esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Inverse Model Identification of Ship Motion  
According to Equation (5), the process of ANFM learning is shown in Figure 5. ANFM is used to learn the inverse model of ship course control with time-varying uncertainty in the modelling parameters, namely the dynamics of \(\psi\rightarrow\delta\).  
When learning, the system first generates suitable sample data \((\mathbf{X}_{i},o_{i})\). Here \(\mathbf{X}_{i}\) and \(o_{i}\) denote the input and output values of the ANFM corresponding to the ith sample data, respectively. For the generation of sample data, sine and sawtooth waves can be used in this paper. The ship module in the figure is set to model conditions where the parameters \(K\), \(T\), \(\alpha\), \(\beta\) are time-varying with speed \(V\). Let the input data of the ship be \(\delta(k)\), and each input rudder angle \(\delta(k)\) produces an output course response \(\psi(k)\) within a certain learning time \(N_{k}\). \(\psi(k)\) together with its previous step \(\psi(k-1)\) and \(\delta(k)\) are used as the input quantity of the ANFM learning algorithm module for determining the structure of ANFM, namely the number of fuzzy rules at each moment, and further from which the center \(C_{j}\), the width \(\sigma_{j}\) and the weight matrix \(W\) of the output layer of each fuzzy rule, namely the RBF neural unit, are determined. The identification of ANFM as \(\psi(k)\) and \(\psi(k-1)\) as two input quantities is to expand the sample learning range of ANFM, thus effectively improving its  
Figure 4: Flow of ANFM learning algorithm.  
Figure 5: Progress of ANFM learning.  
generalization ability. Once the learning algorithm has determined these parameters, the ANFM response module produces the corresponding network output values \(\delta_{ANFM}(k)\). The difference between the input rudder angle of the ship \(\delta(k)\) and the output rudder angle of ANFM \(\delta_{ANFM}(k)\) is the discrimination error \(err(k)\), which characterizes the learning efficiency and approximation performance of ANFM. ANFM is considered to be able to approximate the inverse dynamics of \(\psi\rightarrow\delta\) for ship course control during this learning process when \(err(k)\) enters within the set desired error range \([-err_{d},err_{d}]\) and can be maintained until the end of learning. The trained ANFM can fix the network structure, that is, the number of RBF nodes in the third layer, and further use it in the online control system in the next stage.  
The flowchart of the dynamic approximation inverse dynamics model is shown in Figure 6. After the training, the model can be obtained and the number of fuzzy rules and the initial weights, centers and widths are obtained for the following work.
## 2 Quality Control Algorithms for GPS Observations
### Multi-Path Effects  
Multi-paths are radio signals whereby a GPS signal is reflected off some object reaching the GPS receiver's antenna. These signals take longer to reach the receiver than if they had travelled along a direct path. As a result, multiple copies of the transmitted signal are present in the tracking loop of the receiver [9]. This can result in the GPS miscalculating its position because the signals may have traveled from centimeters to meters further to reach their targets than a direct line-of-sight signal path would have travelled.  
Multi-paths affect not only the code range and carrier-phase measurements but also the measured signal power, which is an average of the composite signal power due to the direct and reflected signal carrier. Although the multi-path effect can be reduced by choosing sites without multi-path reflectors or by using choke-ring antennas to mitigate the reflected signal, it is difficult to eliminate all multi-path effects from GPS observations. Further, multi-path errors are also unique for each receiver and are uncorrelated between signals. Some unmodeled biases remain in GPS observations, even after such data differencing. Therefore, multi-path effects are very difficult to calculate. Multi-path effects are a major residual error source in the double-differenced GPS observables, and they can have a significant impact on the positioning results. If we know the magnitude of multi-path effects, then it will be possible to carry the quality control of GPS observations.  
The magnitude of multi-path effect is defined as the ranging error caused by the reflected carrier-phase GPS signal. The multi-path effects on the pseudo-range observables of L\({}_{1}\) and L\({}_{2}\) frequencies (_MP1_ and _MP2_, respectively) can be written as linear combinations as follows.  
\[MP_{1}=P_{1}-\Phi_{1}+\frac{2}{1-\alpha}(\Phi_{1}-\Phi_{2}) \tag{8}\]  
\[MP_{2}=P_{2}-\Phi_{2}+\frac{2\alpha}{1-\alpha}(\Phi_{1}-\Phi_{2}) \tag{9}\]  
where \(P_{1}\) and \(P_{2}\) are L\({}_{1}\) and L\({}_{2}\) pseudo-range (m), \(\[PERSON]_{1}\) and \(\Phi_{2}\) are L\({}_{1}\) and L\({}_{2}\) phase measurement (m), \(\alpha=(f_{1}/f_{2})^{2}\) and \(f_{1}\) and \(f_{2}\) are L\({}_{1}\) and L\({}_{2}\) phase frequencies (_Hz_), respectively.  
Multi-path analysis creates numerical summaries and graphical displays of pseudo-range multi-path effects for both single and dual frequency data.
# The Synergy of Renewable Energy and Desalination:
## 7 Conclusions  
A review of desalination systems driven by renewable energy sources is provided, outlining the status of available and emerging technologies, study findings, and the use of both traditional and innovative approaches in the desalination sector. Numerous studies were reviewed, with a particular emphasis on the application of solar and wind energy. This analysis examined over eighty research papers and articles devoted to the study of the performance and operation of desalination technologies, renewable energy sources, energy storage technologies, and pumping systems that were published between 1982 and 2024.  
The primary conclusions of this study emphasize the advantages of the application of hybrid energy storage systems, particularly hybrid battery storage systems, which offer a coordinated approach to reduce the influence of the intermittent nature of renewable energy sources, maximize desalination effectiveness, and reduce environmental impacts. The need to use cutting-edge performance prediction control strategies is also emphasized in this paper. According to several recent research publications, the pumping system can achieve improved controllability based on energy management criteria and predictive control techniques. This contributes to the optimal operation of the desalination plants.  
New developments and evaluations of control systems based on artificial neural networks have been covered.  
Finally, combining advanced desalination technologies with renewable energy is an essential step toward achieving sustainable water management. By utilizing the combined potential of hybrid energy storage systems with the advanced machine learning-based energy management technology for centrifugal pumping units, desalination systems can demonstrate improved energy efficiency, environmental sustainability, and economic viability.  
Conceptualization, L.G.; methodology, J.L.D.-G. and L.T.; investigation, L.G.; writing--review and editing, L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the postdoctoral fellowship programme Beatriu de Pinos, funded by the Secretary of University and Research, from the Department of Enterprise and Knowledge (Government of Catalonia), with the grant N\({}^{\circ}\) Ref. 2020 BP 00134.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
This project has received funding from the postdoctoral fellowships programme Beatriu de Pinos, funded by the Secretary of University and Research from the Department of Enterprise and Knowledge (Government of Catalonia), with the grant N\({}^{\circ}\) Ref. 2020 BP 00134).  
The authors declare no conflicts of interest.
## 3 MDM-FSO System Simulation Setup Model
\begin{table}
\begin{tabular}{c c c} \hline  & **Parameter** & **Value Used** \\ \hline \multicolumn{2}{c}{Transmitter Section} \\ \hline Aperture & Transmitter aperture & 20.2 cm \\ \hline \multirow{5}{*}{FSO-Channel} & FSO-Segment length & 1.1 km \\ \cline{2-3}  & Variance & \textless{}0.28 \\ \cline{2-3}  & Beam aperture size & 12.6 cm \\ \cline{2-3}  & Beam divergence & 5 mrad \\ \hline \multirow{5}{*}{SW-Laser} & Center wavelength & 1550 nm \\ \cline{2-3}  & Optical power & 9.8 mW \\ \cline{2-3}  & Modulation bandwidth & 75 MHz \\ \hline \multirow{2}{*}{Data source} & Delay & \textasci{}1 bit \\ \cline{2-3}  & Modulation format & Modified Duobinary \\ \hline \multicolumn{2}{c}{Receiver Section} \\ \hline \multirow{5}{*}{Spatial photodetector} & Active area & \textasci{}1 mm\textasci{}^{2}\) \\ \cline{2-3}  & Receiver aperture & 16.3 cm \\ \cline{2-3}  & Spectral sensitivity & 0.6A/W at 1550 nm \\ \cline{2-3}  & Boltzmann constant & 1.3806505 \(\times\)\(10^{-23}\) \\ \cline{2-3}  & Quantum efficiency & 0.75 \\ \cline{2-3}  & Electronic bandwidth, B\({}_{\text{e}}\) & 2 GHz \\ \cline{2-3}  & Rise \& fall time & \textasci{}5 ns \\ \cline{2-3}  & Half-angle field of view & \(\pm\)80 Deg \\ \cline{2-3}  & Background radiation power & \multirow{2}{*}{\(1.6\times 10^{-19}\) W/Hz} \\ \cline{2-3}  & spectral density (N\({}_{\text{b}}\)) & \\ \cline{2-3}  & Filter bandwidth & \(\text{R}_{\text{b}}\) = 1 Gb/s \\ \cline{2-3}  & Temperature & 300k \\ \hline \end{tabular}
\end{table} 
Table 2: System parameters.
# A Next-Generation Core Network Architecture for Mobile Networks
## 6 Re-Direction
### Scenario with eNodeB
#### 6.1.2 IMS Bearer Update  
As already stated, IMS and EPC use different bearers. When UE attaches to the network or when an SIP registration occurs, a default IMS bearer is established. When a UA moves from the SN to a VCN, the default IMS bearer is updated, while other actions must be performed at the application layer. In particular, the SIP Proxy's IP address used by the SIP UA for connecting to the IMS must be updated with the address of SIP Proxy server of the VCN. However, this task can be easily solved by letting the two SIP Proxy addresses coincide. This is possible without any conflict since the bearer establishes a dedicated circuit pointing either the SN or the VCN, and the SIP Proxies must not be reachable by the UA at the same time.
## 5 Results
### Objective Results  
The trial completion times (Figure 4, top left) include both object manipulation and marking. The results had some variation, but the distributions of the median values for each of the interaction methods were similar and there were no significant differences between the mouse and hands methods (\(t=-1.50,p=0.16\)), between the mouse and controller+stylus methods (\(t=1.28,p=0.22\)), or between the hands and controller+stylus methods (\(t=1.58,p=0.14\)). The degrees of freedom was 11 in all pairs. The completion time varied slightly depending on how much VR experience the participant had before, but there were no statistically significant differences.  
The number of markings performed before the task completion varied between the interaction methods (Figure 4, top right). The median values for the mouse, hands, and controller+stylus conditions were 6.5, 12, and 7 markings, respectively. However, there were no statistically significant differences between the mouse and hands methods (\(t=-1.02\), \(p=0.33\)), between the mouse and controller+stylus methods (\(t=1.30,p=0.22\)), or between the hands and controller+stylus methods (\(t=2.32,p=0.04\)). The degrees of freedom was 11 in all pairs. Some participants performed many markings at a fast pace (2-3 markings per second) leading to a high number of total markings.  
There were some clear differences in the final marking accuracy between the interaction methods (Figure 4, bottom). The median values for the mouse, hands, and controller+stylus methods were 3.2, 5.9, and 4.2 mm, respectively. The variability between participants was highest with the hands method. We found a statistically significant difference between the mouse and hands methods (\(t=-3.68,p=0.004\), [PERSON]'s \(d=1.178\) ([PERSON]'s \(d\geq 0.8\) is considered a large effect size)) using a paired \(t\)-test and Bonferroni-corrected \(p\)-value limit 0.017 (=0.05/3). The degrees of freedom was 11 in all pairs. There were no statistically significant differences between the mouse and controller+stylus methods (\(t=0.98,p=0.35\)) or hands and controller+stylus methods (\(t=2.70,p=0.021\)).  
Figure 4: Box plot visualizations of the objective measurements. The trial completion times for different conditions (**top left**): The median values for each participant are rather similar between the methods. There were two outlier values (by the same participant, for mouse and hands conditions) that were removed from the visualization. The number of markings per five targets (**top right**): There were some differences between the interaction methods (the median value for hands was higher than for the other methods), but no significant differences. The marking accuracy (**bottom**): There was a significant difference between mouse and hands conditions, but no other significant differences.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 2 System Model  
Although previous studies have typically been conducted on systems with many clusters containing numerous users, in this paper, we consider only one cluster with two users because it explains how a new encoding and decoding method is better than the existing methods. Figure 1 shows the NOMA wireless communication system in a downlink scenario with one base station (BS) and two users. Each node has a single antenna. The channels between the BS and the users experience independent and identically distributed Rayleigh fading. It is assumed that the channel state of user 1 is better than that of user 2, i.e., user 1 is considered as the cell-center user, and user 2 is the cell-edge user. The received signal of the users is as follows:  
\[\mathbf{Y}=\sqrt{P_{\text{t}}}\mathbf{H}x+\mathbf{n}, \tag{1}\]  
where \(P_{\text{t}}\) denotes the BS transmit power, \(\mathbf{H}\in\mathbb{C}^{2\times 1}\) denotes the channel between the BS and users, and \(\mathbf{n}\) denotes additive white Gaussian noise (AWGN) with a zero mean and \(\sigma^{2}\) variance. \(x\) is a signal sent from the BS, which is described later in Sections 2.1, 2.2 and 3.1.
# Fitness Landscape Analysis of Product Unit Neural Networks
## 7 Empirical Analysis of Loss Surface Characteristics
### Oversized Architectures  
Recall that oversized architectures are investigated to analyze the effect of overfitting behavior on the PUNN loss surfaces. The loss surfaces produced by PUNNs with oversized hidden layers are referred to as complex PUNN landscapes (CPLs) for the purposes of this section. The landscapes of PUNNs with optimal architectures are referred to as optimal PUNN landscapes (OPLs).  
Most of the differences between CPLs and OPLs are a result of the differences in dimensionality: CPLs tend to have larger gradients, as indicated by larger \(G_{avg}\) values for most problems. CPLs have larger \(G_{dev}\) values, which is indicative of more sudden ravines and valleys in the landscape. Smaller \(M_{1}\) and \(M_{2}\) values for CPLs show that OPLs are more neutral than CPLs. This can be attributed to the larger gradients of CPLs. \(FDC_{s}\) values tend to be smaller for CPLs than for OPLs. This a result of the dimensionality differences, as discussed in the previous section, as well as the fact that the oversized architectures have irrelevant weights, introducing extra dimensions to the search space. The extra dimensions do not add any extra information and only divert the search, thus making the landscape less searchable. Larger \(DM\) values are obtained from CPLs, indicating that they have multi-funnel landscapes. Therefore, the global underlying structures of CPLs are more complex than OPLs, which is in agreement with the fact that CPLs are less searchable than OPLs, and is the case with multi-funnel landscapes. There is a mixed result with respect to the micro-ruggedness of the CPLs: even though CPLs tend to have larger gradients than OPLs, which is usually an indication of a smoother landscape, CPLs produce larger \(FEM_{0.01}\) values than OPLs for XOR, Iris, \(f_{3}\), and \(f_{4}\). The macro-ruggedness \(FEM_{0.1}\) values of CPLs tend to be larger than OPLs, which suggests that CPLs experience more variation in the landscape with larger step sizes than OPLs. Therefore, CPLs possess higher variability across the landscapes than OPLs.
# Measurement of Head-Related Transfer Functions: A Review
## 4 Multi-Loudspeaker-Based Fast HRTF Measurement Methods
### Step-Wise Measurements
#### 4.1.1 Multiple Exponential Sweep Method (MESM)  
Interleaving and Overlapping Mechanisms  
One mechanism of the MESM method is the interleaving, which utilizes the time interval between the linear impulse response and the 2 nd order harmonic distortion of the measured system impulse response. Multi-channel systems are excited by exponential sweeps with a short time delay relative to each other, and, after the deconvolution process, a group of linear impulse responses of identified systems are placed between the beginning of the linear impulse response and the end of the 2 nd order harmonic distortion of the last system [73].  
The other strategy of the MESM method is to excite the exponential sweep for the subsequent system before the end of the previous sweep signal (overlapping). If the highest harmonic distortion of the system response does not disturb the measurement result of the previous system, the sweep signals can overlap in the time domain.  
The combination of these two mechanisms formed the MESM. To identify N loudspeaker systems, M systems can be treated under the interleaving mechanism and the resulting N/M groups are overlapped between each other. A series of impulse responses over time can be calculated after the linear deconvolution, and the linear impulse response of each system can simply be extracted by applying a time window. Some parameters such as the length of the linear impulse response and the 2 nd order harmonic distortion, and the order of the highest harmonic distortion should be pre-determined by a reference measurement prior to the formal measurement. These parameters are then used to optimize the total MESM measurement time towards either the minimal measurement time or the maximal SNR of the measurement results [157].
## 4 Experiment Results and Discussions
### Datasets  
We evaluated the effectiveness of the proposed method using a dataset derived from images captured by the GaoFen-4 remote sensing satellite. The original images have a resolution of 10,240 \(\times\) 10,240 pixels, a spatial resolution of 50 m, and a spectral range of 0.45-0.52 \(\upmu\)m. GaoFen-4 was selected for its suitability in large-scale maritime monitoring, providing adequate resolution for detecting large maritime vessels, such as aircraft carriers and warships, which typically range from 120 to 300 m in size. Although Sentinel-2 has a higher resolution of 10 m, its focus on land and vegetation monitoring makes it less ideal for detecting large maritime targets in complex oceanic environments. Due to the relatively low spatial resolution, the targets are primarily large maritime vessels, with sizes corresponding to 2-6 pixels in the images. To facilitate analysis and maintain the continuity of target sequences across image boundaries, the original images were divided into smaller patches of 1024 \(\times\) 1024 pixels with a 15% overlap.  
The ground truth dataset was constructed through a meticulous process of visual interpretation, conducted by professional analysts with expertise in ship target identification. These experts utilized multiple sources of information, such as spatial features, contextual information, and spectral characteristics, to accurately differentiate ship targets from surrounding features. This rigorous approach ensured that the resulting annotations were highly reliable. To enhance the diversity of the dataset and simulate realistic conditions, cloud cover with varying types and thicknesses was introduced into the background of some images. A multi-scale Gaussian filter was applied to seamlessly blend the clouds with the underlying imagery, producing samples that closely resemble real-world scenarios. All generated samples underwent a stringent quality control process, and only those meeting the reliability and practicality criteria were included in the final dataset.  
The dataset consists of 2490 annotated samples, including 936 positive samples (ship targets) and 1554 negative samples (non-targets), representing a total of 1,368 unique ship targets, with a positive-to-negative sample ratio of 1:2. To train and evaluate the proposed method, the dataset was divided into a training set (1743 samples, 70%) and a validation set (747 samples, 30%). The inclusion of negative samples in both sets is essential for improving the model's ability to distinguish actual ship targets from visually similar non-target objects, thereby reducing false positives and enhancing its robustness for deployment in real-world applications. The details of the dataset are further illustrated in Figure 7.