esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Integration of RDF and LPG in SKATEBOARD
### RDF: Semantic Structure  
RDF is a widely adopted data format for representing structured knowledge within the context of the World Wide Web. Its triple-based schema (subject, predicate, object) provides an ideal framework for expressing information semantically and flexibly. This schema is well suited for defining ontologies, entity relationships, and complex data.  
SKATEBOARD extensively employs RDF for specifying ontologies, classes, properties, and relationships between entities. Thanks to RDF, we can create a structured and interoperable representation of knowledge, allowing users to define custom ontologies tailored to their domains. This approach offers unparalleled flexibility, enabling data to be represented in intricate detail within specific contexts.  
Furthermore, RDF provides deep semantics, enabling the definition of semantic relationships between entities and attributes, thereby enhancing the comprehensibility and recognizability of knowledge. This semantic richness enables seamless integration with different knowledge bases, enriching and disambiguating knowledge across diverse sources.
# Conditional Measurements with Silicon Photomultipliers
## 2 Materials and Methods
### Experimental Setup and Detection Apparatus  
Here we provide a description of the experiment we performed and that we will discuss in Section 4 to test our theoretical predictions.  
The setup used to produce conditional states is shown in Figure 1. The fundamental and the third harmonic of a Nd:YLF laser regeneratively amplified at 500 Hz are sent to a \(\beta\)-barium-borate nonlinear crystal (BBO1, cut angle = 37 deg, 8-mm long) to generate the fourth harmonic (262 nm, 3.5-ps pulse duration) by sum-frequency generation. This field is used to pump parametric down conversion in a second BBO crystal (BBO2, cut angle = 46.7 deg, 6-mm long) to produce TWB states in a slightly non-collinear interaction geometry. Two twin portions are spatially and spectrally selected by means of two irises and two band-pass filters centered at 523 nm. The selected light is then delivered to a pair of PNR detectors through two multi-mode fibers having a 600-\(\upmu\)m core diameter. As to the detectors, we employed two commercial SiPMs (mod. MPPC S13360-1350 CS) operated at room temperature with an overvoltage of 3V. According to the datasheet [30], in such conditions, the detectors are endowed with a quantum efficiency of 40% at 460 nm, a moderate dark-count rate (\(\sim\)140 kHz), and a low cross-talk probability (\(\sim\)2%). The output of each detector is amplified by a fast inverting amplifier embedded in a computer-based Caen SP5600 Power Supply and Amplification Unit, synchronously integrated by means of a boxcar gated integrator (SR250, Stanford Research Systems) and acquired. In order to reduce as much as possible the effect of SiPMs drawbacks, the light signal was integrated over a short integration gate width (10-ns long), which roughly corresponds to the width of the peak of the output trace of the detector. Thanks to this choice, the possible contributions of dark counts and afterpulses can be neglected.  
A half-wave plate (HWP) followed by a polarizing cube beam splitter (PBS) is placed on the pump beam in order to modify its intensity and thus the mean number of photons of the generated TWB states. For each mean value, 100,000 single-shot acquisitions are performed.
# Password Cracking with Brute Force Algorithm and Dictionary Attack Using Parallel Programming
## 2 Literature Review
When using additional GPUs, the results likewise demonstrated linear performance acceleration. Their future work will concentrate on fixing the algorithm's remaining flaws, such as poor portability and a lack of password search improvements. Finally, [PERSON] and [PERSON] [8] suggested a new accelerator design for sha256 crypt password recovery that employs hybrid CPU-FPGA devices. The design was tested on the Xilinx Zynq 7000 XC7Z030-3 SoC, and the experimental results demonstrate that the proposed accelerator significantly improves energy efficiency, achieving a 2.54\(\times\) improvement compared to Hashcat running on an NVIDIA GTX1080 Ti GPU. The accelerator also improves energy and resource efficiency by 1.64\(\times\) and 1.69\(\times\), respectively, compared to the pure FPGA-based implementation in John-the-Ripper. Additionally, the paper outlines the difficulties in using pipeline techniques and presents various strategies to reduce hardware resource overhead. The techniques can be applied to other key derivation functions (KDFs) with comparable features. The researchers also plan to address structural weaknesses and optimize the system using partial reconfiguration technology in the future. Table 1 provides a summary of the related work, including the dataset used, the number of samples included, the number of threads or processors employed, the techniques utilized, and the best results achieved. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Dataset** & \
\begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Threads/Processors** \\ \end{tabular} & **Techniques** & **Best Result** \\ \hline \multirow{4}{*}{[11]} & 3906 for & & & Using CUDA with GPU for & \\  & modulo & & 32, 64, 128, & parallel password cracking & \\  & and 4160 & None. & and 256 threads per block. & and optimized shared & 4.77 speedup. \\ \cline{1-1}  & for bitwise. & & & memory for 6-byte & \\ \cline{1-1} \cline{3-5}  & & & numeric passwords. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary table of related work based on different criteria.
# Vibration Control of an Aero Pipeline System with Active Constraint Layer Damping Treatment
## 3 Results and Discussion
### Results of the FEM-Based Damping Characteristics Analysis  
A fixed constraint is applied at both ends of the finite element model of the active constrained layer damping pipeline, and all nodes on both sides are selected to constrain all degrees of freedom of the node. As shown in Table 4, through the modal analysis of the pipeline with ACLD treatment, the first three natural frequencies and mode of vibration of the pipeline are obtained. The first three modes of the pipeline of ACLD patches are bending, which is the same with for vibration mode of ordinary hydraulic pipelines.  
Figure 14 shows the effect of shear modulus of different viscoelastic materials on the natural frequency and modal loss factor of the pipeline. The shear modulus of the viscoelastic layer varies in \(10^{5}\)-\(10^{10}\). As can be seen from the figure, the natural frequency does not change with the change of the shear modulus; the loss factor varies greatly, especially the first and third orders. The loss factor shows a peak at \(10^{8}\).  
Figure 15 shows the effect of the viscoelastic layer thickness on the natural frequency and loss factor. As can be seen from the figure, as the thickness of the viscoelastic layer increases, the natural frequency changes a small amount. The first-order loss factor and the third-order loss factor of the pipeline both show a peak when the thickness of viscoelastic layer reaches 0.75 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order of the Modals** & **First** & **Second** & **Third** \\ \hline Natural frequency (Hz) & 375.8 & 1049.2 & 1967.3 \\ Mode of vibration & Bending & Bending & Bending \\ \hline \hline \end{tabular}
\end{table}
Table 4: Natural frequency of pipeline with ACLD patches.  
Figure 14: Influence of shear modulus of viscoelastic layer on (**a**) modal frequency and (**b**) loss factor.
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 5 Experimental Setup
### Metrics  
**Binary Classification**. In the first experiment, we view DLP as a binary classification task. For each positive event, we draw a negative event at random, following a specific NS strategy. We thus obtain as a result a list of labeled events, where the label indicates whether it is an event that actually occurred or whether it is a negative sample. Considering a given threshold on the prediction scores, a **confusion matrix** such as the one in Table 2 may then be constructed, measuring the amount of positive/negative events that are scored higher (positive prediction) or lower (negative prediction) than the threshold. By varying the threshold, we can then draw the corresponding receiver operating characteristic (ROC) and compute the associated **area under the curve (AUC)**. Note that this is typically conducted per batch of events, as the prediction scores may not be comparable across batches corresponding to different time spans. The reported AUC is the _average_ of the AUCs obtained on the different batches.  
**Ranking**. In order to assess which negative edge type is more likely to deceive the model at a given time, in a second experiment we consider ranking as a measure of performance. More precisely, we suppose that for each positive event, we assign it a certain number \(K\) of negative events, obtained through various NS strategies. Then, we calculate for each of these entries the rank of the positive but also of the associated negatives. Thus, for each event \((u,v,t)\) in the test data, we have a list of ranks corresponding to the positive event as well as to different NS strategies, such as NS1, NS2, etc.  
In order to obtain a regularly sampled time series, we then partition the time interval \([0,T]\) time into \(B=50\) bins \(I_{1},\ldots,I_{B}\) of equal size. For each interval, we calculate the mean average rank (MAR) of all the positives within that interval and perform the same for each NS strategy. As a result, we obtain for each event type (positive, NS strategy 1, NS strategy 2...) a time series of the ranks of the associated events in the different intervals.  
For instance, suppose that the first interval \(I_{1}\) contains 4 positive events, to each of which we adjoin 2 negative events (coming from 2 different NS strategies, NS1 and NS2). Now, using the DLP algorithm, we obtain scores for each of the positives and the negatives. Suppose that, as a result, the ranks of the 4 positive events are 1, 3, 2, and 1, the ranks of the  
\begin{table}
\begin{tabular}{c|c c} \hline \hline
**Predicted/Actual** & **Actual Positive** & **Actual Negative** \\ \hline
**Predicted Positive** & True Positive (TP) & False Positive (FP) \\
**Predicted Negative** & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
negative obtained using NS1 are 2, 1, 3, and 2, and the ranks for NS2 are 3, 2, 1, and 3. Then, for this interval, the MAR for the positive NS1 and NS2 events are \(\frac{7}{4}\), 2, and \(\frac{9}{4}\), respectively.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology  
### Overview of the Approach  
In this section, we illustrate the overview of proposed method to retrieve the vertical structural variations through the layer-wise strategy while keeping the detailed information from 3D planar primitives. Since the man-made environment is ubiquitous with planar structures, it is reasonable to consider planar primitives as the starting point of building reconstruction.  
Our major motivation of this layer-wise method using 3D planar primitives is behind Figure 1. Figure 1a shows the filtered point clouds using the 2D projection approach and Figure 1c indicates the 2D projection from vertical structures. If we only observe the top-down view of point clouds, it seems like a correct 2D projection from 3D points. However, some problems emerge when we check the point clouds in a side-view. Specifically, some structures that are not connected to the floor are considered as walls or are even discarded. As Figure 1b shows, the red and blue rectangles denote the bay windows which are not connected to the floor. The orange rectangle is an irregular structure containing a vertical gap instead of a flat ceiling. Therefore, we develop a reconstruction method to keep such types of vertical information through 3D planar primitives. Despite maintaining the vertical structures such as bay windows and irregular ceilings, this method can be applied to both mobile and terrestrial point clouds.  
As shown in Figure 2, this approach contains four primary steps. To reconstruct the indoor space, we employ a multi-label method to detect the planar primitives through a global optimization based on the region-growing and random sample consensus (RANSAC) algorithms. Subsequently, the point clouds are sliced into several pieces along the vertical direction. Given the representative slices of the points, the contours of these slices can be extracted by a shrink-wrapping method. Finally, we assemble these 2D contours to constitute 3D building models.  
Figure 1: Multiple views of point clouds: (**a**) Top-down view of filtered point clouds. (**b**) Side-view of filtered point clouds. (**c**) The 2D projection of point clouds (the rectangles with different colors denote the corresponding structures in multi-view figures).
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Neural Networks
What is more, when the ship length is decreased, the type of accident tends to increase to a superior code, but it is independent of the ship length further away from codes 1 (meet), 2 (does not meet) and 3 (one of the two ships do not meet). In this sense, based on the clustering analysis and neural network modelling procedure for a particular region, it is possible to define a series of recommendations to identify the probability of a nearby accident. For instance, examples of these recommendations can be associated with identifying the number of crew members on ships that ensure a decreased number of accidents. Finally, it must be highlighted that there are some limitations in this study. In this sense, the data employed belong to the CIAIM database related to the SAR region, where the influence of weather conditions is blurred by the navigation requirements designed for this area by the Spanish Ministry itself. In consequence, mathematical models and neural network models can be employed for civil ships that navigate this region. The main intention of this work was to define a methodology that could be useful for the prediction of accidents in the SAR region and that could be extrapolated to other ones. Despite this, it must be investigated and tested in future research works. Other limitations of this research were the number of data employed and the variables employed. The variables employed are indeed the typically employed variables in most maritime accident reports, but there are probably still some pending new variables to  
Figure 14: Three-dimensional chart of the type of accident versus minimum crew and ship length. be incorporated that can let us obtain more accurate models about the human factor and its causal factors. In this sense, new variables like the experience and training of the crew members or maintenance of the ship could be of interest to obtain new, more accurate models. Despite this, the main results obtained could be indirectly associated with the human factor, which, in clear accordance with previous research works, is associated with most maritime accidents.
## 5 Probe Automatic Switching Scheme
### Switching Scheme Based on the Minimum Deviation Angle Criterion  
Through simulation calculations, using a 45-degree angle between the probe and the geomagnetic field as a reference, the switching scheme based on the minimum deviation angle criterion can determine the probe number that outputs the minimum deviation angle under any heading and geomagnetic inclination conditions. The simulation results are shown in Figure 16.  
The switching of the three optically pumped probes is achieved automatically through software, and the switching process is shown in Figure 17. During the switching process, the system calculates geomagnetic elements, attitude, and other related parameters in real time based on fluxgate data and makes switching decisions based on these parameters. Specifically, the system uses the fluxgate to calculate the current magnetic inclination and declination angles, and combined with the known installation angles of the optically pumped probes and the fluxgate, it can determine the angles between the three optically pumped probes and the geomagnetic field. When the angle between the geomagnetic field and the optically pumped probe is 45\({}^{\circ}\), the system will prioritize the optically pumped probe with the optimal working angle. To achieve this, the system subtracts 45\({}^{\circ}\) from the angles between each of the three optically pumped probes and the geomagnetic field and then selects the smallest value as the basis for switching.
## 5 Discussions
### Technical Speculations for the New Generation of Nightlight Satellite Sensors
#### 5.2.2 Spatial Resolution  
The optimal resolution settings of remote sensing sensors should be 30 m or 10 m, similar to those discussed in the Nightsat proposal [57]. The spatial resolution of UAV nightlight sensors and the Jilin-1 sensor are now able to achieve spatial resolutions finer than 1 m, which is significant for small-scale urban characterization, especially the extraction of street lights [60]. However, the higher the spatial resolution is, the smaller the field of view is. Based on previous analyses and discussions, we suggest setting the optimal resolution at 10 m or 30 m due to the following reasons: (1) there are now a large amount of daytime optical Landsat series and Sentinel-2 data, which could be well applied to both urban refinement mapping and global mapping; (2) traditional optical remote sensing, however, is not available for research with nightlight Earth observation; (3) nightlight imagery can be effectively combined with daytime optical imagery to increase the capability to characterize land surfaces [61; 62]. Our future nocturnal and daytime optical data will be complementary, enabling more applications of circadian studies. Furthermore, setting similar spatial resolutions for both nightlight and daytime optical imagery will also reduce the burden of image processing.
## 2 Design and Implementation of On-Line PD Monitoring System
### Partial Discharge Detectors
#### 2.2.2 Ultra-High Frequency Antenna  
In the power transformers, the registration of the radio signals in the VHF/UHF band generated by the partial discharges is carried out with the use of antennas mounted in special dielectric windows [36; 37] or antennas put in the tank interior through oil valves [38; 39]. There are also new solutions known, in which, antennas are placed outside the tank and detect the leakage of electromagnetic waves through the gap (insulation gasket between the upper or lower ladle cover and the main tank) [40] or bushing taps [41]. Antennas placed in the dielectric windows usually allow to obtain the highest sensitivity in PD detection. The obstacle in the global popularization of this solution is the fact that the new power transformers are still very rarely equipped with dielectric windows. The factory installation of the interior UHF antennas takes place exclusively on demand of the purchaser. Additionally, the exchange of the revision hatches in the transformer being exploited to mount dielectric windows and antennas is an expensive, time-consuming, and complicated operation, both regarding technical and logistic issues. Unfortunately, the remaining solutions, i.e., antennas placed in the tank interior through oil valves and external antennas, regarding numerous limitations are not a valuable alternative [39]. The first ones are simple monopole antennas of weak amplification and a very narrow transmission band. Additionally, the shielding effect of the oil drain valve and unfavorable placement inside the tank (usually it is a sidewall of the tank, near the bottom), have a negative impact of their efficiency, because they may hamper the registration of the radio signals generated by PDs [42]. In turn, the external antennas are vulnerable to the influence of external electromagnetic disturbances, mainly corona from HV transmission lines. A significant problem is also the fact that the signal registered by the antenna may have very low energy because of the EM wave attenuation on the way from the PD source to the antenna [40]. To minimize the problem, it is necessary to apply advanced algorithms of signals denoising, filtration, and recognition. It is worth mentioning that only a small part of the useful signal gets out through gaps (if they are present in the given tank construction at all) and bushing insulators--outside the transformer tank. Having considered all advantages and disadvantages of the available methods of UHF signals registration, finally, it was decided that for the needs of the monitoring system the antenna adjusted to installation in the dielectric window would be elaborated. High EM signals detection sensitivity, which directly translates into high effectiveness of the PD monitoring system operation, determined its choice.