esa-sceva/satcom-chunk-collection huggingface dataset:
# Predator-Prey Model Based Asymmetry Resource Allocation in Satellite-Terrestrial Network
## 6 Conclusions  
This paper proposes a predator-prey-based resource allocation model in satellite-terrestrial networks, where the satellites are divided into two groups. One kind of satellite is the prey-satellite, which can obtain mobile services from users and can offload the mobile services to the other kind. The other kind of satellite is the predator-satellite. They can obtain mobile services from the prey-satellites and serve mobile users. Based on the simulations, we find that our model can achieve load balancing between prey-satellites and the predator satellites. We note that all the simulations of the paper are the numerical results under the associated parameter settings. Hence, we did not carry out the comparison with other results. In future works, we will design a simulator or a platform written in Java to evaluate our proposed schemes.  
Methodology, M.L.; software, Q.W.; formal analysis, Z.L.; investigation, Z.L.; resources, M.L.; writing--original draft preparation, M.L.; writing--review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data is contained within the article.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 2 Related Work
### Capsules Network  
Although convolutional neural networks (CNN) have achieved substantial successes in many important applications, there are some constraints, i.e., efficiency [20]. By treating the information at the neuron level as vectors, Capsules Networks have shown excellent performance and are applied in a number of areas, including image classification [54; 55], target recognition [56; 57], object segmentation [58; 59], natural language understanding [60; 61], industrial control [62], and more [63]. For instance, researchers propose an associated spatio-temporal Capsules Network for gait recognition [64], in which, particularly, a relationship layer is built to measure the relationship between lower level features and its corresponding higher level features, and it achieves great performance on benchmark datasets. Besides applications of Capsules Networks in various challenging areas, researchers also explore different routing algorithms to accelerate the training process [65]. Dynamic routing by agreement significantly slows down the training process in the Capsules Network. Therefore, various replacement algorithms are investigated, for instance, grouping equivalent routing, K-Means routing, etc..  
There are reported applications of Capsules Networks in the area of urban traffic prediction [66]. A Capsules Network is bundled with a Nested Long Short Term Memory (NLSTM) Network for transportation network forecasting [67], where the Capsules Network is trained to extract spatial features of traffic networks, and the NLSTM is leveraged to evaluate temporal dependencies in traffic sequence data. The experimental results show that the combination of CapsNet and NLSTM outperforms the CNN and NLSTM. The Capsules Network was adopted to predict traffic speed in complex transportation networks [68]. Spatial-temporal traffic sequence data are converted into matrices and then fed into the Capsules Network. However, to the best of our knowledge, there are not any reported efforts that apply Capsules Networks to detect anomaly behaviors on the road. We hope this work will attract more attention and inspire more discussions in the application of Capsules Networks in the smart transportation area, specifically the safety surveillance.
## 2 Materials and Methods
### Gilder Deployment and Data Collection  
The three vehicles were deployed along LoVe from March to September 2018, a period of high biological activity (see below). The vehicles were piloted by Maritime Robotics (Wave Glider), Offshore Sensing (Sailbuoy), and Cyprus SubSea Consulting and Services (Seaglider(r)). Initial deployment of the vehicles occurred from the R/V Hakon Mosby, offshore Bodo, Norway. The vehicles were retrieved twice during the survey period to repair a technical problem with the memory storage, and at the same time, offload the acoustic data. A well-known challenge with autonomous acoustic surveys is the identification of the acoustic targets based only their acoustic signal. To identify targets, additional sampling might be performed by trawl, net, or occasionally optical sampling [15; 37; 38]. Zooplankton composition, abundance, and vertical distribution in an area overlapping with the track of the Sailbuoy in June 2018 (6 stations west of Vesteralen, 10 stations north on Tromsoflaket) was assessed during an independent research cruise aboard RV Helmer Hanssen (see [[https://lofoten-research.no/](https://lofoten-research.no/)]([https://lofoten-research.no/](https://lofoten-research.no/)) accessed on 7 October 2021) using a Hydrobios Multinet (mesh size 180 um, opening area 0.25 m\({}^{2}\)), a Tucker Trawl (1500 um, 1 m\({}^{2}\)), a Video Plankton Recorder (Seascan Inc.), and a Laser Optical Plankton Counter (ODIM-Brooke Ocean Rolls Royce Canada Ltd.) following the methods described in [39].
## 3 Results and Discussions
### Dispersive Phantom
Note that the axial peak-pressure deviation is quite small for the fundamental components, but it gradually becomes significant when more harmonic components appear. Thus, the overall peak-pressure deviation observed in Figure 4a is mainly caused by higher-order harmonic components, indicating that the significance of the theoretical modification relies on high nonlinearity, such as in HIFU. This phenomenon could be addressed with the results shown in Figure 3, where higher-order harmonic waves that occupy higher frequency bands exhibit more notable sound velocity/attenuation dispersions, thus play a more dominant role in the modification of the KZK model. Meanwhile, it can be seen from Figure 4 that the modified equation has larger variation than the KZK equation. The effect of the mKZK equation on the simulation results can be thus clarified as providing more precise prediction for experimental results. The results in Figure 4 give persuasive proof that theoretical modifications made previously are necessary and valid. The beam narrowing effect caused by data normalization still exists. However, in actual treatment more attention is paid to the location of the focus point, because the location determines the heat distribution area, which significantly alters the biological properties of the treatment area. Among existing studies, most researchers have focused on the thermos-lensing effect [23] and bubble formation induced focal region distortion [25; 26], while some also mentioned the acoustic radiation force induced tissue displacement [27]. With the results presenting the dispersion of sound  
Figure 4: The normalized acoustic pressure distributions along the axis of the HIFU transducer, with the phantom placed at 37.7 mm away from the transducer. (**a**) the overall pressure; (**b**) the fundamental component; (**c**) the second harmonic; (**d**) the third harmonic. velocity and attenuation in tissues, the deviation of focal spots can be well explained in combination with the strong nonlinearity nature of HIFU.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 4 Optimization Algorithms  
Distribution network reconfiguration is a nonlinear integer programming problem, which presents significant challenges in terms of solution complexity and computational cost. Intelligent optimization algorithms are commonly employed to address these challenges, owing to their superior performance in solving optimality-based problems.  
The Equilibrium Optimizer (EO) is a novel intelligent optimization algorithm introduced by [PERSON] et al. [19] in 2020. It is inspired by the control volume of strong hybrid dynamic mass balance in physics and has been successfully applied across various engineering domains, including power systems. EO is particularly suitable for solving the distribution network reconfiguration problem, a complex optimization challenge that requires finding the optimal solution while satisfying multiple constraints. Building on this, the present study proposes an improved version of the Balanced Optimizer algorithm tailored for the distribution network reconfiguration problem.
# The Future for End-Stage Kidney Disease Treatment: Implantable Bioartificial Kidney Challenge
## 1 Introduction
### Bioreactors for Artificial Kidneys  
Bioreactors may in the future provide metabolic support to the filtration and absorption activities determined by current dialysis filters. Combined pragmatic water filtration systems and ultrafiltrate and blood reprocessing devices are the basis of more complex devices directed at blood purification (Figure 1). The use of cultured proximal cells on artificial membranes in the implantable Bioartificial Kidney (iBAK) aims at mimicking proximal tubule function. However, cell source availability, distribution, storage, and the following reconstruction represent a challenge. The chance to design an experimental bioartificial renal epithelial cell system in niobium-coated carbon disks covered with renal epithelial cells from adult progenitor kidney cells discloses the possibility of cryopreserve and cryostorage this device so that it can be carried and thawed at the end-use location [36]. The combination of immortalized proximal tubule epithelial cells on functionalized hollow fibers with conventional hemodialysis filters makes this technology able to mimic glomerular filtration, tubular secretion, and reabsorption, thus allowing the most \"physiologic\" and complete replacement of kidney functions [37; 38]. The availability of such technology has enabled the realization of an animal iBAK prototype consisting of an SNM hemofilter with sub-10 nm wide slit pores in series with a porcine renal cell (LL-CPK1) bioreactor unit cultured on as SNM.  
Currently, the only device with relevant clinical results for the future development of an iBAK appears to be the WAK designed by [PERSON] [16; 39]. The WAK has been tested only in three small clinical trials so far in two different hemodialysis duration regimens, shorter (up to eight hours) and longer (24 h), respectively. Ultrafiltration and clearances of urea, creatinine, and phosphorus have been revealed to be effective. The major criticalities of this system concern the formation of bubbles, coagulation of the filter and lines, and the need for continuous treatment in order to achieve an effective and clinically relevant middle molecule clearance in a diffusive modality. The combination of diffusion and convection can increase purification efficiency, but in wearable and implanted devices, it is limited by the low volumes of dialysate and the inability to regenerate ultrafiltrate. This problem may be partially overcome by the implementation of a \"push-pull\" pump, a process that produces an alternating transmembrane pressure with a small volume of dialysate back and forth across the dialyser, thus generating a convective force that improves middle molecule clearance. The integration of this system with microc halves and standard silicon and microelectronics technology would allow the opening and closing of fluidic channels and fluid pumping, rendering it available for wearable devices [40].
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 3 Classical Networks Based on Deep Learning
### PSPNet  
PSPNet [46], a common semantic segmentation model, uses dilated convolutions to expand the receptive field and retain abundant spatial position information, as shown in Figure 5. The algorithm combines multiscale features by using an image pyramid and its variants to improve segmentation performance. This method is effectively applied to complex scene analysis, in which global pyramid pooling features provide additional contextual information and a deep supervision optimization strategy based on ResNet-FCN is introduced. The algorithm performs better than common semantic segmentation models such as FCN, SegNet, and DeepLab V1.  
Figure 4: SegNet network structure [44].  
However, the PSPNet algorithm still has shortcomings. First, the dilated convolution method used in this algorithm requires a large amount of computational resources, which may lead to increased training and inference time. Second, the PSPNet model is relatively complex and requires large amounts of computational resources and storage space. Lastly, the algorithm is sensitive to the size and scale of the input image, and preprocessing and scaling are required for segmentation. Therefore, the PSPNet algorithm needs to be improved to address these issues.  
The improvement directions are as follows. First, a lightweight network structure can be used to reduce the consumption of computational resources. Second, new receptive field-expansion methods can be explored. Lastly, the changes in the size and scale of the input image need to be handled properly to improve the stability and applicability of the model.
## 2 Background
### Sixth-Generation Requirements: Energy Efficiency, Latency, and Trustworthiness  
Next-generation 6G networks are expected to support unprecedented scale, reliability, and adaptability, making performance and trust critical pillars of their design [29]. In particular,  
* **Energy Efficiency:** With the increase in edge devices and AI workloads, energy-saving mechanisms such as sleep-mode scheduling for Distributed Units (DUs) and lightweight inference are essential to lower operational costs and environmental impact [30, 31].
* **Latency:** Applications like telesurgery, autonomous driving, and haptic communications require latency in the sub-millisecond range. Achieving this calls for decentralized, real-time optimization strategies, often relying on edge inference using ML modelsite [32, 33].
* **Trustworthiness:** As networks evolve toward greater autonomy and openness, ensuring that every decision--especially those made by AI--can be verified and explained becomes critical to maintain user confidence and regulatory compliance [34].  
Despite existing progress, meeting these stringent requirements in tandem remains a challenge. For example, securing low-latency FL in a decentralized, multi-vendor RAN is still an open research question. Table 2 outlines key performance requirements and benchmarks for 6G networks, including energy efficiency, latency, and trustworthiness targets.  
In light of these demands and challenges, it becomes evident that a unified, intelligent, and secure architecture--one that integrates Zero Trust and FL into O-RAN--is both necessary and timely. This motivates the research direction pursued in this work.
## 2 Materials and Methods
### Method Comparison and Evaluation  
We expect our CGAN to learn the representation between the source (L8) and the target set (S2) to predict S2-like images spatially and spectrally close to the original S2. Therefore, we evaluated the translations by CGAN against the original S2 spectral bands. We evaluated the performances of our CGAN both quantitatively and qualitatively. We used metrics that are widely used in the field of remote sensing to quantitatively evaluate satellite images, including Erreur Relative Globale Adimensionnelle de Synthese (ERGAS; [50]), spectral angle mapper (SAM; [51]), spatial correlation coefficient (SCC; [52]), peak signal-to-noise ratio (PSNR [53]), root mean squared error (RMSE), and Universal Quality Image Index (UQI; [54]). ERGAS and SAM are useful for assessing spectral differences, whereas SCC and PSNR evaluate spatial reconstruction. RMSE and UQI provide a more holistic assessment of the quality of translations. We visually observed the differences between the predicted and the target images for qualitative evaluation. Table 3 describes each metric and its ideal score. In addition to the results from the architectures, we compared the results with the original Landsat 8 images as a baseline to measure the actual improvements by these methods.
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 2 Problem Definition
In summary, the underlying target tracking is a POMDP and involves an \(N\)-step ahead process of sensor trajectory planning, which computes the optimal action sequence \(\mathbf{u}_{k+1:N}^{*}=\{\mathbf{u}_{k+1}^{*},\cdots,\mathbf{u}_{k+N}^{*}\}\) such that the tracking error is minimised at the future time \(t_{k+N}\). A generic recursive process of sensor trajectory scheduling and target state estimation at discrete-time \(k\geq 1\) involves the steps listed in Algorithm 1:  
```
1:Inputs: The posterior at \(k\), \(p(\mathbf{x}_{k}|\mathbf{\beta}_{1:k})\); the sensor locations \((x_{k}^{s_{i}},y_{k}^{s_{i}})\) and course corrections \(u_{i,k},\ i=1,2\). 2:Propose the set of admissible sensor actions \(\mathcal{U}_{k+1:N}\)
3:Perform scheduling based on an optimality criterion and obtain the optimal sequence of future sensor actions \(\mathbf{u}_{k+1:N}^{*}\in\mathcal{U}_{k+1:N}\) for a future time period \((t_{k+1},t_{N})\). 4:Steer each sensor \(i=1,2\) to the computed location \((x_{k+1}^{s_{i}},y_{k+1}^{s_{i}})\) at speed \(v_{i}\) and course correction \(u_{i,k}\). 5:Take (bearings-only) measurements at time \(t_{k+1}\) and then compute the posterior PDF of target \(p(\mathbf{x}_{k+1}|\mathbf{\beta}_{1:k+1})\) via a suitable tracker. 6:Outputs:\(p(\mathbf{x}_{k+1}|\mathbf{\beta}_{1:k+1})\); \((x_{k+1}^{s_{i}},y_{k+1}^{s_{i}})\) and \(u_{i,k+1},\ i=1,2\). ```  
**Algorithm 1** Sensor scheduling steps at discrete-time \(k\)
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 1 Introduction
(2) It avoids the waste of satellite resources and energy, and improves the cost-efficiency ratio of the LEO megaconstellation. (3) The space cloud computing infrastructure has been built with independent operation and elastic destruction resistance, which is not limited by natural disasters and geographical environment. (4) Provides edge computing services seamlessly and anywhere in the world. Figure 1: The satellite–terrestrial integrated Internet of Things scenario. However, mega LEO constellation edge cloud network is a resource-constrained system, in which energy supply and computing capacity are much lower than the ground edge computing cloud. In order to improve the user capacity that the entire edge cloud can serve, the utilization of system resources needs to be optimized. On the one hand, the energy consumption of the system when the user offloads the task needs to be reduced; on the other hand, the computing resource utilization of the satellite in the LEO edge cloud needs to be balanced to avoid local congestion and overload. To the best of our knowledge, there are no studies that provide a task offloading strategy for edge computing cloud across the entire LEO constellation network. In this work, we propose a joint optimization strategy of energy consumption and computing load for the LEO constellation edge cloud processing offloading task of user. Our main contributions are summarized as follows. (1) The system model of LEO constellation edge cloud network is established. The model dispatches the computing resources of all satellites in the constellation to perform computing offloading for user tasks. Unlike other satellite edge computing models, the satellite terminal only considers offloading tasks to directly connected or neighbor satellites. (2) The influence of the network path on computational offloading performance is considered, and the energy consumption caused by the inter-satellite multi-hop path is taken as part of the system cost. However, the influence of the path on computational offloading is often ignored in the existing literature. (3) A cost model for combined optimization of energy consumption and computational load is proposed. Under the condition of limited computing resources in the low-orbit constellation, the system performance of the entire constellation edge cloud network is optimized by minimizing the total energy consumption of all users and balancing the computing load of all satellites in the constellation. The numerical simulation results show that the proposed strategy is superior to the benchmark strategy. The remainder of this paper is organized as follows. In Section 2, an overview of the existing work is given. Section 3 describes the system model of the LEO constellation edge cloud network. The joint optimization problem formulation and the operation mechanism are presented in Section 4. Section 5 shows the simulation results with the proposed strategy. Finally, this work is concluded in Section 6.
## 3 Case Study
### Collection of Processing Data Based on CM
#### 3.2.2 State Membership Degree Calculation  
The assessment period was divided into 60 discrete slices, with each lasting 1 min. Within each of these time slices, the state membership degrees of the datasets were fed into the DBN as prior probabilities. Initially, the cloud characteristic parameters of the data were extracted using Equation (5), and cloud drops were generated via the FCT for comparisons with the evaluation standard clouds of node states. Considering the cloud conversion in the first minute as an example, Figure 9 shows a comparison of the cloud distribution between the root node data and evaluation standards. The membership degree calculation was based on Equations (4) and (7). Figure 10 shows the membership changes in the monitoring data of nodes X2 and X4 relative to different states within 60 min, while the membership degrees of node X1 and X3 data for state S0 are always one. Moreover, because expert scoring data are not updated within 60 min, the state membership degrees of the nodes from X5 to X8 remain unchanged. Table 6 lists the cloud characteristic parameters and the state membership degrees of the expert scoring data.  
Figure 9: Comparison of cloud distribution between the root node data and the evaluation standards in the first minute.