esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Computational Complexity Comparison  
The computational complexity of ADMM, AltMin, and the proposed MIDE algorithms depends on the number of iterations \(K\). The compared algorithms had a different number of iterations to reach convergence with different antenna configurations. We fixed the number of receiving antennas to \(N_{r}=128\), and the number of transmitting antennas \(N_{t}\) was increased from 16 to 84. Further, we set \(K=5,\) 14, and 10 for the conventional ADMM algorithm, the AltMin algorithm [20], and the proposed MIDE algorithm, respectively, based on the convergence simulations. Figure 6 illustrates the total  
Figure 4: BER performance comparison of the conventional ADMM and the proposed algorithm.  
Figure 5: BER performance comparison of the AltMin algorithm, the LMMSE algorithm, and the proposed algorithm.  
number of multiplications vs. the number of transmitting antennas, which was based on the analysis of the computational complexity of the algorithms in Section 3.3.  
From Figure 6, we can see that the computational complexity increased with the number of users in all compared algorithms. However, the proposed MIDE algorithm achieved the lowest computational complexity among all compared algorithms under various antenna configurations. Specifically, the MIDE algorithm showed a relatively lower computational complexity than the AltMin algorithm, which was proven to have a low complexity detection for uplink massive systems in [20]. In addition, from Figure 6, the proposed MIDE algorithm achieved much lower computational complexity than the LMMSE and ADMM detection algorithms when the dimension of the MIMO system became larger. As a consequence, the proposed MIDE detection is much more applicable for massive MIMO systems with its low computational complexity.
## 1 Introduction
[19] provided two new hybrid intelligent techniques (i.e., M5 Rules-PSO and M5 Rules-GA) to predict the CL and HL of EEB systems with high accuracy, i.e., RMSE of 0.0066, 0.0548, and R\({}^{2}\) of 0.999, 0.998, for the M5 Rules-PSO and MSRules-GA, respectively. Additionally, many other studies used/applied/developed AI techniques for evaluating and predicting energy consumption as well as its efficiency [20; 21; 22; 23; 24]. According to the best review of the authors, meta-heuristics algorithms with a combination of ANN model was considered and developed in many areas with high reliability [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]; however, they are not still considered and prepared for estimating the HL of EEB systems. Therefore, this study developed and proposed four novel hybrid models based on four meta-heuristics algorithms and ANN model, for estimating the HL of EEB systems, namely PSO-ANN, GA-ANN, imperialist competitive algorithm (ICA)-ANN, and artificial bee colony (ABC)-ANN models. Four meta-heuristics algorithms were considered in this study, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models.
## 2 Materials and Methods
### TEM Analysis  
TEM analyses were conducted for a detailed examination of possible ultrastructural changes in the cells after being treated with different concentrations of EEP and control solutions. After determining the cytotoxic concentration of EEP extract, the following samples were evaluated using TEM:  
1. Cells exposed to culture medium only (untreated negative control);
2. Cells exposed to 3 M 12.5% EEP (lowest concentration that was cytotoxic);
3. Cells exposed to 3 M 6.25% EEP (highest concentration that was non-cytotoxic);
4. Cells exposed to 6 M 12.5% EEP (lowest concentration that was cytotoxic);
5. Cells exposed to 6 M 6.25% EEP (highest concentration that was non-cytotoxic).  
Transmission electron microscopy was not performed on the 2.5% NaOCl group due to profound cell damage and lysis, which resulted in the absence of a cell pellet for analysis. The sample preparation for TEM in this study was performed in accordance with the protocol by [PERSON] et al. [43]. The M3 CT3-E1 cells were cultured in a tissue culture flask (Thermo Fisher Scientific, Waltham, MA, USA) in a humidified atmosphere at 37 \({}^{\circ}\)C, 95% air and 5% CO\({}_{2}\). After incubation with 3 M 12.5% EEP, 3 M 6.25% EEP, 6 M 12.5% EEP, 6 M 6.25% EEP and control solutions for 24 h, a single cell suspension for each treatment group was collected via trypsinisation with 0.25% trypsin/EDTA (Thermo Fisher Scientific, Waltham, MA, USA). The cell suspension was then centrifuged at 125\(\times\)\(g\) for 10 min. The cell pellet collected was fixed in 4% glutaraldehyde (EMS, Hatfield, PA, USA) in 0.1 M PBS (Thermo Fisher Scientific, Waltham, MA, USA) for 12 h at 4 \({}^{\circ}\)C. Post-fixation was performed with 1% osmium tetroxide (EMS, Hatfield, PA, USA) for 2 h at 4 \({}^{\circ}\)C. After that, the samples were dehydrated in ascending order of acetone (HmbG, Petaling Jaya, Malaysia) and embedded in epoxy resin (Agar Scientific, Stansted, UK). The ultrathin sections (60-100 nm) were prepared with an ultramicroome and treated with uranyl acetate (Ted Pella, Inc., Redding, CA, USA) and lead citrate (BDH, Dubai, UAE) stains before being analysed under TEM (Carl Zeiss, Oberkochen, Germany).
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Oil Condition Monitoring  
Oil condition monitoring is used to measure engine oils, lubricating oils, and other fluids for detection of lubricant engine wear and related problems to reduce downtime [108]. Continuous oil condition monitoring in machineries and rotary systems is one of the rapidly growing areas for both predicting and preventing their failure. Ahmadi and Mollazade [109] demonstrated the effectiveness of oil condition monitoring techniques in determining the best oil for Dump Truck HD325-5 (used in transportation of minerals). Similar oil condition monitoring systems are available in the literature [110; 111; 112]. Du and Zhe [113] used a high-throughput inductive pulse sensor for online oil debris monitoring. In another study, Xia and Huo [114] developed oil monitoring methods based on information theory.  
Mackos et al. [115] developed a fluid quality sensor to monitor the oil quality applicable for commercial, military, and off-highway vehicles.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 6 Conclusions and Future Work
The improved multi-GNSS PPP-AR processing technique is also intended to be applied to next-generation low-cost, single- and dual-frequency receivers currently being released. There is also the need for an adaptive approach in the application of correct and realistic ionospheric constraints. Experiments and results were conducted and generated, respectively, by [PERSON]; [PERSON] reviewed and revised the paper. This research was funded by Natural Sciences and Engineering Research Council of Canada. Authors acknowledge the contribution of data from International GNSS Service (IGS), German Research Centre for Geosciences (GFZ) and Centre National d'Etudes Spatiales (CNES). The authors declare no conflict of interest.
## 5 Results
### Optimal Design Parameters  
As outlined in Sections 4.5 and 4.6, the well-trained ANN model was applied to a larger dataset to predict the averaged reflection coefficient, while the WSM was used to address the two objectives in the optimization process. Table 3 lists the top 10 combinations of design input features for an inclined porous plate, ranked according to their overall scores. These combinations yield a lower averaged reflection coefficient with a minimal spatial footprint, based on their assigned weightings, for three different wave steepness values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{S. No} & \multicolumn{6}{c}{100\%:0} & \multicolumn{6}{c}{80\%:20\%} & \multicolumn{6}{c}{60\%:40\%} \\ \cline{2-11}  & \(\frac{l}{h}\) & \(\theta\)(deg) & \(R_{f}\) & \  
Figure 10 shows the reflection coefficient against the wave period for the top-ranked optimal design parameters of the wave absorber (see Table 3) selected for each wave steepness (\(H/\lambda\)) with a weighting factor ratio of (80% : 20%).  
Figure 10: _Cont._
## 3 Proposed Architecture
### Network Architecture
Similarly, every node has its unique identifier strings including \"nodeld\" and \"preamble\" which is always used by the nodes to ID themselves while checking their formatted packet strings separated by a specific delimiter such as:  
\[\text{String Data = nodeld+ `.^{\prime}$ + preamble + `.^{\prime}$ + dataLength() + `.^{\prime}$ + data + `.^{\prime}$ + originNodeld + `.^{\prime}$ + endBit; + `/n';} \tag{3}\]  
Interference  
The standard parameter for detecting and thereby handling the interferences includes packet error rate (_PER_), bit error rate (BER), signal-to-interference-plus-noise ratio (SINR), Received Signal Strength Indicator (RSSI), Throughput, and time delay. In this study, we have applied a frequency agility-based interference avoidance algorithm. This algorithm enables the ZigBee to detect the interference and flexibility to shift the nodes to a safe channel with minimum energy consumption and latency during handling interference. In this algorithm, the end devices measure _PER_ with a transmission period of at least 20 packets and if the _PER_ exceeds the 25% level, the interference information is communicated to the parent router to evaluate the link quality indicator (LQI). Here, the parent router identifies that the LQI value is less than 100, then it directs to perform interference detection of available channels by scanning energy detection (ED). The _PER_ is calculated with the following Equation (3):  
\[PER=\left(\frac{Number\ of\ failed\ messages}{Number\ of\ attempted\ measurements}\right)*100\% \tag{3}\]  
Other than this, all of our nodes in the network have their unique ID parameters such as nodeld and preamble. All nodes only communicate if the ID parameters included in the received string from the other node trying to communicate have all the ID parameters valid. Otherwise, the communication is rejected and ignored. This allows the system to make sure that only the nodes that are allotted to them or are desired to communicate can communicate with each other. Figure 5: Mechanism of monitoring end device with 2.4 GHz-based ZigBee and 433 MHz based LoRa.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 3 Methods  
### System Construction  
A multiple-diffraction subtractive double-monochromator (MSDM) mount composed of two multiple-diffraction monochromators can significantly enhance spectral resolution while effectively suppressing stray light. Figure 4a shows the MSDM mount. We considered double diffractions as an example to demonstrate the principle and analysis of the proposed mount. This approach involves two double-diffraction monochromators, FM and SM, connected by slit S\({}_{2}\) in series. S\({}_{2}\) is the exit slit of the FM and the incidence slit of the SM. The light to be tested from Fiber F enters the FM through the entrance slit S\({}_{1}\). After collimation by Lens L\({}_{1}\), it is dispersed by the reflective grating G for the first time. Subsequently, the diffracted light is reflected off Mirror M and returned to the reflective grating G for the second diffraction. The repeatedly diffracted light passes through Lens L\({}_{1}\) again; however, this time, it is focused to form monochromatic images on the plane of S\({}_{2}\). Only narrow-bandwidth light can pass through S\({}_{2}\), which determines the spectral resolution and prevents most of the stray light from entering the SM. In the SM, the light is diffracted twice again, then passes through the exit slit S\({}_{3}\), and is finally detected by Detector D. FM and SM have the same optical elements, only that the light path is reversed. They share M and G and have identical lenses, L\({}_{1}\) and L\({}_{2}\). The incidence angle of the SM is equal to the diffraction angle of the FM. Thus, the optical paths in the FM and the SM are symmetric about S\({}_{2}\). Due  
Figure 3: Subtractive double monochromator. (**a**) Schematic of subtractive double-monochromator configuration. F1, fiber; S\({}_{2}\), S\({}_{3}\), slits; L\({}_{1}\), L\({}_{2}\), L\({}_{1}\)\({}^{\prime}\), L\({}_{2}\)\({}^{\prime}\), lenses; M\({}_{1}\), M\({}_{2}\), mirrors; G\({}_{1}\), G\({}_{2}\) gratings; D, detector. S\({}_{2}\) measures single-monochromator stray light; S\({}_{3}\) measures subtractive double-monochromator stray light. (**b**) Stray-light level in subtractive double-monochromator mount, which is square of that of single monochromator.  
to the reversibility of light, the rays on G in the FM are divergent; however, in the SM, they are confluent, as shown in Figure 4b,c. The wavelength neighboring the sharp spectral line that passes through S\({}_{2}\) merges again in the SM, which effectively reduces the optical noise. Briefly, the multiple-diffraction mount increased the spectral resolution, and the symmetric FM and SM suppressed the stray light.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Low-Power Thrusters
#### 4.1.4 Magnetic Nozzles  
Like electrosprays, interest in microwave and RF thrusters as a practical electric propulsion solution has reignited in recent decades. The s-curve in Figure 3 depicts the relative immaturity of low-power RF nozzled thrusters until recently. As these devices have no plasma-wetted electrodes, they can be used with a variety of propellants; further, they can be easily scaled to low power due to the efficiency of inductive plasma generation. However, high performance (high specific impulse) versions of these devices are still at the lower end of the s-curve. Early work on these devices showed promising performance, but high specific mass due to the power electronics involved [34; 35]. The intervening decades have seen a miniaturization and commodification of these electronics, such that RF nozzled thrusters are now being targeted for use on small satellites. Although an example of this technology has yet to fly, studies of magnetic nozzles are slowly improving their efficiency above single digits [36], and commercial developers are already validating flight units [37]. Yet, there are still many outstanding questions about the fundamental operation of these devices, the foremost being the detachment of electrons from magnetic field lines necessary for thrust generation [38; 39; 40]. Figure 7 shows an example of a modern laboratory microwave thruster that utilizes electron cyclotron resonance for plasma heating [41].
## 3 Utilization of Precursor Compounds from Primary Metabolism in Secondary Metabolism
### Supply of Key Precursors Carbon, Nitrogen, Phosphate and Sulfur for Secondary Metabolism
#### 3.2.4 Supply of Sulfur for Secondary Metabolism  
Sulfur is an important building block for secondary metabolites. The oxidized form of sulfur is present as a sulfuryl moiety (\(-\)SO3-) that can transform amines in proteins, polysaccharides and lipids. Inorganic sulfate (SO42-) can be taken up by active transport across the cell membrane, e.g., by the CysTWA SubI ABC transporter complex in mycobacteriateria. In further steps, sulfate can be assimilated. For instance, in mycobacteria, this process involves ATP sulfurylase (encoded by _cysND_) or APS kinase (encoded by _cysC_) [115].  
The transfer of -SO\({}^{3-}\) to hydroxyl or amino groups of biomolecules is crucial for the regulation of cell-cell communication and metabolism. Sulfite can be reduced to sulfide (S\({}^{2-}\)) by sulfite reductase (encoded by _nirA_), which is used for the biosynthesis of the sulfur-containing metabolites cysteine, methionine, coenzymes and mycothiol [59]. Sulfur availability has been demonstrated to be important for the methylation reactions catalyzed by SAM. Sulfur can occur in some secondary metabolites, e.g., in lantibiotics, and as a component of the amino acids cysteine and methionine [115].  
In _Streptomyces_ spp., sulfotransferase enzymes, which transfer a sulfate group onto an acceptor molecule, have been shown to be responsible for the formation of sulfated liponucleoside antibiotics [116]. Mycolithiol has been found in all Actinobacteria and it regulates the cellular redox status and is thus essential for _M. tuberculosis_ survival [117]. Furthermore, reduced sulfur-containing metabolite coenzyme A (CoA) is required for lipid and cell wall metabolism [59].
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Satellite Data for Coastal Monitoring
[34] combined Sentinel-2 and Landsat imagery in order to observe coastal erosion on Senegal's Langue de Barbarie. Combining historical and modern-day satellite imagery made it possible to map significant shoreline displacement due to anthropogenic disturbances and sea-level rise. Similarly, [PERSON] et al. [35] combined Landsat, Sentinel-2, and ancillary data in order to map long-term shoreline retreat due to coastal degradation and subsidence on the northern coast of Java in Indonesia. Together, these studies highlight the efficacy of Sentinel-Landsat synergy in facilitating coastal monitoring activities [18]. The combined datasets enhance the spatial resolution without compromising historical depth for vigorous trend analysis. Not only is this union instrumental in increasing the accuracy of shoreline detection under varying environmental circumstances, but it is also an adaptable and open-access solution for coastal area management. As illustrated in the literature surveyed, multi-sensor fusion is useful for both retrospective analysis and near-real-time applications, making it invaluable for climate adaptation, spatial planning, and evidence-based decision-making for vulnerable coastal areas. Complementing each other, Sentinel's temporal frequency and Landsat's temporal extent ensure an excellent foundation for shoreline monitoring [51].
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 2 Methodology
### Applied Algorithms for Predicting Lung Cancer
#### 2.12.7 Gaussian Naive Bayes  
When working with continuous data, it is often assumed that the values for each class follow a Gaussian (normal) distribution. To handle this, the training data are divided by class, and the mean and standard deviation for each class are calculated. By using this, the probabilities for the continuous data can be estimated using the following formula [38].  
1. Bayes theorem Consider \(Y\) as the class variable (lung cancer status) and \(X=(x_{1},x_{2},\ldots,x_{n})\) as the feature vector. \[P(Y|X)=\frac{P(X|Y)P(Y)}{P(X)}\] (8)
2. Gaussian likelihood function The likelihood \(P(X|Y)\) is expressed as \[P(X|Y=y)=\prod_{i=1}^{n}P(x_{i}|Y=y)\] (9)For Gaussian-distributed features, \(P(x_{i}|Y=y)\) follows the Gaussian probability density function:  
\[P(x_{i}|Y=y)=\frac{1}{\sqrt{2\pi\sigma_{y}^{2}}}\exp\left(-\frac{(x_{i}-\mu_{y}) ^{2}}{2\sigma_{y}^{2}}\right) \tag{10}\]
3. Class-conditional probability The class-conditional probability, therefore, takes the following form: \[P(Y=y\mid X)\propto P(Y=y)\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi\sigma_{y}^{2}}} \exp\left(-\frac{(x_{i}-\mu_{y})^{2}}{2\sigma_{y}^{2}}\right)\] (11)
4. Prediction The predicted class \(\hat{y}\) is obtained by maximizing the posterior probability: \[\hat{y}=\operatorname*{argmax}_{y}P(Y=y)\prod_{i=1}^{n}P(x_{i}|Y=y)\] (12)
## 2 Parallelisation of DEM
### GPU Parallelisation of \"Calculate Contact Forces\"
#### 2.2.3 Hybrid Scheme  
A hybrid scheme to detect the contact pair list is developed here, which follows the two-step framework of the advanced algorithm (Figure 3). In the first phase of the contact detection operations, the computational domain is divided into a grid of coarse cells. The sorting of particles into each cell can be parallelised across the GPU threads over the particles. However, a data race may be induced at the common cell that is shared by neighbouring particles: the result of writing different particle IDs (i.e., \(1\ldots N\)) concurrently into a common memory address is unexpected. Therefore, an expanded particle list of each cell is adopted by further dividing the coarse cell into a number of finer ones (Figure 2b). Each fine cell corresponds to a specific memory address of the particle list of a coarse cell and accommodates only one particle or less. That means the data race can be avoided by writing the particle IDs into different memory addresses of the particle list, which is determined by the coordinates \(X_{i}\) of the particles (as in Algorithm 3, where \(N_{c}\) is the total number of the coarse cells). The size of the fine cells should be smaller than \(2R^{\text{min}}\) to avoid accommodating more than two particles in one fine cell, where \(R^{\text{min}}\) is the minimum radius of the particles. The expanded particle list is sparse due to a large number of empty spaces, which means a heavy memory requirement. An additional compression step is then performed to obtain a dense particle list. The potential contact pair list will be updated in a number of incremental steps, in between which the particles would not cross different coarse cells. The number of incremental steps to regenerate the contact pair list can be determined by trial calculations and empirical predictions.  
At the second phase, the essential contact force on each particle is calculated by checking all the particles in its neighbour coarse cells, which is parallelised across the GPU threads over the particles. For a specific particle \(i\), it may be in contact with all the particles in its range of \(R_{i}+R^{\text{max}}\), where \(R^{\text{max}}\) is the maximum radius of the particles. Therefore, we construct the coarse cells with a uniform size of \(2R^{\text{max}}\) and all the particles in 27 neighbouring cells for three-dimensional analysis are involved in the calculations. For each contact pair of particles, Equations (1)-(12) are used to calculate the contact forces between the particles.  
Figure 3: Flowchart of parallelisation operations in each incremental step.