esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Data Governance and Data Quality  
Due to the growing awareness of the importance of using data correctly, many organizations are setting up data governance (DG) policies. Even if DG is often regarded as a pure information technology (IT) function, many authors believe that it should be an organization-wide concern [14]. The DG framework of the Data Governance Institute (DGI) defines DG as \"the exercise of decision making and authority for data related matters\" [15], and the principal benefits for organizations that adopt DG are (1) increased revenue and value, (2) managed cost and complexity; and (3) ensured compliance, security, and privacy risk control.  
The DGI data governance framework discusses DQ in terms of DG's focus on DQ [15]. This framework possesses six main focus areas, one of them being data quality. The idea is that DG programs aimed at improving DQ would normally involve the application of DQ software and could be applied locally to one department or throughout an organization/enterprise. Applying the data governance framework requires the involvement of data stewards and data stakeholders, and it is very often quite a challenging process. Other authors argue that having DG helps toward improving DQ [16]. They state that data governance officers can help improve the consistency and quality of data during the data life cycle instead of relying upon the IT department to achieve this enormous responsibility. The IBM Data Governance Council Maturity Model highlights data quality management as one of the core disciplines of effective data governance. It defines data quality management as \"methods to measure, improve and certify the quality and integration of data\" [17]. Other authors also list data quality as part of the decision domains for data governance [18]. Their logic is that DG should help set DQD-related standards, such as accuracy and completeness, but it should also create policies to facilitate the proper communication and evaluation of DQ measures.  
The few research studies that have focused on Big Data governance also seem to indicate a clear link with DQ, e.g., [15; 18; 19]. Reviewing some of the major challenges and opportunities of Big Data, [PERSON] [19] states the opinion that the _volume_ and _velocity_ of data will decrease the certainty associated with data. Furthermore, [PERSON] claims that existing data quality technologies cannot cope with the uncertainty issues caused by the _volume_ and _velocity_ characteristics of Big Data.
## 6 Application Scenarios
### Combining Robots with Projectors  
Designing robots with a screen for a face is becoming popular because it makes it possible to display a broad range of expressions that are easy to identify. Screens can easily be related to computers, and computers are machines like robots. For this reason, screens can be considered to be appropriate parts of robots. Projectors work in a way similar to screens because they make it possible to display multiple kinds of information. The difference between them is related to the way the user interacts with the interface. Embedded screens are constrained to the size of the robot's body, and thus are commonly small. This causes users to become immersed in the contents of the screen. On the other hand, projectors can be used to display large images that can be shared by multiple people sharing the same space. It is interesting to explore the social aspect of robots with projectors, and their effect on human interaction.  
Projected images have a substantial visual impact and they allow to keep the robot structure simple. Moreover, projected images create opportunities to share the experience with more than one user sharing the same space, using a small robot body. By combining robots and projectors, it is also possible, for example, to share photos and memories with people in the same place and those in different locations, opening different possibilities for future designs (Figure 14B). Enhancing a robot's capabilities using a projector is not limited to facial expressions. Robots with no limbs like Pepita have limited body's capabilities, and projectors can be used to display animations of body gestures. For example, avatars with pointing gestures projected from the robot could potentially allow users to perform actions together regardless of whether they share the same space (Figure 14A).  
However, projected images are hard to see in a bright environment, and they can be occluded when the robot is hugged. One possible solution to overcome these limitations could be by notifying the user when a message arrives by combining LEDs and vibrations. By doing this, the user can adapt and move the robot to a darker place if necessary, or uncover the lens of the projector. Other limitations are related to the heat generated by the lamp which could be a problem if it is used continuously for  
Figure 13: Social context for the proposed companion robot Pepita: (**Top**) as a tangible emoticon; (**Bottom**) as a presence indicator.  
extended periods of time. Future similar projects need to consider the problem heat can represent for the application carefully.
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 2 Materials and Methods
### Custom Python Block Development  
To facilitate the implementation of OFDM with channel aggregation, two important Python blocks were developed, namely the \"byte splitting\" and \"byte combining\" blocks. These blocks, which ensure both effective distribution and accurate recombination of data streams, are central to managing the flow of data across multiple channels. Developed in Python, the block utilizes the NumPy library for efficient array management, crucial for handling large volumes of data at high speeds.  
Byte splitter block: This block makes the distribution of data across different frequency channels and is responsible for splitting the incoming data stream into two separate paths.  
Byte combiner block: Complementing the byte splitter, this block combines the separate data streams into a single output monitoring the packet headers. This process is critical to ensure that the aggregated data are correctly synchronized and integrated after they have been transmitted over multiple channels.  
A notable technical achievement within the GNU Radio framework has been the development of custom Python blocks for OFDM channel aggregation. These blocks address the complex data handling requirements of a channel-aggregated OFDM system, thereby enabling enhanced communication capabilities and contributing to the advancement of modern HF digital communication systems.
## 4 Analysis of LDP Characteristics Under Different Stratigraphic Parameters
### Numerical Modeling Framework  
(1) Geometric and Meshing Parameters  
The horseshoe-shaped tunnel cross-section was composed of three circles with distinct radii, as illustrated in Figure 3a. Full-face excavation with an advance length of 1 = 1 m was implemented, maintaining the computational domain configuration shown in Figure 1a. Key parameters included the following: burial depth: H = 49 m; excavated section length: \(L_{1}\) = 99 m; unexcavated section length: \(L_{2}\) = 101 m; unsupported distance behind the face: \(L_{3}\) = 6 m; and LDP data were collected post-excavation. The mesh configuration (Figure 3c) comprised 21,728 elements.  
(2) Stratigraphic and Lining Parameters  
The support system was simplified to a single lining layer with the following properties: density: 2500 kg/m\({}^{3}\); Young's modulus: 28.5 GPa; and Poisson's ratio: 0.25. Stratigraphic parameters remained consistent with those in Section 2.1, encompassing three distinct parameter sets as specified in Table 1. Stratigraphic combination schemes are detailed in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination Scheme** & **Parameters of the Surrounding** & **Parameters of the Surrounding** \\
**Number** & **Rock in the Initial Excavation** & **Rock in Front of the Tunnel Face** \\ \hline
1 & A & A \\
2 & A & B \\
3 & A & C \\
4 & B & A \\
5 & B & B \\
6 & B & C \\
7 & C & A \\
8 & C & B \\
9 & C & C \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics table of different stratum combination schemes.  
Figure 2: (**a**) Comparison results of LDP curves under Condition A. (**b**) Comparison results of LDP curves under Condition B. (**c**) Comparison results of LDP curves under Condition C.  
(3) LDP Monitoring Point Configuration  
To simulate practical construction conditions, monitoring points were distributed along a finite-length section ahead of the tunnel face. A total monitoring line length, \(D\) = 8 m, was established, with specific positioning shown in Figure 3d. Deformation data were recorded at 0.25 m intervals.
## 5 Conclusions  
In this paper, we introduce the FA-MMAC-DA protocol for a directional FANET with multiple channels to achieve fast neighbor discovery and avoid communication interruption. To address the challenge of neighbor discovery for a FANET under the circumstances of being without coordination or prior information, the BR-DA and BR-DA-FANET protocols are proposed to reach the theoretical supremum and achieve quick neighbor discovery during the initial access phase. Subsequently, we present the ND-LP and ACI-LP protocols to enable efficient neighbor discovery and mitigate communication interruptions. Through simulations, we demonstrate that the FA-MMAC-DA protocol outperforms existing approaches by reducing both neighbor discovery delay and communication delay. The protocol proposed in this paper is applicable to a dedicated communication directional ad hoc network with multiple nodes and multiple channels, such as collaborative reconnaissance with multiple UAVs, coordinated disaster relief efforts, and more. For instance, in scenarios where multiple UAVs engage in collaborative communication to facilitate coordinated reconnaissance in disaster-stricken areas, communication among UAVs is established using the FANET. This protocol can be employed to achieve rapid neighbor discovery within the FANET network, thereby reducing neighbor discovery delay and transmission delay.  
This research was accomplished by all the authors: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]) conceived the idea, performed the analysis, and designed the scheme; [PERSON], [PERSON] and [PERSON] conducted the numerical simulations; [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]) cowrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grants: 62001483 and 61931020) and the project was supported by the Provincial Natural Science Foundation of Hunan (grant: 2022 JJ10068).  
Data Availability Statement: Data is contained within the article.  
Figure 31: The simulation for an ACI-LP protocol.  
Conflicts of Interest: [PERSON] and [PERSON] are employed by the 54 th Research Institute of China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Conclusions  
* The average fracture toughness value obtained using the ENDB, SCB, SENB and ENDC specimens at \(-25\)\({}^{\circ}\)C were obtained equal to 1.05, 0.99, 0.93 and 0.7 MPa\(\surd\)m, respectively. Similarly, for \(-5\)\({}^{\circ}\)C, the corresponding values of the average K\({}_{\text{lc}}\) for the same mixture were 0.76, 0.73, 0.67 and 0.49 MPa\(\surd\)m, respectively. These results demonstrate the effect of testing methods and temperature on the low temperature fracture resistance of asphalt mixtures.
* Results obtained from the bending type samples (ENDB, SCB and SENB specimens) were in a narrow bound and with slight differences. However, the K\({}_{\text{lc}}\) value obtained from the ENDC (loaded in diametral compression) was noticeably less than (about 25%) the results of other testing methods. It can be concluded that the type of the applied loading (i.e., bending or compression) has more significant effect than the shape of sample (disc or beam) on K\({}_{\text{lc}}\) value of asphalt mixtures.
* Fracture toughness results were increased by decreasing the test temperature from \(-5\)\({}^{\circ}\)C to \(-25\)\({}^{\circ}\)C by about 40 percent. However, the fracture toughness ratio (K\({}_{\text{lc}}\)(@-25\({}^{\circ}\)C)/K\({}_{\text{lc}}\)(@-5\({}^{\circ}\)C)) was nearly constant for the whole HMA tested samples under mode I with different shapes and loading conditions.
* The results of the ENDB specimen were very close to the fracture toughness obtained from the SCB specimen that is a recommended testing method by the ASTM. Therefore, among the investigated test samples the ENDB testing method can also be recommended for conducting low temperature fracture toughness test on asphalt mixtures.  
Conceptualization, M.R.M.A.; data curation, M.R.M.A.; formal analysis, H.R.K.; funding acquisition, D.Y.; investigation, H.R.K.; methodology, D.Y., H.R.K. and M.R.M.A.; project administration, M.R.M.A.; resources, D.Y.; supervision, M.R.M.A.; visualization, D.Y. and H.R.K.; writing--original draft, H.R.K.; writing--review and editing, M.R.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study did not involve tests on humans or animals.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) Analysis  
The SEM analysis was performed in order to observe the adhesion between the restoration material and the dental tissues when there was no dye penetration (scores = 0) as well as when the dye penetrated the composite/dental tissue interface (scores > 0).  
Analysing the Score 0 samples, adhesive interfaces completely adapted to the restoration material were observed. SEM images show a uniform and efficient polymerization on the entire restored cavity. The hybrid layer has a thin, homogeneous and denser structure in electrons with a relatively uniform thickness (~2 \(\upmu\)m), which closely followed the tooth surface and formed a hybrid layer penetrating the thickness of the dental tissue, in some cases on a depth of 10 \(\upmu\)m. The high degree of homogeneity of the polymerized adhesive layer was also noticed. It was observed that the resin extensions, which intersect the hybrid layer, have an electronic density similar to the overlying adhesive layer, which demonstrated a continuity of the nanoparticle concentration into depth. Thus, the periphery of the dental tissue was also hybridized and the hybrid layer extends in the entire depth of the demineralized area (Figures 5 and 6).  
Figure 5: SEM images for Score 0 in Group A: (**a**) Composite (C)—enamel interface (E); (**b**) Composite (C)—demin (D) interface; HL—hybrid layer. EDX mappings are given below for each SEM electron image.  
Figure 6: SEM images for Score 0 in Group B: (**a**) Composite (C)—enamel interface (E); (**b**) Composite (C)—demin (D) interface; HL—hybrid layer. EDX mappings are given below for each SEM electron image.  
Regarding the samples with a score higher than 0, there can be noticed cracks at the dental tissue/hybrid layer or composite/hybrid layer interfaces, with a varying length for different samples. Gaps and fissures were observed at the composite/dentin interface more than at the composite/enamel interface, indicating that the adhesion to the dentin is likely to be influenced by several factors than the enamel (Figure 7).  
The EDX images indicate a high content of atomic oxygen (O), calcium (Ca), silica (Si), carbon (C) and phosphorus (P) and a medium content of barium (Ba). The analysis indicated a high concentration of phosphorus and calcium in dentin which suddenly drops at the dentin/composite interface. Regarding the adhesive layer, an increase in carbon was observed for all samples, suggesting that the monomers contained that element. In addition, the images revealed small concentrations of aluminum and silica in dentin as well as in the adhesive layer.
## 5 Experimental Results and Analysis
### Performance
#### 5.5.2 Comparison Study on SCB-U Dataset
The model's high precision (0.868) and balanced recall (0.701) indicate that SBD-Net not only minimizes false positives but also reliably detects a wide range of student behaviors, even in challenging conditions such as occlusion or subtle gestures, as shown in Figure 8. The combination of advanced data augmentation techniques, meticulous annotations, and the integration of task-specific modules in SBD-Net significantly contributed to its enhanced performance. Moreover, the model's ability to generalize well across different classroom settings, despite being trained on a dataset with a fixed camera angle, highlights its robustness and adaptability. This further confirms SBD-Net's potential to be deployed in real-world educational environments, where diverse behaviors and interactions need to be accurately monitored and analyzed. Overall, the consistent performance across metrics reaffirms the effectiveness of SBD-Net as a highly capable model for student behavior detection and classification.
## 2 Status of Smart Grids
### Popularization of the Smart Grid  
#### 2.1.1 Europe  
One of the largest deployments of a smart grid system was initiated by Enel Distribuzione, an Italian multinational producer and distributor of electricity and gas. The Telegestore project, by Enel and completed in 2005, was regarded as the first commercial scale use of smart grid technology to the home at an investment expense of EUR2.1 billion, including R&D, production and distribution of smart meters and concentrators, and construction of IT systems. It was considered unusual at that time, when independent companies designed and manufactured their own systems including metering and integrating devices without considering compatibility with ones manufactured by other companies. Since the liberalization of the electricity market in Italy, Enel has built its first smart grid capable of adjusting the two-way flow of electricity generated from renewable sources. Enel also established a strategic partnership agreement with the NEC Corporation for the development of new technologies and solutions in this field.  
InovGrid, an innovative project in Evora Portugal, aims to equip the electricity grid devices with information to automate grid management, improve service quality, reduce operating costs, improve energy efficiency for building sustainable environments, and develop renewable energy. By allowing energy suppliers and companies to use this technology platform to offer consumers value-added energy products and services, the status of the entire distribution grid can be controlled and managed at any given moment of time. Portugal and Electricidade de Portugal (EDP) are at the cutting edge of  
Figure 1: An overview of Smart Grid Architecture.  
technological innovation and service provisioning in Europe through this project to install an intelligent energy grid.  
In Germany, the city of Mannheim used real-time broadband over power lines (BPL) communications in its Model City Mannheim (moma) project. A government sponsored E-energy technology competition identified six model regions, including Mannheim, to carry out research and development activities with the main objective of creating an Internet of Energy.
## 4 Development of LR Liquefaction Probability Model
### Model Construction  
A logistic regression model was employed to develop the new method using five parameters: \(V_{s1}\), \(CSR_{7.5}\), \(M_{W}\), Z, and \(FC\). The model was optimized based on parameter importance rankings, as shown in Equation (23):  
\[P_{L}=\frac{1}{1+e^{-(\theta_{1}\times V_{S1}+\theta_{2}\times\text{LN}(CSR_{7.5})+\theta_{3}\times\text{LN}(M_{W})+\theta_{4}\times\text{LN}(Z)+\theta_{5} \times FC+\theta_{6})}} \tag{23}\]  
where \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), \(\theta_{4}\), \(\theta_{5}\), and \(\theta_{6}\) are the model parameters estimated using the maximum likelihood with 924 samples. The likelihood function is articulated as follows:  
Figure 7: Ranking of parameters’ importance.  
\[\begin{array}{l}\left(\prod\limits_{a=1}^{n_{L}}\frac{1}{1+e^{-(\theta_{1}\times V _{S1}+\theta_{2}\times\text{LN}(CSR_{75})+\theta_{3}\times\text{LN}(M_{W})+ \theta_{4}\times\text{LN}(Z)+\theta_{5}\times\text{FC}+\theta_{6})}}\right)^{w_{ L}}\\ \ast\left(\prod\limits_{b=1}^{n_{NL}}\left[1-\frac{1}{1+e^{-(\theta_{1}\times V _{S1}+\theta_{2}\times\text{LN}(CSR_{75})+\theta_{3}\times\text{LN}(M_{W})+ \theta_{4}\times\text{LN}(Z)+\theta_{5}\times\text{FC}+\theta_{6})}}\right] \right)^{w_{NL}}\end{array} \tag{24}\]  
where \(n_{L}\) represents 417 liquefaction datasets; \(n_{NL}\) represents 507 non-l liquefaction datasets; \(w_{L}\) is the correction coefficient for the discrepancy in the liquefaction data quantity; and \(w_{NL}\) is the correction coefficient for the discrepancy in the non-liquefaction data quantity, calculated based on [PERSON]'s recommendations [41]:  
\[w_{L}=\frac{n_{L}+n_{NL}}{2\times n_{L}} \tag{25}\]  
\[w_{NL}=\frac{n_{L}+n_{NL}}{2\times n_{NL}} \tag{26}\]  
Using this database, \(w_{L}=1.10\) and \(w_{NL}=0.91\) were calculated. The MATLAB programming was employed to complete the construction process and the maximum likelihood estimation was specifically employed to obtain the model parameters for the logistic regression. In the maximum likelihood estimation, the quasi-Newton method was used as an optimization technique, with the Hessian matrix being used to accelerate convergence. Several formulae were derived. By excluding the formulae that violated the natural laws of liquefaction and adjusting them based on engineering experience, the final formula was obtained as follows:  
\[P_{L}=\frac{1}{1+e^{-(-0.0696\times V_{S1}+2.2606\times\text{LN}(CSR_{75})+7.980 5\times\text{LN}(M_{W})-1.3489\times\text{LN}(Z)-0.0244\times\text{FC}-3.4025)}} \tag{27}\]
## 3 Design Principles of the Helical Phased Array
This deviation can be justified by observing Figure 18b, where the radiation pattern changes from its original shape. As a consequence, the front-to-back ratio in this scenario decreases to 9 dB at 20 MHz. For the same reason, as illustrated in Figure 18c, the front-to-back ratio decreases to 5 dB at 30 MHz. To effectively address this issue, the beamformer should be engineered to feed the helices with a phase difference (\(\Delta\Phi\)) that varies in proportion to the spacing between them (S\({}_{\lambda}\)). This adjustment ensures a consistent 180\({}^{\circ}\) phase shift to the backward direction of the array across the frequency range. Further elaboration on this approach is provided in Section 3.1.