esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To the best of our knowledge, a few previous studies [19; 20] have reported the use of voxel-based dosimetry methods to determine the absorbed dose to the lungs, and have investigated the effects of applying these different methods in the liver/lungs interface region. In this scenario, a Monte Carlo simulation would certainly be the most robust method for dose estimation; the simulation of radiation transport through any medium is possible using several MC codes, and provides an accurate dose estimation [21]. Even if computerhardware is becoming more powerful, absorbed dose calculations based on MC simulations using patient-specific characteristics are still time-consuming, especially when attempting to assign absorbed dose at the voxel level for reporting dose-volume histograms. Despite continuous improvements in hardware and software, fast approaches (DPK and VSV) are still more attractive. Recently, software implementing a modified version of the MC code dose-planning method, optimized to run on Graphics Processing Units (GPUs), have become available [22; 23; 24]. This improves the computational efficiency of MC simulations to facilitate large-scale research use and clinical applications [17; 25; 26]. These software allow us to evaluate the dosimetric effect of using water-equivalent or patient-specific densities in \({}^{90}\)Y TARE dosimetry, especially for the lungs' region. The aim of this work is to compare the dosimetric results obtained by two dedicated software implementing a water-equivalent dose calculation and a fast MC simulation, with special emphasis on possible differences at the lungs' level and their impact on \({}^{90}\)Y-TARE dosimetry. In order to assess this purpose, a first comparison was performed on phantom simulation images, and then on a retrospective selection of 24 patients with primary HCC who are treated with \({}^{90}\)Y-TARE.
## 4 Conclusions  
This study presents a 4 \(\times\) 4 CP ME dipole antenna array with wide bandwidth designed in the mm-wave band. To improve the bandwidth of the element, eight metallic walls are sequentially placed around the ME dipole, which work as parasitic radiators. To reduce the number of substrate layers, a compact microstrip 1-to-16 power divider is applied to excite the ME dipole array. The measured impedance bandwidth and AR bandwidth are 35.7% and 30.1%, respectively. The overlapping impedance bandwidth and AR bandwidth achieves 30.1% with a maximum gain of 19.2 dBic at 30 GHz. Due to the advantages of the planar structure, low cost, wide bandwidth, and high gain, the proposed array is very convenient to deploy on servo for beam scanning, thus it has great potential in 5G mm-wave and satellite communications.  
Conceptualization, B.S.; methodology, B.S.; software, B.S.; validation, B.S.; formal analysis, B.S.; investigation, B.S.; resources, C.D.; data curation, C.D.; writing--original draft preparation, C.D.; writing--review and editing, X.C.; visualization, C.D.; supervision, C.D.; project administration, C.D.; funding acquisition, C.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Contract 62071037.  
All data are included within manuscript.  
The authors declare no conflict of interest.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 4 Functional Blocks of the Proposed System
### The BFT Block  
Each BFT block has \(N\) inputs and \(N\) outputs. Let \(BFT_{l}\) be the generic block composing the system, with \(l{\in}[0,b-1]\). The set of samples composing the \(l\)-th bit slice are inputs, while the DFTs of the \(l\)-th bit slices are outputs. The block performs all the operations indicated in expression (8).  
The logical scheme in Figure 4 shows some details of this block. In particular, the output enabled is the ripple carry output of a modulus \(N\) counter, having the ripple carry output of the modulus \(b\) counter as count-enabled.  
Figure 2: Block diagram of the parallel approach.
## 2 Materials and Methods
### Feature Extraction and Selection
#### 2.5.2 Vegetation Index Characteristics  
Vegetation indices have indirect connections with many ecological factors in the environment and can reflect tree species information, such as canopy structure, leaf area index (LAI), chlorophyll, and net primary productivity (NPP) [10]. The differences in vegetation indices among the different vegetation types with respect to various parameters provide the possibility of using vegetation indices for tree species classification [35]. This allows researchers to select vegetation index features as variables for tree species classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Altitude** & **Band Weighting** &
\begin{tabular}{c} **Scale** \\ **Parameters** \\ \end{tabular} & **Shape Factor** & **Compactness** \\ \hline
1600 & 1, 1, 1, 1 & 300 & 0.4 & 0.7 \\
1800 & 1, 1, 1, 1 & 350 & 0.5 & 0.7 \\
2000 & 1, 1, 1, 1 & 260 & 0.4 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal combinations of segmentation parameters.  
In this study, we comprehensively reviewed previous research results and selected six vegetation index features. The calculations were performed using the Python platform, and each index is introduced in detail as follows:  
Normalized difference vegetation index (_NDVI_):  
\[NDVI=\frac{\rho_{NIR}-\rho_{Rad}}{\rho_{NIR}+\rho_{Rad}} \tag{5}\]  
Soil-adjusted vegetation index (_SAVI_):  
\[SAVI=\frac{(1+L)(\rho_{NIR}-\rho_{Red})}{(\rho_{NIR}+\rho_{Red}+L)} \tag{6}\]  
Ratio vegetation index (_RVI_):  
\[RVI=\frac{\rho_{NIR}}{\rho_{Red}} \tag{7}\]  
Infrared percentage vegetation index (_IPVI_):  
\[IPVI=\frac{\rho_{NIR}}{\rho_{NIR}+\rho_{Red}} \tag{8}\]  
Normalized greenness (_Norm G_):  
\[Norm\ G=\frac{\rho_{Green}}{\rho_{Green}+\rho_{Red}+\rho_{Blue}} \tag{9}\]  
Normalized green-red ratio (_Norm GR_):  
\[Norm\ GR=\frac{\rho_{Green}-\rho_{Red}}{\rho_{Green}+\rho_{Red}} \tag{10}\]
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### Lessons Learned with Sap Flow  
Low-cost wireless sap flow monitoring is a challenge for environmental research. The delicate nature of the thermal dissipation sap flow sensor, not often surviving more than a single season, and the price of the commercial sap flow sensor, which is too high for large deployments with tight budgets, lead researchers to building their own sensors. While cost effective [39], there are challenges to manufacturing working sensors, which require a good deal of patience and careful attention to detail. Once manufactured, sensors should undergo calibration to account for slight variations in workmanship and care must be taken during transport and installation, during which time the heating filament can be easily damaged. The cost effectiveness of these self-made sensors outreaches the drawbacks of their tedious manufacturing and delicate installation.  
There is also the issue regarding the integration of sap flow sensors into WSNs. Early WSN sap flow studies were based on experimental hardware burdened with power limitations and software development issues [28; 100]. These days, good wireless implementations are becoming more and more ubiquitous and more seamless in terms of user experience.
## 3 Solver Optimization
### Discretization  
Substituting the flux derivative with its discrete version of difference over time difference, the flux status \(\mathbf{\varphi}\) can be calculated using recursive algorithm every calculation step. Sub-index \(k\) indicates the step. Starting from (12), putting \(\mathbf{\varphi}_{c}=0\) and operating some grouping of parameters, the discrete machine equation is (16).  
\[\dot{\mathbf{\varphi}} =-[R][T]^{-1}[L]^{-1}[T]\mathbf{\varphi}+\mathbf{v} \tag{14}\] \[\frac{\mathbf{\varphi}_{k}-\mathbf{\varphi}_{k-1}}{T_{c}} =-[R][T]^{-1}[L]^{-1}[T]\mathbf{\varphi}_{k}+\mathbf{v}_{k}\] (15) \[\frac{\mathbf{\varphi}_{k}-\mathbf{\varphi}_{k-1}}{T_{c}} =[A_{k}]\mathbf{\varphi}_{k}+\mathbf{v}_{k} \tag{16}\]  
With \([A_{k}]=[A(\theta(t))]=-[R][T]^{-1}[L]^{-1}[T]\) and \(T_{c}\) the calculation step time.  
From here, the recursive step can be evaluate in (17), with \([I]\) the identity matrix.  
\[\mathbf{\varphi}_{k} =T_{c}[A_{k}]\mathbf{\varphi}_{k}+T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k-1} \tag{17}\] \[\mathbf{\varphi}_{k} =([I]-T_{c}[A_{k}])^{-1}(T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k-1})\]  
This algorithm allows the integration of the fluxes, but it contains the time-dependent matrix inversion \(([I]-T_{c}[A_{k}])^{-1}\) that is very time consuming. Focusing on this problem, the coefficient can be reworked as in (18).  
\[([I]-T_{c}[A_{k}])^{-1} =([I]+T_{c}([R][T]^{-1}[L]^{-1}[T]))^{-1}= \tag{18}\] \[=([T]^{-1}([T][T]^{-1}+T_{c}[R][L]^{-1})[T])^{-1}=\] \[=[T]^{-1}([I]+T_{c}[R][L]^{-1})^{-1}[T]=\] \[=[T]^{-1}([R][L]^{-1}([L][R]^{-1}+T_{c}[I]))^{-1}[T]=\] \[=[T]^{-1}([L][R]^{-1}+T_{c}[I])^{-1}[L][R]^{-1}[T]\]  
Thus, introducing the new matrix \([M]\)  
\[[M]=([L][R]^{-1}+T_{c}[I])^{-1}[L][R]^{-1} \tag{19}\]  
Figure 2: Implementation scheme for the function calculating the machine flux status.  
the integrating algorithm becomes (20).  
\[\mathbf{\varphi}_{k}=[T]^{-1}[M][T](T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k-1}) \tag{20}\]  
This approach has two main advantages:  
* \([M]\) does not depend on the position \(\theta\), thus it is time invariant in case of constant switching frequency (\(T_{c}=const\)).
* \([M]\) allows using infinite resistance coefficient for machines with no rotor currents because the matrix \([R]\) is always used as inverse. \([R]\) is diagonal and in case of a coefficient \(r=\infty\) the corresponding element in \([R]^{-1}\) is simply 0.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 5 Application Results and Discussion
### Combined Weights Calculation and Discussion  
Through summarizing and organizing the calculation results of subjective and objective weights, the weight matrix \(\mathbf{U}\) is constructed as follows:  
\[\mathbf{U}=\left(u_{ij}\right)_{3\times 3}=\begin{pmatrix}0.3796&0.2441&0.3763\\ 0.4157&0.3023&0.2820\\ 0.4624&0.2966&0.2410\end{pmatrix}\]  
According to Equation (26) and [PERSON]'s rule, the Lagrange function is established and its derivative is solved. The results yield \(\mathbf{\alpha}=\) (0.3336, 0.3382, 0.3282) and \(\lambda=-0.0100\). \(\mathbf{\alpha}\) represents the weight vector obtained from the three weighting methods. By substituting the weight matrix \(\mathbf{U}\) and the weight vectors from the three weighting methods into the following equation, the final combined weights for the target layer are determined as A1(0.4190), A2(0.2810), and A3(0.3000).  
The weighting methods for each level of the indicator layer and the data layer have been previously outlined and will not be reiterated. The combined weights for each layer are shown in Tables 10 and 11.  
Through the calculation of combined weights, it is observed that the significance of operations and services has escalated. This implies that smart highways should be directed towards management and services in the future, accentuating data utilization and dissemination and emphasizing the integration of system functionalities and user experiences.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Indicator Level** & \multicolumn{3}{l}{**Combined Weight Values**} & & & & \\ \hline First-level indicators & B11(0.18) & B12(0.40) & B13(0.32) & B14(0.10) & B21(0.30) & B22(0.30) \\  & B23(0.20) & B24(0.20) & B25(0.10) & B31(0.58) & B32(0.42) & \\ \hline  & B111(0.25) & B112(0.10) & B113(0.25) & B114(0.10) & B115(0.05) & B116(0.15) \\  & B117(0.10) & B121(0.35) & B122(0.30) & B123(0.15) & B124(0.05) & B125(0.05) \\ Second-level indicators & B126(0.05) & B127(0.05) & B131(0.30) & B132(0.30) & B133(0.30) & B134(0.05) \\  & B135(0.05) & B141(0.35) & B142(0.10) & B143(0.25) & B144(0.20) & B145(0.10) \\  & B311(0.27) & B312(0.45) & B313(0.17) & B314(0.11) & B321(0.29) & B322(0.44) \\  & B323(0.14) & B324(0.13) & & & & \\ \hline  & B3111(0.41) & B3112(0.30) & B3113(0.29) & B3121(0.38) & B3122(0.62) & B3131(0.29) \\ Third-level indicators & B3132(0.33) & B3133(0.38) & B3141(1) & B3211(1) & B3221(1) & B3231(0.39) \\  & B3232(0.29) & B3233(0.32) & B3241(0.53) & B3242(0.47) & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Table of final combined weights for Guideline Level B.
## 5 Dynamic Network Resource Allocation Algorithm Based on Accelerated ADMM
### Dynamic Solution Algorithm Based on Accelerated ADMM  
The optimization model is a multi-objective joint optimization problem. In this section, the ADMM algorithm is introduced to solve the model. The ADMM is an efficient algorithm tailored for large-scale distributed optimization scenarios. It integrates the parallel design of dual decomposition with the rapid convergence properties of the augmented Lagrange method, breaking down complex global optimization problems into multiple simpler sub-problems for resolution. The sub-problems are considerably easier to solve than a joint solution. Efficient global convergence is achieved through alternating optimization and multiplier updates. The ADMM algorithm has the characteristics of rapid convergence in distributed computing, high compatibility under complex constraint conditions, robust parameter performance, and excellent scalability.  
In the resource allocation scenario, the algorithm introduces global auxiliary variables \(\text{h}_{z}=\sum\limits_{i=1}^{\text{N}}x_{S_{i}}^{z}\), \(\text{h}_{b}=\sum\limits_{i=1}^{\text{N}}x_{S_{i}}^{b}\), \(\text{h}_{c}=\sum\limits_{i=1}^{\text{N}}x_{S_{i}}^{c}\), and \(\text{h}_{i}\), \(i\in\text{F}_{\text{x}}\), representing the resources allocatedby the current node to three types of services. The augmented Lagrange function in the ADMM algorithm is as follows:  
\[\begin{array}{ll}\min\mathrm{L}_{\rho}&=-\varepsilon_{\mathrm{SRCR}}+\sum \limits_{j\in\{z,b,c\}}\bigg{[}\lambda_{j}\Big{(}\mathrm{h}_{j}-\sum x_{\mathrm{ S}_{\mathrm{S}_{\mathrm{S}}}}^{j}\Big{)}+\frac{\rho}{2}\Big{(}\mathrm{h}_{j}-\sum x_{ \mathrm{S}_{\mathrm{S}}}^{j}\Big{)}^{2}\bigg{]}\\ &=-\sum\limits_{i=1}^{\mathrm{N}}\bigg{(}\frac{\omega_{2}x_{\mathrm{S}_{ \mathrm{S}}}^{j}}{\mathrm{R}_{Z}}+\frac{\omega_{\mathrm{R}}x_{\mathrm{S}_{ \mathrm{S}}}^{j}}{\mathrm{R}_{B}}+\frac{\omega_{\mathrm{C}}x_{\mathrm{S}_{ \mathrm{S}}}^{j}}{\mathrm{R}_{C}}\bigg{)}+\sum\limits_{j\in\{z,b,c\}}\bigg{[} \lambda_{j}\Big{(}\mathrm{h}_{j}-\sum x_{\mathrm{S}_{\mathrm{S}}}^{j}\Big{)}+ \frac{\rho}{2}\Big{(}\mathrm{h}_{j}-\sum x_{\mathrm{S}_{\mathrm{S}}}^{j}\Big{)} ^{2}\bigg{]},\end{array} \tag{21}\]  
where \(\lambda_{j}\) represents the Lagrange multiplier, which is employed to dynamically facilitate the satisfaction of constraints during the optimization process and is constantly updated. \(\rho\) denotes the penalty parameter, which is utilized to expedite the algorithm's compliance with conditional constraints.  
The traditional ADMM algorithm initializes the local variables \(\left\{x_{\mathrm{S}_{\mathrm{S}}}^{z},\ x_{\mathrm{S}_{\mathrm{S}}}^{b},\ x_{ \mathrm{S}_{\mathrm{S}}}^{c}\right\}\), global variables \(\left\{\mathrm{h}_{z},\mathrm{h}_{b},\mathrm{h}_{c}\right\}\), and Lagrange multipliers \(\left\{\lambda_{z},\lambda_{b},\lambda_{c}\right\}
## 5 Conclusions  
In this paper, we have addressed the poor performance encountered by the sink in our previous work. Our aim is to have WSN perform at an optimum rate in a multichannel environment of the 802.11 network for high data rates. In the future we foresee problems with 802.15.4 when the IEEE 802.11n standard becomes popular. We consider a WSN formed by static nodes with increasing the sink nodes and assigning multiple radio interfaces at the sink. We formulated solutions for solving multichannel multi-radio assignments at the sink by using graph techniques and a binary vector. We also created a number of equations to solve the scenarios of odd or even numbers of transmitting nodes sending data directly to the sink.  
From the outcome of our simulations we prove that if we increase the number of sink nodes and/or increase the number of radio interfaces in the sink we can obtain a better performance at the sink, which results in an overall performance improvement within the network. The multi-radio interfaces assignment in the sink node will be the network to consider for the future, even though when we increase the sink nodes with single interface we have seen improvement in performance. The simulation scenario with three sink nodes, each equipped with three radio interfaces using the three non-overlapping channels in IEEE 802.11 is the network to be considered for future static WSNs with streaming data. We have seen from the simulation results that an average of over 90% improvement in performance can be achieved. As such, we can consider this kind of assignment to be more cost effective and energy efficient in the future.  
In the future, we can consider setting up test bed to compare our empirical results with the test bed results. After further testing in the future areas that can benefit from the use of this application or relate to professional and industrial practice of this kind are transportation and traffic management, parks, built-up environment, festivals and sporting events such as Olympics and World Cup football, narcotics and border controls. We will also examine MC-DCF as a more cost effective solution when 802.15.4 is unable to operate in the 2.4 GHz with other standard such as 802.11n.
# Surrogate-Based Optimization of a Centrifugal Pump with
## 1 Introduction  
The role of the centrifugal pump in a car cooling circuit is to guarantee the liquid circulation, and, therefore, to control the engine temperature. The design of a centrifugal pump turns out to be a difficult task for the following reasons: (_i_) the working point varies over a wide range of flow rate and pressure head; (_ii_) automotive companies require ever strict constraints for pump dimensions and geometry coupling, possibly different for each car model. Moreover, traditional design approaches based on empirical correlations or trial and error methods (based on previous developed models) are characterized by low accuracy (the former) and by an excessive time/cost to reach the optimal design (the latter).  
Optimization algorithms coupled with Computational fluid dynamic (CFD) have demonstrated to be a robust and automatic alternative in the design of centrifugal pumps [1], which can overcome the limits of the traditional approaches. However, the works available in literature are often characterized by a simple parametrization, a small number of design variables [2, 3], and only some components are optimized, i.e., the impeller [2, 4, 5, 6, 7, 8], or the diffuser [9], or the volute [10].  
Centrifugal pumps for the cooling system are characterized by a compact layout to meet the downsizing policies of car makers. The location of the suction pipes is usually prescribed by the engine, and, in general, the vaned diffuser is not adopted because it could decrease the pump performance in off-design conditions. The vaneless diffuser is often used only as a connection between the impeller and the volute. A typical pump geometry is made of an impeller, a small vaneless diffuser, and a volute. The objective of this work is to propose an automatic methodology based on the shape optimization to design from scratch a centrifugal pump for the automotive field. A preliminary work has been presented in [11], where some limitations are present: (_i_) a baseline design is not available in an automatic manner and, as a consequence, the range of the design space is difficult to set and can be too wide to be spanned with a genetic algorithm; (_ii_) the vaneless diffuser represents only the gap created by the parametrization of the impeller, and thevolute, i.e., it cannot be controlled directly by the optimization process, and (_iii_) the volume cross section is free to change and its boundaries are parametrized with Bezier curves. The latter choice allows the optimization process to find cross sections with \"original\" shapes, which are often difficult and/or expensive to manufacture. For these reasons, the approach proposed in [11] has been extended. A baseline geometry is generated by a lumped parameter code and optimized with respect to the efficiency with a genetic algorithm. The optimized baseline geometry is parametrized and the 3D model of the pump is created. Finally, a surrogate based optimization strategy is used to optimize the 3D geometry (impeller, vaneless diffuser, and volute).
## 4 Analysis of Anti-Friction Characteristics of Textured Raceway of the Ball Screws
### Analysis of EHL Characteristics of Textured Raceway of the Ball Screws
The proportion of direct contact with the asperity is relatively large when the rotation speed of the ball screws is low, and the proportion of direct contact with the asperity decreases after the screw raceway is textured. Figure 17: Trend of friction coefficient with texture diameter (workpiece#1). The friction coefficient of the textured raceway achieves the minimum at an entrainment speed of 586.4 mm/s and a texture depth of 10 \(\upmu\)m, the proportion of direct contact with the asperity is relatively low, and the dynamic pressure effect in the contact area is enhanced after the screw raceway is textured. With the further increase in the texture depth, the lubricating oil in the surface texture produces a serious backflow phenomenon, and the friction coefficient of the textured raceway increases. The optimal friction reduction effect is obtained at an entrainment speed of 167.5 mm/s and a texture depth of 5 \(\upmu\)m, and the friction coefficient is decreased by 13.9%. The changing trend of the friction coefficient with the areal density of the texture is presented in Figure 19 under three different rotation speeds of the screw (workpiece#3). The thickness of the lubricating oil film is thin, and the proportion of direct contact with the asperity is higher when the rotation speed of the screw is 100 r/min. The proportion of direct contact with the asperity is reduced, and the friction coefficient decreases after the surface texture is applied to the raceway surface of the ball screw. The entrainment speeds of the oil film are 586.4 mm/s and 1005.3 mm/s, and the friction coefficient of the textured raceway increases with the increase in the areal density of the texture. The ball screw is in good lubrication condition at the medium and high rotation speeds of the ball screw. The friction force mainly comes from the shearing force of the oil film, and the surface texture increases the thickness of the oil film in the contact area, increasing the friction coefficient. The optimum friction reduction is achieved when the entrainment speed is 167.5 mm/s, the areal density of the texture is 8%, and the friction coefficient is reduced by 10.2%. Figure 19: Trend of friction coefficient with areal density of the texture (workpiece#3). Figure 13.0 Trend of friction coefficient with texture depth (workpiece#2). The micro-hydrodynamic pressure perturbations effect is caused by the textured surface of ball screws with appropriate geometric parameters according to the analysis of the anti-friction characteristics of the textured surface. The micro-hydrodynamic pressure perturbations effect of the ball screws enhances the thickness of the oil film and reduces the contact friction between the ball and the raceway of the contact area under different working conditions. The surface texture with different geometric parameters and distributions optimize the lubrication state of the ball screws, thereby reducing the friction coefficient.