esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Architecture Description
### Cloud Layer
Indeed, it could additionally contain commands to issue to NB-IoT Nodes (as it will be shown later on (see Section 4.2) within a particular a test scenario). In such a case, the command is prepared to be given to the relative NB-IoT Node by exploiting the MQTT protocol. Finally, regardless of the presence of commands for NB-IoT Nodes, the data within the LoRaWAN packets are stored within the database. Conversely, let us suppose that packets from NB-IoT Nodes are received by the Server. In this case, no controls are performed since only NB-IoT Nodes belonging to the network are allowed to send data to the Network Server. Therefore, the data are processed and then stored within the database. For the sake of completeness, it has to be underlined that the Network Server does not take charge of the data or information flowing whenever a peer-to-peer network is setup. This is due to the fact that, in this condition, only NB-IoT Nodes are included. They directly communicate with each other, relying on the MQTT protocol, thus forming a decentralised network; therefore, only the MQTT broker is involved. The Application Server is carried out by means of Node-RED too. Its routine is far easier than the ones of the other server. Indeed, it just extracts data from the database to makes it available to users by exploiting graphic interfaces that can be requested by Internet browsers running on PCs, tablets, smartphones, etc. Therefore, such a server acts as the network interface.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 4 Experiment  
### Experiment Environment  
To verify the effectiveness of the correlation filter tracking model based on the deep-pruned feature network proposed in this paper, the hardware and software experimental environment were designed and constructed. The hardware environment consisted of a terminal (AMD Ryzen 5 4600H), a server (X10 DRG-Q), and a GTX 1080 Ti graphics card (with a memory size of 12 GB). The software environment was as follows: Ubuntu 20.04 was selected as the operating system; Python was used for programming; and Pytorch based on deep learning was used as the development platform.  
In this paper, we used the official VID dataset provided by the ILSVRC (ImageNet Large Scale Visual Recognition Challenge) competition 2015 [36] for feature network training and pre-pruning, and the OTB2013 [29] dataset for performance testing of the tracking model (including global pruning rate determination, secondary pruning, feature network updating, etc.).
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 4 Error Synthesis and Distribution
### Distribution of Errors According to the Principle of Equal Impact  
Assuming that the errors are random and uncorrelated, the following is true:  
\[\sigma_{total}^{2}=\sigma_{1}^{2}+\sigma_{2}^{2}+\cdots+\sigma_{n}^{2} \tag{15}\]The principle of equal infectivity allocates errors by assuming that each one has an equal effect on the total.  
\[\sigma_{1}=\sigma_{2}=\cdots=\sigma_{n}=\frac{\sigma_{total}}{n} \tag{16}\]  
where \(\sigma_{total}\) represents the total error and \(n\) denotes the number of errors.  
Thus, the \(x,y,z\) axis error is  
\[\sigma_{xe}=\sigma_{ye}=\sigma_{ze}=\frac{\sigma_{e}}{3} \tag{17}\]  
The equal influence principle of assigning errors allows for fast assignment, i.e., assuming that each error has the same effect on the total. This is the preferred method to satisfy low accuracy requirements but can result in some easy-to-measure errors and other harder ones.
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Solutions  
The probabilistic power flow optimization method based on uncertain data is used to solve the day-ahead scheduling plan, and the minimum value of average adjustment cost in the intraday stage is calculated by the deviation power of wind farms, PV stations, and load. To deal with random variables, the two-point estimation method replaces each uncertain value with the value on both sides of the mean, and takes the mean of other uncertain quantities, and then calculate the probability power flow model. Compared with the Monte-Carlo method of random sampling, a two-point estimation method has a smaller calculation amount and higher accuracy, which is used to estimate the average adjustment cost, and the improved bat algorithm is used to solve the random variable optimization model.
## 5 The PoAP Consensus Mechanism
### The PoAP Consensus Process  
Byzantine Fault Tolerance (BFT) serves as a universal solution for addressing fault tolerance issues within distributed systems, as exemplified by the PBFT consensus algorithm [45]. PBFT necessitates multiple phases of node communication to reach consensus, resulting in a relatively high communication complexity of \(O\left(n^{2}\right)\), and is thus typically employed within permissioned blockchain networks with a smaller scale of nodes [46]. Owing to the performance challenges associated with the application of PBFT in large-scale blockchain systems, this paper introduces the PoAP consensus mechanism, which builds upon the foundation of PBFT, addresses its limitations, and is used in conjunction with the specific application scenario of the NexoNet.  
The PoAP consensus mechanism is divided into two stages: the election stage and the execution stage, as depicted in Figure 3. During the election stage, users vote for maintainers they trust, followed by the selection of committee members through a cryptographic sortition algorithm. In the execution stage, the proposer generates and proposes a new block, which are then verified through voting by committee members, culminating in the collective update of the blockchain state by all maintainers.
# A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay
## 2 Optimization Model for Relay Device Setting  
### The Significance of Relay Device Parameter Setting  
Relay protection plays a crucial role in ensuring the safe and stable operation of a power system. They detect abnormal conditions in the circuit and swiftly isolate the faulty section, thereby preventing equipment damage and system accidents. Parameter setting involves the appropriate configuration of parameters in the relay protection device, such as current, time, impedance, etc., based on the actual operating conditions of the power system and the technical characteristics of the protection equipment. This ensures the accuracy and reliability of the protection actions. The significance and necessity of parameter setting are evident in the following aspects:  
* Ensure the accuracy of protection actions: Reasonable parameter setting guarantees that the relay protection accurately detects faults and promptly initiates appropriate actions, avoiding false operations or failures. This safeguards power system equipment from damage;
* Minimize the scope of protection: Precise parameter setting confines the operation of protection devices to the fault area, reducing the risk of widespread power outages caused by protection mis-operations. This minimizes unnecessary equipment replacement and maintenance, resulting in cost savings;
* Enhance system stability: Appropriate protection parameters swiftly isolate faults, preventing their propagation and maintaining system stability;
* Adapt to system changes: Power system loads and network structures may change over time. By employing reasonable parameter setting, protection devices can adapt to these changes, ensuring optimal performance at all times;
* Improve system reliability: Correct parameter setting helps enhance the reliability of the relay protection system, reducing the likelihood of human and operational errors. This ensures the safe and stable operation of the system;
* Optimize protection strategies: Continuous monitoring and analysis enable the optimization and adjustment of protection parameters, further improving the effectiveness of protection strategies.  
Hence, parameter setting holds essential importance in relay protection, serving as a crucial step to ensure the safe, reliable, and efficient operation of a power system.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Consensus Functions  
The consensus functions are the main step in any clustering ensemble algorithm and aggregate the outcome of ensemble members to produce the final data partition or consensus partition \(P^{*}\). Several existing consensus functions have been used to generate final clustering in the literature and they are gathered into two main approaches according to the review study conducted by [PERSON] and [PERSON] [8]: object co-occurrence and median partition.  
The object co-occurrence approach: It first calculates the co-occurrence of objects by identifying which must be the cluster label associated with each object in the consensus partition. To accomplish this, it counts how many times one or two objects belong to the same cluster, and computes the final clustering result through a voting process among the objects. This approach includes methods such as the Relabeling and Voting method [44; 45], the Co-association matrix [46], and the Graph- and Hypergraph-based method [20; 47].  
Median partition: It produces solutions by treating the consensus function as an optimization problem of finding the median partition regarding the cluster ensemble [25]. Regarding [PERSON] and [PERSON] [8], it is described as \"the partition that maximizes the similarity with all partitions in the clustering ensemble\". This approach is categorized into three groups: Non-Negative Matrix Factorization based method [48; 49], Kernel-based method [50], and Genetic-based algorithms [51].  
[PERSON] and [PERSON] [8] explicitly state in their work that consensus functions based on the median partitioning approach are theoretically more proposed than those based on the object co-occurrence approach. Notwithstanding, [PERSON] et al. [52] presents theoretical arguments based on the validity of both approaches. Therefore, we have investigated two methods based on the object co-occurrence approach (Hybrid Bipartite Graph Formulation and Meta-CLustering Algorithm) and one method based on the median partitioning approach (Non-Negative Matrix Factorization consensus clustering) for clustering GPS velocities.
## 4 Conclusions  
(1) The addition of steel fiber decreased the flowability and entrapped air content of fresh UHPC mixtures. The fluidity was less sensitive for lower \(w/b\) mixtures to superplasticizer dosage than that for higher \(w/b\) mixtures. The influence of superplasticizer dosage on entrapped air content was related to the water-to-binder ratio and the addition of steel fiber.  
(2) More addition of steel fiber (from 1% to 3%) led to an increasing compressive strength of UHPC specimens with different water-to-binder ratios and superplasticizer dosages. However, the addition of 1% steel fiber causes little increase in flexural strength, and the addition of 2% and 3% steel fiber provided a remarkable increase in flexural strength.  
(3) To prepare flowable UHPC mixtures, a very high dosage of superplasticizer was added for lower water-to-binder ratios, which may have an adverse effect on strength gain. Additionally, the more addition of superplasticizer increased the strength of the UPHC mixtures with a higher water-to-binder ratio, due to the lower air content caused by the improved flowability.  
(4) For the same water-to-binder ratio, there is a good linear relation between entrapped air content and compressive strength of UHPC specimens containing different dosages of steel fiber. Therefore, the decreasing effect on entrapped air content by the addition of steel fiber has a significant contribution to the improvement of the compressive strength of UPHC.  
This work was supported by the National Natural Science Foundation of China (No. 51578192).  
[PERSON] conceived and designed the experiments, and [PERSON] performed the experiments. Both of the authors analyzed the data and contributed to the writing of the paper.  
The authors declare no conflict of interest.
This algorithm utilizes multiple types of ephemeris information from MSEC to diminish the RE;
* This work is the first to design an MS-FARIMA algorithm, which supports data of the same attribute information at different times and of different objects. By applying the MS-FARIMA method, the accuracy of ephemeris error extrapolation is enhanced. Additionally, the reasons for the improved accuracy of the ephemeris extrapolation error based on MSEC and multi-stream information are explored;
* This work proposes a DFO estimation and compensation algorithm for the LEOSIN based on the employment of the optimized ephemeris. It theoretically derives the impacts of different ephemeris errors on DFO estimation, enhancing the DFO estimation accuracy. The remainder of this paper is organized as follows: Section 2 introduces related research on existing satellite ephemeris extrapolation methods, Section 3 constructs the ephemeris extrapolation system model for the LEOSIN then models and analyzes single-satellite ephemeris extrapolation errors, Section 4 presents the MS-FARIMA algorithm and applies it to improve the ephemeris extrapolation accuracy, Section 5 provides a simulation analysis and refines the algorithm, applying the optimized algorithm to the DFO error compensation, and Section 6 presents the conclusions. Figure 1: The role of the ephemeris and its challenges in the LEO-SIN.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
###### Abstract  
An adaptive and blind audio watermarking algorithm is proposed based on chaotic encryption in discrete cosine transform (DCT) and discrete wavelet transform (DWT) hybrid domain. Since human ears are not sensitive to small changes in the high-frequency components of the audio media, the encrypted watermark can be embedded into the audio signal according to the special embedding rules. The embedding depth of each audio segment is controlled by the overall average amplitude to effectively improve the robustness and imperceptibility. The watermark is encrypted by a chaotic sequence to improve the security of watermark, so only users who hold the correct key can accurately extract the watermark without the original audio signal. Experimental results show that the proposed algorithm has larger capacity, higher imperceptibility, better security, and stronger robustness when combating against signal-processing attacks than the involved audio watermarking algorithms in recent years.  
audio digital watermarking; chaotic encryption; adaptive embedding depth; blind extraction +
Footnote †: journal: Computer Communications Communications
###### Abstract  
In response to the challenges of high noise, high adhesion, and a low signal-to-noise ratio in microscopic cell images, as well as the difficulty of existing deep learning models such as UNet, ResUNet, and SwinUNet in segmenting images with clear boundaries and high-resolution, this study proposes a CellGAN semantic segmentation method based on a generative adversarial network with a Feature Completion Mechanism. This method incorporates a Transformer to supplement long-range semantic information. In the self-attention module of the Transformer generator, bilinear interpolation for feature completion is introduced, reducing the computational complexity of self-attention to O(n). Additionally, two-dimensional relative positional encoding is employed in the self-attention mechanism to supplement positional information and facilitate position recovery. Experimental results demonstrate that this method outperforms ResUNet and SwinUNet in segmentation performance on rice leaf cell, MuNuSeg, and Nucleus datasets, achieving up to 23.45% and 19.90% improvements in the Intersection over Union and Similarity metrics, respectively. This method provides an automated and efficient analytical tool for cell biology, enabling more accurate segmentation of cell images, and contributing to a deeper understanding of cellular structure and function.  
cell recognition; microscopic images; deep learning; feature extraction; CellGAN semantic segmentation Article
Sensors_**2011**, _11_, 11560-11580; doi:10.3390/s111211560  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**ATLAS: A Traffic Load Aware Sensor MAC Design for Collaborative Body Area Sensor Networks**  
**[PERSON] 1, [PERSON] 1,\({}^{\star}\), [PERSON] 1 and [PERSON] 2**  
\({}^{1}\) Department of Computer Engineering, Kyung Hee University, Gyeonggi-do 446-701, Korea;  
E-Mails: [EMAIL_ADDRESS] (M.O.R.); [EMAIL_ADDRESS] (S.L.)  
\({}^{2}\) Department of Computer Engineering, Korea Polytechnic University, Gyeonggi-do 429-793, Korea;  
E-Mail: [EMAIL_ADDRESS] (Y.-C.B.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-31-201-2532; Fax: +82-31-204-9082.  
_Received: 20 October 2011; in revised form: 20 November 2011 / Accepted: 9 December 2011 / Published: 12 December 2011_  
**Abstract:** In collaborative body sensor networks, namely wireless body area networks (WBANs), each of the physical sensor applications is used to collaboratively monitor the health status of the human body. The applications of WBANs comprise diverse and dynamic traffic loads such as very low-rate periodic monitoring (_i.e._, observation) data and high-rate traffic including event-triggered bursts. Therefore, in designing a medium access control (MAC) protocol for WBANs, energy conservation should be the primary concern during low-traffic periods, whereas a balance between satisfying high-throughput demand and efficient energy usage is necessary during high-traffic times. In this paper, we design a traffic load-aware innovative MAC solution for WBANs, called ATLAS. The design exploits the superframe structure of the IEEE 802.15.4 standard, and it adaptively uses the contention access period (CAP), contention free period (CFP) and inactive period (IP) of the superframe based on estimated traffic load, by applying a dynamic \"**wh**\" (**whenever **wh**ich is required) approach. Unlike earlier work, the proposed MAC design includes _load estimation_ for network load-status awareness and a _multi-hop communication pattern_ in order to prevent energy loss associated with long range transmission. Finally, ATLAS is evaluated through extensive simulations in ns-2 and the results demonstrate the effectiveness of the protocol.  
**Keywords:** collaborative sensors; Wireless Body Area Network (WBAN); traffic load; IEEE 802.15.4; superframe; energy; throughput; and delay
# Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems
## 2 Related Work
### Deep Reinforcement Learning Methods
#### 2.2.3 Transformer-Based Methods  
Transformers [40] have emerged as the most effective neural network architectures for handling long-term sequential data, thanks to their superior computational efficiency and remarkable performance improvements. Taking full advantage of these benefits, transformer networks are now increasingly applied to solve combinatorial optimization problems. [PERSON] et al. [25] introduced a groundbreaking deep reinforcement learning (DRL)-based framework that leverages self-attention mechanisms to learn constructive routing strategies across a variety of VRP variants. This model, known as the Attention Model (AM), demonstrates significant potential in solving complex routing problems. Building on this approach, [PERSON] et al. [41] proposed a policy optimization with multiple optima (POMO) strategy, which enhances the robustness of the learned policies, achieving state-of-the-art performance on VRP tasks. Similarly, [PERSON] et al. [26] propose a model based on the encoder-decoder structure, which provides enhanced node embeddings via batch normalization reordering and gate aggregation. Some other works iteratively refine complete solutions by applying local search operators until predefined termination criteria are satisfied. [PERSON] et al. [42] propose NeuRewriter, a model that learns a policy to select heuristics and iteratively improve solutions for combinatorial optimization tasks, demonstrating strong performance in expression simplification, online job scheduling, and vehicle routing problems. Similarly, [PERSON] et al. [43] propose the dual-aspect collaborative transformer (DACT), which learns separate embeddings for node and positional features using a novel cyclic positional encoding, and applies proximal policy optimization with curriculum learning to solve VRPs, outperforming existing transformer-based models. Taken together, these advancements highlight the growing prominence of transformer-based methods in addressing complex optimization challenges across various routing problems.
# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 4 Deep Learning Methods
### Deep Learning for Semantic Segmentation
DeepLab and its later changes such as DeepLabv3+ are representative of a class of models where dilated/atrous convolutions are introduced to enlarge the receptive field of convolutions while maintaining the computational cost, enhancing the ability to capture multi-scale contextual information critical for detailed segmentation tasks [80]. Differently from the previous examples, there are models that include the generation of multi-resolution pyramid-based feature representations to better learn the global context and detect objects at different scale: FPN, Feature Pyramid Network [81], and PSPNet,Pyramid Scene Parsing Network [82], are examples. They are based on multi-resolution image analysis approach that is well suited to the multi-scale hierarchical architecture of a deep CNN. In attention-based models, attention mechanisms are used to selectively focus on relevant input information, even at different scales and positions as in [83]. Transformer architectures [84], which have been successful in natural language processing and are based on self-attention mechanisms, have also been introduced for semantic segmentation with the goal of capturing more contextual information than the receptive field of an FCN allows. They can capture global interactions between elements in a scene, but at a high computational cost when applied to the raw image. Vision Transformer (ViT) like in [85] and Segmenter [86] are applied directly to sequences of image patches, with efficient performance on reference image segmentation datasets and without the use of convolutions. In contrast, other proposed architectures are combined with FCN, such as Swin Transformer [87] and SETR [88]. A large collection of datasets is available to train and evaluate the performance of methods and models for semantic segmentation, that are used also for other computer vision tasks such as classification and object recognition. The annotated data include 2D, RGB-D and 3D images, indoor/outdoor scenes, general and street scenes with a large number of different object classes. The review [65] shows that the Intersection of Union, IoU, that quantifies the overlap between the segmented region and the ground truth, averaged (mIoU) over all methods included in the articles exceeded 70% on the PASCAL VOC datasets [89] and Cityscapes [90] out of 14 data collections. Both datasets are considered the gold standard for image segmentation. Furthermore, from both studies [64; 65] came the result that the state-of-the-art models led to a relative improvement of approximately 25% of the mIoU accuracy on the two datasets compared to the first FCN model. This is a non-exhaustive overview of approaches developed in the domain of semantic segmentation; deep learning cloud masking methods originate from many of them.