esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper addresses the two-dimensional (2D) direction-of-arrival (DOA) estimation issue for signals with known waveforms but unknown amplitudes using uniform circular array (UCA). Unlike maximum likelihood (ML) methods such as decoupled maximum likelihood (DEML), parallel decomposition (PADEC) and so forth, which estimate DOA by spectrum peak search, we propose an efficient interferometer-based method with known waveforms. The proposed method first estimates spatial signature matrix based on ML method whose each column contains 2D DOA information corresponding to a source. Then, an interferometer procedure is performed to obtain 2D DOA estimate of each source from a closed-form solution separately and in parallel. Several simulation results show that the proposed method can achieve a significantly improvement in performance that coincides with the ML method as well as Cramer-Rao Bound (CRB), especially under some poor conditions, such as low SNR, fewer sensors or small snapshots. In addition, the performance will not degrade as the number of sources increases if the source signals are uncorrelated with each other. Meanwhile, it reduces a great amount of computational complexity without loss of too much accuracy. Thus, the proposed method is quite suitable for 2D DOA estimation with known waveforms in practice.  
two-dimensional direction of arrival; known waveform; uniform circular array; interferometer; Cramer-Rao Bound +
Footnote †: journal: Applied  
Article
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results  
The experimental results are divided into three parts. In the first part, a comparison of different feature point detectors is presented with objective to identify the best ones for VS-IR images. Repeatability score and number of correspondences are used as performance metrics for this comparison. The repeatability score is defined as a fraction of feature points, identified as corresponding feature points between two images. In the second part, the two best detectors are paired with seventeen different descriptors one by one in order to find the best detector-descriptor combinations for VS-IR images. In the third part, the proposed Reg-SIFT is computed with LR, DTR, RFR, SVMR and MLPR regression techniques for comparison and the best regression technique is selected for subsequent comparisons where the Reg-SIFT is compared with the best detector-descriptor pair of the second part.
## 3 Homotopy DCD with Non-Convex Regularization Algorithms
**Proposition 2**: Remove the \(t\)-th (\(t\in I\)) element from the support \(I\) according to the rule  
\[t=\arg\min_{k\in I}\frac{1}{2}\big{|}x_{t}\big{|}^{2}\ R_{x_{t}}+\Re\big{\{}x_{t }^{*}b_{t}\big{\}}-\tau\frac{\big{|}x_{t}\big{|}}{1+\frac{a}{2}\big{|}x_{t} \big{|}}\quad sI.\ \frac{1}{2}\big{|}x_{t}\big{|}^{2}\ R_{x_{t}}+\Re\big{\{}x_{t }^{*}b_{t}\big{\}}-\tau\frac{\big{|}x_{t}\big{|}}{1+\frac{a}{2}\big{|}x_{t} \big{|}}<0 \tag{6}\]  
We now prove Proposition 1. _Proof:_ Let the \(t\)-th element \(x_{t}=0\) be activated as \(\hat{x}_{t}=\alpha=|\alpha|e^{jarg\{\alpha\}}\), and the updated solution vector is denoted as \(\hat{\mathbf{x}}\). The update of the cost function Equation (3) is then given by:  
\[\Delta J=J\big{[}\hat{\mathbf{x}}\big{]}-J\big{[}\hat{\mathbf{x}}\big{]}=\frac {1}{2}\big{|}\alpha^{2}\ R_{x_{t}}-\Re\big{\{}\alpha^{*}b_{t}\big{\}}+\tau \frac{\big{|}\alpha\big{|}}{1+\frac{a}{2}\big{|}\alpha\big{|}} \tag{7}\]  
The cost function is reduced if \(\Delta J<0\). For a fixed \(|\alpha|\), \(\Delta J\) achieves a minimum value if \(\arg\{\alpha\}=\arg\{b_{t}\}\), and in this case:  
\[\Delta J=\frac{1}{2}\big{|}\alpha\big{|}^{2}\ R_{x_{t}}-|\alpha|b_{t}|+\tau \frac{|\alpha|}{1+\frac{a}{2}\big{|}\alpha\big{|}} \tag{8}\]  
Let:  
\[\Delta J^{\prime} =4\bigg{(}1+\frac{a}{2}\big{|}\alpha\big{|}\bigg{)}\Delta J\] \[=\big{|}\alpha\big{|}\bigg{\{}aR_{x_{t}}\big{|}\alpha\big{|}^{2}+ 2\big{(}R_{x_{t}}-a|b_{t}|\big{)}\big{|}\alpha\big{|}+4\big{(}\tau-|b_{t}| \big{)}\bigg{\}}\] \[=aR_{x_{t}}\big{|}\bigg{\{}\bigg{[}\big{|}a\big{|}+\frac{R_{x_{t} }-a|b_{t}|}{aR_{x_{t}}}\bigg{]}^{2}-\bigg{(}\frac{R_{x_{t}}-a|b_{t}|}{aR_{x_{t }}}\bigg{]}^{2}+\frac{4\big{(}\tau-|b_{t}|\big{)}}{aR_{x_{t}}}\bigg{\}} \tag{9}\]  
For \(|\alpha|>0\), if \(|\alpha|=-\frac{R_{x_{t}}-a|b_{t}|}{aR_{x_{t}}}\), we have:  
\[\Delta J^{\prime}=\frac{1}{aR_{x_{t}}}|\alpha|\Big{\{}aR_{x_{t}}\tau-\big{(}R _{x_{t}}+a|b_{t}|\big{)}^{2}\Big{\}} \tag{10}\]  
If \(\Delta J^{\prime}<0\), then \(4 aR_{t,t}\tau-\big{(}R_{t,t}+a|b_{t}|\big{)}^{2}<0\). Thus, there exists a value of \(\alpha\) that results in \(\Delta J<0\), _i.e._, in reducing the cost function. It is seen from the last expression that if we want to add a new element to the solution vector, the index \(t\) of the element should correspond to the maximum of \(\frac{a|b_{t}|-R_{x_{t}}}{aR_{x_{t}}^{2}}\Big{\{}\big{(}R_{t,t}+a|b_{t}|\big{)} ^{2}-4 aR_{t,t}\tau\Big{\}}\) over \(t\in I^{c}\). In this case, we will obtain the largest decrement of the cost function.
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 5 The Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
### Experimental Verification
Figure 10 shows the spectrum diagram of the original signal and reconstructed signal with the rolling element fault. Parameter settings used in the DE algorithm consisted of NP = 20, F = 0.5, and CR = 0.75 [37]. The performance of the OSELM algorithm is affected by the choice of activation function. Therefore, to shorten the training time, which is twice as long as that of OSELM with RBF activation [32], the Sigmoid function was selected as the activation function for OSELM. The number of nodes in its hidden layer and bias range were set to 25 and [0, 1], respectively. The input weight was constrained to [\(-\)1, 1]. An amount of 50 training data were used initially, and each learning data block in the following step contained 40 data. The OSELM and DE-OSELM were implemented to investigate the robustness of the latter, with 30 experiments conducted for each group. The experiment results presented in Figure 11 demonstrate the superior classification accuracy of DE-OSELM, as compared to OSELM, indicating better robustness. To substantiate the efficacy of this approach, the results obtained from Fixed Parameter <8, 2000> VMD and MFO-optimized VMD were fed into several machine learning models, namely, ELM, OSELM, KNN, and DE-OSELM, to recognize and classify the data. We repeated the experiment 30 times to ensure its reliability. Classification outcomes of the proposed approach, as well as the other three methodologies, are showcased in Table 5. The results demonstrate that the accuracy of classification achieved through the proposed  
Figure 11: Comparison of the DE-OSELM and OSELM classification results. Figure 10: (**a**) original signal; (**b**) reconstructed signal. method is notably better than the other three methods, affirming the efficacy and superiority of the proposed method.
## 3 Overview of 3 GPP AKMA  
In the evolving mobile communications landscape, 3 GPP introduces a comprehensive framework called AKMA. AKMA leverages the robust authentication infrastructure of 5G to ensure that only legitimate user equipment (UE) can access application services, i.e., 5G cAPPs. It aims to protect the integrity, confidentiality, and authenticity of data exchanged between UEs and 5G cApps through mutual authentication and key exchange. AKMA operates on principles similar to those used by the generic bootstrapping architecture (GBA) in traditional mobile networks [9].  
AKMA is deeply integrated into the 5G security architecture, underpinned by a robust and multi-layered framework. This architecture's heart is the primary authentication process, which establishes mutual trust between the UE and the 5G core network. This process involves the Authentication Server Function (AUSF) and Serving Network Authentication Function (SEAF) in collaboration with the Home Public Land Mobile Network (HPLMN). By utilizing the Subscription Concealed Identifier (SUCI) to protect the Subscription Permanent Identifier (SUPI) and employing the Authentication and Key Agreement (AKA) protocol, the primary authentication process ensures the confidentiality of user identities, prevents unauthorized access, and generates session-specific keys for securing Non-Access Stratum (NAS) and Access Stratum (AS) communications. This foundational mechanism safeguards network connectivity and serves as the prerequisite for subsequent security operations, including AKMA.  
AKMA functions as a secondary authentication mechanism that operates after the completion of the primary authentication process. Building on the secure session established during primary authentication, AKMA enables application-specific authentication and key management, facilitating secure communication between the UE and 5G cApps. By reusing the primary authentication process for implicit authentication, AKMA minimizes redundancy while ensuring high levels of security for application-layer interactions.  
To address security threats and fulfill its intended purpose, the AKMA design in 5G must incorporate several security and functionality requirements. These requirements include establishing a secure authentication framework to ensure that only legitimate UEs can access AKMA application services and incorporating an AKMA Anchor Function (AAnF) in the 5G core for UE authentication and key management. Mutual authentication between the UE and the AAnF should be based on 3 GPP 5G credentials generated from the existing 5G authentication framework [10]. At the same time, interfaces within the AKMA architecture must support confidentiality, integrity, and replay protection. The key management design should also prevent the compromise of existing keys used for primary authentication and NAS/AS security. It is also essential to use different keys for different application providers to prevent a compromised application traffic tunnel from affecting others.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 4 Results and Discussion
### Statistical Analysis
The figure states that the proposed scheme has the lowest entropy values [24; 25; 27; 44]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{5}{c}{**Values**} \\ \cline{2-6}  & **Proposed** & **[PERSON]**[24] & **[PERSON]**[44] & **[PERSON]**[25] & **[PERSON]**[27] \\ \hline Entropy & 0.0032 & 0.0077 & 0.9973 & 1.4735 & 0.4783 \\ Standard Deviation & 3.4406 & 4.9651 & 5.9980 & 4.9960 & 3.9998 \\ Correlation coefficient & 0.9999 & 0.9969 & 0.9973 & 0.9981 & 0.9979 \\ Cosine Similarity & 0.9989 & 0.9893 & 0.9979 & 0.9966 & 0.9977 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Aggregated values of statistical features of various schemes. Histogram analysis is also another method to detect the difference between stego and cover image. Histograms are frequently used in statistics to show the frequency of a particular type of variable within a given range. Figures 13 and 14 show the histogram analysis of the original images and associated stego images, respectively. As a result, it can be inferred from the outcomes of these experiments that the differences between cover and stego are small and the proposed technique is robust enough to defend against attacks on implanted data. Figure 11: PDH plots of Lena for the proposed technique. Figure 12: PDH plots of Baboon for the proposed technique. \(t\)-test is another important statistical tool that is used to perform a hypothesis on one or a pair of datasets. It, indeed, has the capability to assess whether there is a significant difference between the means of two populations. That feature inspires us to use it in our statistical analysis to detect the tolerance level of changes by our stego image. Here, we have compared the mean pixel intensities of a cover image and a stego image using a two-sample \(t\)-test. The equation of \(t\)-test is given in Equation (26)  
Figure 14: Histogram analysis plots of Wheel for the proposed technique. Figure 13: Histogram analysis plots of Wheel for the proposed technique. \[t=\frac{\frac{\frac{\sum(D)}{N}}}{\sqrt{\frac{\sum D^{2}-\frac{\sum(D)^{2}}{N-1}}{N -1}}}; \tag{26}\]  
Here \(D\) is a matrix of differences of pixel values of cover and stego images, \(N\) means degree of freedom. A high \(t\)-value shows a large discrepancy between the two sets of pixel values, pointing to the possibility that the suspicious image contains secret data. The result of \(t\)-test is shown in Table 4. This table shows that, in most cases, the scheme generates a \(t\) value that is smaller than 1.646. From the T-distribution table, we can infer that these images accept the null hypothesis at 5% confidence level. On the contrary, 40% of images reject the null hypothesis at 5% confidence level.
# Fibers of Failure: Classifying Errors in Predictive Processes
## 2 Materials and Methods
### Statistical Modeling
#### 2.4.2 Kolmogorov-Smirnov (KS) Statistic  
The KS statistic can be used to measure dissimilarity between the cumulative distribution functions (CDF) of two samples. This is specifically known as the two-sample KS test and gives the maximum difference between the two distributions [65]. The KS-test is a non-parametric statistical test which is favorable, since many of the parameters governing the EAF process are of varying classes of distributions [66].  
To perform a KS test, the KS-value has to be calculated by using the null hypothesis, \(H_{0}\); i.e., that the two samples have the same distribution. The confidence level, i.e., p-value, is the probability that the two samples come from the same distribution. The KS-value can have values in the range \([0,1]\), where 0 indicates that the distributions of respective sample are identical and where 1 indicates that the distributions are totally different. Hence, a low p-value in tandem with a high KS-value is a strong indicator that the two samples are different  
The two-sample KS test calculation proceeds as follows:  
\[D_{n_{1},n_{2}}=\sup_{x}|U_{n_{1}}(x)-V_{n_{2}}(x)| \tag{5}\]  
where \(U_{n_{1}}\) and \(V_{n_{2}}\) are the two distribution functions. \(n_{1}\) and \(n_{2}\) are the number of instances in each sample from the two distributions, respectively. \(x\) is the total sample space. sup is the supremum function. \(D_{n_{1},n_{2}}\) is illustrated in Figure 3.  
\(H_{0}\) is rejected if the following condition is satisfied:  
\[D_{n_{1},n_{2}}>c(\alpha)\sqrt{\frac{n_{1}+n_{2}}{n_{1}\cdot n_{2}}} \tag{6}\]  
where \(\alpha\) is a pre-determined significance level and \(c\) is the threshold value calculated using \(\alpha\) and the cumulative KS distribution [67].  
The KS-test is used by FiFa to present the variables that separate a specific error group from the rest of the data. Thus, one of the samples will be from the specific error group and the other sample will be from the rest of the data. Using the KS-test on each variable will show the variables whose distributions are the most different between the two samples.  
The main drawback of the KS-test is that it reduces the difference between the two distributions to the point of maximum difference of the CDF. Hence, the maximum difference may not be representative over the complete distribution space. To combat this shortcoming in the analysis, the plotted distributions of the two samples will be provided as a complementary tool.
## 3 The Human Role in Teleoperation
### System Latency
In recent developments, they enabled reducing its tracking subsystem latency to 2 ms, allowing more time for the overall system lag. The Oculus Rift CV1 [71] and HTC Vive [72] headsets have a refresh rate of 90 Hz, meaning that they can update their displays every 11 ms.  
Human performance studies show that a person can detect latency from 10-20 ms [65]. Lags occur in teleoperation of mobile robots or robotic arms when information is transmitted across a communication network (i.e., end-to-end latency). When the latency is higher than 1 s, the operator tends to change his/her control approach to \"move and wait\", rather than continuously commanding, predicting, and trying to compensate the delay [73]. The time delay factor may differently affect the performance of a specific task. The negative effect extends to over-actuation for the variable delay, affecting robot-to-operator direction information flow more than the other way. In a telemanipulation task related to laparoscopy surgery, negative effects were observed in the system usability and the performance of experienced surgeons for a delay \(\geq\)105 ms [74]; in another task related to telesurgery (precision, cutting, stitching, knotting), a degradation in accuracy, precision, and performance was reported for a delay \(\geq\)300 ms [75]; in a driving simulation task, a performance degradation was observed for a delay \(\geq\)170 ms [76]; in social interaction with mobile telepresence robots, the recommendation for teleoperation commands' latency is under 125 ms [7]; in a real teledriving task (six wheel all-terrain rover), evaluating the average speed and the average time stopped for path decision, the negative effect appeared for latency \(\geq\)480 ms [51]; in a car teledriving task on city roads at 30 Km, while analyzing the tracking line, obstacle detection, and performance, problems for delays \(\geq\)550-600 ms were reported [77; 78]. The mitigation of the effects of latencies related to visual feedback in teleoperation systems may involve a predictive display. The goal is to provide immediate visual feedback to the operator, displaying a scene model animated by the commands. Meanwhile, and in parallel, this scene model is updated with measures acquired by the remote teleoperator sensors [79; 80].
## 4 Numerical Experiment
### Impact of Clustering Parameter Selection on Separation Performance in the k-Means Algorithm
Reconstructing diffraction profiles can be achieved by zeroing out different clusters (singular values) when the total number of clusters is 9, 11, and 15. The specific clusters and their corresponding singular values set to zero are listed in Table 3. Through testing and analysis, it can be found that the diffraction separation is most effective in cases 3, 8, and 20, which correspond to the total cluster numbers of 9, 11, and 15, respectively, as shown in Figure 17, where the reconstructed diffraction and reflection profiles exhibit minimal differences. Despite the differences in the total number of clusters set to zero in these three cases, the initial 9 singular values are identical. Therefore, for the MSSA method, the total number of clusters in the \(k\)-means clustering algorithm is recommended to range between 9 and 15. In this sense, the value of \(k\) is typically optimized based on the complexity of the data and the wavefield characteristics. Figure 16: Comparison of the single trace waveforms of the diffraction’s vertex (**a**) and wings (**b**) extracted from Figure 15 (In (**a**), the left arrow indicates the reflection removal effect under different cases, while the right arrow indicates the energy retention effect of the diffraction vertex, and in (**b**), the arrow indicates the energy retention effect of the diffraction wave wings under different cases). Figure 17: Optimal diffraction profiles reconstructed with different total numbers of clusters. (**a**,**b**) show the diffraction and reflection profiles separated in Case 3 with a total of 9 clusters. (**c**,**d**) show the diffraction and reflection profiles separated in Case 8 with a total of 11 clusters. (**e**,**f**) show the diffraction and reflection profiles separated in Case 20 with a total of 15 clusters.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 2 LIDAR Theory  
LIDAR technology is used in various operational contexts, which are adapted to the desired measurement. For the measurement of the wind resource in the atmospheric boundary layer, two categories are distinguished: Pulsed and Continuous-Wave (CW) LIDARs [18]. Both technologies are coherent LIDARs measuring the speed of particles within the atmosphere using the Doppler effect. The principle of coherent LIDARs is to mix the radiation backscattered by the particles, considered as passive tracers, with that of the local reference beam in order to reliably calculate the frequency of the backscattered signal with a light beating process and finally, to deduce the wind speed at a given position [9]. In this study, we focus on the pulsed LIDAR technology and more specifically on the profiler LIDAR designed by Leosphere(r): WindCube v2(r).
## 2 Materials and Methods  
Radon concentration measurements were performed using an Airthings Corentum Pro airborne instrument. Corentum Pro contains four high-precision radon chambers operating in parallel for maximum reliability, accuracy and speed. Instrument samples indoor air through a passive diffusion chamber, using alpha spectrometry to accurately calculate radon concentration. Radon is detected using silicon photodiodes to both count and measure the energy of alpha particles resulting from the decay chain of radon gas. The instrument is calibrated to reference instruments in accredited laboratories and is American Association of Radon Scientists and Technologists-National Radon Proficiency Program (AARST-NRPP) certified. The standard deviation from the exact radon level is less than 7% + 5 Bq/m\({}^{3}\) after 24 hours and less than 5% + 2 Bq/m\({}^{3}\) after 7 days [12]. The instrument is also equipped with sensors for temperature (0.2 \({}^{\circ}\)C resolution, accuracy \(\pm\)0.5 \({}^{\circ}\)C), relative humidity (0.5% resolution, accuracy \(\pm\)4.5%), atmospheric pressure (0.06 kPa resolution, accuracy \(\pm\)1 kPa).  
Measurement of indoor radon concentration was performed continuously at 15 randomly selected locations in the area of northern Croatia (Figure 1a) from July 2018 to July 2019. Figure 1b shows the results of previous radon concentration measurements in Croatia [5; 6]. In this work, radon concentration measurement was performed in objects separated into four categories: residential buildings and family houses as the most occupied, office buildings that are occupied during week (working hours), as well as weekend cottage and small family wine cellar houses that are occupied mainly at weekends. Short-term measurements were used to provide an indication of actual radon concentrations, mainly at intervals of four to eight days at two places (basement room and the ground floor/first-floor room) with one-hour resolution. Instrument was positioned away from doors and windows as well away from radiation sources, in order to catch radon in air in these places without interference. A single measurement was performed in basement where radon is expected to reach its highest concentration and in one room that is often most occupied with the least airflow in the whole building. Room ventilation is reported by occupants. For buildings that are only occupied for part of the day (office buildings), radon is measured during occupied periods and weekends to determine if there are significant diurnal radon variations. The studied samples with description of locations and object are summarized in Table 1.  
Figure 1: (a) Indoor radon measurement locations for randomly selected buildings in Northern Croatia. (b) National map of indoor radon levels in Croatia [5; 13].