esa-sceva/satcom-chunk-collection huggingface dataset:
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 5 Experimental Results and Evaluation
### Vessel Detections Based on EO Images  
In the second part of our experiments, we generated three types of density maps based on EO detections. First, we visualize the density of all vessels detected in satellite images, for both Sentinel-1 and Sentinel-2 images. Then, we visualize the density of all dark vessels detected in satellite images, i.e., vessels that were only detected in satellite images without matching AIS positions. This set of density maps will help us identify \"dark\" areas (e.g., grid cells) with increased traffic of \"dark\" vessels, which can either be areas of low AIS coverage or areas associated with illegal activities (e.g., sanctioned areas). Finally, an improved map based on both AIS and EO detection is provided.
###### Abstract  
As the world's deepest hydraulic tunnels, the Jinping ultra-deep tunnels provide world-class conditions for research on deep rock mechanics under extreme conditions. This study analyzed the time-dependent behavior of different tunneling sections in the Jinping tunnels using the Nishihara creep model implemented in Abaqus. Validated numerical simulations of representative cross-sections at 1400 m and 2400 m depths in the diversion tunnel reveal that long-term creep deformations (over a 20-year period) substantially exceed instantaneous excavation-induced displacements. The stress concentrations and strain magnitudes exhibit significant depth dependence. The maximum principal stress at a 2400 m depth reaches 1.71 times that at 1400 m, while the vertical strain increases 1.46-fold. Based on this, the long-term mechanical behavior of the surrounding rock during the expansion of the Jinping auxiliary tunnel was further calculated and predicted. It was found that the stress concentration at the top and bottom of the left sidewall increases from 135 MPa to 203 MPa after expansion, identifying these as critical areas requiring focused monitoring and early warnings. The total deformation of the rock mass increases by approximately 5 mm after expansion, with the cumulative deformation reaching 14 mm. Post-expansion deformation converges within 180 days, with creep deformation of 2.5 mm-3.5 mm observed in both sidewalls, accounts for 51.0% of the total deformation during expansion. The surrounding rock reaches overall stability three years after the completion of expansion. These findings establish quantitative relationships between the excavation depth, time-dependent deformation, and stress redistribution and support the stability design, risk management, and infrastructure for ultra-deep tunnels in a stress state at a 2400 m depth. These insights are critical to ensuring the long-term stability of ultra-deep tunnels and operational safety assessments.  
long-term creep behavior; numerical implement; excavation-induced response; different depth; stability analysis +
Footnote †: journal: Applied sciences
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 4 Time-Series Analysis to Predict EtEQ
### Comparison of Learning Algorithms Based on Time Series  
As stated before, we explored whether time-series analysis can be used to predict future End-to-End Quality (EtEQ) values. To do this, we applied four well-known approaches: SVM, kNN, RT, and RBR.  
Figure 9 shows the average Mean Absolute Error (MAE) per path using a training dataset of 2016 instances (seven days), a test dataset of 288 instances (one day), and a lag window composed of the last 12 instances. This test was performed to verify whether time-series learning-algorithms could predict consecutive EtEQ values. These results show that we achieved the best accuracy for the Rule-Based Regression (RBR), and the worst for k-Nearest Neighbors (kNN). Regression Trees (RT) and Support Vector Machines (SVM) also moves very close to RBR results. Notice that the maximum EtEQ value is 1 and, therefore, the MAE per link is 0.24 for RBR and 0.5 for kNN. We applied a T-test to mean values for independent samples (at 95% confidence level) in order to compare the classification algorithms using the MAE. After this analysis, \(p\)-values smaller than 0.05 indicate that the means are significantly different, and, therefore, we would reject the null hypothesis of no difference between the means. Consequently, we can claim that time-series analysis achieves high percentages of success, and that among them, RBR seems to be the best candidate to make predictions.  
We also analyzed the error variability of each algorithm and represented the results using box plots. Three of the four algorithms achieved similar performance for most of the links (RT, RBR, and SVM), as shown in Figure 10. Although, RT might present some outliers, the differences among median, first quartile, and third quartile are minimal. On the other side, kNN presented different behavior compared to the others. In this case, outliers presented larger errors that increase the average  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tools** & **Version** \\ \hline Weka & version 3.7.10 \\ TimeseriesForecasting & version 1.1.25 \\ \hline
**Algorithm** & **Class** \\ \hline RT & weka.classifiers.trees.MSP \\ RBR & weka.classifiers.rules.M5 Rules \\ kNN & weka.classifiers.lazy.IBk \\ SVM & weka.classifiers.functions.SMOreg \\ \hline
**Algorithm** & **Parameters** \\ \hline RT & -M 4.0 \\ RBR & -M 4.0 \\ kNN & -K 1 -W 0 -A “weka.core.EuclideanDistance -R first-last” \\  & -I “weka.classifiers.functions.supportVector.RegSMOImproved -L 0.001 \\ SVM & -W 1 -P 1.0E-12 -T 0.001 -V” \\  & -K”weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0” \\  & -N 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental tools and details.  
Figure 9: Average Mean Absolute Error (MAE) of the EtEQ predictions.  
values and change the overall evaluation of the algorithm. In the rest of the paper, we consider the RBR algorithm to show the potential benefits of predicting EtEQ by means of a time-series analysis.
## 4 Potential Decolorizing Enzymes and Their Characteristics
### Lacases  
Laccases (EC 1.10.3.2), also known as benzenediol: oxygen oxidoreductase or polyphenol oxidase, are members of the family of blue multicopper oxidases that catalyze single electron oxidation of four reducing-substrate molecules, accompanied by the four-electron reduction of molecular oxygen into the water. At the end of the nineteenth century, this enzyme was extracted from the Japanese lacquer _Rhus vernicifera_, after which it was named lacase [123, 124]. Lacases have been considered to be ubiquitous in many white-rot fungi studied so far. The reason for their wide distribution is their function: degradation of the complex polyphenol structure that constitutes lignin in order to break the plant's cell wall and uptake the host's nutrients [125, 126]. Fungal lacases also contribute to melanin formation in different fungal species [60, 127]. This enzyme showed a specific function in some insects and plants [128]. Plant lacase, together with peroxididases, contributes to the biosynthesis of lignin during protoplast regeneration [129, 130]. Lacases have also been found in some bacteria, having a function in melanin synthesis and showing stability at high temperatures and pH [131, 132, 133, 134]. Thus, lacases are widely present in nature with a role in either synthetic or degradation processes.  
Fungal lacases have shown large-scale applications for industrial and biotechnological processes because they are extracellular and inducible, and they do not require a cofactor and show low substrate specificity [128]. For example, they utilize oxygen as a cofactor [135] and oxidizing agent and showed the degradation of several compounds with a phenolic structure [136]. Thus, lacases have been utilized in many bioremediation processes as well as in the degradation of a wide number of textile dyes [39, 128].  
Lacase is a monomeric globular glycoprotein comprised of around 500 amino acid residues organized into three consecutive domains (A, B, C) with a Greek key \(\beta\) barrel topology [127].  
The overall reaction that is catalyzed by lacase represents the oxidation of four electrons of a reducing substrate with a two-electron reduction of dioxygen to water [4 RH + O\({}_{2}\)\(\rightarrow\) 4R + 2H\({}_{2}\)O]. The first step is a reduction of the reducing substrate carried out by the Cu\({}^{2+}\) (Cu\({}^{2+}\) to Cu\({}^{+}\)) at the T1 site, which is primarily an electron acceptor. The electrons extracted from the reducing substrate are transferred to the T2/T3 site, with three Cu\({}^{2+}\) ions in total. This results in changing the enzyme state from fully oxidized to fully reduced [124].  
Lacases catalyze the degradation of phenolic structures through the subtraction of one electron from phenolic hydroxyl groups to form phenoxy radicals that lead to the cleavage steps, which results in aromatic ring cleavage. Moreover, this enzyme can oxidize non-phenolic compounds but in the presence of a mediator such as 2,2 azinobis-(3-ethylbenzthiaazoline-6-sulfonate)(ABTS), 1-hydroxybenzotriazole (HBT), and 3-hyroxyanthranilic acid [124].
# Study on Lubrication Performance of Journal Bearing with Multiple Texture Distributions
## 5 Concave-Convex Composite Spherical Texture  
The relationships between texture parameters and lubrication performances of the journal bearing are found based on the above analysis. It is indicated that, with a pure texture form (pure convex or pure concave), the friction coefficient is hardly influenced, regardless of the parameter type and value. However, the friction coefficient is an important factor that should be reduced in the case of journal bearing lubrication. To this end, a concave-convex composite spherical texture is presented in this section. The composite texture distribution along the circumference direction is shown in Figure 6a, where the red solid dot indicates the convex texture and the hollow dot represents the concave texture.  
Figure 5: Effect of texture area density on (**a**) the load capacity and (**b**) the friction coefficient.  
Two rows of concave spherical segments are arranged near the main loading region and the convex spherical segments are used elsewhere. The blue dashed line in Figure 6a is the circumferential film pressure distribution. It can be seen that the film pressure reaches the peak at the boundary where the texture type alternates from convex to concave, and drops sharply near the region where the texture type changes back from concave to convex. Figure 6b is the circumferential film thickness distribution diagram, in which the red line above the blue line represents the film thickness at the position of the concave spherical segment, and that below the blue line represents the film thickness at the position of the convex spherical segment.  
To discuss the lubrication performance of the composite texture, comparisons between the plain, the convex, and the composite textured bearings are carried out. The pure concave texture case is not considered, because its lubrication performance has already been found to be the worst.
## 3 Results
### Validation  
The resultants of both the theoretical analysis and the measurements, are coherent and consistent with the BL theory. The empirical findings were interpreted theoretically, revealing that adding the \(M\) force resulted in a significant growth in values of \(\epsilon_{d,M}\) and \(s_{M}\) compared to \(\epsilon_{d,ref}\) and \(s_{ref}\), accordingly. In addition, the results obtained are in line with the field literature [9; 42; 43]. At low fan frequency, the thickness of the BL increases, and vice versa. When the BL is the thickest there exist high local velocity gradients. As observed,  
Figure 14: Quantity of large droplets determined by the thickness of the boundary layer.  
Figure 15: Total amount of micron and sub-micron droplets that was measured in the \(SCh_{2}\) with two elbows (\(e_{1},e_{2}\)) mounted at its end, where \(\Delta N_{e_{4}-e_{1}}\ =\ \Delta N_{e_{4}}-\Delta N_{e_{1}}\).  
a greater share of larger droplets was present in the main flow when high values of the local velocity gradients were measured. The Magnus lift force acts in the flow regions within which large \(LVGs\) exist. The thicker the boundary layer is the region within which the \(M\) force impacts the movement of droplets is extended. The calculated value of \(\varepsilon_{d,M}\) and \(s_{M}\) increased with the increase in droplet diameter and the \(LVG\) values. Hence, the measured quantity of large droplets (\(dN_{max}\)) present in the mist flow increased in value with decreasing speed of the gas flow and the corresponding growth in the BL thickness (Figure 14). The increase in the measured value of share of larger droplets was reached at low fan frequencies. The increase in \(\varepsilon_{d,M}\) and \(s_{M}\) was noticed after conducting theoretical analysis with the corresponding flow conditions applied. The M force's impact is more significant for larger droplets. When the BL was the widest, the region where the M force acted expanded accordingly. Thus, the M force had an influence on the higher number of droplets. The droplet content in the core of the flow increased as a result of this, as per the measurements. Therefore, the outcomes obtained from empirical and theoretical investigations may serve as their mutual validation. Droplets of similar size as those measured using APSS were also identified using PIV.
###### Abstract  
The work presented in this paper shows that Fiber Bragg Grating (FBG) optical fiber sensors can potentially be used as receivers in a long-range guided wave torsional-mode pipe inspection system. Benefits over the conventional pulse-echo method arise due to reduced total travel distance of the ultrasonic guided wave reflections, since reflections from defects and structural features do not need to propagate a full round trip back to the transmitting collar. This is especially important in pipe configurations with high attenuation, such as coated and buried pipelines. The use of FBGs as receivers instead of conventional piezoelectric or magnetostrictive elements also significantly reduces cabling, since multiple FBG receivers can be placed along a single optical fiber which has a diameter on the order of only around 100 \(\upmu\)m. The basic approach and sample results are presented in the paper. Additionally, a brief overview of some topics in ultrasonic guided waves is presented as a background to understand the inspection problem presented here.  
ultrasonic guided waves; fiber optics; fiber Bragg grating; nondestructive testing; structural health monitoring; pipe inspection +
Footnote †: journal: Applied Physics Letters
## 4 Experimental Results and Discussion
Additionally, conventional PV systems utilizing Si PV cells require dual-axis tracking for seasonal and daily variations in solar incidence angles. While variations exist depending on the country and region, they generate power for approximately \(3.5\sim 4\) h per day on average. Suppose the solar incidence angle during the central time of the power generation period meets the DNI conditions by setting the reference angle of the optical module. In that case, the variation range of solar incidence angles during the power generation period is within \(1\,\alpha\mid <15^{\circ}\)[25; 26]. Therefore, based on the analysis and experimental results, it is suggested that the module proposed in this study, with a concentration efficiency variation within \(1\,\alpha\mid <15^{\circ}\) of daily solar incidence angle variation, within \(15\%\), may not require tracking means for this purpose. It is also anticipated that tracking methods for seasonal variation in solar incidence angles will be applied similarly to conventional methods.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 2 Related Work  
In this section, we highlight various research contributions to enhance the utility of UAVs in different applications. Several leading companies, including Skycatch, Yuneec, DJI, and Amazon, are focusing on UAVs in terms of load carrying efficiency, parcel delivery, and monitoring etc. \"Amazon Prime Air\" was initiated by Amazon to deliver packages of five pound within thirty minutes at a distance of 10 miles [6]. After introducing UAVs,the company is ambitious to achieve 50% of zero-impact shipment till 2030. DHL also started its drone \"Parcelcopter\" to deliver medicine [7]. Later, the DHL Trend Research Team also published a report on new trends, applications, and limitations of UAVs. In 2017, Walmart started drone service and later requested permission from the Federal Aviation Administration (FAA) for commercial delivery drones in 2019 [8]. Similarly, in 2017 UPS started a battery powered drone service for home package delivery [9]. In February 2017, a Chinese-made autonomous flying drone taxi named \"Ehang 184\" was tested in Dubai. It has a top speed of 63 mph and can transport a passenger weighing up to 100 kg as well as a small luggage [10]. In 2019, Zipline International used UAV medical service to distribute life-saving medicines, blood, and vaccines to 2000 healthcare centers in the south of Ghana [11]. Several countries have started promoting drone research and development for multiple services [12; 13]. Several research studies have focused on multiple aspects of drones. For instance, authors in ref. [14] have addressed mobile charging feature to increase the battery life of drones. [PERSON] et al. [15], proposed a model to decrease the weight of a drone by integrating a wireless powered receiver. [PERSON] et al. [16] suggested several techniques to enhance the flight time of drones through wired as well as wireless media. [PERSON] et al. [4] proposed a method to decrease drones congestion at the charging station through an algorithm for queuing time and charging time. The authors in [17] proposed a model for landing and charging of drones on the roof of a building. In this study, the authors have also addressed misalignment losses and possible solutions to overcome them. [PERSON] et al. [18] proposed a mechanism comprised of multiple transmitters and a single receiver for drones charging. Similarly, [PERSON] et al. [19] proposed a model for the allocation of time slots to the drones through an auctioning process. Besides these studies, there are several research surveys that highlight different issues and challenges of UAVs, as shown in Table 1.
# Evaluation of Rock Brittleness Index under Dynamic Load
###### Abstract  
Rock is a typical brittle material, and the evaluation of its brittleness index has important guiding significance for hard rock resource exploitation, unconventional oil and gas resource exploitation, mechanical driving efficiency, rock burst prediction, and dynamic disaster prevention and control. At present, brittleness index often measures the brittleness of rock under static load; thus, whether it is applicable to dynamic load is worth exploring. In this study, static and dynamic uniaxial compression tests and Brazilian splitting tests were carried out on five kinds of rocks, including fine granite, coarse granite, shale, marble, and sandstone, using the INSTRON\(-\)1346 test system and split\(-\)Hopkinson pressure bar (SHPB), respectively. The brittleness index values of different rocks under static and dynamic load were determined, and the changes in the brittleness of rocks under different loading methods and different strain rates were studied. The definition of brittleness and the applicability of existing brittleness indices were also discussed. It was found that the loading rate amplified the variation of the brittleness characteristics of rock. When static load changes to dynamic load, the brittleness of rocks increases, and the brittleness relationship between different rocks remains unchanged. The more brittle the rock is under static load, the greater the range of brittleness enhancement is under dynamic load. It was also found that the brittleness of sandstone had an obvious effect on the strain rate. The brittleness of rock increases with the increase in strain rate, and the greater the strain rate, the greater the brittleness enhancement degree. These research results can provide reference values for dynamic disaster prevention and safe construction of deep rock projects such as mines and tunnels.  
rock mechanics; brittleness index; SHPB; total stress-strain curve
## 3 Methodology  
The evaluation of gamma and neutron dose rates consists of two main stages:  
* Stage No. 1: RBMK-1500 spent fuel characteristics are modeled using the TRITON code of the SCALE 6 computer code system [18]
* Stage No. 2: the obtained spent fuel characteristics are used as input data for the MAVRIC code [19] of the SCALE 6 code system that performs gamma and neutron transport modeling and calculates resulting dose rates.  
The modeling approaches and obtained results of Stage No. 1, i.e., determination of RBMK-1500 spent fuel characteristics, were comprehensively described in [17] and are not presented in this paper. Gamma and neutron source spectrums were calculated for RBMK-1500 SF with different enrichments including 2.0% enriched spent fuel loaded into cast iron and metal-concrete containers.  
The main assumptions made for Stage No. 2 calculations were as follows:  
* The gamma and neutron sources were defined as homogeneous cylinders containing 102 spent FB and the 32M basket structures. Secondary gamma emissions due to (n, \(\gamma\)) reactions and bremsstrahlung, and emissions from neutron-activated steel components  
Figure 1: Cross-sections of the dry storage containers (1—container body (1a,b,d,e,f—metal parts; 1c—heavy concrete); 2—basket; 3—container lid; 4 (4a,b)—guard plates).  
Figure 2: RBMK-1500 fuel assembly.  
of SF assemblies were not taken into account. Neutron activation of the container components was deemed insignificant [20].
* In order to evaluate the impact of certain energy gamma or neutron emissions from the spent fuel on the resulting gamma or neutron dose rates, separate inputs for individual energy groups (19 energy groups for gamma and 27 groups for neutrons) were created. Neutron and gamma energy groups and their energy boundaries are presented in Table 1.
* The minimum cooling period for RBMK-1500 spent fuel after which the spent fuel is allowed to be loaded into the dry storage container is 5 years. Dose rate distributions and variations were analyzed for a storage period from 5 to 300 years. The 5 year period indicated in the figures below means that the container has only just been loaded with spent fuel.
* It was assumed that aging mechanisms do not change the shielding properties of the container components.
* Three dose rate calculation points (at the center of the container's top/bottom and side walls) were located on the outer container surface.
## 4 Discussion
### Difference between OLCI FAPAR and MODIS FAPAR Product  
As described in Section 3.4, the performance of GPPOLCI-fapar and GPPMODIS-fapar varies at different sites. The main difference between GPPOLCI-fapar and GPPMODIS-fapar results from the FAPAR product being incorporated in the model. Therefore, the differences between OLCI FAPAR and MODIS FAPAR, which may lead to inconsistency between GPPOLCI-fapar and GPPMODIS-fapar, are summarized mainly from two aspects. First, the temporal compositing periods of the two products are different. The MODIS FAPAR product is provided in aneight-day interval while the OLCI FAPAR is at a daily interval. For temporal aggregation purposes, MODIS FAPAR is resampled to daily, but its value does not change throughout the eight days.The maximum FAPAR is chosen as the final output value representing the eight days [35]. Second, the MODIS FAPAR and OLCI FAPAR are retrieved from different algorithms. The OLCI FAPAR retrieval algorithm belongs to the family of the JRC (Joint Research Center), which is based on the 1-D RT model, while MODIS FAPAR is derived from the 3-D RT model. The difference between these algorithms is mainly derived from the various definitions and the leaf/wood spectral values assumptions [49].