esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 6 Conclusions and Future Work  
Examining the problems of low efficiency and difficulty in ensuring the accuracy of the traditional CCTV inspection method, we present an improved YOLOv4 model for the detection of sewer pipe defects. By improving the network structure, the detection accuracy of sewer defects is effectively improved. By comparing different bounding box loss functions, the effectiveness of the combination of DIoU loss function and SPP module is verified.  
Overall, the model proposed in this paper has the following advantages: (1) it has a higher detection performance. Experimental results show that the proposed model achieves a mAP of 92.3% and an average recall of 89.0% on the test set. Compared with the state-of-the-art detection models, such as YOLOv7, YOLOv8 and transformer-based models, our model achieves better performance in detecting multiple sewer defects, not only with higher mAP and recall rate, but also with better actual detection results, which means that it can be better adapted to the sewer defect detection tasks. (2) It has good detection speed. Although the detection speed of our model is lower than that of YOLOv3 and YOLOv7, it is still much higher than Faster RCNN. Overall, the model has the highest detection performance and good detection speed, achieving a balance between detection accuracy and speed. (3) It has better detection accuracy for small defects. Due to the addition of the SPP module, the detection and recognition effect on small defects such as cracks has been significantly improved, which avoids the long-term concentration of technicians to detect these difficult-to-find defects. Compared with the manual detection method, our method can reduce the work intensity of technicians, speed up the detection process and improve work efficiency.  
In fact, the majority of the sewer pipes are normal with no defects, and it will undoubtedly waste a lot of time to directly detect the video. In future work, we will investigate the extraction of key frames containing defects from sewer videos to reduce the overall detection time. In addition to the four types of defects studied in this paper, there are many other types of defects such as fraction and barrier in sewer pipes. These defects can also seriously affect the healthy operation of the sewer systems. More images of other types of defects will be collected for future research.  
Methodology, J.Z. and X.L.; investigation, X.Z. and Z.X.; writing--original draft preparation, J.Z.; writing--review and editing, X.L. and S.W.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China University Industry-University-Research Innovation Fund (2021 FNB02001).  
Not applicable.  
For privacy reasons, the data cannot be made fully public. Readers can contact the corresponding author for details.  
The authors declare no conflict of interest.
# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
## 3 Path-Planning Algorithm Design
### Design of A-DQN Algorithm Based on Heuristic Exploration
#### 3.3.4 Hybrid Exploration Strategy Based on A*  
A* is the classical heuristic-based path-planning algorithm that determines paths based on heuristic information. The core of the A* algorithm lies in its evaluation function, f(n) = g(n) + h(n), where n denotes the current node, g(n) is the actual cost from the starting node to the current node (n), and h(n) is the heuristically estimated cost from the current node (n) to the goal node.  
The \(\varepsilon\)-greedy strategy is often used for random exploration in DQN algorithms. However, for large-scale maps, the training data are huge, and this exploration strategy is randomized, resulting in an increase in the difficulty of the environment. The direct use of traditional DQN algorithms cannot solve the problem of the large training data of neural networks and the difficulty of the environment to be explored, the algorithm convergence speed is slow, and so on. In order to make the exploration both directional and random, this paper adopts the hybrid exploration strategy method (A-DQN) that combines the random strategy and A*-based exploration strategy, and the specific pseudo-code of the A-DQN algorithm is shown in Algorithm 1. The first 30% of the training rounds take the actions decided by the A* algorithm with a probability of 0.5, the random decisions are made with a probability of 0.01, and the rest of the actions are decided by the neural network. From 30% to 80% of the training round, the A* algorithm's decision probability gradually decreases to 0, and in the last 20% of the training round, the A* algorithm's decision probability always remains 0.  
The hybrid exploration strategy integrates heuristic guidance from the A* algorithm and stochastic exploration from traditional DRL methods. Initially, A* provides directional guidance, significantly reducing the convergence difficulty. As training progresses, the stochastic exploration component increasingly dominates, allowing for a comprehensive exploration of the state space. This balance effectively addresses the exploration efficiency issues inherent in standard DRL methods, particularly on large-scale terrain.  
Figure 3: Neural network architecture diagram.
# A Deep-Learning-Based Approach to the Classification of Fire Types
###### Abstract  
The automatic detection of fires and the determination of their causes play a crucial role in mitigating the catastrophic consequences of such events. The literature reveals substantial research on automatic fire detection using machine learning models. However, once a fire is detected, there is a notable gap in the literature concerning the automatic classification of fire types like solid-material fires, flammable gas fires, and electric-based fires. This classification is essential for firefighters to quickly and effectively determine the most appropriate fire suppression method. This work introduces a benchmark dataset comprising over 1353 manually annotated images, classified into five categories, which is publicly released. It introduces a multiclass dataset based on the types of origins of fires. This work also presents a system incorporating eight deep-learning models evaluated for fire detection and fire-type classification. In fire-type classification, this work focuses on four fire types: solid material, chemical, electrical-based, and oil-based fires. Under the single-level, five-way classification setting, our system achieves its best performance with an accuracy score of 94.48%. Meanwhile, under the two-level classification setting, our system achieves its best performance with accuracy scores of 98.16% for fire detection and 97.55% for fire-type classification, using the DenseNetl21 and EffecientNet-b0 models, respectively. The results also indicate that electrical and oil-based fires are the most challenging to detect.  
deep learning; fire detection; fire classification; fire-type detection +
Footnote †: journal: Applied sciences
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 2 Methods
### Image Resolution Transformations Based on Station Spacing  
Multiple A-scan traces are collected and recorded at different positions when the GPR device moves along the survey line (see example in Figure 3a). The GPR image is obtained when the amplitude intensity of the multiple A-scan signals is expressed in color or grayscale (see example in Figure 3b), which is the most commonly used data display result in commercial devices.  
Figure 2: (a) The original GPR image; (b–h) are the GPR images that were generated with 0.2, 0.4, 0.6, 0.8, 1.2, 1.4, and 1.6 gain coefficients.  
Station spacing is the distance between each A-scan trace (also known as step size). It is necessary to set the proper station spacing before GPR data acquisition. The choice of station spacing depends on the antenna's center frequency and the medium's dielectric properties. To avoid the reflected signal overlapped, station spacing should be smaller than the Nyquist spacing, which is one-quarter of the EW's wavelength when traveling through the medium [37]. We define the station spacing \(\Delta x\) with the following Equation (4):  
\[\Delta x\leq\frac{\lambda}{4}=\frac{c}{4f_{c}\sqrt{\varepsilon_{r}}} \tag{4}\]  
In Equation (4), \(\lambda\) is the wavelength of the signal, \(f_{c}\) is the center frequency of the antenna, \(c=3\times 10^{8}\ m/s\) is the speed of the light, \(\varepsilon_{r}\) is the relative dielectric constant. In addition, the choice of station spacing should also comprehensively consider the moving speed of antenna, distance between the transmit and receive antenna, sampling rate, etc. In actual field GPR detection, the station spacing should be set as small as possible to increase the number of scans when moving above the target, which can make the reflected signal clearer. However, if the station spacing is set too small, the GPR system may lose traces due to the limitation of GPR hardware. To simulate the GPR device collecting data at different station spacing, we used the traces extraction method which can adjust the station spacing by artificially removing the collected A-scan traces. However, the displays of the target features can be blurry if too many A-scan traces are extracted, so the interval 1, 2, 3, 4, 5, 6, and 7 traces are selected to extract one single signal, respectively, for data augmentation. Figure 4 shows the GPR images of the target feature by extracting signals at different intervals. Figure 4 is the original GPR image, and Figure 4-h is the interval 1, 2, 3, 4, 5, 6, and 7 traces selected to extract one single signal, respectively. It can be seen that as the interval of the extracted signals increases, the morphological characteristics of the target in the GPR image do not show significant changes, while the number of pixels of the image gradually decreases.  
Figure 3: (**a**) Multiple A-scan traces along the survey line; (**b**) the GPR image in grayscale.
## 3 Results and Discussion
### Post-Test Cell Analysis
#### 3.3.2 Surface Characterization  
Based on the SEM measurements of the disassembled electrode of the 3.3 Ah cell, formation of the SEI layer was confirmed to be relatively uniform with respect to the cathodes, although the cathodes of the battery subjected to cyclic charge/discharge using the CCCV charging pattern were thicker due to the presence of an inorganic SEI layer on the organic SEI layer.  
XPS measurements were then performed to identify the composition of the SEI layer and to understand the origin of the increase in anode thickness.  
Figure 12 shows the qualitative analysis of the organic and inorganic components on the anode surface. Despite few differences in the compositions of the SEI layers formed on the cathodes, the cell subjected to the novel charging pattern contained an SEI layer composed of more inorganic components than organic components. This was consistent with the SEM observations. Furthermore, the detection of high numbers of Lix-C bonds suggests that the exposure of graphite below the SEI layer was high, which indicates that the formed SEI layer was thinner on the cell subjected to the proposed charging pattern. These results confirmed that the greater increase in anode thickness for the CCCV step charging cell was due to the formation of a thicker SEI with a higher inorganic content. It was therefore concluded that this thicker SEI layer resulted in a rapid capacity loss due to a lack of optimized current level to reduce the resistance polarization.
## 4 Discussion
Initially, [PERSON] [2] recommended miniplate removal after three months of fixation, which became the established practice. Subsequently, authors leaned toward retaining Vitallium miniplates, which exhibited greater inertness. [PERSON] et al. [41] determined that approximately 18% of Vitallium miniplates necessitated removal for clinical reasons. Titanium emerged as a successful material in numerous surgical procedures, with nearly 100% of it being released at the site during osteosynthesis, remaining stable and clinically inert within surrounding tissues. Hence, routine removal of titanium miniplates, except in cases of infection, dehiscence, or hypersensitivity, was not deemed necessary. Miniplate removal should primarily address miniplate-related symptoms. While some authors advocate routinely removing miniplates to avert interference with jaw function and osteoporosis, most studies suggest removal only when symptoms manifest and discourage the routine removal of asymptomatic miniplates [42; 43; 44; 45; 46; 47; 48; 49; 12; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Amidst the ongoing discourse and implications concerning metal plates, researchers have developed novel bioresorbable and bioactive materials as alternatives. These materials, such as polyglycolic acid, polylactic acid, and copolymers of polyglycolic acid and polylactic acid, incorporated with hydroxyapatite in polylactic acid, stimulate the formation of new bone tissue without inducing mutagenic effects. Despite the appealing biodegradation characteristic of these materials, there are currently insufficient follow-up data from comparative studies between metallic alloys and bioresorbable substances [50; 51]. The question of whether to remove miniplates after fracture healing remains a subject of debate within the medical community. One major influence in this decision is the cost associated with miniplate removal, which can substantially vary among different countries. The cost of miniplate removal for jawbone fracture fixation is a multifaceted issue shaped by a range of factors. While procedural costs are relatively consistent, discrepancies in hospitalization expenses and potential complications can lead to significant cost disparities. Healthcare providers and policymakers should consider these economic factors while strategizing for jawbone fracture management and subsequent implant removal. The limitations of this study encompass the lack of injury specifics due to the predominantly retrospective nature of the studies, as well as the challenge of tracking patient's post-treatment wellbeing for assessing post-operative complications.
## 2 Materials and Methods
### Convolutional Neural Network and Model Implementation  
We designed our neural network to consist of 7 main sections, including the input and the output layers. Three of these main sections employ 3D convolutional layers, which have different numbers of filters and kernel sizes. These hyperparameters are fine-tuned with controlled experiments to reach the model with the best pattern recognition capabilities for our input data. After every 3D convolutional layer, a max pooling layer was employed, reducing the dimensionality of its input to allow for sharper patterns to be recognized. The dimensionality reduction also decreased the network complexity. Finally, batch normalization was integrated at the end of these three main sections. Batch normalization allowed for using much higher learning rates and being less careful about initialization while also acting as a regularizer [37]. A global average pool layer provided the transition from 3D to 1D by averaging the neuron values in 2 dimensions. Global average pooling enforced correspondence between feature maps and categories and acted as a structural regularizer [38]. Fully connected layers were used with dropout layers to prevent overfitting before completing the network with a sigmoid-activated output layer. The network structure is shown in Figure 1.  
Keras API was used with the PlaidML for AMD graphical processing unit (GPU) package as the backend in our implementation. Non-linear activation functions were utilized in every layer, including Leaky ReLu for 3D Convolutional layers and ReLu for fully connected layers. A widely adopted sigmoid classifier with a binary cross-entropy objective function was used to classify inputs in the final layer as low-risk or high-risk. The weight optimization was done with Adamax, a generalized version of the Adam optimizer. The Adam optimizer is easy to implement, computationally efficient, has minor memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for significant problems in terms of data and parameters [39]. A batch size of 2 was used, leading to longer training times and higher model quality. We trained the network on an AMD Radeon Pro 5300M GPU.
## 3 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS) Analysis  
The volatile components were detected using gas chromatography-mass spectrometer (GC-MS) with the Thermo MS DSQ II (Thermo Fisher Scientific, Waltham, MA, USA). Briefly, the volatile components were adsorbed by headspace solid-phase microextraction (HS-SPME), and PDMS/DVB extraction fiber (PDMS/DVB 65 \(\upmu\)m) was first conditioned at 250 \({}^{\circ}\)C for 1 h at the GC inlet. The sample of crushed tea (1.0 g) was placed into a headspace vial (20 mL) and extracted with 5 mL of boiled supersaturated NaCl solution, followed by adding 10 \(\upmu\)L of ethyl decanoate internal standard solution (prepared using anhydrous ethanol, at 7.5 \(\upmu\)g/mL), placing the bottle in a 60 \({}^{\circ}\)C water bath for 60 min after sealing it promptly [45].  
The chromatographic conditions were as follows: a 30 mm \(\times\) 0.25 mm \(\times\) 0.22 \(\upmu\)m DB-5 MS column; a 230 \({}^{\circ}\)C inlet temperature; high-purity (\(\geq\)99.99%) helium carrier gas; a 1.0 mL/min column flow rate; an initial temperature at 45 \({}^{\circ}\)C and holding for 2 min, then 7 \({}^{\circ}\)C/min to 80 \({}^{\circ}\)C with no hold, 2 \({}^{\circ}\)C/min to 90 \({}^{\circ}\)C and holding for 2 min, 3 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C and holding for 2 min, 3 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C and holding for 2 min, 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, and, finally, 10 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C and holding for 5 min; a 40 \({}^{\circ}\)C column chamber temperature; splitless injection mode. The mass spectrometry conditions were as follows: an ion source EI; an electron energy of 70 eV; an ion source temperature at 230 \({}^{\circ}\)C; a mass scan range of \(m\)/z 32-400. The volatile components were characterized by the mass spectra, retention index (RI), and the NIST 2014 database.  
The internal standard semi-quantitative method was employed to quantify the volatile components, with the formula calculated as follows: content of volatile component = peak area of volatile component \(\times\) content of internal standard/peak area of internal standard. The odor activity value (OAV) was calculated as the ratio of the content of a volatile component to the odor threshold of the component.
## 4 Conclusions  
In conclusion, the activated coal gangue with coated amorphous SiC\({}_{\rm X}\)/SiO\({}_{\rm X}\) was firstly synthesized and applied as excellent cathodes for LOBs. The gangue-based cathodes  
Figure 6: (**a**) EIS of gangue-based LOBs after assembled and 1 st discharge–charge states and the corresponding analog circuit diagram; (**b**) Li 1s XPS spectra of activated coal gangue after the first discharge/charge cycle.  
exhibited superior specific capacities and superior cycling stabilization. The significant electrochemical performances of the gauge-based LOBs were verified by photoelectron spectroscopy:  
1. The amorphous SiC\({}_{\text{X}}\)/SiO\({}_{\text{X}}\) layer formed in situ on the surface of the activated gangue is conducive to promoting structural stability, providing an adequate void for product deposition;
2. After acid-alkali activation, abundant functional groups and oxygen vacancies were exposed on the surface of the coal gangue, which could play a key role in improving the formation/decomposition efficiencies of discharge products (Li\({}_{2}\)O\({}_{2}\)) as highly active electrochemical reaction sites. This work provided a new application of coal gangue with high electrochemical catalytic performance in energy storage devices.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13095551/s1](https://www.mdpi.com/article/10.3390/app13095551/s1)]([https://www.mdpi.com/article/10.3390/app13095551/s1](https://www.mdpi.com/article/10.3390/app13095551/s1)), Figure S1: XRD patterns of the intermediate products; Figure S2: N\({}_{2}\) adsorption-desorption isotherms (a) and pore size distributions (b) of as-prepared samples; Table S1: The atomic content of each element for activated coal gangue and crushed coal gangue from XPS; Table S2: Comparison of battery performance of activated coal gangue-based electrode with other reported electrodes. References [32; 33; 34; 35; 36] are cited in the supplementary materials.  
Conceptualization, Z.S. and N.Z.; Methodology, Z.S. and N.Z.; validation, Z.S., M.L. and N.Z.; Formal analysis, Z.S., M.L. and N.Z.; Investigation, Z.S. and N.Z.; resources, N.Z.; Data curation, Z.S., M.L. and N.Z.; Writing--original draft preparation, Z.S., M.L., J.X. and N.Z.; Writing--review and editing, Z.S., W.F., S.L. and N.Z.; Supervision, Z.S., M.L. and N.Z.; Project administration, N.Z.; Funding acquisition, Z.S. and N.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the National Natural Science Foundation of China (52130402), the Fundamental Research Funds for the Central Universities (2022 QN1003), and the China Postdoctoral Science Foundation (2021M703495).  
Not applicable.  
The authors declare no conflict of interest.
# Stationary Target Identification in a Traffic Monitoring Radar System
## 2 Traffic Monitoring Radar System  
### Traffic Monitoring Radar  
In this work, we use a frequency-modulated continuous wave (FMCW) radar that we have developed [16] to monitor road traffic, which is shown in Figure 2.  
This FMCW radar uses a frequency band of 24.05 to 24.25 GHz (i.e., an operating bandwidth of 200 MHz), so the range resolution reaches tens of centimeters. The radar we designed uses a single transmit antenna element and an array antenna system with multiple receiving antenna elements (e.g., four antenna elements), as shown in Figure 3.  
Figure 2: Traffic monitoring radar developed by Bitsensing Inc.  
As the antenna system, a series-fed patch array antenna is used, and the antenna spacing between the receiving antenna elements is \(\frac{3c}{2f_{c}}\), where \(f_{c}\) and \(c\) denote the center frequency of the radar signal and the speed of light. Based on the antenna system of the radar, it can detect vehicles driving in four different lanes.