esa-sceva/satcom-chunk-collection huggingface dataset:
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 2 Background
### Transmission Control Protocol  
Transmission Control Protocol (TCP), as defined in Request For Comments (RFC) 793, is a connection-oriented, end-to-end reliable protocol. Originally, it was designed to fit into a layered hierarchy that supports multi-network applications [6]. The specifications including connection establishment, error-recovery, flow control, and window size negotiations.  
In a typical TCP connection, each device maintains its state and the corresponding sequence number to track the incoming sequences of packets. When an end-host device receives a new packet, it will send back an ACK (acknowledgement) packet, containing an acknowledgement number, which means the device has successfully received the data and is awaiting further incoming data as the number indicates.  
In the establishment of a TCP connection between a client and a server, a TCP three-way handshake process is performed. The process is as illustrated in Figure 1.  
1. The client sends a SYN (synchronize) packet to the server, which has a random sequence number.
2. The server sends back a SYN-ACK packet, containing a random sequence number and an ACK number acknowledging the client's sequence number.
3. The client sends an ACK number to the server, acknowledging the server's sequence number.
4. The sequence numbers on both ends are synchronized. Both ends can now send and receive data independently.
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 4 Experimental Results and Analysis
### Comparison with One-Class Classifier Method Based on Hand-Crafted Features  
In the literature [41], it is proposed to directly use traditional image features to construct feature vectors, and the standard OCSVM method is applied to identify the authenticity of printed anti-counterfeiting patterns. The applied feature metrics include structural similarity (SSIM), correlation coefficient (CORR), Hamming distance (HAMMING), and Jaccard similarity (JACCARD). The following experiment also takes the same 60 positive samples as the training set, while all the positive samples and negative samples are the test set. It calculates the feature metrics of the sample images to construct the feature vector and then inputs OCSVM for training and testing. The obtained classifiers are named C5, C6 and C7. The metrics used to construct feature vectors and the identification result are shown in Table 3.  
Figure 11: Scatter plot and the decision boundary of different classifiers. (**a**) Scatterplot and the decision boundary of C1; (**b**) Scatterplot and the decision boundary of C2; (**c**) Scatterplot and the decision boundary of C3; (**d**) Scatterplot and the decision boundary of C4.  
It can be seen from the table that the commonly used hand-crafted features are used to construct feature vectors for one-class classification according to the method, and there are obviously many misclassifications, which indicates that these features are not suitable for a one-class classification of such anti-counterfeit patterns. It may be necessary to design and apply more complex traditional features to achieve classification in combination with both genuine and counterfeit patterns. And as mentioned above, the equipment used for forgery is becoming more and more sophisticated, so it is difficult to collect all types of forgery during the design process.  
The scatter plots of samples in two-dimensional feature space and corresponding decision boundaries of classifiers using two-dimensional feature vectors are shown in Figure 12a,b. It can also be seen from the figure that many positive and negative sample points are overlapped, which is not conducive to differentiation.
# Scheduling Multiprocessor Tasks with Equal Processing Times as a Mixed Graph Coloring Problem
## 1 Introduction
Finding an optimal coloring \(c(G)\) of the mixed graph \(G=(V,A,\,E)\) is an NP-hard problem even if the equality \(A=\varnothing\) holds [6]. In article [7], it is shown that any job-shop scheduling problem with unit processing times of the operations to minimize the total completion time \(\sum C_{i}\) may be represented as a mixed graph coloring with the minimization of the sum of colors of path-endpoints in the mixed graph \(G=(V,A,\,E)\); see [8; 9]. Hereafter, \(C_{i}\) denotes a completion time of the job \(J_{i}\). The unit-time scheduling problem with the minimization of the makespan is NP-hard even for three dedicated machines [10]. The unit-time job-shop scheduling problem with the minimization of the total completion time \(\sum C_{i}\) is also NP-hard [11]. Articles [12; 13; 14; 15] investigate the complexity of the job-shop scheduling problem with a fixed number of jobs and a fixed number of machines, provided that a job may be processed several times by the same machine. Articles [13; 16] investigate the complexity of the job-shop scheduling problems with any fixed regular objective function. Articles [17; 18; 19; 20; 21; 22; 23] study different types of connections between mixed graph colorings and unit-time shop-scheduling problems. Article [24] is a survey on the mixed graph coloring problems and the equivalent unit-time shop-scheduling problems. In this article, we show that any optimal coloring \(c(G)\) of the mixed graph \(G=(V,A,\,E)\) is equivalent to finding an optimal schedule for the corresponding partially ordered multiprocessor tasks with unit processing times and the minimization of maximal lateness \(L_{\max}\). Contrary to a classical shop-scheduling problem, several dedicated machines must process a multiprocessor task. Along with two types of the precedence constraints given on the set \(V=\{v_{1},v_{2},\,\ldots,v_{n}\}\) of the multiprocessor tasks, it is necessary to process a specified subset of the tasks simultaneously. The equivalence of the considered scheduling problems and the corresponding mixed graph coloring problems implies that most analytical results and algorithms developed for a wide class of the shop-scheduling problems generate analogous results and algorithms for optimal mixed graph colorings \(c(G)\), and vice versa. In what follows, we use the terminology from books [25; 26] for graph theory and that from books [27; 28] for scheduling theory. Closed Results Published on the Mixed Graph Coloring Problems and the Equivalent Unit-Time Shop-Scheduling Problems to Minimize the Makespan  
To classify different scheduling problems, one can use a three-field notation \(\alpha|\beta|\gamma\) introduced in [29], where field \(\alpha\) specifies a processing system along with a machine number, field \(\beta\) job characteristics, and field \(\gamma\) an objective function [28].
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
###### Abstract  
In large-scale disasters, such as earthquakes and tsunamis, quick and sufficient transportation of emergency relief supplies is required. Logistics activities conducted to quickly provide appropriate aid supplies (relief goods) to people affected by disasters are known as humanitarian logistics (HL), and play an important role in terms of saving the lives of those affected. In the previous last-mile distribution of HL, supplies are transported by trucks and helicopters, but these transport methods are sometimes not feasible. Therefore, the use of unmanned aerial vehicles (UAVs) to transport supplies is attracting attention due to their convenience regardless of the disaster conditions. However, existing transportation planning that utilizes UAVs may not meet some of the requirements for post-disaster transport of supplies. Equitable distribution of supplies among affected shelters is particularly important in a crisis situation, but it has not been a major consideration in the logistics of UAVs in the existing study. Therefore, this study proposes transportation planning by introducing three crucial performance metrics: (1) the rapidity of supplies, (2) the urgency of supplies, and (3) the equity of supply amounts. We formulated the routing problem of UAVs as the multi-objective, multi-trip, multi-item, and multi-UAV problem, and optimize the problem with Q-learning (QL), one of the reinforcement learning methods. We performed reinforcement learning for multiple cases with different rewards and quantitatively evaluated the transportation of each countermeasure by comparing them. The results suggest that the model improved the stability of the supply of emergency relief supplies to all evacuation centers when compared to other models.  
umanned aerial vehicle (UAV); humanitarian logistics (HL); disaster resilience; emergency relief supplies; vehicle routing problem (VRP); Q-learning (QL) +
Footnote †: journal: Applied Sciences
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### Off-Chain Data Storage Methods  
Off-chain data storage refers to the practice of storing data outside the blockchain infrastructure while keeping only data references or cryptographic hashes on-chain. In this approach, the actual data are stored in external systems that offer greater storage capacity and reduced costs [75; 76]. This method addresses blockchain scalability and cost concerns, making it a practical solution for handling large datasets while reducing network transaction load [22; 37]. However, storing data off-chain introduces certain security and data continuity risks [15; 46]. Off-chain storage systems can be designed using either centralized or decentralized infrastructures, providing a critical perspective on the advantages and limitations of different storage environments [12; 47]. Off-chain storage methods can be broadly classified into centralized systems (cloud-based storage) and decentralized systems (P2P-based storage), each offering unique advantages and trade-offs [42; 43]. Off-chain data storage methods are shown in Figure 3.  
* _Centralized Storage Systems_: These involve storing data in large data centers, typically managed by commercial providers such as Amazon Web Services (AWS), Google Cloud Platform, or Microsoft Azure [42; 43]. Centralized storage solutions offer high-speed access, reliability, and extensive service options. However, dependence on a central authority can limit data control, creating a single point of failure, which raises concerns about data security and continuity [15; 46]. * _Decentralized Storage Systems_: Aligned with blockchain's decentralization principle, these systems store data distributed across multiple nodes. P2P-based storage operates on a network where data are shared among users and is commonly represented by technologies such as IPFS [77; 78], Swarm, Storj [79], and Sia [80]. These solutions eliminate reliance on centralized servers, offering benefits such as lower costs and enhanced security. However, factors like data persistence and network performance depend on active user participation, making long-term sustainability a key consideration. Off-chain data storage reduces blockchain transaction loads, creating a more scalable and cost-effective infrastructure. However, storing data outside the blockchain introduces security vulnerabilities. These risks can be mitigated through cryptographic techniques such as hashing and encryption. Storing only hash values on-chain while keeping encrypted data in external storage enhances security and ensures data integrity [8; 38]. Large datasets used in various industries, such as healthcare, can be securely and efficiently stored using off-chain solutions. For example, IPFS-based architectures provide a secure and efficient mechanism for storing large-scale digital health records. This approach expands the system's capabilities while preserving data protection [10; 81]. Table 2 presents a detailed classification of off-chain storage methods, highlighting their advantages, disadvantages, and real-world applications.
## 2 Materials and Methods
### Identification of the \"Points of Interest\" Based on Their TF Image Visual Complexity  
We employed two different algorithms to identify the \"points of interest\" in the patient MRI space based on the UAE values of the iEEG TF images, which are also outlined in Figure 3:  
1. Patient- and layer-specific thresholding algorithm: thresholds were identified by fitting each layer's UAE values to an extreme-value distribution ([PERSON] et al., 2015; [PERSON] et al., 2016). The tail of the distribution contains the contacts with lower complexity, which we regarded as our points of interest in the brain. Extreme value theory pertains to the science of modeling and quantifying occurrences that have extremely low probabilities. Extreme value distribution can be generalized to distributions with the following probability density function (\(p\)) that depends on the location parameter (\(\mu\)) and scale parameter (\(\sigma\)). \[p=\sigma^{-1}exp\left(\frac{x-\mu}{\sigma}\right)exp\left(-exp\left(\frac{x- \mu}{\sigma}\right)\right)\] (4) We fit the UAE values to the extreme value distribution to obtain the probability density function and then identified our points of interest (the tail of the distribution) as those with UAE values lower than the distribution's location parameter (Figure 3a);
2. We used the UAE values from all the layers and passed them to a support vector machine's classifier (see Figure 3b) with a linear kernel to classify each contact as a point of interest or not, where the ground truth label consisted of whether a point (contact) was inside or outside the EZ. As a validation strategy, we adopted a three-fold cross-validation, where we split the subjects into three distinct groups with similar class distribution ([PERSON] et al., 2015) (similar proportion of contacts inside vs. outside the EZ). The number of contacts in the two classes (inside versus outside the EZ) for each group was: for Group 1, 259 inside versus 375 outside; for Group 2, 294 versus 378; and for Group 3, 244 versus 378. The groups were defined such that the subjects in each group were distinct (number of subjects per group: 7 in G1, 7 in G2, and 6 in G3).  
To quantify the accuracy of our algorithms in localizing the EZ, we computed the average Euclidean distance between the identified points of interest and the closest EZ margin, as schematized in Figure 3c. This provided an estimate of the localizing capabilities of our algorithms within the context of an epilepsy surgery application.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 4 Results
### Analysis of Data from Electronic Nose Measurements
#### 4.6.2 Classification Models  
As described in Section 3.5, we used the collected data to build a machine learning classification model using the Random Forest method. In our experiments, we trained several models on the entire dataset and output the out-of-bag error to measure classification performance. This gave us an estimate of classification accuracy. In Figure 10, we show the results of this score as a function of the hyperparameter of the Random Forest model, the number of estimators. Separate models were compared for binary discrimination between all pairs of the three categories of samples considered. The accuracy reached values of 60% or higher, and the highest accuracy was obtained for the case of discrimination between two categories of infected samples (G-W) and reached a value just below 63%. The curves in this figure are relatively smooth, which is an indicator that the estimated accuracy was not due to fluctuations but reflected the data pattern.  
One of the possible outputs from the random forest classification algorithm is a list of modeling features most often selected as important for classification, with their relative importance. We present these data in Figure 11, where the modeling features are sorted by their importance in classification model. As we have built individual binary classification models, there are separate lists of the features for each classification.  
Figure 9: Visualization of measured data in the Principal Component space, transforming the 10 the most important modeling features. Variability captured by the PC is indicated in axis labels. Comparison of C–uninfected, G–green, W–white samples. (**a**) uninfected vs. white, (**b**) uninfected vs. green, (**c**) green vs. white.
# Clustering with Nature-Inspired Algorithm Based on Territorial Behavior of Predatory Animals
## 2 Methodological Background
### Nature-Inspired Algorithms in Clustering  
Clustering approaches using heuristic optimization typically use centroid-based representation of clustering solution. It means that solution is being represented by cluster centers:  
\[x_{p}=[u_{1},u_{2},...,u_{C}]. \tag{1}\]  
The problem of clustering is then presented as the standard continuous optimization task, i.e., to find \(x^{*}\) which satisfies:  
\[f(x^{*})=\max_{x\in S}f(x), \tag{2}\]  
where \(S\subset R^{D}\), and \(f(x)\) constitutes solution's \(x\) objective function value.  
Broad range of existing nature-inspired metaheuristics have been already used for clustering. It includes Particle Swarm Optimization [14], Krill Herd Algorithm [15], Gravitational Search Algorithm [16] or Social Spider Optimization [17].  
The well-known \(K\)-means algorithm uses within-cluster variance as an optimization criterion. Using metaheuristics allows us to employ a variety of other indicators. Internal validation indices constitute a natural choice for the objective function in clustering [12]. With this respect, among others, Davies-Bouldin index (e.g., in [9]), Calinski-Harabasz index (e.g., in [18]), were already under investigation. The summary of algorithms and optimization criteria used in metaheuristic clustering can be found in [19].  
The algorithm introduced in this paper is not conventional in that respect, as it does not employ internal validation indices for performance evaluation. It also does not limit the shape of the clusters to the spherical ones. It makes the solution being worked-out here attractive for solving real-world clustering problem instances.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 1 Introduction
Our proposed method achieves the state-of-the-art coding performance compared with other existing learning-based methods learning-based methods and further reduces the performance gap with the latest MPEG G-PCC coding software TMC13 version 14 (TMC13v14). Specifically, for the Bjontegaard Delta rate (BD-rate), the proposed method outperforms Deep-PCAC and SparsePCAC by 50.7% reduction and 6.3% reduction, respectively, and reduces the gap with TMC13v14 to 29.3%. The primary contributions of this paper include:  
(1) This paper explores the application of attention mechanism in point cloud attribute compression. The attention mechanism is integrated in which a non-local attention module is developed to capture the local and global correlations in both spatial and channel dimensions, which is demonstrated to improve the performance of current VAE coding framework. (2) For practical applications, the proposed modulation network adaptively modifies the latent features of different sparse convolutional layers in a single network, which avoids memory cost of storing multiple networks for multiple bitrates. (3) We establish an end-to-end learning-based PCAC framework which achieves the state-of-the-art compression performance compared to other existing learning-based methods and further reduces the gap with the latest MPEG-PCC reference software TMC13 version 14. The remainder of this paper is organized as follows. Section 2 gives a brief review of related works. Section 3 describes the framework of our proposed point cloud attribute autoencoder, the non-local attention module, and the variable rate autoencoder network. Section 4 first depicts the training and testing details. Then, this section presents objective quality comparison results, subjective quality comparison results, and ablation studies. Finally, concluding remarks and future works are described in Section 5.
###### Abstract  
Recently, nanocomposite glass materials embedded with silver particles and lanthanide ions have been widely investigated. The main interest is a surface plasmon resonance (SPR) phenomenon, which, as a result of nanometric particles' interaction with external electromagnetic waves, has led to the enhancement of rare-earth luminescence. In most works, nanoparticles are created in photonic glass by annealing for various times; however, the most discussion of this field in the literature is dedicated to the practical use of plasmonic effect in optical fibers. In this paper, the effect of silver ions on the luminescent properties of europium ions in antimony-germanate-borate (SGB) glass fibers is presented. The glass was synthesized by a standard melt-quenching technique, and glass fiber was drowned at 580 \({}^{\circ}\)C. The analysis of Ag\({}^{+}\) ions content, as well as heat-treatment (hT) time, show an increase of almost 36% in emissions at 616 nm for glass fiber co-doped with 0.1 Ag\({}^{+}\)/0.2 Eu\({}^{3+}\) ions after a 2 h annealing process. In the experiment, the interaction mechanism was investigated in terms of localized SPR, in each step of the glass fiber fabrication process. Moreover, we demonstrate that the self-assembling of silver nanoparticles onto a glass fiber surface is possible only for fiber co-doped with 0.6 Ag/0.2 Eu ions. This non-conventional, bottom-up technique of thin film was analyzed by Scanning Electron Microscopy (SEM) measurements.  
Keywords: antimony-germanate-borate SGB glass; localized surface plasmon resonance; Silver nanoparticles +
Footnote †: journal: Applied Physics Letters
## 2 Experimental Setup and Data Collection
Among these, 70% and 30% of data points are further selected randomly, internally by the ANN and ELM models, for the purpose of training and optimizing the prediction models, respectively;
* Test set of data: the remaining 20% (i.e., 36 data points) is used to evaluate the prediction performance of the proposed prediction models with respect to those previously proposed and developed in [PERSON] et al. [54]. This set of data was never introduced to the prediction models during the training phase. Figure 1: A 7.98 kW photovoltaic (PV) system installed at the Hashemite University, Zarqa, Jordan before and after cleaning (all 28 modules in the foreground were cleaned manually on 24 September 2014. Modules in the background are not part of this study). Figure 2: The dust accumulation (in terms of days of exposure) (**top**), the average daily ambient temperature (**middle**), and the daily system conversion efficiency (**bottom**), for the whole study period of \(n=179\) data points. It is worth mentioning that the division of the data is performed similarly to that in [PERSON] et al. [54] for fair comparison. In other words, the data is split into 80% for developing the prediction models and 20% for further testing and inspection. The 80% split is called the training set and the 20% split is called the blind set (where the model does not use this part of the data during the development process). The split of the training set into training, validation and testing is a well-established method in neural network development and is a built-in feature in Matlab. The ratios for each part (70%-15%-15%) are the defaults to train and optimize the network architectures.