esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper we explored the potentiality of the Sentinel-1 mission in providing data and value-added information for water resource management applications. The study has been supported by the use of a SAR raw signal simulator, achieving an innovative framework for the analysis of Sentinel-1 performances.  
The results expected by the use of 5-meter resolution Sentinel-1 images in water-related projects were compared to those obtained by the use of 3-meter resolution COSMO-SkyMed imagery in the previously developed WARM-SAR project. The SAR raw signal simulator allowed the estimation of the expected differences of performances due to resolution and frequency. The quantification of the differences in the scattering mechanisms between the X- and C-bands was also presented.  
Three classes of applications were investigated: (i) the creation of an interferometric DEM and related products, (ii) the production of land cover maps, and (iii) the monitoring of water volumes retained at reservoirs.  
As for the interferometric applications, the ratio between Cosmo-SkyMed and Sentinel-1 resolution is 0.6. Such a factor quantifies also the loss of resolution on the production of the DEM. In addition, we achieved a quantitative estimation of the degradation of performances that would be obtained by replicating most of the products obtained in WARM-SAR with Sentinel-1 data. In particular, we experimented that the Sentinel-1 and COSMO SkyMed derived DEM provide comparable results in the extraction of the relation between reservoir surface area and retained water volume for all the reservoirs whose extension is higher than one thousand square meters.  
As for the production of land cover maps, the SAR raw signal simulator led us to simulate, present and interpret Sentinel-1 Level-1\(\alpha\) products. Appropriate electromagnetic models were used to demonstrate that the radiometric contrast between water and rough surfaces at the C band is about 2 dB lower than at X band for incidence angles in the range between 15\({}^{\circ}\) and 45\({}^{\circ}\). Such a result demonstrates that land cover maps obtained by Sentinel-1 will guarantee class separation comparable to that obtained by Cosmo-SkyMed images.  
As for the monitoring of water volumes retained by reservoirs, we experimented that the loss in spatial resolution introduced by Sentinel-1 imagery produces a discrepancy in the order of 15%-20% in the estimation of reservoir surface area with respect to COSMO-SkyMed images. However, this error is reduced to about 12% in the estimation of retained volumes since it is limited at the reservoirs' borders.  
In synthesis, this work presented a new framework for simulating Sentinel-1 data and related products. It provides the opportunity to predict and control quality parameters of the Sentinel-1 SAR data and products, whose high informative content, conjugated with the open access policy, represents an extraordinary opportunity for future projects, mainly in low-income countries.
# The EU Directive on Electromagnetic Fields--Practical Experience of Field Measurements
## 6 Uncertainty in Measurements  
In the directive it is said:  
\"If compliance with the ELVs cannot be reliably determined based on readily accessible information, the assessment of the exposure shall be carried out on the basis of measurements or calculations. In such a case, the assessment shall consider uncertainties concerning the measurements or calculations, such as numerical errors, source modelling, phantom geometry and the electrical properties of tissues and materials, determined in accordance with relevant good practice.\"  
In Figure 7, the problem of taking into account the uncertainty in measurements is illustrated. The limit value in the directive is represented by the line; it might be the electric or magnetic field value. The mean value of the measured value is represented by the dot. The uncertainties are added to this value, including both instrumental errors (non-isotropicity in the probe, nonlinearity, etc.) and the calibration error, and the standard deviation of the measured values. If we chose to go with 95 or 99% probability, it will lead to different sizes of the error bars. Next, we must be aware of if the measurement is taken for the employer to show compliance or as part of helping the work inspectorate. In the first case, the conservative approach says that the measured value including the 95% limit should stay below the limit value to show compliance. But, if the measurements are calculated for the work inspectorate, then it works the other way; the value plus the 95% values should stay above the limit to be sure that the limit is exceeded.  
Figure 7: Examples of how to handle the uncertainty in measurement. The line represents a fictive limit value. The dots are the mean values from fictive measurements. Then, the different scenarios show the problems when the measurements are performed for the employer or the work inspectorate. In the first case, one must be certain that the values are below the limit, whereas in the second case, one has to be certain that the values are above the limit. Thus, they display different outcomes of the measurement.  
Using a shared uncertainty budget, it is sufficient that the mean values are below or above the line. This approach is often used when the error bars are small, but in the case of electromagnetic fields, this is certainly not the case.  
For some of the most common instruments used for measuring RF EMF, the manufacturer specifies, as seen in Figure 8, the following: Instrument error: \(\pm 1\) dB frequency response, \(\pm 1\) dB for lack of isotropicity, \(\pm 1\) dB from calibration. Add to that uncertainty in individual measurements.  
After taking all of this into account it is hard to see how RF measurements in industrial settings could be completed without including at least \(\pm 3\) dB to the measured value when comparing with the action levels in the directive.
# BERT4 Loc: BERT for Location--POI Recommender System
## 5 Results and Analysis
### Effectiveness of Different Sampling Techniques  
We test the effectiveness of different sampling techniques that are mostly used in recommender systems [46; 47] in this experiment and show results In Figure 7, we try the following sampling methods:  
* Full ranking: evaluating the model on all sets of items.
* Uniform X (uni-X): uniformly sample X negative items for each positive item in the testing set, and evaluate the model's performance for these positive items with their sampled negative items.
* Popularity X (pop-X): sample X negative items for each positive item in the testing set based on item popularity, and evaluate the model's performance for these positive items with their sampled negative items.  
To provide a high-level summary of these results, we take the average of the top-k values (10, 20, and 50) to show the precision, recall, HR, NDCG, and F1 scores.  
Figure 6: Ablation study, NDCG comparison among different versions of BERT4 LOC. BERT4 LOC-U- is the model without user activity features, BERT4 LOC-D- without user demographic features, BERT4 LOC-C- without item categorical features, and BERT4 LOC-U&C- without both user and item features.  
Figure 7: Average performance of BERT4 Loc using different sampling techniques.  
Figure 7 shows the average performance of BERT4 Loc using different sampling techniques: full ranking, uni-100, and pop-100. We consider the sample size of 100 based on earlier experiments to choose an appropriate size.  
According to the results, the uni-100 technique consistently outperforms both full ranking and pop-100 in terms of average recall, precision, F1-score, HR, and NDCG. This suggests that uni-100 is the most effective sampling technique among the three. Full ranking exhibits a moderate performance compared to the uniform sampling technique. It has higher average scores (though marginal) than popularity sampling but lower than uniform sampling. This indicates that although full ranking provides relatively accurate recommendations, it may not be as effective as uniform sampling. Popularity has the lowest average scores across all metrics, indicating that it is the least effective sampling technique in this experiment. This is because it samples negative items based on item popularity, which may lead to some random recommendations and lower-accuracy scores.  
Overall, we find that given the dataset size and computational resources, using uniform sampling proves to be more effective than both full ranking and popularity sampling for BERT4 Loc.
## 4 Experiment
### Data Sources and Preprocessing
#### 4.2.2 Reference Samples  
This study categorizes water bodies into four distinct types based on their morphology and distribution contexts: rivers, lakes, urban water bodies, and aquaculture, collectively referred to as complex water bodies. To enhance the accuracy of complex water bodies identification, a specialized sample library encompassing these diverse water body types was developed. Illustrative examples are provided (Figure 10), where \"a\" denotes lakes, \"b\" rivers, \"c\" aquaculture, and \"d\" urban water bodies. The sample labeling process, crucial for accurate water body identification, was executed in three strategic phases. Initially, conventional thresholding techniques were employed to derive preliminary water body outlines. Subsequently, integration of ESA land cover products and Digital Elevation Model (DEM) data [45] was undertaken to eliminate features potentially mistaken for water bodies, such as mountain shadows. The final phase involved generating a binary water body sample dataset through meticulous interpretation and accuracy labeling of concurrent spatio-temporal images sourced from Google Maps.  
For model training, 26 images, each measuring 16,053 \(\times\) 16,054 pixels, were chosen. These images are evenly distributed across the Yellow River Basin, encompassing all water body types identified in the small sample library. The images were segmented into 1024 \(\times\) 1024 pixel slices with a 128-pixel overlap, followed by data augmentation on these segments. The slices were then randomly allocated into training, validation, and test sets at an 8:1:1 ratio. Throughout the training phase, the model was built using the training set, while the validation set facilitated hyperparameter tuning. Post-training, the model's generalization capabilities were assessed using the test set. This process resulted in 6231 training images, 1021 validation images, and an equal number of test images.
## 7 Discussion
These trends, while not definitive, suggest a relevant alignment between the self-reported experience and observable behavior. We also assessed whether participants' scores on the UES-SF dimensions were associated with the behavioral measures of distraction recorded by the UBT system. While again no statistically significant correlations were observed, we noted some consistent patterns. For instance, _Focused Attention_ showed a moderate negative correlation with distraction frequency (\(r=-0.25\), \(p=0.158\)), with low statistical power (\(1-\beta=0.30\)), suggesting that those participants who felt more absorbed tended to exhibit fewer observable distractions. No meaningful correlations were found for the remaining UES-SF dimensions, indicating that the convergence between behavioral and self-reported data may be more specific to attentional aspects of engagement. Regarding the lack of statistical significance in these analyses, we believe it may be related to the sample size, and future studies with a larger sample may help to confirm the robustness of these relationships. In sum, we consider that these correlational results provide preliminary support for the idea that self-reported and observed behavioral measures reflect similar aspects of engagement. Moreover, while not conclusive, the observed patterns also suggest that UBT-derived metrics may capture meaningful aspects of user engagement, highlighting their potential utility as unsupervised behavioral indicators in HRI contexts. In the more general context of joint attention mechanisms, this opens up a promising research line. If real-time behavioral engagement can be monitored through UBT or similar systems, this capability could enable robots to dynamically adjust their behavior when engagement drops. Thus, objective engagement tracking might serve as the final component in closing the loop of shared attention in HRI, complementing JJA and RJA with EJA as a new design principle for adaptive social robots. Finally, the qualitative feedback obtained from the semi-structured interviews reinforces the quantitative findings regarding engagement. Many participants in the RJAS Active condition described the robot as attentive and responsive, frequently noting its ability to track their attention and react appropriately to distractions. These impressions align with the observed increase in engagement and suggest that the RJAS contributed to a sense of mutual awareness during the interaction. At the same time, a few participants reported moments where the robot's reactions felt exaggerated or mistimed, particularly when interpreting minor movements as distractions. These observations point to potential areas for refinement in the system's sensitivity thresholds and head pose detector precision. In contrast, the participants in the Inactive condition often described the robot as passive or disconnected, which supports the notion that attentional responsiveness plays a key role in preventing user disengagement and impacts perceived interaction quality in HRI.
###### Abstract  
Guided waves have immense potential for structural health monitoring applications in numerous industries including aerospace. It is necessary to evaluate guided wave dispersion characteristics, i.e., group velocity and phase velocity profiles, for using them effectively. For complex structures, the profiles can have highly irregular shapes. In this work, a direct method for calculating the group velocity profiles for complex, composite, and periodic structures using a wave and finite element scheme is presented. The group velocity calculation technique is easy to implement, highly computationally efficient, and works with the standard finite element formulation. The major contribution is summarised in the form of a comprehensive algorithm for calculating the group velocity profiles. The method is compared with the existing analytical and numerical methods for calculation of dispersion curves. Finally, an experimental study in a multilayered composite plate is conducted and the results are found to be in good agreement. The technique is suitable to be used in all guided wave application areas such as material characterisation, non-destructive testing, and structural health monitoring.  
guidelines, Structural health monitoring; group velocity; layered structures; dispersion curves
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 2 Related Basic Theory and Algorithm
### Maximum Information Coefficient  
The MIC cannot only measure linear relationships and nonlinear relationships, but can extensively excavate non-functional dependencies between variables. MIC mainly works with MI and meshing methods. MI is an indicator of the degree of correlation between variables. Given a variable \(A=\{a_{i},i=1,2,\ldots,n\}\) and \(B=\{b_{i},i=1,2,\ldots,n\}\), where \(n\) is the number of samples, the MI is defined as follow:  
\[\text{MI}(A,B)=\sum_{a\in A}\sum_{b\in B}p(A,B)log_{2}\frac{p(A,B)}{p(A)p(B)} \tag{1}\]  
where \(p(A,B)\) is the joint probability density of \(A\) and \(B\), and \(p(A)\) and \(p(B)\) are the boundary probability densities of \(A\) and \(B\), respectively. Suppose \(D=\{(a_{i},b_{i}),i=1,2,\ldots,n\}\) is a set of finite ordered pairs. It defines a division \(G\), dividing the value range of variable \(A\) into \(x\) segments and also dividing the value range of variable \(B\) into \(y\) segments. Therefore, the \(G\) is a grid with a size of \(x\times y\). Meanwhile, \(\text{MI}(A,B)\) in each grid is calculated. Since the same grid with a size of \(x\times y\) has several ways of dividing, the maximum value of \(\text{MI}(A,B)\) in different ways of dividing is chosen as the MI value of a division \(G\). Additionally, a definition of maximum MI under a division \(G\) is as follow:  
\[\text{MI}^{*}(D,x,y)=max\text{MI}(D|G) \tag{2}\]  
where \(D|G\) denotes data \(D\) are divided by \(G\). Although it utilizes MI to indicate the quality of the grid, MIC is not just simply estimating the MI. A characteristic matrix is formed by maximum normalized MI values under different divisions. The characteristic matrix is defined as follow:  
\[\text{MI}(D)_{x,y}=\frac{\text{MI}^{*}(D,x,y)}{logmin\{x,y\}} \tag{3}\]  
Moreover, the MIC is defined as:  
\[\text{MIC}(D)=\max_{xy<B(n)}\left\{\text{MI}(D)_{x,y}\right\} \tag{4}\]  
where \(B(n)\) is the upper limit of the \(x\times y\) grid. Generally, \(\omega(1)<B(n)<O\big{(}n^{1-\epsilon}\big{)}\), where \(0<\epsilon<1\).
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 4 _Ulva_ Compounds of Interest
### Pigments  
#### 4.2.1 Chlorophylls  
As per [PERSON] (1957) [129], chlorophylls are green photosynthetic pigments employed by photoautotrophic organisms like plants and algae. They absorb light and, in conjunction with carbon dioxide fixation, produce the carbohydrates necessary for the growth of plants and algae [129; 130; 131].  
Structurally, they are composed of a reduced porphyrin ring, with a central magnesium atom, and a long hydrophobic tail (phytol), which confers them low solubility in water. However, due to the presence of ester and carbonyl groups, polar functions are lipid-soluble [130]. The most common types of chlorophylls present in green algae are the a and b, considering that the concentrations exhibit variability contingent upon species, geographical location, and environmental conditions [132].  
Chlorophylls are used in the food industry as natural colorants in foods and beverages; however, other important features are being reported, namely their antioxidant, antitammoral, and antimicrobial activities (according to [PERSON] et al., 2011 [133], [PERSON] et al., 2005 [134], [PERSON] et al., 2015 [135] and [PERSON] 2003 [136], respectively). A recent study conducted by [PERSON] et al., 2018 [137] proved the possibility of chlorophylls to be used as precursors of photosensizers for photodynamic therapy, namely for cancer treatment and inactivation of microorganisms [130].
## 2 Enhanced Dynamic Model
\begin{array}{ll}\mathrm{Y}\Big{(}U-\frac{b}{2}\Big{)}\,\ if\ \dot{U}>0\ and\ U\geq U(t-1)+b/2\\ \mathrm{Y}\Big{(}U+\frac{b}{2}\Big{)}\,\ if\ \dot{U}<0\ and\ U\leq U(t-1)-b/2\\ V(U)_{(t^{-})}\,\ \ otherwise\end{array} \right. \tag{12}\]  
where \(Y\) refers to the slope, \(b\) indicates the size of the dead zone, \(U\) indicates the control torque, and \(V(U)\) indicates the control torque after backlash. This relationship is illustrated in Figure 4.  
Figure 2: Diagram of example payload manipulation process. We can rewrite Equation (12) as follows:  
\[V(U)=YU+\Gamma(U) \tag{13}\]  
where \(\Gamma(U)\) is expressed as  
\[\Gamma(U)=\left\{ \begin{array}{ll}-Y\dot{b}_{2}^{b},&\quad if\ \dot{U}>0\ and\ U\geq U(t-1)+b/2\\ Y\dot{b}_{2}^{b},&\quad if\ \dot{U}<0\ and\ U\leq U(t-1)-b/2\\ V(U)_{(t^{-})}-YU\,&\quad otherwise\end{array} \right. \tag{14}\]  
Our enhanced dynamic model of a dual-axis manipulator under the effects of multiple forms of interference can be expressed as follows:  
\[M_{0}\ddot{q}+C_{0}\dot{q}+G_{0}+\Xi\big{(}q,\dot{q},\ddot{q},t\big{)}+U_{P}=V (U)=YU+\Gamma(U) \tag{15}\]  
where, for convenience, we specify that  
\[\Xi\big{(}q,\ddot{q},\ddot{q},t\big{)}=\Delta M\ddot{q}+\Delta C\dot{q}+\Delta G +f\big{(}\ddot{q}\big{)}+d(t) \tag{16}\]
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 5 RQ3: OpenID Connect Profiles  
This section explores the OIDC profiles that the eID schemes have adopted. We spot the following features:  
**OIDC Core Profile**: [3]: provides guidelines regarding the core OIDC functionality and how to achieve the baseline security;  
**OIDC International Government Assurance (iGov) Profile**: [30]: is based on the OIDC Core and aims to increase baseline security, provide greater interoperability, and structure deployments for public administration and governmental domains;
**OIDC Financial Grade API (FAPI) Profile**: [57; 58]: is a highly secured OIDC profile that aims to provide specific implementation guidelines for security and interoperability. This profile can be applied to any market area that requires a higher level of security than the one provided by standard OIDC Core;
**OIDC Identity Assurance (IDA) Profile**: [31]: aims to provide SPs with identity information, i.e., verified claims along with an explicit statement about the verification status of these Claims (what, how, when, according to what rules, using what evidence). It enables use cases requiring strong assurance, like compliance with regulatory requirements such as anti-money laundering laws or access to health data, risk mitigation, or fraud prevention.  
We report the main results per each eID means considering only the adopted features by these solutions within columns 2-3 of Table 3. From the results, we have that:  
* The Italian CIE and SPID eID means define their national OIDC specification based on the OIDC iGov profile, and they will be the only solutions within the eID means under analysis that follow the OIDC iGov;
* None of the eID means under analysis support either OIDC FAPI [57; 58] or OIDC IDA [31] profiles.
## 2 Conceptual Development of Structural Design
### Finite Element Analysis (FEA) and Environmental Test  
After developing the 3D model of the STM, finite element analysis (FEA) was carried out to check the stiffness both on individual components and assembly levels using the Autodesk Fusion 360 simulation environment. The model was simplified by removing rounds and chamfers. A total weight of approximately 3.6 kg was assumed to resemble the actual total weight of the satellite at the time of the STM phase. In addition to the frequency analysis, static analysis was also conducted. All the boundary conditions were applied according to the JAXA requirement handbook [20].  
The production of the STM was ordered after confirming the analysis results (Figure 8). The lesson learned when placing the production order was that the tolerance requirement of the slots as well as spacers should be carefully specified and checked, as the spacers must easily slide into the slot when assembling the PCBs. The parts were assembled using dummy internal and external subsystems, which have similar mass. Then, a vibration test  
Figure 6: Standard 6 mm, 9 mm, and free-size spacers.  
Figure 7: (**A**) PCB insertion into the slots. (**B**) The top plate encloses the satellite assembly.  
was conducted to verify whether the satellite could survive the severe launch environment. Different launch loads, such as random and quasi-static vibration loads, were applied to the satellite. Since the satellite is expected to be deployed from ISS, a shock test was not necessary. Qualification-level random and sine-burst vibrations were applied to the test article. Modal surveys were taken on each occasion before and after the random and sine-burst vibration to check whether the signature of the fundamental frequency had shifted. The vibration results are discussed in Section 4.1.