esa-sceva/satcom-chunk-collection huggingface dataset:
# Angular Dependence of Photonic Crystal Coupled to Photovoltaic Solar Cell
## 4 Conclusions  
In this research, the optical coupling between 1D photonic crystals and solar cells was analyzed.  
Outdoor measurements revealed three potential dependence factors: (a) incident angle; (b) irradiance spectrum; and (c) natural and linear polarization of the incoming light. We demonstrated that the most relevant factor, from a photovoltaic effect point of view, was the incident angle variation due to the sun shift throughout the day. Solar irradiance harvesting enhancement was observed when the elevation angle of the sun was between 50\({}^{\circ}\) and normal incidence. Out of this angle range, optical performance dropped as a consequence of optical decoupling with the solar cell spectral response: Bragg peak intensity was significantly reduced, Bragg peak position shifted and remained out of the design specifications, and the gaussian profile of the Bragg peak disappeared for high incident angles. All of this negatively affected the efficiency of the photovoltaic device coupled to 1 DPC. As a main conclusion, not only should the normal incident light conditions be considered, but also the influence of these environment conditions should be controlled at the design phase of the device to ensure the proper performance during the operational phase.  
Polarization of the incoming radiation is a second factor that cannot be neglected to estimate the electrical production of the solar cell. It has also been demonstrated that linear polarization and natural polarization exhibit different behavior from the point of view of the reflectivity. This change of intensities was related to the photocurrent generated by the photovoltaic device.  
The use of 1D photonic crystal in solar applications opens a new portfolio of applications, especially for solar thermal energy hybridization (beam splitter) and photovoltaic devices (transparent modules  
Figure 8: Incident angle dependency of PC1D as a function of the polarization of the incoming radiation. Solid lines correspond to natural polarization light, and open circles refer to linear polarization data.  
for building integrated application). These results support the design of these optical filters and to identify critical tolerance to evaluate the performance of the full device in real operating conditions.  
Conceptualization, J.M.D.-S. and I.L.-B.; methodology, J.M.D.-S. and I.L.-B.; software, J.M.D.-S.; validation, J.M.D.-S. and I.L.-B.; formal analysis, J.M.D.-S. and I.L.-B.; investigation, J.M.D.-S. and I.L.-B.; resources, J.M.D.-S.; data curation, J.M.D.-S. and I.L.-B.; writing--original draft preparation, J.M.D.-S. and I.L.-B.; writing--review and editing, J.M.D.-S. and I.L.-B.; visualization, J.M.D.-S. and I.L.-B.; supervision, I.L.-B.; project administration, J.M.D.-S.; and funding acquisition, J.M.D.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
### Methods for the Evaluation of Feature Importance and Effects  
To verify the expectations towards the features, we have analyzed the co-registration results of one randomly selected simulation round. To analyze the effect of a specific feature on the matching probabilities, we have subdivided the results of all plots into three groups. The groups have been defined by the intervals 0-0.33 (low probability), 0.33-0.67 (intermediate probability) and 0.67-1 (high probability). Based on these groups a boxplot has been generated for each feature, which allows conclusions about which values are associated with higher or lower matching probabilities.  
To investigate the effect of different forest characteristics on the co-registration performance, we have evaluated the results of all validation datasets. The effect of a selected variable (e.g., tree species) on the co-registration probability has been assessed by grouping the results using equidistant intervals of the variable. Based on these groups a boxplot of the selected variable has been created and a statistical analysis has been performed to support the conclusions. To decide if the values of a given variable differ between the groups (meaning at least one group differs significantly from another group), we have applied the Kruskal-Wallis rank sum test. In case of just two groups the Wilcoxon rank sum test has been used instead, which also gives information about the direction of the effect. In case of ordered groups, we have also verified the significance of potential (linear) trends by performing an one-way ANOVA.
## 3 Results  
In the case of the sealer marked with porphyrin, a statistical significance was recorded between all experimental groups and in all studied lengths (sections) (\(p=0.0\)). The deepest sealer's penetration was recorded in group 2 (sodium hypochlorite), and the shallowest in group 1 (smear layer).  
When it comes to the sealer marked with fluorescein, the statistically significant differences were recorded between all of the groups in the mid-section. In the apical and coronal section statistically important differences were recorded between group 1 (smear layer) and the rest of the groups (\(p=0.0\)). No significant differences were recorded between group 2 (sodium hypochlorite) and group 3 (alcohol) in the apical and coronal sections (\(\mathrm{p_{a}}=0.97\) and \(\mathrm{p_{c}}=0.89\), respectively).  
For both markers (fluorescein and porphyrin), a statistically significant positive correlation was recorded between the part of the root and the depth of the sealer's penetration (\(p=0.0\)). The hydrophobic or hydrophilic nature of the used dye affected the measured values, but the alcoholic environment in group 3 did not promote more leakage of porphyrin, compared to the aqueous environment in group 2 (sodium hypochlorite).  
The sealer's penetration depth values for tested groups are shown in Table 1 and presented in Figure 2. Figures 3-5 are representative photographs of a samples of groups: group 2 (remaining smear layer)--Figure 3; group 2 (sodium hypochlorite)--Figure 4; group 3 (isopropyl)--Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Marker**} & \multicolumn{3}{c}{**2 mm**} & \multicolumn{3}{c}{**5 mm**} & \multicolumn{3}{c}{**8 mm**} \\ \cline{3-10}  & & **Mean** & **Median** & **SD** & **Mean** & **Median** & **SD** & **Mean** & **Median** & **SD** \\ \hline \multirow{2}{*}{1} & Fluorescein & 106.9 & 85 & 58.0 & 130.9 & 103 & 68.6 & 169.5 & 161.0 & 102.7 \\  & Porphirn & 21.0 & 19 & 11.2 & 22.0 & 20 & 11.6 & 23.0 & 22.0 & 11.9 \\ \hline \multirow{2}{*}{2} & Fluorescein & 143.50 & 155 & 71.9 & 276.6 & 250 & 106.7 & 323.4 & 312.5 & 152.4 \\  & Porphirn & 201.2 & 198 & 35.6 & 230.9 & 237 & 64.3 & 374.0 & 376.0 & 87.9 \\ \hline \multirow{2}{*}{3} & Fluorescein & 145.5 & 134 & 94.4 & 232.5 & 220 & 82.6 & 316.5 & 279.5 & 177.5 \\  & Porphirn & 169.5 & 162 & 52.5 & 231.5 & 235 & 66.5 & 280.0 & 260.0 & 103.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The depth of sealer’s penetration into the dental tubules depending on the group and sealer’s marker [\(\mathrm{\SIUnitSymbolMicro m}\)].  
Figure 3: Representative photography of a group 2 sample (remaining smear layer).  
Figure 2: Median depth of sealer’s penetration for respective groups and subgroups.  
Figure 4: Representative photography of a group 2 sample (sodium hypochlorite).  
Figure 5: Representative photography of a group 3 sample (isopropyl).
###### Abstract  
Underwater acoustic communication networks (UACNs) play a critical role in ocean environmental monitoring, maritime rescue, and military applications. However, they are highly susceptible to performance degradation due to narrow bandwidths, long propagation delays, and severe multipath effects, especially adversarial jamming attacks. Traditional anti-jamming techniques struggle to adapt to the dynamic nature of underwater acoustic channels effectively. To address this issue, an anti-jamming power control and relay optimization method was developed based on transfer reinforcement learning. By introducing relay nodes, the reliability of jammed communication links is enhanced. Transfer learning was used to initialize Q-values and strategy distributions and accelerate the convergence of reinforcement learning in the underwater communication environment, thereby mitigating the inefficiency of random exploration in the early stages. The proposed method optimizes the transmission power and relay selection to improve the signal-to-interference-plus-noise ratio (SINR) and reduce the bit error rate (BER). Simulation results demonstrated that the proposed method significantly enhanced the anti-jamming performance and communication efficiency of underwater acoustic communication even in complex interference scenarios.  
underwater acoustic communication networks; anti-jamming; transfer reinforcement learning; power control; relay optimization
Design, Implementation and Validation of a GNSS Measurement Exclusion and Weighting Function with a Dual Polarized Antenna +
Footnote †: This paper is an extended version of “GNSS Measurement Exclusion and Weighting with a Dual Polarized Antenna: The FANTASTIC project” published in the Proceedings of the 2018 8 th International Conference on Localization and GNSS (ICL-GNSS), Guimaraes, Portugal, 26–28 June 2018.  
[PERSON]  
1 Department of Telecommunications and Systems Engineering, IEEC-CERES, Universitat Autonoma de Barcelona (UAB), Barcelona 08193, Spain; [EMAIL_ADDRESS] (A.T.-C.); [PERSON]SS] (J.A.L.-S.); [PERSON]SS] (G.S.-G.)  
Footnote 1: email: [EMAIL_ADDRESS]; wim.de; [EMAIL_ADDRESS] (W.D.W.); [EMAIL_ADDRESS] (B.B.); [EMAIL_ADDRESS] (J.M.  
For the above reason, the FANTASTIC project was launched in order to develop enabling technologies that allow a leap of professional applications. One of the main tasks of the FANTASTIC project is to demonstrate the improvement of the RTK positioning in harsh environments, where the received signal is likely to be corrupted by obstacles (foliage, buildings, mountains,...). To enhance robustness, the work focuses on a dual circularly polarized multi-band antenna, a new strategy to combine inertial measurement unit (IMU) and GNSS measurements and on interference mitigation algorithms. This paper will focus on the part corresponding to the antenna. It has two orthogonal polarization outputs (Right-Hand Circular Polarization (RHCP) and Left-Hand Circular Polarization (LHCP) components). Since GNSS satellites transmit RHCP signals, the underlying idea with this configuration is that multipath may affect both the RHCP and the LHCP components, while the direct or genuine signal appears only on the RHCP component. The aim of this paper is to show the developed function in the FANTASTIC project to weight and exclude observables based on the above concept. The goal of the proposed weighting and exclusion (WE) function is to reduce the effects of multipath into the Position, Velocity and Time (PVT) solution.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
### Research Methodology  
In this paper, the research methodology is divided into phases as illustrated in Figure 1. These phases are discussed as follows:  
1. Different types of DDoS attacks are investigated and explored. The investigation is narrowed down to focus on SYN-Flood DDoS attacks;
2. A comprehensive literature review has been conducted to find the strength and limitations of previous works;
3. A careful design for the IP traceback algorithm has been proposed considering scenarios for DoS and DDoS attacks. The design periodically collects statistical information about traffic forwarded at the edge routers where the algorithm decides upon initiating the IP traceback or not. Two unique features identify the scheme. The first is the reliability of path reconstruction using hop-by-hop TCP connections. The second feature is the use of one trace packet per attack path, which makes it lightweight by avoiding the injection of extra traffic overhead in the network;
4. Theoretical analysis was performed to find the complexity of the proposed algorithm and analyzing the convergence time of it;
5. Extensive simulation runs were conducted to validate the correctness of the algorithm. Each data point represents the average of 15 simulation runs;
6. The proposed algorithm is compared with a relevant one using different performance metrics.  
The rest of the paper is organized as follows: Section 2 reviews the related work. The proposed attack path tracing algorithm is presented in Section 3. Section 4 discusses the theoretical analysis, simulation methodology, and performance evaluation. Finally, Section 5 concludes the paper, and future work is provided.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 2 The Equation of Motion for Physical Quantities
### Symplectic Structure and Phase-Space Dynamics  
The Hamiltonian equations of motion, succinctly captured as:  
\[\dot{q}=\frac{\partial H}{\partial p},\quad\dot{p}=-\frac{\partial H}{ \partial q}, \tag{3}\]  
underscore the symplectic geometry of phase space. The conservation of phase-space volume (\(d\omega=0\)) reflects the deterministic and reversible nature of classical mechanics.  
The symplectic form \(\omega\) in a symplectic manifold \((M,\omega)\) is defined as:  
\[\omega=\sum_{i=1}^{n}dp_{i}\wedge dq_{i}, \tag{4}\]  
where \(p_{i}\) and \(q_{i}\) are canonical momenta and coordinates, respectively. The condition \(d\omega=0\) (closure of the symplectic form) ensures the preservation of phase-space structure under Hamiltonian flow.
## 1 Introduction
Existing analog approaches often focus only on the extraction of foot parameters for size recommendations in online stores, but not on the full reconstruction of the foot shape. Full reconstruction includes the lower arch, which is particularly important because it is impossible to sew shoes without this information. As such, the creation of a methodology and development of a mobile application based on this information will allow orthopedic enterprises to improve the technological process of manufacturing individual shoes and enable them to fulfill custom orders remotely. The scientific novelty of this work lies in the reconstruction of 3D human foot models, including the lower arch, using photogrammetry and deep NNs. To obtain a high-quality 3D model, a series of RGB images were obtained via processing of a standard video recording. The offered technique can be modified and used in the field of medical diagnostics of orthopedic diseases. Additionally, 3D models obtained based on the presented methodology can be used in the following areas:  
* Orthopedic tailoring of individual shoes. Enterprises can use 3D models for subsequent printing on industrial 3D printers and for the construction of individual shoes based on the original pads;
* Medical diagnostics. Medical staff with orthopedic qualifications will be able to diagnose the initial stages of diseases affecting the human locomotor system via a remote format;
* Online shoe selection. People who actively use online stores often lack necessary information regarding their shoe size or face a discrepancy in the size grid. The methodology allows them to accurately determine their size, according to the dimensional grid, down to the millimeter. The purpose of this study was to develop a technique that involves using a standard smartphone video camera as a tool for obtaining 3D models of the human foot shape, including the lower arch. Section 2 analysis of existing technology solutions provides a summary analysis containing information on current products on the market and technologies for 3D scanning. The details about the proposed methodology are subsequently determined in Section 3. This section presents a methodology of reconstructing the 3D foot shape and describes dataset formation, feature extraction and matching, structure from motion, segmentation foot images, the dataset for training, the neural network, estimation and filtering, depth maps, and a complete scheme of the approached methodology. Information about the experiments performed is contained in Section 4. The experiments section also includes information on training the neural network, the effects of various feature-extraction algorithms and resolutions on quality, and standard deviations. Section 5, Discussion, presents the possible limitations of the developed methodology. A summary of the results is provided in Section 6, Conclusions.
# Impact of Solidified Municipal Sludge as Temporary Covering Soil on the Stability of Landfill Slope
## 2 Modelling Simulation  
### SEEP/W and SLOPE/W Coupling Landfill Slope Stability Analysis  
The impact of solidified municipal sludge as middle covering soil on landfill slope stability was evaluated with the seepage of porous media (SEEP/W) and slope stability (SLOPE/W) analysis of GeoSlope. SEEP/W is widely used to investigate saturated-unsaturated seepage of groundwater in geotechnical engineering, and can be used for unsaturated finite element calculation. Many scholars have used the SEEP/W calculation program to investigate the migration of landfill leachate. SLOPE/W not only has non-arc sliding, but also can search through soft interlayers. In this study, the landfill slope safety factor was calculated using the Morgenstern-Price method. The material parameters in the model were determined according to the effective stress method. The distribution of stagnant water and pore water pressure under different climatic conditions were first calculated by SEEP/W, and then used in SLOPE/W as the background parameter values for subsequent coupling calculations. The deterioration of temporary covering soil was taken into account in the simulation, and the landfill slope safety factor was also obtained.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 4 Proposed rStaple Tracking Method
### Histogram Significant Enhancement  
In conventional tracking tasks, color information of the target generally differs from that of the background, which is a convenient attribute for locating the target more simply. In other words, the target can be found simply via highlighting it in the image. Thus, we can inspect the pixels in the target area with certain rules to significantly enhance the target.  
The color histogram tracking builds the color statistical model of the foreground and background. Firstly, the probabilities that each pixel belongs to the foreground and background are calculated separately to obtain the target probability of each pixel. Then, the target probability in a certain area around the pixel is accumulated as the score of the point. Eventually, the difference in color between target and surrounding background is used for tracking.  
We can increase the histogram score of the pixels with a higher target probability to highlight the target area in the image. When the target probability \(P_{t}\) of a pixel in Equation (14) is greater than a certain threshold \(p_{0}\), we deploy a significant enhancement to this pixel as:  
\[P_{t}=\begin{cases}(P_{t}-p_{0})\cdot r+P_{t},&P_{t}>p_{0}\\ P_{t},&else\end{cases} \tag{15}\]  
where \(r\) denotes a magnification of the enhanced pixel score. By enhancing the high-response pixel, the discrimination of the foreground and background in the searching area can be increased thereby improving the tracking performance. We update the color histogram model in each frame to improve the adaptability of our tracker:  
\[\begin{split} P_{t}(O)&=(1-\eta_{hist})P_{t-1}(O)+\eta_{ hist}P_{t}{}^{\prime}(O)\\ P_{t}(B)&=(1-\eta_{hist})P_{t-1}(B)+\eta_{hist}P_{t}{}^{\prime}(B) \end{split} \tag{16}\]where \(\eta_{hist}\) denotes the learning rate of color histogram, \(P_{t-1}(O)\) and \(P_{t-1}(B)\) denotes foreground model and background model from frame 1 to \(t-1\), respectively, and \(P_{t}{}^{\prime}(B)\) and \(P_{t}{}^{\prime}(O)\) denotes model estimated in frame \(t\).  
As shown in Figure 2, we test our approach in a video sequence in which the target clearly differs from its background. The tracker with histogram enhancement could keep following the target consistently. In contrast, the tracker without histogram enhancement loses the tracking target in the first few frames since the target is small with a fast motion. This proves that color histogram significant enhancement can weaken the boundary effect of correlation filters to some extent.  
Finally, we linearly combine the response map of the translation filter \(r_{t}\) and the color histogram score map \(r_{hist}\) to obtain the final translation score map \(r\) expressed as:  
\[r=\alpha r_{hist}+(1-\alpha)r_{t} \tag{17}\]  
The position of the maximum in the score map \(r\) is the estimated target position in the current frame, and we subsequently perform scale estimation to obtain the final tracking result.
# A Tour Recommendation System Considering Implicit and Dynamic Information
## 4 Implementation
### System Construction  
TripAdvisor is a famous travel review website that provides travel-related information about hotels, attractions, and restaurants worldwide. Thirty-eight tourist attractions, such as Meiji Jingu Shrine, Asakusa, Tokyo National Museum, and Tokyo Disney Sea, have received high ranks and discussion in Tokyo on the TripAdvisor website and are considered famous POIs. Then, the user check-in record will be assigned to the closest POI. Next, the textual description of POIs is collected from Wikipedia, the official website, and websites that introduce the attractions of Tokyo, such as japan-guide.com, live japan, etc. The extraction processing for implicit POI features is divided into two stages. The first stage is textual preprocessing, including tokenization, stop-word removal, stemming, and lemmatization. The appearance frequency of each term appearing in the textual description of the POI will be counted. Then, the corresponding implicit features for each POI represented by TF-IDF will be generated. The dimension of the implicit feature vector for each POI is 5693. Next, an Autoencoder (AE) is used to reduce the dimension of the implicit feature. The activation function used in Encoder is Relu, while the one used in Decoder is Sigmoid. Through the AE, the dimension of the implicit feature vector is reduced from 5693 to 178.  
To group users into clusters based on the similarity of their visiting sequences, it is necessary to transform all user's visiting sequences into a fixed length. The distribution of the user's visiting sequence length is shown in Figure 4. Note that the sequence with a length of two is removed. Based on the observation from the figure, we determine that the length of visiting sequences is fixed as 9. Thus, the visiting sequence vector will be 1602 dimensions since the length of a visiting sequence is 9 and the dimension of each implicit POI feature vector is 178.  
Figure 4: The distribution of length of visiting sequences.  
Figure 3: The distribution of user check-in points in the Tokyo area.  
Next, users will be further clustered into several disjointed sub-groups by K-means based on the visiting sequence similarity between tourists. The silhouette coefficient analysis [35] is conducted to determine the appropriate K value. Figure 5 shows the silhouette coefficient when K is changed from 3 to 10. Based on the figure, the K value is set as 6 because it has the highest silhouette coefficient. After clustering, all users in the dataset are divided into six clusters. There are 1241, 904, 549, 344, 474, and 430 tourists in each group, respectively. For each group, NMF is employed to derive staying time, time slots, and POIs for all users.