esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 6 Conclusions  
LEO satellite networks have been regarded as one of the most promising methods to provide global broadband backhaul in the 6G era. In LEO satellite networks, the frequent feeder link handover invokes unacceptable communication interruptions and affects the backhaul quality. To overcome this challenge, we propose a novel feeder link handover strategy for backhaul in LEO satellite networks to improve the backhaul quality. We formulate the feeder link handover problem to maximize the available backhaul capacity while optimizing the handover frequency. To solve the optimization problem, we design an available backhaul capacity ratio to jointly consider feeder link quality and inter-satellite networks in handover decisions. In addition, we introduce the service time factor and handover control factor to reduce the handover frequency. Based on these handover factors, we propose a handover utility function and a maximum backhaul capacity handover strategy to optimize the long-term backhaul capacity while avoiding frequent handovers. The simulation results have proved that our proposed strategy effectively outperforms the state-of-the-art schemes in terms of the backhaul capacity and handover frequency.  
The proposed handover strategy mainly aims to increase the upper limit of network backhaul capacity. However, advanced network routing and traffic engineering technologies are critical for implementing backhaul optimization in practical scenarios. In future works, we intend to investigate the integration of routing and feeder link handover to realize a more comprehensive backhaul optimization scheme.  
Conceptualization, J.L. and Y.Z.; methodology, Y.Z. and J.L.; validation, Y.Z. and R.Z.; investigation, J.L.; resources, Y.Z. and R.Z.; data curation, R.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, M.O.; supervision, M.O.; project administration, T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Youth Fund (grant number 62201077).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 9: The overall backhaul capacity performance.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Motivation and Novelty  
The core issues related to machine and deep learning that need to be addressed in order to fulfill the five principles of trustworthy AI are listed in Table 1.  
Solution approaches to address issues concerning TAI have been identified in Table 1; however, a unified solution approach addressing all major issues does not exist. Despite the importance of the outlined TAI principles, their major limitation, as identified in [6], concerns the fact that the principles are highly general and provide little to no guidance for how they can be transferred into practice. To address this limitation, a data-driven research framework for TAI was outlined in [6]. However, to the best knowledge of the authors, no previous study presented a unified information theoretic approach to study the privacy, interpretability, and transferability aspects of trustworthy AI in a rig  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**TAI Principle** & **Issue** & **Solution Approach** \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{I1:} & non-availability of large high-quality training data & transfer learning \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{I3:} & use’s inability to quantify model uncertainties lead to indecisiveness regarding the level of autonomy given to AI system & analytical quantification of model uncertainties \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{I6:} & bias of training data & \multirow{2}{*}{federated learning} \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{I7:} & user’s inability to understand model functionality leads to mistrust and obstruction in establishing accountability & interpretable machine and deep learning models \\ \hline \hline \end{tabular}
\end{table}
Table 1: Core issues with TAI principles and solution approaches.  
This motivated us in this study to develop a novel information theoretic approach for addressing the privacy, interpretability, and transferability aspects of trustworthy AI in a rigorous analytical manner. This study introduces a unified information theoretic approach to \"privacy-preserving interpretable and transferable learning\", as represented in Figure 1, for addressing trustworthy AI issues, which is the novelty of this study.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 3 System Model
### Network Model  
In this paper, we study how to use game theory to address the clustering issue in WSNs for energy balancing. To model the network in a real scenario, we assume sensor nodes are heterogeneous in their initial energy in the network. This assumption is very reasonable because that on the one hand, no routing protocols can achieve ideal energy balancing among nodes in the network, and on the other hand, new nodes joining and old nodes leaving also results in energy heterogeneity. We express the initial energy \(E_{i}\) for any sensor node \(i\) as follows:  
\[E_{i}=E_{b}+\alpha\cdot rand \tag{1}\]  
where \(E_{b}\) is the basic energy; \(\alpha\) is the random energy exponent and \(rand\) is a number randomly selected within the range of \((0,1)\).  
We consider the three-level architecture of a model network [30; 31], as shown in Figure 1. The BS is located at the top level, the middle level contains all CHs, and other normal sensor nodes stay at the bottom level. Sensing data of sensor nodes is transmitted from the bottom level to the middle level, and finally is forwarded to the top level.  
For the development of the following protocol, several assumptions about sensor nodes are listed as follows:  
* All sensor nodes are stationary or nearly stationary after they have been deployed into sensing field;  
Figure 1: Network model.  
* A unique ID is labeled on each sensor node that can be used to distinguish data sources;
* Each node has limited energy stored in the battery which cannot be recharged, but no energy limitations are inflicted on the BS;
* Each sensor node has the ability of changing its power level dynamically to adapt to different transmission distances.
## 5 Numerical Results and Discussion
### Complexity Analysis  
We compare the implementation complexity of various detection schemes, where \(N=1\). The structure block diagram of multiplication operation is shown in Figure 3 of [25]. Complex addition is the addition of two complex numbers. It is assumed that a comparison operation is equivalent to an addition operation. As shown in Table 3, compared with the exact LLR scheme, the simplified LLR scheme does not need CSI. Compared with the exact LLR given in (8) scheme, the simplified LLR proposed in (17) scheme does not need to calculate logarithmic function and Bessel function. As shown in Table 4, compared with the exact LLR scheme, the simplified LLR scheme does not require the exponential function. The multiplication/division operation of the exact LLR in (21) is 65.1 times the simplified LLR in (23); the addition/subtraction operation is 3 times the simplified LLR in (23); the modulus operation is 32 times the simplified LLR in (23). The conjugate operation is 16 times that of the simplified LLR in (23); the squaring operation is 80 times of the simplified LLR in (23). Obviously, compared with the exact LLR scheme, the complexity of our scheme is extremely reduced.
## 3 Control Statement
### Airflow Control Problem Formulation  
The PID tuning optimization problem for the airflow control scheme's objective is to compute the best control design parameters \(\mathbf{X}_{B}=(x_{B}(1),x_{B}(2),x_{B}(3))\in\mathbb{R}^{3}\) that represents the PID controller gains, i.e., \(\mathbf{X}=\left(K_{p},K_{i},K_{d}\right)\in\mathbb{R}_{+}^{3}\). This is achieved while minimizing the cost function. The Integral of Absolute Error (IAE) has been adapted as the cost function for this problem [23,24]:  
\[f_{IAE}\left(\mathbf{X}\right)=\int_{0}^{+\infty}\left|e\left(\mathbf{X},t\right) \right|dt, \tag{17}\]  
where \(e\left(.\right)\) is the error between the reference and the controlled variable, as detailed in Figure 6.  
The IAE cost function is minimized by considering some time-domain constraints, associated to the rise and settling times (\(t_{r}\) and \(t_{s}\)), the steady-state error \(E_{ss}\), and the overshoot \(\delta\left(\%\right)\) criteria of the closed-loop step response [23,24].  
Figure 5: Airflow control scheme strategy for OWC plant equipped with Wells turbine [24].  
Figure 6: Airflow-control strategy for Wells-turbine-based OWC using Harmony Search algorithms.  
The PID tuning problem formulation for the airflow control has been formulated as a constrained and nonlinear optimization problem with expression (18). The tuning problem can be solved by the PSO algorithm [23; 24]:  
\[\begin{cases}minimize\ f\left(\textbf{X}\right)\\ \textbf{X}=\left(K_{p},K_{i},K_{d}\right)\in\mathcal{S}\subseteq\mathbb{R}_{+} ^{3}\\ \text{subject to:}\\ g_{1}\left(\textbf{X}\right)=E_{ss}-E_{ss}^{\max}\leq 0\\ g_{2}\left(\textbf{X}\right)=t_{r}-t_{r}^{\max}\leq 0\\ g_{3}\left(\textbf{X}\right)=t_{s}-t_{s}^{\max}\leq 0\end{cases} \tag{18}\]  
where \(f\): \(\mathbb{R}^{3}\rightarrow\mathbb{R}\) represents the cost function, \(\mathcal{S}=\left\{\textbf{X}\in\mathbb{R}_{+}^{3},LB\leq\textbf{X}\leq UB\right\}\) stands for the bounded search space for the control variables, and \(g_{i}\): \(\mathbb{R}^{3}\rightarrow\mathbb{R},\ (i=1,2,3)\) represents the constraints.
# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
###### Abstract  
The detection of road damage is highly important for traffic safety and road maintenance. Conventional detection approaches frequently require significant time and expenditure, the accuracy of detection cannot be guaranteed, and they are prone to misdetection or omission problems. Therefore, this paper introduces an enhanced version of the You Only Look Once version 8 (YOLOv8) road damage detection algorithm called RDD-YOLO. First, the simple attention mechanism (SimAM) is integrated into the backbone, which successfully improves the model's focus on crucial details within the input image, enabling the model to capture features of road damage more accurately, thus enhancing the model's precision. Second, the neck structure is optimized by replacing traditional convolution modules with GhostConv. This reduces redundant information, lowers the number of parameters, and decreases computational complexity while maintaining the model's excellent performance in damage recognition. Last, the upsampling algorithm in the neck is improved by replacing the nearest interpolation with more accurate bilinear interpolation. This enhances the model's capacity to maintain visual details, providing clearer and more accurate outputs for road damage detection tasks. Experimental findings on the RDD2022 dataset show that the proposed RDD-YOLO model achieves an mAP50 and mAP50-95 of 62.5% and 36.4% on the validation set, respectively. Compared to baseline, this represents an improvement of 2.5% and 5.2%. The F1 score on the test set reaches 69.6%, a 2.8% improvement over the baseline. The proposed method can accurately locate and detect road damage, save labor and material resources, and offer guidance for the assessment and upkeep of road damage.  
YOLOv8; object detection; attention mechanism; GhostConv; road damage +
Footnote †: journal: Applied Physics Letters
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 3 Problem Formulation
### Mathematical Preliminaries  
This subsection introduces some important definitions and results required for the application of AEM.  
Considering a general nonlinear system described as  
\[\dot{x}=f(x) \tag{8}\] \[x(0)=x_{0}\in\mathbb{R}^{n}\]  
where \(f:\mathbb{R}^{n}\times\mathbb{R}^{m}\mapsto\mathbb{R}^{n}\) is a suitable right hand side.  
**Definition 1**.: _A set \(\mathcal{H}\) is said to be positively invariant for the system_  
\[\dot{x}=f(x)\]  
_if \(x(t)\in\mathcal{H}\) for every \(x(0)\in\mathcal{H}\) and for all \(t\geq 0\)._  
The class of systems which are suitable for the proposed robust observer design are a particular class of nonlinear systems known as quasi-Lipschitz dynamic models with bounded uncertainties. The formal definition of a quasi-Lipschitz function is presented as follows [24].  
**Definition 2**.: _A vector function \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{k}\) is said to be a quasi-Lipschitz function \(\mathcal{Q}(A,\eta_{0},\eta_{1})\) if there is a matrix \(A\ \in\ \mathbb{R}^{k\times n}\) and nonnegative constants \(\eta_{0}\) and \(\eta_{1}\), such that for every \(x\ \in\ \mathbb{R}^{n}\), the following inequality holds:_  
\[\big{\|}f(x)-Ax\big{\|}^{2}\leq\ \eta_{0}+\eta_{1}\big{\|}x\big{\|}^{2}. \tag{9}\]  
Hence, system (6) can be represented as follows  
\[\dot{x}=Ax+g(x)u+\phi\ \, \tag{10}\]  
where \(x\in\mathbb{R}^{n}\) is the state vector, \(A\in\mathbb{R}^{n\times n}\) is the system matrix, \(g(x)\in\mathbb{R}^{n\times m}\) is the control function, \(u\in\mathbb{R}^{m}\) is the vector of control inputs, and \(\phi=f(x)-Ax\), is an uncertain vector function assumed to satisfy quasi-Lipschitz condition, as stated in (9). To guarantee an appropriate design, the pair \((A,C)\) must be determined such that the system is observable.
## 2 Power Converter Principle
### Boost Mode Principle  
Figure 5 illustrates the synchronous boost circuit and the timing chart. When \(V_{in}\) is lower than \(V_{o}\) (\(V_{in}<V_{o}\)) throughout the operation, \(v_{gs1}\) is at the high-voltage level to turn on \(Q_{1}\), and \(v_{gs2}\) is at the low-voltage level to turn off \(Q_{2}\). Both the time intervals, \(t_{10}\)-\(t_{11}\) and \(t_{t2}\)-\(t_{t3}\), are dead times that can prevent \(Q_{3}\) and \(Q_{4}\) from concurrently turning on and causing the short-circuit. \(v_{gs3}\) is changed to the high-voltage level during the time interval, \(t_{t1}\)-\(t_{t2}\), thus, increasing \(i_{L}\); the operating circuit in the time interval, \(t_{t1}\)-\(t_{t2}\), is depicted in Figure 5b. At \(t_{t2}\), because \(i_{L}\) is greater than the current command inside the SBBC controller, \(v_{gs3}\) is changed to the low-voltage level, thus, turning off \(Q_{3}\). In the time interval, \(t_{t3}\)-\(t_{t4}\), \(v_{gs4}\) is at the high-voltage level and \(Q_{4}\) is turned on, hence, decreasing \(i_{L}\); the operating circuit during \(t_{t3}\)-\(t_{t4}\) is depicted in Figure 5c. When \(i_{L}\) is lower than the current command inside the SBBC controller at time, \(t_{t4}\), \(v_{gs4}\) is changed to the low-voltage level, thus, turning off \(Q_{4}\).
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 4 Discussion
### Analysis of Guided Filter  
Compared with other popular edge-preserving algorithms such as the bilateral filter, the guided filter has better behavior near the edges, a fast and non-approximate linear-time characteristic. Furthermore, the guided filter step, which enhances the structure information's boundaries, is the key factor in determining the quality of restoration, especially for \"Small fonts\" and \"Tiny fonts\". After the fusion of the sequences, the letters are closely arranged, and their edges are mildly shifted and blurred, resulting in the small variance of the window centered on the boundary points. If the selected regularization parameter \(\varepsilon\) is too large, the guided filter will mistake the boundaries between letters as the \"flat area\" to be smoothed, then the loss of boundary information makes it impossible to distinguish letters, whereas if the parameter is too small, then the guided filter will mistakenly regard the ghosting as the gradient edge to preserve, which may cause registration error.  
We tested different combinations of regularization parameter \(\varepsilon\) and window size \(r\), the reconstruction results are compared, as shown in Figure 6. If the \(\varepsilon\) is not selected properly, the boundaries will be deleted and blurred, as marked by the red rectangle, while the selection of \(r\) has little effect on the results. Therefore, we could find that \(\varepsilon\) plays a greater role in edge restoration than \(r\). In the case, \(\varepsilon\) and \(r\) are set to 0.022 and 4, respectively, to achieve better reconstruction result.  
Figure 5: The results after five iterations of our method. (**a**) Use the mean as the reference after guided filtering. (**b**) Use the fused image as the reference after guided filtering.
Seed endophytic bacteria can be horizontally transferred through multiple generations of plants and help to keep seed dormancy status, protect the seed from pathogen infection, promote seed germination in the soil, and improve seedling tolerance to abiotic stress [25; 26; 27; 28; 29; 30]. Leaf endophytic bacterial microbiomes have high variation associated with plant species, leaf sub-locations, and growth stages. They can suppress pathogen attachment on leaves and enhance plant growth and biomass accumulation [31; 32; 33; 34]. Flower endophytic bacteria reside in stigmas, pollens, petals, and nects to function in pollination [35]. Roots harbor far more endophytic bacterial species and abundance than any other organs. These bacteria are highly diverse in both community structure and functions [36]. Both rhizobia and nonrhizobial endophytic bacteria in root nodules are critical for legume growth and resilience [37]. Most plant endophytic bacterial studies, especially those that target peas, focus on roots and root nodules due to their legume and symbiotic nitrogen fixation nature [38; 39; 40; 41]. In contrast, little is known about endophytic bacteria in plant embryos, which may harbor bacteria transmitted from flowers and seeds and facilitate plant nutrient utilization after seed germination. The embryo bacterial microbiome also shapes the blueprint of bacterial communities in the next generation of plants. For example, the high bacterial diversity in watermelon embryos mainly includes spore-forming Firmicutes, Proteobacteria-ria, Bacteroidetes, Actinobacteria, and other minor phyla that are vertically transmittable to seedlings [42]. In addition, embryonic microbiota may be involved in plant disease resistance. For example, bacterial communities in maize embryos are associated with their susceptibility to fusarium ear rot [43]. We previously reported the community structure and associated networks of endophytic bacteria in pea roots throughout the plant life cycle [44]. Here, we collected and investigated the endophytic bacterial microbiomes in edible pea embryos using five representative cultivars and a pea sprout control. Twenty-six NFB were isolated from pea embryos. Some of the NFB isolates are also KSB or PSB. All 26 NFB showed variable levels of indole-3-acetic acid (IAA) secretion. High-throughput 16S RNA sequencing of 40 bacterial collections from pea embryos revealed similar community structures, species abundance, and diversity in most samples. Our work identified nutrient-solubilizing NFB with biofertilizer potential and established an embryo-derived endophytic bacterial pool for seed dormancy and germination research of edible peas.
## Materials and Experimental Procedure
### AE Analysis: From the Descriptor to the Classification  
The descriptor-based approach is based on the assumption that the AE signal is completely described by a set of descriptors. The signals recorded by the acquisition system constitute images of the physical phenomena (fibre rupture, matrix cracking, delamination, etc.). In the case of discrete type acoustic emission, the main parameters, called descriptors, are calculated in real time by the system or in post-processing from the digitized waveforms. Table 1 summarized the main descriptors analysed in this study. The pattern recognition approaches described in a previous paper [42] are used to distinguish several classes. So, AE data are initially described by 25 features or descriptors (Table 1). Descriptors values are then normalized in the range [\(-\)1, 1] in order to process data of all the descriptors with comparable scales. The correlation matrix of the 25 descriptors is calculated and  
Figure 2: Calibration curve with the reciprocity method. Sensibility in reception for a sensor \(\upmu\)80 on steel material for the Rayleigh waves.  
subjected to a complete link hierarchical clustering. The resulting dendrogram enabled the elimination of some redundant descriptors if they are highly correlated with others. To keep significant information about each signal, a maximum correlation of 0.75 is chosen, which corresponds to 1\(-\)R = 0.25 (R being the Pearson coefficient). Then a Principal Component Analysis (PCA) is performed in order to define new uncorrelated descriptors as linear combinations of the selected descriptors and to reduce the data set size. Unsupervised clustering with the k-means method optimized by a genetic algorithm [13] follows this procedure. The procedure is carried out 10 times for a number of clusters ranging from 2 to 10. The optimum solution is selected based on reproducibility and the values of two validation criteria (Davies-Bouldin index and Silhouette).
## 5 Simulation Results and Discussion
### Comparison Results under Varying Irradiance  
Comparative evaluation of the developed controller with the backstepping controller was made as shown in Figure 17 using similar irradiance variation as given in Figure 11. We observed that the proposed controller tracked the MPP in 0.01 s, whereas the backstepping controller attained this in 0.025 s (see the zoomed view in Figure 17). It can also be noted that the developed controller's rise time was 0.002 s, which is less than the backstepping controller. The comparison of solar array system output power is depicted in Figure 18, which portrays that the proposed AOSMC had less power loss and was 5 % more efficient than the backstepping controller.  
Figure 17: Comparison of voltage in PV system under varying irradiance.  
Figure 18: Comparison of the generated output power under varying irradiance.