esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance of Polymer Materials for Low-Temperature Applications
## 2 Materials and Methods  
### Materials  
In the present study, three different polymer materials, namely, polyethylene (PE), acrylonitrile butadiene styrene (ABS), and polyvinylidene fluoride (PVDF), were selected among a variety of polymer materials adopted in piping systems in shipping and offshore industries. PE--a type of polyolefin--comprises hydrocarbons as the main constituent and is a nonpolar material because it does not dissolve in common solvents, showing negligible swelling. Therefore, PE is widely adopted as a plastic piping material in water and natural gas distribution systems because it has a very high impact strength at low temperatures, low erosion, and low decay, even under seawater conditions [20; 21]. High-molecular-weight PE grades of medium-to-high density have become state of the art for industrial piping systems. The grades are classified according to their internal pressure resistance in PE80 (MRS 80 MPa) and PE100 (MRS 100 MPa). In the present study, PE100 was selected for tests because the material is employed in plastic piping systems in the maritime industry and has superior performances, including durability and resistance to external shocks, compared with PE80. ABS can be adopted in the application by varying the composition of its components: acrylonitrile, polybutadiene, and styrene. ABS is thermoplastic and composed of terpolymer-containing acrylonitrile, polybutadiene, and styrene. Acrylonitrile provides strength to the material and gives ABS an improved chemical resistance relative to polystyrene, while the styrene component provides both improved processability and moldability. In addition, as polybutadiene is a synthetic rubber, its properties include flexibility and impact resistance. Further, the chemically bonded polybutadiene rubber particles give the material its toughness and impact strength, even at low temperatures [22]. Owing to these properties, ABS has been adopted in applications in various industries such as electronics and automobile industries [23; 24]. In the present study, ABS was selected for the test because this material is being employed in the piping systems in shipping industries, e.g., cooling water, vacuum, water treatment, and black/gray water, which denotes one of the most superior capabilities and applications at low temperatures. PVDF is a semicrystalline thermoplastic and belongs to the class of fluorinated polymers. It is one of the best chemically resistant polymers [25]. Moreover, among fluorinated polymers, PVDF has outstanding properties such as high mechanical strength, chemical resistance, and machinability because of its high crystallinity [26]. Thus, PVDF has been extensively applied in almost every industrial field, including electronics, chemical, and biotechnology fields [27; 28; 29]. In this study, we performed tensile tests on PVDF to confirm its versatility in the low-temperature range.
###### Abstract  
Autonomous underwater vehicles (AUVs) as an efficient underwater exploration means have been used to perform various marine missions. However, limited by the technologies of underwater acoustic communications and intelligent autonomy, the most current and advanced AUVs only perform a limited number of tasks in the small-scale area and the known underwater environment. Therefore, in this paper, a one path planning model was proposed combining the global path planning and the local path planning for the large-scale complex marine environment. More specifically, the B-spline curve was used to represent the smooth path for the requirement of kinematic constraints of AUVs. After considering the various constraints, such as the energy/time consumption, the turning radius limitation, the marine environment, and the ocean current, the path planning was abstractly modeled as a multi-objective optimization model with the time cost, the curvature cost, the map cost, and the ocean current cost. The swarm hyper-heuristic algorithm (SHH) with the online learning ability was proposed to solve this model with real-time performance and stability. The results showed that the proposed online learning SHH algorithm had obvious advantages in terms of time efficiency, stability, and optimal performance compared with the results of two traditional heuristic algorithms, both particle swarm optimization (PSO) and firefly algorithm (FFA). The time efficiency of the online learning SHH algorithm improved at least 20% compared with PSO and FFA.  
Keywords:AUV; path planning; swarm intelligence; swarm hyper-heuristic algorithm; online learning +
Footnote †: journal: Applied
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
## 2 Results and Discussion
### Bronate Degradation by UV/BiOCl-HAP\({}_{\rm SM}\) in the Presence of Formate  
The merit of BiOCl-HAP\({}_{\rm SM}\) is its ability to integrate the magnetic performance of HAP\({}_{\rm SM}\) and the outstanding photocatalytic capability of BiOCl. Given the affirmation of the mixture's magnetic characteristic (Figures 2 and 3), the photocatalytic efficacy of BiOCl-HAP\({}_{\rm SM}\) was further tested. Formic acid/ formate is a common precursor of reductive radicals in photoreductive processes [34; 35]. As such,  
Figure 4: UV-visible diffuse reflectance absorption spectra of BiOCl-HAP\({}_{\rm SM}\) microflowers.  
Figure 3: Magnetization (M-H) curves for BiOCl-HAP\({}_{\rm SM}\).  
Figure 2: BiOCl-HAP\({}_{\rm SM}\) dispersion in water (**a**) without or (**b**) with a magnet.  
formic acid/formate was selected herein to preliminarily evaluate the degradation efficiency of BrO\({}_{3}^{-}\) in the SOCA-mediated UV/BiOCl-HAP\({}_{\text{SM}}\) process. As shown in Figure 5, 79.0% BrO\({}_{3}^{-}\) was degraded by UV/BiOCl-HAP\({}_{\text{SM}}\) within 15 min of reaction time. Such performance is considered excellent, given that a UV lamp power of 10 W was used and the efficiency was about 35% as the manufacturer claims. That is, BiOCl-HAP\({}_{\text{SM}}\) possesses a good photocatalytic capability. Direct photolysis (UV alone, 16.7%; UV + Formate, 19.3%), adsorption (BiOCl-HAP\({}_{\text{SM}}\) + formate in dark, 1%), or reduction by formate (0.7%) caused slow BrO\({}_{3}^{-}\) degradation (Figure 5). Hence, reductive radicals (e.g., CO\({}_{2}^{\bullet}\)(\(-\)2.0 eV), H - (\(-\)2.3 eV), and O\({}_{2}^{\bullet-}\)(\(-\)0.28 eV)) and the conducted electron (e\({}_{\text{CB}}^{-}\)) were speculated to play important roles [23; 36]. Notably, high initial bromate concentration ([BrO\({}_{3}^{-}\)]\({}_{0}\) = 6.6 \(\upmu\)M (1.0 mg\(\cdot\)L\({}^{-1}\))) was used considering the inexpediene caused by the ultrafast degradation for low concentration (100% degradation of 10 \(\upmu\)g\(\cdot\)L\({}^{-1}\) bromate within 3 min reaction time; Figure 6). Such concentration was also adopted to maintain the consistency
## 4 Discussion
### Cochlear Implant Users and Normal-Hearing Subjects  
Despite the fact that the groups of CI users were heterogeneous, the general recognition behaviour was the same for CI users and NH subjects, whatever implant was used. With NH listeners, for a SNR of +9 dB, the 100% recognition level was reached.  
With CI users, the plateau was not always reached with a SNR of +18 dB; additionally, it was below the 100% measured with NH subjects. For a +9 dB SNR (maximum tested in simulation), the CI users' performances were below the scores observed with NH subjects; the mean scores with CI users ranged from 50 to 75%; this is consistent with previous studies [36].  
The same results were also seen with the \(x_{50\%}\) (sigmoid fitting), which was better in NH subjects than with CI users.  
With the CI users, an inversion occurred between +12 and +15 dB SNR and it was also seen for +18 dB SNR; performances observed with a CP strategy were higher than the performances with an FC strategy.  
The reliability of the data obtained from CI users is a real issue. Is there a link between the plateau and the \(x_{50\%}\)? The scatter point diagram of the four CI user populations is shown in Figure 5. It shows that, for each manufacturer, all possibilities exist, either with a good plateau and a poor \(x_{50\%}\) or vice versa. All intermediate situations were found and the correlation coefficients were not significantly different from zero.  
As the very goal is to provide an opportunity for every CI user to hear in everyday life [28], the work ahead is important. The efficiency of a CI is affected by many factors such as the recognition and linguistic skills, the degrees of nerve survival and the technical choices that are made when fitting the device and the variations are wide with every subject.
## 1 Introduction  
Vehicle production in Mexico has been increasing in recent years [1, 2]. As well as the number of imported vehicles, generating one of the main tasks to be solved by logistics companies: the transport of vehicles to dealerships. Currently, there are several commercial offers that provide a solution to route planning and fleet management. However, the cost of these applications is significantly high because they depend on the number of auto-carriers to route, making the acquisition of application difficult to afford.  
This paper presents a vehicle-routing problem with time windows (VRPTW) in the real-world proposed by a logistics company in Mexico. In the problem, a heterogeneous fleet of auto-carriers departs from the new car storage yard (NCSY), delivers and unloads vehicles in the dealerships within predefined time windows, and finishes at the NCSY as shown in Figure 1. The objective of this research is to design and develop a logistic software to solve it. Considering the restrictions on the transport of vehicles imposed by Mexican traffic regulations [3], capacity, and allocation constraints, optimal performance with delivery time windows and proper planning of transportation routes. The software has a two-phase heuristic algorithm: in the first phase, the heuristic [4] is implemented to design the auto-carrier routes and an algorithm is proposed for the allocation of the vehicles in the auto-carriers. For experimentation, we used a real database of approximately 4000 vehicles, more than 600 auto-carriers, and 44 different dealerships as a destination, obtaining results with the proposed algorithm in a reasonable time.  
The structure of this paper is as follows. A review the relevant literature is provided in Section 2. An overview of the importance of research and the problem are shown in Section 3. The VRPTW is defined and mathematical formulations are presented in Section 4. The proposed solution and the developed methodology are described in Section 5. The experimental results of the algorithm are presented and analyzed with real-world instances in Section 6. Finally, in Section 7 conclusions and future research work are given.
## 5 Accuracy and Uncertainty of COSMIC-2 Refractivity, Temperature, and Water Vapor Retrievals
### General Quality of COSMIC-2 Refractivity and Water Vapor Profiles in the Lower Troposphere  
We have more than 3000 COSMIC-2 -RS41 pairs within the matching criteria from June to October 2019. To see how COSMIC-2 data quality varies with different tracking systems (GPS with the mean L1 SNR of 1250 \(\forall\)\(v\)/ and GLONASS with the mean L1 SNR of 1100 \(\forall\)\(v\)), we separate the comparison results for the COSMIC-2/GPS- RS41 pairs (Figure 8a) and COSMIC-2/GLONASS-RS41 pairs (Figure 8b), respectively. Figure 8 depicts that the mean difference and uncertainty of the COSMIC-2/GPS-RS41 pairs are very close to those of the COSMIC-2/GLONASS-RS41 pairs. The mean difference for the former from the surface to 30 km altitude is equal to \(-0.32\%\) with a std of 1.83%, where those for the latter are \(-0.34\%\) with a std of 1.84%. These results are also very compatible with those from the COSMIC-RS92 refractivity comparison [46]. COSMIC refractivity is also biased lower in the lower troposphere compared to those from RS92.  
The negative refractivity biases relative to those of RS41 are from bending angle biases owing to considerable tracking uncertainty from COSMIC-2, mostly below the SR. Similar comparisons from for COSMIC2 (GPS)-ERA5 bending angle and COSMIC2 (GLONASS)-ERA5 bending angle, which covers both lands and oceans, also show that COSMIC-2 data quality does not change much when receiving signals emitted either from GPS or GLONASS [47]. Hereafter, we will not further separate the COSMIC-2 data with different emitters.  
The TGRS includes a high-gain beamforming RO antenna, which can achieve the highest SNR of RO measurements to date (\(>\)2500 \(\forall\)\(v\)/ in a 1 Hz band). On 16 August 2019 (day of year-DOY 228), all measurements were collected with beamforming on mode. Before that, beamforming tests were conducted on 28-29 June (DOY 209-210) and 1-8 August (DOY 213-219). The default configuration was in beamforming off mode before August 16 except for the beamforming on period. This allows us to test how the high SNR RO measurements would affect the retrieval uncertainty.  
Here we separate the RS41 and COSMIC-2 pairs with beamforming and non-beamforming period. Figure 9 depicts that the beamforming COSMIC-2 water vapor profiles (Figure 9b) are slightly better than those from non-beamforming profiles (Figure 9a).  
Figure 8: The fractional refractivity comparisons for (**a**) the COSMIC-2/GPS-RS41 pairs, and (**b**) COSMIC-2/GLONASS–RS41 pairs. The mean differences are in the red line, and the standard deviations of the mean difference are in the green line. The sample numbers are in the blue line. The standard errors of the means are in the horizontal black lines superimposed on the mean line. The mean and std values are computed from surface to 30 km altitude.
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 2 Materials and Methods
### In Vitro Genotoxicity Study
#### 2.3.2 Chromosomal Aberration (CA) Test  
The CA test is an in vitro genotoxicity test that is conducted using a mammalian cell line [25]. Briefly, the Chinese hamster ovary K1 (CHO-K1) cell line (BCRC 60006, Culture Collection and Research Center, Food Industry Research and Development Institution, Hsinchu, Taiwan) was grown in Ham's F-12 medium supplemented with 10% fetal bovine serum, penicillin (100 unit/mL), and streptomycin (100 \(\upmu\)g/mL) at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) humidified incubator. CHO-K1 cells were seeded in 25 cm\({}^{2}\) flasks at a density of 2.5 \(\times\) 105 cells/mL culture medium and incubated for 24 h. Then, 30% lethal concentration (LC\({}_{30}\)) doses of the test articles, which were determined by the cytotoxicity test (data not shown), were added and incubated for 24 h with or without S9. During the last 6 h of culturing, cell cycles were stopped after exposure with 10 \(\upmu\)L colcemid (10 \(\upmu\)g/mL) (Sigma, Darmstadt CAS No. 64-86-8). The CHO-K1 cells were detached with trypsin (0.05%)-EDTA (0.53 mM) (Gibco, Waltham, MA, USA) and fixed by fixation solution (methanol: acetate = 3:1, ALPS, CSC, Taipei Taiwan) after being swollen by 0.54% KCl (Merck, Darmstadt, Germany). The fixed cells were dropped on slides, stained by Diff-Quik stain (Sysmex, Hyogo, Japan) and observed under light microscopy. A total number of 100 metaphases of cells were counted under oil-immersed microscopy for each dosage. The number of cells with damaged chromosomes was recorded, from which the rate of mutation was calculated. Aberration rate (%) = (number of cells with damage chromosomes/100) \(\times\) 100.  
The results of the CA test studies were analyzed by Student's \(t\)-test and Microsoft Excel to compare the data between groups. A significant difference in the aberration rate was determined if the difference between the negative control and treated groups was more than twofold at \(p<0.05\).
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 1 Introduction
\begin{table}
\begin{tabular}{c l} \hline \hline
**Acronym** & **Explanation** \\ \hline MIMO-OFDM &
\begin{tabular}{l} Multi-input, multi-output wireless orthogonal frequency division \\ multiplexing \\ \end{tabular} \\ \hline ML & Machine learning \\ \hline mMIMO & Massive multiple-input multiple-output \\ \hline MMSE-OSIC & Minimum mean squared error ordered successive elimination constellation \\ \hline mMTC & Massive machine-type communication \\ \hline mRSU & Mobile roadside unit \\ \hline NRZ & Non-return-to-zero \\ \hline NOMA & Non-orthogonal multiple access \\ \hline NTN & Non-terrestrial networks \\ \hline OOK & On-off keying \\ \hline OPL & Optical path loss \\ \hline PA & Process automation \\ \hline PBAS & Pilot-based access point selection \\ \hline RF & Radio frequency \\ \hline RL & Reinforcement learning \\ \hline RSMA & Rate-splitting multiple access \\ \hline RSU & Roadside unit \\ \hline RTBC & Real-time broadband communication \\ \hline SDMA & Spatial division multiple access \\ \hline SDN & Software-defined network \\ \hline SHA & Smart home automation \\ \hline SNR & Signal-to-noise ratio \\ \hline THz & Terahertz (communication) \\ \hline UAVs & Unmanned aerial vehicles \\ \hline UCBC & Uplink centric broadband communication \\ \hline uHDD & Ultra-high data density \\ \hline uHSLLC & Ultra-reliable low latency communications \\ \hline uMUB & Ubiquitous mobile ultra-broadband \\ \hline URLLC & Ultra-reliable low-latency communications \\ \hline V2X & Vehicle-to-everything \\ \hline VANET & Vehicular ad hoc network \\ \hline VLC & Visible light communication \\ \hline VLEO & Very low earth orbit \\ \hline VR & Virtual reality \\ \hline WSN & Wireless sensor network \\ \hline XL-MIMO & Extra-large multiple-input multiple-output \\ \hline XR & Extended reality \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._within the Industry 4.0 and Industry 5.0 paradigms, followed by a discussion of the main limitations of the research to date and key directions for further research and conclusions.
## 3 Dynamics Model of the Helicopter  
The body coordinate frame as well as the inertial coordinate frame of the helicopter are shown in Figure 2. \(P=[x,y,z]^{T}\) is the position vector of the helicopter in the inertial coordinate system. \(V=[u_{a},v_{a},w_{a}]^{T}\) is the velocity vector of the helicopter in the body coordinate frame.  
Figure 1: Control block diagram of the UAM system.  
According to the Newton-Euler method, the flight dynamics equation of the helicopter named T-REX 300X can be established as follows [14]:  
\[m\dot{V}=F_{b}+F_{bg} \tag{1}\]  
\[\dot{\omega}=J^{-1}[M_{b}-\omega\times(J\omega)] \tag{2}\]  
where the helicopter inertia tensor matrix is \(J=diag\big{\{}I_{x},I_{y},I_{z}\big{\}}\) and \(\omega=(p,q,r)^{T}\) is the angular velocity vector in the body coordinate frame. \(F_{b}\) and \(M_{b}\) represent the aerodynamic force vector and the aerodynamic moment vector, respectively.  
\[F_{b}=\begin{pmatrix}X_{mr}\\ Y_{mr}+Y_{tr}\\ Z_{mr}\end{pmatrix} \tag{3}\]  
\[M_{b}=\begin{pmatrix}L_{mr}+L_{tr}\\ M_{mr}\\ N_{mr}+N_{tr}\end{pmatrix} \tag{4}\]  
where \(X_{mr},\ Y_{mr},\ Z_{mr},\ L_{mr},\ M_{mr},\ N_{mr}\) represent the forces and the torques of the main rotor and \(Y_{tr},\ L_{tr},\ N_{tr}\) represent the forces and the torques of the tail rotor, respectively.  
According to [15], when the helicopter is in a low-speed flight state or in a hovering state, the resistance of the fuselage as well as the torque generated by the vertical tail fin are ignored. The projection vector of the gravity is defined in the body coordinate frame as  
\[F_{bg}=\begin{pmatrix}-mg\sin\theta\\ mg\sin\varphi\cos\theta\\ mg\cos\varphi\cos\theta\end{pmatrix} \tag{5}\]
# Computation of Compact Distributions of Discrete Elements
## 3 Methods of This Paper
### Parametric Control of Element Distribution
#### 3.3.1 Changing of Element Sizes  
Our research results indicate that the distribution of the initial point set has a significant effect on the synthesis results. A region with a more compact distribution of initial points usually eventually results in a smaller Voronoi region. Therefore, we propose to control the sizes of the Voronoi polygons by adjusting the parameters while generating the initial points to control the density. However, the experimental results show that if we only use the method described above to control the size, there will be situations in which the size gradually varies in the junctions of large and small polygons. This will create irregular polygons at the boundary (as shown in Figure 11a), which is not desirable. For this reason, in addition to controlling the density of the initial points, we utilize the method of changing the size of the geometry that was adopted during the synthesis process for better results.  
Let \(\alpha\) be the slope of the geometry used during the synthesis process, and take the circular cone in Figure 10 as an example. While generating the Voronoi region, use geometry with a smaller size (geometry with a larger \(\alpha\)) when a smaller region needs to be obtained and geometry with a larger size (geometry with a smaller \(\alpha\)) in other regions. Together with the initial point distribution in this region, better results can be achieved by changing the sizes of the elements (as shown in Figure 11b).
## 3 Results and Discussion
### CBR Test--Phases 1 to 3, Repeated Loading
As mentioned in relation to the recoverable displacement, regardless of the PNF considered, the equivalent modulus had also not yet stabilised at the end of phase 1 (i.e., after 35 load cycles), showing once again that the criterion used to define the number of cycles may need to be adjusted. For the unreinforced soil (PNF = 0%), at the end of phase 1, Eequ was 12.65 (\(\pm\)2.12)MPa (95% confidence interval in brackets), which is within the range of the [PERSON]'s modulus values  
Figure 7: Evolution of Eequ with the number of loading cycles for specimens of soil reinforced with different PNF: 0%, unreinforced; and with 1% and 2%, reinforced (average of three specimens). reported by [PERSON] [24] for silts (2 to 20 MPa). The equivalent modulus at the end of phase 1 of the reinforced soil for both PNF of 1% and 2% increased relative to that of the unreinforced soil, which confirms the beneficial effect of the natural fibre reinforcement. This parameter also indicates that the inclusion of pine needles led to a stiffer response for all reinforced specimens. The higher equivalent modulus was observed for soil reinforced with PNF = 1%. After 35 cycles, i.e., at the end of phase 1, and relative to the unreinforced soil (PNF = 0%), the equivalent modulus increased by 58% for PNF = 1% and 22% for PNF = 2%. The immediate effect of changing the magnitude of the load (phases 2 and 3) was to increase the displacement (both permanent and recoverable) suffered by the specimens. This effect is small but sufficient to reduce the equivalent modulus during the first 2-3 cycles of each phase. After that, the equivalent modulus rises again with a growth rate similar to that of the previous phase. Increasing the load does not seem to significantly modify the evolution of the equivalent modulus. However, it is not possible to guarantee that the resilient response of the material was achieved after 35 load cycles of phase. [PERSON] et al. [2] mentioned that the slope of the equivalent modulus-plunger stress relation can be used as an indication to investigate the failure of the material. According to the authors, if the slope decreases, the soil is approaching its maximum strength. The relation between E\({}_{\text{equ}}\) and maximum force for each of the phases 1 to 3 is presented and analysed in Figure 8 (for all PNF). The slope of the trendline for each phase is reduced: 0.017 for phase 1, 0.015 for phase 2, and 0.010 for phase 3. This reduction indicates that the soil matrix is reaching its maximum strength, regardless of the fibre reinforcement.