esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 3 Multi-Tasks Schedule Formulation and Energy Consumption Model
### Multi-Task Schedule Description  
For convenience, we firstly list all the related acronyms and parameters, as shown in Table 1.  
For any one multi-radio node, suppose there are \(n\) half-duplex RF transceivers equipped on the node and the RF resource set is denoted by \(R=\{R_{1},R_{2},\cdots,R_{n}\}\). At any time, the number of tasks  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Description** \\ \hline \(m\) & the number of waiting to be scheduled tasks \\ \(n\) & the number of RF transceivers \\ \(\gamma\) & the idle RF resource \\ \(N_{i}\) & the \(i\)-th multi-radio node \\ \(R_{j}\) & the \(j\)-th RF transceiver \\ \(R=\{R_{1},R_{2},\cdots,R_{n}\}\) & RF resource set \\ \(T=\{T_{1},T_{2},\cdots,T_{m}\}\) & waiting to be scheduled tasks set \\ \(W=\{W_{i,1},W_{i,2},\cdots,W_{i,l}\}\) & the \(l\)-th packets of \(T_{i}\) \\ \(w_{i}^{R(j)}=\{w_{i,k},w_{i,k+1},\cdots,w_{i,k+h}\}\) & the \(h\) packets allocated to \(R_{j}\) \\ \(T_{i}\) & the \(i\)-th task \\ \(w_{i,q}\) & the \(q\)-th packet of \(w_{i}^{R(j)}\) \\ \(t_{i,q}\) & required time for transmitting \(w_{i,q}\) \\ \(t_{j}\) & total time for transmitting \(w_{i}^{R(j)}\) \\ \(t_{\max}\) & the maximum between \(t_{j}\) \\ \(S_{i,q}\) & energy consumption of transmitting \(w_{i,q}\) \\ \(P_{trans,j}\) & transmission power of \(R_{j}\) \\ \(S_{j}\) & total energy consumption of \(w_{i}^{R(j)}\) \\ \(I_{j}\) & energy consumed for listening of \(R_{j}\) \\ \(p_{i,le,j}\) & idle listening power of \(R_{j}\) \\ \(E_{j}\) & total energy consumption of \(R_{j}\) \\ \(E\) & total energy consumption of \(N_{i}\) \\ \(E_{current}\) & energy consumption of current solution \\ \(E_{new}\) & energy consumption of new solution \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acronyms and parameters used in this paper.  
waiting to be scheduled is \(m\) and is denoted by \(T=\{T_{1},T_{2},\cdots,T_{m}\}\), the number of idle RF resources is \(\gamma\), normally \(m\geq\gamma\). To make the description convenient, assume the number of packets which are transmitted by each task is \(l\). The \(l\) packets of task \(T_{i}\) are denoted by \(W=\left\{W_{i,1},W_{i,2},\cdots,W_{i,l}\right\}\). As shown in Figure 1, the energy efficient task scheduling strategy will quickly allocate the tasks according to the current RF resource and the corresponding packet queue in order to reduce the energy consumption and data latency.
# Optimization of Metal-Ceramic Functionally Graded Plates Using the Simulated Annealing Algorithm
## 4 Optimization with Simulated Annealing  
A general, single objective, optimization problem consists in finding \(n\) design variables \(\boldsymbol{x}=(x_{1},\ x_{2},\ \ldots,x_{n})\ \in\Omega\subseteq\Re^{n}\), which minimize an objective function \(f(\boldsymbol{x})\) as:  
\[min\ _{x\in\Omega}f(\boldsymbol{x}) \tag{9}\]  
where \(\Omega\) denotes a feasible region, which can be defined by \(m\) inequality constraints:  
\[g_{j}(x)\leq 0,\qquad j=\{1,\ 2,\ldots,\ m\} \tag{10}\]  
and bound constraints \(x_{i}^{\,l}\leq x_{i}\leq x_{i}^{\,u},\ i=1,\ldots,n\), where \(x_{i}^{\,l}\) and \(x_{i}^{\,u}\) are, respectively, the lower and upper limits of the design variables. The design variables can also be defined as a discrete set of integer values.  
The simulated annealing (SA) algorithm proceeds iteratively as described by [PERSON] et al. [45], [PERSON] et al. [46] and [PERSON] et al. [47], i.e., a new point \(\boldsymbol{x}\) is accepted or rejected according to the Metropolis criterion: If \(\Delta f=f(\boldsymbol{x}^{\prime})-f(\boldsymbol{x}_{i})\geq 0\), the new point \(\boldsymbol{x}_{i+1}=\boldsymbol{x}^{\prime}\) is accepted; otherwise the new point is accepted with the probability given by \(p(\Delta f)=e^{-\frac{\Delta f}{\Gamma}}\), where \(\Gamma\) is designated as temperature parameter. This acceptance probability is the basic idea behind the search mechanism in SA. The algorithm starts at a user defined temperature \(\Gamma_{0}\) and stops at a temperature parameter such that no more improvement in the objective function can be expected. As mentioned by [PERSON] et al. [46], from an optimization point of view, an iterative search accepting only new points with lowest function values is very likely to be stuck in a local minimum. The SA algorithm permits uphill moves under the control of the temperature parameter. The optimality of the final point cannot be guaranteed, but the method has proved to be able to proceed towards a better minima, even in the presence of many local minima. Detailed explanation of the algorithm implementation can be found in [PERSON] et al. [46], [PERSON] et al. [47]and [PERSON] et al. [48], among other authors. In the present study, the applicability of the SA algorithm to the design optimization of FGM structures is analyzed and discussed.
## 4 Self-Sensing Geosynthetics
### Geosynthetic Production and Fabrication Processes
#### 4.7.6 Screen Printing  
In the screen printing method, a mesh is used to transfer ink (or paste), with the exception of areas made impermeable to the ink by a blocking stencil. The screen printing method is also known as serigraph printing and serigraphy. The open mesh holes on the screen are filled with ink using a blade or squeege motion, and the screen briefly touches the substrate along a line of contact when the motion is reversed. The different electrical circuits can be printed on the surface of the host bed such as fabrics and polymer films. For a design that requires a greater and more delicate level of detail, a finer and smaller aperture for the mesh would be adopted. The mesh might be composed of a synthetic polymer, including nylon. The mesh must be installed on a frame and put under tension to function properly. Before printing, the pre-press procedure must be carried out on the frame and screen. Presses for screen printing typically come in three different configurations: flatbed, cylinder, and rotary. Rotary, flatbed, and cylinder printing are three common types of screen printing presses. Generally, different unsaturated polymeric substrates including polythiophene, polypyrrole, polyaniline, polyacetylene, polyurethane, and polystyrene containing different conductive fillers specifically CNMs are used as conductive inks or pastes [221; 222; 223; 224; 225; 226; 227]. A non-conductive polymer is used on the surface of the host bed as an interface layer to improve the interaction and surface condition before the printing process [221; 228; 229; 230]. The printing of complex circuits with high delicacy, simplicity of the process, economy, and diversity of the host platform are the main advantages of this  
Figure 23: (**a**) Spray coating to deposit conductive filler on glass fibre fabric [65; 215] and (**b**) continuous spray coating to fabricate CNT/glass fibres [221].  
method. However, low control of layer thickness, sensitivity to humidity, and relatively low durability are among the limitations of using this method.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)
#### 3.1.1 Intervention Classification  
This category encompasses studies focused on the automated classification of individual interventions, typically utterances made by teachers or students. These approaches aim to identify pedagogically relevant patterns at the utterance level, such as the presence of questions, argument components, or instructional strategies.  
A common objective is question detection, with early work such as [17] developing machine learning models to identify teacher questions that promote student participation. This direction has evolved to emphasize the detection of authentic questions, as explored by [18; 19] and further discussed in [20; 21].  
Beyond questions, other studies apply natural language processing (NLP) techniques to classify rhetorical or argumentative structures. For instance, [PERSON] and [PERSON] [22] investigates the identification of argument components within student interventions to better understand the structure of classroom discourse. Others, like [23], use low-level acoustic features for the classification.  
Although these studies focus on isolated speech units, their findings often serve as the foundation for higher-level analyses of classroom interaction and engagement.
## 4 Experiments and Results
### The Power Consumption in Sensing Part (\(P_{sense}\))
#### 4.1.1 The Experimental Results  
The data in Table 3 are tested with the normality and Levene's tests. We confirm that all columns are normally distributed and have equal variances. The results of the ANOVA test for all data columns (\(F=15,950.86\), \(p\)-value \(\approx 0.00\)). We interpret that the extremely low \(p\)-value indicates significant differences between the means of at least some of the columns in the dataset. This suggests that the test case groups do not all have the same mean, and at least one group's mean is significantly different.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TC** & \(I_{node}\) & \(I_{pol}\) & \(I_{deep}\) & \(I_{sense}\) & \(I_{send}\) & **Aims** \\ \hline TC1 & x & x & & & & To compare the delay technique in an idle state \\ TC2 & x & & x & & & \\ \hline TC3 & x & x & & x & & To compare the delay technique in idle state and \\ TC4 & x & & x & x & & \\ \hline TC5 & x & x & & x & x & To compare the delay technique in every state \\ TC6 & x & & x & x & x & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The test case of the experiment I.  
Figure 5: The sequence diagram of the software architecture.  
Based on the experimental results, the data in the table for each test case are normally distributed. When comparing the means of each pair, it was found that TC1 and TC2 do not differ significantly, whereas TC3 and TC4 show a significant difference. Similarly, TC5 and TC6 also exhibit a significant difference.  
The boxplot in Figure 6 compares each column's central tendency and spread in the dataset. We found that the means of each test case were consistent with the statistical testing methodology. TC1 and TC2 had the lowest values and similar means, while TC3 and TC4 had higher means, falling within the mid-range group, and showed significant differences between the pairs. This pattern was similarly observed in the TC5 and TC6 experiments.
## 4 Discussion  
### Vegetation Type-Driven Effects on \(CO_{2}\) Fluxes  
We did not observe marked differences between the soils studied, excluding some distinctions in the topsoil layer (Table 1). The soil in the forest had a slightly higher pH in the 0-5 cm layer, as well as a higher \(\mathrm{C_{org}}\) content in the 0-10 cm layer in comparison to grassland soil. A larger amount of \(\mathrm{C_{org}}\) in the top 10 cm of the forest soil against grassland is common for sandy soils [(33)]. In contrast to the differences in \(\mathrm{C_{org}}\) and \(\mathrm{N_{tot}}\) content between forest and grassland soils, the \(\mathrm{C/N}\) ratio in forest soil was systematically higher at all depths. Nevertheless, there was no observed effect of \(\mathrm{C_{org}}\) and \(\mathrm{N_{tot}}\) content on the \(\mathrm{C_{mic}}\). Since overall soil properties varied slightly, we assume that the main differences in soil \(\mathrm{CO_{2}}\) fluxes between these ecosystems are primarily due to the influence of plant communities [(34)], rather than being driven by differences in soil properties. However, higher soil organic carbon and \(\mathrm{C/N}\) ratio in the forest can result in higher basal respiration of the soil, ceteris paribus.  
One of the substantial differences between the ecosystems is the existence of the litter horizon in the forest soil and the sod horizon in the grassland soil. This reflects the predominant pathways of carbon entering the soil, its transformation, and release [(35; 36)]. Additionally, a relative abundance of active thin roots in the grassland provides differences in an SR structure and its dynamics. The second major factor affecting the SR flux dynamics is the difference in the microclimate. Because of strong isolation of soil by vegetation canopy and litter layer from direct solar radiation, the forest soil was cooler and wetter than the soil in the grassland. The low water-holding capacity of the sandy soil also contributes to these differences. Thus, the consequences of weather anomalies on the hydrothermal regime of the soil in the grassland may be stronger than in the forest [(37)]. The microclimate effect can mediate the response of communities to extreme weather conditions [(38)] as well as the \(\mathrm{CO_{2}}\) source ratio in the soil.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Problem Statement and Proposed Solution Framework
#### 2.1.3 Soundscaping  
Finally, in the (third) soundscaping stage, the estimated source signals form the basis of the new acoustic environment of the user. By user-driven re-weighing of these source signals, desired signals can be enhanced and undesired signals can be suppressed. This re-weighing operation seeks a perceptually pleasing balance between residual noise and speech distortion that result from the source separation stage. From a user experience perspective, we envision that the user has access to additional sliders in the smartphone app to tune the gain for each source signal in the enhanced mixture produced by the hearing aid, as shown in Figure 3. The soundscaping stage can be cast as the following inference task:  
\[p(y_{n}\mid x_{1:n},\,\hat{s})\propto\int p(w)\;p(y_{n}\mid w,\,s_{n})\;p(s_{n }\mid x_{1:n},\,\hat{s})\;\mathrm{d}w\;\mathrm{d}s_{n}\,. \tag{5}\]  
On the right-hand side (RHS) of this equation, the factor \(p(s_{n}\mid x_{1:n},\,\,\hat{s})\) is available as output of the source separation stage. The other RHS factors, the prior on the user preferences \(p(w)\) and the function that generates the re-weighted signal \(p(y_{n}\mid w,\,s_{n})\), have already been specified by creating the full generative model (1). Therefore, soundscaping by inference as specified by (5) is computable.  
In summary, in this section we have outlined a probabilistic modeling-based framework for situated soundscaping design. Crucially, all design tasks, namely (2) for source modeling, (3) for source separation and (5) for soundscaping, have been phrased as au  
Figure 3: An overview of the soundscaping stage. The user controls the weights for the individual signals and performs source-specific amplification and suppression.  
Figure 2: An overview of the source separation stage. Based on the observed signal and the trained source models the latent states corresponding to the individual signal are tracked and extracted.
# Hardware Acceleration of Tsunami Wave Propagation Modeling in the Southern Part of Japan
## 2 Data and Methods
### Hardware Code Acceleration  
According to the MacCormack finite difference approximation, one needs to obtain the values of the three functions H, u, and v at each mesh point. To leverage the advantages of the FPGA microchip features, the stream processor architecture was proposed as an implementation of the numerical algorithm. The proposed Calculator contains several processor elements (PEs). Each of these elements forms a pipeline with a sequential data stream. The \"onboard\" memory contains all the necessary  
Figure 1: Isolines of maximal wave height distributions obtained by the exact solution to the shallow water system [15] (brown lines) and the numerical solutions using the field-programmable gate array (FPGA)-based “Calculator” (red dashed lines) and using the Method of Splitting Tsunamis (MOST) package (blue lines). The offshore distance is measured along the vertical axis relative to the figure’s bottom boundary.  
information. The acceleration of calculations by the FPGA architecture is due to the inner Block Random Access Memory (BRAM) access for implementing the stencil buffer.  
The Calculator architecture makes it possible to process several nodes in parallel. At the same time, the user can connect a number of PEs to make several iterations. So, the computation pipeline could be optimized depending on the features of the FPGA microchip in use. MacCormack finite difference approximation fits very well with the Calculator architecture processing one node in one computer clock cycle.  
The originally designed \"Calculator\" hardware, based on a Xilinx Virtex-7 VC709 FPGA microchip, was used for numerical tests; see [13; 14] for details. A general view of the designed printed board is given in Figure 2.  
Table 1 summarizes the achieved performance results of the proposed Calculator (using MOST software) in comparison with those of sequential and several alternative parallel architectures.
# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 3 Solution Algorithm
### Unit Task Segmentation  
In power grid inspections, the complexity and diversity of inspection tasks make it almost impossible for drones to complete all inspections within a single flight range. Therefore, how to effectively divide inspection tasks to ensure that UAVs can complete effective inspections within their safe flight range has become an urgent problem to be solved. To address this issue, this paper proposes a method for dividing inspection tasks based on the endurance capability of a single UAV.  
Firstly, the UAV configuration information and geographical data are input as parameters. Then, a temporary list is created to store the power grid towers in the current unit task. For the first power grid tower that has not yet been added to the list, the algorithm first verifies whether it matches the line number of the current unit task. If it does not match, the task is divided to form a unit task, which is constrained by Constraint (12) to prohibit drones from crossing different transmission lines to avoid confusion during task execution. If it matches, the algorithm proceeds to the next step to calculate the remaining endurance of the drone after removing all the power grid towers from the current list. If Constraints (6) and (7) are not satisfied, the upper limit for task division is set at 80% of the UAV's single-flight endurance capability to divide the task and obtain a new unit task. If the constraints are satisfied, the algorithm continues to check the next tower until all towers  
Figure 3: Framework of the bilevel optimization algorithm.  
have been inspected. Finally, all generated unit tasks are input into the bilevel optimization section. The entire process is shown in the left half of Figure 2.  
Throughout the division process, Constraints (4) and (5) must be satisfied. Constraint (4) ensures that all transmission towers are allocated to the tasks, while Constraint (5) ensures that each tower is assigned to only one task to avoid duplication.
## 4 Autonomous Shovel Technology
### Shovel Trajectory Planning
#### 4.2.1 Resistance Force Determined Trajectory Planning Method
[60], from University of ElectronicScience and Technology of China, established a manipulator model system, which adopted the method of intelligent drag reduction, inserting a shovel on the basis of the minimum energy consumption trajectory. The bucket was stressed less, the energy consumption of the process was effectively reduced and the production was significantly improved under this planning method. The corresponding calculating formula for the uprise velocity for minimum energy consumed trajectory is listed below as Equation (1). \[V_{pu}=-(l_{1}+l_{2})sin\beta_{2}\beta_{2}+[r_{5}\sin(\beta_{2}+\beta_{3})+r_{6 }\cos(\beta_{2}+\beta_{3})]\omega \tag{1}\]  
in which, \(V_{pu}\) is the uprise velocity component of the bucket tooth point P, \(l_{1}\) is the boom length, \(l_{2}\) is the pull rod length, \(r_{5}\) is the depth of the bucket, \(r_{6}\) is the vertical distance between the tooth point and the bottom of the bucket, \(\omega\) is angular velocity of the bucket and \(\beta_{2}\) and \(\beta_{3}\) are the second and third velocity vectors of motion pair, respectively. For a better understanding of those parameters, the manipulator model system is graphically described in Figure 7, in which the blue lines are a simple profile for the reverse six-bar linkage mechanism, with letters A-G representing the movable mechanical joints; the black lines are just for annotations.
## 2 System Modeling
### Solar Irradiance: Theoretical Approach
The signal and noise parameters, including the shot noise (\(\sigma_{s}^{2}\)), dark noise (\(\sigma_{d}^{2}\)), and thermal noise (\(\sigma_{T}^{2}\)), incorporate the gain from the FoV (\(M_{FoV}\)) as follows:  
\[M_{FoV}=\frac{1.5^{2}}{\sin^{2}\theta_{FoV}} \tag{6}\]  
\[\sigma_{s}^{2}=2 qM_{APD}^{2}F(I_{b}+I_{x})B \tag{7}\]  
\[\sigma_{x}^{2}=2 qM_{APD}^{2}F_{Ix}B \tag{8}\]  
\[p=\frac{1}{2}(r_{s}+r_{E}+d_{sun}+d_{max}). \tag{2}\]0  
\[p=\frac{1}{2}(r_{s}+r_{E}+d_{sun}+d_{max}). \tag{2}\]1  
where \(\theta_{FoV}\) is the field of view (\({}^{\circ}\)), \(M_{APD}\) is the APD's gain, \(q\) is the electron's charge, \(B\) is the APD's bandwidth (Hz), \(F\) is the excess noise, \(I_{dg}\) is the surface's dark current, \(I_{ds}\) is the bulk's dark current, \(K_{B}\) is Boltzmann's constant, \(T\) is the temperature (K), and \(R_{L}\) is the  
\[p=\frac{1}{2}(r_{s}+r_{E}+d_{sun}+d_{max}). \tag{2}\]2 Difference between the area of the daytime and that of illuminated satellites by season: spring (a) and summer (b). The orange-colored area (inside): noon on the earth ground and the yellow-colored area (outside): maximum reachable area of solar irradiation to LEO satellites. load's resistance (\(\Omega\)). In this simulation, the 50 \(\Omega\) load resistor is chosen for its standard use in high-frequency systems, ensuring impedance matching and signal integrity at the gigahertz range of operation without compromising the analysis dominated by the shot noise in the system's framework. On the other hand, the signal-to-noise ratio (SNR, \(\gamma\)) is defined as follows:  
\[p=\frac{1}{2}(r_{s}+r_{E}+d_{sun}+d_{max}). \tag{2}\]3  
where \(\sigma_{n}^{2}\), the noise variance, varies, depending on sunlight's presence, as follows:  
\[\sigma_{n}^{2}= \begin{cases}\sigma_{x}^{2}+\sigma_{d}^{2}+\sigma_{T}^{2},&w/o \;sun\\ \sigma_{s}^{2}+\sigma_{d}^{2}+\sigma_{T}^{2},&w/&sun.\end{cases}  \tag{12}\]  
This study assumes \(H(k)\), the channel response, does not affect signals, focusing instead on solar interference effects.