esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### The Comparison of Models  
Similarly, previous researchers have used these four methods to estimate forest AGB and achieve good accuracies, while results of the models' comparison vary compared with this study. [PERSON] et al. (2018), integrating airborne LiDAR and optical data, compared the accuracies of forest AGB models in the upper Heihe River Basin in northwest China, and found that RF was the best (\(R^{2}\) = 0.9, RMSE = 13.4 Mg \(\cdot\)ha\({}^{-1}\)), following by ANN and SVR ([PERSON] et al., 2016). Based on Landsat satellite imagery, [PERSON] et al. (2016) implemented the optimal spatial forest AGB estimation in northwestern Zhejiang Province, China, and RF (\(R^{2}\) = 0.6, RMSE = 26.4 Mg \(\cdot\)ha\({}^{-1}\)) also performed better than SVR ([PERSON] et al., 2017). [PERSON] et al. (2017) developed forest AGB models using GLAS and Landsat data, and also found that RF (\(R^{2}\) = 0.95, RMSE = 17.73 Mg \(\cdot\)ha\({}^{-1}\)) had a better estimation than SVR ([PERSON] et al., 2018). [PERSON] et al. (2018) concluded that ANN performed (RMSE = 27.6 Mg \(\cdot\)ha\({}^{-1}\)) better than SVR and RF, by conducting a comparative analysis of algorithms for forest AGB estimation with ALOS PALSAR and Landsat data ([PERSON] et al., 2018). In a word, ANN and SVR models all showed a close performance for forest AGB modeling in this study, and in previous research. The RF models generally obtained the highest accuracies among the three ML methods, while SVR showed the best performance in this study. This may be due to the smaller sample sizes in this study, and the uniform random distribution of samples in the study area. It also highlighted the powerful capacity of SVR for limited samples. Additionally, due to direct evaluation and the accuracy of the training data, rather than the independent validation set or by cross-validation from limited samples, the models of this study were obviously much more accurate than in previous research. Because the GWR and ML models have not been compared in any previous research, this finding can provide a reference for mapping forest AGB in the future.
## 2 Materials and Methods
### Public Datasets  
In order to establish the utility of our dataset and range the learning capabilities of models trained over the present dataset, the other two publicly available datasets have been used in this work:  
1. CVC-EndoSceneStill [23]. It contains 912 WL images which are manually segmented. A distribution into training, validation and test sets is provided by the owners. Each set contains 547, 183 and 182 images, respectively. This dataset is available at [[http://www.cvc.uab.es/CVC-Colon/index.php/databases/cvc-endoscenestill/](http://www.cvc.uab.es/CVC-Colon/index.php/databases/cvc-endoscenestill/)]([http://www.cvc.uab.es/CVC-Colon/index.php/databases/cvc-endoscenestill/](http://www.cvc.uab.es/CVC-Colon/index.php/databases/cvc-endoscenestill/)).
2. Kvasir-SEG [24]. It contains 1000 WL images which are manually segmented. Distribution into training, validation and test sets has been randomly done, so each set includes 600, 200 and 200 images, respectively. This dataset is available at [[https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)]([https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Items**} & \multicolumn{1}{c}{**Train Set**} & \multicolumn{1}{c}{**Validation Set**} & \multicolumn{1}{c}{**Test Set**} \\ \hline Image type & WL & 1382 & 558 & 192 \\  & NBI & 821 & 340 & 141 \\ Paris Classification & Protruded lesions: 0-Ip & 274 & 81 & 29 \\  & Protruded lesions: 0-Ips & 245 & 41 & 14 \\  & Protruded lesions: 0-Is & 433 & 176 & 31 \\  & Flat elevated lesions: 0-IIa & 1052 & 263 & 49 \\  & Flat elevated lesions:0-IIa/c & 27 & 122 & 25 \\  & Flat lesions: 0-IIb & - & 48 & 58 \\  & N/A & 172 & 166 & 127 \\ NICE classification & Type 1 & 435 & 139 & 114 \\  & Type 2 & 1552 & 592 & 92 \\  & Type 3 & 172 & 166 & 127 \\  & N/A & 44 & - & - \\ Diagnosis & Adencarcinoma & 172 & 166 & 127 \\  & Adenoma & 1552 & 592 & 92 \\  & Hyperplasia & 435 & 139 & 114 \\  & N/A & 44 & - & - \\ Histological stratification & High grade dysplasia & 360 & 217 & - \\  & Hyperplasia & 342 & 139 & 56 \\  & Invasive adenocarcinoma & 172 & 166 & 127 \\  & Low grade dysplasia & - & - & 29 \\  & No dysplasia & 1285 & 375 & 121 \\  & N/A & 44 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frames in each of the sets according to clinical metadata.  
These datasets do not provide clinical information of the images they include. In both cases, for each image, a binary mask indicating the polyp area is provided. While CVC-EndoSceneStill also provides void area binary masks, Kvasir-SEG does not. The polyp binary mask is used at training and testing, but the void binary mask is used to report metrics with respect to the valid endoscopic image, so void binary masks have been manually created for the images included in the test set of Kvasir-SEG.
###### Abstract  
In order to improve the detection accuracy of an algorithm in the complex environment of a coal mine, including low-illumination, motion-blur, occlusions, small-targets, and background-interference conditions; reduce the number of model parameters; improve the detection speed of the algorithm; and make it meet the real-time detection requirements of edge equipment, a real-time obstacle detection method in the driving of driverless rail locomotives based on DeblurGANv2 and improved YOLOv4 is proposed in this study. A blurred image was deblurred using DeblurGANv2. The improved design was based on YOLOv4, and the lightweight feature extraction network MobileNetv2 was used to replace the original CSPDarknet53 network to improve the detection speed of the algorithm. There was a high amount of background interference in the target detection of the coal mine scene. In order to strengthen the attention paid to the target, the SANet attention module was embedded in the Neck network to improve the detection accuracy of the algorithm under low-illumination, target-occlusion, small-target, and other conditions. To further improve the detection accuracy of the algorithm, the K-means++ algorithm was adopted to cluster prior frames, and the focal loss function was introduced to increase the weight loss of small-target samples. The experimental results show that the deblurring of the motion-blurred image can effectively improve the detection accuracy of obstacles and reduce missed detections. Compared with the original YOLOv4 algorithm, the improved YOLOv4 algorithm increases the detection speed by 65.85% to 68 FPS and the detection accuracy by 0.68% to 98.02%.  
YOLOv4 algorithm; Mobilenetv2; SA attention mechanism; focal loss function; K-means++; DeblurGANv2 2023
# Fully Open-Source Meeting Minutes Generation Tool
## 4 Selection of Open-Source Models
### Selection of OCR Programs  
The extracted information and slide image are aligned based on the extracted _OCR_ text. Therefore, an accurate _OCR_ program must be selected. To justify the selection, multiple _OCR_ programs are compared, and the lowest _CER_ is chosen as the _OCR_ for the system.  
Table 5 shows that _PaddleOCR_ consistently delivers the fastest processing time and maintains high accuracy at any image resolution, showing the best overall performance. _Tesseract_ performs well at higher resolutions but suffers from a significant accuracy drop at lower ones, reaching 100% _CER_ below 320 \(\times\) 180, as denoted by the bold values in the Table 5. _EasyOCR_ is the slowest and least accurate, especially at lower resolutions, where error rates rise sharply. Thus, _PaddleOCR_ is the most reliable and is selected as the best fit for the system.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metric** & **KeyBERT [53]** & **RAKE [54]** & **YAKE [55]** & **TextRank [56]** & **RaKUn2 [57]** \\ \hline Cosine Similarity & 28.46\% & 27.83\% & 34.01\% & 33.97\% & 47.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of cosine similarity among keyword extraction models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **T5 [50]** & **PEGASUS [51]** & _BART_[52]** \\ \hline ROUGE-1 & 30.66\% & 30.95\% & 39.34\% \\ ROUGE-2 & 15.05\% & 16.30\% & 22.43\% \\ ROUGE-L & 25.56\% & 26.16\% & 35.35\% \\ Duration (s) & 4.448 & 1.374 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of _ROUGE-N_ and duration among summarization models.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 3 Independent Vortices Search
### Vortex Search Algorithm
This helps to keep the configuration simple, reducing human interference in searching for a solution to a problem. Another interesting characteristic of VS is the transition between global and local search. In many MHs, exploration and investigation occur from the beginning to the end of the execution. This makes  
Figure 1: With an initial radius of size 10 and maximum cycles at 100, the black line shows the decrease of radius size, whereas the colored areas indicate the standard deviations of \(\pm 1\), \(\pm 2\), and \(\pm 3\sigma\). Multivariate Normal Distribution generates about 99.7% of the candidate solutions within these intervals. At around half of the cycles, the search shifts from exploration to investigation. it possible to find a good enough solution, even in the first few cycles. This way, they can interrupt the execution if so desired. Otherwise, the global search continues through to the last iterations, attempting to reduce the risk of local minima. In VS, the radius size is reduced over the cycles using an inverse incomplete gamma function (Equation (22)) and considering the ratio of cycles performed (Equation (21)). Thus, exploration and investigation occur gradually, and so are not present throughout the entire execution, yet still providing an adequate balance. The advantage of this strategy is the fine-tuning of the best solution found in the last cycles. However, by analyzing the VS strategy, we can conclude that the transition between exploration and investigation has two drawbacks. First, it has the local minimum risk: There is no possibility of achieving the global solution in an execution if it limits the radius to a local minimum area. The radius of the vortex becomes small and the candidates get stuck in a local search limitation. The second inconvenience is identifying the dispensability of the processing during the execution. When defining a limit of execution for example, by the number of cycles, it executes them in its totality because only in the later cycles does VS carry out the investigation stage. MVS tries to minimize the local problem by adding several vortices to VS. However, applying Equation (25) does not mean the vortex with the so-far best solution will bring the other centers closer to a global minimum. With these characteristics in mind, we based the proposed IVS method on maintaining solution fine-tuning while trying to circumvent the local minimum problem, all without increasing the computational cost.
## 4 Results and Discussion
### Comparison of Proposed Models Among Themselves
To summarize, in this study, seven pre-trained convolutional neural networks, including AlexNet, GoogLeNet, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50, and a designed model, MSU-CNN, are evaluated with k-fold cross-validation to ensure robust and reliable performance for pistachio type classification. The networks used in this study are distinguished by their different architectures and features. Each has strengths and drawbacks, leading to performance differences. The AlexNet network is a basic architecture for deep networks. It is simple and fast. However, its general characteristics can limit its learning capacity. The GoogLeNet network has a very deep structure, can learn more features, and has high computational efficiency. But the training process may be longer. Proposed MSU-CNN is a network designed specifically for this classification task, it does not have many layers compared to other networks, and it is fast. However, since it is designed for this classification task, it may cause the performance to change on different datasets. The VGG16 network tries to learn in-depth features using filters and its structure is simple, but it uses a large number of parameters. EfficientNet is an advanced network optimization strategy and its smallest model is the EfficientNet-b0 network. It generally provides higher performance with fewer parameters. ResNet-18 and ResNet-50 networks are used to train deep networks using residual connections. ResNet-50 has a deeper structure with 50 layers. Residual connections allow the network to learn effectively despite being very deep. The Inception-v3 network learns features in parallel with filters of different sizes using Inception modules. It provides high accuracy and efficient computation. However, the training process can be long and requires more computational resources. As a result, ResNet-50 and Inception-v3 networks can learn more complex features and thus achieve higher accuracies due to their high depth and parallel processing capacity.
###### Abstract  
This paper investigated the optimization of the hardness and oscillation mode of flexible hydrofoils using bidirectional fluid-structure interaction (FSI) to address the issue of insufficient guidance in engineering applications. A two-dimensional flexible symmetric hydrofoil model of NACA0012 with a chord length of 1 m was constructed for this research. The hydrodynamic characteristics of low-frequency flexible hydrofoils with varying hardness and oscillation modes were analyzed through numerical simulation. The results indicated that the flexible hydrofoil with a Shore hardness of D50 exhibited the most optimal hydrodynamic performance under low-frequency conditions across the five groups of hardness tests. Among the three commonly utilized oscillation modes, the inboard oscillation mode demonstrated the most favorable performance. The hydrodynamic performance of the flexible hydrofoil surpassed that of the rigid hydrofoil in both inward and outward oscillation motions; however, it was inferior in pure pitching motions. Comparative analysis of the vortex structure and velocity distribution in the flow field revealed that the inward oscillation motion effectively enhanced the kinetic energy of the wake vortex and slowed down vortex dissipation, thereby improving the overall flow velocity. These findings provide theoretical support for the study of flexible hydrofoils and contribute to their advancement in pumping applications under actual ultra-low head conditions.  
**Keywords:** flexible hydrofoil; Shore hardness of the hydrofoil; bidirectional fluid-structure interaction; oscillation mode; hydrodynamic performance
## 2 Materials and Methods
### Localization Algorithms  
Two localization algorithms requiring only a limited number of reference nodes were implemented. Reference nodes are also known as beacon nodes because they have accurate information about their accurate locations and are thus used as reference points for the localization of other nodes. For this paper, two localization algorithms were considered and implemented: multilateration and min-max [10].  
Multilateration is a simple range-based, decentralized localization algorithm based on geometric principles. To estimate its position, a node collects beacon messages and estimates its distance to each  
Figure 1: Intelligent off-road vehicle unit configuration.  
beacon. The node then computes its position by determining the intersection of the circles centered at the positions occupied by the beacons with radii equal to the estimated distance between the beacons and the node [6]. The intersection would ideally be a single point, but the intersection typically identifies an area in which the node is located due to accumulated error.  
Min-max is a popular localization algorithm due to its simple implementation. To estimate its position, a node creates an association between each beacon position and the distance to that beacon. The node then creates pairs of horizontal and vertical lines around each beacon such that the minimum distance between each line and the beacon position equals the estimated node-beacon distance. The node locates itself in the center of the rectangular area between the innermost horizontal and vertical lines [6]. The localization error depends on the size of the rectangular area. A smaller intersection enables better localization.
Unlike other works with main emphasis in networking protocols and performance analysis considering a fixed node topology, our work not only covers ad-hoc cases but also focuses on the deployment of MIMO architecture in wireless nodes in order to optimize transmission power and improve network performance without additional spectrum requirements. In this context, it is assumed that node locations may vary according to traffic conditions in the building or other external factors; therefore, Monte Carlo (MC) simulations are performed where at each iteration a different network topology is considered. As integration with 5G standardization and SG infrastructure progresses [16], a physical layer protocol analysis is essential as well, since network nodes will play a dual role: measuring critical parameters and informing the BMS of appropriate actions as previously mentioned, as well as interacting with mobile users in order to provide information on demand. The rest of this manuscript is organized as follows: In Section 2, the mathematical framework for wireless transmission in building environments is provided, with emphasis on multipath exploitation, considering a MIMO-3G infrastructure. Indoor propagation is considered, while performance metrics, such as per frame signal-to-interference-plus-noise ratio (SINR) and transmission power, are derived as well. In Section 3, the simulation framework is provided, while in Section 4 results are presented for various MIMO orientations (i.e., \(2\times 2\), \(3\times 3\), and \(4\times 4\)) and six resolvable multipath components in terms of mean transmission power per mobile node. Finally, concluding remarks and proposals for future work are given in Section 5.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 4 Experiments
### Results of Different Compressors  
In this sub-section, we compare the proposed MCRU-based dynamic DRC with other compressors with regards to the compression ratio (CR), which is defined as  
\[\text{Compression Ratio (CR)}=\frac{\text{uncompressed data size}}{\text{ compressed data size}}. \tag{13}\]  
The hyper-parameters of MCRU are the same as in Table 4. The results are listed in Table 1. Again, for our proposed approach, the median CR over 10 runs is reported.  
As we have mentioned in Section 2.2, dictionary-based entropy coding methods, such as Gzip, are not good at removing redundancy in sensor signals, leading to low CRs. The block sorting-based BSC algorithm, on the other hand, has demonstrated great performance improvement (more than 2 times) against Gzip on signals with a relatively simple pattern, such as BLOND signals. However, this gap shrinks dramatically on signals with a more complex pattern, such as ESC and MPACT signals. As for compressors based on mixed-context strategy, namely PAQ and its successor CMIX, they outperform BSC consistently on all types of signals. Comparing these two methods, the major difference is that CMIX additionally utilizes an LSTM to extract context information, yielding a minimum gain on CR of approximately 0.2 over all signals, which makes CMIX a state-of-the-art general compressor.  
The proposed MCDRC does not show advantages over CMIX on BLOND signals which contain a simple pattern. Nonetheless, on other signals which contain more complex features, MCDRC outperforms the state-of-the-art CMIX consistently. Note that the CR-advantage of MCDRC over CMIX nearly doubles on 14-bit signals compared to their 16-bit counterparts, which once again reveals the superiority of the flexible predictor resolution setting of MCDRC.
# Complex Background Reconstruction for Novelty Detection
## 3 Methodology
### Reconstruction Network (RNet)
In this work, we input the original image and the reconstructed image into the discriminator \(D\) to obtain the corresponding features \(D(x)\) and \(D(\hat{x})\). The feature distribution matching computes the similarity between the distributions of \(D(x)\) and \(D(\hat{x})\), which is measured by the MMD distance. Formally, let \(f(\cdot)\) denote the function that outputs an intermediate layer of the encoder \(D\) for a given input \(x\) sampled from the input data distribution \(\mathbb{P}\); the function of the discriminator \(D\) can be considered as forming a new kernel with \(k\): \(k\circ D(\mathbf{a},\mathbf{b})=k(D(\mathbf{a}),D(\mathbf{b}))=k_{D}(\mathbf{a},\mathbf{b})\). Thus, the adversarial loss can be defined as  
\[\mathcal{L}_{adv}=M_{k\circ D}^{2}(f(x),f(\hat{x})). \tag{2}\]  
**Reconstruction Loss:** In RNet, the reconstructed image is obtained by decoding the latent representation of the samples. In order to optimize the decoder, the reconstruction loss is defined as  
\[\mathcal{L}_{con}=\mathbb{E}_{\mathbf{x}\sim\mathbb{P}\mathbf{x}}\|x-\hat{x}\|_{1}. \tag{3}\]  
**Encoder Loss:** Both losses above are designed to force the generator to reconstruct the input sample better. Moreover, we add a loss to minimize the MMD distance between the distributions of \(E_{1}(x)\) and \(E_{2}(\hat{x})\). The encoder loss is in the same form as the adversarial loss, which is defined as  
\[\mathcal{L}_{enc}=M_{k}^{2}\big{(}E(x),D(\hat{x})\big{)}. \tag{4}\]  
To summarize, the loss function of RNet is a linear combination of the reconstruction loss, the adversarial loss, and the encoder loss:  
\[\mathcal{L}_{RNet}=\mathcal{L}_{con}+\gamma\mathcal{L}_{adv}+\delta\mathcal{L }_{enc}. \tag{5}\]  
where \(\gamma\) and \(\delta\) are weighting factors of the adversarial loss and the encoder loss, respectively.
## 2 Adaptive Time-Delayed Control Schemes
### Conventional Adaptive Time-Delayed Control Scheme
The control gain \(\hat{M}_{t}\) in Equation (6) is constructed as a _discontinuous function_ owing to \(\
u_{t}\), and hence the conventional ATDC of Equation (5) provides an easy response to unknown disturbances or undesired side effects. However, since this approach is based on \(\
u_{t}\), it may cause chattering or fluctuation. Moreover, \(\hat{M}_{t}\hat{M}_{t-L}^{-1}\) may temporarily fall away from 1, and thus the stability criteria in Lemma 1 may not be satisfied. From this point of view, it can also be said that the control gain with adaptive law can be further improved. The control gain with a new adaptive law is proposed in the next section.