esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Evaluation Index  
In this paper, we use four metrics to evaluate the super-resolution quality of solar images: PSNR, SSIM, NIQE [25], and LPIPS [26]. PSNR and SSIM are two metrics primarily used to quantify the similarity between super-resolution images and original images at the pixel level and structural level. NIQE and LPIPS, on the other hand, are used to measure the perceptual quality of images, reflecting their visual preference.  
Given a ground truth (GT) image \(I\) with N pixels and a super-resolved image \(\hat{I}\), the formula for calculating PSNR between \(I\) and \(\hat{I}\) is as follows, where a higher value of this metric indicates a smaller pixel differences between the images:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{L^{2}}{\frac{1}{N}\sum_{i=1}^{N}\left( I(i)-\hat{I}(i)\right)^{2}}\right) \tag{7}\]  
The formula for calculating SSIM is as follows, where a higher value of this metric indicates smaller structural differences between the images.  
\[\text{SSIM}(x,\,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy} +c_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2 }+\sigma_{y}^{2}+c_{2}\right)} \tag{8}\]  
where \(\mu_{x},\mu_{y},\sigma_{x},\sigma_{y}\), and \(\sigma_{xy}\) are the means, variances, and covariance of the GT image and the super-resolved image, respectively.  
The formula for calculating NIQE is as follows, where a smaller value of this metric indicates a higher natural perceptual quality of the image:  
\[\text{NIQE}=\sqrt{\left(\
u_{1}-\
u_{2}\right)^{T}\left(\frac{\Sigma_{1}+ \Sigma_{2}}{2}\right)^{-1}\left(\
u_{1}-\
u_{2}\right)} \tag{9}\]  
where \(\
u_{1}\) and \(\Sigma_{1}\) are the mean vector and covariance matrix obtained from a set of sharp images, and \(\
u_{2}\), \(\Sigma_{2}\) are the mean vector and covariance matrix obtained from the super-resolved image.  
The calculation formula for LPIPS is as follows: the image is fed into a neural network, features are extracted from the L layer and then unit-normalized across the channel dimension. A vector \(\text{w}_{1}\) is used to scale the number of active channels, and the L2 distance is subsequently calculated. The result is averaged spatially and summed across the channels.  
A smaller value of this metric indicates a perceptual quality of the image that is closer to the GT image.  
\[\text{LPIPS}=\sum_{1}\frac{1}{\text{H}_{1}\text{W}_{1}}\sum_{\text{h,w}}\|\mathbf{ w}_{1}\odot\left(g_{hw}^{l}-g_{0\ hw}^{l}\right)\|_{2}^{2} \tag{10}\]
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 5 Conclusions
### Practical Implications  
The findings of this study demonstrate that the proposed method, based on the Incremental Information Function (IIF) and the analysis of its characteristic values, constitutes an effective analytical tool for investigating the properties of iterative steganographic techniques. This approach enables quantitative assessment of key performance metrics, including the robustness and capacity of iterative steganograms. The IIF-based method provides a systematic framework for evaluating and comparing different iterative steganographic algorithms, with the potential to optimize steganographic systems and improve the effectiveness of steganalysis techniques.
# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 4 Results
### Formulation of the Mathematical Model  
In the general mathematical model, for each possible route i, the following costs are calculated:  
\[Z_{i}=F_{i}+H_{i}+W_{i}+D_{i}, \tag{13}\]  
where \(F_{i}\)--fuel costs on the \(i\)-th route, \(H_{i}\)--costs associated with the flight altitude on the \(i\)-th route, \(W_{i}\)--the impact of weather conditions on the \(i\)-th route, and \(D_{i}\)--the impact of air traffic density on the \(i\)-th route.  
In estimating fuel consumption, it can be modeled as a function of speed \(v_{i}\), altitude, and mass, as follows:  
\[H_{i}\text{: }F_{i}=f(v_{i},H_{i},\text{M}), \tag{14}\]  
where the function \(f()\) can be linear or nonlinear.  
The regression model is as follows:  
\[F_{i}=\alpha v_{i}+\beta H_{i}+\gamma, \tag{15}\]  
where \(\alpha\), \(\beta\), and \(\gamma\)--model parameters determined on the basis of historical data.  
In evaluating flight altitude, costs and safety can vary with altitude. (Costs vary with altitude due to air density, and safety varies in order to have more time for decision making for any issues in flight.) This can be described by the following expression:  
\[H_{i}=h_{1}\cdot\exp(-h_{2}H_{i}), \tag{16}\]  
where \(h_{1}\) and \(h_{2}\)--constants that take into account the effect of altitude on fuel consumption.  
In the assessment of weather conditions, the impact of weather conditions on routes can be modeled thusly as a function of temperature \(T_{i}\) and wind \(V_{i}\):  
\[W_{i}=\omega_{1}T_{i}+\omega_{2}V_{i}, \tag{17}\]  
where \(\omega_{1}\) and \(\omega_{2}\)--coefficients that determine the effect of temperature and wind.  
An estimate of air traffic density \(D_{i}\) can be estimated as follows, as a function of the average number of aircraft on a route:  
\[N_{i}\text{: }D_{i}=d\cdot N_{i}, \tag{18}\]  
where \(d\)--the coefficient characterizing the impact of traffic density.
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 4 Energy Efficient GA-Based Multipath Routing
### Multipath Formation Using Genetic Algorithm
#### 4.2.1 Encoding of Chromosome  
A routing path from CH to sink node is encoded as a chromosome. This chromosome is encodedas a sequence of positive integers, which represent each sensor node found in the path from CH to the base station with length \(m\), where \(m\) is equal to cluster size. By using the gene values, for instance, \(\{4,3,\) 5, 6, 8, BS, 9, BS, BS\(\}\), we can find the entire paths from every intermediate node to the sink node. For example, let us look in Figure 4 below to understand how the multipath is encoded in the chromosome. If CH's position is 1, then the path to the base station will be CH\(\rightarrow\)\(4\rightarrow\)\(6\rightarrow\)BS, or if the CH is placed at position three, then the path will be CH\(\rightarrow\)\(5\rightarrow\)\(8\rightarrow\)BS.  
A group of the chromosome is called the population. To begin with, we have initialized the population with the size of the cluster. The validity of a chromosome is assured if two things are satisfied; firstly, if all possible multi-paths in the chromosome donot have any loop, and secondly, if the gene value in the chromosome is among one of the valid possible next hops in the network.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 5 Resource Orchestration
### Remarks  
Table 4 shows approaches successfully applying DRL to perform resource orchestration in NFV-based CN [80; 82] and RAN [81; 84]. Those DRL-based approaches used algorithms such as DQN, DDPG, online DQN, and TD3 to maximize provider revenue and decrease deployment costs while meeting performance metrics such as latency and energy consumption. The worst-case complexity of these algorithms is \(O(HN)\) and depends on \(H\) (number of hidden layers) and \(N\) (number of neurons). Further approaches considering a broader set of QoS and QoE performance metrics are needed.  
Unlike the works cited above, Refs. [85; 87] orchestrated resources in NSL from an end-to-end perspective. It is noteworthy that those works emulated the transport network with OpenDayLight and managed the resource allocation as a function of the available bandwidth to connect RAN, CN, and edge networks. Nonetheless, more investigations in multitenant environments involving RAN, CN, TN, and edge networks are needed to turn 5G/6G network slices into a reality. It is also necessary to provide NSL orchestration solutions for vertical application domains such as UAV, IoT, and tactile Internet. Moreover, providers need multiagent and noncentralized RL/DRL solutions to orchestrate slice resources to multiple tenants using a global network view and provide resource management capabilities inside the slice (i.e., in-slice management). It is noteworthy that various orchestration approaches were tested with real datasets and emulated in controlled scenarios. Therefore, the next step to determine their practicability is to evaluate them in more complex and realistic testbeds.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment
#### 5.1.1 Flight Time for Flight Path  
As our study targets disastrous and emergency environments, the timely and reliable environmental information transfer is emphasized. The time for UAV-to-CH communication is limited owing to the high mobility of the UAV. Hence, reducing the UAV-to-CH communication time affects the network performance and is not acceptable. Therefore, in our study, we optimized the UAV velocity during data and non-data collection periods to minimize the total flight time without degrading the network performance. The UAV uses the maximum speed when it does not communicate with the SNs, and the value changes adaptively during the communication. The total flight time of the network is expressed as  
\[F_{Tot}=flying\ time+hovering\ time+UAV-to-CH\ communication\ time \tag{26}\]
# Stochastic Modeling of the Al Hoseima (Morocco) Aftershock Sequences of 1994, 2004 and 2016
## 6 Temporal Stochastic Modeling for the Al Hocima Sequences
According to the model presented in Figure 11, the estimated best model parameters shown in Table 3 were used to compute the expected cumulative number of earthquakes and error bounds. The computed values were compared with the observed cumulative number of earthquakes. Figure 11 shows that the estimated model matches the observed data well for all three sequences, with the observed values remaining inside the error boundaries throughout the whole sequence. Figure 11: The cumulative number of events above magnitude \(m_{c}\) for the three sequences. Circles: observed values. Lines: mean \(\pm\sigma\) fitted model. The magnitudes of the events included in the sequences are also depicted. Inset stand out the first ten days of the series. range of the released energy, as well as an instrumentation earthquake record. Thus, to reduce this variability, Equation (18) is transformed as follows:  
\[E_{k}^{tr}=\frac{E_{k}}{E_{0}}=\frac{10^{4.8+1.5~{}M_{k}}}{10^{4.8+1.5~{}M_{0}}}=1 0^{1.5~{}(M_{k}-M_{0})} \tag{19}\]  
where \(E_{k}\) and \(M_{k}\) correspond to the energy and magnitude of the \(k\)-th event, respectively, and \(E_{0}\) and \(M_{0}\) are the energy and magnitude of the first event considered, respectively. \(E_{k}^{tr}\) is the Benioff's energy. The approach described above was used to analyze the considered aftershock sequences. Considering the compound Poisson process, given as follows:  
\[Z(t)=\sum_{k=1}^{N(t)}E_{k}^{tr} \tag{20}\]  
Equations (16) and (17) were used to derive the estimation of the mean \(E[Z(t)]\) and the variance \(\mathrm{var}[Z(t)]\) for each series, considering the best-fit model derived and analyzed in the previous section. Figure 12 displays the expected cumulative energy and confidence bounds, mean plus/minus the standard deviation, according to the appropriate best-fit model for each aftershock sequence, and this is compared to the computed cumulative energy released in the sequences. Figure 12: _Cont._  
It should be noted that the stochastic model used follows the two previously quoted assumptions: both the independence of the magnitudes and the independence of the magnitudes with the occurrence time. Figure 12 shows that, for the 1994 sequence, no significant deviation of real data values from the model was observed, which supports the above assumptions. However, deviations of the real data values with the model were observed for the two other aftershock sequences. For the 2004 sequence, the main deviation was observed at the beginning of the sequence, up to the 30 th day, where the clustering of stronger aftershocks was recognized. This reveals that the assumption of the independence of magnitudes with occurrence times is not valid in this case. In addition, another deviation of the observed data out of the error bounds can be observed in the 2016 sequence, where an additional concentration of stronger aftershocks is recognized.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 3 Estimation Methods for DRR, CDC Error, and Phase Inconsistency Errors
### Analysis of DRR Error  
The derotation rate \(k_{\omega}\) is important in the pre-processing because the accuracy of the correction of the beam-steering bandwidth \(B_{s}\) depends on it. The measurement of azimuth steered antenna pattern also requires high precise \(k_{\omega}\)[18]. However, \(k_{\omega}\) is not always known precisely due to the approximation in (5). \(k_{\omega}\) is defined as the partial derivative of the azimuth-varying Doppler centroid with time, which is approximately the Doppler rate of a point located at the VRP. Furthermore, the effective velocity in (5) may introduce an error into \(k_{\omega}\), which will affect the pre-processing. Since the curvature of the orbit and earth, Linear geometric model [30] is commonly used in spaceborne SAR instead of curved geometric model for convenient. In the linear geometric model, the satellite velocity and beam velocity are substituted by the effective velocity in the imaging processing. According to the practical experience, however, those velocities are not exact for calculating derotation rate and the suitable velocity value for DRR is between satellite velocity and effective velocity. Though it is not accurate, the effective velocity is still used to calculate DRR in practice, which will introduce error inevitably. Since the satellite velocity is usually 10% larger than effective velocity for a typical spaceborne SAR satellite with height of 600 km, then the maximum calculating error for DDR will be 5%. If there is a DRR error, the time ranges of different targets in azimuth after pre-processing will not overlap, which will lead to erroneous azimuth antenna pattern correction and signal weighting. More seriously, there will be aliasing in the signal after pre-processing when this error is large enough.  
Define \(k_{\omega}\) with DRR error as:  
\[k_{\omega}^{e}=k_{\omega}+\Delta k_{\omega}=k_{\omega}\left(1+\delta\right). \tag{21}\]  
Correspondingly, the slant range from the sensor to the VRP becomes  
\[r_{s}^{e}=\frac  
This means the overlapped pre-processed signals from different azimuth targets will be separated based on their different azimuth positions when DRR error exists. The shift of the signal from an arbitrary point target is linearly dependent on its azimuth position. The signals from two targets symmetrically located at the scene center will separate symmetrically, so we refer to this effect as a'symmetrical shift'.
# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
###### Abstract  
Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies that are generally designed for specific applications. However, soft robotics presents its own set of challenges in both design and implementation, particularly in sensing and control. These challenges are abundant when dealing with the force control problem of a compliant gripping mechanism. The ability to effectively regulate the applied force of a gripper is a critical task in many control operations, as it allows the precise manipulation of objects, which drives the need for enhanced force control strategies for soft or flexible grippers. Standard sensing techniques, such as motor current monitoring and strain-based sensors, add complexities and uncertainties when establishing mathematical models of soft grippers to the required gripping forces. In addition, the soft gripper creates a complex non-linear system, compounded by adding an adhesive-type sensor. This work develops a unique visual force sensor trained on synthetic data generated using finite element analysis (FEA) and implemented by integrating a non-linear model reference adaptive controller (MRAC) to control gripping force on a fixed 6-DOF robot. The robot can be placed on a mobile platform to perform various tasks. The virtual FEA sensor and controller, combined, are termed virtual reference adaptive control (VRAC). The VRAC was compared to other methods and achieved comparable control sensing and control performance while reducing the complexity of the sensor requirements and its integration. The VRAC strategy effectively controlled the gripping force by driving the dynamics to match the desired performance after a limited amount of training cycles. The controller proposed in this work was designed to be generally applicable to most objects that the gripper will interact with and easily adaptable to a wide variety of soft grippers.  
soft robotics; adaptive control; computer vision +
Footnote †: journal: Applied Sciences
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 6 Conclusions  
An industrial equipment condition monitoring method based on MEST is proposed in this paper, and a novel method for construction of the memory matrix is put forward. The improved similarity that combines relative information in other variables is applied in monitoring. For different equipment and monitoring parameters, the method could be applied. The actual operational data for a blower fan in thermal power plant is used for validation, and the condition estimation performances are discussed. The actual data for failure event are used for validating the effectiveness of incipient failure detection  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Factor** & **Alarm 1** & **Alarm 2** \\ \hline \multirow{3}{*}{Vibration Measurements} & Threshold & 5.1 mm/s & 5.1 mm/s \\ \cline{2-4}  & Current Value & 5.75 mm/s & 5.20 mm/s \\ \cline{2-4}  & Sample Number & 124 & 276 \\ \hline \multirow{3}{*}{Residual} & Threshold & \(\pm\)0.2385 mm/s & \(\pm\)0.00625 mm/s \\ \cline{2-4}  & Current Value & 0.2492 mm/s & 0.007 mm/s \\ \cline{2-4}  & Sample Number & **120** & 263 \\ \hline \multirow{3}{*}{Similarity} & Threshold & 0.908 & 0.901 \\ \cline{2-4}  & Current Value & 0.832 & 0.848 \\ \cline{2-4}  & Sample Number & 121 & **260** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different failure detection method.  
Figure 10: The trend of \(x_{3}\) and \(x_{6}\) while alarms.  
employing proposed condition monitoring and early alarm method, the performance is compared with traditional exceed threshold method.  
The method has advantages such as, it is concise and easy to understand conceptually, which should be attractive to industrial engineers. Irrespective of the steady or dynamic work condition, the proposed memory matrix construction method improves the accuracy of condition monitoring. The proposed method using residual and similarity enhances the alarm timeliness, and proposed method could identify the incipient failure ahead.  
Although the proposed method has been employed in a specific blower fan, the model can be further explored with suitable parameters selection and threshold determination. The method should be further proved for different industrial equipment, and further research is planned for taking the method into account in other industrial equipment.  
[PERSON] proposed the idea and carried out the validation; [PERSON] guided the research; [PERSON] collected the data. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Experimental Design
### Laboratory Experiments  
#### 2.3.1 Stimuli Creation  
Binaural stimuli were created by convolving the same anechoic speech sample previously used in the in-situ test with binaural room impulse responses (BRIRs) captured at the same source and receiver positions as well as head orientation angles used in the first experiment. The BRIRs were acquired using the exponential sine sweep technique [29] in the HAART software [30], with a Neumann KU100 dummy head microphone (Neumann, Berlin, Germany) and a Genelec 8040A loudspeaker. These BRIRs are part of an open-access database ([[https://zenodo.org/record/2641166#.XmxCL_hKhFlY](https://zenodo.org/record/2641166#.XmxCL_hKhFlY)]([https://zenodo.org/record/2641166#.XmxCL_hKhFlY](https://zenodo.org/record/2641166#.XmxCL_hKhFlY))), and more details about that database are provided in [31]. The playback level of the binaurally synthesised stimulus for each position was adjusted to match the perceived loudness of the corresponding stimulus used in the previous in-situ test.  
The experiment allowed a small amount of head rotation to help the subjects resolve any potential front-back confusion, as well as to provide a more natural listening experience, as observed by [PERSON] and [PERSON] [32]. This required the binaural stimuli to be synthesised dynamically according to head tracking. Therefore, for each of the stimuli, five BRIRs ranging between \(-7.2^{\circ}\) and \(7.2^{\circ}\) with the intervals of \(3.6^{\circ}\) were used. A real time interpolation was achieved between each adjacent BRIR by the VBAP method [33]. The head tracker used was based on the method developed by [PERSON] et al. [34], which was adapted using Bluetooth for a wireless transmission of the position information. The head rotation was limited by visual indicators that guided the subject to reposition their head in the centre position if they exceeded the \(\pm 7.2^{\circ}\) limit.
## 3 Optimization Model and Evaluation Indexes
### Degwo-Bpnn
#### 3.3.2 Prediction Flow of DEGWO-BPNN Model  
The process of the BPNN prediction model based on the DEGWO optimization algorithm is shown in Figure 4. Firstly, the collected data samples are classified into the training set and testing set. DE algorithm parameters are initialized to generate the grey wolf initial population. The fitness function is applied to calculate the best fitness of the \(\alpha\), \(\beta\), and \(\delta\) grey wolves. The position of the parent population is updated, and the mutation population and the offspring population are determined by the three basic operations of the DE algorithm. The fitness function is used again to select a new optimal individual and update the position of the population for the \(\alpha\), \(\beta\), and \(\delta\) grey wolves. Finally, the population position of \(\alpha\) grey wolf is treated as the best position. The weight and threshold corresponding to the best position are the optimal weight and threshold. If the result satisfies the termination condition set before, the DEGWO algorithm ends the iteration operation; otherwise, it will be iterated again until the iteration reaches the maximum number. The optimal weight and threshold are transferred to the pre-set BPNN model. The training set data are continuously trained to determine the best training model. Then, the test set is used to test the prediction effect of the model. Once the deviation between the predicted result and the actual value is small, the training operation is completed when the accuracy requirement is met. If the desired outcome is not achieved during the training process, the training will continue until the maximum number of iterations is reached.