esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data
### More Features  
In addition to one's velocity which is represented in the global frame, it is also beneficial to take the information of the front car into account while studying the change in the acceleration of the AV. As mentioned previously, the acceleration and velocity data are provided per bounding box in the vehicle frame; it is necessary to transform them into the global frame to maintain consistency.  
Furthermore, we use a simple geometrical procedure to detect the existence of the front car. As shown in Figure 1, the procedure checks if a hypothetical ray (green) starting from the position of the AV intersects with the front car (the red bounding box). However, one defect is that there could be many misses by using a single ray. Therefore, we optimize this procedure by utilizing a rectangular ray to represent the AV's view with some detection tolerance. As a result, the following quantities are extracted as part of the features: velocity \((v_{Rx},v_{ty},v_{tz})\) and acceleration of the front car \((a_{\text{fv}},a_{\text{fy}},a_{\text{tz}})\) and the relative distance to the front car \((dx,dy)\). Additionally, enlarging the tolerance range may lead to detection mistakes when there are multiple vehicles ahead, thus the number of vehicles is also included to describe the complexity of the scenario, which is denoted as \(num\_v\_labels\). For frames where the front car is not detected, the previously mentioned quantities are set to 0.  
In total, 12 features are calculated as mentioned above and will be used in our experiments. Table 1 shows the features with corresponding explanations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Explanations** \\ \hline \(v_{x},v_{y},v_{z}\in\mathbb{R}\) & The velocity of AV in \(x,y,z\) directions. \\ \hline \(dx,dy\in\mathbb{R}\) & The distance from AV to the front car in \(x,y\) directions. \\ \hline \(v_{\text{fv}},v_{ty},v_{tz}\in\mathbb{R}\) & The velocity of front car in \(x,y,z\) directions. \\ \hline \(a_{\text{fv}},a_{\text{fy}},a_{tz}\in\mathbb{R}\) & The acceleration of front car in \(x,y,z\) directions. \\ \hline \(num\_v\_labels\in\{0,1\}\) & The number (existence) of front vehicles, which describes the complexity of the scenario. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 12 features computed from the data of Waymo Open Dataset.
## 3 Numerical Results
### Shading Patterns Analysis  
In order to evaluate the performance of the proposed Q-learning-based GMPPT method, the shading patterns 1-3 were used during the learning process and then the acquired knowledge was exploited during the execution of the GMPPT process for the rest of the shading patterns. The distribution of incident solar irradiation (in W/m\({}^{2}\)) over each PV module of the PV array for each shading pattern is presented in Figure 6, while the resulting output power-voltage curves of the PV array are presented in Figure 7. Each number (i.e., SP1, SP2 etc.) in Figure 7 indicates the order of application of each shading pattern during the test process. These shading patterns, as well as their sequence of application, were formed such that the power-voltage characteristic of the PV array exhibits multiple local MPPs at varying power levels (depending on the shading pattern). This approach enabled the learning capability of the agent employed in the proposed Q-learning-based GMPPT technique and its effectiveness in reducing the time required for convergence to the GMPP to be investigated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Number of particles & 8 & Inertia weight w & 0.6 \\ Maximum duty cycle & 0.83 & Cognitive coefficient c\({}_{1}\) & 1.6 \\ Minimum duty cycle & 0.19 & Social coefficient c\({}_{2}\) & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Operational parameters of the particle swarm optimization (PSO)-based GMPPT method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Initial duty cycle & 0.71 & Minimum duty cycle & 0.19 \\ \(\tau_{min}\), \(\tau_{max}\) & 0.08, 0.8 & Maximum duty cycle & 0.83 \\ d & 1 & \(k_{1}\), \(k_{2}\), \(k_{3}\) & 10, 25, 0.6 \\ Discount factor & 0.9 & \(N_{max}\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operational parameters of the proposed Q-learning-based GMPPT method.  
Figure 6: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Low Change of Duty** & **Medium Change of** & **High Change of Duty** \\  & **Cycle** & **Duty Cycle** & **Cycle** \\ \hline Increment of \(\Delta D\) & +0.04 & +0.12 & +0.28 \\ Decrement of \(\Delta D\) & -0.04 & -0.12 & -0.28 \\ No change of \(\Delta D\) & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Action-space of the proposed Q-learning-based GMPPT method.
## 4 Discussion
Recent research performed to correct the bias of daily satellite precipitation estimates along the Kelantan River Basin using a deep learning approach managed to decrease the \(RMSE\) to a maximum of 30.0% and \(MAE\) to a maximum of 23.2%, respectively [85]. Although our current research area covers a much bigger area, we managed to reduce the \(RMSE\) and \(MAE\) to maxima of 66% and 59%, respectively. Further investigation can be conducted by using different optimizers to train the data with LSTM modeling. The machine learning algorithm processing time is crucial when dealing with huge datasets [86]. With the automated shell program developed, the model training job was scheduled and automatically run 24 by 7 and it has optimized the performance of the computing resource. Although the multiple layer implementation of the LSTM model is proven to provide better results [87], it could be challenging in making decisions regarding the model parameters during LSTM training. An existing dataset is stored in a relational database system and this will facilitate further research studies of extreme event forecasting with machine learning. Nevertheless, it is worthwhile to consider the potential benefits of utilizing non-relational database systems, such as MongoDB. The scalable data processing and analysis pipeline, especially wherein data are visualized in the pHp program and accessible via a relational database, will more effectively and efficiently facilitate the collaboration and data sharing among relevant researchers and data analysts online. Cloud-based platforms offer flexibility for deep learning architecture training [88]. By utilizing database connectors and the application programming interface (API), AI analysis tools can establish connections and conduct comprehensive analysis as well as forecasting modeling. The development of reliable flood forecasting models can be very helpful in data-scarce regions [89].
# Extension of the Discrete Electron Transport Capabilities of the Geant4-DNA Toolkit to MeV Energies
###### Abstract  
The discrete physics models available in the Geant4-DNA Monte Carlo toolkit are a subject of continuous evolution and improvement in order to meet the needs of state-of-the-art radiobiological research for medical and space applications. The current capabilities of Geant4-DNA for event-by-event electron transport extend up to 1 MeV. In this work, Geant4-DNA's most accurate electron inelastic model for sub-keV energies is improved and extended up to 10 MeV via the Relativistic Plane Wave Born Approximation and other theoretical considerations. Benchmark simulations of the electronic stopping power and range of electrons in liquid water using the new model show almost excellent agreement (at the few % level) with the recommendations of the International Commission on Radiation Units and Measurements (ICRU) up to 10 MeV, offering notable improvement (by a factor of \(\sim\)2) over the default Geant4-DNA inelastic model and an order-of-magnitude higher electron limit. The present development will allow Geant4-DNA users to perform electron track-structure simulations up to 10 MeV, thus, covering a wider range of radiotherapeutic applications (including FLASH-RT) as well as space applications involving MeV electrons which are not currently reachable.  
Monte Carlo; Geant4-DNA; truck structure; stopping power; cross sections +
Footnote †: journal: Applied sciences  
0000-0001-5000-0001-5000][PERSON]
# S2p: Provenance Research for Stream Processing System
## 7 Experimental Evaluation
### Throughput Results  
Figure 12 reports the throughput results for both s2p-flink and normal Flink under various workloads. In general, s2p-flink causes an 11% to 24% decline in throughput, and these increase ratio values fluctuate around 16% (the median) with the variance 0.00014 for the whole decreased ratio for P1, P2, and P3. This also implies good scalability of s2p.  
More precisely, Figure 12a reports the throughput results for P1. For all workloads ranging from 2 GB to 500 GB, s2p-flink brings less than 14% decline in throughput, and even only 11% for some workloads. Figure 12b compares the results for P2. For this application, we observe the throughput decline ratio of s2p-flink no more than 19%, and shows a decreasing trend when the data set is larger than 10 GB. Figure 12c shows the results for P3. The throughput decline ratio of s2p-flink is less than 17% for data sets smaller than 10 GB and stays steady for data sets between 15 GB and 120 GB. The decline ratio slightly increased for large data sets (i.e., larger than 120 GB) but was still no more than 24%.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **P1** & **P2** & **P3** \\ \hline Mean & 0.146 & 0.196 & 0.257 \\ \hline Median & 0.15 & 0.2 & 0.27 \\ \hline Variance (\(\sigma^{2}\)) & 0.0001 & 0.0008 & 0.0021 \\ \hline \end{tabular}
\end{table}
Table 4: Statistical results for increased cost ratio of P1, P2, and P3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**WorkLoads** & **P1** & **P2** & **P3** \\ \hline
2 GB & 1.16X & 1.22X & 1.19X \\ \hline
5 GB & 1.13X & 1.22X & 1.20X \\ \hline
10 GB & 1.13X & 1.24X & 1.20X \\ \hline
15 GB & 1.16X & 1.20X & 1.27X \\ \hline
30 GB & 1.14X & 1.16X & 1.29X \\ \hline
60 GB & 1.15X & 1.21X & 1.28X \\ \hline
120 GB & 1.14X & 1.19X & 1.26X \\ \hline
240 GB & 1.15X & 1.16X & 1.30X \\ \hline
500 GB & 1.16X & 1.16X & 1.32X \\ \hline \end{tabular}
\end{table}
Table 3: Latency comparison results for P1, P2, and P3 applications under various workloads on s2p-flink and native Flink.
###### Abstract  
Rotor winding short circuit faults are common faults for variable-speed pumped-storage generator-motors (VPSGSM). At present, the exciting rotor fault protection of VPSGSM is simple and has low sensitivity. It can only act when the instantaneous value of the rotor phase current reaches three times the rated current. Therefore, it is difficult to cover some rotor winding short-circuit faults with weak fault characteristics. It is urgent to study a novel rotor winding short-circuit-fault protection method for VPSGSM. In this paper, a protection method that combines the stator and rotor currents with different frequencies is proposed. The characteristics of the stator and rotor currents before and after the fault is analyzed by using Clark transformation. On this basis, a specific protection criterion is constructed based on the discrete integral operation, which is easy to implement and not affected by the change of rotor speed. Then, the calculation method of the protection setting is proposed, considering the effect of unbalanced voltage and sensor measurement error. Simulation results show that the proposed method can reliably realize the protection of rotor winding faults. It has faster protection action speed than other methods in the same field. The protection coverage rate is over 90%.  
variable-speed generator-motor; rotor winding short-circuit fault; Clark transformation; protection method combined the stator and rotor currents
## 3 Results and Discussion
### Qualitative Analysis  
Embedding Space Visualization  
As illustrated in Figure 2, the GRU-based model's t-SNE projection displays distinct clusters for Job Description (blue), Positive Resume (green), and Negative Resume embeddings (red). Although some overlap is observable--especially where resumes partially match certain job descriptions--green points generally lie closer to their corresponding blue anchors than red points do, indicating that the GRU managed to separate the most well-aligned resumes from clearly incompatible ones.  
In Figure 3, the LSTM-based model yielded a broadly similar distribution pattern, although its clusters appear slightly more spread out. The LSTM's ability to capture longer-range dependencies in textual data (e.g., detailed resumes with multiparagraph experience and intricate job requirements) often yields tighter local groupings of green (positive) points around the blue (job) points, while red (negative) points drift farther away. However, some mixed regions suggest that additional context, such as domain-specific skills, might still be challenging to isolate purely via textual embeddings.  
Figure 13: Transformer—validation accuracy vs. epoch.  
Finally, Figure 4 showcases the Transformer-based model's t-SNE embedding. Here, clusters of blue and green points are more clearly discernible, and negative resumes (red) are distributed more distinctly. The self-attention mechanism appears to help encode nuanced relationships, causing them to cluster more cohesively. In particular, candidates with highly specialized or precisely matched skill profiles show very close proximity to the relevant job descriptions.  
Overall, this comparative view highlights that while all three sequential heads (GRU, LSTM, and Transformer) achieved effective separations in embedding space, the Transformer-based model demonstrated a consistently sharper distinction between positive and negative resumes.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 4 Results
### MAM Test Results  
A real GNSS signal was combined with an RFI signal generated by a signal generator. Then, the combined signal was injected into a GNSS receiver. GNSS observables extracted from the receiver were sent to the SPM, where algorithms based on detecting RFI from GNSS observables were applied. The GIMAD system can detect single-tone interference centered at L1 (1575.42 MHz) with a \(-\)40 dBm.  
The GNSS data received from a satellite varies depending on its trajectory. Taking this into account, the MAM algorithm is conceived as an adaptive algorithm in the sense that it adapts to the evolutionary changes of the C/N\({}_{0}\), phase, and pseudorange of the satellites. Figure 7 below shows the RFI detection through the abnormal deviations of the AGC and the phase. Similar results for L5 have been obtained.
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 3 Application of the Geophysical Surveys and Processing Results
### Seismological Station at Atalanti, Ethiopia (ATAL)  
Station _ATAL_, situated about 5 km north of Atalanti town (Figure 15), in Fthioidida, Central Greece, was installed on 26 September 2007. The site of this station is important for the monitoring of seismicity in the northern Euboekos Gulf. Station _ATAL_ is presently equipped with a _Guralp CMG-DM24S6-EAM_atalogger and a _CMG-3T/120s_ broadband seismometer. The geophysical line could not be executed closer to the station due to the rough geomorphology of the area in its vicinity and the vegetation.  
Figure 14: SRT and MASW (**top**) and ERT (**bottom**) sections at the site of the seismological station _THAL_.  
Figure 15: Location of the seismological station _ATAL_ and the geophysical measurements along with the geology of the area.  
The carbonate bedrock, where the station is based (Figure 15), consists of Middle and Lower Jurassic limestones and dolomites (_Ji-m kD_), according to [73].  
Similar to the case of station _THAL_, station _ATAL_ also presents a fairly flat HVSR curve (Figure 16). However, the base level of amplification is lower, with an average value equal to 1.1. A frequency peak at \(f\) = 1.0 Hz, with \(A\) = 1.3 \(\pm\) 0.3, is present in some cases, but it is not clear. A higher peak frequency at \(f\) = 13.6 Hz, with \(A\) = 1.2 \(\pm\) 0.2, is also distinguished in some of the cases.  
Concerning the SRT technique (Figure 17) at _ATAL_ seismological station, the subsurface consists of three seismic layers. The first and second layers have an average thickness of 1.0 m each and P-wave velocities of \(V_{P1}\) = 1200 m/s and \(V_{P2}\) = 1900 m/s, respectively. At depths greater than 2.0 m, the seismic bedrock dominates along the section with \(V_{P3}\) > 2700 m/s. Additionally, the S-wave velocity of the seismic bedrock was calculated at 1940 m/s, as derived from the 1D MASW model (Figure 17), while the \(V_{S30}\) value was determined at 2016 m/s. The results of the ERT (Figure 17) reveal a significantly resistant (>8000 Ohm.m) formation across almost the entire section.  
Figure 16: Average HVSR curves (solid lines) determined for station _ATAL_ on different days, from 60 s long windows selected after the application of STA/LTA anti-triggering on 6 h long records. Circles denote the global frequency on each curve, while diamond symbols mark the average of the frequency band denoting the range of peak frequencies of the HVSR lines from the individually selected windows (light/dark gray band in Figure 2b). Squares mark the prominent global and secondary peak frequencies recognized from the HVSR spectra, with error bars indicating the uncertainty of the amplification factor (Table A1).
## A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 2**  
\({}^{1}\) School of Computer and Information, Hefei University of Technology, Tunxi Road 193,  
Hefei 230009, China  
\({}^{2}\) School of Electronic and Information Engineering, Beihang University, XueYuan Road 37,  
Beijing 100191, China; E-Mails: [EMAIL_ADDRESS] (T.J.); [EMAIL_ADDRESS] (D.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-551-6290-1552.  
Academic Editor: [PERSON]  
_Received: 2 June 2015 / Accepted: 24 August 2015 / Published: 2 September 2015_  
**Abstract:** Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner-Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments.  
keywords:Global Navigation Satellite System (GNSS); interference detection and mitigation; spectrogram; Wigner-Ville distribution (WVD); reassigned method; notch filter +
Footnote †: journal: Journal of Instrumentation and Measurement
## 4 Experiment and Evaluation
### Correlation Analysis Between Pose Estimation and CAD Retrieval
This simple equation gives a positive reward score if the image similarity rank from the proposed method is higher (rank number becomes small) than that from PoseContrast or PosefromShape. On the contrary, this equation gives a negative score if the image similarity rank from the proposed method is lower (rank number becomes large). \[s_{i,corr}=r_{i,Conventional}-r_{i,Proposed} \tag{32}\]  
For example, if the matching CAD model is ranked 1 st using the proposed network and 3 rd using a conventional method, the score is +2. On the contrary, if the conventional method yields a better rank, we give a negative score. If both results yield the same rank, the score is 0. The proposed correlation score is based on the rank difference between the two networks rather than the absolute rank obtained from each network. After adding all scores from all RGB images, we get the average by dividing by the number of all RGB images. Tables 7 and 8 show the correlation score of the proposed method with two image similarity measurements. In both measurements, a positive score is obtained in the 'Better' case, while a negative score is obtained in the 'Worse' case. Additionally, the 'SS' case yields a score close to zero. This quantitative analysis shows that the performance improvement from the proposed pose estimation method also improves the performance of CAD model retrieval. In addition, the pose estimation failures also negatively affect the CAD model retrieval. As shown in Table 8, correlation scores with respect to PosefromShape, the positive correlation scores in the 'Better' case are higher than those in Table 7. This means that the correlation score evaluation with respect to PosefromShape is much higher than PoseContrast. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Better** & **SS** & **Worse** \\ \hline Bag (144) & 68 & 25 & 37 \\ Bed (110) & 15 & 36 & 46 \\ Bench (146) & 87 & 20 & 31 \\ Bottle (184) & 109 & 3 & 8 \\ Car (188) & 75 & 89 & 6 \\ Chair (199) & 95 & 73 & 16 \\ Clock (173) & 51 & 19 & 26 \\ Motorbike (170) & 70 & 81 & 7 \\ Stove (176) & 116 & 31 & 14 \\ Table (72) & 47 & 21 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the qualitative evaluation compared to PosefromShape (number of detected images is in parenthesis). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Similarity Measure** & **Better** & **SS** & **Worse** \\ \hline SM-AT & 0.519 & 0.0 & \(-\)0.364 \\ SM-NI & 0.338 & 0.110 & \(-\)0.226 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Correlation score per sample between pose performance and similarity measurement. Pose performance comparison is with respect to PoseContrast.