esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Method for Ship Target Detection Based on YOLOv4
## 2 Related Works  
For the past few years, convolutional neural networks have also been continuously applied in remote sensing image object detection, and the object detection algorithm based on deep learning is a cutting-edge technology for ship detection.  
To enhance the detection performance of multi-scale ships, features are usually fused. [PERSON] et al. [16] achieved good detection results with a SAR ship detection network constructed using an improved FPN, which consists of four unique FPNs to fuse features to improve SAR ship detection performance. [PERSON] et al. [17] used an improved FPN (Feature Pyramid Network) and PANet (Path Aggregation Network) to fuse features from the backbone network. The two modules can integrate feature maps of different layers, combine context information on multiple scales and strengthen feature information.  
To improve small target detection, two types of methods are commonly used. The first method is to expand the number of small target samples by data enhancement. [PERSON] et al. [18] proposed a Gaussian hybrid Wasserstein GAN using gradient penalty to generate small ship samples with sufficient information; the CNN was then trained on the original and generated data to achieve accurate real-time detection of small ships. The improvement of data enhancement on small target detection is limited. Another approach is to make improvements to the network structure. [PERSON] et al. [19] added recombination and routing layers to the improved YOLOv2 network, bringing together shallow and deep feature maps in forward propagation to improve the detection of small ships. To improve the detection accuracy of small targets, [PERSON] et al. [20] added a detection scale to the YOLOv3 network to enhance the sensitivity to small targets, and anchors to small targets were assigned at the increased shallow feature scale. This approach increases the amount of computation and decreases the speed of reasoning.  
To address the problem of false and missed detection of ships because of cloud interference, [PERSON] et al. [21] proposed an offshore ship detection method based on scene classification and saliency-tuned YOLO-Net. Firstly, the images were divided into four categories. Secondly, targets were extracted from different images by the saliency detection method. Finally, they designed the saliency-tuned YOLOv4 network to detect ships. This method takes a lot of time to classify the samples.  
Real-time ship detection has high requirements for accuracy. As an excellent representative algorithm of the YOLO series, YOLOv4 can detect targets at multiple scales and can achieve a balance of accuracy and speed, which makes it a valuable research tool. In this paper, YOLOv4 was used for detecting ships. To address the problem of detecting small targets with poor accuracy and complex background interference, improvements were made to the structure and detection accuracy.
# Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
### Moisture Damage and Potential Distress  
Moisture damage is one of the main forms of potential distress that results in strength loss, stripping, and deformation of pavements [9]. It is generally caused by segregation in the construction process, which is presented as poorly bonding and compaction [10]. This category of distress is so-called potential damage because it is hard to detect with little change in its extrinsic feature at the initial phase but the strength has been loosened [11]. When it bears certain loading, a sudden breakdown may happen on the road surface [12]. Because of this hidden distress, maintenance work and driving safety burden more pressure than the average routine [1].  
In order to solve the problem, traditional methods, such as geological radar and falling weight deflectometer (FWD), are used for in-field projects to detect potential failure inter road structures [13; 14; 15]. Lots of effort has been made in making a long-term detective routine [16]. However, accuracy of these methods is not achievable. Because moisture damage and potential distress cause multiple driving problems, they cannot be determined by tests from a one-directional aspect under the wide-range and complex in-field conditions [9]. A simple linear relationship cannot construct and explain the correlations between properties and in-field failure of pavements. Furthermore, predictions and classifications derived from the traditional methods may be unstable because of the personal experiences of judgment for potential damage by most nondestructive detection methods [17]. For example, even though the geological radar technology has developed more automatic and precise for pavement detection and the radar images are processed by high advanced software, the detection results are easily affected by the different detection conditions, which will lead to misjudgment from the engineers with less experience.
# Adaptive Driver Face Feature Fatigue Detection Algorithm Research
## 4 Experiment and Analysis
### Training and Evaluation Index for Target Detection  
The training dataset was the YawDD dataset. The learning rate was set to 0.01 for the first 20 iterations and 0.001 for the final 20. The training procedure was iterated 50 times.  
The evaluation metrics selected in the experiments include precision, recall, and F-score.  
Figure 7: YawDD dataset: (**a**) Images of male drivers. (**b**) Images of female drivers.  
(1) _Precision_  
Equation (4) illustrates the formula which is used to gauge the precision of recognizing driver weariness.  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
The phrase _TP_ represents how many times the motorist was correctly identified as being exhausted, while _FP_ indicates how many times the driver was incorrectly identified as being fatigued.  
(2) _Recall_  
The recall rate serves as a barometer for fatigue detection. Equation (5) gives the formula for this quantity, which displays the rate of missed driver fatigue detection by the system.  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
The \(FN\) in the calculation represents the number of times the system mistakenly interprets the driver's state as non-fatigue when in fact the driver is in a state of fatigue.  
(3) _F-Score_  
The _F-score_ is a comprehensive index designed to measure the performance of the system for detecting driver tiredness more thoroughly while balancing the influence of the precision and recall rates. The greater the _F-score_, the better the performance; the expression is shown in Equation (6). It is a way of combining the precision and recall of the model, and it is defined as the harmonic mean of the model's precision and recall.  
\[F-Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{6}\]
# A Star-Identification Algorithm Based on Global Multi-Triangle Voting
## 4 Conclusions  
In this paper, a novel star-identification algorithm is proposed to solve the \"lost-in-space\" problem for star sensors, which utilizes several triangles as a voting unit to obtain the candidates in the initial match, and then a follow-up filter procedure acquires the final results. As the simulation shows, the proposed algorithm is more robust to position noise, magnitude noise, as well as false stars compared with the geometric voting and grid algorithms. Moreover, our algorithm is efficient, and its memory size is acceptable. Therefore, the GMTV algorithm is suitable for future astronautic missions. We will focus on running our algorithm on a hardware system of a star sensor to evaluate its on-orbit performance.  
Funding acquisition, J.Z.; Investigation, X.Y., K.Z., and X.L.; Methodology, X.Y.; Supervision, J.Z.; Validation, X.Y.; Writing--original draft, X.Y.; Writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grants no. 61890961) and the Natural Science Basic Research Program of Shaanxi (program no. 2018 JM6008).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Our Method** & **Grid** & **Geometric Voting** \\ \hline Run time & 18.36 ms & 72.71 ms & 96.86 ms \\ Memory size & 1.5 MB & 0.5 MB & 0.6 MB \\ \hline \end{tabular}
\end{table}
Table 2: Time and memory size.
## 3 Results and Discussion
### ESI-MS of Uranyl Complexes with Glycine, Aspartic Acid, Cysteine, and Histidine
#### 3.2.2 Anionic Uranyl-Amino Acid Complexes
Two combinations are possible for complexes comprising one deprotonated amino acid and two neutral amino acids: [UO\({}_{2}\)(AA-H)(AA)(AA\({}^{\prime}\))]\({}^{+}\) and [UO\({}_{2}\)(AA\({}^{\prime}\)-H)(AA)\({}_{2}\)]\({}^{+}\). In the absence of laborious computational studies to deduce the most favorable isomers, we initially assume predominant deprotonation of the most acidic amino acid based on gas-phase acidities: GA[Asp] \(>\) GA[His] \(>\) GA[Cys] \(>\) GA[Gly] (see Table S3). However, the CID results may contradict this simple assumption and suggest two isomers. In negative ion mode, there is no doubt as to the deprotonation site as all the amino acids are deprotonated in [UO\({}_{2}\)(AA-H)\({}_{2}\)(AA\({}^{\prime}\)-H)]\({}^{-}\), [UO\({}_{2}\)(AA-H)(AA\({}^{\prime}\)-H)\({}_{2}\)]\({}^{-}\), and [UO\({}_{2}\)(Cl)(AA-H)(AA\({}^{\prime}\)-H)]\({}^{-}\).
## 4 Discussion
### Limitations of the Proposed Method  
As previously mentioned, the feasibility and reasonability of the proposed method for the dynamic risk assessment of cable fires were verified in this study. However, this study has several considerable limitations. In the assessment case, the fire risk factors of the cables in the power distribution room, including 15 risk factors at two levels, were determined. However, due to some risk factors, such as external fire sources existing in the distribution room, the identified risk factors may not be sufficiently comprehensive. Furthermore, due to the lack of sufficient data, the risk transfer mechanism and CPTs were primarily determined by referring to experts' opinions and the relevant literature. Thus, a certain degree of subjectivity is inevitable in risk assessment. To reduce the subjective influence, structural learning and parameter learning [50,51] can be used in actual assessments to further improve the DBN. Moreover, the fire risk associated with cables is intrinsically connected to energy fluctuations within the electrical system and the reliability of equipment. To mitigate the subjectivity in fire risk assessment and bolster the quantitative interpretation and analysis of risks, we intend to include multivariate models of the terminal energy flow transmission situation and associated equations within our future research endeavors. Moreover, we only analyzed the importance of single-node risks for risk control, and investigating the importance of risk control in multi-node joint cases will be a focus of our future research.
## 3 Proposed Methods
### Energy Efficiency Using Balanced Energy-Efficient and Reliable Algorithm  
In this model, the energy is dissipated from the transmitter and receiver for the operation of power amplifiers and electronic circuits. Equations (1) and (2) indicate energy consumption when sending and receiving n-bit data over a distance d0 that exists between the transmitter and receiver. According to the energy model, each node's energy consumption should be proportional to 2d if the distance between the transmitter and receiver is small compared to the threshold distance d0; otherwise, energy consumption is proportional to 4d.  
Energy Model: The energy model in WBAN is designed at the physical and MAC layers for the following reasons: In the physical layer, there is a loss due to channel distance, and in the MAC layer, the energy efficiency is affected due to idle listening, overhearing, etc. These factors are considered when estimating the energy loss of every sensor node during communication. The two-channel propagation models used are the free-space and the multipath fading channel model. The former is used for direct transmission power loss (one hop) and the latter is used for packet transmission via multi-hop power loss. As a result, the amount of energy required to send an l-bit packet over distance d is calculated as per Equation (1).  
\[E_{TX}(l,d)=(lE_{elec}+l_{fs}d^{2},d<0)\left(lE_{elec}+l_{mp}d^{4},d>0\right) \tag{1}\]  
where free-space energy loss is denoted by efs, multipath energy loss is denoted by emp, the distance between the source and the destination node is denoted by \(d\), and crossover distance is denoted by Equation (2)  
\[d_{0}=\sqrt{e_{fs}/e_{mp}} \tag{2}\]  
The energy required by the radio to receive the message is calculated as per Equation (3).  
\[E_{R}X(l)=lE_{elec} \tag{3}\]  
Therefore, the physical and MAC layers of WBAN are designed with respect to the transmitting and receiving of power energy levels. The energy model in this proposedmethodology uses the Balanced Energy-Efficient and Reliable (BEER) algorithm, which is used for transmission within the cluster heads of a network. There will be an improvement in data transmission when the network's cluster heads' routing is optimized, and this includes a Balanced Energy-Efficient and Reliable (BEER) algorithm that will optimize energy consumption. This will increase network throughput and energy efficiency while decreasing end-to-end delay. Routing with SOPR will minimize the traffic within the network and enhance the packet-delivery ratio.
## 5 Case study
### ES Device Capacity Optimization
It is easy to achieve good performance, but hard to be perfect. This characteristic can provide guidance for decision-makers to obtain optimal planning schemes under different conditions with particular requirements on reliability and economic efficiency.
# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
## 3 The Proposed Relocation Protocol for Rugged Terrains  
Although the distributed-based relocation protocol [19] has made up for the drawbacks of the previous central-based relocation protocols, questions still remain whether it is suitable for the real world. In fact, it should be noted that the environment in which hopping sensors are deployed would be different from the general terrain. In the relocation protocol [19], topographical information on obstacles around the cluster zones were not taken into account. The movement of hopping sensors fails due to obstacles and the relocation cannot satisfy the movement requested from the cluster header of sensing hole. Thus, the number of messages continuously requested may increase to overcome the persisting sensing hole. This could put a heavy load on the entire network, and it would be very negative in terms of energy. Therefore, we propose a relocation protocol for more realistic hopping sensor networks by indirectly predicting the topographic information around sensing holes.
## 4 Results and Discussion
### Maintenance
Figure 7: The e-road impacts divided per phase and sub-phase. According to the previously performed fatigue analysis, eight wear layer rehabilitations should be conducted during the lifetime of an e-road. However, the endurance of the wear layer is the most uncertain quantity and can vary from approximately 2 years to no more than 5 years (from 3 up to 9 rehabilitations in 20 years of the design life). The average endurance of the entire asphalt layer (e.g., binder and wear) can be set at approximately 10 years. Thus, the rehabilitation of the binder layer over 20 years of design life is generally only done once. The cumulative energy demand of the e-road is shown in Figure 8 considering the different wear rehabilitation frequencies. As can be seen from Figure 8 and Table 15, the frequency of the maintenance operation can halve the final results, thus suggesting that future work should be done to reduce the related uncertainty, and to optimise the structural and material features. The frequency of the wear layer rehabilitation is still uncertain and influences the impact assessment of an e-road to a great extent. This aspect requires refinement when more data from e-road monitoring are available. The impacts of the e-road have been compared with those of a t-road in order to provide a reference. The obtained results refer to a traditional t-road which has undergone a wear and binder layer rehabilitation at time 0, when the e-road was built by revamping a traditional road, as explained in Section 3. The results of this comparison are reported in Table 16. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CED [M]/km** & **AD—Fossil Fuels [M]/km** & **Climate Change [kg CO\({}_{2}\)-Eq/km]** \\ \hline E-road & 22,672,230 & 21,600,052 & 473,882 \\ T-road & 11,814,331 & 10,512,842 & 227,122 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The impacts of an e-road construction and maintenance for a lifetime of 20 years. Figure 8: The cumulative energy demand of 1 km of e-road considering the different wear layer rehabilitation frequencies. The results show that the e-road presents almost double the impacts of a traditional road in almost all the analysed impact categories as a result of the production and installation of the WPT components and of the more frequent maintenance operations. However, these figures should be considered simply as a reference. As mentioned in the methodological section, this impact needs to be considered from a larger perspective that also considers the effect of the infrastructure on the other three axes. In fact, compared to traditional roads, e-roads have a greater impact, but they fulfil an additional function to a t-road, thus making the comparison unfair when it is limited to the declared unit (km of t-road/e-road).
# Identification of Factors Influencing Fluoride Content in Tea Infusions: A Systematic Review
## 3 Results
### Main Study Outcomes
#### 3.3.4 Influence of Tea Mass and Leaf Fragmentation  
The degree of leaf fragmentation is another important factor influencing fluoride content. Teas in bagged or powdered form--characterized by finer particle size and greater surface area--release significantly more fluoride than loose leaf teas [19; 28; 31; 32; 49; 50; 51; 52; 54; 58; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. [PERSON] et al. [28] reported particularly high fluoride levels in infusions made from granulated or bagged black teas, highlighting the role of processing in fluoride release. (see Table 2)
## 3 Case Study  
### Overview of the Engineering Project  
The deep foundation pit in this project pertains to the main excavation of the Sanbao Station on the SG9-3 section of the Phase I civil construction of Hangzhou Metro Line 9. Sanbao Station serves as a \"T\"-shaped transfer station between Line 6 and Line 9. The Line 6 section of Sanbao Station (Zones 1 to 5) is an underground four-story island-style station with a total length of 175 m and a standard width of 25.7 m. The Line 9 section (Zones 6 to 11) is an underground three-story island-style station with a total length of 201.2 m and a standard width of 24.5 m. Both sections adopt a double-column, three-span reinforced concrete box-frame structure with full waterproofing. The diaphragm walls and interior lining walls are designed as composite walls. The station is primarily constructed using the open-cut method. Entrance passages, ventilation shafts, and other auxiliary structures are mainly constructed using the open-cut method after the main structure is completed. For Entrance C, where it connects to the already constructed underpass tunnel, the horizontal freezing method is employed, followed by underground excavation. During the excavation and underground construction, special consideration is afforded to minimizing the impact on the surrounding environment. A schematic diagram of the environment surrounding Sanbao Station is shown in Figure 5.
## 4 Signal Combining Schemes for ADR Solutions
### Receiver Scenario with a Single PD (\(N_{pd}\) = 1)
#### 4.2.1 Select Best Combining Scheme  
The SBC scheme is initially proposed in [44]. Adapted for our mining VLC system, the \(R_{x}\), equipped with any of the ADRs presented, must select the best of the PDs in the ADR, which we will call \(PD_{i_{op}}\) to establish a link with \(T_{x_{ap}}\). This process allows the fulfillment of the objective of maximizing the SINR, which is denoted in the optimization problem that follows [22; 23; 44]  
\[\left(T_{x_{ap}},PD_{i_{op}}\right)=\underset{T_{x_{ap}},PD_{i_{op}}}{arg\ max}\ \gamma_{T_{x_{ap}},PD_{i_{op}}}, \tag{35}\]  
where \(\gamma_{T_{x_{ap}},PD_{i_{op}}}\) is the SINR of the link between \(T_{x_{ap}}\) and \(PD_{i_{op}}\), denoted by  
\[\gamma_{T_{x_{ap}},PD_{i_{op}}}=\frac{\left(\tau\sigma\xi P_{a_{op}}H_{T_{x_{ap }},PD_{i_{op}}}\right)^{2}}{\sum\limits_{a=1,a\
eq a_{op}}^{N_{T_{x}}}\left( \tau\sigma\xi P_{a}H_{T_{x_{ap}},PD_{i_{op}}}\right)^{2}+\left(n_{PD_{i_{op}}} \right)^{2}}. \tag{36}\]  
In this technique, ADRs will only consider the signal received by the PD that has the highest SINR. Therefore, for its physical implementation, it is necessary to install a circuit in the ADR that continuously monitors the SINR in each PD. In addition, a quick switching system is required for the selection of the desired PD. Theoretically, by selecting the best PD in the ADR, a higher SINR can be achieved; however, this scheme does not perform signal combination, thus the interference you will select will also be high.
## 6 Research Areas
### Autonomous Navigation
#### 6.1.3 Navigation  
Construction sites are highly cluttered environments. During the navigation, the robot must be aware of its surroundings and make minor adjustments to its path [212]. [PERSON] et al. [213] compared laser sensors, ultrasonic sensors, and infrared sensors for mapping a floor for robot navigation. [PERSON] et al. [214] and [PERSON] et al. [212] used deep-learning for vision-based obstacle avoidance of the robot. [PERSON] et al. [90] developed an autonomous arm for a UGV for detecting and removing obstacles from the robot's path. They used a CNN-based segmentation model for object detection. This is useful for removing small obstacles up to the payload capacity of the attached arm.  
[PERSON] and [PERSON] [165] developed a wall-following algorithm using LiDAR sensors and a ground-based wheeled robot to run along the walls and find defects. This is a simple algorithm that does not need any pre-planning of the path, but it cannot move away from the walls. In large halls or auditoriums, the wall-following feature can be a limitation. [PERSON] et al. [215] used visual servoing with UAV to maintain a safe distance from the wall. Visual servoing uses a feedback loop between the camera image and robot control to dynamically control the robot with the changing condition [215].