esa-sceva/satcom-chunk-collection huggingface dataset:
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 3 PRNet Framework
### Computational Complexity Analysis  
The main computation of the network structure is focused on the PRF module and IF-Fusion. The computational complexity of the projection operation is \(O(N)\), where \(N\) is the number of points in the point cloud. KD-Tree is used in the KNN algorithm, so the computational complexity of point searching is \(O(NlogN)\).e computational complexity of the sampling operation is \(O(N\times K)\), where \(K\) is the number of nearest neighbors of each point. The computational complexity of the fusion operation is \(O(N\times K\times C)\), where \(C\) is the number of channels of the feature. The computational complexity of each layer of the PRF module is \(O(N)+O(NlogN)+O(N\times K)+O(N\times K\times C)\). Computational complexity of IF-Fusion is \(O(L\times H\times W\times C\times C^{\prime})+O(M\times H\times W\times C)+O(6 \times H\times W\times C)\). Where \(O(L\times H\times W\times C\times C^{\prime})\) is the complexity of feature extraction, \(O(M\times H\times W\times C)\) is the complexity of paired fusion, and \(O(6\times H\times W\times C)\) is the complexity of cascade fusion. Where \(H\times W\) is the spatial size of the feature map, \(C\) is the number of input channels, \(C^{\prime}\) is the number of output channels, and is the number of layers of the convolutional neural network.  
The total compute capacity of the PRF module is approximately 4.5 million FLOPs. The total compute capacity of the IF-Fusion module is approximately 1541 million FLOPs. Because of the CPU's powerful linear task computing efficiency, the CPU is used to compute the PRF module, and the GPU is used to compute IF-Fusion. The inference time for each frame is about 90 ms on two NVIDIA GTX 1080 Ti GPUs (NVIDIA, Santa Clara, CA, USA).
## 3 Results
### Field Reference Data and Definition of Shorebird Intertidal Habitats  
A total of 1023 polygons, corresponding to 25,841 pixels (10 \(\times\) 10 m), were sampled within the 170 sampling sites defined throughout the intertidal flats of the Bijagos Archipelago (Figure 1). This represented 0.6% of the total intertidal area considered in this study. Overall, 580 sediment samples were analysed, representing 766 polygons and 21,649 pixels of exposed sediments. In these sediment samples, the mud content ranged between 0 and 75%, but samples with more than 30% were very scarce (Figure S4). Therefore, we created two sediment classes: sandy, with 0-10% mud content, and mixed, in which we grouped all the sediment samples with 10% or more of mud content. Using the quantitative data collected during the ground truthing and the results from the sediment analysis, we defined seven intertidal habitat types for the supervised classifications, based on a set of percent cover thresholds (rocks, shell beds, macroalgae, and FCB) and mud content (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Habitat Types** & **Defining Thresholds** & **Median Water** & **Mud Content** & **n** & **n** \\  & & **Film Cover (\%)** & **(Mean \% \(\pm\) SD)** & **Polygons** & **Pixels** \\ \hline Rocks & \(\geq\)30\% of rock cover. & 0 & N/A & 88 & 428 \\ Shell beds & \(\geq\)30\% of shell cover. & 20 & N/A & 53 & 609 \\ Macroalgae & \(\geq\)30\% of macroalgae cover and & & 30 & N/A & 116 & 3155 \\  & \(\leq\)50\% of high-density FCB cover and & & & & \\ Bare & \(\leq\)30\% of rocks, shells, and macroalgae & 35 & 2 \(\pm\) 2.3 & 408 & 12,777 \\ sediment sandy & cover and \(<\)10\% of mud content. & & & & \\ Bare & \(\leq\)50\% of high-density FCB cover and & & & & \\ sediment mixed & \(\leq\)30\% of rocks, shells, and macroalgae & 30 & 15.3 \(\pm\) 6.5 & 36 & 1219 \\  & cover and \(\geq\)10\% of mud content. & & & & \\ FCB sandy & \(\leq\)30\% of rocks, shells, and macroalgae & 0 & 4.7 \(\pm\) 2.9 & 179 & 4119 \\  & cover and \(<\)10\% of mud content. & & & & \\ FCB mixed & \(\leq\)30\% of rocks, shells, and macroalgae & 5 & 19.9 \(\pm\) 10.3 & 143 & 3534 \\  & cover and \(\geq\)10\% of mud content. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Habitat types defined for the supervised classifications of intertidal habitats, with a description of the defining thresholds and features (from the field reference data and sediment analysis) and sample sizes (polygons and pixels). Mud content is the percentage weight of sediment \(<\)63 \(\upmu\)m. FCB: fiddler crab burrow areas.
## 3 Results
### Scaling and Reliability
#### 3.4.3 Analysis of Stress Testing Results
For equivalent size VLM (**Qwen 7B**) at **100 users** load, multiple instances can respond in approximately **16 s**, while single instances may take around 68 s. Furthermore, for smaller 1B models (**Llama 1B**), we can serve as many as **150 concurrent users** with an approximately **20 s response time** compared to almost 90 s with a single instance. These outcomes highlight the robustness, scalability, and reliability of JAVIS. By leveraging Ray's scaling capabilities, JAVIS ensures that even under substantial concurrency, performance remains stable and responsive, allowing for a significantly enhanced user experience. Figure 4: Single-instance (non-Ray) vs. multiple-instance (Ray) response time results per user. Each bar inside each bar plot group represents one model in the following order: qwen2 1b, qwen2 7b, llava 13b, llama 1b, llama 3b, and llama 8b. The same order is preserved for the blue plot represented by the models leveraged by autoscaling with Ray. It is important to note that these results were achieved on a relatively modest server with four A6000 GPUs. Therefore, increasing GPU availability would significantly enhance the capacity to handle larger user loads. Additionally, the results were based on simultaneous queries, meaning the actual user load that can be supported is likely much higher, as users typically have pauses between messages.
## 3 Deep Learning Architecture and Signal Reconstruction
### Generative Adversarial Network
The generator is given the vibration signal as the input, then the generator produces the pressure signal as the output. The discriminator’s input is the result of the concatenation of the vibration signal and either the generator’s output or the expected pressure signal. The discriminator is only given one or the other at a time, shown using dotted arrows. The discriminator classifies the output as real or fake, which is then used in the loss calculation. the expected output of the SmN model would be the corrected signal without false spikes or noise. The SmN should learn what kinds of errors are produced by the original model and correct them. For each of the previously trained models, nine different smoothing models were trained. The architecture of three of these models was based on the ANN with dense layers, The remaining models were based on the CNN architecture, with two AE models and two U-net models. Furthermore, the models were trained twice, using Binary Cross-Entropy (BCE) and MSE loss. The most important part of the mapping from vibrations to pressure signals is the correct detection of pressure spikes. As such, utilizing the BCE loss can allow the model to treat the task as a binary classification task to find spikes as positive classes and no spikes as negative classes. Figure 5 shows the idea behind utilizing the smoothing model on another model's output. Table 4 shows the tested smoothing models. Each model shown in the table was trained based on the output of every model shown in Table 3. A smoothing model is given as input, the pressure signal of the model it was paired with in the training, then the smoothing model aims to output an improved recreation of the pressure signal by removing errors and noise from the signal originally produced by the model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters** & **Loss Functions** & **Activation Functions** \\ \hline Autoencoder–small & 17,121 & & \\ Autoencoder–big & 188,193 & & \\ U-Net–small & 2,046,545 & MSE & ReLU/Sigmoid \\ U-Net–big & 16,992,849 & & \\ \hline GAN-Generator & 2,046,545 & 1—Discriminator Loss; weighted & \\ (U-net–small) & 2,046,545 & Sum between 1—Discriminator Loss; & ReLU \\  & & and MAE/MSE & \\ \hline GAN-Generator & 16,992,849 & 1—Discriminator Loss; weighted & \\ (U-net–big) & 16,992,849 & Sum between 1—Discriminator Loss; & ReLU \\  & & and MAE/MSE & \\ \hline GAN-discriminator & 2,586,897 & BCE & Sigmoid \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A general summary of the models used in the experiments, which were trained to reconstruct the engine’s pressure signal based on the input vibration signal. Figure 5: This diagram illustrates the idea behind utilizing the smoothing model to improve the output generated by the original model. The input to the model is the vibration signal, and the output is the predicted pressure signal, which then becomes an input to the smoothing model. The smoothing model’s output is the predicted, possibly improved pressure signal.
# Considered Factors of Online News Based on Respondents' Eye Activity Using Eye-Tracker Analysis
## 3 Research Methodology
### Data Processing and Analysis  
In this study, there were two types of data: quantitative and qualitative. Quantitative data were obtained from the Eye-Tracker and Beck Anxiety Inventory experimental process. Raw fixation data were recorded in the form of XY coordinates, fixation duration, and timestamps of the respondent's eyes for each fixation at a time [67]. Raw data were filtered to find the ratio along with the duration of fixation on the predetermined AOI, namely news text and images. Processing fixation data produced detailed gaze characteristics of each respondent, which also showed the nature of each respondent's attention [68]. Then, data were compared with anxiety level data from the Beck Anxiety Inventory instrument. From these two areas, we were able to compre the ratio of each level of anxiety to the AOI text or image.  
Qualitative data in this study were raw data recording respondents' answers to several of the questions asked. The questions posed came from a related research study and aimed to obtain appropriate qualitative data for this study [69]. The following three questions were asked to the respondents:  
1. Which news makes you the most anxious?
2. What are the reasons for your anxiety?
3. What is your opinion on the spread of news in the Indonesian news media today?These three questions were asked because they had their respective goals to find out the respondents' point of view when reading news that gave them anxiety. Question (A) intended to focus the interview process further on a particular story that induced the most anxiety for the respondent. Question (B) was asked to find out the reasons why the news played a role in arousing respondents' anxiety. This question allowed broad answers, and they depended on the background of the respondent's perspective and the news, particularly to the answer to question (A). Finally, question (C) was asked to determine the respondent's views on the news that has spread across Indonesia from various sources and platforms. This answer gave ideas and criticisms on the development of news media in Indonesia in the future. For our research, this answer also uncovered respondents' perspectives on news that gave them anxiety, and it tells certain factors about current news in Indonesia that are uncomfortable to news consumers [70].  
Qualitative data were then processed with qualitative data processing software to find the C coefficient (CC) and co-occurrence (CO) of respondents' answers to the three questions asked. CO can be interpreted as an association between concepts, their intensity, their meaning, and their role in constructing phenomena in a study. CC shows the strength of the relationship between two codes that are similar to CO. The results of this processing are expected to show the correlation of relationships answering the problems that arise in delivering news that hurts consumers. Quantitative and qualitative data were triangulated, and results are explained in the discussion section.
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 5 Experimental Results and Discussion  
Table 3 exhibits the _g-mean_ values for each method and dataset. Friedman ranks were added in order to simplify the understanding of the results (see Section 4.3). Table 3 confirms previous machine learning works, in the sense that other problems, such as class overlapping, the small disjuncts, the lack of density or the noisy data, among others, increase the effect of class imbalance on the classifier performance. For example, see the Salinas dataset, which is a high imbalanced dataset and its _g-mean_ value (without a preprocessing method) is relatively high (0.8848); i.e, it is affected by the class imbalance, but this problem does not induce a poor classification performance (as it occurs in other datasets). These results highlighted the need to study a heuristic methods that allow one improve the quality of the training dataset, such as ENN, or strategies such as TL or CNN (see Section 3), in order to reduce the negative effect of the class imbalance on the classifier performance. For the rest of datasets, the classifier performance is clearly impacted by the class imbalance problem, as it can be observed in Table 3. When the classes distribution was balanced, a substantial performance improvement was reached (see ROS and SMOTE results). In accordance with Friedman ranks, it can be noted that ROS presents better results than SMOTE. In machine learning scenarios, SMOTE traditionally overcomes ROS's performance [24], but in the big data and deep learning context, results (of this work) show that this expectation was not sufficiently fulfilled. This behavior has been observed in other works related to big data and deep learning [1; 48]. However, the performances of both ROS and SMOTE were practically equivalent. Even for the KSC dataset, SMOTE _g-mean_ outperforms ROS, although there does exist a clear trend to obtain better results with ROS than with SMOTE. RUS overcomes the classification performance of MLP trained with the original dataset; notwithstanding, it only improves the _g-mean_ results of TL and ENN. I.e., RUS is an effective technique to deal with the class imbalance problem, but it is not the best strategy. RUS eliminates a considerable amount of samples and keeps acceptable values of _g-mean_ in Salinas, Pavia and PaviaU. These results exhibit that only a few samples are necessary to obtain a good classifier performance; thus, this information gives relevance to the study of heuristic sampling data. On the other hand, Table 3 shows that in the most datasets, the strategies ENN and TL present the worst results. In Indian, Pavia, Botswana and KCS datasets it is not enough only to improve the quality
# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 4 Results and Discussions
### The Fault Detection by SVM Binary Classification Method
From Table 2, the application of different kernel functions has an evident influence on the performance SVM. In the application of linear kernel function [25], the fault detection accuracy rate of both PCA and 2 DPCA combined with SVM reach 100%. However, the detection results obtained by the 2 DPCA + SVM are better than those of PCA + SVM in the application of other kernel functions. In the case of the polynomial kernel function and sigmoid kernel function, the accuracy rate for the fault samples under the 2 DPCA+SVM is also 100%. In order to investigate the performance of SVM with PCA and 2 DPCA at different reconstruction thresholds, and under various kernel functions, we also conducted a multi-classification experiment. In the multi-classification experiment, we regarded each subclass in Table 1 as an independent category, such as transformer AE signals containing rain AE signals and transformer AE signals containing wind AE signals in normal samples as an independent category, the recognition results are listed in Table 2.  
The results presented in Table 2 were found the best results exist in 2 DPCA + SVM with RBF kernel [23] at low reconstruction threshold. For SVM classifier, the accuracy and sensitivity under different reconstruction thresholds and different kernel is different. When using the linear kernel, the PCA + SVM is almost unaffected by the reconstruction threshold, but the 2 DPCA + SVM obviously changes with the reconstruction threshold when using linear kernel. As the reconstruction threshold increases (the projection axis increases), the performance of the 2 DPCA + SVM is becoming worse. It can be seen from Table 2 that the performance of fault detection using the linear kernel function is still ideal whether using the PCA + SVM or the 2 DPCA + SVM. However, when the other three kernel functions are applied, the performance of the PCA + SVM is lower than the linear kernel function. For the 2 DPCA + SVM method, apart from the Sigmoid kernel function, the linear kernel function and the polynomial kernel function can still make the accuracy rate reach 100%.
## 3 Results
### Feature Importance Analysis  
The feature importance analysis for U-Net, SE-UNet, UNet3+, and SE-UNet3+ models at lead times of 24 h, 48 h, and 72 h is presented in Figure 10 (showing only the ten most important features) for 24 h, and Figures S5 and S6 for 48 h and 72 h, respectively (see details in Supplementary Materials). The results suggest that the GFS precipitation forecast data is the most important variable among the 25 input variables, followed by the land and sea mask (lsm), which is a topography-related variable. Additionally, variables 500u and 500w are among the top ten most critical variables in each model at lead times of 24 h, 48 h, and 72 h, indicating the significance of the GFS precipitation forecast data, lsm, zonal wind speed at 500 hPa, and vertical speed at 500 hPa in the neural network training for the U-Net, SE-UNet, UNet3+, SE-UNet3+ models.  
Figure 9: Comparison between the accumulated precipitation forecasts (mm) by PM and GFS models with different lead times, and the precipitation observations by GPM for TC Ma-on at 113\({}^{\circ}\) E, 20.5\({}^{\circ}\) N-precipitation forecasts by PM with different lead times of (**a**) 24 h, (**d**) 48 h, (**g**) 72 h; precipitation forecasts by GFS with different lead times of (**b**) 24 h, (**e**) 48 h, (**h**) 72 h; accumulated precipitation observation by GPM within different periods of (**c**) 24 h, (**f**) 48 h, (**i**) 72 h. The red star denotes the TC Ma-on location.
## 1 Introduction
With reference to CLA, which has beneficial effects in various disease conditions such as diabetes, obesity, cancer, and atherosclerosis, studies are showing that the stage of the lactation period affects, by a small percentage, the production of CLA in milk fat ewes [18]. This was also observed in [PERSON] et al. [31], who argued that the CLA had maximum values at the end of the lactation period of sheep. Fatty acids, according to [PERSON] et al. [32], were affected by grazing and the seasonality. The research in sheep, goats, and cows have concluded that fresh grazing increases CLA, oleic acid, and monounsaturated fatty acids (MUFA), while reducing the saturated ones in the fat of milk [33; 34]. The grazing at early spring (young vegetative stage) contains linolenic acid, more than 40% of the total fatty acids, while at the end of spring (mature vegetative stage), the linolenic acid decreases. Sheep that consumed by grazing in young vegetative stage showed higher CLA concentrations in milk fat from when eating in pasture, in mature vegetative stage [33; 35; 36]. The importance of pasture was also pointed out in a study, where they observed the lowest concentration of CLA in fat cows from Nordic countries, in relation to European countries [37]. Most research so far on this topic is focused on cow's milk, yet sheep and goat milk are of special interest for Mediterranean countries such as Greece. Sheep and goat farming has been the main pastoral activity in Greece since ancient times and has survived until today as it is associated with products having deep cultural roots and characterized as designated origin. The main objective of this study was to compare the fatty acid profile and the fat-soluble vitamin (A and E) content of milk produced from organic and conventional dairy sheep and goat farms in Greece, taking under consideration the geographical vicinity, seasonality, feeding scheme, and other farming practices.
# A Practical Approach for Picking Items in an Online Shopping Warehouse
## 3 Grasp Planning
### Height Accumulated Features (HAF)  
As the name suggests, the HAF algorithm utilizes the heights of surface points, gathered from the point cloud data, relative to their neighbors in order to learn how to grasp the objects. The authors stress three important advantages of the algorithm; _segmentation independent_, _integrated path planning_ and use of _known depth regions_[10; 11].  
The term _height_ refers to the measure of the perpendicular distance from the table plane to the points on the top surface of the object. The input point cloud is first discretized and the height grid \(H\) now contains a \(1\times 1\) cm\({}^{2}\) cell that saves the highest z-valued points with corresponding x and y values [33]. HAF features are defined similarly to Haar Basis functions. All height grid values of each region, \(R_{i}\), on a height grid \(H\), are summed up. The sums \(r_{i}\) are individually weighted by \(w_{i}\) and then summed up. The regions and weights are dependent on the HAF feature that are defined by an SVM classification. A feature value, \(f_{i}\), is defined as the weighted sum of all regions. The \(j\)th HAF value \(f_{j}\) is calculated as:  
\[f_{j}=\sum_{i=1}^{nrRegions_{j}}w_{i,j}\cdot r_{i,j} \tag{1}\]  
\[r_{i,j}=\sum_{k,l\in\mathbb{N}:H(k,l)\in R_{i,j}}H(k,l) \tag{2}\]  
The paper claims to have tested 71,000 features (70,000 of which are automatically generated) and finally selected 300 to 325 with an F-score selection [11]. Figure 5 visualizes the calculations in process for our implementation of the HAF algorithm; it corresponds to one of the objects in ARC'17.  
The green bars indicate the identified potential grasps, whereas the height of the bars represents the grasp evaluation score. The frame (green, red and blue segments) represents the final grasp hypothesis chosen by the algorithm and indicates the final position where the end effector should go.