esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardness of an Asymmetric 2-Player Stackelberg Network Pricing Game +
###### Abstract  
Consider a communication network represented by a directed graph \(G=(V,E)\) of \(n\) nodes and \(m\) edges. Assume that edges in \(E\) are partitioned into two sets: a set \(C\) of edges with a fixed non-negative real cost, and a set \(P\) of edges whose costs are instead priced by a _leader_. This is done with the final intent of _maximizing_ a revenue that will be returned for their use by a _follower_, whose goal in turn is to select for his communication purposes a subnetwork of \(G\)_minimizing_ a given objective function of the edge costs. In this paper, we study the natural setting in which the follower computes a _single-source shortest paths tree_ of \(G\), and then returns to the leader a payment equal to the _sum_ of the selected pricable edges. Thus, the problem can be modeled as a one-round two-player _Stackelberg Network Pricing Game_, but with the novelty that the objective functions of the two players are _asymmetric_, in that the revenue returned to the leader for any of her selected edges is not equal to the cost of such an edge in the follower's solution. As is shown, for any \(e>0\) and unless \(\mathsf{P}=\mathsf{NP}\), the leader's problem of finding an optimal pricing is not approximable within \(n^{1/2-e}\), while, if \(G\) is unweighted and the leader can only decide which of her edges enter in the solution, then the problem is not approximable within \(n^{1/3-e}\). On the positive side, we devise a _strongly_ polynomial-time \(O(n)\)-approximation algorithm, which favorably compares against the classic approach based on a _single-price_ algorithm. Finally, motivated by practical applications, we consider the special cases in which edges in \(C\) are unweighted and happen to form two popular network topologies, namely _stars_ and _chains_, and we provide a comprehensive characterization of their computational tractability.  
Keywords:communication networks; shortest paths tree; stackelberg games; network pricing games +
Footnote †: This work was partially supported by the “Fondo di Ateneo per la Ricerca 2019” grant of the University of Sassari and by the project E89C20000620005 “ALgorithmic aspects of BLOckchain TECHology”
## 3 Results and Discussion
### Antibacterial Activity
faecalis_ for the RS extract, respectively. This fact indicates the antibacterial potential of sprouted legumes and radish extracts, with very promising results supporting their use as natural antibacterial agents in the food industry. The study of the antibacterial activity of some extracts obtained from sprouts has been approached to a small extent in the literature, especially regarding certain legumes or radishes. Studies have demonstrated the antimicrobial properties of various sprouted  
Figure 7: The percentage of inhibition of the analyzed extracts, at the maximum concentration applied (100 \(\mu\)L/mL), against the 2 sensitive strains (_S. aureus_ and _E. faecalis_). PS—peak sprouts, RS—radish sprouts, BS—bean sprouts, LS—lentil sprouts. vegetables against human pathogenic bacteria. [PERSON] et al. 2014 reported that sprouted radish, kohlrabi, and red cabbage exhibited inhibitory zones against _Escherichia coli_, _Salmonella Typhimuriumium_, _Stigella flexneri_, _Proteus mirabilis_, _Klebsiella pneumoniae_, _Pseudomonas aeruginosa_, and _Staphylococcus aureus_[14]. Research on different Brassica oleracea sprouts, including red cabbage, broccoli, Galega kale, and Penca cabbage, revealed that red cabbage and broccoli extracts exhibited the strongest antibacterial activity [40]. [PERSON] et al. 2015 also found a strong correlation between this activity and the organic acid composition of the sprouts [40]. Additionally, the study on methanolic extracts of mixed leguminous sprouts (Chickpea, Green gram, and Horse gram) showed the highest inhibition zone (30 \(\pm\) 0.6 mm) against Shigella flexneri and the lowest (3 \(\pm\) 0.3 mm) against Escherichia coli [41].
###### Abstract  
Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions.  
digital twin; federated digital twin; concurrency control; priority inversion; deep reinforcement learning Article
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 4 The Proposed Algorithm
### Joint CP Tensor Decomposition
Consider (15) as an example and transform to its iterative sequence as follows:\[\mathbf{A}^{(n+1)^{\mathrm{T}}}=\mathbf{A}^{(n)^{\mathrm{T}}}-\Big{(}\Big{(} \mathbf{C}^{(n)}\odot\mathbf{B}^{(n)}\Big{)}^{\mathrm{H}}\Big{(}\mathbf{C}^{(n) }\odot\mathbf{B}^{(n)}\Big{)}+\lambda\mathbf{I}\Big{)}^{-1}\Big{(}\Big{(} \big{(}\mathbf{C}^{(n)}\odot\mathbf{B}^{(n)}\big{)}^{\mathrm{H}}\Big{(} \mathbf{C}^{(n)}\odot\mathbf{B}^{(n)}\Big{)}+\lambda\mathbf{I}\Big{)}A^{(n)^{ \mathrm{T}}}-\Big{(}\mathbf{C}^{(n)}\odot\mathbf{B}^{(n)}\Big{)}^{\mathrm{H}} \mathbf{Y}_{(1)}^{\mathrm{T}}\Big{)} \tag{18}\]  
From the above iterative sequence, one can easily get back to (15) when (18) approaches the convergence. Equations (15)-(17) are the well-known ridge regression solutions. However, there is a bias issue according to [28,29]. For example, the bias can be demonstrated by transforming the least squares solution (15) as follows,  
\[\min_{\hat{\mathbf{A}},\ \hat{\mathbf{B}},\ \hat{\mathbf{C}}} \|\mathcal{Y}-\mathcal{X}\|_{F}^{2}+\lambda\Big{(}\|\hat{ \mathbf{A}}\|_{F}^{2}+\|\hat{\mathbf{B}}\|_{F}^{2}+\|\hat{\mathbf{C}}\|_{F}^{2 }\Big{)}\]0  
When the solution converges, \(\mathbf{A}^{(n)}\rightarrow\ \hat{\mathbf{A}}\), \(\mathbf{B}^{(n)}\rightarrow\ \tilde{\mathbf{B}}\), \(\mathbf{C}^{(n)}\rightarrow\ \hat{\mathbf{C}}\), \(\mathbf{Y}_{(1)}^{\mathrm{T}}\approx\big{(}\hat{\mathbf{C}}\odot\tilde{ \mathbf{B}}\big{)}\tilde{\mathbf{A}}^{\mathrm{T}}\)(with noise in \(\mathcal{Y}\)). Substitute \(\hat{\mathbf{B}}\), \(\hat{\mathbf{C}}\), \(\mathbf{Y}_{(1)}^{\mathrm{T}}\) into the right-hand side of (19), we can get \(\big{(}\hat{\mathbf{C}}\odot\tilde{\mathbf{B}}\big{)}^{\mathrm{H}}\big{(} \hat{\mathbf{C}}\odot\tilde{\mathbf{B}}\big{)}\tilde{\mathbf{A}}^{\mathrm{T}}\). Substitute \(\hat{\mathbf{A}}\), \(\hat{\mathbf{B}}\), \(\hat{\mathbf{C}}\) into the left-hand side of (19), we can get \(\big{(}\hat{\mathbf{C}}\odot\tilde{\mathbf{B}}\big{)}^{\mathrm{H}}\big{(} \hat{\mathbf{C}}\odot\tilde{\mathbf{B}}\big{)}\tilde{\mathbf{A}}^{\mathrm{T}} +\lambda\mathbf{I}\tilde{\mathbf{A}}^{\mathrm{T}}\). If we compare two sides, it leads to (19) invalid due to common term \(\big{(}\hat{\mathbf{C}}\odot\tilde{\mathbf{B}}\big{)}^{\mathrm{H}}\big{(} \hat{\mathbf{C}}\odot\tilde{\mathbf{B}}\big{)}\tilde{\mathbf{A}}^{\mathrm{T}}\) exists in both sides. Therefore, \(\lambda\mathbf{I}\mathbf{A}^{(n+1)\mathrm{T}}\) in (19) is called the bias term. It is desirable to compensate for the effect of the bias term by introducing an additional term as the algorithm converges to the desired solution.
## 5 Discussion  
### Summary of Findings  
Based on the model analysis and simulation, the results of the study can be summarized in the following five points:  
1. Stochastic perturbation factors and initial strategy probability play a significant role in the gaming system's evolutionary situation. When the environment in which the PBSC operates is unstable, it lengthens the fluctuation interval and significantly reduces the speed of strategy evolution. Increasing the proportion of the initial carbon emission reduction group is more conducive to accelerating the system's evolution.
2. The agents' abatement benefits and costs significantly affect the choice of strategy. It is most favorable to promote the abatement strategy when the party's abatement benefit and cost are at \([E_{i}^{*},E_{i}^{\prime}]\) and \([C_{i}^{\prime},C_{i}^{*}]\), respectively. Among them, the Production and Construction Party (Y) is the most sensitive to the benefits and costs of abatement. Consequently, these factors influence Y's decision to implement abatement strategies.
3. The operational status of the PBSC is a key factor in determining participants' strategies. X at the top and Y at the bottom of the PBSC are most affected by its synergistic capabilities. Establishing an appropriate monitoring mechanism within the PBSC is conducive to increasing the parties' willingness to reduce emissions and keeping the penalty for non-compliance to less than twice the amount of the base case is conducive to improving the overall effectiveness of PBSC in reducing emissions.
4. The external environment's effect on the agents' strategies is mainly in terms of the losses caused and the help provided. Continued external pressure on Z to reduce emissions can be an effective driver for continued abatement by Z. For other agents, the external loss is within \([G_{i}^{\prime},G_{i}^{*}]\) to push them to implement abatement strategies. External incentives and aids impact the speed of evolution, most prominently on Y, followed by X. Sections 4.5.2, 4.5.6 and 4.5.7 show that, with other factors constant, equal changes in negative and positive incentives lead to different results: Negative incentives may cause subjects to abandon emission reduction, while positive incentives  
Figure 16: Evolution pathways of the four parties under different information contents. (**a**) Low information content; (**b**) High information content.  
tives only affect the system's convergence speed. External losses are more effective than incentives and aid in promoting emission reduction by agents.
5. The abatement information content is an essential factor affecting decision-making. The information content positively correlates with the agent's willingness to reduce emissions. It is optimal to keep the information content \(D\in[5,10]\) to ensure that each agent implements an abatement strategy and avoids wasting resources. The agent's ability to process information is positively related to the evolutionary speed. It has the most significant effect on X. When \(\gamma_{X}\lambda_{X}\leq 0.08\), X rejects abatement.
###### Abstract  
Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson's disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson's disease.  
activity recognition; classification schemes; non-steady-state locomotion; Parkinson's disease +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Independent System Calibration of Sentinel-1B
## 7 Radiometric Calibration  
The transformation of the image intensity values into maps of RCS or backscattering coefficient is performed by measuring the entire end-to-end SAR system against reference targets with well-known RCS, e.g., corner reflectors or transponders. The key parameter of this transformation is the absolute calibration factor which is determined during the calibration process and finally annotated in the  
Figure 21: Plot analogue to Figure 19 for averaged EW measurements. Here, the antenna model prediction has been adjusted using the offset defined by the difference between the mean modeled and mean measured EW3 gain. Hence, the beam-to-beam differences can be compared with respect to EW3 for both model and measurement.  
Figure 22: EW3-mean-removed difference between averaged measured EW data and the corresponding antenna model prediction. The distinct monotonous variation over elevation for each sub-swath signifies a potential influence of antenna mispointing.  
SLC product. For this purpose, all data have to be calibrated first in a relative radiometric sense, i.e., the complete internal calibration (drift compensation, channel imbalance, etc.) and the antenna pattern correction (shape and beam-to-beam gain offset compensation) have to be applied correctly.
## 3 Results
### Classification Results Based on Preprocessing and Modeling Methods
Given the comparable performance of SVM, RF, and XGBoost under the CWT preprocessing condition, model selection alone was insufficient to yield significant performance gains. Therefore, the subsequent analysis focused on incorporating wavelength selection strategies to further optimize classification outcomes and assist in identifying the most suitable model combination.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 4 Results and Analysis
### Experimental Results
In the CG category, YOLOv9 and YOLOv8 perform similarly, with Precision rates of 77.4% and 76.9%, respectively, ranking highest among all algorithms. However, our method leads in Recall (90.9%), with YOLOv11 following at 90.4%. YOLOv9 achieves the best overall AP (92.1%), while our method achieves 91.9%, ranking second. For CVN targets, most algorithms exceed 95% Recall and 99% AP (except YOLOv10, Faster-RCNN, and CenterNet), with minimal performance gaps due to large target sizes and distinct features. For the LST and SP categories, which have smaller sample sizes, algorithm performance varies widely. In LST, YOLOv9 achieves the highest Recall (84.3%) and AP (87.2%), demonstrating strong small-sample feature capture. Our method achieves an AP of 85.3%, surpassing the others, indicating improved small-sample recognition capabilities. For SP (smallest sample size), YOLOv9 performs worst (Precision < 50%), while YOLOv10, YOLOv11, Faster-RCNN, CenterNet and our method achieve 100% Precision but low Recall rates, indicating high-confidence threshold filtering at the cost of Recall. Our method achieves the highest Recall (71.7%) and AP (78.1%), demonstrating robust feature extraction for small-sample categories. The key to achieving higher precision with our model lies in mechanisms such as strip pooling and DLKA attention, which allow the model to extract more key features from the limited small-sample categories. Additionally, the ATFL loss function adaptively assigns greater weights to the small-sample categories, ensuring the model places more focus on these categories, which in turn leads to higher precision. Further analysis of AP metrics shows that YOLO-SWD excels in all categories, achieving the best results in three of six categories and second-best in the remaining three, with minimal gaps to the top performers. This highlights its significant precision advantages in complex near-shore scenarios, effectively addressing occlusion and visual diversity. Finally, comprehensive comparison of mAP across models shows that our YOLO-SWD method achieves the highest 83.9%, significantly outperforming the others. This conclusively demonstrates its superior comprehensive performance in occlusion-heavy and complex scenarios. YOLO-SWD's improved network architecture and optimized feature extraction capabilities enable better adaptation to dynamic changes and multi-scale targets in near-shore occlusion environments.
# Study of the Effects of Plasma Pretreatment on the Microstructure of Peanuts
###### Abstract  
In this study, cold plasma treatments are employed to modify peanuts. This study systematically investigates the effects of various plasma treatment conditions, including power, duration, and gas type, on the microstructure of peanut seed coats and embryos. Observations under a scanning electron microscope (SEM) reveal that as plasma treatment power increases from 100 W to 500 W, the etching level of peanut seed coats significantly intensifies, surface roughness deepens, and concavities become more pronounced. Additionally, micro-pores on the seed coat gradually enlarge and form cracks. Specifically, when the plasma treatment is set at 200 W for 60 s, the oxygen (O\({}_{2}\)) treatment group shows interconnected cracks on the peanut seed coat surface, with lipid particles exiting and protein particles and polymers decomposing. In contrast, the helium (He) treatment group displays clear cell structures and deep grooves, with no noticeable lipid particles exiting around surface cracks. The argon (Ar) treatment group exhibits a distinct rectangular cell structure with clear boundaries, and although surface cracks form, only a few protein particles escape from the cracks. The embryo surface structure becomes looser after plasma treatment, leading to the disintegration of lipid particles, protein particles, and polymers, affecting the fusion and migration of large and small lipid bodies within the peanut's internal structure. Increasing treatment duration intensifies the etching phenomenon, resulting in more lipid particles exiting, which indicates a positive correlation between lipid particles exiting and treatment duration. This study sheds light on the mechanisms underlying changes in peanut microstructure due to cold plasma treatment, providing scientific evidence for improving peanut quality, enhancing oil extraction efficiency, and optimizing food processing techniques.  
plasma; treatment power; treatment duration; gas type; peanut microstructure; etching; roughness +
Footnote †: journal: Applied sciences
## 1 Introduction
The \(Q\)-learning-based FEKF integrated navigation algorithm was developed, enabling real-time parameter tuning and fine-tuning the state and observation noise error parameters for this navigation system according to the operational state environment of the probe during the cruising phase. The research is different from the X-ray pulsar and the Doppler velocity dual measurement model integrated navigation given by [33] in that the federation filter composed of the primary filter and sub-filter is adopted to realize the information fusion of multiple navigation modes and guarantee the estimation performance of autonomous integrated navigation. Compared with the above research [39; 40; 41; 42], the intelligent \(Q\)-learning algorithm used to tune the parameters of the state and observation noise covariance is also improved accordingly. The reward mechanism and \(Q\)-table of the reinforcement \(Q\)-learning is designed according to the sub-filter characteristics of the federated filter, and the iterative period of the \(Q\)-learning is taken as the algorithm to evaluate the cumulative reward. The optimal state and observation noise covariance parameters are obtained by adjusting the grid size of the \(Q\)-table and the learning parameters. The proposed intelligent integrated navigation method based on QLEKF has the potential to realize high precision and autonomy. The main research work for this paper involves in-depth research on the high accuracy and feasibility of the intelligent integrated navigation for the cruise phase of the SSBE based on the X-ray pulsar/solar and target planetary Doppler velocity measurements of the QLEKF algorithm. This paper is divided into six sections. After the introduction of the first section, the orbital dynamics model and the integrated navigation measurement model based on the X-ray pulsar and two-dimensional Doppler velocity measurements of the cruise phase are modeled in Section 2, and the state and measurement equations intelligent navigation for the cruise phase are given. In Section 3, the navigation information obtained from the fusion state estimation algorithm based on QLEKF is developed with the combination of reinforcement \(Q\)-learning and the conventional Kalman filter. InSection 4, the performance of the QLFEKF-based intelligent integrated navigation using X-ray pulsar/solar and target planetary Doppler velocity measurements is simulated and analyzed. The results are compared with the EKF and the FEKF integrated navigation algorithms. The high accuracy performance of the suggested intelligent integrated navigation algorithm is confirmed with simulation and analysis. Finally, the conclusions are presented in Section 5.
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 4 Proposed Method
### Feature Extraction
#### 4.2.1 Horizontal and Vertical Straight Lines (HVSL)  
This descriptor has been designed to separate Square-Kufi from other styles. It mainly describes how frequently horizontal and vertical lines appear in a text image. Square-Kufi differs from other styles by the high number of vertical and horizontal lines as shown in (Figure 5).  
HVSL is not extracted from the input image, but rather from the edge image resulted from the preprocessing step. The final HVSL descriptor holds the following features: (a) The appearance frequency of vertical and horizontal (i.e., V/H) lines, which is the main cue used to distinguish Square-Kufi, (b) The difference ratio between the number of the pixels that constitute the texts edge and the sum of V/H lines appearance frequency.