esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 2 Proposed Facial Expression Recognition System Using the Mixture of CNN and SVM Classifier
### Fusion of CNN-Based Facial Motion Classifier and SVM-Based Geometry Displacement Classifier  
Both CNN-based classification of facial expressions using facial motion flow and SVM-based facial geometry displacement classification provide the probabilistic likelihood of seven facial expression classes. For the mixture of the CNN and SVM classifier, we applied a weighted summation of the two classifiers, assuming that both of the classifiers provide probabilistic outputs of each class. When \(P_{cnn}\) represents the CNN-based facial motion flow classification output, and \(P_{sum}\) represents the SVM-based facial geometry displacement classification result, the final output of the mixture classifier can be represented by  
\[P_{i_{ALL}}(t)=P_{i_{cnn}}(t)\times w_{cnn}+P_{i_{sum}}(t)\times w_{sum}, \tag{12}\]  
where \(w_{cnn}\) is the weight for the CNN-based classification, \(w_{sum}\) is for the SVM-based classification, and \(w_{cnn}+w_{sum}=1\). For each frame of the facial expression sequence, the  
Figure 5: Average landmark flow in different expressions: (**a**) Anger, (**b**) Contempt, (**c**) Disgust, (**d**) Fear, (**e**) Happiness, (**f**) Sadness, (**g**) Surprise. Blue line shows average intensity in each landmark point. Yellow dot shows the landmark flow in the image space.  
final classification label is selected by determining the maximum probabilistic value after the heterogeneous fusion of the CNN and SVM classifier by weighted summation.  
\[\text{Class Label}=argmax_{i}(P_{i_{ALL}}) \tag{13}\]  
For the final classification of the class label for a given facial expression sequence, the majority voting scheme is used. The class with the maximum number of class labels for the given sequence is chosen as the final class label for the given sequence.  
The prediction complexity of the RBF kernel SVM is \(\mathcal{O}(kd)\), where \(k\) is the number of support vectors, with \(d\) being the number of input dimensions. If we consider that the matrix computational complexity is \(\mathcal{O}(d^{3})\) and vector multiplication is \(\mathcal{O}(d^{2})\), in the CNN model, forward convolution layers are \(\mathcal{O}(d^{4})\), and fully connected layers are \(\mathcal{O}(d^{2})\). Therefore, the computational complexity of the CNN model is much higher than the SVM model. The computational complexity of the proposed model is dominated by the CNN model.
## 4 Improved PSO-Optimized Fuzzy-BPNN Control Algorithm
### Improved Particle Swarm Algorithm
#### 4.2.1 Standard Particle Swarm Algorithm  
The particle swarm algorithm (PSO) is an algorithm for population intelligence optimization. It works by constructing a population of M particles in a D-dimensional space, where each particle represents a potential optimal solution to the desired value. The system  
Figure 9: Fuzzy-BPNN-PID algorithm control schematic.  
sets each particle (\(i\) = 1, 2, \(\ldots\), M) to have a position parameter \(x_{i}\) and a velocity parameter \(v_{i}\), as well as a fitness value obtained according to the objective function. The particle evaluates the individual optimal position \(P_{id}^{k}\) and the population optimal position \(G_{id}^{k}\) of the particle based on the fitness value, and each time the particle is updated, it obtains a new fitness value as well as \(P_{id}^{k}\) and \(G_{id}^{k}\), thus gradually finding the historical optimal position \(P_{id}^{k}\) of the particle and the historical optimal position \(P_{gd}^{k}\) of the particle population [30]. The updated iterative equation is calculated as:  
\[V_{id}^{k+1}=\omega V_{id}^{k}+c_{1}r_{1}\left(P_{id}^{k}-X_{id}^{k}\right)+c _{2}r_{2}\left(P_{gd}^{k}-X_{id}^{k}\right) \tag{10}\]  
\[X_{id}^{k+1}=X_{id}^{k}+V_{id}^{k+1} \tag{11}\]  
where \(\omega\) is the inertia weight; \(d\) = 1, 2, \(\ldots\), D; \(i\) = 1, 2, \(\ldots\), M; \(V_{id}\) is the velocity of the particle; \(c_{1}\) and \(c_{2}\) are acceleration factors; and \(r_{1}\) and \(r_{2}\) are random numbers in the interval [0, 1].  
When using the PSO algorithm for parameter optimization, the performance evaluation index of the fitness function needs to be specified. The purpose of the algorithm in this paper is to reduce the value of deviation; therefore, this paper selected the time multiplied by the absolute value error integral criterion index as the fitness function \(J\) of the particle swarm algorithm, and the calculation formula is:  
\[J=\int_{0}^{n}t\cdot|e(t)|dt \tag{12}\]  
where \(n\) is the total number of iterative steps of the particle swarm.
## 1 Introduction
For example, the \(\Delta\)Ex3 variant is frequent in childhood leukemia, and its incidence correlates with shorter survival [34]. Contrarily, survivin variant 2B correlated with better survival in adult leukemia patients [34]. A response to chemotherapy depends on the presence of characteristic mutations. In particular, survivin promoter activity is potentiated by the AML1/ETO fusion gene [23; 35], which characterizes AML with a t(8,21) subtype. Importantly, survivin downregulation mediated by MUC1-C/\(\beta\)-catenin pathway targeting was reported to sensitize AML cells to cytarabine (Ara-C) treatment [33]. Similarly, downregulation of survivin induced apoptosis in acute lymphocytic leukemia (ALL) cell lines and significantly potentiated the antileukemic effect of chemotherapy [36]. In this study, we report on enhanced survivin level in a panel of AML-derived cell lines treated with Ara-C, which is a part of standard AML treatment, called \"7 + 3\". In this regimen, Ara-C is combined with an anthracycline. From the most used anthracycline variants, doxorubicin was mostly found to attenuate survivin level [16; 18; 37], whereas idarubicin (IDA) treatment upregulated survivin [38; 39]. Survivin silencing with small interfering RNA (siRNA) or short hairpin RNA (shRNA) potentiated an effect of chemotherapy on cell proliferation and survivin expression [35; 36]. Therefore, targeting survivin with specific inhibitor may improve cancer treatment. The first and the most tested drug is a small-molecule survivin inhibitor YM-155 [40]. It inhibits survivin transcription through direct interaction with the promoter of survivin gene. Clinical trials investigating the safety and tolerability of YM-155 were conducted with encouraging results [41; 42]. It was found to be effective in prostate carcinoma, melanoma, or NSCLC [43; 44; 45], albeit usually not as a single agent but in combination with chemotherapy. Several studies reported that YM-155 had more off-target effects, e.g., Mcl1 or XIAP inhibition [39; 46], DNA damage [27], or NF-xB inhibition [47]. We tested an effect of YM-155 and its combination with Ara-C on the AML cell lines, and we found a complementary effect of IDA and YM-155 on cell viability and survivin expression. We conclude that IDA effectively reverses the effect of Ara-C on survivin expression in the majority of leukemia cell lines. Moreover, survivin suppression occurs after YM-155 treatment in the cells resistant to IDA. Importantly, altered levels of other antiapoptotic proteins, Mcl1 and XIAP, considerably affect the response of AML cells to IDA or YM-155 treatment, either alone or in combination with Ara-C.
# Effect of Surface Roughness on Aerodynamic Loads of Bluff Body in Vicinity of Smoothed Moving Wall
## 2 Mathematical Formulation
### Computational Details  
The quantities and parameters that are input data for the numerical simulations, associated with the physical phenomenon or the numerical method, are as follows: the Reynolds number, \(\text{Re}\sim O(10^{4})\) and \(\text{Re}\sim O(10^{5})\); incident flow velocity in the X direction (see Figure 1), i.e., with zero angle of attack, \(\alpha=0^{\circ}\); initial dimensionless core size, \(\sigma_{0}=\sqrt{2\Delta t/\text{Re}}\); time increment, \(\Delta t=2k_{a}\pi/\text{Nc}\)[61], equal to 0.05 [15]; number of flat panels for discretizing the bodies: \(\text{Nc}=104\) panels for a smooth surface, \(\text{Nc}=48\) panels for a rough surface [15], and \(\text{Np}=100\) panels for a moving wall (the total number of panels is \(\text{Nt}=\text{Nc}+\text{Np}\) in the presence of the ground effect; otherwise \(\text{Nt}=\text{Nc}\)); averaged relative roughness \(\varepsilon/\text{D}\); cut-off width k; total simulation time \(\text{t}_{S}=50\) for 1000 computational steps and \(\text{t}_{S}=100\) for 2000 steps; and the gap-to-diameter ratio, \(G/D\).  
A value of 1000 was adopted for the large gap-to-diameter ratio, \(G/D=\infty\). The gap-to-diameter ratio \(G/D=0.5\), which is the transition gap of the large-gap and intermediate-gap regimes, was set for the investigations of the ground effect linked with the variation in the mean roughness height. The dimensionless time interval adopted for calculating the average values of the circumferential pressure, drag, and lift coefficients is \(15\leq\text{t}\leq 50\) or \(15\leq\text{t}\leq 100\), depending on the total simulation time adopted, and only complete oscillation cycles are considered in these intervals.
## 2 Materials and Methods
### Data Analysis  
As this trial involved preliminary and exploratory data, an a priori power analysis prior could not be performed. A sample size of n = 20 per group was chosen based on comparison with other studies using identical designs and evaluating similar outcome measures [57; 58]. No adjustments for multiple testing were performed. Data analysis was performed in SPSS (29.0.2.0, IBM Corp, New York, NY, USA). Data normality was checked (Shapiro-Wilk test) to determine whether parametric or nonparametric analyses would be performed. For aims one to three, PRE, POST, and PRE-POST (delta) evaluations were performed. A one-way ANOVA/Kruskal-Wallis test was used for comparisons between groups. A two-sided paired \(t\)-test/Wilcoxon test was used for comparisons between two sessions. To evaluate aim four, Pearson and Spearman correlation tests were executed to determine bivariate correlations between independent variables (PRE cPPT, delta cPPT, PRE TS, delta TS, PRE CPM, and delta CPM) and dependent variables (PRE IL-6, delta IL-6, PRE TNF-\(\alpha\), and delta TNF-\(\alpha\)). For absolute values of r, 0-0.190 was regarded as very weak, 0.200-0.390 as weak, 0.400-0.590 as moderate, 0.600-0.790 as strong, and 0.800-1 as very strong correlation [59]. As these correlations might be confounded by other variables, partial correlations were also performed to evaluate the influence of either demographics (age, BMI) or psychosocial factors (DASS-21, PSQI) as covariates.
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 6 Conclusions  
With the help of continuously improving deep learning models, considerable progress has been made in reconstructing HDR images by fusing multi-exposure LDR images. There are many challenges in reconstructing HDR images by fusing multi-exposure LDR images, especially the ghosting, blurring, noises, and detail distortion. After comparing with the state-of-the-art models in both of qualitative and quantitative experiments, it is confirmed that the MDHDR model proposed in this paper performs well in reconstructing HDR images by fusing multiple-exposure LDR images. The HDR image reconstructed by our proposed MDHDR has no ghosting, retains the details that are easily lost in high-brightness and low-brightness areas, and has better clarity. Our proposed MDHDR provides a practical and effective solution for reconstructing an HDR image by using multi-exposure LDR images.  
Our future research focuses on two main tasks. The first task is that we will base on following two points to adjust the network architectures to recover the lost image details. The first point is to extract short- and medium-exposure images to compensate lost image details in long-exposure images. The second point is to extract long- and medium-exposure images to compensate lost image details in short-exposure images. The second task is to simplify the super-resolution model to reduce the number of network parameters and inference time.  
Conceptualization, K.-C.H., S.-F.L. and C.-H.L.; Methodology, K.-C.H.; validation, K.-C.H.; writing--original draft preparation, K.-C.H. and S.-F.L.; writing--review and editing, K.-C.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Thanks to friends who are keen on reconstructing HDR images for their suggestions on my study. Thanks to all scholars who provide open sources on [[https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan.git](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan.git)]([https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan.git](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan.git)) (accessed on 29 January 2025) and [[https://github.com/tkd1088/ESBN.git](https://github.com/tkd1088/ESBN.git)]([https://github.com/tkd1088/ESBN.git](https://github.com/tkd1088/ESBN.git)) (accessed on 15 October 2024). The authors declare no conflicts of interest.
## 1 Introduction
Previous research has emphasized the significance of urban morphology in influencing lightning activity and thunderstorm processes [32; 33; 34]. The evolving urban landscape, marked by expanding city sizes, plays a crucial role in shaping thunderstorm systems [34]. The type of urban land use has been observed to alter the spatial patterns of lightning activity [28]. Additionally, building heights within urban areas can also impact lightning activity [35]. However, a comprehensive understanding of the influence of the urban underlying surface on lightning activity and thunderstorm processes remains elusive, lacking a systematic description and mechanistic explanation [6; 8]. The impact of urbanization on lightning activity and thunderstorm processes has been a long-standing and challenging research topic, serving as a crucial scientific foundation for the precise forecasting of intense local thunderstorms in terms of timing, location, and magnitude [36; 37; 38; 39; 40]. With the continuous expansion of the urban scale, the impact of various factors, such as the air pollutant concentration, heat island intensity, rough underlying surface, and high plot ratio of built-up areas, on lightning activity cannot be overlooked. This paper poses the following pivotal questions: What are the distinct features of lightning activity within built-up areas? How can we systematically delineate the predominant influence of urbanization on lightning activity and thunderstorm processes? And what is the mechanism driving these observed patterns? In addressing these concerns, this paper reviews the research on the impact of urbanization on thunderstorm processes and lightning activity from various perspectives, including aerosols, urban thermal effects, urban dynamic effects, and building morphology. The aim is to provide a valuable theoretical basis and technical support for potential urban lightning prediction, early warning, and risk assessment.
## 4 Results and Discussion
### YOLOv11n-seg Model Selection and Comparison with Other Models
#### 4.4.4 Limitations of Using Mask R-CNN for Real-Time Industrial Segmentation Tasks  
A preliminary study was also conducted on Mask R-CNN, where it was compared with other segmentation architectures. While this model is well known for its accurate pixel-wise segmentation, it was found to be unsuitable for our high-speed industrial application due to several critical limitations.  
First, Mask R-CNN is built upon the Faster R-CNN architecture and employs a two-stage processing pipeline. In the first stage, it generates regional proposals; in the second stage, it classifies and segments them. This architecture significantly increases computational complexity compared to one-stage models, like YOLOv8 or YOLOv11, leading to higher latency in image processing. As a result, the model is generally inappropriate for real-time or near-real-time systems [84].  
Moreover, the original Mask R-CNN paper reported an inference speed of only five frames per second (FPS) using a ResNet-101-FPN backbone. In our scenario--where a conveyor belt moves at over \(1\) m per second--such a low speed is insufficient to segment and classify small, fast-moving objects effectively [84]. Additionally, efficient training and inference with Mask R-CNN require high-end GPUs, particularly when handling high-resolution images or large datasets. This imposes limitations in terms of deployment on resource-constrained industrial hardware, such as embedded systems (e.g., Jetson Nano or Raspberry Pi) [85].  
Another primary concern is that Mask R-CNN performs relatively poorly on small objects. Given that our target materials often range from 1 to 4 mm in size, even minor inaccuracies in segmentation can lead to significant degradation in overall system performance [86].  
Finally, while Mask R-CNN performs well in static domains, such as medical imaging (e.g., tumor or organ segmentation), document layout analysis, and labeled static datasets (like Cityscapes in autonomous driving), it is not optimized for fast-paced environments. Therefore, it is ill suited for dynamic recycling systems, where the critical requirements are high-speed conveyor belts and low-latency decision making.
## 4 Results and Discussion
### Method Performance  
The proposed method was able to successfully co-register about 69.5% of the synthetic inventory plots, while it linked 81.5% of the tree pairs correctly. By applying a pure distance-based tree assignment (ignoring GNSS errors), only 41.0% of the trees were linked correctly, and the criterion of at least 50% matching tree pairs was fulfilled for only 32.5% of the plots.  
The original BWI plots showed similar results. By applying the pure distance-based tree assignment, 288 tree pairs were identified, but only 107 (37%) of these pairs were classified as matching. Only 29 of the plots (22%) fulfilled the criterion of at least 50% tree pairs classified as matching. By applying the proposed co-registration algorithm, 517 tree pairs were assigned. Of these tree pairs, 261 (50%) were classified as matching. The algorithm indicated that 80 of the 133 inventory plots (60%) were co-registered correctly, with 414 assigned tree pairs and 230 (72%) pairs classified as matching. Based on these results, the investigated original BWI plots were characterized by horizontal GNSS errors of up to 21 m, while 80% of the horizontal GNSS errors were in a range between 1.4 and 8.7 m.  
Figure 9(a) illustrates the number of correctly co-registered original BWI plots as a function of the applied co-registration probability. Due to the GNSS correction, the number of co-registered plots increased and higher probabilities were achieved.  
Figure 9: Effect of (**a**) the dominant tree species; (**b**) the number of tree species; (**c**) the height of the tallest tree; and (**d**) the variability of the tree heights on the probability of a correct co-registration. The numbers in brackets correspond to the number of plots. The whiskers extend to 1.5 times the interquartile range. Outliers are marked by circles.  
Figure 10b illustrates the effect of the year of survey on the co-registration probability. A time-lag between the field survey and the ALS flight campaign (three to four years) might have a negative effect on the co-registration results, since trees might have grown, toppled, or been harvested in this period of time. Although the probabilities of 2011 seem to be slightly lower than the probabilities of 2012, the two-tailed Wilcoxon-test has shown no significantly lower values for 2012 (\(p\) = 0.13). However, these results indicate that better results could be achievable if the time lag between inventory and ALS data acquisitions is small.
# Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm
## 3 Experimental Results Analysis
### Comparison of Test Results  
#### 3.4.1 Comparison of Segmentation Results for Different Color Stones  
Based on the detection results in Figure 10, the improved model shows finer performance in complex edge segmentation compared to the original YOLOv8. The dynamic snake convolution improves contour adaptation for curved targets, enhancing mask boundary continuity and reducing jagged artifacts. The U-NetV2 backbone strengthens the integration of local details and global semantics through multi-scale feature fusion. This allows better target integrity in occlusion scenes, as shown by clearer crack edges in dense regions. The DySample module improves resolution adaptability, boosting sensitivity to small-scale objects while reducing feature dilution from traditional upsampling. Compared to Attention U-Net, YOLOv8-Seg offers a better trade-off between accuracy and computational cost, avoiding overhead from attention modules. Against DeepLabV3, the snake convolution's geometric constraints lower false detection rates and improve segmentation robustness.