esa-sceva/satcom-chunk-collection huggingface dataset:
# Reasoning Algorithms on Feature Modeling--A Systematic Mapping Study
## 5 Results
### Answers to RQs
#### 5.1.6. RQ6: What Problems Does the Algorithm Solve? This research question highlighted which problems had more solutions and which did not. The source of the classification was an open categorization, considering the following levels:  
* Null FMs (NFM)
* Valid partial configuration (VC)
* Valid product (VP)
* Other. * Unable to decide (UTD). In reviewing the categories declared by the selected papers, we faced similar problems to those faced in RQ2. Forty-three papers (app. 61%) were included in the category of _Other_. This fact did not allow for a more detailed analysis of the types of problem solved by the proposals. After a detailed review of the papers, a new categorization was proposed. We had to consider another aspect--cases in which one study aimed to solve more than one problem, so that the same paper contributed to more than one category. For example, the authors of SP3 aimed to solve the following types of problems: _Void FM, Valid product, All products, Number products, Commonality, and Variability factor_. The new categorization is presented in Table 15, including the selected papers on each category. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **\#Papers (\%)** & **Selected Papers** \\ \hline \multirow{3}{*}{Valid partial configuration} & \multirow{3}{*}{11 (17\%)} & SP1, SP6, SP12, SP27, SP36, SP37, SP43, SP46, \\  & & SP64, SP67, SP71 \\ Anomaly detection & 10 (15\%) & SP1, SP4, SP6, SP8, SP23, SP27, SP31, SP46, \\  & & SP95, SP64 \\ Void FM & 7 (11\%) & SP3, SP12, SP30, SP31, SP46, SP59 \\ Synthesizing feature models & 7 (11\%) & SP15, SP18, SP44, SP45, SP49, SP54, SP60 \\ Valid product & 6 (9\%) & SP3, SP25, SP28, SP30, SP31, SP59 \\ Core Features & 6 (9\%) & SP6, SP29, SP30, SP46, SP59, SP54 \\ Feature model relations & 6 (9\%) & SP11, SP3, SP29, SP59, SP61, SP62 \\ All products & 5 (8\%) & SP3, SP23, SP30, SP59, SP64 \\ SPL testing & 5 (8\%) & SP5, SP20, SP42, SP62, SP32 \\ Optimization & 5 (8\%) & SP30, SP50, SP55, SP57, SP66 \\ Number of products & 4 (6\%) & SP3, SP12, SP30, SP59 \\ Commonality & 4 (6\%) & SP3, SP27, SP59, SP64 \\ Model checking & 4 (6\%) & SP19, SP41, SP65, SP70 \\ Variability factor & 4 (6\%) & SP3, SP40, SP59, SP64 \\ Filter & 4 (6\%) & SP52, SP53, SP59, SP64 \\ Dependency analysis & 3 (5\%) & SP23, SP43, SP56 \\ Variant features & 3 (5\%) & SP29, SP30, SP59 \\ Explanations & 2 (3\%) & SP8, SP71 \\ Multi-step configuration & 2 (3\%) & SP38, SP64 \\ Atomic set & 2 (3\%) & SP46, SP59 \\ Change impact analysis & 2 (3\%) & SP29, SP47 \\  & & SP7, SP9, SP11, SP13, SP27, SP29, SP33, SP39, \\ Other & 14 (21\%) & SP40, SP45, SP46, SP58, SP59, SP69 \\ UTD & 8 (12\%) & SP2, SP10, SP21, SP35, SP42, SP63, SP68, SP72 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Selected papers and FMs used for the proposals.
## 4 The TRAJ-Q-GPSR Algorithm
### Traj-Q-GPSR Algorithm  
Traj-Q-GPSR integrates trajectory information and two-hop neighbor awareness into routing, enabling forward-looking Q-learning for efficient UAV data delivery. This section details the implementation of the Traj-Q-GPSR algorithm from four aspects: the design of the state and action spaces, the construction of the reward function, the dynamic adjustment strategy for Q-learning parameters, and the pseudocode representation.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### _Informal Analysis_
#### 5.2.3 Stolen-Verifier Attack  
If the cloud server database is leaked to the attacker, the attacker can obtain the pseudo identity and the random value \(x_{i}\) of the vehicle user and the identity and random value \(y_{j}\) of the fog node. The attacker may endeavor to calculate the session key using these values. However, without knowing the short-term keys \(r_{1}\), \(r_{2}\), and \(r_{3}\), the attacker cannot compute the session key. Furthermore, the pseudo identity of the vehicle is masked with its identity, password, and \(s_{0}\), and the attacker cannot derive sensitive information from the identity and the random values \(x_{i}\) and \(y_{j}\) of the fog node.
# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 4 Architecture Definition  
Developing an architecture for an SDR-MM requires a systematic approach. In this approach, CONOP and system requirements were used to determine the desired functionality and performance of the SDR-MM. Three alternative architectures were developed from the stated CONOP using the following methodology which aligns closely with the last three steps of OOSEM.  
The process begins with functional decomposition, which involves breaking down the system's use case(s) into smaller, more manageable subfunctions. The decomposition of the main SDR-MM function is an essential step in understanding how the functions of the SDR-MM interact with each other and contribute to its overall operation. Based on the CONOP and stakeholder needs, the main purpose of the SDR-MM is to receive analog signals from LNA and perform digital conversion (Level 0 function); however, the three architectures differ at Level 1 and Level 2 functions, specifically transforming analog RF signal to digital data and managing heat. The SysML activity decomposition map is an effective tool for displaying the functional decomposition and breakdown at multiple levels, and activity decomposition map examples are provided in the following three subsections, one for each architecture alternative.  
Next, the Level 1 and 2 functions are allocated to SDR-MM elements. This involves creating a high-level conceptual architecture that outlines the major components and interfaces of the module, including any hardware components and external interfaces. The SysML activity diagram can be used for this purpose. The functions, flows, and interfaces can be realized and allocated to the conceptual elements. Finally, the SDR-MM architecture is tested and evaluated against the requirements and CONOP to ensure that it meets the desired performance and functionality. Analytical models are used to calculate and predict the transformation of inputs into outputs and system parameters. To enable simulation capabilities, SysML constraint blocks are used to define constraints on the system behavior. They are linked to the model elements that they constrain, allowing for traceability and analysis throughout the development process.  
As part of the validation process, the'satisfy' relationship between MOEs and their associated requirements was established [30]. For example, for the design constraint of 'the total cost of the SDR-MM shall be less than USD 15,000' will be formalized as totalCost < USD 15,000. A sample for the architecture design validation process is shown in Figure 6.  
Figure 6: A sample of requirement validation.
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
###### Abstract  
High-quality geomagnetic measurements are widely used in both fundamental research of the magnetic field and numerous industrial applications. However, vector data measured by fluxgate sensors show a dependency on temperature due to sensitive coil core material and components of the sensor electronics. Here, we propose a new method for detecting and eliminating temperature dependence in magnetic observatory data. The method is designed to correct temperature drifts in variation vector magnetometer measurements when preparing quasi-definitive data according to an INTERMAGNET standard. A special feature of the method is the semi-automatic adjustment of localization intervals for temperature correction, which prevents boundary jumps and discontinuities in the course of sequential data processing over long intervals. The conservative nature of the approach implies the minimization of the original data amount subjected to correction. The described method is successfully applied in the routine monthly preparation of quasi-definitive data of the Saint Petersburg Observatory (IAGA-code SPG) and can be efficiently introduced at other magnetic observatories worldwide.  
magnetic observatories; temperature correction; quasi-definitive data; INTERMAGNET; absolute measurements; variometer; semi-automated algorithm +
Footnote †: journal: Applied  
0023 by the authors.  
Liconse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Deep Learning-Based FSS Spectral Characterization and Cross-Band Migration
###### Abstract  
Conventional design methodologies for Frequency Selective Surfaces (FSSs) are often plagued by challenges such as difficulties in determining unit cell structures, a plethora of optimization parameters, and substantial computational demands. In response, researchers have developed deep learning-based approaches for FSS design, highlighting their advantages in terms of high efficiency and low resource consumption. However, these methods are typically confined to designing FSSs within the spectral ranges defined by their datasets, significantly limiting their applicability. This paper systematically analyzes the impact of material and geometric parameters of FSSs on their spectral characteristics, thereby establishing a theoretical foundation for the cross-band transfer learning capability of neural networks. Building on this foundation, we utilized COMSOL (Version 6.0) and MATLAB (Version R2021b) co-simulations to recollect 600 sets of FSS data in the millimeter-wave band. Using only 23.1% of the data volume, we achieved training results comparable to those obtained with the full dataset in a significantly shorter time frame, with a mean absolute error of 0.07 on the test set. This demonstrates the feasibility of transfer learning and successfully implements cross-band transfer learning of convolutional neural networks from the terahertz band to the millimeter-wave band. The findings of this study provide valuable insights for the integration of deep learning with FSSs, enhancing data utilization efficiency, and further advancing the development of efficient, concise, and universal FSS design methodologies. This advancement extends the scope from solving specific problems to addressing a broader class of issues.  
transfer learning; frequency selective surfaces; convolutional neural networks (CNN) +
Footnote †: journal: applied sciences  
Article
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 5 Understanding the Impact of Instrument Operating Modes
### SRAL Acquisition Mode Comparisons for Sea State  
Another study looked at sea state measurements (wind speed (WSP) and significant wave height (SWH)). Comparisons were made between S3A, S3B measurements, buoy in-situ measurements from the US National Data Buoy Center [29] online archive, and ERA5 [30] model data. Analyses included scatter plots, such as presented in Figure 3 below, and triple collocations using the methodology described in Section 4 (see [19] for details). Such comparisons allowed not only to verify the consistency of S3A and S3B measurements but also to assess the relative precision of different acquisition modes for sea state.  
Overall, the results indicate a better agreement of SARM SWH with in-situ or model, while for wind speed, the agreement is slightly better for LRM or PLRM.
# Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network
###### Abstract  
Correctly measuring the importance of nodes in a complex network is critical for studying the robustness of the network, and designing a network security policy based on these highly important nodes can effectively improve security aspects of the network, such as the security of important data nodes on the Internet or the hardening of critical traffic hubs. Currently included are degree centrality, closeness centrality, clustering coefficient, and H-index. Although these indicators can identify important nodes to some extent, they are influenced by a single evaluation perspective and have limitations, so most of the existing evaluation methods cannot fully reflect the node importance information. In this paper, we propose a multi-attribute critic network decision indicator (MCNDI) based on the CRITIC method, considering the H-index, closeness centrality, k-shell indicator, and network constraint coefficient. This method integrates the information of network attributes from multiple perspectives and provides a more comprehensive measure of node importance. An experimental analysis of the Chesapeake Bay network and the contiguous USA network shows that MCNDI has better ranking monotonicity, more stable metric results, and is highly adaptable to network topology. Additionally, deliberate attack simulations on real networks showed that the method exhibits high convergence speed in attacks on USAir97 networks and technology routes networks.  
complex networks; node importance metric; multi-attribute integrated measurement; CRITIC method +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
This article used mudstone and slag as cementitious materials and waste rock materials produced by mining areas as an aggregate, and generated geopolymer composite materials under alkaline activator conditions. Uniaxial compression tests, uniaxial cyclic loading and unloading tests, SEM scanning tests, and permeability tests were carried out on the MSWCs with different mudstone proportions. The effects of the mudstone proportion and uniaxial cyclic loading and unloading times on the mechanical properties, permeability, and microstructure of the MSWCs were studied. According to the research results, the following conclusions can be drawn:  
1. The MSWCs have qualified uniaxial compressive strength and low permeability, which fulfils the basic requirements of being a water barrier layer for a spoil disposal site, proving that mudstone and slag can be used as cementitious materials to construct artificial water barrier layers in mining areas.
2. The failure modes of MSWC specimens changed from brittle failures to plastic failures with an increasing percentage of mudstone and number of uniaxial cyclic loading and unloading.
3. Based on the proportion of mudstone and the number of uniaxial cyclic loading and unloading as variables, a coupling model of uniaxial compressive strength of the MSWC is established. With an increase of mudstone proportion, the uniaxial compressive strength of the MSWC gradually decreases; and with an increase of uniaxial cyclic loading and unloading times, the uniaxial compressive strength of the MSWC shows a trend of increasing first and then decreasing, and the uniaxial compressive strength reaches a maximum at about 50 cycles.
4. The microstructure of the MSWCs showed that mudstone cementitious material develops relatively slowly and has strong plastic characteristics, low uniaxial compressive strength, and low permeability; while slag cementitious material develops relatively fast and has strong resistance to deformation, high uniaxial compressive strength, and low permeability.
5. When the mudstone proportion is 0.6, the MSWC has strong uniaxial compressive strength and low permeability, which fulfils the requirements of reconstructing a water barrier layer for a spoil disposal site in a mining area.  
Conceptualization, Y.C. ([PERSON]); Data curation, Y.C. ([PERSON]), Y.Z., J.Z. and T.C.; Funding acquisition, Y.C. ([PERSON]); Methodology, Y.C. ([PERSON]); Supervision, Y.C. ([PERSON]); Writing--original draft, Y.C. ([PERSON]); Writing--review and editing, Y.C. ([PERSON]), Y.C. ([PERSON]), Y.Z., J.Z. and T.C. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Key R&D Program of China (2022 YFC2903902) and the National Natural Science Foundation of China (51974295).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Measurement Methods  
The measurement process itself is very simple with low-cost equipment. The assembled device, made of an Insta360 X3 camera and a low-cost GNSS, was placed on a geodetic pole at a height of about 2 m so that the surveyor covers a small part of the camera's field of view. The low-cost GNSS device was paired with the phone via Bluetooth and the GNSS Controller mobile app, and after a short initialization, the GNSS RTK and RAW data collection started. For post-processing time synchronization between the images and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**St. Dev. XY [mm]** & **St. Dev. Z [mm]** & **St. Dev. XYZ [mm]** \\ \hline
49 mm & 28 mm & 56 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard deviations of the differences between the GNSS RTK points and the points given from the Mavic 3E point cloud.  
Figure 3: Map of the test site: Lipová street. **Left—**controlled points; **right—**an orthophoto (adapted from http://mapycz, accessed on 7 March 2025).  
GNSS, the mobile device screen was recorded for a few seconds before each data collection session began in order to record a graphical clock with a very precise time (Figure 4).  
The area of interest was then measured by the walk method with the device at a speed of 4-5 km/h in a closed loop. During the measurements, an effort was made to avoid places with an obscured view of the sky to prevent degradation of the GNSS signal, but this was not always possible. To ensure sufficient accuracy in locations with poorer GNSS signals and corner areas, the carrier speed was partially reduced.
# Active Noise Control over Space: A Subspace Method for Performance Analysis
## 4 Subspace Method
### Projection of the Primary Noise Field into the Subspace  
Below we project the wave-domain coefficients of the primary noise field into the subspace \(\mathbf{O}\).  
For a new primary noise field represented by vector \(\mathbf{\beta}\), by projecting \(\mathbf{\beta}\) into the subspace \(\mathbf{O}\), we can obtain  
\[\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}=\sum_{b=1}^{B}<\mathbf{\beta},\mathbf{o}_{b}>\mathbf{o}_{ b}=<\mathbf{\beta},\mathbf{o}_{1}>\mathbf{o}_{1}+\cdots+<\mathbf{\beta},\mathbf{o}_{B}>\mathbf{o}_{B}, \tag{26}\]  
where \(\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}\) denotes the projection of vector \(\mathbf{\beta}\) into subspace \(\mathbf{O}\). The matrix form of the projection is represented by  
\[\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}=\mathbf{O}\mathbf{y}, \tag{27}\]  
where \(\mathbf{y}=\{y_{1},y_{2},\ldots,y_{B}\}^{T}\) are the primary noise field coefficients in the subspace, and \(y_{b}=<\mathbf{\beta},\mathbf{o}_{b}>\).  
Therefore, the primary noise field can be separated by two parts: the projected part and the remaining part,  
\[\mathbf{\beta}=\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}+R(\mathbf{\beta}), \tag{28}\]  
where \(R(\mathbf{\beta})\) is the orthogonal complement of the subspace \(\mathbf{O}\). The projected part indicates the primary noise field which can be cancelled in this system setup, and the orthogonal complement indicates the primary noise field which cannot be cancelled in this system.  
If \(R(\mathbf{\beta})=0\), \(\mathbf{\beta}\) lies in the subspace, then the primary noise field can be completely cancelled by the loudspeaker array.  
In more general cases, \(R(\mathbf{\beta})\
eq 0\). This indicates the limitation of noise cancellation over the region of interest, under the particular loudspeaker placement and acoustic environment.  
Next, we design the driving signal of loudspeaker \(d_{l}(k)\) to cancel the primary noise field projected into the subspace (\(\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}\)).
## 3 Results
### From Measurements to Continuous and Retrospective Perceived Pleasantness
#### 3.2.2 Global Sound Pleasantness Estimation Based on Sound Level Time Series  
In a practical case, the available data will more likely be a time series of sound levels, instead of instantaneous sound pleasantness values. Therefore, this section aims to estimate the global sound pleasantness of a walking trip, based on its sound level time series. The two proposed approaches consist of: (i) estimating \(GP\) from the instantaneous \(P\) values, which are themselves estimated in terms of noise level time series; (ii) directly estimating \(GP\) in terms of the noise level time series.  
A model has been proposed in the previous section for estimating the instantaneous sound pleasantness based on the last 30 s sound level time series through SIMPLE modeling. Section 3.1.2 showed that the \(GP\) value can be estimated as the arithmetic average of these instantaneous estimated pleasantness values, \(P_{\text{estimated}}\). Combining these two results enables one to estimate \(GP\) values from sound level measurements. Based on the 10 real sound sequences of the second experiment, the resulting model, built on the unique predictor \(P_{\text{mean,estimated}}\) (\(b^{*}\) = 0.81, \(t\)(8) = 3.8, \(p\) < 0.005), explains 60% of the total variance (\(R^{2}\) = 0.65, \(F\)(2,8) = 14.9, \(p\) < 0.005), with a Root Mean Square Error (_RMSE_) of 0.72. This model has the advantage of considering the short-term recency effect, but this, in return, makes the \(GP\) value dependent on the direction of the walking trip. Figure 7 presents the estimated global pleasantness obtained with this approach, versus the actual assessed global pleasantness averaged over the participants.  
Figure 6: Pearson correlation coefficient between pleasantness and \(L_{50}\), \(L_{90}\), \(L_{10}\), and \(L_{\text{eq}}\), with varying reaction and integration times.  
Table 5 presents the relations between the sound level time series (\(L_{\text{eq,1s}}\)) and the \(GP\), relative to a 3-min sequence through simple indicators that neglect the recency effects. These models take advantage of simplifying the \(GP\) estimation by giving it the same value, whatever the direction is.  
The tested models rely on three different indicators, namely the median and the arithmetic average of sound levels and the \(L_{\text{eq}}\), which is often used for exposure assessment through the widely used Sound Exposure Level indicator (SEL). The two indicators \(L_{\text{mean}}\) and \(L_{50}\) enable good \(GP\) estimates. Inversely, there is no significant correlation between the \(L_{\text{eq}}\), and the \(GP\) values calculated over the sequences. \(L_{\text{eq}}\), contrary to \(L_{\text{mean}}\) and \(L_{50}\), is impacted by noise peaks, explaining the poor correlation. This suggests again that there is no peak-effect on the global sound sequences assessments in the present study, in accordance with [29].