esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric Remote Sensing with GNSS
## 2 GNSS Observables and Ranging Errors  
In order to achieve PNT, a standard GNSS receiver's measurement module must generate GNSS observables and the receiver's positioning module must properly model each ranging error for GNSS observables. GNSS observables refer to the basic measurementsproduced from a GNSS receiver, including pseudorange, carrier-phase, and Doppler shift. Together with the epoch time and the carrier-to-noise density ratio (often denoted as C/N\({}_{0}\)), these data are conventionally stored in a observation file based on the receiver independent exchange format (RINEX) [10].  
Pseudorange (also known as code-range) refers to the sum of the real range and the range equivalent of various ranging errors or offsets. Pseudorange (\(P\)) can be defined as:  
\[P^{m}=R^{m}+c(\delta T^{m}+\delta t)+I^{m}+T^{m}+M^{m}+D^{m}+W^{m} \tag{3}\]  
where \(m\) denotes a specific GNSS satellite number. \(R\) is the true geometric range from the satellite to receiver's antenna phase center in meters, \(c\) is the speed of light in vacuum in meters per second, \(\delta T\) is the satellite clock bias in seconds, \(\delta t\) is the receiver clock bias in seconds, \(I\) is the ionospheric signal delay in meters, \(T\) is the troposphere signal delay in meters, \(M\) is the multipath effect delay in meters, \(D\) is the error caused by the geometric dilution effect in meters, \(W\) refers to all other noise effects (e.g., receiver noise, relativity effects). The standard unit of \(P\) is meters.  
Doppler shift (\(f_{D}\) or simply Doppler) is the frequency shift caused by the relative motion between a GNSS satellite (transmitter) and receiver (antenna). \(f_{D}\) is related to the satellite radial velocity (\(v_{r}\)), which is equal to the pseudorange rate:  
\[f_{D}^{m}=\frac{v_{r}^{m}}{\lambda}=\frac{p^{m}}{\lambda} \tag{4}\]  
where \(\lambda\) is the wavelength of the GNSS signal being used. \(f_{D}\) is typically not used to compute navigation solutions, but instead used to determine the receiver velocity. The standard unit of \(f_{D}\) is Hz.  
Carrier-phase (or beat carrier-phase) refers to the time integral of the carrier Doppler shift. Carrier-phase (\(CP\)) can be defined as:  
\[CP^{m}=\frac{1}{\lambda}[R^{m}+c(\delta T^{m}+\delta t)-I^{m}+T^{m}+M^{m}+D^{m }+W^{m}]+N^{m} \tag{5}\]  
where \(N\) is the carrier-phase measurement ambiguity (sometimes known as integer ambiguity). \(CP\) typically is measured in number of cycles of the carrier signals being received and tracked. The relationship between \(f_{D}\) and \(CP\) can be expressed as:  
\[CP^{m}=N^{m}+\int_{0}^{t}(f_{D}^{m})dt_{\Delta} \tag{6}\]  
where \(t_{\Delta}\) is the elapsed time during a carrier-phase measurement.
## 4 New Classification of Methods for the Detection of Water Network Operating Conditions
### Hardware Methods (Direct)  
#### 4.1.1 Acoustic Methods  
Acoustic methods are among the oldest methods for identifying leaks in the water supply system. Initially, listening sticks and microphones were used to detect pipeline leaks [28]. This method involves listening to sounds along the route of the pipe and finding areas where the acoustic band has changed due to leaking water.  
Today, leak detectors are widely used for condition surveillance of linear facilities around the world [17; 29]. Surveillance is very often carried out using satellite communications. Modern devices allow for leak detection with an accuracy of up to 1 m in up to 15 min [30]. The premise of microphone-based line leak detection was originally developed for gas pipeline operations by [PERSON] in 1935 (after [31]). Sound analyzers for leak detection in water supply networks began to be used in the late 1970s [32]. According to [33], during failure, the frequency of sounds increases to above 200 Hz and is usually contained in the 400-500 Hz band.  
Leak noise correlators (LNC) are used to process the sounds recorded by a pair of serially mounted microphones. Sound correlators use computational techniques of signal  
Figure 5: Proposed classification of methods for detection of water supply network operating conditions (own elaboration).  
analysis. Therefore, this method can be classified as a hardware-software method. The sound signals are usually sampled using a Hanning window, and then, using fast Fourier transform (FFT), periodograms are calculated in the form of a signal power spectrum as a function of frequency. A cross-correlation is calculated for the discrete signal power spectra from the two microphones, which is a measure of the similarity between the two signals [34].  
Another acoustic method has its origins in mechanical scrubbers used to remove sediment from pipelines. These are known in the literature as pipeline inspection gauges (PIGs) [35,36]. With the development of sensors called hydrophones, which allow for the recording of sounds propagating in water, floats equipped with these sensors began to be introduced into water pipes in place of scrubbers. Methods using this technique are known as pipe-mounted acoustic (PMA). An example of a PMA method is the so-called smart ball used since 2007 [37]. Before testing, the device should be calibrated each time. The ball is equipped with sound detectors and a GPS receiver to locate it in the water pipe. Simultaneously with the measurements carried out by the ball as it moves along the water pipe, the recording of acoustic waves is carried out by sensors mounted on the outer walls of the pipe in order to eliminate ambient sounds reaching up to 70 dB [38].
## Evaluation and Results
### Grid Network Formation
#### 5.4.2 Results--Average Energy Consumption  
Figures 6-9 show the average energy consumption across significant nodes. This is seen as important as these are the nodes which then took on the role of hotspot, therefore reducing energy consumption in these nodes was beneficial both in terms of overall network performance and network lifetime. The comparisons shown are between the evaluation results, where the standard duty cycling with CCA and check interval was in use, and results where the MADCAL algorithm was applied, such that a dynamic threshold was created within significant nodes for communication with the MSN.  
Figure 6 illustrates significant energy saving when the MADCAL algorithm was in use. The larger interference range in use here would normally result in considerable overlap of communication between significant nodes causing competition for communication with the MSN. This, subsequently,  
Figure 6: Average energy consumption (mWs), grid topology, significant nodes. Interference range 77.52 m.  
results in wasted energy consumption, with some nodes awake but unable to communicate with the sink. However, with a communication threshold established by MADCAL, although overlaps of threshold are still possible depending on node position, nodes are less likely to seek channel access at the same time. Hence there is less extraneous energy consumption.  
In Figure 7 interference range is reduced to 69.13 m. Results remain a significant improvement, however, we can now observe how as interference range reduces it becomes more difficult to improve energy consumption.  
Figure 8: Average energy consumption (mWs), grid topology, significant nodes. Interference range 62.02 m.  
Figure 7: Average energy consumption (mWs), grid topology, significant nodes. Interference range 69.13 m.  
There is little difference between Figure 8 from Figure 7 with benefits still to be seen in energy consumption when MADCAL is in use. One main observation is that it can be seen that it is easier to save energy when the sink is moving more slowly. In this case larger thresholds are calculated, but with the sink moving at only 2 mps, there is more time to put nodes to sleep before the sink node reaches. The counter to this is that it could result in increased delay of packet delivery.  
Figure 9 is significant in that the interference range is now strained to the extent that it is only marginally greater than the distance between nodes and the greatest distance to the sink--50 m. However, despite the reduction in communication overlap when standard duty cycling is in use, the MADCAL algorithm still results in improved energy consumption, which again is most in evidence when the sink node is moving slowly.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Results
#### 5.3.1 Evolutionary Search on Seosan Data  
Table 3 compares the results of the previous study (C language) [8] and this study (Python). CMA-ES showed particularly good performance compared to the previous study. Overall, we could improve the performance of evolutionary search by using new software libraries.  
Table 4 shows the CPU time to build the prediction models for Seosan data. Since inference time is usually much shorter than training time, actual performance can be more important than training speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Evaluation**} & \multicolumn{5}{c}{**Case**} \\ \cline{3-6}  & & **1** & **5** & **6** & **7** \\ \hline DE & MAE & 0.0920 & 0.0828 & 0.0392 & 0.0653 \\ (Previous) & NCLS & 0.9026 & 0.7965 & 0.9220 & 0.8826 \\  & MAE & 0.0587 & 0.0973 & 0.0380 & 0.0568 \\ DE & NCLS & 0.9451 & 0.7686 & 0.9225 & 0.8974 \\ PSO & MAE & 0.0907 & 0.0820 & 0.0385 & 0.0622 \\ (Previous) & NCLS & 0.9044 & 0.7984 & 0.9232 & 0.8878 \\  & MAE & 0.0603 & 0.0973 & 0.0380 & 0.0567 \\ PSO & NCLS & 0.9441 & 0.7686 & 0.9226 & 0.8976 \\ CMA-ES & MAE & 0.1303 & 0.1393 & 0.0761 & 0.1304 \\ (Previous) & NCLS & 0.8631 & 0.6513 & 0.8437 & 0.7297 \\  & MAE & 0.0582 & 0.0973 & 0.0380 & 0.0567 \\ CMA-ES & NCLS & 0.9457 & 0.7686 & 0.9226 & 0.8976 \\ MOHID model [10] & MAE & 0.1352 & 0.1238 & 0.0656 & 0.0434 \\  & NCLS & 0.8633 & 0.7134 & 0.8480 & 0.9229 \\ \hline \hline \end{tabular}  
* The lower the mean absolute error (MAE) values, the better. The higher the NCLS values, the better.  
\end{table}
Table 3: Parameters of evolutionary computation methods.  
Figure 4: Formula to calculate skill score (ss) of NCLS.
## 2 Materials and Methods
### Support Vector Machine (SVM)  
In this study, SVM was used as the operation engine. In many machine learning algorithms, SVM belongs to the family of kernel-based classifiers, and they are very powerful classifiers, as they can perform both linear and non-linear classification simply by changing the \"kernel\" function utilized [23]. SVM has been widely used in the realm of EEG [24; 25; 26; 27]. The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see paper [28]; for more details on SVM, see [29].  
In this study, the LIBSVM package was used as an implementation of SVM [30]. Furthermore, the radial basis function (RBF) was taken as the kernel function to study the classification results, which is commonly used in support vector machine classification. The RBF kernel on two samples \(x_{i}\) and \(x_{j}\), represented as feature vectors in some input space, is defined as follows [31]:  
\[K(x_{i},x_{j})=\exp(-\frac{\left|x_{i}-x_{j}\right|^{2}}{2\sigma^{2}}) \tag{21}\]  
where \(\left|x_{i}-x_{j}\right|^{2}\) may be recognized as the squared Euclidean distance between the two feature vectors, and \(\sigma\) is a free parameter. When \(\sigma^{2}\rightarrow\infty\), the classification accuracy of using the RBF kernel is at least as good as using the linear kernel after selecting a suitable parameter. The RBF kernel may be the most used kernel in training nonlinear SVM, so we also take it as SVM kernel function.
## 3 Proposed Methodology
### Adversary Attack Model
#### 3.5.1 Sending Fake Information by the Black Hole Node  
Initially, a black hole node enters in a sneaky manner, without participating in the routing process of the network. It carefully monitors the behavior of the normal nodes by listening to the incoming and outgoing traffic and keeping these packets in its memory for a short time. Whenever a source node doesn't have a valid router towards the destination, it sends RREQ packets in the network to find routes towards the destination. As soon as the black hole node receives the RREQ packet, it takes advantage of that time and shows itself off as a legitimate node, claiming that it has a valid and short route. It then sends the RREP with the highest sequence number and the lowest hop count. Based on the bogus reply of the black hole node, the source node establishes a route and it starts sending data packets to that node. After getting into that route, the black hole node drops all of the data packets instead of forwarding them to the destination node.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 1 Introduction
The time domain correlation analysis according to distance and frequency shows that the time-varying characteristics of the shallow water channel become stronger as the distance becomes longer and as the frequency increases [26]. For this reason, the time reversal technique can achieve good performance even if the scattering environment is complicated. As the time-varying property of the clutter signal becomes stronger, the detection performance improves because the time coherence of the clutter signal is decreasing while the target signal is time-invariant. In this paper, a method is proposed to improve the detection performance using the time reversal technique in the situation where the clutter is mixed in the shallow water. By using the wave focusing effect of the time reversal technique, the peak response of the target can appear prominently. In this work, it is considered that the cylindrical object is resting on the sea floor. In this case the sea floor consists of soft sediment and there are gravel, sand, and seaweed in the current, whose scattering can cover target scattering to mask detection via active sonar. It is assumed that these clutters are time-varying and K-distributed. Therefore, when the time reversal technique is applied to the received signal from the clutter with time-varying characteristics, it is difficult to obtain a high peak response. However, for the scattering waveform of the cylindrical object considered in this work, because the time-varying characteristic of the target response is weaker than that of the reverberation by the clutter, the time reversal technique can increase the peak of the target signal. The paper is organized as follows. The peak enhancement using the time reversal technique and the proposed method are described and the probability of detection is numerically evaluated in Section 2. Section 3 presents the cylindrical target model and the clutter model. Section 4 presents the performance evaluation through computer simulation. Finally, Section 5 concludes the paper.
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 4 Experiments  
### Datasets  
In order to verify the effectiveness of StructSim, we conducted extensive experiments on the classic datasets YAGO [5] and DBLP [11]. YAGO is a huge semantic knowledge base system derived from Wikipedia, WordNet and GeoNames [5]. Now, it contains more than 10 million entities and 120 million facts. In this paper, we mainly adopt the \"Core Facts\" part in YAGO, denoted by Yago-Core [27], which includes the fact tuples of entity and relationship such as <[PERSON], directed, War Horse (film)>, and tuples of entity type. Table 2 shows the statistics of the Yago-Core dataset. DBLP contains the major conferences in four research fields including database, data mining, information retrieval and artificial intelligence. It has 14,475 authors, 14,376 papers, 8593 terms and 20 conferences, the labelled data of which is 4057 authors, 20 conferences and 100 papers.
## 4 Experimental Results and Discussion
### Effect of the Longitudinal Tilt Angle of the Secondary System on Useful Heat  
Figure 15 shows the useful heat for the longitudinal tilts under study and the 400 (W) power not converted into electricity by the \(PV\) cells.  
Figure 15 suggests the following conclusions as concerns the useful heat:  
1. Useful heat obviously decreases as the wind speed increases. This effect is more noticeable as the longitudinal tilt angle increases.
2. When the wind speed is 10.03 (m/s), the useful heat gain (\(UHG\)) of the longitudinal tilt configuration is between \(-\)21.30% (Medellin) and \(-\)33.91% (Almeria) with respect to the base configuration. For this wind speed, there is a noticeable difference in UHG between the lower latitude and higher latitude locations.
3. If the wind speed is 4.99 (m/s), the \(UHG\) is between \(-\)17.27% (Medellin) and \(-\)18.77% (Almeria). The difference in useful heat gain between the most extreme locations decreases considerably with this wind speed.
4. When the wind speed is 2.55 (m/s), the useful heat gain in the longitudinal tilt configuration is still lower than in the base configuration. \(UHG\) is approximately \(-\)8%. In this case, the difference in useful heat gain between the most extreme locations is negligible. The same is true for a wind speed of 1.05 (m/s).
5. When the wind speed is 0 (m/s), the useful heat gain in the longitudinal tilt configuration is similar to that of the base configuration.
6. It can be concluded that the presence of wind is detrimental to the longitudinal tilt configuration at all locations as concerns the useful heat. However, it is important to note that this type of technology also generates electricity. Furthermore, according to Table 5, the longitudinal tilt configuration performs better as concerns incident solar irradiation on the \(PV\) system. This will be assessed in the study below.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Cities** & \(H_{b_{C_{B}}}\) (kWh) & \(H_{b_{C_{B}}}\) (kWh) & \(H_{b}G\) (\%) \\ \hline
1 & Medellin (Colombia) & 235.43 & 248.10 & 5.38 \\
2 & Bangkok (Thailand) & 310.30 & 354.67 & 14.30 \\
3 & Morelia (Mexico) & 452.55 & 532.21 & 17.60 \\
4 & Karachi (Pakistan) & 418.83 & 528.83 & 26.26 \\
5 & Cairo (Egypt) & 472.14 & 648.10 & 37.27 \\
6 & Almeria (Spain) & 443.08 & 680.19 & 53.51 \\ \hline \end{tabular}
\end{table}
Table 5: Beam solar irradiation incident on PV cells.  
Figure 16 shows the useful heat for the longitudinal tilts under study and the power not converted into electricity by the photovoltaic cells of 300 (W). Figure 13 shows similar trends in useful heat as Figure 15. The trends for useful heat gain are also true.
## 2 NOMA System Model
### Jain's Fairness Index  
_JFI_ is a well-known metric applied to wireless communications to evaluate a system's fairness. The efficient resource allocation scheme can provide a flexible system to achieve fair data distribution in NOMA systems [24]. Therefore, the second objective is to generate a balanced data rate as [PERSON]'s fairness objective. _JFI_ is defined as follows:  
\[JFI=\frac{\sum_{i=1}^{U}R_{i}}{U\sum_{i=1}^{U}R_{i}^{2}}, \tag{22}\]  
where \(R_{i}\) is the user data rate and \(U\) is the total number of users. This indicator varies in accordance with the number of users and the achieved throughput. Given that two users are multiplexed on the same channel as high and low channel gains, the minimum user data rate for the user with low channel gain is ensured. Thus, fairness is an indicator of the QoS in NOMA systems.
## 2 Preliminaries  
### The Global Monitoring Network and Local Magnetic Field Data  
The Global Coherence Monitoring Network [21] comprises five magnetometers which are continuously collecting data on Schumann resonance amplitudes at five locations around the globe (the numbers of the stations denote latitude and longitude in degrees): Baisiogala, Lithuania, Eastern Europe (55.638929, 23.722195); Boulder Creek, CA, USA (37.4192, \(-\)122.057); Alberta, Northern Canada (53.364561, \(-\)113.41565), Hofuuf, the eastern region of Saudi Arabia (25.383333, 49.583333); and Northland, the north island of New Zealand ( \(-\)38.526368, 175.675718). The distribution map of the five magnetometers is shown in Figure S1. The magnetometer used in this study has two ANT4 magnetic field detectors (Zonge Engineering Inc., Zonge International, 3322 E. Ft. Lowell Road, Tucson, AZ 85716, USA) which are positioned in north-south and east-west orientations. The signals are digitized with a 24-bit data acquisition system at an average rate of 130 Hz. Thus, it is a vector magnetometer. The spectral power is computed on the east-west component. The same applies for each magnetometer. At each location, two magnetometers are positioned in the north-south and east-west orientations to measure the time-varying magnetic field. The magnetometers have a large bandwidth, yielding a flat response in the range 0.01-300 Hz, but the measurement is performed (according to [PERSON]'s theorem) only for frequencies up to 65 Hz [21]. The Schumann resonance amplitude data are collected by data-collecting equipment, time-stamped using the Global Positioning System, and sent to a central server. The measurement procedure of the local Schumann resonance amplitude data is explicitly explained in [21; 22].
# A Review of Deep Learning Applications for Railway Safety
## 6 Operation Safety
### Managing Accident Reports  
Accident reports can help minimize risk factors to prevent future accidents. Accident reports mostly contain diverse input field entries, such as fixed field entries, which include the primary cause of accidents, or a narrative field, which is a short text description of the accident. The narratives can provide more information than a fixed field entry, but the terminologies used in the reports are not easy to understand by a non-expert reader. [PERSON] et al. [89] applied word embedding methods, such as Word2 Vec and GloVe, to narrative texts in train accident reports. As shown in Figure 9, the proposed method classifies accident cause values for the primary cause field based on embedding vectors about the narrative text. This NLP approach can help label accidents more accurately and consistently.