esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 5 Discussion
### Influence of Multi-Granularity Structure  
We also conducted an ablation study to analyze the effects of applying the Local Focus Scheme (LFS) and further verify the performance of different granularities. Specifically, we only use one of three branches to obtain the final results, respectively. Next, we test the performance of different combinations. The ablation studies in Table 4 show the importance of applying multi-granularity on the overall results, Dual Sub-Pooling (DSP) has a performance gain compared with global pooling (GP), and LFS outperforms both GP and DSP across all performance metrics. Moreover, the combination of them has a peak superiority. The above results indicate that LFS can learn fine-grained representation to mine more discriminative local information, and each granularity can be viewed as the complementary features of each other.
## 3 Verification by Comparison with Previous Experimental Results
### Scope of the Application of the RC Beam Plane Conservation Assumption
#### 3.2.3 Investigation of the Estimation Accuracy Based on the Plasticity Ratio \(R_{\rm p}\)  
The value obtained by dividing the maximum deflection \(\delta_{\rm u}\) by the tensile rebar yield deflection, \(\delta_{\rm y}\), is defined as the plasticity ratio \(R_{\rm p}\). It is an index generally used to evaluate ductility in the seismic design of RC piers.  
\[R_{\rm p}=\delta_{\rm u}/\delta_{\rm y} \tag{19}\]  
Figure 10 shows the relationship between the estimated and experimental values of the plasticity ratio. The figure shows that when the plasticity ratio \(R_{\rm p}\) is large, the estimated ratio is higher than  
Figure 8: Comparison between the estimated and measured maximum deflections.  
Figure 9: Investigation of the estimation accuracy based on the deflection ratio \(R_{\rm D}\).  
the experimental result \(R_{\rm p.exp}\). It is observed that the maximum deflection \(\delta_{\rm u}\) can be estimated with relatively high accuracy when the plasticity ratio \(R_{\rm p}\leq 10\). In addition, if \(R_{\rm p}>10\), the estimation accuracy is low. A high plasticity ratio indicates that the RC beam has a high degree of damage.  
The estimated value of \(R_{\rm p}\) is calculated by assuming a bending deformation based on the plane conservation of the section of the RC beam in the calculation. Therefore, it is assumed that all transmitted energy is consumed by the bending deformation of the beam. In contrast, in the experiment, energy is usually consumed by not only the bending deformation, but also damage to the concrete at the collision point, pull-out of the reinforcing steel, and opening of shear cracks. Therefore, the estimated \(R_{\rm p}\) tended to be higher than \(R_{\rm p.exp}\) when the plasticity ratio \(R_{\rm p}\) was high.
## 2 Materials and Methods
### Image Enhancement  
The pixel values of remote sensing images are generally stored in 10 or 11 bits. In order to facilitate subsequent superpixel segmentation, the images are converted to 8 bits by 2% linear stretching. Then, the logarithmic stretching is used to expand the darker part of the image by utilizing its property of steep change in a low-value range so that the difference between real shadows and non-shadowed dark regions in the enhanced image is more obvious. The logarithmic function is defined as follows:  
\[g(x,y)=c\cdot\log_{(1+v)}(1+v\cdot f(x,y)) \tag{1}\]  
where \(c\) is a constant coefficient, usually set to 1. The larger \(v\) indicates the higher degree of logarithmic stretching, and the pixel value of the original image at \((x,y)\) is \(f(x,y)\in[0,1]\). Figure 2 illustrates the logarithmic curves corresponding to different values of \(v\).  
Figure 3 illustrates the images and their corresponding histograms after different degrees of logarithmic stretching. The values of dark vegetation and shadow in the image before stretching are similar, which cannot be effectively distinguished by the naked eye, while after logarithmic stretching, the dark vegetation and shadow are easier to distinguish visually. It can be seen that logarithmic stretching can show more details of the low gray part of the image and highlight the shadow in the image, which is important for the subsequent superpixel segmentation and shadow detection.  
Figure 1: Flowchart of the shadow detection method combining topography and spectra (CTS).  
Figure 2: The logarithmic stretch functions of different \(v\).
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
###### Abstract  
Photovoltaic (PV) power generation may vary with respect to several factors such as solar radiation, temperature, power conditioning units, environmental effects, and shading conditions. The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. The main reason for efficiency reduction under partial shading conditions is the creation of multiple local maximums and one global maximum operating point. The classical Maximum Power Point Tracking (MPPT) algorithm fails to determine the global maximum operating point to prevent power losses under partial shading conditions. In this study, a novel hybrid MPPT method based on Perturb & Observe and Particle Swarm Optimization that mainly aims to determine global operating point, is proposed. The proposed hybrid MPPT method is tested under different partial shading conditions and variable irradiance levels. In this manner, the dynamic response of the system is remarkably increased by the proposed MPPT method. To show the superiority of the developed method, a performance comparison is conducted with the P&O- and Kalman-Filter-based MPPT methods. The obtained results illustrate an improvement around 1.5 V in undershoot voltage and 0.2 ms in convergence speed. In addition, the overall system efficiency of the PV system is increased around 2% when compared to the P&O- and Kalman-Filter-based MPPT methods. Consequently, the proposed method seems to be an efficient method in terms of undershoot voltage, convergence time, tracking accuracy, and efficiency under partial shading conditions.  
1  
comparison of MPPT algorithms; MATLAB Simulink; Maximum Power Point Tracking (MPPT); MPPT methods; partial shading situations
## 3 Results
### A Universal Prediction Model for SSC of Multiple Blueberry Varieties
#### 3.5.2 PLSR Model Based on UVE Algorithm  
In order to screen out the characteristic wavelengths that are highly correlated with blueberry SSC and reduce the influence of irrelevant variables on model stability, the UVE algorithm was used to select the features of the jointly preprocessed spectral data (224 data points). Two hundred random noise variables were introduced into the original spectral matrix, and the \(CV_{max}\) of the random noise variables was used as the threshold to eliminate the uninformative variables, and finally, 117 variables were screened for modeling, and the processing results are shown in Figure 7.  
As can be seen in Figure 7a, the vertical black solid line distinguishes the actual spectral feature wavelengths on the left side from the random noise variables on the right side, and each scatter in the figure represents the CV value of a single variable. The horizontal dashed line delineates the CV threshold range, and variables with CV values below the threshold are all uninformative variables for the blueberry SSC prediction model, while variables with CV values above the threshold are retained as valid characteristic wavelengths. Based on this criterion, 117 highly correlated wavelengths were screened out, and the results of the PLSR prediction model were constructed as illustrated in Figure 7b, whose predicted set dispersion point distributions showed a significant linear trend and a good fitting effect. In order to comprehensively evaluate the optim  
Figure 6: PLSR model prediction results based on S-G, MSC and SNV after joint processing.  
Figure 7: UVE processing results. (**a**) Results of UVE screening variables. (**b**) PLSR model predictions after wavelength selection by UVE.  
algorithm, Table 3 compares the model performance metrics for the three types of spectral data (original full spectrum, preprocessed full spectrum, and UVE-screened spectrum).  
Analysis of the data in the table shows that after wavelength screening by the UVE algorithm, the number of variables in the model was reduced from the original 224 to 117, and at the same time, the number of LVs was also optimized from 21 to 20, which realized the streamlining and optimization of the model structure. Compared with the preprocessed full-spectrum model, the prediction accuracy of the model after UVE screening decreased slightly, with the \(R_{P}^{2}\) decreasing from 0.94 to 0.93, and the RMSEP increasing from 0.33% to 0.35%, with a decrease in accuracy of about 6.1%. However, compared with the original full-spectrum model, the model after UVE screening still showed excellent prediction performance, and the RPD was kept at a high level of 3.70, indicating that the model still had high quality. Meanwhile, UVE screening also improves the computational efficiency of the model and reduces the development cost of efficient detection instruments.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### GroEL1  
GroELs (or Cpn60s) are a group of chaperonins that help newly translated proteins fold and maintain specific structures [41]. Mycobacteria express two forms of GroEL--GroEL1 and GroEL2--through nonadjacent genes [42]. GroEL1 possesses a histidine-rich C terminus, and the attB site and has been verified to be important to mycobacteria biofilm maturity by research on two aspects. Firstly, integrating _M. smegmatis_ phage Bxb1 at the attB site inhibits the function of GroEL1 and impairs the ability of _M. smegmatis_ to form biofilm [29; 42; 43; 44]. Secondly, the _M. smegmatis_ mutant lacking the _groEL1_ gene (_\(\Delta\)groEL1_) fails to construct mature biofilm. Specifically, [PERSON] et al. clarified that GroEL1 is crucial for biofilm maturation instead of the initial attachment because GroEL1 modulates the synthesis of mycolic acids that are vital to biofilm maturation [29]. Though Mtb can encode similar GroEL1 proteins, it is still unclear whether the Mtb \(\Delta\)_groEL1_ mutant can exhibit similar defects in biofilm formation. [PERSON] et al. showed that \(\Delta\)_groEL1_ in slow-growing _M. bovis_ BCG generates immature biofilm, indicating thatGroEL1 plays a crucial role in the biofilm formation in _M. bovis_[30]. Furthermore, GroEL1 can counteract Cu\({}^{2+}\) ions, which inhibits mycobacterial biofilm growth [45]. These studies show that GroEL1 has a significant impact on mycobacterial biofilm formation.
## 4 Discussion
### Temperature Sensitivity of Re Components  
Our results showed that temperature sensitivity of Re_EC (Q\({}_{10\text{-}\text{Re}}\)) was 2.26, ranging from 1.95 to 2.74 from 2009 to 2011 (Figure 9a-c), which is slightly lower than 2003 (3.2) and 2004 (3.4) in this alpine meadow [26]. For one reason, warmer (about 1 \({}^{\circ}\)C) and drier (about 150 mm annual precipitation) climatic conditions played crucial roles for the decreased Q\({}_{10\text{-}\text{Re}}\) in this study compared to in 2003 and 2004, which could be evidenced in many relevant studies [89; 90; 91; 92]. Indeed, we also found that the year with cooler and wetter climate showed a greater Q\({}_{10\text{-}\text{Re}}\) amid these three years (Figure 8). For another reason, the higher Q\({}_{10\text{-}\text{Re}}\) was estimated only during the GS of 2003 and 2004, while we estimated Q\({}_{10\text{-}\text{Re}}\) in this study based on annual scale. Thus, the vegetation seasonality or the different contributions of Ra to Re was likely to exert important impacts on Q\({}_{10\text{-}\text{Re}}\) variation [93; 94].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Method** & **Year** & **Ra/Re (1 \(-\)\(\beta\)*\(\gamma\))** & **References** \\ \hline Alpine meadow, Tibet & Regression & 2011 & 0.20–0.91 & [45] \\ Alpine meadow, Qinghai & Plant exclusion & 2003 & 0.39–0.46 & [83] \\ Alpine meadow, Haibei & Regression & 2009 & 0.53–0.80 & [79] \\ Alpine meadow, Tibet & Satellite-based & 2009–2011 & 0.27–0.56 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-site comparison of autotrophic contribution to ecosystem respiration (Ra/Re) in the alpine meadows on the Qinghai-Tibet Plateau.  
Figure 9: Relationships between ecosystem respiration components and soil temperature from: 2009 (**a**); 2010 (**b**); and 2011 (**c**); and (**d**) a graph for data from all years, 2009 to 2011.  
After partitioning of Re components at seasonal scale, we found that temperature sensitivity of Ra_EC (Q\({}_{10,\text{Ra}}\)) with an average of 5.20 was significantly higher than that of Rm_EC with an average of 1.50 (Q\({}_{10,\text{Rm}}\)) (Figure 9). Our results confirmed that autotrophic and heterotrophic contributions of Re differed in their responses to temperature change [94; 95]. Furthermore, variation of Q\({}_{10}\) in Rm (1.42 to 1.60) was more stable than that in Ra (4.14 to 7.31) (Figure 9a-c), which implying that the ecosystem acclimation of Ra might be stronger than heterotrophic contributions of Re response to temperature change [95; 96; 97].
## 1 Introduction
In order to decrease gear vibration and obtain better transmission performance, many researchers have studied the nonlinear dynamic characteristics of the gear system [14; 15]. [PERSON] [16] and [PERSON] [17] analyzed the influence of several parameters on dynamic transmission errors and provided error excitation data to control gear vibration and noise. [PERSON] [18] studied the nonlinear frequency-response characteristics of the spur gear system and analyzed the nonlinear vibration characteristics under multiharmonic excitation. [PERSON] [19] investigated the influences of gear wear and bearing clearance on the dynamic responses of a gear system, and obtained a trend of factors such as translation displacement, meshing stiffness, and bearing clearance under gradual wear. In addition, [PERSON] [20] analyzed the nonlinear dynamic characteristics of a gear system from the perspective of system stability through bifurcation diagrams, phase diagrams, Poincare maps, and time-domain diagrams. [PERSON] et al. [21; 22] revealed the bifurcation and chaos of a multistage gear system. [PERSON] [23] and [PERSON] [24] analyzed the effect of tooth surface wear on the steady-state response of the gear system. [PERSON] [25] studied the pressure angle and backlash changes caused by bearing deformation in order to determine whether the gear system falls into a chaotic and unstable state. [PERSON] [26] studied the influence of excitation frequency, backlash, meshing damping ratio, and other factors on the nonlinear dynamic characteristics of a system with the aid of the stability criterion. Nonetheless, most of them ignored the nonuniformly distributed meshing force and its coupling with shaft deformation, dynamic center distance, and time-varying meshing stiffness. Therefore, it is difficult for them to provide effective guidance for designing the gear systems used in complicated working environments. In this paper, the dynamic modeling and nonlinear analysis of a spur gear system, considering a nonuniformly distributed meshing force, are researched. This paper consists of four sections. Following the introduction, a nonuniformly distributed meshing force model is developed in Section 2 via an 18-degrees-of-freedom bending-torsion-swing-coupled dynamic model of a pair of involute spur gears. Section 3 analyzes the influence of meshing frequency, stiffness excitation, damping, and error excitation on the nonlinear dynamic characteristics of the gear system. Reasonable parameter ranges that guarantee the gear system is in a stable motion are extracted. In the last section, the conclusion is summarized.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Implementation Details  
The proposed network was developed using the PyTorch framework, taking advantage of its dynamic computational graph and GPU acceleration features. The training was executed over 100,000 iterations on a cluster of four NVIDIA RTX 3090 GPUs (NVIDIA Corporation, Santa Clara, CA, USA), which facilitated efficient parallel processing and accelerated convergence times. The Adam optimization algorithm was employed to update  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Component** & **Layer \#** & **Layer Type** & **Output Shape** & **Activation** \\ \hline \multirow{4}{*}{U-Net Encoder} & 1 & Conv2D (3 \(\times\) 3, 64 filters) & (64, 64, 64) & ReLU \\  & 2 & Conv2D (3 \(\times\) 3, 128 filters) & (32, 32, 128) & ReLU \\  & 3 & Conv2D (3 \(\times\) 3, 256 filters) & (16, 16, 256) & ReLU \\  & 4 & Conv2D (3 \(\times\) 3, 512 filters) & (8, 8, 512) & ReLU \\ \hline U-Net Bottleneck & 5 & Conv2D (3 \(\times\) 3, 512 filters) & (8, 8, 512) & ReLU \\ \hline \multirow{4}{*}{U-Net Decoder} & 6 & ConvTranspose2D (3 \(\times\) 3, 256 filters) & (16, 16, 256) & ReLU \\  & 7 & ConvTranspose2D (3 \(\times\) 3, 128 filters) & (32, 32, 128) & ReLU \\  & 8 & ConvTranspose2D (3 \(\times\) 3, 64 filters) & (64, 64, 64) & ReLU \\  & 9 & ConvTranspose2D (3 \(\times\) 3, 3 filters) & (128, 128, 3) & Sigmoid \\ \hline \multirow{4}{*}{Uncertainty Estimation} & 1 & Conv2D (3 \(\times\) 3, 32 filters) & (64, 64, 32) & ReLU \\  & 2 & Conv2D (3 \(\times\) 3, 64 filters) & (32, 32, 64) & ReLU \\  & 3 & Conv2D (3 \(\times\) 3, 128 filters) & (16, 16, 128) & ReLU \\  & 4 & Conv2D (3 \(\times\) 3, 256 filters) & (8, 8, 256) & ReLU \\  & 5 & Conv2D (3 \(\times\) 3, 256 filters) & (8, 8, 256) & ReLU \\  & 6 & Conv2D (3 \(\times\) 3, 256 filters) & (8, 8, 256) & Sigmoid \\ \hline Patch-Wise Regularization & - & SVD \& Rank Loss & - & - \\ \hline Optimizer & - & Adam (LR = \(1\times 10^{-4}\), \(\beta 1\) = 0.9, \(\beta 2\) = 0.999) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed network architecture of the proposed LLIE framework.  
the network weights, and was selected for its adaptive learning rate mechanism and robustness in handling sparse gradients. An initial learning rate of \(1\times 10^{-4}\) was set and reduced by 20% every 5000 iterations to ensure stable convergence and prevent oscillations around the minima.
## 5 Experimental Results
### Expanded Scenarios for Validation
#### 5.3.2 Scenario 2: User in Nabeul  
* **User Location**: Nabeul (latitude: 40.7528, longitude: \(-\)73.9725). **Stores:** **Store DD** (Nabeul): Rating 4.3, Distance 2.5 km. **Store EE** (Sousse): Rating 3.8, Distance 5.0 km.  
Figure 7: Sequence diagram.  
* **Results:**  
Store DD receives the highest recommendation score due to its proximity and relatively high rating, as shown in Table 4.
## 3 Experimental Scenarios
### Scenario II: Experimental Results
As was also expected, the evaluation results outperformed those of their edge-based counterpart, as summarized in Table 9. While maintaining statistically significant improvements (all \(p<0.01\)), the adjusted effect sizes still revealed substantial practical differences. The \(256\to 8\) model showed a medium effect (d = 2.25), with the \(512\to 16\) variant demonstrating marginally greater impact (d = 2.40). These effects exceeded [PERSON]'s threshold for large effects (d \(>0.8\)) by 1.8-5 times, confirming the engineering significance of the model's temporal depth scaling. The cloud-based GRU models consistently outperformed their edge counterparts across all input-output configurations while using the same datasets, data preprocessing, and training setups. In the smallest setup (\(64\to 2\)), the cloud GRU achieved an RMSE of 0.468, representing a 21.9% improvement over the edge model's 0.599. As the sequence length increased, the performance advantage of the cloud GRUs became even more apparent. For the \(128\to 4\) configuration, the RMSE was 34.1% lower, and, for \(256\to 8\), the reduction remained significant at 32.8%. Even in the largest configuration (\(512\to 16\)), where gains typically diminished, the cloud GRU model still achieved a 13.7% reduction in RMSE compared to the edge model. These results highlight the scalability and increased effectiveness of GRU models when more computational resources and memory are available. The cloud GRUs learned more robust temporal patterns due to their greater capacity. As a result, the cloud-based GRUs outperformed the edge GRUs in terms of predictive accuracy across all tested configurations, offering up to 34% lower RMSE in mid-range settings and still delivering gains even in high-timestep conditions, with comparable inference times across both environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **RMSE** & **MSE** & **\(p\)-Value** & **Effect Size** \\
**(Input \(\rightarrow\) Output)** & **(Mean \(\pm\)\(\sigma\))** & **(Mean \(\pm\)\(\sigma\))** & **(vs. 64\(\rightarrow\)2)** & **(Cohen’s d)** \\ \hline \(64\to 2\) & 0.468 \(\pm\) 0.032 & 0.226 \(\pm\) 0.021 & – & – \\ \(128\to 4\) & 0.334 \(\pm\) 0.018 & 0.114 \(\pm\) 0.009 & \(<\)0.008 & 1.45 (small) \\ \(256\to 8\) & 0.213 \(\pm\) 0.011 & 0.046 \(\pm\) 0.003 & \(<\)0.007 & 2.25 (medium) \\ \(512\to 16\) & 0.201 \(\pm\) 0.009 & 0.041 \(\pm\) 0.002 & \(<\)0.009 & 2.40 (medium) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance measures with statistical validation for the GRU architectures (mean \(\pm\) std over 10 runs). Figure 9: The training vs. validation loss curve of the GRU model 4: \(512\to 16\), showing smooth training and validation loss curves over epochs.