affinity of CPs on the MPs, at pH 7, was obtained for 4-chlorophenol and 2,4-dichlorophenol on powdered polyethylene standard (q\({}_{\text{t}}\) = 221 ug/g), while the lowest was obtained for the adsorption of pentachlorophenol on polyethylene terephthalate (q\({}_{\text{t}}\) = 25 ug/g). On the other hand, the pH value of the water matrix did not affect the adsorption of halogenated benzenes and PAHs on MPs. The pseudo-second-order rate model fit the adsorption kinetics data of all experiments. The results obtained for the adsorption of CPs on MPs indicated a lower sorption affinity of CPs with MPs at pH 4 and pH 10 compared to pH 7. The Langmuir isotherm, at pH 7, implied that 4-chlorophenol's adsorption affinity was not significantly influenced by the type of MPs. On the other hand, at pH 7, the adsorption of 2,4-dichlorophenol, 2,4-dichlorophenol and pentachlorophenol varied greatly, with powdered MP types showing the highest affinity for CP adsorption. Furthermore, the obtained adsorption isotherm results imply that electrostatic attraction, hydrogen bonds, \(\pi\)-\(\pi\) interactions and van der Waals interactions, are an integral part of adsorption mechanisms of the CPs on the MPs.  
microlasticals; biodegradable plastic; chlorinated phenols; halogenated benzenes; polycyclic aromatic hydrocarbons; adsorption; pH effect Article
## 4 Results and Discussion
### Cohesion Work  
Cohesive work is an indicator to characterize the cohesive properties of asphalt. The greater the cohesion work, the better the cohesion properties of the asphalt and the stronger the resistance to cracking. The change in cohesion work for SK base asphalt, Evotherm M1 WMA, Sasobit WMA and different aging methods is shown in Figure 5.  
Figure 4: Influence of aging on SFE of aged asphalt.  
Figure 5: \(Cont.\)  
According to the description in Section 2, the cohesion work is numerically equal to two times the SFE value. Therefore, the cohesive work of asphalt is consistent with the change trend of SFE with the addition of Sasobit and Evotherm warming agent. That is, the cohesion work of the three kinds of asphalt gradually decreases with the aging time, and the cohesion work of Evotherm M1 WMA tends to be stable after 150 h of UV aging. The addition of the warming agent makes the viscosity of the asphalt change differently. Compared with the base asphalt, the cohesion work of the asphalt decreased by 22.19% after adding Sasobit. The difference is that the cohesion work of the asphalt increased by 36.04% after adding the Evotherm M1 warming agent. Compared with the cohesion work of the neat asphalt, after 200 h of UV aging, the cohesion work of Evotherm M1 WMA decreased from 216.52 to 59.05 mJ/m\({}^{2}\) with a decrease of 72.73%. The cohesion work of Sasobit WMA decreased from 123.85 to 22.87 mJ/m\({}^{2}\) with a decrease of 81.53%. After PAV aging, the cohesion work of Evotherm M1 WMA decreased to 110.79 with a decrease of 48.83%. The cohesion work of Sasobit warm asphalt decreased to 39.27 mJ/m\({}^{2}\) with a decrease of 68.29%.  
By comparing the changes in cohesion work of the three kinds of asphalt, it is found that the Evotherm M1 warm asphalt has the strongest resistance to cohesion failure, while Sasobit warm asphalt has the greatest possibility of cohesion failure. From a cohesiveness standpoint, the former exhibits relatively better resistance to water damage.
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
## 3 Results
### Compound Encapsulation by Nanoliposomes and Their Cytotoxicity  
To determine the cytotoxicity of free malB and malB-nanoliposomes on non-small cell lung cancer cell lines, the viability of different cell lines was examined by sulforhodamine B (SRB) assay with malB treatment at several concentrations. As shown in Figure 2 and Table 3, both free compound and malB-nanoliposomes inhibited cell growth in a dose-dependent manner and significantly compared with the control (\(p<0.005\)). High doses of malB and malB-nanoliposomes (7.5 \(\upmu\)M and 15 \(\upmu\)M) remarkably inhibited viability of all tested lung cancer cell lines. However, the cytotoxic effects of malB and malB-nanoliposomes on all cell lines were decreased at a dose of 3.75 \(\upmu\)M, and minimal effects were observed at a dose of 1.875 \(\upmu\)M. Based on the IC\({}_{50}\) values, malB-nanoliposomes exhibited the strongest cytotoxicity against the A549 cell line. Free malB showed stronger cytotoxic activity than that of the malB-nanoliposomal formulation on all examined cell lines. However, the difference was insignificant (\(p>0.05\)). Blank nanoliposomes showed no significant effects on cell growth (cell survival \(>\)90%). Therefore, the IC\({}_{50}\) value of blank liposomes could not be determined.  
Figure 1: TEM morphological images of malB-loaded nanoliposomes: nanoliposome C (malB entrapped in nanoliposomes at 0.25 mg, EE = 78.73% \(\pm\) 9.52%); nanoliposome D (malB entrapped in nanoliposomes at 0.50 mg, EE = 51.33% \(\pm\) 16.53%). Blank nanoliposomal particles.  
Figure 2: Cont.  
Figure 2: Cell viability after 48 h under the treatment of malB-nanoliposome C or free compound on different lung cancer cell lines. Cultured cells (\(2\times 10^{3}\) cells/well) were treated with different concentrations of either malB-nanoliposomal or free malB. Normal saline served as the negative control. Each value represents the mean \(\pm\) SEM. One-way ANOVA was used for analyzing statistical differences between groups. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) when compared with negative control. # \(p<0.01\) when compared with blank liposome.
## 5 Topology and Overload Detection
### Proposed Protocol
For example, the cyclic synchronization and distributed clock mechanisms of EtherCAT are well aligned with the parallel execution model of segmented control blocks, allowing for each slave device to assume a localized monitoring role. Similarly, the producer-consumer model inherent in PROFINET communication can be utilized to distribute and aggregate segment-level load metrics across functional nodes in real time. To ensure broader applicability, future research will investigate the implementation of the proposed architecture within time-sensitive networking (TSN)-enabled ethernet infrastructures, thereby enabling end-to-end validation of determinism, latency bounds, and real-time observability. This direction aims to extend the utility of the proposed overload detection and control optimization framework toward deployment in next-generation industrial networks characterized by strict timing constraints and decentralized process control.