esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
The light-scattering effect in turbid waters can lead to a significant increase in the bit error rate [14], while real-time scenarios, such as vehicle networking, demand algorithms that prioritize low latency and high computational efficiency. Traditional likelihood-based (LB) and feature-based (FB) methods often suffer from insufficient robustness and high computational complexity in dynamic channels. In recent years, deep learning (DL) has transcended the limitations of traditional models, which rely on prior mathematical assumptions, by automatically learning the intricate relationships between channel features and modulation formats, demonstrating significant advantages. This paper systematically reviews existing research and the latest advancements in VLC modulation format identification technology within the context of 6G. It focuses on analyzing various feature-extraction methods, as well as the potential and challenges of machine learning algorithms. By comparing the performance differences between traditional methods and DL approaches, this paper highlights the significant improvements in noise robustness, classification accuracy, and cross-scenario adaptability achieved through DL's automatic feature extraction and nonlinear mapping capabilities. Furthermore, it explores the adaptability of different algorithms in dynamic channel environments, particularly emphasizing the innovative applications of multi-domain feature fusion, lightweight architectures, and meta-learning paradigms in developing lightweight, high-precision, and real-time MFR technologies. These advancements are crucial for enhancing the performance of 6G space-air-ground-sea integrated networks. Future research directions are also envisaged in alignment with the demands of 6G networks for intelligence and heterogeneous integration, addressing challenges in hardware deployment, real-time requirements, and dataset adaptability.
# Evolution towards Smart and Software-Defined Internet of Things
## 5 Evolution of IoT Architecture
### Vertical and Horizontal IoT Models  
Currently, there are two different IoT models, vertical and horizontal IoT models [82]. In the vertical IoT model, the IoT node, gateway, and cloud are primarily managed by one single entity, achieving better compatibility among various elements. However, the vertical model has disadvantages as well, because the user is completely dependent on the vendor, and a different setup is required for each different task. This led to the development of the horizontal model, where multiple providers can work with the same underlying framework.  
[PERSON] et al. in [83] state that the focus of IoT applications has been to develop dedicated domains for all applications which resulted in data redundancy and problems in interoperability. It is difficult to introduce new services in the system and the changes are slow. The concept of horizontal IoT places all applications on a single domain which addresses these challenges profoundly. However, it decreases security and isolation in the system.  
[PERSON] et al. in [66] provide a solution to the main problems that can hamper Industrial 4.0 adoption for a medium high-factory (heterogeneity, management, congestion, and latency). They have used an open-source software solution architecture based on Open and Distance Learning (ODL) together with the IoT Data Management  
Figure 6: SDN Architecture of IoT based on Fog Computing.  
(IoTDM) to orchestrate I4.0 infrastructure enabling interoperability and management of the diverse Industrial IoT (IIoT) devices. The authors have used SDN to enhance the programmability of the network. Scalability has been enhanced for the industrial IoT by this solution. The downsides are those important requirements of quality-of-service, energy-efficient scheduling, and security have not been adequately addressed.
# The Concept of Using 2D Self-Assembly of Magnetic Nanoparticles for Bioassays
## 2 Materials and Methods
### Synthesis of Magnetic Fe\({}_{3}\)O\({}_{4}\) Nanoparticles  
The process involves the dissolution of 3.81 g of FeSO\({}_{4}\).7 H\({}_{2}\)O in 100 mL of a 0.02 M hydrochloric-acid aqueous solution and dissolving 7.41 g of FeCl\({}_{3}\).6 H\({}_{2}\)O in 100 mL of distilled water. The resulting solutions were combined in a flask and subjected to stirring at 400 rpm within a nitrogen atmosphere. After about one hour, the speed of the mechanical stirrer was increased to 800 rpm, and 25 mL of a 25% ammonia solution was added dropwise (at a rate of 1 droplet per second). Mixing at 800 rpm was continued for another 30 min. An appropriate volume of the suspension was then extracted. This was followed by a thorough washing procedure with distilled water. During the washing process, the separation of the nanoparticles from the suspension was facilitated by the use of a neodymium magnet. The nanoparticles were left in suspension for the subsequent functionalization step.
# Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping
## 4 Conclusions  
A computer vision method for automatically identifying the readings of multiple pointer meters is proposed in this paper. The extrinsic parameters of the meter plane are calibrated by the pixel coordinates and world coordinates of the mid-points of the measured meter scale instead of the traditional calibration pattern. The meter image is made parallel to the surface of the meter by inverse perspective mapping. The mapping is finished by rotation transformation of the meter image, and the rotation transformation matrix is obtained, eliminating the perspective effect of the meter image. The transformed image is measured by the visual detection method to obtain the readings of the pointer meter, correcting the coordinates of the rotation center and the end point of the pointer. From the results of the measurement, the readings of the pointer meter are hardly affected by the non-coplanarity of the imaging and pointer meters.  
Conceptualization, Q.T. and H.B.; methodology, Q.T.; software, H.B. and S.L.; validation, S.L. and J.M.; formal analysis, Q.T.; investigation, J.M.; data curation, H.B.; writing--original draft preparation, H.B. and Q.T.; writing--review and editing, H.B. and Q.T.; visualization, S.L.; supervision, J.M.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Reference Values 1** & **Traditional Measurement Results 1** & **Measurement Results 1** \\  & **/Reference Values 2** & **/Traditional Measurement Results 2** & **/Measurement Results 2** \\  & **\(g_{0}{}^{(1)}\)/\(g_{0}{}^{(2)}\) (MPa)** & **\(g_{1}{}^{(1)}\)/\(g_{1}{}^{(2)}\) (MPa)** & **\(g_{2}{}^{(1)}\)/\(g_{2}{}^{(2)}\) (MPa)** \\ \hline
1 & 1.3/0.3 & 1.101/0.312 & 1.238/0.325 \\
2 & 5.6/0.8 & 6.098/0.712 & 5.610/0.768 \\
3 & 8.1/0.9 & 8.791/0.834 & 8.126/0.920 \\
4 & 12.8/1.2 & 13.013/1.149 & 12.765/1.240 \\
5 & 16.7/1.5 & 16.847/1.467 & 16.654/1.476 \\
6 & 19.1/1.6 & 19.863/1.611 & 19.111/1.638 \\
7 & 22.7/2.0 & 22.910/1.935 & 22.670/1.981 \\
8 & 24.8/2.2 & 24.996/2.246 & 24.815/2.248 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Measurement readings of two pointer meters by two methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & **Absolute Error \(\Delta f_{1}{}^{(1)}\)/Absolute Error \(\Delta f_{1}{}^{(2)}\) of** & **Absolute Error \(\Delta f_{2}{}^{(1)}\)/Absolute Error \(\Delta f_{2}{}^{(2)}\) of** \\  & **Conventional Measurement** & **Measurement** \\  & \(\Delta g_{1}{}^{(1)}\)=\(g_{1}{}^{(1)}\)–\(g_{0}{}^{(1)}\)/\(\Delta g_{1}{}^{(2)}\)=[\(g_{1}{}^{(1)}\)–\(g_{0}{}^{(2)}\)] (kPa) & \(\Delta g_{2}{}^{(1)}\)=[\(g_{2}{}^{(1)}\)–\(g_{0}{}^{(1)}\)] (kPa) \\ \hline
1 & 199/12 & 12/5 \\
2 & 489/88 & 10/2 \\
3 & 691/66 & 6/1 \\
4 & 213/51 & 5/1 \\
5 & 147/33 & 16/4 \\
6 & 763/11 & 11/2 \\
7 & 210/65 & 2/9 \\
8 & 166/46 & 10/2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Measurement absolute errors of two pointer meters for two methods.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 4 Results and Discussion
### Performance of Single-Frequency PPP
The results also indicate that the integration of the multi-GNSS can improve the single-frequency PPP performance in terms of convergence time and positioning accuracy. As it is shown in Figure 5 and Table 5, the GRAPHIC and UC approaches have much the same performance for the equivalence of two solutions. By introducing external ionosphere information, the average convergence time of PPP solutions is reduced. For instance, the improvement in convergence time of BIM-, NTCM- and GIM-constrained quad-constellation b2 single-frequency PPP is 15.2%, 24.8% and 28.6%, respectively, compared with the UC solution. The positioning accuracy of the quad-constellation single-frequency PPP can reach 1-3 cm horizontally and 8-9 cm vertically. The improvement in the positioning accuracy of the ionosphere-constrained PPP, for giving higher weighting after convergence, was not significant. The positioning accuracy of the GIM-constrained PPP solutions is the best and  
Figure 4: Comparison of positioning error of b2 single-frequency PPP with different schemes for GPS, GLONASS and GPS/Beidou/GLONASS/Galileo solutions at station MRO1 (DOY 245/2018, \(\mathrm{Kp}\approx 1.17\)). The corresponding satellite numbers and PDOP values are also shown.
# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
###### Abstract  
In this study, an IoT-based fire detection system was developed to detect and prevent damage from forest fires at an early stage. In Korea, forest fires spread quickly due to the dry climate and winds in spring and autumn, so quick detection and prevention is necessary. To quickly detect and prevent forest fires that occur periodically, a real-time fire detection system was developed by combining a Raspberry Pi and a fisheye camera. A lens with a 220\({}^{\circ}\) angle of view was installed, and an image analysis algorithm was implemented using the OpenCV library. The location of the fire was estimated by calculating the polar coordinates of the omnidirectional images. Using the Wi-Fi communication function of the Raspberry Pi, the acquired continuous images were transmitted to the Firebase database, and the images were analyzed to identify the movement path of the forest fire. The developed system was applied to a mountainous area near the Samcheok Campus of Kangwon National University. As a result of the experiment, when the location of points about 25.9 m (average) away from the observation point was predicted, the positional error was analyzed to be about 1.1 m. If the system is improved in the future, it is expected that it will be able to contribute to the early prevention of forest fires with fast and accurate responses.  
fire detection; fisheye lens; image analysis; Raspberry Pi; digital surface model +
Footnote †: journal: Applied Sciences  
1
## 4 Numerical Example
### Bridge System Reliability Estimation of the Single Failure Criterion
#### 4.2.1 Failure Criterion I  
Failure criterion I involves a simple series system. It is assumed that the failure of any one slab segment will cause the bridge system failure. Since the bridge structure is symmetrical, the repetitive pairs of failure mode pairs are ignored. According to the copula theorem, the copula parameters of the selected copula functions were evaluated, as shown in Table 5. The AIC and BIC values were calculated according to Equations (4) and (5), and the results are shown in Table 6. Then, the principle of minimum AIC and BIC values was used to select the best-fitting copula function. It illustrates that the Gaussian copula was the best-fitting copula for the two performance functions, as seen by comparing the AIC and BIC values.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Slab Segment Number** & **Failure Probability** & **Slab Segment Number** & **Failure Probability** \\ \hline
1 & \(4.57\times 10^{-8}\) & 6 & \(4.46\times 10^{-6}\) \\
2 & \(1.56\times 10^{-7}\) & 7 & \(6.22\times 10^{-7}\) \\
3 & \(6.22\times 10^{-7}\) & 8 & \(1.56\times 10^{-7}\) \\
4 & \(4.46\times 10^{-6}\) & 9 & \(4.57\times 10^{-8}\) \\
5 & \(1.14\times 10^{-4}\) & & \\ \hline \end{tabular}
\end{table}
Table 4: The failure probability of each slab segment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Slab**} & \multicolumn{2}{c}{**Resistance \(R\)**} & \multicolumn{2}{c}{**Dead Load Effect \(G\)**} & \multicolumn{2}{c}{**Live Load Effect \(Q\)**} \\ \cline{2-7}  & **Mean** & **Standard** & **Mean** & **Standard** & **Mean** & **Standard** \\
**Number** & **(kN-m)** & **Deviation** & **(kN-m)** & **(kN-m)** & **(kN-m)** & **(kN-m)** \\ \hline
1 & 796.04 & 91.783 & 212.86 & 9.68 & 85.42 & 12.71 \\
2 & 796.04 & 91.783 & 221.79 & 9.68 & 96.42 & 14.35 \\
3 & 796.04 & 91.783 & 223.91 & 9.68 & 116.8 & 17.38 \\
4 & 796.04 & 91.783 & 230.74 & 9.68 & 144.4 & 21.49 \\
5 & 796.04 & 91.783 & 260.69 & 9.68 & 180.9 & 26.92 \\
6 & 796.04 & 91.783 & 230.74 & 9.68 & 144.4 & 21.49 \\
7 & 796.04 & 91.783 & 223.91 & 9.68 & 116.8 & 17.38 \\
8 & 796.04 & 91.783 & 221.79 & 9.68 & 96.42 & 14.35 \\
9 & 796.04 & 91.783 & 212.86 & 9.68 & 85.42 & 12.71 \\ \hline \end{tabular}
\end{table}
Table 3: Statistical parameters of the random variables [20].  
Then, based on the Gaussian copula function, the joint failure probability of each pair of failure modes was obtained using Table 4 and Equation (7), as shown in Table 7. According to the binary Gaussian copula and Equation (8), the system failure probability \(P_{f_{1}}\) of the case bridge under failure criterion I was \(1.099\times 10^{-4}\). For comparison, the system failure probability based on the other three copulas were also calculated, which were \(P_{f_{\rm{I-Cayton}}}=1.097\times 10^{-4}\), \(P_{f_{\rm{I-Gumbel}}}=1.139\times 10^{-4}\) and \(P_{f_{\rm{I-Frank}}}=1.245\times 10^{-4}\).
## 3 Communication Delay Issues with Combined TSC and TCR Controller
### Performance Expectations  
Additionally, temporal delays were introduced during the continuous computation of such partial derivatives for the TKED, due mostly to either data propagation across optical fibers or wireless methods, A/D transition, or determination of the W cumulative, as well as period derivation of the TKED or signal data communication using GPS. Time delays could have an influence only on control logic, affecting the minimization of shaft torsional oscillations. As a result, while studying a decrease in the shaft torsional waves, such constraints must be considered. In most circumstances, time delays might vary from several microseconds to a few more hundred milliseconds [38; 39; 40; 41]. Comprehensive simulations were performed in this work, utilizing a variety of common time delay settings. Section 5.2 describes some of the simulated occurrences that corresponded to a 900 ms significant delay.  
Modeling the Problem to Suggest a Solution, Including Developing Communication Delay Reduction Solutions
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Performance Requirements  
To evaluate the performance of blockchain-based copyright protection applications, several metrics should be taken into consideration. These are defined as follows:  
* Transaction throughput: Throughput can be defined as the number of transactions/second in each block, whereas transaction throughput can be defined as the rate at which valid transactions are committed by the blockchain in a defined time period.
* Scalability: The size and frequency of the blocks along with the number of transactions that the system can process to cope with the increased workload.
* Transaction latency: The amount of time required to add a block of transactions in the blockchain. This includes the propagation time and any settling time due to the consensus mechanism in place.
* Storage: On-chain storage of users' data (such as personal information, copyright information or keys) or off-chain mechanism (such as InterPlanetary File System (IPFS) or data lake) to store large amount of copyrighted data.
* Transaction fee: In Ethereum, an execution fee is used to compensate miners for the computational resources required to perform different smart contract functions (e.g., content registration or delivery). The gas price is measured in terms of ether. Hence, the total transaction fee is the gas consumed multiplied by the gas price. In Bitcoin, the transaction fee is the cost that a user incurs when he/she sends coins from one bitcoin wallet into another. This fee is based on the size (in bytes) of the transaction and the age of its inputs. The miners responsible for mining the block that includes the transaction are being paid with the transaction fee in BTC.
* Robustness of content protection schemes against different types of attacks: The copyright protection schemes (based on encryption, DRM, watermarking or fingerprinting) must be resistant against all possible threats, such as security, privacy, signal processing, tampering, communication and collusion attacks, among others. These threats are discussed in Section 2.6.
* Transparency of copyrighted multimedia content: In the copyright protection schemes based on watermarking/fingerprinting, the embedded watermark/fingerprint must be transparent and should not introduce distortion, which can cause quality degradation.
* Computational costs in generation of copyright content: These are on- or off-chain costs associated with the process of generating copyrighted content (encrypted with access control, watermarked or fingerprinted).
* Total response time: It includes the time taken by the content owner in registering the copyrighted content on the blockchain so as to make it available for the clients, and the time taken in delivering the content to the client upon final payment settlement.
# Nonlinear Optimal Control Law of Autonomous Unmanned Surface Vessels
## 3 Results
\tag{17}\]  
From Equation (16), optimal value \(\mathbf{u}_{2}^{*}(\mathbf{e},t)\) of the second term of the nonlinear optimal control law \(\boldsymbol{\tau}_{\eta 2}\) in Equation (9) can be further formulated as:  
\[\mathbf{u}_{2}^{*}(\mathbf{e},t)=-\mathbf{W}_{2}^{-1}\mathbf{S}^{T}\mathbf{V}_ {2}\mathbf{e}. \tag{18}\]  
Derivations of Optimal Parameters \(\mathbf{W}_{2}\) and \(\mathbf{V}_{2}\) for \(\boldsymbol{u}_{2}^{*}(\mathbf{e},t)\)  
By choosing  
\[\mathbf{W}_{2}={a_{2}}^{2}\mathbf{I}_{3\times 3}, \tag{19}\]  
\(a_{2}>0\). Positive definite symmetric matrix \(\mathbf{Q}_{2}\) can also be factorized by Cholesky factorization as  
\[\mathbf{Q}_{2}=\left[ \begin{array}{cc}\mathbf{Q}_{211}{}^{T}\mathbf{Q}_{21 1}&\mathbf{Q}_{212}\\ \mathbf{Q}_{212}{}^{T}&\mathbf{Q}_{222}{}^{T}\mathbf{Q}_{222}\end{array} \right] \tag{20}\]  
By substituting matrices \(\mathbf{W}_{2}\), \(\mathbf{S}\) and \(\mathbf{Q}_{2}\) to Equation (17), the following equalities can be obtained:  
\[\mathbf{Q}_{211}{}^{T}\mathbf{Q}_{211}-\frac{1}{{a_{2}}^{2}}\mathbf{V}_{211}{ }^{T}\mathbf{V}_{211}=0_{3\times 3} \tag{21}\]  
\[\mathbf{K}_{2}+\mathbf{Q}_{212}-\frac{1}{{a_{2}}^{2}}\mathbf{V}_{211}{}^{T} \mathbf{V}_{212}=0_{3\times 3} \tag{22}\]  
\[\mathbf{K}_{2}+\mathbf{Q}_{212}-\frac{1}{{a_{2}}^{2}}\mathbf{V}_{212}{}^{T} \mathbf{V}_{211}=0_{3\times 3} \tag{23}\]  
\[\mathbf{Q}_{222}{}^{T}\mathbf{Q}_{222}-\frac{1}{{a_{2}}^{2}}\mathbf{V}_{212}{ }^{T}\mathbf{V}_{212}=0_{3\times 3} \tag{24}\]  
By solving Equations (21) and (24), we get submatrices \(\mathbf{V}_{211}=a_{2}\mathbf{Q}_{211}\) and \(\mathbf{V}_{212}=a_{2}\mathbf{Q}_{222}\). The matrix \(\mathbf{V}_{2}\) can then be expressed as:  
\[\mathbf{V}_{2}=\left[ \begin{array}{cc}a_{2}\mathbf{Q}_{211}&a_{2}\mathbf{Q}_ {222}\\ 0_{3\times 3}&\mathbf{I}_{3\times 3}\end{array} \right]. \tag{25}\]  
For satisfying the condition of Equation (7), matrix \(\mathbf{Q}_{211}\) is expressed as a diagonal form as:  
\[\mathbf{Q}_{211}=q_{211}\mathbf{I}_{3\times 3}. \tag{26}\]  
For some positive scale \(q_{211}\), scale \(\rho_{2}\) for the optimal tracking problem of AUSVs can then be represented as  
\[\rho_{2}=a_{2}q_{211}. \tag{27}\]  
On the basis of the above derivations, weighting matrix \(\mathbf{W}_{2}\) was chosen as Equation (19) for any finite \(a_{2}>0\), allowing weighting matrix \(\mathbf{Q}_{2}>0\) to be analyzed with \(\mathbf{Q}_{211}\), \(\mathbf{Q}_{212}\), and \(\mathbf{Q}_{222}\), which satisfies the requirements in Equations (25) and (26). The nonlinear optimal tracking problem of AUSVs is then solved by the following nonlinear optimal control law:  
\[\boldsymbol{\tau}_{\eta 2}=\mathbf{T}_{2}(\mathbf{e},t)+{\rho_{2}}^{-1}\mathbf{u}_{ 2}{}^{*}(\mathbf{e},t), \tag{28}\]where  
\[\mathbf{u_{2}}^{*}(\mathbf{e},t)=-\frac{1}{a_{2}}\Big{[}\ \ \mathbf{Q}_{211}\ \ \ \mathbf{Q}_{222}\ \Big{]}\mathbf{e}. \tag{29}\]
# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 4 Classification Scheme
### Classification of Communication Technology  
In our mapping study on the trustworthiness of large language models (LLMs) in communication technology, we examined advanced cellular generations along with non-cellular technologies. Based on the initial categorization for communication and the process of keywording, the following classes were extracted:  
* Cellular;
* Non-cellular;
* Not Mentioned.  
Although we initially considered all cellular generations, including 2G and 3G, we ultimately concentrated on 4G and beyond due to their compatibility with AI-driven applications. LLMs are particularly relevant to 4G, 5G, and 6G, which support advanced data processing and real-time decision-making [70].  
* **4G--**With significantly higher data rates, 4G enabled new applications such as IoT and industrial automation. LTE is another common term used for 4G.
* **5G--**Offering low-latency communication and very high data rates, 5G broadens the range of use cases across diverse sectors.
* **6G--**Expected to offer ultra-low latency, terabit-per-second data rates, and AI-driven network optimization, 6G aims to enable futuristic applications such as holographic communications, pervasive intelligence, and seamless global connectivity.
* **Cellular (not-specified)--**This category includes studies that do not specify which particular cellular technology is employed in the T-A-K fields.  
Moreover, non-cellular technologies play a crucial role in applications where LLMs can enhance security, automation, and efficiency [71].  
* **WiFi--**Encompasses technologies based on the IEEE 802.11 standard, including vehicular ad hoc networks (VANET) and dedicated short-range communication (DSRC).
* **Radio-frequency Identification (RFID)--**A short-range communication method using electromagnetic fields to transmit data.
* **Bluetooth--**A technology designed for low power consumption, short-range communication, and low data transfer rates.
* **Satellite--**A long-range communication method that is sensitive to interference and relies on a clear line of sight.
* **Non-cellular (not-specified)--**Includes studies that do not specify the non-cellular technology used in the T-A-K fields.