esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This work enhanced the performance of the optimization algorithm based on the mating behavior of NMRs. The proposed ENMRA used enhanced exploration and exploitation properties, concepts of hybridization, and a division of population strategy to improve the performance of a basic NMRA optimizer. A GWO-based exploration strategy and LNS- and DE-based exploitation characteristics helped attain good results compared to competitive algorithms. The neighborhood search around the best solution caused the algorithm to converge to the global optimum faster than the competitive algorithms. The effectiveness of the proposed algorithm was evaluated using a CEC 2019 benchmark test suite. The superiority of ENMRA was confirmed using the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The overall effectiveness (\(OE\)) summarized the performance of ENMRA over compared algorithms.  
The interaction of essential underground communications functions was also addressed in this study, and a distributed cross-layer framework called DECEN for the effective utilization of bandwidth-limited MI channels was presented. The DS-CDMA technique provided reliable transmissions links with little network information by leveraging ENMRA. Furthermore, statistical delay constraints were guaranteed, and the best link performance was realized through analytical cross-layer design resolution for the given code length. According to simulation outcomes, the DECEN had lower latency, higher throughput, and lower energy usage.  
Other modified variants of the suggested ENMRA can also be proposed to tackle optimization issues with binary, discrete, or multi-objective problems. The suggested method can also be integrated with opposition-based learning, mutation, disruption, and Levy flight for improved performance. Finally, research into the use of ENMRA in other disciplines, such as industry and engineering applications, signal denoising, cloud computing task scheduling, image-processing applications, resource management applications, big data applications, feature selection, neural networks, and other real-world applications, would be a valuable contribution.  
Conceptualization, P.S., R.P.S., and Y.S.; methodology, P.S., R.P.S., and Y.S.; software, P.S., R.P.S., and Y.S.; validation, P.S., R.P.S., and Y.S.; investigation, P.S., R.P.S., Y.S., and M.F.I.; writing--original draft preparation, P.S., J.S., and M.F.I.; writing--review and editing, P.S., Y.S., J.S., and M.F.I.; visualization, R.P.S. and Y.S.; supervision, R.P.S., Y.S., and M.F.I.; project administration, P.S., R.P.S., J.S., and M.F.I.; funding acquisition, J.S., and M.F.I.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by Sejong University Research fund.  
Not applicable.  
Data Availability StatementData are available from the authors upon reasonable request.  
[PERSON] would like to thank the Deanship of Scientific Research, Prince Sattam bin Abdul Aziz University, for supporting this work.  
The authors declare no conflict of interest.
# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
## 3 Experiments and Results
### Performance Comparison with Other Environment-Aware Methods
Results show that the proposed CGBNN-5 obtains the enhanced SDR values of \(6.74\) dB in \(0\) dB SNR cases for four specific noise interferences. The enhanced SDR values of NC-DNN, NC-OMLSA, and NC-MMSE are lower under the same conditions, which are \(6.05\) dB, \(6.30\) dB, and \(6.13\) dB, respectively. For the evaluation of residual noise levels, the enhanced SNR of CGBNN is \(4.85\) dB, that is, higher than NC-DNN (\(4.12\) dB), NC-OMLSA (\(3.29\) dB), and NC-MMSE (\(2.63\) dB). CGBNN-5 yields better speech quality and intelligibility than the NC-DNN model, while reducing the memory storage burden by \(35\%\), as shown in Table 2.
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
## 4 Methodology of the Proposed Three-Layer Granule Models
### Granulating in the Input Space  
DBSCAN can generate clusters that have arbitrary shapes, and its output is non-parametric. This causes difficulty on its extension for online clustering, unlike \(k\)-means or fuzzy \(c\)-means (FCM) [45], which can utilize cluster centers for online clustering directly. In the literature, several incremental DBSCAN algorithms were proposed to utilize core points for clustering updated data. However, the number of core points in big data is still huge. Therefore, this paper proposed to granulate core points, and to use prototypes of granules to direct online clustering. Contrary to incremental DBSCAN algorithms based on grids and partitions, granules in this study have noting to do with shapes of original clusters, but they could be used to reconstruct the shaped clusters. On the other hand, since  
Figure 2: Diagram of the basic DBSCAN clustering.  
an information granule is a set of similar points, the computation cost will be reduced by only considering the prototypes of granules in GrC.
## 4 Experiments
### Glaucoma Classification  
For glaucoma classification, both the ROI images (cropped) and whole images (uncropped images) were used as the inputs for the proposed RF-based and CNN-based models. To train the RF-based model, deep features were extracted by feeding all retinal images, both those cropped with the YOLO and those uncropped, to the trained CNN models. Previous layers from the fully connected layer of trained DenseNet201, ResNet50V2, InceptionV3, and VGG19 models were used to extract features. The number of features obtained from these layers is as follows: 1920 features from the \"global_average_pooling2d_12\" layer of the DenseNet201 model, 2048 features from the \"global_average_pooling2d\" layers of the ResNet50V2 and InceptionV3 models, and 25,088 features form the VGG19 model.
## 5 Conclusions  
This paper analyzed the alignment tolerance of double-diffracted spatially separated heterodyne grating interferometer with a mechanical fixture of our design. The double-diffraction structure by corner-cube retro-reflectors doubles the optical fold factor and also converts the angular mismatch into a position mismatch, which improves the alignment tolerance. We analyzed the alignment tolerance from three aspects: quantitative research, qualitative simulation and experiments. The results depicted that the designed structure stays accurate with the \(\pm\)1100 arcsec roll movement, \(\pm\)440 arcsec yaw movement, \(\pm\)280 arcsec pitch movement and \(\pm\)0.6 mm stand-off error when -10 dB intensity loss is afforded. Besides, the output results for different given crucial structural parameters shows that to achieve higher alignment tolerance, shorter diffracted beam length and wider laser beams are required. Since the separating distance does not affect the interfering intensity, the conclusions are also suitable for a common optical path configuration.  
[PERSON] established the simulation model, designed the experiments, and prepared the original draft. [PERSON] designed the structure and took part in the formal analysis. [PERSON] contributed to the conceptualization, project administration, and resources. [PERSON] took part in the experiments, data curation, and visualization. [PERSON] contributed to the supervision and funding acquisition.  
The authors acknowledge gratefully the support of the National Natural Science Foundation of China (Project codes: 51605120, 51675138 and 61675058) and the National Science and Technology Major Project (Project code: 2017 ZX02101006-005).  
The authors cordially thank our co-workers [PERSON], [PERSON], and [PERSON] for their helpful assistance and valuable suggestions.  
The authors declare no conflict of interest.
## 4 Survey Results and Analysis
### Exploratory Factor Analysis
#### 4.3.5. Summary Factor Analysis  
We also notice that \"the rotation converged in 3 iterations\".  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Extraction Sums of Squared**} & \multicolumn{3}{c}{**Rotation Sums of Squared**} \\ \cline{2-10}
**Component** & \multicolumn{3}{c}{**Total**} & \begin{tabular}{c} **\% of** \\ **Variance** \\ \end{tabular} & \begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Variance** \\ \end{tabular} & \begin{tabular}{c} **\% of** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Variance** \\ \end{tabular} & \
\begin{tabular}{c} **\% of** \\ **\%** \\ \end{tabular} &
\begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} \\ \hline
**1** & 7.569 & 58.220 & 58.220 & 7.569 & 58.220 & 58.220 & 5.774 & 44.415 & 44.415 \\
**2** & 1.469 & 11.299 & 69.519 & 1.469 & 11.299 & 69.519 & 3.264 & 25.105 & 69.519 \\
**3** & 0.995 & 7.656 & 77.175 & & & & & & \\
**4** & 0.559 & 4.300 & 81.475 & & & & & & \\
**5** & 0.431 & 3.318 & 84.793 & & & & & & & \\
**6** & 0.387 & 2.981 & 87.774 & & & & & & & \\
**7** & 0.344 & 2.646 & 90.420 & & & & & & & \\
**8** & 0.292 & 2.242 & 92.663 & & & & & & & \\
**9** & 0.260 & 2.000 & 94.663 & & & & & & & \\
**10** & 0.218 & 1.674 & 96.337 & & & & & & \\
**11** & 0.196 & 1.506 & 97.843 & & & & & & \\
**12** & 0.147 & 1.128 & 98.971 & & & & & & \\
**13** & 0.134 & 1.029 & 100.00 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total variance with Principal Component Analysis (PCA).  
Figure 1: Scree plot for QI Analysis.  
There are two Strategic directions (S1, S2) with eigenvalues which are greater than 1.00 and which explain 69.519% of the total variance. This is greater than the threshold of 60% which is required for satisfactory construct validity [48].  
Table 8 shows the results from the rotated component matrix. It can be noted that all values are greater than 0.45 [48].  
The first direction strategy accounted for 44.415% of the variance (with rotation sums of square loadings) and contains the QIs I.13, I.11, I.12, I.10, I.14, I.09, I.05, I.06 and I.08. This strategic direction 1 will be called \"Availability for online training\" (S1).  
The second direction strategy accounted for 25.105% of the variance (with rotation sums of square loadings) and contains the QIs I.02, I.01, I.03 and I.04. This strategic direction 2 will be called \"Challenges of online training\" (S2).  
According to the data presented in Table 7, the [PERSON]'s alpha coefficient value for strategic direction 1 is 0.930. This index does not increase if any indicator is removed. The value of [PERSON]'s alpha for strategic direction 2 is 0.864. This index also does not increase if we remove any indicator. In both cases, we obtain a value higher than 0.70, which shows that the items have a high internal consistency.
## 5 Maximum Likelihood Estimation  
A Brownian motion depends both on its starting point and the underlying Riemannian metric. We can consider both parameters of the model and, given the data, seek to estimate the parameters by the maximum likelihood (MLE). The resulting optimal starting point will in this case be a diffusion mean [31]. As visualized in Figure 1, the pushforward measure of a Brownian motion generated by a non-invariant metric induces distributions on the quotient space, and these distributions will be anisotropic if the metric on the top space is not bi-invariant. Here, we describe a setting for estimation of the underlying metric by the maximum likelihood.  
Consider i.i.d. observations \(y^{1},\ldots,y^{n}\) on \(G\) or \(G/H\). Let \(p_{t}(\cdot|\theta)\) and \(\pi_{*}p_{t}(\cdot|\theta)\) be the densities of Brownian motions with parameters \(\theta=(g,A)\), where \(g\) represents the starting point and \(A\) the metric tensor at \(g\). The inverse \(A^{-1}=\Sigma\) can be thought of as the covariance of the model. We obtain a likelihood as  
\[\mathcal{L}(\theta|y^{1},\ldots,y^{n};T)=\prod_{i=1}^{n}p_{T}(y^{i}|\theta), \tag{27}\]  
and, similarly, \(\pi_{*}\mathcal{L}=\prod\pi_{*}p\).  
The bridge sampling scheme introduced above yields approximations of the intractable transition densities in (27). In the \(d\)-dimensional Euclidean case, importance sampling yields the estimate [9]  
\[p_{T}(u,v)=\sqrt{\frac{\det(A(T,v))}{(2\pi T)^{d}}}e^{-\frac{\|u-v\|_{A}^{2}} {2T}}\mathbb{E}[\varphi_{T,v}],\]  
where \(\|x\|_{A}=x^{T}A(0,u)x\). Thus, from the output of the importance sampling, we obtain an estimate of the transition density. Similar to the Euclidean case, we here obtain an expression for the heat kernel \(p_{T}(e,v)\) as \(p_{T}(e,v)=q(T,e)\mathbb{E}[\varphi_{T,v}]\), where  
\[q(T,g)=\sqrt{\frac{\det A(v)}{(2\pi T)^{d}}}\exp\left(-\frac{d(g,v)^{2}}{2T} \right)=\sqrt{\frac{\det A(T,v)}{(2\pi T)^{d}}}\exp\left(-\frac{\|\text{Log}_ {g}(v)\|_{A}^{2}}{2T}\right), \tag{28}\]  
where the equality holds almost everywhere and \(A\in\text{Sym}^{+}(\mathfrak{g})\) denotes the metric \(A(e):=A(0,e)\). The \(\text{Log}_{g}\) map in (28) is the Riemannian inverse exponential map \((\text{Exp}_{g})^{-1}\).  
Algorithm 1 provides a detailed description of the iterative MLE approach. Visual examples of the iterative MLE can be found in Figures 4 and 6.
## 4 Energy Efficient Protocols, and Scheduling in WBSNs
### Energy Efficient Protocols in MAC Layer
The MAC frame structure (TDMA-based) consists of three parts: Beacon, Downlink, and Uplink. Beacon is used for synchronization, and also contains network information that is broadcasted to nodes periodically. Downlink comprises the transmission (unicast or broadcast) from the gateway to the nodes. Uplink is divided in a Contention Access Part (CAP) and a Contention Free Part (CFP) for access to the channel. The duration of these parts is adjusted adaptively by the gateway according to the traffic characteristics. Band allocation schemes (Burst Bandwidth, Periodic Bandwidth and Adjust Bandwidth) are used by the gateway in order to decide how long the downlink and uplink periods should be and how to share the bandwidth resource. The use of a flexible band allocation improves energy efficiency by reducing the possibility of collisions and the overhead of radio transmission times, idle listening and control packets. The main energy is wasted when nodes have to stay awake to receive potential data. Better performance of BodyMAC protocol compared to that of IEEE 802.15.4 in energy consumption and end to end delay is obtained. The medical medium access control, MEDMAC in [45], is a TDMA based MAC protocol with some features such as: contention free channel access, and time slots dynamically adjusted. A Multi-Superframe is suited where a beacon period establishes the number of time slots from 2 to 256 including the contention free period and an optional contention access period. The synchronization mechanism
## 2 Results
### Transcriptional Analysis of Genes Involved in the AsA Metabolic Pathways
Fruits were harvested from early development in June (Jn) to overmutative fruit in June of the next year (indicated as Jn*). The genes analyzed were _GMP_, _GME_, _GGP_, _GPP_, _GallDH_, and _GallDH_ of the L-galactose biosynthesis pathway; _GallR5_, _GallR8_, _GallR10_, _GallR12_, _GallR14_, and _GallR17_ of the D-galacturonic bio-synthesis pathway; _MIOX_, of the _mpo_-inositol pathway; _AO_, _APX1_, _APX2_, _APX3_, and _APX4_ involved in AsA degradation; and _MDHARI_, _MDHAR2_, _MDHAR3_, _DHARI_, and _DHAR2_ involved in AsA recycling. An expression value of 1 was arbitrarily assigned to the values obtained in the flavedo of fruit in June. Results are the mean \(\pm\) S.E of at least three replicates. Of the genes involved in AsA degradation, _AO_ experienced a marked reduction in July to undetectable levels in the remaining stages of fruit development. The expression of other degradation genes (_APX1_, _APX2_, and _APX4_) increased during development and maturation or experienced slight fluctuations (_APX3_) during the whole process (Figure 6B). Genes from the recycling pathway exhibited two patterns of expression: (i) a gradual increase until mature fruit in February (_MDHAR2_ and _MDHAR3_); or (ii) high transcript levels during early development which declined gradually during maturation (_MDHAR1_, _DHAR1_, and _DHAR2_) (Figure 6C). In the pulp, most transcripts related to the biosynthetic pathways accumulated at high levels in immature fruits and displayed two pattern of expression during development: (i) genes that were downregulated and reached low transcript levels in the pulp of mature fruits (_GalDH_, _GalUR5_, _GalUR10_, _GalUR12_, _GalUR17_, and _MIOX_); and (ii) genes that decreased their expression during fruit enlargement up to November, but increased it again during maturation (January to February) (_GMP_, _GPP_, _GME_, _GGP_, _GalLDH_, _GalUR8_, and _GalUR14_) (Figure 7A). Similarly, genes involved in AsA degradation (Figure 7B) and recycling (Figure 7C) also followed two expression patterns: (i) a downregulation after initial fruit development (_AO_, _APX1_, _APX3_, and _DHAR1_); and (ii) a reduction followed by an enhanced expression to reach high levels in the pulp of mature fruits (December to February) (_MDHAR1_, _MDHAR2_, _MDHAR3_, _APX4_, and _DHAR2_). Finally, the comparison of the expression of the different genes analyzed in the four tissues of Valencia Late oranges revealed a higher expression of most genes in leaves than in the flavedo, in the flavedo than in petals, and in petals than in the pulp. Genes of the L-galactose biosynthetic pathway and the recycling gene _MDHAR1_ were especially stimulated in leaves than in any other tissue, whereas expression of _APX2_ was substantially higher in fruit tissues than in leaves or petals. These results suggest that the content of AsA in each orange tissue appears to be coordinated by a complex and specific interplay between the relative expression of genes involved in the biosynthetic and recycling pathways and those involved in the degradation.
## 4 Implications for Digester Technologies Treating RMP Wastewater
### High-Rate Digesters:  
1. [label=()]
2. Covered high-rate anaerobic lagoons (CoHRAL) are an evolution of the low-rate CAL in which a degree of mixing and or heating has been incorporated. These systems still have a comparatively large footprint, whereby volumes of 20,000 m\({}^{3}\) are common. Mixing is typically achieved through the input of waste, the recirculation of sludge or the pumping of biogas through the sludge layer. While better mixing provides better degradation of FOG-rich substrates, it is unlikely that the turbulence generated by these methods will be sufficient to disrupt a floating crust layer.
3. Continuous stirred-tank reactors (CSTR) are typically operated at either mesophilic (38 \({}^{\circ}\)C) or thermophilic (55 \({}^{\circ}\)C) temperatures. These reactors are continuously stirred and, accordingly, the HRT and SRT are coupled [48]. Consequently, if biomass recycling is absent, high hydraulic loading reduces both the HRT and SRT and results in short-circuiting and sludge washout [48]. Under normal operating conditions, as demonstrated by [PERSON], [PERSON] [11] and [PERSON] et al. [27] CSTRs treating RMP waste perform well under high FOG loads.  
3. Upflow anaerobic sludge blanket (UASB) reactors decouple the SRT from the HRT and allow for high hydraulic throughputs, commonly 3-4 times the effective volume of the reactor in a 24 h period [49]. However, FOG disrupts formation of granular sludge and is consequently incompatible with UASB reactor technology [50]. Increasing the recovery of FOG from waste streams could improve the viability of UASB reactors as a treatment option for residual organics in the wastewater stream. The high protein content in RMP wastewater presents an added risk of ammonia inhibition.
4. Expanded granular sludge bed (EGSB) reactors are a subset of UASB reactors which are built taller to accommodate a larger volume of granular sludge which is typically developed on a bed of sand or other inert material, effectively enabling a higher organic loading rate. As with UASB reactors, a high hydraulic throughput can be achieved, but FOG is still problematic in establishing and maintaining a granular sludge bed [51]. The high protein content in RMP wastewater presents an added risk of ammonia inhibition.
5. Anaerobic filter reactors (AFR) contain a high surface-area packing medium which acts as a filter upon which a bacterial biofilm forms. Wastewaters are pumped through this filter and solids are retained and degraded by the bacteria grown in the filter material. Reactors are heated and additional mixing is unnecessary due to reactor design. FOG degradation appears to be good in other industries [52]. The high protein content in RMP wastewater presents an added risk of ammonia inhibition.
6. Anaerobic membrane bioreactors (AnMBR) decouple the SRT from the HRT by capturing solids using microfiltration membranes, allowing for high hydraulic throughputs [16]. Temperature is typically controlled at mesophilic (37 \({}^{\circ}\)C), and degradation of moderate FOG loading appears to be high [16; 53].
## 1 Introduction
[32] who implemented a parallelized thermo-elastic PFM algorithm in Abaqus/Explicit; and [PERSON] et al. [33] who established 3D thermo-mechanical PFM models for ceramic thermal shock fracture. While these simulations demonstrate satisfactory experimental agreement, they predominantly focus on idealized ceramic materials. Studies addressing thermal fracturing in geological materials--particularly reservoir rock with inherent mineralogical heterogeneity and differential thermal expansion properties--remain scarce. Furthermore, computational efficiency limitations persist: divergent numerical implementations within finite element frameworks yield significant variations in solution convergence rates and computational cost. Consequently, enhancing PFM's algorithmic efficiency constitutes a critical research thrust within the computational fracture mechanics community. The evolution of fracture networks under external loading is governed by the solution of diffusion-type equations subject to specific boundary constraints. Given COMSOL Multiphysics' demonstrated efficacy in handling multiphysics phenomena [34; 35; 36; 37], this study implements a TM-PFM within this platform to simulate thermal fracture evolution. Crucially,we leverage COMSOL's state variables to dynamically update the historical strain field--a methodological advancement that eliminates the need for auxiliary ODE/DAE interfaces required in conventional implementations. Utilizing this optimized framework, we simulate two fundamental thermal fracturing mechanisms in reservoir rock: (i) fracture initiation driven by steep thermal gradients during instantaneous thermal shocks; (ii) crack propagation induced by differential thermal expansion of heterogeneous mineral assemblages. Quantitative analysis of fracture topology evolution provides new insights into these distinct failure modes. The resulting mechanistic understanding offers actionable guidelines for optimizing thermal stimulation protocols in EGS, particularly regarding fracture network design for permeability enhancement.