esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Scalability
#### 3.4.1 Storage  
Storage is one of the main issues of the integrated architecture between IoT and BC. For instance, to add a new node, all the previously used nodes need to store the complete chain to validate the new node because the underlying BC framework is constantly becoming larger, which makes it a complex and error-prone process [69]. Every member in the BC network keeps a local copy of the entire distributed ledger. When a new block is verified, it is broadcast over the whole P2P network, and each node adds the approved block to its ledger. This places a strain on the storage capacity available to IoT devices. If 1000 users trade a single 2 MB image every day in a BC application, a BC node would require around 730 GB annually [58]. As a result, when BC works with IoT data, the difficulty is to manage the growing data storage requirements. Moreover, unlike traditional paradigms, cryptographic algorithms must be constrained to function within the restricted storage capacity of IoT devices. The storage requirements for broadcasts necessary for exchanging keys must be met to ensure effective employment of communication protocols and security for IoT [85]. One of the consequences of a BC-enabled environment is that accounting ledgers may need to be kept for an infinite period. This necessitates offloading accounting data from IoT devices to local or distant servers in the case of IoT devices. Although storage prices are continually decreasing, solution providers must select where and when these data are kept [69].  
Filtering, compressing, and normalizing IoT data have been recommended using several approaches. Saving data supplied by the IoT is useful on many levels since it covers target services, communication, and embedded devices. Data compression reduces the amount of time it takes to process, transmit, and store created data. Furthermore, as demonstrated by Bitcoin-NG, BC's consensus mechanism, which generates bottlenecks, may be changed to increase bandwidth and minimize transaction latency, leading to better IoT transitions [74]. Another concept is to merge the BC with the current P2P storage, which enables storing huge amounts of data off the chain, to alleviate the storage problem. Using the DHT, the method creates off-chain storage. The raw data are kept on the DHT off-chain, with just the data references remaining on the BC. The SHA-256 hash is used as a reference. The InterPlanetary File System (IPFS) is a P2P dispersed file system that combines principles from earlier P2P systems such as BitTorrent protocol, DHT, Git, and self-certified filesystems. Filecoin acts as an incentive layer on top of IPFS to provide a completely distributed file storage system [26]. Desema, a decentralized service marketplace system based on Ethereum and IPFS, has been presented. Service metadata and big data are kept off-chain in the Desema system, with Ethereum merely storing data references [26].
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 6 Stages of the Quantification of QoE in CIAs
### Output Stage
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Video Metric** & **Audio Metric** & **Time Metric** &
\begin{tabular}{c} **Other QoS** \\ **Metrics** \\ \end{tabular} & **QoE** \\ \hline [PERSON] et al. (2003) [49] & Yes & Yes & & \\ \hline [PERSON] et al. (2011) [43] & Yes & Yes & & \\ \hline [PERSON] et al. (2014) [31] & & Yes & Yes & \\ \hline [PERSON] et al. (2015) [51] & Yes & Yes & & \\ \hline [PERSON] et al. (2017) [46] & Yes & & & \\ \hline [PERSON] et al. (2018) [52] & & Yes & & \\ \hline [PERSON] et al. (2018) [38] & Yes & Yes & & \\ \hline [PERSON] et al. (2018) [44] & & Yes & & Yes \\ \hline [PERSON] et al. (2018) [53] & & & Yes & Yes \\ \hline [PERSON] et al. (2019) [42] & Yes & & & Yes \\ \hline [PERSON] et al. (2019) [40] & & Yes & & \\ \hline [PERSON] (2019) [57] & & & Yes & \\ \hline [PERSON] et al. (2019) [33] & Yes & Yes & Yes & \\ \hline [PERSON] et al. (2019) [54] & & & Yes & \\ \hline [PERSON] et al. (2019) [41] & & & Yes & \\ \hline [PERSON] et al. (2020) [36] & & Yes & & Yes \\ \hline Nvidia (2020) [58] & Yes & Yes & & \\ \hline [PERSON] et al. (2020) [35] & & & Yes & \\ \hline [PERSON] et al. (2020) [47] & & Yes & & \\ \hline [PERSON] et al. (2021) [39] & Yes & Yes & Yes & \\ \hline Leo Bodnar Electronics (2021) [55] & & Yes & & \\ \hline Leo Bodnar Electronics (2021) [56] & & Yes & & \\ \hline [PERSON] et al. (2021) [50] & Yes & & & \\ \hline [PERSON] et al. (2021) [48] & & Yes & Yes & \\ \hline [PERSON] et al. (2021) [34] & & Yes & Yes & \\ \hline [PERSON] et al. (2021) [9] & & Yes & & \\ \hline [PERSON] et al. (2018) [38] & Yes & Yes & & Yes \\ \hline Exoprise (2024) [45] & & & Yes & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Proposals from the literature and the output metrics provided. using proprietary or previous models to map metrics from the other categories of output metrics onto the ACR/MOS scales. The most commonly used QoS metrics at the output stage were those indicative of the state of computational resources such as CPU, RAM, GPU, or disk usage. Other proposals provided network metrics such as bandwidth usage, bytes sent or received, number of packets used, or RTT. The main deficiency of these output metrics is that most works did not establish the relationship between QoS and QoE metrics.
## 2 Methodology
### Signal Processing Stage  
It is difficult to accurately monitor the impact damage to large structures with single sensor information. Monitoring signal fusion can reflect more impact damage information, which is an important way to improve diagnostic accuracy. Therefore, the measured data of multiple sensors are fused in a novel way to obtain more impact information. The specific conversion process is shown in Figure 2:  
Figure 1: General framework of the proposed multi-task 2D-CNN approach.  
Figure 2: Flow chart of the 2D image processing method.  
The impact signal received by the sensor network is drawn as a three-dimensional surface diagram of sensor number-time-voltage amplitude by mesh algorithm. On the sensor channel dimension, the values between the two sensors are generated through linear interpolation. In the three-dimensional surface map, the color reflects the voltage amplitude of sensor data, and multiple sensor data points are connected through a straight line to achieve multi-sensor data fusion. Then, the 2D color image is obtained by vertical projection to the plane composed of the sensor number and time of the 3D surface map. In the 2D color images, eight vertical dividing lines can be observed, representing the impact signals received by the sensors numbered 1-8 from left to right. The image points between each demarcation line are generated by linear interpolation during the drawing of the 3D surface and then transformed by projection. The 2D color images retain the time information, amplitude information of each sensor data point, and the relationship between each sensor. The 2D color images have three color component values \(R_{\mathrm{ij}}\), \(G_{\mathrm{ij}}\), \(B_{\mathrm{ij}}\), \((\mathrm{i}=1,2,3\cdots,m,j=1,2,3\cdots,n)\), where \(\mathrm{m}\) and \(n\) represent the number of points in a row and the number of points in a column of a color image, respectively. It is also necessary to carry out grayscale processing on the image to make the proposed multi-task 2D-CNN model easy to train and iterate. The grayscale value signal \(I_{\mathrm{ij}}\) is defined with Formula (1):  
\[I_{\mathrm{ij}}=R_{\mathrm{ij}}*299/1000+G_{\mathrm{ij}}*~{}587/1000+B_{ \mathrm{ij}}*114/1000 \tag{1}\]  
After the 2D color image is converted to a gray image, the number of data are reduced, which improves the calculation efficiency. Multi-sensor data fusion is realized, which provides a data set for the subsequent multi-task 2D-CNN model. The impact position information is related to time, and the relative position relationship of \(I_{\mathrm{ij}}\) represents the time domain information, such as the propagation time and arrival time, so the damage location can be realized. \(I_{\mathrm{ij}}\) expresses the signal amplitude of the Lamb wave, which is related to the impact energy and can realize the impact energy monitoring.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods
### Computational Models
#### 2.2.3 Exposure Scenarios  
Figure 1 illustrates the exposure scenarios. As shown in Figure 1, the separation between the dipole antenna and the surface of the head model was 5 mm for the rat, and 5, 15, and 25 mm for the human. The frequencies considered here were 3, 6, and 10 GHz, which are the upper frequency range at which the SAR is used. The reason for choosing frequencies above 3 GHz is that the whole-body-averaged SAR cannot be neglected at lower frequencies, making it difficult to discuss the local temperature elevation and its time course. The antenna-head separation for the rat model was only chosen at 5 mm to realize local exposures [7; 17; 25]. The length of the antenna was adjusted to half the wavelength of each frequency in free space. The duration of the RF exposure corresponded to 50 min so that temperature elevation was in the steady state condition. For comparison, the output power of the antenna was adjusted so that the peak SARs averaged over 10-g tissue were 2, 10, 20, 30, 40, and 50 W/kg. The guideline/standards were set at 2-W/kg local RF exposure limit for the  
general public and unrestricted environments, and at a 10-W/kg limit for restricted environments or occupational exposure.
# Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving
## 1 Introduction
Finally, the cost function of LTV-MPC is designed with the weight adjustment coefficient, the optimal auxiliary torque and generalized additional yaw moment are obtained, and the coordination between man-machine and vehicle handing stability in the auxiliary driving process is realized. Lower layer torque allocation, while meeting the requirements of tracking the target generalized longitudinal force and additional yaw moment, introduces the stability weight adjustment coefficient to achieve multi-objective optimization of tire load rate and energy efficiency. Then, an optimal energy efficiency point-based tracking method is proposed to achieve the optimization of multi-axle vehicle efficiency, avoiding the nonlinearity caused by considering energy consumption models and efficiently achieving torque distribution. Figure 1: The overall control framework. This study effectively improves the integrated control performance of distributed multi-axle vehicle chassis under man-machine cooperative driving conditions. The main contributions are as follows:  
1. The stability region of the vehicle is obtained by the phase plane method. Based on this, the stability weight adjustment coefficient is obtained by fuzzy control mapping. At the same time, the LTV-MPC optimization problem is designed by combining the auxiliary driving and stability weight adjustment coefficient, then the multi-objective cooperation between man-machine co-driving and vehicle handling stability is realized. 2. Coordinate the multi-objective optimization between tire load rate and energy efficiency in torque distribution through the stability weight adjustment coefficient. The optimal energy efficiency point tracking strategy is adopted to achieve energy efficiency optimization, avoid nonlinear optimization solutions, and improve torque distribution efficiency.
## 6 Discussion
### Effect of Distance Between Source and Landing Site  
The maximum values of \(P_{RX}\) are located around the jammer's location regardless of whether the flight vehicle passes overhead or beneath the _Source_. So, if the _Source_ is assumed to be located in the vicinity of the landing site, it can considerably deteriorate the navigational capabilities of the GNSS receiver, as indicated by the \(P_{RX}\) graph in Figure 10. On the other hand, it should be noted that the GNSS is a secondary navigational tool in some real-world applications, while the INS is always the primary source of navigational information if it is being used.  
The accumulative navigational error of the INS system is part of why the GNSS receiver is needed, and also accounts for why a distant _Source_ can also result in malfunctioning of the navigational pair of GNSS and INS. If a distant _Source_ makes the GNSS navigational service unavailable, the INS will be unable to eliminate its internal error, and the blind-flying distance makes this error large enough to prevent the flying platform from reaching its landing site.
# Antimicrobial Activity of Human C-Type Lectin Domain Family 3 Member A (CLEC3A)
## 2 Materials and Methods
### Antimicrobial Activity  
To investigate the antimicrobial activity of CLEC3A and its protein domains, we used viable count assays. Bacterial strains were cultivated in tryptic soy broth (TSB) (Merck, Darmstadt, Germany) overnight at 37 \({}^{\circ}\)C. Incubation was stopped at mid-logarithmic phase (OD \(\approx\) 0.5 (630 nm)) and bacterial culture was mixed with Tris-HCl-glucose-buffer (10 mM Tris-HCl, 5 mM glucose; pH = 7.4; TG-buffer) (S. aureus and P. aeruginosa) or with phosphate-glucose buffer (10 mM K\({}_{2}\)HPO\({}_{4}\), 5 mM glucose; pH = 7.4; PG-buffer) (_MRSA_). These buffer conditions were optimized previously for P. aeruginosa, S. aureus, and MRSA_[15; 19]. After centrifugation (4500 g, 5 min), the supernatant was discarded, and the pellet resuspended with TG- or PG-buffer. 50 uL of bacterial solution (concentration: 2 \(\times\) 10\({}^{6}\) colony forming units (CFUs)/mL) were incubated with CLEC3A, CLEC3A Ex23, and CLEC3A Ex3 protein solutions, the well-investigated AMP LL-37 (positive control), and CLEC3A Ex2 (negative control) in 1:2 dilutions at 37 \({}^{\circ}\)C for 2 h. Double-distilled water and TG- or PG-buffer served as untreated control. Matn3, COMP, and ORF8 were further control proteins and were incubated with 50 uL of the respective bacterial solution in 1:2 dilutions at 37 \({}^{\circ}\)C for 2 h. Subsequently, solutions were diluted with a TG or PG-buffer, plated out onto pre-warmed TSB agar plates, and incubated overnight at 37 \({}^{\circ}\)C. Bacterial growth was determined by counting CFUs. For each protein concentration, the percentage of colonies compared with the untreated control was determined. Furthermore, minimal inhibiting concentrations (MIC50s), which are defined as the concentration of the antimicrobial proteins or peptides at which only 50% of colonies survived compared to the untreated control, were calculated. Experiments were performed three times for each protein and each bacterial strain.
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
## 1 Introduction
In addition to its central role in energy metabolism, CoQ10 enhances mitochondrial mass [12], modulates the permeability transition pore [10], improves mitochondrial function [13], and reduces the generation of ROS [14]. Its critical involvement in mitochondriadrial health [15, 16], coupled with its potent antioxidant properties, positions CoQ10 as a promising therapeutic agent, especially in clinical trials targeting neurodegenerative diseases [17, 18]. CoQ10 levels typically increase during early life, but they decline with aging [10, 19]. CoQ10 plays an essential role in protecting the neurological system from degeneration by facilitating electron transport, supporting energy metabolism, and enhancing oxygen utilization, particularly in the musculoskeletal and nervous systems. Previous research has highlighted the therapeutic potential of CoQ10 in preventing and managing a range of neurological, cardiovascular, and metabolic disorders [10, 20, 21, 22, 23]. This study investigates the short-term effects of exposure to CoQ10 on mitochondrial respiration, fatty acid metabolism, oxidative stress-related gene expression, and sirtuin expression in young (P5) and aged (P16) MSCs. For these experiments, MSCs were derived from stem cells of human exfoliated deciduous teeth (SHEDs). The Oroboros system was used to assess the mitochondrial function. Gas chromatography-mass spectrometry (GC-MS) analysis evaluated the fatty acid profiles in young and aged MSC cells. Further, quantitative polymerase chain reaction (qPCR) demonstrated passage-dependent changes in the expression of oxidative stress-related genes and sirtuins in response to CoQ10 treatment. These findings highlight, for the first time, CoQ10's passage-specific effects on oxidative stress metabolism, suggesting its potential to mitigate mitochondrial dysfunction and metabolic shifts associated with cellular aging.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result
### Evaluation Metrics  
To evaluate the performance of the proposed bug localization method, three evaluation metrics [2] were employed: Accuracy, Mean Reciprocal Rank (MRR), and Mean Average Precision (MAP). These metrics were chosen because they effectively capture different aspects of the model's ability to recommend and rank buggy files accurately.  
Accuracy is defined as the ratio of correctly predicted buggy files to the total number of bug reports, as represented by the equation:  
\[Accuracy=\frac{\#\text{ of }\text{Corrected }\text{Buggy }\text{Files}}{Total\text{ of }\text{Bug }\text{Reports}} \tag{2}\]  
This metric provides a straightforward measure of how often the model correctly identifies the buggy file corresponding to a given bug report. Mean Reciprocal Rank (MRR) evaluates the ranking quality of the recommended items, focusing on the position of the first correct prediction in the ranked list. It is calculated as follows:  
\[MRR(O,U)=\frac{1}{|U|}\sum_{u\in U}\frac{1}{k_{u}} \tag{3}\]  
Here, U represents the set of bug reports, and ku is the rank position of the first correctly predicted buggy file for a bug report u. For instance, if the correct buggy file appears at the 7 th position in a list of 10 recommendations, the reciprocal rank is 1/7. MRR emphasizes the importance of ranking the correct result as high as possible.  
Mean Average Precision (MAP) provides a more detailed evaluation by considering the precision of the ranked list at each level. It is defined as:  
\[MAP(O,U)=\frac{1}{|U|}\sum_{u\in U}AP(O(U)) \tag{4}\]  
The AP(O(U)) denotes the average precision for a single bug report u, calculated across all relevant items in the ranked list. Dissimilar to basic precision, MAP accounts for the position of each correctly predicted item in the recommendation list, assigning higher penalties for errors in the higher-ranked items. This ensures that the model prioritizes accuracy for items at the top of the list, where they are more likely to be considered by users.  
These metrics were selected because they align with the goals of bug localization, which involves not only predicting the correct buggy files but also ranking them in a way that facilitates efficient debugging by developers. Accuracy measures the overall success rate, MRR highlights the effectiveness of ranking the first relevant file, and MAP evaluates the quality of the entire ranked list. Together, these metrics provide a comprehensive assessment of the model's performance and its practical utility in real-world debugging scenarios.
## 1 Introduction
### Paper Scope and Structure  
This paper aims to provide a comprehensive and forward-looking review of how 6G enabling technologies can be adapted and integrated into MCNs. The primary scope includes three core contributions. First, the paper introduces and analyzes a reference architecture for heterogeneous MCNs that spans space, air, sea surface, ground, and underwater layers, emphasizing the multi-domain interactions and technological heterogeneity of future maritime systems. Second, it surveys key communication technologies and techniques from the 5G/6G ecosystem, such as Reconfigurable Intelligent Surfaces (RIS), Non-Orthogonal Multiple Access (NOMA), UAV relaying, THz communications, and AI-driven networking. The paper also discusses their potential applicability and adaptation to the unique challenges of maritime environments, including mobility, intermittent coverage, and harsh propagation conditions. Third, the paper identifies a set of representative optimization and resource management scenarios arising in MCNs, formulates them mathematically, and maps them to suitable solution methodologies, ranging from traditional optimization (e.g., convex programming, graph theory) to modern approaches such as machine learning, reinforcement learning, and federated learning.  
Beyond these technical focuses, the paper also aims to bridge the gap between academic research and practical deployment by highlighting relevant applications, such as autonomous navigation, emergency response, remote monitoring, and smart shipping. It further explores different AI-based concepts that can be adapted to support and optimize maritime paradigms. The remainder of the paper is organized as follows: Section 2 presents the evolution of MCNs and introduces the proposed reference architecture. Section 3 reviews the fundamental enablers and emerging technologies of 6G by providing a multi-level vision of 6G networks. Section 4 outlines the emerging AI-driven concepts that can benefit maritime operations and optimization problems. Section 5 discusses potential applications that can be realized in future 6G MCNs. Section 6 proposes detailed technical solutions and optimization problems aligned with 6G capabilities. Finally, Section 7 concludes the paper and outlines future research directions.