esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents an extended comparison study between 16 different linear and non-linear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the Savitzky--Golay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters.  
wine grape berries; hyperspectral imaging; linear and non-linear regression methods; penalized regression; variables importance +
Footnote †: journal: Applied Sciences  
1  
CITAB--Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Inov4 Agro--Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, Universidade de Tras-os-Montes e Alto Duoure, 5000-801 Vila Real, Portugal  
[EMAIL_ADDRESS] (V.G.); [EMAIL_ADDRESS] (A.M.-F.)  
Department of Chemical Engineering, University Coimbra, CIFQPF, Rua Stivo Lima, Polo II--Pinhal de Marroccos, 3030-790 Coimbra, Portugal  
[EMAIL_ADDRESS] (M.S.R.)  
WM&B--Laboratory of Wine Microbiology & Biotechnology, Department of Biology and Environment, Universidade de Tras-os-Montes e Alto Duoure, 5000-801 Vila Real, Portugal  
Bollis-Bosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal  
Douro, 5000-801 Vila Real, Portugal  
Correspondence:[EMAIL_ADDRESS]; Tel.: +351-2-5935-0753
# Three Dimensional CS-FEM Phase-Field Modeling Technique for Brittle Fracture in Elastic Solids
## 2 Methods
### Review of Phase-Field Model for Brittle Fracture  
Following the derivations in [25; 27; 39], we discuss briefly about the formulations of phase-field approximations for diffuse fracture modeling.  
According to the variational theory of fracture, the crack propagates in such a way the total energy of a system is always minimized. Thus, we approach by minimizing the energy functional. The total energy of a discontinuous system is given by  
\[\Pi=\psi_{\epsilon}(\mathbf{e})+\psi_{f}-\psi_{ext}(\mathbf{u}) \tag{8}\]  
where \(\psi_{\epsilon}(\mathbf{e})\) is the elastic strain energy as given in Equation (7) integrated over the entire volume \(\Omega\), \(\psi_{f}\) is the fracture surface energy and \(\psi_{ext}(\mathbf{u})\) is the external potential energy.  
The fracture surface energy \(\psi_{f}\) is given by  
\[\psi_{f}=G_{c}\int_{\Omega}\gamma(c)d\mathbf{\Omega} \tag{9}\]  
where \(G_{c}\) is the critical energy release rate proposed by [PERSON], \(c\) is the phase-field parameter and \(\gamma(c)\) is the density function which was calculated to be [28]  
\[\gamma(c)=\frac{1}{2}[\frac{1}{l_{c}}c^{2}+\frac{l_{c}}{2}|\
abla c|^{2}] \tag{10}\]  
where \(l_{c}\) is the length scale parameter.  
The external potential energy is given by the summation of the body force \(\mathbf{b}\) and the surface traction \(\mathbf{t}_{\Gamma}\):  
\[\Psi_{ext}(\mathbf{u})=\int_{\Omega}\mathbf{b}\mathbf{\cdot}udV+\int_{\Gamma}\mathbf{t}_{ \Gamma}\mathbf{\cdot}ud\mathbf{A} \tag{11}\]  
The total internal potential energy of a system can be written as the summation of the bulk energy and the energy required for the formation of crack [24; 28; 39]  
\[\Psi(\mathbf{u},c)=\int_{\Omega}[(1-c)^{2}+d]\psi(\mathbf{\epsilon})d\mathbf{\Omega}+\int _{\Omega}\frac{G_{c}}{2}[l_{c}\
abla c\cdot\
abla c+\frac{1}{l_{c}}c^{2}]d\bm {\Omega} \tag{12}\]  
where \(d\) is a numerical stabilization parameter.  
By variation of the external and internal energy potentials (using Equations (8)-(12)) and thereby imposing the principal of virtual displacements we obtain the governing equations of the model:  
\[[(1-c)^{2}+d]\frac{\partial\sigma_{ij}}{\partial\mathbf{x}_{i}}+\mathbf{b}_{j}=0\quad \text{in}\quad\mathbf{\Omega} \tag{13}\]  
\[[(1-c)^{2}+d]\mathbf{n}_{i}\sigma_{ij}=\mathbf{t}_{j}\quad\text{in}\quad\mathbf{\Gamma}_{ \mathbf{t}} \tag{14}\]  
\[\mathbf{u}_{j}=\mathbf{\bar{n}}_{j}\quad\text{in}\quad\mathbf{\Gamma}_{\mathbf{u}} \tag{15}\]\[-G_{c}l_{c}\frac{\partial^{2}c}{\partial x_{i}\partial x_{i}}+[\frac{G_{c}}{l_{c} }+2\psi(\epsilon)]c=2\psi(\epsilon)\quad\text{in}\quad\mathbf{\Omega} \tag{16}\]  
\[\frac{\partial c}{\partial x_{i}}n_{i}=0\quad\text{in}\quad\mathbf{\Gamma} \tag{17}\]
# A Preliminary Analysis on the Insecticidal Effect of Cyantraniliprole against Stored-Product Pests
## 1 Introduction  
The protection of stored agricultural products (cereals, legumes, flours, dried fruits, etc.) from insect pests is based on the use of two main categories of insecticides: fumigants and residual insecticides [1]. After the phase-out of methyl bromide, its main alternative aerial insecticide, phosphine, has been the most important fumigant for use on stored-grains and other durable products [2]. However, the continuous use of phosphine has certain disadvantages, such as corrosiveness and resistance development [2, 3, 4, 5]. Moreover, new fumigants that can be used with success against stored-product pests (SPP) do not appear in the market due to reduced effectiveness or the high cost of the application and registration process [6]. In order to delay the development of resistance, new insecticides with different action mechanisms could be rotated with existing insecticides.  
Cyantraniliprole is a second-generation ryanodine-receptor insecticide discovered after chlorantraniliprole by DuPont Crop Protection and belongs to the diamide family, group 28 [7, 8, 9, 10, 11]. New insecticides with different modes of action are necessary today to replace pyrethins and other commonly used but less effective alternatives [8]. This new insecticide group acts by binding and activating the ryanodine receptors in insect striated muscle cells, provoking calcium release from internal stores and causing continuous muscular contraction, paralysis, and death [6, 7, 8]. Cyantraniliprole has been reported to be effective against a broad spectrum of insect pests, including lepidoptera, dipteran leafminers, aphids, leafhoppers, psyllids, beetles, whitfflies, thrips, and weevils [8, 12, 13, 14, 15, 16]. Owing to its structural specificity to insect ryanodine receptors over mammalian counterparts,cyantraniliprole has also been shown to be safe for non-target vertebrates [8; 16]. No studies on its efficiency against SPP have been carried out until today.  
The aim of the present study is to investigate, for the first time, the insecticidal effect of cyantaniliprole against common SPP beetle species, such as the mealworm beetle (_Tenebrio molitor_ L.), the confused flour beetle (_Tribolium confusum_ [PERSON]), the lesser mealworm (_Alphitobius diaperinus_ ([PERSON]) (Coleopterara: Tenebrionidae)), the lesser grain borer (_Rhyzopertha dominica_ ([PERSON]) (Coleopterara: Bostrychidae)), and the khapra beetle (_Trogoderma granarium_ [PERSON] (Coleopterara: Dermestidae)). Our results are discussed in the context of exploring new strategies of pest management in storage facilities and minimizing the damage caused by SPP.
## 3 SAR Payload Design
### System Parameter Design
Therefore, the antenna length of the satellite in the S-STEP was selected as 2 m for the stripmap mode operation with a high resolution of 1 m. Based on the iterative design optimization process, the SAR payload system parameters were selected, as listed in Table 3. Based on the above description, the design workflow of the S-STEP SAR payload is illustrated in Figure 5.  
Figure 5: Design workflow of the SAR payload of S-STEP according to the required resolution, swath width, and NESZ sensitivity where \(\rho_{r,req}\) required range resolution, \(\rho_{a,req}\) required azimuth resolution, \(\sigma_{NE,req}\) required NESZ, \(W_{s,req}\) required swath width in range, \(B_{d}\) Doppler bandwidth, \(A_{a}\) antenna area, \(L_{a}\) antenna length, \(W_{a}\) antenna width, \(f_{t}\) transmitted frequency, \(\eta\) antenna efficiency, \(f_{p,min}\) minimum PRF, \(f_{p,max}\) maximum PRF, \(R_{n}\) near range of swath, \(R_{f}\) far range of swath, \(W_{s}\) swath width in range, \(\tau_{w}\) time duration of swath, \(\tau_{m}\) guard time, \(G\) universal gravitational constant, \(M_{e}\) mass of the Earth, \(R_{e}\) radius of the Earth, and \(H_{s}\) satellite altitude, respectively.
## 5 Experiments
### Discussion
Simulated mappings with many tiles have a lower error when the Message Level (ML) model is used (Sobel, 4 Tiles and JPEG, 7 Tiles). Because the CA and TL models are very explicit, small errors can accumulate to huge errors when lots of communication takes place. The ML model models a communication phase in total, so that the error is independent from the amount of polling attempts and communicated tokens. Figure 8: Comparison of the simulation results to the observed behavior of the Use-Cases on a real platform. The impact of the communication model on the simulation results can be seen in Figure 8b. While using the transaction level model can lead to negative errors, the message level model always leads to positive errors. The ML model becomes more accurate for high communication load (many tokens to communicate, much inferences). Beside the higher accuracy, the ML model improves also the simulation speed. The results in Table 3 show that simulation using the message level communication model is fastest. They also show that there is only an increase in simulation time between single and multi-processor systems. The increase in simulation time between a 2 Tile and a 4 Tile system for the Sobel experiment, or the 3 Tile and 7 Tile mapping for the JPEG experiment is minimal. The fast yet accurate simulation allows using our approach for Design Space Exploration (DSE) over different architectures and mappings.
## 2 Materials and Methods
### Measurement
#### 3.1.4 MSCR Test  
MSCR tests were conducted on pre-cut SBS-modified asphalt samples mixed for different amounts of time. The experimental results are presented in Figure 6.  
Figure 5: Temperature sweep test of pre-cut SBS-modified asphalt mixed for different amounts of time. (**a**) Complex modulus, (**b**) phase angle.  
The rutting recovery rate is related to deformation recovery capability. In Figure 6a, it can be observed that the rutting recovery rate of the pre-cut SBS-modified asphalt mixed for 15 min was weaker than that of the conventionally prepared SBS-modified asphalt. This was attributed to the inadequate mixing of the modifier with the asphalt, resulting in a poor modification effect. However, after mixing for more than 30 min, the rutting recovery rate of the pre-cut SBS-modified asphalt exceeded that of the conventional-process asphalt. This suggests that after mixing for 30 min, the elastic recovery performance of pre-cut SBS-modified asphalt can replace that of conventionally prepared SBS-modified asphalt. When mixed for 30-60 min, longer mixing times resulted in higher rutting recovery rates and stronger deformation recovery capability. This was due to the filamentous structure of the SBS, which increased the interlocking effect and strengthened the spatial network structure. After mixing for 120 min, the rutting recovery rate decreased due to the atomized particle form of the SBS, which weakened the strength of the network structure.  
Non-recoverable creep compliance reflects resistance to deformation. In Figure 6b, it can be seen that the non-recoverable creep compliance of the pre-cut SBS-modified asphalt mixed for 15 min was greater than that of the conventional-process asphalt. This indicates that at this stage, SBS was unevenly dispersed with a large particle size, resulting in a poor modification effect due to inadequate mixing with the asphalt. However, after mixing for 30 min, the non-recoverable creep compliance of the pre-cut SBS-modified asphalt was lower than that of the conventional-process asphalt, demonstrating an improved resistance to deformation which surpassed that of the conventional SBS asphalt. However, after 60 min of mixing, the deformation resistance was weaker than that at 30 min, indicating that when SBS is in a filamentous state, smaller filament width and granular diameter lead to poorer deformation resistance. After mixing for 120 min, the deformation resistance was further weakened due to the atomized particle form of SBS.
## 3 Experiments
### Static Route Planning Experiments by SPA*
The algorithms in traditional path planning do not bypass the medium-risk area of the path; in the SI-based path planning algorithm, ACO falls into the local optimal solution and crosses the high-risk area. At 400 \(\times\) 400, SPA* also identifies and avoids risk areas. The algorithms in traditional path planning and SI-based path planning began to show serious performance deficiencies. Most of the paths in them appear as straight lines without planning purpose, just like the shortest path without considering risk. In particular, all the planned routes in SI-based path planning have a route very close to the island, which undoubtedly increases the risk of ship grounding. In general, the time of SPA* is reduced by about 5\(\sim\)12,425 times compared to each algorithm, and the R-VIMO of it is always maintained at 0. At the same time, in the control of \(f_{1}\) and \(f_{2}\), the performance of SPA* is improved by 7.68%-25.14% and 8.44%-14.38%, respectively, compared with each algorithm. According to the experiments in this section, SPA * shows excellent and fast risk planning ability in static navigation path planning.
## 3 Immersion and Presence  
Immersion is an important attribute of a VR experience and commonly defined as disconnecting the user from the real world and giving them a sense of being/presence in the virtual world [2; 11; 22] and is often referred to as Spatial Immersion [19; 23]. Contrary to popular belief, immersion and presence are different from each other [24]. [PERSON] [25] explains the difference through the use of colours, where a human's colour perception is different (presence), although that particular colour has a unique wavelength which defines it (immersion). In terms of VR, people can experience different levels of presence from the same immersive experience, similar to the colour example, and is defined as \"human reaction to immersion\" [25] (p. 2). Hence, presence relates to the amount of sensory modalities engagement, which are activated by the physical properties and the close resemblance of the virtual environment to the real one, known as immersion [25]. On the other hand, [PERSON] [26] argues that immersion is not limited to the physical dimensions or properties of the system, rather, it is related to the user's response to the narrative. In [PERSON]'s explanation, immersion is a result of our brain's indulgence in the narrative to an extent that it transports us from the current space to the virtual environment [27]. [PERSON] [26] further argues that there are levels of immersion and that the system does not need to have a complete \"photo- and audio-realism\" (p. 68) for it to be immersive; rather, the narratives are set to align the user's expectations and actions to the conventions of the virtual world to achieve immersive experience. In contrast, [PERSON], et al. [1] argue that presence as a term is often confused, and its definition depends on the academic discipline. Hence, they prefer using the term spatial presence as it closely matches the VR effect of being present in the virtual world [1]. They also argue that spatial presence can be achieved by closely mimicking the real environment visually and audially, as well as through the psychological involvement of the user through the activation of sensory modalities [1]. By looking at all these arguments, we can deduce that: the VR application must utilize features such as stereo sound, stereoscopic vision, realism, large field of view, high frames refresh rate (FPS), and head tracking, in order to engage sensory modalities to both immerse and transport the user away from the real to imaginary environment. It also must include narrative elements that sets users' expectations and aligns it with their actions, with the conventions of the virtual world to maximize their immersive experience.
# Three-Dimensional Scaffolds Designed and Printed Using CAD/CAM Technology: A Systematic Review
## 1 Introduction
### Advantages of 3D-Printed Scaffolds for Bone Regeneration  
1. Customization: three-dimensional printing enables the production of patient-specific scaffolds that are suited to the individual's requirements in terms of histocompatibility, transplant size and bone growth pace [11].
2. High porosity: three-dimensional-printed scaffolds have high porosity, allowing for the penetration of cells and nutrients, which promotes tissue regeneration [11,12].
3. Controlled release of biomolecules/drugs: three-dimensional-printed scaffolds can be configured to release biomolecules and medications in a controlled manner, potentially improving bone regeneration [13].
4. Mechanical strength: adequate mechanical strength ensures integrity after implantation. Bone scaffolds must have temporary mechanical stability, and 3D printing enables the fabrication of scaffolds with mechanical qualities similar to those of real bone [14].
5. Bioprinting technology enables the insertion of living cells and/or growth factors into scaffolds with the goal of emulating the structure and function of natural bone [15].  
Additive manufacturing offers the advantage of enabling the creation of scaffolds with a variety of micro- and macrostructures. This is significant because the size and direction of the material's pores, as well as its composition at the microscopic level, influence the formation of the vascular and bone structures of the tissue to be regenerated [3; 4; 13].
## 8 Cross-Layer Optimization
### Summary  
There are generally two categories of cross-layer optimization [132]. The first one retains the overall existing architecture and extends interactions among layers, as illustrated in the conceptual model of this paper. There is already a multitude of works dedicated to exploitation of such opportunities to adapt to various requirements in the context of _IoT_. The inefficiency of traditional protocol architectures also gives rise to the second type of optimization, which takes more radical approaches [128]. Both cross-layer module (XLM) [133] and content-centric networking (CCN) propose novel architectures. XLM incorporates all network layers into one singular entity, and conventional cross-layer communication is made intrinsic. Content-centric networking (CCN), on other hand, is built upon the core abstraction which is shifted from the location identifiers such as the IP address to the content name. Compared with the first type where optimization is selectively done within the existing framework, CCN provides a holistic revolution which can potentially be the infrastructure of future _Internet of Things_.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## Appendix C Proof of Theorem 2
\begin{split}\mathcal{M}_{\phi_{k}}(1-s)&\leq\epsilon_{k}^{ d}+\left(1-\epsilon_{k}^{d}\right)\int_{0}^{\infty}\left(\frac{1+\hat{\gamma}_{k}}{ \exp\left(\sqrt{\frac{\hat{\gamma}_{k}^{2}+2\hat{\gamma}_{k}}{(1+\hat{\gamma}_ {k})^{2}\hat{\gamma}_{k}}Q^{-1}(\epsilon)}\right)}\right)^{-\frac{s}{\ln^{2}} }f_{\hat{\gamma}_{k}}(x)dx+\left(1-\epsilon_{k}^{d}\right)F_{\hat{\gamma}_{k}}( \gamma_{0})\\ &\overset{(d)}{\approx}\epsilon_{k}^{d}+\left(1-\epsilon_{k}^{d} \right)\int_{0}^{\infty}\zeta_{k}^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}}(x) dx+\left(1-\epsilon_{k}^{d}\right)F_{\hat{\gamma}_{k}}(\gamma_{0})\\ &=\epsilon_{k}^{d}+\left(1-\epsilon_{k}^{d}\right)\int_{0}^{ \infty}e^{-\frac{s}{\ln^{2}}\ln^{2}\hat{\gamma}_{k}}f_{\hat{\gamma}_{k}}(x) dx+\left(1-\epsilon_{k}^{d}\right)F_{\hat{\gamma}_{k}}(\gamma_{0})\\ &\overset{(e)}{\approx}\epsilon_{k}^{d}+\left(1-\epsilon_{k}^{d} \right)\int_{0}^{\infty}e^{-\frac{s}{\ln^{2}}(\hat{\gamma}_{k}-1)}f_{\hat{ \gamma}_{k}}(x)dx+\left(1-\epsilon_{k}^{d}\right)F_{\hat{\gamma}_{k}}(\gamma_{ 0})\\ &\overset{(f)}{\approx}\epsilon_{k}^{d}+\left(1-\epsilon_{k}^{d} \right)M_{\gamma_{k}}\left(-\frac{s}{\ln^{2}}\right)+\left(1-\epsilon_{k}^{d} \right)F_{\hat{\gamma}_{k}}(\gamma_{0})\\ &=\mathcal{I}_{k}\Big{(}s,\epsilon_{k}^{d}\Big{)}+\mathcal{G}_{k }\Big{(}\gamma_{0},\epsilon_{k}^{d}\Big{)}\\ &\quad\text{ where }\mathcal{I}_{k}\Big{(}s,\epsilon_{k}^{d} \Big{)}=\left(1-\epsilon_{k}^{d}\right)M_{\hat{\gamma}_{k}}\Big{(}-\frac{s}{ \ln^{2}}\Big{)},\mathcal{G}_{k}\Big{(}\gamma_{0},\epsilon_{k}^{d}\Big{)}= \epsilon_{k}^{d}+\left(1-\epsilon_{k}^{d}\right)F_{\hat{\gamma}_{k}}(\gamma_{ 0}).\;(d)\;\text{and }(f)\;\text{are obtained from }\hat{\gamma}_{k}\gg 1.\;(e)\;\text{is approximately obtained in terms of the first-order expansion of the Taylor formula. Theorem 2 is proven. \\ \end{split} \]
## 5 Environmental Analysis of GERO Perforated Brick with Olive Stone Doped Mortar
### Olive Stone Grinding  
In order to use ground olive stones as fine aggregate in the mortar that makes up the GERO-type perforated bricks, they must undergo pretreatment. This process consists of three subprocesses or stages. Firstly, the olive stone from olive oil production contains olive pulp, peel and water attached to it. This mixture is called olive pomace and has a high moisture content. Therefore, the first subprocess will be a drying process to reduce the moisture content, to later submit it to a grinding process to the dimensions of fine aggregate and, finally, a screening process to obtain homogeneous raw material. The machines used consume only electrical energy. The LCI of the olive grinding preparation is obtained from the research conducted by [PERSON] et al., where they evaluated the use of olive pomace in composite materials [55]. Electricity consumption has been adapted to Spain's national grid. According to this research and selecting only the processes that affect the production of olive stone grinding, the consumption of 0.21 kWh/kg of olive stone grinding stands out [55].  
Transportation  
This section justifies the distances between the raw material production site and the point of manufacture of the prepared mortar mix. The unit of transport defined is the tone per kilometer (tkm). Commonly, raw materials are transported with a 16-32-ton truck using a diesel engine. In order to obtain the values of the distances, they are based on the research of [PERSON] et al., where they evaluate the preparation, transport and placement of concrete in Spain [57].  
For fine aggregates, an average range of 15-60 km from the quarry to the preparation plant is established. In this research, a value of 30 km is chosen. In the case of cement, the distance is considerably increased due to the fact that there are not as many cement production plants. [PERSON] et al., indicate a mean transport range of 50-400 km [57]. Therefore, it was decided to choose the value of 200 km. Finally, the olive stone must travel from its place of production in the oil mill to the mortar production plant. For this purpose, the distance of 30 km is chosen again, the same as for fine aggregate. Table 12 shows the values of the transport variable.