esa-sceva/satcom-chunk-collection huggingface dataset:
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
###### Abstract
This policy allows different machines to cache diverse content after considering the popularity of this content. The popularity of content is fundamentally subject to the download counter of that content, despite the fact that a few other factors need to be assessed because the popularity of the same content can be different at varying machines. The original CCN concept proposed caching all the accessed content at all the intermediate machines [2], which is inefficient due to leaving needless copies of those content and may exhaust the repository spaces. At the same time, all repositories have a fixed storage size, and as the content are continuously being generated, requested, and stored, cache overflow is just a matter of time. Therefore, when there is not enough space for caching content, an effective content removal policy needs to be executed to erase one or more existing content. Several cache replacement schemes have been proposed over time, and most of the schemes' main idea is to remove content based on the access time of that content. These schemes run the content removal procedures until there is enough free space to cache the incoming content. Therefore, various existing schemes for content caching and cache removal face issues such as cache overflow due to inefficiently the caching content that were not necessary to be cached and cache miss due to removing the potential popular content that is requested frequently. In this paper, we propose content popularity ranking (CPR) mechanism to determine the popularity of existing content at content server and rank this content based on the calculated popularity. Furthermore, we propose content caching scheme that caches the content selectively at different machines based on the introduced CPR mechanism to reduce the cache overflow and increase the cache hit ratio. In addition, we also propose content removal scheme based on the CPR mechanism that removes content from the cache repository when there is a lack of space. The proposed scheme considers the popularity of the existing content and removes the content with the least popularity. Therefore, the probability of cache hits increases, the probability of cache misses decreases, and the content delivery times can be reduced. The rest of the paper is organized as follows. Section 2 addresses various related work, and then Section 3 describes the proposed CPR mechanism, content caching, and content removal schemes. After that, diverse performance results are presented in Section 4, and finally, Section 5 concludes the paper.
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 3 Preliminaries  
### Cryptographic Algorithm  
AES [27] is a specification for the encryption of electronic data chosen by the National Institute of Standards and Technology (NIST) in 2001. It is based on a design principle known as a substitution-permutation network (SPN), and it is efficient in both software and hardware. AES is a symmetric block ciphers that takes plaintext in blocks of 128 bits and converts them to ciphertext using keys of 128, 192, and 256 bits. The AES-128 encryption algorithm consists of 10 rounds for 128-bit keys. Every round consists of four processing steps, except the last round, which omits the MixColumns step. The four processing steps are as follows:  
\(SubBytes\)step: The \(SubBytes\)step offers the nonlinearity in the cryptographic algorithm. Every byte in the state array is substituted based on rules dictated by a predefined substitution table.  
\(ShiftRows\)step: The \(ShiftRows\)step operates on the rows of the state; it cyclically shifts the bytes in every row by a certain offset except the first row.  
\(MixColumns\)step: The \(MixColumns\)step operates on the column of the state, the four bytes of every column of the state are combined using an invertible linear transformation. The \(MixColumns\)function takes four bytes as input and outputs four bytes, where every input byte affects all four output bytes.  
\(AddRoundKey\)step: In the \(AddRoundKey\)step, the subkey is added by combining every byte of the state with the corresponding byte of the subkey using bitwise XOR.  
The AES-128 encryption algorithm takes plaintexts in blocks. The plaintexts are masked with a 128-bit fixed key before the first round. After four processing steps are done repeatedly, the plaintexts were converted to ciphertexts. The AES algorithm is considered secure in mathematical proofs, so it is a worldwide standard.
## 2 System Description and Cancellation Problem
### System Description  
As mentioned in the previous chapter, the cancellation signal occurs in multi-PV operation. To simplify, an operation system with two PVs is used for analysis, as shown in Figure 1.  
Figure 2 shows the diagram of the system under analysis. This system consists of a 100 kW PV array, output capacitor \(C\), DC/DC converter with maximum power point tracking (MPPT), and islanding detection program. The DC load is shown as an equivalent resistance and the DC bus is modeled as a constant voltage source. The specifications of the PV module are listed in Table 1. The inverter is used to connect the AC and DC grids. A VSC controller is modeled as a voltage source converter (VSC-detail model) to regulate the DC bus voltage (keep stable at 500 V).  
The sampling time is \(T_{s}\) = 5 \(\times\) 10\({}^{-5}\). The simulation time is 2 s and the simulation is performed with the two following steps:  
* Step 1: Start the injection of perturbation signal at 0.4 s after system startup.
* Step 2: The islanding event is activated at 1.2 s after system startup by disconnecting the AC grid.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Information** & **Value** \\ \hline Open circuit voltage & 64.2 V \\ Short circuit current & 5.96 A \\ Voltage at maximum power point & 54.7 V \\ Current at maximum power point & 5.58 A \\ Maximum power & 305.226 W \\ Parallel strings & 66 \\ Series-connected modules per string & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of SunPower SPR-305E-WHT-D (SunPower, San Jose, CA, USA) PV module.  
Figure 1: Cancellation testing system.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design  
The growing deployment of Industrial Internet of Things (IIoT) systems in sectors such as manufacturing, energy, and logistics has introduced critical challenges related to data integrity, device authentication, and system resilience. Traditional centralized architectures are increasingly inadequate in addressing these concerns due to their vulnerability to single points of failure, cyberattacks, and scalability limitations--particularly when managing real-time, high-volume data from resource-constrained devices. To overcome these issues, blockchain technology offers a decentralized, tamper-proof, and trustless framework that enhances security and transparency in IIoT environments.  
This section presents a comprehensive blockchain-based architectural framework specifically designed for Industrial Internet of Things (IIoT) systems. The proposed de sign integrates edge computing, smart contracts, and distributed ledger technology to ensure secure, scalable, and efficient operations. It is structured into multiple layers--from physical devices to application-level interfaces--each contributing to secure data acquisition, intelligent processing, trusted storage, and fine-grained access control. Additionally, the architecture introduces a collaborative DDoS mitigation mechanism that combines deep learning models, edge node filtering and blockchain-based smart contracts to ensure continuous and trustworthy system operation.  
By leveraging edge computing for localized processing, smart contracts for automated policy enforcement, and blockchain for immutable logging and auditability, the framework addresses key IIoT security and performance requirements. The following subsections outline the layered architecture, system components, controller modules, blockchain structure, and integrated defense mechanisms that collectively enable a robust and intelligent Industrial Internet of Things (IIoT) ecosystem.
## 3 AdViSED Fast Measuring System
### Pre-Alarm Signal and Smoke Alarm  
At runtime, if the smoke is present in the scene caught on camera, it is natural to think that multiple boxes are overlapped. Therefore, we want to count the number of overlaps. The algorithm counts for each pixel how many bounding boxes it belongs to. An index called overlap takes the maximum of these values, and this overlap index is used to generate the final smoke alarm.  
A threshold is used to generate a binary signal from the prealarm, also known as overlap index. We chose \(THR_{overlap}=7\), so we need at least seven bounding boxes overlapped to generate a logical one in this comparison. To activate the smoke alarm, we want the signal to be greater than the threshold for some seconds in a sliding temporal window. The selected time window has a size of 3 s, and the overlap index must exceed the threshold for at least 1.5 s inside the window. If this happens, the smoke alarm is generated. The implementation of the latter is done implementing the M of N decision algorithm, where M is the number of detected smoke frames in 1.5 s and N is the number of frames in 3 s.  
### Pre-Alarm Signal and Smoke Alarm  
The proposed technique has been tested with a large set of test videos from \(320\times 240\) to \(1920\times 1440\) pixels/frame, while the frame rate is between 10 fps and 60 fps (including all intermediate formats usually present in the state-of-art systems). Hereafter, we show the pictures of the output processed videos for three outdoor cases (Figures 2i and 3a,c) and one indoor case (Figure 3b) representing both the case of true positive (Figures 2i and 3b) and negative (Figure 3a,c) conditions. Purple boxes in the figures refer to detected blobs. The alarm is only generated when the red circle appears on the top left of the image (Figures 2i and 3b); otherwise the circle displayed is green.  
Figure 2 is an example application of a smart city antifire system showing the output mask of each processing step already described. Figure 3a is related to an intelligent transportation system with antifire safety features. Figure 3b is an example of an active antifire home alarm. Figure 3c is an example of a forest fire prevention.  
Figure 2: Cont.  
In the elaboration of Figures 2i and 3b, the smoke alarm is activated while in Figure 3a,c the smoke alarm is not activated. This algorithm, different from [14; 16; 17; 18; 20; 21], surrounds the smoke blobs and examines their bounding boxes evolution. This way, AdViSED can reach better performance in terms of accuracy of the alarm prediction.  
Figure 3: (**a**) Outdoor transportation scenarios with few bounding box overlaps; (**b**) indoor scenarios with smoke alarm activated; and (**c**) outdoor scenarios with smoke alarm not activated.  
Figure 2: Detection of smoke on a test video. (**a**) Current Frame n; (**b**) motion detection; (**c**) color segmentation; (**d**) logic AND pixelwise; (**e**) median filter; (**f**) feature extraction; (**g**) bounding box extractor; (**h**) bounding boxes applied on the current frame n; and (**i**) final result at runtime with many overlaps and smoke alarm activated.
## 3 Results and Discussion
### Effect of Pin Fin
#### 3.4.2 Effect of Pin Fin Spacing  
It studied the effect of the pin fin spacing on the heat transfer at the pin fin height of 3 mm. Five kinds of pin fin spacings including 18.6 mm, 21.0 mm, 23.4 mm, 25.8 mm and 28.2 mm are investigated in this part. The average Nusselt number of the lower surface under various pin fin spacings at \(Re\) = 2 \(\times\) 10\({}^{3}\)-6 \(\times\) 10\({}^{4}\) is shown in Figure 17. Figure 18 is \(Nu\) distribution of the lower surface at \(Re\) = 2 \(\times\) 10\({}^{4}\) under different pin fin spacings.  
When the pin fin spacing is 25.8 mm, the maximum \(Nu\) on the lower surface is 56.948, which is the largest among the five spacings. Combined with Figures 17 and 18, there is little difference in the heat transfer in the range of 18.6 mm\(\sim\)28.2 mm. Small spacing can hinder airflow and increase viscous dissipation, which is more obvious at high \(Re\) than that at low \(Re\). At high \(Re\), the impingement cooling effect of large pin fin spacing is better than that of small pin fin spacing.  
Figure 19 is a comparison of frictional resistance under various pin fin spacings. The larger the pin fin spacing, the greater the flow resistance and the pressure drop. The average Nusselt number of the lower surface with the pin fin spacing of 25.8 mm increases by 7.5% compared with that of 18.6 mm. That of 25.8 mm decreases by 1.1% compared with that of 18.6 mm. The flow resistance of the impingement cavity with the pin fin spacing of 25.8 mm is 3.5% higher than that of 18.6 mm. That of 25.8 mm is 1.5% lower than that of 28.2 mm. Considering the heat transfer and pressure drop, the pin fin spacing can be selected as 25.8 mm.  
Figure 17: Comparison of the average Nusselt number of the lower surface under various pin fin spacings.  
Figure 18: \(Nu\) distribution of the lower surface under different pin fin spacings at \(Re\) = 2 \(\times\) 10\({}^{4}\).
## 3 Computational Approach
### Weight Calculation  
The weight calculation phase of this study is dedicated to understanding the optimal ways to assimilate both tag and time details from the CTS into the resultant weights. This exploration has culminated in the proposition of three distinctive schemes designed to shape the weights within a user-resource matrix. To begin with, there is the tag weight, denoted as \(\text{W}_{\text{tag}}\). The assigned weight for every element in the user-resource matrix is derived from the tags that a user utilizes for bookmarking, combined with the frequency of those tag uses. This ensures that the weight gives prominence to both the relevance of the tag and its recurrence in the user's interactions. The second scheme, known as time weight or Wtime, is a tad more intricate. It employs an adaptive exponential forgetting function. The beauty of this function lies in its ability to inherently mirror the shifts in a user's interests over time. This ensures that the weightings remain dynamic and relevant, adjusting to the ebb and flow of user interests. Lastly, the study integrates both the tag-based and the time-based weights to formulate a unified weight, termed as the hybrid weight or Whybrid. This hybrid scheme is developed with an aim to balance and optimize the contributions from both the tag-specific information and the temporal dynamics, resulting in a more holistic measure for the modified ratings.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 2 State of the Art
### TMA Design under a Signal Processing Perspective  
This section is divided into two parts. First, the initial approaches that have jointly considered TMA and wireless communications are introduced. Second, the models involving TMA together with linear digital transmitter and receivers are addressed. Multipath fading wireless channels together with diversity exploitation at the receiver are also considered. Finally, the efficiency of the TMA as well as how to improve it are taken into account, leading to the so-called enhanced time-modulated array (ETMA).
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 2 Materials and Methods
### Neural Networks  
Neural networks, which are inspired by the brain's information processing, allow computers to handle complex tasks such as decision-making, prediction, object recognition [36], and time series prediction [37; 38]. Among these, LSTM (long short-term memory) networks have become the go-to model for time-series predictions [39; 40; 41; 42] due to their ability to handle long-range dependencies. LSTM networks overcome the limitations of standard recurrent neural networks (RNNs), such as eliminating the gradient problem, by utilizing a gating mechanism to regulate the flow of information.  
The LSTM architecture ([[https://www.youtube.com/watch?v=8](https://www.youtube.com/watch?v=8) HyCNIVRbSU]([https://www.youtube.com/watch?v=8](https://www.youtube.com/watch?v=8) HyCNIVRbSU), accessed on 3 December 2024) consists of four key components: input gate; forget gate; output gate; and memory cell. Each gate plays a pivotal role in controlling the information passed through the network and retained for future predictions. The input gate filters incoming information; the forget gate determines what information is discarded, and the output gate controls the information released from the memory cell. As a result of these mechanisms, LSTMs excel in various domains, such as stock forecasting [43; 44], oil-production forecasting [45], weather forecasting [46], and video descriptions [47].  
In recent years, several researchers have proposed LSTM-related studies, such as natural language processing [48] and parameter and architecture optimization [49; 50].  
Figures 3 and 4a,b illustrate the LSTM architecture and the key functions used within the gates, such as the sigmoid and tanh activation functions, respectively.  
Figure 4: Graphs of (**a**) sigmoid and (**b**) tanh functions.  
Figure 3: LSTM architecture.
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 2 Materials and Methods
### UWG Evaluation
#### 2.3.1 Impact of the Rural Station  
As mentioned in Section 2.2.2, three different rural stations have been chosen: Centre equestre, Meteorole, and Mondouzil. The performance of UWG was evaluated according to the three rural stations by comparing the RMSE and the MBE between predicted values of temperatures \(\hat{T}\) and measured ones \(T\) in Carmes station.
## 2 Experimentation  
### Experimental System  
As shown in Figure 2a, the experimental system is mainly composed of a centrifugal fan, a developing section, a transition section and a test section. Driven by the pressure difference, direct leakage occurs from the high pressure air side to the low pressure gas side through the gap between the sealing sheets and the outer casing. The centrifugal fan can ensure adequate pressure and flow rate of air. A butterfly valve by which the air pressure can be adjusted was installed between the outlet of the centrifugal fan and the inlet of the developing section. The developing section was to ensure air reach the fully developed state and ensure accurate measurement. The transition section was installed between the developing section and the test section to ensure a good connection. In addition, a grid was installed at the entrance of the transition section to obtain uniform and stable flow field at the inlet of the test section. The test section (Figure 2b) is rectangular cross section with a width and a height of 400 mm and 175 mm, respectively. The inner upper surface of the test section was to model the inner surface of the sector plate or circular arc plate of the actual air preheater. The distance between the upper end of the sealing sheets and the inner upper surface of the cover plate could be adjusted to model the leakage gap size of the actual air preheater. The relevant parameters of the sealing sheets were also adjustable. Besides, all connecting flanges in the experimental system were sealed with ethylene-vinyl acetate copolymer sponge. The practical sealing sheet (Figure 2c) was made of Corten Steel and characterized by high strength and corrosion resistance.  
The distance (\(d\)) between adjacent sealing sheets gradually increases along the radial direction, so the sealing sheet length of 400 mm is selected to study the direct air leakage at different segmentation in the radial direction. As shown in Table 1, the structural and operating parameters in the experiment,  
Figure 2: Sketch of (**a**) direct air leakage test system: (**b**) test section and (**c**) real sealing sheet.  
the distance between adjacent sealing sheets \(d\), the sealing number \(Z\), the gap size \(x\), the bending angle \(\alpha\), and the total pressure difference \(\Delta p_{\text{tot}}\) were set based on the widely used real RAPH.
## 3 Proposed Watermarking Framework
### Structure of Watermarking Network to Be Trained  
As mentioned before, our intentions with the proposed NN-based watermarking scheme are simplicity in the network structure and depth, host resolution adaptability, WM adaptability, and controllability between invisibility and robustness of WM. Also, to increase the quality of the watermarked image, WM invisibility, and balanced training, we use several techniques such as maintaining the host image's resolution, average pooling for processing the WM data, MAE loss for the extractor network. Those are more focused in the following explanations.  
Among the functional blocks in Figure 1, only the solid-lined ones are implemented in the network; those in the dotted blocks are processed in off-the-network. This network structure is shown in Figure 2, which includes pre-processing networks for host and WM, WM embedding network, and WM extraction network. Here, the attack simulation process is included for training. This network's first structural feature is that it consists of only the simple CNNs with a relatively shallow depth that the highest depth has only 13 CNNs. Each of the consisting networks and their components was determined empirically based on the tremendous experiments' data. The second feature is that the WM pre-processing network increases the resolution to that of the host image, while most previous works reduce the host image's resolution to that of the WM. This is to maintain the host image's information to increase the invisibility of the WM, which is based on our experimental results that it is more challenging to obtain invisibility performance than robustness, and the scheme maintaining the host resolution was superior in invisibility with the same robustness.  
Other features of our network are dealt with in detail in the following sub-sections to explain each network.  
Figure 1: Proposed digital watermarking system: (**a**) watermark embedding, and (**b**) watermark extraction.