esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Generation and Measurement of BC Particles  
The BC particle standards were generated by a miniature combustion aerosol standard (miniCAST) burner [41; 42]. The miniCAST uses a co-flow diffusion flame of hydrocarbon to produce BC particles. The flame is quenched with dry nitrogen, which freezes the combustion process and stabilizes the BC particles produced during the combustion process. The diameter and concentration of the BC particles can be adjusted by changing the flow rate of the fuel (propane), oxidant (compressed air), and quenching gas (nitrogen).  
During the experiment, the miniCAST was adjusted to produce 70 nm particles, approximating the diameter of observed vehicular BC particle emissions [28; 33]. These particles were introduced into the flexible aluminum duct containing two turbulence mixing blades, which generated the necessary turbulence mixing. Then, particles passed through a mesh to produce a uniform particle concentration airflow before entering the cabin filter housing unit. Particles were alternatively sampled from either the pre- or post-filter locations through two multi-hole probes, which directed sampled air to the various particle instruments. Particle number size distributions (6-523 nm) were measured by the TSI Engine Exhaust Particle Sizer (EEPS) [43]. Real-time BC mass was measured by the Artium LII300 laser-induced incandescence (LII) [44; 45], while particle mass was estimated using the Dekati mass monitor (DMM) [46]. In this study, each particle filtration experiment was done once on each filter.  
The ambient concentration of BC and ultrafine particles observed from three Canadian cities (Toronto, Montreal, and Vancouver) during traffic rush hours was reported from the Urban Transportation Exposure Study (UTES) [47; 48]. Median values of various pollutants, including the ultrafine particles, BC, and PM, observed at Toronto were used as target exposure concentration references (27,869 particles/cm\({}^{3}\) UFP, 1.4 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) BC, 8.7 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) PM\({}_{2.5}\), and 12.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) BTEX).
# Extraction of Humic Acid from Lignite by KOH-Hydrothermal Method
## 3 Results
### Effects of Reaction Time and Temperature  
The effect of the reaction time on HA yield was further investigated after the optimal alkali-to-carbon mass ratio was determined. As shown in Figure 3, the HA yield increased from 54.4% to 90.2% as the reaction time varied from 3 to 7 h. At reaction times exceeding 7 h, the HA yield decreased from 90.2% to 84.7%. A short time resulted in an inadequate reaction, and HA easily decomposed at high temperatures and pressures. Therefore, 7 h was selected as the optimal time.  
After determining the optimal reaction time, the effect of the temperature (130-190 \({}^{\circ}\)C) was investigated (Figure 4). The HA yield increased sharply from 20% to 90.2% as the temperature increased from 130 to 190 \({}^{\circ}\)C and then increased slowly until the temperature rose to 210 \({}^{\circ}\)C. Most of the intermolecular forces in coal are physical associations and are weak. A high temperature resulted in the destruction of the molecular structures, thereby releasing HA in the lignite. However, high temperatures also led to the breaking of noncovalent bonds (e.g., hydrogen bonds), the hydrolysis of weak covalent bonds (e.g., ether bonds and ester bonds), and the cleavage of side-chain bonds (e.g., aromatic rings and fat bonds). With increasing hydrothermal temperature, the permittivity and polarity of the water in the reactor decreased, the dissolving capacity of the intermediate polar and nonpolar organic compounds in the lignite was enhanced, and the amount of HA dissolved in lye increased. When the temperature was greater than 210 \({}^{\circ}\)C, the increase in the HA yield was not very obvious. A higher temperature corresponds to greater energy consumption and economic costs. Therefore, 190 \({}^{\circ}\)C was selected as the optimal reaction temperature.  
Figure 3: Effect of the reaction time on the HA yield.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 5 Detailed Elaboration of Each Layer
### Functions of User Terminals  
User terminals have several functions, including user data management, access and band request, and receiving satellite broadcasting or control information. In response to this information, user terminals perform various actions. The access network relies heavily on access protocols, which are divided into two main types: random access and on-demand access. Random access allows user terminals to initiate communication without prior coordination, while on-demand access requires coordination between the user terminal and the network before communication can begin.
_Appl. Sci._**2012**, \(2\), 602-628; doi:10.3390/app2030602  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Optical Current Sensors for High Power Systems: A Review**  
**[PERSON] 1, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,3,**  
**[PERSON] 4, [PERSON] 1,2, [PERSON] 1 and [PERSON] 1,**  
* 1 INESC Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal; E-Mails: [EMAIL_ADDRESS] (R.M.S.); [EMAIL_ADDRESS] (H.M.); [EMAIL_ADDRESS] (P.J.)
* 2 Dept Fisica e Astronomia da facultade de Ciencias da Universidade do Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal; E-Mail: [EMAIL_ADDRESS] (I.N.); [EMAIL_ADDRESS] (J.L.S.)
* 3 Centro de Competencia de Ciencias Exatas e da Engenharia, Universidade da Madeira, Campus da Penteada, 9000-390 Funchal, Portugal; E-Mail: [EMAIL_ADDRESS]
* 4 Faculty of Health Sciences, University Fernando Pessoa, R. Carlos da Maia 296, 4200-150 Porto, Portugal; E-Mail: [EMAIL_ADDRESS]
* 5 Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +351-220-402-301; Fax: +351-220-402-734.  
_Received: 23 May 2012; in revised form: 15 June 2012 / Accepted: 18 June 2012 / Published: 2 July 2012_  
**Abstract:** The intrinsic advantages of optical sensor technology are very appealing for high voltage applications and can become a valuable asset in a new generation of smart grids. In this paper the authors present a review of optical sensors technologies for electrical current metering in high voltage applications. A brief historical overview is given together with a more detailed focus on recent developments. Technologies addressed include all fiber sensors, bulk magneto-optical sensors, piezoelectric transducers, magnetic force sensors and hybrid sensors. The physical principles and main advantages and disadvantages are discussed. Configurations and strategies to overcome common problems, such as interference from external currents and magnetic fields induced linear birefringence and others are discussed. The state-of-the-art is presented including commercial available systems.  
**Keywords:** optical current sensors; magnetooptic current sensors; fiber optic current sensors; Faraday effect
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 2 Physical Components and Methods
### FSO Communication--The Ground Station's Side  
The telescope's tracking process consists of three main parts:  
* Coarse tuning: adjusting the telescope angle to the area where the satellite should pass. There, it waits for the satellite to continue tracking it.
* Accurate tracking: creating a continuous communication link between the telescope and the satellite using image processing for precise attitude control.
* Continuous link: using motion prediction to create accurate, high-quality tracking.  
Figure 4(a) illustrates the communication link between the ground station and the satellite; an actual image of the International Space Station (ISS) captured from our ground station is shown in Figure 4(b).
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Traffic Prediction with an LSTM ANN  
For the time series prediction, an LSTM ANN uses a sliding window containing \(n\) most recent observations, as defined in (5). From every set of the traces listed in Table 1, we extracted ten overlapping sub-traces every 30 min. The models were trained on the first 10 min of each sub-trace and tested on its remaining part. We concatenated the obtained predictions, which allowed us to input their values into a video player and have uninterrupted playback.  
For the prediction, we used a many-to-one architecture, where a throughput sequence \(\{r_{t}\}\) is ANN's input, and a single throughput value \(r_{t+1}\) is a prediction. Firstly, we normalize the traffic flow sequence \(\{r_{t},r_{t-1},\dots,r_{t-n}\}\), i.e., remove trends and differentiate it obtaining \(\{x_{t},x_{t-1},\dots,x_{t-n}\}\), which is then delivered to the LSTM ANN. Both our LSTMs, unidirectional and bidirectional, consist of an input, two hidden and output layers. The top LSTM layer supplies its output to a conventional feed-forward ANN, which maps them into a single value that corresponds to an unprocessed prediction at time \(t+1\). Finally, we denormalize the output values from the feed-forward ANN, obtaining the prediction of network throughput \(\hat{r}_{t+1}\) at \(t+1\). This process and the structure of the LSTM ANN used for predicting traffic flow are presented in Figure 6.  
An LSTM ANN minimizes the NRMSE defined in (7) through training, which iteratively changes the weights and biases of the network to decrease the differences between the model output and observed values. For this purpose, we chose _Backpropagation Through Time_ (BPTT) algorithm. The BPTT unfolds an LSTM ANN and converts it into a feed-forward ANN to apply the backpropagation algorithm. Then, the backprop  
Figure 6: Architecture of the LSTM ANN used for network throughput prediction.  
the gradient of the cost taking network parameters as the input, which in the case of an LSTM are the same for every unfolded time step. The algorithm aggregates the errors from the unfolded time steps, updates the weights and folds the ANN back. Due to good performance, relative simplicity and implementation in popular software, the BPTT is popularly used for the RNN ANNs, including their LSTM variants. More information about the algorithm and its comparison with others can be found in [32].
## 8 Numerical Results
### Solutions  
Since the DAEs representing the dynamics of the manipulator are transformed to ordinary differential equations (ODEs) through partitioning, the solution can be easily obtained by integrating the ODEs. The second order ODEs were transformed to a state space representation as described earlier and subsequently integrated numerically by using MATLAB ODE45. The integration was performed within 0 to 4 s. Figures 8 and 9 show the end-effector position and orientation of the manipulator subject to the sinusoidal loads. Both figures depict the end-effector position and orientation of the manipulator modeled as rigid body and flexible body. The flexible body model used in Figure 8 is based on the Rayleigh-Ritz method whereas that used in Figure 9 is based on the finite element method.  
into account, not the elastic displacements alone. It is shown that the flexibility of the manipulator links results in infinitesimal displacement in both the translational DOFs and the rotational DOF in addition to the rigid body displacement of the end-effector. This infinitesimal displacement should be taken into account when dealing with a high precision application such as machining. Furthermore, it is shown that the elastic displacement obtained by both the methods have some degree of agreement, with some difference in the magnitude.  
Figure 10: The elastic displacements and the orientation of the end-effector obtained by using the two methods in the case of sinusoidal loads.  
Figure 9: End-effector position obtained by using rigid body and flexible body models based on finite element method in the case of sinusoidal loading.  
It can also be seen that the elastic displacements in the \(y\)-direction is larger than that in the \(x\)-direction. The former is roughly twice the latter. This can be related to the manipulator stiffness. The larger elastic displacement in the \(y\)-direction might be caused by smaller manipulator stiffness in the \(y\)-direction. This can be due to less depth of the manipulator in the \(y\)-direction compared to its depth in the \(x\)-direction during the performed motion. In fact, the stiffness of the manipulator in the \(y\)-direction is at its best when the manipulator is standing tall and therefore has a large depth in the \(y\)-direction. In such a posture, the stiffness in the \(x\)-direction will be reduced. The varying stiffness corresponding with the changing posture is a typical characteristics of parallel manipulators.  
Moreover, the magnitude of the elastic displacements which represents the flexibility of the manipulator is shown to be unrelated with the methods as it is shown that the finite element method gives fewer elastic displacements in the case of sinusoidal loading but it gives larger elastic translational displacements in the case of triangular pulse loading. This implies an inconsistent relation between the flexibility of the manipulator and both the methods given a variety of loadings.
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 2 Underwater Target Polarization Detection Mechanism
In this way, only the first three parameters are required, a quarter of the wave plate with phase delay effect is not needed, only a linear polarizer is needed, and the angles of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\) are taken, at the same time, the light intensity \(I_{1}\), \(I_{2}\), \(I_{3}\), \(I_{4}\) of the three parameters can be measured separately, that is, Equation (7). The formula for calculating the degree of linear polarization can be simplified by Formula (10). \[DOP=\frac{\sqrt{Q^{2}+U^{2}}}{I} \tag{10}\]  
At the same time, the polarization azimuth can be expressed by Formula (11). \[\alpha=\frac{1}{2}\text{arctan}\bigg{(}\frac{U}{Q}\bigg{)} \tag{11}\]
## 3 Results
### Edge Detection and Enhancement  
Once seismic data takes the optimal form for discontinuity analysis, an edge detection and enhancement procedure is applied to capture the major and minor faults and discontinuities in the seismic data. Advancements in modern seismic interpretation technologies offer several seismic attributes to compute structural attribute volumes and to enhance faults in seismic data for automatic fault extraction. Some attributes compute the continuity of seismic reflections, such as semblance [2], or coherency [5; 31; 32; 33; 34], while others measure the discontinuity, such as variance [4; 35], or gradient magnitude [36], etc. These attributes are dependent either on amplitude contrast or reflector's continuity and highlight faults only when the set criteria are fulfilled.  
For this study, we tested multiple structural-based volume attributes to probe the structural variations across the study area, e.g., variance, curvature, ant-tracking, edge enhancement, edge evidence and chaos. The objective was to compare and choose the best ones to combine. To begin with, the attributes were tested using a few inlines and crosslines with varying parameters. This helped to select the best attribute with suitable parameters. Figure 5a-h shows time-slices at 1550 ms from the attributes tested for this study; (a) shows input seismic data where the faults are hardly visible; and (b) shows the variance attributes, revealing the faults as shown by the red pointers. Variance can map discontinuities and gas chimneys effectively. (c) The curvature attributes may detect subtle structural changes but the results were not helpful for this study. (d) Chaos attributes were not beneficial either, as evident from the figure; it is mainly used to detect faults and fractures. (e) Edge Enhancement attributes were able to detect only major faults; (f) Edge Evidence attributes enhance edge detection by improving continuity. It detected major faults along with unnecessary information from seismic data. (g) A Dip Steering Median Filter (DSMF) preserves the edges for effective detection of discontinuities. The fault traces are slightly visible as shown by the pointers in the figure. (h) Ant-tracking showed comparatively better results and detected several major and minor discontinuities.  
As evident from Figure 5, the application of the most traditional discontinuity attributes was not successful in fault delineation in the gas chimney area. Consequently, a few new attributes were integrated and the results were remarkable. These provided the maximum information about the discontinuities and structural variations in the study area. A brief account and the results of the selected attributes are presented below.
## 3 Indoor Positioning Systems
### Systems Based on Computer Vision  
Indoor positioning systems (IPSs) that use computer vision use the information collected by cameras and a set of image processing techniques to identify, track, and navigate people in indoor environments [108; 109]. Initially, we identified two classes of IPSs based on computer vision: fixed cameras, arranged in the infrastructure, and mobile cameras, available in wearable devices, smartphones, etc.  
Another way of describing IPS is by observing the use of the cameras and the types of targets (passive or active). In structural systems, cameras are used actively, locating, and monitoring people in indoor environments. That is, the user does not need to load any device. Mobile systems can also be passive, in which the camera is used by the user to be monitored and captures images or videos from that user's perspective. The captured information can be compared with previously stored files or learned models (decision trees, neural networks, etc.). Although the terms \"active\" and \"passive\" are not common in computer vision terminology, we maintain them to standardize the approaches to other technologies. In this review, we consider the terms \"structure\" and \"mobile\" to differentiate active and passive strategies.
## 5 Discussion
### Effect of Pore Structure on Shale Adsorption Capacity  
The pore structure characteristics of shale affect the occurrence state of shale gas and are also key factors affecting shale adsorption capacity [55]. The correlation between pore structure parameters and V\({}_{\text{L}}\) of Taiyuan Formation shale was analyzed, leading to the conclusion that the relationship between shale adsorption capacity and pore structure parameters is significantly weaker compared to that with organic matter. Specifically, V\({}_{\text{L}}\) shows a strong positive correlation with total pore SSA and a weak positive correlation with total PV, with respective correlation coefficients of 0.6290 and 0.3450. Macroporous PV and SSA do not show a significant correlation with V\({}_{\text{L}}\), while mesoporous PV and SSA exhibit a weak positive correlation with V\({}_{\text{L}}\) (Figure 14). Research shows that the greater the total PV and SSA of shale in the Taiyuan Formation, the stronger the shale adsorption capacity. The main reason is that the adsorption of shale gas molecules is a physical process, and it has a certain effective range for adsorption. Meanwhile, macropores in shale primarily serve as storage spaces for free gas and act as migration channels for shale gas [56]. The mesopores are widely developed in the pores of the Taiyuan Formation shale, providing  
Figure 13: The relationship between kerogen type and Ro and V\({}_{\text{L}}\) for the Taiyuan Formation shale in the SNCB. (**a**) kerogen type and V\({}_{\text{L}}\); (**b**) Ro and V\({}_{\text{L}}\).  
the main PV and SSA for the shale. This increases the number of adsorption sites on its surface and provides the primary storage space for gas adsorption, therefore enhancing the shale adsorption capacity.
## 2 Related Work
### ROI Extraction  
The ROI extraction phase plays an important role in palmprint verification, since the accuracy of the whole process is highly dependent on ROI extraction, where several findings have been discussed in previous work [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. In [18], ROI extraction steps consist of (i) skin-color thresholding, (ii) hand valley detection, and (iii) finding palm region. The authors used [PERSON]'s skin-color model in the thresholding phase. In the second and third phases, they proposed the competitive hand valley detection (CHVD) algorithm to locate valley key-points. Thereafter, the output was exploited to locate the ROI. ROI extraction based on skin-color segmentation is used in [19; 20]. [PERSON] et al. [19] extended the CHVD algorithm to simultaneously track palmprint and knuckle-print ROIs. The most challenging issue in these approaches is poor segmentation for images with a skin-colored background. To address this problem, statistical segmentation models were employed. In this way, Reference [21] exploited active shape model (ASM), while [22; 23] proposed their methods based on the active appearance model (AAM). These approaches suffer from the probability of important features loss due to small extracted ROIs.  
[PERSON] et al. [14] presented an ROI extraction technique including several steps. First, they applied a low-pass filter to the original image. Thereafter, boundaries of the gaps between the fingers were obtained using a boundary tracking algorithm. Finally, based on the coordinate system, a fixed-size sub-image was extracted. Similarly, the methods of [PERSON] [15], [PERSON] [16], and [PERSON] [17] used the finger gaps to extract the ROI. Although these methods are famous and widely used, they depend on the gaps between the fingers as reference points to determine the coordinate system, which means that all fingers must be spread and the hand should be facing toward the camera. To tackle this problem, [PERSON] et al. [24] proposed a five-step method that can be summarized as follows: (i) binarization of the input image, (ii) combination of binarized image and edge, (iii) key point candidate detection using the radial-distance function, (iv) optimal key-point selection, and (v) palm region extraction. This approach suffers from the probability of important features loss due to small extracted ROIs.
## 5 Proposed Dynamic Cluster Head Election Algorithm
### Proposed Optimum Cluster Head Election Method
#### Schemes of Sensing  
The wireless sensor concept means the possibility of physical sensing, and the ability to process the data obtained and communicate the results wirelessly. In recent years, many power-saving schemes have been introduced which assume that data acquisition and processing consume far less power than communication [34]. All network sensors are constrained, and their components consume a specific energy amount, taking into account the increasing growth network. Therefore, the process of supplying power and ensuring the proper operation of WSN becomes very difficult. Hence, building structures of effective wireless networks for energy efficiency networks is critical.  
By the network scheme switching, power is saved substantially at the network level. Two different network schemes are reviewed as follows:  
**Scheme 1 (Single Cluster Algorithm):**  
The data points obtained from all sensor nodes are transmitted to the CH, and \(E_{allSens\text{-}CH}\) energy consumption can be calculated, in this case, as:  
\[E_{allSens\text{-}CH}=\sum_{n=1}^{N}\left[(E_{trans}+eamplifcoe\text{-}dist_{n \text{-}sn}^{\text{*}})\cdot N_{trans\_dbits}\right] \tag{4}\]  
where \(N\) indicates the number of nodes, \(dist_{n\text{-}sn}\) indicates the distance between nodes and the sink node, and \(N_{trans\_dbits}\) indicates the obtained data bits.  
For example, Figure 4 illustrates the Single Cluster Algorithm. Consider it as a process of managing and monitoring the precision agriculture platform, where sensors obtain humidity and temperature. In each platform, the sensed humidity and temperature are directly transmitted to the communications management unit SN without relay and routing.