esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions to complex problems as the Traveling Salesman Problem (TSP). In such a problem, the target positions are usually static and punctually defined. The objective is to minimize a cost function as the minimal distance, time or energy. However, in some applications, as the one addressed in this paper--namely the data collection of buried sensor nodes by means of an Unmanned Aerial Vehicle--the targets are areas with varying sizes: they are defined with respect to the radio communication range of each node, ranging from a few meters to several hundred meters according to various parameters (e.g., soil moisture, burial depth, transmit power). The Unmanned Aerial Vehicle has to enter successively in these dynamic areas to collect the data, without the need to pass at the vertical of each node. Some areas can obviously intersect. That leads to solve the Close Enough TSP. To determine a sub-optimal trajectory for the Unmanned Aerial Vehicle, this paper presents an original and efficient strategy based on an evolutionary algorithm completed with geometrical heuristics. The performances of the algorithm are highlighted through scenarios with respectively 15 and 50 target locations. The results are analyzed with respect to the total route length. Finally, conclusions and future research directions are discussed.  
Close Enough Traveling Salesman Problem; evolutionary algorithm; genetic algorithm; Wireless Underground Sensor Networks; Unmanned Aerial Vehicle; optimal trajectory planning
## 4 Simulation Results
### Discussion  
The extensive experimental evaluation shows that the proposed SCEHO-IPSO algorithm has achieved better task scheduling in the cloud platforms by considering parameters like load balancing and resource utilization. The traditional optimization algorithms have poor convergence speed in multi-objective problem statements and become trapped in local optima problems. Compared to existing optimization algorithms like ALO, GA, ACO, PSO, GWO, MVO, and EHO, the proposed SCEHO-IPSO algorithm has the advantages of strong search ability and faster convergence speed, particularly in the context of task scheduling. The performance measures of cost, execution time, makespan, latency, and memory storage demonstrate the efficacy of the proposed SCEHO-IPSO algorithm over other algorithms, which is specifically stated in Tables 2-4. Additionally, related to other platforms, Kafka is extremely fast and massively scalable because it efficiently decouples the data streams, which results in lower latency, cost, execution time, makespan, and sufficient memory storage. The Kafka replicates and distributes partitions across several servers that protect against server failures.
## 2 Multiphysics-Coupled Analysis-Based Induction Hardening Process
### Multiphysics-Coupled Analysis  
Induction hardening involves two primary phases: the heating phase, during which a workpiece is heated using electricity applied to a coil, and the quenching phase, where the heated workpiece is rapidly cooled in water. During the heating phase, interactions such as electromagnetic and thermal effects are analyzed using distinct solvers within a unified simulation platform. Electromagnetic interactions, which involve the application of a current, are addressed using an electromagnetic solver. Thermal interactions, including heat transfer and thermal stress generated during heating and cooling, are analyzed using a thermal solver. Structural interactions, such as deformation resulting from temperature variations, are evaluated using a structural solver. Given that structural deformations caused by induction hardening require a comprehensive analysis of all these interactions, a multiphysics-coupled approach is indispensable.  
Moreover, because induction hardening involves temperature changes exceeding 1000 \({}^{\circ}\)C, it is crucial to incorporate material properties that dynamically adapt to temperature variations in real time to ensure accuracy. Traditional electromagnetic-thermal coupled analyses are typically conducted using a one-way coupling method, as depicted in Figure 2a. In this method, results from one analysis are sequentially passed to the subsequent analysis, limiting the ability to capture real-time changes in temperature-dependent properties. To address these limitations, this study employs a two-way coupling method, illustrated in Figure 2b. This method enables real-time data exchange among electromagnetic, thermal, and structural analyses, thereby accurately capturing the dynamic effects of temperature on material properties, such as electrical conductivity.  
Figure 2: Process flowchart of multiphysics coupling analysis through one-way and two-way loops. (**a**) One-way coupling approach (**b**) Two-way coupling approach.  
However, the two-way coupling method significantly increases computational resource requirements and complexity, particularly as the number of coupled interactions grows. This added complexity may also result in convergence issues that are proportional to the intricacy of the interactions. FE results were obtained using a NVIDIA DGX STATION (Future Automotive Intelligent Electronics Core Technology Center, Cheonan, Republic of Korea).  
To validate the accuracy of this multiphysics-coupled model, experimental comparisons were conducted. The simulated temperature distribution matched well with infrared camera measurements, as shown in Figure 3. This ensures reliable predictions for future optimization.
Photonics_**2014**, \(1\), 67-82; doi:10.3390/photonics1020067  
_photonics_  
ISSN **2304-6732**  
www.mdpi.com/journal/photonics  
**Transport Schemes for Fiber-Wireless Technology: Transmission Performance and Energy Efficiency**  
**[PERSON] \({}^{\star}\), [PERSON] and [PERSON]**  
Department of Electrical and Electronic Engineering, University of Melbourne, VIC 3010, Australia;  
E-Mails: [PERSON]ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (A.N.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +61-3-8344-4486; Fax: +61-3-8344-6678[PERSON]  
_Received: 23 December 2013; in revised form: 21 March 2014 / Accepted: 24 March 2014 / Published: 16 April 2014_  
**Abstract:** Fiber-wireless technology has been actively researched as a potential candidate for next generation broadband wireless signal distribution. Despite the popularity, this hybrid scheme has many technical challenges that impede the uptake and commercial deployment. One of the inherent issues is the transport of the wireless signals over a predominantly digital optical network in today's telecommunication infrastructure[PERSON] Many different approaches have been introduced and demonstrated with digitized RF transport of the wireless signals being the most compatible with the existing optical fiber networks. In this paper, we review our work in the area of digitized RF transport to address the inherent issues related to analog transport in the fiber-wireless links and compare the transmission performance and energy efficiency with the other transport strategies.  
**Keywords:** fiber-wireless; radio-over-fiber; digitized RF transport
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum MIMO Transmission and Quantum Channel
#### 3.4.1 Bit-Flip Noise  
Bit-flip noise introduces errors similar to classical bit errors by randomly flipping the state of a qubit from \(|0\rangle\) to \(|1\rangle\) or vice versa. The likelihood of such an error occurring is denoted by \(p_{x}\), and its impact on a quantum state \(\rho\) is described by Equation (14).  
\[\mathcal{N}_{X}(\rho)=p_{x}X\rho X^{\dagger}+(1-p_{x})\rho \tag{14}\]  
Here, \(X\) is the Pauli-X operator, responsible for flipping the qubit state.
## 5 Discussion and Future Scope
The action is based on learning a policy through behavior cloning [127] by mapping the multi-sensory information with the respective optimal beam based on the pre-trained dataset. The reward is then determined from the action to select the best scheduling policy for a low-latency data delivery. Hence, the goal of achieving the trade-off between beam alignment and data transmission in such scenarios for low-latency communication in AR/VR is made possible by IL. The algorithms as shown in Figure 7 will help to decide the priority-based transmission scheduling policy based on the user's behavior determined by multi-modal interactions. Multi-modal interactions, involving unimodal inputs such as visual-based, sensor-based, kinetic-based, and audio-based inputs, are widely analyzed based on human behavior for immersive communication in a virtual environment. The TI can be considered with 5G/5 GB technology for the transmission of multi-modal sensory information to increase the perceptual performance. However, the latency, transmission rate, and sampling rate are affected due to their asynchronous nature, which leads to the requirement of an effective multiplexing scheme to integrate these different modalities. The investigation of suitable AI/ML techniques to improve the timeliness of data delivery should be performed to deploy various multiplexing schemes and facilitate the transmission over the packet-switched networks in AR/VR wireless TI networks. Even the advancement of such ML techniques can be used to enable highly accurate predictions of channels, traffic, states, and other key performance indicators in an URLLC wireless virtual environment [128]. Figure 6: The block diagram depicts a futuristic approach that employs several advanced 5G/5 GB technologies for instant packet delivery of prioritized information in AR/VR to ensure ultra-low-latency transmission. The 5 GB beamforming technology is introduced by utilizing a holographic MIMO to produce guided beams to track the multi-modal sensory information of the user. Then, the tracking information will undergo a learning algorithm based on the scenario to decide the optimal route for the timely delivery of the buffer packets to reduce the latency.
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 2 Data  
### Raman Lidar  
The ARM RL operates at 355 nm and measures the elastic and Raman scattered light of nitrogen and water vapor in addition to two Raman rotational channels [40; 41; 42]. The retrieval algorithm used to retrieve cloud and aerosol properties from the RLs is feature detection and extinction retrieval (FEX) [38; 39]. RL-FEX applies thresholds to multiple quantities to identify features and distinguish those features into clouds and aerosols. The multiple quantities stem from the multiple channels and two fields of view included in the instrument design of the RL, which results in increased information to identify features and determine the extinction. RL-FEX uses the elastic and nitrogen channels of the RL, and the multiple quantities include the scattering ratios derived using the elastic and nitrogen channel signals from both the narrow (0.3 mrad) and wide (2 mrad) fields of view, and the scattering ratio derived using only the elastic channel and the total volume depolarization ratio. Features are classified based on the corresponding thermodynamic state of the atmosphere from radiosonde measurements in addition to their scattering properties (i.e., the lidar ratio, backscatter, and depolarization ratio). RL-FEX uses a best-estimate approach for extinction retrievals to minimize the uncertainty. The primary method utilizes directly-retrieved extinction profiles from the Raman method. Other methods include the transmission method, and the methods relying on inference from nearby directly-retrieved measurements or assuming the lidar ratio based on a climatological value. The best-estimate extinction approach results in the majority of ice cloud particulate extinction to be directly retrieved (i.e., no assumptions are necessary for the lidar ratio) [8; 39]. The RLs have been shown to detect high clouds well, which allows for the detection of optically very thin ice clouds [8; 37; 38; 39].  
The RLs used in this analysis are located at the Southern Great Plains (SGP) site in Lamont, Oklahoma (36.61\({}^{\circ}\) N, 97.49\({}^{\circ}\) W) and the Tropical Western Pacific (TWP) site in Darwin, Australia (12.43\({}^{\circ}\) S, 130.89\({}^{\circ}\) E). We only analyze the ice clouds containing randomly oriented ice particles. The time period extends from 1 August 2008 to 31 August 2016 at the SGP site and 15 December 2010 to 1 January 2015 at the TWP site. While the RL default averaging resolutions are temporally 10 min/vertically 30 m [8], we use 8 min/60 m and 24 min/60 m to match CALIPSO's resolutions for ice cloud extinction retrievals over SGP and TWP, respectively [8].
## 2 Materials and Methods
### Method Validation
#### 2.5.5 Stability  
Two levels of QC samples (LQC and HQC) under different processing and storage conditions were analyzed in six replicates to evaluate the stability of the method. Processing conditions and the short-term stability of ERDA were evaluated after storage for 10 h at room temperature (approximately 24 \({}^{\circ}\)C). The freeze-thaw stability was determined using QC plasma samples after three cycles of freeze at \(-\)80 \({}^{\circ}\)C and thaw at room temperature. The long-term stability was evaluated by analyzing QC plasma samples after storage at \(-\)80 \({}^{\circ}\)C for 4 weeks. The auto-sampler stability was evaluated by re-injecting the reconstituted QC samples after 24 h of its storage in autosampler. The acceptable limits for QC samples should be \(\pm\)15% for accuracy and \(\leq\)15% for precision.
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 3 The Extended COST-231 Hata Model  
In contrast to COST-231 Hata, the expanded version of the empirical model proposes a general path propagation equation to replace the signal intensity caused by path loss and to span a wider frequency range. The general free-space model [31] encompasses two terms: a logarithm part based on distance and another logarithm part based on frequency, as shown below:  
\[PL_{E}(d)=PL_{E}(d)=32.44+20\text{log}(f)+20\text{log}(d) \tag{1}\]  
Here, \(E\) is zero for a free space model according to the assumed code given in Table 1 for each type of environment \(E\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment Type \((E)\)** & **Code** \\ \hline Free Space & 0 \\ Urban & 1 \\ Suburban & 2 \\ Dense Urban & 3 \\ Rural & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Codes assumed for each environment.  
However, the propagation loss equations are derived in this study for different realistic environments, such as dense urban, urban, suburban, and rural environments, based on the propagation equation given in [32] as follows:  
\[\begin{array}{c}PL_{E}=PL_{E}(d)=46.3+33.9\text{log}(f)-13.82\text{log}(h_{b})-a (h_{m})\\ +(44.9-6.55\text{log}(h_{b}))\text{log}(d)+CF_{E}\end{array} \tag{2}\]  
**Notations.** We introduce \(CF_{E}\) as a correction factor for each environment type \(E\). \(PL_{E}\) is the path loss for environment type \(E\) in dB, as assumed in Table 1. \(f\) is the frequency in MHz. \(h_{b}\) is the height of the base station or transmitter in meters, \(m\). \(h_{m}\) is the height of the mobile or receiver in meters. \(d\) is the distance between the transmitter and receiver in kilometers, Km. \(a(h_{m})\) is the correction factor of the mobile antenna.  
Note that in (2), the propagation loss equation differs in each environment concerning two parameters, namely \(a(h_{m})\) and \(CF_{E}\).  
Therefore, the propagation loss equations [32] are derived using (2) to obtain the following:  
1. The propagation model for an urban environment is as follows: \[PL_{1}=46.3+33.9\text{log}(f)-13.82\text{log}(h_{b})-a(h_{m})+(44.9-6.55\text{ log}(h_{b}))\text{log}(d)\] (3) where \[a(h_{m})=(1.1\text{log}(f)-0.7)h_{m}-(1.56\text{log}(f)-0.8)\ \ \ \text{dB}\]
2. The propagation model for the suburban environment is as follows: \[PL_{2}=PL_{1}-2(\text{log}(f)-\text{log}\,28)^{2}-5.4\] (4)
3. The propagation model for the dense urban environment is as follows: \[\begin{array}{c}PL_{3}=46.3+33.9\text{log}(f)-13.82\text{log}(h_{b})-a(h_{m })\\ +(44.9-6.55\text{log}(h_{b}))\text{log}(d)+3\end{array}\] (5) where \[a(h_{m})=3.2(\text{log}(11.75\cdot h_{m}))^{2}-4.97\ \ \ \text{dB}\]
4. The propagation model for a rural environment is as follows: \[PL_{4}=PL_{1}-4.78(\text{log}(f))^{2}+18.33\text{log}(f)-40.94\] (6)
# Failure Mechanism of the Yizhuxiang Collapse under the Joint Effect of Freeze-Thaw and Mining
## 4 Deformation and Failure Mechanism
### Collapse Evolution Process  
The collapse deformation process can be divided into four stages by the deformation and failure characteristics.  
Fracture formation stage: during underground mining, cracks were formed in the rock mass under the joint effect of vibration loading and unloading. In addition, the continuous deformation of the pillars caused cracks in the rock mass. (Figure 10a).  
Rear edge fracture formation stage: As dolomite was eroded and softened by precipitation, the existing internal fractures gradually developed and became connected. In addition, mining promoted the gradual expansion of the fractures to form dominant fractures. The top of the rock mass was bent and toppled under the action of gravity, and the fractures on the rear edge of the rock mass gradually expanded to the deeper part (Figure 10b).  
Collapse formation stage: due to the deformation of safety pillars and the blasting of the perilous rock, the internal cracks became connected, and the rock mass was bent and toppled, and the master fractures on the rear edge gradually expanded. The precipitation infiltrated along the fractures, generating hydrostatic pressure, reducing the mechanical strength between the rock mass and the structural plane, increasing the downward and outward movement force of the unstable rock mass, and finally leading to collapse (Figure 10c).  
Fracture penetration stage: Compared with previous years, the weather in the winter of 2021 changed greatly, with an increased snowfall and a low temperature, leading to more significant freezing and thawing, which caused the destruction of the rock mass structure, the gradual penetration and expansion of the fractures, and the accelerated deformation of the rock mass, and finally resulted in the complete penetration of the fractures on the rear edge of Yizhuxiang and the failure of the rock mass (Figure 10d).  
Figure 9: Pillar cracking and failure evolution process: (**a**) Pillar corner stress concentration, resulting in peeling; (**b**) Pillar surface peeling, internal pressure crack; (**c**) Pillar surface continued to peel off, producing oblique shear stripes; (**d**) Pillars failed, leaving little residual strength.
## 3 Results and Discussion
### Ball Rebound Test
#### 3.3.2 Test Results  
The ball rebound test was repeated 3 times for each specimen type, with the average and standard deviation of the test results shown in Figure 14. For materials A and B, which were printed with only a single composite material, the rebound height ratios were 36.59% and 4.05%, respectively, which showed an approximate 9-fold difference due to the differences in the mechanical properties of the base resin. In particular, in this test, even for the half-and-half structure, the properties of the surface in contact with the steel ball changed significantly depending on the printing order, resulting in a difference of more than 3 times at the rebound height ratio. Although the material ratio and structure were the same, it could be seen that the case where the steel ball contacted hard material A had significantly higher resilience than the case where the steel ball contacted soft material B. Through this, it was found that the half-and-half structure behaved as if two plates with different properties were simply joined together, as in the previous 3-point bending test. On the other hand, the laminar composite structure showed similar properties to a new material through the plate-reinforced composite effect, as evidenced by having the same rebound height ratio regardless of the direction of the steel ball impact.  
Figure 13: Specimens in the ball rebound test.
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 5 Conclusions and Perspectives  
Waist-worn accelerometers can classify activities below a certain intensity threshold (e.g., count) as static behavior, while it is difficult to separate different body postures (e.g., sitting-standing) from each other. As standing may confer some health benefits compared with sitting and laying, it needs to be classified and estimated.  
The proposed Poincare plot-based angle variance analysis approach can be a substitute to the Actigraph count-based method (which calculates activity intensity) for both (1) the single waist-worn HAR dataset and (2) the chest-worn MHealth (walk-run) dataset with a tri-axial accelerometer.  
In this paper, we showed that our proposed method has good-to-excellent performance in identifying different body postures of sedentary activities as well as moderate (walk) to vigorous (run) activities, which may be deemed less computationally complex and cheaper to implement in wearable devices for ambulatory activity classification platform.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
###### Abstract  
Bisphenols are well-known endocrine disruptors that can easily migrate from plastic and can containers to food. Due to the complicated matrix and ultra-low concentrations of bisphenols in food, samples require extensive preparation before instrumental analysis. In this paper, an environmental sensitive microgel was employed as a sorbent for the preconcentration of four bisphenols, bisphenol A (BPA), bisphenol B (BPB), bisphenol E (BPE) and bisphenol F (BPF), from liquid food samples. Liquid chromatography with fluorescence detection (LC-FLD) was used for the quantification of bisphenols. By applying microgel solid-phase extraction procedure, the limits of detections achieved in liquid food samples can be lowered to 0.9 \(\mu\)g-L\({}^{-1}\) for BPF and BPA, 2.3 \(\mu\)g-L\({}^{-1}\) for BPF and 2.9 \(\mu\)g-L\({}^{-1}\) for BPF. Only 5 mg of microgel was sufficient to achieve good recoveries (70.5-109%) with precision (RSD 0.21-501%, \(n=3\)) for different analyzed liquid food samples spiked at concentration levels of 50 \(\mu\)g-L\({}^{-1}\). In five out of twelve of the analyzed samples (pineapple, mandarin, peach, mushroom and picks), they were contaminated with BPA, and the determined concentration was in the range of 6.2-22 \(\mu\)g-L\({}^{-1}\); however, these results are below the specific migration limit (SML) set for BPA (50 \(\mu\)g-kg\({}^{-1}\)).  
microgel; bisphenols; solid-phase extraction; chromatography; food samples +
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