esa-sceva/satcom-chunk-collection huggingface dataset:
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 3 Materials and Methods
### Squeeze-and-Excitation Module
#### 3.3.3 Driver Behavior Analysis  
When the eyes and mouth are open, their aspect ratio is usually constant, while when they are closed the value of the aspect ratio is close to 0. This ratio does not change with individual human differences. Therefore, in the proposed model, we use EAR [23] and MAR [24] to assess the fatigue state of the driver.  
In the research of [PERSON] et al. [23], the concept of EAR was first proposed. The obtained feature map contains six key points of the human eye as shown in Figure 6. When the person blinks, the distance of these six points will change; the distance relationship of these six points can be used to judge whether there is blinking behavior and the length of blinking time can be judged by the number of frames. Similarly, we use a similar algorithm to evaluate mouth opening and closing (MAR). To this end, we designed a full binary tree SVM classifier to classify the states of eyes and mouths and the final outputs include: (a) closed eyes; (b) yawning; (c) sleepy-nodding. To speed up the efficiency, we use the strategy of transfer learning [25] to pre-train the SVM on the SMIC database [26] (the Spontaneous Micro-expression database). The SMIC database contains a large number of images of human facial expressions and the pre-trained models can be directly used for our classification task after fine-tuning.  
We use the aspect ratio of the eyes to evaluate the opening and closing of the eyes. The formula for the aspect ratio of the eyes is as follows:  
\[\text{EAR}=\frac{\|P_{2}-P_{6}\|+\|P_{3}-P_{5}\|}{2\|P_{1}-P_{4}\|} \tag{7}\]  
Likewise, the formula for the aspect ratio of the mouth looks like this:  
\[\text{MAR}=\frac{\|P_{51}-P_{59}\|+\|P_{53}-P_{57}\|}{2\|P_{49}-P_{55}\|} \tag{8}\]  
where \(P_{i}\) represents the position of the key points of the eyes and mouth. In [23], the aspect ratio threshold of the eyes is set to 0.2 and in [24] the aspect ratio threshold of the mouth is set to 0.8. In this model, two thresholds are obtained experimentally.  
The average blink time for a normal person is 0.2-0.4 s, while the blink time in a fatigued state usually lasts for 2 s. The value of MAR is always changing during normal speech and if the value of MAR is stable and consistently exceeds the threshold, we assume that the driver has made a yawning movement. Therefore, we can determine whether the driver is in a fatigue state based on the number of frames in the video. If the number of frames with blink time longer than 2 s exceeds 60%, or the number of frames with yawn exceeds 40%, we assume that the driver is in a fatigue state.
# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System
### Data Acquisition  
This stage is dedicated to gathering the data coming from both network and location-sources. Network information encompasses the measurements and events generated in the communication between the UEs and the cells and it is classically obtained from the signaling of control and management planes. Furthermore, new dataflows of information are required to gather UE context information, particularly the UE position. This might be acquired from the UEs, the cellular network, or 3 rd party localization services [7; 21]. Additionally, the data regarding the position of the BSs and details of the scenario can be obtained from the operator's OSS or other network sources.
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
## 3 Selection of the Best TEG + DC/DC Converter Combination
### Experimental Tests  
The tests were carried out indoors at an ambient temperature of about 20 \({}^{\circ}\)C. The TEGs were mounted on a flat rectangular aluminum busbar that was heated from room temperature to approximately 50 \({}^{\circ}\)C using a power supply and heating resistors, as shown in Figure 3. Next, to determine the performance of the devices during the cooling phase, the power supply was disconnected, whereby the aluminum bar cooled by natural convection. The electrical output power of the TEGs and DC/DC converters was determined by multiplying the respective voltages and currents.  
Figure 4 shows the performance of TEG #1 combined with DC/DC converter #1. According to Figure 4b,c, with the same temperature difference between the busbar and the ambient, more power is generated during the heating cycle than during the cooling cycle.  
Figure 3: Experimental setup to compare the performance of TEGs and DC/DC converters.  
This hysteresis response is mainly due to the delayed thermal diffusion from the ceramic plate to the hot junction of the TEGs [40].  
Figure 5 shows the experimental performance of each TEG, DC/DC converter and the different combinations of TEG + DC/DC converter. It is noted that the TEG #1 + DC/DC converter #1 combination has a better performance than the others, this being the most suitable combination for applications with very low temperature gradients. Therefore, the next sections take a closer look at the TEG #1 + DC/DC converter #1 combination.
## 2 Results and Discussion
### K Values  
Inosine 5'-monophosphate (IMP) provides a sweet and meaty flavor to improve the quality of fish, however, its transformation to inosine (HxR) and hypoxanthine (Hx) results in unpleasant bitterness reflecting the initial stage of autolytic degradation and subsequent microbiological spoilage. K value is strongly affected by the transformation rate of HxR and Hx and commonly used to evaluate the freshness of fish [23]. The K value of fresh turbot samples was 5.38% and increased during storage. The K value of AP treated samples reached 65.31%, exceeding the rejection limit of 60% at day 15. However, the VP and MAP treatments significantly delayed the increase of K-value (\(p<0.05\)), which remained below the rejection limit until the end of storage (day 27). These results indicated that  
Figure 1: Changes in water holding capacity (WHC) (**a**), TVB-N value (**b**), TBA value (**c**) and K value (**d**) of samples under different treatments during storage period (AP: air packing; VP: vacuum packing; MAP1: 70% CO\({}_{2}\)/30% N\({}_{2}\); MAP2: 60% CO\({}_{2}\)/10% O\({}_{2}\)/30% N\({}_{2}\); MAP3: 50% CO\({}_{2}\)/15% O\({}_{2}\)/35% N\({}_{2}\); MAP4: 55% CO\({}_{2}\)/5% O\({}_{2}\)/40% N\({}_{2}\)).  
VP or high CO\({}_{2}\) concentration combined with superchilling could suppress the degradation of ATP and keep the quality of turbid samples at acceptable levels during storage.
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 5 Conclusions  
In the previous aeromagnetic compensation work, although the classical BP-ANN is stronger than traditional regression algorithms in terms of fitting ability, the BP-ANN is a global approximation network, with limited generalization ability, and there are problems, such as falling into a local minimum easily, gradient disappearance, and an overfitting problem in magnetic compensation which affects the accuracy of aeromagnetic compensation. In order to improve the accuracy of compensation, on the basis of the T-L model, we proposed a compensation algorithm based on BRF-ANN, in which the hidden layer node uses the distance between the input mode and the central vector as the independent variable of the function, and uses the radial basis function as the activation function, which has the characteristics of local approximation and better generalization ability, avoids the problem of local minimum effectively, and improves the accuracy of magnetic compensation to a certain extent. We verified the feasibility of this method in simulated data and measured data experiments.  
Z.S., J.J. and C.Y.: Conceptualization, Methodology, Software; J.J. and S.Z.: Resources Data curation; C.Y.: Writing--Original draft preparation; C.Y. and Z.S.: Visualization; Z.S., C.Y., P.Y. and S.Z.: Writing--Reviewing and Editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China (No. 2020 YFE0201300), National Natural Science Foundation of China (No. 42204141), the Natural Science Foundation of Jilin Province (No. 20210508033 RQ), National Natural Science Foundation of China (No. 42274187), Interdisciplinary training Program for Young teachers and students of Jilin University (415010300086).  
The authors declare no conflict of interest.  
Figure 13: Flight D compensation results: (**a**) BP-ANN and BRF-ANN compensation results; and (**b**) compensation for the resulting sampling points 1000 to 1500.
## 4 Discussion
### Comparison to Similar Studies  
The fraud and scam detection problem has been extensively studied in the scientific literature for many years. One of the types of such fraud is web phishing, where fraudsters try to steal user data or money by creating fake websites. Machine learning algorithms and methods are usually used to identify phishing websites together with publicly available datasets such as PhishTank [8] and VirusTotal [30]. Most of these studies do not distinguish online shops as a separate object of study and are more focused on methods for detecting phishing pages. In contrast to our study, these works use different features that focus more on sophisticated URL analysis [24] than website content analysis. Therefore, these phishing detection methods are not suitable for detecting fake online shops, because the latter requires evaluation of their content data. For the same reason, existing datasets are not suitable for training fraudulent online shop classifiers, so we had to create our own dataset for this study.  
To our knowledge, there are few scientific works that are dedicated solely to identifying fake online shops. Unlike our proposed solution, [32; 37] use only features extracted from the content and a complex analysis of the entire website code and its structure. The authors of [17] also perform textual and image analysis of online shops. Contrary to our study, this analysis is not a simple keyword search, but is based on natural language processing (NLP) methods. Although high accuracies (0.998, 0.97, and 0.987, respectively) are achieved in all these studies, these methods can be difficult to apply in practice due to the high processing and memory requirements. Therefore, in our study, we propose using features that can be obtained directly by querying third-party services (e.g., WHOIS [33]), analyzing the web address string (e.g., number of digits), or simply searching the source code of the webpage for specific parts and keywords (e.g., contact email, payment information).  
The work most similar to our study is [PERSON] et al. [38]. The authors of [38] also proposed to use features obtained from the source of the website and third parties, including Trustpilot [31] and WHOIS [33], but in addition they use data from social network platforms together with features obtained from the metadata of the website. These features include high discounts, social media footprint, domain age, registration date, SSL names, country and issuer, Trustpilot score and review, e-commerce technologies and policies. Contrary to our proposal, this study does not include URL-based features.  
The method proposed in [38] achieved 0.86 accuracy using a custom made dataset containing 282 records.
## 3 Results and Discussion
### Adsorption of Single Molecule, Double Molecules on Pt-Graphene
#### 3.2.1 Analysis of Adsorption Structures and Electronic Properties
It is shown that in the adsorption process, electrons are transferred from the surface of Pt-doped graphene to gas molecules, and the gas molecules SO\({}_{2}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) all exhibit the characteristics of electron acceptors. The amount of charge transfer after adsorption of SO\({}_{2}\) and SOF\({}_{2}\) mixed gas is \(-\)0.321 \(e\), 0.127 \(e\), indicating that electrons are transferred from the surface of Pt-doped graphene to SO\({}_{2}\) gas molecules, and SO\({}_{2}\) is an electron acceptor but the SOF\({}_{2}\) is an electron donor.
## 6 Discussion
### Implications for Practitioners
This might be a more cost-efficient and effective way of improving an inspection system. Organisations might consider implementing advanced technologies when aiming for one or more of the following: (a) improving inspection quality, i.e., the accuracy and consistency, (b) increasing efficiency by reducing inspection time that is considered as waste, (c) streamlining inspection processes, (d) reducing labour cost, (e) avoiding manual handling either to protect the part (sensitive) or the operator (unsafe environment), (f) monitoring the inspection process in real time or (g) reducing human error by eliminating subjective evaluation. The findings of this study could be useful for organisations that are considering investing into advanced technologies. While the present work specifically addressed the visual inspection of engine blades, the applied approach and insights might be transferable to other part inspections, processes and industries. The SWOT analysis and weighted factor analysis have been proven to be useful when comparing factors that are difficult to quantify and for unequal inspection agents, such as humans and technologies. Since the three agents were applied to three different types of inspection, companies could repeat the assessment using those methods and their own list of criteria that are relevant to the specific organisation and operational situation. It is a matter of debate whether new technologies such as artificial intelligence will cause job losses or will create new and possibly more interesting jobs. The World Economic Forum predicts that significantly more jobs will be created than lost [106]. Advanced technologies such as automated inspection machines have the potential to take on the highly repetitive and tedious tasks and free up the human operator. For the operator this means less strain and being able to focus on their expertise, higher skills and more fulfilling tasks, which may provide more motivation and excitement, leading to higher job satisfaction. From an organisation perspective, employees can do more difficult and value-added activities that cannot be automated and require fundamental knowledge and more sophisticated decision-making abilities, e.g., development of new innovations to enhance productivity and processes. There is also potential for 'hybrid intelligence', as discussed later.
## 2 Background
### Overlay Sensor Network  
In traditional approaches, an overlay network is a computer network which is built on the top of another network. Different types of distributed systems such as cloud computing, peer-to-peer networks, and client-server applications are overlay networks because their nodes run on top of the Internet. The Internet was built as an overlay upon the telephone network [23]. An overlay sensor network is a type of sensor network that creates a virtual topology on top of the physical topology of a wireless sensor network. Nodes in an overlay network are connected through virtual links which correspond to paths in the underlying network. Overlays are typically implemented in the application layer, though various implementations at lower layers of the network stack do exist. In [24] the authors have proposed a novel access architecture using a sensor network overlay. By embedding sensing, computation and transmission capabilities, sensor nodes can cooperate to serve as an effective monitoring and data gathering technology. In the scheme, the transmission of control messages is done at the sensor plane, in parallel with data transmission in the data plane.
###### Abstract  
Dentin hypersensitivity is a common disease of the oral cavity, which renders the tooth extremely sensitive to stimuli. These symptoms usually result from the exposure of the dentinal tubules to the external environment. There is a need for a treatment for dentinal hypersensitivity that can overcome the shortcomings of the existing agents. This study thus aimed to assess the therapeutic efficacy of no-ozone cold plasma (NCP), which was developed for safe use in the oral cavity, in conjunction with 1.23% acidulated phosphate fluoride (APF) and hydroxyapatite (HA), which are widely used conventional treatments of hypersensitivity. The fluoride content was evaluated using electron probe micro-analyzer (EPMA) analysis. Moreover, we confirmed the effect of NCP pre-treatment on the dentinal tubule occlusion by APF and HA as follows: scanning electron microscopy and energy dispersive X-ray spectrometry were employed to analyze the exposed dentinal tubules, and the calcium and phosphorus content were measured. Furthermore, an additional experiment was conducted using a metal mesh to analyze the working elements of NCP. All experimental results were analyzed by one-way analysis of variance and then by using the Turkey test as a post hoc test. EPMA analysis confirmed that the fluoride content of the APF and NCP group was significantly higher than that of the APF group (\(p<0.001\)). The fluoride content of the group treated with APF and NCP equipped with a metal mesh was significantly lower than that in the group treated with APF and NCP and the group treated with APF and NCP equipped with a cotton mesh (\(p<0.01\)). Moreover, the group treated with NCP pre-treated with HA and APF exhibited significantly greater dentinal tubule occlusion than the other groups (\(p<0.05\)). The same result was confirmed by calculating the calcium/phosphorus ratio (\(p<0.05\)). Pre-treatment of the enamel and dentin surfaces with plasma improved hypersensitivity by enhancing fluoride deposition with APF and dentinal tubule occlusion with HA.  
acidulated phosphate fluoride; dentin hypersensitivity; dentin tubule occlusion; hydroxyapatite; no-ozone cold plasma Article
# Research and Application of Edge Computing and Deep Learning in a Recommender System
## 2 Research Status of the Recommender System
Moreover, mobile phones and edge devices continually advance, equipped with specific computing power. While preserving the accuracy of deep learning-based recommender systems, research priorities now include model compression, segmentation, and collaborative training. [PERSON] et al. [14] provided a comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison, and applications. [PERSON] et al. [15] identified limitations in the coupled formulation of knowledge distillation and proposed decoupled knowledge distillation (DKD), achieving significant improvements across CIFAR-100, ImageNet, and MS-COCO datasets for image classification and object detection tasks. The concept of knowledge distillation originated with [PERSON] et al. [16], aiming to use a vast and accurate teacher model to instruct a small but fast student model. Based on this idea, the intention was to select cutting-edge recommender systems such as DeepFM [17], the DCN [18], and xDeepFM [19] as the teacher model, guiding the training of user-defined student models. This approach was first applied in recommending train number lists on a travel platform. Through knowledge distillation, the resultant model reduced the required number of parameters and network runtime, while maintaining accuracy. It has been demonstrated that allowing a small accuracy drop significantly reduces model deployment costs. Finally, the model was segmented to alleviate data traffic, with the segmented models deployed on both cloud and terminal devices. This approach enables high-precision reasoning under delay constraints, particularly suitable for practical production lines as a lightweight front-end deployed model.
## 2 Methods  
### Modeling  
#### 2.1.1 Problem Description and Hypothesis  
In the multi-UAV cooperative task assignment problem, the distribution task assignment and flight path planning of a multi-UAV in the multi-demand point scenario are studied. Assume that there are UAVs \(K=\{K_{1},K_{2},\ldots,K_{m}\}\) and demand points \(B=\{B_{1},B_{2},\ldots,B_{n}\}\), and the UAVs \(K\) need to perform a delivery service for each demand point \(B_{j}\). Because the UAV is constrained by its performance, such as load, volume, and mileage, each UAV can only complete a limited number of demand point distribution tasks.  
Under the premise that each UAV meets the load constraint, load volume constraint, and time window constraint, the distribution center assigns several UAVs to deliver materials for the demand point. Each UAV starts from the distribution center, provides materials for the demand point, and recovers materials from the demand point simultaneously. After completing the established task, it returns to the distribution center. The distribution scheme is planned with a minimum total cost, minimum total task penalty, and optimal balance of using UAVs.  
In this paper, we make the following assumptions:  
1. There are multiple drones and one distribution center.
2. The speeds of the drones are known and constant.
3. Drones can serve all customers.
4. The drone flight endurance is known and fixed regarding travel time.
5. The drone must leave customer locations immediately after delivering or picking up parcels.
6. The UAV must be able to fulfil the service demand of at least one demand point.
7. The UAV has an entire initial power supply and does not consider any midway charge or change.
8. The distribution center's cargo reserves are deemed unlimited.