esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Efficient Neural Architectures for Linguistic-Acoustic Mapping in Text-To-Speech +
## 2 Recurrent Neural Network Based Linguistic-Acoustic Decoder  
To study the introduction of our proposed networks into a TTS system, we employed our previous multiple speaker adaptation (MUSA) framework [8; 27; 37]. This is a two-stage RNN model influenced by the work of [PERSON] and [PERSON] [7], in the sense that it uses unidirectional LSTMs to build the duration model and the acoustic model without the need of predicting dynamic acoustic features. A key difference between our works and that of [PERSON] and [PERSON] is the capacity to model many speakers and adapt the acoustic mapping among them with different output branches, as well as interpolating new voices out of a common representation. Nonetheless, for the current work, we did not use this multiple speaker capability and focused on just single speaker models (one model per speaker) for the new architecture design on improving the acoustic model. This way we can decouple architectural effects from shared representations, training each speaker model with a comparable amount of data and the same number of parameters.  
Figure 1 depicts the linguistic-acoustic decoder of MUSA across the different structures explored in this work. The input to the model is a sequence of frames, each one containing a mixture of linguistic and duration features). Then, each structure outputs a frame of 43 acoustic parameters per time-step. Both linguistic and acoustic features used in this work are detailed in Section 5.2. The MUSA framework with RNNs is shown in Figure 1a. As a first step, we have a pre-projection, fully-connected layer with a ReLU activation. This embeds the mixture \(\mathbf{x}_{t}\) of different input types into a common dense representation \(\mathbf{h}_{t}\) in the form of one vector per time step \(t\). Hence, the transformation \(\mathbb{R}^{L}\rightarrow\mathbb{R}^{H}\) is applied independently at each time step \(t\) as  
\[\mathbf{h}_{t}=\text{max}(0,\mathbf{W}\mathbf{x}_{t}+\mathbf{b}), \tag{1}\]  
where \(\mathbf{W}\in\mathbb{R}^{H\times L}\), \(\mathbf{b}\in\mathbb{R}^{H}\), \(\mathbf{x}_{t}\in\mathbb{R}^{L}\), and \(\mathbf{h}_{t}\in\mathbb{R}^{H}\). After this projection, we have the recurrent core formed by an LSTM layer of \(H\) cells and an additional LSTM output layer. The output is recurrent, as this prompted better results than using dynamic features to smooth cepstral trajectories in time when using RNNs [7].
## 4 Experiments
### Data Normalization  
In general, the data need to be normalized when the sample data are scattered and the sample span is large, so that the data span is reduced to build the model and prediction. In the SVM modeling, in order to improve the accuracy of prediction and smooth the training procedure, all of the sample data are normalized to fit them in the interval [0,1] using the following linear mapping formula \((-x_{id\max},x_{id\max})\):  
\[X^{\prime}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}},\ \ \ \ \ i=1,2,3\cdots,N, \tag{10}\]  
where \(X^{\prime}\) is the mapped value; \(x\) is the initial value from the experimental data; \(N\) is the total number; \(x_{i}\) is \(i\) of input data; \(x_{\max}\) and \(x_{\min}\) denote the maximum and minimum values of initial data, respectively.
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### ET Estimation
The use of remotely-sensed-vegetation indices has been tested to predict crop coefficients at different scales. In fact, [PERSON] and [PERSON] [139] found that the NDVI and SAVI were strictly related to LAI, vegetation ground cover and biomass. These relationships were obtained by radiometric measurements obtained from satellite and ground-based platforms. The relationship is simple and robust and may be used for studying WUE and water productivity at a regional level. The statistical analyses of field data and remote sensing data, derived from multispectral imagery using an UAV, confirms the feasibility of applying the proposed methods to assess physiological and structural properties of Citrus under water and saline stress. The novelty of this study was to show that diurnal changes in physiological and structural traits of citrus irrigated with RW combined with RDI could be determined by multispectral images from UAVs. NIR was a useful indicator of s, A and g on both crops. The NDVI was strictly related to gas exchange in both species and on mandarin [144]. [PERSON] et al. [145] hypothesized that the index PRI (normalized by canopy chlorophyll content), might be a strong indicator of the diurnal dynamics of water stress. They acquired narrow-band multispectral and thermal imageries over a vineyard cultivated under three different irrigation treatments, and evaluated the relationships between the normalized PRI, field-physiological parameters (stomatal conductance) and plant water potential. Relationships with CWSI, which were used as a remote sensing benchmark for water stress detection, were also discussed by the authors, who reasoned that CWSI can be used as a remote sensing benchmark for water stress detection. Some critical points were suggested for integrating VIs derived by UAVs and everyday water resource management, such as the integration of differential global positioning and the development of intelligent control of UAV platforms to improve positional accuracy and spatial data capture. A review by [PERSON] et al. [146] set up the first worldwide dataset to analyze the global LAI-VI relationships at different spatial and temporal scales. Analysis was derived from low-resolution (30 m) satellite Landsat images, with the goal of producing large-scale fine resolution LAI maps, essential for agricultural applications, especially in mosaic agriculture. The authors reported that the LAI-VI relationships were crop-specific and may explain why the largest amount of variance in field-measured LAI was obtained using only remotely sensed observations.
## 4 Materials and Methods
### Sample Preparation and HS-SPME-GC-MS Analysis  
Ginseng samples (\(n=6\)) were cut into 0.2-0.3 cm slices and dried at 37 \({}^{\circ}\)C for 16 h before the slices were crushed and screened through a 40-mesh sieve. A total of 100 mg of ginseng powder was transferred into a 4-mL headspace vial containing 400 \(\mu\)L of 20% NaCl solution that was used to disrupt the enzymatic activity of ginseng samples [22]. 2-heptone, as the internal standard, was also added into the vial with a final concentration of 0.125 ng/\(\mu\)L. For the volatile component analysis, a 100-\(\mu\)m fused silica fiber coated with DVB/PDMS/CAR was used and preheated at 40 \({}^{\circ}\)C for 5 min before being exposed to the headspace at 40 \({}^{\circ}\)C for 25 min. HS-SPME-GC-MS analysis was performed with a 7890A GC (Agilent, Palo Alto, CA, USA), equipped with a 5977B single quadrupole mass detector (Agilent, Palo Alto, CA, USA). The chromatographic separation was performed on a DB5-MS mass spectrometry column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m, Agilent, Palo Alto, CA, USA). The instrumental method was a modified version of [PERSON] and [PERSON]'s work [23,24]. The injector (splitless mode) temperature was set at 270 \({}^{\circ}\)C. The oven temperature was initially set at 40 \({}^{\circ}\)C, held for 5 min, and increased to 150 \({}^{\circ}\)C at the rate of 5 \({}^{\circ}\)C/min, and then the temperature was ramped up to 260 \({}^{\circ}\)C at the rate of 15 \({}^{\circ}\)C/min and held for 7 min. The temperature of the quadruple mass analyzer was set at 150 \({}^{\circ}\)C. The EI ion source was used for MS data acquisition with a temperature of 230 \({}^{\circ}\)C and the full-scan acquisition range was from 50 to 600 amu. Helium was used as the carrier gas at a flow rate of 1 mL/min (constant flow).
## 4 Results and Discussion
### MSW Management Case Study  
#### 4.2.1 Fangshan  
For the data of Fangshan District, this paper was solved using MOEAs and NSGA-II, respectively, where MOEAs obtained four non-dominated solutions and NSGA-II also obtained four non-dominated solutions, these solutions on the three objective functions are shown in the following table, where MLD denotes the average distance of the new waste treatment center from the customer point, CO\({}_{2}\) denotes the number of CO\({}_{2}\) emitted in the scheme, and COST denotes the total cost of the program:  
Table 3 shows that the non-dominated solutions found by NSGA-II algorithm dominate the solutions found using the MOEA. Figure 6 illustrates the distribution of the solutions obtained by the two algorithms during the search, where a, b are the MOEAs and NSGA-II algorithms, respectively, the red dots indicate the non-dominated solutions found and the blue dots indicate some elite solutions in the search process. The pictures  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Indicators**} \\ \cline{2-5}  & **MAE** & **MSE** & **MAPE** & **R\({}^{2}\)** \\ \hline Bayesian ridge & 13.75594 & 404.61622 & 33.34671 & 0.80929 \\ Linear regression & 13.83695 & 402.21477 & 33.30782 & 0.81042 \\ Elastic net & 13.83486 & 402.21509 & 33.30374 & 0.81042 \\ SVR & 25.13753 & 1723.37975 & 49.88354 & 0.18771 \\ GBR & 2.34633 & 11.63561 & 7.24244 & 0.99452 \\ RF & 3.62245 & 31.8622 & 8.46075 & 0.98498 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different methods in predicting waste production.  
Figure 5: Results of Kriging interpolation spatialization.  
also show that the NSGA-II algorithm is less likely to fall into local optima and more likely to search for global optima when searching in the objective function space compared to the MOEA. The optimal solution is about 15% lower in CO2 emissions, 30% lower in total cost, and about 44% higher in average distance from residential areas compared to the worst solution. Figure 7 below represents one of the optimization schemes solved by NSGA-II.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MLD** & **CO2** & **COST** \\ \hline MOEA & 5038.65 & 88,215.21 & 3,570,108.51 \\ MOEA & 5038.65 & 80,242.39 & 3,607,206.57 \\ MOEA & 10,524.51 & 90,962.80 & 4,319,379.33 \\ MOEA & 10,524.51 & 92,007.21 & 4,307,333.79 \\ NSGA-II & 10,524.51 & 79,557.84 & 3,591,459.74 \\ NSGA-II & 10,524.51 & 79,447.70 & 3,598,231.44 \\ NSGA-II & 8148.23 & 78,870.39 & 3,585,352.43 \\ NSGA-II & 7259.81 & 78,421.20 & 3,541,808.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results in Fangshan District.  
Figure 6: Comparison of the algorithm’s search on the objective function space. (**a**) The solutions obtained using the MOEA are distributed across Fangshan District. (**b**) The solutions obtained using the NSGA-II are distributed across Fangshan District.
## 2 Backgrounds
### Existing Vertical Federated Deep Learning (VFDL) Methods
#### 2.4.2 Data-Driven Transfer Approach  
Transfer learning [30] is a mechanism that pre-trains a task in a source domain similar to the target domain and then trains the task in the target domain using the knowledge obtained from the source domain. Features or weights pre-trained in the source domain can be transferred to the target domain. In the target domain, additional fine-tuning can be performed based on features or weights transferred from the source domain. Transfer learning is generally applied in situations where there are sufficient data.  
Existing VFDL methods of the data-driven transfer approach train the target task by transferring features trained by each party through unsupervised representation learning to other parties [3; 8; 9; 10]. In [3], passive parties transfer features trained through unsupervised representation learning to the active party, and the active party trains the target task based on this. There is a VFDL method for vertically partitioned image data [8]. The active party transfers the feature extractor trained with the labeled partial image to each passive party, and each passive party trains an autoencoder based on it. Features obtained from the autoencoder of each passive party are transferred back to the active party and used for training the target task. In [9], each guest party trains an overcomplete autoencoder on its own data. The host party trains the target task based on expanded features extracted from the overcomplete autoencoder of each guest party. Each guest party in [10] uses PCA and an autoencoder for representation learning.  
As in [3; 8; 9; 10], existing VFDL methods of the data-driven transfer approach generally utilize an autoencoder for unsupervised representation learning. Since DT-VFDL-AE in Figure 2 is a representative mechanism of existing VFDL methods that utilize a data-driven transfer approach, an autoencoder is used for unsupervised representation learning at each guest party. In existing VFDL methods of data-driven transfer approach, only one step of communication occurs to transmit the trained features from each guest party to the host party. Due to this one-shot communication mechanism, existing VFDL methods of data-driven transfer approach are communication-efficient. However, they generally show relatively lower performance than centralized training.
## 2 Materials and Methods
### NK Cell Activities  
The activities of NK cells in the spleen and peritoneum were evaluated using the \({}^{51}\)Cr release assay [30; 31]. Peritoneal cells were recovered by intraperitoneal washes, whereas spleens were homogenized, and the homogenate supernatants were collected by centrifugation. Red blood cells were lysed in cold 1% ammonium oxalate for 10 min. Cells obtained from the spleen and peritoneum were washed with Hanks Solution (Gibco BRL, Grand Island, NY, USA) and cultured overnight. HTLA-230 neuroblastoma target cells were labeled with Na\({}_{2}\)O\({}_{4}\) for 2 h (100 \(\mu\)Ci/L \(\times\) 10\({}^{6}\) cells) (ICN Biomedicals, Asse, Belgium), and were thereafter treated with splenocytes or peritoneal cells, as effector cells, for 6 h at 37 \({}^{\circ}\)C, which were applied at 100:1 and 10:1 effector:target cell ratios, respectively. The amounts of radioactivity released into the supernatants were determined using a Cobra 5002 gamma counter (Canberra Packard, Meriden, CT, USA). Percentage target cell lysis was calculated using the following formula:  
\[[(\mathrm{Exp}-\mathrm{S})/(\mathrm{M}-\mathrm{S})\times 100]\%, \tag{1}\]  
where Exp is the released \({}^{51}\)Cr value, S is the spontaneously released \({}^{51}\)Cr value, and M is the highest \({}^{51}\)Cr value [29].  
Figure 1: Study experimental design. In this study, we examined the effects of treatments on six groups comprising 10 mice each. In the five groups initially treated with CPA, mice were intraperitoneally administered 150 and 110 mg/kg of body weight CPA on days 3 and 1 day prior to oral treatment. The experimental compounds were orally administered four times at 12-h intervals, commencing on the day following the second CPA administration. Control mice without initial CPA treatment were intraperitoneally administered an identical volume of sterile saline. CPA: Cyclophosphamide, HM: Hemomine (60% LJG305).
## 4 Masked Addressing
### Chip Identification  
Chip identification occurs when the system needs to locate a particular chip from a group of chips. Following identification, the chip can reply with a simple acknowledgement, and any other data to be collected.  
1. To identify chip \(A\), the external reader transmits \(C_{A}\ \ \&\ \ M_{A}\) and \(M_{A}\). All chips within range receive this message.
2. Each chip activates its ID generator, with zero voltage difference applied to the ID cells, producing a generated code \(G\).
3. Each chip evaluates the statement \[C_{A}\ \ \&\ \ M_{A}=G\ \ \&\ \ M_{A}\] (14)
4. If the preceding step evaluates to true, the chip concludes it is being addressed.
## 6 Experimental Results and Analysis
### Experiment on Simulated Noisy Images  
In this experiment, the public breast ultrasound image dataset [27] is used, the data included breast ultrasound images of women aged 25 to 75, and the magnitude of patients is 600 women suffering from a given condition. The dataset is made up of 780 images with a size of 500 \(\times\) 500 pixels. For the convenience of comparison, the test data are cropped to a grayscale image with a size of 256 \(\times\) 256, and speckle noise is simulated on it. To quantitatively evaluate all the compared algorithms, 3 speckle noise levels \(\delta\) = {1.0, 0.3, 0.5} are tested. \(PSNR\) and \(MSSIM\) indicators are selected for evaluation, and the specific outcomes are displayed in Table 4.  
Table 5 displays the consequence of \(PSNR\) and \(MSSIM\) for diverse noise classes. In contrast, the improved Neighbor2 Neighbor improves the \(PSNR\) compared with Neighbor2 Neighbor, and the denoising influence can exceed Neighbor2 Neighbor. The mean structural similarity of images would take the edge off Neighbor2 Neighbor. With the raising of the noise level, the advantage of the proposed algorithm in structural similarity is also prominent, notably with regard to severe noise.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **PSNR/dB** & **MSSIM2** \\ \hline Neighbor2 Neighbor & 28.6445 & 0.6177 \\ Neighbor2 Neighbor + \(L_{ssim}\) & 29.6093 & 0.6525 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average quantitative results of different loss functions on the test dataset.  
Figure 5: Results of ultrasound image denoising with different loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Denoising Algorithms**} & \(\delta\) = **0.1** & & \(\delta\) = **0.3** & & \(\delta\) = **0.5** \\ \cline{2-7}  & **PSNR/dB** & **MSSIM** & **PSNR/dB** & **MSSIM** & **PSNR/dB** & **MSSIM** \\ \hline Noisy & 33.0975 & 0.7554 & 29.3456 & 0.6845 & 28.8437 & 0.6371 \\ Neighbor2 Neighbor & 35.0696 & 0.8589 & 31.4945 & 0.7887 & 28.9461 & 0.7564 \\ Neighbor2 Neighbor + \(L_{ssim}\) & 44.4122 & 0.8521 & 40.7540 & 0.8284 & 34.9397 & 0.8136 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different noise level denoising methods (\(PSNR\), \(MSSIM\)).
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 2 Related Work
### Error Control Techniques for Ao Reduction  
Besides packet concatenation, several reliability-enhancing mechanisms can also be used to improve the reliability of short-packet communications, including forward error correction (FEC); automatic repeat request (ARQ); a combination of FEC and ARQ, namely hybrid ARQ (HARQ); and network coding. Recent studies have explored combining HARQ mechanisms with two-hop links to optimize AoI [23; 24]. Nonetheless, these methods still depend on explicit feedback and iterative decoding, which increase the real-time computational load with every retransmission and require relay nodes to maintain multi-packet buffers. Network-coding-based schemes improve performance by replacing retransmissions with algebraic mixing of packets [25; 26]; however, they also demand finite-field matrix operations and additional memory to store coding coefficients and partially decoded blocks. In contrast, the scheme proposed in this paper preserves the packet-level real-time processing path and confines added computations to a one-time offline optimization step. Consequently, it retains nearly the same runtime complexity and buffer requirements as the approach in [5], while delivering substantial AoI gains under finite blocklength constraints.
## 2 Materials and Methods
### Characterization of 4 Azw  
The physicochemical properties of the solid material were determined using standard analytical techniques. To obtain the specific surface area and pore structure of the material, the Nitrogen adsorption-desorption isotherms at 77 K were measured with an automated gas adsorption analyzer ASAP2000 (Micromerities, Norcross, GA, USA) with \(\pm\)5% accuracy. The total contents of chemical components were determined by X-ray fluorescence spectrometry (XRF) using a Philips PW-2400 X-ray spectrophotometer with Rh and Au excitation tubes. The X-ray diffraction (XRD) analysis of the samples was carried out via a powder X-ray diffractometer (Bruker AXS) in the angular range of 2-60\({}^{\circ}\) in steps of20 with a scan rate of 0.025\({}^{\circ}\)/s. For Fourier-Transform Infrared spectroscopy (FT-IR, Bruker ALPHA), the spectrum was recorded in the 400-4000 cm\({}^{-1}\) range.  
The pH of 4 AZW was measured as follows: A 1:2 (w:v) 4 AZW: distilled water suspension was shaken for 24 h at 30 \({}^{\circ}\)C, then filtered, and the pH of the filtrate was determined by a pH-meter [26]. The cation exchange capacity (CEC) of 4 AZW was determined by the BaCl\({}_{2}\)-triethanolamine procedure [27]. To determine the point of zero charge (pHPZC) of the material, the 'drift method' was applied [28]: 0.2 g of adsorbent was added to 40 mL of the 0.1 mol/L NaCl solution at different initial pH values (pH\({}_{\rm i}\)) in the range of 3 to 12, and agitated for 24h at room temperature; the final pH (pH\({}_{\rm f}\)) values of solutions were then measured, and \(\Delta\)pH is plotted vs. pH\({}_{\rm i}\).
## 4 Results
### Acquisition and Tracking of GNSS Signals in Real Time
#### 4.3.2 Tests Results Using Weak Galileo E1b/c Signals  
The test results using weak signals are summarized in Table 16. The receiver successfully acquired and tracked weak Galileo E1b/c signals, including those with a \(C/N_{0}\) down to 20 dB-Hz, obtaining navigation solutions with TTFF values ranging from 1 to 2 min. The receiver used the observations obtained from signals with a \(C/N_{0}\) at or close to 20 dB-Hz to compute the navigation solutions.  
The results demonstrate that the receiver can detect weak Galileo E1b/c signals with a \(C/N_{0}\) as low as 20 dB-Hz in real-time and obtain navigation solutions. However, while many measurements yielded a TTFF between 1 and 2 min, there were instances where the receiver faced challenges in determining position using the observables derived from weak signals.
## 1 Introduction
### Main Contribution
The only drawback of this method is the interpolation process required to obtain the appropriate input shape for the U-Net model. All the details of these methods are explained in the dedicated sections of this paper. We believe that the second approach represents the main contribution of this work, as it overcomes the disadvantages associated with the traditional use of a simple FFNN in the context of the FIML paradigm. To implement these methods, we utilized two test cases: the NACA0021 and SD7003 airfoils. We selected these airfoils primarily because experimental results are available for these configurations. Furthermore, these airfoils are widely recognized for their significance in studying separation and transition phenomena at low Reynolds numbers, as they are used in various applications. This makes them ideal candidates for evaluating our strategies.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Extracted Fingerprint Feature Analysis  
The physician's fingerprint features are encrypted and embedded into the reference image. During the decryption scheme, the features are extracted and reconstructed through the LSTSQ algorithm. To evaluate the correctness of the feature reconstruction, the relative error between the original feature vector \(f\) and the reconstructed feature vector \(f^{\prime}\) has been computed following Equation (18).  
\[e_{r}=\frac{|f-f^{\prime}|}{|f|} \tag{18}\]  
The relative error computed after the fingerprint feature reconstruction for each test image is equal to 8.32411 \(\times\) 10\({}^{-10}\). As shown, the values of the relative error between \(f\) and \(f^{\prime}\) are very low. Therefore, the reconstructed feature vector can be used for an accurate fingerprint matching with the fingerprint stored in the database.