esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Analysis
### Computational Complexity
#### 4.1.3 Complexity of the Data Forwarding Algorithm  
To forward a data packet, the node may need to perform the following tasks:  
1. Determining the base path candidate set (i.e., in the case that the node is the source node).
2. Determining the Euclidean routing path (i.e., in the case that the node is the source node).
3. Determining the next hop by using the greedy forwarding algorithm.  
To determine the base path candidates, the node needs to identify the hole containing the source and the destination. This job requires the computational complexity of \(O(h\times n)\). Then, the node constructs the visibility graph whose vertices are the vertices of the core polygons that do not containing the source and the destination, and the vertices of the holes containing the source and the destination, and whose edges do not intersect the interior of the core polygons and holes. As the number of the vertices of the visibility graph does not exceed \(2v+(h-2)n\), the computational complexity for constructing such a visibility graph does not exceed \(O\left((2v+(h-2)n)^{3}\right)\). Based on the visibility graph, the node determinesall the base path candidates by using breath-first search whose computational complexity is \(O\left(2v+(h-2)n+(2v+(h-2)n)^{2}\right)\). Therefore, the computational complexity of the first task (i.e., finding the base path candidates) is \(O\left(h\times n+(2v+(h-2)n)^{3}+2v+(h-2)n+(2v+(h-2)n)^{2}\right)\) which is equivalent to \(O\left((2v+(h-2)n)^{3}\right)\) (4).  
Let \(c\) be the number of the base path candidates, then the complexity for choosing a base path from the candidate list is \(O(c)\). To scale up the base path using the homothetic transformation, the node needs to determine the transformation centers, scale factors and the coordinates of the images. The computational complexity for these jobs is \(O(m)\), where \(m\) is the number of vertices of the base path. As \(m<2v+(h-2)n\), the computational time complexity for the second task does not exceed \(O(c+2v+(h-2)n)\) (5).  
Having determined the Euclidean path, the next hop is determined by using greedy algorithm whose computational complexity is only \(O(b)\), where \(b\) is the number of 1-hop neighbors (6).  
From (4), (5), and (6), the time complexity of data forwarding algorithm is \(O\left((2v+(h-2)n)^{3}+c+b\right)\).
# A Novel Method for Spectrometry Based on Imaging Systems
## 2 Materials and Methods
### DoI Estimator Parameter  
The DoI estimation method used in this work is based on the \(N/I\) parameter, previously proposed in [17]. This estimator is defined as the ratio of the integral of the scintillation light distribution (\(N\)) to its maximum (\(I\)).  
The detected light distribution originated by each scintillation event in a monolithic crystal can be expressed through the Scrimger and Baker equation [25]:  
\[f(x,y)=\frac{I}{\left(1+\frac{x^{2}+y^{2}}{t^{2}}\right)^{3/2}} \tag{1}\]  
where \(I\) is the maximum of the distribution and \(t\) is the distance between the point of interaction and the photodetector (related to the DoI through the formula \(DoI=T-t\), where \(T\) is the crystal thickness) and \(r^{2}=x^{2}+y^{2}\) is the distance in the plane from the normal (a scheme is shown in Figure 5).  
Figure 5: Scheme to illustrate the parameters in Scrimger and Baker formula.  
It is possible to find the relation between \(N\), \(I\) and the \(DoI\) by integrating Equation (1):  
\[N=\int dx\int dy\frac{I}{\left(1+\frac{x^{2}+y^{2}}{t^{2}}\right)^{3/2}} \tag{2}\]  
applying a conversion to polar coordinates it is possible to write the expression as follow,  
\[N=\int d\theta\int dr\frac{I\cdot r}{\left(1+\frac{r^{2}}{t^{2}}\right)^{3/2}} =2\pi\left[\frac{I\cdot t^{2}}{\sqrt{(1+\frac{r^{2}}{t^{2}})}}\right]_{0}^{+\infty} \tag{3}\]  
Hence, the relation between \(N\), \(I\) and the \(DoI\) parameters results in,  
\[N=2\pi\cdot I\cdot t^{2}\Longrightarrow N/I\propto t^{2} \tag{4}\]  
The chosen electronic read out allows to know the charge value detected by each anode. These values fill up an \(8\times 8\) charge matrix, whose maximum represents \(I\) in the \(N/I\) ratio.  
Starting from the obtained \(\gamma\)-ray spectra, an evaluation of the charge distributions and consequently of the \(N/I\) distribution for each visible energy peak was conducted. These distributions were divided into four different depth regions for each of which the resulting spectrum was obtained.
## 1 Introduction  
Water-lubricated bearings are important support devices for the propulsion shaft systems of underwater vehicles. Improving their lubrication conditions and load-bearing capacity are important measures to improve the safety and concealment of underwater vehicles [1; 2; 3; 4]. In practice, water-lubricated bearings generally exist in dynamic pressure lubrication or mixed lubrication states [5; 6]. However, at low speeds, under heavy loads and starting and stopping conditions, the formation of a hydrodynamic film between the bearing and the shaft will be difficult, causing some areas to be under a boundary lubrication state or even experience dry friction. This will lead to a rapid deterioration in interface lubrication, which in turn causes abnormal wear of the bearing lining [7; 8]. Over the past two decades, the surface microtexture has been proven to be one of the effective technological tools in tribology for improving lubrication, increasing load capacity, and reducing friction and wear [9; 10; 11; 12; 13; 14].  
Ultra-precision machining technology is capable of achieving sub-micron-level shape accuracy and nanometer-level surface roughness precision, representing the highest level of manufacturing precision [15]. Ultra-precision machining technology can achieve the high-precision fabrication of functional surfaces with complex micro- and nanostructures [16]. Thanks to this, it is possible to customize micro- and nano-textures on the surface of water-lubricated bearings using ultra-precision machining technology to improve the tribological properties of water-lubricated materials [17; 18].  
In this study, a micron-scale stripe texture was designed, a mold with negative microstripe textures was processed by femtosecond laser etching, and the microtextures were fabricated onto the surfaces of two types of water-lubricated material (Thordon and polyurethane) by a precision thermoforming method. Tribological tests revealed that the microstripe-textured water-lubricated materials exhibit lower friction and less wear than the original materials, and the results demonstrated that the presence of the microstripe texture effectively improves the friction and anti-wear properties of the water-lubricated materials. This paper provides a new idea for the design and fabrication of water-lubricated materials with superior water-lubricating and anti-wear properties.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
###### Abstract  
This article reviews numerous published studies on geographic information system (GIS)-based assessment and mapping of mining-induced subsidence. The various types of mine subsidence maps were first classified into susceptibility, hazard, and risk maps according to the various types of the engineering geology maps. Subsequently, the mapping studies were also reclassified into several groups according to the analytic methods used in the correlation derivation or elements of the risk of interest. Data uncertainty, analytic methods and techniques, and usability of the prediction map were considered in the discussion of the limitations and future perspectives of mining subsidence zonation studies. Because GIS can process geospatial data in relation to mining subsidence, the application and feasibility of exploiting GIS-assisted geospatial predictive mapping may be expanded further. GIS-based subsidence predictive maps are helpful for both engineers and for planners responsible for the design and implementation of risk mitigation and management strategies in mining areas.  
Keywords:mine subsidence; mine hazards; mine reclamation; GIS; geospatial predictive mapping +
Footnote †: journal: Applied Sciences
## 4 Simulation Results and Analysis
### Effects of Rotor Offset  
In this section, the effects of different rotor offsets on the dynamic responses are studied. The static displacement eccentricity is ignored and the distance between the rotor and the left bearing A is set to be \(a=0.33l\), \(a=0.34l\), \(a=0.35l\) and \(a=0.36l\), respectively. The rotor shaft orbits and bearing shaft orbits for different rotor offsets are displayed in Figure 9. It can be seen that the orbits are center circles and they get smaller when the distance becomes large. Thus, in order to reduce the vibration, the rotor should be placed in the middle as much as possible. Figure 10 shows that the dynamic responses decrease with the distance and the change regulations in the \(x\)-direction and \(y\)-direction are similar. It can be inferred that the effects of rotor offset on the dynamic responses of the system are similar in the time domain. In Figure 11, the spectra of dynamic responses (\(x\), \(\theta_{x}\) and \(x_{A}\)) for different rotor offsets are displayed. It can be observed that the frequencies are simple when the static eccentricity is ignored. Figure 11 shows the spectra of \(x\) and the main frequency is \(\Omega\). The combination of VC frequency and rotating frequency can be observed when the rotor offset is \(a=0.33l\) and \(a=0.34l\), while the combined frequencies disappear when the rotor offset is \(a=0.35l\) and \(a=0.36l\). The spectra of \(\theta_{x}\) in Figure 11 also show the same regulations and the combined frequencies are more obvious. The combined frequencies \(f_{\rm{vc}}+3\Omega\), \(2f_{\rm{vc}}+3\Omega\), \(2f_{\rm{vc}}+5\Omega\) and \(3f_{\rm{vc}}+5\Omega\) can also be seen as the combination of VC frequency, rotating frequency and even multiples of power frequency, which means the coupling effects of bearing forces, unbalanced mass force and UMP also exist when the rotor offset is \(a=0.33l\) and \(a=0.34l\). As Figure 9 shows, the dynamic responses of the bearings are larger when the rotor offset is \(a=0.33l\) and \(a=0.34l\), which means the radial deformations of the bearings are larger and the bearing forces will be formed. Thus, the coupling effects of bearing and rotor exist in these two cases, while the bearing forces cannot be formed and the coupling effects do not exist when \(a=0.35l\) and \(a=0.36l\). It also can be concluded that the coupling effects on the dynamic angle responses are greater than the displacement responses. In Figure 11, the frequencies of \(x_{A}\) are similar to \(\theta_{x}\) and they are more obvious when \(a=0.33l\) and \(a=0.34l\), which indicates that the coupling effects on the bearings are greater.  
Figure 8: The spectra of dynamic responses for different static displacement eccentricities. (**a**) The spectra of \(x\); (**b**) The spectra of \(\theta_{x}\); (**c**) The spectra of \(x_{A}\).  
Figure 11: _Cont._  
Figure 10: Time history of the dynamic responses for different rotor offsets.  
Figure 9: Rotor shaft orbits and bearing shaft orbits for different rotor offsets. (**a**) Rotor shaft orbits; (**b**) Left-bearing shaft orbits; (**c**) Right-bearing shaft orbits.
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### Magnetic Stirring  
Many researchers used the magnetic stirring technique combined with ultrasonication during the preparation of nanofluids [70; 71; 72], but some used it separately to disperse nanoparticles with a low concentration. In this technique, a stir bar also called a \"flea\" is used to generate the stirring action. The stir bar is submerged into the solution and spins very quickly under the action of the rotating magnetic field created by stationary electromagnets or a set of rotating electromagnets underneath the vessel in a stirring apparatus.  
[PERSON] et al. [73] used the sol-gel method for the preparation of TiO\({}_{2}\) nanoparticles by using titanium tetra-isopropyloxide; magnetic agitation was used for proper mixing of nanoparticles with different volume concentrations into the base fluid. The prepared nanoparticles were well dispersed in the base fluid without any settlement at the bottom of the flask. [PERSON] et al. [74] prepared transformer oil based TiO\({}_{2}\) nanofluid and used magnetic stirring for mixing of the base fluid and dispersant and then nanoparticles of titanate. They prepared six samples of nanofluid by varying the amount of dispersant (0.1-1.5 %vol.) and observed that dispersant addition played an important role in the dispersion of nanoparticles in the base fluid. [PERSON] et al. [75] used the magnetic stirrer to homogenize the mixture of SDS surfactant and distilled water before adding the TiO\({}_{2}\) nanoparticles into the prepared solution under ultrasonication. This technique is usually used for pre-homogenization of nanofluids and the homogenization of the mixture of surfactant and base fluid.
## 4 Results
### Experimental Platform  
The sensor nodes used in the experiments were Sun SPOTs nodes (Sun Small Programmable Object Technologies) [22] that have the following main characteristics: 32-bit ARM920T processor running at 180 MHz, 512 KB RAM, 3.7 V 720 mAh rechargeable battery, 4 MB flash memory, six analog inputs readable by an Analog Digital Converter, five general I/O pins, and an IEEE 802.15.4 compliant radio TI CC2420 transceiver [24].  
Figure 4 shows the specific Sun SPOT network stack protocol. A Sun SPOT's radio chip is configured to accept all packets bearing this address or the broadcast address. Packets with other addresses are discarded. To provide routing, meshing, and fragmentation, the Sun SPOT stack relies on the LowPAN protocol [25]. Multi-hop connectivity can be accomplished using a sophisticated routing protocol for _ad hoc_ networks, AODV [26]. Sun SPOTs feature a combination of LowPAN and AODV as network protocols. In the transport layer, the radiogram protocol and the radiostream protocol allow Sun SPOT applications to access the network.  
Figure 4: Sun SPOT network stack.
# Grain Boundary--A Route to Enhance Electrocatalytic Activity for Hydrogen Evolution Reaction
## 3 Results and Discussion
### Theoretical Prediction of GB-Assisted HER
#### 3.1.2 Limiting Potentials  
To further evaluate the HER performance, we compared the limiting potential (U\({}_{\text{L}}\) = \(-\Delta\)G\({}_{\text{H^{*}}}\)/e) of various metal materials with different exposed facets (Tables S3 and S4). Figure 2b presents a volcano plot, which typically correlates E\({}_{\text{B}}\)(H*) with U\({}_{\text{L}}\). GB-free Au(110) is located far from the top on the right side of the volcano plot, indicating that Au(110) binds H* weakly enough not to form a stable intermediate, which readily desorbs off the surface; thus, a much higher overpotential is required to yield gaseous hydrogen [10], which is in agreement with the previous discussion. When GBs are formed on Au(110), HER activity moves close to the top of the volcano. The active sites at the GBs (s1-3) possess superior HER performance to almost all the metal catalysts considered here. Additionally, the U\({}_{\text{L}}\) required is also reduced for sites near the GBs (s4 and s5). In particular, s1, s2 and s3 lie close to the top of the volcano plot near Pt, although PDS is different, suggesting that their HER activity is similar to that of the Pt extended surface. The enhanced HER performance is mainly due to the increase in the ability to bind H* on the Au(110) surface with GBs. Therefore, the GBs on the Au(110) surface can dramatically improve H* binding strength and, thus, boost the HER process.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 5 Titanium-Based Nanomaterials
### Doping
Non-metal dopants create new levels near the valence band of TiO\({}_{2}\) and do not function as charge carriers, which is a significant limitation of metal doping. This characteristic makes non-metal doping advantageous. The introduction of new levels in the band gap narrows it, allowing non-metal dopants to expand TiO\({}_{2}\)'s adsorption capacity in the visible light range. These dopants replace oxygen in the TiO\({}_{2}\) lattice, and the resulting oxygen vacancies can serve as recombination centers for charge carriers, reducing photocatalytic efficiency [80]. Moreover, non-metal doping can enhance the durability and stability of the photocatalysts [141]. Nitrogen-doped TiO\({}_{2}\) has been particularly studied for its high stability, similar atomic radius to oxygen, and lower ionization energy compared to other non-metal dopants [80]. For example, the photocatalytic degradation of p-xylene has been explored using Ultra Small Porous Nitrogen-doped Titanium Dioxide (USPNT) under simulated indoor illumination. This photocatalyst achieved a maximum degradation rate of 94.4% for p-xylene removal under low-power daylight conditions, whereas P25 reached only 38% (Figure 15). USPNT demonstrated high crystallinity, which contributes to its enhanced photocatalytic activity. Furthermore, photoluminescence spectra revealed that the USPNT sample exhibited significantly lower photoluminescence intensity and, consequently, a much lower  
Figure 14: UV–vis absorption spectra of Mn/TiO\({}_{2}\), Co/TiO\({}_{2}\), Mn/Co/TiO\({}_{2}\), and un-doped TiO\({}_{2}\) samples [88]. Figure 13: Benzene removal using P25, TiO\({}_{2}\), and TiO\({}_{2}\) doped with various metals [129]. recombination rate compared to P25. The intermediate levels introduced by N-doping act as hole reservoirs, promoting charge carrier separation and enhancing photocatalytic efficiency [85]. Carbon doping in TiO\({}_{2}\) photocatalysts (Figure 16) [148] can also improve photocatalytic activity by stabilizing the anatase phase and enhancing pollutant adsorption on the TiO\({}_{2}\) surface. The introduction of C can improve the conductivity of TiO\({}_{2}\), increasing the charge transfer rate from the bulk to the surface. Research comparing the photocatalytic activity for methylene blue degradation among C-doped TiO\({}_{2}\), N-doped TiO\({}_{2}\), and C and N co-doped TiO\({}_{2}\) revealed that doping limits the growth of TiO\({}_{2}\) crystallites. UV-vis adsorption spectra indicated an increase in visible light adsorption capacity compared to undoped TiO\({}_{2}\). The degradation rates increased in the following order: undoped TiO\({}_{2}\) < N-TiO\({}_{2}\) < C-TiO\({}_{2}\) < C-N-TiO\({}_{2}\). Thus, TiO\({}_{2}\) doping enhances photocatalytic activity due to the presence of C and N atoms that facilitate electron transfer, with co-doped TiO\({}_{2}\) exhibiting the highest photocatalytic activity due to the synergistic effects of the two dopants.
## 2 Materials and Methods
### Analysis of Yanggaeng
#### 2.3.3 Antioxidant Properties of Yanggaeng  
The total polyphenol content was measured using a modified version of the Folin-Denis method. A 20 \(\upmu\)L aliquot of the sample was mixed with 500 mL of Folin & Denis reagent (Sigma Aldrich, St. Louis, USA) and 7.5% Na\({}_{2}\)CO\({}_{3}\) solution (Samchun, Seoul, Republic of Korea). The mixture was allowed to react in the dark for 30 min. The absorbance was measured at 765 nm using an ELISA reader (EPOCH2 Plate Reader/Spectrophotometer; BioTek, Winooski, VT, USA). The results are expressed as milligrams of gallic acid (Sigma Aldrich) equivalents (mg GAE) per gram of sample [24]. DPPH radical-scavenging activity was measured as previously described [25]. Briefly, 100 \(\upmu\)L of the sample was mixed with 100 \(\upmu\)L of 0.2 mM DPPH solution (Sigma Aldrich), and the mixture was allowed to react in the dark for 30 min. Absorbance was measured at 517 nm using an ELISA reader, following a previously published protocol [26]. The ABTS radical-scavenging activity was measured as previously described, with slight modifications [25]. Yanggaeng samples (0.5 g) were homogenized in 50 mL of distilled water using a blender, followed by centrifugation at 2002\(\times\)\(g\) for 10 min. The supernatant was collected for further analysis. ABTS cation radicals were generated by mixing 7.4 mM ABTS with 2.6 mM potassium persulfate and incubating the mixture in the dark for 24 h. The ABTS solution was then diluted with distilled water to achieve an absorbance of 1.4 at 735 nm, using the molar extinction coefficient. For the assay, 40 \(\upmu\)L of Yanggaeng extract was added to 140 \(\upmu\)L of the diluted ABTS solution, incubated in the dark for 60 min, and absorbance was measured at 735 nm using an ELISA reader. Total polyphenol content (originally determined as mg GAE/g), DPPH radical-scavenging activity (originally determined as mg AAE/g), and ABTS radical-scavenging activity (originally determined as mg AAE/g) were all converted to \(\upmu\)GAE/mg and \(\upmu\)AAE/mg to provide more precise numerical representation of the relatively low antioxidant concentrations observed in the samples and to facilitate accurate statistical analysis.
## 2 Materials and Methods
### In Situ Biological Field Test  
The in situ field test was conducted at a metal processing plant in central Taiwan. The plant had used TCE for surface processing without proper contamination control measures, resulting in TCE contamination at the site. The site's geological structure consisted of 0-1.5 m of sandy silt, 1.5-2 m of silty sand with sporadic clay, 2-4 m of silty clay with sandy clay, 4-5.5 m of silty medium-to-fine sand, 5.5-6 m of silty clay, and 6-10 m of silty medium-to-fine sand with sporadic thin layers of silt. The interbedded geologic structure was frequently observed at the site and the groundwater level was from 4 m to 5 m below the ground surface. The groundwater flow direction was mainly in the northeast to southwest direction. The hydraulic conductivity of the site was approximately 2.8 \(\times\) 10\({}^{-5}\) to 1.6 \(\times\) 10\({}^{-6}\) m/s. The injection well and monitoring well distribution is presented in Figure 1. At the upstream spot 1 m away from the injection well (IW), an observation well (OW1) was established. OW2 and OW3 were established at downstream spots 3 m and 5 m away from the IW, respectively. In addition, a contaminated monitoring well was selected as the control well (CW) within the contamination range of the site. The CW was approximately 30 m away from the in situ biological field test site and was free from the effects of the in situ chemical injection.  
Given that the geologic structures were silty fine sand and interbedded silt and clay, the double packer injection method was adopted. The double packer injection well structure is depicted in Figure 2b. The outer layer of the Manchette tube was Bentonite cement slurry. On the Manchette tube, injection holes were designed at 50 cm intervals. During injection of the chemicals at the target depth, the double packer system was extended to the predetermined injection depth and filled with water in the water bag at both ends to ensure chemical exposure at each contamination depth. During injection, the injection pressure was controlled at 2-4 bar to avoid cracks on soil pores and the formation of short-circuiting flows. During the testing period, chemicals were injected once every 2 months. The amount injected was adjusted according to the TOC detection results during the testing period. During the entire testing period, water quality characteristics including TOC, VOCs, nitrate, nitrite, ammonia nitrogen, sulfate, sulfides, total iron, and iron (II) were periodically monitored.  
Figure 1: In situ biological field test well distribution.