esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Validation of Effectiveness  
1. Validation of engineering allowable error  
Taking a 40% new energy penetration rate as an example, to validate the effectiveness of the proposed method, it was compared with the traditional fitting method. The comparison was based on the error defined as the average ratio of the distance between all critical points (injection power vectors) and the boundary of the hyperplane to the magnitude of the injection power vector at that moment.  
\[err_{j}=\sum\limits_{j=1}^{m}\frac{1}{m}\frac{d_{j}}{\left\|x_{j}\right\|^{2}} =\sum\limits_{j=1}^{m}\frac{1}{m}\frac{\left|\sum\limits_{i=1}^{n}a_{ LSi}x_{j}-1\right|}{\sqrt{\sum\limits_{i=1}^{n}a_{LSi}^{2}}\sqrt{\sum \limits_{i=1}^{n}x_{j}^{2}}} \tag{44}\]  
Figure 10: The SSR under new energy penetration rates ranging from 5% to 50%.  
In the equation, \(d_{j}\) represents the distance from the critical point to the system hyperplane, \(\|x_{j}\|^{2}\) represents the module of the critical point in the system (module of the power vector), and \(a_{LSi}\) represents the coefficients of the system hyperplane.  
The comparison of errors between the method used in this article and the traditional fitting method is shown in Table 2.  
Out of the 268 critical points in the three-dimensional safe region, 249 points have a relative error of less than 0.5%, accounting for 92.91% of the total; 19 points have a relative error between 0.5% and 1.5%, accounting for 7.09% of the total; and the maximum relative error is 1.365%. All errors are within the permissible engineering tolerance (5%).  
(2) Boundary Time Domain Validation  
Taking a 40% new energy penetration rate as an example, a number of running points are found at the boundary point for time domain simulation, and then the boundary is constructed accurately based on whether the simulation result is in a stable state and whether the region where the point is located is a safe region. In order to further verify the accuracy of the result, several points near the critical surface of the safety domain are taken for time domain simulation verification.  
The time domain simulation at operating points 2, 9, and 10 resulted in an unstable state, and these points are located outside the hyperplane, not satisfying the stability constraints. Based on the results of the time domain simulation in Table 3, the determination obtained through the stochastic-batch gradient descent with the gradient accumulation strategy remains consistent with the time domain simulation, indicating that the use of the improved stochastic-batch gradient descent and gradient accumulation strategy effectively searches for the SSR with the required accuracy.
# Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid
## 3 Test Results
1.37 to 1.07, and for cell pressure 100-300 kPa ranged from 1.07 to 1.01 (Table 4). Based on these results, further tests were performed only at cell pressures of 20, 50, and 70 kPa.  
In several tests with a geogrid reinforcement at the lowest cell pressure of 20 kPa, two peak deviatoric stresses appeared during shear (Figure 4). The first peak appeared when the vertical strain was equal to 2-3%. This tendency was common in all soils reinforced with geogrids. The second peak began to appear at a 5% vertical strain and reached its maximum at the end of the test (15% vertical strain). Notably, the deviatoric stress value for the second peak was greater than that of the first peak.  
In cases where two peak values appeared, it was clear that the load was primarily carried by the geogrid itself, with minimal contribution from the surrounding soil. This phenomenon was also evident in post-test images of the geogrid (Figure 5b), which showed substantial damage and deformation of the geogrid. Conversely, in cases where only a single deviatoric stress peak was observed, the geogrid returned to its initial state without significant long-term damage (Figure 5a).  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\(\sigma_{3}\), kPa} & \multicolumn{2}{c}{**Deviatoric Stress (\(\sigma_{1}\)-\(\sigma_{3}\)), kPa**} & \multirow{2}{*}{**Reinforcement**} \\ \cline{2-2} \cline{4-4}  & **Without Geogrid** & & **With Geogrid** \\ \hline
20 & 106.94 & 145.98 & 1.37 \\
50 & 208.04 & 244.19 & 1.17 \\
70 & 339.94 & 364.48 & 1.07 \\
100 & 393.21 & 419.12 & 1.07 \\
200 & 697.33 & 742.13 & 1.06 \\
300 & 1146.01 & 1154.49 & 1.01 \\ \hline \end{tabular}
\end{table}
Table 4: Effect of cell pressure on maximum deviatoric stress for soil No. 4.  
Figure 3: Influence of cell pressure on the mechanical properties of soil No. 4.  
These observations suggest a complex interplay between the geogrid and the surrounding soil and could have significant implications for the performance of geogrid-reinforced structures. Only the deviatoric stress values of the first peak were evaluated for further analysis of the results.
## 3 Materials and Methods
### System Pipeline
#### 3.2.1 Pre-Processing  
The quality of digital wound images is damaged due to noise because of inappropriate illumination, shadows, and camera flash [22]. Therefore, the quality of the images was enhanced, prior to image segmentation, in two pre-processing stages: color correction and noise removal. Color correction was performed by a hybrid approach combining gray world assumption and retinex theory [23], which are the most common techniques used in the literature. The gray world assumption method is based on the average intensity value of the color channels, while retinex theory is based on maximum intensity values [24]. The hybrid approach maximizes each method's efficacy by finding the correlation coefficients that will simultaneously satisfy both methods. Due to noise in digital images caused by reflections and shadows, noise removal is vital to removing white and black pixels in the images. For this reason, a median filter was applied to all images since it is a compelling method for removing salt and pepper noise while preserving the surgical site's edges. The results of pre-processing are displayed in Figure 4.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 4 Discussion
### Privacy and Data Protection  
Among the themes identified, privacy emerged as a top concern influencing the adoption of digital contact-tracing technology [21; 39]. Most of the studies have shown a negative correlation between privacy concerns and adoption rates [44; 45; 49; 120]. The privacy concerns that resulted in low adoption varied with the nature of the data collected [50; 52; 62; 91; 95]. Specifically, there were concerns about data misuse and long-term use of data. In addition, surveillance measures by authorities mainly affected people's willingness to participate in digital contact-tracing [90; 101]. However, with time, respondents showed an increasing likelihood of adopting contact-tracing apps when they believed that their data collected by these apps were secure [95].  
Surprisingly, certain studies have reported that there was no relationship or no significant impact on privacy conditions and download intention [71; 131]. However, research has shown that individuals are more likely to use contact-tracing apps if they believe their privacy is protected [80]. There was a positive relationship between knowledge about the app's privacy features and app usage [44], and participants value consent as a key privacy protection mechanism [91]. Overall, it was clear from the literature that robust privacy policies and assurances about data handling could create a positive attitude towards contact-tracing applications. In addition, it was evident that people were not fully aware of the contact-tracing mechanism.  
**Recommendations for government**. Governments should establish and enforce strict data privacy regulations with transparent oversight mechanisms to reassure citizens. This is because they are responsible for safeguarding public trust by ensuring personal data are protected, thereby addressing fears of data misuse, and ultimately promoting CTA adoption. Governments should also launch public education campaigns explaining the data privacy features of the apps, including anonymisation and encryption processes. This is because increasing awareness about privacy-preserving technologies can reduce scepticism and increase app adoption.  
**Recommendations for app developers**. App developers should implement privacy-by-design principles, such as anonymised data collection. This is because app developers are directly responsible for ensuring technical measures address privacy concerns. These features reduce the risk of data misuse and demonstrate a commitment to safeguarding user data, thereby promoting adoption. App developers should also provide clear and concise privacy policies in-app, using accessible language and visual aids to explain how data will be protected and used. This is because transparency in privacy practices empowers users to make informed decisions, addressing the negative correlation between privacy concerns and app adoption.
# Query Processing in Blockchain Systems: Current State and Future Challenges
## 7 Future Research Challenges
### Legal Restrictions  
As illustrated in Section 3.1, blockchains are becoming increasingly popular for storing sensitive data, such as health data. However, such private data are protected by data protection laws, such as the _General Data Protection Regulation (GDPR)_[139]. Although blockchains are ideal for the secure storage and distribution of sensitive data in terms of immutability and tamper-resistance, they are fundamentally in conflict with data privacy principles [140]. Special categories of personal data, such as health data, however, are subject to a particularly high degree of protection--here data subjects must be granted full control over their data. To this end, comprehensive adjustments to a blockchain are necessary [141]. In particular, the _right to be forgotten_ is in conflict with the immutability of a blockchain, and the _right to restriction of processing_ contradicts the fully decentralized distribution of data to nodes that manage them autonomously. Moreover, it is impossible for data subjects to exercise their right to data minimization against individual data processors, since the data are tamper-resistant available in a blockchain [142].  
However, such adjustments to make a blockchain GDPR-compliant also have a significant impact on query processing in blockchains. These implications concern two aspects in particular. On the one hand, due to the right to be forgotten DELETE statements are required. In the context of blockchains, however, this is technically difficult not only due to immutability, but also because of expandable objects. If such an object has to be deleted, initially all components of the object have to be found. These components can be distributed arbitrarily over all blocks of the blockchain. To support DELETE statements efficiently, data structures and block structures are required that exceed auxiliary structures found in current blockchains systems significantly. On the other hand, the access control in blockchain systems must be considerably refined in order to grant data subjects the legally guaranteed control over their data. Data subjects must be able to make fine-grained decisions about who should have access to which data. As a consequence, queries regarding the change history of objects become much more complex in particular. If a user has restricted access to some of the changes, only, it must be resolved how a history query can be executed in this case without having to process the restricted data. Expandable objects constitute a special challenge in this respect as well, since they can only be queried and assembled if all components can be accessed. If this cannot be guaranteed due to access restrictions, the data models and also the query processing itself have to be revised.
## 2 Materials and Methods
### Regression Analysis and Modeling  
To develop a model focused on STICKRATIO and MSE behavior and optimization, it is necessary to perform a regression analysis embracing input and output drilling mechanical parameters. It allows modeling relationships between variables for the purpose of output prediction, while the Spearman and Pearson correlations, for instance, have a focus on measuring the strength and direction of association between two variables. Although both address relations between variables, regression can correlate two or more variables, which makes it easier to understand the behavior of STICKRATIO and MSE in relation to variable associations.  
In the context of analyzing and modeling datasets, the parameters under verification must be standardized first. Since each drilling mechanic parameter has different minimum  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **STICKRATIO** & **MSE** \\  & **[-]** & **[-]** \\ \hline RPM & \(-0.24\) & \(0.10\) \\ FLOW & \(0.19\) & \(-0.03\) \\ WOB & \(0.08\) & \(-0.21\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spearman correlation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **RPM** & **FLOW** & **WOB** & **STICKRATIO** & **MSE** \\  & **[rev/min]** & **[L/min]** & **[tf]** & **[\%]** & **[kpsi]** \\ \hline Mean & 160.00 & 2543.00 & 8.58 & 0.27 & 258.00 \\ Median & 160.00 & 2365.00 & 8.51 & 0.17 & 245.00 \\ Standard deviation & 26.30 & 553.00 & 3.06 & 0.27 & 132.00 \\ Variance & 690.00 & 305,762.00 & 9.37 & 0.07 & 17,444.00 \\ Range & 81.90 & 2013.00 & 20.50 & 1.12 & 1441.00 \\ Minimum & 98.70 & 1996.00 & 1.03 & 0 & 55.80 \\ Maximum & 181.00 & 4009.00 & 21.50 & 1.12 & 1497.00 \\
25 th percentile & 159.00 & 2224.00 & 6.39 & 0.07 & 178.00 \\
50 th percentile & 160.00 & 2365.00 & 8.51 & 0.17 & 245.00 \\
75 th percentile & 180.00 & 2411.00 & 10.20 & 0.40 & 299.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive and dataset details.  
and maximum values, the standardization uses \(-1\) as the minimum reference value and \(+1\) as the maximum reference value. The minimum and maximum values for RPM, FLOW, and WOB are presented in Table 5.  
After encoding the dataset, the regression analysis was performed using Microsoft(r) Excel(r) software (Table A1--Appendix A), which presents the setup used to conduct the regression with the already standardized data. The regression statistics analyses were then obtained and are presented in Table 6. It is observed that the model for STICKRATIO and MSE shows an adjusted R\({}^{2}\) of 81.61 [%] and 90.34 [%], respectively. It indicates that the model represents and explains with more than 80% assurance the behavior of STICKRATIO and MSE through the following input drilling mechanic parameters: RPM, FLOW, and WOB.
None of the studies, however, proposed to include emotion recognition in renovation decision support systems or tools. None of the currently known decision-making systems or tools integrate emotion recognition into renovation solutions. To close the gap the present study proposes an innovative decision-making framework for sustainable renovation solutions, based on emotion recognition, and uses a case study to illustrate its possible application. The problem of the research is the selection of the most efficient sustainable renovation alternatives, based on sustainability criteria and emotional data. This research aims to propose a methodology and a system that considers the dimensions of sustainability, including emotions, when stakeholders make decisions on renovation alternatives. Objectives of the research: (1) to develop the methodological framework for the assessment of renovation alternatives based on the multiple-criteria assessment, emotion recognition methods and BIM integration; (2) to develop an intelligent decision support system; (3) to apply the system for selection of the sustainable renovation alternatives for the building in Ukraine; (4) to validate the research results; (5) to distinguish the potential future research directions, including new applications for emotion recognition in sustainable construction and renovation projects. The methodological framework and the decision support system developed as part of this research can be adopted in all stages of renovation from decision making through investigation and design to construction activities. The results of the study can be useful for practitioners and researchers who investigate emotional satisfaction of stakeholders with renovation solutions. The rest of this study is organised as follows. A literature review is presented in Section 2 on the tools, methods and systems available for decision making in the field of sustainable renovation. Section 3 describes the proposed framework for the selection of renovation solutions, including emotion recognition methods. Section 4 uses the case study of renovation solutions for a building located in Ukraine to demonstrate how the proposed methodology works and how the decision support information can be integrated with the BIM model. Section 5 presents a discussion and comparisons with the findings of other authors. Section 6 draws the final conclusion.
## 2 Materials and Methods
### MODIS Dataset  
The MODIS sensor onboard the Terra and Aqua satellites, developed by the National Aeronautics and Space Administration (NASA), aims to continuously monitor the Earth's surface, having 36 spectral bands ranging from 0.4 \(\upmu\)m to 14.4 \(\upmu\)m in three different spatial resolutions (250, 500 and 1000 m) [32]. We used the following products derived from the MODIS sensor: (a) MOD13Q1, which provides the enhanced vegetation index (EVI) at 250-m spatial resolution and 16-day composite; (b) MOD09Q1, surface reflectance (bands 1 and 2) at 250-m spatial resolution and in the 8-day composite; (c) MOD09A1, surface reflectance (bands 1 to 7) at 500-m spatial resolution and 8-day composite; (d) MOD11A2, surface temperature at 1-km spatial resolution and 8-day composite; and (e) MOD17A2, GPP at 500-m spatial resolution and 8-day composite. Data acquisition used the NASA website ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/))). The definition of the surface albedo (\(\alpha_{s}\)) used the MOD09Q1 and MOD09A1 products according to the equation [33]:  
\[\alpha_{s}=0.160_{p1}+0.291_{p2}+0.243_{p3}+0.116_{p4}+0.112_{p5}+0.081_{p7}- 0.0015 \tag{1}\]  
where \(p_{i}\) is the surface reflectance of the MODIS bands (1-7), except band 6.  
MODIS data were from the period 2001-2016, eliminating low-quality pixels from the available quality parameters. We resampled the products with 500- and 1000-m spatial resolution to 250-m by the nearest neighbor interpolation method and the 8-day composite to 16-days from the mean value. The Savitzky-Golay filter [34] was applied to reduce noise in the time series. This filter has been widely used in remote sensing time series, using local polynomial regression to remove noise and retain the waveform from the natural characteristics of vegetation [35; 36; 37].
## 7 Performance Evaluation of Multicast R-GOOSE over Public WAN
### Sender and Receiver Sides  
In the sender side, Raspberry Pi with the Ubuntu 20.04 Server 64-bit operating system is used to retransmit multicast R-GOOSE messages using the strategy defined in IEC61850-8-1. For this purpose, an application that generates multicast datagrams (UDP/IP) is programmed in C language and is able to create non-secured or secured (signed) R-GOOSE messages. Each R-GOOSE message contains several fields in the session layer, as shown in Figure 10. This paper pays particular attention to two fields: SPDU number in the session header and timestamp (t) inside GOOSE PDU in the session payload. These fields are used for measuring the latency of each unique R-GOOSE message. While SPDU number is an integer number, the t is Coordinated Universal Time (UTC), which presents the time value in accordance with the IEC61850 timestamp structure. For each R-GOOSE retransmission, the C application increments the SPDU number and acquires an updated time value from the host device (Raspberry Pi) via the use of the C-standard sys/time.h library. It then incorporates a fresh time (t) into the associated field of each R-GOOSE message. Furthermore, the C application can also publish signed R-GOOSE messages. For signed R-GOOSE, the HMAC calculation is carried out over session layer data, and the Hash value is also appended at the end of the message as shown in Figure 11b. HMAC calculation requires an authentication key, which is normally provided by KDC. However, KDC was not used in this test, so the C application uses a static key (that is not refreshed) for the HMAC calculation and to generate the Hash value for each signed R-GOOSE retransmission. The signature is calculated using the HMAC-256 algorithm with a 32-bit static key using OpenSSL 1.1.1f.  
The C application retransmits non-secured R-GOOSE or signed R-GOOSE with a certain IP-multicast (IPmc) address (in our experiment, IPmc = 239.1.1.20). These messages are sent to the local router (FHR) that has been configured for tunneling to the LHR in both P2M architectures (GRE and mGRE over IP/IPSec) over both 4G and 5G Internet.  
In the receiver side, the Linux PC (Ubuntu 20.04 operating system) is configured to signal the LHR about its interested multicast group membership (i.e., IPmc = 239.1.1.20) by sending an IGMPv3 message containing the IPmc address. Then, the LHR forwards the received messages with the IPmc address (i.e., published R-GOOSE by Raspberry) to the Linux PC. This PC has Wireshark [50] software installed to record the received R-GOOSE messages. The latency for each R-GOOSE message can be calculated by comparing the timestamp (t) value of the message (incorporated by the C program in Raspberry) with its arrival time in the Linux PC recorded by Wireshark. This necessitates time synchronization  
Figure 19: Test setup for communication latency measurement of R-GOOSE messages.  
to a common time reference between the sender (Raspberry) and the receiver (Linux PC) devices.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 2 Secure and Resilient Smart Grid Frameworks  
The development of secure and resilient smart grid frameworks is essential for a comprehensive understanding of the power system structures, functions, and techniques. Such frameworks enable a systematic analysis of the vulnerabilities and existing cyber threats, facilitating the application of targeted protection, detection, and mitigation methods. This strategic approach is vital for safeguarding the specific functions, services, and infrastructures within the power system. It underscores the importance of these frameworks in ensuring the operational integrity, reliability, and security of our energy infrastructures against a backdrop of evolving cyber challenges.
## 3 System Model and Basic Assumptions
### D&F MIMO Signal Model
At the UE, baseband matrix \(\mathbf{G}_{\mathrm{BB}}\in\mathbb{C}^{\mathrm{N}_{\mathrm{d}}\times\mathrm{ N}_{\mathrm{R}}^{\mathrm{D}}}\) and RF receive chains matrix \(\mathbf{M}_{\mathrm{DR}}\in\mathbb{C}^{\mathrm{N}_{\mathrm{R}}^{\mathrm{D}} \times\mathrm{N}_{\mathrm{R}}^{\mathrm{D}}}\) are used to obtain the desired symbols and can be expressed as  
\[\mathbf{\hat{S}} =\mathbf{G}_{\mathrm{BB}}^{\mathrm{H}}\mathbf{M}_{\mathrm{DR}}^{ \mathrm{H}}(\mathbf{H}^{2}\mathbf{\hat{X}}+\mathbf{N}_{\mathrm{DF}}^{2}), \tag{20}\] \[=\mathbf{\hat{S}}+\mathbf{N}_{\mathrm{D}}\]  
where \(\mathbf{N}_{\mathrm{D}}=\mathbf{G}^{\mathrm{H}}\mathbf{N}_{\mathrm{DF}}^{2}\) and \(\mathbf{G}=\mathbf{G}_{\mathrm{BB}}\mathbf{M}_{\mathrm{DR}}\). In (20), the first term is the desired signal and the second term is the noise, which represents the noise in access link at the Decode-&-Forward (D&F) RNCS, respectively.