esa-sceva/satcom-chunk-collection huggingface dataset:
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 7 Discussion
### Convexity for Loss Functions  
To analyze the convexity of the loss functions and the total loss function presented (29), we examine the properties of each individual component and their interactions. Convexity is important in ensuring that optimization procedures converge to a global minimum, which is essential for training neural networks.  
The financial governing loss \(\mathcal{L}_{\text{financial}}\) is defined as the mean squared error of the residual \(\mathcal{R}^{2}(S_{i},t_{i})\) over a set of sampled points \((S_{i},t_{i})\), given by (21). If the residual function \(\mathcal{R}\) is differentiable and the model is well-specified, then the squared residual is convex with respect to the parameters \(\theta\) because the square of any differentiable function is convex. Therefore, \(\mathcal{L}_{\text{financial}}\) is convex, provided that \(\mathcal{R}(S_{i},t_{i})\) itself is a convex function of the model's predictions \(u_{\theta}(S,t)\).  
The initial value loss \(\mathcal{L}_{\text{initial}}\) measures the difference between the neural network's output at \(t=0\) and the payoff function \(\max(S_{i}-K,0)\), given by (21). The function \(\max(S_{i}-K,0)\) is convex in \(S\), and the square of a convex function is convex. Therefore, \(\mathcal{L}_{\text{initial}}\) is convex in \(u_{\theta}(S_{i},0)\). The overall loss will remain convex if the neural network model \(u_{\theta}(S,t)\) is sufficiently smooth and the architecture is designed to preserve convexity, such as in the case of linear activations or simpler architectures.  
The boundary loss consists of two components: one for the zero boundary condition and one for the infinity boundary condition, including zero boundary loss (24) and infinity boundary loss (25). Both of these losses are squared terms, which are convex. Therefore, the boundary loss function as a whole is convex in terms of the model's output, provided that the underlying neural network architecture does not introduce non-convexities (e.g., non-convex activation functions like ReLU or sigmoid).  
The total loss function is the weighted sum of the individual losses (29). Since each of the individual loss components is convex, the weighted sum of convex functions remains convex as long as the weights of \(\lambda_{\text{initial}}\), \(\lambda_{\text{boundary}}\), and \(\lambda_{\text{financial}}\) are non-negative.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 2 The Proposed Method
### Pole Segmentation Based on Improved Mask R-CNN  
#### 2.2.1 Mask R-CNN  
Mask R-CNN is a two-stage model. In the first stage, the pole image is sent to the Backbone network to extract features, in which Mask R-CNN uses the feature pyramid network (FPN) [29] for reference to fuse the feature maps of different stages. Then, the region proposal network (RPN) is used to regress the anchor, and the proposal layer and non-maximum suppression (NMS) are combined to filter out the region of interest (ROI). In the second stage, ROI Align is performed on ROI to replace the original ROI Pooling. All floating-point numbers are retained by bilinear interpolation to ensure feature resolution. Then, the head network is implemented to achieve the classification and segmentation of the final object. The mask branch of the head network adopts a fully convolutional neural network (FCN) [30], which uses convolution and deconvolution to build an end-to-end network to classify each pixel for achieving better results. The loss of Mask R-CNN is a multi-target loss term, as shown in Equation (1), including classification loss, bounding box regression loss, and mask loss.  
\[L=L_{cls}+L_{box}+L_{mask} \tag{1}\]where, \(L_{cls}\) is the classification loss, \(L_{box}\) is the bounding box regression loss, and \(L_{mask}\) is the mask loss.
# Digitized Reconfigurable Metal Reflectarray Surfaces for Millimeter-Wave Beam-Engineering
###### Abstract  
Digitized beam-forming metal reflectarray antennas are designed for the millimeter-wave region. The phase control of antennas has been implemented by the reconfiguration of rectangular grooves on a metal plate. The antenna has 1147 elements arranged in an aluminum metal plate. The depths of all metal grooves are manipulated for designed phase control of high-gain beam-aimed reflectors. We have demonstrated a digitized reconfigurable metal reflectarray to steer a re-radiated millimeter-wave field from the reflector in a two-dimensional scanning plane from \(-20^{\circ}\) to \(20^{\circ}\). The far-field patterns show that the measured gain of the 2-bit reflectarray is only 1 dB lower than that of a non-digitized reflectarray antenna. The measured peak gain is higher than 31.7 dB, and the measurements show that the gain of the full \(40^{\circ}\) scanned beam is 31.7 dB and well-defined scanned beams are obtained with a maximum scan gain loss of 0.2 dB. The proposed reconfigurable antennas can be a useful candidate for high-gain beam-aimed antennas for practical reflecting surfaces and a variety of wireless and satellite communication systems.  
reflectarray antenna; millimeter-wave; digitized antenna; beam-steering; metal-only antenna +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Digitized Reconfigurable Metal Reflectarray Surfaces for Millimeter-Wave Beam-Engineering.  
_Appl. Sci._**2021**, _11_, 8811. [[https://doi.org/10.3309/app1135811](https://doi.org/10.3309/app1135811)]([https://doi.org/10.3309/app1135811](https://doi.org/10.3309/app1135811))
## 4 Experiments and Results Discussion
### Comparison of Different Architectures  
In addition to the scheme in this paper, three other common or possible architectures are given for performance comparison:  
Architecture I: Single-blockchain architecture based on PoW consensus. The average bpi of PoW in our experiments was 12-13 s, referring to the Ethereum main network.  
Architecture II: Single-blockchain architecture based on DPoS consensus (one election per epoch).  
Architecture III: Triple-blockchain architecture based on DPoS consensus (each blockchain holds elections separately).  
Architecture IV: Scheme in this paper (three blockchains share the same election results).  
By submitting sufficient OS transactions (A, B, C, D and E) and necessary election transactions (applyCandidate, exitCandidate, vote and withdrawVote) in an epoch, Figure 17  
Figure 16: Average query delay as block height increases.  
Figure 15: Average response time and confirmation time under different offline rates of BPNs.  
shows the OS transaction throughput under each architecture. We simulate a case where each node submits two election transactions to the blockchain that holds elections in an epoch. Although there are no election transactions in PoW, the OS transaction throughput in DPoS-based blockchains is higher. Compared with single-blockchain architecture, triple-blockchain architecture can increase throughput. Architecture IV only retains elections in the RSCchain, which further increases the OS transaction throughput. It seems that there is not much improvement because there are only 150 nodes in the experiment, but the number of active users in the OS ecosystem is far more than this. Therefore, in the real world, Architecture IV can reduce a lot of unnecessary election costs. Although multi-blockchain architecture is highly scalable, the OSCMS does not provide too many services at present, and unnecessary blockchain splitting will increase the time cost and resource consumption of cross-chain.
## 2 Robot Manipulator Dynamics  
### Manipulator Dynamic without Actuators  
The electro-hydraulic manipulator which is depicted in Figure 1 is a 3-DOF robot manipulator driven by two hydraulic rotaries in the link \(i^{th}(i=1,2)\) and one cylinder in the last link.  
Firstly, the manipulator dynamics in the joint coordinate are expressed by:  
\[M(\theta)\ddot{\theta}+C\Big{(}\theta,\dot{\theta}\Big{)}\dot{\theta}+G(\theta )+d(t)=\tau \tag{1}\]  
where \(\theta,\dot{\theta},\ddot{\theta}\in R^{3}\) are position, angular velocity and angular acceleration vectors of each joint, respectively, \(M(\theta)\in R^{3\times 3}\) is the symmetric and positive definite matrix of inertia, \(C\Big{(}\theta,\dot{\theta}\Big{)}\in R^{3\times 3}\) denotes the Coriolis and Centrifugal term matrix, \(G(\theta)\in R^{3}\) is the gravity term, \(\tau\) is torque acting on joints, \(\dot{M}(\theta)-2C\Big{(}\theta,\dot{\theta}\Big{)}\) is a skew-symmetric matrix [29], that is given as \(\theta^{T}\Big{(}\dot{M}(\theta)-2C\Big{(}\theta,\dot{\theta}\Big{)}\Big{)} \theta=0\), and \(d(t)\) stands for the disturbance induced in the hydraulic actuator and external factor while operating.  
In fact, the robot dynamic parameters are not well known. Its dynamic is affected by the mass distribution, oscillation in the process of operation. Let's define \(M=M_{0}+\Delta M\), \(C=C_{0}+\Delta C\), \(G=G_{0}+\Delta G\) where \((.)\) represents for estimated parameters which are clearly presented in Appendix A and \(\Delta(.)\) acts as uncertainties of the model. Suppose that all \(\Delta(.)\) are bounded, i.e., \(\|\Delta\|.\|\|_{\infty}\leq\xi(.)\), then the dynamic equation of the manipulator in (1) can be rewritten as:  
\[M_{0}(\theta)\ddot{\theta}+C_{0}\Big{(}\theta,\dot{\theta}\Big{)}\dot{\theta}+ G_{0}(\theta)+\Delta U+d(t)=\tau \tag{2}\]  
where \(\Delta U=\Delta M(\theta)\ddot{\theta}+\Delta C\Big{(}\theta,\dot{\theta} \Big{)}\dot{\theta}+\Delta G(\theta)\) denotes the uncertainties of the system.  
Figure 1: The structure of the 3-DOF manipulator.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System
### Other Control Techniques  
Active force control (AFC) is a disturbance rejection control technique that has been broadly used in the robotic system applications [105]. It was first introduced by [PERSON] and [PERSON] in [106]. Recently, the AFC was utilized in the active suspension system to ensure the passenger ride and car stability [107]. Hence, the active force control method was proposed by [108] to increase the robustness of the active seat suspension. The artificial neural network (ANN) was used to approximately estimate the seat and human body weight. The main advantages of the AFC are its disturbance rejection efficiency and simple control algorithm. However, the system can potentially get damaged when the high force is applied. Figure 18 shows the block diagram of the AFC scheme. The skyhook damped system was integrated with ground-hook damped system in [50]. The skyhook was used to reduce the vibration transmitted to the driver, while the ground-hook was implemented to reduce the relative deflection of the seat suspension. According to Reference [27], a different type of control method was presented to suppress the driver's vertical acceleration in the whole low-frequency as well as the human body sensitive frequency range using the power spectrum density (PSD) scaling factor based on the projective chaos synchronization (PCS). The Lyapunov theory was used to validate the stability of the PCS. The primary and secondary controllers were proposed to attenuate the active vibration of the active seat suspension [109]. The reverse dynamics of the force actuator were considered as a secondary controller where the force feedback was used to track the force of the system. The active seat suspension was considered as a single degree of freedom. The multi-criteria optimization technique was utilized to evaluate the primary controller. Two different masses were selected to examine the performances of  
Figure 17: Hybrid structure of adaptive neuro fuzzy interference system (ANFIS) and super twisting sliding mode controller (ASTSMC) [63]. Figure 18: Representation block of active force control scheme [108]. the proposed controllers. SEAT method was selected as an optimization technique for different driver masses and different suspension travel range. The experimental results indicated that the vibration of the active seat suspension was reduced by 44.8% compared with that of a passive seat suspension. The optimal control algorithms were designed to minimize the vertical acceleration at the driver's hip point where the linear quartic regulator was used as a controller [110]. Table 3 summarizes the existing work in the active seat suspension control in the vertical direction. While Table 4 shows the pros and cons of each control algorithm used in the active seating system.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods  
### Study Area  
The study was performed in the northern region of the state of Minas Gerais, Brazil, classified as AS climate--tropical with dry summer [24] (Figure 1). The sugar cane crop is under 150 irrigated fields using center pivot irrigation used in this study.  
This area was chosen because it is a research partnership area with little to no precipitation (Figure 2) that favors the acquisition of a larger number of cloud-free images, thus being able to acquire a volume of spectral information of the area, mainly coinciding with Landsat-8 and Sentinel-2.  
Figure 1: Location of the study area with the central pivots used for training, testing, and applying the models highlighted.
## 3 Results
Even acetic acid and caffe acid increase in November, while ITP decreases in November; 1,2-propanediol and monoacylglycerolglycerols decrease in October and then remain stable in November. To better understand the metabolic changes occurring in carrots harvested after the fourth month, PCA was conducted on the samples of roots collected in November and December (Figure 5). This analysis provided a six-component model whose PC1 accounted for 29% of the overall variance and whose PC2 accounted for 19%. Even in this model, it was possible to identify a time axis that was inversely proportional to the PC1 values since all samples harvested in November had a positive PC1 value, while samples harvested in December showed negative PC1 values. From the analysis of the loading plot (Figure 6), the variables that were positively correlated (\(p<0.05\)) with PC1, and thus that decreased from November to December were leucine, carotenoids, aspartic acid, 1,2-propanediol, and \(\omega\)-6 polyunsaturated fatty acids. Metabolites that increased during late harvest (\(p<0.05\)) were alanine, falcarinol, GABA, NAD, luteolin 7-O-glucoside, tyrosine, chlorogenic acid, and fumaric acid. Figure 5: PCA scores of the purple carrot. Carrots harvested in November are in green and December in black. The univariate analysis conducted on these molecules and reported in Table 2 confirmed what was observed in the multivariate analysis: the carrots harvested in December had greater amounts of alanine, GABA, falcarinol, luteolin 7-O-glucoside, chlorogenic acid, NAD, tyrosine, and fumaric acid while having lower levels of leucine, carotenoids, 1,2 propanediol, aspartic acid, and \(\omega\)-6 polyunsaturated fatty acids. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metabolite** & \multicolumn{2}{c}{**Amount (\(\mu\)mol/g)**} \\  & **November** & **December** \\ \hline Leucine & \(1.93\pm 0.14\) & \(1.18\pm 0.13\) ** \\
1,2-Propanediol & \(0.25\pm 0.03\) & \(0.13\pm 0.02\) ** \\ Alanine & \(1.19\pm 0.11\) & \(2.61\pm 0.12\) ** \\ GABA & \(1.27\pm 0.13\) & \(3.05\pm 0.33\) ** \\ Aspartic acid & \(0.94\pm 0.11\) & \(0.51\pm 0.05\) * \\ Lutéolin 7-O-Glucoside & \(0.83\pm 0.08\) & \(1.18\pm 0.12\) * \\ Chlorogenic acid & \(1.14\pm 0.15\) & \(2.09\pm 0.41\) * \\ Fumaric acid & \(0.30\pm 0.03\) & \(0.65\pm 0.18\) * \\ Tyrosine & \(1.08\pm 0.08\) & \(1.68\pm 0.25\) * \\ NAD & \(0.054\pm 0.002\) & \(0.11\pm 0.02\) * \\ \(\omega\)-6 unsaturated fatty acids & \(2.62\pm 0.24\) & \(1.42\pm 0.26\) * \\ Falcarinol & \(0.26\pm 0.04\) & \(0.54\pm 0.03\) ** \\ Carotenoids & \(0.009\pm 0.002\) & \(0.0031\pm 0.0004\) ** \\ \hline \hline \end{tabular}  
* \(p<0.05\); ** \(p<0.01\).  
\end{table} 
Table 2: Comparison of the carrots harvested in November and December for the molecules resulting to be significant by PCA analysis, and one-way-ANOVA was employed to assess statistical differences. Figure 6: PC1 normalized loadings of purple carrot harvested in November and December. Variables with \(p<0.05\) are presented in black and molecule abbreviations are reported in the Abbreviations.
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 5 Conclusions and Future Work  
In this paper, we studied a novel community evolution prediction algorithm. The existing community evolution prediction algorithm only obtains the features of the community from a single level and then uses it for community evolution prediction. These approaches are neither comprehensive nor detailed enough, so they cannot obtain good results. To solve these problems, the MCEP algorithm is proposed to obtain features from two layers: the node layer, and the community layer. At the node layer, we propose the global and local community role-extraction algorithm. MCEP can accurately divide the nodes with different characteristics in the community using GLRE. Second, at the community layer, MCEP discovers community mesoscopic features by constructing a community hypergraph and using NMF. The related experimental results verify the effectiveness of the MCEP algorithm.  
Although the MCEP can improve the effectiveness of community evolution prediction, the algorithm still has some shortcomings. First, in the stage of calculating community features, the algorithm needs to manually select relevant features. Obviously, this is a very inefficient way, and it is difficult to obtain the best results. Second, the MCEP algorithm has two parameters: the number of roles, and the number of mesoscopic features. These two parameters also need to be set in advance. In many cases, choosing an optimal parameter is quite difficult. Therefore, how to automatically select effective features and how to automatically select algorithm parameters is the future research direction from this paper.  
Conceptualization, Z.W. and W.L.; Data curation, Q.X.; Formal analysis, Z.W.; Funding acquisition, W.L.; Investigation, Q.X.; Methodology, Z.W.; Project administration, W.L.; Resources, Z.W.; Software, Z.W.; Supervision, Q.X. and W.L.; Validation, Q.X.; Visualization, Q.X.; Writing--original draft, Z.W.; Writing--review & editing, W.L. All authors have read and agreed to the published version of the manuscript.  
The research presented in this paper is supported by the National Key R&D Program of China (No. 2017 YFE0117500) and the National Natural Science Foundation of China (No. 61762002).  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://networkrepository.com/ia-reality-call.php](https://networkrepository.com/ia-reality-call.php)]([https://networkrepository.com/ia-reality-call.php](https://networkrepository.com/ia-reality-call.php)) (accessed on 12 March 2022); [[http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz](http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz)]([http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz](http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz)) (accessed on 12 March 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Comparison of algorithm effects in different dimensions: ia-reality-call.
## Appendix A The Derivation of the Proposed 3D Localization Model  
Let us consider the 3D positioning model in Figure 11. The origin of the Cartesian coordinate system is one of the spacecrafts equipped with a hybrid-ISL that can measure the range \(R\) and compute the angle from phase difference measurements.  
The coordinates of the \(T\) spacecraft along the \(x\)- and \(y\)-axis are straightforward to compute. One can consider the projections of point \(T_{on}\)\(x\)- and \(y\)-axes to be \(T_{x}\) and \(T_{y}\), respectively. The right-angle triangles \(CTT_{x}\) and \(CTT_{y}\) have known angles \(\alpha\) and \(\beta\) and a known hypotenuse equal to the range \(R\). Thus, the segments \(CT_{x}\) and \(CT_{y}\), that are the coordinates on the \(x\)-, \(y\)-axis, respectively, can be computed as follows:  
\[CT_{x}=R\cdot cos\alpha;CT_{y}=R\cdot cos\beta. \tag{11}\]  
Let us consider the projection \(T^{\prime}\) of point \(T\) on the reference plain defined by \(x\)- and \(y\)-axes. The length of diagonal \(CT^{\prime}\) of rectangle \(CT_{x}T^{\prime}T_{y}\) is as follows:  
\[CT^{\prime}=R\cdot\sqrt{cos^{2}\alpha+cos^{2}\beta}. \tag{12}\]  
Applying Pythagoras theorem on the triangle \(CTT^{\prime}\) with a length of \(TT^{\prime}\) that is equal to the \(z\)-axis coordinate yields the following:  
\[TT^{\prime}=R\cdot\sqrt{sin^{2}\alpha+cos^{2}\beta}. \tag{13}\]
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 3 Intelligent Tensioning System Design
### Operation Flow  
Taking multiple prefabricated beam fields as application scenarios, each beam field has multiple tensioning devices, and the working time of each tensioning device is random. On-site data transport mainly depends on mobile terminal networks. thus, the transport speed, and transport cost of the network application layer should be considered. The operation flow comprises four steps. First, the control engine is started, and each sensing node is activated and is in ready mode, which will respond to the data acquisition instruction by the control engine at any time and then send the current measured value back to the control engine. Second, the monitoring sub-service of the transport layer continuously monitors the transport requests from the operation site. When a tensioning job is started at the operation site, the on-site PC controller will send a data push request to the remote server. The server decomposes and verifies the request data immediately when monitoring the received request data. If the data are confirmed a legitimate request, then the server will give confirmation feedback and prepare for data reception. If no data are detected from the pushing end for a long time, then the transport channel will be automatically closed, thereby maximally saving the resources and costs of network communications. Third, the tensioning process packet has a high sampling frequency, large data volume, and other features. The data-receiving service needs to receive data from different tensioning devices at the same time. At the receiving side, the temporary data table is used as a buffer area. When receiving data, the data is immediately written to the temporary table and the corresponding service of the data processing layer is started, thus improving the speed of data writing and service response. After the end of the tensioning operation, the data in the temporary data table is transferred to the historical data table. Fourth, the job monitoring service of the mobile terminal Appis monitoring the tensioning operation. When the tensioning data is monitored, the tensioning process data is displayed in a curved manner, thereby realizing remote monitoring.