esa-sceva/satcom-chunk-collection huggingface dataset:
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 4 Experimental Results
### Performance Analysis for DCS-DDOA  
Similarly, we first present the behavior of DOA estimation for DCS-DDOA in a direct way. Assume that there are two fixed sources with incident angles \(\theta_{1}=-60^{\circ}\) and \(\theta_{2}=30^{\circ}\). The input SNR is 10 dB. The DOA spatial spectrum corresponding to different packet sizes is shown in Figure 15. When the  
Figure 14: The DOA RMSE and detection frequency for DCS-DOA versus \(M\) under different packet sizes.  
Figure 13: The comparison of detection frequency between CS-DOA and DCS-DOA under different packet sizes.  
Furthermore, we make a comparison between DCS-DOA and DCS-DDOA under a varying SNR, given that the packet size is 64 and the number of received data samples from each array sensor is \(M=64\). The results are shown in Figure 18, where each point is based on an average of 500  independent trials. Obviously, the DOA estimation performance of DCS-DDOA is always better than DCS-DOA regardless of the noise level. This results from the avoidance of the error propagation and the exploitation of a joint sparse representation. However, it costs something else to obtain such a satisfactory DOA estimation behavior. Now, we conduct a statistical analysis to get the computation time spent on one DOA estimate for the two techniques. In accordance with previous simulation results, when the packet size is 64, the behavior of DOA estimation is nearly perfect for both methods when \(M\) reaches 128, so we just explore the computation time in cases where \(M=64\), 128. By the same token, the cases of \(M=32\), 64, 96, 128 are investigated when the packet size is 32. The results are shown in Figure 19, which is obtained based on an average of 500 independent trials.  
Figure 17: The comparison of detection frequency among CS-DOA, DCS-DOA, DCS-DDOA under different packet sizes.  
Figure 18: The comparison of DOA estimation RMSE and detection frequency between DCS-DOA and DCS-DDOA under a varying SNR with the packet size being 64 and \(M\) being 64.  
As depicted in Figure 19, it takes much more time for DCS-DDOA to complete one DOA estimation process. Thus, it would be inapplicable in some scenarios where there is an urgent need for real-time target localization and tracking. In practice, if the quality of wireless links can be estimated in real time, we can combine it with the application requirements to decide which approach to adopt. If the link quality is sufficiently good, we will choose DCS-DOA for a faster DOA estimation process. Otherwise, we need to utilize DCS-DDOA to ensure the DOA estimation accuracy.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### Towards Microsecond Real-Time XPCS at Storage-Ring Sources
#### 4.1.3 Kinetics during Gelation  
Many studies on dynamics in gels and during gelation revealed a reduction of the experimental speckle contrast [105; 122; 123]. This indicates that a faster dynamic process appears during gelation that falls outside the accessible time scale of the detector. Such a two-step relaxation process is typical for gels and glasses, often denoted as \(\alpha\)- and \(\beta\)-relaxation [124]. The fast \(\beta\)-relaxation describes the dynamics of the single particles, i.e., rattling of particles, while the \(\alpha\)-relaxation reflects the collective dynamics, e.g., due to the structure or jamming. In many gel studies the \(\beta\)-relaxation was not accessible. However, the lack of contrast is frequently modelled with a localization length [66] that is a measure of the range of localized particle motion. With the avenue of measuring faster dynamics, direct access to the full relaxation process will be in range.  
This was first demonstrated by [PERSON] et al. [125; 126]. Using a prototype of the UFXC32k chip that has become part of the XSPA-500k detector (see Table 1), the formation of a colloidal gel was followed with few \(\upmu\)s time resolution. Gel formation of octadecyl-grafted silica particles suspended in decalin was followed after temperature quenches around the gel temperature of 278 K. A unified scaling between the depth of the quench \(\Delta T\) and the gel formation was observed. The effective interaction derived from the data is much stronger than anticipated from the interaction potential. This is consistent with a collective behavior connected to the gel formation.  
[PERSON] et al. recently revealed the kinetics of the heat-induced gelation of hen-egg white [38]. Conventional hen-egg white from chicken eggs was used and heated to 80 \({}^{\circ}\)C. The subsequent gelation was measured in USAXS geometry, allowing to use a large X-ray beam and thus reducing the radiation-induced damage. A reaction-limited aggregation process was found with an average network mesh size of about 400 nm. At this length scale, the relaxation time grew exponentially with experimental time. This was followed by a steady state after about 400 s that was characterized by a compressed exponential correlation function and temporal heterogeneity. This study demonstrates the possibility of investigating processes on length scales from nm to um which are particularly relevant for the food industry.
## 4 Analysis and Discussion
### PUF-Based Authentication Challenges and Solutions
#### 4.4.5 Challenge 5: Memory Consumption  
Conventional security protocols cannot be implemented on IoT devices as they have small ROM, RAM, and processing power. Apart from that, they have power constraints. Therefore, there is a need for the development of power--as well as memory-efficient algorithms. A lightweight PUF-based authentication system was proposed and tested on a wireless sensor network [81]. The performance analysis shows that the proposed algorithm saves up to 45% power and uses 12% less memory compared to datagram transport layer security (DTLS) handshake authentication.
# On the Use of PEDOT as a Catalytic Counter Electrode Material in Dye-Sensitized Solar Cells
## 1 Introduction
It can be seen in Figure 1A that over the last five years, the number of publications on \"dye-sensitized solar cells\" progressively decreased. As expected, this can be ascribed to the increasing interest in the new perovskite solar cells, as depicted by the rise in publications related to this topic. Figure 1B outlines that since 2015 among each component of DSSCs, the topic \"counter electrode\" covered an almost constant value of about 20%. This suggests that there is still a significant interest in the improvement of this specific component of DSSCs if compared to other subject areas, such as sensitizers, photoanode, or electrolyte [2]. In this contribution, we review the historical developments and the properties that have made PEDOT-based materials one of the most widely used substrates for DSSC counter electrode fabrication.
# Ontology-Driven Semantic Analysis of Tabular Data:
## 4 Software Implementation
### Development of OWL-Ontology in the Field of Monitoring Water Resources of Kazakhstan  
The subject area of the ontology is \"Water resources of the Republic of Kazakhstan\". This data includes information about water resources: rivers, lakes, reservoirs, their main indicators: length/area, water level, water class, etc. The ontology also contains data on regions--the number of people in cities, regions, birth, and death rates. Figure 2 shows the schema of the ontology.  
The purpose of creating an ontology is to find links between the economic indicators of regions and water bodies. The ontology should help answer the question of how water resources affect the standard of living of the population.  
The ontology contains two main classes of objects--Region and Water_Object. The regions are the regions of the Republic of Kazakhstan. In addition, cities can be located in regions--for this, a separate City class has been made, which is the successor of the Region class.  
The Region and City classes have the following property structure:  
1. Population
2. Male population
3. Female population
4. Urban population
5. Male urban population
6. Female urban population
7. Rural population
8. Male rural population  
Figure 2: Scheme of the developed ontology on Protege.  
Female rural population  
Each property is timestamped with a Timestamp, allowing for the data by year, month, or even day to be entered. This allows for tracking the trends of a particular property of an object over time.  
There is also a structure of relations between objects:  
1. Located_In--located in (reverse to Contains)
2. Contains--contains (reverse to Located_In)  
The Located_In and Contains properties are reciprocal--if a region contains a lake, then it must be true that the lake is located (Located_In) in the region.  
The Water_Objects class includes 3 types of objects--Basin (pool), Lake (lake), and River (river). These water bodies are connected both with each other (for example, a river is in a basin) and with regions.  
For water bodies, there is this property structure:  
1. Area (lake, basin)/Length (river).
2. Water level.
3. Class of water (according to the degree of pollution).
4. Elevation difference (river).
5. Water temperature.
6. Location (latitude, longitude).
7. Water resources.  
The remaining object classes (other than Region and Water_Object) describe additional information needed in the analysis. All data are interconnected using a timestamp or coordinates, which allows for analysis both for a specific territory and for a given period of time.
## 4 Results and Discussion
Further research is being carried out on the effect of pointing error on channel capacity. The differences of average capacity between strong and weak turbulence conditions are shown in Figure 9b. It can be seen from the graph that the smaller the pointing error parameter, the larger the maximum difference of average channel capacity. For instance, when \(\xi=10\), the maximum difference is 0.39 when SNR = 11 dB, but the maximum difference is 0.49 at SNR = 18 dB when \(\xi=1\). When the average SNR is greater than 11 dB, the channel capacity difference of \(\xi=1\) is larger than that at \(\xi=10\), though the gap between them will gradually shrink as the SNR value increases. We can conclude that a larger pointing error parameter results in a higher channel capacity, and it can aggravate the effect on capacity caused by atmospheric turbulence. Figure 8: Average BER of 16 PPM–BPSK–SIM against average SNR per hop for different turbulence strength and pointing error parameters. The fixed relay gain is \(C=1\).
## 3 Results
### Performance of Final ML Models
#### 3.3.2 Dosimetry  
See Table 9 for a dosimetric summary of \(\rm{MCO_{gs}}\) against the three AP solutions. Furthermore, see Figure 4 for an illustration of dosimetric differences from \(\rm{MCO_{gs}}\) for key dose-related metrics for each patient in the validation dataset and Figure 5 for an example dose distributions of each solution. At a population level all three methods provided excellent correspondence with \(\rm{MCO_{gs}}\) with deviations either not statistically significant (at the 95% level), or of a small magnitude. For example, PTV coverage and hotspot metrics were within 0.28 Gy of \(\rm{MCO_{gs}}\), and OAR objectives within 0.66% and 0.34 Gy for volume and dose metrics, respectively. The most noticeable statistically significant deviation was \(\rm{CI_{PTV48}}\) for \(\rm{PGAP_{std}}\) which was an improvement on \(\rm{MCO_{gs}}\) by +0.01; however, this was not considered a clinically significant difference. At patient level, deviations in the performance of the three methods was observed, with \(\rm{PGAP_{std}}\) and \(\rm{PGAP-ML_{clus}}\) highly comparable and \(\rm{PGAP-ML_{reg}}\) the poorest performer. For \(\rm{PGAP-ML_{reg}}\) the most notable deviations from \(\rm{MCO_{gs}}\) were for PTV48 D50%, \(\rm{CI_{PTV60}}\), \(\rm{CI_{PTV48}}\), rectum \(\rm{D_{mean}}\) and bladder \(\rm{D_{mean}}\) with deviation ranges of [\(-\)1.05, 1.22] Gy, [\(-\)0.114, 0.0378], [\(-\)0.172, 0.0925], [\(-\)2.05, 1.93] Gy and [\(-\)1.52, 2.03] Gy, respectively. For \(\rm{PGAP_{std}}\) and \(\rm{PGAP-ML_{clus}}\), bladder deviations were similar to \(\rm{PGAP-ML_{reg}}\), but substantially reduced for other metrics with PTV48 D50%, \(\rm{CI_{PTV60}}\), \(\rm{CI_{PTV48}}\) and rectum \(\rm{D_{mean}}\) deviations less than \(\pm\)0.60 Gy, \(\pm\)0.02, \(\pm\)0.06 and \(\pm\)1.18 Gy, respectively. In general, for \(\rm{PGAP-ML_{clus}}\) and \(\rm{PGAP_{std}}\) deviations from \(\rm{MCO_{gs}}\) across all patients were considered small and likely not of clinical significance.
# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 4 Discussion  
### Rainfall Forecasting  
The tendency observed in this study is that the forecast accuracy decreases with longer lead-time and higher rainfall intensities as shown in Figure 8. The first forecasted hour shows a high forecast ability, however with the increase of lead-time the ability is rapidly reduced. The forecast skill also decreases with higher rainfall thresholds, indicating that high rainfall intensities are more challenging to forecast and are subject to more errors. In most cases the ROC curve is below the random forecast line for a threshold of 3 mm/h, demonstrating that in these situations the model fails to forecast higher rainfall intensities efficiently. The ROC curves shown in Figures 4-7 demonstrate how the forecast efficiency decline after the first hour forecasted for a threshold of 3 mm/h. For a rainfall threshold of 0.1 mm/h, all the events for both probabilistic forecasts produce skilful predictions up to 3 h lead-time. With an increased lead-time however, the forecast ability also decreases.  
The events analysed produced good forecasts up to 1 h lead-time for all rainfall thresholds, but overall the radar ensembles performed slightly better than the STEPS ensembles during this period. The STEPS ensembles lose forecasting skill less rapidly and more constantly between the time-steps used when the thresholds are between 0.1 mm/h and 1.0 mm/h. Significant forecast accuracy is lost after the first hour for the radar ensembles. The area beneath the ROC curve is reduced by approximately 20% for rainfall intensities higher than 0.1 mm/h. For a threshold of 3.0 mm/h, the area beneath the ROC curve is reduced by around 24% after the first forecasted hour. For lead-times longer than 1 h, the STEPS ensembles perform better than the radar ensembles in most cases. Seeing as radar errors are the predominant source of uncertainties in the first hour forecasted, an ensemble generator based on the modelling of the radar residual errors is expected to produce more accurate results at the beginning of the forecast [14; 50].  
For higher rainfall intensities, the decrease in forecasting accuracy can be up to 27% per hour after 1 h lead-time. In most cases with a 3.0 mm/h threshold and lead-time longer than 2 h, the forecast is unable to predict the rainfall intensities accurately at these small spatial scales and presents areas beneath the ROC curve equal or lesser than 0.5. The loss of efficiency in the forecast is consistent with other studies [9; 56]. Due to the fact that regions with high-intensity rainfall are smaller there is a decline in the performance of the ensemble rainfall forecasts for higher rainfall thresholds. The area beneath the ROC curves for the radar ensembles are on average 10% higher than the STEPS up till 30 min lead-time. The difference falls to 6% when the forecasting lead-time is up to 1 h.
## 4 Adaptive \(\alpha\)-Value Computation
### [Cl/Co,F/NC] and [Cl/NC,F/NC] Cases  
In Figure 8a,b, two instances of these cases are depicted, respectively.  
Proposition 1: _When \(T_{j}\) and \(T_{k}\) belong to \([Cl/Co,F/NC]\) or \([Cl/NC,F/NC]\), the values of \(\alpha\) and \(\beta\) do not make any difference in the selection process._  
Proof: This claim can be derived directly from the following facts:  
Figure 8: Two robots are carrying out an exploration mission. It is assumed that \(R_{2}\) has already chosen the task \(T_{4}\) whereas \(R_{1}\) is still making its decision. The communication and sensory ranges are drawn around the robots with red and green dashed lines, respectively. Dotted lines are used to show the sight-line between \(R_{2}\) and the tasks. The corresponding Euclidean distance is also shown. (a) [Cl/Co,F/NC] case: robot \(R_{1}\) is selecting from targets \(T_{2}\) that is the closest and keeps it connected and \(T_{3}\) that is the furthest and cause a disconnection; (b) [Cl/NC,F/NC] case: robot \(R_{1}\) is selecting from targets \(T_{1}\)—the closest—and \(T_{2}\)—the furthest—given that both targets cause a disconnection.  
Figure 7: Two robots are carrying out an exploration mission. The communication and sensory ranges are drawn around the robots with red and green dashed lines, respectively. It is assumed that \(R_{2}\) has already chosen the task \(T_{4}\) whereas \(R_{1}\) is still selecting from \(T_{1}\), \(T_{2}\), and \(T_{3}\). Dotted lines are used to show the sight-line between \(R_{2}\) and the tasks. The corresponding Euclidean distance is also shown. _HO-Threshold_ is set to 6.  
* in the [Cl/Co,F/NC] case the furthest task \(T_{k}\) makes the robot disconnected, and then applying (7) to \(T_{j}\) and \(T_{k}\) leads to: \[\Phi_{i}(\alpha,T_{k},R)=\alpha\cdot\Psi_{i}(T_{k})\leq\alpha\cdot\Psi_{i}(T_{j}) +\beta\cdot\Omega_{i}(T_{j},R)=\Phi_{i}(\alpha,T_{j},R),\ \forall\alpha\] (10) s.t. \[\Omega_{i}(T_{j},R)>0,\Omega_{i}(T_{k},R)=0\] \[\Psi_{i}(T_{k})\leq\Psi_{i}(T_{j})\]
* in the [Cl/NC,F/NC] case both tasks make the robot to be disconnected, and thus the \(\Phi\) function value will depend only on the \(\Psi\) term: \[\Phi_{i}(\alpha,T_{k},R)=\alpha\cdot\Psi_{i}(T_{k})\leq\alpha\cdot\Psi_{i}(T_{j })=\Phi_{i}(\alpha,T_{j},R),\quad\forall\alpha\] (11) s.t. \[\Omega_{i}(T_{j},R)=0,\Omega_{i}(T_{k},R)=0\] \[\Psi_{i}(T_{k})\leq\Psi_{i}(T_{j})\]  
In conclusion, in any of these cases, the task selection is not affected by \(\alpha\).
## 4 Selection and Analysis of Resonant Compensation Network
### Operating Characteristics of Resonant Compensation Networks  
Based on the analysis above, S-S and LCC-S resonant compensation networks were selected for the inverter output for multi-load WPT applications. By designing corresponding parameters, the CC output of the S-S topology and the CV output of the LCC-S topology could be achieved.
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 2 Mining Conditions and Methods
### Analysis of Station Network Accuracy and System Wave Speed  
Set the seismic event source as unknown.  
\[x=\{t_{0},x_{0},y_{0},z_{0}\}^{t} \tag{1}\]  
where \(t_{0}\) is the time of the seismic event \(x_{0}\), and \(y_{0}\) and \(z_{0}\) are the 3D coordinates of the seismic event.  
The optimization of sensor station locations depends on the covariance matrix \(C_{x}\) of \(x\).  
\[C_{x}=k\left(AA^{T}\right)^{-1} \tag{2}\]  
Figure 1: Structure diagram of microseismic monitoring system. Green and gray represent drilling and measurement points.  
where \(k\) is a constant, and the value of \(A\) is expressed as  
\[A=\begin{bmatrix}1&\frac{\partial T_{1}}{\partial x_{0}}&\frac{\partial T_{1}}{ \partial y_{0}}&\frac{\partial T_{1}}{\partial z_{0}}\\ \vdots&\vdots&\vdots&\vdots\\ 1&\frac{\partial T_{1}}{\partial x_{0}}&\frac{\partial T_{1}}{\partial y_{0}}& \frac{\partial T_{1}}{\partial z_{0}}\end{bmatrix} \tag{3}\]  
where \(T_{i}\) (\(i=1,\,\ldots,\,\)n) is the calculated seismic arrival time and n is the number of sensor viewing stations.  
Covariance can be interpreted graphically using a confidence ellipsoid, and the eigenvalues of the covariance matrix form the length of the main axis of the ellipse. Solving for station optimization involves solving for a station arrangement that minimizes the ellipsoid volume. Because the product of the smallest volume of this ellipsoid is proportional, the optimized station location should minimize the following equation for all seismic events recorded by the monitoring network:  
\[obj=\min(\sum_{i=1}^{n_{e}}p_{h}(h_{i})\lambda_{x0}(h_{i})\lambda_{y0}(h_{i})_ {z0}(h_{i})\lambda_{t0}(h_{i})) \tag{4}\]  
where \(n_{e}\) is the number of seismic events, located in the region where the seismic activity will be monitored; \(P_{h}(h_{i})\) is the relative importance of the event, whose source is \(h_{i}=\{x_{i},y_{i},z_{i}\}^{T}\), which can be the probability function of an event occurring adjacent to that location; and \(\lambda_{x0(h_{i})}\) is the eigenvalue of \(C_{x}\).  
According to the geological conditions of the Maoping mine, the mine velocity model recommended by the IMS microseismic monitoring system (\(V_{p}=5500\) m/s, \(V_{s}=3500\) m/s) was used to carry out the system positioning error analysis. Comparing the calibration blast coordinates to the microseismic positioning coordinates revealed a significant spatial positioning error of approximately 20 m. This error requires the use of Trace software to correct for wave velocity. The results of the calibration of the blast waveform and positioning are shown in Figure 2. The positioning calibration procedures are listed in Table 1.  
Figure 2: Calibration blast waveform and positioning results.
## 3 Network Slicing Concepts
### Adaptation Layer  
This module provides a significant backbone function of the slicing mechanism, that of the operation abstraction and communication with the various layers. Different technology domains are exposed in a unified manner to the Slice Manager, without hardwired modifications and explicit rule enforcement. It comprises of VIM, NFVO, NMS and monitoring plugins, each one accommodating the different domain of the MANO paradigm and Katana Slice Manager implementation. Finally, the individual responsibility of its operation relies on the corresponding plugin, where it receives a request and translates it to the proper API call.
# Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant-Oil-Water Systems
## 2 Methodology and Materials
### Data Collection and Physics-Based Model Tuning  
We leveraged experimental data from the literature to fine-tune the modified hydrophilic-lipophilic difference net-average curvature (HLD-NAC) model, a physics-based approach for predicting surfactant-oil-water phase behavior ([PERSON] et al., 2015; [PERSON] et al., 2015; [PERSON] et al., 2015). The experimental data from [PERSON] et al. (2015) ([PERSON] et al., 2015) include salinity scans and solubilization ratios of water and oil in the microemulsion phase. These data calibrated the model to ensure it accurately reflects real-world phase behavior for a given system.  
The modified HLD-NAC model incorporates key surfactant parameters, such as surfactant structure, oil type, salinity, temperature, and pressure, allowing us to account for the intricate balance between hydrophilic and lipophilic forces. By optimizing the model parameters, we ensured that the model provides robust predictions for the phase behavior of surfactant-brine-oil systems under varying conditions ([PERSON] et al., 2015). We note here that the models typically assumed pure excess phases, i.e., the oil phase is pure oil and the aqueous phase is pure water pseudo component.