esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparative Experiments of Various Attention Mechanisms  
The subsequent trial examined the influence of various attention mechanisms on the performance of the YOLOv11 model for detecting maize diseases. The trials challenged six common attention mechanisms, including CBAM, EC, EMA, GAM, SA, SimAM, and SK; each was integrated into the YOLOv11 model for performance evaluation. The proposed YOLOv11-RCDWD model in this study incorporates the CBAM attention mechanism within the YOLOv11 model. The corresponding experimental results are presented in Table 3. The CBAM attention mechanism demonstrated excellent performance across multiple metrics, particularly excelling in precision (90.5%), F1 score (84.1%), [EMAIL_ADDRESS] (86.2%), and memory consumption (17.3 MB). Although the detection speed slightly decreased from 1.3 ms to 1.6 ms, the model's parameter count (8.49 M) was reduced by approximately 2.03 M compared with the baseline model's 10.52 M. Additionally, its computational load (20.6 GFLOPs) is slightly lower than the baseline model's 21.2 GFLOPs, giving it a clear ad  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1 Score/\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]-0.95/\%** & **Detection Speed/ms** & **Parameter/M** & **GFLOPs** & **MemoryCost/ MB** \\ \hline YOLOv11s- & 87.7 & 78.0 & 82.6 & 85.3 & 63.5 & 1.3 & 10.52 & 21.2 & 19.2 \\ YOLOv11s- & 87.3 & 79.8 & 83.4 & 85.6 & 63.9 & 3.2 & 8.89 & 22.7 & 18.1 \\ YOLOv11s- & 86.5 & 80.2 & 83.2 & 85.5 & 63.3 & 2.4 & 9.04 & 23.7 & 18.5 \\ YOLOv11s- & 87.3 & 77.1 & 81.9 & 84.6 & 62.6 & 3.3 & 8.57 & 21.2 & 17.6 \\ YOLOv11s- & 83.2 & 80.9 & 82.0 & 85.8 & 63.5 & 4.4 & 10.60 & 34.8 & 21.6 \\ VIPB3 & & & & & & & & & \\ YOLOv11s- & 89.1 & 79.9 & 84.2 & 87.1 & 66.7 & 3.2 & 9.85 & 25.2 & 20.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maize Disease Detection Performance of Different Backbone Network Models.  
vantage in overall performance. Therefore, CBAM is the most suitable attention mechanism for maize disease detection tasks.  
The EMA attention mechanism performed well in recall (81.1%) and [EMAIL_ADDRESS]-0.95 (64.6%) but with disadvantages in detection speed (3.3 ms) and parameter count (9.08 M), making it suitable for tasks with high recall requirements. The GAM attention mechanism performed well in precision (88.7%) and F1 score (82.2%), although its parameter count (11.91 M), computational load (23.3 GFLOPs), and memory consumption (24.2 MB) were found to be relatively high and its detection speed is slower (4.6 ms), making it suitable for tasks less sensitive to model complexity. The SA and SimAM attention mechanisms performed moderately across multiple metrics, particularly in precision (SA: 84.8%, SimAM: 85.5%) and F1 score (SA: 83.1%, SimAM: 82.0%), indicating they may not be suitable for maize disease detection tasks.  
In summary, the YOLOv11s-CBAM model performs the best in maize leaf disease detection tasks, maintaining high detection accuracy while effectively reducing model parameter count and memory consumption, rendering it appropriate for implementation in real-world applications.
## 3 System Design and Methods
### Network Architecture Design
#### 3.1.1 Feature Projection Networks  
In order to effectively manage the inherent heterogeneity present within the feature spaces of both the source and target domains, we devised a comprehensive strategy involving the creation of two distinct feature projection networks, \(f_{\text{s}}(\cdot)\) and \(f_{\text{t}}(\cdot)\), one meticulously tailored to each domain's unique characteristics. Each network's input is drawn from the corresponding dataset, i.e.,  
Figure 1: Deep network architecture for heterogeneous transfer learning  
\[\mathbf{X}_{s} =[\mathbf{x}_{1}^{s},\mathbf{x}_{1}^{s},\cdots,\mathbf{x}_{n_{l}}^{s}], \tag{5}\] \[\mathbf{X}_{t} =[\mathbf{X}_{l};\mathbf{X}_{u}]\] \[=[\mathbf{x}_{1}^{t},\mathbf{x}_{2}^{t},\cdots,\mathbf{x}_{n_{l}}^ {t},\mathbf{x}_{n_{l}+1}^{t},\cdots,\mathbf{x}_{n_{l}+n_{u}}^{t}], \tag{6}\]  
and the projected output is given by  
\[\mathbf{\tilde{X}}_{s} =f_{s}(\mathbf{X}_{s}), \tag{7}\] \[\mathbf{\tilde{X}}_{t} =[\mathbf{\tilde{X}}_{t};\mathbf{\tilde{X}}_{u}]=f_{t}(\mathbf{X }_{t})=f_{t}([\mathbf{\tilde{X}}_{l};\mathbf{\tilde{X}}_{u}]). \tag{8}\]  
Then, the labeled projected data \([\mathbf{\tilde{X}}_{s};\mathbf{\tilde{X}}_{t}]\) together with their corresponding labels \(\mathbf{\tilde{Y}}=[\mathbf{Y}_{s};\mathbf{Y}_{t}]\) are sent to the classification network, where  
\[\mathbf{Y}_{s} =[y_{1}^{s},y_{2}^{s},\cdots,y_{n_{s}}^{s}], \tag{9}\] \[\mathbf{Y}_{l} =[y_{1}^{t},y_{2}^{t},\cdots,y_{n_{l}}^{t}]. \tag{10}\]  
At the same time, all the transformed data are sent to compute the maximum mean discrepancy (MMD) in order to align the probability distribution of the project data from two domains.  
These dedicated networks assume the pivotal role of not only acquiring domain-specific representations but also orchestrating the transformation of input feature data into a unified and shared feature space. This concerted effort is strategically engineered to alleviate the potentially detrimental effects stemming from differences in feature spaces, thereby safeguarding the model's overall performance. Each of these feature projection networks is thoughtfully structured, comprising a cascade of fully connected layers, each with its own set of learnable parameters. This hierarchical arrangement empowers the networks to progressively acquire increasingly abstract and discriminative representations of the input data, ensuring that the nuances and subtleties of the feature space peculiar to each domain are effectively captured.  
This meticulous process yields the output of the feature projection networks, which manifests as the transformed feature data. These transformed data find their home in the shared and harmonized feature space, where they seamlessly coexist with their counterparts from the other domain. This pivotal transformation effectively bridges the feature space gaps between the source and target domains, laying the foundation for the smooth and effective transfer of knowledge and insights across domains.
# Scale-Free Random SAT Instances
## 6 Unsatisfiability by Small Cores
Another argument in this direction is as follows:  
Let \(C(V)\) be the subset of clauses only containing variables of the subset \(V\) of variables. The greater \(|C(V)|/|V|\) is, the higher is the probability to have an unsatisfiable core inside  
Figure 5: Estimation of the number of clauses that are needed to make unsatisfiable 50% of the formulas generated for distinct values of \(\beta\) and \(k=3\) as a function of the number of variables. \(C(V)\). In the case of scale-free random k-SAT formulas, let \(C_{r}\) be the set of clauses only containing variables \(\{1,\ldots,r\}\). We can estimate  
\[E\left[\frac{|C_{r}|}{r}\right]=\frac{m}{r}\left(\frac{\sum_{i=1}^{r}i^{-\beta} }{\sum_{i=1}^{m}i^{-\beta}}\right)^{k}\approx\frac{m}{r}\left(\frac{r^{1-\beta }-1}{n^{1-\beta}-1}\right)^{k}\]  
For \((1-\beta)k\geq 1\), i.e., \(\beta<1-1/k\), the maximum of this function is \(r=\infty\). For \((1-\beta)k<1\), i.e., \(\beta>1-1/k\), the maximum is finite:  
\[r=(1-(1-\beta)k)^{-1/(1-\beta)}\]  
Notice that \((1-\beta)k\) is the exponent predicted by Theorem 5, and that for 2-SAT, \(1-1/k=1/2\). Therefore, we obtain another proof that at \(\beta=1-1/k\) we obtain a change in the behavior of scale-free random k-SAT formulas. When \(n\rightarrow\infty\), for \(\beta\leq 1-1/k\) the most probable is to obtain a very large core that involves a fraction of the whole set of clauses. For \(\beta>1-1/k\) the most probable is to obtain a small core only involving a finite set of clauses and variables \(\{1,\ldots,(1-(1-\beta)k)^{-1/(1-\beta)}\}\).
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 3 Material and Method  
### Image Dataset  
Most of the real-world images are usually affected by the complex and varied nature of the blur. In such cases, blur assessment and segmentation turns out to be a challenging task. Generally, different blur measure operators proposed in the literature are sensitive/receptive to different type of features/characteristics/attributes of the blurred image. Consequently, if the performance of these operators is to be compared, a large dataset is imperative that strives to incorporate broadly varied nature of the blur. Out of the available on-line resources, the dataset of [11] seems an appropriate choice and has been considered in this comprehensive study. This dataset contains 1000 real-world partially blurred images. These are of different resolutions and are collected from the Internet. As humans are the ultimate objects to perceive the images, the human labeled ground-truths are also provided for the blurred and sharp regions. There are 704 images corresponding to defocus blur and 296 images representing the motion blur with different magnitudes of defocus and motion respectively. These images cover various scenarios and comprise of numerous attributes like nature, people, vehicles, man-made structure and other living beings. This dataset provides an ample test-bench to evaluate different blur measures. Few images with ground-truths are shown in Figure 1.
## 2 Materials and Methods
### Data-Driven System Identification
#### 2.4.1 Optimal Excitation Signal Generation  
Utilisation of wide-band, deterministic, and periodic excitation signals is assumed for the identification experiments due to several advantages they inherently bring in the process of plant model derivation:  
* Simultaneous excitation over the frequency band of interest resulting in shorter experiment duration;
* Improved signal-to-noise ratios compared to stochastic random noise excitations;
* Periodic nature allowing to measure multiple realisations of the executed motion trajectory to mitigate noise and transient leakage effects
* Possibility of optimisation of testing signal power spectrum, e.g., to minimise the resulting crest factor, shaping the energy fed to the feedback loop under closed-loop experimental conditions.  
A multi-sine signal comprising the number of base harmonic functions with different frequency, amplitude, and phase delay is considered in the form of  
\[r(t)=\sum_{k=1}^{N}A_{k}\cos(2\pi kf_{0}t+\varphi_{k}). \tag{20}\]  
The amplitudes \(A_{k}\) and spectrum \(kf_{0}\) of the excitation trajectory can be optimised based on the requirements of the execution time of the experiment, required frequency resolution of the non-parametric model and assumed control bandwidth.
## 3 Takeover Experiment Results and Discussion
### Driver Biosignals  
During all the takeover experiments, we collected driver biosignal data and attempted to reduce the error caused by driver movement through the filtering process The raw data of each sensor described in Section 2.2 were processed to measure the respiration rate, heart rate, and NDRT concentration while driving (Figure 17). We conducted t-tests to determine whether there were significant differences in the drivers' heart rate or respiration rate in manual driving and autonomous driving situations. First, since there were 30 or more data points in each set of data, normality was assumed by the central limit theorem, and homogeneity of variance was confirmed from [PERSON]'s test. For heart rate and respiration rate, the difference according to driving mode was confirmed by an independent sample t-test under the assumption of equal variance.  
Figure 18 shows the average and change in heart rate for each driver for a total of 1021 min of manual driving and 1476 min of autonomous driving. The heart rate is generally lower during autonomous driving (M = 62.96, SD = 18.27) than that during manual driving (M = 64.29, SD = 18.81). However, the difference was not statistically significant with the t-test of the two groups (\(p\) = 0.81). The respiratory rate decreased slightly during autonomous driving (M = 20.35, SD = 3.49) compared to during manual driving (M = 20.73, SD = 3.35). The difference in the t-test was not significant (\(p\) = 0.49). Therefore, the use of heart rate and respiration rate as criteria for directly judging concentration  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **w/ DMS** & **w/o DMS** \\ \hline Takeover failure & 14\% & 37.2\% \\ Reaction time & Mean & 1.18 s & 2.09 s \\ NDRT percentage & SD & 0.88 s & 1.59 s \\ NDRT percentage & 29.8\% & 79.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Driving results according to the request to look forward.  
Figure 16: Driving results according to driver availability warnings (\(n_{w/}=50\), \(n_{w/o}=43\)).  
on driving behavior is inappropriate. It is better to use them for identifying abnormal conditions such as drowsiness or shock.
## 3 Results and Discussion
The addition of zeolite to the sewage sludge prior to composting may have adsorbed heavy metals, which caused them to remain in the sludge and subsequently be detected during the analysis. Notably, artificial accelerated freezing and composting did not significantly affect the concentrations of metals, which corroborates numerous findings from previous studies indicating that the use of wastewater sludge as fertilizers (even post-composting) carries risks of heavy metal contamination in the soil [29; 33]. The results suggest that the primary influence on the characteristics of the sludge stemmed from biochemical and microbiological processes inherent to both composting and natural drying of the sludge under real conditions. The accelerated freeze-thaw-drying cycles impacted the physical properties of the sludge (moisture content) and the  
Figure 10: Boxplots of metal concentrations in the analyzed sludge: (**a**) copper concentration range; (**b**) nickel concentration range; (**c**) aluminum concentration range. concentration of volatile substances. However, this study did not address the issue of microbiological contamination in the sludge, which could potentially be mitigated through physical methods. According to Russian regulatory standards, the use of treated sewage sludge as fertilizer is strictly controlled to prevent soil and crop contamination, ensuring environmental and public health safety. Standards such as GOST R 17.4.3.07-2001 [45] and SanPiN 2.1.7.573-96 [46] set maximum allowable concentrations (MACs) for heavy metals, organic pollutants, microbial contaminants, and radioactive substances in sludge. These requirements specify, for instance, that copper should not exceed 750 mg/kg, nickel 200 mg/kg, and zinc 1750 mg/kg. In this study, the treated sludge contained 32.97 mg/kg of copper, 15.06 mg/kg of nickel, and 34.77 mg/kg of zinc, all well below the regulatory limits, confirming the suitability of this treated sludge for use as fertilizer in the Russian Federation. However, practical application depends significantly on the properties of the initial sludge, which may require further treatment, such as metal recovery for reuse as secondary resources in industry, to meet safety and sustainability standards.
# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
## 2 Materials and Methods  
### Dataset  
To ensure the applicability of the algorithm for detecting defects in aircraft engine components, the dataset used in this study was collected offline in workshops and factories. The collection method involved placing an industrial camera vertically 20 cm above the test specimen. An LED ring light was positioned between the industrial camera and the test specimen to provide illumination. The position of the camera was adjusted to ensure that the field of view covered the central channel of the ring light completely. To conform to the image input requirements of the YOLOv5 algorithm, the output resolution of the industrial camera was adjusted to 640 \(\times\) 640, and the test specimens were photographed.  
A total of 1200 original images were obtained through sampling. The LabelImg ([PERSON], National Taiwan University, Taiwan, China) software was used for annotation to generate the txt files required by YOLOv5, which contain the categories and locations of the defects in the images. These images were then filtered and classified into four defect categories: pits, cracks, scratches, and roughness. Based on the proportion of each defect category in the images, there were 397 images in the pit category, 199 images in the crack category, 283 images in the scratch category, and 321 images in the roughness category. Each image contained varying numbers of defects, ranging from 1 to 20. This is illustrated in Figure 1. The defects in the figure have been labeled and some of the markings have been enlarged for easy viewing.  
Figure 1: Pictures of the four types of defects in the dataset.  
It can be seen that the defect features in this dataset cover a wide range, including small target defects, commonly found industrial defects, and difficult-to-detect defects. The dataset includes a significant proportion of small target defects, such as the pit category, which accounts for one-third of the total data. These are small defects with a diameter of less than 1 mm, occupying only a minimal number of pixels in the detection images. Crack defects often occur due to processing errors or component fatigue and have a relatively large proportion in practical situations. Roughness defects vary in size and shape and have a high degree of overlap with the background, making them difficult to distinguish.  
The images of each defect category in the dataset were divided into training, validation, and testing sets in an 8:1:1 ratio. This resulted in 960 training images and 120 images each for validation and testing.
## 1 Introduction  
Phloroglucinol derivatives are a large class of secondary metabolites widely distributed in plants and brown algae. Over a thousand phloroglucinol derivatives have been characterized to date. As an example, 429 phloroglucinol derivatives have been isolated from the genus _Hypericum_ alone [1]. Phloroglucinol derivatives found in plants and brown algae have extremely diverse structures, ranging from the simple grandinol, an acylphloroglucinol produced by several _Eucalyptus_ species, to the more complex phlorotannins found in several families of brown algae [2, 3]. These compounds often exhibit antiviral, antibacterial and antifungal activity [2]. Phloroglucinol derivatives are also produced by some microorganisms [2, 4]. By contrast with the phloroglucinol derivatives found in plants and brown algae, phloroglucinol derivatives of microbial origin are rather simple. Some _Pseudomonas_ strains produce 2,4-diacetylphloroglucinol (DAPG) alongside its biosynthetic intermediates monoacetylphloroglucinol (MAPG) and phloroglucinol.  
DAPG-producing _Pseudomonas_ spp. have received particular attention due to their ability to control numerous soil-borne plant diseases, including take-all of wheat, tobacco black root rot and sugar beet damping-off [4, 5]. These bacteria also play an important role in natural disease suppresses found in several soils across the world. Besides their presence in the rhizosphere, DAPG-producing _Pseudomonas_ spp. are also known to colonize various environment, including the phyllosphere [6], the skin surface of certain amphibians [7] and the surface of marine algae [8]. This review specifically covers rhizosphere-inhabiting DAPG-producing _Pseudomonas_ spp.  
Several reviews have been previously published on rhizosphere-inhabiting DAPG-producing _Pseudomonas_ spp. and their role in take-all decline [4, 9]. In this review, we discuss the biosynthesis and regulation of phloroglucinol derivatives in the genus _Pseudomonas_. Then, we tackle the role that DAPG-producing _Pseudomonas_ spp. play in several soilsnaturally suppressive to soil-borne plant diseases. We also discuss the mode of action of phloroglucinol derivatives, which can act as antibiotics, signalling molecules and, in some cases, even as pathogenicity factors. Finally, we discuss the genetic and genomic diversity of DAPG-producing _Pseudomonas_ spp. as well as its importance for improving the biocontrol of plant pathogens.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 7 Results & Discussion
### Use of Adapters  
Using Adapters means estimating a set of speaker-specific modules previously injected in multiple parts of the model architecture while keeping the original model backbone frozen [44]. As suggested in Section 5.2, one of our purposes was to study different Adapter sizes while considering the percentage of learnable parameters that each one implied (see Table 1). Figure 5 depicts this analysis when using the training set and the development set. According to these results, the following conclusions can be inferred:  
* In data scarcity scenarios, i.e., when using the development set for estimation, the Adapters-based adaptation method was able to significantly improve the MST-based model performance reported in Table 2. However, no significant differences were observed when using different Adapter sizes.
* When the training set was used, more remarkable differences could be observed regarding the Adapter size, leading to the conclusion that 32 hidden units (estimating around 0.52% of parameters with respect to the entire end-to-end model; see Table 1) provided the best recognition rate. However, the Adapters-based method did not outperform the MST-based model in this scenario, as Table 2 confirms.  
Nonetheless, it should be noted that by considering the best Adapter size for each speaker we were able to obtain recognition rates of around 50.8 \(\pm\) 1.4 and 32.9 \(\pm\) 1.3% WER when using the development and training datasets, respectively. These results present significant differences with respect to the MST-based system (see Table 2), which shows the effectiveness of the Adapters-based method when adapting VSR systems to a specific speaker. In addition, the fact that each speaker required a tailored Adapter size could be related to the behaviour observed in Figure 4, where all the fine-tuning strategies obtained different recognition rates depending on the speaker evaluated. Furthermore, this finding highlights the flexibility that Adapters-based adaptation methods can provide.