esa-sceva/satcom-chunk-collection huggingface dataset:
# Model for Wall Shear Stress from Obliquely Imping Planar Underexpanded Jets
## 2 Methodology
### Model Validation  
Validation of the CFD result is challenging in the absence of the experimental or DNS data for compressible planar impinging jets needed for direct comparison. We evaluated two flow regions: (i) impinging jet and (ii) wall jet region.  
Impinging jet region validation: The implementation of \(k-\omega\) SST jet in the impinging jet region was previously validated with underexpanded axisymmetric jets using Schlieren photography. The shape and the shock structures were shown to be in excellent agreement with experimental observations [23]. Additionally, the CFD simulations were compared with pressure profiles on the impingement surface from pressure-sensitive paint (PSP) experiments [24]. PSP utilizes the emission spectra of a luminophore by relating the emission intensity at specific wavelengths to the partial pressure of oxygen at the surface. Images were taken under wind-on and wind-off conditions, and the images' intensity ratios were related to pressure. The CFD approach was evaluated using oblique planar jet impingement against the PSP pressure measurements. The oblique jet produces an uphill shift in the impingement point from the geometric center [32]. Figure 2 shows the computed pressure profiles overlayed on the PSP measurements. The CFD simulations show good agreement in the shape and magnitude of the pressure profile.  
Wall jet region validation: The \(k-\omega\) SST simulations were compared to two separate DNS studies. First, the model was used to replicate DNS data from normal planar impinging jet conducted by [PERSON] et al. [37], reporting wall jet profiles downstream of the impingement point. Figure 3a shows the agreement in velocity profile from [PERSON] et al. and the \(k-\omega\) SST computation. Second, the model was evaluated against the DNS of a classical wall jet conducted by [PERSON] et al. [41]. Figure 3b-d compares the development of the wall jet thickness, \(y_{1/2}\), the maximum velocity, \(U_{m}\), and the wall shear stress.  
Figure 2: Normal pressure profiles from CFD (red) and pressure-sensitive paint (blue) experiments for h = 30 mm, d = 1 mm, NPR = 1.0 with the impingement angle of 30 degrees (**left**) and 15 degrees (**right**), as shown originally in [PERSON] et al. [24].  
In summary, the \(k-\omega\) SST was found to be an acceptable model for use in the parametric study because of the excellent agreement with the PSP measurements, validation from two DNS studies describing wall jet development, and previously reported Schlieren photography comparison for the axisymmetric impinging jet [23].
## 2 Materials and Methods
### Preparation of Essential Oil Solutions  
To evaluate the electrical performances of tea tree, clary sage, and cinnamon bark EOs with the fabricated textile-based microfluidic chip sensor, different concentrations of EO solutions were prepared. Briefly, 0.2% solutions of cinnamon bark, tea tree, and clary sage essentials oils were prepared in 10% ethanol solution containing distilled water (\(v/v\)), followed by ultra-sonication for 20 min at room temperature. Here, ethanol was used to solubilize essential oils into the water so that the formulation was blended homogeneously, ensuring that the composition was not separated into distinct ingredients. Additionally, 4% and 10% solutions of these EOs were also prepared.
## 3 Experimental Results  
### Innovative Nutrient Mixer Cartridge System  
The Nutrient Mixer system developed in this research focused on precise nutrient dilution control. Experimental results demonstrated that the Nutrient Mixer could accurately control dilution amounts down to nanoliter precision when combining six primary fertilizers with water. The precise dilution and combination of fertilizers within the cartridge did not directly deliver nutrients to plant roots but were controlled accurately during the dilution process. As depicted in Figure S8, this process achieved an effect resembling a fine mist dispersion.  
Figure 7: Performance testing of the new drainage manager in vertical cultivators. The **left** diagram illustrates the vertical growing chamber and the remisting system. The **right** image displays the actual vertical growing chamber with plants.  
This system, as shown in Figure S9, was able to precisely control the necessary concentration of fertilizers through nanoliter-scale dilution. This capability of precise dilution control demonstrates the potential to scientifically adjust and optimize the nutrients required by plants in hydroponic cultivation. The data obtained from the experiments confirmed that this system could very precisely control and provide the trace nutrients needed for plant growth. This represents a potential in smart farm technology to implement customized nutrient supply schedules tailored to different plant species.  
However, during the experiment, as shown in Figure S10, clogging of the print head nozzles occurred due to crystallization reactions in the nutrient solution, which disrupted the normal operation of the system. This issue highlights the structural vulnerabilities of the nozzles and calls for experimental modifications in the composition and concentration of the nutrient solution under various conditions to minimize nozzle clogging. These results underscore the need for further research and development to solve potential issues in nutrient mixer technology while significantly enhancing the ability to micro-manage nutrients in smart farms.
## 2 Exosome Features
### Exosomal Cargo  
There are more than 4000 structural, transport, and signaling proteins (Tsg101, tetraspannins, and Alix) (MHC I and MHC II) involved in exosome formation. Transport proteins, cytokines, and, recently, a large number of nucleic acids have been described in exosomes [20], mainly miRNA. Among the cargo molecules found in exosomes, chaperones stand out, including HSP90, HSP70, and HSP60. Studies suggest that exosomes containing HSP70 are associated with the progression of inflammatory diseases by activating receptors such as TLR4 [21]. Furthermore, the presence of HSP70 has also been reported on the membrane of exosomes secreted by cancer cells [22].  
About 1500 miRNAs have been identified in the human genome [23; 24]. However, the exact number of miRNAs within all exosomes is unknown; at least 100 miRNAs have been identified in exosomes derived from mast cells [25]. Cargo molecules play physiological roles in cellular communication and are often involved in several diseases, including cancer, metabolic disorders, immunological conditions, neurological diseases, and cardiovascular disorders [26].
# A Survey on Fault Diagnosis of Rolling Bearings
## 3 Rolling Bearing Fault Detection
### Deconvolution-Based Fault Detection Methods  
The deconvolution-based detection method is to find an inverse filter to eliminate the transmission path influence in the signal acquisition process and extract the fault pulse from the noise-contaminated vibration signal. The research on deconvolution-based methods can be dated back to 1980. [PERSON] et al. [85] proposed the minimum entropy deconvolution (MED) method to maximize the kurtosis of filtered signals and used it to analyze seismic signals. However, MED is easily affected by a random pulse with a large amplitude, making it impossible to accurately extract the periodic pulses corresponding to the fault in the signal [86]. To address this, [PERSON] et al. [87] developed a new index called correlation kurtosis to evaluate the periodicity and sparsity of signals and proposed maximum correlated kurtosis deconvolution (MCKD) for maximizing the correlation kurtosis value. MCKD overcomes the shortcomings of MED and can effectively extract the periodic pulse corresponding to the fault when there is a single abnormal pulse in the signal. However, the processing performance of MCKD is affected by two parameters, i.e., the inverse filter length and the fault cycle size. Whether the parameter setting is accurate directly affects the final processing result of MCKD. To address this issue, [PERSON] et al. [88] proposed sparse maximum harmonics-noise-ratio deconvolution (SMHD), which can adaptively estimate the fault period by calculating the harmonic noise ratio of the envelope of the filtered signal. However, SMHD generally suffers diminished performance when analyzing the signals with harmonic components. MCKD and SMHD require a long calculation time due to the deconvolution operation based on iteration analysis. Therefore, [PERSON] et al. [89] proposed a method that does not require iterations, namely, multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), which can complete the deconvolution in a short time but is adversely affected by the periodic oscillations of fault pulse. Recently, [PERSON] et al. [90] introduced the second order cyclostationary index to deconvolution methods and proposed the maximum second order cyclostationary blind deconvolution (CYCBD) method. The performance of CYCBD is better than that of MCKD and MOMEDA, but the fault cycle frequency needs to be set accurately to ensure the processing effect. In order to overcome the shortcomings of these methods, researchers have proposed some improved deconvolution methods by combining other processing methods or using optimization algorithms to determine the optimal parameters required for deconvolution, such as EMD combined with MED [91], PSO optimized MCKD [92], and CS optimized CYCBD [93].
# Augmented Reality-Based Interface for Bimanual Robot Teleoperation
## 1 Introduction
### Previous Research  
#### 1.2.1 Bimanual Robotics  
_Dual-arm robotic systems_ are being used in a wide range of domestic, industrial, and healthcare tasks. The main reason for this is their flexibility and manipulability. In addition, they have a behavior quite similar to that of the human, which makes it possible for humans to relate to their movements more intuitively [9, 10, 11].  
More specifically, _bimanual robotics_ consists of the coordination of two robotic arms that interact physically in order to achieve a common goal [9]. Many applications of bimanual robotics can be found in the literature--for instance, the handling of deformable objects [12, 13, 14], objects with unknown shape [15, 16], or objects whose geometry requirestwo grasping points [17; 18; 19]; the emulation of human bimanual tasks [20; 21; 22]; assistive robotics [23; 24]; assembly operations [25; 26; 27]; surgery tasks [28]; and simultaneous manipulation and cutting [29], manipulation and fastening [10], or manipulation and surface treatment [30; 31], which is the case considered in this paper.  
The vast majority of contributions in bimanual robotics present fully automated tasks based, for instance, on artificial intelligence techniques [21; 23], motion planing techniques [14; 15; 26; 32; 33], or other low-level control approaches [19; 24; 25; 27; 28].  
However, the presence of the human interacting with the bimanual robotic system is very interesting due to the possibility of exploiting the human's natural knowledge of bimanual configurations and motions in order to improve the task performance [31; 34; 35]. For this reason, human-robot interaction (HRI), which is the main focus in this work, is nowadays a trending research topic in bimanual robotics.  
Some interesting approaches can be found related to HRI in bimanual robotics. For instance, the authors in [34] proposed to improve the transportation of a large workpiece, typically performed by two users, by using a bimanual robot attached to a mobile platform. In this approach, the mobile platform moved through a pre-defined trajectory, while the user was able to arbitrarily adapt this trajectory by means of an impedance control. The authors in [35] proposed a multi-layered prioritized shared controller to maintain the robot hands' orientation and contact with the manipulated surface, while the user was able to teleoperate the bimanual robot hands on a plane. The authors in [31] presented a similar approach based on the task priority and sliding mode control techniques to perform surface treatment tasks using a bimanual robotic system. In this case, the user was able to teleoperate all six Degrees of Freedom (DoF) of one robotic arm that held the workpiece, whose movement was limited in the 3D workspace, and to teleoperate two DoF of the other robotic arm, which held the surface treatment, maintaining the appropriate tool orientation and pressure. Due to the relevance of this application for this work, more details can be found in Section 2.
## 5 Results
### Measurement by the Pipette with Gap
#### 5.1.1 Influence of the Excitation Pressure Variations  
In the previous studies by [PERSON] et al. [40] and [PERSON] et al. [39], the excitation pressure was kept constant over the whole frequency range. These studies showed that the calculated elastic modulus does not depend on the variation of the excitation pressure amplitude. This was examined by using two different samples and pipette radii with two different pressure variation approaches. First, using the smaller pipette, the 1:1:2 mixture excited by the excitation pressure in the frequency range varied in two separate spans, from 2.8 Pa to 12.2 Pa and from 4.2 Pa to 19.2 Pa. Second, the 1:1:3 sample excited in the frequency range by constant 3 Pa pressure and by various pressures from 1 Pa to 12 Pa using the larger pipette. Figure 6, charts (a) and (b), demonstrates amplitudes of the two excitation pressure ranges and the generated displacement. The chart (c) demonstrates the calculated modulus of elasticity by Equation (1) for the 1:1:2 sample in the first approach. The red line in the chart (c) belongs to the calculated modulus of elasticity with the 2.8 Pa to 12.2 Pa range and the blue dotted line denotes the calculated modulus of elasticity with excitation pressure in 4.2 Pa to 19.2 Pa range. It is clear from the results, although the pressure and the generated displacement are different in the two excitation conditions, that the calculated modulus of elasticity remains constant with an acceptable approximation.  
For the second approach, the larger pipette with a bigger radius was utilized to excite the 1:1:3 mixture in the experimental setup shown in Figure 1b. The displacement was measured and the modulus of elasticity calculated. The red line of the chart in Figure 7 demonstrates the calculated elastic modulus of the sample by various excitation pressure amplitudes from 1 Pa to 12 Pa and the blue line indicates the calculated modulus of elasticity when the sample is excited with a 3 Pa constant pressure in all the frequency steps. The two lines are completely identical showing that the diversity of the excitation pressure has no effect on the calculated modulus of elasticity using the analytic approximation (1). Comparing Figures 6a and 7 reveals that the calculated modulus of elasticity is independent of the excitation pressure variations using both the pipettes. Therefore, the excitation pressure and the generated displacement amplitude have a linear relation at a fixed frequency.  
Figure 5: Calculated modulus of elasticity of the four mixtures in pipette with gap boundary condition; (a) 1:1:0 mixture; (b) 1:1:2 (blue); 1:1:3 (green); 1:1:4 (red) mixtures.  
Figure 6: Influence of variation in the excitation pressure on the calculated modulus of elasticity; (**a**) disparate pressure amplitudes; (**b**) the measured displacement amplitude; (**c**) the modulus of elasticity of the two excitation ranges.  
Figure 7: Calculated modulus of elasticity by the two pressure ranges.
## 3 Methods and Results  
In this study, we used data from multiple LEO missions to estimate the expected radiation levels in an LEO and assess the use of COTS components in these missions. The data collected by various radiation assessment systems on board these missions were analyzed and compared to computational models to estimate the actual radiation levels in the LEO. We then assessed the performance of COTS components in these radiation environments to determine their suitability for use in LEO missions. By combining these data and analysis, we hope to provide meaningful insights into the radiation environment in LEOs and improve our understanding of the use of COTS components in these missions.  
We first performed a deep search for LEO missions that carry reliable and calibrated in situ radiation monitors with an output in terms of doses (mainly rads or Grays). The goal was to compare the measured dose with the one provided by the computational models. It is important to remark that the TID endurance of an electronic part is given in rads (Si) in its datasheet. For this reason, we did not analyze particle fluxes. This posed a problem, since most of the missions we found carried some sort of particle detector, but not dosimeters. The selected missions are ISS, MIR, PROBA-V, and FOTON-M4.  
Next, the same orbit, shielding thickness and time period were modeled in Spenvis ([[https://spenvis.oma.be/](https://spenvis.oma.be/)]([https://spenvis.oma.be/](https://spenvis.oma.be/)), accessed on 1 June 2023) and Satellite Tool Kit from Ansys ([[https://www.ansys.com/products/missions/ansys-stk](https://www.ansys.com/products/missions/ansys-stk)]([https://www.ansys.com/products/missions/ansys-stk](https://www.ansys.com/products/missions/ansys-stk)), accessed on 1 June 2023). STK is capable of modeling the space radiation environment thanks to the Space Environment and Effects Tool (SEET). The STK SEET Radiation Environment incorporates the APEXRAD, CRRESRAD, CRRESELE, CRRESPRO, NASAELE, and NASAPRO models. In order to compare the modeled results against experimental results, we transformed the doses in a common unit of uGy per day, assuming the worst case scenario. The orbit is modeled using two-line element (TLE) data for the given period.
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
###### Abstract  
This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study.  
wireless acoustic sensor network; low-cost sensor; meshed network; acoustic quality; real-time signal processing; Raspberry Pi; acoustic signal processing 2022
# Analyzing Data Reduction Techniques: An Experimental Perspective
###### Abstract  
The exponential growth in data generation has become a ubiquitous phenomenon in today's rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.  
data reduction; IoT, Big Data; data compression; energy efficiency; bandwidth consumption +
Footnote †: journal: _sciences_  
002420241434634363  
sent to the cloud. This approach significantly reduces bandwidth usage and improves efficiency. Alternatively, if energy and computation power are not an issue, data reduction should be applied directly at the sensor level using techniques such as compression or filtering. These techniques offer valuable ways to optimize data handling and improve overall performance.  
This paper presents a study and experimental evaluation of various data reduction techniques using different types of datasets to analyze their performance. As part of this research, we also introduce a new taxonomy for data reduction techniques, including lossy and lossless types. By categorizing them based on the data loss characteristics, we provide insights into the trade-offs between data reduction efficiency and the preservation of critical data features. Our experimental evaluation aims to provide a comprehensive understanding of the strengths and limitations of each technique. The main contributions of this work are the following:  
* New data reduction techniques' taxonomies.
* Comparison of different data reduction techniques.
* Experimental evaluation of data reduction techniques.  
The rest of this paper is organized as follows: Section 2 provides some background information. Section 3 discusses related work and compares data reduction techniques. Section 4 presents the proposed taxonomy. Section 5 presents the datasets, and the metrics used in the experiments are discussed in Section 6. The data reduction techniques are presented in Section 7, and the experimental evaluation of these techniques is provided in Section 8. Section 9 presents the discussion of the results. Finally, Section 10 focuses on the main conclusions of this study and proposes future work.