esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis and Results
### Changes to Object Geometry  
In Table 5 we present the summary of the number of consecutive versions with the combinations of invalid or valid geometries. We analyze consecutive versions \(v_{i}\) and \(v_{j}\) of the object \(P\) where \(i<j\) and \(i\geq 1\). The number of consecutive version pairs where the same contributor \(u\) created both \(v_{i}\) and \(v_{j}\) is also included. The validity of geometries is tested in the PostGIS database using the ST_IsValid() function. This PostGIS function is compliant with the Open Geospatial Consortium's (OGC) OpenGIS Specifications where according to these specifications, a valid geometry is one such that there are \"no anomalous geometric points, such as self intersection or self tangency and primarily refers to 0 or 1-dimensional geometries\". All spatial databases implementing the OGC OpenGIS Specifications, such as Oracle Spatial or Microsoft SQL Server, will provide a function with this functionality. Therefore geometries invalid in PostGIS will also be returned as invalid in OGC OpenGIS compliant databases. We used this PostGIS function to investigate if invalid geometries were being created by contributions and edits to objects. In Table 5 there are \(91\%\) of versions (\(v_{i}\) followed by \(v_{j}\)) which are both valid. In \(83\%\) of these cases the same contributor edited the object. However the number of versions with consecutive invalid geometries is a non-trivial \(8\%\). Of this \(8\%\)\(21,843\) (or \(87\%\)) of these consecutive versions were created by the same contributor. This indicates that either these contributors were not aware of the invalidity of the polygons/polylines they had created or for some other reason had not fixed these problems. In the current versions \(v_{n}\) of all \(15,640\) objects there are \(14,891\) valid polygons/polylines while \(749\) are invalid. In Table 6 we present the summary of the number of consecutive versions with node edits. We look at consecutive versions \(v_{i}\) and \(v_{j}\) of the object \(P\) where \(i<j\) and \(i\geq 1\). The number of consecutive version pairs where the same contributor \(u\) created both \(v_{i}\) and \(v_{j}\) is also included.  
Figure 6: Scatter plot of the number of tags against the number of versions (final) for all objects in the case study area.  
Remarkably, the contribution of additional spatial detail occurs in \(79\%\) of edits. Of these edits \(90\%\) were carried out by the same contributor which could potentially indicate that contributors incrementally contribute spatial data to objects over time. This could potentially be the result of contributors \"saving\" work as they work through a series of edits or returning at a later time to incrementally edit their data.
# Aiding Dictionary Learning Through Multi-Parametric Sparse Representation
## 5 Results
### Water Networks  
Dictionary learning has only recently been applied for modelling water networks, showing promising results at fault detection and isolation (FDI) tasks [23]. Represented as a graph, where the edges are pipes and the nodes' distribution junctions, the water network main characteristic for FDI is the recorded pressure at each of its nodes. Given a known nominal pressure vector \(\mathbf{p}_{0}\), whose elements represent the nominal pressure in each junction, we compare it to the current measurement \(\mathbf{p}\) leading to the pressure residue  
\[\mathbf{r}=\mathbf{p}-\mathbf{p}_{0}. \tag{31}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sparsity/Seed Size** & \(\mathbf{r=15}\) & \(\mathbf{r=150}\) & \(\mathbf{r=1500}\) \\ \hline \(s=3\) & 33.67 & 43.33 & 84.40 \\ \(s=4\) & 21.33 & 43.87 & 86.67 \\ \(s=5\) & 5.33 & 41.13 & 89.87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of regions as a percentage of the total number of signals ([%]).  
Figure 6: Evolution of the computation time versus the number of iterations (case \(s=3\), \(r=15\)).  
Thus, faults are detected by the network nodes by investigating the residue vectors over time.  
In our experiments, we choose the benchmark Hanoi water network that consists of 31 junctions. We iterate through each of the nodes and produce faults of various magnitudes. For each scenario, we record the pressure residue (31) and store all of them as columns in the matrix \(R\). A single fault is expected to have impact on the measured pressure of its node and its first and second level neighbours. See [23] for a thorough description of the water network and the data generation process.  
We generate 16 faults for each node leading to \(n=496\) training signals of \(m=31\) entries each. We perform DL on a dictionary of \(n=124\) atoms with sparsity \(s=\{3,5\}\) for 50 iterations and depict in Figure 7 the last sparse representation stage during training. In Figure 7a, we can see that the number of regions is around 36% of the total number of signals, which is confirmed by the number of region re-utilization depicted in Figure 7b.
# A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring
## 2 Background Knowledge
### Neumann Series Approximation  
Given a matrix inverse \(\mathbf{A}^{-1}\), it can be expanded as the following Neumann series [21]  
\[\mathbf{A}^{-1}=\sum_{n=0}^{\infty}\left(\mathbf{X}^{-1}(\mathbf{X}-\mathbf{A}) \right)^{n}\mathbf{X}^{-1}, \tag{9}\]  
which holds if \(\lim_{n\to\infty}\left(\mathbf{I}-\mathbf{X}^{-1}\mathbf{A}\right)^{n}= \mathbf{0}\) is satisfied. In our case, suppose  
\[\mathbf{A}=\mathbf{K}+\sigma_{n}^{2}\mathbf{I}\triangleq\mathbf{D}_{A}+ \mathbf{E}_{A}, \tag{10}\]  
where \(\mathbf{D}_{A}\) is the main diagonal of \(\mathbf{A}\) and \(\mathbf{E}_{A}\) is the hollow. If we substitute \(\mathbf{X}\) in Equation (9) by \(\mathbf{D}_{A}\), we get  
\[\mathbf{A}^{-1}=\sum_{n=0}^{\infty}\big{(}-\mathbf{D}^{-1}\mathbf{E}_{A}\big{)} ^{n}\mathbf{D}_{A}^{-1}, \tag{11}\]  
which is guaranteed to converge when \(\lim_{n\to\infty}\big{(}-\mathbf{D}_{A}^{-1}\mathbf{E}_{A}\big{)}^{n}= \mathbf{0}\). We investigated the convergence condition in [17], where we proved that if \(\mathbf{A}\) is diagonally dominant, then Neumann series can approximate \(\mathbf{A}^{-1}\) both fast and accurate. In case \(\mathbf{A}\) is not diagonally dominant, we also provided a way to convert it into a diagonally dominant matrix in [17], such that \(\mathbf{A}^{-1}\) can still be approximated by Neumann series. When Neumann series given in (11) converges, we can then approximate \(\mathbf{A}\) with only the first \(L\) terms. The \(L\)-term approximation is computed as follows:  
\[\widetilde{\mathbf{A}}_{L}^{-1}=\sum_{n=0}^{L-1}\big{(}-\mathbf{D}_{A}^{-1} \mathbf{E}_{A}\big{)}^{n}\mathbf{D}_{A}^{-1}, \tag{12}\]  
For instance, when \(L=1,2,3\), we have the approximations  
\[\widetilde{\mathbf{A}}_{L}^{-1}=\left\{\begin{array}{ll}\mathbf{D}_{A}^{-1},&L=1\\ \mathbf{D}_{A}^{-1}-\mathbf{D}_{A}^{-1}\mathbf{E}_{A}\mathbf{D}_{A}^{-1},&L=2 \\ \mathbf{D}_{A}^{-1}-\mathbf{D}_{A}^{-1}\mathbf{E}_{A}\mathbf{D}_{A}^{-1}+ \mathbf{D}_{A}^{-1}\mathbf{E}_{A}\mathbf{D}_{A}^{-1}\mathbf{E}_{A}\mathbf{D}_ {A}^{-1}.&L=3\end{array}\right. \tag{13}\]
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 6 Open Research Challenges
### IA in CR Networks with Reinforcement Learning  
The key to performing IA in CR without CSI is the use of reconfigurable antennas (RA), which are capable of dynamically switching among a fixed number of radiation patterns to introduce artificial fluctuations in the channel [41]. The radiation patterns used to realize blind IA have significant impacts on the overall performance of the system [42]. Hence, intelligent antenna pattern selection strategy is a crucial research issue for practical RA-based blind IA implementation using reinforcement learning techniques.
# Investigation of Earthquake-Induced Pipe Damage in Liquefiable Soils
## 2 2023 Kahramanmaras, Turkey, Earthquake  
On 6 February 2023, two significant earthquakes occurred consecutively on the Eastern Anatolia Fault Zone near the center of Kahramanmaras. The first earthquake had a magnitude of Mw 7.7 and occurred at 04:17 local time, followed by another event of Mw 7.6 at 13:24 local time on 7 February 2023. Following these earthquakes, another destructive earthquake occurred on 20 February 2023, at 20:04 local time in Hatay, centered along the Dead Sea Fault, with a magnitude of Mw 6.4. The 2023 earthquakes in Kahramanmaras occurred in the transition zone between the Dead Sea and East Anatolian faults, and Hatay was one of the regions most affected by these earthquakes (Figure 1). Figure 1 shows the magnitudes and surface ruptures of the earthquakes ([PERSON] et al., 2018).  
Figure 1: (**a**) Global seismic hazard map (Kahramanmaras, 2023); (**b**) 6 February 2023 Pazarck (Kahramanmaras, 2023) Mw 7.7 and Elbistan (Kahramanmaras, 2023) Mw 7.6 earthquakes and aftershocks ([PERSON] et al., 2018).  
After 1988, Iskenderun Port was extended by landfill construction. In Study Area 1, the soil conditions in the Sarseki zone, including the extended landfill areas were taken into consideration (Figure 2a). For Study Area 2, the soil conditions around Ataturk Boulevard in the city center of Iskenderun were used. Severe damage occurred in the drinking water and stormwater pipelines and facilities on Ataturk Boulevard.  
Hatay is located within the Antakya-Samandag depression and has soil conditions which originated from different tectonic regimes. The alluvial units are commonly encountered in the Amik Plain and are observed in coastal areas near the Iskenderun district [41]. The alluvial units located along the coast of Iskenderun (grey shaded areas in Figure 2b) contain gravel, sand, clay, and silt [42; 43; 44].
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Model Performance Comparison  
We compare the performance of R50A3-LWBENet model with U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet, and MU-Net for lake water body segmentation through three aspects: visual observation, performance evaluation metrics, and model convergence, and finally get the enhancement effect of the R50A3-LWBENet model in this process. U-Net is a classical network model that possesses symmetric encoding and decoding structures, where the contextual information of the image is extracted during encoding, while during decoding the up-sampled features are fused with the different scale features generated by the encoding process to obtain rich contextual information [17]. Attention U-Net (AU-Net) proposes an Attention Gate (AG) model that automatically learns target structures of different shapes and sizes. By using a model trained with AG, irrelevant regions in the input image can be suppressed while highlighting salient features that are useful for a specific task, integrating the model into the standard U-Net structure and improving the sensitivity and prediction accuracy of the model [35]. Recurrent U-Net (RU-Net) incorporates Recurrent Convolutional Layers (RCLs) in each convolutional encoding and decoding unit of U-Net. Feature accumulation is performed through the Recurrent Convolutional Layers to ensure a more accurate presentation of the features required for the segmentation task [36]. Attention RU-Net (ARU-Net) applies both AGs and RCLs to U-Net, which both enhances the network's ability to model contextual dependencies in the image encoding and decoding process and better focuses on image regions of interest. The SER34 AUNet model used the U-Net semantic segmentation network and is improved by adding the DANet module and the SE module, which ultimately realizes high-precision water body segmentation [22]. MU-Net is a hybrid MixFormer architecture, where the MixFormer module is embedded into U-Net to model the local spatial detail information and global contextual information of an image, while the attention mechanism module is added to refine the features generated by the encoder [23].
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### False Data Injection Attacks
#### 3.1.3 Attack 3: Zero SoC Reporting Over a Specified Time Period  
In this attack, malicious consumers falsely report a zero SoC during specific time periods, even though their actual SoC is greater than zero. During other periods, they report their true SoC values. By mixing true and false reports, the attack becomes stealthy and harder to detect. The attack deceives the system into believing the consumer's battery is fully depleted, prompting the RL agent to prioritize charging them immediately. This strategy can create significant spikes in energy demand, especially if multiple malicious consumers launch this attack. Equation (11) is used to calculate the reported SoC values based on the actual SoC.  
\[f_{3}(SoC_{i}^{(t)})=\begin{cases}0&\text{if }t\in[t_{s},t_{e}],\\ SoC_{i}^{(t)}&\text{otherwise}\end{cases} \tag{11}\]  
where:  
* \(t_{s}\) and \(t_{e}\) represent the start and end times of the period during which the consumer falsely reports zero SoC values. Outside of this time frame, the consumer reports the actual SoC.
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 1 Introduction
### The Main Contributions of This Paper  
As a result of the analysis of articles devoted to developing the fast DHT-II algorithms, the following should be noted [5]. Firstly, the mathematical notations used by the authors to describe the proposed fast algorithms are often quite complicated. As a result, the understanding of the essence of the suggested solutions may be difficult for practitioners unfamiliar with the polynomial theory, number theory, Galois field theory, etc. Secondly, the mentioned theories and the developed algorithms concern specialized tasks. However, reducing the computing operations is desirable when a wide range of problems are solved.  
To lessen these disadvantages, in [5], a structural approach was proposed to develop fast algorithms for matrix-vector multiplication. It was based on a deep analysis of the structure of base transform matrices, identifying individual features of the arrangement of identical entries. Then, if necessary, the matrix structure was changed for subsequent use of the submatrix factorization [5]. The structural approach did not limit the length of the original data sequence, for example, to a power of two or three [5]. The number of multiplication and addition operations necessary for matrix-vector product computation could be reduced using this incredibly simple and effective approach. Since DHT-II coefficient matrices are structured, this research aimed to develop reduced-complexity DHT-II algorithms for input sequences of lengths \(N=2\), \(3\), \(4\), \(5\), \(6\), \(7\), and \(8\) based on a structural approach.  
This paper is structured as follows. Section 1 introduces the problem and defines the subject of this study. Section 2 contains a short mathematical background and defines the system of used notations and symbols. Section 3 contains the derivation of algorithms for DHT-II transform sizes from \(2\) to \(8\). Sections 4 and 5 contain a discussion of this study's results and a comparative evaluation of the computational costs required to implement the developed and known algorithms. Conclusions are presented in Section 6.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 3 Overview of Previous Studies on IRS-Assisted UAV Communications
### Improvement of Energy Efficiency
\begin{table}
\begin{tabular}{c l l c} \hline \hline
**Reference** & **Objective** & **Design Variables** & **Scenario** \\ \hline \multirow{3}{*}{[68]} & Maximize the received & \begin{tabular}{l} UAV active beamforming, \\ IRS passive beamforming, \\ UAV trajectory \\ \end{tabular} & (a) \\ \hline \multirow{3}{*}{[69]} & Minimize the energy & \begin{tabular}{l} Movement of UAV, \\ IRS phase shift, \\ power allocation policy \\ \end{tabular} & (a) \\ \hline \multirow{3}{*}{[70]} & Maximize the & \begin{tabular}{l} Transmit power of users, \\ BS active beamforming, \\ IRS passive beamforming \\ \end{tabular} & (d) + (e) \\ \hline \multirow{3}{*}{[71]} & Maximize the & \begin{tabular}{l} UAV trajectory, \\ task offloading, \\ cache with the phase shift, \\ design of IRS \\ \end{tabular} & (a) \\ \hline \multirow{3}{*}{[72]} & Maximize the secrecy & \begin{tabular}{l} UAV trajectory, \\ IRS phase shift, \\ user allocation, \\ transmit power control \\ \end{tabular} & (e) \\ \hline \multirow{3}{*}{[73]} & Minimize the average & \begin{tabular}{l} Resource allocation strategy, \\ UAV trajectory, \\ UAV velocity, \\ UAV velocity, \\ IRS phase shift \\ \end{tabular} & (a) \\ \hline \multirow{3}{*}{[74]} & Maximize the number & \begin{tabular}{l} UAV trajectory, \\ IRS phase shift, \\ user allocation and transmit power \\ \end{tabular} & (a) \\ \hline \multirow{3}{*}{[75]} & Minimize the weighted & \begin{tabular}{l} Transmit power, \\ time slot scheduling, \\ task allocation \\ \end{tabular} & (c) \\ \hline \multirow{3}{*}{[76]} & Minimize the energy & \begin{tabular}{l} UAV deployment, \\ IRS phase shift, \\ user association, \\ IRS association \\ \end{tabular} & (a) \\ \hline \multirow{3}{*}{[77]} & Minimize the total & \
\begin{tabular}{l} UAV trajectory and velocity, \\ IRS phase shift, \\ subcarrier allocation \\ \end{tabular} & (d) + (e) \\ \hline \multirow{3}{*}{[47]} & Improve the coverage &
\begin{tabular}{l} Outage probability, \\ average bit error rate (BER), \\ average capacity \\ \end{tabular} & (b) \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of previous works relevant to improvement of energy efficiency. UAVs, a novel framework of visible-light-communication-enabled UAV multicell networks was proposed, leveraging widespread IRSs. The results showed that the proposed system efficiently reduces energy consumption as compared to a counterpart without IRSs. The work in [77] presented a novel approach of heterogeneous networks (HetNets) supporting dual connectivity, by adopting multiple UAVs as manual relays with IRSs. The authors of [47] investigated how to improve the coverage and reliability of UAV communications systems using IRS-assisted UAVs. To model the statistical distribution of IRS-enabled ground-to-air links, an approximation method was developed for the probability density function of the instantaneous signal-to-noise ratio (SNR).
## 9 Experimental Assessment and Analysis
### Experiment 2: Subjective Evaluations
During the test the subjects must determine which of the two words were pronounced. The subjective results are then given on a 1 to 5 scale. However, as for the naturalness, we adopted a much simpler criterion, which is to establish whether the Comprehension of the word is _acceptable_ or it is too difficult. Comparison is made with the Votrax speech synthesizer [74] and we again assume that visual synthesis is performed with the algorithm described in Reference [60]. A further subjective test concerns the synchronization between synthetic speech and facial movements. While we tested synchronization of face movements with speech synthesized with \(Fuzzy-Genetic\) algorithm, comparison was made with spoken speech degraded by speech-shaped noise added to the spoken speech. The SNR of noise was \(-18\) and \(-20\) dB as described in Reference [60]. However, while audiovisual results reported in Reference [60] are related to the Viseme-Driven algorithm our results, they obtain \(Naturalness\); \(Rating\) and not synchronization as we do. However, we assume that synchronization quality can be also expressed in terms of naturalness of the audiovisual information delivered to the human. The subjective results are described later. Note, however, that in Reference [60] naturalness results with nonsense words are not available, and therefore we indicate NA (Not Available) for them. In conclusion of this section, the tests carried out concern the measurement of the fundamental subjective characteristics of the artificial voice generated by the \(Fuzzy-Genetic\) algorithm such as: naturalness and comprehension of artificial words. Moreover, above all, the quality of facial movements, such as the perceived quality of the synchronization between artificial voice and facial movements. Naturalness and understanding of artificial voice have been measured with and without facial movements. Naturalness is reported in Table 2. Comprehension is shown in Table 3; of course, it emerges that comprehension of the artificial words is greater for words with meaning. It also emerges from the measurements that the facial movements helps in the understanding of artificial words. The evaluation of the synchronization of facial movements with the artificial voice is shown in Table 4. From this evaluation it emerges that synchronization is best perceived in meaningless words than in meaningful ones, probably because facial movements are appreciated as an aid in trying to understand the words. In any case, all the objective and subjective results are very close to that obtained with existing systems and thus we are quite sure that the algorithm produces audiovisual information of high quality.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 3 Methodology
### Gas Leak Detection  
In this section, the proposed method for visually-enhance gas emanation and gas leak detection is explained in detail. As described in Algorithm 2, firstly, a set of preprocessing steps is applied on the image frames to improve the result of optical flow analysis using an adaptive histogram-based adjustment technique. For this method, the first frame is considered as the reference to adjust the histogram of the remaining frames. Next, all pixels with illumination differences lower than a predefined threshold (\(T_{\gamma}\)) are omitted. The threshold (\(T_{\gamma}\)) determines the algorithm's flexibility on temporal illumination changes and it is set manually based on the environmental conditions such as the existing noises in the image, the level of motions, and illumination variations. Additionally, adaptive thresholding [33] is used to binarize the frames and remove the outliers. For binarization of the frames, a parameter (\(T_{s}\)) is used to determine the sensitivity of the thresholding process. For the experiments, it is set by comparing the results of using different values.  
In the next step, Farneback optical flow [34] is used to create the motion map representing the orientation and magnitude of local changes. Then, the magnitude map is processed to filter out the motions with magnitudes smaller than a defined threshold (\(T_{a}\)). \(T_{a}\) determines the algorithm's sensitivity for lower motions and its value is determined based on the magnitude and volume of expected gas emanations. Later, the series of magnitude maps are combined to form a probabilistic map \(P^{M}\) determining the probability of the occurrences of local motions as shown in Equation (2), where \(M_{i,j,k}\) is the magnitude map of \(k\)th frame. In this way, the effect of noises and small changes is minimized. Additionally, the probabilistic map is processed to remove sudden and inconsistent local motions based on a defined threshold (\(T_{p}\)). The elements of the probability map demonstrate the occurrence rate of local motions. Thus, the higher threshold value causes the algorithm to lean on the more repetitive local motions with higher occurrence rates and magnitudes. Therefore, its value can be determined based on the pattern of gas emanation, the severity of the expected leak, and the weather situation. Finally, the hotspot area which contains the highest probability values and the area with the highest flow intensity are determined based on the probabilistic map.  
\[P_{i,j}^{\text{M}}=\frac{1}{K}\sum_{k=1}^{K}\left\lceil M_{i,j,k}\right\rceil,0\leq M_{i,j,k}\leq 1 \tag{2}\]