esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Soft Tissue Analysis  
Measurements from the point tragus to point pogonion in millimeters for every participant were taken on 3D face scans before and after the treatment. The measurements are visible in Table 5. Figure 4 shows a typical head scan before (left) and after treatment (center), and the measurement of the tragus-pogonion distance (right).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & \
\begin{tabular}{c} **Tragus–Pogonion (mm)** \\ **T0** \\ \end{tabular} &
\begin{tabular}{c} **Tragus–Pogonion (mm)** \\ **T1** \\ \end{tabular} \\ \hline  
\hline \hline \end{tabular}  
\end{table}
Table 5: Measurements of the soft tissue changes in the length of the mandible.  
Figure 3: The reference cephalometric points marked on latero-lateral cephalogram of an example of a case before treatment (**left**) and after the Twin Block treatment (**center**); on the (**right**): pre- and post-treatment lateral cephalograms superimposed at SN line (center at S) with drawn lines representing maxilla and mandible, upper and lower frontal teeth, and first permanent molars (red—before treatment; green—after treatment).  
Kolmogorov-Smirnov test for normality showed normal distribution of all variables visible in Figure 5.  
There is a high statistically significant difference in the results of the paired \(t\)-test for the measurements of the soft tissue changes in the length of the mandible. On average, for the first measurement (T0) the mean is 148.71 and the standard deviation is 26.43. For the second measurement (T1), on average, the mean is 153.16 and the standard deviation is 19.86, \(p<0.001\). After the superimposition of 3D face scans before and after the treatment for every patient, the greatest recorded distances in the chin area are shown in Table 6.  
Figure 4: Examples of a typical head scan before (**left**) and after treatment (**center**), and measurement of the tragus–pogonion distance shown with black line (**right**).  
Figure 5: Distribution of data normality for the soft tissue tragus–pogonion measurements.  
The color-coded heat map indicated that the most significant soft tissue changes following treatment were observed in the protrusion of the chin and the elongation of the mandible. Typical changes are visible in Figure 6.
## 1 Introduction  
The concept of a \"digital twin\" refers to a model that uses technical and managerial processes that combine real and virtual environments to manage and process data and to control physical systems. Digital twins have become widely used in the fields of manufacturing, production and operations management and have also been introduced into the fields of construction, infrastructure and civil engineering [1]. The idea of a digital twin is built around the role of a virtual copy of a target physical entity that is used to conduct experiments or simulations and predict future behaviors to help decision-makers make up their minds or to help automated decision systems determine appropriate solutions to the expected problems [2]. A digital twin can be used in construction project management processes by providing its database with information and data through sensors that monitor the execution of work and the behavior of the commissioned facility after construction. In this way, the database provides the ability to compare the ideal situation to reality and the possibility to track the work through specific monitoring cycles and produce necessary reports and alerts for managers [3].  
In this study, the digital twin is discussed and analyzed within a structured framework, starting from the goal of being able to respond intelligently to expected events. The work presented here aims to link the results of exiting studies to obtain a clear vision of executive construction project management and support the development of a comprehensive digital system that employs digital twins to solve specific construction project issues, such as budget and timeframe overruns, worker safety and environmental impact. By obtaining an integrated digital support system for managing operations at construction sites that is based around a coherent vision, the authors expect to provide companies operating in the field with a deeper understanding of the gains they could experience by moving away from outdated management approaches.  
This study assumes that the smart construction model simulates reality, can predict technical and economic risks and has the possibility of using digital twin functions in distinct phases, such as intelligent analysis in design (e.g., for structural engineering or utility management), on-site project implementation, the operation of buildings and facilities etc. This is due to the fact that using sensor data for processing, analysis and deploying the correct software solution depends on the lifecycle stage of the building, thus varies in scope, complexity and purpose.  
The main contribution of the current research consists of defining an overarching model for digital twins for construction projects (DTCP) that integrates mature, emerging and future approaches within the field into maturity stages that a company can follow. The framework is based on an integrative outlook that pursues the organizational and project management benefits of using a diverse array of digital technologies depending on their adequacy for certain applications, ranging from reducing costs to tracking health and safety hazards.
# Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
## 2 Model Development
### Dispersion Parameters and Relative Concentration Intensity
Given the scarce knowledge on the subject, a 1D model has normally been adopted for the estimation of \(i_{\alpha}\) relating this parameter only to the downwind distance. [PERSON] et al. (2015) considered for \(i_{\alpha}\) both a 1D model and a 3D model; however, they have shown how only with a 1D model the relationship (14) can be analytically integrated. Since our goal is to obtain a plume model easy to use and suitable for carrying out preliminary impact assessments, supported also by the observations that a 1D model for \(i_{\alpha}\) leads to reasonably correct results [54], only the 1D models according to which \(i_{\alpha}=i_{\alpha}(x)\) will be considered below. Analysis [49] of experimental measurements [89, 90] did not allow to univocally identify a relation for \(i_{\alpha}\) and operationally the cited work hypothesized to place \(i_{\alpha}=1\), so \(\lambda=1\). This assumption, not contradicted by the experimental evidence, implies the representation of the concentration fluctuations of the instantaneous plume as an Exponential distribution because a Gamma distribution is reduced to it when \(\alpha=1\). In practice, this means that at great distances from the emission all concentration fluctuations depend on the internal fluctuations of the instantaneous plume and meandering is negligible. In the light of the experimental evidence considered, this assumption for \(i_{\alpha}\) while being realistic was not completely satisfactory, although from the analyses made in the work cited this simple model for \(i_{\alpha}\) leads to a reasonably limited error. Crucial at this regard are the measurements made inside a wind tunnel in near-neutral conditions [55]. Based on them, the authors developed a new 1D model that makes \(i_{cr}\) depend on the normalized downwind distance \(\xi=x/z_{i}\), (\(z_{i}\) is the PBL top) according to  
\[i_{\alpha} = \xi\frac{p_{1}\xi^{2}+p_{2}\xi+p_{3}}{\xi^{3}+q_{1}\xi^{2}+q_{2} \xi+q_{3}} \tag{30}\]  
where the coefficients \(p_{1},p_{2},p_{3},q_{1},q_{2},q_{3}\) depend on the height of the source, its characteristic dimension \(\sigma_{0}\) and the vertical extension of the PBL. The experimental measurements of refer to three different types of sources [55]. In applications related to olfactory annoyance it is recurring to consider low source without buoyancy. Accordingly, it would seem appropriate to use the lower-level source (LLS) coefficients in the model here developed. The values of the coefficients are indicated in Table 1.
# Time Series Feature Selection Method Based on Mutual Information
## 2 Methodology  
### Problem Description  
This paper focuses on the feature selection of multivariate time series. Each sample in the dataset is a set of time series, and the length of each time series may vary. A set of univariate time series can generally be defined as follows:  
\[T=t_{1},t_{2},\cdots,t_{n} \tag{1}\]  
where \(n\) represents the length of the time series. Correspondingly, an \(m\)-dimensional multi-variable time series can be written as:  
\[\begin{array}{l}T_{1}=t_{11},t_{12},\cdots,t_{1n_{1}}\\ T_{2}=t_{21},t_{22},\cdots,t_{2n_{2}}\\ \cdots\\ T_{m}=t_{m1},t_{m2},\cdots,t_{mn_{m}}\end{array} \tag{2}\]  
In the feature selection of time series, the dimension of the monitoring data is the number of features in the time series. When processing time series data, if the number of features in the dataset is too large, it can seriously affect the effectiveness of model training, leading to the so-called \"curse of dimension\" [43]. Effective feature selection can lay a good foundation for subsequent anomaly detection, fault diagnosis, condition identification, remaining useful life prediction, etc.
###### Abstract  
The production of minimally processed vegetables generates large amounts of by-products whose concentrations in bioactive compounds is comparable to those of the edible part. The aim of this work was the optimization of sustainable processes for the extraction of phenolic compounds from chicory and fennel by-products using water as solvent. The results were compared with those obtained through a conventional extraction performed with a 70% ethanol aqueous solution as extraction solvent. The ultrasound-assisted extraction (UAE) and microwave-assisted extractions (MAE) were established by developing two Box-Behnken designs, respectively, a four-factor, three-level design and a three-factor, three-level design. A quadratic polynomial model was useful in optimizing both the ultrasonic (R\({}^{2}\) 0.8473 for chicory and R\({}^{2}\) 0.9208 for fennel) and microwave (R\({}^{2}\) 0.9145 for chicory and R\({}^{2}\) 0.7836 for fennel) extraction of bioactive compounds as well as the antioxidant activity of extract (R\({}^{2}\) 0.8638 for chicory and R\({}^{2}\) 0.9238 for fennel treated with ultrasounds; R\({}^{2}\) 0.9796 for chicory and R\({}^{2}\) 0.7486 for fennel submitted to MAE). The UAE conditions able to maximize the total phenolic concentrations were: 10 g/100 mL, 55 \({}^{\circ}\)C, t: 60 min, 72 W for chicory (9.07 mg gallic acid/g dm) and 15 g/100 mL, 45 \({}^{\circ}\)C, t: 40 min, 120 W for fennel (6.64 mg gallic acid/g dm). Concerning MAE, the highest phenolic concentrations were obtained applying 7.5 g/100 mL; 2 min; 350 W for chicory (8.23 mg gallic acid/g dm); 7.5 g/100 mL; 3 min; 160 W for fennel (6.73 mg gallic acid/g dm). Compared to conventional solvent extraction, UAE and MAE allowed the obtainment of (a) chicory extracts richer in phenolic compounds (+48% and +34%, respectively), in less time (4-fold and 90-fold reduction, respectively) and (b) fennel, extracts with slightly lower amount of phenolics (\(-11.7\%\) and \(-10.5\%\), respectively) but halving the extraction time (UAE) or reducing it to 60-fold (MAE).  
c 25 February 2023  
Revised: 23 March 2023  
Accepted: 24 March 2023  
Published: 25 March 2023
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 1 Introduction
Secondly, addressing the challenge of target recognition and localization in dynamic environments, this study innovatively proposes the Dynamic Environment Target Identification and Localization (DETIL) algorithm. This algorithm introduces a strategy for identifying unknown obstacles when encountered and effectively improves the recognition rate and localization accuracy of points of interest. In the realm of multi-task mixed control, this research designs a comprehensive control scheme that integrates the use of the adaptive heuristic function A path planning algorithm and the novel DETIL algorithm*, set against the backdrop of sparse maps and complex environmental conditions. Through simulation validation and performance analysis, the proposed scheme demonstrates its effectiveness and feasibility in practical applications. These contributions provide valuable references for the aerospace industry, offering insights for the development of autonomous navigation and control systems in future extraterrestrial exploration missions. This study not only provides new theoretical and practical perspectives for path planning and target recognition and localization techniques in extraterrestrial body exploration but also offers valuable experience and reference for the design of autonomous control systems for more complex exploration missions in the future. Through in-depth research into path planning under sparse map conditions, target recognition and localization in dynamic environments, and the design and implementation of multi-task mixed control schemes, this study aims to achieve further optimization and application in autonomous systems for future extraterrestrial exploration missions. In summary, the contributions of this study include the following:Section 2 analyzes the impact of different heuristic functions on the A* algorithm's path planning under sparse map conditions and designs an A* algorithm with an adaptive heuristic function tailored for sparse maps. * Section 3 introduces the theory of target recognition and localization in dynamic environments and designs the implementation of both components of the DETIL algorithm. * In Section 4.2, a multi-task intelligent agent mixed control scheme under sparse map and complex environmental conditions is designed and simulated on the Webots platform, demonstrating the superiority of the adaptive heuristic function A* algorithm and the DETIL algorithm. * Section 5 presents some conclusive conclusions.
# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 4 Discussion
### Application Potential of Crop Growth Process Monitoring  
Corn is an important grain crop in North China Plain. According to planting time and variety, corn can be roughly divided into spring corn and summer corn. In the two-year three-cropping area, the planting area of summer corn is much larger than that of spring corn. The growing season of summer corn is from June to October. The rainy season is in July and August, and it is also the key period of maize growth, which is closely related to the final yield. The lack of clear sky observations from optical satellite due to weather factors such as clouds and smog is widespread in other agricultural regions of China (2016). Because of the shortcomings of temporal resolution or spatial resolution, single sensor remote sensing satellite cannot satisfy the requirement of long-term monitoring of crop accurately. It is one of the important methods for crop growth monitoring in the field scale based on time-series spatial-temporal fusion images harmonized by multi-source remote sensing data sources.  
Previous studies (2; 9; 26; 41) have demonstrated that the availability and great potential of long time series remote sensing images in crop growth monitoring. In this paper, the result manifested that the improved Kalman filter could be used to harmonize the time-series multi-source remote sensing images for summer corn growth monitoring. Finally, we analyzed the growth situation of summer corn in the study area based on NDVI time-series dataset synthesized from multi-source remote sensing data and discussed how the temperature and moisture of the environment changed with the growth stage. The growth process of summer corn obtained from synthetic remote sensing images could be directly applied to follow-up studies, such as precise farmland management of irrigation, fertilization and pesticides and yield estimation over large areas. Furthermore, our results suggested that the application potential of synthesizing multi-source data in crop growth monitoring is enormous and it was a low-cost method to obtain complete time-series images in the field scale at the present stage.
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results
### Tree Topology Results
As shown in Figure 30a, the two middle output ports have similar amplitude responses and are approximately 5 dB higher than the other two ports at the center frequency of 29 GHz. Figure 30b shows the phase differences between the adjacent ports, which are approximately 90\({}^{\circ}\) with a minor deviation of \(\pm 3^{\circ}\) at the center frequency. The second tree exhibits similar behavior. Figure 30c shows its amplitude responses where the difference of the two middle ports compared to the two outer ports is approximately 5 dB. The phase differences, similar to the first tree, maintain the required 90\({}^{\circ}\) between the adjacent ports with a slight deviation of \(\pm 3^{\circ}\). Lastly, Figure 30e shows the amplitude distribution for the ports of the third tree topology. As is shown, the amplitudes follow the same trend with cases 1 and 2, while the phase differences, as shown in Figure 30f, are at 0\({}^{\circ}\) with a minor deviation of \(\pm 3^{\circ}\). Figure 31 shows the array factor that was calculated using the S-matrix values of our network. It can be seen that the 19 dB achieved SLL is close to the desired 21 dB. The beam crossover level is at 3 dB and the beams point to broadside, \(+30^{\circ}\), and \(-30^{\circ}\) with negligible deviations. Figure 30: _Cont._  
Figure 29: Response of the unequal split power divider: (**a**) amplitudes of the unequal split power divider output ports; and (**b**) phase difference between the unequal split power divider output ports.
## 3 Case Study
Figure 1: (**A**) Old mining infrastructures and, behind, the S. Pedro da Cova waste pile; (**B**) general view of the burning slope of S. Pedro da Cova waste pile where the gaseous emissions and burnt vegetation are visible; (**C**) detailed view of the burning slope showing the neoformed minerals in the surface of the slope (yellow—sulfur; white—salammoniac); (**D**) selected area for the prototype installation. The comparison of geochemical composition of the burnt/burning waste material and the unburned material allow for the identification of enhancement or depletion behavior of elements due to the combustion process [22]. The enrichment of elements may result from the elements' concentration due to the volatilization of other elements and organic matter consumption during the combustion process. The depletion of elements may be associated with their volatilization during combustion. However, other reasons for enrichment or depletion, such as the element's mode of occurrence, weathering, and/or leaching, should be considered. A cluster analysis considering data about the chemical composition of samples reveals a clear geochemical differentiation between burnt/burning waste material and unburned material [22]. The chemical composition of leachates obtained from S. Pedro da Cova waste material presents a higher concentration of elements in burning/burnt material when compared with unburned material, indicating that the combustion process affected the elements' mode of occurrence and their availability to be leached [24]. The higher element concentrations in leachates from burning/burnt waste material, the depletion of low-molecular-weight polycyclic aromatic hydrocarbons concentrations, and a greater abundance of high-molecular-weight polycyclic aromatic hydrocarbons from the unburned coal waste material are attributed to the geochemical changes induced by the burning process [24; 25]. The combustion process in the S. Pedro da Cova waste pile gave rise to atmospheric emissions of high concentrations of a wide variety of volatile organic compounds, including BTEX compounds (benzene, toluene, ethylbenzene, and xylene) and to the formation of sulfur and salammoniac, which results from the exhalation and condensation of gas by interaction with surface water, atmospheric gases and surrounding rocks [22].
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 1 Introduction
### Contribution and Organization  
In this paper, our objective is to maximize the sum capacity of all CUEs while guaranteeing allocation fairness among CUEs and the transmission reliability for each VUE pair. Note that we are using the terms CUEs and V2I links, VUEs and V2V links interchangeably in this paper. Our main contributions include the following:  
1. We utilize the effective capacity theory and LVP to measure the reliability of the V2V link and formulate the joint power control and RBs resource allocation problem with the objective of maximizing the sum data rate of CUEs with the \(\alpha\)-fair function. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem of NP-hard complexity.
2. To solve the formulated problem, we propose a novel matching algorithm based on the exchanged preference profiles to obtain the optimal spectrum allocation results using the matching game theory. We also analyze the optimality and convergence of our proposed algorithm.
3. Extensive simulations are conducted to evaluate our proposed algorithm's fairness, achievable throughput, and outage ratio performance under different vehicular networks. The simulation results show that our proposed algorithm is highly affected by the parameter \(\alpha\) in the \(\alpha\)-fair function and can obtain better efficiency and fairness trade-offs compared to the classical allocation method in [8].  
The rest of this paper is organized as follows. Section 2 establishes the system model. Section 3 introduces the \(\alpha\)-fair function and formulates the joint power control and spectrum resource allocation problem. The resource allocation algorithm is presented in Section 4. Section 5 discusses and analyzes the simulation results, and Section 6 concludes the paper.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 4 Results
### Number of Supported Peripheral Connections  
Table 1 shows the number of connections sustained for each configuration tested over three different distances: 0.5 m, 4.5 m, and 9.0 m. In general, increasing the connection interval results in more peripheral connections for a given event length. When looking at the event length, decreasing the event length for a given connection interval leads to more connections sustained. It was observed that, as the distance between the central and peripheral nodes was increased from 0.5 m to 9.0 m, the number of connections decreased by 50% or more for most of the combinations considered for a transmit power of +0 dBm. Notable exceptions to this trend are the connection interval-event length pairs {10 ms, 5000 us} and {10 ms, 7500 us}, wherein the number of connections was fully constrained by the event length. For example, only one transmission event of length 7500 us fits within a 10 ms connection interval, limiting the number of connections to one, regardless of distance.  
The Kolmogorov-Smirnoff test showed that the number of connections compared across the three distances were not normally distributed. The Friedman test, applied to compare the number of connections for each distance, detected statistically significant differences. To compare on a pair-wise basis, the Nemenyi post hoc test was applied. Statistically significant differences were found between 0.5 m and 9.0 m (\(p\) = 0.001) and 4.5 m and 9.0 m (\(p\) = 0.008). When comparing results from 0.5 and 4.5 m, no statistically significant differences were found. No statistical analysis was warranted as a function of the remaining parameters (connection interval and event length), since these results were not stochastic.