esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Currently, closed circuit television (CCTV) methods [16; 17] are one of the most common ways to survey the pipe. Several fast and efficient alternative methods to analyse the condition of a sewer pipe wall objectively were proposed [18; 19; 20; 21; 22; 23]. These methods are acoustic [18; 8; 10], acoustic optical [19], electromagnetic [20; 21], electroacoustic [22], ultrasonic [5; 7; 23] and laser-based [8] methods. These methods are not designed to work in CSOs. The only sensors which are installed in  
Figure 1: Sectional internal view of a traditional combined sewer overflow (CSO) chamber (adapted from [15]). CSO are ultrasonic water level meters [24; 25] and optical fibre-based condition monitoring systems [26]. The ultrasonic water level sensor monitors the water levels to inform the operator the frequency of spill incidents when water level is high enough to be discharged via the outlet pipe as shown in Figure 1, while the fibre optical sensor is used to detect the changes of humidity and temperatures. However, none of these methods is suitable for the analysis of CSO screen conditions. Therefore, there is little or no knowledge about the propensity of the CSO to discharge unscreened wastewater into the receiving watercourse. Therefore, there is a clear need for a new instrument to continuously monitor the real-time condition of CSO screens. This technology will present the opportunity for screens to be cleaned before or shortly after they have become seriously blinded, preventing sewage debris from hardening and ensuring that they are clean and able to pass sufficient flow before the next wastewater discharge event. This also will ensure that the screen is operating at its design performance at all times, hence reducing the risk of unconsented spills. This paper describes the operation principle and six-month-long field experiments with a new low-cost acoustic sensor that is able to access rapidly the condition of a static screen. The data provided by this type of sensor will enable water companies to manage their sewage infrastructure at a significantly lower cost by proactively responding to the potential of CSO events. The structure of the remainder of this paper can be summarised as follows. In Section 2, the details of the system architecture and the design methodology are presented. The results from the two phases of field trials, i.e., short-term and longer term, for the proposed acoustic instrument are discussed in Section 3. Finally, conclusions are drawn in Section 4.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Error Prediction Results  
#### 4.3.1 Static Prediction Results  
To reduce the common errors, receiver errors and multi-path effects more effectively on the basis of WLS and Kalman filter, the LSTM-based error prediction was performed on the integrated positioning. The prediction result can be used to correct the errors caused by multi sources.  
The prediction results of LSTM-based error prediction performed on WLS is presented in Figure 10, while that performed on Kalman filter is shown in Figure 11. The error data were divided into training set and testing set, and the ratio of the training set to the testing set was 2:1. In Figures 10 and 11, the blue curves denote the original data, the orange
# Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
## 2 Detection Process of Surface Defects in SiCp/Al Composites
### Training and Evaluation of Defect Detection Model
#### 2.3.2 Training Process  
A transfer learning approach is used to train defect detection models. Transfer learning is the use of existing similar datasets or already trained models for the training of a new model. A pre-trained model is obtained through some other datasets similar to the surface defect dataset, and then this pre-trained model is used to train our own dataset. Transfer  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Map** & **Anchors** \\ \hline
76 \(\times\) 76 & (34, 43) (66, 79) (81, 144) \\
38 \(\times\) 38 & (153, 125) (130, 245) (234, 210) \\
19 \(\times\) 19 & (239, 351) (177, 413) (492, 227) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Anchors for each feature map.  
learning not only speeds up the training of the model but also increases the diversity of the dataset and the amount of data to some extent.  
The acquired processed surface images were divided into training and validation sets in a 9:1 ratio. First, the training was frozen; that is, the feature extraction parameters of the backbone network were frozen with the pre-trained weights, and the learning rate was set to 0.001 iterations for 30 epochs to speed up the training process. In the unfreezing phase, the learning rate was set to 0.0001 iterations for 40 epochs and then to 0.00001 iterations for 30 epochs. Gradually decreasing the learning rate slows down the gradient descent and reduces the gradient descent step size, which makes it easier to approach the minimum point. In addition to setting different learning rates as described above, this paper also employs learning rate cosine annealing. This utilizes the idea of an annealing optimization method, using linear ascent in the ascent phase and simulated cosine function descent in the descent phase. Using the learning rate cosine annealing method, the network can avoid becoming trapped in the local optimal solution.  
After the training was complete, the trained model was used to detect images in the validation set. The surface defect detection results for SiCp/Al composites at different magnifications are shown in Figure 5. It was found that regardless of the size of the defects, the detection model could detect their locations and label them with the defect types and the correct prediction probability for detection results. Since only pit defects were detected, the AP and mAP values are equal. The AP values obtained for the defect detection dataset are calculated to be 97.4%, and the average detection time per image is only 47 ms.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 5 Conclusions  
In this paper, we proposed a video description model based on temporal-spatial and channel attention. In detail, we fully utilized the essential characteristics of CNN and added channel features into the attention mechanism of the model. Therefore, the model can use visual features more effectively and ensure the consistency of visual features and sentence descriptions to enhance the effect of our model. Moreover, our experimental results show that the model has achieved good performance on MSVD dataset. To analyze the model on a larger dataset, we evaluated the model on the MSR-VTT dataset. The results show that our model also has a certain improvement on the MSR-VTT dataset. We also proposed a video visualization model based on the video description and visually demonstrated its effectiveness.  
Conceptualization, J.X. and H.W.; methodology, J.X. and L.L.; validation, J.X. and L.L.; formal analysis, H.W. and L.L.; data curation, L.L. and Q.F.; writing--original draft preparation, J.X., H.W. and L.L.; writing--review and editing, Q.F. and J.G.; visualization, J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program (Grant No. 2016 YFB0800105), Sichuan Province Scientific and Technological Support Project (Grant Nos. 2018 GZ0255, 2019 YFG0191). The authors would like to thank the National Key Research and Development Program and the Sichuan Province Scientific and Technological Support Project for financially supporting this research.  
The authors declare no conflicts of interest.
# Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7
## 4 Experiments and Results Analysis
### Dataset  
This paper is based on the publicly available AIZOO dataset. Additionally, Python web scraping techniques were employed to gather images of individuals wearing masks in various complex scenarios from the internet, expanding the dataset. After reorganization, the dataset consisted of over 10,000 images with diverse sizes, angles, and lighting conditions. The images were reannotated using Labellmg software to create a dataset specifically for scenarios involving mask wearing in complex scenes, including images from real-life situations such as stations, schools, hospitals, and malls. Some examples are shown in Figure 9. The dataset includes three classes: faces without masks (face), masks (mask), and faces with masks (face_mask). The dataset was split into training and testing sets at a 4:1 ratio.
# Speaker Diarization: A Review of Objectives and Methods
## 3 Analysis of Speech Signals  
Most speech applications try to extract useful information from input audio via some form of analysis or processing. The focus of speech analysis is generally both F0 and the amplitude spectral envelope of the VT transfer function, e.g., some version of the series of formants, which can be efficiently noted in terms of their center-frequencies and bandwidths. Speech input, in the form of continuous air pressure waves from a VT, is mapped into a digital sequence by analog-to-digital conversion, for use by digital computers. Input speech data are sampled periodically at a suitable rate (at least the _Nyquist rate_, which is twice the highest frequency in the signal), to preserve information over a relevant frequency range for a given application (e.g., for speech by telephone, up to almost 4 kHz) [26].  
While various speech applications have a wide range of objectives, very similar methods have been commonly used for most of speech analysis, to transform high-dimensional speech data (e.g., 64 kilobits/s for basic digital telephone speech: 8000 samples/s at 8 bits/sample) to much lower-dimensional classification (e.g., dozens of spectral param eters at 100 frames/s). This readily applies to ASR, SV, language identification, emotion recognition, and SD.  
Many recent neural-based SD systems (called _end-to-end_, or E2E) avoid all direct (spectral or intonational) speech analysis, on the assumption that any specific processing that is not directly linked to the final task objective may possibly discard pertinent information. Nonetheless, as much of SD is not E2E and uses some form of analysis, we discuss this here. As SD relies both on local cues (e.g., at speaker changes) and long-range comparisons of speaker characteristics, considerations of analysis should examine long- and short-range scopes.
## 4 Synthesis and Summary
### Future Directions
#### 4.4.3 Extended Endurance and Resilience  
For intelligent systems, the endurance and resilience are important in case of fatal failure. Effective human-AI interaction design is predicated on increased cross-disciplinary efforts, requiring reconciling productivity with resilience [84]. To investigate the fragile problems of the controller, a new non-fragile fault-tolerant scheme was provided against multiplicative and additive controller gain perturbations [85]. An adaptive error constraint line-of-sight guidance law was originally proposed with better transient and steady-state performance, and simplified the design of the controller by not relying on interference observers [86]. A new approach is suggested for the high-level processing of received data to evaluate their consistency, which is agnostic to the underlying technology of the individual sensory input [87]. This approach [87] demonstrates the feasibility of identifying anomalies in nominal operations when the navigation sensor is subjected to adversarial attacks or experiences malfunctions.  
Furthermore, it is concluded that deploying multiple USVs as a formation fleet benefits great fault-tolerant resilience [88]. A new algorithm named the 'angle-guidance fast marching square' (AFMS) was presented, and a novel priority scheme based upon the distance to the closest point of approaching (DCPA) has been proposed and developed to efficiently and effectively navigate the USV formation [88]. A comprehensive training framework encompassing multiple tasks has been formulated for the management of formation control, tailored to the dynamic behavior of Unmanned Surface Vehicles and employing the leader-follower approach. Within this framework, the Soft Actor-Critic (SAC) reinforcement learning algorithm has been modified to facilitate the development of agent constructs [89].  
Last but not least, the development of more efficient battery technology and the integration of renewable energy sources and power systems will also incessantly enable USVs to achieve extended endurance. This will allow them to carry out longer-duration missions, such as ocean monitoring and research [90]. A hybrid power system [91] comprising a solar array, an ocean wave energy converter, a fuel cell system, a diesel generator and a lithium ion battery pack was designed. The results show that optimization has been achieved with a 19.6% contribution from solar power during daylight hours and a 5.53% contribution of the wave energy harvester to meet the load demands [91]. And, an AutoNaut uncrewed surface vehicle powered entirely by renewable energy has been invented as the first USV to regularly run scientific missions off the coast of the UK [92].  
Therefore, research related to fault-tolerant schemes and resilience amelioration by means of the USV formation has always been a heated discussion in the topic of USVs. And, in a more practical perspective, the industry will call for an increasing innovation of power systems in the future.
## 5 Discussion  
### Key Findings  
The current state of VR in education is a rapidly evolving field, with new technologies and applications emerging regularly. Recent studies, in particular, emphasize the growing role of VR in social skills training and highlight the effectiveness of VR-based social skills programmes, particularly when high levels of immersion and realistic interaction are provided [6,7]. However, many interested parties from the education sector lack an informative overview of possible commercial providers that could potentially be used to design social skills acquisition scenarios themselves or that have such scenarios in their sales repertoire. This article attempts to address this lack of overview in a constantly changing field. Therefore, with this article, we are trying to give readers a structured, action-oriented overview of ten commercial VR providers relevant to social skills training in educational contexts. By analyzing key technical and pedagogical features, such as communication modes, avatar customization, language support, and content authoring tools, we mapped the current VR landscape with a focus on usability for non-technical clients. Our findings highlight the diversity of available solutions. One of the most striking aspects of our results is the even distribution of VR and social VR environments among the providers. This suggests that there are practical case studies for both, and that the choice between them will depend on the specific needs of the clients. Furthermore, the analysis of technical requirements and communication features reveals that most providers offer a combination of different communication styles, including scripted dialogues, AI-powered communication, and interaction with real individuals. This suggests that the field is rapidly evolving and that providers are continually adapting to new technologies and user needs. Our results also highlight the importance of language customization in VR social skills training. While some providers offer limited language availability, others utilize AI-driven dialogue systems that can support a wide range of languages. This suggests that language customization is a critical factor in the development of effective VR training platforms. Next, the examination of avatar creation and customization reveals that most providers offer some level of customization, although the extent of customization varies widely. This proposes that avatar creation is an important aspect of VR social skills training and that providers should prioritize the development of customizable avatars. Finally, the results of our analysis highlight the importance of content libraries and authoring tools. While some providers offer extensive content libraries, others focus on the creation of industry-specific VR content. This suggests that the development of effective VR training platforms requires a balance between the provision of pre-existing content and the ability to create customized content.
# Dynamic Single-Electron Transistor Modeling for High-Frequency Capacitance Characterization
## 2 Method of Modeling
### Proposed Model
We also try to generalize the results by normalization, i.e., when the operation condition is described, temperature \(T\), gate-source voltage \(V_{\text{gs}}\), and drain-source voltage \(V_{\text{ds}}\) are normalized as \(K_{\text{g}}T/(e^{2}/2C_{\text{$\sum$}})\), \(C_{\text{g}}V_{\text{gs}}/e\) and \(C_{\text{$\sum$}}V_{\text{ds}}/e\), respectively, and enclosed in square brackets [... ] if these are put down with raw values. The normalized \(V_{\text{gs}}\) ranges from 0 to 1 electron in the simulation since only \(n\)'s from \(-1\) to 2 are considered in solving the master equation. Two cases of small and moderate normalized \(V_{\text{ds}}\) of 0.01 and 0.5 are considered. The normalized temperature is set to 0.05 in most of the simulations in order not to thermally disturb the Coulomb blockade condition. \begin{table}
\begin{tabular}{l l} \hline \hline Gate, drain and source capacitances & 1 aF \\ \(C_{\text{g}}C_{\text{d}}=C_{\text{s}}\) & \\ \hline Tunneling resistances & \\ \(R_{\text{d}}R_{\text{s}}=R_{\text{t}}\) & 25 M\(\Omega\) \\ \hline Cutoff frequency & 1 GHz \\ \(f_{\text{c}}=1/(2\pi R_{\text{E}}C_{\text{$\sum$}})\) & \\ \hline Gate—source voltage & 0–162 mV [0–1] \\ \(V_{\text{gs}}\) & \\ \hline Drain—source voltage & 0.534 mV [0.01], 26.7 mV [0.5] \\ \(V_{\text{ds}}\) & \\ \hline Temperature & 15.5 K [0.05] \\ \(T\) & except for Figure 6. \\ \hline \hline \end{tabular}  
* Square brackets [... ] indicate normalized values.  
\end{table} 
Table 1: The simulation parameters.
## 3 Materials and Methods
### Fermentation of Goji Juice  
A mixture of dried fruit and water at a ratio of 1:5 (_w/v_) was homogenized after soaking for 12 h. Next, 0.15% pectinase (_w/v_) was added to the goji juice and incubated at 50 \({}^{\circ}\)C for 2 h. The seeds were removed by filtering through a gauze and 5% sucrose was added, before the pH was adjusted to 6.5 with baking soda. After pasteurizing the goji juice, it was cooled to room temperature. The bacterial mixtures were prepared according to the ratios shown in Table 4 and added to the juice as an inoculum at 0.5% (_v/v_), before fermenting for 20 h under anaerobic conditions at 37 \({}^{\circ}\)C. Non-fermented goji juice was treated as a control sample.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Name** & **Strain Combination** \\ \hline SLV & _L. rhamnosus_, _L. reuteri_, _B. velezensis_ \\ SZP & _L. rhamnosus_, _L. plantarum_, _B. licheniformis_ \\ SZVP & _L. rhamnosus_, _L. plantarum_, _B. velezensis_, _B. licheniformis_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different combinations of microbial strains used in the study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\(p\)-Hydroxybenzoic Acid} & \multicolumn{2}{c}{**Chlorogenic Acid**} & \multicolumn{2}{c}{**Caffeic Acid**} & \multicolumn{2}{c}{_p-Coumaric Acid_} & \multicolumn{2}{c}{**Ferulic Acid**} \\ \hline  & Free & Bound & Free & Bound & Free & Bound & Free & Bound & Free & Bound \\ \hline DPPH & 0.639 \({}^{**}\) & \(-\)0.783 \({}^{**}\) & 0.711 \({}^{**}\) & \(-\)0.411 & \(-\)0.091 & 0.400 & \(-\)0.503 \({}^{**}\) & 0.619 \({}^{*}\) & 0.700 \({}^{**}\) & \(-\)0.031 \\ HFR & \(-\)0.548 \({}^{*}\) & 0.490 & \(-\)0.565 \({}^{*}\) & 0.541 \({}^{*}\) & 0.039 & 0.062 & 0.128 & \(-\)0.188 & \(-\)0.459 & 0.342 \\ \hline  & **Rutin** & \multicolumn{2}{c}{**Quercetin**} & \multicolumn{2}{c}{**Naringenin**} & \multicolumn{2}{c}{**Luteolin**} & \multicolumn{2}{c}{**Kaempfer**} \\ \hline  & Free & Bound & Free & Bound & Free & Bound & Free & Bound & Free & Bound \\ \hline DPPH & 0.728 \({}^{**}\) & 0.288 & \(-\)0.187 & 0.142 & 0.672 \({}^{**}\) & \(-\)0.803 \({}^{**}\) & 0.000 & 0.246 & 0.000 & \(-\)0.363 \\ HFR & \(-\)0.615 \({}^{*}\) & 0.057 & 0.110 & 0.236 & -0.241 & 0.454 & 0.000 & 0.098 & 0.000 & 0.254 \\ \hline \multicolumn{2}{c}{DPPH means 2,2-diphenyl-1-pricrylhydraz! free radical scavenging power; HFR means hydroxyl radical scavenging power; Data with \({}^{**}\) and \({}^{**}\) means statistically significant at \(p\leq 0.01\) and \(p\leq 0.05\), respectively.} \\ \end{tabular}
\end{table}
Table 3: [PERSON]’s correlation coefficients of phenolic compounds and antioxidant capacities.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 4 Results
### Validation and Evaluation  
The field campaign focused on collecting rock and soil samples from locations identified as having maximum values based on the fuzzification results. Figure 11 (Appendix A) shows a comparison between a high-resolution image (Figure 11,c) obtained through drone reconnaissance and the fuzzification output, where the sampling locations can be observed (Figure 11,d). This study focuses on the spectral analysis of the collected soil samples.  
In Figure 9, the first column shows the soil samples from which the spectra were obtained. The second column presents the corresponding spectra graphically.  
Figure 10 shows the location of the ROIs about geological faults, with ROIs 2, 4, and 6 located near these structures, suggesting a possible structural influence on the alteration processes identified at these sites.  
Figure 8: Ratio maps comparing PS band ratios with Sentinel-2. (**a–f**), ASTER (**g–l**), and Landsat 9 (**m–r**) in the selected ROIs (test 2). The maps illustrate areas of agreement and disagreement between sensors in detecting Fe\({}^{3+}\) iron oxides.  
Figure 9: Spectral characterization of soil samples. The first column (**a**,**c**,**c**,**e**,**g**,**i**,**k**) displays the soil samples collected from various ROIs. The second column (**b**,**d**,**f**,**h**,**j**,**l**) shows the corresponding spectral reflectance curves.