esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations and Boundary Conditions
### Nonlinear Least Squares Levenberg-Marquardt Algorithm (Nlls)  
Due to its high convergence efficiency to obtain the global optimal solution, the \(Nlls\) algorithm has been widely used [30; 31; 32; 33]. By considering the equation \(C_{R}=f\bigg{(}\frac{L}{D_{s}},log(R_{e}),\ p\bigg{)}\), the problem-solving nonlinear equations are expressed as:  
\[F(x,p)=C_{R}-f\bigg{(}\frac{L}{D_{s}},log(R_{e}),\ p\bigg{)} \tag{9}\]  
The nonlinear equation \(f(x,p)\) needs to be solved, where \(x\) is the time series, \(y\) is the observations, and \(p\) is the nonlinear equation parameters. A nonlinear model is fitted by minimizing the sum of the square of errors, which can be expressed as:  
\[min\sum_{i=k}^{n}R-f\bigg{(}\frac{L}{D_{s}},log(R_{e}),\ p_{k}\bigg{)}=min\sum _{i=k}^{n}\epsilon_{k} \tag{10}\]  
where \(p_{k}\) is the parameter of the \(k\)th iteration, and \(\epsilon_{k}\) is the residual of the \(k\)th iteration. Taylor expansion was used to approximate the nonlinear function of \(f(x,p_{k+1})\) for solving Equation (10) as follows:  
\[f\bigg{(}\frac{L}{D_{s}},log(R_{e}),\ p_{k+1}\bigg{)}=f\bigg{(}\frac{L}{D_{s} },log(R_{e}),\ p_{k}+\Delta\bigg{)}=f(x,p_{k})+J(p_{k})\Delta \tag{11}\]  
where \(J\) is the Jacobian matrix and \(\Delta\) are the steps of the \((k+1)\)th iteration. By solving the following equation, we can get the residual of the \((k+1)\)th iteration as follows:  
\[C_{R}-f\bigg{(}\frac{L}{D_{s}},log(R_{e}),\ p_{k+1}\bigg{)}=C_{R}-f\bigg{(} \frac{L}{D_{s}},log(R_{e}),\ p_{k}\bigg{)}-J(p_{k})\Delta=\epsilon_{k}-J(p_{k })\Delta=0 \tag{12}\]  
Figure 4: \(ANN\) structures with two neurons in the hidden layer.  
It is known as [PERSON]'s method. This method, however, cannot be used to solve equations with overdetermined matrices. This problem was solved by multiplying a transposed matrix to reduce the overdetermined matrix's dimension by Gauss-Newton (Equation (13)).  
\[J(p_{k})^{T}J(p_{k})\Delta=J(p_{k})^{T}J(p_{k})\epsilon_{k}=g \tag{13}\]  
By developing the Levenberg-Marquardt (_LM_) algorithm, the Gauss-Newton method was improved, since it does not work when the Hessian matrix is singular. To use the _LM_ method, a constant, the trust-region radius, must be included in the equation as follows.  
\[\Delta_{k}^{LM}=-\Big{(}J(p_{k})^{T}J(p_{k})+\mu_{k}I\Big{)}^{-1}g \tag{14}\]  
The parameter \(\mu_{k}\) is an iteration parameter introduced to overcome constraints caused by singularities or near singularities of \(J(p_{k})\). This parameter is also used to preventing \(\Delta_{k}^{LM}\) from being too large when the Hessian matrix (\(J(p_{k})^{T}J(p_{k})\)) is nearly singular. \(\Delta_{k}^{LM}\) is well defined in this case if \(\mu_{k}\) is positive. In comparison to the steepest gradient method, [PERSON]'s method, and the Gauss-Newton method, the _LM_ method is the most widely used nonlinear fitting method. Near to the solution, \(\mu_{k}\) may be very small. In contrast, when the solution is far away, the value of \(\mu_{k}\) may be very large; therefore, controlling \(\mu_{k}\) can lead to an optimal solution.
# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 5 Discussion  
Through the experimental results, we can find that the best combination of prediction features is the prediction method based on the height and time characteristics of basketball movement. In real prediction modeling, the optimal prediction data length is about one-third of the overall trajectory motion process. This time node can basically ensure that the overall prediction accuracy is stable and has a faster prediction time. Our proposed method can not only capture the free-throw trajectory of the basketball accurately, but can also predict the whole shooting trajectory based on partly trajectory. The acceleration of the basketball during the shooting process was collected by an accelerometer. The experimental results showed a shooting accuracy of 86.3%. However, the method extracts insufficient feature information, which requires a lot of data to improve the accuracy. The use of wearable sensors for data collection and analysis helps improve shooting accuracy by providing feedback on parameters such as bounce speed and release angle. However, the shooting accuracy can only reach up to 71%. In addition, the basketball shooting gesture recognition method based on image processing and machine learning collects basketball players' action pose data and extracts multidimensional motion pose features from the time and frequency domains. The average shot hit rate of this method can reach 91.2%.  
The research in this paper also has certain limitations. The object of our study is the free-throw shooting, which limits the player standing at the free-throw line. In free-throw shooting, we specify the basketball player to shoot directly towards the basket at the free-throw line to avoid the ball's displacement in the third dimension. When the player changes position to shoot again, our method no longer applies. At the same time, since the image-based trajectory extraction method is easily affected by environmental factors, its accuracy has further potential for improvement. Therefore, our next step is to improve our algorithm to reduce the influence of environmental factors on our results.  
Figure 8: Prediction results under different interpolation points.
# TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
## 2 Time Series Vision Transformer
### Embedding Layer
#### 2.1.1 Time Series Patch Embedding  
Vibration signals typically exist in the form of one-dimensional or three-dimensional time series data. In contrast, Vision Transformers usually process three-channel RGB images. The utilization of time series patch embedding empowers the TSViT model to directly handle time series data in both one-dimensional and multi-dimensional formats, capturing local features effectively. The basic principle of time series patch embedding is explained as follows.  
A time series vibration signal sample can be denoted as \(X\in R^{L\times C}\), where \(L\) is the length of samples and \(C\) is the dimension of the vibration signal. First, we split the input time series data into fixed-sized patches, similar to image segmentation in ViT [19]. Each patch can be denoted as \(p\in R^{L_{p}\times C}\), where \(L_{p}\) is the length of each patch. We can obtain \(L=L_{p}\times n\), where \(n\) is the number of patches. Afterward, convolution is applied to each patch, as shown in Equation (1) [6]. The size of the convolution kernel is the same as that of \(p\), and the stride is \(L_{p}\).  
\[e_{p}^{k}=\sum_{i=0}^{L_{p}-1}\sum_{j=0}^{C-1}p(i,j)\times weight^{k}(i,j)+b^{k },\ 1\leq k\leq m \tag{1}\]  
where \(weight^{k}\) indicates the weight of the \(k\)-th convolution kernel, \(b^{k}\) indicates the bias of the \(k\)-th convolution kernel, \(m\) is the number of convolution kernels and the dimension of the time series patch embedding, and \(e_{p}\) represents the embedding result of patch \(p\), as depicted in Equation (2). The result of patch embedding on \(X\) is as shown in Equation (3). Here, the utilized convolution is one-dimensional, implying that the convolution operates in a single direction. It is important to note that one-dimensional convolution refers to the dimensionality of the convolution's direction, not the dimensionality of the input data.  
\[e_{p}=\left[e_{p,1},e_{p,2},\cdots,e_{p,m}\right] \tag{2}\]  
\[PE(X)=\left\{e_{1},e_{2},\ldots,e_{n}\right\} \tag{3}\]
## 6 Liposome Applications
### Pharmaceutical Applications
#### 6.1.1 Systemic Liposomal Drugs  
The encapsulation of drugs within liposomes affords protection from enzymatic degradation while the drugs are in circulation. To illustrate, penicillin and cephalospor that have been encapsulated in liposomes are protected from \(\beta\)-lactamase enzymes, thereby extending their therapeutic effectiveness [85].  
The use of liposomes facilitates targeted drug delivery, which is particularly useful in the context of cancer treatment. By modulating the distribution of the drug within the body, liposomes facilitate the enhancement of drug efficacy at reduced doses while simultaneously reducing the incidence of adverse effects [86]. This targeted delivery is particularly important in the context of chemotherapy, where the reduction of toxicity represents a fundamental objective.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 5 Simulations and Numerical Results  
We performed simulations of the proposed scheme in MATLAB. We considered hexagonal small-cell scenarios with uniform UEs randomly distributed in the coverage areas of SCBS as shown in Figure 4. Moreover, we changed the coverage area and transmission power to simulate the MCBS scenario. The physical channel between \(Tx\) and \(Rx\) was modeled as \(H_{(Tx,Rx)}=D_{(Tx,Rx)}^{\beta}\big{|}h_{Tx,Rx}\big{|}\), where \(D_{(Tx,RX)}\) is defined in (4), \(h_{(Tx,RX)}\) is the Rayleigh coefficient, and \(\beta\) is the path-loss exponent. We assumed each UE has \(Q\) number of social attributes such as \(\alpha_{1}\)s, \(\alpha_{2}\)s,..., \(\alpha_{Q}\)s which include _SFI_, _SCI_, and _ISI_ and physical attributes such as \(\alpha_{1}\)P, \(\alpha_{2}\)P,..., \(\alpha_{Q}\)p which include \(\gamma\), \(D\), and \(m\) as defined in Sections 3.2.1 and 3.2.2. We classified _SFI_, _SCI_, _ISI_, \(\gamma\), and \(m\) as desired attributes and normalized them using (6). Similarly, \(D\) was classified as an undesired attribute, and we normalized it using (7). Among the social attributes, the most important is _SFI_. Hence, we assigned it 0.5 weight, and 0.25 (i.e., 0.5/2) to each of the remaining two social attributes. Our proposed algorithm worked even if there is no friend UE in the proximity by selecting a D2D peer based on common interests. Hence, it selected the UE with maximum _ISI_ value. Similarly, for physical attributes, we assign 0.5 to \(D\) as it is the most important physical attribute and 0.25 to each of the remaining two physical attributes.  
For social attributes, we assigned _SFI_ value as a random number between 0 and 1 [18]. To calculate _SCI_, we assumed a friends' list (for \(Tx\) and \(Rx\), i.e., \(\Phi_{Tx}\), \(\Phi_{Rx}\)) as \(\Phi=\{F_{1},F_{2},\ldots,F_{N}\}\), where \(F_{i}\in[1,\ldots,N]\) and \(N\in\mathbb{N}\)[22]. This meant that \(Tx\) and \(Rx\) can have any number of friends in the range of 1 and \(N\). For any value of \(N\), the value of _SCI_ ranges between 0 and 0.5. However, for simplicity, we assume \(N\) in the range of 1 to 20. Similarly, to compute _ISI_, we assumed \(\Psi\) (individual interests list for \(Tx\) and \(Rx\)--i.e., \(\Psi_{Tx}\), \(\Psi_{Rx}\)--or mutual interests' list \(\Psi_{TxRx}\)) as \(\Psi=\{I_{1},I_{2},\ldots,I_{N}\}\), where \(I_{i}\in[0,1]\) and \(N\in\mathbb{N}\)[22; 23]. For physical attributes, we calculated \(\gamma\) by assuming \(\Gamma=20\) s, and \(\sum_{i=1}^{c}t_{i(Tx,Rx)}\in[0,N]\) and \(N\in\mathbb{N}\)[24]. Similarly, \(D\) was calculated based on the polar coordinates of uniformly distributed UEs in the coverage area using (5) [17].  
Figure 4: Proposed model with BS, DUEs, and CUEs.  
We evaluated the performance of the proposed scheme by showing the numerical results in terms of the algorithm computation time, selection of best peer, \(\mathfrak{I}\), and \(\varepsilon\varepsilon\) of D2D communications as given in the subsequent subsections. The parameters used for simulation are given in Table 3.
## 1 Introduction
[PERSON] [13] analyzed the bonding property between the steel bar and the CBGM affected by CBGM strength, surface shape, diameter, anchorage lengths of steel bars and stirrup length and spacing through central pull-out testing. Moreover, it was found that the bonding property between the steel bar and CBGM was significantly better than that of the steel bar and concrete by comparing the result to the archetypal bond strength formulas. [PERSON] et al. [14] investigated the effect of bond lengths on the bond behavior between basalt fiber-reinforced polymer (BFRP) sheets and concrete, and the results showed that the failure load between the BFRP sheets and concrete increased in correlation with the increase in the bond length when the bond length was not increased beyond the effective bond length. [PERSON] et al. [15] analyzed the bond properties of the specimens with different concrete ages and strength grades, and empirical bond stress-slip models of deformed steel bars in concrete during construction under reversed cyclic loading were proposed. Based on the above-mentioned existing achievements, we can conclude that most of the existing studies focused on the influence of concrete, CBGM, steel tube restraint and steel bars on the bonding property. High-strength bolts have the advantage of simple construction, good mechanical performance, removable, fatigue resistance, and not being loose under dynamic load; thus, the application of high-strength bolts in engineering is becoming increasingly common. With the increasing application of CBGM and high-strength bolts in engineering structures, it is important to investigate the bonding property between high-strength bolts and CBGM [16]. At present, the common experimental methods regarding the bonding property include the central pulling test, central compression test, beam test and beam end test, etc. At the same time, the plate and column tests are occasionally used [17; 18; 19]. Moreover, the center pulling test has the advantages of ease of fabrication, low cost, convenient loading, and ease of obtaining experimental data; therefore, it is recommended by GB/T 50152-2012 for use in engineering applications [20]. In this study, the central pull-out tests used for 24 specimens composed of pre-cast concrete and the high-strength bolt connected by the CBGM in a reserve hole or square steel tube were carried out. The objective of this study is to investigate the influence of the bolt anchorage lengths and steel tube restraint on the ultimate load, failure mode and bonding property of experimental specimens. Moreover, a formula for calculating the bond strength between high-strength bolts and CBGM was proposed, and a bond-slip constitutive model was created.
## 2 Materials and Methods
### Drying Experiments  
#### 2.2.1 Experimental Site and Raw Material  
Three experimental batches of maize were dried using both an ISD and under direct sun (DSD) near the city of Gombe in the Wakiso District of Uganda. The experimental site is located at coordinates 0\({}^{\circ}\)32\({}^{\prime}\)01.2\({}^{\prime\prime}\) S and 31\({}^{\circ}\)37\({}^{\prime}\)39.0\({}^{\prime\prime}\) E. The experiments took place during the months of July and August, the driest months in the Lake Victoria Crescent AEZ, with an average 67 mm of rainfall. The average solar radiation of 438.4 W m\({}^{-2}\) was measured throughout the experiments using a solar data logger (DL-131 LUX, Voltcrraft, Hirschau, Germany). Maize was supplied by local farmers in bags and was loaded into the ISD and spread on the bottom surface in a 2-to-3-cm-thick layer. For DSD, maize was spread on a tarpaulin with the same grain layer thickness next to the ISD. The experiments  
Figure 1: Inflatable solar dryer (ISD): (**a**) view from outside fully loaded with 1000 kg of maize and (**b**) inside view during the drying experiment showing the location of different sensors and mesh bags (MB).  
were conducted during the daytime and ended when the moisture content of the grains reached the recommended level (14% w.b.). The moisture content was measured at the beginning of the drying experiment (to account for the difference in the moisture content as they were harvested at different periods of time) and at constant intervals until the end of the experiment.
## 6 Numerical Analysis of Test
### Parameters Affecting the Performance of TMD Installed on the Powerhouse Structure  
First, a parametric study was performed of the powerhouse structure system to investigate the effects of various control system parameters on the response control. The optimum design parameters of the TMD scheme were studied and evaluated by changing the tuning frequency ratio. Tuning frequency ratios of 0.5, 0.75, optimal, 1.25, and 1.5 were used. The high-frequency vibration of the uncontrolled structure and the vibration damping structure were calculated. The time-history curves of displacement, velocity, and acceleration of the top and bottom layers of the structure are shown in Figure 11. It is worth noting that increasing TMD did not necessarily result in a reduced response by the structure. When the tuning frequency ratio was 0.5 and 0.75, we found that the displacement, velocity, and acceleration response of the top layer of the structure were greater compared to the uncontrolled structure. The TMD system with these parameters not only had no good control effect, but also increased the vibration response of the structure. However, the increase in response reduction was significant up to the optimal tuning frequency ratio. It was also found that the vibration velocity of the TMD system increased as the tuning frequency ratio approached the optimum, as shown in Figure 12. Therefore, it was concluded that an optimum tuning frequency ratio exists for the TMD system.  
Figure 10: Finite element model.  
Figure 11: Comparison of time history curves at different tuning ratios: (**a**) displacement time history curve of the top structure; (**b**) displacement time history curve of the bottom structure; (**c**) velocity time history curve of the top structure; (**d**) velocity time history curve of the bottom structure; (**e**) acceleration time history curve of the top structure; and (**f**) acceleration time history curve of the bottom structure.  
Table 4 summarizes the structural responses of different tuning frequency ratios under sinusoidal excitation. It can be seen from the data that the vibration reduction effect was the most obvious when the parameters were optimal. At this point, TMD could play a better role in vibration absorption and energy dissipation.
# Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
## 2 Materials and Methods
### Dosimetric Evaluation  
All treatment plans were analyzed and compared using dose-volume histograms (DVHs). The quality of the plans was evaluated based on the dosimetric parameters for the target and sparing of the left lung and heart tissues. The dosimetric parameters for the target were the V95% (target coverage), the conformity index (CI), and the homogeneity index (HI). The CI is a metric used to determine how tightly the prescription dose conforms to the target and is calculated as follows:  
\[\text{CI}=\frac{\text{TV}_{\text{PTV}}\times\text{TV}_{\text{PTV}}}{\text{TV} \times\text{V}_{\text{RI}}}\]  
where V\({}_{\text{PTV}}\) is the target volume enclosed by the reference isodose, TV is the target volume, and V\({}_{\text{RI}}\) is the reference isodose volume. HI refers to the uniformity of dose distribution within the target volume and is determined as follows:  
\[\text{HI}=\frac{\text{D}_{2\%}-\text{D}_{98\%}}{\text{D}_{50\%}}\]  
where D\({}_{2\%}\) is the near-maximal dose, D\({}_{98\%}\) is the near-minimal dose, and D\({}_{50\%}\) is the median dose [22]. For the lung, we analyzed the V\({}_{20\text{Gy}}\) and mean dose, and for the heart, we analyzed the V\({}_{25\text{Gy}}\) and mean dose.  
Figure 2: In-house phantom with (**a**) patient-specific bolus and (**b**) SuperFlab bolus.
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
###### Abstract
[22] proposed the PAGE-Net, which mainly included two modules: pyramid attention and salient edge detection. With an enlarged receptive field, PAGE-Net obtained the edge information by predicting the edge of significant objects through supervised learning. The model proposed by [PERSON] et al. [23] applied joint training to the two almost opposite tasks of SOD and COD(Camouflaged Object Detection). The Dual ReFinement Network (DRFNet) proposed by [PERSON] et al. [24] can be directly applied to high-resolution images. DRFNet consisted of a shared feature extractor and two effective refinement heads, which could obtain more discriminative features from high-resolution images. However, the salient object is not necessarily the only possible salient target in the graph. Other complicated factors should be considered. In addition to its wide range of  
Figure 1: Two saliency detection tasks: (**a**) Original image, (**b**) Saliency prediction task, (**c**) Salient object detection task. applications, the saliency prediction task is related to human vision itself, and it is closely related to neuroscience and psychology. Consequently, saliency prediction has been widely used in interdisciplinary and emerging subjects. The main contributions of this study are as follows:  
* This research focused on the task of saliency prediction, analyzed the psychological and physiological mechanisms related to saliency prediction, introduced the classic models that have been affected by saliency prediction, and determined the impact of these theories on deep learning models. * The visual saliency model based on deep learning was analyzed in detail, and the performance evaluation measures of the representative experimental datasets and the model under static and dynamic conditions were discussed and summarized, respectively. * The limitations of the current deep learning model were analyzed, the possible directions for improvement were proposed, new application areas based on the latest progress of deep learning were discussed, and the contribution and significance of saliency prediction with respect to future development trends were presented.
# Effect of CeO2@GNPs on the Corrosion Properties of 2024 Alloy
## 4 Conclusions  
1. The YS and UTS of the 0.5 wt% CeO\({}_{2}\)@GNPs nanocomposite fabricated using the hot-press sintering method were 21.1% and 24.7% higher than the matrix, respectively. The existence of CeO\({}_{2}\) nanoparticles on the surfaces of the GNPs led to a further improvement in the mechanical properties of the composites.
2. The addition of raw GNPs weakened the corrosion resistance. However, the corrosion current density (2.776 \(\times\) 10\({}^{-3}\) A\(\cdot\)cm\({}^{-2}\)) and polarization resistance (13,971.37 \(\Omega\) cm\({}^{2}\)) with CeO\({}_{2}\) nanoparticle-layered GNPs exhibited a significant decreasing trend compared to the nanocomposites without CeO\({}_{2}\) (3.540 \(\times\) 10\({}^{-3}\) A\(\cdot\)cm\({}^{-2}\), 10,199.72 \(\Omega\) cm\({}^{2}\)).  
Figure 8: Sketch of the corrosion principle of the nanocomposites in a NaCl solution.  
3. The corrosion types of sintered matrix, nanocomposites reinforced with 0.5 wt% GNPs and CeO\({}_{2}\) modified GNPs in a 3.5 wt% NaCl solution were dominated by pitting and intergranular corrosion. The presence of GNPs in the composite destroyed the integrity of the oxide film on the surface of the material. The potential of the GNPs was higher than that of the Al matrix, which constituted a micro-coupled corrosion and accelerated the dissolution of Al. The presence of the corrosion inhibitor CeO\({}_{2}\) particles on the surface of the GNPs acted as a passivation film to impede the charge transfer. It improved the interfacial bonding between the reinforcement and the Al matrix, which reduced the generation of microcracking and contributed to the improved corrosion resistance of the nanocomposites.  
Conceptualization, Q.W. and Z.G.; writing, Q.W.; formal analysis and data curation, J.C. and L.Y.; funding acquisition, N.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangxi Provincial key laboratory open fund project, grant number \"EJ202003431\" and Key R&D Projects in Jiangxi Province, grant number \"20192 BBE50002\".  
Not applicable.  
The data from this experiment are not published in other journals or databases at this time.  
The authors declare no conflict of interest.