esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Search Algorithms
### Overview of Flower Pollination Algorithm  
FPA was developed by [PERSON] in 2012 [43] and is considered to be an excellent optimization method. The algorithm is based on copying or mimicking the process of pollination or reproduction of flowering plants. This process can occur through various forms and through different pollinators, manifesting a high degree of adaptation and specialization. There are two types of pollination according to how the pollen is sent or arrives at the plant: biotic pollination and abiotic pollination. Biotic pollination is done by pollinators such as insects (bees, wasps, ants, flies and butterflies) or some animals (mice, bats, and some birds, such as the hummingbird) in flowering plants. Abiotic pollination does not require the transfer of pollen by alive organisms; this is done by water, wind, or gravity. The biotic or cross-pollination is considered to be global pollination process with pollen carrying pollinators performing Levy flights. Global pollination can be formulated as  
\[x_{i}^{t+1}=x_{i}^{t}+\gamma L(\lambda)(g^{*}-x_{i}^{t}) \tag{18}\]  
where \(x_{i}^{t}\) denotes pollen \(i\) or the solution vector \(x_{i}\) at iteration \(t\), \(g^{*}\) is the current best solution among all current generation solutions, \(\gamma\) is a scalar factor that is used for controlling the step size and \(L(\lambda)\) is the Levy flights based step size that corresponds to the strength of the pollination. Since insects can fly over a long distance with steps of different distance, where the length of each step or jump follows the levy probability distribution function.  
\[L\approx\frac{\lambda\Gamma\left(\lambda\right)\sin\left(\pi\lambda/2\right) }{\pi}\frac{1}{s^{1+\lambda}}\qquad(s\gg s_{0}>0) \tag{19}\]  
where \(\Gamma\left(\lambda\right)\) is a standard gamma function, and this distribution valid for large steps \(s>0\). The strength of pollination is generally considered to be \(\lambda=1.5\). For the local pollination, may be represented as follow \[x_{i}^{t+1}=x_{i}^{t}+\epsilon(x_{j}^{t}-x_{k}^{t}) \tag{20}\]  
where \(x_{j}^{t}\) and \(x_{k}^{t}\) are pollen from different flowers of the same plant species. For a local random walk, \(x_{j}^{t}\) and \(x_{k}^{t}\) come from the same species and \(\epsilon\) is drawn from a uniform distribution as \([0,1]\). The FPA is summarized in the Algorithm 3:  
```
Find the best solution in the initial population  while (\(t<\text{MaxGeneration}\)) for\(i=1:n\) (all \(n\) flowers in the population) if\(\text{rand}<p\),  Draw a step vector \(L\) from a Levy distribution  Do global pollination else  Draw \(\epsilon\) from a uniform distribution in \([0,1]\)  Do local pollination endif  Evaluate new solutions If new solutions are better, update them in the population endfor  Find the current best solution end
```  
**Algorithm 3**Flower Pollination Algorithm
###### Abstract  
TiO\({}_{2}\)-based materials are commonly employed as photocatalysts for industrial wastewater treatment. The primary reasons of employing TiO\({}_{2}\) include cost effectiveness, ready availability, eco-friendliness, non-toxic behavior, and exceptional resistance towards photo-corrosion. However, the wider band gap of pure TiO\({}_{2}\) restricts its performance because of its optical absorption of solar light to the ultraviolet (UV) region only, and to some extent of photo-excited charge recombination. In the present work an attempt is made to develop a facile synthesis approach by using urea, a cheap chemical precursor, to form nitrogen doped TiO\({}_{2}\) with the key objective of extended light absorption and thus enhanced photocatalytic performance. It was also observed that the urea-induced anatase phase enrichment of TiO\({}_{2}\) is another key factor in promoting the photocatalytic performance. The photocatalysts prepared by varying the amount of urea as a nitrogen dopant precursor, are characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and photoluminescence (PL) to evaluate their crystallinity, morphology, functional groups, and charge separation properties, respectively. Moreover, the surface area was also estimated by physicochemical adsorption. The maximum nitrogen-doped sample yielded >99% photodegradation efficiency of methylene blue (MB) dye-simulated wastewater as compared to a pure TiO\({}_{2}\) sample which exhibited 6.46% efficiency. The results show that the simultaneous factors of nitrogen doping and anatase phase enhancement contributes significantly towards the improvement of photocatalytic performance.  
photocatalysis; nitrogen doping; anatase phase enrichment; textile wastewater treatment Article
## 2 Materials and Methods
### Irradiation Experiments  
All devices were irradiated with 6 MV photon beams on a Varian TrueBeam (Varian Medical Systems, Inc., Palo Alto, CA, USA) medical linear accelerator and with a kilovoltage (100 kVp) photon beam on an Xstrahl 300 orthovoltage X-ray unit (Xstrahl Ltd., Surrey, UK).  
For 6 MV irradiations, the transistors in the 3D-printed sample holder were placed at isocenter with a 5 \(\times\) 5 cm\({}^{2}\) field size on top of a 15 cm stack of Solid Water (Sun Nuclear Corp., Melbourne, FL, USA) for backscatter and below an additional 5 cm of Solid Water, as shown in Figure 1a. A PLA frame with a cap was used to protect the device from physical compression by the Solid Water and to minimize the air gap (\(\approx\)1 mm) between the device and the solid water placed on top. The source-to-detector distance was set to 100 cm for all irradiations. The output of the accelerator under these measurement conditions was verified using a calibrated ion chamber. Devices were exposed to doses ranging from 1 to 10 Gy of 6 MV photon beams.  
In the orthovoltage unit an applicator cone of 5 cm diameter, 30 cm length was used to deliver dose directly to the diode. Diodes were placed on top of a 15 cm stack of Solid Water for backscatter as shown in Figure 1b. These measurement conditions differed from the 6 MV beam due to the less penetrating nature of the 100 kVp photons compared to the 6 MV photons. Doses between 1 and 30 Gy were delivered.  
For wireless readouts, all transistors were programmed with a \(-\)80 V bias applied to the Gate electrode (V\({}_{\mathrm{g}}\)) for three seconds at V\({}_{\mathrm{d}}\) = 0 V to write the charges to the polymer electret. Transistors were then irradiated, and after each delivered dose, the transfer characteristic was scanned to readout the drain current change at a fixed bias to quantify changes in the I-V curve under these specific conditions. When programmed charges were depleted by radiation, transistors were reprogrammed for repeated use.  
The real-time readout in a wired configuration was conducted after programing with a \(\mathrm{V_{g}}=-80\) V bias for three seconds. The drain current was measured at an electric field at \(\mathrm{V_{g}}=-15\) V; \(\mathrm{V_{d}}=-15\) V. After the drain current was stabilized, the 6 MV photon beam was turned on and off at different dose rates of 600, 400, 300, 200, and 60 cGy/min for each step.
## 5 Conclusions  
This paper proposed a dynamic scheduling and operation reduction scheme for efficiently processing dynamic graphs in GPU environments. Unlike existing methods that prioritize only active vertices, our approach introduces a priority-based scheduling technique that incorporates tentative active vertices, optimizing GPU load ordering. By dynamically adjusting the execution order, our method reduces redundant computations and maximizes GPU utilization. Additionally, to address the inefficiencies caused by repeatedly processing overlapping snapshot data, we implemented an operation reduction technique, which has been experimentally validated to be particularly effective in environments with frequent edge insertions and deletions. Experimental results showed that the proposed method achieves up to a 348% speedup over Subway and a 120% improvement over EGraph, primarily due to memory optimizations and enhanced parallel execution strategies that minimize unnecessary CPU-GPU data transfers.  
The proposed scheme is expected to play a crucial role in various applications such as real-time social network analysis, financial transaction monitoring, cybersecurity threat detection, and intelligent transportation systems. As data structures become increasingly complex and interconnected, dynamic graph processing technology will be essential for rapidly and efficiently handling large-scale data flows.  
Future research will focus on expanding the proposed scheme to distributed platforms to evaluate its performance in large-scale graph processing environments. Additionally, the applicability of the scheme across various graph algorithms will be explored, and optimized techniques will be developed to maximize performance in real-time dynamic graph processing environments.  
Conceptualization, S.S., J.C., D.C. ([PERSON]), H.L., D.C. ([PERSON]), J.L., K.B. and J.Y.; methodology, S.S., J.C., D.C. ([PERSON]), K.B. and J.Y.; software, S.S. and J.C.; validation, S.S., J.C., D.C. ([PERSON]), H.L., D.C. ([PERSON]), J.L., K.B. and J.Y.; formal analysis, S.S., J.C., K.B. and J.Y.; data curation, S.S., J.C. and D.C. ([PERSON]); writing--original draft preparation, S.S. and J.C.; writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2024-2020-0-01462, 50%) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and was carried out with the support of the 'Cooperative Research Program for Agriculture Science and Technology Development' (Project No. RS-2021-RD010195, 50%) by the Rural Development Administration, Republic of Korea.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Subway (Baseline)** & **EGraph** & **Proposed Method** \\ \hline CC & 100\% & 35\% & 28\% \\ BFS & 100\% & 29\% & 25\% \\ SSSP & 100\% & 38\% & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative Analysis of Transfer Volume by Algorithm.
## 4 Conclusions  
With the help of the N\({}_{2}\)-O\({}_{2}\) dual-gas reactive sputtering system, Ti/(Mo-TiAlN)/(Mo-TiAlON)/Al\({}_{2}\)O\({}_{3}\) tandem absorbing films were deposited, matching the theoretical design. The complex films include an infrared reflectance layer, interference absorptive layers with different metal doping amounts, and an anti-reflectance layer. The layer thicknesses of Ti, Mo-TiAlN, Mo-TiAlON, and Al\({}_{2}\)O\({}_{3}\) were found to be approximately 100, 300, 200 and 80 nm, respectively.  
Figure 12: Reflectivity variation with Mo content after thermal oxidation in atmosphere at 500 \({}^{\circ}\)C for 8 h (Al content is 10 at.%).  
When Al target current was increased, it tended to form metallic oxide. With an increase of Al content to 12 at.%, the ratio of N/O was nearly 0.1. Nitride is continuously changed to oxide. When Mo content was increased in the film, the ratio of metallic elements (Ti,Al) and non-metallic elements (N,O) changed slightly. XRD and HRTEM results showed that a diffraction peak appears at 2\(\theta\) = 40\({}^{\circ}\), demonstrating that Mo element aggregates in substitutional solid solution (Ti,Al)(O,N) columnar grain in the form of metal Mo.  
TiMoAlON films have low reflectivity in the spectrum range of 300-900 nm, and they increase sharply in the near-infrared spectrum range. Absorptivity in the visible spectrum increases with Al content. When Al content was more than 10 at.%, absorptivity was almost in the spectrum range from visible to infrared, but absorptivity decreased in the ultraviolet spectrum range. When Al content was increased to 12 at.%, absorptivity, \(\alpha\), decrease by 0.05 in the experimental conditions. After baking in atmosphere at 500 \({}^{\circ}\)C for 8 h, the film had the highest absorptivity when doped with 2 at.% Mo. In the visible-light range, \(\alpha\) = 0.97, and in the whole ultraviolet-visible-light near-infrared spectrum range, \(\alpha\) = 0.94, and emissivity (\(\varepsilon\)) = 0.02 at room temperature and \(\varepsilon\) = 0.10 at 500 \({}^{\circ}\)C.  
Conceptualization, methodology and writing, H.G.; validation, H.Y.; investigation, X.C.; data curation, S.D. All authors have read and agreed to the published version of the manuscript.  
The authors are thankful for the valuable contributions and equipment support by [PERSON], Xi'an University of Technology.  
The authors declare no conflict of interest.
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 4 Experimental Details  
### Ultrasonic Scattering Hardware  
In the experiment, the frequency bands generated by the transducer are practically limited, unlike in numerical studies. In addition, the corresponding scattering regime is based on the wavelength of acoustic waves. The size of fine dust compared to the applied wavelength (\(\lambda\)) is calculated as the size parameter approximation (\(x\)), which is expressed as:  
\[x=\frac{2\pi a}{\lambda} \tag{10}\]  
where \(a\) is the radius of the scatterer. The size effect is categorized by the scatterings of Mie and Rayleigh when the size parameter is approximately one and smaller than one, respectively. The ultrasonic transducer was carefully selected with a fine dust diameter of 10 \(\upmu\)m (PM10). The applied transducers have a center frequency of 5 MHz and wavelength of approximately 68 \(\upmu\)m in air (with an acoustic wave velocity of 343 m/s at 20 \({}^{\circ}\)C). The size parameter approximation is approximately 0.46, which is considered under Rayleigh scattering with an acceptable ISA range.  
To measure ultrasonic multiple scattering through air with fine dust, an air-coupled testing scheme is designed based on the suggested attenuation measurement. Figure 2a presents a schematic illustration of the experimental set-up. The experimental hardware includes a pair of air-coupled transducers (Ultran NCT5-D13), power amplification (E&I 210L), waveform generator (Keysight 33500B), and digitizer (NI-PXIe-1071). To obtain the attenuation of the coherent wave, the distance between the transmitter and receiver is continuously and accurately increased using a DC-micromotor (Fraubaber 3242024 CR). The DC-micromotor is carefully controlled by a pulse, where 10 pulses are required for a linear motion in a 0.2 \(\upmu\)m distance (encoder: 1000 pulse and gear ratio: 43:1). Practically, 1500 pulses are selected and the corresponding moving distance is 300 \(\upmu\)m. The developed controller system is presented in Figure 2b, which shows the control button and monitor.  
Figure 1: Conceptual illustration of signal processing for scattering attenuation.
## 2 Integrated Seat Suspension and MR Damper Model
### MR Damper Controller  
The force generated by the MR damper '\(F_{mr}\)' depends on the control input \(u\) which is related to the supply voltage \(v\). The supply voltage was controlled by the Heaviside step function adopted from [PERSON] et al. (2018). The governing equation of the Heaviside step function is given by Equation (18):  
\[v=V_{max}H((F_{c}-F_{mr})F_{mr}) \tag{18}\]  
\[F_{c}=K_{p}e(t)+K_{i}\int e(t)dt+K_{d}\frac{d}{dt}e(t) \tag{19}\]  
Here, \(V_{max}\) in Equation (18) is the saturation voltage of the coil in the MR damper and \(H\) is the Heaviside step function. The value of the supply voltage \(v\) depends on \(F_{c}\), i.e., the desired controlled force, which is estimated by the FLST-PID and the force produced by the MR damper. In Equation(19), \(K_{p},~{}K_{i}\)_and_\(K_{d}\) are the PID gain which were determined through fuzzy logic. \(e\) is the sprung mass velocity error. Equation (19) allows to calculate the controlled force \(F_{c}\) (desired force) which feeds into Equation (18). Equation (18) permits to determine the input voltage of the MR damper.  
When \(F_{c}>F_{mr}\), it means that the requirement of the control force \(F_{c}\) (desired force) is greater than the force produced by the MR damper \(F_{mr}\). Therefore, the voltage must be increased to its maximum value to satisfy the requirement. When \(F_{c}=F_{mr}\), the control force (desired force) is equal to the MR damper force \(F_{mr}\). There should be no change in the voltage to maintain the voltage. In cases other than those mentioned above, the voltage remains zero. In this case, there is no requirement for activating the MR damper.  
If the force produced by the MR damper is equal to the desired control force, then the supply voltage should remain constant [24].  
If the magnitude of the MR damper force is less than the desired force and if both forces are in the same direction, then the applied voltage is the maximum to increase the force generated by the MR damper. Otherwise, the supply voltages are sets to zero [24]. The parameters of the MR damper model used for the numerical simulation were taken from References [19; 26] and are mentioned in Table 2.
# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 2 Related Works
However, muscle and adipose tissue are large structures that can be more easily perceived. The authors did not research any other small issues that may lead to potential lesions. Reference [35] performed conditional Generative Adversarial Networks (cGANs) as a robust and potentially improved method for semantic segmentation than U-Net. Their works showed better results. However, they only consider three types of anatomical structures. The target objects are still apparent in MRI segmentation. Moreover, the attainment of cGANs is generally harder than U-net because the model requires more hyperparameters, and more training attempts were involved in adversary. Reference [36] analysed how 2D U-net functioned on cartilage and meniscus segmentation of knee MR imaging data for morphology and relaxometry compared with manual segmentation. They found that U-Net demonstrates efficacy and precision in quickly generating accurate segmentations that can be used to monitor and diagnose osteoarthritis. However, their model only considers cartilage and meniscus, and their experiments did not achieve 3D modelling. To our knowledge, it is very difficult to detect many small targets simultaneously in one model, and there is no existing research performing many small target segmentation in the knee joint MRI dataset. As per the biomedical aspect, especially MRI segmentation, researchers focus on improving the segmentation accuracy of an anatomical structure. [PERSON] et al. proposed ResNet-14 CNN, which achieves higher performance on knee ligament segments [37]. [PERSON] et al. utilised a Dilated CNN to construct a cardiac MRI segmentation network that has produced the most satisfactory evaluation on dice coefficient [38]. [PERSON] et al. introduced AssemblyNet, a large ensemble CNN network for whole-brain MRI segmentation [39]. Their networks defeated the U-Net baseline, which is also one of our baseline models. However, although these models generally give better performance on testing images, they may ignore recognising specific small anatomical structures. We also found that CNN-based models have played a significant role in, and positively influenced, real-world medical applications [37; 38; 39; 40]. This paper takes advantage of the above-mentioned encoder-decoder architecture to build improved networks, i.e., U-net variants and DeepLabv3 plus variants. They showed a greater capability in experiments to segment small structures without losing the accuracy of large structures.
## 2 Requirements Specifications and Measurements Gaps
### Sea Ice Cover  
This variable requires a revisit time of 3 h, with a minimum requirement of 12 km of spatial resolution and an accuracy of 5%. In the time frame 2020-2030, Sentinel-1 with a C-band SAR and Sentinel-3 with an SRAL radar altimeter are the Copernicus missions capable of measuring this variable. At high latitudes, Sentinel-1 presents a revisit time \(<\)1 day, and its utilization period is from 2014-2030. MetOp is a contributing Copernicus mission that can provide sea ice cover data thanks to the Micro-Wave Imager (MWI, instrument operated by ESA and EUMETSAT), with global coverage once per day and a spatial resolution of 25 km. The utilization period for MWI is from 2022-2043. The Sentinel-1 and MetOp missions do not meet the 3-h revisit time at a 12-km horizontal spatial resolution. In this regard, the formulation of potential technologies to measure sea ice cover is necessary. In this way, microwave radiometers, SAR, SAR altimeters and GNSS-R are technologies capable of measuring this variable.  
Microwave radiometers can measure this variable using the 19.35-, 37- and 90-GHz channels (e.g., the Special Sensor Microwave, SSM/1; the Special Sensor Microwave-imager/sounder, SSMIS, with a spatial resolution of 25 km) [36]; or the 18.7- and 89-GHz ones (the Advanced Microwave Scanning Radiometer for Earth Observation System, AMSR-E, with a spatial resolution of 12.5 km) [37]. However, in order to achieve the high spatial resolution required, the antennae of these instruments should be enlarged. Currently, dual-polarization GNSS-R has emerged as a promising technique to measure sea ice cover and thickness. Experiments with the mission TechDemoSat-1 have demonstrated the capability and high accuracy of this instrument to conduct these types of measurements in [22; 38; 39].
## Results
### Satellite-Scale: Results for Various Plant Functional Types
#### 3.4.2 Relationships of Cl\({}_{\rm green}\) and P\({}_{\rm max\_capacity2000}\)  
Figure 10 presents the linear relationships between Cl\({}_{\rm green}\) and P\({}_{\rm max\_capacity2000}\) for the seven plant functional types. For CA-Let, JP-TMK, JP-TKY, JP-Mase, and JP-FJY, the R\({}^{2}\) values of the Cl\({}_{\rm green}\)-P\({}_{\rm max\_capacity2000}\) correlations were 0.81, 0.84, 0.67, 0.95, and 0.70, respectively. A linear correlation was not found for TH-SKR, as Cl\({}_{\rm green}\) and P\({}_{\rm max\_capacity2000}\) changed very little. Additionally, a linear correlation was not observed for US-Dk1. The linear regression functions (\(y=\) ax \(\pm\) b) for CA-Let, JP-TMK, JP-TKY, JP-Mase, and JP-FJY are summarized in Table 4. Differences in these empirical equations reveal the characteristics of each plant functional type.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Site name** & & **VPD** & * **P\({}_{\rm max\_capacity2000}\);** & \\
**(Year 2003)** & **Respiration (Rec) Equation** & **Threshold** & **mg-CO\({}_{2}\)-m\({}^{-2}\)-s\({}^{\dagger}\)** & **ntial slope** \\  & & **(kPa)** & **(Growing Season)** & **(\(\mathbf{a_{slope}}\))** \\ \hline CA-Let & Rec = 0.29 exp(0.037T\({}_{\rm air}\)) & 2 & 0.47 (Apr–Sept) & 0.0029 \\ JP-TMK & Rec = 0.3 exp(0.08T\({}_{\rm soil}\) ) & 2 & 1.37 (May–Oct) & 0.0016 \\ JP-TKY & Rec = 0.23 x 0.23 exp(0.08T\({}_{\rm air}\)) & 2 & 0.97 (May–Sept) & 0.0023 \\ JP-Mase & Rec = 0.18 exp(0.06T\({}_{\rm air}\)) & 2 & 0.96 (May–Aug2) & 0.0017 \\ JP-FJY & Rec= 0.25 x 0.25 exp(0.08T\({}_{\rm air}\)) & 2 & 1.00 (Apr–Oct) & 0.0014 \\ US-Dk1 & - ** & 2 & 0.70 (Apr–Oct) & 0.0035 \\  & Rec = & & 1.18 (Apr–Oct, & \\ TH-SKR & 0.025 \(\times\) 2.57 exp((T\({}_{\rm air}-10)/10)\) & 2 & except May2 and & 0.0009 \\  & & & June) & \\ \hline \hline \end{tabular}  
* Averaged values (averaged over growing season), T\({}_{\rm soil}\) = Soil temperature, T\({}_{\rm air}\) = Air temperature, VPD = Vapor Pressure Deficit; ** Rec was not calculated because AmeriFlux provided GPP for this site.  
\end{table}
Table 3: Summary of the growing season average P\({}_{\rm max\_capacity2000}\) and initial slope, and the VPD threshold for the seven plant functional types.  
Figure 10: Relationships between Cl\({}_{\rm green}\) calculated from 16-day period MOD09A1 data and P\({}_{\rm max\_capacity2000}\) and maximum light-use efficiency (LUE\({}_{\rm max}\)) for the seven plant functional types in 2003.