esa-sceva/satcom-chunk-collection huggingface dataset:
# Hot-Pressing of Ti-Al-N Multiphase Composite: Microstructure and Properties
## 1 Introduction  
The advancement of industry heavily relies on the availability of materials with exceptional functional properties, such as resistance to abrasive wear, corrosion under both low- and high-temperature conditions, and erosion, and high hardness values. The design of new materials or the enhancement of existing ones is a critical driver of technological progress. In the tool industry, particular emphasis is placed on coatings that exhibit high hardness and resistance to abrasive wear, erosion, and corrosion. Widely used coatings include TiN, TiAlN, TiCN, and diffusion layers of titanium nitride, particularly for cuttingtools exposed to extreme mechanical and chemical stresses [1; 2; 3; 4; 5]. These coatings and diffusion layers are produced using various deposition technologies, including Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD). These methods enable the creation of coatings with high hardness and excellent wear resistance, making them especially suitable for machining applications [6; 7; 8; 9; 10]. However, despite their numerous advantages, these coatings face limitations, particularly in resisting corrosion caused by ball mill (Fritsch Pulverisette 5, FRITSCH, Idar-Oberstein, Germany). A 250 mL tungsten carbide milling chamber and 10 mm WC grinding balls were employed. The milling process was carried out with a ball-to-powder ratio of 10:1, a rotational speed of 200 rpm, and alternating cycles of 10 min of milling followed by 20 min of cooling. The process concluded after 4 h of milling. Particle size analysis of the processed powders was performed using a Malvern Mastersizer 3000 particle size analyzer (Malvern Instruments Ltd., Malvern, UK). The initial AlN particles (average size 1.65 \(\upmu\)m) and TiN particles (average size 14.2 \(\upmu\)m) underwent planetary mixing, resulting in a tri-modal particle distribution with an average particle size of 2 \(\upmu\)m (Figure 1). A small addition of BN did not significantly affect the particle size distribution. The analysis conducted via Scanning Electron Microscopy (SEM) (Figure 2) confirmed that the particle sizes of the examined powders are highly diverse. The smallest particle diameter was observed for aluminum nitride (Figure 2a). SEM observations of the powder particles also revealed their morphology. Particle analysis (Figure 2b-d) showed that the particles have an angular, sharp-edged, and plate-like structure. The HP sintering process demonstrates minimal dependence on the morphology of the initial powders. Even with a low flowability, a sintered bulk material density exceeding 99% was achieved.
## 5 Scalability  
Finally, the extent of carbon capture associated with a large-scale process is to be discussed. The reported data were obtained with a lab-scale module featuring only one lighting module. Considering the industrial device (four lighting modules), one can anticipate a multiplication by 4 of the produced biomass, as well as a multiplication by 4 of the emissions associated with lighting. Nevertheless, carbon dioxide emissions linked to utilities (gas sparging and mixing) would be unchanged. Therefore, the CO\({}_{2}\) captured per day would be around 2.89 \(\pm\) 0.75 gCO\({}_{2}\)/day, while the emissions induced by the processwould be 383 gCO\({}_{2}\)/day. The process would only capture 0.51 \(\pm\) 0.20% of its emissions. Increasing further the size of the photobioreactor would not change this value. Indeed, the need for utilities would start to increase in a volumetric manner, like the lighting module number (because of geometrical constraints).  
Finally, optimizing the setup using red/blue lighting and only accounting for the electrical power directed toward the lighting system, the fraction of absorbed CO\({}_{2}\) could reach 1.02 \(\pm\) 0.40% of the process emission. Taking into account the potential gain of photosynthetic efficiency if reaching the theoretical maximum, the carbon capture could reach 2.80% of its emission. However, as stated before, such a leap is quite audacious since the theoretical maximum is far from reachable in any industrial process [31]. Leads exist to increase even beyond this value of carbon fixation [10], notably in the field of genetics enhancement, but a lot of work is still needed before maturity. The same comment can be made for the development of less energy-intensive processes [35]. More generally, microalgal carbon uptake is limited by the same scale-up difficulties that any photosynthetic bioprocess faces [36].
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Energy Expenditure
#### 3.5.2 RGB Depth  
One of the noticeable technologies used for image capturing is the RBG-Depth camera, which captures objects in three dimensions. [PERSON] [69] presented a framework for the estimation of vision-based energy expenditure using a depth camera and validating the method with oxygen uptake measures. The method was found suitable for monitoring in a controlled environment, showing advantages as pose-variant and individual-independent way of measuring energy expenditure, in real time and remotely.  
The deep learning technique has been considered one of the best tools to estimate, classify, and analyze quantitative data. Its application in sports has been increasing in recent years. This method is suitable for implementation in controlled environments, where the system first detects the presence of humans and then tracks the human body. This process is followed by a CNN-based feature extraction, then activity recognition and, finally, prediction of the calories produced [69].  
A novel approach using a fully contactless and automatic method, based on computer vision algorithm, was presented by [PERSON] [67]. The RGB-Depth images are captured using Microsoft Kinect during exercise and a histogram of Oriented Optical Flow (HOOF) descriptors are extracted from the depth images and are used to predict heart rate. It feeds a regression model that finally estimates the energy consumption.
## 1 Introduction
Additionally, the SNR and BER performances of the proposed systems are measured to compare the system performances for all investigated distribution LED patterns. The paper also studies the impact of increasing the number of arrays to further investigate the factors affecting system performance. Then, due to the bandwidth limitation of the blue phosphorus white LED used, this paper proposes a new designed pre-equalization circuit in order to overcome such limitations. The proposed analog pre-emphasis circuit is used to further extend the LED bandwidth with the aid of blue filtering to reach 416 MHz, which is the widest bandwidth reported in the literature to the best of the authors' knowledge. In the following section, the simulated MISO VLC system model using MATLAB is first presented, and then, the impacts of the studied parameters of the different distribution patterns on the system performance are compared. In Section 3, the equalization circuit is described, and the corresponding results are displayed and discussed in Section 4. Section 5 is devoted to the main findings of the paper.
# Conceptual Design of a Compact Divertor Heat Load Simulation Device: HIT-PSI
## 2 Designs of the Device and Its Sub-Systems  
### Scientific Objectives  
HIT-PSI is designed and constructed for experimentally simulating the extreme plasma environment with an intensive heat flux over 10 MW/m\({}^{2}\), and a high-density particle flux over \(10^{24}\) m\({}^{-2}\)s\({}^{-1}\) in steady-state, for divertor physics studies, particularly with detached mode plasma parameters in ITER-like future fusion reactors, such as CFETR [5, 22]. The major scientific objectives of the device are listed as follows:  
* To investigate strongly coupled PSI regimes under high fluxes close to extreme first-wall plasma conditions similar to that of ITER-like reactors;
* To examine the effects of chemistry and atomic physics in such boundary plasmas with high densities and low temperatures;
* To explore low-temperature plasma diagnostic technologies under the D-SOL plasma and magnetic field conditions;
* To develop high-parameter plasma source technologies in a strong magnetic field environment.  
HIT-PSI is schematically depicted in Figure 1. It mainly consists of a cascaded arc plasma source, a superconducting magnet, a target holder, as well as a vacuum, and other related auxiliary subsystems. The design specifications of HIT-PSI are listed in Table 1, where plasma parameters of serval typical linear plasma simulator devices and the ITER-like divertor are also represented for comparison.  
Figure 1: The conceptual drawing (**left**) and schematic design (**right**) of HIT-PSI.
# Power Control Optimization of an Underwater Piezoelectric Energy Harvester
Afterward, many researchers focused on piezoelectric energy harvesting using flow-induced vibration ([PERSON] et al., 2012; [PERSON] et al., 2013; [PERSON] et al., 2013; [PERSON] et al., 2013). [PERSON] et al. (2015) studied a macro fiber composite piezoelectric energy harvester in the water vortex at a fluid velocity of 0.5 m/s, and 1.32 uW of power was generated. [PERSON] et al. (2016) investigated a piezoelectric beam induced by the vortex shedding from an upstream cylinder and the power output generated at fluid velocities of 2.5 m/s and 5 m/s were 200 uW and 3 mW, respectively. In the present work, a system for powering sensors to monitor physical and chemical parameters in a water pipeline is modelled. Carbon nanotube-based sensors are usually used to measure the pH of water and chemical compounds, such as chloride. Furthermore, Micro-Electro-Mechanical Systems (MEMS) sensors are also employed to measure fluid temperature and velocity, conductivity, flow rate, pressure, etc. Thus, as presented in ([PERSON] et al., 2012; [PERSON] et al., 2013) no batteries are implemented in order to provide self-powering capability to wireless sensor devices. Hydraulic turbines, such as Pelton and Darrieus, are used for capturing the energy of the water flux, but we have chosen a circular cylinder on a pipeline to take advantage of the vibrations induced by the Von Karman Vortex street generated in the wake of a circular cylinder at low Reynolds numbers due to the simplicity of the device. The main goal of the current study is to demonstrate how a novel optimization system implemented into an underwater energy harvester increases the captured energy by the device. Due to this objective, a new piezoelectric control law has been designed using exhaustive search algorithms. In the literature, there are a number of control algorithms designed to increase the captured power such as [PERSON] et al. [39]. In our study, we have introduced a piezoelectric voltage based control law which highly enhances the mean power captured by the energy harvester.
## 3 Instruments Used and Data Capturing  
### Selection of Instruments  
Low budgets and transportation problems have forced the use of miniaturized instruments. For this reason, it was not possible to use classic total geodetic stations or larger laser scanners on tripods. Therefore, the most modern methods of contemporary geomatics were employed using portable measuring instruments.  
In Morocco, there is also a problem with the authorization of drones for photogrammetric documentation; drones or in general RPAS (Remotely Piloted Aerial System) were very suitable for some hardly accessible objects or areas in this project [26]. Of course, there are more possible technologies. Remote sensing or aerial images can be used instead of the often-problematic use of RPAS. However, the problem here is that most of the objects of interest are roofed and the aerial photographs do not give much information, only about the shape of the entire building system from the outside, but not from the inside.  
Furthermore, easily portable devices such as 3D scanners and both stop-and-go and mobile systems are ideal for basic documentation [27]. Modern close-range photogrammetry can also be used. It turns out that it is ideal to use more technologies, because each documentation project contains several types of objects, and different procedures of their measurement are suitable [28].  
Figure 3: Map of Morocco with the location of both studied objects.  
The constructions we documented during the expedition were visible from the outside like monotonous walls of unfired clay blocks. The aim was to document the interior spaces especially, which were extensive, dark, and narrow. These are conditions that are not suitable for the use of close-range photogrammetry. Therefore, PLS technology was used.
## 5 Conclusions  
This study employed diffuse reflectance and diffuse transmittance devices to collect spectra for evaluating the SSC of kiwifruit, leading to the following conclusions: (1) SSC prediction models developed using diffuse transmittance and diffuse reflectance spectra demonstrated excellent performance across the entire wavelength range. The application of the CARS method for feature wavelength selection further improved the prediction accuracy, reduced model errors, and significantly decreased the computational load. (2) Validation with an independent set revealed that the PLSR model based on diffuse reflectance spectra outperformed the diffuse transmittance model in both accuracy and error metrics. In summary, the diffuse reflectance method shows greater potential for SSC spectral analysis in kiwifruit, offering higher accuracy and reliability due to its ability to capture comprehensive information about the fruit internal structure. Conversely, diffuse transmittance spectroscopy, which requires light penetration, provides limited insight into internal tissues, with varying source-detector distances affecting spectral analysis. Future work will focus on optimizing source-detector distances to enhance the precision and efficiency of kiwifruit detection instruments.  
Writing--review and editing, Y.X.; writing--original draft preparation, W.Z.; visualization, T.C.; validation, J.H.; software, S.C.; formal analysis, W.L.; data curation, J.K.; funding acquisition, W.T.; resources, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 6203285; the Young Talent Fund of the Xi'an Association for Science and Technology, grant number 959202413041; the Xi'an Science and Technology Plan Project, grant number 23 NYGG0070; and the Natural Science Basic Research Program of Shaanxi, grant number 2022 JQ-181.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to them being necessary for future essay writing.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (62203285), the Young Talent Fund of Xi'an Association for Science and Technology (959202413041), the Xi'an Science and Technology Plan Project (23 NYGG0070), and the Natural Science Basic Research Program of Shaanxi (2022 JQ-181).  
The authors declare no conflicts of interest.
# Wireless Home Assistive System for Severely Disabled People
## 2 Materials and Methods
### The Human-Machine Interface  
This study designs two kinds of human-machine interfaces of MCT for the user: PC-based AAC software and the home appliance control interface. The PC-based AAC software programmed in LabVIEW graphic language includes a big dialog database with words, phrases, or sentences and the corresponding voice database. Basically, the database of PC-based AAC can be classified into 3 kinds of dialogs: the dialog for daily life, the dialog for greetings, and the dialog for emotional expression. It's very easy and flexible to change or update new option of words, phrases, or sentences for the interface of PC-based AAC. In general, the PC-based AAC is quicker and more effective for the severely disabled to reply to the caregiver or family. More and more useful and popular words, phrases, sentences, voices, and even different languages can be appended to the database as long as the hard drive or disk is big enough. The voice database built into the PC-based AAC includes a synthesized computer voice and a real human voice, e.g., the voice of the family or caregiver, or even the real voice of the severely disabled. The PC-based AAC can be operated with the help of MCT, the user can click the option on the interface shown in Figure 6 when MCT is set in mouse mode or typing word in keyboard mode. Compared with the general standalone AAC products for the disabled with speech disorders, the PC-based AAC is easier and it is more feasible to update or increase the scale of the word database or phrase database without hardware limitations.  
The home appliance control interface shown in Figure 7 includes three parts of dialogs, which are used for general home appliance control, air conditioner control, and TV channel selection. Besides the software interface mentioned above, there are still a few hardware components such as an infrared  
Figure 5: The entity of MCT: (**a**) The front side of the panel, (**b**) The back side of the panel, and (**c**) MCT product.  
Figure 6: The user interface of personal computer-based alternative augmentative communication (PC-based AAC) with voice output included in WHAS.  
transceiver, Arduino module, Bluetooth module, and a power on/off control box (for controlling general home appliances, e.g., fan, music, light etc.). The user can control the general home appliance, air conditioner, and TV channels through the home appliance control interface by themselves. Two kinds of wireless communication protocols are adopted to finish the wireless home appliance control: one is infrared protocol, and the other is Bluetooth protocol. The user can operate the functions of the home appliance target by using an assistive input accessory and MCT according to the home appliance control interface on the computer screen. After that, the home appliance control system will automatically choose the infrared protocol (for air conditioner, TV channel selector) or Bluetooth protocol (for general home appliances, e.g., fan, music, light, etc.) to communicate with home appliances and complete home appliance control automation.
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 3 Methods
### Detailed Process of OI-EleAttG-GRU in HAR  
In the learning process for identifying human activity from sensor signal data, as depicted in Figure 4, there are two analysis methods that can identify human activity using data from various types of sensors (gyroscopes, magnetometers, and accelerometers) obtained at a specified sampling rate. The traditional method is shown in flow chart A. Informative features are extracted in a manually produced manner, followed by applying a sliding window method with variable size on real-time acceleration data. The processed data is then fed into the classifier for training and testing of the classification model (k-means, artificial neural network, naive bayes, random forest, etc.). Flowchart B shown in Figure 4 illustrates a typical process using a deep learning approach where data can be processed from different types of deep network structures, and where results are ultimately obtained using SoftMax classification. The method shown in Flowchart B was used in this study.  
The deep network in Flowchart B uses a new OI-EleAttG-GRU structure to achieve accurate human activity recognition. The network structure of proposed OI-EleAttG-GRU combines a new O-Inception convolutional structure with EleAttG-GRU blocks, which can be divided into three modules: sensor data preprocessing, deep learning model training, and prediction result classification output. This creates the OI-EleAttG-GRU Deep Learning Model which is shown in Figure 5.  
When extracting features from the waveform data of the sensor, after the convolution and pooling operation was repeated four times, we used the O-Inception module introduced above. O-Inception reduces noise interference and improves network judgment accuracy through recognition rate improvement. After that, flatten and double-layer EleAttG-GRU are used to extract the time features. Finally, this is transferred to the SoftMax classifier to classify the results.  
In addition to the high recognition rate, this model also has a good fault tolerance performance because of the EleAttG-GRU network we added after CNN's O-Inception structure. When there are more wrong data or illegal data in the initial data, the recognition rate of the traditional CNN network will drop significantly or even appear to be untrained. On the other hand, EleAttG-GRU has the time-dependent characteristics of GRU, which correspond to multiple feature maps at various times. When the wrong data is input, EleAttG-GRU can predict the wrong data according to the correct data of other feature maps through time-dependent characteristics, thereby weakening the impact of the wrong data on the recognition rate during the training process.  
Figure 3: EleAttG-GRU structure with Element-Wise-Attention.
## 1 Introduction
However, ELM algorithm itself has some shortcomings, such as the random selection of the input weights and hidden layer threshold which in general would lead to a poor stability of the network. In order to improve the prediction accuracy of the algorithm, it is necessary to increase the number of the hidden layer nodes [6], and the increase will inevitably reduce the computing speed of the ELM learning algorithm. Thus, the application of ELM has to overcome this contradicting pair of operational needs. Researchers have studied and improved the above problems of ELM in recent years. [PERSON] et al. [12] introduced differential evolution (DE) algorithm into ELM to obtain the optimal connecting weights between the input layer and hidden layer, and the optimal threshold of hidden layer. These optimal parameters can improve the stability of the network. [PERSON] et al. [13] proposed a regularized extreme learning machine (algorithm) based on discriminative information (called IELM), which can significantly improve the classification performance and generalization ability of ELM. [PERSON] et al. [14] proposed an incremental method for sparsifying the ELM using a newly devised indicator driven by the condition number in the ELM design matrix, called sparse pseudoinverse incremental-ELM (SPI-ELM), which exhibits better generalization performance and lower run-time complexity compared to ELM. Although they improved the computational speed, training accuracy and generalization performance of ELM algorithm, the least squares-based ELM algorithm still has the problem of randomness of the parameters, which will affect the stability of the network. Based on the above evaluation of algorithms that exist, a BOD soft sensor based on an improved extreme learning machine is proposed in this paper. Adverse effects of randomness of extreme learning parameters on prediction results and stability of the network are considered while proposing such soft sensor. The parameters of ELM are coded as the cuckoo nest locations, with the corresponding fitness values of the root mean square error (RMSE) between the actual value and the prediction value,to obtain the optimal parameters of the ELM by using an improved cuckoo search (ICS) algorithm [15]. The fuzzy rough monotone dependence (FRMD) algorithm proposed by [PERSON] et al. [16] to reduce the dimensionality of the data from the BSM1 simulation model [17; 18] is used in Matlab simulation. The reduction data is used as the input of the soft sensor and effluent BOD is used as the output of it.