esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method of Calibrating Micro-Scale Parameters of PFC Model and Experimental Validation
It is found that the normal strength of bond, bond stiffness, and particle friction are the dominant parameters influencing the strain at failure. Figure 1: Type of bond model: (**a**) contact-bond model and (**b**) parallel-bond model. [PERSON] et al. [24] conducted a parametric study to identify the effect of parallel bond-contact stiffness ratio, bond area, and bond strength on stress-strain response of PFC3D model. Results show that the overall stiffness increases with the increase of the first two parameters, but is not affected by the bond strength. [PERSON] et al. [25] summarized that the macroscopic Young's modulus is controlled by the modulus of the bond and particle, the Poisson's ratio depends on the ratio of particle normal to shear stiffness, and the uniaxial compressive strength is related to the bond shear strength. Also, the linear equations between them are fitted. [PERSON] et al. [26] argued that bond cohesion has a remarkable impact on the model strength. [PERSON] et al. [27] further examined the effects of maximum to minimum particle size ratio and particle size distribution on the macroscopic mechanical properties of PFC3D, which show good agreement with the results of [PERSON] et al. [28] using similitude rules. Besides, based on numerous uniaxial compression simulations, [PERSON] et al. [29] utilized artificial neural networks to predict the micro-properties required for PFC3D modeling under three given macro-properties. By means of gray relational analysis and factorial screening, [PERSON] and [PERSON] [30] obtained the dominant micro- parameters influencing the uniaxial compressive strength, Young's modulus, and Poisson's ratio of coal. Moreover, [PERSON] and [PERSON] [31] used an improved simulated annealing algorithm to find the approximate values of these micro-parameters, which does not require establishing the relationship between microscopic and macroscopic parameters. [PERSON] et al. [32] further developed FFD-ANN (Full Factorial Designs and Artificial Neural Networks) method to predict the micro-parameters of PFC3D model for triaxial compression tests. From the literature review, it can be concluded that relationship between partial micro- and macro-parameters has been revealed. However, so far, it is still unclear that how these parameters interact with each other and collectively affect each macro-scaled mechanical property. Moreover, information regarding effective methods for finding the micro-scale parameters quickly is relatively rare. Therefore, in this research, a novel experimental design method incorporating Box-Behnken response surface methodology and desirability function optimization (BB-DFO) approach was proposed to conduct sensitivity analysis and optimization of micro-scale parameters of parallel-bond model. After that, the fracture development around circular, square, and D-shaped openings in rock specimens loaded in uniaxial compression was simulated using the calibrated PFC model and then was compared with the results of experimental investigation.
# A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm
## 3 Multi-Objective Inverse Kinematics Problem Statement
### The Forward Modeling of the 5-DOF-Robotic Arm  
A 3D-printed robotic arm with 5-DOF excluding the gripper joint, as in Figure 2a, and the detailed kinematic representations and frames decomposition are illustrated in Figure 2b. The first three rotational joints starting from the base are actuated using an MG996R servomotor, while the two joints of wrist roll and wrist pitch are actuated with a smaller micro-servo, the SG90. The gripper grasping is also controlled by a SG90 servo; however, it is not included in the model. The system is controlled using an Arduino board and a set of H-bridges to avoid any overload when controlling servos. Such a robotic arm has a low cost and can be used for a variety of educational applications. It allows us to understand the main issues of robotics arms' motions pasification, which are similar in industrial arms applications.  
Figure 2 represents a 3D rendering of the used 5-DOF arm (Figure 2a) as well as its kinematics scheme (Figure 2b). The Denavit-Hartenberg (DH) convention is used to obtain the forward kinematics expression of the 5-DOF robot manipulator; the DH parameters of the arm are reported in Table 1 and the relative frames displacements and rotations are indicated in Figure 2b.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 4 Modulation Technology in VLC System
### Power Requirements and Spectrum Efficiency  
The power requirement for an OWC system is proportional to the square root of the SNR, as shown in Equation (19) [117]:  
\[P=\frac{1}{R}\sqrt{\sigma_{N}^{2}SNR} \tag{19}\]  
where \(R\) is the responsivity of the photodetector and \(\sigma_{N}^{2}\) is the total noise power in the detector current.  
Hence the normalized average power requirement of M-PAM, BPSK, QPSK is shown in Equations (20)-(22), respectively:  
\[\frac{P_{M-PAM}}{P_{NRZ-OOK}}=\frac{M-1}{\sqrt{\log_{2}M}} \tag{20}\]  
\[\frac{P_{BPSK}}{P_{NRZ-OOK}}=\frac{1}{2\sqrt{2}} \tag{21}\]  
\[\frac{P_{QPSK}}{P_{NRZ-OOK}}=\frac{1}{2\sqrt{2}}\frac{erfc^{-1}(BER)}{erfc^{-1 }(2 BER)} \tag{22}\]  
Nowadays, the most commonly used modulation schemes are the M-QAM OFDM, NRZ-OOK, and M-PAM. Among them, M-PAM has better spectral efficiency but requires higher SNR to maintain targeted BER, as illustrated in Figure 14.  
Figure 14: Spectral efficiency vs. SNR requirement for a BER of \(10^{-3}\).  
Figure 13: Chromaticity diagram. © 2015 IEEE. Reprinted, with permission from [114].
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Land Cover Data  
This paper utilizes the Landsat series of remote sensing data from the United States Geological Survey (USGS) Earth Explorer ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 7 August 2023)). Landsat-based remote sensing data were used to extract information on land cover types. The data comprise two types: Landsat 7 ETM (2001-2013) and Landsat 8 OLI (2014-2020). Remote sensing images with consistent high vegetation coverage during the period from June to September each year and with cloud cover below 5% were selected. Orthogonal signal correction, layer overlay, sharpening, etc. were performed on the extracted images. By combining the characteristics of MOD17 data with the actual distribution in the Songnen Plain area using the land cover data classification system, the land cover types were classified into five types: forest land (FOR), grassland (GRA), swamp (SWA), dry land (DRY), and paddy field (PAD). Dry land refers to cultivated land without irrigation facilities, primarily relying on natural precipitation to grow drought-resistant crops. Based on the characteristics of colors and textures presented in the images, the remote sensing automatic classification markers of different ground cover types were established. Based on a geographical information system platform and by using the automated classification method, major land cover data in the Songnen Plain were obtained (Figure 2b, taking 2020 as an example).
## 3 Methodology
### Feature Extraction
#### 3.3.5 Harris Corner Detection (HCD)  
The Harris corner point's features are utilized to separate the background and foreground. Rotating the window in every orientation along a corner point should produce a significant shift in luminance. The point intensity of Harris's point in the fingerprint foreground areas is much greater than in the background regions. The [PERSON] method, originally proposed by [PERSON] et al., is a variation or extension of the Moravec corner detection algorithm that extracts corner points using grey disparity between images [39]. Assuming that a greyscale image \(I\) and window \(w(x,y)\) are considered to be removed (through movements of \(u\) in the \(x\) direction, and \(v\) in \(y\) direction), the intensity variation is calculated as Equation (10):  
\[E(u,v)=\sum_{x,y}w(x,y)[I(x+u,y+v)-I(x-y)]^{2} \tag{10}\]  
where \(I(x,y)\)and\(I(x+u,y+v)\) are intensities at position \((x,y)\) and at moving window \(x+u,y+v\), correspondingly. Because windows with corners have been explored with a wide range of intensity, we decided to focus on them. As a result, the component from Equation (10) is rewritten as Equation (11):  
\[\sum_{x,y}[I(x+u,y+v)-I(x-y)]^{2} \tag{11}\]Equation (12) is attained by Taylor extension:  
\[E(u,v)\approx\sum_{x,y}\bigl{[}I(x,y)+uI_{x}+vI_{y}-I(x,y)\bigr{]}^{2} \tag{12}\]  
Escalating Equation (12) and eliminating the appropriate products leads to Equation (13):  
\[E(u,v)\approx\sum_{x,y}u^{2}l_{x}^{2}+2 uvI_{x}I_{y}+v^{2}l_{y}^{2} \tag{13}\]  
By evaluating Equation (13) in matrix form, we attain Equation (14):  
\[E(u,v)\approx[u\ v]\left(\sum_{x,y}w(x,y)\begin{array}{cc}I_{x}^{2}&I_{x}I_{ y}\\ I_{x}I_{y}&I_{y}^{2}\end{array}\right)\begin{bmatrix}u\\ v\end{bmatrix} \tag{14}\]  
Alternatively, consider \(M=\sum_{x,y}w(x,y)\begin{array}{cc}I_{x}^{2}&I_{x}I_{y}\\ I_{x}I_{y}&I_{y}^{2}\end{array}\).  
In this instance, Equation (14) is revised and presented as Equation (15):  
\[E(u,v)\approx[u\ v]M\begin{bmatrix}u\\ v\end{bmatrix} \tag{15}\]  
The eigenvalues of matrix \(M\) are stated as \(\lambda_{1}\) and \(\lambda_{2}\), which creates a rotationally invariant description. At this time, three points need to be considered:  
* As soon as an eigenvalue \((\lambda_{1}\) or \(\lambda_{2})\) is high and significantly superior, an edge occurs.
* Pixel point is stated to be in a flat area when \(\lambda_{1}\approx\lambda_{2}\) and its values are low.
* Pixel point is stated to be in a corner when \(\lambda_{1}\approx\lambda_{2}\) and its values are high.
* A score can be determined for every window to evaluate whether it is likely to represent a corner, as shown in Equation (16): \[R=\det(M)-k(trace(M))^{2}\] (16)  
where \(\det(M)=\lambda_{1}\), \(\lambda_{2}\), \(trace(M)=\lambda_{1}+\lambda_{2}\), and constant \(k\) is fixed in the range of 0.04 to 0.06. A window is obtained through score \(R\) that is greater than a positive threshold rate, i.e., considered to consume a corner.
## 3 Results  
In this section, the ATR-FTIR and DRIFT results are included for each pigment, grouped according to their chemical nature, i.e., silicates, oxides, carbonates, sulphides and acetates. For each subsection, tables are included, comparing the characteristic bands identified for each modality, the vibration modes and the mineralogical assignment made for each band. Due to the lack of knowledge in the use of DRIFT modality, some of the assignments were made by the authors by considering the position of the bands identified in ATR-FTIR modality, which were already reported by other authors. Figures comparing the spectra in both modalities are also included for each pigment.  
Considering the FTIR spectra obtained, it must be considered that the bands in the region between 2400 and 1900 cm\({}^{-1}\) in the ATR-FTIR spectra were assigned to the signal from the diamond crystal from the benchtop equipment used [25]. As for the DRIFT spectra, they generally showed high intensity bands due to atmospheric CO\({}_{2}\), around 2400-2300 cm\({}^{-1}\) (marked in the figures with \"CO\({}_{2}\)\"), as well as broad bands between 4000 and 3100 cm\({}^{-1}\), assigned to -OH [25,26]. Moreover, many of the DRIFT spectra showed bands between 3100 and 2800 cm\({}^{-1}\), 2650 and 2500 cm\({}^{-1}\) and 1780 and 1270 cm\({}^{-1}\) related to organic compounds [25,26]. Their presence could be related to the extraction process of the pigment from the mineral ore, as it was the case of natural blue ultramarine (UL-N), explained below. In other cases, the organic components could be due to a levigation process, i.e., mineral particle separation. This method was developed by [PERSON] (MP) for pigment commercialization in 1997 as a technique for separating powder pigment into various pigment sizes, thus obtaining different shades [27]. The MP method involved washing the pigment with different mediums: e.g., egg yolk for azurite and malachite, rabbit glue for cinnabar or casein for realgar [27]. This could justify the presence of characteristic bands generally assigned to organic compounds, such as methylene groups or amino acids, which were present in many of the pigment spectra (marked in the figures with \"OM\", i.e., organic matter). However, these \"OM\" bands were not included in Tables 2-6 since their assignment to one organic component or another was not essential for pigment allocation nor within the aims of this study. In addition, in order to identify the compound nature, the application of other analytical techniques such as chromatographic techniques, such as gas chromatography (GC) and High Performance Liquid Chromatography (HPLC), would be necessary.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 4 Simulation Setup and Experimental Results
### Performance Evaluation of Energy Consumption  
The energy consumption analysis across 100 communication rounds for all the datasets and scenarios confirms the efficiency of the QESIF in reducing operational overhead, as shown in Figure 6. Under clean channel conditions, the QESIF recorded the lowest average energy usage at 13.4 mJ, compared to QuIDS at 15.9 mJ, QRHE at 17.3 mJ, HyQuSec at 18.2 mJ, and QLSN at 19.6 mJ. In noisy conditions, the QESIF maintained an average of 15.8 mJ, whereas QuIDS, QRHE, and HyQuSec consumed 18.7 mJ, 20.1 mJ, and 21.6 mJ, respectively, with QLSN peaking at 22.9 mJ. The difference became more pronounced in the eavesdropped scenario, where the QESIF's adaptive protocol stack and entropy-aware session management held energy usage at 17.6 mJ. In contrast, QRHE rose to 22.3 mJ, HyQuSec to 24.0 mJ, and QLSN to 25.7 mJ due to increased cryptographic overhead and communication retries.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 3 RFID Link Budget Calculation
### Downlink (Reader to Tag) Communication
#### 3.1.2 Propagation under Real Conditions
Next, note that, since the wavenumber k\({}_{\rm c}\) is usually small (e.g., k\({}_{\rm c}\approx 19\) at f\({}_{\rm c}=866\) MHz), the variation of r(t) is slow in comparison with that of \(\cos(\omega_{\rm c}\)t) and, henceforth, in this paper we will assume that r(t) is constant, i.e., r(t) = r, at least over the time required to complete a transaction between the vehicle and the RFID tag (which is typically on the order of a few milliseconds). Under this assumption, it follows from (11) that \(r^{2}{}_{rms}=r^{2}\) is the sum of the squares of two independent Gaussian variables I and Q, with means (see (11))  
\[t_{1}=t-\sqrt{\left(vt\right)^{2}+\left(1-\left(v/c\right)^{2}\right)\left(d^ {2}+\left(h_{1}-h_{2}\right)^{2}\right)}/c, \tag{7}\]5  
and  
\[t_{1}=t-\sqrt{\left(vt\right)^{2}+\left(1-\left(v/c\right)^{2}\right)\left(d^ {2}+\left(h_{1}-h_{2}\right)^{2}\right)}/c, \tag{7}\]6  
where \(a\), \(a_{1}\) and \(a_{2}\) were defined in (12). Consequently, the distribution of \(r^{2}{}_{rms}\) is non-centrally chi squared with two degrees of freedom, i.e.,  
\[t_{1}=t-\sqrt{\left(vt\right)^{2}+\left(1-\left(v/c\right)^{2}\right)\left(d^ {2}+\left(h_{1}-h_{2}\right)^{2}\right)}/c, \tag{7}\]7  
where \(l_{0}\) is the Bessel function of the first kind and zero order. Finally, using (13), the probability that the power at the RFID tag will exceed a predetermined detection threshold \(\gamma\), i.e., \(P(p_{R}\geq\gamma)=P(r^{2}{}_{rms}\geq\gamma^{\prime})\), where \(\gamma^{\prime}=960\pi^{2}\gamma/\!g_{R}\lambda^{2}\), becomes  
\[t_{1}=t-\sqrt{\left(vt\right)^{2}+\left(1-\left(v/c\right)^{2}\right)\left(d^ {2}+\left(h_{1}-h_{2}\right)^{2}\right)}/c, \tag{7}\]8  
where \(Q(\cdot)\) is the Marcum \(Q\) function [40]. Observe that Equation (14) gives the probability that the link will be available through the whole time interval.
## 2 Research Background  
### Project Profile  
The Wuyang Mine, located in Lu'an City, Shanxi Province, China, is depicted in Figure 2. The mine has a designed production capacity of 3.6 million tons per year. The primary extraction target is the No. 3 coal seam, using a comprehensive mechanized mining method that combines one-time full-height, full-caving extraction with strike longwall and retreating low-level cavity techniques. The No. 3 coal seam features a simple structure with a dip angle ranging from +2\({}^{\circ}\) to +7\({}^{\circ}\), averaging +5\({}^{\circ}\).  
The 76 working face is flanked by goafs on both sides, classifying it as an isolated working face due to its encirclement by mined-out areas. The arrangement of the working face is depicted in Figure 3.  
The 76 working face is buried at a depth of approximately 605 m and has a width of 295 m. There are no significant folds, faults, or other structural anomalies above the working face, indicating a stable coal seam. The coal seam thickness ranges from 3.1 to 7.9 m, averaging 5.5 m, and has a compressive strength of 10.8 MPa. The immediate roof consists of sandy mudstone with a thickness of 6.7 m, while the main roof is composed of fine-grained sandstone with a thickness of 13.4 m. The main key stratum is coarse-grained sandstone, with a thickness of 26.25 m and located approximately 351.8 m from the coal seam. The roof and floor lithology primarily consists of argillaceous mudstone and fine-grained sandstone. The compressive strength of the mudstone roof ranges from 21.3 to 51.9 MPa, that of the fine-grained sandstone roof ranges from 57.0 to 74.1 MPa, and the sandy mudstone floor has a compressive strength of 38.9 MPa. Neither the coal seam nor its roof and floor exhibit any tendency for coal bursts. The comprehensive borehole histogram near the working face is illustrated in Figure 4.  
Figure 3: Mining area layout.  
Figure 2: Geographic location of the Wuyang Mine.  
In the preliminary design phase before mining, coal pillars 8-12 m wide were established on both sides of the working face, with each roadway having a width of 5 m. This decision was influenced by the specific geological conditions and previous mining experience at Wuyang Mine. Although narrower coal pillars (3 to 5 m) are typically recommended to prevent strain energy accumulation, past operations at Wuyang Mine have shown that wider pillars are more effective in managing high in situ stress and reducing the risk of coal bursts. Given the issues related to coal burst resistance, it is necessary to make determinations in conjunction with the research findings presented in this paper.
## 2 Refrigeration Cycles, Models and Assumptions
### CO\({}_{2}\) Refrigeration Cycle with Mechanical Subcooling (MS Cycle)  
Essentially, the CO\({}_{2}\) refrigeration cycle with mechanical subcooling corresponds to a main CO\({}_{2}\) single-stage cycle that uses an auxiliary cycle, with small capacity, to provide subcooling at the exit of the gas-cooler/condenser [17]. This cycle operates in subcritical or transcritical conditions depending on the heat rejection temperature (T\({}_{\rm H}\)) and on the high-pressure established by the back-pressure valve (P\({}_{\rm high}\)).  
The transition from subcritical to transcritical conditions was investigated by [PERSON] et al. [20], [PERSON] et al. [21], [PERSON] el at. [22] and [PERSON] et al. [23] for standard CO\({}_{2}\) refrigeration cycles, however,  
Figure 1: Schematic representation of cascade and CO\({}_{2}\) with mechanical subcooling.  
no reference for the transition was found when the CO\({}_{2}\) cycle uses a mechanical subcooling system. For the analysis of the MS cycle, the transition from transcritical to subcritical was established in terms of the maximum COP value reached by each operating mode although this transition in real plants would be difficult. The considerations are the following:  
If saturation pressure of CO\({}_{2}\) at T\({}_{\rm H}\) is lower than the pressure fixed by the back-pressure (P\({}_{\rm high}\)) and this last is lower than the critical pressure of CO\({}_{2}\) (73.773 bar), the optimum operation conditions will be in subcritical-mode with liquid subcooling. These boundary conditions are detailed by Equation (1), and the corresponding pressure-enthalpy diagram of CO\({}_{2}\) represented in Figure 2. In this type of operation, the first CO\({}_{2}\) heat exchanger acts as condenser (point 2 to 3) and the subcooler subcools liquid CO\({}_{2}\) (points 3 to 4). The case of partial condensation in the CO\({}_{2}\) heat exchanger (point 2 to 3*) is possible, but the best energy results are obtained for complete condensation.  
\[{\rm P_{sat,CO_{2}}(T_{H})}<{\rm P_{high}}\leq{\rm P_{crit,CO_{2}}} \tag{1}\]  
\[{\rm P_{high}}>{\rm P_{crit,CO_{2}}} \tag{2}\]  
For pressures fixed by the back-pressure (P\({}_{\rm high}\)) higher than the critical pressure, Equation (2), the optimum operating conditions are in transcritical mode, as represented by Figure 3. For this mode of operation, the first CO\({}_{2}\) heat exchanger acts as gas-cooler (point 2 to 3) and the subcooler subcools gas or liquid depending on the high-pressure and T\({}_{\rm H}\) temperature (in red). The intermediate temperature (T\({}_{\rm I}\)) corresponds to the evaporating level of the high pressure cicle, in green, and is always lower than T\({}_{\rm H}\).  
Figure 3: Pressure-enthalpy diagram of MS cycle in transcritical conditions.  
Figure 2: Pressure-enthalpy diagram of mechanical subcooling (MS) cycle in subcritical conditions.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### The Methods for Evaluation  
The accuracy of the proposed algorithm is evaluated by running the algorithms presented in Section 2.2 and the proposed algorithm on the datasets mentioned in Section 2.1. Then, the F-measure scores were calculated by the mir_eval Python library [33], and the results were ordered so that they could be compared with each other. As mentioned above, the onset points are not exact times but a range of acceptable times. Therefore, to calculate F-measure scores, each of the estimated onsets' points should be compared with a range of points around the ground truth points. Thus, six different window sizes (10, 50, 100, 150, 200, and 250 ms) were considered to calculate the F-measure scores. Furthermore, the F-measure scores' average, variance, and ANOVA were calculated to better understand the results.