esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 2 Shearer Cutting Unit Detection in Infrared Thermal Images  
In the mining process of a shearer, the cutting unit motor drives the roller to achieve coal breaking. The cutting load is very high, resulting in faster temperature rise of the cutting part. Meanwhile, the drastic friction between the roller and coal wall produces a large amount of heat, which makes the temperature of the whole cutting unit higher than that of the surrounding objects. When the shearer is in different cutting states, the temperature change of the cutting unit is very sensitive, and can be called a sensitive region. Therefore, the first step of our proposed method was to detect this sensitive region in an infrared thermal image based on mathematical morphology theory.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 1 Introduction
Section 2 presents the proposed method for enhancing the multimode vibration damping of composite beam structures. More precisely, in Section 2.1, a finite-element model for laminated composite piezoelectric beams is developed, Section 2.2 briefly describes the \"current-flowing\" multimode shunt-damping method and Section 2.3 provides the state-space representation of the shunt-damping system when a \"current-flowing\" shunt circuit is connected to the top piezoelectric patch. The section ends with the presentation of the proposed design optimization approach for multimode vibration control. The method considers the capacitances and the shunting branch inductors as new design variables along with the resistor damping components. The proposed optimization approach uses a particle swarm optimization (PSO) algorithm to minimize the \(H_{\infty}\) norm of the damped system. To investigate in detail the effect of these variables on the damping performance, as well as for comparison with the other methods proposed previously in the literature [15, 17, 18], two additional numerical examples with fewer design variables are addressed. Simulation results are shown in Section 3 to verify the proposed design optimization approach. In addition, a comparison of the proposed PSO method with the GA algorithm is provided in Section 3.6 to demonstrate the capability of the proposed PSO method to improve the performance of shunt-damping circuits. Finally, in Section 4, a review of the work and a summary of the principal conclusions of this study are outlined.
## 5 Conclusions  
The actual spectrum sensing performance of the well-known detectors employed in CR networks based on antenna array, such as the ED and MF deviates from the theoretical results owing to the noise power uncertainty and _SNR_ wall phenomenon. This phenomenon has a negative impact on the spectrum sensing performance and on the receiver operation characteristic (ROC) when increasing in the sensing time has no any compensating effects. In this paper, we demonstrate that under implementation of the GD in CR networks based on antenna array there is no \(\textit{SNR}_{\textit{wall}}^{GD}\) in the case of noise power uncertainty that is confirmed by the real scenario simulation. The GD can calibrate the noise power uncertainty problem by the compensation channel (see Figure 2) using the reference noise forming at the GD AF output. The GD is able to detect the PU signal at any low _SNR_ value with increasing in the number of samples that is still not the ideal solution under fast spectrum sensing. Thus, the GD implementation in CR networks based on antenna array allows us to reduce some negative effects caused by the noise power uncertainty and improve the PU signal detection performance and robustness. The probability of error \(\ P_{\sigma}\) as a function of the normalized optimal detection threshold is evaluated for the ED and GD both under presence and absence of the noise power uncertainty. The GD demonstrates the better probability of error \(P_{\sigma}\) performance in comparison with the ED in both cases. Finally, as is demonstrated by the simulation results, with an increase in the noise power uncertainty, the probability of error \(P_{\sigma}\) increases as well.  
This research was supported by the Kyungpook National University Research Fund, 2014. Additionally, the authors would like to thank the anonymous reviewers for the comments and suggestions that helped to improve a quality of the present paper.  
[PERSON] and [PERSON] proposed the theoretical definition and description of the GD spectrum sensing performance, sample complexity, and optimal GD threshold. [PERSON] performed the simulation. Both authors analyzed the simulation results and compared them with theoretical investigation and contributed in writing the paper.  
The authors declare no conflict of interest.
## 3 CNQF Architecture
### CNQF Hierarchical Structure  
While functional elements within the subsystems are designed to be integrated horizontally, they also form part of a hierarchical structure as is typical of PBNM systems [2]. The hierarchical structure is also depicted in Figure 1. The top level consists of tools that provide centralized administrative capabilities such as Graphical User Interfaces (GUI) for interactive high level configuration, and policy entry/editing; visualization tools for network-wide configuration and status monitoring; and central repositories. The PDL (Policy Decision Layer) comprises of various Policy Decision Points (PDPs) _i.e._, Resource Brokers of the RMS; these are centrally managed via the PAL (Policy Administrative Layer). The bottom layer is the MCAL (Measurement, Control and Adaptation) layer that consists of elements that directly interface with the Policy Enforcement Points (PEPs) in the transport network. The MCAL elements include the various RMS Resource Controllers, the ADs and the various MMS NMs.
## 5 Conclusions  
This paper introduces a novel THz band gain-stable 2-D wide-angle scanning PAA. According to the 8 \(\times\) 8 PAA's simulated results, a 2-D wide-angle scanning of \(\pm\)48\({}^{\circ}\) was achieved from 106 to 114 GHz, while in the 115-119 GHz range, the scanning range reached \(\pm\)40\({}^{\circ}\) on the E-plane and H-plane. Additionally, the normal peak gain was stably maintained at 21.9-22.8 dBi across the operating frequency band. Compared with other works, the advantages of this work are mainly reflected in 2-D beam scanning and a wider scanning range. Due to its stable gain and 2-D wide-angle scanning performance, the proposed PAA has a broad application prospect in THz wireless-communication equipment. The next step will involve designing a feeding network for the proposed PAA, then seeking available beam control chips, and, finally, fabricating a prototype to measure the beam-scanning performance in order to accelerate the application of the proposed PAA in THz wireless communication.  
This research received no external funding.  
Data Availability Statement: The datasets created and analyzed during this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Western Blot  
For this experiment, MDA-MB-231 cells were grown to a density of 4 \(\times\) 10\({}^{6}\) cells and treated with polyphenol extract of _L. graveolens_ at 150 \(\upmu\)g/mL for 24, 48, and 72 h. A total of 250 \(\upmu\)M of cisplatin was used as a positive control for apoptosis, and untreated cells were used as a negative control. To extract protein for western blot analysis, cells were washed with 1X phosphate-buffered saline (PBS) and then lysed using 600 \(\upmu\)L of RIPA lysis buffer, supplemented with a protease inhibitor (Complete, Roche, Copenhagen, Denmark). The extracts were clarified by centrifugation for 20 min at 9465\(\times\)_g_ at 4 \({}^{\circ}\)C. Protein concentrations were determined by the Bradford protein assay according to the supplier's specifications (Merck, St. Louis, MO, USA). Soluble protein extracts (20 \(\upmu\)g) were mixed with an appropriate amount of 2X or 4X Laemmli sample buffer, then boiled for 10 min, separated by SDS-PAGE, and subsequently transferred to polyvinylidene difluoride (PVDF) membrane (Bio-Rad, Hercules, CA, USA). Membranes were blocked with 5% nonfat dry milk in 1X Tris-buffered saline supplemented with 0.1% Tween-20 and probed overnight using an appropriate primary antibody (1:1000 dilutions were used for the primary antibodies). Dilutions of 1:3000 of anti-rabbit antibody conjugated to horseradish peroxidase were used for detection. Finally, immune complexes were developed using the highest sensitivity substrate SuperSignal West Pico or West Femto (Thermo Fisher Scientific, Waltham, MA, USA) with a ChemiDoc imaging system (Bio-Rad, Hercules, CA, USA) [45]. Most of the PVDF membranes were stripped and reprobed for \(\upbeta\)-actin. Antibodies for PARP (#9542), caspase-9 (#9502), caspase-3 (#9662), \(\upbeta\)-actin (#4967), and secondary anti-rabbit IgG (#7074) were purchased from Cell Signaling Technology (Danvers, MA, USA).
# An Efficient Algorithm to Determine Probabilistic Bisimulation
## 3 A Partition Refinement Algorithm for Probabilistic Bisimulation (Outline)
### Stability of Blocks and Partitions
As \(B\) is stable under \(C\), \(t\stackrel{{ a}}{{\rightarrow}}C\) and hence, \(t\stackrel{{ a}}{{\rightarrow}}C\backslash C^{\prime}\). The following property, called the _stability property_, says that a partition stable under itself induces a probabilistic bisimulation. In general, partition based algorithms for bisimulation search for such a stable partition. **Lemma 3**: **Stability Property. ** _Let \(\mathcal{A}=(\,S,\rightarrow\,)\) be a PLTS. If a partition \(\mathcal{B}\) for \(\mathcal{A}\) is stable under itself, then the corresponding equivalence relation \(\mathcal{B}\) on \(S\) is a probabilistic bisimulation._  
By the first condition of Definition 3 and stability of all blocks in \(\mathcal{B}\) we have that either \(B\subseteq S\) or \(B\subseteq U\), for each block \(B\in\mathcal{B}\). We write \(s\mathcal{B}t\) iff \(s,t\in B\) for some \(B\in\mathcal{B}\). Note that used in this way \(\mathcal{B}\) is an equivalence relation on \(S\). Suppose \(s\mathcal{B}t\) for some \(s,t\in S\) and \(s\stackrel{{ a}}{{\rightarrow}}f\). Let \(u\in U\) correspond to \(f\). Say \(s,t\in B\) and \(u\in B^{\prime}\) for some blocks \(B,B^{\prime}\in\mathcal{B}\). Then, \(s\stackrel{{ a}}{{\rightarrow}}B^{\prime}\). By stability of \(B\) for \(B^{\prime}\), it follows that \(t\stackrel{{ a}}{{\rightarrow}}B^{\prime}\). Hence, \(v\in B^{\prime}\) and \(g\in\mathcal{D}(S)\) exist such that \(v\) corresponds to \(g\) and \(s\stackrel{{ a}}{{\rightarrow}}g\). Therefore, for any block \(B^{\prime\prime}\in\mathcal{B}\) we have \(f[B^{\prime\prime}]=u[B^{\prime\prime}]=v[B^{\prime\prime}]=g[B^{\prime\prime}]\) since the block \(B^{\prime}\) of \(u\) and \(v\) is stable under each block \(B^{\prime\prime}\) of \(\mathcal{B}\). Thus, the stable partition \(\mathcal{B}\) induces an equivalence relation that satisfies the conditions for a probabilistic bisimulation of Definition 2, as was to be shown.
## 3 Results and Discussion
### Effect of Coir Fiber Addition on the Flexural Strength of Geopolymer Specim  
Fibers are added to matrices to form composites with improved strength and toughness. Crack arrest at the fiber-matrix interface and increasing crack path via fibers with high aspect ratios are among the mechanisms for improved toughness. The effect of various fractions of coir fibers on the flexural strength of metakaolin-based geopolymer composites synthesized is presented in Figure 8. From Figure 8, it is clear that incorporating pre-treated coir fibers at different percentages (0.5% to 2%) enhanced the bridging effect and generally increased the flexural strengths of metakaolin-based geopolymer composites. This may also be attributed to the high tensile strength and elastic modulus of the coir fibers, as well as the possibility of stress transfer from the specimen to the coir fiber through the interface of the geopolymer matrix [19]. The basic mode of failure of the coir-fiber-reinforced geopolymer specimen formulated is mode I [15] (opening mode: tensile stress is orthogonal to the local plane of the crack surface) (Figure 9). When a fiber-reinforced composite is subjected to bending load(s), the bending moment causes tensile stresses. At the fiber-matrix interface, the tensile stress transforms into shear stress, which builds resistance at the fiber/matrix interfaces. As fiber contents are increased, the formation of microcracks within the composite structure is anticipated instead of a few large macroracks. For the MGP-0.5% geopolymer composite formulation, a flexural strength of 9.74 N/mm\({}^{2}\) was recorded. At higher coir  
Figure 6: Compressive strength of specimens at different coir fiber contents tested after 28 days.  
Figure 7: Mode of failure of cubes after compressive strength test.  
fiber content (1%), a significant increase (28%) higher than the control sample without coir fibers was observed. [PERSON] et al. [19] reported a similar increase in flexural strength of high-class fly ash geopolymer mortar as fiber content increased from 0% to 1% volume fraction, although the strength values in this current study were higher.  
Compared with the work of [PERSON] et al. [1], a reduced value in the flexural strength was recorded when 1% coir fiber content was used as a reinforcing element in fly-ash-based geopolymer. The flexural strength observed for 1% coir fiber addition was the highest (10.39 N/mm\({}^{2}\)) in this study. Flexural strength tends to decrease with increasing coir fiber content from 1% to 2%. The flexural strength of the 1.5% and 2% fiber contents were 9.13 N/mm\({}^{2}\) and 8.58 N/mm\({}^{2}\), respectively. Compared with the control sample (MGP Control) that gave 7.60 N/mm\({}^{2}\), these values are still higher. At higher fiber content (1.5% and 2%), a weak composite interface might have been created due to increased fiber-to-fiber interactions. The coir fibers in the composite may not have perfectly aligned with the geopolymer matrix. Hence, at higher flexure stress, weak interface cracks could form easily leading to failure.
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
## 2 Materials and Methods
### HPOM Design  
When HPOM is set based on the eye being a sphere, the relationship between the ORCP and PCP satisfies a sphere Equation (1). This can be expressed in the spherical coordinate system shown in Figure 1. The gaze direction is indicated by the g-axis that originates from the ORCP to terminate at the PCP. When the g-axis is oriented in any given direction, the positional relationship between the PCP and ORCP can be described by a radius R and angles \(\theta\) and \(\phi\) as commonly seen in spherical coordinate systems. D is the line segment of the line connecting ORCP and PCP projected onto the XY plane of the spherical coordinate system. This can be calculated as shown in Equation (2) using a trigonometric function. \(\theta\) is the angle between the g-axis and z-axis. It can be expressed as an angle in the range of \(0\) to \(\pi\). \(\phi\) is the angle formed by the line segment D with the x-axis. It can be expressed as an angle in the range of \(0\) to \(2\pi\).  
\[\left(\mathrm{x_{ORCP}-x_{PCP}}\right)^{2}+\left(\mathrm{y_{ORCP}-y_{PCP}} \right)^{2}+\left(\mathrm{z_{ORCP}-z_{PCP}}\right)^{2}=\mathrm{R}^{2}. \tag{1}\]  
\[\mathrm{D}=\sqrt{\left(\mathrm{x_{ORCP}-x_{PCP}}\right)^{2}+\left(\mathrm{y _{ORCP}-y_{PCP}}\right)^{2}}=\mathrm{R}\times\sin\theta \tag{2}\]
## 2 Quality Assessment of COSMIC-2 GPS Observations
### Single Noise Ratio  
The signal-noise ratio (SNR) is the ratio of the carrier signal intensity received in the receiver to the noise intensity. That can reflect the signal quality of the carrier. The higher the SNR is, the smaller the error is [27]. In the process of TEQC detection, SNR1 and SNR2 are the average signal-noise ratios on L1 and L2 carriers respectively, and the thresholds are set to 6 and 4 respectively [28; 29]. The mean SNR of every COSMIC-2 satellite every day and the mean SNR of six COSMIC-2 satellites every day in 5 days is plotted in Figures 3 and 4 respectively.  
As can be seen from Figures 3 and 4, both SNR1 and SNR2 of the six COSMIC-2 satellites for 5 days are much larger than the threshold value, the SNR is relatively smooth, and there is no significant fluctuation, besides, SNR1 is somewhat larger than SNR2, which indicates that the signal quality of L1 is better than that of L2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**DOY** & **Sat-1** & **Sat-2** & **Sat-3** & **Sat-4** & **Sat-5** & **Sat-6** & **Mean** \\ \hline
138 & 7 & 9 & 8 & 8 & 7 & 7 & 8 \\
139 & 8 & 10 & 9 & 9 & 8 & 8 & 9 \\
140 & 7 & 10 & 9 & 9 & 6 & 7 & 8 \\
141 & 10 & 10 & 11 & 9 & 8 & 7 & 9 \\
142 & 8 & 11 & 11 & 10 & 9 & 7 & 9 \\ Mean & 8 & 10 & 10 & 9 & 8 & 7 &
## 3 Results and Discussion
### Heavy Metals Content  
Table 3 reports the heavy metals content (mg kg\({}^{-1}\) d.w.) for the analyzed basalt aggregate for concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Aliquot ID**} & \(\mathbf{C_{Ra}}\) & \(\mathbf{C_{Th}}\) & \(\mathbf{C_{K}}\) & \(\mathbf{C_{Cs}}\) \\  & **(Bq kg\({}^{-1}\) d.w.)** & **(Bq kg\({}^{-1}\) d.w.)** & **(Bq kg\({}^{-1}\) d.w.)** & **(Bq kg\({}^{-1}\) d.w.)** \\ \hline
1 & 53.6 \(\pm\) 6.1 & 36.4 \(\pm\) 4.9 & 498 \(\pm\) 57 & \textless{}0.18 \\
2 & 63.6 \(\pm\) 7.1 & 44.9 \(\pm\) 5.7 & 510 \(\pm\) 66 & \textless{}0.24 \\
3 & 58.6 \(\pm\) 6.6 & 35.7 \(\pm\) 5.1 & 491 \(\pm\) 50 & \textless{}0.21 \\
4 & 61.9 \(\pm\) 6.8 & 45.7 \(\pm\) 5.5 & 505 \(\pm\) 64 & \textless{}0.27 \\
5 & 55.3 \(\pm\) 6.4 & 40.7 \(\pm\) 5.3 Of note, the obtained results are lower than the threshold limits [44]; hence, they can be regarded as no pollutants and do not compromise the well-being of the environment.
## 3 Proposed Model
### System Model and Problem Formulation  
A mobile edge capture and analytics (MECA) is the combination of the wireless network, mobile edge server and cloud server [29]. The mathematical notations are marked in Table 1. In MCA a work flow, mobile health care application is modeled as consumption weighted directed acyclic graph (DAG), i.e., G (V, E). Whereas, each task \(v_{i}\) is represented by a node \(v_{i}\in V\). An edge \(e(v_{i},v_{j})\in E\) represents communication between \(v_{i}\) to \(v_{j}\). A task \(v_{i}\) could be started until all associated predecessors complete [30]. \(v_{1}\) and \(v_{n}\) are two dummy tasks (i.e., entry task and exist task). A task could be started until associated predecessors complete. In simple words \(v_{j}\) cannot be started in anticipation of \(v_{i}\) to get the job done and \(i<j\). A set of servers can be represented by \(K=\{k_{1},\ldots,k_{n}\}\). It is presupposed that each k server holds different \(VM\) type, that all \(VM\) instances are heterogeneous, every VM has dissimilar  
Figure 2: Mobile cloud architecture.  
computation speed which are illustrated as \(\zeta_{j}=(j=1,\ldots,M)\). A set of VM instances can be shown by \(V_{K}=(v_{k1},\ldots,v_{kn})\), in which \(K_{i}^{\kappa k}\) is the VM assignment for task \(v_{i}\). Each workflow application task has workload \(W_{i}=(i=1,\ldots,N)\) with deadline \(D_{iu\ a}\). To minimize the power consumption of the submitted workflow tasks, each application task is assigned to the lower speed VM while meeting the deadline \(D_{i}\), because the lower speed VMs always leads to lower power consumption [31]. Since a task \(v_{i}\) can only be performed by one VM j, a decision variable \(x_{ij}\in[0,1]\) is utilized, \(x_{ij}=1\) only if the task \(v_{i}\) is assigned to the VM \(v_{j}\). The task \(v_{i}\) has two execution costs (i.e., local and cloud), one cloud execution time is determined by the speed \(\zeta_{j}\) and power consumption \(P_{w}\) e.g., \(T_{i}^{e}\), i.e., \(C_{i}^{e}=\sum_{j=1}^{V}x_{ij}\times\frac{W_{i}}{\zeta_{j}}\times P_{cw}\) and task execution time on mobile \(M_{i}^{e}=\sum_{j=1}^{\leftarrow}x_{ij}\times\frac{W_{i}}{\zeta_{m}}\times P_{ mw}\).
## 2 Materials and Methods
### Methods
#### 2.3.3 Validation Methods of Reconstruction Accuracy  
To assess the accuracy of the reconstruction results based on DINEOF and CS-DINEOF algorithms, the approach described in Section 2.3.1 was employed. For each sub-dataset and time step, 1% of valid pixels were randomly selected for cross-validation. The original chl-a values at these points were recorded, and the values in the dataset under reconstruction were marked as missing. The four parameters of RMSE, Mean Absolute Error (MAE), Pearson correlation coefficient (r), and Signal-to-Noise Ratio (SNR) were used to verify the accuracy by comparing the reconstruction results with original values at cross-validation points. These parameters are defined as follows:  
\[RMSE=\sqrt{\frac{\sum(X^{c}-X^{re})^{2}}{N}}\]  
\[MAE=\frac{\sum|X^{c}-X^{re}|}{N}\]\[r =\frac{Cov(X^{c},X^{re})}{\sqrt{D(X^{c})}*\sqrt{D(X^{re})}}\] \[SNR =10 log_{10}\left(\frac{\sum(X^{re})^{2}}{\sum(X^{c}-X^{re})^{2}}\right) \tag{5}\]  
where \(X^{c}\) represents the original chl-a values of the cross-validation set, and \(X^{re}\) denotes the reconstructed chl-a values. \(N\) is the number of cross-validation points.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 2 System Model and Overview
### Discussion, Threats, and Solutions  
Nevertheless, the aforementioned approaches are mainly centrally oriented evacuation-planning methods, meaning that all available information from the field of disaster area must be gathered at the Emergency Control Center for optimal planning, either based on high computing demands but accurate optimal solutions, or by executing heuristic algorithms for approximate but fast decisions. The outcomes of evacuation planning are then announced (or even enforced) to the evacuees to guarantee their safety. However, in real-life evacuation planning, evacuees may not follow the proposed evacuation plan and retain their distributed and autonomous decision-making. Our research work aims at filling this gap and focuses on the autonomous and distributed operation of the Public Safety System. This is achieved by allowing evacuees to make optimal decisions about themselves regarding the selection of the optimal evacuation route in a dynamically changing disaster area by learning from their past actions and considering the reaction of the surrounding environment.