esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Data Analysis  
#### 4.2.1 Gaze-Target Distancing: Condition Type Influence over Eye Position  
In the first group, salience was applied after subjects had to adapt for 15 min to an advanced scotoma simulation. The Kruskal-Wallis test found that there is a significant difference between the three conditions (\(\chi^{2}\) (2) = 7.19, \(p\) = 0.03) for the distance between gaze and target. The post-hoc Dunn's revealed that the cued scotoma induced significant changes in the gaze-target distance (\(p\) = 0.02) compared to the normal condition. No significant difference was found for the scotoma condition compared to the other two conditions (Figure 9, G1).  
Figure 8: Inclusion and exclusion criteria for different subjects. Positive and negative predictive values were looked at to identify the trend where, irrespective of the scotoma radius, both values reached a plateau (the positive predictive value did not increase and the negative predictive value did not decrease anymore). The black arrow indicates where this plateau was reached for the majority of subjects. In this example, a demonstration is shown for subjects 1 and 2. For those where this trend was not observed, they were excluded. This was the case, for example, for subjects 5 and 12.  
Figure 7: Bar plots of positive and negative predictive values of all subjects across different scotoma radii. The black arrow inside the box plots indicates the point from which a plateau is starting to emerge. The plateau is starting to emerge at 5\({}^{\circ}\).  
For the second group, where subjects were presented first with the scotoma simulation together with the cued salience, there was a significant effect between the three conditions as well (\(\chi^{2}\) (2) = 7.20, \(p\) = 0.03). The post-hoc Dunn's revealed that compared to the normal condition, the central scotoma changed significantly the gaze-target distance (\(p\) = 0.02, Figure 9, G2).
## 2 Materials and Methods
### Simulation Design
#### 2.1.2 Wavelet Transforms  
The transient fault signal obtained from the simulation program is used as input into a sub-program written in MatLab. This program uses the wavelet toolbox within MatLab to perform a five-level decomposition of the sample signal with [PERSON]'s wavelet family as the mother wavelet. By decomposing the signal into five frequency bands, a better image of the fault can be obtained by studying the various waves. The wavelet transform of each phase is obtained with the MatLab command wavedec. This command allows a five-level decomposition to be made using [PERSON]'s db5 as the mother wavelet.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Parameter Design
#### 4.1.3 Target Design  
Our observation object was a target group comprising one principal target named center and 29 secondary targets named bait. The trajectory parameters are outlined in Table 5. We assumed that the target trajectory was an arc that starts at 129.67\({}^{\circ}\) E, 40.86\({}^{\circ}\) N and ends at 118.40\({}^{\circ}\) W, 34.10\({}^{\circ}\) N after 1614 s. The ground distance of the trajectory was 9179 km, with a maximum altitude of 825.45 km. The complete trajectory of the target in the J2000 coordinate system was generated using the Runge-Kutta method. These methods incorporate random directions and sizes of tangential velocities to the position and velocity of the primary target at the release time. Figure 7c shows the scene of target group diffusion in this simulation scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline FOV/deg & \(1\times 1\) & Pupil diameter/cm & 40 \\ Pixel number & \(128\times 128\) & NEI/(W \(\times\) cm\({}^{-2}\)) & \(1\times 10^{-18}\) \\ Number of intervals & 6 & SEI/(W \(\times\) cm\({}^{-2}\)) & \(3\times 10^{-15}\) \\ F number & 2.5 & Max distance/km & 6000 \\ Effective focal number/cm & 100 & Min elevation angle/deg & \(-32.0\) \\ Min altitude/km & 250 & Min solar exclusion angle/deg & 60.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensor main parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** \\ \hline Trajectories begin & (129.67\({}^{\circ}\) E, 40.86\({}^{\circ}\) N) \\ Trajectories end & (118.40\({}^{\circ}\) W, 34.10\({}^{\circ}\) N) \\ Max altitude/km & 825.45 \\ Random velocity/(m/s) & [2; 5] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Targets trajectory parameters.  
Each type of target has different parameters. In terms of shape, cone and sphere are generally considered to be the most common target types, and the cylinder is a container for placing targets. In terms of attitude, we assumed that cones type targets perform spin and precession, and sphere and cylinder type targets perform rolling [41]. In terms of temperature, we set the initial temperature of the target to 350 K and gradually reduced it to 328 K as the target moved [42; 43; 44]. Table 6 provides specific parameters for the above settings, and Figure 7d shows the shape of the main targets in the simulation scenario.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 6 Experimental Validation
### Assessment of Online Cluster Adaptation
#### 6.3.1 Cluster Balancing through Roaming
Convergence was reached after only two roaming cycles, with the timing of CM decisions coinciding with the broadcast interval of CH messages. In larger topologies that exhibit more imbalance after initial cluster formation, significantly more roaming steps may be required for balancing. Allowing parallel roaming operations, such as that of the CMs of the yellow cluster between second 0 and 3, can thereby influence the convergence time either positively or negatively, thus providing scope for further optimization. Figure 20: Cluster balancing in the \(5\times 5\)-node grid (**a**: constellation after initial clustering, **b**: constellation after one roaming cycle, **c**: balanced constellation after two roaming cycles). Reprinted from ref. [18].
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 8 Performance-Based Design for PEF
### Additional Insights  
In addition to underscoring the need for integrating PEF considerations into building codes, the literature also discusses several critical advancements and considerations:  
* Advanced Numerical Tools: The utilization of advanced numerical tools, such as SAFIR and ABAQUS, is essential for conducting sequential analysis of structures under PEF conditions [68; 98]. These tools provide a detailed understanding of how structures respond to the combined effects of seismic and fire loads, thereby enabling more precise and effective design strategies.
* Performance-Based Fire Curves: The development of performance-based fire curves, such as the iBMB curve, allows for more realistic fire simulations in PEF analysis [24]. These curves are specifically tailored to reflect the actual fire behavior observed in post-earthquake scenarios, offering a more accurate basis for assessing structural resilience.
* Vertical Fire Spread: The significance of considering vertical fire spread in tall buildings is emphasized [75]. Understanding how fire propagates vertically in high-rise structures, especially after an earthquake, is crucial for devising effective fire mitigation and evacuation strategies.  
* Innovative Solutions: The application of innovative materials, such as fiber-reinforced polymers (FRPs), is highlighted for enhancing the PEF resistance of both existing and new structures [65; 66]. FRPs offer significant benefits in strengthening and retrofitting structures to withstand the dual impacts of earthquakes and fires.
* Further Research Needs: There is an urgent need for additional research on the performance of various structural systems and connections under PEF conditions. This includes investigating different construction materials, structural designs, and connection types to identify the most effective solutions for enhancing fire resilience post-earthquake.
* Data Collection and Risk Prediction: The importance of collecting post-earthquake fire data is underscored to improve risk prediction models and validate existing research findings. Comprehensive data collection will enhance the accuracy of predictive models and inform future building codes and standards, ensuring they are grounded in empirical evidence.  
In conclusion, addressing these considerations and incorporating PEF analysis into building codes will significantly enhance the resilience of urban structures in earthquake-prone regions. Advanced numerical tools, realistic fire simulations, innovative materials, and comprehensive data collection are all crucial in developing robust and reliable design methodologies for mitigating the risks associated with post-earthquake fires.
## 3 Results and Discussion
### Correlation of SAR Variables with Soybean Yield
Among the backscatter-related variables, DPSVI, RVI, and \(VH\) were selected due to their lower correlations with each other (Table 6), providing complementary information for the prediction scenarios. Regarding the variables from polarimetric decomposition, alpha was excluded, and only entropy was used. Thus, for the prediction scenarios, the selected SAR variables were DPSVI, RVI, \(VH\), entropy, and EVI across the three stages, using the Boxcar filter \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Harvest 1—Stage 2**} & \multicolumn{4}{c}{**Harvest 2—Stage 2**} & \multicolumn{4}{c}{**Harvest 2—Stage 2**} \\ \hline DPSVI & \(-\)0.67 & 0.67 & \(-\)0.67 & 0.31 & \(-\)0.96 & DPSVI & \(-\)0.68 & 0.68 & \(-\)0.68 & 0.22 & \(-\)0.96 \\ - & Pol & \(-\)1 & 1 & \(-\)0.45 & 0.83 & - & Pol & \(-\)1 & 1 & \(-\)0.51 & 0.85 \\ - & - & RFDI & \(-\)1 & 0.45 & \(-\)0.83 & - & - & RFDI & \(-\)1 & 0.51 & \(-\)0.85 \\ - & - & - & RVI & \(-\)0.45 & 0.83 & - & - & - & RVI & \(-\)0.51 & 0.85 \\ - & - & - & - & - & \(VH\) & 0.001 & - & - & - & - & \(VH\) & 0.033 \\ - & - & - & - & - & \(VV\) & - & - & - & - & - & - & \(VV\) \\ \hline \multicolumn{8}{c}{**Harvest 1—Stage 3**} & \multicolumn{4}{c}{**Harvest 2—Stage 3**} \\ \hline DPSVI & \(-\)0.58 & 0.58 & \(-\)0.58 & \(-\)0.43 & \(-\)0.97 & DPSVI & \(-\)0.62 & 0.62 & \(-\)0.62 & \(-\)0.29 & \(-\)0.96 \\ - & Pol & \(-\)1 & 1 & \(-\)0.42 & 0.76 & - & Pol & \(-\)1 & 1 & \(-\)0.52 & 0.80 \\ - & - & RFDI & \(-\)1 & 0.42 & \(-\)0.76 & - & - & RFDI & \(-\)1 & 0.52 & \(-\)0.80 \\ - & - & - & RVI & \(-\)0.42 & 0.76 & - & - & - & RVI & \(-\)0.52 & 0.80 \\ - & - & - & - & \(VH\) & 0.21 & - & - & - & - & \(VH\) & 0.039 \\ - & - & - & - & - & \(VV\) & - & - & - & - & - & \(VV\) \\ \hline \multicolumn{8}{c}{**Harvest 3—Stage 1**} & \multicolumn{4}{c}{**Harvest 3—Stage 3**} \\ \hline DPSVI & \(-\)0.63 & 0.63 & \(-\)0.63 & \(-\)0.36 & \(-\)0.96 & DPSVI & \(-\)0.61 & 0.61 & \(-\)0.61 & \(-\)0.32 & \(-\)0.95 \\ - & Pol & \(-\)1 & 1 & \(-\)0.45 & 0.81 & - & Pol & \(-\)1 & 1 & \(-\)0.47 & 0.80 \\ - & - & RFDI & \(-\)1 & 0.45 & \(-\)0.81 & - & - & RFDI & \(-\)1 & 0.47 & \(-\)0.80 \\ - & - & - & RVI & \(-\)0.45 & 0.81 & - & - & - & RVI & \(-\)0.47 & 0.80 \\ - & - & - & - & \(VH\) & 0.12 & - & - & - & - & \(VH\) & 0.11 \\ - & - & - & - & - & \(VV\) & - & - & - & - & - & - & \(VV\) \\ \hline \multicolumn{8}{c}{**Harvest 3—Stage 2**} & \multicolumn{4}{c}{**Harvest 3—Stage 3**} \\ \hline DPSVI & \(-\)0.68 & 0.68 & \(-\)0.68 & 0.25 & \(-\)0.97 & & & & & \\ - & Pol & \(-\)1 & 1 & \(-\)0.41 & 0.83 & & & & & \\ - & - & RFDI & \(-\)1 & 0.41 & \(-\)0.83 & & & & & \\ - & - & - & RV & \(-\)0.41 & 0.83 & & & & \\ - & - & - & - & \(VH\) & 0.12 & & & & \\ - & - & - & - & - & \(VV\) & - & - & - & - & - & \(VV\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Correlation between variables derived from polarimetric decomposition.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Efficiency and Backbone Analysis  
The training time and floating point of operations (FLOPs) are used to evaluate the efficiency on the UCMD dataset with a 20% noise rate using different backbones. The experimental results are presented in Table 7. On the one hand, it is evident that the FLOPs of our improved APL method do not increase significantly. This is because AWNet is a shallow neural network with significantly fewer parameters than deep learning models. As a result, the training time of our improved APL method does not increase significantly compared to the original APL. Considering that the original APL method has to try at least 12 different weighting parameters, it actually takes longer to train than our improved APL. On the other hand, it can be seen that our improved APL method achieves better retrieval results than the original APL method under different deep learning models. This indicates that our method has better generalisation.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 6 Open Research Challenges
That is, for security and resource allocation reasons, one would overwhelmingly prefer to be able to uniquely identify a source as well as to determine which group or groups this source may belong to.
# Optical Angle Sensor Technology Based on the Optical Frequency Comb Laser
## 4 Angle Detection Based on the High Pulse Energy of a Mode-Locked Femtosecond Laser
The laser beam introduced into the setup is at first collimated by a collimating lens and is then made incident to a nonlinear crystal mounted on a rotary table. As can be seen in Equation (8),  
Figure 16: The matching angle in the second harmonic generation (SHG) [59]: (**a**) Refractive index ellipse of a negative uniaxial crystal; (**b**) Phase mismatched case; (**c**) Phase matched case. the intensity of the second harmonic light to be generated in a nonlinear crystal depends on the crystal material, as well as the light wavelength. In the setup, barium borate (BBO) crystal with a similar matching angle for each mode in the femtosecond laser has thus been employed as the nonlinear crystal. The optic axis of the nonlinear crystal is adjusted to be approximately 20 degrees. It should be noted that, regarding Equation (7), the fundamental light is made to focus in the BBO crystal so that the second harmonic light can be generated effectively. The second harmonic light generated by SHG is then condensed onto a photodiode by a condenser lens, and the photocurrent from the photodiode is converted into voltage signal through a trans-impedance amplifier to monitor the signal by an oscilloscope. It should be noted that not only the second harmonic light but also the remaining fundamental light would come out from the BBO crystal. In the setup, a polarizer is placed in front of the photodiode so that the fundamental light will not be detected. For the verification of the angular displacement of the BBO crystal, a commercial laser autocollimator is also employed as a reference sensor. Figure 17: Measurement of the angular displacement of an object based on the characteristics of the matching angle in the second harmonic generation (SHG) [59]: (**a**) Schematic of the optical setup; (**b**) The angle to be measured by the method. Figure 18: Measurement of the angular displacement of a rotary table based on SHG: (**a**) Optical configuration of the setup; (**b**) A photograph of the setup [59]. Figure 19a shows the change in the intensity of the second harmonic light as the change in the angular displacement of the BBO crystal observed in experiments, where three types of focusing lens with focal lengths of 40 mm, 75 mm and 150 mm are employed. As can be seen in the figure, a peak can be observed at the matching angle in each of the plots. Figure 19b shows the variation of the intensity of second-harmonic light as the change in the angular position of the BBO crystal. As can be seen in the figure, the angular displacement in a step of 0.4 arc-seconds has clearly been distinguished. These results demonstrate the feasibility of measuring small angular displacement by the SHG. It has also been verified that the shorter focal length of the focusing lens contributes to obtaining second-harmonic light with larger intensity, since the power of the fundamental beam can be further concentrated at the beam waist of the focused beam in the BBO crystal.
## 4 Discussion  
As elaborated in the previous section, the presented method provides three main advantages. Firstly, the algorithm is capable of determining which initial states cannot be distinguished from which particular initial state in the case that the system is not entirely observable on a given set. This can provide further understanding why a given system is not entirely observable. Thus, the method supports the designing of a technical system by providing information on how to modify the output function in order to achieve global observability on the desired state space. Hence, the usage of different sensors can be checked whether they provide observability. Secondly, the algorithm delivers the initial states, which are observable on \(D_{x}\) and which are probably not. Therefore, if the nonlinear system is only observable on a subset \(\mathcal{B}\subset D_{x}\), this subset \(\mathcal{B}\) can be determined. This is the case even if the subset \(\mathcal{B}\) is of a complex structure. Thirdly, the algorithm is capable of considering system uncertainties and can determine the observability in dependency of these uncertainties. This is particularly useful if parameters of the system are unknown or the measurement function contains design parameters due to different types of sensors that could be used. From the results it is possible to extract which sensors benefit the observability of the states that are of interest for the user. This is mostly archived by the interval based numerical approach. Furthermore, interval based methods deliver guaranteed results and are robust against rounding errors [17].  
The runtime of the algorithm highly depends on the considered nonlinear system and whether the states are observable or not. This was especially noticeable in Example 2. For an increase of 0.92% over 3 d and 5 h of computational time was necessary, while the first 628,000 iterations only took 8 min and 48.084 s. This drastic increase of computational time per iteration occurs when the initial states are indistinguishable. In that case, the list of the objects (13) grows for many elements by every iteration, which slows the algorithm down. Nonetheless, in case of complex nonlinear systems, an adaptation of the algorithm to parallelize the execution might be worthwhile to enhance the performance. However, the determination whether the nonlinear system is observable is a task that needs to be done once before the technical system is used. Therefore, the runtime of the method is not of immediate importance. The here presented work aims to demonstrate the benefits of interval based numerical approaches.  
Conceptualization, T.P.; Data curation, T.P.; Formal analysis, T.P.; Investigation, T.P.; Methodology, T.P.; Software, T.P.; Supervision, B.T.; Validation, T.P.; Visualization, T.P. and S.L.; Writing--original draft, T.P., S.L., M.D. and R.D.; Writing--review & editing, T.P., S.L., M.D. and R.D. 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.
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 2 Background  
The PRN code has high correlation values for the same PRN codes and low correlation values for different PRN codes. To generate a PRN code that has the correlation characteristic, there are three methods: a linear feedback shift register (LFSR), a Legendre sequence, and a memory code [5]. The LFSR is a design method using linear combinations of shift registers, the Legendre sequence is a method based on a mathematical sequence, and the memory code is a method of storing and reading predetermined sequences in memory. Table 1 shows the frequency bands and PRN code generation methods for representative GNSS signals. As shown in Table 1, most of the signals are generated based on the LFSR, while the GPS LIC signals [8] and the BDS B1C signals [9] are generated using the Legendre sequence, and the code of the Galileo E1 signals [10] is generated based on the memory code. This paper focuses on a structure that can support various signals by sharing hardware for LFSR, which is the most widely applied. As shown in Figure 1, the LFSR-based code generator basically consists of shift registers, a Feedback Tap circuit, an Initial read-only memory (ROM), and an output circuit. The LFSR-based PRN generation method for GNSS is subdivided into M-sequence, gold code, and kasami code according to the detailed configuration of LFSR [6].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GNSS** & **Frequency Band** & **Signal** & **Code Structure** & **Code Type** \\ \hline \multirow{4}{*}{GPS} & \multirow{2}{*}{L1} & L1C/A & LFSR (10b, 10b) & Gold code \\ \cline{3-5}  & & L2 & L2C (L2 CM/ L2 CL) & LFSR (27b) & M-sequence \\ \cline{2-5}  & & L5 & L5 I & LFSR (13b, 13b) & Gold code \\ \cline{3-5}  & & L5 Q & LFSR (13b, 13b) & Gold code \\ \hline \multirow{4}{*}{BDS} & \multirow{2}{*}{B1} & B1I & LFSR (11b, 11b) & Gold code \\ \cline{2-5}  & & L1 & B1C & Legendre sequence & Weil code \\ \cline{2-5}  & & B2 & B2I & LFSR (11b, 11b) & Gold code \\ \cline{2-5}  & & L5 & B2A (B2 ad/B2 ap) & LFSR (13b, 13b) & Gold code \\ \cline{2-5}  & & B3 & B3I & LFSR (13b, 13b) & Gold code \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary the code generation method of GNSS signals [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22].
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 3 Methods and Materials  
### Space-Time Prediction  
Let us denote by \(p(\mathbf{s}_{i},t_{j})\) the PM\({}_{2.5}\) pollution at site \(\mathbf{s}_{i}=(x_{i},y_{i})\), with \(i=1,\ldots,S\), where \((x_{i},y_{i})\) represents the vector of spatial coordinates, and time \(t_{j}\), with \(j=1,\ldots,T\). While in the temporal prediction the main goal is to forecast the level of pollution in a given station \(\mathbf{s}_{1}\) for \(n-\)ahead time steps (\(n\geq 1\)), \(\{p(\mathbf{s}_{1},t_{T+1}),\ldots,p(\mathbf{s}_{1},t_{T+n})\}\), considering the historical information of pollution in the same site \(\mathbf{s}_{1}\), \(\{p(\mathbf{s}_{1},t_{1}),\ldots,p(\mathbf{s}_{1},t_{T})\}\), the spatio-temporal prediction consists of predicting the pollutant concentration at new site \(s_{0}\) where there are not monitoring stations--that is, the past information is not available, but the past information of the nearest stations is used. Such prediction can be extended to every point on a regular grid for producing a prediction map. Figure 1 shows an example of the difference between temporal and spatio-temporal predictions. Mathematically, the space-time prediction can be expressed as a nonlinear transformation \(f(\cdot)\) of the past pollution observations at different sites using no linear functions. Hence, the proposed model can be written as  
\[\hat{p}(\mathbf{s}_{0},t_{T+n})=f(p(\mathbf{s}_{k},t_{T-m}),\mathbf{X}(\mathbf{ s}_{k},t_{T-m})),\]  
where \(\hat{p}(\mathbf{s}_{0},t_{T+n})\) is the prediction of the PM\({}_{2.5}\) concentration at the station \(\mathbf{s}_{0}\) and time \(t_{T+n}\); \(p(\mathbf{s}_{k},t_{T-m})\) represents the sequence of past observations of PM\({}_{2.5}\) at the sites \(\{\mathbf{s}_{k}\}\), with \(k=1,\ldots,K\), and at the past times \(\{t_{T-m}\}\), with \(m=0,1,\ldots,M\)--that is, \(p(\mathbf{s}_{k},t_{T-m})=\{p(\mathbf{s}_{1},t_{T-1}),\ldots,p(\mathbf{s}_{n},t _{T-m})\}\); finally, \(\mathbf{X}(\mathbf{s}_{k},t_{T-m})\) is a sequence of covariates given by past meteorological and pollutant variables--that is, \(\mathbf{X}(\mathbf{s}_{k},t_{T-m})=\{X(\mathbf{s}_{1},t_{T-1}),\ldots,X( \mathbf{s}_{n},t_{T-m})\}\).