esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin
## 2 Materials and Methods
### The Motion Model of Caudal Fin  
The self-propulsion model has the difficulties in conducting corresponding experimental investigation. Most of the experimental research usually gives the incoming flow velocity or the towing velocity of fin. In other words, the motion of the fin is imposed. Here, we adopt the imposed motion model. The fin is towed forward at constant speed \(U\) and is allowed to move in two degrees of freedom:  
1. motion transversely to the direction of towing, or heave \(y(t)\); and
2. angular motion about a spanwise axis, or pitch \(\theta(t)\).  
The heave motion of the fin can be described by the equation:  
\[y(t)=h\cos(2\pi ft) \tag{1}\]  
where \(f\) is the flapping frequency, \(t\) is the time, and \(h\) is amplitude of the heave motion. In addition, the pitching angle \(\theta\) is given by:  
\[\theta(t)=\alpha(t)+\theta_{m}(t) \tag{2}\]  
where  
\[\theta_{m}(t)=\arctan\left(\frac{\dot{y}(t)}{U}\right) \tag{3}\]  
The angle \(\alpha(t)\) is the angle of attack and given by:  
\[\alpha(t)=\alpha_{max}\sin(2\pi ft) \tag{4}\]  
where \(\alpha_{max}\) is amplitude of the angle of attack. It can be seen that the angle of fin motion \(\theta_{m}\) is dependent on the velocity of the fin in the global reference frame. The definitions of the different angles are shown in Figure 2.  
The chord Reynolds number and the Strouhal number \(St\) is defined by:  
\[Re=\frac{UL}{\
u} \tag{5}\]  
\[St=\frac{2 fh}{U} \tag{6}\]  
where \(\
u\) is the fluid kinematic viscosity. As is shown in [8], the forces and torque are converted to dimensionless forms and should be described by:  
\[C_{x}(t)=\frac{F_{x}(t)}{0.5\
u L^{2}S} \tag{7}\]  
Figure 2: Definition of variables.  
\[C_{y}(t)=\frac{F_{y}(t)}{0.5\rho U^{2}S} \tag{8}\]  
\[C_{m}(t)=\frac{M_{\Theta}(t)}{0.5\rho U^{2}SL} \tag{9}\]  
Here, \(\rho\) is the density of the fluid, \(F_{y}\) is the transverse force, \(F_{x}\) is the thrust force, \(M_{\Theta}\) is the torque, and \(S\) is the projected area of the fin (one-sided). As is shown in [8], the propulsive efficiency \(\eta\) is defined as the ratio of output power \(P_{o}\) to input power \(P_{e}\) which can be written as:  
\[P_{o}(t)=\frac{1}{T}\int_{0}^{T}F_{x}(t)Udt \tag{10}\]  
\[P_{e}(t)=\frac{1}{T}\int_{0}^{T}\left[F_{y}(t)\dot{y}(t)+M_{z}(t)\dot{\Theta}( t)\right]dt \tag{11}\]  
\[\eta=\frac{P_{o}}{P_{e}} \tag{12}\]  
where \(T\) is the period of the motion of the fin.
## 1 _Applied sciences_
### _Con-Thermal Technologies for Food Processing_
The physical properties of the juice, including pH, acidity, and Brix, remained largely unaffected by the treatments, except for colour, which displayed changes [7]. These findings suggest that a UV-C dose of 15.13 mJ/cm\({}^{2}\), when applied using a continuous system, can serve as a viable alternative for processing red prickly pear juice at a pH of 3.6. This approach effectively extends the shelf life of the juice while preserving its key quality attributes [7]. Lastly, [PERSON] et al. reached significant conclusions regarding the effective reduction of polyphenol oxidase or polyphenodoxidase (PPO) activity, an enzyme responsible for enzymatic browning, and the production of high-quality fruit products [3]. In their review entitled \"Thermal, High Pressure, and Ultrasound Inactivation of Various Fruit Cultivars' Polyphenol Oxidase: Kinetic Inactivation Models and Estimation of Treatment Energy Requirement\", they found that the resistance to different treatments varied across fruit types and cultivars. When compared to thermal treatment alone, the combined use of high pressure and ultrasound with heat proved more effective in PPO inactivation in fruits. Heat-assisted high-pressure processing predominantly followed a first-order kinetic behavior, although some fruit cultivars exhibited biphasic inactivation patterns [3]. Estimating the specific energy requirements for achieving over 80% PPO inactivation highlighted the dependence on equipment setup, process scale, and design. The thermal process alone indicated energy requirements ranging from 87 to 255 kJ/kg, while high-pressure processing indicated a range of 139 to 269 kJ/kg. Ultrasound processing, on the other hand, showed a wider energy range of 780 to 10,814 kJ/kg [3]. These findings emphasize the efficacy of combining high pressure and ultrasound with thermal treatment for reducing PPO activity and producing high-quality fruit products. However, it is crucial to consider the specific fruit cultivars and optimize the energy requirements based on the processing equipment, scale, and design.
## 3 Orphan and Abandoned Wells Distribution in USA
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Number of Wells Investigated** & **Emission Rate 1.6 g hr\({}^{-1}\)** & **References** \\ \hline Colorado &
\begin{tabular}{c} 128 plugged and 206 unplugged \\ abandoned wells \\ \end{tabular} & 0 and 586 & [19] \\ \hline Pennsylvania & Unplugged 53 and 35 plugged & 21.7 and 14.8 & [20] \\ \hline West Virginia & Unplugged 147 and 112 plugged & 3.42 and 0.10 & [21] \\ \hline Several areas in The USA Wyoming, Colorado, Utah, and Ohio & Unplugged 19 and 119 plugged & 10.02 and 0.002 & [17] \\ \hline Pennsylvania & 31 orphan wells, 9 of them buried & 3.75 to 174.2 & [22] \\ \hline California & 121 wells: 97 abandoned, 17 idle, and 6 active & 0.286, 35.4, and 189.7 & [23] \\ \hline Oklahoma & Abandoned wells (n = 179) & 4.0 \(\pm\) 17.9 and, 2.7 \(\pm\) 12.3 & [24] \\ \hline Entire United States & Unplugged 19 and 119 plugged & 240 and 0.462 & \\ \hline U.S. & Unplugged 239 and 276 plugged & 11 and 1.6 & \\ \hline Oklahoma & Unplugged 53 and 276 plugged & 17 and 1.6 & \\ \hline Pennsylvania & Unplugged 48 and 53 Plugged & 19 and 9.6 & \\ \hline Colorado & Unplugged 293 and 276 plugged & 11 and 1.6 & \\ \hline West Virginia & Unplugged 147 and 112 plugged & 3.2 and 0.1 & \\ \hline East & Unplugged 228 and 162 plugged & 9.6 and 2.8 & \\ \hline West & Unplugged 65 and 114 plugged & 15 and 1.8 \(\times\) 10\({}^{-3}\) & \\ \hline North & Unplugged 257 and 276 plugged & 8.8 and 1.6 & \\ \hline South & Unplugged 53 and 276 plugged & 17 and 1.6 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methane emission from plugged and unplugged orphan wells. The scatter plot in Figure 5 displays the depth of wells drilled over time, from the mid-1800s to the present. Each point represents a well, with its position on the vertical axis showing the depth in meters, and the color gradient indicating the depth in feet. The plot reveals a clear trend of increasing well depths over time, particularly from the 1950s onwards,  
Figure 3: Distribution of the abandoned and orphan wells across the United States with each well represented by a dot. The color of each dot represents the depth of the well, as indicated by the color bar on the right side of the plot, with depths ranging from approximately 2500 ft to 20,000 ft. Darker colors (toward blue) represent deeper wells, while lighter colors (toward yellow-green) indicate shallower wells [18]. Figure 2: Documented orphan wells in the USA (225,287 wells). The data were collected from each state commission’s website. reflecting advancements in drilling technology and the pursuit of deeper hydrocarbon resources. The concentration of deeper wells in more recent decades, as seen in the upper part of the plot, highlights the industry's evolution towards more challenging drilling operations. This trend underscores the increasing complexity and technical demands of well drilling over time. Figure 4: The potential CCS project in the State of Louisiana, the data are from [26]. Figure 5: The abandoned and orphan wells’ depth and their completion time [18].
## 1 Introduction
The gradient-based techniques for plume tracing could be ineffective in a turbulent environment given the too coarse sampling performed by the UAV, relative to the spatial and temporal rates of change in the environment. In addition, some proposed strategies are based on the behavior used by insects or bacteria for foraging or pheromone tracing [21; 22], hence the naming of these algorithms as bio-inspired [23; 24]. However for biological entities (e.g., moths), the strategy of plume tracing may not be based on olfactory sensor alone, but could be aided by vision, sight, auditory, and/or tactile cues. This work proposes a metaheuristic algorithm for pollutant source localization using a UAV that combines the gradient-based strategy and a probabilistic method. The gradient-based component is used to trace the plume of the pollutant, while the probabilistic component helps to find the pollutant plume, concentrating the efforts on searching areas with a higher probability of finding the source location. The gradient-based component is a metaheuristic method that has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component is based on the work presented in Reference [25], which uses the Bayesian method to build and update a probability map of the pollutant source location, with each new sensor information available while the UAV navigates in the environment. To test the proposed algorithm, the polluted environment was simulated using a model of particle distribution including turbulence for wind effects. The experimental tests were performed using a real quadrotor which added constraints related to movements and pollutant sensor measurements. The results showed that the UAV was able to find the pollutant plume and trace it towards the source location, even with high variation of the environment and movement constraints of the UAV. The remaining content of this work is organized as follows. Section 2 describes the pollutant distribution model that is used in the comparative analysis of the metaheuristic algorithms to trace pollutant concentrations and to test the proposed solution of pollutant source localization. Section 3 presents the approach proposed to solve the pollutant source localization. Section 4 presents the comparative analysis of classical metaheuristics to trace the plume of pollutant. Section 5 presents the design of the algorithm to localize the pollutant source, using the metaheuristics selected in the comparative analysis and including the probabilistic strategy. Section 6 presents the results of the experimental test. Finally, the conclusions are drawn in Section 7.
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 8 Antioxidant Defence in Antarctic Fungi  
### Enzymatic Antioxidant Defense  
Accelerated ROS production in Antarctic fungi induced by cold stress activates their antioxidant enzyme defense mechanisms. Their ability to survive in extremely cold ecosystems is associated with a cellular response that includes increased levels of SOD, CAT, and GPx. In particular, psychrophilic and psychrotolerant fungal strains isolated from Antarctica show higher enzyme activities compared to mesophilic strains from the same region [52; 163]. In contrast, the situation of a drastic decrease in growth temperature led to a more significant increase in enzyme activity in mesophilic Antarctic strains than in their psychrophilic counterparts. Enhanced enzymatic antioxidant defense has also been observed in Antarctic strains _P. olsonii_[170], _P. grisefulvum_[171], and _A. fumigatus_ I-9 [167]. Of interest are transcriptomic studies of a psychrophilic strain of _M. psychrophila_, which showed a lack of up-regulation of genes responsible for SOD synthesis at temperatures of 4 \({}^{\circ}\)C and 20 \({}^{\circ}\)C. Thirty microbial strains isolated from the Bulgarian Antarctic base \"St. Kliment Ohridski\" on Livingston Island were screened for the synthesis of antioxidant enzymes, specifically SOD and CAT [43]. Species belonging to the genera _Cladosporium_, _Penicillium_, _Monodictys_, _Geomyces_, _Aspergillus_, _Alternaria_, _Lecanicillium_, _Epicoccum_, and _Rilizopus_ have been included in the study. The analysis revealed that six of these strains exhibited significantly higher enzyme activity at 15 \({}^{\circ}\)C than at 30 \({}^{\circ}\)C, with _A. glaucus_ strain 363 being recognized as the most proficient producer of cold-active SOD.  
An up-regulation of the CAT gene has been observed in the psychrophilic yeast strain _Rhodotorula_ sp. (USM-PSY62) when subjected to a temperature downshift to 0 \({}^{\circ}\)C. This phenomenon coincides with a rise in ROS concentration and variations in the antioxidant defense mechanisms [196]. Cold-adapted fungi belonging to the species _Chrysosporium pannosum_, _Cylindocarpon magusianum_, _Mortierella minutissima_, _P. chrysogenum_, _P. cyclopium_, and _P. islandcium_ exhibited increased production of extra- and intracellular CAT at 15 \({}^{\circ}\)C compared to 20 \({}^{\circ}\)C [195]. Arbuscular mycorrhizal fungi (AMF) have been observed to respond to cold stress by increasing the levels of several antioxidant enzymes, including SOD, CAT, ascorbate peroxidase (APX), and glutathione reductase (GR) [230]. The gpx gene, essential for GPx synthesis in _V. volcanoes_, showed expression levels critical for fruiting body formation under low-temperature conditions [197]. Conversely, overexpression of this gene has been shown to enhance fruiting body regeneration in degenerated strains of _V. volcanoes_ T19. Transcriptomic analysis of _A. alternata_ revealed that exposure to cold stress significantly altered its transcriptional profile, particularly affecting the glutathione and thioredoxin systems [231].
## 3 Results
### Hptc-Dpph Fingerprint  
HPTLC fingerprint at 254 nm showed clear and distinct bands for all the standards, including caffe acid (R\({}_{\mathrm{f}}\) 0.51), chlorogenic acid (R\({}_{\mathrm{f}}\) 0.033), _p_-coumaric acid (R\({}_{\mathrm{f}}\) 0.58), gallic acid (R\({}_{\mathrm{f}}\) 0.42), and protocatechuric acid (R\({}_{\mathrm{f}}\) 0.50). The intensity of the bands was consistent, with all standards being well-visible under these conditions, indicating that this wavelength provides optimal visualization for detecting the tested phenolic compounds (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline Sample & \begin{tabular}{c} **Caffeic Acid** \\ **(mg/g)** \\ \end{tabular} & \begin{tabular}{c} **Chlorogenic** \\ **Acid (mg/g)** \\ \end{tabular} & \begin{tabular}{c} _p-Coumaric_ \\ **Acid (mg/g)** \\ \end{tabular} & \begin{tabular}{c} **Ferulic Acid** \\ **(mg/g)** \\ \end{tabular} & \begin{tabular}{c} **Gallic Acid** \\ **(mg/g)** \\ \end{tabular} & \begin{tabular}{c} **Protocatechuric** \\ **(mg/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Syringic Acid** \\ **Acid (mg/g)** \\ \end{tabular} &
\begin{tabular}{c} **Vanillic Acid** \\ **(mg/g)** \\ \end{tabular} \\ \hline A1 & 0.003 \(\pm\) 0.001 & 0.005 \(\pm\) 0.001 & 0.012 \(\pm\) 0.001 & 0.001 \(\pm\) 0.001 & 0.977 \(\pm\) 0.015 & 0.169 \(\pm\) 0.002 & 0.012 \(\pm\) 0.001 & 0.24 \(\pm\) 0.01 \\ A2 & 0.122 \(\pm\) 0.004 & 0.114 \(\pm\)At 365 nm, the visibility of the standards varied significantly. While caffeic acid (R\({}_{\rm f}\) 0.51) and chlorogenic acid (R\({}_{\rm f}\) 0.033) exhibited clear and prominent fluorescence, gallic acid (R\({}_{\rm f}\) 0.42) and protocatechuric acid (R\({}_{\rm f}\) 0.50) were not visible under this wavelength. \(p\)-Coumaric acid (R\({}_{\rm f}\) 0.58) was faintly visible with very low fluorescence intensity, indicating a reduced ability to detect certain phenolic compounds under these conditions (Figure 4).  
Under white light with DPPH staining, the standards demonstrated varying intensities of radical scavenging activity. Caffeic acid (R\({}_{\rm f}\) 0.51), chlorogenic acid (R\({}_{\rm f}\) 0.033), gallic acid (R\({}_{\rm f}\) 0.42), and protocatechuric acid (R\({}_{\rm f}\) 0.50) exhibited strong and distinct bands, indicative of high antioxidant activity. However, \(p\)-coumaric acid (R\({}_{\rm f}\) 0.58) showed only a very faint band with low intensity compared to the other standards, suggesting a weaker radical scavenging response under these conditions (Figure 5).  
Figure 4: HPTLC fingerprint (365 nm, without derivatization) for _Acer_ (A1–A10) samples and phenolic acid reference bands. For A4 and A7 samples, the red bands that appear in the solvent front are due to chlorophyll content. CAF: caffeic acid; CGA: chlorogenic acid; COU: \(p\)-Coumaric acid; GAL: gallic acid; PRO: protocatechuric acid.  
Figure 3: HPTLC fingerprint (254 nm, without derivatization) for _Acer_ (A1–A10) samples and phenolic acid reference bands. CAF: caffeic acid; CGA: chlorogenic acid; COU: \(p\)-Coumaric acid; GAL: gallic acid; PRO: protocatechuric acid.
### Case Study  
Kaohsiung City is an important industrial city in Taiwan with the third-largest population of over 2.7 million. So far, over 55% of domestic wastewaters in Kaohsiung City are still directly discharged to rivers without treatment due to the uncompleted construction and household connection of sewage systems. Moreover, a lot of industries and factories, such as electronic industries, chemical manufacturing factories, metal processing plants, foundries, paint and dye industries, motor vehicle plating and finishing plants, and paper and board mills, are located in or around Kaohsiung City [13]. Those industries may discharge a considerable amount of untreated and minimally treated wastewaters into the adjacent rivers. Fengshan River, one of the most important rivers flowing through the downtown of Kaohsiung City, receives domestic, industrial, agriculture, and husbandry wastewater from the residential areas, factories, and stock farming in the watershed. In terms of the total amount of pollution, industrial wastewater accounts for the most contribution. The characteristics of heavy metal contamination strongly depend on the industries along the river. The situation leads to the need to evaluate the heavy metal contamination in the Fengshan River sediments and assess the potential environmental risk. Despite this great need, no studies have studied the heavy metal contamination in Fengshan River sediments and its associated ecological impacts.  
Therefore, this study investigated spatial and temporal variations in heavy metals in the surface sediments of Fengshan River to assess the degree of heavy metal contamination and the potential ecological risk using multiple sediment pollution indices. The relationship between sediment properties and heavy metals, and the major factors controlling the spatiotemporal variations in sediment heavy metal concentrations were also determined.
# Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
## 5 Description of the Investigated Slope  
The Liangyeshan landslide is located in Wuping county, Fujian province, China. It is a typical drag medium-sized landslide. The landslide is composed of silty clay, residual sandy clay, fully weathered granite, and fragmented strong weathered granite (Figure 2).  
The natural grade of the slope varies greatly, which is about 5-25\({}^{\circ}\), and the vegetation on the slope is developed. The sliding body is in the shape of a chair and is located on the right side of the main line of the expressway. The width of the landslide is about 60 m. The elevation of the trailing edge of the slope is 352.85 m, the elevation difference is about 36.6 m, and the azimuth of the main slide direction is about 337\({}^{\circ}\). There are the typical characteristics of landslide accumulation, with thin leading and trailing edges, and thick  
Figure 2: Engineering geological profile.  
middle. The initial sliding surface can be defined as the interface between residual sandy clay and fully weathered granite.  
There are buildings of the highway service area near the foot of Liangyeshan landslide (Figure 3). In order to evacuate personnel and property in time and take emergency treatment measures in case of sudden danger, it is necessary to predict the short-term displacement during the slope-sliding process.  
Before the slope slides, we arranged ten displacement-monitoring points, D1-D10, in the direction of the main sliding line, as shown in Figure 4. Slope displacement is automatically collected by the displacement-monitoring system installed on the slope, and then transmitted remotely to the database of the monitoring platform through the Internet of Things (Figure 5). We also buried soil-moisture sensors in the slope, so as to monitor the moisture content of soil.  
Figure 4: Monitoring-point layout.  
Figure 3: Study area background.  
We started monitoring the slope on 1 June 2013. We recorded monitoring data of precipitation, soil moisture content and slope displacement during rainfall and after the rain through the Internet of Things. The slope had been monitored for two years. After it rained for six days from 2 June, the slope soil reached a saturated state, and the slope started to slide on 8 June. In this paper, we selected 151 sets of slope displacement and time collected for 20 h during the sliding process as the training dataset.  
For the purpose of mitigating the disaster caused by the landslide, we used XGBoost, RNNs and SVR to predict the displacement corresponding to the predetermined sliding time of the slope after the slope reached the saturation state. Furthermore, we investigated the variation law of slope displacement with time during the slope-sliding process. We collected 151 sets of data of the resultant displacement of the slope after the soil was saturated to carry out the displacement-prediction research by using XGBoost, RNNs and SVR.
# Simulation Tests of the Passing Distance of Ships on a Two-Way Fairway
###### Abstract  
One of the components necessary to determine the width of a safe maneuvering area on two-way fairways is a safe passing distance. Existing methods do not consider modern model studies of interactions between passing vessels, additionally, they ignore the influence of the vessel's position accuracy and navigators' qualifications. This paper presents a method to determine the passing distance, which is free of the drawbacks of the methods used so far. The proposed method is based on simulation research carried out using an FMBS-type (Full Mission Bridge) simulator. The tests were carried out for three loaded vessels (bulk carrier, tanker, and sea ferry), on four sections of the fairway with different parameters and aids-to-navigation available. The results obtained allowed the modification of the authors' previous, but still widely used, deterministic-probabilistic MTE (Marine Traffic Engineering) method for determining the width of a safe maneuvering area.  
passing distance; safety of navigation; design of waterways; full-mission ship simulator; maritime traffic engineering; safe maneuvering area; two-way fairway