esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Rock Characterization  
The coquina was classified as grainstone (Figure 1). It was composed of robust and medium shell fragments, with a size varying from 0.3 to 9.0 mm. The rock was well-selected, with most bioclasts ranging up to 4 mm. It was observed that rare siliciclastic grains were present (3%). There were also rare fragments of disjointed ostracodes (2%) and traces of microcrystalline pyrite. The porosity was classified as intraparticle, interparticle, vug, moldic, intercrystalline, and breccia, in which the predominance was of intraparticle, interparticle, and intercrystalline porosities.  
The coquina sample was characterized by its petrophysics characteristics. The porosity of the sample was 19.8% and the permeability was 765.1 mD. Other properties can be found in Table 3.  
Figure 1: Thin section of coquina sample classified as a grainstone. With the zoomed images on the right, it is possible to observe the different pore types and the heterogeneity of the rock constituents. Magnification of 2.5 times.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Pore** & **Porosity** & **Permeability** & **Grain** & **Diameter** & **Length** & **Weight** \\  & **Volume (cc)** & **(\%)** & **(mD)** & **Density (g/cc)** & **(cm)** & **(cm)** & **(g)** \\ \hline Coquina & 6.8 & 19.8 & 765.1 & 2.69 & 3.5 & 3.6 & 74.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Routine petrophysics of the studied coquina sample.  
The sample was digitalized and 4304 microCT images were generated with 9.97 \(\upmu\)m/voxel. During the processing, a non-local means filter [62] was used to smooth the image and remove artifacts from the acquisition process. After the image processing, it was possible to identify the different components of the rock. The shells and minerals described in the thin section appeared as different shades of grey. The pore space was identified as black and dark grey (Figure 2).  
To produce the digital capillary pressure and relative permeability curves, it was necessary to segment the microCT images into pore and rock. For that, the Kittler and Illingworth method [63] was used. With the segmented images, the digital total porosity was calculated, and the value obtained was 15.3% for the whole digital sample (Figure 3). After that, a region of interest (ROI) was defined to be used in the simulation process.  
The NMR data acquisition, interpretation, and transformation of T2 to pore size distribution are discussed elsewhere [51]. It can be observed in Figure 4 that 82% of the pore size distribution had a radius larger than 10 \(\upmu\)m, which means that the microCT images at a resolution of 9.97 \(\upmu\)m captured the dominant porous space of this sample.  
Figure 3: The volume of interest used in the digital sample. (**a**) microCT images of the coquina, and (**b**) rock matrix in grey and segmented pores in blue. (**c**) Segmented pore system.  
Figure 2: Slices from the micro-computed tomography (microCT) coquina sample with a pixel size of 9.98 \(\upmu\)m.
## 6 Simulation Results and Discussions  
We investigated the decoding performance of various TT-NOMS, TT-NSMS, TR-NOMS and TR-NSMS for Hamming code, BCH codes and LDPC code. The parameters of the simulation are shown in Table 1. Denote \(E\) as the edge in graph, \(T\) as the number of iterations and \(B\) as the size of batch. \(E_{b}/N_{0}\) represents the signal-to-noise ratio. The optimal multiplicative coefficient is set to 0.8 in the normalized min-sum (NMS) algorithm, and the optimal offset coefficient is set to 0.15 in the OMS algorithm.  
Because the short loops in the tanner graph affect the performance of decoding, the information transmitted by the CNs layer are unreliable, and the VNs layer transmit reliable information. The performance of these layers does not improve due to the increase in weight. Therefore, the weight \(w^{t}_{v^{\prime},c^{\prime}}\), \(w^{t}_{v}\) are set to 1 in Equations (8) and (9), which is similarily done in [3]. \(w^{t}_{v^{\prime},c^{\prime}}\), \(w^{t}_{v}\) are not decomposed by TT or TR. All of \(w\) in Equations (22), (23), (21) and (22) are set to 1. Tensorly [25] of low rank decomposition is used in our codes.
## 6 Conclusions  
In this paper, an ABFT-SMM system is established, and the approximation characteristic of the system is discussed. The concepts of agent and fuzzy T-S model are first addressed to describe the structure of the ABFT-SMM system. Different from the traditional method, the parameters and the membership value of the agent can be adjusted along with the process, which lead to application results better than the existing studies. Then, both the approximation capability of continuous and discrete systems are analyzed with the famous Stone-Weierstrass theorem. Finally, chaotic time series prediction, water heater identification and waste heat utilizing process modeling are presented to illustrate the viability and efficiency of the mentioned approach.  
The following conclusions can be drawn:  
1. The ABFT-SMM system is an h-consensus network.
2. The structure of the model can be adjusted by the number of the agents and their corresponding weights.
3. The proposed modelling method can approximate any linear or nonlinear system at arbitrary accuracy.
## 4 Discussion  
Micro-implants are clinically and statistically more effective in reinforcing orthodontic anchorage [31] with a high rate of success ranging from 71.4% to 100% [32]. The micro-implant success rate of 85.9% observed in our study was similar to those obtained in previous studies [32; 33; 34]. The success rate did not differ significantly according to sex, which is also mostly in line with previous reports [16; 26; 29; 35]. However, [PERSON] et al. [36] have communicated that the success rates are higher in women compared to those in men. Age has been reported as a significant factor affecting micro-implant survival [29]: adolescents have a higher risk of micro-implant failure [37]. However, we did not detect a significant effect of the patients' age on the success rate in the present study, which may be due to our cohort of young adults with a mean age of 23.6 \(\pm\) 5.8 years. The anteroposterior and vertical skeletal patterns had no significant associations with the micro-implant success in this study; previous studies have, however, reported conflicting results. [PERSON] et al. [38], [PERSON] et al. [16], and [PERSON] et al. [39] have concluded that anteroposterior skeletal pattern is not associated with the micro-implant success. [PERSON] et al. [36] have reported that the success rate for Class III malocclusion patients is lower compared to Class I and II patients. In contrast, according to [PERSON] et al. [25], patients with a retruded mandible have significantly higher rates of micro-implant failure. [PERSON] et al. [38] found that patients with larger gonial angles and Frankfort-mandibular plane angles are at a greater risk of micro-implant failure. Similar results have been reported by [PERSON] et al. [16] in a study of 129 miniscrews and concluded that miniscrews placed in patients with a high mandibular plane angle carry a greater risk of failure. [PERSON] et al. [25] also reported a similar tendency for micro-implant failure in patients with a high mandibular plane angle, although this result only trended towards statistical significance. Therefore, thin cortical bone has been suggested as a possible factor responsible for the increased incidence of micro-implant failure in long-faced patients [16], as cortical bone thickness plays an important role in conferring primary stability [40]. [PERSON] et al. [41] reported that patients with a high mandibular plane angle and a decreased cortical bone thickness at
###### Abstract  
To investigate the effect of wind deflectors on air flow and heat transfer performance of an air-cooling tower under crosswind conditions, an experimental system based on a surface condenser aluminum exchanger-type indirect air-cooling tower is established at a 1:100 proportional reduction. A 3-D computational fluid dynamics simulation model is built to study the air flow and temperature fields. The air flow rate into the cooling tower and the heat transfer rate of the radiators are used to evaluate cooling performance. Rotating wind deflectors are adopted to reduce the influence of crosswind on the cooling tower performance. The effects of the rotating wind deflectors on the thermal-hydraulic characteristics of the air-cooling tower under different environmental crosswind speeds are studied. Results indicate that the wind direction in the tower reverses as the rotating speed of the wind deflectors increases. The thermal performance of an air-cooling tower under crosswind conditions can be improved by using rotating wind deflectors. The heat transfer rate of a cooling tower with eight wind deflectors begins to increase when the rotating speed exceeds 2 r/min.  
air cooling tower; heat transfer rate; air flow rate; rotating wind deflector; crosswind +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
Figure 2 presents the schematic of the voltage source inverter connected to the grid and the voltage control system examined in this paper. To focus exclusively on voltage control, the grid interaction is excluded from this study, and the inverter is connected to a local resistive load. The controller processes measurements of the output voltage, filter current, and output current, converting them into the \(dq\) reference frame. These \(dq\) variables are subsequently used to formulate a quadratic programming (QP) problem. The optimal control problem is solved using the OSQP solver with a receding horizon approach. The resulting control actions are then transformed back into the \(abc\) reference frame and applied using PWM modulation. The parameters of the laboratory experimental setup used in this study are summarized in Table 1.  
Figure 3 shows the experimental setup used to evaluate the proposed MPC control for the voltage source inverter (VSI). In this setup, the energy resource (ER) is emulated using a Chroma 62060 regenerative DC power supply, which provides the necessary DC-link voltage for a Semikron three-phase inverter. An LC filter is  
Figure 2: Schematic setup for MPC in voltage source inverters.  
high-frequency harmonics in the three-phase voltage (\(\mathbf{v_{s}}\)) and produce a smooth output capacitor voltage (\(\mathbf{v_{c}}\)). The VSI configuration includes an output transformer, modeled as an unknown series impedance for control purposes, that is connected to a star-connected resistor bank with a resistance of 47 \(\Omega\) per phase. Furthermore, a star-connected resistor bank of 23 \(\Omega\) is connected in parallel through a breaker, allowing the introduction of a load disturbance into the system. Filter currents, output voltages, and output currents are measured using custom-designed sensor boards. The dSPACE MicroLabBox acquires these measurements via its analog-to-digital (ADC) channels for control purposes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System Parameters** & **Value** & **Remark** \\ \hline \(\
u_{\mathrm{dc}}\) & 300 V & DC Bus \\ \(i_{\mathrm{n}}\) & 10 A & Nominal current \\ \(L_{\mathrm{f}}\) & 5 mH & Filter inductance \\ \(C_{\mathrm{f}}\) & 12 \(\mu\)F & Filter capacitor \\ \(R_{\mathrm{f}}\) & 0.065 \(\Omega\) & Filter resistance \\ \(\omega\) & 2\(\pi\)50 rad/s & Nominal frequency \\ \(T_{\mathrm{s}}\) & 200 \(\mu\)s & Sampling time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the system.  
Figure 3: Experimental setup for MPC in voltage source inverters.
# Optimal Mixture Designs for Heavy Metal Encapsulation in Municipal Solid Waste Incineration Fly Ash
## 3 Results and Discussion
### Heavy Metal Leaching  
#### 3.3.1 MSWIFA and Water Mixture  
Figure 4 shows the concentration of heavy metals in the leachate of MSWIFA mixed with water at a liquid-to-solid ratio of 10, and the corresponding EPA drinking water limits. The concentrations of several metals are close to or higher than EPA limits. If MSWIFA is landfilled without any treatment, it may lead to severe pollution, specifically for Cr, Pb and Cd, which show concentrations above the EPA drinking water limit. The worst behavior is seen with Pb, with a concentration 1350 times greater than the limit. The concentrations of Zn and Hg approach the limit, and they may exceed it under more concentrated conditions. To landfill such hazardous material without any treatment clearly poses a significant pollution risk.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{3}{c}{**Ettrinigite**} & \multicolumn{3}{c}{**Friedel’s salt**} & \multicolumn{3}{c}{**Gypsum**} & \multicolumn{3}{c}{**Sturmanite**} & \multicolumn{3}{c}{**C-S-H and C-S-A-M**} \\ \cline{2-11}  & **3d** & **7d** & **28d** & **3d** & **7d** & **28d** & **3d** & **7d** & **28d** & **3d** & **7d** & **28d** \\ \hline
**C70B0** & \(\surd\) & \(\downarrow\) & & \(\times\) & & \(\times\) & \(\surd\) & \(\downarrow\) & \(\surd\) & \(\downarrow\) & \(\downarrow\) \\ \hline
**C42B28** & \(\times\) & \(\surd\) & \(\downarrow\) & \(\times\) & & \(\times\) & \(\surd\) & \(\uparrow\) & \(\downarrow\) & & \(\surd\) & \\ \hline
**B70M20** & \(\times\) & \(\uparrow\) & & \(\times\) & \(\surd\) & \(\downarrow\) & & \(\times\) & & \(\times\) & \\ \hline
**B30M60** & \(\times\) & & \(\surd\) & & \(\surd\) & & \(\times\) & \(\surd\) & \(\downarrow\) & \\ \hline
**L40B20** & \(\surd\) & \(\uparrow\) & \(\surd\) & \(\uparrow\) & \(\times\) & \(\surd\) & \(\uparrow\) & \(\surd\) & \(\uparrow\) \\ \hline
**L60B0** & \(\surd\) & \(\downarrow\) & \(\surd\) & \(\uparrow\) & \(\times\) & & \(\surd\) & & \(\surd\) & \\ \hline
**G2B28** & \(\surd\) & & \(\surd\) & \(\times\) & & \(\times\) & & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Qualitative changes in amount of hydrates of representative mixtures. The notations are as follows—\(\surd\): Is present; \(\times\): Is absent; \(\uparrow\): Increases; \(\downarrow\): Decreases.
## 4 Results
### Pattern of Arriving Ship's Trajectory
#### 4.1.1 Port Entry Phase  
The port entry phase consisted of the process prior to entering the Gadeog Sudo waterway after piloting outside the port. According to Figure 11a, Leg 1 is the step in which ships approach to enter the waterway from various trajectories. Turning Section 1 is the stage just before entering, where the course of the vessel is aligned with the direction of the waterway. The average SOG in this phase is 8.23 knots, and the results of the frequency counting are shown in Figure 12a. As a result of the analysis, 8-9 knots was the most common, with 171 counts, followed by 7-8 knots at 147 counts. The average COG was 328.84, and the results of the frequency counting are shown in Figure 12b. Therefore, the course of 330-335 degrees corresponds to 187 counts, which was determined through analysis to be the largest distribution.  
The time series analysis of this phase was performed by adding the boxplot of the turning section (TS1) to that of Leg 1, divided into 11 stages, from 0 to 10, with the results shown in Figure 13. According to Figure 13a, ships moving at an average of 9.23 knots at the starting point gradually decrease their SOG and finally decrease to an average of 7.32 knots at the turning section. The change in the time series of the COG is shown in Figure 13b, and the COG, which changed in various courses, is ultimately determined as having an average of 337.25, similar to the direction of the waterway in the turning section. The summarized descriptive statistics are shown in Table 5, below.  
Figure 11: Plotting the arriving ship’s trajectory data on the British Admiralty Chart: (**a**) all data; (**b**) passing the left side of Todo; and (**c**) passing the right side of Todo.  
Figure 12: Frequency analysis of the port entry phase: (**a**) speed over ground (SOG); (**b**) course over ground (COG).  
Figure 13: Boxplot time series analysis of the port entry phase: (**a**) SOC; (**b**) COG; * the time series analysis for ship trajectories in Leg 1 is expressed in 11 stages, from 0 to 10, by multiplying the result scaled in Equation (1) by 10. This phase is expressed as a 12 stages time series graph by adding a Turning Section 1 in addition to Leg 1 divided into 11 stages. The boxplot was used to visually express the descriptive statistics for 12 stages.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scaled**} & \multicolumn{6}{c}{**Speed Over Ground**} & \multicolumn{6}{c}{**Course Over Ground**} \\ \cline{2-11}  & **Mean** & **Std.** & **Min** & **Median** & **Max** & **Mean** & **Std.** & **Min** & **Median** & **Max** \\ \hline
0 & 9.23 & 2.41 & 4.10 & 9.55 & 14.00 & 330.03 & 10.09 & 310.60 & 329.00 & 349.80 \\
1 & 8.41 &
## 3 Results and Validation
### Experimental Validation
#### 3.2.1 Description and Preparation of the Test Specimens  
The curved beams were obtained from a polyvinyl chloride (PVC) cylinder with a nominal internal diameter and thickness equal to 150 mm and 2.7 mm, respectively. The cylinder was cut across two mutually orthogonal planes, obtaining four curved parts with a nominal internal radius equal to 75 mm, in agreement with the scheme reported in Figure 7. Curved beams of 30 mm in length were cut from the curved parts for the test specimens.  
The piezoelectric ceramic transducers, manufactured by Physik Instrumente, have a 20 \(\times\) 20 mm square plan form and a thickness of 3 mm. The geometry of the transducer was chosen accordingly to the experimental case study, after preliminary theoretical and numerical analyses. The material of the curved beams was selected by taking into account the relatively low stiffness of the PVC versus the piezoelectric transducer, thereby magnifying the displacements and strains induced by the transducer on the structure in the experimental tests.  
\begin{table}
\begin{tabular}{c c} \hline
**Transmission Factor on Curved Beam** & **Transmission Factor on Flat Beam,** \\
**(Internal Curvature Radius: 75 mm)** & **Theoretical Value, Equation (9)** \\ \hline \(2.89\times 10^{-3}\) & \(3.13\times 10^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 8: Transmission factor comparison.  
Figure 7: Details of the curved beams.  
In light of the results from the Crawley-de [PERSON] theory, the adhesive layer between the transducer and the surface above which it is applied assumes a relevant influence on the effectiveness of the transmitted forces and strains; the adhesive typology, its physical and chemical properties, and the adhesive layer thickness and conditions can affect the interaction between the piezoelectric transducer and the beam [23]. In this research activity, the piezoelectric ceramic transducers were bonded on the top surface of each curved beam through the bi-component epoxy adhesive Araldite(r) 420 A/B by Huntsman. The mechanical properties of the adhesive and its processing procedures were provided by the supplier.  
It is known from the literature that the low surface energy materials, such as polymers, are difficult to bond [27]; therefore, with the aim of improving the adhesion, roughening of the surface was carried out, ensuring an increase in the surface area and leading to more molecular bonding interactions and mechanical interlocking [28]. The curing was performed at room temperature for 24 h to obtain a full cure and an adhesive layer of about 100 \(\upmu\)m. Each piezoelectric transducer was applied at a given distance from the end edge of the curved beam to ensure the proper area was equipped for the following tests. The test specimen, consisting of a piezoelectric ceramic transducer and a curved beam above which it is bonded, is shown in Figure 8.
## 3 Results
### Applications Across Industrial Domains  
To provide a structured overview, the 60 selected studies were categorized into 7 sectors based on industrial focus. Four sectors were classified based on clearly defined scopes: buildings and heating, ventilation and air conditioning (HVAC) systems, power generation and distribution, wind energy, and semiconductor manufacturing. The remaining studies were grouped into broader categories: multi-industry manufacturing, industrial equipment, and transportation. As shown in Figure 5, the number of studies included in each category is indicated in parentheses.  
Figure 4: Annual publication trend of studies.  
Figure 5: Categorization of reviewed studies by sector.  
Figure 3: Word cloud of frequent terms in selected studies.  
The multi-industry manufacturing category comprises studies whose methodologies, datasets, or implementation frameworks demonstrated relevance across at least three distinct industrial sectors. These include works utilizing cross-sector datasets, proposing generalized PdM frameworks, or presenting solutions explicitly designed for application in diverse manufacturing environments. In contrast, the industrial equipment category consists of studies focused on specific types of machinery or subsystems (such as motors, compressors, bearings, or robotic systems) regardless of the broader industrial domain. These studies typically focus on diagnostics at the equipment level using sensor-based approaches and are defined by their application within a specific industrial domain.
## 3 Results and Discussion
### Parametric Study
#### 3.3.6 Effect of Pile Spacings  
Figure 12 shows the effects of pile spacings on solidified soil embankments. As the pile spacing increased, the total settlements increased significantly in Figure 12a. When the pile spacing increased from 2.5 m to 5.5 m, the total settlement at the top of the embankment increased from 11 mm to 64 mm. With increasing pile spacing, the vertical stress decreased obviously, and there was a notable change in the distribution of the vertical stresses in Figure 12b. Notably, the vertical stress at point C remained relatively small. The total settlements along AC and AB, including the part belonging to the pile, also increased with the increasing pile spacing shown in Figure 12c,d. The axial force increased obviously with the increasing pile spacing in Figure 12e, meaning that more loads were transferred to the piles.