esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bandgap Algorithm of the PMPIC by Extended Plane Wave Expansion Method
### Topology Optimization of the PMPIC  
In order to validate the topology optimization algorithm, the procedure shown in Figure 4 is carried out by MATLAB Software. Firstly, the inductor is fixed as \(L\)\(=\)\(1\) H, and the unit cell is divided into \(10\)\(\times\)\(10\) small elements (e.g., \(M\)\(=\)\(10\)). Parameters of the GA are set as the population size, the selection rate, the crossover probability, and the mutation probability, which are equal to \(50\), \(0.5\), \(0.7\), and \(0.001\), respectively.  
In order to show the structural topology clearly, two colors are applied for different materials. The Blue area denotes the host base structure, and the yellow area denotes the piezoelectric patch. The optimal topology configuration of the unit cell is shown in Figure 9a. Moreover, the first four dispersion curves are plotted in Figure 9b, and the maximum bandgap width is equal to \(36.6891\) Hz ([124.1, 160.8] Hz). Obviously, the  
Figure 8: The dispersion curves under configuration B: (**a**) the first four disperse curves and (**b**) comparison of the first bandgap.  
Figure 7: The unit cell of configuration B.  
maximum bandgap width is much larger than that of configuration B, and the structural topology of the piezoelectric patch is nonrectangular.  
Secondly, it is desired to optimize the structural topology and the interface circuit simultaneously. Here, the central frequency is assumed to be 200 Hz, and the unit cell is also divided into 10 \(\times\) 10 small elements (e.g., \(M\)\(=\) 10). The optimal topology configuration of the unit cell is shown in Figure 10a, the first four dispersion curves are plotted in Figure 10b, and the maximum bandgap width is equal to 22.4 Hz ([192.3, 214.7]Hz). At the same time, the inductor is calculated as \(L=1.221\) H. Obviously, \(f_{0}\)\(=200\) Hz is close to the center of the optimal bandgap. Four isolated host structures in the optimal result can be used for local masses.  
In the end, it should be emphasized that \(M\) is a key parameter and the precision of material distribution increases with \(M\). However, large \(M\) will cause the amount of optimization calculation to increase rapidly. Without loss of feasibility, this paper pays less attention on calculation efficiency. As for this problem, it may be overcome by using advanced calculation tools such as supercomputing platforms or fast optimization algorithms.  
Figure 10: The optimal result under \(f_{0}\)\(=200\) Hz: (**a**) topology configuration of the unit cell and (**b**) the first four dispersion curves.  
Figure 9: The optimal result under \(L\)\(=1\) H: (**a**) topology configuration of the unit cell and (**b**) the first four dispersion curves.
## 3 Derivation of the 10\(N-9\) Phase-Shifting Algorithm
### Error Analysis  
Research was done by [PERSON] and [PERSON] on the error analysis within the consideration of the coupling error [46; 47]. In phase-shifting interferometry, [PERSON] found that the measurement accuracy can be influenced by harmonics, phase shift errors, or a combination of both. He derived a kind of analytical formula in order to check the ability to suppress both the phase shift errors and the coupling errors. Therefore, the RMS (Root Mean Square) phase error resulting from phase shift miscalibration is described as follows  
\[\sigma_{m}=\frac{1}{2\sqrt{2}}\left|\frac{iF_{1}(v)}{F_{2}(v)}-1\right|, \tag{17}\]  
Figure 5: Sampling functions of [PERSON]’s \(2N-1\) algorithm (\(N=10\)).  
Figure 6: Sampling functions of \(10N-9\) algorithm (\(N=10\)).  
where \(F_{1}(v)\) and \(F_{2}(v)\) can be obtained by Equations (15) and (16), and \(i\) is an imaginary unit. The RMS error resulting from coupling error can be given by  
\[\sigma_{c}=\frac{1}{2}\sum_{\text{m=2}}^{\infty}\frac{\gamma_{m}}{\gamma_{1}} \sqrt{\left[\frac{iF_{1}(mv)}{iF_{1}(v)}\right]^{2}+\left[\frac{F_{2}(mv)}{F_{ 2}(v)}\right]^{2}}, \tag{18}\]  
where \(\gamma_{m}\) can be obtained by Equations (2)-(5). Therefore, the entire RMS error can be given by  
\[\sigma=\sqrt{\sigma_{m}^{2}+\sigma_{c}^{2}}, \tag{19}\]  
In Figure 7, we can see the trend of the RMS error (reference surface reflective \(r_{1}=4\%\); sample surface reflective \(r_{2}=30\%\)). When measuring a spherical surface by a wavelength-tuning interferometer, \(\varepsilon_{0}\) is especially important, since the reference surface of the spherical is curved, and a non-uniform phase shift occurs along the axial translation in the field of view [48,49], thus the value of phase shift miscalibration \(\varepsilon_{0}\) can be estimated to be around 0.3 [50].  
Figure 7 also shows that a synchronous detection algorithm has the largest RMS error because of its inability to compensate for the coupling error. In comparison with [PERSON]'s \(2N-1\), [PERSON]'s \(2N-1\) has a smaller RMS error because it is a modified and improved algorithm based on the theory of [PERSON]'s \(2N-1\). Except for \(10N-9\), [PERSON]'s \(5N-4\) algorithm outperforms the other three algorithms. Obviously, \(10N-9\) has the smallest RMS error value (lower than 1 nm, about 0.7 nm) of all algorithms, which means that the \(10N-9\) algorithm can more effectively suppress the RMS error than other conventional algorithms, even for a 30% phase shift miscalibration. Therefore, the \(10N-9\) algorithm is appropriate for measuring the surface profile of a highly reflective silicon wafer.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 4 Evaluation  
Dataset A (Mixed Host) contained 17,711 domains across multiple autonomous systems, ensuring broad applicability across diverse hosting environments. Dataset B (Cloudflare CDN) consisted of 5368 domains exclusively from Cloudflare AS 13335, evaluating the technique's effectiveness in CDN environments, where traditional TLS fingerprinting is less granular. Cloudflare was specifically chosen, due to its roll-out of Encrypted Client Hello (ECH) in August 2024 [31], which presented challenges to the widely adopted JARM fingerprinting approach. Dataset C (Malicious) included 4475 known malicious domains from verified sources, assessing the abilities of the approaches to identify similarities within known malicious infrastructure.  
Although only three datasets were used, they were constructed by aggregating data from multiple sources (Table 2), to provide a representative evaluation across distinct technical fingerprinting challenges. These included variations in hosting environments, the impact of privacy-enhancing measures in CDN deployments and the classification of known malicious infrastructure. The datasets were, however, temporal, as many malicious domains are. They represented a snapshot of domain behaviour at a given point in time, and they focused on specific deployment scenarios rather than long-term trends.
## 1 Introduction
### Compression and Expansion Machines  
Isentropic Ltd. has developed positive displacement machines that can operate as both a compressor and expander, reducing the number of machines from four to two. However, these machines require custom designs with a large bore-to-stroke ratio and are more suitable for smaller capacities (<5 MW\({}_{\text{el}}\)). A prototype developed by Isentropic Ltd. and the University of Newcastle achieved efficiencies of 92-94% [2]. Axial turbomachinery is more cost-effective and efficient for larger capacities (>50 MW\({}_{\text{el}}\)) and is better suited for larger installations. Conventional turbomachinery is either a compressor or an expander. The reduction in the number of machines from four to two also offers potential cost savings, although it is not yet the current state of the art. The outlet temperature of the charging compressor is the highest temperature in the cycle process and simultaneously presents one of the greatest technical challenges in the development of the sub-components and thus for the overall Brayton battery system.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 5 Evaluation
### Experiment Design
#### 5.1.4 Sensitivity Analysis  
This study conducted a sensitivity analysis on control methods across various traffic demands, different CRs of CBCVs, and unbalanced traffic demand.  
The traffic demand level is quantified through the Volume-to-Capacity (V/C) ratio. In the balanced traffic flow scenario, the specific demand levels analyzed are 0.2, 0.5, and 0.8.  
The CR of CBCVs are set to 10%, 30%, 50%, and 70%. Sensitivity testing for imbalance traffic demand was conducted with a CBCV CR of 30%. The imbalance in traffic demand reflects the combination of traffic flows in different directions, which can be categorized into the following four types:  
1. Balanced traffic demand in all directions.
2. Uneven straight-through traffic demand on arteries.
3. Uneven left-turn traffic demand on main arteries.
4. Uneven traffic demand of both straight-through and left-turn traffic on main arteries.  
The details are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length of the segment under control (m) & 300 \\ Simulation duration (s) & 1200 \\ The update time interval of signal control (s) & 1 \\ Duration of the clearance time (s) & 3 \\ Minimum green time interval (s) & 15 \\ Maximum green time interval (s) & 50 \\ Saturation flow rate (veh/h) & 1440 \\ Maximum acceleration (m/s\({}^{2}\)) & 3.5 \\ Minimum acceleration (m/s\({}^{2}\)) & \(-4\) \\ Time lag in the Newswell model (s) & 1.6 \\ Spatial lag in the Newswell model (m) & 6 \\ Speed limit of the road (km/h) & 60 \\ The average length of vehicles (m) & 4.5 \\ The transmission interval for CBCVs speed guidance (s) & 3 \\ Execution cycle of the rolling horizon scheme (s) & 15 \\ The safety interval in the rolling horizon scheme (s) & 5 \\ The gradient of the road segment & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation settings.
## 3 Results  
### Forest Fire Protection  
Landscape monitoring is one of the key elements in forest fire protection. It is possible to conduct it by calculating NBR and dNBR indices. At first glance, cloud coverage may interfere with vegetation monitoring because the Sentinel 2 satellite cannot record the situation below clouds. Croatia has favorable climate conditions for applying Sentinel 2 data in vegetation monitoring. There are many sunny hours, especially during the warm part of the year when grass and bushes grow the fastest, and the probability of forest fire ignition is the highest [50]. There is a high probability that two or three data retrievals will be useful for further analysis and calculation of NBR and dNBR indices. The successful applicability of the NBR and dNBR indices in the case study of Croatia is described and well documented in [41].  
Other important segments are fire detection and prediction of fire-spreading danger. Comparing FRP values and FRP uncertainty in different buffers around the fire polygon indicates that the FRP's highest mean values are in the fire polygon and one-kilometer buffer around the fire polygon. There is no significant difference between the FRP mean value in the 5 km and 11 km buffers. Most of the FRP values recorded outside of the fire polygon were within a three-kilometer radius of the fire polygon. Accordingly, it is possible to define an area of 3 km around the recorded group of FRP data as a potential fire location (Figures 3 and 4).  
Investigation of the Sentinel 3 LST observations showed that, for each revisit time, data are not recorded precisely at the same place. Also, a small number of recorded LST values was recorded inside the fire polygon (Figure 5). It was decided to observe the dataset recorded within a five-kilometer radius of each fire polygon. Enough LST values were recorded in that area to enable anomaly detection and adequate further analysis.  
Further analysis showed a linear correlation between the Canadian FWI and recorded LST values in a 5 km area around the weather station, the data of which were used for the FWI calculation. Based on this correlation, the equation for the FWI was calculated and adjusted to Croatian climatological conditions (2), where \(\overline{\text{LST}}\) presents the mean value of the recorded LST values in a 5 km radius around each weather station. The adjusted FWI has a different value range than the original Canadian FWI, so it was necessary to adjust the danger classes (Table 1).  
\[FWI_{adjusted}=0.871\;*\;\overline{\text{LST}}-9.549 \tag{2}\]  
Figure 4: The buffer around FRP pixels indicates the area of increased fire probability.  
Figure 5: Spatial distribution of LST values recorded on different dates for the fire event detected on 27 May 2021 at 12:06. The red line presents the fire polygon, and the blue dots present locations of measured LST values.  
Figure 3: Recorded FRP (blue dots) and fire polygon (orange).
## 5 Energy-Efficient Resource Matching Algorithm for D2D Relay Communications
### Three-Dimensional Matching Algorithm  
Given the RB allocation of the second hop, the joint optimization problem (8) is equivalent to a three-dimensional matching problem involving the matching among D2D TRs, RSs and RBs for the first hop. To simplify the problem, we combine one RS and one RB (CUE) as a one-dimensional unit RC. Owing to the existence of \(R\) RSs and \(C\) RBs, there are \(R\times C\) different combinations, denoted as \(\mathcal{RC}=\{RC_{r,c_{f}}\}_{r=1,c_{f}=1}^{r=R,c_{f}=C}\). Thus, the three-dimensional matching problem is reduced to a two-sided matching with \(M\) D2D TRs on one side and \(R\times C\) RC units on the other side, which can also be solved by the proposed pricing-based matching approach. We give the definition below:  
**Definition 3**.: _A matching \(\tilde{\mathcal{X}}\) is a one-to-one correspondence, denoted as \(\tilde{\mathcal{X}}\): \(\mathcal{M}\cup\mathcal{RC}\rightarrow\mathcal{M}\cup\mathcal{RC}\cup\emptyset\), such that \(\tilde{\mathcal{X}}(m)=RC_{r,c_{f}}\) means that D2D TR \(m\) is matched with unit \(RC_{r,c_{f}}\) and \(\tilde{\mathcal{X}}(m)=\emptyset\) means that \(m\) remains single. \(\tilde{\mathcal{X}}(m)\) is the matching partner of \(m\)._  
Since the matching in the first hop D2D link is a one-to-one three-dimensional matching among D2D TRs, RSs and RBs, if \(\tilde{\mathcal{X}}(i)=RC_{r,c_{f}}\), for \(\forall i^{\prime}\in M-\{i\}\), we have \(\tilde{\mathcal{X}}(i^{\prime})=\{\mathcal{RC}-RC_{r,c_{f}}\}\cup\{\emptyset\}\). Similar to the definition of the two-dimensional stable matching, the three-dimensional matching is stable if there exists no blocking pair. That is, there exists no TX and RC unit that have not been matched with each other under \(\tilde{\mathcal{X}}\) and that prefer each other more than their current matching partners.  
Figure 3: Flowchart of the two-dimensional matching algorithm.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Joint Factor Analysis  
High-dimensional general recognizers such as _Support Vector Machines_[39] have been adopted to more complex classifiers called _Joint Factor Analysis_ (JFA) and _i-vectors_[40]. In representing a speaker, JFA obtains a low-dimensional set of parameters (the i-vector) as a compact embedding. The means of speaker-dependent GMMs form an aggregate _supervector_. The JFA model acts as Gaussian pdfs on speaker- and session-dependent HMM supervectors, where most variance is accommodated with a few hidden variables (speaker and channel factors). A speaker supervector has four components: speaker-independent, speaker-dependent (_eigenvoice_), channel-dependent (_eigenchannel_), and residual. These high-dimensional supervectors (tens of thousands of dimensions), from stacking first-order statistics for each mixture component, are projected into a low-dimensional fixed-length representation (i-vector, of 200-600 dimensions). The i-vector thus models a speaker by removing the UBM mean supervector and projecting into a low-dimensional space (a _total variability_ space).
# Burnup Credit Evaluation for BWR Spent Fuel from Full Core Calculations
## 5 Results and Discussion
### Burnup Credit Analysis for the Realistic BWR FA Model
#### 5.2.1 Criticality Estimation for Single BWR FA
In this case, the credit comes not only from the fuel burnup and residual gadolinium, as in the case of the peak reactivity method, but also from the increase in FA model complexity, which allows us to follow the changes in the axial power distribution while relying on calculated coolant properties and fuel temperatures. For further analysis, we will call this approach \"peak reactivity of the realistic FA model\". The development of the burnup credit methods intends to reduce the cost of spent fuel management. Hence, the gain from the BUC should be recognisable when either boral plates, which are used in the storage and transport tasks as neutron absorbers, can be removed or their number can be substantially reduced, or the spent fuel storage system's capacity can be increased.
## 3 Results
### The Most Important Factors for Gully Modeling  
The average merit (AM) values calculated by information gain ratio (IGR) technique are summarized in Table 3. The results indicate that all factors can be included in gully erosion susceptibility modeling because their AM values are greater than zero. However, rainfall, with an AM value of 0.225, is the most effective factor for gully erosion susceptibility mapping in the study area. It is followed by elevation (AM = 0.186), river density (AM = 0.106), distance to river (AM = 0.093), land use (AM = 0.086), lithology (AM = 0.083), distance to road (AM = 0.038), profile curvature (AM = 0.031), aspect (AM = 0.028), NDVI (AM = 0.023), slope (AM = 0.020), and plan curvature (AM = 0.016).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Collinearity Statistics**} \\ \cline{2-3}  & **Tolerance** & **VIF** \\ \hline Land use & 0.184 & 1.525 \\ \hline Lithology & 0.674 & 1.354 \\ \hline NDVI & 0.628 & 2.047 \\ \hline Plan curvature & 0.492 & 1.254 \\ \hline Profile curvature & 0.398 & 2.673 \\ \hline Rainfall & 0.712 & 1.951 \\ \hline River density & 0.420 & 2.322 \\ \hline River distance & 0.324 & 1.875 \\ \hline Road & 0.583 & 1.840 \\ \hline Slope & 0.809 & 1.245 \\ \hline Aspect & 0.856 & 1.030 \\ \hline Altitude & 0.198 & 2.329 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-collinearity statistics for the gully erosion affecting factors.
# A Survey of Information Extraction Based on Deep Learning
## 4 Multi-Model IE Based on Deep Learning  
Multi-modal information extraction is the combination of multi-modal learning and information extraction technology [94]. Traditionally, the study of IE has focused on extracting entities and relationships from pure text, where information is mainly represented in the format of natural language text [95]. However, the rapid development of the Internet has resulted in massive amounts of data, including text, audio, images, video, and other modalities. Multi-modal information on the Internet, in some scenarios, only for the text of the data information extraction, may cause the loss of data information; therefore, researchers began to discuss how to extract the required information from multi-modal data. Existing work has demonstrated that the addition of visual modal information can play an important role in work, such as knowledge graph completion and triplet classification, and that multi-source information has shown potential for reasoning on the knowledge graph.  
\"Modality\" is widely defined, which can be intuitively understood as different types of multi-media data or as a more fine-grained concept. The key point to distinguishing modalities can be understood as whether data are heterogeneous. For example, for an actor, relevant information can be found on the Internet, including text introductions, personal pictures, film and television works, and film and television audio. These four types of data correspond to text, picture, video, and sound, respectively, which can be understood as multi-modal data of the object [96]. In a multi-modal data environment, cross-modal data have both modal characteristics and semantic commonalities. Multi-modal IE is a combination of multi-modal learning and IE technologies. At present, the existing unimodal representation learning methods have achieved good results, laying a foundation for the acquisition of multi-modal representations. The development of deep learning has also provided convenience for multi-modal research. The following two subsections consider multi-modal named entity recognition and multi-modal relationship extraction.
## 6 Simulation Setup and Results
### Simulation Results
#### 6.2.2 Dual-IMM Results with B034 Dataset
Analyzing part (b) and based on the peaks observed on the figure, it is evident that the dual-SIF-IMM makes use of the faster degradation rates to which it has access, resulting in a better estimation process. However, it does not show a better performance than the dual-KF-IMM algorithm. To further compare all results, Figure 20 depicts all the battery estimation curves in one plot. It can be seen that the dual-IMM strategies performed better than their dual-only counterparts, as they had access to faster aging conditions. The dual-SIF performed better than the dual-KF because of the deltas to which it had access. Lastly, the dual-KF-IMM performed the best across all algorithms despite not having the advantage of the delta parameters. In this case, tuning of the deltas in the dual-IMM strategy might be a limiting factor in the estimation process. \begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **RMSE** \\ \hline Dual-KF & 0.3677 \\ Dual-KF-IMM & 0.1758 \\ Dual-SIF & 0.2891 \\ Dual-SIF-IMM & 0.2198 \\ \hline \end{tabular}
\end{table} 
Table 5: B034 RMSE: Battery Capacity Estimation. Figure 18: (a) Dual-KF and dual-KF-IMM internal resistance estimates. (b) Dual-SIF and dual-SIF-IMM internal resistance estimates. It is important to mention that if the minimum battery capacity value estimated at each cycle were to be selected to generate a plot such as Figure 20, the dual-IMM algorithms completely capture the measured battery capacity, as demonstrated by Figure 21. Furthermore, the battery capacity RMSE values detailed in Table 6 further demonstrate the accuracy of the proposed dual-IMM strategy. Figure 21: Battery capacity estimation results of each algorithm. Figure 19: (**a**) Dual-KF and dual-KF-IMM battery capacity estimates. (**b**) Dual-SIF and dual-SIF-IMM battery capacity estimates. Figure 20: Battery capacity estimation results of each algorithm. Lastly, Figure 22 shows the mode probability results of the dual-IMM strategies. Part (a) demonstrates the mode probability of the dual-KF-IMM, and part (b) depicts the mode probability of the dual-SIF-IMM. When comparing Figure 22 to Figure 13, the dual-IMM strategies favor the _fast_-aging mode slightly more than the B036 dataset. However, neither algorithm makes full use of the faster degradation to which they have access (switch to _fast_ mode). Some reasons could be due to the nature of the estimation process. Battery capacity only affects the estimation process indirectly. Furthermore, the faster degradation rates do not fully represent the new dynamics of the entire system, only partially. The faster degradation rates provide a way to adapt faster to the new conditions to which the battery may be subjected.