esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Performance Evaluation
### Experimental Test  
To verify the proposed scheme and its performance, WSN nodes and devices have been developed (Figure 11) and deployed along a 25 km transmission line in Guangzhou Province to build RCC. A single-phase-to-ground fault happens at 0.16 s and the recorded differential current of each phase is shown in Figure 12. The trip signal is activated at 0.2813 s, which takes 121.3 ms since the fault happens. The time consumption takes end-to-end latency into account as well as the protection algorithm cost. Although the time delay is longer than optical-fiber based communication, it is acceptable as an emergency communication channel under the condition that the main communication channel fails.  
Figure 11: Experimental prototype of wireless sensor devices. (**a**) wireless sensor node in shell; and (**b**) and printed circuit board (PCB) design of wireless sensor module.  
Figure 10: Routing protocol performance with node removal.  
Figure 9: Routing Protocol performance different network scale. (**a**) average, maximum and minimum end-to-end delay; and (**b**) packet delivery ratio.
## 3 Results
### ANN Performance Results  
In this study, the ANN model, like the ANFIS model, was trained using arc light as input and current data as output. The estimation of the quality of welding processes has been performed using ANN models in several studies [40; 41; 42; 43; 44; 45; 46].  
The designed ANN model structure is shown in Figure 12. The model consists of three hidden layers with 40, 30, and 20 neurons, respectively, and one output layer. The Levenberg-Marquardt backpropagation algorithm was used for training.  
In Figures 13 and 14, the ANN estimations made at current levels of 140 A, 120 A, 100 A, and 80 A using training data obtained from electrodes with diameters of 3.25 mm and 2.50 mm were compared with the target current signals.  
According to the data in Figure 13a, the RMSE value between FSANNCO and the target FCS was found to be 33.6504 A, and the R-squared value was 0.6842. In Figure 13b, the maximum cross-correlation value between FSANNCO and the target FCS was found to be 0.9559. According to the data in Figure 14a, the R-squared value was 0.7364, and the RMSE value was 31.1072 A. In Figure 14b, the maximum cross-correlation value was calculated to be 0.9550.  
The R-squared value between FSANNCO and the target FCS was calculated as 0.6417 and 0.5412, respectively, for the 3.25 and 2.50 mm electrode ANN model test data. The RMSE was found to be 33.7037 A and 40.9669 A, respectively. The maximum cross-correlation value was found to be 0.9554 and 0.9349, respectively.  
Figure 12: ANN model.  
Figure 13: Performance analysis of the ANN model with training data for the 3.25 mm electrode: (**a**) comparison of target FCS with filtered shifted ANN current output (FSANNCO); (**b**) cross-correlation between the target FCS and FSANNCO. Note: FSANNCO, filtered shifted ANN current output.
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 6 Experimental Results
### Experiment II: The Influence of Pressure on the PAD Subsystem Accuracy  
#### 6.3.1 PAD Subsystem Accuracy: Pressure versus Ordinary Presentations  
In order to identify the influence of pressure on the PAD subsystem accuracy, BPCER20 is used to compare the PAD subsystem accuracy considering the scenarios of ordinary presentations and presentations with pressure. The error rates, reported in [23] for ordinary presentation and in the previous experiment for presentations with pressure, are shown in Figure 8 to compare the PAD subsystem accuracy for both scenarios.  
The majority of tests in Figure 8 reveal that additional finger pressure during the presentation results in a reduction in BPCER20. This reduction could be significantly large, as  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extractor**} & \multicolumn{6}{c}{**APCER\({}_{PAI}\) (\%)**} \\ \cline{2-9}  & **PlayDoh** & **White Glue** & **Spray Rubber** & **Polish Nail** & **Nails Hardener** & **Gelatin** & **Latex** \\ \hline \(\text{VLPQ}_{\text{3-3}}\) & 1.11 & 32.22 & **0** & 1.11 & **0** & **0** & **0** \\ \hline \(\text{VLPQ}_{\text{5x5}}\) & 2.22 & 26.67 & **0** & 1.11 & **0** & 2.22 & 1.11 \\ \hline \(\text{VLPQ}_{\text{5-7}}\) & 4.44 & 7.78 & 1.11 & 2.22 & 3.33 & 3.33 & 12.22 \\ \hline \(\text{LPQ}\text{-  
noticed when examining the PAD subsystem using GIST 3-D descriptor, where the BPCER was reduced over 80% when compared with ordinary presentations for both sensors. On the other hand, a few tests in the figure show an advantage for ordinary presentations. Those scenarios are further investigated in the next subsection.  
The most intriguing observation to emerge from Figure 8 is the significant accuracy enhancement when using \(VLPQ_{77}\) for the optical sensor and \(GIST3-D\) for the thermal sensor. Thus, Figures 9 and 10 demonstrate the score distributions for bona fide and attack presentations considering both cases. The influence of pressure can be simply noticed by comparing the median values for attacks, and the median values for bona fide. Considering the optical sensor, the median of attacks dropped from \(-1.34\) to \(-2.28\) and the median of bona fide increased from 0.21 to 0.30. On the other hand, the thermal sensor does not show a noticeable change in the median of attacks but the median of bona fide increased from 0.21 to 0.37. Another interpretation can be seen by the decreased misclassified presentations when pressure is considered.  
Figure 8: Scenario comparison for the proposed PAD subsystem considering the 5 feature extractors.  
Figure 9: PAD scores distribution for the optical sensor using VLPQ\({}_{7}\)-\(\gamma\) features.
## 6 Conclusions
The following abbreviations are used in this manuscript:  
SAR  Synthetic Aperture Radar  
FFSAR  Fully Focused SAR  
UFSAR  UnFocused SAR  
WL  Water level  
WL\({}_{i}\)  Water level from a single radar waveform  
WL\({}_{a}\)  Water level obtained from aggregating all WL\({}_{i}\)s of a single satellite pass  
WLE  Water level error  
OCOG  Offset Center of Gravity  
NM  Nadir measurement  
AM  Across-track measurement  
RMC  Range Migration Compensation  
OLTC  Open Loop Tracking Command  
S6  Sentinel-6 [PERSON]  
LRM  Low-Resolution Mode  
CS2  CryoSat-2  
S3A/B  Sentinel-3 A/B  
VS  Virtual Station  
PISA  Precise Inland Surface Altimetry  
NTC  Non-Time Critical  
GSW  Global Surface Water  
GPP  Ground Processor Prototype  
WTC  Wet Troposphere Correction  
DTC  Dry troposphere correction  
IC  Ionosphere correction  
GIM  Global Ionospheric Map  
SETC  Solid earth tide correction  
PTC  Pole tide correction  
LTC  Load tide correction  
RMSE  Root mean square error  
MAD  Median absolute deviation  
SDE  Standard deviation error
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 4 Experimental Study
### Performance Evaluation  
To evaluate the effectiveness of CAMP, we compared our proposed approach with the state-of-the-art approaches to find out the quality of the maintenance performed by existing strategies. We evaluated on the basis of the maintenance cost, quality and cache hit rates.
## 5 Case Study: Experimental Results
### Thematic and Temporal Analysis
#### 5.1.3 Comparison with Textual Topic Modeling  
We also compared our proposed modeling with classical text-based topic modeling. For this comparison, we exploited the topic modeling method, using the textual content of the articles. Figure 3 shows the results of both user-based and text-based topic modeling. We depicted the relative portion of topics in order to show the temporal variation of the distribution clearly.  
In the text-based topic modeling, the topics are more evenly distributed along the time axis compared to the user-topic modeling. This shows that text-based topic modeling is not appropriate for representing temporal factors, which change dynamically over time, in tasks such as event detection. On the other hand, our user-topic model shows more variation with time. It deals with the dynamic nature of topics in a way which addresses the concerns raised in the literature [33]. While other researchers have tackled this dynamicity problem using more complex probabilistic models [33; 34], our proposed method can be used for dynamic analysis without any further modification other than the substitution of words with users.  
\begin{table}
\begin{tabular}{c c c l} \hline \hline
**Topic** & **Rank** & **Prob** & **Title** \\ \hline \multirow{4}{*}{1} & 1 & 0.96 & Sending Angry Emails Just Makes You Angrier \\  & 2 & 0.95 & Connected Gun Lets Anyone Watch What Or Who You Are Shooting \\  & 3 & 0.95 & Judge: Warrantless Airport Seizure of Laptop ‘Cannot Be Justified’ \\  & 4 & 0.94 & Secret Service Plans New Fence, Full Scale White House Replica, But No Moat \\  & 5 & 0.94 & New Molecular 3D Printer Can Create Billions of Compounds \\ \hline \multirow{4}{*}{2} & 1 & 0.97 & Top Democrats Request FBI Investigation of [PERSON] Campaign Ties To Russia \\  & 2 & 0.96 & Scientists: What We’re Doing To The Earth Has No Parallel In 66 Million Years \\  & 3 & 0.96 & Climate Deal: US and China Join Paris Climate Accords \\  & 4 & 0.95 & Climate Change Is Altering Global Air Currents \\  & 5 & 0.95 & Ice Loss In West Antarctica Is Speeding Up \\ \hline \multirow{4}{*}{3} & 1 & 0.97 & Michigan Lets Autonomous Cars On Roads Without Human Driver \\  & 2 & 0.96 & Virtual Zuck Fails To Connect \\  & 3 & 0.96 & DNA Testing For Jobs May Be On Its Way, Warns [PERSON] \\  & 4 & 0.95 & India’s Infosys To Hire 10,000 American Workers After [PERSON] Criticism \\  & 5 & 0.94 & Let Consumers Sue Companies \\ \hline \multirow{4}{*}{4} & 1 & 0.98 & Devuan’s Systemed-Free Linux Hits Beta 2 \\  & 2 & 0.97 & Windows Switch To Git Almost Complete: 8500 Commits and 1760 Builds Each Day \\ \cline{1-1}  & 3 & 0.97 & Mozilla Will Deprecate XUL Add-ons Before the End of 2017 \\ \cline{1-1}  & 4 & 0.97 & Debian 8 Jessie Released \\ \cline{1-1}  & 5 & 0.96 & Ubuntu 15.04 Released, First Version To Feature systemd \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top-5 articles per topic in Slashdot data (\(K=4\)).  
Figure 3: Comparison of user–topic and text-topic modeling methods in the Slashdot dataset.
## 6 Conclusions  
This study describes a turbulent downslope wind storm and rotor event observed on 11-12 January 2013 in the lee of the MBM in southeastern Wyoming. The UWKA research aircraft made four passes along the low-level wind across the mountain during a five-hour period. The airborne measurements include a profiling radar able to map vertical and along-track wind in a vertical transect below flight level at a very fine resolution (\(\sim\)30 m). This case was numerically simulated using WRF at a resolution of 1 km. The following are the main findings:  
* Even though the simulated vertical profiles of wind and stability departed somewhat from observed ones just upwind of the mountain range, with no critical level in the simulated wind profile, the model did produce supercritical wind in the lee, followed by a strong updraft flanked by counter-rotating vortices. This updraft had the appearance of a hydraulic jump and was followed by a decaying series of trapped lee waves. Intense turbulence was simulated near the plunging jet and the wave breaking area surrounding the hydraulic jump.
* WCR observations reveal persistent, intense cross-crest acceleration of the low-level flow, a plunging leeside jet separating from the boundary layer some 25 km downwind of the crest to produce a deep hydraulic jump (with updrafts up to 10 m s\({}^{-1}\)) flanked by counter-rotating vortices, and (at least in one transect) a shallow rotor with reverse flow near the surface.
* Flight-level and WCR observations indicate persistent moderate to severe turbulence in the vicinity of the plunging jet and the wave breaking area.
* Upstream conditions were generally conducive to downslope windstorm formation, at least early on, before flight measurements. During the flight the critical level and the suitable stratification (more stable below, less stable above mountaintop) vanished upwind, suggesting that a self-induced critical level may have sustained the downslope windstorm.
* A sampling of 19 other winter storms over the same mountain range suggests that the predictability of downslope windstorms based on coincident upstream conditions alone is poor.  
This research and the 11-12 January 2013 UWKA flight were funded by the National Science Foundation grant AGS-1058426.  
[PERSON] analyzed all data used in this study, led the WRF simulations, and wrote the paper. [PERSON] assisted with the WRF model output analysis and [PERSON] assisted with the WCR dual-Doppler synthesis. [PERSON] provided oversight of the project and the write-up.  
The authors declare no conflict of interest.
#### Article
###### Abstract  
This study develops a high-accuracy indoor positioning system using ultra-wideband (UWB) technology and the time-of-arrival (TOA) method. The system is built using Arduino Nano microcontrollers and DW1000 UWB chips to measure distances between anchor nodes and a mobile tag. Three positioning algorithms are tested: the triangle centroid algorithm (TCA), inner triangle centroid algorithm (ITCA), and the proposed intersection midpoint algorithm (IMA). Experiments conducted in a 732 \(\times\) 488 \(\times\) 220 cm indoor environment show that TCA performs well near the center but suffers from reduced accuracy at the edges. In contrast, IMA maintains stable and accurate positioning across all test points, achieving an average error of 12.87 cm. The system offers low power consumption, fast computation, and high positioning accuracy, making it suitable for real-time indoor applications such as hospital patient tracking and shopping malls where GPS is unavailable or unreliable.  
ultra-wideband (UWA); time-of-arrival (TOA); indoor positioning; triangle centroid algorithm (TCA); intersection midpoint algorithm (IMA) Article
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 5 Simulations
### Performance Comparison
Transforming Figure 12 to the radar raw R-A-E measurement space with inverse coordinate transforms, we obtain the detection and tracking results. As shown in Figure 13a, the detected trajectory of ETH highly coincides with the true value. The ETH is a TBD method since detection and tracking are announced simultaneously. In fact, although the primary threshold is lowered and the clutter becomes massive, this also means that the probability of detection of the target becomes higher. In order to compare the performance, Figure 13b shows the tracking results of traditional data processing via a detect-before-track (DBT) mode. In Figure 13b, the track initialization is three-point differentiation, the association algorithm is nearest neighbor (NN), and the filtering algorithm is interacting multiple model (IMM). The filter parameters are adjusted to the best. Nevertheless, as can be seen from Figure 13b, the traditional DBT method tracks a large number of temporary tracks. Specifically, the tracks of the target are often intermittent. In fact, there are a total of 18 tracks that belong to the same target, and the total number of temporary tracks reaches 253. This not only wastes radar tracking resources, but also poses a challenge for subsequent target discrimination. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**False Alarm Rate** & \(n_{a}n_{b}n_{\theta}=200^{3}\) & \(n_{a}n_{b}n_{\theta}=400^{3}\) & \(n_{a}n_{b}n_{\theta}=600^{3}\) \\ \hline \(\text{P}_{f}=0.01\) (924) & 0.3814 s & 0.9579 s & 1.7943 s \\ \hline \(\text{P}_{f}=0.1\) (3557) & 1.3723 s & 3.9687 s & 7.5323 s \\ \hline \(\text{P}_{f}=1\) (7042) & 2.7169 s & 7.8832 s & 14.9521 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational Load with Different Parameters. Figure 12: Detection and tracking results of EHT, with SNR=0 dB, primary threshold \(\eta=0\), and secondary threshold \(\eta^{\rm Hough}=10\). Figure 14 gives the accumulation effect of multiple targets, with Figure 14a the accumulation effect in the Hough parameter and Figure 14b the detection result in THIS-CS. The parameters are still Scheme II, Plane-constrained integration, with \(q=0.001\), time interval [150, 250] s, primary threshold \(\eta=0\), and secondary threshold \(\eta^{\rm Hough}=10\). The difference between these two targets is the difference in release altitude of 10 km. As can be seen, our method can also detect multiple targets at the same time. In fact, the Hough transform is based on a voting mechanism where the accumulation between targets is independent; thus, the Hough transform has implied parallelism, as well as multi-target detection capability. We also find through simulation that our method is able to detect the same target even if the trajectory passes through the surveillance area and then returns to the surveillance area.
## 3 Identification of QBIE Temperature Events
### Extracting QBWO and ISO Signals  
The next step is to identify the dominant QBWO and ISO signals of temperature variability in Hong Kong. We first applied wavelet spectral analyses to detect the dominating periodic signals of QBWO and ISO of temperature variability in Hong Kong. Analyses were carried out to data of all 14 weather stations. The results, as shown in Figure 2, indicate that the most dominant period of QBWO and ISO signals range from 10 to 25 days, while some individual cases are featured with longer periodic signals of up to 30-60 days. This suggests that, on the intraseasonal timescales, temperature variability in Hong Kong is primarily driven by QBWOs. While ISO signals overall play a lesser role, they may contribute to unusually long-lived QBIE events as shown later in our analyses. Although the temperature series may exhibit slight discrepancies across stations, they all show similar wavelet spectra, highlighting significant signals within the 10-60-day period.  
Based on these results, in order to take into account most possibilities, we take 10-60 days as the periods of interest for the QBWO and ISO signals in this study and define QBIE temperature events based on data being bandpass filtered within this range. A 121-point Lanczos bandpass filter was applied to the daily anomaly series to extract the QBWO and ISO signals. The Lanczos filter was chosen because it can reduce aliasing artificial signals well during filtering calculations and is widely used for meteorological time series, especially for extracting ISO signals [29; 30; 31; 32].  
Figure 2: Wavelet power spectra of daily mean temperature of the (**a**) HKO, (**b**) PEN, (**c**) SLW, and (**d**) WLP stations. Black contours denote the cone of influence (COI) of the spectra.
# Online Uniformly Inserting Points on the Sphere +
## 3 Results
### Analysis of the Lower Bound
#### 3.2.3 More Point Sequence Insertion  
**Lemma 2**.: _For more than three points, the gap ratio is no less than \(1.78\)._  
Proof of Lemma 2.: In each step of the online insertion, we compute the gap ratio and hope it to be the lowest. After inserting the first three points, the gap ratio can be expressed as \(r=\frac{d_{est}}{d_{min}}\), where \(d_{est}\) is the arc corresponding to the maximal empty spherical area, and \(d_{min}\) is the minimum arc. We use sequence \(\delta_{3},\delta_{4},\cdots,\delta_{n-1},\delta_{n}\) to denote the center angle for the largest spherical area and sequence \(\widehat{d_{3}},\widehat{d_{4}},\cdots,\widehat{d_{n-1}},\widehat{d_{n}}\) to denote the distance for the minimum arc at each step.  
Similarly, the minimum gap ratio will exist on the condition that \(r_{2}=r_{3}=r_{4}=\cdots=r_{n-1}=r_{n}\), which is equal to \(\frac{R\delta_{3}}{\widehat{d_{3}}}=\frac{R\delta_{4}}{\widehat{d_{4}}}= \cdots=\frac{R\delta_{n-1}}{\widehat{d_{n-1}}}=\frac{R\delta_{n}}{\widehat{d_ {n}}}\). Since we insert the point in order to ensure the gap ratio is the lowest in each step, we have \(\delta_{3}=\delta_{4}\). Moreover, as \(0\leq d_{n}\leq d_{n-1}\leq\cdots\leq d_{3}\), we have \(0\leq\gamma_{n}\leq\gamma_{n-1}\leq\cdots\leq\gamma_{4}\leq(\pi-\gamma_{4})\). Thus, we have \(\gamma_{3}\leq\pi/2\). Since \(r_{3}\) is a decreasing function about \(\gamma_{3}\), the minimum gap ratio will not be less than \(1.78\) regardless of the values of \(\alpha\) and \(\beta\).  
**Theorem 2**.: _The minimal gap ratio of inserting points on the sphere is no less than \(1.78\)._
## 2 Background
### Configuration  
Configuration refers to the process of assembling customized systems from generic, mass-produced components, as described in [1]. This task is widely performed across industries such as computer manufacturing, automotive production, furniture design, telecommunications, and travel services, where components are standardized yet allow for personalized solutions. Customers provide specific requirements, and configurations are created by selecting and adapting component instances to meet these requirements. Constraint programming, particularly through constraint satisfaction problems, has emerged as an effective approach for configuration tasks due to its ability to systematically explore solution spaces while managing combinatorial complexity. CSPs excel at defining clear boundaries for possible configurations and identifying component interactions. Instantiations (on the left in Figure 2) or partial instantiations (on the right in Figure 2) are starting points for configurations. Both refer to the solved CSP on the right side of Figure 1.  
The suitability of CP/CSPs for configuration stems from four key advantages: First, their flexible, declarative modeling approach allows for natural problem representation. Second, they can diagnose over-constrained scenarios by explaining why no solution exists and suggesting constraint relaxations. Third, they support interactive configuration processes where user choices dynamically constrain subsequent options through constraint propagation. Finally, they can incorporate and reason about user preferences during the configuration process. These capabilities make CP/CSPs particularly valuable for developing customized solutions while maintaining efficiency and user satisfaction.  
Figure 3 shows an example of a configuration task. There are eight available slots and two types of boards: Board A and Board B. Board As can only go into odd slots, and Board Bs can go into any slot, but there cannot be three or more together. There are three Board As and five Board Bs. The task is to allocate all boards to available free slots.  
Figure 1: An example of a CSP and its solution.  
Figure 2: Instantiations and a partial instantiation from Figure 1.