esa-sceva/satcom-chunk-collection huggingface dataset:
# Gated Recurrent Attention for Multi-Style Speech Synthesis
## 3 Gated Recurrent Attention
In Equations (13)-(15), \(U_{*}\), \(V_{*}\), and \(W_{*}\) denote parameter matrices and \(b_{*}\) denotes a bias vector. The score \(e_{n,t}\) weighs how much the \(n\)-th encoded input should be relatively attended by the \(t\)-th decoder state given as follows,  
\[e_{n,t}=v^{T}\tanh\left(W_{e}s_{t-1}+V_{e}x_{n}+U_{e}\left(r_{t}\odot f_{n,t-1 }\right)+b_{e}\right) \tag{15}\]  
where \(v\) is a vector and \(\odot\) denotes element-wise multiplication. The attention alignment \(\alpha_{n,t}\) is also given by Equation (8) as in the LSA. The recurrent state \(f_{n,t}\) is updated by the previous recurrent state \(f_{n,t-1}\), alignment \(\alpha_{n,t}\), and update gate \(z_{t}\):  
\[f_{n,t}=\left(1-z_{t}\right)\odot f_{n,t-1}+z_{t}\odot F*\alpha_{n,t} \tag{16}\]  
where \(F\) is a convolutional filter and \(*\) denotes the convolution operation. The attention mechanism we propose in this work, GRA, is a gated recurrent variant of the LSA [21]. The recurrences in the vanilla RNN, GRU, LSA, and GRA are illustrated in Figure 2 for comparison. The GRU is designed to update the recurrent hidden state \(h_{t}\) to be a weighted average of the previous recurrent hidden state \(h_{t-1}\) and the candidate recurrent hidden state \(\tilde{h}_{t}\) with the update gate \(z_{t}\), which is a function of the input at time \(t\) and the previous recurrent hidden state \(h_{t-1}\) as in Equation (1). Similarly, in Equation (16), the GRA updates the recurrent state \(f_{n,t}\) to be a weighted average of the previous recurrent state \(f_{n,t-1}\) and the convolved alignment \(F*\alpha_{t}\) with the update gate \(z_{t}\), which is a function of the query, key, and the previous recurrent state \(f_{n,t-1}\). The candidate recurrent hidden state \(\tilde{h}_{t}\) in Equation (3) is a function of the input at time \(t\) and the multiplication of the previous recurrent hidden state \(h_{t-1}\) and the reset gate \(r_{t}\), which is computed by Equation (4). The score \(e_{n,t}\) in Equation (15) is a function of the query, key, and the product of the previous recurrent state \(f_{n,t-1}\), which comes from the previous alignment \(\alpha_{n,t-1}\), with the scoring gate \(r_{t}\). The proposed GRA is expected to generate better alignment between the encoded input and the decoder state than the LSA by reflecting the **long-term context** and creating shortcuts between multiple temporal steps, as the GRU models a sequential data better than the vanilla RNN [26]. The update gate \(z_{t}\), scoring gate \(r_{t}\), and score \(e_{n,t}\) in GRA are **sensitive to the location** as they are computed conditioned on the previous recurrent state as well as the content. Each of the two gates controls **how much the recurrent state is updated** or **how much the score is sensitive to the recurrent state**.
# Spatial Pattern Simulation of Antenna Base Station Positions Using Point Process Techniques
###### Abstract  
Spatial statistics is a powerful tool for analyzing data that are illustrated as points or positions in a regular or non-regular state space. Techniques that are proposed to investigate the spatial association between neighboring positions are based on the point process analysis. One of the main goals is to simulate real data positions (such as antenna base stations) using the type of point process that most closely matches the data. Spatial patterns could be detailed describing the observed positions and appropriate models were proposed to simulate these patterns. A common model to simulate spatial patterns is the Poisson point process. In this work analyses of the Poisson point process--as well as modified types such as inhibition point process and determinantal Poisson point process--are presented with simulated data close to the true data (i.e., antenna base station positions). Investigation of the spatial variation of the data led us to the spatial association between positions by applying [PERSON]'s K-functions and L-Function.  
point process; Poisson point process; antenna base stations; inhibition point process; determinantal Poisson point process; Ripley's K-function; L-Function
# A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra
## 3 Results and Discussion
Factor 1) of the developed PLS-DA model about FTIR spectra for MCF7 (blue circles) and MDA (red circles) cells, showing the calibration (filled circles) and projected test set (hollow circles) samples in (**a**). The root mean square error for cross-validation (RMSECV) is 0.31. One misclassified sample of the test set is enclosed inside a circle and labelled in (**a**). Regression coefficients for the PLS model with two components in (**b**). As evident from the above discussion, the PCA-LDA and PLS-DA techniques are widely used for the analysis and classification of spectral measurements, together with other multivariate data classification techniques [37; 39; 42; 43; 44]. However, they have been mainly used for single datasets including spectra of different types to obtain classifications (e.g., discrimination of spectra from healthy and diseased cells). On the contrary, in the present study, the two classification techniques were both used on three different types of very different spectral datasets, in order to obtain a comparison of the predictions, as independent as possible from the single dataset. This comparison is useful for choosing the optimal method. The comparison pointed out the good performance of both methods, with a prevalence of PCA-LDA which is able to classify FTIR spectra with better accuracy and specificity than PLS-DA.
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 5 Performance Evaluation and Analysis
### Evaluation Setup  
Our simulation experiments are performed on Matlab, conducted in a 1000 \(\times\) 1000 m\({}^{2}\) field. The sink node is located in the center of this field, and the sensor nodes are randomly distributed. The maximum transmission range of each node is 100 m. The number of nodes varies from 100 to 500, the active ratio of nodes (i.e., the percentage of nodes generating packets) varies from 10% to 100%, and the percentage of fault nodes is set to 2.5%, 5%, 7.5%, or 10%.  
The _Accuracy_ is defined as \(\frac{Num_{correct\_Recovery}}{Num_{rectified}}\times 100\%\), reflecting the effectiveness of the path recovery algorithm. The Gain-Loss Ratio is defined as \(\frac{Cain}{Loss}\times 100\%\), which reflects the tradeoff between the overhead and the benefit of the path recovery algorithm.  
We compare LiPa with the iPath and CSPR algorithms. iPath utilizes the information of the parent node carried by packets and the hash value identifying the path in a collection cycle, recovering the routing path by correlating the paths between different packets. CSPR employs a compression sensing technique, allowing each packet to carry partially encoded information, then collects and decodes information of path recovery in the multi-collection cycles to obtain the routing paths.
The use of VLC mitigates interference with medical devices, while the RSS method and PSO scheme collectively outperform alternative proximity estimation and optimization techniques. The integration of these elements results in a comprehensive solution that offers superior accuracy, reduced costs, and improved compatibility within the complex and sensitive hospital setting [31; 32]. The proposed method stands out from existing solutions through its strategic integration of VLC using three LEDs with different wavelengths, coupled with a PSO-enhanced trilateration algorithm. Unlike conventional technologies, such as WiFi, Bluetooth, or RFID, the system takes advantage of the inherent lighting infrastructure in hospitals, minimizing deployment costs and possible interference with medical devices. The use of different wavelengths facilitates multiplexing and reduces interference, addressing the challenges faced by single-wavelength solutions. Furthermore, the precision of localization is refined through the RSS method. These collective features offer a comprehensive solution tailored to the complex needs of hospital environments, surpassing existing methods in precision, cost-effectiveness, and compatibility. In the following, we detail each of the advantages of these system components in Table 1.  
Compared to traditional methods like WiFi, Bluetooth, or RFID, which may struggle with accuracy, interference, and dynamic environmental changes, the proposed combination of multiwavelength LEDs, RSS, and PSO stands out as a more sophisticated and adaptive solution, tailored to meet the stringent demands of precise localization within the sensitive and ever-changing hospital environment. In this sense, the main contributions provided by this work can be summarized as follows:  
* To propose a system based on Color Shift CSK modified modulation for localization, utilizing three transmitting LEDs with different wavelengths, enabling differentiation without transmitting any information. * To implement a trilateration algorithm incorporating the PSO algorithm for precise localization. * To compare the proposed positioning algorithm with several conventional trilateration and localization methods used in other research.
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 6 Time History Analyses on 3D Models and Comparison to IIS Design Spectrum
### Nonlinear THA: Nonlinearity in the Lower Structure  
A comparison analogous to the one in Figure 8a is given in Figure 8b, where the markers provide the result of nonlinear THA on 3D models of IIS, with the LS described through the Pivot-type model, as reported in Section 6.1. For both the US and the isolation system, linear modelling is adopted. The results seem in agreement with the RSA curve for cases B (\(T_{ISO}\) = 0.5 s, \(T_{1}\) = 0.63 s) and C (\(T_{ISO}\) = 1 s, \(T_{1}\) = 1.06 s); some scatters are  
Figure 11: RSA on lumped mass models vs. THA on 3D FE models with (**a**) linear behaviour of both LS and ISO and (**b**) nonlinear ISO behaviour.  
Figure 10: RSA on lumped mass models vs. THA on 3D FE models with \(\eta_{i}\) = 5% vs. THA on 3D FE models with \(\eta_{i}\) = 2%.  
observed for cases D (\(T_{ISO}\) = 1.5 s, \(T_{1}\) = 1.54 s) and E (\(T_{ISO}\) = 5 s, \(T_{1}\) = 5.01 s). However, it is worth observing that these displacement ratios are smaller than the RSA counterparts. Therefore, even when the lower structure has nonlinear behaviour, the _IIS spectrum_ can be used for the preliminary design of IIS, since it provides an estimate _on the safe side_ of the expected behaviour.
## 5 Simulation Results  
Through simulations, we evaluated the efficiency of the proposed algorithm and drew conclusions about motion control parameters: velocity and acceleration of the whole trip. All experiments were performed in MATLAB 2018a in Windows 10 on a computer with Intel i7-750U, 3.5 GHZ, and 8 GB RAM. Based on fractional Brownian motion, we drew several 3-dimensional maps via a simple algorithm with a strictly geometrical interpretation.  
The inputs for our algorithm were \(k\) groups of information spots in a map of size \(x_{max}*y_{max}*h_{max}\). Spots in each group obey a Gaussian distribution. For each dimension out of x,y,z, the covariance \(COV_{i}\) for the \(i_{th}\) group is denoted as \(COV_{i}=h_{max}/2*r\), where \(r\) means a random real number varies from 0 tp 1. The expectations \(e_{x,y,z}^{i}\) are explained in Equation (18):  
\[\begin{split} e_{x}^{i}&=x_{max}*rand(K,1)\\ e_{y}^{i}&=y_{max}*rand(K,1)\\ e_{z}^{i}&=h_{max}*rand(K,1)\end{split} \tag{18}\]  
The number \(num\) that describes the amount of spots that each group contains is also randomly defined as an integral number at given ranges: \(num\in[1,2x_{max}]\).  
Furthermore, we set a case that take the current fields into consideration. The current fields are created based on real-world data and have velocity in 3 dimensions. They may cost more or less energy cost of the HAUV mission, which refers to the direction and value of the currents surrounding the HAUV. An example of the current fields in our work is as Figure 14.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 4 Evaluation
### Application Execution Time  
We use Sniper [9] to simulate a 64-core high performance CMP that runs the SPLASH-2 [8] workloads. The configuration of this simulation is in Table 2. This configuration mimics a high-performance X86-64 processor with high memory bandwidth similar to the Intel Xeon Phi processor. We evaluate the application performance of baseline, SWIFT and the proposed SlideAcross network. DSR is not included, as it is not a virtual-channel router and may require multiple physical networks to isolate the traffic of different message classes.  
Figure 12 shows the execution time of SPLASH-2 workloads using different NoCs. The result is normalized to that of the baseline NoC. SlideAcross reduces applications' execution time by an average of 29.2% and 16.9% compared to the baseline and SWIFT. For the low-injection rate application Barnes, SlideAcross reduces the execution time by 38.1%, showing the effectiveness of bypassing. The SPLASH-2 workloads have low average network utilization [18], so the performance under low-load conditions largely determines the system performance.
## 2 Materials and Methods
### Bioaccessibility Determination  
The standardized INFOGEST 2.0 in vitro digestion protocol [10] simulating oral, gastric, and small intestine phases was employed to assess the bioaccessibility of the bioactive compounds in nanoemulsions. Briefly, nanoemulsion samples were mixed with simulated saliva, gastric fluid, and intestinal fluid, each containing the appropriate digestive enzymes, and incubated at 37 \({}^{\circ}\)C for the specified times, as described by the digestion protocol. After protocol completion, samples were centrifuged at 12,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C to separate the micellar phase, which contained the bioaccessible compounds to be quantified. A 3:2 petroleum ether/methanol solution was used for the extraction of lycopene and phenolics from the bioaccessible fraction of the digesta. The concentration of lycopene was measured as detailed in Section 2.3. The total phenolic content (TPC) and antioxidant activity were determined using the Folin-Ciocalteu method and DPPH assay, respectively. Bioaccessibility (%) was then calculated according to Equation (2):  
\[\text{Bioaccessibility}(\%)=\frac{\text{Concentration in digestible fraction}}{\text{Concentration in the undigested sample}}\ \times\ 100 \tag{2}\]
# Damage Imaging in Lamb Wave-Based Inspection of Adhesive Joints
## Materials and Methods  
### Description of Specimens  
The object of the research was a single lap adhesive joint of steel plates. Dimensions of each adherend were 270 mm \(\times\) 120 mm \(\times\) 3 mm. The length of the overlap was 60 mm (the half-width of the plate). The geometry of the analyzed joint is shown in Figure 1. The plates were degreased with Loctite-7063 cleaner and dried. Then, the surface of the overlap was treated with abrasive paper (type P120) and was degreased again just before applying the glue. The adhesive layer was prepared with the use of the epoxy-based glue Loctite EA 9461. The mean value of the bondline thickness for all of the specimens was equal to 0.216 mm and the standard deviation had a value of 0.043 mm, which resulted in the coefficient of variation being equal to 0.198.  
Investigations were conducted for nine types of specimens (Figure 2): one intact (#1) and eight (#2-#9) with different defects in the form of partial debonding. The defects were modelled as a lack of glue on a part of the overlap surface. A PTFE tape of 0.2 mm thickness was used to prevent the glue from leaking to the debonding areas of the damaged joints. The adherends of each specimen were separated after the experimental measurements to determine the exact geometry of defects in the prepared joints. Photographs of the disconnected joints are presented in Figure 3.  
Figure 1: Geometry of the analyzed single lap joint: (**a**) plan view; and, (**b**) side view.  
It is visible in Figure 3 that the defects had slightly different shapes and areas than was assumed. The glue that leaked onto the surfaces covered with the PTFE tape (i.e., intended voids) did not affect the WRMS results because the tape prevented the plates from connecting even though there were some adhesive leakages. However, in addition to the intended voids, some additional areas resulting from the improper adhesion of the adhesive to the adheren (so-called kissing defects or low volume defects [8]) were identified for specimens #3 to #9. The kissing defect regions are outlined in Figure 3 by the yellow line. To quantify the real defect areas, including both the intended voids and kissing defects, the photographs seen in Figure 3 were processed using the ImageJ program [37]. The results are given in Table 1.
## 3 Results and Discussions
### Neutron Dose Rates as a Function of Atmospheric Pressure  
Figure 4 shows the relationships of the measured neutron dose rates versus the barometric atmospheric pressures along the inward and outward routes between the areas near Syowa Station and near Dome Fuji Station in the period from 18 November 2018 to 23 January 2019. The counts of neutron signals obtained at each plot ranged from 1300 (for 7.2 h) to 25,000 (for 35 h), and thus the standard deviation of each point was calculated to be less than 3%.  
Figure 4: Relationship of the measured neutron-dose rates and barometric atmospheric pressures along the routes between the coastal and inland areas (shown in Figure 1) in the period from November 2018 to January 2019. The standard deviation of each dose-rate plot was less than 3%.  
Figure 3: Time course of the neutron dose rate measured with WENDI-II (circle marker/solid line) and atmospheric pressure (square marker/dotted line) measured with a barometer near Dome Fuji Station (location A in Figure 1) at 3762 m in altitude in December 2018. The standard deviation of each dose-rate plot was less than 2%.  
As seen in the figure, the quite reproducible relationships were obtained between the cosmic-neutron intensities and atmospheric pressures, regardless of the measurement timings; as mentioned above, there was a time interval of about 20 days between the end of the inward route measurements and the start of the outward route ones. These results imply that the employed portable system can stably work for measuring the cosmic- neutron intensities with good accuracy even in a harsh environment such as Antarctica.
# Experimental Investigation of Vertical Density Profile of Medium Density Fiberboard in Hot Press
## 2 Materials and Methods  
The raw material used for the production of MDF is wood and glue. Generally, three types of wood are used with different percentages: popular (Populus csapica) with 60%, Ghaz (Tamarix aphylla) with 20%, and lachi (eucalyptus) with 20%.  
For the required binding of the board, gluing is a crucial step in the manufacturing of MDF board. Other factors that depend on the glue content are board looseness, area roughness, bending, and internal bonding [19]. The glue used is procured from various certified suppliers, and it is comprised of urea and a formaldehyde mixture. The glue is applied through nozzles with diameters from 2 mm to 3 mm with respect to the ratio of fiber/pulp; i.e., if the pulp is transferring in the blow line of 25 kg, then 15 kg glue is applied.  
The process of hot pressing in MDF involves applying pressure in a specified pattern with respect to time, with the platens being heated up to the optimal temperature [20]. A pressure versus time graph in hot pressing is shown in Figure 2.  
Figure 1: Medium density fiberboard manufacturing process.  
Figure 2: Hot press cycle graph.  
Experiments were carried out on 16 mm medium density fiberboard with variations in hot press parameters the ranges specified in Table 1.  
Afterwards, the density profile test, bending test, internal bonding test, and vertical density profile or X-ray graph test were performed on the sample of the 16 mm board.
# Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks
Different shapes of conductors are buried in one half-space and the electromagnetic is incident from the other half-space is incident. By inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module for training, the shape of the conductor can be reconstructed promptly. Most of the published articles in free-space apply U-Net, which requires an initial guess in the training process to reconstruct the image. However, since the object is buried in half-space, the performance of U-Net is poor because the lower part of the object cannot be measured. Numerical results present that our proposed method is efficient as well as timely compared with conventional methods. Since DCNN can avoid the computational complexity of the Green's function, real-time imaging can be achieved in time as soon as the module is trained. To the best of our knowledge, DCNN implementation on buried conductors in the frequency domain is the first to be rolled out. In addition, we believe that it would have various beneficial practical applications in medical and military or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.
## 2 Methods
### Feature Extraction and Sensitivity Analysis  
To derive a data-driven SoH model, informative features are calculated from the NFRA data that cover the identified relevant frequency range. Various characteristics can be determined addressing geometrical, frequency, time-frequency, and statistical measures [40; 41]. In this study, the geometrical features, \(y\)-axis intercept of the extrapolated NFR and the slope of the NFR spectra, are extracted for each ageing cycle. In detail, a simple linear regression analysis of the extracted NFR data in the sensitive frequency range is executed. Please note that the linear regression analysis is a low-cost and easy to implement approach, that is, an ideal method for the extraction of the relevant ageing features. In principle, additional features can be added, processed and translated to fewer but informative measures using principal component analysis and similar techniques [42; 43]. Here, we directly select the most informative feature via a sensitivity study, that is, determining which feature changes the most with the cycle numbers. Only for this feature, an empirical SoH model is derived based on SVR.