esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, we investigated the allelopathic effect of _Secale cereale_ cultivars on different weeds that grow in the cultivated fields of _Perilla_ frutscens. Two _S. cereale_ cultivars, Paldong and Singhi, were used to test the allelopathic effect on in vitro grown _Digitaria ciliaris_, _Chenopodium album_, _Amaranthus lividus_, _Portulaca oleracea_, _Pinellia temata_ and _Commelina communis_. The results indicated that _S. cereale_ extracts affect callus growth of weeds in terms of fresh weight and percentage of growth inhibition. The inhibitory effects of both _S. cereale_ cultivars combined with grass cover extracts were higher than using grass weeds alone. Concentrations of all identified phenolic compounds were significantly higher in the leaves extracts of Paldong compared to Singhi. Particularly, syrginic acid in leaves extract of the Paldong cultivar were 12.87-fold higher than in the Singhi cultivar. The other predominant phenolic compounds such as salicylic acid, \(p\)-coumaric acid, vanillic acid, and \(p\)-hydroxybenzoic acids were 3.30, 4.63, 3.11, and 1.28 times higher, respectively, in the leaves extracts of Paldong compared to Singhi. Principal component analysis (PCA) results indicated that the composition of phenolic compounds was significantly related to cultivar types and plant parts used. In addition, biomass increase caused increased weed inhibitory capacity of _S. cereale_ both in tillage and no-tillage regimes. These results suggest that the biomass of cover crops negatively influenced weed density.  
allelopathic properties; cultivars types; cover crop; weed inhibition; callus growth Article
# CLensRimVision: A Novel Computer Vision Algorithm for Detecting Rim Defects in Contact Lenses
## 2 Background
### Computer Vision for AOI of Contact Lenses  
AOI is being used to shorten the process of the mass production of lenses in terms of minimizing the presence of defective products. There are several techniques and combinations inside the AOI sector. In the context of contact lens defect detection, [PERSON] et al. use computer vision algorithms to utilize edge points to calculate the distance from the center and employ a radius abnormality function [21]. This approach involves computing the distance between the radii of adjacent parts of the circle and evaluating the absolute difference. Finally, the radius abnormality function is applied by defining an offset threshold. However, during the actual experimental setup, there may be situations in which the contact lens is not properly placed in the contact lens holder (we will explain this in Section 3.1), leading to an uneven thickness along the circular rim of the contact lens (we will discuss this in Section 4.4.1). This scenario motivates us to further develop the algorithm to accurately detect defects even under such circumstances, a consideration that [PERSON] et al. may not have considered.  
Image Processing Techniques  
* Color space and morphological operations can extract information as compared to intensity or HSV color-space-based segmentation, as [PERSON] et al. proved in their research [22]. The HSV (hue, saturation, and value) color space is utilized for color-based object segmentation. The hue channel represents the color category, while the saturation ranges from unsaturated to fully saturated levels. The value channel conveys the color intensity. Morphological operations, such as erosion and dilation, modify images using a structuring element [23; 24; 25]. [PERSON] et al. use morphological operations and prove that this technique can detect the edge of objects [26]. Moreover, the usage of erosion reduces noise and isolates elements, while dilation merges dissimilar elements and detects irregularities.
* Polar Coordinate Transformation: The conversion of Cartesian coordinates (x, y) to polar coordinates (r, \(\theta\)) has various effects and facilitates specific image processing tasks. This transformation is useful for radial distortion correction, circular pattern analyses, and the transformation of images into a polar representation. The open library from CV2 [27] that supports this algorithm is Warp polar [28].  
* Circle Detection: The algorithm aims to identify the best-fit circle that matches a given set of points. A circle in 3D space can be represented using a parametric equation, involving the center, radius, normal unit vector, and perpendicular vector. These parameters provide a comprehensive description of the circle in three-dimensional space. [PERSON] et al. use circle detection to detect outliers in their research [PERSON] et al. (2018); [PERSON] et al. (2018), which is similar to our aim to detect burr defects on the circular edge of contact lens.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 5 Experimental Results  
In this part, various numeral simulations are enacted to assess the effectiveness of the proposed method according to the visual quality perception. PSNR and robustness evaluation has assessed these to several typical geometric, image filtering, and combination attacks conducted using NC and BER to measure the degree changes among pixels in the extracted binary watermark. The proposed method is tested over color host images of size 256 \(\times\) 256 (shown in Figure 4) and color medical images selected from datasets [57; 58; 59] (shown in Figure 5). Binary watermark images are shown in Figure 6.  
The attained numerical results are compared with other similar moment-based watermarking methods [28; 29; 41; 60; 61].  
Additional comparisons with statistical watermarking methods [6; 9; 16; 18; 33] are also conducted to ensure the robustness and visual invisibility of the proposed methodology.  
We document our experimental results according to the best and correct choice of \(\alpha\), where 0\(<\alpha>\) 3 and \(\alpha\in R^{+}\).  
Correspondingly, we tested different parameters values at \(\alpha=0.7,\ 1.1,\ 2.3,\) and 2.6; from different experimental results, we noticed that the value of \(\alpha\) at \(\alpha=1.1\) is the optimum choice, where there are no readjustments while using the optimum value \(\Delta=0.2\).  
Experimental results documented using different image sizes (128 \(\times\) 128 and 256 \(\times\) 256) are depicted in the following sub-sections. The watermark logo images are of sizes 32 \(\times\) 32 and 10 \(\times\) 10.  
Figure 4: The host standard color images (**a**) Lena; (**b**) Peppers; (**c**) Baboon; (**d**) F16; (**e**) San Diego miramar; (**f**) San Diego north island; (**g**) Man; (**h**) Elephant; (**i**) Monarch; (**j**) Museum.  
Figure 5: Selected host color medical images from datasets [57; 58; 59].  
Figure 6: Selected binary watermark images (**a–j**).
# Non-Autoregressive End-to-End Neural Modeling for Automatic Pronunciation Error Detection
## 2 Literature Review
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Terms** & **Description** & **Terms** & **Description** \\ \hline AAPM & Articulatory Acoustic-Phonemic Model & APM & Acoustic Phonological Model \\ AR & AutoRegressive & ASR & Automatic Speech Recognition \\ ARI & Adjusted Rand Index & CALL & Computer-Assisted Language Learning \\ CAPT & Computer-Assisted Pronunciation Training & DM & Dication Model \\ DNN & Deep Neural Network & E2E & End-to-End \\ ERNs & Extended Recognition Networks & GMM & Gaussian Mixture Mode \\ GoP & Goodness of Pronunciation & GOPT & Goodness Of Pronunciation feature-based \\ HMM & Hidden Markov Mode & LASO & Listen Attentively and Spell Once \\ L2 & Second Language & MDD & Mispronunciation Detection and Diagnostic \\ MHSA & Multi-Head Self-Attention & MT-APM & Multi-Task APM \\ NAR & Non-AutoRegressive & NMI & Normalized Mutual Information \\ PDS & Position Dependent Summarizer & PM & Pronunciation Model \\ S2S & Speech-to-Speech & T2S & Text-to-Speech \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations used in this paper. data using a label-shuffling strategy for a unique data enhancement operation. For each sentence in the original speech training dataset, the label of the phone at each point in its reference transcript is either left untouched or randomly replaced with an arbitrary anti-phone label. Domain speech without labels aims to improve mispronunciation detection and diagnosis (MDD) accuracy. Pseudo-labeling in the speech domain (PL) has been a popular technique for semi-supervised ASR. Within the pseudo-label generation approach, methods may be categorized as either offline or online PL. Offline PL approaches apply pseudo-labels to unlabeled samples using a teaching model developed independently. Then, a student model is trained on samples that are both labeled and pseudo-labeled [41]. It has been demonstrated that filtering heuristics [42; 43] and repeated training [44] can increase PL quality. In online PL approaches, however, pseudo-labels are created in real time by the online model itself [45; 46; 47]. In contrast to [45], we combine PL with wav2 vec 2.0 fine-tuning and phonemes as targets. Although various mispronunciation detection models are discussed in this section, most models use autoregressive neural modeling techniques. Autoregressive neural modeling techniques use left-to-right forward computation to generate the output depending on the input or the previous block output. This will increase processing time and degrade performance when there are fewer data. In this study, we use non-autoregressive neural modeling techniques to reduce the processing time and use a pronunciation model to improve performance.
## 4 Results
### Datasets
#### 4.1.1 Petrified Forest  
The petrified forest of Lesvos is located on the western part of Lesbos Island and was formed from the fossilized remains of trees and plants. It constitutes an area of high geological importance, and thus, its mapping at frequent time intervals is of high importance for conservation and protection purposes. Thus, a documentation mission was organized using the Phantom 4 Pro drone. The flight took place on 31 January 2022 in the petrified forest area of Lesvos Geopark, and the drone flew at an altitude of 25m above the sea level. The produced dataset consists of 208 files (geo-tagged photos) of 4.8 GB.
# Developing Security Assurance Metrics to Support Quantitative Security Assurance Evaluation
## 4 Security Assurance Analytics
### Security Assurance Scoreboard
While assigning the risk factor for each vulnerability criterion, we consider the standard risk model:  
\[Risk=Likelihood\times Impact.\]  
In this case, we adopt the data factors defined in OWASP Top 10 categories [36], which are systematically derived using CVSS v3. To calculate the corresponding risk of an OWASP Top 10 vulnerability, we take the data factor \"Average Incidence Rate\" as the _likelihood_, while _impact_ uses \"Average Weighed Impact\". Table 8 shows the snapshot data factors of the vulnerability \"Broken Access Control\" in OWASP Top 10. According to the table, the initial risk factor of the vulnerability is calculated as 3.18% \(\times\) 5.93 = 0.225. After the risk  
Figure 5: Security assurance scoreboard—vulnerability metrics. value is derived, we also place it on a 0-10 scale, with the same range as the weighting factor. As a result, the final risk factor becomes 2.25. Except for the metrics introduced in the previous sections, several new ones are added to the scorecard to enhance the analysis capabilities, including the security requirement fulfillment metrics (divided into full, partial, and weak fulfillment), vulnerability existence metrics (divided into existence and non-existence), and the ranking numbers. The ranking numbers are generated following the ascending order of the criteria. As we can see in the rank, the security requirement \"Stored Cryptography\" has the highest priority, meaning it does not perform very well and is currently the most crucial in security assurance evaluation, concluded in line with our previous analysis. Therefore, the stakeholders should emphasize more on this criterion to improve the security level of the assurance target.
## 4 Experiments and Analyses
### Ablation Study
#### 4.5.2 Effect of Model Components  
We conducted experiments on the Weibo and OntoNotes 4 datasets to evaluate the impact of various components contained in CGR-NER on model performance. As shown in Table 9, after removing the Chinese character glyph ('-w/o Glyph') from CGR-NER, the F1 scores on the Weibo and OntoNotes 4 datasets are significantly reduced by 2.3% and 1.63%, respectively, which verifies the effectiveness of the Chinese character glyph in improving the recognition performance of our model. When the gate mechanism is removed ('-w/o GM') and the contextual representation and glyph representation after CMTE interaction are directly concatenated, the F1 scores on Weibo and OntoNotes 4 are decreased by 0.6% and 0.22%, respectively, which indicates that the importance of the representation from different modals varies, and the gate mechanism could effectively balance them. After removing the CMTE ('-w/o CMTE') from the fusion layer, the F1 scores on the Weibo and OntoNotes 4 datasets are reduced by 0.54% and 0.28%, respectively, which indicates that the interaction information between different modal representations could help the model to learn more semantic knowledge, thus improving the recognition performance of the model. When CMTE and the gate mechanism are removed at the same time ('-w/o CMTE+GM'), and the contextual representation is directly concatenated with the glyph representation, the F1 scores on the Weibo and OntoNotes 4 datasets are reduced by 0.85% and 0.55%, respectively, verifying the necessity of CMTE and the gate mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Kernel Size**} & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**OntoNotes 4**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline
3 & 70.23 & **71.17** & **70.70** & **82.16** & **83.79** & **82.97** \\
5 & **71.35** & 69.47 & 70.37 & 80.28 & 83.40 & 81.82 \\
7 & 68.72 & 70.69 & 69.70 & 81.08 & 82.69 & 81.88 \\ \hline BERT- & & & & & & \\ BiLSTM- & 69.86 & 67.2 & 68.4 & 80.51 & 82.23 & 81.34 \\ CRF & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of CGR-NER under different kernel sizes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Layer Size**} & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**OntoNotes 4**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline
1 & 70.21 & 69.95 & 70.08 & 81.56 & 83.77 & 82.65 \\
2 & **70.23** & **71.17** & **70.70** & **82.16** & **83.79** & **82.97** \\
3 & 70.01 & 69.66 & 69.83 & 79.98 & 83.65 & 81.77 \\ \hline BERT- & & & & & & \\ BiLSTM- & 69.86 & 67.2 & 68.4 & 80.51 & 82.23 & 81.34 \\ CRF & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of CGR-NER under different crossmodal attention layers.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 1 Introduction
We make the following contributions: (1) the collection of a dataset of drowsiness and non-drowsiness from images and videos monitored through a camera for experiments; (2) the proposal of two methods for drowsiness detection and prediction using facial landmarks and deep learning. In the facial landmark-based method, we improve drowsiness detection by determining the appropriate thresholds of blinks and yawns for each driver. In the deep learning-based method, we propose the use of two adaptive deep neural networks with the transfer learning approach for drowsiness detection. We designed and perfected these networks developed on the advanced networks of MobileNet-V2 and ResNet-50V2, which are more efficient in terms of memory and complexity. The proposed networks are very good feature extractors, since they can capture and learn relevant features of drowsiness automatically; (3) the use of a transfer learning approach to well solve the problem of fast training, a small training dataset and accuracy improvement; (4) the comparison and discussion the performance, accuracy, and advantages of the proposed methods with other methods. The experimental results show that the proposed methods achieve an accuracy of 97%. It can be concluded that the proposed method using deep learning has similar efficiency to other effective methods using a combination of behavioral and physiological features, but it is more feasible and has outstanding advantages. The rest of the paper is presented as follows: Section 2 presents the background on identifying facial features and deep learning models. Details of the proposed methods are presented in Section 3. We provide some experimental results and evaluation in Section 4. Finally, we give conclusions in Section 5.
## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
Also, confusion matrices and ROC curves, when applying the proposed TT-VFDL-ST to each classification dataset on three target task models distinguished by the type of FM part, are displayed in Figure 9. HUMAN_ACTS, which achieved 100% accuracy for all types of target task models, showed false-positive and false-negative values of 0 and ROC curves with an AUC value of 1.00. 98.31 to 100% accuracy, showed false-positive and false-negative values of 2 and ROC curves with an AUC value of minimum 0.99. Figure 9: Confusion matrices and ROC curves (with AUC values) when applying the proposed TT-VFDL-ST on each classification dataset with three target task models distinguished by type of FM part structure. Methods using task-driven features, namely the proposed TT-VFDL-ST, our previous work [7], refs. [12; 13; 14; 15], ref. [34], and BASE, showed false-positive and false-negative values in balance. However, refs. [8; 9; 10; 11] and CMP, which use data-driven features, showed that the results were biased towards false-negative values rather than false-positive values. From these detailed evaluation values, it could be said that task-driven features are more effective in improving performance in the IoT domain, rather than data-driven features. In Table 10, we compared the communication costs of each method in megabytes. Each communication cost is the cost for each method to achieve optimal performance. TT-VFDL-ST, our previous work [7], refs. [8; 9; 10; 11], and CMP need only one communication during the training since they take transfer learning for their distributed learning. Due to the preparation steps, TT-VFDL-ST and our previous work [7] need slightly more communication costs than [8; 9; 10; 11] and CMP on the same dataset. Refs. [12; 13; 14; 15] or [34] requires very large communication costs since the communication occurs in each training loop. As a result, TT-VFDL-ST and our previous work [7] could reduce communication costs 745.56 times at maximum and 19.31 times at minimum rather than [12; 13; 14; 15] or [34] while achieving the highest performances.
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
## 4 Shock and Vibration Tests  
To validate the accuracy of the optical fiber sensor, experimental tests were conducted by subjecting a beam to base excitation, impact and drop tests. In the following tests, the FBG sensor is attached on a cantilever beam subjected to bending. The bending strain can be transferred from the beam to the FBG sensor, resulting in a shift of the Bragg wavelength reflected from the FBG sensor. The bending strain in the cantilever is position dependent. The main influence exerted by the position of the FBG in the cantilever beam is that the strain of the FBG varies with the position of the FBG on the beam. In this work, the FBG sensor was placed at the center of the beam. The effects of bending on the FBG include strain-induced wavelength shift and curvature-induced power loss. In this work, the bending curvature of the cantilever is small; therefore, the power loss can be neglected.
# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Experiment
#### 4.4.2 Comparative Experiment
The improved BGF-YOLOv10 further enhances performance, achieving a [EMAIL_ADDRESS] of 32.0%, with 2.0 M parameters and 8.6 G FLOPs, demonstrating its advantages in small object detection while maintaining resource efficiency. In this experiment, the improved algorithm (Ours) demonstrates superior performance across key metrics. It effectively balances accuracy and efficiency by achieving a [EMAIL_ADDRESS] of 38.2% with 28.98 MB parameters and 16.2 GFLOPs. Compared to YOLOv9-c ([EMAIL_ADDRESS] of 37.0%, 102.1 GFLOPs), our model delivers higher accuracy while reducing computational complexity by over 80%, making it ideal for resource-constrained UAV platforms. Similarly, it surpasses YOLOv9s ([EMAIL_ADDRESS] of 37.6%, 26.7 GFLOPs) with 0.6% higher accuracy and 40% less computational demand. Against lightweight models like YOLOv8n ([EMAIL_ADDRESS] of 31.4%, 8.2 GFLOPs) and YOLOv10n ([EMAIL_ADDRESS] of 29.0%, 8.2 GFLOPs), Ours improves accuracy by nearly 7-9% while maintaining manageable computational complexity. It also outperforms YOLOv5s (33.3% [EMAIL_ADDRESS], 15.9 GFLOPs) and YOLOv7-tiny (35.3% [EMAIL_ADDRESS], 13.9 GFLOPs) with higher accuracy (4.9% and 2.9%, respectively) and minimal computational overhead. Traditional models like YOLOv3-tiny (19.9% [EMAIL_ADDRESS]) and YOLOv4-tiny (25.7% [EMAIL_ADDRESS]) perform poorly in complex UAV scenarios. Similarly, Swin-transformer (31.6% [EMAIL_ADDRESS], 58.0 GFLOPs) and Faster R-CNN (22.8% [EMAIL_ADDRESS], 180 GFLOPs) fail to balance accuracy and efficiency. In contrast, Ours achieves the highest [EMAIL_ADDRESS] among all tested models with much lower computational costs, making it highly suitable for dynamic UAV scenarios. This combination of high detection accuracy, reduced computational demand, and adaptability makes the proposed algorithm ideal for UAV-based object detection in resource-constrained and dynamic environments.