esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Analysis of PD Signal Denoising
### K-SVD Simulation  
In this paper, an initial dictionary is constructed as a zero matrix with dimensions corresponding to the signal's sampling length multiplied by a configurable dictionary size \(N\), ensuring dictionary redundancy as long as \(N\) is greater than 1. Subsequently, samples from the original PD data are randomly populated into the dictionary matrix to establish the initial learning dictionary.  
Subsequently, based on the K-SVD and the OMP algorithm described in the previous section, the dictionary is updated column by column, which constitutes the entire process of dictionary learning. The learned dictionary is then used for sparse decomposition and reconstruction of the original signal, with the results depicted in Figure 9; at the same time, it displays the comparison of the original noiseless PD signal and the signal reconstructed using the AO-VMD and K-SVD algorithms, in both the time domain and time-frequency spectrum.  
The comparison demonstrates that the noise in the PD signal has been effectively removed, and the original noiseless PD signal almost overlaps with the reconstructed signal, validating the efficacy of the proposed method in this paper.  
In the evaluation criteria for dictionaries, sparsity and reconstruction error are two important indicators. Sparsity demonstrates the dictionary's ability to represent signals with a minimal number of non-zero coefficients. A higher sparsity indicates that the signal can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IMF** & **IMF1** & **IMF2** & **IMF3** & **IMF4** \\ \hline Kurtosis & 2.7609 & 3.2024 & 2.9276 & 2.8780 \\ \hline
**IMF** & **IMF5** & **IMF6** & **IMF7** & **IMF8** \\ \hline Kurtosis & 3.2310 & 12.3905 & 1.5268 & 2.1598 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The kurtosis of IMFs.  
Figure 9: Comparison of original and reconstructed signal.  
be represented with fewer elements, thus achieving a more efficient representation. On the other hand, the reconstruction error reflects the accuracy with which the original signal can be reconstructed using the dictionary. A lower reconstruction error signifies a more accurate representation of the original signal, indicating the effectiveness of the dictionary in preserving signal integrity during compression and reconstruction processes [33]. Figure 10 compares the performance of the K-SVD and Discrete Cosine Transform (DCT) dictionaries in denoising the same noisy PD signal using these two metrics.  
The results demonstrate that the K-SVD learning dictionary is not only markedly effective in removing noise from PD signals but also exhibits superior performance compared to traditional analytical dictionaries such as DCT. This underscores the applicability and effectiveness of K-SVD in the denoising of PD signals.
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
###### Abstract  
In this article, we propose a new scheme for a fuzzy logic controller, which includes acceleration as one of its linguistic variables, as opposed to other techniques and approaches that have been developed and reported in the literature. This method is used for controlling the tracking of the trajectory followed by the joints of a 2-DoF manipulator robot. To this end, a complete simulation environment is developed through the MatLab/Simulink(r) software. The dynamic model of the manipulator robot includes a vector that consists of the estimate of the friction forces present in the joints. Then, a controller based on fuzzy logic is designed and implemented for each joint. Finally, the performance of the developed system is assessed and then compared to the performance of a classic PID controller. The incorporation of the fuzzy variable acceleration significantly improved the system's response.  
fuzzy logic controller; linguistic variables; industrial robotics; trajectory-tracking control 1
## 4 Convolutional Neural Network-Based Cooperative Spectrum Sensing  
In this paper, we exploit the strength of the convolutional neural network, a particular type of deep neural network, to design a new cooperative spectrum sensing solution for the FC to determine the state of the PU on the primary channel. The process of cooperative spectrum sensing is illustrated with the following steps:  
1. The FC trains the CNN using historical sensing data represented by the local spectrum decisions provided by the SUs.
2. At the beginning of each time slot, all the SUs are required to perform local spectrum sensing by using an energy detection method and reporting their sensing outcomes to the FC via a control channel.
3. The FC uses the new sensing data as input for the trained CNN to make a global decision about the PU state on the channel of interest, and then feeds back the final decision to the SUs.  
Figure 3: Comparison between the normal Poisson distribution and the transformed Poisson distribution (TPD) with \(e_{h,avg}=8\) and different values of \(e_{h,min}\).  
Accordingly, the problem of neural network-based cooperative spectrum sensing is divided into two important parts: local spectrum sensing by the SUs and global decision making by the FC using the trained CNN.
## 3 Topographic Feature Extraction Methods for ML Application  
### Input Data Processing Methods for Applying DNN  
The number of input nodes is equal to the length of the input vector, and the number of output nodes is the number of classes to be identified. Since the altitude information is too large to put itself as an input value of the DNN, the PMF value of the altitude information is extracted and used in the DNN as an input feature value. Both absolute and relative altitudes can be the input of the DNN. If the number of input nodes is \(N\), to obtain the absolute-altitude PMF value, divide the altitude range from 0 to the preset maximum height value into \(N\) ranges and calculate the probability value of the corresponding altitude range. Since the minimum altitude value is 0 m when the highest altitude value is \(h_{max}\), the interval for each altitude section is \(\Delta_{A}=h_{max}/N\), and the probability value corresponding to the \(n\)-th altitude section according to the absolute-altitude PMF, \(p_{A}(n)\), is expressed as Equation (1), where \(P[\cdot]\) is the probability function.  
\[p_{A}(n)=P[(n-1)\Delta_{A}\leq altitude\leq n\Delta_{A}],\ \ \ \ \ 1\leq n\leq N. \tag{1}\]  
Since there is a significant difference in the average elevation and the elevation difference by topography in each country, the elevation difference in the analysis area may be more important than the absolute elevation value. If \(h_{min,R}\) is the lowest altitude, and \(h_{max,R}\) is the highest altitude within the terrain type analysis area, the interval between each altitude section is \(\Delta_{R}=(h_{max,R}-h_{min,R})/N\), and the probability value corresponding to the \(n\)-th altitude section according to the relative-altitude PMF, \(p_{R}(n)\), is expressed as:  
\[p_{R}(n)=P[h_{min,R}+(n-1)\Delta_{R}\leq altitude\leq h_{min,R}+n\Delta_{R}],\ \ \ \ \ \ 1\leq n\leq N. \tag{2}\]  
If the absolute and relative-altitude PMFs are used simultaneously, after obtaining each PMF of \(N/2\) ranges, we can combine them as \(N\) values and apply them as the input of the DNN.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Evaluating Causes of Striping and Nonuniformities
#### 3.2.3 Electronic Effects--Hysteresis and Crosstalk
This occurs because the detectors are read out one at a time from both sides with a delay of one sample time per detector, and the aggregation mode switching produces crosstalk into the LGS readout electronics. Also, we have observed a streaking effect for very bright point sources at night, especially gas flares and lightning. We believe that this is due to some type of hysteresis effect. None of these effects should surprise anyone with experience working with CCD arrays. These temporal effects are the price one pays for the extremely high resolution and sensitivity that CCD arrays provide. In particular, the DNB CCD read-out and signal aggregation process is complicated by the many ASNs that it cycles through and by its Time Delayed Integration [36]. Timing becomes critical, and is believed to be the cause of the fixed-pattern offsets in the Earth-view [12; 15].
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Median Absolute Error (MedAE)  
\[MedAE=Median(|y_{i}-\hat{y_{i}}|) \tag{15}\]  
MedAE is another accuracy metric. It measures the median of the absolute differences between actual and predicted values. It is robust to outliers.  
**Mean Absolute Percentage Error (MAPE)** MAPE is an additional accuracy metric used to evaluate forecasting performance. It is calculated as the mean of the absolute percentage differences between actual (\(y_{i}\)) and predicted (\(\hat{y_{i}}\)) values, expressed as  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{y_{i}-\hat{y_{i}}}{y_{i}}\right| \times 100 \tag{16}\]  
MAPE provides insight into the average magnitude of errors relative to the actual values. It offers a forecasting accuracy measure that considers each prediction's proportional error.  
**Mean Squared Logarithmic Error (MSLE)**  
\[MSLE=\frac{1}{N}\sum_{i=1}^{N}(\log(1+y_{i})-\log(1+\hat{y_{i}}))^{2} \tag{17}\]  
MSLE assesses the accuracy of models for data with exponential growth patterns. It measures the average squared difference between the logarithm of actual and predicted values.  
**Root Mean Squared Logarithmic Error (RMSLE)**  
\[RMSLE=\sqrt{MSLE} \tag{18}\]  
RMSLE is a variant of MSLE. It assesses the accuracy of data with exponential growth. Lower RMSLE values indicate better accuracy.  
**Huber Loss**  
A Time Factor (TF) based on the four input features, the Huber Loss can be expressed as  
\[L_{\delta}(y,f(x))=\left\{\begin{array}{ll}0.5(y-f(x))^{2},&\text{if }|y-f(x)|\leq \delta\\ \delta(|y-f(x)|-0.5\delta^{2}),&\text{if }|y-f(x)|>\delta\end{array}\right. \tag{19}\]  
where \(L_{\delta}(y,f(x))\) represents the Huber Loss, \(y\) represents the true Time Factor (employee capability), \(f(x)\) represents the predicted Time Factor by the model based on the four input features, and \(\delta\) is the hyperparameter that controls the threshold at which the loss transitions from quadratic (squared error) to linear (absolute error) behavior.  
The choice of the \(\delta\) parameter depends on the specific problem and is a trade-off between robustness to outliers and the smoothness of the loss function. It is often determined through experimentation, cross-validation, or domain knowledge. Smaller values of \(\delta\) make the Huber Loss more robust to outliers, while larger values make it behave more like the Mean Squared Error (MSE) loss.  
**Mean Bias Deviation (MBD)**  
\[MBD=\frac{1}{N}\sum_{i=1}^{N}\frac{y_{i}-\hat{y_{i}}}{y_{i}} \tag{20}\]  
MBD assesses bias in predictions. It measures the average relative bias between actual and predicted values. A positive value indicates overestimation and a negative value suggests underestimation.
## 4 Radiomics and ML in ASCC
### Mri
This extended model demonstrated 34.8% error reduction beyond baseline clinical model in terms of disease recurrence prediction and 18.1% error reduction in terms of 2y-DFS post CRT in an independent cross validation analysis [31]. Finally, in a recent prospective study led by [PERSON] et al., 25 patients with non-metastatic ASCC underwent multiparametric 3-T MRI incorporating diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) sequences at baseline, weeks two and four of treatment, and eight weeks after the end of standard CRT. Standard radiomics and delta-radiomics, a measure of change in radiomics between several imagings, were performed on manually segmented volumes, using only first-order statistical-based features (histogram analysis of the multi-parametric maps). Local recurrence was correlated with a few apparent diffusion coefficient (ADC) metrics extracted from DW-MRI: baseline skewness (\(p\) = 0.04, ROC AUC 0.90) and standard deviation (SD) (\(p\) = 0.02, ROC AUC 0.90), week two skewness (\(p\) = 0.02, ROC AUC 0.91) and SD (\(p\) = 0.01, ROC AUC 0.94), week four kurtosis (\(p\) = 0.01, AUC 0.92) and SD (\(p\) = 0.01, ROC AUC 0.96). Delta-radiomics changes in minimum ADC between baseline and week two (\(p\) = 0.02, ROC AUC 0.94), and between baseline and week four (\(p\) = 0.02, ROC AUC 0.94) were also prognostic of local recurrence. K-trans min at second follow-up (\(p\) = 0.05, AUC 0.84) was the only DCE-MRI feature associated with local recurrence. For any recurrence, minimum ADC (\(p\) = 0.02, ROC AUC 0.87) and SD (\(p\) = 0.01, ROC AUC 0.85) at baseline, and maximum ADC (\(p\) = 0.03, ROC AUC 0.77) and SD (\(p\) = 0.02, ROC AUC 0.81) at week four were found to be of interest. After least absolute shrinkage and selection operator (LASSO) logistic regression was performed, minimum ADC and SD at baseline were retained for any recurrence. No other machine-learning algorithm was tested. The authors argue that use of higher-order radiomics is warranted and could further investigate the highlighted associations with disease recurrence [32].
## 2 Methodology  
### Overall Research Design  
The proposed watermarking system is composed of two main phases: watermark embedding and watermark extraction. The system works as follows: The watermark image and the host image are first pre-processed and then input into the watermarking system. In the embedding phase, the host image is transformed into the frequency domain by applying DTCWT. The embedding process is optimized to choose the most suitable coefficients for watermark insertion. The watermark is then inserted into the least significant bit (LSB) of the selected coefficients. In the extraction phase, the watermark image is extracted by reversing the embedding process. To evaluate the proposed approach, the similarity between the watermark image and the extracted watermark is measured by computing the normalized cross-correlation (NCC). Additionally, the imperceptibility of the proposed approach is evaluated by calculating the peak signal-to-noise ratio (PSNR) of the watermarked images. Figure 2 illustrates the framework of the proposed approach.
## 4 Discussion
### RR Measurements at Different Body Postures  
The body posture could significantly influence the lung volume [12; 31; 32; 33] and the consequent RR detection. The lung has the largest volume capacity at the sitting posture [31]. In lateral recumbent posture, lung and chest wall compliances decrease, which limits the amplitude of thoracic respiration movement [25]. Small respiratory movement amplitude could result in miscounting of RR. In stretchable RR sensors, due to the difference in strain between different postures, the accuracy of RR could be different.  
However, in this study, IJP sensor showed high accuracy at different postures. Firstly, the skin-like characteristics of the PDMS surface allow the IJP sensor to closely attach to the body surface and follow the topographical changes [34; 35]. Secondly, the polymer-based strain gauge sensors are very sensitive (gauge factor > 100) compared with typical strain gauge (2 \(\leq\) gauge factor \(\leq\) 5). Thirdly, silver nanoparticles are highly sensitive which increased the accuracy of the IJP sensor [36]. Finally, during the measurement, test subjects did not move, talk or laugh, which largely reduced the movement artefact in respiratory signals.
# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 5 Challenges and Future Directions
### Challenges
#### 5.1.3 The Lack of Consistent Evaluation Methods for the Model Qualities  
The aim of BMC is to automatically assess the model quality, with the current research focus being on the implementation process. However, the specific metrics used to evaluate the model qualities have not yet been defined. For example, the level of detail (LOD) [101] and level of geometry (LOG) [124] are used to assess the level of details in a model, with a higher LOD indicating richer information. However, the models with a higher LOD are not necessarily superior to those with a lower LOD, as different applications require different levels of detail. For instance, when considering BIM or CIM models for city-level disasters, only LOD 100 or 200 is required, instead of LOD 400, which is used for fabrication [185]. The lower LOD models have lower computational and storage costs, provided meeting application requirements. On the other hand, the consistency of BIM is sometimes defined as the consistency between BIM models and 2D drawings, sometimes as the consistency across multiple stages of a BIM model, and sometimes as the consistency between BIM models and physical buildings.  
Most studies defined evaluation metrics for BMC research as the percentage of rules that are correctly identified. However, in some studies related to machine learning, some classic metrics, such as accuracy, precision, and F1 score are more heavily focused on. In the presence of multiple dimensions of measurements coexisting, the measurement of the weights for each dimension has not been thoroughly researched. This has resulted in the absence of a unified standard for measuring the qualities of BMC systems, making it difficult to fairly compare the outcomes of different studies.
## 5 Conclusions  
This research proposed, fabricated and tested a new optics, the QFP prism for simultaneously paralleling multiple diffraction beams for grating interferometry application. To get a compact and effective QFP prism we calculated the angle \(\alpha\), the key parameter of the prism, according to the wavelength, the reflective index and grating patch, and designed a QFP prism with \(\alpha=12.924^{\circ}\). The fabricated QFP prism was tested in a grating ruler system to evaluate the prism performance in terms of shape parameters and optical functionality. The testing results preliminary proved that the design and fabrication process are feasible. The less complexity in optical configuration and assembly procedure with usage of the QFP prism will be beneficial for mass production of this kind of grating rulers.
## 1 Introduction  
In September 2015, the 2030 Agenda for Sustainable Development was signed by 193 United Nations (UN) countries. Specifically, the 14 th goal (Life below water) is dedicated to: \"Conserve and sustainably use the oceans, seas and marine resources for sustainable development\". In the targets defined by the 2030 Agenda to reach goal 14, the target 14.a aims to: \"Increase scientific knowledge, develop research capacity and transfer marine technology, taking into account the Intergovernmental Oceanographic Commission Criteria and Guidelines on the Transfer of Marine Technology, in order to improve ocean health and to enhance the contribution of marine biodiversity to the development of developing countries, in particular small island developing States and least developed countries\" [1]. Furthermore, 2021-2030 was declared by the United Nations as the \"Decade of Ocean Science for Sustainable Development\" and among the social outcomes to be achieved is \"Data Transparency and Accessibility\", which aims to achieve an ocean with open access to data, information and technologies for nations, stakeholders and citizens [2].  
Unfortunately, obtaining data and information from underwater environments with proprietary instrumentation is often difficult. In particular, bathymetric data, which are the subject of this work, are expensive data in terms of instrumentation, specialised staff, logistics, time and mode of execution [3; 4; 5; 6]. These elements may be an obstacle to achieving the sustainability goals of the 2030 Agenda and the Decade of the Ocean. However, the emergence in the consumer market of new smart and Internet of Things (IoT) technologies for mapping aquatic and marine environments is an opportunity to disseminate low-cost instrumentation for research purposes, democratisation and access to data even in remote areas, and involvement of the population in citizen science projects.  
For these reasons, this paper intends to investigate the Single Beam Echo Sounders (SBES) sonar (SOund Navigation And Ranging) produced by Deeper UAB. These SBES, called Deeper Smart Sonar, are inexpensive, lightweight devices that are easy to use even by non-experts, can be mounted on Unmanned Aerial Vehicles (UAVs)/Unmanned Surface Vehicles (USVs) and, as will be seen in the literature review section, found some interest in scientific applications.
## 3 Results and Discussion
### Analysis of Time Series of Inflow to the Volga-Kama Cascade Reservoirs  
To assess the changes that have occurred, all the studied inflow series (flood inflow, summer low-water inflow, winter low-water inflow, pre-flood drawdown) were divided into two parts in accordance with the selected state change point, and each part was processed by applying the Kritsky-Menkel distribution. The calculation results are presented \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistics** & **Annual** & **HW** & **SLW** & **WLW** & **PFD** \\ \hline Mean (mln m\({}^{3}\)) & 234,688 & 150,750 & 56,310 & 18,552 & 9077 \\ \hline SEM & 5627 & 4139 & 2263 & 727 & 581 \\ \hline Median & 237,862 & 150,467 & 53,481 & 17,789 & 7267 \\ \hline Standard deviation \(\sigma_{B}=\sqrt{\,D_{B}}\) & 44,662 & 32,849 & 17,960 & 5767 & 4610 \\ \hline Sample variance \(D_{B}\) & 1,994,655,640 & 1,079,041,861 & 322,556,913 & 33,255,047 & 21,252,457 \\ \hline Kurtosis & 0.33 & \(-\)0.34 & 0.31 & 2.28 & 0.54 \\ \hline Skewness & 0.15 & 0.15 & 0.74 & 1.33 & 1.20 \\ \hline Minimum (mln m\({}^{3}\)) & 141,759 & 83,444 & 28,271 & 10,486 & 3981 \\ \hline Maximum (mln m\({}^{3}\)) & 360,820 & 234,826 & 106,056 & 38,481 & 22,003 \\ \hline Sample size \(N\) & 63 & 63 & 63 & 63 & 63 \\ \hline
**\% annual runoff** & & **64\%** & **24\%** & **8\%** & **4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The distributions’ numerical characteristics for the 1916–1978 series. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistics** & **Annual** & **HW** & **SLW** & **WLW** & **PFD** \\ \hline Mean (mln m\({}^{3}\)) & 275,250 & 152,656 & 75,661 & 33,465 & 13,468 \\ \hline SEM & 5668 & 4174 & 2897 & 1180 & 940 \\ \hline Median & 269,833 & 154,065 & 71,498 & 31,784 & 11,403 \\ \hline Standard deviation \(\sigma_{B}=\sqrt{\,D_{B}}\) & 36,734 & 27,048 & 18,776 & 7650 & 6089 \\ \hline Sample variance \(D_{B}\) & 1,349,366,302 & 731,577,879 & 352,551,273 & 58,528,168 & 37,077,255 \\ \hline Kurtosis & 1.23 & \(-\)0.14 & 0.49 & 0.73 & 4.36 \\ \hline Skewness & 0.46 & \(-\)0.04 & 0.92 & 1.10 & 1.98 \\ \hline Minimum (mln m\({}^{3}\)) & 197,877 & 91,738 & 46,836 & 22,824 & 6222 \\ \hline Maximum (mln m\({}^{3}\)) & 374,650 & 208,240 & 130,728 & 54,563 & 35,438 \\ \hline Sample size\(N\) & 42 & 42 & 42 & 42 & 42 \\ \hline
**\% annual runoff** & & **55\%** & **27\%** & **12\%** & **5\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The distributions’ numerical characteristics for the 1979–2020 series. in Figure 9 by the reliability graphs of inflow and in Table 9 and Figure 10 in the form of distribution quantiles. The data show a significant increase in annual inflow, summer-autumn low water, winter low water, and pre-flood drawdown. The flood volume has not changed much. In  
Figure 10: The volume of inflow for two long-term series by seasons and years of different reliability by inflow.