esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing
## 3 Attribute-Based AC Method Incorporating Blockchain and CSP  
Based on the attribute-based AC method, this paper proposes a distributed AC method based on blockchain and Internet of Things technology, as shown in Figure 2.  
In this model, the following entities are mainly included: an attribute authorization authority (AAA), an IBC (identity-based cryptograph) [17] server, a cloud service provider (CSP), an IoT gateway, an IoT device, and a federated blockchain network.  
The attribute authorization authority (AAA): The AAA is an attribute authorizer of IoT devices. It authorizes \(i\) attribute value pairs for each sensing layer terminal, based on its role, identity, etc. Attribute value pairs (_prop_n\({}_{i}\), _prop_v\({}_{i}\)_) constitute the set \(S\) of attribute value pairs of the device, and the set \(S\) is submitted to the blockchain for consensus in the form of transactions, which will be permanently stored on the federated chain after the consensus is passed. The whole system can have multiple attribute authorization authorities.  
The IBC server: Every IoT device must complete registration with the IBC server in advance, before joining the system. After successful registration, the device will receive a pair of IBC-based public-private key pairs.  
Cloud service provider (CSP): The CSP mainly provides cloud computing and storage services for IoT devices; the IoT data are usually stored with encryption.  
The IoT gateway: With strong computing and storage capabilities, it is directly connected to the IoT sensing layer device, and is the intermediary between the IoT terminal and the blockchain and cloud service providers. This largely solves the problem of the limited computing and storage resources in the IoT terminal.  
Figure 2: Blockchain-based IoT AC system model.  
IoT devices: IoT perception layer devices that collect, process, and transmit various device-generated data.  
Federated blockchain network: this gives consensus, confirms and stores the transactions submitted to the Fabric network, and also provides a smart contract function to realize the access control function.
## 4 Results and Discussion
### Disengaging Uniformity  
The friction pair gaps after the disengaging process with different ATF temperatures are presented in Table 2. It can be found that the disengaged friction pair gaps decrease from the first friction pair to the sixth friction pair in sequence. However, it is not intuitionistic to directly evaluate the disengaging uniformity through the value of friction pair gaps for a wet multi-disc clutch. Therefore, the NUC is proposed to characterize the disengaging uniformity, which is expressed as:  
\[\text{NUC}=\frac{1}{Z}\sum_{i=1}^{Z}\frac{1}{1+\delta_{i}/\delta^{*}} \tag{20}\]  
where \(\delta^{*}\) is the ideal uniform disengaging gap. The NUC is inversely proportional to the value of friction pair gaps, the value range of which is 1 \(>\) NUC \(>\) 0.5. The smaller the NUC is, the more evenly distributed the disengaged friction pairs are.  
The variation of the NUC in the disengaging process with different ATF temperatures is shown in Figure 5f. During the disengaging process, the NUC first decreases slowly then decreases rapidly and fluctuates with the fluctuation of friction pair gaps, finally stabilizing. With the decrease of the NUC, the discs separate more and more rapidly. The NUCs after the disengaging process with different ATF temperatures are presented in Table 2.  
As the ATF temperature increases from 60 \({}^{\circ}\)C to 80 \({}^{\circ}\)C, the NUC decreases from 0.5825 to 0.5761. However, as the ATF temperature increases from 80 \({}^{\circ}\)C to 120 \({}^{\circ}\)C, the NUC sees a slight increase from 0.5761 to 0.5784. As the ATF temperature continues to increase, the NUC can hardly be changed. Therefore, with the increase of ATF temperature, the disengaging uniformity first increases, then decreases slightly, and the disengaged friction pair gaps at 80 \({}^{\circ}\)C distribute most uniformly.
# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
## 3 Materials and Methods
### Training Procedure
#### 3.2.1 Datasets
Because the annotations were crowdsourced, a two-stage cleansing process was incorporated to make them compatible with the ADT task: First, the alignment of the annotations was improved by adjusting the positions of the annotated beats following the work of [PERSON] et al. [29]. Second, the video game annotations were mapped to their corresponding drum instrument(s) following an expert system informed by the guidelines of the community of annotators. After the cleansing process, we observed that the dataset trained a model better than the non-synthetic datasets (ENST, MDB, and RBMA), but only as well as [PERSON]'s two-step process with TMIDT. Moreover, the results indicated that ADTOF-RGW likely contains annotation errors despite the cleansing process. In this work, we built a new dataset consisting of high-quality annotations chosen for their superiority by the community of rhythm game players and including virtually no duplicated tracks from ADTOF-RGW. We named this set ADTOF-YT because many annotated tracks were shared by players and annotators on platforms such as YouTube and Twitch. To cleanse the dataset, we adopted the same process as for ADTOF-RGW. Although the original video game annotations supported up to ten instruments as well as two levels of dynamics (normal and quiet notes), inconsistencies due to crowdsourcing persuaded us to use a smaller five-class vocabulary without velocity information: bass drum (KD), snare drum (SD), toms (TT), hi-hat (HH), and crash and ride symbols (CY + RD). Moreover, we noticed that ADTOF-YT is biased toward the metal and rock musical genres, as displayed in Figure 5. A quantitative analysis of the cleansing procedure is provided in Appendix A, and the modalities and ethical considerations of both the ADTOF-RGW and ADTOF-YT datasets are discussed in Appendix B.  
Figure 5: Genre distribution for both ADTOF datasets.
## 4 Proposed Architecture  
In our proposed architecture, a smart contract is created that provides metadata about record data, ownership, and permissions. Cryptographically signed instructions for controlling these characteristics are included in our system's blockchain transactions [50; 51; 52; 53]. Only legal transactions ensuring data alternation are used by the contract's state-transition functionalities to carry out the rules. If a medical record can be represented computationally, laws may be built to enforce any set of rules governing it. For example, before providing third-party viewing access, a policy may require separate consent transactions from patients and healthcare providers. For complicated healthcare workflows, we created a solution based on blockchain smart contracts. Smart contracts are created to manage data access permissions across different actors in the healthcare ecosystem, as shown in Figure 4.  
This will make it easier for all stakeholders to exchange and communicate information. Smart contracts include data authorization restrictions. It can also assist in tracing all actions associated with a unique ID from the point of origin to the current instance of time. There will be no need for a centralized organization to supervise and authorize the operation because this can be done directly through the smart contract, considerably  
Figure 4: Blockchain and IPFS-supported healthcare ecosystems.  
Figure 3: Benefits of employing blockchain technology in the healthcare industry.  
lowering the cost of administration. To ensure performance and economic sustainability, all medical record data are saved in interplanetary file storage, and the content identifier (CID) of the record is committed to the chain. The smart contract includes the registration of the entities, uploaded documents, treatment process details, and insurance claim process. The tools used in the implementation of the proposed work are shown in the Figure 5. It includes Solidity for smart contract development. It uses Truffle suit for the local blockchain environment. It employs web3.js library to connect the smart contract with the front end. Metamask wallet is used to enable the interaction of the code with the blockchain.
## 2 System Model
### Signal and Interference Model  
For UD-LSC systems, the received signal to interference and noise ratio (SINR) of each ground LBC can be expressed as (1). In this paper, we propose a multi-satellite cooperative scheme to serve ground \(M\) LBCs. In Formula (1), the beam association matrix \(V\in\mathbb{Z}^{K\times M}\), which is composed by elements \(v_{k,m}\), is used to indicate that the LEO satellite \(k\) serves the beam \(m\) when \(v_{k,m}=1\) and otherwise \(v_{k,m}=0\). \(P_{k,m},G_{tx},G_{rx}\), and \(P_{rx}\) denote the transmit power from satellite \(k\) to beam \(m\), the transmit antenna gain, the received antenna gain, and the received power of ground user terminals, respectively. \(h_{k,m}\) denotes the channel gain from the LEO satellite \(k\) to LBC \(m\). In this paper, we consider a 3 GPP-NTN based channel model in [1] as (2).  
\[R_{k,m}=\frac{v_{k,m}P_{k,m}G_{tx}h_{k,m}G_{rx}P_{rx}}{I_{L}(k,m)+I_{G}(k,m)+N _{0}\cdot BW} \tag{1}\]  
In (1), \(I_{L}(k,m)\) and \(I_{G}(k,m)\) represent the interference from LEO satellites and the GEO satellites, respectively. \(N_{0}\) is the power spectrum density of the noise, and \(BW\) is the beam bandwidth. The interference from the UD-LSC system to the GEO communication system is expressed as (3) and (4).  
Figure 1: A typical coexistence scenario of the UD-LEO satellite system and a GEO satellite system.  
\[h_{k,m}=10^{(\frac{PL_{k,m}^{S}+PL_{k,m}^{A}+PL_{k,m}^{SM}+PL_{k,m}^{SL}+PL_{k,m}^{AD }}{10})} \tag{2}\]  
In (2), \(PL_{k,m}^{FS}\) is free space path loss, \(PL_{k,m}^{A}\) is atmospheric path loss due to gases and rain fades, \(PL_{k,m}^{SM}\) is shadowing margin, \(PL_{k,m}^{SL}\) is scintillation loss, and \(PL_{k,m}^{AD}\) is additional loss, for example degradation due to feeder links [14].  
\[I_{L}(k,m)=\sum_{i=1}^{K}\sum_{j=1,j\
eq m}^{M}v_{i,j}P_{i,j}G_{tx}\Big{(} \alpha_{i,j}^{k,m}\Big{)}h_{i,j}G_{rx}\Big{(}\beta_{i,j}^{k,m}\Big{)}P_{rx} \tag{3}\]  
\[I_{G}(k,m)=\sum_{g=1}^{G}v_{k,m}P_{g}^{G}G_{tx}^{G}\Big{(}\gamma_{k,m}^{g} \Big{)}h_{g,m}^{G}G_{rx}\Big{(}\theta_{k,m}^{g}\Big{)}P_{rx} \tag{4}\]  
In Figure 2, we can observe that \(\alpha_{i,j}^{k,m}\) and \(\beta_{i,j}^{k,m}\) represent the off-axis angles from the LEO satellite \(i\) to LBC \(m\) at the UD-LSC transmitter and the UD-LSC receiver, respectively. \(\gamma_{k,m}^{g}\) and \(\theta_{k,m}^{g}\) represent the off-axis angle from GEO satellites to LBC \(m\) at the GEO transmitter and the UD-LSC receiver, respectively. The antenna gain of the GEO and LEO satellite systems can be modeled as (5) [13].  
\[G(\theta)=G_{0}\bigg{[}\frac{I_{1}(u(\theta))}{2u(\theta)}+36\frac{J_{3}(u( \theta))}{u(\theta)^{3}}\bigg{]}^{2} \tag{5}\]  
where \(\theta\) denotes the off-axis angle and \(G_{0}\) denotes the maximum beam gain.\(J_{1}(\cdot)\) and \(J_{3}(\cdot)\) represent first kind and third kind Bessel function, respectively. Here, \(u(\theta)=2.07123 sin(\theta)/sin(\theta_{3 dB})\).
# The Democratization of Artificial Intelligence: Theoretical Framework
## 1 Introduction  
Artificial intelligence has rapidly evolved from a niche academic discipline to a transformative technology with wide-reaching applications. As AI becomes more integrated into various sectors, there is a growing movement to democratize AI, ensuring that its benefits are not limited to a select few but are distributed across society. Democratization of AI can be described as a reality where \"every person and every organization\" has access to technology powered by AI [1; 2], or as \"to the demand that AI be made widely available or useable or that individuals should be empowered to develop or use AI. Training data should be accessible, AI technologies should be affordable, and their use should be encouraged [3]. Other authors describe the democratization of AI as providing widespread access to open data and AI models for non-technical individuals. This access allows them to effectively use AI models in specific contexts without needing deep technical expertise. In this context, some questions are raised by previous studies: Are big companies the adequate players to manage open data and deploy AI models for governing such ecosystems [4]? Are there ethical, societal, and safety challenges being addressed when we use open data and open-source models [5]? Are the main stakeholders being taken into consideration [6]?  
We live in a context of classification of AI usage, particularly now. Every company, public organization, and nation can easily use Generative AI models to pursue its objectives. As such, this paper aims to provide a comprehensive overview of AI democratization, highlighting its significant key dimensions that are part of a proposed theoretical framework for democratizing AI and exploring the main application usage contexts. This paper brings two contributions from theory. First, it identifies the main dimensions upon which the democratization of AI can be studied and identifies the main domains that each dimensionattains when studying the democratization of AI, serving as a solid base to cover the principal aspects this topic entails. Future studies may use this framework to organize in-depth studies on each of the dimensions of democratization of AI.  
This study is organized into eight sections. The paper presents a comprehensive overview of the democratization of artificial intelligence (AI), which makes AI technologies accessible to a broader range of people and organizations. It discusses the historical context of AI, current trends (Section 2), key stakeholders (Section 3), ethical considerations, challenges, and opportunities (Section 4). The paper proposes a theoretical framework (Section 5) for democratizing AI, highlighting its significance, key drivers, and general challenges. The framework includes key components such as accessibility, affordability, usability, ethical and regulatory considerations, and drivers, challenges, and strategies for effective democratization. This is followed by the exposition of possible scenarios of AI democratization (Section 6), and discussion (Section 7), finishing with conclusions (Section 8).
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 3 Seismic Resolution Improvement
### Principal Component Filtering  
Strike-slip faults manifest in seismic profiles as distinct discontinuity points along the same phase axis, thus enhancing the continuity of effective seismic reflections and bolstering the fault identification capability of the seismic data. However, the presence of noise can significantly impact the quality of the seismic data and subsequently hinder accurate interpretation. Consequently, denoising seismic datasets is of critical importance.  
One effective approach employed for denoising is the principal component analysis (PCA) algorithm, which leverages eigenvalues as features in orthogonal signal space orthogonal decomposition. This enables the enhancement of coherent energy while suppressing interference [37]. The methodology involves calculating and analyzing the covariance ma  
Figure 3: Quality improvement of the seismic data. (**a**) The original seismic profile. The burial depth map of the Tarim Ordovician strata is in the upper left corner, and the red line shows the location of the seismic section. The green arrow indicates the north direction. (**b**) The seismic profile after PCA+ structure-oriented filtering. This methodology involves calculating and analyzing the covariance matrix of seismic traces within a defined time window, facilitating a robust denoising process and improving the overall quality of seismic data for further analysis and interpretation. (**c**) The seismic profile of PCA+ structure-oriented filtering + fault-focused imaging. By maintaining the essential characteristics of the original seismic signal while improving its signal-to-noise ratio, this method effectively emphasizes the discontinuity along the in-phase axis.  
trix of seismic traces within a defined time window, facilitating a robust denoising process and improving the overall quality of seismic data for further analysis and interpretation.  
\[R=\begin{bmatrix}r_{11}&r_{12}&\cdots&r_{1p}\\ r_{21}&r_{22}&\cdots&r_{2p}\\ \cdots&\cdots&\cdots&\cdots\\ r_{p1}&r_{p2}&\cdots&r_{pp}\end{bmatrix} \tag{1}\]  
\[r_{ij}=\frac{\sum_{k=1}^{n}\left(x_{ki}-\overline{x}\right)\left(x_{ki}- \overline{x_{j}}\right)}{\sqrt{\sum_{k=1}^{n}\left(x_{ki}-\overline{x}\right) ^{2}\sum_{k=1}^{n}\left(x_{ki}-\overline{x_{j}}\right)^{2}}} \tag{2}\]  
In these expressions, the eigenvalues of the covariance matrix are obtained, and \(n\) is the number of samples. \(k\) is the serial number of the \(i\) variable. \(p\) eigenvalues are obtained by solving the eigenequation of the correlation coefficient matrix \(R\): \(\left(R-\lambda I_{p}\right)=0\). Then, the unit eigenvector \(b_{j}^{\alpha}\) is obtained by solving the equation \(Rb=\lambda j\)[38].  
Finally, the features of the weighted reconstruction signal of the eigenvector corresponding to each eigenvalue are analyzed as follows:  
\[U_{ij}=z_{i}^{T}b_{j}^{\alpha} \tag{3}\]  
The reconstructed signal representing the interference signal is eliminated to achieve noise filtering.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 4 Discussion
Apart from that, GNSS measurements could be used for the comparison and validation ground-based method for InSAR. A more accurate displacement assessment could show more sufficient results in the following years since the number of satellite images will be extended, and electronic transponders could installed to further investigate Limassol, as it is the perfect city for studying the urban environment through SAR techniques. All the procedures could be automatized, and the possibility of the real-time monitoring of land subsidence could be realized. Through the CyCLOPS (INFRASTRUCTURES/1216/0050) program, corner reflectors have been installed all around Cyprus with promising results.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 1 Introduction
The OAGS mechanism dynamically adjusts the Kalman gain based on the matching coefficient, prediction deviation, and matching inconsistency, minimizing the adverse impact of incorrect matches on state estimation. This enhancement enables the proposed matching-based autonomous navigation approach to more effectively adapt to complex environmental variations, ensuring robust and reliable localization, as validated by the experimental results in Section 4.2 (Adaptability Evaluation) and Section 4.3.2 (Comparison of Filters). (3) Building on the contributions above, we propose a novel and practical long-distance autonomous navigation approach for land vehicles. The proposed method is evaluated on the R\({}^{3}\)LIVE [3; 4] and NCLT [12] datasets, demonstrating its effectiveness in diverse real-world scenarios. Experimental results show that the proposed method enables real-time global localization and effectively addresses the issue of position drift in LIV-SLAM systems. It achieves an average RMSE of 2.03 m across all sequences of the NCLT dataset in outdoor environments and 2.64 m on the R\({}^{3}\)LIVE dataset under the same condition. To clarify the scope of this work, it should be noted that the goal of our research is not to present a universally optimal global localization solution. Rather, we aim to address a practical and increasingly common scenario: autonomous navigation in GNSS-denied environments where LiDAR and visual sensors are available.
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
## 3 Results and Discussion
### Electrical Characterization  
The measurement setup to determine the high-frequency performance of the fabricated waveguides is depicted in Figure 8. By using a VNA (Rohde & Schwarz(r) ZVA 50) with E-band frequency converters (Rohde & Schwarz(r) ZVA Z90E), the scattering parameters were obtained and the results are shown in Figure 9. The reference planes were calibrated to the flange-interfaces of the frequency converters by a TOSM (Through, Offset-Short, Short, Match) calibration algorithm with waveguide calibration standards. Based on the scattering parameters, the attenuation coefficients \(\alpha\) of the different samples can be calculated according to  
\[\alpha=-\frac{10}{l}\text{log}_{10}\Big{(}\frac{|s_{21}|^{2}}{1-|s_{11}|^{2}} \Big{)}\text{dB} \tag{1}\]  
whereby \(l\) corresponds to the length of the device under test. In order to give a comparison to a conventionally fabricated waveguide, a commercial WR12 test port adaptor (Rohde & Schwarz(r) HP/A) is used as a reference and is additionally shown in Figure 10. Due to the higher surface roughness and smaller waveguide cross-section (both height and width), the epoxy resin and ceramic waveguide feature a higher attenuation compared to the test port adaptor. However, geometry deformation is a major contribution to the relativity high attenuation which would (in case of a straight, non-deformed waveguide) exhibit similar attenuation levels as the test port adaptor as shown in [14; 23]. The additional attenuation, which is introduced by the ceramic waveguide, compared to the epoxy resin-based one, stems from the additional surface roughness (\(R_{\text{q,e}}=2.43\) um vs. \(R_{\text{q,c}}=0.53\) um).  
Figure 8: Photography of the measurement setup for determining the scattering parameters of the fabricated waveguides.  
Figure 9: Measured return loss \(s_{11}\) and insertion loss \(s_{21}\) of the fabricated WR12 waveguides.  
The measured normalized phase coefficient \(\frac{\beta}{P_{0}}\)[42] in Figure 11 still exhibits the typical waveguide behavior but indicates significant deviations in geometry, which can be seen, for example, in the shift of the cutoff frequency. \(\beta_{0}\) was calculated from the ideal dimensions of a WR12 waveguide as stated in [1]. The test port adaptor exhibits a \(\frac{\beta}{P_{0}}\)-ratio near one, since its dimensions are very close to an ideal WR12 waveguide. As already pointed out in Section 3.1, the epoxy resin prototype was adapted to the geometry deformations of the ceramic specimen and, therefore, includes the impact of irregularities along the propagation direction. However, the ceramic pendant still shows a higher deviation from \(\beta_{0}\), indicating deviations of its cross-section along the propagation direction.  
Therefore, the suitability of the novel AM process is clearly demonstrated. It can be stated that the geometric distortion due to the subsequent processes, i.e., debinding and sintering, is most critical for reproducible manufacturing of waveguide components.