esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Appropriate Geometry for Thermally Efficient CO\({}_{2}\) Adsorption Beds
## 2 Adsorption Beds and Model Description
### Mesh Independence and Model Validation  
The mesh independence is studied for four mesh schemes listed in Table 2. It could be observed that the mesh number of \(>\)72,000 is sufficient to ensure precise results. For this study, the mesh size was taken \(>\)100,000 for all investigated cases. The model results are validated against experimental data of our previous work [23], as shown in Figure 2. An excellent agreement is observed between the numerical results and the experimental breakthrough curves of CO\({}_{2}\) and N\({}_{2}\) concentration ratios. The investigated numerical cycle is taken as the stable cyclic one when the difference between adsorption and desorption uptakes remains constant.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Bed length, m & 0.20 \\ Bed cross-sectional area, cm\({}^{2}\) & 3.1416 \\ Bed density (kg/m\({}^{3}\)) & 297 [16] \\ Adsorbent thermal conductivity (W/m\(\cdot\)K) & 0.3 [35] \\ Adsorbent heat capacity (J/kg\(\cdot\)K) & 900 [35] \\ Particle density, \(\rho_{p}\) (kg/m\({}^{3}\)) & 911 [35] \\ Bed porosity, \(\epsilon\) & 0.674 [16] \\ \(\theta_{\text{t=CO2}}\) (Equation 4) (mol/kg) & 6.80 [16] \\ \(\theta_{\text{t=CO2}}\) (Equation 4) (mol/kg) & 9.9 [16] \\ \(\theta_{\text{t=U2}}\) (Equation 4) (mol/kg) & 14.0 [16] \\ \(\theta_{\text{t=2U}}\) (Equation 4) (mol/kg) & 0 [16] \\ \(k_{\text{t,U2}}\) (Equation 4) (0/1/Pa) & 2.44 \(\times\) 10\({}^{-11}\) [16] \\ \(k_{\text{t=2U}}\) (Equation 4) (1/Pa) & 1.39 \(\times\) 10\({}^{-10}\) [16] \\ \(k_{\text{t,U2}}\) (Equation 4) (1/Pa) & 4.96 \(\times\) 10\({}^{-10}\) [16] \\ \(k_{\text{t,U2}}\) (Equation 4) (1/Pa) & 0 [16] \\ \(k_{\text{t,U2}}\) (Equation 4) (0/mol) & \(-\)42,000 [16] \\ \(k_{\text{t,U2}}\) (Equation 4) (0/mol) & \(-\)18,000 [16] \\ \(k_{\text{t,U2}}\) (Equation 3) (1/s) & 0.1213 [16] \\ \(k_{\text{t,U2}}\) (Equation 3) (1/s) & 0.3055 [16] \\ CO\({}_{2}\) adsorption heat (Equation 8), \(\text{{{\text{{\text{{\text{{\text{{\text{\text{\text{\text{\text
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Comparison Results  
In this section, we present the benchmark results on the DanceTrack and MOT20 datasets. Our baseline model was ByteTrack, and compared to the baseline model, our method only added a small amount of additional computational cost.
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 3 Developments in Innovative Detection Methods
### Improvements and Developments in Real-Time and Online Analysis Techniques
#### 3.4.2 Surface Plasmon Resonance
With few probes, it is possible to detect a wide range of species within less than 18 h from a low concentration sample. The method can be used in real-time monitoring without the need of sampling and the associated contamination risks. SPRi has a multitude of advantages in bacterial detection. There is no need for multiple sampling and no breach of containment. Each breach of containment can potentially lead to a risk of contamination of the sample, whether in the production of advanced therapy drugs or industrial production of complex media. The analysis is performed in real-time and is extremely interesting in the quality control of health products. Despite this progress, the conditions required for applications in the health and pharmaceutical industries are not yet in place. Advances in sensor and device design, as well as improvements in ligands, are future prospects in this area. Future research in SPRi can provide this technique a promising role in the pharmaceutical and food industries. Other techniques are under development, in particular the Localized Surface Plasmon Resonance (LSPR) technique [128] with very promising results in the detection of viruses [129] or in the detection of biomarkers in cancer [130]. If broad-spectrum probes are validated, they can be combined with more specific probes within SPRi sensors. This provides broad-spectrum detection on the one hand, and specific identification of selected relevant strains on the other. A significant advantage of SPRI is that it makes it possible to probe the sample without altering it and then inject the patient with the cell therapy. Another possibility would be to copy the automatic detection model of blood culture bottles and apply it with an SPRi chip. One could proceed to an automated analysis of the final product of ATMP while leaving the possibility of complementary analysis with other conventional techniques from the same sample. Originally the main limitation of SPRi was the limited recognition spectrum due to the ligands used, the antibodies. The use of antimicrobial peptides in complex biological media provides future perspectives in research and development of microorganism detection methods. As described along this review and summarized in this Table 3, there are many techniques for microorganism detection. Nevertheless, choosing the right technique for complex samples depends on different factors such as the sensibility toward bacteria and the limit of detection (LOD), the analysis time, the specificity of the technique, its cost, and its advantages and disadvantages.
# Piezoelectric Sensor with a Helical Structure on the Thread Core
## 2 Fabrication  
### Fabrication of a Piezolectric Sensor with a Helical Structure  
We fabricated a piezoelectric sensor by attaching a PVDF film with a helical structure on a thread. Figure 2a-e show the manufacturing process of the piezoelectric sensor. A PVDF film (Measurement Specialties Inc., Hampton, VA, USA) was cut with a thickness of 28 \(\upmu\)m in an ax shape, as illustrated in Figure 2a. Then, a customized flexible printed circuit board (FPCB) electrode was attached to the PVDF film using a carbon conductive adhesive tape (Nisshin EM Co., Ltd., Tokyo, Japan). Specifically, the FPCB electrode was connected to a wider part of the PVDF film, as shown in Figure 2b. Then, the PVDF film was curled on a thread (length: 85 mm; diameter: 3 mm; cotton: 100%) as a helical structure, as depicted in Figure 2c. Then, the combined structure of the thread and the PVDF film was coated with a polyethylene terephthalate (PET) film tape (thickness: 20 \(\upmu\)m; Saehan Co., Ltd., Ansan, Korea), as shown in Figure 2d. Finally, the wrapped surface was surrounded with six threads (see Figure 2e). The fabricated sensor had a thickness of 4 mm and a capacitance of 212 pF, measured by a \(7^{\frac{1}{2}}\)-digit graphic sampling multimeter, Keithley DMM7510 (Keithley Instruments, Cleveland, OH, USA).  
Figure 1: Schematic of the textile-based piezoelectric sensor (green) in the smart textile under flexural loading.
## 3 Results
### A Systematic Description of the T3 SD Metamodel
#### 3.1.4 The Implementability Perspective (Wymore's Implementability Cotyledon)  
The union of IOR and TYR yields the space of _implementable system designs_ (ISDs). This implies that the space of the implementable system designs is the intersection of the space of FSDs with the space of BSDs. In essence, an ISD is a BSD that can exhibit the behavior of a given FSD. The space of ISDs captures BSDs that implement the behavior of FSDs, which then undergo production synthesis to serve as the basis for the real system [35]. Whereas not every FSD will be buildable and not every BSD will provide the required functionality, the ISD guarantees both conditions (which are necessary to produce a real system): a buildable system (BSD) that has at least one system mode (ZS) with a homomorphic relationship to the functional system model (Z) of the FSD. This is captured in the metamodel through the _implementation artifacts_ (IA) that, in addition to ZS, include a _homomorphic mapping of states_ (HS), _homomorphic mapping of input_ (HI), and _homomorphic mapping of output_ (HO).  
Selection of a specific ISD within the space of ISDs is driven by the TR. Whereas IOR and TYR define a space of ISDs, TR defines an order in such space. In [PERSON]'s words, TR is used as a \"means of which any two [alternative ISDs] can be compared consistently with respect to a trade-off between the performance [PR] and cost [CR] requirements\" [20]. As indicated in [39], the required tradeoff may provide a preference toward performance over cost, a preference toward cost over performance, or equal preference between performance and cost.  
These relationships are shown as a subset of the metamodel in Figure 7.  
Figure 7: A description of the T3 SD Implementable Perspective showing designs that meet the required input/output, have a homomorphic relationship between the functional and buildable designs, meet the required technology, and are ordered according to the preferred tradeoff between performance and cost
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 3 EconLedger: Rationale and Architecture
### Hybrid On-Chain and Off-Chain Storage  
To address issues of high storage overhead incurred by directly saving raw data into DLTs, EconLedger utilizes a hybrid on-chain and off-chain storage solution. Figure 2 illustrates the block and off-chain data structure used in EconLedger. The block is the basic unit of on-chain storage, which includes block header and the orderly transactions list. The \(MT\_root\) in the block header stores the hash root of a Merkle tree to maintain the integrity of all transactions. In each transaction, the \(swarm\_hash\) only stores references to the data rather than the data themselves. As references are hash values with fixed length such as 32 or 64 bytes, all transactions have almost the same size even if linked raw data have large sizes or require different formats, such as ENF signals or multimedia recordings.  
Off-chain storage relies on a Swarm network in which all sites cooperatively construct a DDB system. In EconLedger, a site refers to a fog/edge server. The data uploaded to Swarm are cut into pieces called _chunks_, which is the basic unit of storage and retrieval in the Swarm network. Each chunk can be accessed at a unique address, which is calculated by its hashed content. All data chunks use their chunk hash to construct a Merkel hash tree for which its root is the reference to retrieve raw data. Swarm implements a specific type of content addressed distributed hash tables (DHTs), called Distributed Pre-image Archive (DPA), to manage chunks across distributed sites. All Swarm sites have their own base addresses with the same size as the chunk hash, and the sites closest to the address of a chunk not only serve information about the content but actually host data [35]. All sites in the Swarm network use the Kademlia DHT protocol [36], which synchronizes chunks in a P2P manner, to ensure data persistence and redundancy.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Overview of the Zeus Banking Malware  
Zeus, also known as Zbot, is a notorious banking malware designed to steal financial information such as online banking credentials through methods like keylogging, screen capturing, and the real-time manipulation of web sessions (man-in-the-browser attacks). It spreads via phishing emails, malicious downloads, and software vulnerabilities. Once installed, Zeus establishes persistence, evades detection using rootkits and encrypted communication, and connects to a remote Command and Control (C&C) server to send stolen data and receive instructions [19]. Known for its modular architecture, Zeus can be customized for specific targets and often forms botnets for large-scale cybercrime operations. Its source code leak in 2011 led to numerous variants, making it one of the most impactful and studied banking malware families in cybersecurity history.  
Once the Zeus malware gets onto a device, it needs to perform several actions to infect the device. The Zeus bot inserts malicious code into the winlogin.exe process after copying itself to the system 32 directory, and this is achieved by escalating its privileges and manipulating the winlogin.exe and svchost processes. Two files are created, local.ds, which is used to download the configuration file, and user.ds, which is used to transmit stolen data back to the threat actors' C&C servers. The additional code injected into the svchost process is used by the Zeus bot for communication purposes, and Zeus communicates using a Command and Control (C&C) channel which can either use a centralized or P2P architecture. In the centralized architecture, the IP address of the C&C server is hardcoded into the bot's binary file, which leaves the bot vulnerable, because if the C&C channel is discovered and blocked, the Zeus bot becomes inactive and is unable to recover [20]. Modern-day variants of the Zeus malware use a P2P architecture, as this is more resilient to disconnections and is much harder to detect and block [21]. One reason is simply because the IP address is not hardcoded into the bot binary and because, in the P2P network, multiple bots can act as C&C servers. This architecture also allows stolen data to be routed through the bot network via these intermediary C&C bots and, crucially, allows bots to recover from failures [22]. This recovery is possible, because each peer C&C bot can essentially provide support to a failed bot, for example, by sending the failed bot an updated IP address to help it resume malicious communications.
## 1 Introduction
The contributions of this paper can be summarized as follows:  
* We consider a multiple-UAV-aided NOMA network where each UAV acts as a mobile FD relay in mmWave UL for 6G cellular systems;
* We propose the use of the MUSIC algorithm at the hybrid beamforming to detect the DoA estimations for each UAV and improve the performance of the UAV-aided NOMA cellular system;
* We propose an optimization procedure in order to maximize the average achievable sum rate of the UL mmWave UAV relay network by taking into accounts LoS obstruction, channel time-varying condition, the DoA information at the hybrid beamforming due to the different spatial directions of UAVs, as well as the requirements in terms of quality of service (QoS) for each user. The paper is organized as follows. Section 2 presents the proposed system model, channel model, the mmWave hybrid beamforming, the MUSIC algorithm and the problem formulation to optimize the global UL sum rate of the UAV-network-aided NOMA cellular system. In Section 3, the numerical results providing a comparison with the NOMA cellular system without the use of multi-UAV relays are shown. Finally, Section 4 concludes the paper and outlines future research activities.
## 2 Experiment and Methods
### Prediction Method
#### 2.3.2 Genetic Algorithm  
Genetic algorithm (GA) is a method to search for the optimal solution by simulating the natural evolution process. Through mathematical methods and computer simulation operation, the solving process of the problem is transformed into a process similar to the crossover and mutation of chromosome genes in biological evolution [43; 44]. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, better optimization results can be obtained faster. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control, and other fields.  
The basic operation flow of GA is as follows:  
1. Initialization: set the evolution algebra counter \(t=0\), the maximum evolution algebra \(T\), randomly generate \(n\) individuals as the initial population \(g(0)\), and find an appropriate coding scheme to encode individual \(a_{i}\) in the population.
2. Individual evaluation: Define the objective function and calculate the fitness \(g(a_{i})\) of each individual in the population.
3. Selection operation: select the individuals participating in reproduction according to the size of fitness. Take the proportion of fitness as the selection standard. For a population of given size \(n=\){\(a_{1}\), \(a_{2}\),..., \(a_{n}\)}, the fitness of individual \(a_{i}\) is \(g(a_{i})\). Then, its selection probability is \[p(a_{i})=\frac{g(a_{i})}{\sum_{i=1}^{n}g(a_{i})},\ i\ =\ 1,\ 2,\ \ldots,\ n.\] (2)
4. Crossover operation: According to crossover probability \(Pc\), two matched individuals are selected to exchange part of their genes, thus forming two new individuals. Crossover operation is an important feature of genetic algorithm which is different from other evolutionary algorithms. It plays a key role in genetic algorithm and is the main method to generate new individuals. Crossover operators generally adopt single point crossover operators.
5. Mutation operation: according to the mutation probability \(Pm\), some gene values in the individual coding string are replaced with other gene values to form a new individual. The mutation operation in GA is an auxiliary method to generate new individuals, which determines the local search ability of GA, while maintaining the diversity of the population. The cross operation and mutation operation cooperate with each other to jointly complete the global search and local search of the search space.
6. Population \(g(t)\) gets the next generation population \(g(t+1)\) after selection, crossover, and mutation operations.
7. Termination condition: when \(t=T\), the individuals with optimal fitness obtained in the evolution process are output as the optimal solution and the calculation is terminated. The GA calculation process is shown in Figure 6.
# Semantic Communication on Digital Wireless Communication Systems
## 1 Introduction
Lastly, based upon this minimum Manhattan distance constellation mapping scheme, we propose a hybrid transmission scheme to adapt different quantization levels, which can separate the semantic quantization output from the modulation order. Meanwhile, this hybrid transmission scheme can improve the transmission quality of semantic communication at the low SNR range while leveraging the bandwidth-saving advantage of semantic communication [14; 20; 23; 24]. The rest of this paper is organized as follows. In Section 2, we propose the specific physical layer procedure design for the bit-conversion-based JSCC transmission framework for semantic communication and introduce the corresponding simulator. In Section 3, we propose a novel physical layer indicator IER based on the bit-conversion-based JSCC transmission framework, as well as its simulation verification. In Section 4, the optimization methods for the bit-conversion-based JSCC transmission framework and their simulation verification are presented, including minimum Manhattan distance constellation mapping and hybrid JSCC and SSCC transmission schemes. Finally, conclusions are drawn in Section 5.
###### Abstract  
To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an indepth analysis of the latest research and developments, offering insights into enhancing blasting efficiency, improving safety, and optimizing tunnel design. Building on this foundation, we introduce a digital identification method for assessing half porosity through 3D image reconstruction. Utilizing the Structure from Motion (SFM) technique, we re-construct the 3D contours of tunnel surfaces and bench faces after blasting. Curvature values are employed as key indicators for extracting 3D point cloud data from boreholes. The acquired postblasting point cloud data is processed using advanced software that incorporates the RANSAC algorithm to accurately project and fit the borehole data, leading to the determination of the target circle and borehole axis. The characteristics of the boreholes are analyzed based on the fitting results, culminating in the calculation of half porosity. Field experiments conducted on the Huangtai Tunnel (AK20 + 970.5 to AK25 + 434), part of the new National Highway 109 project, provided data from shell holes generated during blasting. These data were analyzed and compared with traditional onsite measurements to validate the proposed method's effectiveness. The computed half porosity value using this technique was 58.7%, showing minimal deviation from the traditional measurement of 60%. This methodology offers significant advantages over conventional measurement techniques, including easier equipment acquisition, non-interference with construction activities, a comprehensive detection range, rapid processing speed, reduced costs, and improved accuracy. The findings demonstrate the method's potential for broader application in tunnel blasting assessments.  
3D point cloud; half porosity; tunnel blasting; image 3D reconstruction; motion structure; recovery method +
Footnote †: journal: Applied Sciences
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
###### Abstract  
The Paleogene Engine Formation in the Huizhou Sag, Pearl River Mouth Basin, has been identified as a key target for deep oil and gas exploration. However, research on the diagenesis of these deep reservoirs still remains limited. This study evaluated the role played by diagenetic processes on the reservoir quality of the Paleogene Enping Formation in the Huizhou Sag, Pearl River Mouth Basin, from braided river deltas to meandering river deltas. A core observation, thin section examination, cathode luminescence analysis, scanning electron microscopy, mercury penetration, porosity-permeability test, and other analytical methods were performed to analyze the diagenesis and its impact on the physical properties of the deep, low-permeability sandstone reservoirs in the Enping Formation within the study area. It was shown that the reservoir composition maturity of the Paleogene Enping Formation in Huizhou Sag is relatively high, and the reservoir space is dominated by dissolved pores, accounting for more than 48.2%. The deep and ultra-deep elastic reservoirs are typically characterized by \"low porosity, low permeability, and strong heterogeneity\". In particular, the reservoir space of the deep, low-permeability reservoir of the Enping Formation is significantly affected by diagenesis in which mechanical compaction notably altered the porosity of the Enping Formation reservoir, with a reduction in pore volume ranging from 12.5 to 27.2% (average 18.9%); cementation usually enhances pore reduction by between 2.1 and 28.7% (average 11.7%), while dissolution has resulted in an increase in pore volume ranging from 1.4 to 25.6% (average 10.1%). A further analysis revealed that the deep reservoir type in this region is characterized by \"densification\", as evidenced by the correlation between reservoir porosity-permeability evolution and hydrocarbon accumulation.  
Huizhou Sag; deep reservoir; diagenesis; Pearl River Mouth Basin; tight sandstone +
Footnote †: journal: Applied sciences  
0000-0002-4300-3300][PERSON]  
0000-0002-0002-3300][PERSON]  
0000-0002-3300-3300][PERSON]