esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description  
### Description of the Model  
The process of analyzing the model was highly nonlinear, and therefore, involved very complex calculations as two non-linearities intervened at the same time: the geometric nonlinearity and the nonlinearity of the material's behavior in terms of the stress-strain curve. The decreasing branch of the resistance to rupture was included in this nonlinearity.  
To build the wireframe model, the module APDL of Ansys (Version 16.2 and 17.2, Company: Ansys Inc., Canonsburg, Pennsylvania, United States, 2015), whose research license is held at the Department of Civil Engineering of the University of Alicante (Alicante, Spain). The intention was to simulate three prototypes (see Table 1):  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prototype** & **Typology** & \begin{tabular}{c} **Concrete Sections** \\ **(mm \(\times\) mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Rebar** \\ **Reinforcement** \\ \end{tabular} &
\begin{tabular}{c} **Cross-Sections** \\ **Supports (mm)** \\ \end{tabular} \\ \hline P03 & RC & \(300\times 250\) & 4 o 12 & - & 3300 \\ P04 & SRC & \(300\times 250\) & 4 o 12 & HEB-100 & 3300 \\ P05 & RC & \(300\times 250\) & 2 o 16 + 2 o 20 & - & 3300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table summarizing the performed tests, RC: reinforced concrete, SRC: steel-reinforced concrete.  
The experimental tests are detailed in [PERSON] et al. [17]. The first model, P03, aimed to describe reinforced concrete. The model P04 had the same reinforcement by including an HEB-100 cross-section in the central part (which covered a length of 2000 mm). The third model, P05, had a reinforced concrete beam capable of supporting a similar load to the P04 model, but without the steel section (Figure 1).  
The characteristics of the joint in the reinforced concrete structures with the embedded steel cross-sections were essential to understanding their behavior because it increased the rigidity and ductility at the most vulnerable point, mainly in order to withstand seismic activity.  
As the applied deformations were progressively increased, the following states represented in Figure 2 could be distinguished.
## 3 Results and Discussion
### Water Absorption Index  
The _WAI_ characterizes the amount of water absorbed by the material [27]. It can be seen from Figure 1 that the _WAI_ increased when the working screw speed decreased, and the effect of the amount of chokeberry pomace and the feed moisture were insignificant in the investigated range. Figure 2 shows that the effect of the amount of chokeberry pomace was higher at low level of feed moisture than at high level. The screw speed influenced the process strongly at high level of feed moisture (20%), and slightly at low level (14%). [PERSON] et al. [24] established values of _WAI_ from 1.6 g/g to 2.9 g/g during extrusion of chokeberry pomace with a twin-screw extruder. Our values were higher, from 4.28 g/g to 6.56 g/g, which can be explained by the use of rice as main raw material for extrusion in our study. [PERSON] et al. [7] found that the increase in chokeberry amount in the initial mixture increased the water absorption of the snack pellets. This is in accordance with our results and can probably be explained by the increase in the number of components able to absorb water, like fibers and proteins [43]. [PERSON] et al. [33] established that the _WAI_ decreased significantly (_p_ < 0.01) as the percentage of tomato pomace increased. This means that the relationship between the pomace level and _WAI_ depends on the type and composition of pomace.
## Materials and Methods  
### Materials  
Desmopan(r) 6064A TPU (density = 1.09 g/cm\({}^{3}\), melting temperature = 200 \({}^{\circ}\)C) was kindly provided by Covestro Srl (Milano, Italy) in the form of pellets. This injection-molding-grade TPU is used in various applications, such as shoe soles and automotive interiors, due to its excellent mechanical properties, low abrasion tendency, complete lack of plasticizers, and microbial resistance. MWCNTs were provided by Nanocyl SA (Sambreville, Belgium) under the trade name Nanocyl NC7000 TM. They were produced via catalytic chemical vapor deposition (CCVD) and supplied as a dry powder. The manufacturer's datasheet reported an average diameter of 9.5 nm and a length of 1.5 \(\upmu\)m, a BET specific surface area of 250-300 m\({}^{2}\)/g, and a volume resistivity of 10\({}^{-4}\)\(\Omega\)-cm. Microtek MPCM6D microencapsulated paraffin (named M6D in this work), provided by Microtek Laboratories Inc. (Dayton, OH, USA), was utilized as PCM. In this PCM, paraffin wax characterized by a melting temperature (Tm) of 6 \({}^{\circ}\)C and a crystallization temperature (Tc) of about \(-\)10 \({}^{\circ}\)C was encapsulated inside a stable melamine formaldehyde polymeric shell. The resulting particles had a mean diameter of 17-20 \(\upmu\)m and a density of 0.9 g/cm\({}^{3}\) and were 85-90 wt% paraffin. Their melting enthalpy, according to the producer's datasheet, was in the interval of 157-167 J/g. All these materials were used as received.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Preparation of PNIPAM/Magnetite Nanoparticles  
PNIPAM is synthesized by free-radical polymerization in aqueous solution using NIPAM monomer and an initiator to trigger the reaction at 60 \({}^{\circ}\)C. Briefly, the monomer was dissolved in water (5%), and after the complete dissolution of the monomer, the solution was purged by bubbling nitrogen for 20 min. After that, the initiator KP (5 \(\times\) 10\({}^{-3}\) mol/mol) was added to the solution, and the reaction vessel was kept at constant temperature for 18 h. The obtained PNIPAM was dialyzed against distilled water to remove the unreacted monomer. Furthermore, the polymer was dried at 60 \({}^{\circ}\)C for 24 h. Next, PNIPAM aqueous solutions with various concentrations were prepared (Table 1) by dissolution into phosphate-buffered saline solution (pH 7.45) at room temperature. The magnetite nanoparticles were also dispersed in PBS by sonication. Polymer solution was added dropwise in a water-miscible organic solvent, acetone, under high stirring to prepare the PNIPAM/magnetite nanoparticles. The nanoprecipitation procedure involved the nanoparticles assembling at the contact with the non-solvent phase (acetone). The nanoprecipitation was performed at three different temperatures for non-solvent phase (30, 34, and 38 \({}^{\circ}\)C) to show the influence of the temperature sensitive PNIPAM behavior on the nanoparticle's characteristics.  
The drug-loaded nanoparticles were obtained by encapsulation of the anti-cancer drug, doxorubicin. Briefly, the doxorubicin was dissolved in PNIPAM aqueous solutions with different concentrations (Table 1). The PNIPAM/drug aqueous solution was nano precipitated according to the above procedure. The drug-loaded nanoparticles were recovered by centrifugation (9000 rpm) and oven-dried at 50 \({}^{\circ}\)C. The dried drug-loaded nanoparticles were further used for drug release analysis (all samples) and biological assessment (only sample prepared at 34 \({}^{\circ}\)C).  
2.2 Characterization of Magnetic Nanoparticles by X-ray Diffraction (XRD) and Transmission Electron Microscopy (TEM)  
X-ray diffraction (XRD) spectra for synthesized magnetite nanoparticles were registered on a PANalytical X'ERT MPD X-ray Diffractometer in the range 20 = 10-80. An X-ray beam characteristic to Cu K\(\alpha\) radiation was used (\(\lambda\) = 1.5418 A). Geometrical evaluation (size and shape), including the crystalline structure of magnetic nanoparticles, was investigated by high-resolution transmission electron microscopy (HR-TEM) using a TECNAI F30 G2 S-TWIN microscope operated at 300 kV with Energy Dispersive X-ray Analysis (EDAX) facility.
## 4 Experiment Verification
### Experimental Results
#### 4.2.1 Comparison without Transfer with Individual Models
F is a good index to evaluate models comprehensively. Figure 11 shows the test data set F of DTLCL and other different models. The F value of the DTL model is greater than 75% for every error, especially for N, OF, IF, RF, GPF in Test 1 and Test 2. Most of the other models are below 70%. The above results also confirm that compared to the other models without transmission, the DTLCL approach performs better. Figure 10: R of different models. Based on the above results, it can be assumed that the indicators of accuracy, precision, detection and comprehensive assessment are improved by DTLCL. Moreover, the results of DTLCL are more precise, consistent, and generalizable depending on the number of samples. From the analysis of the above results, it can be inferred that DTLCL has improved the indicators of accuracy, precision, detection and comprehensive assessment. Moreover, depending on the number of samples, the results of DTLCL are more repeatable, accurate and have high generalisation ability. The accuracy rises with the size of the sample. Figure 11: F of different models.
# Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer
## 2 Materials and Methods
### The Employed Object Detection Model as Part of the Depalletization System
#### 2.2.1 The EfficientDet-D0 Architecture  
The authors considered the EfficientDet-D0 architecture because of both its relatively high performance on COCO, with respect to the Average Precision (AP) metric (\(AP\), \(AP_{50}\), \(AP_{75}\) equal to 34.6, 53.0, 37.1, respectively) and, especially, its very low inference time, i.e., 10.2 ms with an NVIDIA(r) Tesla(r) V100 [33]. In fact, both the number of parameters and the FLOP value of the selected architecture are very low and equal to 3.9 M and 2.5 B, respectively. It is worth reporting that such values of the AP are related to the 91 classes of the COCO Dataset and are lower for almost 20% with respect to the performance of the best EfficientDet-D7 [33], which is characterized by an inference time equal to 122 ms (exploiting the same GPU card), a FLOP value equal to 325 B and 52 M of the parameters. Considering the fact that the studied application is based on only one object class and that time is a critical resource in the depalletization system, in the authors' opinion, the D0 version represents a good solution. Moreover, by running several inference tests on a workstation based on an i7 family CPU (32 GB RAM, Intel(r) Core i7-9700E processor and an Integrated Intel(r) UHD graphics 630), it emerged that the average inference time was equal to 0.652 s. The inference time represents 7.70% of the whole process, starting from the 2D and 3D scans of the environment to the pick and place and assuming a robot movement time of 5 s.  
Figure 3 shows EfficientDet's architecture [33], which is the one-stage detector. EfficientNet-B0 was employed as the backbone network, with the transfer learning of ImageNet. This architecture presents a novel feature network, the BiFPN, which takes Level 3-7 features \(\{P_{3},P_{4},P_{5},P_{6},P_{7}\}\) from the backbone network and applies bottom-up and top-down bidirectional feature fusion several times. These fused features are the input of the class and box prediction layers, which perform the object classification and bounding box.  
The backbone network adopts the same width/depth scaling coefficient of EfficientNet-B0 [34] such that it is easy to reuse the pretrained ImageNet checkpoints. The BiFPN has a width and depth:  
\[W_{BiFPN}=64(1.35^{\phi})=64,\quad D_{BiFPN}=3+\phi=3 \tag{1}\]  
keeping in mind that \(\phi=0\) for EfficientDet-D0. The box/class prediction network has a width and depth, respectively:  
\[W_{pred}=W_{BiFPN}=64,\quad D_{box}=D_{class}=3+int(\phi/3)=3 \tag{2}\]The input image resolution must be divisible by \(2^{7}\) since Feature Levels 3-7 were used, and for this reason, \(R_{input}\) is calculated using the equation below:  
\[R_{input}=512+128\cdot\phi=512 \tag{3}\]
## 1 Introduction
Although GAN-based methods can achieve better fusion results, problems have gradually emerged during research. Existing methods only perform feature fusion at a single layer and do not consider the interaction of information between different scales, which to some extent limits the fusion performance of their networks. To address the limitations of existing methods, we propose a cross-scale differential features attention generative adversarial network for infrared and visible light image fusion. This method is an end-to-end model based on Wasserstein generative adversarial networks(WGAN) [10] and dual discriminator conditional generative adversarial network (DDcGAN) [11]. Firstly, unlike existing simple fusion strategies, we designed a cross-modal attention module to measure the corresponding activity levels at different scales separately. Secondly, to enable the interaction of feature information across different scales, we designed a cross-scale attention decoder. This decoder is capable of fusing feature information across different scales in a sequential manner from deep features to shallow features. We proposed a parallel path of differential  
by the AE network. To alleviate the domain difference problem, [PERSON] et al. proposed a self-supervised IVIF feature adaptive network framework: SFA-Fuse [15]. This network includes a self-supervised feature adaptive network and an enhanced network coordinated by edge details and contrast-based loss functions. To further improve the fusion performance, some researchers have introduced attention mechanisms into autoencoders. [PERSON] et al. [8] proposed the NestFuse fusion method, introducing a nested connection architecture and a spatial/channel attention model into the AE network. Subsequently, [PERSON] et al. proposed a symmetric encoder-decoder network with residual blocks, named SEDRFuse [16]. They designed a feature fusion strategy based on attention maps, thereby preserving more information from different modality images. [PERSON] et al. [17] proposed a fusion network based on dense Res2 net and dual non-local attention models. Thereafter, they introduced UNFusion [18]. Dense skip connections are introduced into the AE network to extract the feature correlation of the intermediate layer. The problem of insufficient fine features and coarse-scale features is solved by designing a multi-scale network structure. The Lp normalized attention model is used as the fusion strategy. These methods propose attention-based fusion strategies and use these attention models to refine and integrate features.
## 2 Research Method
### Numerical Model  
#### 2.1.1 Simulation Method  
In this paper, a 3D tunnel-strata simulation model was established using MIDAS/GTS 12 [23; 24; 25]. In the numerical model, eight-node hexahedron elements are used to simulate the strata, primary support, secondary lining, track plate, concrete support layer, and inverted arch filling layer. The model of steel rails is not established, and the wheel-rail superposition coefficient and rail dispersion coefficient are used to reflect the influence of the wheel-rail force acting on the steel rail instead [26]. The reinforcement effect of the anchor rod on the surrounding rock is simulated by improving the mechanical parameters of surrounding rock in the anchor zone to avoid the influence of bolt setting on the efficiency of dynamic analysis. Material properties of strata are described by the elastoplastic model and the Mohr-Coulomb yield criterion; other parts are described by the elastic model.  
To deal with the dynamic problems in this paper, the static and dynamic internal forces of the surrounding rock and tunnel lining were calculated separately under the action of dynamic and static loads, and the total internal stress and displacement of tunnel lining were obtained by superimposing those under the dynamic and static loads linearly. The influence of the 5 parameters was analyzed, which are rock level (\(\lambda\)), the way the train passes through the two tunnels (\(\kappa\)), train speed (\(v\)), tunnel cross angle (\(\theta\)), and the rock thickness between the two tunnels (\(H\)). The total number of numerical models is 18, as is shown in Table 1.  
Taking the intersection of upper and lower tunnels as the center, a distance of 50 m was selected from the center to the left, right, front and back boundaries, as is shown in Figure 2. The burial depth of the upper tunnel is 32 m, and the bottom boundary is 50 m, away from the tunnel inverted arch. To eliminate the wave reflection at left, right, front, back, and bottom boundaries, tridimensional viscoelastic artificial boundaries were adopted in this paper, and springs and damp elements were used to achieve this function. The normal and tangential damping ratios are 1.0 and 0.5, respectively. Analysis sections are selected with an interval of 10 m along the longitudinal direction of both the upper tunnel and the lower one, and measuring points are located at the tunnel vault, side wall, and tunnel inverted arch in each section.  
The strata, tunnel lining, and track slab were considered as a continuously homogeneous medium. Table 2 shows the mechanical parameters of the strata used in the numerical models. The mechanical parameters of secondary lining, concrete foundation, filling layer, primary support, and track plate, in turn, refer to that of C35, C30, C25, C25, and C45 concrete, which are the  
Figure 1: Stress diagram of the tunnel inverted arch.  
in Chinese code GB50010-2010 [27]. For example, concrete with strength grade C30 refers to the compressive strength of standard cubic specimen of concrete not less than 30 MPa.
## 3 Open-Area Environmental Interference Suppression Model  
A deep-learning-based environmental interference suppression method is proposed in this section, named the EMC environmental interference suppression algorithm (\(E^{2}ISA\)), which can suppress the environmental interferences from the internal signal accurately and efficiently.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanofluids
#### 3.3.2 Temperature  
Some reports indicate that the thermal conductivity is enhanced with the temperature increase, while others reported a reduction of the thermal conductivity with the same effect. Few others stated that no changes were observed with temperature variations. For instance, [PERSON] et al. [53] and [PERSON] et al. [54] registered a dependent behavior between the thermal conductivity of the NFs and temperature, while [PERSON] et al. [55] reported an independent behavior. [PERSON] et al. [56], on the other hand, observed a decrease of the thermal conductivity with increasing temperature in a water-based TiO\({}_{2}\) NFs. The enhancement of the thermal conductivity with increasing temperature is thought to be caused by the improvement of the Brownian motion and the reduction of the surface energy of the particles. On the contrary, a reduction of the thermal conductivity with increasing temperature was reported for NFs with non-spherical particles, which might indicate that the aspect ratio of the particle has a relevant influence [5; 26].
###### Abstract  
The stability of riverbank slopes is crucial in watershed ecology. The morphology and tensile strength properties of plant roots play a significant role in slope stability, which is of great importance for the ecological stability of riverbanks. The Jinsha and Yalong River basins are the largest hydropower bases in China and are in the ecologically fragile areas of the dry and hot river valleys, yet fewer studies are available on these basins. Further studies on the growth morphology and root mechanical properties of plant roots in the riparian zone at different elevations have not been reported. Therefore, we selected the dominant species of _Cynodon dactylon_ root as the research subject, analyzed the root morphology, and conducted indoor single-root tensile tests to study its root structure and mechanical properties at various elevations. The results showed that the root morphology of _Cynodon dactylon_ was positively correlated with elevation. Compared to low elevations (L and M), the root length increased by 57.3% and 21.47%, the root diameter increased by 24.85% and 13.92%, the root surface area increased by 93.5% and 67.37%, and the total root volume increased by 119.91% and 107.36%. As the elevation gradient increased, the flooding time decreased, leading to more developed plant roots for _Cynodon dactylon_. The Young's modulus ranged from 148.43 to 454.18 MPa for Ertan Cynodon dactylon roots and 131.31 to 355.53 MPa for Guanyingyan Cynodon dactylon roots. The maximum tensile strength, ultimate tensile strength, ultimate elongation, and Young's modulus of the plant root of the _Cynodon dactylon_ showed a power function relationship with the diameter. The maximum tensile strength increased as the diameter increased, while the remaining properties decreased following a power function relationship. The maximum tensile strength, ultimate tensile strength, and Young's modulus of _Cynodon dactylon_ were positively correlated with elevation, while the ultimate elongation was negatively correlated with elevation. The results elucidate the influence of elevation on the root morphology and mechanical properties of dominant riparian species. This provides a theoretical basis for managing and protecting riparian slopes in ecologically fragile areas.  
riparian zone; _Cynodon dactylon_; root morphology; tensile properties; elevation
## 3 Materials and Methods
### Information Model  
The design of the information model for the data records required the consideration of many factors. The model had to take into account the needs of the users while meeting the assumptions concerning the construction of the metadata system (based on the relational-graphic model, taking into account the requirements of the concept of linked open data, LOD) and the limitations related to the preparation and provision of associated data. By assumption, every data record consists of:metadata--a set of mandatory (e.g., name, description, author, keywords) and optional elements (e.g., links to related resources). Some of these elements are of a simple type (can be represented by literals), while others are references to existing objects using URIs. Such URIs should make it possible to create links to reference registers (internal or external).
* data--an optional part, which might be represented by one or many files.
* license --information about license defined at a record level.  
Additionally, every data record must have a unique identifier and a publicly accessible URL once published.  
The work undertaken was divided into stages. In the beginning, based on users' interviews, the types of data records were identified and assigned into the groups as follows (groups' names are italicized, the types requiring multimedia processing are bolded):  
* _Data_: Catalogue; Chemical analysis; Dataset, database; **Dendrological collection**; Flow (NetFlow) from a firewall device; **Herbarium sheet**; **Histological preparation--human; Histopathological preparation--animal**; Source code; Threat log;
* _Documents_: Article, chapter; Book; Journal; Legislation; Legislation collection; Map; Map collection, atlas; Synopsis; Thesis;
* _Materials_: Archival material; Educational material; Manuscript; Note; Other documents;
* _Multimedia_: **3D, foo360; Artistic, architectural work; Audio; Photo; Presentation; Video;**
* _Portfolio_: Expert; Research equipment; Research laboratory; Research offer.  
Every data type was then characterized by metadata attributes and possible types of data constituting its payload. Such descriptions were mapped into the relational schema and graph model compliant with specially developed ontology. The mapping to other standards (as schema.org and Dublin Core) was done as well. Finally, the implementation of tools for depositing records with attached data has begun.