esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities
## 4 Use Case--Analyzing Media Criticism
### Research Background  
Mass media play a pivotal role for democratic societies. However, a number of recent national and international debates (e.g., on the media coverage of the 2020 US election and the COVID-19 pandemic) have shown that the performance of mass media is highly contested and trust in journalism is on the decline. A study that was published by Gallup in September 2020 ([[https://news.gallup.com/poll/321116/americans-remain-distrustful-mass-media.aspx](https://news.gallup.com/poll/321116/americans-remain-distrustful-mass-media.aspx)]([https://news.gallup.com/poll/321116/americans-remain-distrustful-mass-media.aspx](https://news.gallup.com/poll/321116/americans-remain-distrustful-mass-media.aspx)), accessed on 21 February 2021) shows that only 9% of U.S. citizens have a great deal of confidence in media reporting, while more than 60% of the respondents described their confidence in news media as \"not very much\" or \"none\". The performance of the media is also highly contested in Germany, where the term _Ligeppresse_ (lying press) was elected as the worst German word of 2014 ([[https://www.unwortdesjahres.net/fileadmin/unwort/download/pressemiteilung_unwort2014.pdf](https://www.unwortdesjahres.net/fileadmin/unwort/download/pressemiteilung_unwort2014.pdf)]([https://www.unwortdesjahres.net/fileadmin/unwort/download/pressemiteilung_unwort2014.pdf](https://www.unwortdesjahres.net/fileadmin/unwort/download/pressemiteilung_unwort2014.pdf)), accessed on 21 February 2021) and is still used by some ideological groups.  
Measuring and understanding the issues, dynamics, and impact of media criticism is a challenging task that can tremendously benefit from systematic studies that cover media criticism from major stakeholders, such as (i) mass media, (ii) media-critical agents, and (iii) social media across different outlets and media. An analysis of the daily output produced by these stakeholders makes it clear that it is no longer feasible to manually collect and analyze these document streams by hand and that a collaborative research design that integrates methods from computer sciences is required.
# Space Habitat Data Centers--For Future Computing
## 5 Performance Formulation  
The performance model assumes that small satellites have limited computing capability necessitating accessing additional computing resources. In existing work [19; 44], the data requiring processing is transmitted to the terrestrial cloud computing platform. The formulated metrics are the computing platform access latency and the accessible computing resources in the space segment. This section has two parts. The first and second parts formulate cloud platform access latency and accessible computing resources in the space segment respectively.
## 5 Repair Qualification Study
### First Stage--Preliminary Selection of Bonding  
The first and simplest selection of a bonding system consists of verification of the nature of the damage (which components of the bonding layer were damaged) during the static tearing off of the shield (Figure 10). It was assumed that further, more advanced testing is feasible only in those bonding systems with the decohesive fracture type in the bonding layer or rubber. It would indicate that the surface preparation and primer were correctly selected and that the maximum transferring load was via the bonding layer or rubber.  
The test results were as follows:  
1. Bonding 2216B/A--deadhesive fracture surfaces between the metal and bonding layer;
2. Bonding 2216B/A + Rafil primer--decohesive fracture surfaces of the primer;
3. Bonding 2216B/A + BR-127 primer--decohesive fracture surfaces of the bonding layer;
4. Bonding DP490--no damage to the bonding layer, decohesive fracture surface of the rubber;
5. Bonding DP490 + Rafil primer--decohesive fracture surfaces of the primer;
6. Bonding DP490 + BR-127 primer--deadhesive fracture surfaces between the metal and bonding layer.  
According to the above criterion, only the following systems were further tested:  
* Bonding 2216B/A + BR-127 primer;
* Bonding DP490  
Figure 10: Scheme of tearing off the shield during preliminary tests: (a) crack initiation with a chisel, (b) tearing off the shield with universal pliers.
## 3 Results and Discussion
### Sensitivity Analysis of the Selection Criteria
Table 8 shows that for the CON system RT64 HC is the first option in the vast majority of cases (95.6%) in both scenarios, while PureTemp 63 is the second-best option in 75% of the cases in Scenario 1, and in 83.3% in Scenario 2. This means that, except for very few cases that correspond to specific combinations of values for the weights of the decision parameters, which deviate from the reference values considered by the authors, the selection of RT64 HC as the best option for the CON concept is quite robust and justified.
## 7 Results
### Discussion  
In Section 7.4, we demonstrate the applicability of ML attestation for a model architecture proposed in the literature and used on TinyML devices. We assess how various model hyperparameters influence the attestation time and the size of the ML-EAT token. Additionally, we evaluate the benefits of standardization or consensus between devices when comparing integer-encoded claims to string-encoded ones.  
Based on the use cases described in Section 6.3, the following insights can be drawn:  
1. In the first scenario, the token generation time of 27.8 ms on the device does not affect the real-time requirements of the anomaly detection system. Given that the inference time is between 15 and 20 ms and the attestation is not performed frequently, the impact remains negligible. Furthermore, the token size is small, consisting of 1084 bytes, which includes both the PSA and ML-EAT tokens.
2. In the second scenario, it is evident that both the ML-EAT token size and the encoding type of the claim keys play a crucial role. For the evaluated model architecture, the ML-EAT token size can be reduced from 9468 bytes to 4218 bytes, resulting in shorter attestation times and a smaller token size that needs to be transmitted.  
Moreover, for the models evaluated, the attestation time may impact the real-time requirements of the anomaly detection system on the device, as the attestation time can be up to six times longer than the assumed inference time. However, in a TinyML federated learning system, the attestation process may occur less frequently, since the model may not require frequent updates due to the stability of the patterns being learned across updates.
# Insights into PCDD/Fs and PAHs in Biomass Boilers Envisaging Risks of Ash Use as Fertilizers
## 3 Experimental Part
### Conformity of PCDD/Fs and PAHs in Ashes with the Proposed Limits for Fertilizers  
In view of the proposed limits of contamination in ashes to be used as fertilizers [65], there are some concerns regarding the studied ashes. While content of Cl in bottom ashes (BA) varies from 0.004-0.22%, fly ash (FA) streams present 0.14-8.1% Cl, exceeding the recommended Cl limit of 3% in some cases. The \(\Sigma_{16}\) PAHs of fly ashes (1.5-2.5 mg/kg) and bottom ashes (0.06-0.1 mg/kg) comply with the proposed limit of 6 mg/kg. In this case, the handling of ND data has no significant impact on the conformity evaluation. Regarding PCDD/Fs, the fly ashes present contents between 789-24,052 ng/kg, corresponding to 35-1139 ng WHO\({}_{2005}\)-TEQ/kg, assuming the MB option for ND values. Even assuming the LB option for ND, the values vary from 26-1139 ng WHO\({}_{2005}\)-TEQ/kg. This means that the fly ashes unequivocally exceeded the proposed limit of 20 ng WHO\({}_{2005}\)-TEQ/kg. In the case of bottom ashes, judgment on whether TEQs exceed the limit depends on the option used for ND. While for the MB option the TEQs range, 14-20 ng WHO\({}_{2005}\)-TEQ/kg, indicate that bottom ashes may reach the limit; considering LB option (0-7.3 ng WHO\({}_{2005}\)-TEQ/kg) they comply, but considering the UB option (27-32 ng WHO\({}_{2005}\)-TEQ/kg) all bottom ashes exceed the limit. This highlights the need for the requirement of low detection limits for such measurements. The sensitivity analysis reveals that when there are left-censored data, the chosen options for handling ND results affect the judgment of compliance with limit values. When there are many ND cases, as in this study, the assignment of EQL fractions to ND is perhaps questionable [249]. Given the special case of PCDD/Fs, choice of another analytical methodology or laboratory could be advisable, in view of the target values and sample matrix. It becomes evident that any regulation of contaminants must detail requirements of analytical methodologies, namely sensitivity/detection limit and criteria to present results, including how to handle ND values. This is especially important when limit values are aggregated quantities such as TEQs and sum (\(\Sigma\)). Other ND handling methods could be
## 2 Background Study
### Rain Attenuation Parameters  
Developing a rain fade model involves mathematical analysis of rain attenuation phenomena by reasoning and cause-based interaction. A broad range of variables that can impact the rain attenuation is given in [15; 18; 19; 20; 21; 22; 23; 24; 25; 26]. The parameters that affect the rain attenuation are rainfall rate, frequency, path length in the precipitation, temperature, humidity, wind velocity, wind direction, visibility, polarization, raindrop radius, raindrop size, latitude, elevation angle, station height, raindrop cross-section, depolarization loss, and refractive index of the air.  
\begin{table}
\begin{tabular}{l l l} \hline
**Factors** & **Earth–Space** & **Terrestrial** \\ \hline Rain & ✓ & ✓ \\ \hline Cloud, sky noise, scintillation, latitude, Earth radius & ✓ & ✗ \\ \hline Hop length & \begin{tabular}{l} Varies according to orbit \\ • \\ • \\ \end{tabular} & \begin{tabular}{l} Varies according to orbit \\ • \\ MEO: \(\approx\)8000–12,000 km \\ • \\ \end{tabular} & \
\begin{tabular}{l} Varies according to necessity: \\ • \\  • \\ \end{tabular} \\  &
\begin{tabular}{l} Varies according to necessity: \\ • \\ RAe: Maximum 200 km \\ \end{tabular} \\ \hline Path length reduction & \multicolumn{2}{l}{Effective slant path} & Effective terrestrial path \\ \hline \end{tabular}
\end{table}
Table 2: Similarity and contrast between Earth–space to terrestrial links.
## 3 Modeling Uncertainty
### EGARCH Price Model  
The GARCH price model has limitations in capturing asymmetry in volatility. To overcome this shortcoming, the EGARCH model is introduced. In the EGARCH model, the asset return has same equation as Equation (6), though the conditional variance is modeled as  
\[\ln(\sigma_{t}^{2})=\omega+\sum_{p=1}^{p}\alpha_{p}\Bigg{(}\bigg{|}\frac{ \epsilon_{t-p}}{\sigma_{t-p}}\bigg{|}-\frac{\sqrt{2}}{\pi}\Bigg{)}+\sum_{o=1}^ {Q}\gamma_{o}\frac{\epsilon_{t-o}}{\sigma_{t-o}}+\sum_{q=1}^{Q}\beta_{q}\ln( \sigma_{t-q}^{2}) \tag{11}\]  
Rather than working with the complete specification, a simpler version, an EGARCH(1,1,1) with a constant mean is defined as \[r_{t}=\mu+\epsilon_{t} \tag{12}\] \[\epsilon_{t}\sim N(0,\sigma_{t}^{2})\] (13) \[\ln(\sigma_{t}^{2})=\omega+\alpha_{1}\left(\left|\frac{\epsilon_{t -1}}{\sigma_{t-1}}\right|-\frac{\sqrt{2}}{\pi}\right)+\gamma_{1}\frac{\epsilon _{t-1}}{\sigma_{t-1}}+\beta_{1}\ln(\sigma_{t-1}^{2}) \tag{14}\]  
For using the EGARCH(1,1,1) model, five parameters \(\mu\), \(\omega\), \(\alpha\), \(\gamma\), and \(\beta\) need to be estimated. Similar to the previous subsection, the Maximum Likelihood (ML) method is employed to estimate these parameters based on historical data.
# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
###### Abstract  
In this paper, we propose a novel simultaneous Correlative Interferometer (CI) technique that elaborately estimates the Direction of Arrival (DOA) of multiple source signals incident on an antenna array. The basic idea of the proposed technique is that the antenna-array-based receiver compares the phase of the received signal with one of the candidates at each time sample and jointly exploits these multiple time samples to estimate the DOAs of multiple signal sources. The proposed simultaneous CI-based DOA estimation technique collectively utilizes multiple time-domain samples and can be regarded as a generalized version of the conventional CI algorithm for the case of multiple time-domain samples. We first thoroughly review the conventional CI algorithm to comprehensively explain the procedure of the direction-finding algorithm that adopts the phase information of received signals. We also discuss several technical issues of conventional CI-based DOA estimation techniques that are originally proposed for the case of a single time-domain sample. Then, we propose a simultaneous CI-based DOA estimation technique with multi-sample diversity as a novel solution for the case of multiple time-domain samples. We clearly compare the proposed simultaneous CI technique with the conventional CI technique and we compare the existing Multiple Signal Classification (MUSIC)-based DOA estimation technique with the conventional CI-based technique by using the DOA spectrum as well. To the best of our knowledge, the simultaneous CI-based DOA estimation technique that effectively utilizes the characteristics of multiple signal sources over multiple time-domain samples has not been reported in the literature. Through extensive computer simulations, we show that the proposed simultaneous CI technique significantly outperforms both the conventional CI technique in terms of DOA estimation even in harsh environments and with various antenna array structures. It is worth noting that the proposed simultaneous CI technique results in much better performance than the classical MUSIC algorithm, which is one of the most representative subspace-based DOA estimation techniques.  
6G; wireless positioning; Direction of Arrival (DOA); direction finding; array antenna; Uniform Circular Array (UCA); Correlative Interferometer (CI); Root Mean Squared Error (RMSE) +
Footnote †: journal: _Sensors_**2023**, 23, 8938. [[https://doi.org/10.3390/s23218938](https://doi.org/10.3390/s23218938)]([https://doi.org/10.3390/s23218938](https://doi.org/10.3390/s23218938))
## 2 Literature Review
For instance, the island of Crete can serve as a prime example of how local communities are advancing towards energy independence using diverse renewable resources. The development of community energy labs (CELs) across the island, as part of the 'Renewable Energy Valleys' (REVs) initiative, aims to fully meet local energy demands through renewable energy sources such as solar, wind, geothermal, and biomass. This initiative is not only a demonstration of a data-driven multi-carrier grid management system but also an indicator of the renewable energy potential in Greece ([PERSON] et al., 2018). Classic optimization algorithms, while foundational in various fields, exhibit several notable drawbacks that limit their effectiveness in complex problem-solving scenarios, such as repetition in the local minimum ([PERSON] et al., 2018). These limitations stem from the reliance of these algorithms on specific assumptions and conditions, which can lead to suboptimal resultswhen applied to diverse or dynamic problems. The GREENIA framework as a holistic integration provides several advantages over existing methods. Firstly, GRUs effectively model temporal relationships, making them well suited for forecasting energy production based on historical meteorological data, which is essential for accurately predicting renewable energy output. Additionally, we use the Jaya evolutionary algorithm for optimizing the placement of RES. Unlike traditional gradient-based methods, Jaya can efficiently find global optima without becoming stuck in local optima, which is particularly beneficial for the complex, non-convex optimization landscapes typical in renewable energy scenarios. Finally, classical methods often lack transparency for stakeholders without technical expertise. The proposed framework, however, integrates SHAP and NLE, which provide interpretability and actionable insights, ensuring that the model's outputs are accessible and understandable for both technical and non-technical stakeholders. In summary, GREENIA transcends traditional methods by offering an integrated, AI-driven, and explainable framework for optimizing the placement of renewable energy sources (RES) across varied geographic settings. It utilizes evolutionary algorithms to determine optimal RES placement, specifically applied to a case study in Greece. The study is organized as follows: Section 1 introduces the problem, laying out the challenges in RES deployment. Section 2 reviews the existing literature and methods currently employed. Section 3 details the methodology underpinning the GREENIA implementation, while Section 4 presents the results of applying the GREENIA approach, comparing them to other state-of-the-art models. Section 5 provides a comparative analysis between the outcomes of the proposed framework and those from existing literature. Section 6 discusses the limitations of the approach and future research directions, and, finally, Section 7 offers a comprehensive conclusion summarizing the implementation and the overall contribution of the GREENIA framework.
## 3 Results
### Question 2: Why Is the IEQ Model Needed?
overall IEQ performance (or occupants' overall satisfaction with the indoor environment) and the performance of each IEQ factor (or occupants' satisfaction with each factor) [14; 40], we must identify the specific IEQ aspect that has the most significant (negative/positive) effect on the occupants' satisfaction [1; 39; 41], and compare the IEQ performance between different indoor spaces [10; 42]; for this reason, the IEQ model is needed. Apart from the impact of IEQ, the standards relating to IEQ are also worth our attention. To date, the majority of IEQ standards and guidelines only focus on one IEQ aspect (i.e., thermal [43], visual [44], acoustic [45], or IAQ [46] factors) or provide separate categories for these different IEQ aspects [47]. Since no unified calculation method exists for an integrated IEQ index, the standards seldom stipulate the types for the overall IEQ performance. This makes evaluating and comparing the overall IEQ conditions in different buildings difficult. To develop an integrated IEQ standard, a unified IEQ index calculation method must be established, namely, an IEQ model. Based on this model, the IEQ evaluation system can be confirmed, promoting work at the design stage and making the IEQ more comfortable for the target occupants.
## 4 Construction of Deep Learning Database
### ERT Data Preprocessing  
Prior to inversion, the raw ERT measurements undergo a series of preprocessing steps to reduce noise and correct artifacts: (1) Data filtering and outlier removal: Extremely high or negative apparent resistivity values, often resulting from poor electrode contact or instrumental errors, are identified and removed using a threshold-based approach and manual quality inspection; (2) Stacking and signal averaging: Multiple measurements are stacked to improve the signal-to-noise ratio (SNR), particularly in regions with high contact resistance; (3) Geometric and topographic corrections: Electrode positions are adjusted based on differential GPS data and corrected for terrain effects using a topographic correction module in the inversion software; and (4) Reciprocity checks: Forward and reverse measurements are compared to identify inconsistent data, which are then excluded  
Figure 8: Relative resistivity variation maps: (**a**) Line 1-3; (**b**) Densified Line 2.  
from inversion. These preprocessing techniques significantly enhance data reliability and ensure stable convergence during the subsequent inversion and imaging stages.
## 3 Experimental Analysis
### Displacement Analysis
#### 3.2.1 Average of the Shield Ring Point Cloud Displacement  
The average point cloud displacement across 102 shield rings ranges from 0.10 mm to 0.99 mm, illustrating the variability in tunnel deformation. Figure 7 highlights significant displacement peaks around rings 8, 16, 21, 29, 40, 53, 71, 73, and 90, indicating areas of increased misalignment. These peaks suggest localized structural deformation that may require further investigation. While the highest displacements approach 0.99 mm, most values remain moderate, with an average displacement clustering around 0.4-0.7 mm. This distribution reflects the strong alignment between the two-point cloud datasets and confirms the accuracy of the SILSS in capturing tunnel surface deformations. The results validate the methodology's effectiveness for structural monitoring and maintaining tunnel safety.  
Figure 6: MLS point cloud (\(P_{i}\)). (**a**) Raw tunnel cross-section point cloud. (**b**) Cross-section lining point cloud after denoising.
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 4 Results and Discussion
### Discussion
SHAP is a technique for interpreting the output of machine learning models [42]. Among other things, SHAP values are a way to assess the importance of features relative to other features in the model and can be used to quantify the contribution of each feature to the predicted output. As illustrated in Figure 21, the HSP data contributed the most to the prediction of surrounding rock grades, followed by the 3D spatial data, and finally the rock fragmentation index and TBM data. This finding aligns with the results of the causal analysis, though there remain discrepancies. Firstly, it demonstrates that horizontal sonic profiling (HSP) can effectively identify the fracture and integrity of the rock mass. Secondly, it shows that the rock condition is also affected by the spatial distribution to a certain extent, because the lithology as well as the yield of different rocks have certain spatial distribution patterns. Secondly, for TBM data, it is usually necessary to indirectly reflect the rock fragmentation through the calculation of the rock fragmentation index (such as TPI, FPI, etc.). Among the various boring parameters, the variation of cutterhead speed (RPM) is frequently more significant for predicting surrounding rock grades. Consequently, the integration of the outcomes of  
Figure 20: The PCA results and impact on model performance. the causal analysis and SHAP method demonstrates that all three data sources contribute to the prediction of surrounding rock grades to a certain extent. In this regard, the focus should be directed towards HSP data, 3D spatial data and rock fragmentation indicators (FPI, TPI).