esa-sceva/satcom-chunk-collection huggingface dataset:
The data processing energy consumption can be calculated from the energy consumption rate of data processing and the amount of data processed, as shown in Equation (1) [9; 10]:  
\[E_{a}(i)=i\cdot Ex \tag{1}\]  
where \(i\) is the amount of data processed, and \(Ex\) is the energy consumption of the data processing circuit for processing unit data. We calculate the energy consumption of data reception based on the amount of data received, as shown in Equation (2) [9]:  
\[E_{b}(i)=i\cdot Ey \tag{2}\]  
where \(i\) is the amount of data processed as in the previous Equation, and \(Ey\) is the energy consumption of the transmitting circuit. The energy consumption of the data transmission is mainly related to the amount of data sent and the distance sent, as shown in Equation (3) [9]:  
\[E_{Z}\left(i,d\right)=\left\{ \begin{array}{c}E_{b}(i)+i\cdot E_{m}*d^{2}, \ (d<d_{0})\\ E_{b}(i)+i\cdot E_{n}\ d^{4},\ (d\geq\ d_{0})\end{array} \right. \tag{3}\]  
where \(d\) is the distance between two sensors, \(d_{0}\) is a constant, and the energy dissipation is adopted; the energy consumption of sending data is relative to the square of distance when the distance between sending and receiving nodes is less than \(d_{0}\)[8; 9]. \(E_{m}\) is the energy consumption coefficient of the free space power amplifier. When the distance between the sending and receiving node is greater than or equal to \(d_{0}\), then the multi-channel model for energy dissipation is adopted. The energy consumption of sending data is relative to the fourth power of distance [9; 10]. \(E_{n}\) presents the energy consumption coefficient of multi-channel power amplifiers. Figure 2: Energy consumption structure of wireless sensor nodes. The novelty of this research lies in the innovative integration of KNN machine learning algorithms into WSN protocols to address long-standing challenges in energy efficiency, network reliability, and clustering optimisation. The proposed modifications to existing protocols (LEACH-KNN, SEP-KNN, TEEN-KNN, and DEC-KNN) represent a significant advancement in WSN design by combining the strengths of grouping algorithms with practical network requirements. This study presents new architectures, models, and distributed algorithms that optimise cluster head selection and placement, reduce energy consumption, and extend network lifetime.
# CenterInst: Center-Based Real-Time Instance Segmentation
## 4 Experimental Results
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(L\) & \(n\) & \(N\) & \(\mathbf{AP_{val}}\) & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) \\ \hline
1 & 36 & 36 & 37.4 & 58.9 & 39.1 & 16.0 & **41.0** & 58.9 \\
3 & 8 & 24 & 37.6 & 59.4 & 39.3 & 16.4 & 40.9 & 59.4 \\
3 & 12 & 36 & **37.8** & **59.5** & **39.5** & **16.9** & 40.9 & 59.5 \\
3 & 16 & 48 & 37.6 & 59.3 & 39.4 & 16.5 & 40.8 & **59.6** \\ \hline \hline \end{tabular} \({}^{*}\)_L_\({}^{*}\) represents the number of scale feature maps to be sampled, \({}^{*}\)\(n^{*}\) represents the number of sampling points per scale feature map, and \({}^{*}\)\(N^{*}\) represents the total number of sampling points. Bold data indicates that this method outperforms others in this metric.  
\end{table} 
Table 6: Sampling point number. Employing multiscale sampling and an appropriate number of sampling points can lead to better model performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{AP_{val}}\) & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) \\ \hline CenterInst & **37.8** & **59.5** & **39.5** & **16.9** & **40.9** & **59.5** \\ - high resolution & 37.6 & 59.3 & 39.4 & 16.1 & **40.9** & 59.4 \\ - bilinear & 37.2 & 58.8 & 38.9 & 16.0 & 40.7 & 59.1 \\ interpolation & & & & & & \\ \hline \hline \end{tabular} Note: \({}^{*}\)- high resolution\({}^{*}\) denotes not extracting queries from high-resolution pixel features; \({}^{*}\)- bilinear interpolation” indicates not using the bilinear interpolation method to extract queries. Bold data indicates that this method outperforms others in this metric.  
\end{table} 
Table 5: Query proposal. We ceased the use of high-resolution feature map sampling in the query selector and the use of center point coordinates for query sampling. After ceasing the use of center point coordinates for query sampling, we employed query sampling based on the pixel position of the center point in the feature map.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 3 Framework for Security Monitoring and Multi-Domain Trust Management
### Trust Level Agreement between Slices and Domains  
SDNs in different domains provide different security levels. For instance, operators may have different security practices and security may be customized for applications. The users of 5G slices can be organizations, service providers, end-users, or cooperating operators. They all need to be continuously and strictly aware of how trustworthy the network services they get are, and whether the network fulfils their security requirements.  
Security information shared between domains must be at least near-real-time and thus support dynamic scenarios where the client, which is, for example, an end-to-end multi-domain orchestrator, may at any time change to another operator or network. However, disclosing all security-related information is not often feasible due to the volume and complexity of information as well as the sensitivity of the information for operators--it may reveal company secrets, privacy critical data, or weaknesses that could be utilized by attackers.  
The Trust Level Agreement (TLA) mechanism enables sharing of near-real time measurements that can be used in end-to-end connectivity management and orchestration. The TLA mechanism enables 5G users to specify and track the required level of security for the network. Users can specify arbitrary trust models and requirements that must be fulfilled before they accept the network. The TLA is implemented by the Trust Metric Enabler. It ensures that trust requirements are continuously fulfilled and informs users of critical changes in the monitored trust levels. The enabler may also be used to hide domain-specific complexities and details that may be sensitive from a privacy or operator perspective. There may be explicit controls on what information is disclosed. Also, as the trust metrics are calculated per application-specific slice, the enabler inherently ensures that information on other slices and applications does not leak.  
The messaging diagram, in Figure 4, illustrates four simplified phases of TLA communication. In the first phase, the Trust Metric Enabler monitors network for security relevant events and measurements. In the second phase, a client, wishing to use a 5G slice, requests the enabler to resolve whether the given slice fulfils the client's trust demands. In the third phase, the client has found a network slice it trusts. The service provider and the client agree the use of it and establish a secure tunnel for communication. The fourth phase illustrates how the Trust Metric Enabler continuously monitors the network and informs the client of critical status changes.
# Birds Detection in Natural Scenes Based on Improved Faster RCNN
## 3 Materials and Methods
### Soft Non-Maximum Suppression  
In the process of object detection, the Non-Maximum Suppression algorithm [32] generates a series of detection box sets \(B\) and the corresponding fraction \(S\) in the detected image. When the detection box \(M\) with the maximum score is selected, \(M\) is removed from the set \(B\) and is incorporated into the final detection result set \(D\). Meanwhile, any detection box in the set \(B\) whose overlap rate is greater than the threshold value \(N_{t}\) is also removed. The NMS algorithm is calculated as shown in Formula (2):  
\[s_{i}=\left\{\begin{array}{l}s_{i},I_{ou}(M,b_{i})<N_{t}\\ 0,I_{ou}(M,b_{i})\geq N_{t}\end{array}\right. \tag{2}\]  
where \(I_{ou}\) represents the overlap rate. However, this method has an obvious problem: if there is a high degree of overlap among birds in the same area of the image, and some bird detection box fractions are set to 0, the detection of this bird will fail and the average detection rate (mAP) of the algorithm will be reduced, as shown in Figure 3.  
In Figure 3, the score value of the left detection box is 0.77 and that of the middle detection box is 0.95. The threshold value for setting the overlap rate of birds is 0.5, while the overlap rate of the two detection boxes in the figure is 0.54. According to the NMS algorithm, the detection box with a low score that the overlap rate exceeds the threshold will be removed, resulting in the failure to detect the birds in the middle. Aiming at the existing problems of NMS, a soft-NMS algorithm was used in this paper. Set an attenuation function for adjacent check boxes based on the amount of overlap rather than setting their fraction to zero completely. Simply put, if a check box overlaps most of \(M\), it will have a low score. However, if there is only a small overlap between the detection box and \(M\), its original detection score will not be greatly affected. In addition, Soft-NMS does not require additional training and is easy to implement, so it is easy to be integrated into the model. The Soft-NMS score attenuation function is shown in Formula (3):  
\[s_{i}=\left\{\begin{array}{l}s_{i},I_{ou}(M,b_{i})<N_{t}\\ s_{i}(1-I_{ou}(M,b_{i})),I_{ou}(M,b_{i})\geq N_{t}\end{array}\right. \tag{3}\]
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 3 Methodology
### Loss Function  
It is well known that choosing an appropriate loss function plays a key role in training a DNN. In this subsection, we devise three mean squared error (MSE) losses to formulate a compound loss function aimed at addressing the corresponding optimization problem. The MSE metric computes the squared difference between pixel values of images, thereby promoting smoothness and contrast in the resulting image output. Notably, in the augmentation of the existing MSE loss function within the N2N model, we introduced two additional loss items for image content and noise, thus crafting a more potent composite loss function. The standard MSE loss definition is as follows:  
\[MSE(I_{1},I_{2})=\frac{1}{M\times N}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}\big{[}I_{ 1}(i,j)-I_{2}(i,j)\big{]}^{2} \tag{7}\]  
where \(I_{1}(i,j),I_{2}(i,j)\) denote the input images, and \(M\) and \(N\) represent the size of input images. In this work, there are three loss functions: \(Loss_{N2N}\), \(Loss_{content}\), \(Loss_{noise}\). When we feed the noisy image \(I_{noisy1}\) into the network, we can obtain the clean image \(I_{content1}\) and the noise \(I_{noise1}\), and, when we feed the noisy image \(I_{noisy2}\) into the network, we can obtain the clean image \(I_{content2}\) and the noise \(I_{noise2}\).  
N2N loss: The N2N loss, which is similar to the loss function in the original N2N, evaluates the difference between the generated content \(I_{content1}\) and another noisy picture \(I_{noisy2}\). The expression is as follows:  
\[\begin{split} Loss_{N2N}&=MSE\big{(}f_{content} \big{(}I_{noisy1}\big{)},I_{noisy2}\big{)}\\ &+MSE\big{(}f_{content}\big{(}I_{noisy2}\big{)},I_{noisy1}\big{)} \end{split} \tag{8}\]  
Content loss: When our network takes a denoised image \(I_{content1}\) as an input, we expect the model to predict the same image as output without any noise terms. Similarly, when \(I_{content2}\) is the input, we expect the output image to be free of noise. Therefore, the loss function that constrains the content branch of the image decomposition is defined as follows:  
\[\begin{split} Loss_{content}&=MSE\big{(}f_{content }(I_{content1}),I_{content1}\big{)}\\ &+MSE(f_{content}(I_{content2}),I_{content2})\end{split} \tag{9}\]  
Noise loss: When we take noise \(I_{noise1}\) as an input, we expect that the output is noise without content. Similarly, when \(I_{noise2}\) is the input, we expect the output image to be free of content. Therefore, the loss function that constrains the noise branch of the image decomposition is defined as follows:  
\[\begin{split} Loss_{noise}&=MSE\big{(}f_{noise }(I_{noise1}),I_{noise1}\big{)}\\ &+MSE(f_{noise}(I_{noise2}),I_{noise2})\end{split} \tag{10}\]  
In contrast to the loss functions employed by the N2N denoising model, the enhanced loss function utilized in this paper is capable of constraining the network to generate ideal content and noise components, thereby achieving complete separation between image content and noise.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
###### Abstract
Turboelectric systems have been proposed for hybrid vertical take-off and landing (VTOL) UAM systems [9; 10]. These generators, when augmented with batteries in a hybrid configuration, can be used to power electric lift rotors that utilize the high-power density of batteries during take-off. There are a variety of engines that can be utilized for hybrid vehicles, but gas turbine engines generally offer a superior power-to-weight ratio for the aircraft scale typical of UAM systems. There has been research focused on modeling hybrid VTOL UAM systems [11]; however, there is a distinct lack of published work on fabrication and flight testing such systems to address practical issues for safety and effectiveness.
## 5 Conclusions  
In this study, soil surface images were analyzed to predict two soil properties of WC and BD using multiple regression analysis and DNN regression models. In situ images of the soil surface were obtained and various features were extracted from them, including color, texture, and shape features. A total of 18 soil surface image features were extracted, and their relationships with soil properties were investigated. These features were used as input variables for both multiple regression analysis and DNN regression models.  
The results show that WC had a stronger relationship with image-based features than BD. After removing the features that could affect multicollinearity, multiple regression analysis was performed with seven selected features as independent variables. The results showed that the multiple regression analysis models provided reasonable performance for WC and BD prediction.  
Furthermore, DNN regression models were developed to predict soil properties. The results showed that the DNN models outperformed the multiple regression analysis models in terms of accuracy for both WC and BD predictions. The accuracy of the DNN models slightly decreased when using the combined dataset compared to using individual datasets. However, the DNN models still provided more accurate predictions than the multiple regression analysis models, demonstrating their potential usefulness in soil analysis and prediction.  
In conclusion, this study demonstrated that soil surface images can be used to predict WC and BD, and the accuracy of the predictions can be improved by using DNN regression models. The results suggest that soil surface imaging combined with DNN models has great potential in soil analysis and prediction, which can be beneficial for precision agriculture and environmental monitoring. Further studies can be conducted to investigate the application of this method in various soil types and environments.  
Conceptualization, D.K. and Y.S.; methodology, D.K.; software, D.K.; validation, T.K. and J.J.; formal analysis, T.K.; investigation, T.K.; resources, J.J.; data curation, J.J.; writing--original draft preparation, D.K.; writing--review and editing, Y.S.; visualization, D.K.; supervision, Y.S.; project administration, Y.S.; funding acquisition, D.K. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A03045271) and Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Agricultural Energy Self-Sufficient Industrial Model Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (321007-02).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Transport Study of Cr(VI) through Aliquat 336/PolyHIPE Membrane
#### 3.4.2 pH Effect on the Transport of Cr(VI) through the Aliquat 336/PolyHIPE Membrane
The low initial flux at pH 2 may be due to that the formation of chromate aggregate complex in the membrane phase. The complexed Cr(VI) may form condensation products of chromate aggregate as (R\({}_{3}\)CH\({}_{3}\)N\({}^{+}\))\({}_{2}\)Cr\({}_{3}\)O\({}_{10}\)\({}^{2-}\) and/or (R\({}_{3}\)CH\({}_{3}\)N\({}^{+}\))\({}_{2}\)Cr\({}_{4}\)O\({}_{13}\)\({}^{2-}\)[38]. Then, the \(J/\!\!^{0}\) increases with increasing pH, and levels off at pH 4. Since within the pH range of this study, the Cr(VI) species distribution is almost the same (as determined by Mineql +4.6), the extraction step in this study can be expressed as following reactions:  
\[\mathrm{R_{3}CH_{3}N^{+}+}\mathrm{HCrO_{4}}^{-}\rightarrow\left(\mathrm{R_{3 }CH_{3}N^{+}}\right)\mathrm{HCrO_{4}}^{-} \tag{10}\]  
\[2\left(\mathrm{R_{3}CH_{3}N^{+}}\right)\mathrm{HCrO_{4}}^{-}\rightarrow\left( \mathrm{R_{3}CH_{3}N^{+}}\right)_{2}\mathrm{Cr_{2}O}\gamma^{2-} \tag{11}\]  
\[2\left(\mathrm{R_{3}CH_{3}N^{+}}\right)\mathrm{+}\mathrm{Cr_{2}O}\gamma^{2-} \rightarrow\left(\mathrm{R_{3}CH_{3}N^{+}}\right)_{2}\mathrm{Cr_{2}O}\gamma^{2-} \tag{12}\]  
\[\left(\mathrm{R_{3}CH_{3}N^{+}}\right)_{2}\mathrm{Cr_{2}O}\gamma^{2-}\mathrm{ +}\mathrm{HCrO_{4}}^{-}\mathrm{+}\mathrm{H}^{+}\leftrightarrow\left(\mathrm{ R_{3}CH_{3}N^{+}}\right)_{2}\mathrm{Cr_{3}O_{10}}\mathrm{{}^{2-}+}\mathrm{H_{2}O} \tag{13}\]  
\[\left(\mathrm{R_{3}CH_{3}N^{+}}\right)_{2}\mathrm{Cr_{2}O}\gamma^{2-}\mathrm{ +}\mathrm{2 HCrO_{4}}^{-}\mathrm{+}\mathrm{2H}^{+}\leftrightarrow\left(\mathrm{ R_{3}CH_{3}N^{+}}\right)_{2}\mathrm{Cr_{4}O_{13}}\mathrm{{}^{2-}+}\mathrm{2H_{2}O} \tag{14}\]  
Based on the initial flux, the transport efficiency of Cr(VI) through Aliquat 336/PolyHIPE membrane is comparable to other studies (Table 3). It is also worth pointing out that even for trace concentrations of Cr(VI), the residual amount of Cr(VI) is less than 0.5 ppm after 10 h of separation, meeting the environmental regulation standard of Taiwan. Figure 6: The variations of Cr(VI) concentrations with respect to time. \(R_{f}\) (\(\blacklozenge\)), \(R_{m}\) (\(\blacktriangle\)), and \(R_{s}\) (\(\bullet\)). Feeding phase: [Cr(VI)] = 53 mg/L; stripping phase: NaOH solution at pH 12; stirring rate: 450 rpm.
## 1 Introduction
### Research Objectives and Paper Structure  
This paper aims to systematically review the research progress on non-conventional lane design and control coordination optimization at urban road intersections, analyze existing research achievements and deficiencies, and envision future development trends. To provide a comprehensive overview of the research framework, Figure 1 illustrates the systematic approach adopted in this study. The framework encompasses four primary research dimensions that form the theoretical and methodological foundation for non-conventional lane analysis. The research methodology integrates mathematical modeling, simulation analysis, field studies, and control strategies to address the complex challenges in non-conventional lane implementation. The framework progresses systematically from theory development through model construction, optimization design, and empirical validation to practical application, ultimately contributing to spatiotemporal reconstruction theory, integrated optimization frameworks, and innovative applications in connected and automated vehicle environments.  
The paper structure is arranged as follows: Section 2 introduces the concepts, forms, and classification of non-conventional lanes, systematically sorting out the basic characteristics and development history of various forms, including contraflow left-turn lanes, outside left-turn lanes, exit lanes for left-turn, variable lanes, and tandem intersections. Section 3 analyzes the operational mechanisms and traffic flow characteristics of non-conventional lanes, presenting in-depth explorations at three levels: microscopic driving behavior, mesoscopic traffic flow characteristics, and macroscopic operational patterns. Section 4 evaluates the applicability and effects of non-conventional lanes, including analysis of applicable conditions, traffic efficiency evaluation, traffic safety evaluation, environmental and social benefit evaluation, and the construction of a comprehensive evaluation system. Section 5 discusses the optimization design and control methods of non-conventional lanes, covering geometric design optimization, signal control optimization, and spatiotemporal coordination optimization methods. Section 6 summarizes typical application cases and empirical studies, analyzing the current status of non-conventional lane applications at intersections, implementation effect assessment, and lessons learned. Section 7 envisions future development trends and research directions, including technological development trends, theoretical research directions, and application prospect outlook. Section 8 is the conclusion, summarizing research findings and proposing future research recommendations.  
Figure 1: Research framework.
## 4 Experiments and Results
### Parametric Analysis
#### 4.3.2 The Effect of Atom Number  
A class-specific dictionary is composed of multiple atoms. In theory, a large number of atoms will make the dictionary have a better representation ability, but it would significantly increase the computational complexity. Here, we set the number of atoms to be 30, 40, 50, 60, 70 and 80, and train these class-specific dictionaries, respectively. On the basis of these dictionaries, the recognition accuracy under Scenario_A is calculated, which is shown in Figure 7. In addition, in this experiment, the iteration is 45, and the weight factors \(\lambda_{1}\) and \(\lambda_{2}\) are set to 0.1 and 0.01, respectively.  
It can be found that, when the number of atoms is 40, 55, 70, and 85, the recognition accuracies of several models in seven tasks are basically the same. This leads to two conclusions. First, when the number of atoms is 40, it can meet the requirements of the experiments. Second, our proposed method is robust, and it is insensitive to the number of atoms. Based on the above analysis, we set the number of atoms to 40 in the several experiments above.  
Figure 5: The loss curve during dictionary learning (BMP2).  
Figure 6: The recognition accuracy under different iterations (Scenario_A).
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 2 Literature Review
### Main Methods for Detecting Workload
#### 2.2.2 Task Performance Measurement of Workload  
Task performance measurement assesses workload based on the performance of subjects in completing predetermined tasks. For instance, [PERSON] and others [44] have evaluated psychological effort and cognitive load levels through subjects' reading and vocabulary scores; and [PERSON] and others [45] found that the accuracy and response times in tasks effectively reflect workload levels. However, current task performance measurements are mainly used for reading or memory tasks and are not very suitable for assessing pilots' workload during flight tasks. Furthermore, according to the theory of workload-task performance relationship proposed by [PERSON] [46], there is no absolute negative correlation between them. Similarly, this type of performance evaluation is also a post-evaluation, which cannot detect real-time workload during the process.