esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulated Underwater Environment
### Reverberation Models for Seabed  
In this study, it is assumed that the patches where scattering occurs are evenly distributed on the seabed. Thus, a model for the scattering of patches within a resolution cell range is required. The resolution cell of sonar in general form and a model for the patches are illustrated in Figure 2.  
The Rayleigh distribution is applied using the central limit theorem, in which the sum of sufficient random distributions converges to a Gaussian distribution under the assumption that sufficiently many scatterers are distributed in the resolution cell. However, for active sonar systems with high resolution that have recently become available owing to the current development of technology, the number of reverberation components that influence the given resolution cell could be reduced. Therefore, the central limit theorem cannot be applied and a non-Rayleigh distribution is applied.  
Figure 2: A model for a resolution cell and patches.  
As shown in Figure 1, a shallow water environment with a depth of 100 m is assumed and the LFM signal with a band from 6 kHz to 20 kHz is used for the transmit signal of active sonar. Because the swarm size is not large on the seabed, a non-Rayleigh distribution can be applied considering a finite number of scatterers. [PERSON] and [PERSON] derived reverberation signals by applying the K-distribution instead of the Rayleigh distribution under this assumption [20]. The distribution of the envelope of a matched filtered signal, which is received from a finite number of scatterers, becomes the K-distribution. The shape parameter of the K-distribution is related to the number of scatterers while the scale parameter is determined by the mean size of the scatterers [27]. The K-distribution is convenient in various environments and situations according to the shape parameters and scale parameters. In this study, the shape parameter is set to 1 and the scale parameter is set to 1. Then, the clutter signal power becomes 1 since the multiplication between the shape and scale parameters is the expectation of clutter power. For a given TCR, the clutter signal is scaled with respect to the target signal power [28]. Note that for the shape parameter greater than 50, the distribution converges to the Rayleigh distribution.
## 5 Summary and Conclusions  
The authors' research focuses on designing, modeling, and controlling mechanical systems subjected to complex force fields [74; 75]. Therefore, the mutual interactions between multibody system dynamics, applied system identification, and nonlinear control fall within the scope of the authors' research domain. In this work, on the other hand, the sensitivity analysis of a deep Reinforcement Learning (RL) controller applied to the cart-pole swing-up problem is performed. Through extensive numerical experiments, the effectiveness of the proposed controller is analyzed in the case of the presence of friction forces. Subsequent works will focus on studying environments characterized by randomized parameters during the training procedure to further improve the robustness of the resulting control system.  
Future research works will be devoted to further refining the numerical procedure for developing the control algorithm proposed in this paper, so that it can be readily applied to more complex dynamical systems, such as articulated mechanical systems modeled within the multibody framework that have an underactuated structure. For instance, an immediate extension of this work, which is focused on the swing-up problem of a cart-pole system, is the development of a robust controller similarly based on the use of the deep RL approach for the Furuta pendulum, as well as for an inverted pendulum controlled through a gyroscope mounted on a gimbal. In fact, the benchmark problems mentioned before are closer to practical systems employed in engineering applications, and the development of a robust controller for such systems seems quite promising.  
In summary, the current research work focuses on the computational aspects of the swing-up problem of the cart-pole system. The paper aims to develop a nonlinear controller based on reinforcement learning techniques simulated in a virtual environment. Therefore, the experimental verification using a test-rig of the proposed controller is outside the scope of the present work. However, this issue will be addressed in future investigations specifically dedicated to developing experimental results for supporting the theoretical controller obtained using the proposed approach. Additionally, the development of an in-depth analysis of the DRL-based the sensitivity and robustness of the control system and its experimental implementation, using advanced dry friction models capable of exhibiting stiction, the pre-sliding, and the Stribbeck effect, are topics that will be considered as future extensions of the present work.  
This research paper was principally developed by the first author ([PERSON]) and by the second author ([PERSON]). The detailed review carried out by the third author ([PERSON]) considerably improved the quality of the work. The manuscript was written through the contribution of all authors. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Results and Discussions
### Scattering Microstructure and Modeling of Its Reflective Properties  
The scattering of a beam reflected in the visible range from a surface is a complex process that depends on the properties of the reflecting material and the topography of the reflecting surface. In this case, reflection can occur both from a clean surface and from a surface that has a layer of reflective, partially reflective or transparent coating. In the practical experiment, we used plates, processed using femtosecond lasers. This allowed us to ensure the formation of a relief microstructure with various reflective properties.  
The comparative irradiation of the studied surfaces with a narrowly directed light beam of a red helium-neon laser with a wave length 632.8 [nm] and diameter of about 3 mm indicates that the presence of a complex surface microstructure makes it possible to improve the scattering properties of the surface (Figure 6).  
As shown in Figure 6, pos. b, a surface with a pronounced periodic structure forms a diffraction pattern with clearly defined diffraction maxima (d), negatively affecting the unmasking surface's property. When scattering by a surface with a complex structure (Figure 6c, area 3), one can also notice a diffraction pattern, which is weakly mapped against the background of the scattered spot, formed by the periodic component of the surface structure.  
Figure 6: The nature of scattering of a reflected narrowly directed beam of a helium–neon laser (in the visible part of the spectrum, with a wave length 632.8 [nm]): (**a**) from the flat surface, (**b**) from the surface with a periodic microstructure, formed with femto laser processing, (**c**) from the surface with a complex microstructure. 1—the projection screen, 2—the reflective surface, 3—the diffraction pattern observed against the background of a diffused spot, d—the diffraction maxima.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Symbolic Representation of Transition Systems
#### 2.11.4. Symbolic Representation of Transition Systems  
Given a transition system \(\mathcal{TS}\stackrel{{\text{def}}}{{=}}\langle S,S_{0},R,AP,L\rangle\), we define a symbolic representation of the whole system as follows.  
**Definition 74** (Symbolic Transition System).: _A symbolic transition system encoding \(\mathcal{TS}\) is the tuple \(\mathcal{M}=\langle V,\mathcal{I},T\rangle\), where_  
* \(V\) _is a set of propositional state variables, with_ \(|V|=\lceil log_{2}|S|\rceil\)_, encoding the states_ \(S\) _of_ \(\mathcal{TS}\)\(\mathcal{I}\) _is a propositional formula over_ \(V\) _representing the initial states_ \(S_{0}\) _of_ \(\mathcal{TS}\)_._
* \(T\) _is a propositional formula over_ \(V\cup V^{\prime}\) _representing the transition relation_ \(R\) _of_ \(\mathcal{TS}\)_._  
All definitions provided in Section 2.8 about reachability on transition systems naturally extends to their symbolic counterparts.  
**Example 9**.: _Given the hardware design and its transition system described in Example 1, the overall encoding of the transition system \(\mathcal{TS}\) will be the tuple \(\mathcal{M}=\langle V,\mathcal{I},T\rangle\) defined as follows:_  
\[V=\{v_{0},v_{1}\} \tag{34}\]  
\[\mathcal{I}=\
eg v_{0}\wedge\
eg v_{1} \tag{35}\]  
\[T=(\
eg v_{0}\wedge\
eg v_{1}\wedge v_{0}^{\prime}\wedge\
eg v_{1}^{\prime})\,\vee \tag{36}\]  
\[(v_{0}\wedge\
eg v_{1}\wedge\
eg v_{0}^{\prime}\wedge\
eg v_{1}^{\prime})\,\vee\] \[(v_{0}\wedge v_{1}\wedge\
eg v_{0}^{\prime}\wedge\
eg v_{1}^{ \prime})\,\vee\] \[(v_{0}\wedge v_{1}\wedge\
eg v_{0}^{\prime}\wedge\
eg v_{1}^{ \prime})\]
## 3 Offset Correction  
In this section, we present two distributed recursive calibration algorithms for estimation of the calibration parameters, \(b^{i}\), in (2), assuming that the sensor gains are ideal, i.e., \(\alpha^{i}=1\). The algorithm for isolated offset calibration has been formulated in [44]; it represents a special case of the calibration algorithms presented in [33; 34; 44]. Isolated offset estimation can often be encountered in practice, since the sensor offset is usually the most sensitive parameter requiring re-calibration. We also present the basic mathematical tools used throughout the paper.
## 3 Experiment 2
### Results  
We first compared the perceived visual speed with the actual running speed using a paired \(t\)-test, as both running and perceived visual speed were normally distributed (please refer to Supplementary Materials S1-T2 for the data.) This allowed us to determine how visual speed was estimated compared to the actual running speed, i.e., treadmill speed. Participants set visual speed significantly higher than the actual treadmill speed (t(19) = \(-4.8743\), \(p<0.001\)). Figure 5 shows the difference between running speed and perceived visual speed.  
We then tested whether the presentation and manipulation of the optical flow affected spatiotemporal parameters in treadmill running using a \(4\times 3\) (Condition[noOF, matchOF, fastOF, slowOF]*minute[1,2,3]) mixed analysis of variance (ANOVA). The results showed no main effect of the condition for all the spatiotemporal parameters, i.e., step length, step duration, step frequency, and step width. Even if there was no influence of the condition, the results showed a significant effect of the minute of the acquisition. There was no interaction between the main factors. Table 2 reports all the results.  
\begin{table}
\begin{tabular}{c c} \hline
**Spatiotemporal Parameters** & **Estimation** \\ \hline Step duration (s) & The duration between the local minima of the left and right heel \\ Step length (m) & Treadmill speed (m/s) \(\times\)Step duration (s) \\ Step frequency (steps/s) & 1/step duration \\ Step width (cm) & The distance on the x-axis between the left and right heel \\ \hline \end{tabular}
\end{table}
Table 1: Gait parameters and their method of estimation.  
Figure 5: Visual speed underestimation. Speed of the visual scene that was perceived as matching running/treadmill speed. The bars represent the means and the error bars the 95% confidence interval of those means.  
A [PERSON]'s post-hoc test indicated that the difference was significant when comparing minute 1 to minute 3 for step duration (\(p<0.05\)), step frequency (\(p<0.05\)) and step width (\(p<0.01\)), while there was only a tendency for step length (\(p=0.061\)).  
For the HR, since a plateau was observed in all participants, the mean value was calculated as average of 30 s once the plateau was reached. A repeated-measures ANOVA showed a significant difference for mean HR [F(3,57) = 5.7465, \(p<0.01\)], while the starting HR did not differ between conditions \(\left[\chi^{2}(3)=0.6528,p=0.8842\right]\). A [PERSON]'s post-hoc test indicated a significant difference between the noOF and the VR conditions (noOF-matchOF:\(p<0.01\), noOF-fastOF:\(p<0.05\), noOF-slowOF: \(p<0.001\)) as shown in Figure 6.  
Last, a repeated-measures ANOVA performed on perceived effort values (as measured with the Borg RPE scale) showed no difference between the different OF conditions \(\left[\chi^{2}(3)=2.4296,\ p=0.4882\right]\).
## 4 Evaluation Metrics  
Considering \(T_{P}\), \(T_{N}\), \(F_{P}\), and \(F_{N}\) represented true positive, true negative, false positive, and false negative, respectively, the precision \((\frac{T_{P}}{T_{P}+F_{P}})\), the recall \((\frac{T_{P}}{T_{P}+F_{N}})\), the F1-score \((2\times\frac{P\times R}{P+R})\), the accuracy \((\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}})\), and the receiver-operating curve-area under the curve (ROC-AUC) were calculated.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Sequential Estimation  
#### 4.3.1 Results for the First Estimation Using RSSI  
The cumulative distribution function (CDF) summarizing the results of the second estimation using RSSI, AOA, and HYBRID after the first estimation with RSSI is shown in Figure 28. The figure indicates that using AOA or HYBRID for the second estimation leads to faster CDF convergence and higher localization accuracy compared to using RSSI for both estimations. Similar to the first estimation, using RSSI for the second estimation results in a larger number of significant outliers with high localization errors. This suggests that the second estimation was not sufficient to correct these large errors.  
This improvement is particularly noticeable in the reduction of outliers and in the steeper slope of the CDF. These results support the idea that incorporating directional information in the second step--either through AOA or HYBRID--helps to compensate for the limitations of RSSI, which is highly sensitive to multipath and noise. This observation aligns with findings from [PERSON] et al. (2018), who showed that the fusion of multiple features, including RSSI and AOA, enhances localization robustness under NLoS conditions. Similarly, [PERSON] et al. (2018) demonstrated that sequential Bayesian estimation methods, such as particle filters, can effectively suppress outliers by continuously refining the estimate. Our results are consistent with this finding, especially when comparing the RSSI\(\rightarrow\)HYBRID combination with RSSI\(\rightarrow\)RSSI, which leaves more sources with large localization errors. When HYBRID is applied with a strong weighting toward RSSI, the RSSI\(\rightarrow\)HYBRID approach improves accuracy compared to RSSI\(\rightarrow\)RSSI. However, it still includes some sources with large localization errors, indicating that the second estimation is not always sufficient to correct poor initial estimates. This limitation is also noted by [PERSON] et al. (2019), who emphasized the need for high-quality initial estimations in sequential localization using Gaussian processes.
# Linking Off-Road Points to Routing Networks
## 4 Implementation  
The key extension of SQL with respect to geoscience is the technical report \"Simple Features for SQL\" of the Open Geospatial Consortium (OGC) [4; 28]. Common databases already offer an addition to its core framework to address this concept. For PostgreSQL, there exists the extension PostGIS [29] that follows OGC's described standard. Hence, we describe our implementation in terms of the OGC standard.  
While the extensions pgRouting and PostGIS are written in C/C++, our middleware was implemented in Java. The linkage and routing queries were conducted by JDBC commands with OGC's SQL extension. By restricting ourselves to JDBC calls, our solution was independent of a specific relational database management system, as long as OGC's spatial extensions to SQL are provided.  
We have already employed the implementation described below in one demonstration [30]. There, the end user could mark arbitrary locations as favorable points by simple mouse-clicks on a map overlay. According to the generated, geometric objects, a database query on a routing network was issued. Because the user was not restricted to selecting points on the routing network, a linkage had to be performed. For this scenario, we used the OSM map of Munich (of the year 2013) that we imported statically into the PostgreSQL database. Access to the routing network was obtained by the additional extension pgRouting [31], which sits on top of PostGIS. Figure 6 depicts the software layers of our employed solution. Note that there are also other frameworks with similar concepts, cf. [32]. The translation of our linkages to the language of OGC was performed easily:  
* \(d\) was given by ST_Distance.
* \(\ell\) was computed by a shortest path algorithm on pgRouting's network. Common algorithms such as Dijkstra and A* were available.
* ST_ShortestLine represented the geodesic function \(g\).
* \(\operatorname*{argmin}_{a\in A}d(a,v)\) with \(A\subseteq E\) or \(A\subseteq V\): We used ST_DWithin as a pre-filter for distant edges/vertices before the exact distances to all remaining edges/vertices were calculated.
* The split of an edge \((u,v)\) into \((u,v^{\prime})\) and \((v^{\prime},u)\) with \(i(v^{\prime})\in i(u,v)\) was performed by calling ST_Line_Locate_Point and then generating both new edges with two calls of ST_Line_Substring.  
On a query, the network would be modified in such a way that the user's specified geolocations were represented in the routing network. After the query was evaluated, we restored the network back to its initial state.
###### Abstract
A research was performed to improve the IMERG product with ML over the Brahmaputra River Basin, which used rain gauge observations as a reference [55]. Similarly, another study was performed to improve the satellite rainfall estimation from MSG data in northern Algeria with machine learning using rain gauge observation as reference [56]. The framework of this study was applied to the east coast of Peninsula Malaysia for generating daily precipitation estimates based on the rain gauge observations and IMERG satellite estimations, and the performance before and after enhancement is evaluated. The details of this methodology are explained in the next section. The findings are then discussed, and the final section concludes this research.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results
### Experimental Setup
#### 5.2.3 Split/Padding Method  
In the experiments, each utterance had three choices on its prefixed segment length, i.e., 100 frames, 200 frames, and 300 frames. The detailed split/padding methods are shown below. In the padding method (a), for utterances whose length was less than the prefixed value, the front frames were copied and filled to the end of the utterance until reaching the prefixed length. For example, if the specified length was 300 frames, an utterance with 80 frames was extended to 300 frames by repeating its frames, as illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subset** & **Speakers** & **Genuine** & **Spoof** \\ \hline
**Training** & 10 & 1508 & 1508 \\
**Development** & 8 & 760 & 950 \\
**Evaluation** & 24 & 1298 & 12008 \\ \hline
**Total** & 42 & 3566 & 14466 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed information of the ASVspoof 2017 Version 2.0 dataset. The number represents the quantity of utterances in each subset.  
In the split method (b), for utterances whose length was longer than the prefixed length, the utterance was split. For example, if the prefixed length was 100, an utterance with 250 frames was first extended to 300 frames, according to method (a), and was then be split into three segments, each with 100 frames, as is shown in Figure 5.
## 3 Deep Learning-Based PV Power Forecasting
### Training Procedure  
The eight models investigated in this study can be categorized into two classes: autoencoders and recurrent neural networks. The autoencoders represented include RBM, VAE, and SAEs while the RNN-based models contain RNN, LSTM, GRU, BiLSTM, and ConvLSTM. The dataset used for training and testing are normalized first, and more data preprocessing is needed for the autoencoder models. For instance, data reshaping is needed to transform the univariate PV power time-series data to a two-dimension matrix to be used as input for the autoencoders including the SAE, VAE, and RBM. The main difference between the two classes in the training phase is the learning way, the RNNs are entirely supervised trained while the auto-encoders are first pre-trained in an unsupervised manner and then the training is completed based on supervised learning. Specifically, RNNs models are trained in a supervised way by using a subset of training as input sequence (\(\mathcal{X}~{}=~{}x_{1},\ldots,x_{k}\)) and an output variable \(\mathcal{Y}=x_{k+1}\). The sequence length \(l\), called the lag, is a crucial parameter used in the data preparation phase. The mapping sequence to the next value is constructed using a window sliding algorithm. The value of \(l\) is determined using the Grid Search approach [51]. Here, the value of \(l\) is chosen 6, which is the lowest value that maximizes the overall performance of the proposed approach.  
RNN--based models are trained to learn the mapping function from the input to the output. After that, these trained models are used to forecast new data that complete the sequence. On the other hand, the greedy layer-wise unsupervised plus fine-tuning were applied to RBM, VAE, and SAES. It should be noted that PV power output forecasting based on autoencoder is accomplished as a dimensionality reduction. That is these models do not have the possibility to discover time dependencies or model time series data. [PERSON] [44] shows that a greedy layerwise unsupervised learning for each layer followed by a fine-tuning improves the features extraction and learning process of the neural networks dedicated to prediction problems or for dimensionality reduction like autoencoders. The VAE-driven forecasting procedure including the pretreatment step is illustrated in Figure 4.  
Figure 3: Schematic presentation of deep learning-based photovoltaic (PV) power forecasting.