esa-sceva/satcom-chunk-collection huggingface dataset:
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 2 Thermal Conductivity of NSN Nanofluids
### Experimental Data Evaluation
#### 2.1.1 CNTs/Nanofibers/Nanowires  
CNTs are the most popular suspension medium for nanofluids since they display an average thermal conductivity of 4000 W/(m\(\cdot\)K), which is higher than that of other nanoparticles [18]. Many articles and reviews [19; 20; 21] focused on nanofluids with CNTs, and only representative work is listed in Table 1. The type of nanofluid with elongated nanoparticles shows excellent performance in TCE due to the large size and aspect ratio of CNTs, which are hundreds of nanometers in diameter and at least tens of microns in length [15; 22]. Nevertheless, the addition of large-aspect-ratio particles into a base fluid could significantly increase viscosity compared to the continuous phase [23]. Moreover, CNTs, another nanofibrous polymer [15], and nanowires [24] entangle with each other, which could cause a complex particle morphology and heat transfer properties in different experiments. [PERSON] et al. [21] recently reviewed the thermal conductivity of commonly used particles and base fluids for fabricating nanofluids and showed that carbon-based nanofluids hold superior thermal features to those prepared by metal oxides and metals.  
Figure 2 displays a number of points showing the enhancement of heat conduction in varying ranges. [PERSON] et al. (2015, 2016) reported doubled thermal conductivities after suspending 2.5 wt.% metal oxide nanoparticles. They compared three nanofluids with cubic, nanorod, and spherical nanoparticles; they concluded that cubic particles displayed the best performance. Their abnormal results are significantly more excellent than others represented in Figure 2a. Thus, they are not plotted in the figure. The TCE shows two distinct trends that the black dotted line can distinguish in the figure in a wide range of volume fractions. Several studies reported abnormal TCE within 1% particle loading. [PERSON] et al. (2017, 2018) prepared nanowires with high aspect ratios (25 for Ag and approximately 82 for CuO nanowires) and measured an intriguingly high thermal conductivity increment at low volume fractions.  
In contrast, the TCE for nanofluid containing CuO nanospheres remains low. Therefore, thermal conductivities for NSN nanofluids can be much higher than those of nanofluids containing spherical nanoparticles. The study also showed that the TCE linearly increases with the volume fraction when the nanowire volume fraction is lower than 1%, and the authors discovered that their data effectively conforms to the Hamilton-Crosser (H-C) model. Intriguingly, [PERSON] et al. (2019) reported a TCE of 20.8% by suspending only 0.0174% Ag nanowires, which shows a performance approximately 20 times better than the results of [PERSON] et al. (2017). Overall, nanofluids with CNTs/nanofibers/nanowires demonstrate the best performance in different types of nanofluids. Specifically, nanofluids prepared with Ag nanowires at low concentrations show abnormal TCE.
## 3 Experimental Results and Discussion
### In-Plane Test  
#### 3.2.1 Quasi-Static Compression  
Two sizes of specimens were studied. The loading speed was 2 mm/min; thus, the strain rate for \(10\times 20\times 20\) specimens was \(1.67\times 10^{-3}\pm 3.7550\times 10^{-6}\), and the strain rate for the \(8\times 8\times 8\) specimen was \(4.18\times 10^{-3}\pm 3.0301\times 10^{-7}\). As shown in Figure 19a, the fracture stress and strain of the bigger specimens were lower than those of the smaller specimens. As shown in Figure 19b, it is obvious that there were three phases during the loading. In the first phase, maybe because the pores among the specimen were compressed, the stress-strain curve was non-linear and the slope increased rapidly. In the second phase, the stress-strain curve could be seen as linear. The modulus values of these two sizes of specimens were similar. In the third phase, the stress-strain curve seems to present material softening before failure.  
As shown in Figures 20 and 21, there were kink bands [18] along with fibers in both small and big specimens. The kink bands showed two kinds of typical features. One feature is shown in Figures 20a and 21a, where one major kink band ran through the specimen and delamination began to generate at the upper or lower side. The other feature is shown in Figures 20b and 21b, where two kink bands in an X shape ran through the specimen and delamination began to generate in the middle part of the main body. In addition, by comparing Figures 20 and 21, for the smaller specimen, the whole specimen was involved in the fracture, while, for the bigger specimen, only the local part was involved in the fracture.  
Figure 19: The quasi-static compressive behavior under in-plane loading: (**a**) stress–strain curve; (**b**) first derivative of stress vs. strain curve.  
Figure 20: The fracture pattern of the smaller specimen: (**a**) side view of one kind of kink band; (**b**) side view of another kind of kink band.
## 3 Experiment Setup
Instead, Figure 3b-d show that the spectral content of SE, SSK, and SSE smoothly faded, providing a better visual interpretation of failures. Figure 3: Examples of envelope spectra (obtained from power spectral density (PSD)) performed by simulated signal. Top row, spectral-kurtosis (SK) and spectral-entropy (SE) estimators; bottom row, their sliding versions (sliding spectral kurtosis (SSK); sliding spectral entropy (SSE)). Order represents shaft-speed harmonics.
# Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite
## 3 Thermal Design Strategy  
The primary goal of thermal design is to ensure that the SAR antenna can capture images for over 100 s in the stripmap mode with a resolution of 2 m within each orbit cycle. At the same time, the temperature difference of all T/RMs on the SAR antenna should be within 5 degrees Celsius at all times. In addition, under extreme space environments and satellite operating modes, the thermal control system (TCS) must ensure that all components remain within the permissible temperature range (Table 2), while minimizing the power consumption of the heaters.  
The surface of RUs needs to maintain a flatness of \(\pm\)2 mm; however, thermal deformation is a significant factor affecting the flatness. During the assembly, integration, and testing (AIT) process of the SAR satellite, the ambient temperature stands at 20 \({}^{\circ}\)C. To eliminate the temperature variation of the structure between its in-orbit and AIT states, which could potentially degrade the shape and positional accuracy of the antenna mounting surface, it has been decided to maintain the +Z plate's in-orbit temperature at 20 \({}^{\circ}\)C. Furthermore, a temperature control accuracy of 5 \({}^{\circ}\)C is proposed to avoid excessive thermal deformation caused by uneven temperatures at the mounting points.
## 5 Application to the Liquid Bulk Terminal in the Port of Cartagena (Spain) and Its Evaluation  
This section describes a use case application of the optimization model explained in the previous section, which served as a feasibility study for HADES. The study has two parts:  
* Estimation of the maximum theoretical anchoring delay reduction attainable by terminal operators and its monetary value if the HADES system was used with the current port traffic.
* Estimation of the maximum theoretical additional traffic (and thus, income) that the Port of Cartagena could serve using the same infrastructure, without increasing the current service levels (i.e., anchoring congestion delays), thanks to using HADES.
# Verdet Constant of Magneto-Active Materials Developed for High-Power Faraday Devices
## 2 Characterization of the Verdet Constant as a Function of Wavelength and Temperature
### Model Function for the Verdet Constant Temperature-Wavelength Dependence
For instance, the contribution of the diamagnetic terms is usually very weak compared to the paramagnetic and mixing terms if the described spectral region is sufficiently far from any resonance line. Therefore, the diamagnetic contributions may be neglected in these cases. Furthermore, assuming that only one dominant transition is contributing to the Verdet constant, together with neglecting the diamagnetic term and considering the temperature is fixed, Equation (10) will reduce to the widely-used single-transition model  
\[V(\lambda)=\frac{F\lambda_{0}^{2}}{\lambda^{2}-\lambda_{0}^{2}}+G\quad. \tag{13}\]  
Equation (13) is used in the vast majority of the reported Verdet constant investigations at room temperature since the needed criteria for its use are usually very well met (e.g., [41; 46; 58; 59; 60]). However, when the investigated spectral range contains additional resonance lines, they should be included in the model function, especially when the resonance strongly affect the overall Verdet constant dependence. Careful analysis of the measured data, as well as knowledge of the spectroscopic characteristics of the investigated compound, may provide valuable insight into the considerations about the selection of the lines which need to be taken into account [48]. Verdet Constant Investigations of Magneto-Active Materials Developed for High-Power Faraday Devices  
In this section, a brief overview of the reported Verdet constant investigations at the room temperature is given. The overview is divided into three subsections, corresponding to different spectral regions: (1) UV region (\(\lambda<400\) nm); (2) VIS-NIR region (\(400<\lambda<1100\) nm); and (3) NIR-MIR region (\(\lambda~{}>~{}1100\) nm). This allows us to discuss the current status and possible future development of the covered materials more conveniently. In each of the subsections, the historical development of the reported materials is briefly discussed along with a table providing:  
* Fitting parameters \((F,G,\lambda_{0})\) of the single-transition model in Equation (13), which could be used for the description of the listed materials' Verdet constant wavelength dependence within the discussed spectral region. * Values of the Verdet constant at two wavelengths selected from the spectral region. * Additional references leading to further investigations of the parameters related to the high-power benchmarking (e.g., Verdet constant temperature dependence, Q and P constants, optical anisotropy parameter, thermal properties or LIDT tests) or to studies dealing with the optimization of the fabrication process of the listed materials. The following Tables 1-3 list only the absolute values of the Verdet constant to make the comparison of the listed materials more convenient.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Ballistocardiography (BCG) and Seismocardiography (SCG)  
Ballistocardiography is a non-invasive method based on the measurement of the body motion generated by the ejection of the blood at each cardiac cycle. A ballistocardiograph records the micro-vibrations of the body caused by the mechanical activity of the heart by using external pressure or strain sensors, whereas a seismocardiograph records the local vibrations of the chest wall in response to the heartbeat. The application of BCG sensors for driver monitoring has many similarities to BCG monitoring in a chair. In this posture, the sensor is normally placed on the chest (Figure 5a). The attempts to integrate BCG sensors into car seats began in 2011 [72] (Figure 5b). The BCG signal was detected by a capacitive sensor mat based on a quasi-piezoelectric force transducer (EMFi sensor). The placement of this BCG sensor in the passenger seat of a SMART car was successfully demonstrated. With the car engine turned off, the mechanical action of the heart was visible in the acquired BCG signal, although the signal-to-noise ratio (SNR) of the BCG signal was not very high. However, data provided in [72] show that movements and engine vibrations make BCG monitoring difficult in a running vehicle, and the superimposed artefacts do not allow correct signal interpretation. In 2018, [PERSON] and [PERSON] [77] presented a method for measuring HR and RR using a BCG sensor placed in the passenger seat (Figure 5c). The sensor was a liquid-filled bladder connected to a pressure transducer. Initial validation of this non-invasive seat sensor for HR and RR monitoring was performed in a controlled laboratory environment. In 2019, [PERSON] et al. [78] investigated the performance of a three-axis accelerometer and a three-axis gyroscope collecting SCG signals for continuous recording of vital signs in daily life. The sensors were mechanically attached to the participants' chests through direct connection using attachment belts or textiles. The authors reported that the best accuracy for estimating the blood pre-ejection period was achieved by combining the gyroscope and accelerometer signals. Later, in 2022, [PERSON] et al. [79] proposed a SCG measurement system consisting of two accelerometers. The primary accelerometer measures chest vibrations in response to heartbeat. It is integrated into the seat belt located on the driver's chest. The second accelerometer is used to eliminate the effects of external disturbances. It is located on the driver's seat to measure vehicle vibrations and driver body movements.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
###### Abstract  
Landslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based on variational mode decomposition and a genetic algorithm optimization of the Elman neural network (VMD-GA-Elman). First, using VMD, the landslide displacement sequence is decomposed into the three subsequences of the trend term, the periodic term, and the random term. Then, appropriate influencing factors are selected for each of the three subsequences to construct input datasets; the rationality of the selection of the influencing factors is evaluated using the gray correlation analysis method. The GA-Elman model is used to forecast the trend item, periodic item and random item. Finally, the total displacement is obtained by superimposing the three subsequences to verify the performance of the model. A case study of the Shuizhuyuan landslide (China) is presented for the validation of the developed model. The results show that the model in this paper is in good agreement with the actual situation and has good prediction accuracy; it can, therefore, provide a basis for early warning systems for landslide displacement and deformation.  
Keywords:landslide displacement prediction; variational mode decomposition; genetic algorithm; Elman neural network; gray relational analysis +
Footnote †: journal: Applied
## 1 Introduction and Motivation
The CH disseminates the information on the list of PNs and requests the PNs to collect data from their respective subclusters. After partitioning the network, the AUV initiates a data-gathering tour with the probe interval in a predetermined neighborhood and communicates with the CH to acquire the list of PNs. Then, the AUV visits each PN to collect available data. The proposed data-gathering scheme, which is named AUV-visits-PN (AUV-PN), is evaluated and compared in terms of total amount of energy consumption, the standard deviation of each node's energy consumption, latency to gather the data at a sink, and controlling overhead with the following possible alternatives for data gathering:  
1. AUV-visits-CH (AUV-CH): In this scheme, the CH collects data from MNs and acts as a data relaying node for the cluster. During a data-gathering tour, the AUV only visits the CH to collect the data. 2. Domingo routing protocol [4]: This is a data-gathering approach where no mobile sink is employed. A network is partitioned into a number of clusters, each of which contains a CH that collects the data from respective MNs and forwards it to the sink in a single or multihop manner. The main contributions of this paper can be summarized as follows:  
1. An approach for designing the deterministic AUV data-gathering neighborhoods is presented, keeping in mind the absence of global information. 2. The proposed scheme selects relay nodes from the set of already deployed sensor nodes, thus relieving the scheme from special installation requirements [11]. 3. The performance of the proposed scheme is compared with the existing schemes in terms of standard deviation of energy consumption, latency, and overhead. The preliminary version of this work was presented in a prior conference paper [12]. The present paper extends the conference version with additional results related to energy consumption during the network partitioning phase, protocol overhead, and distribution of energy consumption in the network. The rest of the paper is organized as follows. In Section 2, an acoustic link budget is presented. In Section 3, system description is given. Then, Section 4 presents the operation of the proposed AUV-based data gathering protocol. After that, protocol performance evaluation is presented in Section 5. Finally, Section 6 concludes this paper.
## 3 Results
### Effect of Grinding Conditions on the Particle Size Distribution Curves of the Mill  
The volume proportion of the four populations in the powders varies according to the type of mechanical stress during grinding (roller mill and ball mill) and to the setting parameters (Figure 6). The results show significant differences between the two mills used. The very large particles (population 1) are only present only under certain grinding conditions associated with moderate pregrinding intensities. In all tests with the roller mill (Figure 6a), the coarse particles (population 2) are the major fraction (volume fraction > 50%). This is at the expense of the fine (population 3) and very fine (population 4) particles. In all of the ball mill tests (Figure 6b), the proportions of population 3 particles are more balanced, with significant diversity depending on the mill settings. Populations 2 and 3 are the most common, with a lower proportion of population 4 particles. The proposed experimental design, using two different types of mills, has produced powders with significant differences between the volume proportions of the four populations.  
Figure 6: Proportion (%) of the four different particle populations of different pea powders ground (**a**) with a roller mill and (**b**) a ball mill (P1 = population 1; P2 = population 2; P3 = population 3; P4 = population 4) under different grinding conditions. Values with different letters in the same particle population fraction are significantly different (\(p\) < 0.05). Please refer to Tables 2 and 3 for details of grinding conditions e1 to e8 for roller mill and ball mill essays, respectively.  
Ball milling also appears to produce a higher proportion of fine particles (populations 3 and 4) than roller milling (Figure 6).  
With regard to the characterization results obtained in terms of cell structure dimensions of pea cotyledons, it can be assumed that under our grinding conditions, the following are true:  
* Compression/shear grinding (combined with roller milling) is more likely to have preserved the cellular integrity of the cotyledons due to its coarser particle size (i.e., producing powders with particles predominantly associated with population 2 (Figure 6), grouping together particles of sizes larger than the cell size).
* Impact grinding (combined with ball milling) produced a higher proportion of cell fragments (Figure 6) associated with open cells and the release of subcellular components (i.e., a high proportion of particles < 55 \(\upmu\)m (population 3) within the powders produced).
## 4 Simulation and Discussion
### Simulation of the Vehicle Crossing the Water Surface for Takeoff
The yaw angle can be observed in the figure as being driven more quickly and accurately to the expected value because it does not have a degree of freedom coupled to the position loop. In addition, due to the large deviation between the initial position and the tracking position, the calculated expected roll angle and pitch angle produce large oscillations in the first few seconds and can be stabilized in a short time. In Figure 8, the simulation results show that the speed and accuracy of the feedforward PD controller are weaker than those of the ANFTSMC. As shown in Figure 8, the trajectory tracking errors and angle tracking errors converge to zero in a short time, and the tracking errors are stabilized in a small, bounded area near zero. Figure 9 shows the changes in the HAUV's theoretical control input. It can be observed that the control signals are generally smooth (by means of an adaptive algorithm and by replacing the sign function in the controller with a saturation function). Furthermore, \(u_{1}\) was small 13 s ago, which is due to buoyancy canceling out most of the gravity during underwater maneuvering. The HAUV has the characteristics of low energy consumption and strong maneuverability when under water. It has been proven that buoyancy generated by a HUAV's watertight structure is of great significance and must be considered in controller design. \(u_{1}\) mutates at around 13 s, at which time the HAUV is maneuvering through the water-air trans-medium process, and \(u_{1}\) presents a linear increase trend and finally stabilizes, which is consistent with our linear hypothesis. In addition, it can be clearly seen in Figures 10 and 11 that the adaptive parameter estimation of the position ring and attitude ring is converging to a constant value, which guarantees the boundedness and convergence of the estimated parameters. Figure 12 shows the change curve of the non-singular fast terminal sliding-mode surface. As shown in Figure 12, the sliding-mode surface rapidly converges to zero, which means that the system error can quickly reach the sliding state, thereby verifying that the designed controller has the advantage of a fast response. In addition, Figure 13 shows the changing trend of the switching function after linearization. It can be observed that the switching process of the system model has been completed during the exiting-water process. Figure 11: Estimated values of the adaptive parameters of the position loop. Figure 9: Theoretical control input during the process of exiting water.
## 4 Experimental Validations  
In a second step of validation, experimental measurements have been carried out on ferritic steel components. First, TOFD inspections of large flaws involving 3D effects (sometimes very important) have been considered.