esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 1 Introduction
### Contributions
#### 1.2.3 Block and Thread Mapping Strategies  
Using the CUDA framework, we implemented four GPU parallel thread mapping methods to maximize computational efficiency and GPU resource utilization. A brief description of each of the following methods is given below.  
* **BBM**: **Basis-Block Mapping**: each CUDA block is assigned to a Basis-ERI, and all CUDA threads within a CUDA block comput a Basis-ERI in parallel. This implementation is identical to the one proposed in our previous study and is used as the baseline for comparison.
* **BTM**: **Basis-Thread Mapping**: each CUDA thread is assigned to a Basis-ERI, with each CUDA thread independently evaluating a Basis-ERI.
* **Shell-Block Mapping**: each CUDA block is assigned to a shell-based ERI, and all threads within the CUDA block collaboratively compute a shell-based ERI in parallel.
* **Shell-Thread Mapping**: each CUDA thread is assigned to a shell-based ERI, with each CUDA thread independently evaluating a shell-based ERI.  
The definitions of Basis-ERI and shell-based ERI are provided in Section 2 and Section 4.1, respectively.
## 2 Materials and Methods
### Flow Field Hydrodynamics
To mitigate external effects of measurement system on the sensor body, it was mounted on a 1 m long rectangular steel pipe, and the large mass of the arm relative to the upstream flow was found to effectively dampen against oscillations of the mounting system. Bulk velocity estimates (\(n\) = 24, range: 0.3-1.2 m/s) used in Equation (2) were calibrated using a linear regression fit (\(R^{2}\) = 0.73) based on the time-averaged data obtained from an acoustic Doppler current profiler (ADCP Workhorse 1200 kHz; RDI Teledyne). The software WinRiver II (Teledyne Marine, Poway, USA) and Agila 7.6 (Bundesanstalt fur Gewasserkunde, Koblenz, Germany) was used for processing the ADCP data. Once calibrated using field data, the high-speed (50 Hz) velocity fluctuations from the DBox provide a flow turbulence distribution, which is not possible when using a moving ADCP [51]. The probe thus provides a new source of spatially distributed flow turbulence data. When combined with the ADCP time-averaged velocity field and fish spatial preferences, it becomes possible to study a wider range of hydrodynamic stimuli, which fish experience upstream of trash racks. Spatially resolved data is needed in order to understand the underlying mechanisms, which control fish migration in terms of the timing, direction and distances associated with underwater stimuli [52]. Figure 6: (**a**) Computational fluid dynamics (CFD) simulation results showing the spatial distribution of the pressure coefficient, \(C_{p}\), for a freestream velocity of 50 cm/s. The solid circle corresponds to the stagnation point, and the open circles the two differential ports \(\Delta\mathrm{P}_{1}\) and \(\Delta\mathrm{P}_{2}\). (**b**) Photograph of the device including the two differential pressure sensors, as well as the 9V power supply and SD data storage card. (**c**) Image of the DBox in the field at the HPP Kirchbichl, mounted on the large debris removal arm. Water flow direction shown as a blue arrow.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 2 Submarine Optical Networks Basics and Demonstrations Technologies on Submarine Cable Systems
### SDM Transmission Technologies in Long Haul Transoceanic Systems
#### 2.4.1 Features of SDM-Based Submarine Cable Systems
In the near future, we expect that SMFs can operate with a less than 200-\(\upmu\)m coating diameter [10], which technically can be achieved either with a thicker cladding diameter (<125 \(\upmu\)m) and the same coating thickness or by using 125-\(\upmu\)m cladding diameter and reduced coating thickness in ranges less than 200 \(\upmu\)m. For this, the mechanical reliability of new fiber design has to be guaranteed. In both cases, additional tests for bend performance have to be conducted.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 3 Advanced Applications of NBs in Precision Surface Cleaning  
NBs have become a phenomenal tool for improving surface cleaning processes in various fields. NBs provide effective and environmentally acceptable ways to remove pollutants because of their special qualities, including small size, large surface area, and capacity to produce ROS [19]. These bubbles work particularly well in removing stubborn organic or inorganic residues, dissolving biofilms, and cleaning hard-to-reach surfaces. They are used in many fields including food safety, wastewater treatment, medical cleansing, and semiconductor fabrication [8].  
By utilizing their physical and chemical interactions, such as improved adherence to pollutants, mechanical agitation during collapse, and the generation of ROS, which break molecular bonds and break down tough residues, NBs increase cleaning performance [6]. This makes them ideal for sensitive surfaces that can be damaged by traditional chemical or abrasive cleaning techniques. The following studies demonstrate how NBs can be used in various creative ways for surface cleaning, showcasing their adaptability and potential to completely transform cleaning methods across sectors globally.
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### Channel Attenuation Coefficient  
The Channel Attenuation Coefficient \(H_{ij}\) is modeled considering the effects of signal absorption and dispersion as it travels from the mirror to the receiver. This coefficient can be expressed as follows:  
\[H_{ij}(t)=\frac{1}{(1+d_{ij}(t)/d_{0})^{n}}\exp(-\alpha_{env}(t)d_{ij}(t)) \gamma_{ij}(t). \tag{20}\]  
where \(d_{0}\) is a reference distance; \(n\) is the channel attenuation exponent, which depends on the environment; \(\gamma_{ij}(t)\) represents the temporal effect of channel conditions on signal transmission; and \(\alpha_{env}\) is the environmental absorption coefficient that describes the signal loss due to the surroundings.
# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 3 Research Methodology  
To begin this research, raw images containing characters from A to Z and numbers from 1 to 9 were collected. The research methodology employed in this study combines both descriptive and experimental research. The first phase of the study involved a pre-processing step using the technique of skin masking to segment the hands in the images, which were then passed to Canny edge detection for sharp edge detection.  
Following this, feature extraction was carried out using the SURF model, resistant to rotation, perspective scaling, occlusion, and variation. The SURF features were clustered using mini-batch k means clustering and fed into the bag of visual words model. The output of the BoVW model was then classified using labels and matched with the appropriate sign language words. The combination of descriptive and experimental research methodologies allowed for a comprehensive analysis of the effectiveness of the proposed system. The system's accuracy was evaluated using several classification algorithms, including naive Bayes, logistic regression, K nearest neighbors, support vector machine, and convolutional neural network.  
The purpose of this research is to analyze the existing study and conduct even more versatile research based on modern tools and techniques that can take in gestures from the camera and convert them to text which will help to reduce the communication barrier between mute people and the rest of society.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Simulation Models
#### 3.7.2 Energy Model  
Energy consumption of devices plays a vital role in the D2D communication-based IoT 5G network, since devices are battery operated with limited battery energy. The device's mobility increases the energy consumption of the devices due to the increased control overhead signaling in the network. The maximum number of bits that can be transmitted is defined by the energy consumption ratio in transmitting one bit to the total available battery energy. There are four leading states of the device in the wireless network: transmission, reception, idle and sleep; each state's energy consumption is different. Thus, a Generic Energy Model [51] has been utilized to assess the energy consumption based on power consumption and time duration in each state through the equations below:  
\[E_{transmission}=P_{transmission}\times t_{transmission} \tag{9}\]  
\[E_{reception}=P_{reception}\times t_{reception} \tag{10}\]  
\[E_{idle}=P_{idle}\times t_{idle} \tag{11}\]  
\[E_{sleep}=P_{sleep}\times t_{sleep} \tag{12}\]  
where \(E_{transmission}\), \(E_{reception}\), \(E_{idle}\), and \(E_{sleep}\) denote the energy consumption for transmission reception, idle and sleep, respectively. \(t_{transmission}\), \(t_{reception}\), \(t_{idle}\), and \(t_{sleep}\) are the time duration of the device and \(P_{transmission}\), \(P_{reception}\), \(P_{idle}\), and \(P_{sleep}\) stand for the power consumption in the states.  
Following the _Generic Energy Model_, the parameters \(P_{reception}\), \(P_{idle}\), and \(P_{sleep}\) are configurable according to the deployment condition, while \(P_{transmission}\) contains the signal transmitted power, which could be calculated through the following:  
\[P_{transmission}=\alpha VP_{t}+P_{CO} \tag{13}\]  
Figure 3: A general network scenario to validate and verify the MBMQA routing scheme.  
where \(P_{t}\) is the power of the transmitted signal, \(\alpha\) stands for the power amplifier coefficient, \(V\) denotes the voltage supply in volts and \(P_{CO}\) refers as the power consumption of the signal in the entire path. For the purpose of the simulation, the power consumption of the devices in sleep and idle mode was not considered in the simulation. These modes consume a limited amount of energy as compared to when the device is transmitting or receiving any data. Therefore, the total energy consumption (\(E_{total}\)) of a device for transmission and reception of packets can be calculated as:  
\[E_{total}(i)=E_{transmission}+E_{reception} \tag{14}\]  
where \(E_{transmission}\) is the total energy consumed during transmission and \(E_{reception}\) is the total energy consumed during reception. The general energy model provides parameter values such as time spent and energy consumption in various states and significantly affects the routing scheme. The MBMQA routing scheme utilizes the battery energy parameters for selecting the optimal source and destination pair route. In addition, these parameter values were used in lifetime models of devices.
## 3 Results and Discussion
### Results of the Two-Level System of Multi-Objective GAs  
#### 3.2.1 Results for Level 1: Multi-Objective GA Based on the ARIMA Model  
In this part of the study, we tested four populations individually using the multi-objective GA based on the ARIMA model to generate forecasting data for the two different cost objects. The forecasted data for each population obtained with 15 different generations were then evaluated using the second level. The second level evaluation helped in deciding the best generation of the forecasted data. In this section, we present only the best forecasted curves with the historical data because of the huge number of possibilities considered in this study.  
Figure 4 shows the forecasted labour data curve for 20 months from 2013 to 2015, for the second population and, specifically, for generation 13. In addition, it shows the historical data with the polynomial trend to illustrate the relationship between the independent variables over a timeline with monthly intervals. The selected labour data show a better forecasting than that obtained with the ARIMA model in that the forecasted data are close to the actual data and the polynomial trend. The ARIMA parameters for the selected labour cost data covering 47 months were \(p=0.22\), \(d=1\) and \(q=0.23\).  
Figure 4: Labour cost data forecasting using the multi-objective GA based on the ARIMA model.  
Figure 5 shows the curve for the forecasted material data for 20 months from 2013 to 2015, for the third population and, specifically, for generation 10. In addition, this figure shows the original data with the polynomial trend to illustrate the relationship between the independent variables over a timeline with monthly intervals. The selected material data show better forecasting than that obtained with the ARIMA model in that the forecasted data are close to the actual data and the polynomial trend. The ARIMA parameters for the selected material cost data covering 52 months were \(p=0.39\), \(d=1\) and \(q=0.43\).  
The forecasted data for the labour and material cost objects were evaluated using the second level, applying a multi-objective GA based on the statistical forecasting error rate. The model for the forecasting accuracy evaluated the forecasted data for 20 months from 2013 to 2015 based on the actual values of this period. Implementing level 2, the accurate forecasted data were found, i.e., the proper selection of data for each object to be used for forecasting.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 3 Realization of Fault Diagnosis Real-Time Processing System
### Fault Diagnosis and Information Missing Analysis
The operation status of _Px_ can be analyzed according to the difference between action expectation and actual action value, as shown in Equation (12):  
\[\left\{ \begin{array}{cc}-0.2&<\;E_{P_{x}}\;-\;T_{P_{x}}\;<\;0.2\;P_{x}\;operates\; normally.\\ 0.2&\leq\;E_{P_{x}}\;-\;T_{P_{x}}\;\leq\;1\;\;P_{x}\;\;maloperation.\\ -1&\leq\;E_{P_{x}}-\;T_{P_{x}}\;\leq\;-0.2\;P_{x}\;refuses\;action.\end{array} \right. \tag{12}\]  
According to the above method to determine faulty elements and the method to analyze operation statuses of protections, an algorithm defined on the power grid network \(G\) is given below. The fault diagnosis of each element in _V_e3_ is performed, and the operation statuses of related protections are analyzed. The implementation flowchart of Algorithm 3 is shown in Figure 8.  
Figure 8: Algorithm 3 flow chart. In the following, the simple grid fault shown in Figure 3 is used as an example to show the algorithm process. Initialize \(G\) and input \(V\_e3\) = [B1]. From Step 3, obtain \(CB\_1\) = {CB4}, \(CB\_2\) = {}. B1.BP_B1.\(p\) = 0.0275, B1.RBP_12_B3.\(p\) = 0.9312 are calculated in Step 4. B1.\(p\) = 0.9331 is calculated in Step 5, which exceeds 0.8. And \(V\_e4\) = [B1]. In Step 8 and Step 9, the action exceptions of protections related to B1 fault are calculated, respectively, and BP_B1.\(E\) = 0.9098, RBP_12_B3.\(E\) = 0.8957. As BP_B1.\(T\) = 1, RBP_12_B3.\(T\) = 1, the difference between the action expectations and actual values of the two protections is in the range of (\(-\)0.2, 0.2). BP_B1 and RBP_12_B3 operate normally. In summary, the faulty element in the power grid is B1. CB4 refuses to trip. There is no protection maloperation or refuse-operation phenomenon.
## 2 Materials and Methods
### Development Stages of the Crop  
SAR and optical images were obtained at three development stages of the soybean crop. These stages were selected based on the vegetative peak, determined by a time series based on the EVI (Figure 2b). The maximum value of the EVI coincides with the vegetative peak [6]. Moreover, this index is sensitive to dense vegetation, effectively reducing the atmospheric and soil background noise [26]. The three crop development stages of interest for image collection were:  
* Stage 1: Between planting and the vegetative peak;
* Stage 2: During the vegetative peak;
* Stage 3: Between the vegetative peak and harvest.  
SAR images were acquired on dates close to the optical images to minimize differences in the crops' phenological stages, with additional care taken to ensure no rainfall occurred during data collection, as the C-band can be affected by precipitation [4].  
During the first development stage (Stage 1), the plants are just beginning to grow, and the radar (SAR) signal may be influenced by soil exposure, resulting in a low backscatter coefficient. Nevertheless, the type of soil also influences the SAR signal response, as well as soybean yield, potentially indicating a relationship between these factors. In the second development stage (Stage 2), when the crop reaches its vegetative peak and fully covers the area, the radar signal is influenced by the dense plant canopy. The backscatter coefficient tends to increase due to multiple scattering within the vegetation and between the vegetation and the soil. The density and structure of the canopy at this stage lead to a more complex interaction with radiation, increasing the SAR's sensitivity to biomass and vegetation water content. Additionally, the vegetative peak correlates well with the EVI vegetation index and soybean yield [27], suggesting that incorporating SAR data at this stage could enhance predictive performance. In the third development stage (Stage 3), the area is fully senescent, and both volume and moisture content begin to decrease. Despite this, the radar signal may still be influenced by the remaining plant structure. At this stage, SAR can detect changes in the texture and composition of senescent plants by interacting more with the dry branches and pods, which tends to correlate with the final crop yield. Figure 2a shows the similarity between EVI and _VV_ and _VH_ backscatter coefficient time-series.
## 4 Discussion
### Antimicrobial Potential of Biosurfactant  
Several microorganisms have been used for a wide range of BS production some of which with some antimicrobial properties [3; 4; 7; 11]. However, limited number of reports documented antimicrobial activity of BS obtained from Lactobacillus spp. CFBS obtained from few Lactobacillus spp., displayed antimicrobial activity at low concentrations [14]. Antiadhesive and antib Biofilm potential, however CABS are popular for their antiadhesive property rather than antimicrobial potential (concentration ranging between 4 and 50 mg/mL) [11]. In this work we have demonstrated antimicrobial activity of CFBS at a concentration of 625 ug/mL. More than 80% of antimicrobial effect of CFBS was achieved by the test organisms (_S. aureus_, _Ps. aeruginosa_, _B. subtilis_ and _E. coli_) used in this study. [PERSON] et al. [9] showed 76.8% antiadhesive activity at a 50 mg/mL of CABS isolated from _L. paracasei_ spp. _paracasei_ A20 against _S. aureus_.  
The growth of bacteria like _E. coli_ ATCC and _Y. enterocilitica_ were inhibited at a concentration of 25 mg/mL for CABS isolated from _L. plantarum_ CFR 2194. The same surfactant at both concentrations was found to be ineffective against _S. typli_. The possible mode of action of CFBS needs thorough investigation. BS can act on microbial system through interfering the membrane functions and energy generating mechanisms. BS can reduce the cell surface hydrophobicity and microbial adherence to surfaces. Thus, microbial colonization can be prevented or reduced. BS can also increase the cell permeability and can cause the leakage of the metabolites. Alteration in physical membrane structure as well as interference of the protein conformations was also usually reported [4].
ensure that the minimum acceptable thickness is obtained in places exposed to operating conditions. Furthermore, there are a lot of sophisticated and advanced solutions such as laser cladding [15; 16] and laser hybrid [17] deposition, which provide smooth and sound coatings, but application on large areas and geometrically complicated surfaces such as heat exchangers or steel tubes in power generation boilers are not applicable because of the high cost of producing.  
CMT (Cold Metal Transfer) as a variant of the MAG welding process from FRONIUS is considered as interesting for thin metal sheet welding [18] and the protective or remanufacturing coatings deposition processes [19]. Low welding power and a safely low dilution of the deposited material with substrate material as well as good stability and wettability, together with a relatively high productivity demonstrate its potential use for applying metallic coatings by pad welding [20]. Subsequently, the surfaces of the deposited coatings were remelted by the TIG process to obtain smooth surfaces without gaps and irregularities, characteristic of MAG/CMT surfacing. There are scientific reports about the significant influence of the protective coating's surface-shape imperfections on its wear resistance and corrosion resistance in boiler-work conditions [21]. Abrasive wear, as defined by ASTM, is due to hard particles that are forced against and moved along a solid surface. Wear is defined as damage to a solid surface that involves progressive loss of material, and is due to relative motion between that surface and a contacting substance [22]. The tribological performance of cladded material is usually related, with the properties of microstructure [23].  
Literature analysis of the problem of surfacing nickel alloys [24] confirms the validity of the CMT method [16]. Generally, it is possible to obtain flawless and high-quality padding welds. The Heat Affected Zone (HAZ) could be thin, when compared to the similarly produced weld by classical MIG (Metal Inert Gas) welding. The size and geometry of crystallites in the weld zone, i.e., large dendrites, are similar to those obtained in a classic MIG process. The residual stresses are found to be minimal. This work by [PERSON] et al. demonstrates that the CMT welding is fully suitable for the welding of high nickel alloys [17].  
Four protective coatings were made on the substrate of 16 Mo3 steel from various high-alloy materials, such as Inconel 718, Inconel 625, Stellite 6 and Alloy 33, which are used by default to work in increased and high-temperature applications [25]; however, in this work, tests were carried out to verify the wear resistance at room temperature, because previous reports for this temperature level are few.