esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Point-of-Conflict Prediction  
To accurately simulate the navigation of a pedestrian, the incorporation of the prediction is necessary. This prediction might not be accurate, as humans in real life usually make inaccurate predictions. As a result, the prediction process in our model also focuses on replicating a similar prediction mechanism.  
We proposed a concept called _point-of-conflict_ (POC), a location within the environment that the agent thinks could collide with the obstacle or at the predicted position of the obstacle when it is closest to the agent [31]. Even in the case of a low chance of collision (e.g., when the agent and the obstacle are navigating on two sides of the road), a POC is still predicted. The motivation is that, when the human has already learned the appropriate prediction method, the prediction process would occur in most cases. This would happen naturally inside human cognition without much reasoning.  
When the prediction task is handled, the agent would use the information from the POC instead of the actual obstacle in the path-planning training task as introduced in Section 4. The location of the POC will be predicted by the agent depending on the obstacle's type, which will be demonstrated in more detail subsequently. Figure 4 illustrates the path-planning process of the agent after the prediction task is utilized.  
The position of the POC depends on the type of obstacle. For example, if the obstacle is _stationary_, the POC's position should be the same as the position of the obstacle. Apart from stationary obstacle, we define two other obstacle's types: _single diagonal movement_ obstacle, and _pedestrian_ obstacle. Each type of obstacle has a different method of calculating the POC's position. To simplify the prediction of the POC, we assume the agent has the information of the obstacle's speed and heading direction. It is worth noting that the heading direction is the direction toward the obstacle's destination instead of its current orientation. This is because when moving, the pedestrian may not always heading toward his destination, but could turn in another direction for various reasons (e.g., steering to the left-hand side). There have been several studies addressing the problem [24; 25; 39], which could be applicable to our study.
# Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
## 3 Materials and Methods
### Proposed Model  
The model proposed in this paper mainly consists of two subnetworks. The first subnetwork is the characterization network, responsible for extracting low-dimensional spatial features from multivariate time-series data and calculating reconstruction errors. The second subnetwork is the prediction network, designed to capture time-dependent relationships. The characterization network encodes spatial information from multivariate time-series data into a low-dimensional representation. The incorporation of multi-head attention aims to focus on crucial information in the low-dimensional feature space. Subsequently, the characterization network calculates reconstruction errors. These reconstruction errors are then provided to the prediction network, which is based on an attention mechanism. The prediction network captures the temporal dependencies of the reconstruction errors from the characterization network and utilizes external attention to focus on important temporal information. Finally, both the characterization network and the prediction network undergo end-to-end training. For normal data, the reconstruction values generated by encoding the data are like the original input sequence, and the predicted values are like the future values of the time series. In contrast, for anomalous data, there is a significant deviation in both reconstruction and prediction values. Therefore, during the inference process, anomalies are precisely detected by calculating the anomaly score in the composite model.  
We use the POT algorithm [29] to automatically calculate the anomaly threshold. When the anomaly score exceeds the threshold, we identify it as an anomalous event; otherwise, we consider it as a non-anomalous event. Figure 1 illustrates the proposed network architecture. In the figure, \(S^{\prime}\) represents the output of the characterization network, and \(R\) denotes the reconstruction error of the characterization network, computed as the square difference between the original input data \(S\) and \(S^{\prime}\). \(R^{\prime}\) signifies the extracted low-dimensional key feature information \(D^{\prime}\) combined with the downsampled features of \(R\). \(P\) represents the prediction error of the prediction network, calculated by subtracting \(P_{out}\) from \(R\).
## 5 Conclusions  
The elimination of the gear clearance of C-axis of heavy duty machine tool is proposed based on dual servo motor driving system in order to improve the position accuracy of rotary table of ultra-heavy vertical milling lathe. The process of clearance elimination is presented by adjusting the driving torques of the master-slave motors. The dynamic model of the drive system of the dual servo motor with gear clearance is established, and then the simulation and optimization of model parameters are achieved to eliminate the gear clearance. Experiments are carried out to verify the validity of the proposed anti-backlash method. After parameters adjusting and optimization based on the proposed method, the precision of worktable C-axis of ultra-heavy vertical milling lathe is improved by more than 56% under different working conditions.  
For the novelty and the advantages of this article, it established a general dynamic model in order to reflect the gear clearance in the actual work and optimized the control parameters of the dual servo motor system. Furthermore, it can not only effectively eliminate the gear gap error of heavy machine tool table, but also improve the rotary positioning accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Test Items** & **Tolerance (\({}^{\prime\prime}\))** & **Traditional Worktable (\({}^{\prime\prime}\))** & **Double Motors Worktable (\({}^{\prime\prime}\))** \\ Bidirectional positioning accuracy & 10 & 8 & 4 \\ Repeated positioning accuracy & 6 & 5 & 4 \\ Reverse difference & 8 & 4 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Indexing accuracy test of the worktable (C-axis) under no-load conditions.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]): Computer simulation, [PERSON] ([PERSON]): Formal analysis, H.F.; Data curation, Z.H.; Investigation, H.F.; Writing-original draft, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Writing-review and editing, Z.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (51805116).  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their careful, constructive and insightful comments to improve the quality of the present contribution.  
**Conflicts of Interest:** The manuscript entitled \"Elimination of Gear Clearance for the Rotary Table of Ultra Heavy Duty Vertical Milling Lathe Based on Dual Servo Motor Driving System\" has not been previously published, is not currently submitted for review to any other journal, and will not be submitted elsewhere before a decision is made by this journal. Herewith I confirm, on behalf of all authors, that we have no potential conflict of interest.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 3 Path Planning Design
* Each target cell in the network can only be accessed once by one UAV. * The data in the target cell is not required to be transmitted back to the central base station in real-time. The UAV only needs to visit all the target cells in a period of time and to bring the data back to the central base station. Figure 3: Three multi-UAV research methods [40]. * The energy carried by a single UAV is limited and equal; this energy can ensure that the UAV can access at least one target cell in the network, complete data acquisition, and return to the central base station.
# Silicon Compounds in Sponges
## 4 Results and Discussion  
Morphological feature and the ultrastructure of the spicules by SEM in silicon sponges were studied by many authors [18; 20; 24; 29; 30; 34; 37; 38; 39]. We only tried to research the comparative chemical composition in sponges with different spicule morphology. Spicules of two types are morphologically distinguished in glass sponges. Some of them are organized as if a cylinder in a cylinder: samples 1 (_Schulzeviella gigas spinosum_) (Figure 2), 2 (_Serciogluus sp._) (Figure 3), 3 (_Asconema setubalense_) (Figure 4), 4 (_Euplectella aspergillus_) (Figure 5), 5 (_Euplectella suberia_) (Figure 6). Spicules of samples 6 (_Dactyloxyl sp._) (Figure 7), 7 (_Sarostega culdata_) (Figure 8), 8 (_Farrea sp._) (Figure 9) show the coalescence of layers into a continuous monolithic aggregate. The spicules of the freshwater common sponge (Classis Demospongia) (sample 9 _Lubomitskia bacialensis_) from Lake Baikal are small monolithic spicules with a central channel (Figure 10).  
gigas spinosum_ (sample 1), the elements \(Cu\), \(Pb\), \(Cr\) and \(Ni\) are absent; arsenic (\(As\)) was not detected in samples _Euplectella suberia_ (sample 5), _Dactylaclya sp._ (sample 6) and _Sarotsegia oculata_ (sample 7), and chromium in _Asconema setubalense_ (sample 3) and Farrea sp. (sample 8). The highest calcium (\(Ca\)) concentration was recorded in spicules of _Dactylaclya sp._ (sample 6), and zinc (\(Zn\))--in spicules of _Euplectella aspergillus_ (sample 4). The nature of these features is not yet clear.  
Analysis of the composition of the inorganic part of the skeleton of the common freshwater sponge 9 showed a similarity with sea sponges. The main element of sample 9 is silicon, but there are no elements characteristic of marine organisms: chlorine (\(Cl\)), bromine (\(Br\)), arsenic (\(As\)), and manganese (\(Mn\)).  
The surface composition of sponge spicules was studied by X-ray photoelectron spectroscopy XPS for two samples--2 (_Sericololplus_ sp.) And 7 (_Sarotsegia oculata_). As follows from the analysis of X-ray photoelectron spectra (Table 2), the surface layer (\(\sim\)30 A thick) of sample 7 contains mainly carbon (\(C\)), oxygen (\(O\)), to a lesser extent nitrogen (\(N\)), silicon (\(Si\)), and sodium (\(Na\)). Most of the observed carbon is, according to the values of the binding energy (\(\mathrm{Eb}\), \(\mathrm{Eb}\)), of an aliphatic nature; the rest is in a more oxidized state. Oxygen (\(O\)) is partly part of hydroxyl groups, partly part of silicon dioxide (\(SiO_{2}\)) and sodium oxide (\(Na_{2}O\)). The oxidation state of silicon is +4. A noticeable content of nitrogen in the surface layer is also observed. XPS spectra of sample No. 2, the state of the elements and their content are similar to those described above.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Performance Metrics for Classification
#### 3.3.2 Recall and Precision  
Recall, or sensitivity, is a critical metric in medical diagnostics, quantifying the proportion of actual positives correctly identified by a classification model. This metric, also known as the True Positive Rate, is essential where the cost of missing a positive diagnosis, such as a disease, could be grave. A model with high recall ensures comprehensive identification of positive cases, which is vital in medical scenarios where overlooking a condition can result in severe health consequences or even fatality. High recall demonstrates that a model is adept at detecting positive cases, a crucial capability in medical testing environments where failing to diagnose a condition timely can be detrimental.  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
Precision, on the other hand, measures the accuracy of positive predictions made by a model, calculated as the ratio of TPs to the total predicted positives (sum of True and FPs). High precision indicates a high probability that a positive classification by the model is correct, which is especially important in medical contexts where FPs can lead to undue stress, unnecessary invasive procedures, or expensive treatments. The challenge often lies in balancing precision with recall, a dynamic known as the precision-recall trade-off. Enhancing recall may increase FPs, reducing precision, while optimizing precision could cause the model to miss some TPs, thus lowering recall. This balance is particularly critical in healthcare, where the consequences of FNs (missed diagnoses) and FPs (unnecessary treatments) must be carefully weighed. Decision makers must therefore judiciously adjust the model to ensure an optimal balance, reflecting the specific demands and potential repercussions inherent in their diagnostic applications.
## 3 Results and Discussion  
### Analysis of Machine Learning Approach  
The correlation coefficients among the pairs of parameters are depicted in the correlation chart depicted in Figure 1. The slope of a least-squares reference line for every pair of parameters serves as the correlation coefficient in each off-diagonal scatter subplot. A histogram representing the distribution of every parameter is shown along every diagonal subplot. [PERSON]'s coefficient, used to determine the degree of linear correlation among two sets of data, served as the basis for this graph. When a value is assigned to the correlation coefficient, it must be from \(-1\) to \(1\). Linear correlations between a pair of parameters are said to be positively sloped when the values are positive, and vice versa. Coefficients approaching \(1\) indicate robust correlations, whereas those near \(0\) point to poor ones.  
In addition, the correlation chart showed that the mechanical properties of hardness (H) and ultimate tensile strength (\(\sigma_{\text{u}}\)) were affected by the rising number of passes (N), where strong correlations of \(0.81\) and \(0.44\) were obtained, respectively, whereas the ductility (D\({}_{\text{u}}\)) demonstrated a moderate correlation of \(-0.37\). Moreover, the grain size (Gr) demonstrated a strong correlation of \(-0.67\) with N. Regarding the die angle (\(\phi\)), it affected H strongly and \(\sigma_{\text{u}}\) moderately with \(-0.63\) and \(-0.35\), respectively, while affecting D\({}_{\text{u}}\) moderately and Gr strongly with \(0.47\) and \(0.83\), respectively. Moreover, the route type (Rf) weakly affected all four responses: H, \(\sigma_{\text{u}}\), D\({}_{\text{u}}\), and Gr. Calculation of the correlation coefficients is a popular approach in ML for data analysis and mining. It is capable of extracting the important features from a given collection of input and output characteristics, which subsequently play a considerable role during model training and fitting. In this study, the most importantsystem feature was the number of passes, followed by the die angle, and finally the route type, which was the least important parameter.
## 3 Method
### Attention Module 1 and 2  
After the initial feature extraction, the feature \(X_{4}\) is further refined through two attention modules, each composed of an SCA block followed by an STA block. These modules allow the network to adaptively highlight discriminative features along both the channel and temporal dimensions. The flow of computation through the attention modules is as follows:  
Figure 1: Overall architecture of the proposed SpikeCAN.  
\[X_{5} = \text{SCA}(X_{4}), \tag{6}\] \[X_{6} = \text{STA}(X_{5}),\] (7) \[X_{7} = \text{SCA}(X_{6}),\] (8) \[X_{8} = \text{STA}(X_{7}),\] (9) \[\text{ATT} = X_{4}+X_{8}, \tag{10}\]  
where the final attention output of the attention stage, denoted as ATT, is computed by applying spike-element-wise addition between the output of the second STA block (\(X_{8}\)) and the input of the first attention module (\(X_{4}\)). This residual connection helps preserve the original feature information while integrating refined attention-enhanced features.
## 4 Discussion  
In the liquid extracts of _M. memorosum_ herb, nineteen phenolic compounds were identified, including two phenolic acids, five hydroxycinnamic acids, nine flavonoids, and three tannin metabolites. Among the phenolic acids, benzoic acid was predominant. The main hydroxycinnamic acids were \(p\)-coumaric acid and _trans_-cinnamic acid. Rutin and quercetin were identified as the dominant flavonoids. Epicatechin was the major tannin metabolite. The most potent anti-inflammatory compounds among the identified compounds are chlorogenic acid, caffeic acid, quercetin, rutin, naringin, naringin, and apigenin. These substances act via modulation of signalling pathways like NF-kB, inhibition of cytokines, and reduction of inflammatory enzyme expression. Chlorogenic acid exhibits notable anti-inflammatory properties by inhibiting the activation of nuclear factor kappa B (NF-kB), a key transcription factor involved in the expression of pro-inflammatory cytokines; reducing the production of inflammatory mediators such as TNF-\(\alpha\), IL-6, and IL-19; modulating oxidative stress and suppressing the expression of COX-2 and iNOS, enzymes involved in inflammation [40]. Caffeic acid also exhibits strong anti-inflammatory effects by inhibiting pro-inflammatory cytokines (e.g., TNF-\(\alpha\), IL-6) and enzymes such as COX-2 and iNOS. Its mechanism of action involves the suppression of NF-kB activation and oxidative stress [41, 42]. Several identified flavonoids in these liquid extracts also exhibit pronounced anti-inflammatory activity through diverse molecular mechanisms. Quercetin has been shown to inhibit key pro-inflammatory cytokines such as IL-18, IL-6, and TNF-\(\alpha\), as well as enzymes like COX-2, primarily by suppressing the JAK1/STAT3/HIF-1\(\alpha\) and NF-kB signalling pathways [43, 44]. Rutin exerts similar effects by downregulating TNF-\(\alpha\), IL-19, COX-2, and iNOS expression, and modulating NF-kB, MAPK, and Nrf2/HO-1 pathways [45, 46]. Naringin has demonstrated the ability to suppress IL-6 and TNF-\(\alpha\) production and inhibit NF-kB and MAPK/ERK signalling, contributing to its anti-inflammatory and antioxidant effects [47, 48]. Naringenin, a related flavanone, reduces the expression of inflammatory me
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 4 Results and Discussion
### Comparison Analysis  
The proposed SCCA module is compared with nine existing state-of-the-art methods. For a fair comparison, different methods are trained using the same parameters. The number of batches, weight decay, number of training rounds, and initial learning rate are set to 16, 1 \(\times\) 10\({}^{-4}\), 50, and 1 \(\times\) 10\({}^{-3}\), respectively. The top-three outstanding performers  
Figure 8: Training results of the DUTS dataset with added attention modules: encoder, CA (channel attention), SCCA (split coordinate channel attention), CBAM (convolutional block attention module), SE (squeeze-and-excitation Network), and triplet. (**a,b**) shows the comparison results of F\({}_{1}\)-score and MAE, respectively.  
under the single measure are highlighted in bold. The comparison results are shown in Figure 9 and Table 1.  
The encoder makes predictions in the shortest time, taking only 0.387 and 0.020 s to predict a single image, respectively. This illustrates that the original U2 Net model utilizes convolutional coding in a reasonable and efficient manner. The SCCA training model has the smallest MAE, being the only model with less than 0.07 and 0.03. SCCA achieves the highest F\({}_{1}\)-score measure, while CA and SENet are tied for second place on the DUTS dataset, both with an F\({}_{1}\)-score measure of 0.802. The module sizes of SENet, SCCA, and GCB are the smallest, at 0.50 MB, 0.57 MB, and 0.57 MB, respectively. In the comprehensive evaluation, SCCA demonstrates the best performance, achieving the top-three marks in all four indicators. Encoder and SENet perform well on the HKU-IS dataset but still have potential in improving detection precision and reducing training error. Therefore, the SCCA module has the best detection effect among all attention modules. It not only boasts a high prediction accuracy and few parameters but also has a short prediction time, making it very suitable for deployment on mobile devices for salient object detection tasks.
# Detection of Parking Slots Based on Mask R-CNN
## 2 Research Status
### Machine Learning and Deep Learning  
The rapid development of machine learning in the field of image processing has also been applied to parking-slot detection. For instance, [PERSON] et al. [35] proposed a parking-slot detection method, which uses a dataset of marking-points trained on multiple corresponding detectors with AdaBoost cascade classifier, where the parking slots can be inferred with six Gaussian templates. [PERSON] et al. [36] also used the LSD algorithm to detect parallel line pairs first, then T-shaped or L-shaped marking-points of parking slots determined by the trained detector. Compared to the approach in [PERSON] et al. [35], only one detector needs to be trained. Although the above two methods were based on learning image features that improved the accuracy and stability of detecting the marking-points to a great extent, they only detected parallel and vertical parking slots in terms of the ability to extract image features. These approaches extracted the marking-points by artificially selecting low-level image features (such as Haar, LBH) for training, which had a certain gap with deep learning.  
With regard to the use of deep learning algorithms for the detection of parking slots, in a preliminary attempt, [PERSON] et al. [37] roughly detected four vertices of parking slots on the AVM image using Faster R-CNN. [PERSON] et al. [38] also proposed a method using semantic segmentation and vertical grid encoding to detect parking slots and static obstacles based on the AVM system. A lot of the algorithms that use deep learning to detect parking slots used static images taken from a camera [39; 40; 41]. These approaches are typically used to detect vacant slots for parking-lot management and are not applicable to dynamic environments, as they rely on strong a priori knowledge in the location of the slots. Therefore, our study seeks to fill this gap by developing a parking-slot detection approach using Mask R-CNN to detect parking slots under dynamic environments and various illuminating conditions.