esa-sceva/satcom-chunk-collection huggingface dataset:
# A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space-Time Block Code
## 3 Proposed Algorithm  
### Estimation of the Noise Variance  
Taking the squared Euclidean norm and mathematical expectation of both sides of (1), the autocorrelation of the received signal is obtained as  
\[\mathrm{E}\{\mathbf{r}^{H}\mathbf{r}\} =\mathrm{E}\{(\mathbf{H}\mathbf{x}+\mathbf{w})^{H}(\mathbf{H} \mathbf{x}+\mathbf{w})\},\] \[=\mathrm{E}\{\mathbf{x}^{H}\mathbf{H}^{H}\mathbf{H}\mathbf{x}+2 \mathbf{x}^{H}\mathbf{H}^{H}\mathbf{w}+\mathbf{w}^{H}\mathbf{w}\}, \tag{7}\]  
where \(\mathrm{E}\) means the expectation of random variables. If the noise \(\mathbf{w}\) and the signal \(\mathbf{x}\) are uncorrelated, the second term of the right hand side is neglected. (7) is rewritten as  
\[\mathrm{E}\{\mathbf{r}^{H}\mathbf{r}\} =\mathrm{E}\{\mathbf{x}^{H}\mathbf{H}^{H}\mathbf{H}\mathbf{x}\}+ \mathrm{E}\{\mathbf{w}^{H}\mathbf{w}\}\] \[=\mathrm{E}\{\mathrm{Tr}\{\mathbf{D}\mathbf{x}^{H}\}\}+4\sigma_{w }^{2}, \tag{8}\]  
where \(\mathrm{Tr}\) means the trace of the matrix. In terms of the noise variance, (8) is rearranged as  
\[4\sigma_{w}^{2}=\mathrm{E}\{\mathbf{r}^{H}\mathbf{r}-\mathrm{Tr}\{\mathbf{D} \mathbf{x}^{H}\}\}. \tag{9}\]  
If the modulated input signal occurs with equal probability, then the expectation of cross-correlation between \(x_{i}\) and \(x_{j}\) for \(i\
eq j\) is zero, i.e., \(\mathbf{x}\mathbf{x}^{H}\approx\sigma_{x}^{2}\mathbf{I}\). By using this circumstance, the estimation of the noise variance is calculated as  
\[\sigma_{w}^{2}\approx\frac{\mathrm{E}\{\mathbf{r}^{H}\mathbf{r}-4\sigma_{x}^{ 2}\alpha\}}{4}. \tag{10}\]The expected value of the signal is estimated using a moving-average method [12]. Therefore, the noise variance is obtained as  
\[\hat{\sigma}_{w}^{2}(n)=\zeta\hat{\sigma}_{w}^{2}(n-1)+(1-\zeta)\frac{\mathbf{r}^ {H}\mathbf{r}-4\sigma_{x}^{2}\alpha}{4}, \tag{11}\]  
and \(\zeta\) (\(0\leq\zeta<1\)) is a forgetting factor. However, several samples of \(\sigma_{w}^{2}\) may be negative in early stages because of the fluctuation of an estimate. In this case, \(\gamma\) cannot be calculated. To solve this drawback, (11) is modified as  
\[\hat{\sigma}_{w}^{2}(n)=\begin{cases}\hat{\sigma}_{w}^{2}(n-1)&\text{ if }\hat{\sigma}^{2}(n)<0\\ \zeta\hat{\sigma}_{w}^{2}(n-1)+(1-\zeta)\frac{\mathbf{r}^{H}\mathbf{r}-4\sigma _{x}^{2}\alpha}{4}&\text{others}\end{cases}. \tag{12}\]
## 3 Methodology
### Construction of Reference MD Space  
MD space is essentially the distance between an unknown data point and a given population, indicating the similarity between them. The construction of a good reference MD space is crucial in order to obtain good classification performance. For instance, the reference MD space in MTS is constructed on the basis of a supervised learning process, using a given set of normal observations. A threshold to distinguish the two classes is then properly defined and used to classify the unknown samples. If the observed MD is less than the threshold, the unknown sample has statistically similar characteristics to the normal population of the samples.  
The main objective of this paper is to investigate the potential of constructing the reference MD space not only by considering normal samples but also abnormal samples, as well as a combination of them. Three different thresholds will be introduced based on three different reference MD spaces. Classification performance on the testing dataset is then compared and evaluated for these three different MD spaces.  
The three different classification scenarios are schematically presented in Figure 2. Figure 2 illustrates MD values based on a reference MD space that is constructed by considering only normal observations. The green triangles and red circles represent normal and abnormal observations, respectively. By introducing a suitable decision threshold, indicated as a blue dashed line, the two categories (normal/abnormal) of the given samples can be differentiated. Figure 2 presents the classification scenario by employing a reference MD space that considers only abnormal observations. Scenario 3, shown in Figure 2, employs a reference MD space that is constructed by using both normal and abnormal observations. As such, this approach enables us to introduce a two-dimensional linear decision boundary.  
In the following context, \(MD^{normal}\) and \(MD^{abnormal}\) stand for the Mahalanobis distance based on normal and abnormal observations, respectively.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 4 Cognitive Radio
### Cognitive Radio Network  
CR network (CRN) is a network that comprises more than one CR device equipped with cognitive capability and reconfigurability, which intercommunicate by changing their transmitter parameters based on their respective interaction with the radio operating environment [74]. There are three different CRN schemes namely: Interweave CRN, Underlay CRN, and Overlay CRN, briefly discussed as follows:  
1. **The Interweave CRN :** The Interweave CRN enables a CR device to use spectrum only if no PU is active. Thus, PU activities are constantly being monitored to prevent interference from CR transmissions.
2. **The Underlay CRN:** In the Underlay CRN, CR devices transmit with low power in the presence of PU activities. However, CR power is strictly ensured below a predefined noise/temperature level to prevent interference [75]. Ultra wide band communication approaches are typically used in Underlay CRN scheme.
3. **The Overlay CRN:** In the Overlay CRN, CR devices use code books and messages to identify the PU, and then mitigate interference by relaying their messages based on a difference code book [75]. The CR power level is typically not an issue of concern in the Overlay scheme.  
CRNs are indeed to be beneficial to the development of IoT in general, and towards enhancing the performance of LPWAN technologies. Consequently, in subsequent sections, we shall discuss the burgeoning concept of CR in LPWAN.  
Figure 2: The cognitive radio system.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas
### SAR Requirement  
As explained in Section 2.6, SAR is used as a metric to guarantee the safety of biological tissues in the event of severe electromagnetic exposure. The standard SAR levels are maintained by IBBDs by considering low output power. In general, the specific absorption (\(SA\)) per pulse can be calculated by [87]  
\[SA=SAR\times T_{p} \tag{13}\]  
where \(T_{p}\) represents the pulse duration. The EM power absorbed by the biological tissue medium can raise the temperature of the tissue. It must be noted that the temperature of the biological tissues adjacent to the implanted device should not rise more than 1-2 \({}^{\circ}\)C.
# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
## 7 Conclusions  
This paper has proposed an on-line approach to predict the remaining useful life (RUL) of power connectors, intended to ease and simplify the application of predictive maintenance plans. This simple on-line strategy, while presenting a reduced computational burden, allows forecasting the electrical behavior of the connector, thus enabling planning when the connector needs to be replaced because it has reached its end-of-life (EOL). The proposed strategy is based on monitoring the electrical resistance of the connectors, since it is a reliable indicator of their health condition. The proposed method predicts the RUL of the connectors based on the degradation path of the electrical resistance. The RUL of power connectors is determined by applying the autoregressive integrated moving average (ARIMA) method to the electrical resistance time series data. From experimental results based on heat cycle tests, which were used to force an accelerated degradation of the connectors, it has been shown that both AR (autoregressive) and ARMA (autoregressive moving average) models offer both simplicity and accuracy in predicting the RUL of the power connectors. Although accelerated ageing tests were performed to acquire reliable degradation data, in a real application, they are not required because the proposed method is based on the on-line data acquired for each connector. In addition, the RUL calculated by the proposed method is adapted to each particular connector, because it only considers the time series data of the particularly analyzed connector.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Ministerio de Ciencia, Innovacion y Universidades de Espana, grant number RTC-2017-6297-3, and by the Generalitat de Catalunya, grant number 2017 SGR 967.  
The authors declare no conflict of interest.  
Figure 6: Autoregressive integrated moving average (ARMA) (\(p=2\), \(q=2\)) RUL prediction of the analyzed connectors (#1 to #7) for time instants (**a**) 1, (**b**) 2, and (**c**) 3.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Results
#### 3.5.5 Energetic Masking  
The consequences of choosing a specific adjustment level on speech audibility in noise can be assessed by estimating the amount of energetic masking at each frequency resulting from that specific adjustment choice. Figure 4 plots the proportion of glimpses of the target speech at each frequency as a function of the adjustment level. Glimpses were computed using the extended glimpse proportion metric defined in Tang and Cooke [32]. It is evident from this visualisation that a listener's reallocation strategy in the face of a speech-shaped noise masker is to make adjustments that ensure the availability of speech glimpses in the frequency region above 1 kHz. This is clearly seen for the low and high bands and for spectral tilt, where attenuation and boosting, respectively, lead to a high glimpse density in the 2-4 kHz region in particular. The middle 1-2 kHz band represents an interesting compromise; here, many listeners chose to keep the energy level close to that of the original speech (as evidenced by Figure 3), presumably because boosting this band leads to a loss of glimpses at higher frequencies, while attenuation conversely produces a paucity of glimpses in the central frequency region itself. The average preference made by listeners here enables the maintenance of glimpses in the 1-2 kHz range.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 3 Materials and Methods
### Mesh Refinement  
The computational grid employed for solving the numerical scheme within the fluid domain is shown in Figure 2. It consisted of tetrahedral elements, which, as previously mentioned, utilized fourth-order shape functions for the resolution of acoustic pressure and velocity fields. The mesh was predominantly structured in a uniform manner throughout the domain. The element size was chosen based on the wavelength associated with the liquid domain. A sensitivity analysis of the computational grid was conducted, to optimize the element size by considering the dimensionless attenuation,  
\[\bar{\alpha}=\alpha d=10\log_{10}\left(\frac{E_{1}}{E_{2}}\right), \tag{16}\]  
which reflected the attenuation of the signal through the emitter-receiver path. Here, \(E_{1}\) and \(E_{2}\) represent the energy of the signal detected at the surface corresponding to the emitter (where the boundary condition for the pulse was applied: purple in Figure 1b) and the receiver (light blue in Figure 1b), respectively. Surface pressure probes recorded the temporal evolution of acoustic pressure, and the energy was computed using Equation (8). In the mesh sensitivity analysis, the parameter \(\Delta\bar{\alpha}\) was considered to assess the convergence of the numerical scheme and was calculated as  
\[\Delta\bar{\alpha}=\left(1-\frac{\bar{\alpha}_{\text{fine}}}{\bar{\alpha}_{ \text{coarse}}}\right)\times 100,\]  
where \(\alpha_{\text{fine}}\) refers to the attenuation value for a finer mesh and \(\alpha_{\text{coarse}}\) is the attenuation value for a coarser mesh. In Figure 3, the trends of the degrees of freedom (DOFs) and the parameter \(\Delta\bar{\alpha}\), which accounted for the percentage variation of attenuation with respect to mesh element size, are shown on a dual y-axis plot. It is evident that the variation of \(\Delta\bar{\alpha}\)  
Figure 1: Numerical setup: (**a**) fluid, solid, and PML domain; (**b**) boundary conditions.  
remained below 5% up to an element size equal to \(\lambda\). This value was ultimately selected as the reference size for subsequent analyses, as it minimized the number of DOFs from 2,078,160 for a \(\lambda/2\) size to 111,580 for \(\lambda\), thereby significantly reducing the computational cost of the entire simulation.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 5 Experimental Results and Analysis
### Visualization  
For the purpose of displaying the experimental results more intuitively, t-SNE [32] is applied to visualize the embeddings of the nodes. We paint nodes according to their true labels, and the nodes belonging to the same category will be painted in the same color. From the second row of Figure 4a, we can clearly see that the classic VGAE (i.e., assuming that the prior is a standard normal distribution) cannot distinguish different categories of nodes in the latent space. This is because the standard normal distribution prior will encourage the centers of all categories to approach the origin, resulting in the inability to make full use of the latent space. Similar problems also exist in EVGAE and ARVGE, the second row of Figure 4b,c is more obvious. However, the ideal prior should have as little impact as possible on the posterior distribution and encourage the posterior distribution to learn parameters from the input data. The NPIVGAE we put forward can make full use of the latent space to learn the node representations while distinguishing categories relatively well as shown in Figure 4d.  
Figure 4: Visualization on (**a**) variational graph autoencoder (VGAE); (**b**) epitomic variational graph autoencoder (EVGAE); (**c**) adversarially regularized variational graph autoencoder (ARVGE), (**d**) NPIVGAE. The first row and the second row are the visualization results on Cora and Citeseer datasets, respectively.  
Figure 3: The area under the ROC curve (AUC) and average precision (AP) for each iteration in the link prediction task.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
###### Abstract  
The performance of photovoltaic (PV) arrays are affected by the operating temperature, which is influenced by thermal losses to the ambient environment. The factors affecting thermal losses include wind speed, wind direction, and ambient temperature. The purpose of this work is to analyze how the aforementioned factors affect array efficiency, temperature, and heat transfer coefficient/thermal loss factor. Data on ambient and array temperatures, wind speed and direction, solar irradiance, and electrical output were collected from a PV array mounted on a CamnetENERGY facility in Varennes, Canada, and analyzed. The results were compared with computational fluid dynamics (CFD) simulations and existing results from PVsyst. The findings can be summarized into three points. First, ambient temperature and wind speed are important factors in determining PV performance, while wind direction seems to play a minor role. Second, CFD simulations found that temperature variation on the PV array surface is greater at lower wind speeds, and decreases at higher wind speeds. Lastly, an empirical correlation of heat transfer coefficient/thermal loss factor has been developed.  
solar PV; photovoltaic; thermal loss factor; heat transfer coefficient; wind speed; wind effect; cooling Article
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.2.2 Gross Primary Productivity (GPP)  
The GPP data were derived from the MOD17A2 product produced by NASA's Land Processes Distributed Active Archive Center (LPDAAC) with 500 m spatial resolution and 8 days temporal resolution. In this study, GPP data for the 2019 rice growing season (1 May-31 August) in Tianjin were cropped and resampled to 10 m spatial resolution by bilinear interpolation method using the QGIS 3.22.1 software.
###### Abstract  
In this study, the effects of the structural parameters (SPs) of the guide cone, such as the surface inclination and the material recirculation gap size, on the two-phase flow field and classification performance of a real-sized industrial-scale micron air classifier were investigated. This was achieved using the two-way coupling of a computational fluid dynamics-discrete phase model in ANSYS 2022 R2, with the assistance of a high-performance system (HPC). The objective of this study was to determine the optimal SPs of the guide cone so as to achieve the best classification efficiency and satisfy the required particle size distribution curve, named the know-how curve (KHC), for the particle size range (\(0\div 400\) um) used in producing quartz-based artificial stone. The bottom diameter (\(d\)) of the guide cone (CHL) was altered while keeping the outer diameter of the feeding tube unchanged. As a consequence, the material recirculation gap size was changed, and the size, shape, position, and rotational direction of the vortices formed in the secondary classification space and classification chamber were also changed. These vortices significantly affected the classification performance. Specifically, the classifiers with different guide cone structures, named CHL1, CHL2, CHL3, and CHL4, yielded Newton efficiencies of \(75.06\%,87.26\%,95.5\%\), and \(94.02\%\), respectively. According to the simulation results, the best guide cone structure is recommended to satisfy objectives such as (i) the highest classification efficiency, the smallest cut size, and the smallest classification sharpness index and (ii) those in (i) under the constraint of the required KHC.  
full-scale industrial classifier; CFD; DPM; particle classification; classification efficiency; KHC (required particle size distribution curve) +
Footnote †: journal: Applied sciences
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
###### Abstract  
With the popularity of network and the continuous development of multimedia technology, saving of network bandwidth and copyright protection of multimedia content have gradually attracted people's attention. The fragile watermark for integrity authentication of image data and protection of copyright has become a hotspot. In the storage and transmission process, image data must be compressed to save network bandwidth. As a result, semi-fragile watermarking techniques, which can be used to distinguish common image processing operations from malicious tampering, are emerging. In this paper, semi-fragile watermarking algorithms for image authentication are surveyed. The basic principles and characteristics about semi-fragile watermarking algorithms are introduced, and several kinds of attack behaviors are also included. Aiming at several typical image-authentication algorithms, advantages and disadvantages are analyzed, and evaluation indexes of various algorithms are compared. Finally, we analyze the key points and difficulties in the study on semi-fragile watermarking algorithms, and the direction about future development is prospected.  
image watermarking; semi-fragile watermark; content authentication; tamper detection; recovery; evaluation indexes +
Footnote †: journal:  
_Future Internet_
# A Shearlets-Based Method for Rain Removal from Single Images
## 6 Some Discussion for the Proposed Method  
### Computation Complexity  
The computation complexity of our algorithm includes two Shearlets transforms computation and four sub-problems with closed-form solutions. For two Shearlets transforms, its computation complexity is about \(O(N\log N)+O(N)\). The computation complexity of four sub-problems is \(O(N)\), thus the total complexity for Algorithm 1 is \(O(N\log N)+O(N)\), where \(N\) represents the total number of pixels. Table 2 shows the computation time comparison of four methods. For 'Pic1' with size \([559\times 314\times 3]\), the computing time of the proposed method is 2.39 s, while the 15 ICCV and 16 CVPR methods take 72 s and 946 s respectively, and the 18 UTV method only takes 0.87 s (without direction rotation). Our explanation is that the computation of Shearlets transform needs more computing time than UTV. Thus, we should strike a balance between the algorithm efficiency and computation complexity.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 3 Secure Transmission Strategies  
In this section, we focus on the SNR received by the receiver and eavesdroppers in the mmWave wiretap network. We assume that the transmitter is able to detect the existence of eavesdroppers within the sector secrecy guard zone. We first analyze that the eavesdroppers are not in the sector secrecy guard zone, and then consider the eavesdroppers in the sector secrecy guard zone. It is worth mentioning that whether there are eavesdroppers in the sector secrecy guard zone or not, the transmitter transmits useful information signals. When the eavesdroppers are in the sector secrecy guard zone, we further exploit AN and produce positive effects through power adjustment control.
## 2 Materials and Methods
### Method Validation  
Linearity, precision and trueness, limits of detection (LODs) and limits of quantification (LOQs) were assessed and evaluated based on existing international method performance and validation guidelines [19; 20]. Thus, linearity was assessed with standard mixtures at six levels of concentration for each analyte, while calibration was based on matrix-matched calibration curves in food simulant and evaluated by the linear regression coefficient (R\({}^{2}\)). The method's LODs and LOQs were based on the chromatographic signal-to-noise ratio (S/N). The mean value and standard deviation of the S/N were obtained from 5 chromatograms of the lowest calibration level for each substance. The LOD was calculated as 3 times the S/N, while the LOQ was calculated as 3 times the LOD [19; 21].  
Trueness and precision were assessed in food simulant D1 (50% _v/v_ ethanol on H2O), the one with the highest amount of organic solvent and often the most problematic during method validation due to solubility issues [3]. This food simulant was fortified at 3 concentration levels for all the selected analytes based on their linear range. Repeatability was evaluated by performing six replicates of the fortified samples within the same day and by the same operator, while for reproducibility, six replicates of the samples were analyzed on three consecutive days by the same operator. Assessment of analytical method's method trueness was based on calculating the percent relative recovery as the amount found in the fortified sample divided by the known amount added. The three tested concentrations for repeatability and reproducibility were selected based on the method's LOQs and linear range (low, medium, high).