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  
Thermal conductivity is an essential physical property of nanofluid conduction. Several studies have been performed to enhance nanofluids' thermal conductivity. For NSN nanofluids, Table 1 summarizes experimental studies on the thermal conductivity enhancement of NSN nanofluids. The experimental conditions vary in the nanofluid type, particle shape, size, volume fraction, and temperature. Distilled water (DW) was commonly used for aqueous nanofluids, and oil was used as the base fluid. Most of the experiments were performed by transient hotwire technology in the lab. The insulation treatment of the sensor and control of surfactant is significantly essential [15]. As shown in the table, all of the experimental results showed the thermal conductivities of nanofluids were enhanced after nanoparticles were suspended in the base fluid. Results showed that the thermal conductivity of nanofluids increases as the particle loading grows. However, not all data demonstrated the abnormal thermal conductivity capacity of nanofluids with non-spherical nanoparticles. Figure 2 presents the experimental data in Table 1 to compare the TCEs of different NSN nanofluids at room temperature. Considering the continuous variation in TCE with temperature, data were obtained by interpolation within the temperature range.  
The shapes of non-spherical nanoparticles used in studies can be divided into three categories based on the dimensionality of particle geometry [13; 16]. Carbon nanotubes (CNTs), nanofibers, and nanowires are approximated as one-dimensional elongated particles with a large aspect ratio (AR, ratio of length to diameter of particles). Their cross-section size is in the nanometer range. However, the length is several microns or even longer. In addition, their bending states differ due to different materials.  
Nevertheless, these particles with large aspect ratios are easily intertwined, inducing a network effect [17]. Their rheological characteristics are significantly apparent. Rod-like and ellipsoidal particles are categorized as two-dimensional nanomaterials with a moderate aspect ratio of 1 to 10. If the length is much less than the cross-sectional diameter, the particle usually appears as a disk shaped like a platelet or a blade, as shown in Figure 1c. Additional types of particles with arbitrary shapes such as bricks, diamonds, and other polygonal shapes are three-dimensional classes. From the average slope of the three zones, the approximate enhancement effect by three types of non-spherical nanoparticles on thermal conductivity is 4.5:2.5:1. The thermal conductivity characteristics of three types of NSN nanofluids are discussed.
# How Soft Polymers Cope with Cracks and Notches
## 2 Experimental Tests on Macroscopically Flawed Thin Plates  
In this section, we present the results of tensile tests on thin plates made of various elastomeric polymers, carried out under displacement control up to complete failure. The plates contain flaws with different geometrical features, ranging from cracks to notches, denoted here as geometric discontinuities with a finite radius of curvature. Rubber-like polymers have been chosen because their behavior is highly representative of a vast range of soft materials. For instance, silicone rubber is often employed as a substitute of human skin, since it does not show the strain-induced stiffening of natural rubbers, maintaining comparable values of tensile and tearing strength [32]. The stress-strain curves obtained from tensile testing of silicone rubbers are typical of a hyperelastic, almost incompressible behavior. Several models have been proposed [33], which usually show good agreement at low to moderate stretches (generally below 1.5). During our experiments we have found that the one-parameter neo-Hookean model offers a satisfactory approximation in the stretch range of interest (see, for instance, Figure 1).  
The purpose of the experiments herein described is to evaluate the effect of initial flaws on the tensile strength of the material, considering both the effect of the flaw size and of the applied strain rate. The response of the specimens during the experimental tests is monitored by measuring the applied force and corresponding displacements, and by using the Digital Image Correlation (DIC) technique. The DIC is a contactless technique widely used to get full-field displacement and strain maps in experiments, through numerical reconstruction of the kinematic field shown by the surface points of the samples. For an optimal use of such a technique, the surface of the specimens needs to be covered with speckle patterns before testing. Several parameters affect measurement accuracy and spatial resolution, included optical measures connected to the camera and lens resolution, image magnification, mean size of the speckle pattern, and factors depending on the correlation algorithm, such as the image subset size and the gray-level interpolation. In particular, the resolution of the displacement measurement is governed by the subset size, whose lower bound is limited by the speckle size and, consequently, by the available pixels [34; 35]. In the present work, the images are acquired with a high-resolution digital camera (maximum resolution of 5184 \(\times\) 3456 pixels) mounted  
Figure 1: Typical curve obtained from tensile testing of a silicone rubber (experimental and fitted data). The enlarged inlet shows the region \(\lambda\) = 1.2–1.4, with excellent agreement between the different models.  
on a tripod, and lights are used to ensure a uniform illumination of the samples. The sequence of images is treated by means of the freeware software Ncorr [36], developed in MATLAB environment, for monitoring the displacement and the strain fields within the specimen.
## 5 Experiment Verification
In contrast, frequency responses of the VSM with \(T_{G}\) = 0.1 s under a 3% step-up load change are demonstrated in Figure 13. It is clear that all the requirements are satisfied for both the case of \(H\) = 5 s, i.e., the optimal design, and the case of \(H\) = 10 s. Being different from Figure 12, \(T_{G}\) = 0.1 s represents a condition that the time constant of speed governor is relatively small, where the system can be overdamped, i.e., \(\zeta\) > 1. Under this condition, it is not advisable to employ a large inertia coefficient, e.g., \(H\) = 10 s, because doing so will only slow down the frequency dynamics. Therefore, the proposed optimal design of inertia again leads to better frequency regulation performances. To further illustrate this point, frequency responses of the VSM under a 3% step-down load change when \(T_{G}\) = 7 s and \(T_{G}\) = 0.1 s are, respectively, presented in Figures 14 and 15. Under this frequency event, the frequency \(f_{r}\) becomes larger than its nominal value of 50 Hz. Nevertheless, the expressions of performances indices derived in Section 3 are still applicable, and similar conclusions can be drawn from these figures. For the underdamped system (see Figure 14), a larger inertia coefficient is desirable as it translates into fewer oscillations. In contrast, a smaller \(H\) is preferable for the overdamped system (see Figure 15) because it allows faster frequency recovery. Figure 11: Steady-state experimental waveforms. Figure 12: Experimental results of frequency regulation under a 3% step-up load change (\(T_{G}\) = 7 s).
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 2 Signal Model  
The received GNSS signal can be transformed into a complex baseband signal. When no spoofing signals are present, the baseband signal can be expressed as follows [23; 24]:  
\[x_{n}=\sum_{i=1}^{M}a_{i}D_{i}(nT_{s}-\tau_{i})c_{i}(nT_{s}-\tau_{i})e^{i(2\pi f _{i}nT_{s}+\theta_{i})}+w(nT_{s}),\ n=0,1,\ldots,K-1. \tag{1}\]  
Here,  
\(F_{s}=1/T_{s}\) is the sampling frequency;  
\(M\) stands for the number of in-view satellites;  
\(K\) stands for the number of samples;  
\(a_{i}\) is the amplitude of the \(i\)-th satellite signal;  
\(D_{i}(t)\) is the navigation data of the \(i\)-th signal at time \(t\);  
\(c_{i}(t)\) is the pseudorandom code of the \(i\)-th signal at time \(t\);  
\(\tau_{i}\) is the propagation delay of the \(i\)-th signal;  
\(f_{i}\) is the Doppler frequency offset of the \(i\)-th signal;  
\(\theta_{i}\) is the carrier phase of the \(i\)-th signal;  
\(w(t)\) is complex zero-mean additive white Gaussian noise (AWGN) with variance \(\sigma^{2}\).  
Choosing \(K\) samples as observation data, Equation (1) can be rewritten as [23]:  
\[\mathbf{x}=\mathbf{G}(\mathbf{\tau},\mathbf{f})\mathbf{a}+\mathbf{w}. \tag{2}\]  
Here,  
\(\mathbf{x}=[x_{1},x_{2},\ldots,x_{K}]^{T}\in\mathbb{C}^{K\times 1}\) is the measurement vector;  
\(\mathbf{G}(\mathbf{\tau},\mathbf{f})\) can be expressed as \([\mathbf{g}_{1},...,\mathbf{g}_{M}]\), being \(\mathbf{g}_{i}=[g_{1,i},...,g_{K,i}]^{T}\), and \(g_{k,i}=D_{i}(kT_{s}-\tau_{i})c_{i}(kT_{s}-\tau_{i})e^{[2\pi f_{i}kT_{s}},\ k=1, 2,\ldots,K,i=1,2,\ldots,M\);  
\(\mathbf{\tau}=[\tau_{1},\tau_{2},\ldots,\tau_{M}]\), \(\mathbf{f}=[f_{1},f_{2},\ldots,f_{M}]\). During the observation period, \(\mathbf{\tau}\) and \(\mathbf{f}\) can be regarded as constants under static or low dynamic condition;  
\(\mathbf{a}=[a_{1}e^{\theta_{1}},a_{2}e^{\theta_{2}},\ldots,a_{M}e^{\theta_{M}}]^ {T}\in\mathbb{C}^{M\times 1}\) represents complex amplitudes of the received signals;  
\(\mathbf{w}=[w(T_{s}),w(2T_{s}),\ldots,w(kT_{s})]^{T}\in\mathbb{C}^{K\times 1}\) is the AWGN vector, and each element has a variance \(\sigma^{2}\) during the observation period.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
###### Abstract
Furthermore, the deep learning algorithm reduces the access times and control-plane latencies, which meets the ultra-low delays, as defined by the 3 GPP targeted at 1 ms. This improves the quality of service (QoS) and enables the implementation of mmWave standalone networks. Moreover, the technique can be adopted in wireless local area networks (WLAN) as part of the IEEE 802.11 ay standard and mmWave links for vehicle-to-everything (V2X) after adding the mobility component. Moreover, the deep learning algorithm will enable the use of highly directional beams without the reliance on wide-beam codebooks; this in turn eliminates the vulnerability of bean-blockage due to low directivity. Namely, the MS will be able to use narrow beams when transiting from sleep (off) mode to idle or active mode in the control plane, thus helping to reduce the beam search time. As a result, high data rates can be supported here, i.e., leveraging the high channel capacities and aggregated antenna gains. This paper is organized as follows. Section 2 presents the beamforming, signals and channel models. Then the beam access scheme is proposed in Section 3. Performance evaluation is presented in Section 4, along with conclusions in Section 5.
## 1 Introduction  
Red beds, characterized by reddish hues due to iron oxides, are globally widespread sedimentary sequences formed under oxidizing conditions ([PERSON], 1980) [1]. Marine red beds (MRBs), found in marine settings, are distinct from continental red beds (CRBs) ([PERSON] and [PERSON], 1977; [PERSON], 1982) [2,3]. MRBs are significant in sedimentology, paleoceanography, paleontology, and paleoclimatology, offering insights into Earth's redox history and climatic shifts ([PERSON] and [PERSON], 1977; [PERSON] et al., 2005; [PERSON] et al., 2021) [2,4,5].  
First identified in the 19 th century in the Mediterranean, Europe, and North Africa ([PERSON], 1833) [6], MRBs were initially described lithologically. Mid-20 th century advances linked their red coloration to hematite (Fe\({}_{2}\)O\({}_{3}\)) and oxidizing conditions. The late 20 th century saw MRBs integrated into studies of oceanic anoxic events (OAEs), carbon cycling, and mass extinctions ([PERSON], 1976; [PERSON] et al., 1987) [7,8].  
Since the 21 st century, high-resolution geochemical techniques and numerical modeling have advanced MRB research, revealing their roles in paleoenvironmental reconstruction, plate tectonics, and biological evolution ([PERSON] et al., 2011; [PERSON] et al., 2019) [9,10]. MRBs are categorized into oceanic (basin), deeper water (slope), and shallow water (shelf) types based on depositional environments ([PERSON] et al., 2009; [PERSON] et al., 2021) [5,11].  
Cretaceous oceanic red beds (CORBs), first identified in the Carpathians and eastern Alps ([PERSON], 1860; [PERSON], 1861) [12,13], are globally distributed in the Atlantic, Pacific, Indian, and Tethyan Oceans ([PERSON] and [PERSON], 2005; [PERSON] et al., 2005) [4,14]. Their formation is linked to Late Cretaceous oceanic oxidation, iron cycle changes, global cooling, and sea-level fluctuations ([PERSON] and [PERSON], 2022) [15]. CORBs mark the transition from anoxic to oxic marine conditions ([PERSON] et al., 2011) [16].  
In the northern Tethyan Himalaya (NTH), CORBs are exposed as reddish limestones, malstones, shales, siltstones, and radiolarian cherts, with key outcrops in Yamdrok Lake, Gyangze, Kangmar, Sakya, Gyirong, and Zanda (Figure 1). These deposits, dated from late Santonian to early Paleocene, exhibit a progressive east-to-west age trend ([PERSON] and [PERSON], 2002; [PERSON] et al., 2005; [PERSON] et al., 2011) [9,17,18].  
Figure 1: Geological structure map of the Tethys Himalayas and its adjacent areas (the base map is after [PERSON], 1964) [19] showing the sections with Cretaceous oceanic red beds (CORBs) and location of the Zhangguo section in Gyangze, southern Tibet of China.  
This study aims to (1) analyze the well-preserved CORB sequence in the Zhangguo section (Gyangze, NTH), (2) establish stratigraphic correlations with global CORB deposits, and (3) evaluate the synchroneity and paleoceanographic implications of Upper Cretaceous CORB deposition.
# Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT
## 5 Analysis of Vibration Signals of the Motor Bearing Rolling Element
### Feature Extraction Based on the Hilbert Transform  
The power spectrum represents the relationship between signal power and frequency, that is, the distribution of signal power in the frequency domain. In this paper, the Hilbert transform was used to  
Figure 5: Frequency domain waveform.  
Figure 6: Center frequency of each IMF (intrinsic modal functions) component under different scales.  
solve the envelope signal of each IMF component, then the power spectrum of the envelope signal was solved, and the frequency value with the largest power spectral density is marked in the figure, which was used to represent the fault characteristic frequency, rotating frequency, and frequency multiplication of the signal extracted by power spectrum analysis. The fault characteristic frequency of the motor bearing rolling element was 141.0751 Hz. The optimal modal number, \(K=4\), was determined according to the center frequency method of the multi-threshold. In order to verify the validity of the optimal modal number, for the motor bearing rolling element data, the power spectrum frequency values of each IMF component under \(K=3\), 4, and 5 are shown in Table 3.  
As can be seen from Table 3, 29.2969 Hz represents the rotating frequency, 146.4844 Hz represents the rotating frequency of five times, 140.625 Hz represents the rolling element fault characteristic frequency, 70.3125 Hz represents the rolling element fault characteristic frequency of 1/2, and 11.7188 Hz represents the rolling element fault characteristic frequency of 1/12. The value of 52.7344 Hz represents the difference between the rolling element fault characteristic frequency and the frequency tripling of the rotating frequency. When \(k=4\), the four IMF components can obtain the rotating frequency, the rotating frequency of five times, the rolling element fault characteristic frequency, and the rolling element fault characteristic frequency of 1/2. When \(k=3\), the rotating frequency cannot be extracted. When \(k=5\), the rolling element fault characteristic frequency cannot be extracted. Therefore, \(k=4\) was determined as the optimal modal number of the VMD method for the rolling element data, and the power spectrum of the corresponding IMF component is shown in Figure 7.  
From the experimental results, it is proven that the validity of \(K\) is the optimal modal number of the VMD decomposition determined by the center frequency the of multi-threshold by solving the power spectrum in this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **IMF1(Hz)** & **IMF2(Hz)** & **IMF3(Hz)** & **IMF4(Hz)** & **IMF5(Hz)** \\ \hline \(K=3\) & 146.4844 & 140.625 & 70.3125 & & \\ \(K=4\) & 146.4844 & 29.2969 & 140.625 & 70.3125 & \\ \(K=5\) & 146.4844 & 29.2969 & 52.7344 & 29.2969 & 11.7188 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The frequency values of the power spectrum of each IMF component.  
Figure 7: Power spectrum of each IMF component under \(K=4\).
## 7 Limitations of the Study  
The proposed approach has several limitations that should be taken into account. Firstly, the quality of geographic data is crucial to its efficacy; inaccurate data might result in suggestions that are not relevant and lower customer happiness. The system's implementation necessitates a large investment in GIS infrastructure, which small- and medium-sized businesses may find difficult. Furthermore, even though the mobile agents allow for real-time adaption, a connection delay could make suggestions less responsive. Additionally, because it might not adequately capture quickly evolving behaviors across various consumer groups, the system struggles with the variety of user preferences. Moreover, it might be difficult and resource-intensive to integrate mobile agents with current GIS services and e-commerce systems, necessitating careful planning. Another issue is user adoption, as prospective users may be reluctant to embrace mobile agent technology because of misunderstandings or perceived difficulties with user interactions.
## 3 Results and Discussion  
### The Effect of Pepper Pre-Treatment on the Count of LAB After Fermentation  
In all fermented samples, an increase in the number of lactic acid bacteria was observed, which indicates that the fermentation process had taken place (Figure 1). The count of lactic acid bacteria ranged from 8.73 log CFU/g for the sample not treated with ultrasound to 10.55 log CFU/g for the sample pre-treated with ultrasound in a water bath. In the case of ultrasound generated using a sonotrode, no effect of pre-treatment time on the differences in the count of microorganisms was observed (ranging from 9.53 to 9.57 log CFU/g). However, after using ultrasound in a water bath for a longer time, an increase of about 1 logarithmic cycle in the count of microorganisms was observed in the fermented samples (ranging from 9.74 to 10.55 log CFU/g). This may indicate a greater efficiency at loosening the plant tissue, which allows microorganisms to penetrate inside this tissue.  
Using starter cultures enhances the likelihood of proper microbial development during fermentation, resulting in products with desirable sensory characteristics and consistent quality. The count of lactic acid bacteria indicates a successful lactic acid fermentation process [33]. Additionally, this count is inversely related to undesirable microorganisms in fermented products, such as pathogenic and saprophytic microorganisms, that can be transmitted through water and food. This relationship occurs because the pH of the product decreases during fermentation, inhibiting the growth of other microorganisms [34].  
In other studies, ultrasound was used after the inoculation of Citrus reticulata cv. _Bakraei_ juice with _Limosilactobacillus reuteri_ bacteria. A positive effect on the number of LAB was observed after the application of ultrasound for up to 5 min, while extending this process to 6 min resulted in a reduction of the number of _L. reuterii_[19]. In turn, ultrasound pre-treatment of pumpkin increased the number of _Lacticaseibacillus rhamns_ GG bacteria during the fermentation process compared to the untreated raw material by about 1 logarithmic cycle, but did not significantly increase the number of _Lactiplantiucillus plantarum_ 299v [7]. The increase in the number of _L. plantarum_ in this study after ultrasound treatment may be due to the different chemical compositions of pumpkin and pepper  
Figure 1: Number of lactic acid bacteria in fermented peppers after 7 days of fermentation. Different letters indicate statistically significant differences according to [PERSON]’s test at \(\alpha=0.05\). Sample codes are described in Table 1.  
and the use of a different strain of these microorganisms. [PERSON] et al. indicated that using ultrasound may damage the cell membrane and release nutrients, contributing to the increased growth of LAB [35].
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 1 Introduction
This paper presents the development of a mathematical model that focuses the lateral stability of a mobile robot in the active model. An active suspension that can adjust its ground clearance with input (linear actuator length) is described and designed. The mobile robot can actively adjust its posture using the two active suspensions, which are on both sides of the robot. Furthermore, when the locomotion of the robot is leveled, its equipped devices can work smoothly. Then, a mathematic model of a mobile robot on side slopes is presented, and lateral stability analysis associated with the active input of the active suspension is conducted. Thus, the main contribution of this study is the development of a mathematic model representing the relationship between the input of the active suspension and the robot's lateral stability. This active suspension can be easy controlled by a linear actuator while ensuring the coincident motions of the front and rear wheels. Another contribution is the parameter design of this active suspension. It provides a method to choose an appropriate linear actuator for the active suspension. In addition, the designed parameters can be adapted to the mathematic model. The mathematic model developed here can also be adapted for controlling the robot in order to increase its stability. The rest of this paper is organized as follows. Section 2 describes a mobile robot with an active suspension. Section 3 designs the active suspension while achieving the input-output relationship. Section 4 describes the lateral stability of the mobile robot with an active suspension. Section 5 includes case studies for both the active suspension and the robot's lateral stability. Section 6 describes the manufacture and test of a prototype.