esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Influence of the Superpixel Size  
As the main parameter involved in the proposed superpixel version of bilateral filtering, the superpixel size may greatly influence the final processing results. Generally speaking, segmentation with large superpixels can only capture the coarser details or edges of size larger than the superpixel size, and the finer details would be smoothed after reconstruction. In contrast, segmentation with small superpixels can keep more fine details but degrades the de-noising ability and raises the computational cost. Figure 4 shows the reconstructed image patches, derived from Figure 1, with different superpixel sizes. We can clearly see that, with an increase of superpixel size, a mosaic pattern gradually appears in the reconstructed images; which is quite similar to pseudo-Gibbs artifacts. In contrast, smaller superpixels can capture small edges well, but more noise appears. So, considering the various organs of different sizes which are contained in the UI images, we experimentally set the superpixel size to 1% of the minimal size of the horizontal and vertical image sizes, and lower than 7 pixels, in this work.
## 5 Case Study and Analysis  
### Background  
The Qingdao Bohai agriculture development corporation is a subsidiary of Shandong Bohai industry group, which is a grain and oil processing enterprise integrating grain and oil processing, international trade, warehousing, and logistics. Taking \"Serving the Three Rural Areas and Benefiting the Society\" as mission, the corporation continues to pool global industrial superior resources, and is growing gradually in the process of agricultural industrialization. Furthermore, its deep processing fields include soybeans, rapeseed, cereals, oils, and vegetable fibers. For the sake of deep processing high quality soybean better, the management board wants to select a high-grade soybean supplier.  
Four soybean suppliers \(\{\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4}\}\) with the ability to meet the corporation's requirements are entitled to this selection. The evaluation criteria are the most representative summarized from the existing literature on green supplier selection, as well as combining with the company's situation in the reality. Then, the selected six criteria are determined as follows [42; 43]: price of green agricultural product (\(C_{1}\)); quality of green agricultural product (\(C_{2}\)), production capacity and technology (\(C_{3}\)), delivery (\(C_{4}\)), environmental management (\(C_{5}\)), and design for environment (\(C_{6}\)).
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements
### Configuration, Scenario, and Results
These figures also include two sets of constellation diagrams, one for the scenario without equalization and one with the QR-RLS adaptive algorithm. Each set had two constellation diagrams corresponding to the values in the center of the illuminated area and at the measurable threshold (350 cm for QR-RLS and 325 cm for the scenario without equalization). By comparing both figures, it can be seen that wider bandwidths had a significant impact on \(\rm E_{b}/N_{0}\). Figure 13 shows the better results of \(\rm E_{b}/N_{0}\) by approximately 4 dB in comparison to Figure 14. However, wider channels also offered higher transmit speeds. Figures 15 and 16 show the comparison of the BER and EVM parameters. Both figures displayed a bit error rate for the 1 MHz bandwidth and 4-32 QAM modulation formats. Some waveforms are not present in the figures, as they tended to copy the horizontal axis (their BER was out of the platform measuring range). This fact mainly affected the 4-QAM modulation scheme in combination with various adaptive algorithms and was caused by the limited number of transmitted symbols, effectively influencing the range of the measurable bit error rate. Modulated signals without equalization had significantly higher error rates. The 32-QAM modulation scheme at 300 cm never exceeded the order of \({}^{-5}\), when the QR-RLS algorithm was used. In comparison, the whole platform easily exceeded the order of \({}^{-4}\) at 150 cm without any equalization algorithms. The 4-QAM modulation would need to be used to reach a distance of 300 cm without equalization algorithms; therefore, the effective transmit speed would be lowered significantly. The system with this configuration might be able to transmit even on 325 cm, but the BER decreased rapidly (\({}^{-4}\) at 325 cm). The EVM comparison in Figure 13 shows that the QR-RLS exhibited the best results. QR-RLS managed to improve EVM by 1.4% at the center of the illuminated area and by up to 10% at 300 cm. LMS and NLMS had very similar results: LMS improved EVM by 0.02% at the center and 5% at 300 cm, while NLMS improved EVM by 0.25% at the center and 7% at 300 cm. Figure 14: Dependence of \(\rm E_{b}/N_{0}\) on the distance and adaptive algorithm used, bandwidth 4 MHz, 4-QAM. All the above figures show that QR-RLS appeared to be the best adaptive algorithm, improving the measured parameters significantly. It was followed by NLMS and LMS, which had very similar results. Part of the results can be seen in Tables 3 and 4. Figure 16: Dependence of EVM on the distance and used adaptive algorithm, bandwidth \(1\) MHz, 4-QAM. Figure 15: Dependence of BER on the distance and used adaptive algorithm, bandwidth \(1\) MHz, 4-32-QAM.
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 2 Related Work
### Single Frequent Subgraph Mining Algorithm  
The frequent subgraph mining algorithms designed for single-machine environments include SUBDUE [20], VSGRAM [21], gSpan [22], and GRAMI [16]. SUBDUE, an early representative algorithm, addresses the challenge of mining frequent subgraphs within a single, large graph. It achieves graph compression by substituting the vertices of the input graph with frequent patterns. These algorithms employ heuristic search strategies and approximate processing methods. However, it is important to note that these methods may not discover all frequent subgraphs, leading to the loss of many frequent patterns during the mining process. Additionally, their computational load is substantial, making it challenging to scale up to large graph pattern mining. The VSGRAM algorithm introduces a novel pruning strategy designed for processing sparse graphs, but it exhibits limitations in terms of scalability. gSpan employs a minimal Depth First Search (DFS) code for subgraph identification, utilizes the rightmost path for path expansion, and effectively prunes candidate subgraphs. On the other hand, the GRAMI algorithm stands out as the most proficient single-machine frequent subgraph mining algorithm for a single graph. It transforms the frequent subgraph mining problem into a constraint satisfaction problem model, enabling the efficient mining of single graphs with millions of vertices, surpassing the performance of other single-machine frequent subgraph mining algorithms.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.2 Zero-DM Filter  
The zero-DM filter first de-disperses the time series at a DM of 0 pc cm\({}^{-3}\) and then subtracts these values from every frequency channel. Any broadband RFI that exists in the data--which is strongest at a DM of 0 pc cm\({}^{-3}\), while any astrophysical signal is expected to have a positive, non-zero DM--is mostly removed. While this recipe is commonly used in standard searches for FRBs [8], there are certain drawbacks. This is especially the case for sources with a high signal-to-noise ratio (S/N), that can be detected in every frequency channel. As we are effectively subtracting the mean of the channels from the time-observing frequency intensity data from the telescope (henceforth called a dynamic spectrum), and these high-S/N cases, one also removes some amount of the true signal. This results in a reduction in the significance of the signal. Secondly, for sources that have very small DMs, the dispersed signal resembles broadband zero-DM RFI and such a subtraction can remove a significant amount of astrophysical signal. But, overall, since FRBs are expected to have very large DMs, and the S/N per channel is usually low, this method is a simple and effective tool for search engines.
## 4 Discussion
### Probiotics as a Treatment Option for Halitosis
Both studies noted no significant differences at week 4; however, by week 8 of the intervention, the placebo group displayed an increase in BBI scores, whereas the probiotics group exhibited a reduction in BBI scores. BBI scores are evaluated by a self-estimation of bad breath using a scale of 1 to 5, with a higher score indicating worsening of symptoms after intake [20; 22]. Regarding periodontal parameters, [PERSON] et al. determined that there were no statistically significant differences observed in the average probing depth (PD), bleeding index (BI), and plaque index (PI) between and within the two groups when comparing baseline measurements to those taken on day 14, before and after treatment [21]. Similarly, [PERSON] et al. analyzed the variables PI and GI and observed that both scores were not significantly influenced by the probiotic administration [30]. In contrast, [PERSON] et al. observed an improvement in periodontal parameters, namely, probing pocket depth (PPD), clinical attachment loss (CAL), bleeding on probing (BOP), PI, and GI [26]. This is coherent with the findings of the study carried out by [PERSON] et al. in which they observed that the probiotic group showed significant improvement in the parameters PI, modified gingival index (MGI), and BI at 3 months compared with the control group [24]. Furthermore, [PERSON] et al. discovered that the average PPD significantly improved during the probiotic period compared to the placebo period, and intra-period comparison revealed a significant improvement in the number of BOP sites from day 0 to day 14 in the probiotic period, whereas no such improvement was observed in the placebo period [28]. The contradicting findings between these studies could be explained by the different probiotic strains the authors used; [PERSON] et al. and [PERSON] et al. administered the probiotic _S. salivarius_, whereas [PERSON] et al. and [PERSON] et al. administered the strains _L. reuteri_ and _L. salivarius_[21; 24; 26; 30]. [PERSON] et al. also administered _L. salivarius_[28]. Furthermore, the study conditions vary between the different clinical trials. [PERSON] et al., for instance, performed their study on patients wearing orthodontic braces [30], whereas [PERSON] et al. and [PERSON] et al. realized periodontal therapy (scaling and root planning, SRP) on their patients [24; 26]. Given this heterogeneity among these studies might explain the incongruuity of the results.
# Student Behavior Recognition in Classroom Based on Deep Learning
## 5 Experiment and Analysis
### Dataset  
In deep learning, the quality of the dataset construction directly impacts the effectiveness of the model recognition. The selection criteria for the dataset are as follows: 1 the chosen dataset must have clear pixel resolution; 2 it should avoid overexposure; 3 interference from poor camera angles (such as severe tilt or the lens facing a wall) must be eliminated; and 4 priority should be given to datasets that feature large groups, excluding scenarios where there are too few or no students present in the classroom.  
Currently, a comprehensive public dataset for classroom behavior detection has not yet been fully established. The original data used in this study come from a classroom teaching surveillance video, with a total duration of 550 min, that is publicly available on the Internet. Most of the students in the footage are primary school students. From this, 3650 datasets were selected. To address the issue of model overfitting due to the high correlation between image frames, we preprocessed the images by removing correlations using techniques such as principal component analysis and then constructed the final dataset.  
The purpose of this paper is to analyze student behavior in the classroom. Five common behaviors have been selected to assess students' engagement: Raising_hand, Standing_up, Writing, Slippage, and Listening. The criteria for evaluating these behaviors are presented in Table 2.  
After data cleaning and labeling, the number of tags in each category was as follows: Raising_hand (643), Standing_up (165), Writing (278), Slippage (458), and Listening (3845). In order to better display the prediction results, each category tag was encoded accordingly, ensuring that the results were presented intuitively. The encoding scheme is shown in Table 3 below.  
Two well-known machine learning algorithms, Faster R-CNN and YOLOv5, were used as benchmark methods for performance comparison.
## 2 Methodology  
In this section, we start with the GPS dual-frequency ionosphere-free (IF) undifferenced observation equations. Then, the satellite clock estimation models considering code biases are presented. Finally, we give the detailed strategies for satellite clock estimation and PPP.
# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 4 Experiments
### Comparative Experiments
#### 4.2.1 Using 3D CNN Embedding without Pre-Training  
In Figure 7, the horizontal axis of the graph indicates the number of training epochs (each consisting of 5000 iterations), and the vertical axis indicates the average accuracy for the validation set. After nine epochs of training on the UCF101 dataset (i.e., 45,000 tasks), our method achieved the highest validation accuracy. By contrast, MR [15] and ARN [11] required 11 and 13 epochs of training, respectively. Similarly, our model required nine epochs of training on the HMDB51 dataset (i.e., 45,000 tasks), while MR [15] and ARN [11] required 15 and 16 epochs, respectively. Thus, our method required fewer epochs to train the model than the compared methods. This may be because the Kinter, along with STM and CM, in our method provided more accurate information, accelerating the training.  
Figure 7: Accuracy curve of the validation set during training. The horizontal axis of the graph indicates the number of training epochs (each consisting of 5000 iterations), and the vertical axis indicates the average accuracy for the validation set. (**a**) Results under 5-way 1-shot on the UCF101. (**b**) Results under 5-way 1-shot on the HMDB51.  
As shown in Table 1, in the 5-way 1-shot and 5-way 5-shot experiments on the HMDB51, HiTIM achieved recognition accuracies of 46.92% and 61.74%, respectively, which were higher than those of the other methods by 1.22% to 8.87%. In the 5-way 1-shot and 5-way 5-shot experiments on the UCF101, HiTIM achieved accuracies of 66.54% and 84.71%, respectively, which were 0.22% to 9.49% higher than those of the other methods. Compared with the methods that use feature-enhanced techniques and relation network in matching module, the proposed method consistently achieved better performance under different settings on UCF101 and HMDB51 datasets, indicating that our STM and CM are efficient.  
In Table 1, ARN+SsSA-rotation refers to ARN with self-supervised learning and data augmentation, which enhances the model's generalization performance by increasing the amount of additional prior knowledge and training data. In contrast, our method does not use data augmentation or self-supervised learning; nonetheless, it achieved higher recognition accuracy than ARN+SsSA-rotation under different task settings for UCF101, HMDB51, and Kinetics. This may be because the dynamic generation of adaptive parameters for the Kinter improved the model's ability to learn differences between tasks, along with its generalization ability. The MR is based on ARN and adds three dynamic parameter generation networks between the four layers of the feature extraction module. Compared with the MR, the proposed method only includes a Kinter and achieves higher recognition accuracies than MR under different task settings for UCF101 and HMDB51. This may be because our structure of Kinter is more concise and efficient than the corresponding module in MR, and the use of D3D enhanced the model's representation capability.
## 2 Impact Test and Results
### Experimental Results and Discussion  
#### 2.2.1 Tool Failure Mode  
Figure 2 below presents the tool failure mode after the test, which mainly includes noticeable wear of the rake and flank face, deformation, and breakage in the tip area. An indented groove appears on the tool flank face. This formation of grooves on the friction surface parallel to the direction of relative motion is called abrasive wear, a common form of wear on HSS tools. The unevenness on the tool's rake face is typical adhesion wear. Due to the alternating stress, thermal stress, and structural defects of the tool's face layer, the material particles on the tool's face are gradually stuck away by chips or workpieces, which causes adhesion wear of the tool. There are multiple collapses and deformations near the tip of the tool. In the cutting process, the tip and cutting edge will undergo plastic deformation and collapse due to the action of high temperature and high pressure, called transformation wear. It is the main wear form of HSS tools when the cutting temperature exceeds 873 K. There is severe breakage at the tool's tip. This form of brittle breakage rarely occurs in HSS and other tools with strong toughness. When they face uneven cutting temperatures and shock, if the tool is not sufficiently resistant to thermal cracking impact, excessive thermal stress can form cracks and brittle breakage. Deep grooves, a typical feature of boundary wear, appear near the tip at the junction of the tool's main and secondary flank faces. The air is not easily accessible in the tool-chip close contact area, and the oxygen supply is sufficient at the edge of this contact area. The tool material reacts with the oxygen in the air to form low-hardness oxides on the surface once the cutting process is at a high temperature. They are easily wiped off by chips or workpieces and form grooves, called oxidation wear, which can seriously lead to the loss of the cutting ability of the tool [13].  
Therefore, mechanical wear, including abrasive and adhesion wear, causes the cutting edge's rake and flank face wear. The area in and around the tool's tip is dominated by thermal wear, such as transformation wear and oxidation wear. The most severe deformation and breakage of the tool are near the tip, indicating that thermal wear effects have a more significant impact on the tool than mechanical wear. Therefore, thermal wear is the primary tool failure mode in this cutting energy-absorption process.  
Figure 1: (**a**) Layout schematic, (**b**) cutting-type energy-absorbing structure, (**c**) test trolley, and (**d**) force cells.  
Figure 2: Tool failure mode.
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 6 Computational Complexity  
In this section, we discuss the computational complexity of our protocol. We focus on the part of the protocol regarding the ring. Indeed, for the rest of the protocol, involving just a Tor circuit, the reader may refer to the results available in the literature [1].  
The communication phase requires, besides the hop-by-hop encryption of the messages (which is standard in any protocol supporting secure communication), the attempted decryptions that the intended proxy node has to perform before sending the message outside the ring. On average, there are \(\frac{k-1}{2}\) decryptions applied only to the first token of a given communication (recall that the size of a token is about 500 bytes). In the worst case, there are \(k-1\) decryptions. This overhead does not appear relevant, as it regards only the proxy node, and for good privacy levels (e.g., \(k=100\)), the extra time required is small. Observe that the magnitude of an AES encryption/decryption is \(10^{2}\) Mbytes per second on standard personal computers.  
Now, we consider the set-up phase.  
First, consider the protocol without fault tolerance (see Section 4). Similarly to the Tor set-up phase, we require \(k\) key exchanges for the forward keys and \(k(k-1)\) key exchanges for sender/proxy keys. For values of \(k\) guaranteeing a good anonymity level, the cost of this phase is not prohibitive. When fault tolerance is included, we pay a price in terms of complexity of the set-up phase. Indeed, we require \(j(j-1)\) key exchanges per layer in the pre-set-up phase, and then \(j^{2}\) forward key exchanges per pair of adjacent layers (executed in parallel) plus \(k(k-1)\) exchanges for sender/proxy key exchanges. In summary, we increase the previous cost by \(j(j-1)+j^{2}\). Due to the fact that we expect that \(j\) is very small, this computational overhead does not appear as an actual issue for the protocol. Recall that the set-up phase, differently from Tor, is not done for each communication, but it is done to set-up the network, so it can be considered an operation with a long-term lifetime.
###### Abstract
Additionally, considering that many vegetation indices are calculated from the same spectral bands with different mathematical formula, these indexes are correlated. Therefore, scientists have attempted to synthetize information included in this correlated vegetation indices by using various approaches such as feature selection via machine learning approaches [21; 22] or by applying Principal Component Analysis (PCA), which allows to reduce redundant information within correlated variables [22; 23; 24]. However, various scientists report various effects on accuracy by applying PCA. Authors of [22] reported that models with the PCA-based features selection perform even worse than no feature selection. In contrary, [24] showed that Support Vector Machine classification with feature extraction (PCA) on individual image dates produced the most accurate classification (96.2%). Therefore, additional evaluation of this aspect is needed. Considering these various approaches, the question arises whether a wider number of spectral indexes helps increase the accuracy of automatic crop detection and which of these indexes are the most valuable for crop mapping. Therefore, the main objective of the study was to evaluate which of the spectral indexes were the most valuable for detection of specific plant types in our study area, which was mostly cultivated with maize and winter wheat. Moreover, considering that vegetation indices are correlated with each other, principal component analysis (PCA) enables a reduction in redundant information between features. Therefore, the secondary goal of this study was to evaluate whether dimensionality reduction within indices will influence the accuracy of crop mapping. Furthermore, random forest enables the evaluation of feature importance; thus, the additional goal of this study was to evaluate the importance of the Sentinel-2 acquisition dates on crop differentiation. As an experimental test site, we selected an agricultural area close to the town of Jelcz-Laskowice in Poland, utilizing the random forest classification of 14 Sentinel-2 images and 12 vegetation indices.