esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Figure 12 illustrates the flow structure in a vertical diffuser for nozzle air supply through tubes of different configurations at a flow rate of \(q=0.047\) m\({}^{3}\)/s. Figure 1:5 Thermograms of the flow structure in a vertical diffuser with nozzle air supply through tubes with different cross-sectional shapes (flow through the system \(q=0.018\) m\({}^{3}\)/s): (**a**) round; (**b**) square; (**c**) triangular. Figure 12a shows that an increase in flow rate through the system with a nozzle air supply and round tubes does not lead to a change in the flow structure in the vertical diffuser. There is also intense air movement at the base of the diffuser, which is gradually distributed over the entire volume of the cylindrical part with some displacement to the right side. Figure 12b demonstrates that an increase in flow characteristics through the system with nozzle air supply and square tubes does not cause changes in the flow structure compared to \(q=0.018\) m\({}^{3}\)/s. In this case, there is a small area of intense air movement in the lower part of the diffuser and a nearly uniform distribution of air in the rest of the diffuser volume. Figure 12c illustrates that an increase in air flow through the triangular nozzle tubes actually led to a central air flow in the vertical diffuser. In this case, the flow structure is the most uniform compared to nozzle tubes with round and square cross-sections. Based on the presented data, it can be stated that when air is supplied through a nozzle into the diffuser, the influence of transverse profiling of the tubes on the flow structure is not very significant. However, it was previously found that the use of square and triangular tubes increases the intensity of turbulence by up to 30% compared to air supply through round tubes [40]. This can have a positive impact on heat exchange processes in real devices since the higher the intensity of turbulence, the higher the level of heat transfer [41]. Figure 13 shows the flow structure in a vertical diffuser for nozzle air supply through tubes of different configurations at a flow rate of \(q=0.057\) m\({}^{3}\)/s. Figure 13 illustrates that an additional increase in air flow through the system confirms the general patterns of the creation of flow structure in a vertical conical diffuser when air is supplied through nozzle tubes with round, square, and triangular cross-sections. Thus, the evolution of changes in flow structure in the diffuser with nozzle air supply is shown, as is the influence of cross-sectional shape on the distribution of air flow in the diffuser. By choosing the initial conditions and geometric dimensions of the nozzle tubes, it is possible to control the flow structure in a vertical diffuser. In practical terms, this will allow for the customisation of chemical or technological processes to meet the needs of production. Figure 1:6 Thermograms of the flow structure in a vertical diffuser with nozzle air supply through tubes with different cross-sectional shapes (flow rate through the system \(q=0.047\) m\({}^{3}\)/s): (a) circle; (b) square; (c) triangle.
## 5 Prototype Design and Installation on the Demo Site  
In order to test the proposed inspection procedure, a small pilot plant is available. The pilot, shown in Figure 6a, consists of 11 PV panels with several damaged cells. The datasheet contains the main specifications of the panels: every panel has a maximum peak power of 175 W, a \(V_{oc}\) of 44.35 V and a \(I_{sc}\) of 5.45 A. Summarising, the full plant achieves a maximum power point (MPP) of 2.1 kW, with 488 V \(V_{oc}\) and 5.45 A \(I_{sc}\). It is important to emphasise that these characteristics are provided by the manufacturer for the newly manufactured panels, but the pilot plant have several defects, and therefore the showed values may vary significantly.  
A three-level NPC I-type converter, which is prepared to allow the bidirectional current flow, has been developed for the purpose of performing an EL inspection on the pilot plant. The inverter, shown in Figure 7, achieves a maximum power of 3 kW. As Figure 6b shows, the inverter AC side has been connected to the 400 V three-phase grid, whereas the PV side can set its voltage between 330 and 550 Vdc. During the EL test execution, this voltage range is enough to allow the current control between 10% \(I_{sc}\) and 100% \(I_{sc}\). Besides, the DC-link pre-charge allows the panels direct polarisation in night time conditions.  
Figure 5: Complete system flow diagram for the maintenance of USPVPs.
# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
## 3 System Testing
### NB-IoT Network  
The NB-Iot network is chosen as the main network option, due to the fact that it is designed specifically for IoT applications and it is lightweight, low-powered and performs better on low data rates. It is observed that the NB-IoT network is able to provide connectivity to devices with a received signal strength indicator (RSSI) value as low as \(-105\) dBm as compared to \(-95\) dBm of long-term evolution (LTE) [22]. In order to test the range of the connectivity of the NB-IoT SIM7020 module that is used on the smart tag, measurements of the signal's strength, known as the RSSI, are acquired. The connection was tested at a building of the National and Technical University of Athens, which consists of three floors, a terrace and a basement. Measurements were taken every 30 s for 5 min on each one of the floors, the terrace, the second floor, the first floor, the ground floor and the basement. As a result, we acquired 10 measurements of RSSI on each floor that are graphed on Figure 8.  
As we expected, the signal strength is higher on the terrace, where the device is not surrounded by walls and it reaches \(-85\) dB. The RSSI values on the first and second floors do not appear to have substantial differences. The signal starts to become low on the ground floor and reaches its minimum strength in the basement, where it reaches \(-110\) dB. Despite the fact that the signal is not strong, it is sufficient, and the device manages to connect to the NB-IoT network and transmit the gathered data. This proves that the NB-IoT network indeed offers long-range connectivity and can normally operate at places where common cellular networks, such as 4G/3G networks, struggle to do so.  
Figure 7: User’s interface.
# Optimization Method of Assembly Tolerance Types Based on Degree of Freedom
## 7 Discussion  
From the content of the related works in Section 2, it is clear that the tolerance design method based on rule-based reasoning makes it easy to achieve the automatic generation of tolerance specifications. In particular, the ontology-based rule-based reasoning is more widely used in the application of the automatic generation of tolerance specifications. However, the above methods recommend multiple tolerance types for the same assembly feature surface of the assembly body. Taking the part P4 shown in Figure 8 as an example, [PERSON]'s [27] method, [PERSON]'s [8] method, and [PERSON]'s [33] method are selected for tolerance specification design. With different tolerance design methods, the assembly feature element P4S2 will receive different numbers of recommended tolerance types as shown in Table 5. The initial tolerance types obtained by the above methods are 7, 9, and 8, respectively. After screening by the tolerance band freedom preference method proposed in this paper, five types of tolerance design solutions are finally obtained. The four shape-tolerance types obtained by the rule-based reasoning method are reduced to two, and the excluded tolerance types have obvious characteristics and their tolerance zones belong to two-dimensional tolerance zones. When defining the DOF of the two-dimensional tolerance bands, the variation of the third dimension is defined as a constant degree, and for this reason it is preferentially excluded from the Boolean screening due to the small number of DOFs. Of the various tolerance-band generation algorithms mentioned above, the preferred method proposed in this paper has obvious implications and increases the decision difficulty for designers.  
Figure 9: Form and position tolerance scheme of P1 and P2.P3 in Assembly Model.
###### Abstract  
Tepenes are critical components of the floral fragrance component in _Dendrobium chrysotoxum_, synthesized by terpene synthase (TPS). Analysis of the _D. chrysotoxum_ genome and transcriptional data revealed that the gene _DcTPSb1_ was significantly up-regulated during flowering periods, showing a strong correlation with the accumulation of aromatic monoterepenes in the floral components of _Dendrobium chrysotoxum_. Consequently, the _DcTPSb1_ gene was selected for further analysis. _DcTPSb1_ exhibited elevated expression levels in flowers among four organs (roots, stems, leaves, flowers) of _D. chrysotoxum_, with the highest expression observed during the blooming phase, which aligned with the accumulation of volatile terpenes during flowering. DcTPSb1, located in the chloroplasts, was identified as a member of the TPS-b subfamily associated with monoterepenes synthesis, showing close phylogenetic relationships with homologous proteins in related plant species. An analysis of the promoter region of _DcTPSb1_ indicated that it may be regulated by methyl jasmonate (MeJA) responsiveness. Functionally, DcTPSb1 was shown to catalyze the conversion of geranyl diphosphate (GPP) to linalool, ocimene, and (-)-\(\alpha\)-pinitol in vitro. Overexpression of _DcTPSb1_ in tobacco resulted in a significant increase in terpenoid release during the blooming stage; however, the up-regulated substances did not include their catalytic products. The classification of DcTPSb1 as a terpene synthase capable of producing multiple products provides valuable insights into the complex biosynthesis of terpenes in orchids. These findings enhance our understanding of the functional diversity of _DcTPSb1_ and the processes involved in terpene biosynthesis in orchids.  
Dendrobium chrysotoxum; DcTPSb1; terpene synthase; floral fragrance
## 3 Performance Evaluation and Analysis
### Datasets Information, Validation Criteria and Experimental Setting  
The 10 datasets information are summarized in Table 2.  
The rand index (RI) [15], the Xie-Beni index (XB) [16] and Within-Between index (WB) [17] are used for evaluating the performance of the proposed WFCS algorithm. The WB index is a recently proposed one. RI index is defined to evaluate the accuracy of partition--the higher the value is, the higher accuracy we get. XB and WB index are to evaluate the with-cluster compactness and between-cluster separation--the smaller the XB and WB values are, the better the clustering results is.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Datasets** & **Size of Dataset** & **Number of Dimensions** & **Number of Clusters** \\ \hline Australian & 690 & 14 & 2 \\ Balance-scale & 625 & 4 & 3 \\ Breast Cancer & 569 & 30 & 2 \\ Heart & 270 & 13 & 2 \\ Iris & 150 & 4 & 3 \\ Pima & 768 & 8 & 2 \\ Vehicle & 846 & 18 & 4 \\ Wine & 178 & 13 & 3 \\ ENGINE & 186 & 3 & 2 \\ LTT & 300 & 3 & 2 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of 10 datasets.  
The parameter setting is: \(\alpha\!<\!0\) or \(\alpha\!>\!1\)[18], \(m\!>\!1\), \(\varepsilon\)=\(10^{-6}\) and \(\beta\!\in\!\left\{0.005,0.05,0.5,1\right\}\). Parameter values in experiments are tabulated in Table 3, which is based on the best clustering results in terms of the means and standard deviations of the RI index. We conduct each algorithm 10 times. All experiments were implemented on a computer with 2.5 GHz CPU and 8 GB RAM.
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 2 Elastoplastic Representation of Mechanical Properties
### Yield Function and Plastic Shear Strain  
As shown in Figure 1b, the rock exhibits a shear failure mode with a fracture angle \(\theta\) under conventional triaxial compression. This is due to the rock material being strong in compression but weak in tension. The lateral confinement under deviatoric loading counteracts some of the internal tensile stress, causing the rock to undergo plastic deformation along shear fractures. Therefore, Figure 1c assumes that after yielding, the stress state of the rock always remains on the Mohr-Coulomb yield surface. The shear strength \(\tau\) is  
Figure 1: Conventional triaxial compression procedure diagram for rock: (**a**) stress–strain curve; (**b**) failure mode; (**c**) yield surface loading path; (**d**) dilatancy angle; (**e**) non-associated flow rule.  
represented by the yield surface tangent line, with the intercept as the cohesion \(c\) and the inclination as the internal friction angle \(\varphi\) (\(\varphi=2\theta-\pi/2\)). The yield surface rises during the hardening stage and falls during the softening stage, expressed by the yield function as follows [3,4]:  
\[f=\sigma_{1}-\sigma_{3}N_{\varphi}-2c(\gamma_{p})\sqrt{N_{\varphi}}=0 \tag{3}\]  
where \(N_{\varphi}=\left[1+\sin\varphi(\gamma_{p})\right]/\left[1-\sin\varphi(\gamma_ {p})\right]\), \(\gamma_{p}\) is the plastic shear strain, and \(c\big{(}\gamma_{p}\big{)}\) and \(\varphi\big{(}\gamma_{p}\big{)}\) are functions of \(\gamma_{p}\) representing cohesion and internal friction angle, respectively. \(\gamma_{p}\) can be expressed as follows:  
\[\gamma_{p}=\epsilon_{1}^{p}-\epsilon_{3}^{p} \tag{4}\]  
\[\epsilon_{1}^{p}=\epsilon_{1}-\epsilon_{1}^{t} \tag{5}\]  
\[\epsilon_{3}^{p}=\epsilon_{3}-\epsilon_{3}^{t} \tag{6}\]  
\[\epsilon_{V}^{p}=\epsilon_{1}^{p}+2\epsilon_{3}^{p} \tag{7}\]  
where \(\epsilon_{1}\) and \(\epsilon_{3}\) are the first and third principal strains, \(\epsilon_{1}^{p}\) and \(\epsilon_{3}^{p}\) are the first and third principal plastic strains, and \(\epsilon_{V}^{p}\) is the plastic volumetric strain. Substituting Equations (1), (5), and (6) into Equation (4) yields the following:  
\[\gamma_{p}=\epsilon_{1}-\epsilon_{3}-(1+\mu)(\sigma_{1}-\sigma_{3})/E \tag{8}\]  
According to Equation (8), the plastic shear strain, as a plastic parameter that incorporates deviatoric stress, not only effectively records the degree of plastic yielding at the Mohr-Coulomb yield surface but also reasonably reflects the stress state in which it exists.
# Thermogravimetric Study of Refuse Derived Fuel Produced from Municipal Solid Waste of Kazakhstan
## 3 Results and Discussions
### Effect of Heating Rate  
Effect of heating rates on the combustion behavior of paper, plastic, textile, and RDF mixture was presented with three different heating rates of 10, 20, and 30 \({}^{\circ}\)C/min (see Figure 5a-d, respectively). The results reveal that the weight loss curves of RDF samples were delayed and shifted towards high temperatures with an increase of the heating rates from 10 to 30 \({}^{\circ}\)C/min. These results with delay of weight loss curves and shift of weight loss rate to a higher temperatures were consistent with studies of [PERSON] et al. [13] and [PERSON] et al., [43]. In addition, the effect of heating rate was more significant for the temperature between 250 and 550 \({}^{\circ}\)C, for all the samples. Although, in comparison to the weight loss curves of paper (Figure 5a), plastic (Figure 5b), and textiles (Figure 5c), the effect of heating rate on RDF samples (Figure 5d) were not so significant, which can be explained by the presence of mixture of components in the RDF.  
Figure 4: Weight loss curves (TG) and weight loss rates (DTG) of samples at 10 \({}^{\circ}\)C/min.  
Table 3 presents the DTG peaks and peak characteristics for all the four samples under study at different heating rates. Three major peaks were observed for paper during all three heating rates, namely, at 320-329 \({}^{\circ}\)C, 434-471 \({}^{\circ}\)C, and 640-679 \({}^{\circ}\)C (Figure 5a). The difference of first DTG peaks was 10%/min between the tests with heating rates of 10 to 20 \({}^{\circ}\)C/min. Then this difference was decreased to 6%/min when heating rate was further increased to 20 to 30 \({}^{\circ}\)C/min. This delay of weight loss at the higher heating rates is due to the high decomposition rate of paper within the temperature range of 320 to 329 \({}^{\circ}\)C. Figure 5b shows that there are five weight loss peaks in the DTG profile in 10 \({}^{\circ}\)C/min and 20 \({}^{\circ}\)C/min, while in 30 \({}^{\circ}\)C/min, there are only four weight loss peaks. This may result from the reaction overlap occurring in the range of 400-450 \({}^{\circ}\)C or it could also be due to the mixed content of plastic. The maximum weight loss rate increases from 9.4%/min to 26.1%/min for paper, from 5.5%/min to 9.7%/min for plastic, from 7.3%/min to 19.7%/min for textile and from 7.3%/min to 23.3%/min for RDF, when the heating rate varied from 10 \({}^{\circ}\)C/min to 30 \({}^{\circ}\)C/min, as is shown in Table 3. We also note that the increase of the heating rate results in higher weight residuals, but some inconsistencies do occur due to the heterogeneous content of sample. In general, the heating rate could affect the samples combustion. Furthermore, since the mass of samples were less than 20 mg for  
Figure 5: Effect of heating rate on TG/DTG curves of (**a**) mixed paper, (**b**) plastic, (**c**) textile, (**d**) RDF.
## 2 Materials and Methods
### Technical Specifications of the Examined IMMSs
#### 2.2.1 Kaarta Stencil (Handheld)  
The system [18] was tested on 19 September 2017.  
The basic configuration (the one we tested) consists of an industrial PC, a Velodine VLP-16 linear scanner, a MEMS-based INS, and an industrial camera called \"black and white\" (in truth, acquiring 640 \(\times\) 480 grey-scale images), mounted on a base with a standard photography thread size. A small tripod was used to support the system. The measurement principle of this system, conceived by [PERSON] and [PERSON] from the Robotics Institute at Carnegie Mellon University [19], is different from the others considered in this study. It is called \"Lidar Odometry And Mapping\" (LOAM): by finely using two algorithms running in parallel, it allows a practically real-time solution. An odometry algorithm estimates the velocity of the TLS, therefore correcting the errors in the point cloud, while a mapping algorithm matches and registers the point cloud to create a map.  
The Stencil is proposed commercially as a modular system, offering the opportunity to be configured with different sensors. It is possible to connect a tablet or a screen for real-time checks of the acquired data. New versions of the Stencil will be soon proposed, also including a GNSS receiver and/or a 360\({}^{\circ}\) panoramic camera for texturing purposes. Different mounting platforms can be used to scan from a backpack, a vehicle, or a drone. No accuracy specifications are available on the technical sheet [20]; a white paper [21] presents some accuracy tests by comparing the Stencil to static measurements with a Faro Focus 3D TLS.  
Figure 1: The systems under testing during the field acquisitions: (**a**) Kaarta Stencil; (**b**) Leica Pegasus Backpack; (**c**) GeoSlam Zeb-Revo.  
In the tested configuration, the system was initially set up for use with the feature-tracking camera: this function is supposed to be helpful in scenes dominated by planar areas, like long and flat facades, tunnels, etc. The idea of using images for the localization problem, already mentioned in Section 2.1 for MMS, is proposed again by [PERSON] and [PERSON] in [PERSON] in [PERSON] and Singh (2017), with \"visual odometry\" integrating the \"lidar odometry\" of [PERSON] and [PERSON] (2017). Nevertheless, during the first acquisitions in the test field performed with the Stencil with the feature-tracking camera turned on, significant drifts and misalignments were noticed on surfaces acquired twice. Since the other tested IMMSs do not allow this option, we preferred to acquire data with the camera turned off.
###### Abstract  
The significance of predicting the dynamic response and damage of an existing concrete tunnel during underground blasting has increased owing to the close proximity between the newly built and existing tunnels. Peak particle velocity (PPV) is a commonly used criterion in the assessment of blast-induced structural damage. However, such structural damage is also associated with the frequency content of the blast wave. Nevertheless, the recommended threshold PPVs, which are based on empirical criteria, predict conservative estimations. Using stringent and regulated blasting methods often results in project delays and escalates the total project expenditure. In this paper, a three-dimensional finite element model of an underground tunnel has been developed in LS-DYNA to analyze damage to the concrete tunnels under blast loading. A suite of analyses was performed to examine the potential damage induced in the underground tunnel. A lower frequency load was found to have a greater potential for producing damage compared with a high frequency blast load. The results showed that the location of the cracking within the tunnel, such as the arch foot or tunnel wall, was also influenced by the frequency of the blast wave. The maximum allowable PPV for the concrete tunnel was determined for a range of frequencies based on predicted free field PPV and additional factors of safety of 1.2 and 1.5 were established, depending on the safety needs and importance of the tunnel construction. Thus, our findings provide useful information for improving the evaluation of tunnel damage and guaranteeing the safety of underground tunnels.  
Keywords:drill-and-blast tunnel; dynamic response; tunnel blasting; damage prediction; concrete lining; peak particle velocity; frequency; numerical modeling +
Footnote †: journal: Applied Sciences
# Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science
## 4 Numerical Lagrangian Models
### Ichthyop  
Ichthyop is a free Java-based particle tracking numerical model which was originally developed to study ichthyoplankton dynamics [97]. Ichthyop has also been employed for modeling the fate and transport of microplastics and toxic algae [76; 119]. The model can use three-dimensional hydrodynamic information including temperature, velocity, and salinity, simulated by oceanic numerical models such as the Regional Oceanic Modelling System, called ROMS, and the Model for Applications at Regional Scale, named MARS, as input [97]. Ichthyop is capable of providing two types of microplastics simulations: (1) modeling plastic particle trajectories, and (2) ensemble simulation (i.e., executing a series of numerous particle tracking simulations) [97]. Ichthyop can also simulate the trajectory of more than one million particles as passive tracers [119].  
Similar to D-WAQ PART, Ichthyop does not consider the integration of complex particle transformation processes including biofouling, different types of aggregation, degradation, and fragmentation.  
Ichthyop uses the Runge-Kutta method [97] for solving the advection equation [76]. Horizontal and vertical advection and dispersion can be simulated in Ichthyop [76; 97; 113; 119; 120]. The model follows D-WAQ PART and uses Equation (11) to simulate the horizontal advection. Ichthyop utilizes a terminal velocity (\(v_{part}\)) in order to address the vertical movement of particles, as defined in Equation (17) [114].  
Ichthyop uses Equation (18) for the consideration of horizontal diffusion. Accordingly, horizontal diffusion is defined as a function of the turbulent dissipation rate and the cell size of the computational domain [115]. Furthermore, the vertical diffusion of particles is predicted based on a proposed random-walk simulation [121].  
Ichthyop is incapable of simulating some other effective physical processes on microplastics' fate and transport, such as beaching and washing-off, windage, resuspension, and aggregation, which can be regarded as an important limitation.  
Hydrodynamic parameters are simulated on a rectangular structured mesh in some hydrodynamic models such as ROMS and MARS. Then, they are interpolated linearly and extracted at time steps in order to be prepared as inputs for Ichthyop.