esa-sceva/satcom-chunk-collection huggingface dataset:
# Features of Bread Made from Different Amaranth Flour Fractions Partially Substituting Wheat Flour
## 2 Materials and Methods
### Factorial Design and Statistics  
The study of PS and AF addition level effects on wheat dough rheological and bread characteristics and the optimization process were performed using Design-Expert software (Stat-Ease, Inc., Minneapolis, MN, USA). A full factorial design was used to study the main and interaction effects of two factors on 24 responses. The studied factors were three amaranth flour particle sizes (large, medium, and small) and five addition levels to wheat flour (0, 5, 10, 15, and 20%). The responses considered were the following: Falling Number (FN) index, the water absorption of the composite flour (WA), dough development time (DT), stability (DT), the consistency reached during the protein weakening stage (C1-2), the consistency reached during the starch gelatinization stage (C3-2), the consistency reached during the stability of hot starch gel (C3-4), the consistency during the starch retrogradation stage (C5-4), dough tenacity (P), dough extensibility (L), dough strength (W), alveograph configuration ratio (P/L), the maximum height of the gas release curve (H'm), the total volume of CO2 production (VT), the volume of the gas retained in the dough at the end of the test (VR), the retention coefficient (CR), elastic modulus (G\({}^{\prime}\)), viscous modulus (G\({}^{\prime\prime}\)), loss tangent (tan \(\delta\)), maximum gelatinization temperature (T\({}_{\rm max}\)), maximum creep compliance (Jc\({}_{\rm max}\)), maximum recovery compliance (Jr\({}_{\rm max}\)), bread volume (BV), and bread firmness (BF).  
An experimental design that consists of fifteen combinations was conducted and the coded versus the real values of the factors are presented in Table 1. The simultaneous effect of these two factors on the responses was investigated through response surface methodology (RSM). The effects of PS and AF addition levels on dough and bread properties were evaluated through mathematical modeling. The most suitable model for predicting data variation for each response was selected according to \(F\)-test results, the coefficient of determination (\(R^{2}\)), and adjusted coefficients of determination (_Adj-R\({}^{2}\)_). The effects of the factors and their interactions were underlined using Analysis of Variance (ANOVA), considering a significance level of 95%.  
In order to establish the optimal value of the factors, amaranth flour particle size, and addition level, a multiple responses analysis was applied to the predictive models in conjunction with the desirability function approach. For the numerical optimization applied in this study, the desired goal established for each response included: addition level, ST, C3-4, H'm, VT, VR, CR, W, Jr\({}_{\rm max}\), and BV at maximum value, while C1-2, C5-4, P/L were minimized, and the levels of all remaining responses which are considered in this study were kept within range.
# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 5 Conclusions  
In this paper, we proposed a new adaptive source coding method to enhance the quality of surveillance video transmission under limited bandwidth conditions. The advantage of the proposed method is the reduction of both network bandwidth usage and decoder complexity, and the range of coding bitrate can be adaptively determined as required. We develop a linear model to represent the relationship between video quality and moving object's dynamics. The relationship was learned from a regression algorithm. This learned model could be applied to enhance video quality, by adaptively adjusting coding bitrate according to object dynamics. Our experimental results show that more than 68% of the scenarios can save coding bitrate and can be reduced by 194.29 Kbps on average.  
The proposed method is a general framework that can be extended to any type of network environment, with new learning data provided for the specific network environment. However, more object dynamics, such as background changes, texture complexity, and the color of moving objects, can be incorporated into the proposed linear model and could enhance the accuracy of the bitrate prediction. Nonlinear modeling and more advanced regression methods are also promising for future work.  
Conceptualization, T.-M.P. and Y.-K.W.; methodology, T.-M.P. and Y.-K.W.; software, T.-M.P.; validation, T.-M.P. and Y.-K.W.; formal analysis, T.-M.P. and Y.-K.W.; writing--original draft preparation, T.-M.P.; writing--review and editing, T.-M.P., K.-C.F. and Y.-K.W.; supervision, K.-C.F. and Y.-K.W.  
This research received no external funding.  
The authors declare no conflict of interest.
# Cross Sensor Simulation of Tomographic SAR Stacks
###### Abstract  
This paper presents an algorithm for simulating tomographic synthetic aperture radar (SAR) data based on another stack actually gathered by a real acquisition system. Through the procedure here proposed, the simulated system can be evaluated according to its capability to image complex natural media rather than reference point targets. This feature is particularly important whenever the biophysical properties of the target of interest must be preserved and cannot be easily modeled. The system to be simulated may be different from the original one concerning resolution, off-nadir angles, bandwidth and central frequency. The algorithm here proposed handles these differences by properly taking into account the wavenumbers of the target illuminated by the real survey and requested by the simulated one. The complex images constituting the synthetic stack are associated with the effective vertical interferometric wavenumber peculiar of the geometry to be simulated, regardless of the original data. Furthermore, the three-dimensional resolution cell of the simulated tomographic system is consistent with the simulated geometry concerning size and spatial orientation. These two latter features cannot be guaranteed by simply filtering the original stack. The simulator here proposed has been used to simulate the tomographic stack expected from the forthcoming European Space Agency (ESA) BIOMASS mission. The relationship between baseline distribution and 3D focusing capability was explored; special attention has been paid to the robustness of tomographic power at being a good proxy for the above ground biomass in tropical regions.  
SAR; tomography; simulation; wavenumbers; orbits; biomass Article
## 4 Discussion
### Practical Applications  
This study's findings have several practical applications that can benefit athletes, coaches, and individuals looking to optimize their training and improve performance. Firstly, this research highlights the effectiveness of HIIT in inducing physiological adaptations, such as increased heart rate and RPE. This information can be used by coaches and athletes to design training programs that target specific performance goals, taking advantage of the acute physiological responses associated with HIIT. Secondly, this study challenges the conventional belief that acute physical fatigue leads to a decrease in muscular performance. Instead, it reveals that HIIT can lead to improvements in muscular strength, particularly in muscles actively engaged in exercise. This insight can guide athletes and trainers in developing strategies to enhance muscle function, ultimately contributing to improved athletic performance.  
Additionally, this study's findings regarding cognitive function are noteworthy. Despite the physical stress induced by HIIT, the stability of reaction times suggests that acute physical fatigue does not necessarily impair cognitive functions related to processing speed. This has implications for individuals who need to balance physical training with cognitive demands, such as students or professionals [40]. Lastly, the increase in pain threshold observed following HIIT sessions provides an interesting perspective. It suggests that acute physical fatigue may enhance pain tolerance, possibly due to increased sympathetic modulation. This finding could be valuable for individuals dealing with pain or discomfort, offering a potential non-pharmacological approach to increase their pain tolerance [41].
## 3 Methodology
### Convolutional Neural Network Classification  
The convolutional neural network (CNN) remote sensing image segmentation method performs semantic segmentation of an image--that is, it classifies each pixel. Therefore, its basis is still the image classification problem based on the combination of the scale space and the spectral relationship of the remote sensing image feature extraction [72]. A CNN primarily comprises a convolutional layer and a pooling layer. The essence of a CNN is to construct multiple filters that can extract data features, and to extract these data structure features through convolution and pooling operations. Convolutional layer neurons extract features by acquiring local information and then combining higher layers to obtain global information. The pooling layer is based on the convolutional layer, which down-samples the extracted data features to improve the generalization of the extracted data.  
Glacier mapping using the CNN model to achieve the target segmentation of glacier remote sensing images includes the following steps:  
Figure 5: Process of debris-covered glacier classification based on the RF classifier.  
1. Use the 'TensorFlow' and 'Keras' packages in Python to build a CNN framework to construct a remote sensing image glacier segmentation model based on deep learning [73; 74]. The glacier segmentation model based on remote sensing images has 24 inputs as training samples, including 23 grayscale images and 1 corresponding label file.
2. According to the calculation performance of the computer's graphics card and the number of model parameters, set the training batch size = 128 and learning rate = 0.001, use the training function and the training set to iteratively train the remote sensing image glacier segmentation model, and use the validation set and test set to verify and test the remote sensing image glacier segmentation model after each round of training, respectively. When the remote sensing image glacier segmentation model converges, save the trained remote sensing image glacier segmentation model.
3. After outputting the segmentation results of the trained remote sensing image glacier segmentation model, the segmentation results are fine-tuned using the guided filter (GF) [75] and conditional random field (CRF) model [76]. Among them, GF uses the label file as a guide map, and uses the original image as the input image to optimize the boundary of the glacier extraction results to eliminate salt-and-pepper noise. The binary potential function in the CRF constrains the color and position between any two pixels, making it easier for pixels with similar colors and adjacent positions to have the same classification. Further, edges are smoothed according to the smoothness between adjacent pixels.  
The technical process for debris-covered glacier classification based on an RF-CNN classifier is shown in Figure 6.  
Figure 6: Technical process for debris-covered glacier classification based on the CNN classifier.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 5 Secure Homomorphic K-Means Clustering Scheme
### Scheme Details  
1. Data Preprocessing Data Encryption: Encrypt each data point \(x_{i}\) from the original dataset using the Paillier encryption algorithm to obtain encrypted data \(E(x_{i})\).
2. Secure K-means Clustering Initialization: Randomly generate \(K\) initial cluster centers on the cloud server. Encrypt the cluster centers using the Paillier encryption algorithm to obtain encrypted cluster centers \(E(c_{j})\). Iterative Update: Repeat the following steps until a stopping condition is met (e.g., reaching the maximum number of iterations or cluster centers no longer change): 1. Compute Distances: Perform the secure computation distance (SCD) protocol on the user side to compute the encrypted distances \(E(d_{ij}^{2})\) between each data point and the encrypted cluster centers.  
Figure 1: The framework of the scheme.  
* Secure Cluster Assignment: Execute the secure cluster assignment protocol on the user side to assign each data point to the nearest cluster based on the encrypted distances obtained.
* Update Cluster Centers: Update the encrypted cluster centers on the cloud server using the secure update cluster center Protocol.
* Convergence Check: Check for the convergence of cluster centers on either the user side or the cloud server. If the stopping condition is met, end the iterations.
* Result Decryption Decrypting Cluster Results: Decrypt the encrypted cluster centers \(E(c_{j})\) to obtain the original cluster centers \(c_{j}\), and decrypt the encrypted cluster indices \(E(\lambda_{i})\) to obtain the original cluster indices \(\lambda_{i}\) using the Paillier decryption algorithm.
* Result Analysis Obtaining Cluster Results: Based on the decrypted cluster centers and cluster indices, derive the final cluster results on the user side for further analysis.
## 3 Fundamentals: Workpiece and Moving Head Kinematics
### Control with Cartesian Coordinates  
For interoperability of the moving head with other systems and equipment, it is necessary to control it with three-dimensional Cartesian coordinates instead of directly controlling the angles of each movement axis. The general approach follows the transformation equations from Cartesian coordinates to spherical coordinates. The equations for the \(pan^{\circ}\) and \(tilt^{\circ}\) angles are noted in Equations (1) and (2) and are generalized for point \(P_{n}=[x_{n},\ y_{n}m,\ z_{n}]^{T}\), as shown in Figures 2 and 4.  
\[pan^{\circ}_{n}=sgn(^{MH}y_{n})\cdot arccos\left({\frac{MH}{\sqrt{x_{n}^{2}+ y_{n}^{2}}}}\right)+360^{\circ} \tag{1}\]  
\[tilt^{\circ}_{n}=arcsin\left({\frac{{{m}_{n}}}{{||\vec{P}_{n}||}}}\right) \tag{2}\]  
Some changes and simplifications of the spherical coordinate transformation equations were made to adapt to the specific use case of a moving head: The \(pan^{\circ}\) equation in Equation (1) omits points with \(z_{n}<0\) since the \(tilt\) movement is software limited to the positive half space. Furthermore, the full 0-540\({}^{\circ}\) movement range of the \(pan\) axis is not used; instead, to avoid a full turn around of the moving head between certain orientations, the \(pan\) axis is offset by 360\({}^{\circ}\) to a range of 180-540\({}^{\circ}\). The \(tilt^{\circ}\) Equation (2) is defined in a way that 0\({}^{\circ}\) and 180\({}^{\circ}\) are on the \(xy\) plane, and 90\({}^{\circ}\) is parallel to the \(z\) axis, which deviates from the typical definitions of spherical coordinates.  
With the Cartesian control of the moving head established, it is important to define the notations and relations between the Cartesian coordinate system of the moving head and the world or reference coordinate system, from which the control inputs for the moving head will be provided.  
Figure 3: **Left**: Original static Gobo wheel. **Right**: New static Gobo wheel with various aperture sizes.
## 6 Simulation Results  
This section introduces the traffic environment and provides performance analysis via MATLAB simulations. Several arguments (e.g., the cone angle \(\theta\), transmission range and the speed of vehicle) are explored to observe network behaviors in several different environments. Here, we compare the performance of forming the communication group and information routing with three schemes, including: (1) the distance method [9]; (2) the cone-based method [8]; and (3) the proposed HDTC method. The relationship between the arguments and the communication group and the relationship between the arguments and the routing group are discussed, respectively. The default settings in the simulation are: \(\theta=75^{\circ}\) (_i.e._, half of the cone angle); the average speed is 21 m/s; the connection distance is 150 m. Let \(\theta^{\prime}\approx(2/3)\theta\) for the purpose of selecting the routing group. In [34], a long-range radio is applied for the sensors in outdoor applications, and the experimental results show that the average communication range is about 200 m. Accordingly, the proposed scheme is examined with varying transmission ranges from 110\(\sim\)200 m. In the proposed system, the short timer of the modified cone-based scheme is set to be 4 s and the threshold distance \(d_{0}\) of the time-slot method is set to be the transmission range. The default lane headway is assumed to be 20 m. Assume the upper and lower bounds of \(|G_{u}|\) are UB = 20 and LB = 5. Table 2 depicts the values of simulation parameters.
## 3 Elasto-Plastic Constitutive Model  
### Method to Construct the Revised SMP Model  
To construct the elasto-plastic constitutive model of an UGM under repeated loads, the UGM is regarded as a continuous medium and in a state of small deformation. It is assumed that the UGM produces only elastic and plastic strains without creep [5; 6; 16; 23]. Moreover, the hardening modulus of the UGM remains unchanged during the process of repeated loads. To simplify the model, the anisotropic characteristics are not considered in this study. According to the plastic increment theory, three aspects are required for the construction of the elastic-plastic model, which are yield surface, hardening law and plastic flow rule [3; 4; 5]. The detailed processes were displayed in the literature [5]. In this study, the key points were briefly introduced to understand the construction of the revised SMP elasto-plastic constitutive model of UGM, and a flowchart for the construction was drawn, as seen in Figure 1.  
Among the three aspects, the yield surface is the primary concern. The SMP criterion is chosen as the yield criteria of the UGM. Shakedown theory and stress-strain analyses are involved in the establishment of the yield surface. There are five main steps to find out the features of the yield surface. Starting from the deformation behavior of UGM, shakedown theory was used to evaluate the plastic state under the current load [7; 24; 25]. In plastic shakedown range, the plastic strain rate and resilient strain was analyzed considering mul  
Figure 1: Flowchart of the construction of the revised SMP elasto-plastic constitutive model.  
tiple factors and the corresponding prediction models were constructed [11; 26; 27]. After analyses of the relationship between the yield stress and strains and modulus, the yield surface function was obtained through the prediction models of plastic strain rate and resilient strain [5; 28]. Then, the variation features of yield surface were built under repeated loads, which were used for the construction of elasto-plastic model.  
To characterize the relationship between stress and strain during the plastic stage, three hardening methods are used, the isotropic hardening, kinematic hardening and mixed hardening law, respectively. Actually, the mixed hardening law is the most suitable method to describe the actual hardening phenomenon, as the mixed hardening approach is usually too complicated for application and calculation. A simple hardening method was introduced in the revised SMP model. The kinematic and isotropic hardening was separately conducted without interaction effect. In this hardening law, kinematic hardening law was considered only during the reloading and isotropic hardening was used in the single loading period.  
According to the relationship of yield surface and plastic potential surface, associated and non-associated flow rule were used to describe the direction of plastic stain. For the civil materials, the non-associated flow rule is commonly used for the plastic flow rule [3; 4]. Then, the non-associated flow rule was used for the elasto-plastic constitutive model of an UGM.
# Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
## 6 Conclusions  
This paper has described and validated a simulation environment designed to predict the performance of indoor localization systems based on dual-band DoA approach in a complex multi-path scenario. The proposed approach is implemented by means of a virtual environment defined through a set of full-wave simulations and controlled via an ideal circuital network. The latter, by connecting both the Target and his corresponding Image antenna, enables the full implementation of the two-ray reflection model.  
As illustrated in Section 5, the validation process of the simulated results is reached by modeling the reference scenarios described in previous papers and exploiting the ability to predict the corresponding measurements, obtained experimentally.  
This approach allows consideration of complex multi-path effects that are extremely difficult to model in real experimental scenario. Consequently, the use of the proposed simulation environment lets the designer to avoid a lengthy and difficult measurement campaign as well as long design step and makes it a predictive optimization tool for localization devices.  
Future works could be done regarding the inclusion of a multi-path model, with a statistical meaning, as for example those generated by reverberation chambers. In addition, the development of a simulation environment for ultra-wideband DoA-based indoor positioning systems may be investigated.  
Conceptualization, A.C., G.C., S.M. and M.P.; methodology, S.M., M.P.; software, E.C. and M.P.; validation, A.C. and G.C.; formal analysis, S.M.; investigation, E.C., G.C., M.P.; resources, G.C.; data curation, S.M.; writing--original draft preparation, S.M.; writing--review and editing, E.C. and G.C.; visualization, A.C. and G.P.; supervision, A.C., G.C. and G.P.; project administration, G.P.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Regione Toscana POR CreO FESR 2014-2020-Action 1.1.5.a3 and resources of the Regione Toscana, Bando FAR-FAS 2014 under project \"SUMA-Struttura Urbana Multifunzionale Attiva\".  
The data presented in this study are available in the present article.  
The authors declare no conflict of interest.
## 5 Simulation Results and Discussion
### Sensitivity Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Controller Parameter Variation**} & \multicolumn{2}{c}{**\%Change**} & \multicolumn{2}{c}{**Setting Time (Soc) for \%S. band**} & \multicolumn{2}{c}{**Peak Overshoot**} & \multicolumn{2}{c}{**Peak Undershoot (\(-\)\(\sigma\))**} & \multicolumn{2}{c}{**TAE**} \\ \cline{2-11}  & \(\Delta f_{L}\) & \(\Delta f_{L}\) & \(\Delta h_{\text{relax}}\) & \(\Delta h_{\text{relax}}\) & \(\Delta f_{L}\) & \(\Delta h_{\text{relax}}\) & \(\Delta h_{\text{relax}}\) & \(\Delta f_{L}\) & \(\Delta h_{\text{relax}}\) & \(\Delta h_{\text{relax}}\) \\ \hline Nominal & No change & 5.325 & 4.371 & 8.750 & 0.0034 & 0.0033 & 0.0 & 0.0243 & 0.0307 & 0.0053 & 0.7057 \\ \hline Loading condition & +50 & 5.892 & 5.670 & 8.801 & 0.0055 & 0.0035 & 0.0 & 0.0283 & 0.0353 & 0.0081 & 0.8718 \\  & \(-\)50 & 4.924 & 4.001 & 8.475 & 0.0014 & 0.0015 & 0.0 & 0.0172 & 0.027 & 0.0011 & 0.5308 \\ \hline \(\mathcal{K}\) & +50 & 5.531 & 4.382 & 8.740 & 0.0034 & 0.0033 & 0.0 & 0.0243 & 0.0307 & 0.0053 & 0.7228 \\  & \(-\)50 & 5.315 & 4.365 & 8.760 & 0.0055 & 0.0034 & 0.0 & 0.0243 & 0.0307 & 0.0053 & 0.6913 \\ \hline \(T_{\text{cl}}\) & +50 & 6.027 & 5.341 & 8.750 & 0.0035 & 0.0034 & 0.0 & 0.0228 & 0.0315 & 0.0054 & 0.7648 \\  & \(-\)50 & 4.507 & 3.925 & 8.732 & 0.0025 & 0.0012 & 0.0 & 0.0264 & 0.0269 & 0.0045 & 0.6576 \\ \hline \(T_{\text{cl}}\) & +50 & 5.325 & 4.371 & 8.750 & 0.0035 & 0.0034 & 0.0 & 0.0243 & 0.0307 & 0.0053 & 0.7110 \\  & \(-\)50 & 3.325 & 4.371 & 8.750 & 0.0034 & 0.0 & 0.0243 & 0.0307 & 0.0053 & 0.6982 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sensitivity analysis for a wide range in plant parametric uncertainties.