esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 6 Survey Findings and Possible Research Directions
Although the position of the BS can be application specific, there should be a unified position for the BS based on each sensor network application. As shown in the WSN representation of Figures 1,2,4 and 7-10, we assumed different positions for the BS. Likewise, in the studied routing protocols, the authors assumed different convenient positions for the BS. In fact, if the position of the BS is changed in some of the algorithms, the algorithms will not perform as expected. This means that such algorithms are dependent on the position of the BS. Even so, some algorithms [139; 141] assume that the BS changes position in order to prevent the energy-hole phenomenon. Whereas, some algorithms assume that the BS is stationed at thecentre of the sensor field, others assume that the BS is situated at a corner of the sensor field. Thus, the position of the BS is very important in designing a load-balanced routing protocol. The assumptions of the position of the BS by different researchers is quite conflicting. As previously mentioned, let us assume that the position of the BS is application specific; each application should have a unified position for the BS in order to verify the performance of any proposed routing protocol. We admit that researches are ongoing on node placement optimization problems relating to WSN. However, we emphasise that more research should focus on finding an optimal position for the BS. This is because, no matter how properly we place the SNs, if the BS is poorly cited, the SNs will deplete their battery energy quickly during data transmission. Besides, the energy-hole problem might be more pronounced in such a network. Additionally, determining an optimal unified position for the BS will create a suitable platform to verify the performance of any proposed energy-efficient and energy-balanced routing protocol. Lastly, in this paper, we presented a detailed survey on the common input decision variables used by researchers to provide a formalism of mathematical optimization structure for the routing problem relating to an energy-efficient and energy-balanced WSN. As previously mentioned, optimization plays a vital role in WSNs. A detailed optimization problem consists of the input decision variables, constraints, objective functions and the outputs. Therefore, future researches can provide a general formalism of mathematical optimization structure for the routing problem relating to an energy-efficient and energy-balanced WSN. In this case, similar tables (Tables 2-6) can be developed for the constraints and objective functions. With such tables, a unifying and formal mathematical optimization structure can be developed for any class of routing protocol for a WSN based on the use of energy-efficient and energy-balanced criteria.
###### Abstract  
In the context of grid connected photovoltaic (PV) generation systems, there are two paramount aspects regarding the Maximum Power Point Tracking (MPPT) of the photovoltaic units and the continuity of the service. The most diffused MPPT algorithms are based on either perturb and observe, or on an incremental conductance approach and need both PV current and voltage measurements. Several topology reconfigurable converters are also associated with the PV plants, guaranteeing fault-tolerant features. The generation continuity can also be assured by interleaved inverters, which keep the system operating at reduced maximum power in case of failure. In this paper, an evolution of a hysteresis based MPPT algorithm is presented, based on the measurement of only one voltage, together with a novel space vector modulation suitable for a two-channel three-phase grid connected interleaved inverter. The proposed MMPT algorithm and modulation technique are tested by means of several numerical analyses on a PV generation system of about 200 kW maximum power. The results testify the validity of the proposed strategies, showing good performance, even during a fault occurrence and in the presence of deep shading conditions.  
interleaved inverter; MPPT algorithm; space vector modulation; hysteresis control Article
# Research on Control Technology of Large-Section Water-Bearing Broken Surrounding Rock Roadway
## 1 Introduction
Through similar testing, numerical simulations, and field surveys, they analyzed the instability characteristics of the surrounding rock. Based on their findings, they proposed an optimization scheme for support that involves increasing the length of the anchor cable to achieve improved support effectiveness [18]. [PERSON] et al. conducted an analysis of the non-uniform deformation mechanisms affecting the surrounding rock of weak roadways through field tests, numerical simulations, and orthogonal tests. Based on the results obtained from the numerical simulations, they proposed a combined support method aimed at enhancing the stability of the surrounding rock in soft rock roadways [19]. Through the theories of [PERSON] and others, [PERSON] and his colleagues analyzed the relationship between support resistance and the deformation of surrounding rock in roadways under high ground stress. They addressed the issue of significant deformation in the surrounding rock of deep roadways and proposed a step-by-step combined support scheme that effectively enhanced the stability of the surrounding rock in these roadways [20]. Some scholars have proposed an improved support optimization scheme for roadway support in water-bearing weak surrounding rock, based on comprehensive analysis and research [21; 22]. The aforementioned research has made significant progress in understanding the deformation and failure modes, as well as the support technologies for the water-bearing, fractured surrounding rock in underground engineering. Numerous effective support schemes have been proposed and successfully implemented in engineering practice. However, there remains a lack of theoretical frameworks concerning the construction of a surrounding rock control theory system and the coupling mechanisms of pore water pressure and surrounding rock in water-bearing strata. Consequently, this paper focuses on the Y1 belt conveyor roadway of the 830 working face Y1 in the underground mining area of the Yingping deep phosphate mine at the Wengfu phosphate mine as the engineering context. It investigates issues related to surrounding rock deformation, side collapse, and the stability of supporting structures during the construction of roadways in water-bearing layers. There is an urgent need to systematically explore the coupling mechanisms of the surrounding rock-support synergy. By developing a dynamic constraint model for discontinuous deformation rock masses and supporting structures, this study aims to enhance the spatial locking effect of the support system on the extended boundaries of the fractured zone. This approach seeks to achieve precise control over the extent of the surrounding rock damage and to ensure long-term stability maintenance.
# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
## 2 Related Work
### Deep-Learning-Based Object Detection  
Object detection models based on neural networks are largely divided into two-stage detector and one-stage detector. A two-stage detector is a structure in which the region proposal and classification stage are separated and operated sequentially, and R-CNN-based deep learning models are applicable. The R-CNN model is a model that separates region proposal, CNN, classifier, and regression and has a disadvantage in that the object detection performance is excellent, but the learning speed is slow. To solve this problem, fast R-CNN, which improves learning speed and performance by learning CNN, classification, and regression in a network by applying an end-to-end technique to a model, was proposed. However, for R-CNN and fast R-CNN, the process of generating a candidate region was performed independently of CNN, leaving inefficient problems in terms of learning speed. In order to improve this, the faster R-CNN to which the region proposal network (RPN) was applied was proposed, and the computational performance of the R-CNN model was enhanced. A one-stage detector is a structure in which regional proposals and classifications are made simultaneously, and it can solve both classification and localization problems at the same time. Representative deep learning models using one-stage detectors include the single-shot multibox detector (SSD) [26] and YOLO. YOLO is a model that divides the input image into a grid and predicts the bounding box and class probability for each cell. It is superior to R-CNN in terms of learning speed. However, if an object smaller than the grid is detected, there is a problem that the accuracy is lowered, so the version has been continuously upgraded. SSD is a model proposed to solve using multiscale feature maps and anchors [27]. In summary, the two-stage detectors are suitable for tasks that require a slow but high detection accuracy because the region suggestion and classification proceed sequentially. The one-stage detectors perform two steps at a time, so they learn faster but are used for tasks that require real-time detection due to their low accuracy.
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements
### Capacitive Sensors
#### 3.3.4 Optical Sensors  
These sorts of devices convert light rays into electrical signals. The sensor measures the quantity of light and transforms it into a form that can be read by an instrument. Some advantages of using optical sensors are: immunity from electromagnetic interference; great sensitivity; and multiplexing capabilities. Optical sensors can be affected by external light sources and temperature variation [29].  
In this regard, a research work presents a low-cost wearable gait monitor that uses optical fiber [30]. The sensor detects transmittance changes when the plastic optical fiber is bent. The wearable sensor is attached to the knee of the patient where it can measure the joint angles. The system uses a photodetector that measures the amount of light received and, after a calibration stage, it calculates the flexion angle. The authors choose fiber optic sensors because they are immune to electromagnetic interference, low weight and flexible. The results of the tests performed demonstrate that the system is sensitive enough to detect small angles. The authors believe that it can also detect hyper-extension, although further tests are needed.  
The use of plastic fibers for developing wearable sensors enables the development of low cost, robust and easy-to-handle devices. Optical wearable sensors can be an important tool for rehabilitation and performance monitoring of professional athletes.
## 2 Materials and Methods
### Set of Mobile Platform Configurations Assessed  
This section describes eleven mobile platform configurations whose omnidirectional capabilities are analyzed in the paper. All of them have three identical omni wheels with the same radius (\(r_{k}=r\)), roller orientation (\(\gamma_{k}=0^{\circ}\)), and distance from the centroid of the mobile platform (\(R_{k}=R\)), but they have different wheel distributions (\(\delta_{k}\)) and relative orientations (\(\varphi_{k}\)). Table 1 shows the configurations assessed, providing an identification name, a top-view schematic representation of the distribution and orientation of the three omni wheels, and the angular distribution of each wheel (\(\delta_{k}\)) and its relative orientation (\(\varphi_{k}\)). The axes of the platform frame are represented with green (\(x_{b}\)) and black (\(y_{b}\)) arrows, and the axes of the wheel frames are represented with pink (\(x_{w_{b}}\)) and black (\(y_{w_{b}}\)) arrows.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Configuration** & **Representation** & \(\delta_{1}\)(\({}^{\circ}\)) & \(\delta_{2}\)(\({}^{\circ}\)) & \(\delta_{3}\)(\({}^{\circ}\)) & \(\varphi_{1}\)(\({}^{\circ}\)) & \(\varphi_{2}\)(\({}^{\circ}\)) & \(\varphi_{3}\)(\({}^{\circ}\)) \\ \hline
3A & 60 & 180 & 300 & 0 & 0 & 0 \\ \hline
1A-2B & 60 & 180 & 300 & \(-90\) & 0 & \(-90\) \\ \hline
1A-2C & 60 & 180 & 300 & 30 & 0 & \(-30\) \\ \hline
1A-1B-1C & 60 & 180 & 300 & 0 & \(-90\) & \(-30\) \\ \hline
1A-1B-1D & 60 & 180 & 300 & 0 & \(-90\) & 60 \\ \hline
3E & 60 & 180 & 300 & 49.37 & 49.37 & 49.37 \\ \hline
3B & 60 & 180 & 300 & \(-90\) & \(-90\) & \(-90\) \\ \hline
2A-1B & 60 & 180 & 300 & 0 & \(-90\) & \(0\) \\ \hline
1B-2C & 60 & 180 & 300 & 30 & \(-90\) & \(-30\) \\ \hline
1B-2D & 60 & 180 & 300 & \(-60\) & \(-90\) & 60 \\ \hline
1A-2D & 60 & 180 & 300 & \(-60\) & 0 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobile platforms analyzed.
# Attenuation Zones of Two-Dimensional Periodic Foundations Including the Effect of Vertical Loads
## 3 Validation of Computational Method
### AZ of 2D Bragg Scattering Periodic Structure  
Using the finite difference method, [PERSON] et al. [24] studied the AZs of a 2D Bragg scattering periodic structure that was made of periodic Pb inclusions in rubber matrix, which is a special case of the present work by taking the core and the coating layer as the Pb inclusion. The mass density and elastic coefficients \(C_{11}\) and \(C_{44}\) of Pb were 11,600 kg/m\({}^{3}\), 72.1 GPa, and 14.9 GPa, respectively. The mass density and elastic coefficients \(C_{11}\) and \(C_{44}\) of rubber were 1300 kg/m\({}^{3}\), 680k Pa, and 40k Pa, respectively. Figure 2 shows the dispersion curves calculated by the present method and the finite difference method, where the filling ratio of Pb in the typical unit cell was 0.36. In Figure 2, the horizontal ordinate represents wave vectors along the boundary of the first irreducible Brillouin zone, and the vertical ordinate represents wave frequencies. Each pair of wave vector and frequency corresponded to a wave propagation mode. For waves with frequencies in the shaded area in Figure 2, there were no corresponding wave propagation mode, which means that these waves were forbidden in the periodic structure. Thus, the frequency range of the shaded area was named as AZ. The solid lines and scatters in Figure 2 represent the results obtained by the present method and the finite difference method, respectively. It can be seen from Figure 2 that the AZs determined by the present method matched well with those by the finite difference method.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Family** & **Material** & **Fixation Degree (\%)** \\ \hline Acids & Polyamide/Wool & 80–95 \\ Azoics & Cellulosic & 75–90 \\ Basics & Acrylic & 95–100 \\ Direct & Cellulosic & 70–95 \\ Scattered & Polyester & 90–100 \\ Premetallized & Wool & 85–95 \\ Reagents & Cellulosic & 60–90 \\ Sulphurous & Cellulosic & 60–86 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fixation percentages of different families of colorants and their estimated loss in effluents [20]. Figure 1: Loss in the effluent percentage. The basic advantages of these technologies can be classified as follows: metal oxides, carbon nanotubes, and nanoclays are low-cost; nanotubes and membranes have high-efficiency; and membranes and photocatalysts are easily applied [31]. The textile industry employs increasingly more synthetic dyes because they are easy to use, cheap, and offer good stability, and they come in a wide range of colors compared to natural dyes [41]. Artificial dyes are synthetic organic compounds that are molecularly dispersed and bind to textile substrates thanks to intermolecular forces. More than 10,000 dyes are available on today's market. Most are not easily biodegradable, given their complex aromatic molecular structure and synthetic origin [42]. As dye baths are not biodegradable in nature, and are toxic and inhibitory, they significantly and negatively affect both water and land. The fact that certain dyes or their metabolites are toxic or mutagenic and carcinogenic should also be taken into account [43]. Records worldwide [44; 45] demonstrate the sensitive aspect of exposure to these synthetic substances at both high and low concentrations. Thus, they need to be removed before water is discharged. For some years now, adsorption has become the most efficient technique to remove different dye types. The action of these absorbents is clearly affected by several parameters, such as dye concentration, the time the action lasts, temperature, dye particle size, and the concentration of the adsorbents, etc. Adsorption is clearly an efficient process in most physical, environmental, chemical, biological, and natural settings [46; 47]. In industry, adsorption techniques are being applied by solids acting as adsorbents to purify wastewater and polluted water. In recent decades, different types of these adsorbents have been used to remove several types of pollutants from water [48]. This paper reviews the works that have focused on highly-efficient wastewater recovery. In line with their conclusions, it proposes reusing hybrid compounds in a circular economy concept. It shows the possibility of dye recovery using different adsorbents and presents a cheap and efficient procedure based on the advantages of nanoclays. In it, a future vision is proposed for the reuse of recovered dyes as new hybrid pigments protected by nanoclays, or via their desorption, to reuse them as dyes in other dyeing textile baths.
# Effect of Gradation Segregation on Mechanical Properties of an Asphalt Mixture
## 3 Results
### Effect of Gradation Segregation on the Asphalt Mixture
#### 3.2.3 Vertical Stress Transfer Performance  
In order to investigate the vertical stress transfer performance (VSTP) of the asphalt mixture at different segregation levels, the discrete element model was established according to the method described in 1.2. The width of the upper wall was set to 22 cm to better simulate the actual situation where the wheel load acts on the road surface. The heights of the model were taken as 4, 6 and 8 cm for comparative analysis.  
After the model was established, the upper wall was given a velocity of 0.1 mm/s to load downwards until the vertical deformation of mixture reached 2 mm. After finishing load, the average stress (\(\overline{\sigma_{l}}\)) of the upper wall and the average stress (\(\overline{\sigma_{d}}\)) of the bottom wall were measured, and their ratio (\(\delta\)) was calculated. The VSTP is analyzed by comparing the difference of \(\delta\) values. The result is shown in Figure 10.  
Figure 10 shows the effect of gradation segregation and model dimensions on the VSTP of asphalt mixture. The data presented in Figure 10 illustrate that with the gradation from coarse to fine, the VSTP of asphalt mixture gradually decreases. In addition, when segregation of coarse aggregates occurs, the VSTP of the asphalt mixture is significantly improved. It is interesting to note that the increase rate raises results from the increase of segregation level. When segregation of fine aggregates occurs, the VSTP of the asphalt mixture has a slight decline and the difference between asphalt mixtures at different segregation levels is not significant. This indicates that the segregation of coarse aggregates has a greater impact on the VSTP of the mixture than the segregation of fine aggregates. In addition, as the height of the model increases, the vertical transfer distance of stress increases and the VSTP of asphalt mixture gradually weakens.
## 5 Rotor Temperature Modeling and Prediction
### Rotor Temperature Prediction Experiment Under Medium-Sized Samples
EO-SVR's prediction performance is significantly influenced by the data, demonstrating its weak robustness. Therefore, it is clear that RIME-XGBoost performs best in terms of prediction accuracy, generalization ability, and stability. In contrast, SO-BiGRU, despite its high R-squared value on the training set, exhibits unstable performance on the test set and has extremely high computational complexity. EO-SVR and SMA-RF show relatively good overall performance, but their prediction accuracy is lower than that of RIME-XGBoost. Figure 4: _Cont_. The robustness of RIME-XGBoost under varying temperature conditions highlights its potential for real-world industrial applications. Unlike SMA-RF and EO-SVR, which exhibit significant bias in specific temperature ranges (e.g., EO-SVR's underprediction in high-temperature regions), RIME-XGBoost's minimal bias across the entire temperature spectrum suggests its adaptability to diverse operational scenarios. Figure 4: (**a**) Prediction on the training set using RIME-XGBoost. (**b**) Prediction on the training set using SMA-RF. (**c**) Prediction on the training set using SO-BiGRU. (**d**) Prediction on the training set using EO-SVR. It is critical for EV applications to be diverse where sudden temperature fluctuations (e.g., during rapid acceleration or braking) are common. Furthermore, RIME-XGBoost's computational efficiency aligns with real-time monitoring requirements in high-speed motor systems. It is a viable candidate for embedded systems with limited computational resources. Under medium-scale sample conditions, RIME-XGBoost demonstrates excellent prediction performance. However, whether it can maintain high accuracy in rotor temperature prediction under small-sample conditions remains to be further verified. Therefore, the next section will test and discuss the small-sample modeling performance of RIME-XGBoost.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
###### Abstract  
Dispersed micro- to nano-particle-laden gas flows are common in many engineering and environmental applications. Characterizing both their dispersed and carrier phase using experimental methods is very important for determining their properties and behavior. This paper reviews techniques for measuring the carrier phase, as well as the dispersed particles ranging from the micro- to the nano-scale. We focus not only on the developments of specific techniques over the last 20 years, but also on relationships and comparisons among these techniques. In addition to a systematic description and classification of these methods, we discuss the parameters they measure, such as particle velocity, size, composition and concentration. A more detailed review is provided for several important measurement techniques, including particle image velocimetry, the phase-Doppler particle analyzer and light-scattering intensity measurements for microparticles, as well as the scanning mobility particle size, the fast mobility particle size and the electrical low pressure impactor for nano-particles. The founding principles, development histories, various applications of these techniques and where they are going are summarized. This article provides a resource for investigators that plan to study micro- or nano-particle-laden gas flows in various contexts.  
micro- and nano-particles; particle-laden gas flows; particle characterization; measurement techniques +
Footnote †: journal: Applied sciences
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Parameters for Measuring the Performance and Quality of the Data Transmission Network
#### 2.2.1 Delay  
Delay or latency through wireless networks is the time interval required by the packets to propagate from the source to the destination. The end-to-end packet delay is made up of the summation of route discovery (source-processing delay), queuing (network delay) and propagation and transfer time (destination delay). Many applications require an end-to-end delay guarantee for time sensitive data [42]. Equation (1) expresses the delay (\(\sigma\)) as the average difference between a number of delivered data (\(\eta\)) and the throughput (\(\tau\)) [41].  
\[\sigma=\frac{\eta}{\tau} \tag{1}\]