esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### MLR Model Developed under Op4  
The constructed MLR model is:  
\[y=-1.100+1.299x_{1}+30.18x_{4}-0.2127x_{1}^{2}-332.9x_{4}^{2}-0.7592x_{1}x_{ 2}-1.040x_{1}x_{3}-19.03x_{2}x_{4}+e\] (54) \[(0.277)(0.174)\)\((8.92)\)\((0.0211)\)\((71.7)\)\((0.182)\)\((0.371)\)\((5.10)\)\((0.0991)\)The performance measures are shown in the fourth column of Table 3. The diagnostics tests produce the following:  
(a) CODPA (see Section 3.4) was applied at every step in Op3. In the final model, four outliers were identified: observations 40, 42, and 84 in the first cycle and observation 124 in the second sample. That is why the final model was constructed with a training set containing \(n=122\) observations.  
(b) The stepwise regression algorithm MSRA converged in 14 steps all in phase one over uniquely purged data.  
(c) The ANOVA test for validity of the regression produced test statistics \(F_{stats}=62.6\) which resulted in \(p\)-\(value_{F}<2.2(10^{-16})\). The conclusion of the test is that the model is valid. The same results from the ANOVA test were acquired in the previous 13 steps of MSRA.  
(d) The expected error nullity test produced test statistics \(t_{stats,null}=-0.0234\) resulting in a \(p\)-\(value_{null}=0.9814\). The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the expected error in the regression model is zero. The same results for the expected error nullity test were acquired in the previous 13 steps of MSRA.  
(e) The heteroskedasticity testing algorithm MHTRA produced valid auxiliary regression with ANOVA in \(p\)-\(value_{F}=3.05(10^{-3})\). However, its adjusted coefficient of determination is \(R_{adj}^{2}\)=0.117. The conclusion of MHTRA is that the constructed regression is statistically significantly heteroskedastic, but the latter is practically insignificant. Similar conclusions for the heteroskedasticity were acquired in the previous 13 steps of MSRA.  
(f) The modified Jarque-Bera Monte-Carlo test for error normality produced test statistics \(JB_{stats,null}=3.604\) which resulted in \(p\)-\(value_{B}=0.124\). The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the error in the regression model is normal. The same results for the modified Jarque-Bera Monte Carlo test were acquired in the previous 13 steps of MSRA.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Embedding Domain
#### 2.1.7 Optimal Pixel Adjustment Process  
To eliminate the distortion produced by LSB replacement, the authors in [46] suggested a simple and efficient optimal pixel adjustment process (OPAP) approach. If message bits are contained in the right-most \(r\) LSBs of an \(m\)-bit pixel, the other \(m-r\) bits are modified by a straightforward assessment, according to their manner. These \(m-r\) bits are either replaced by the adjusted result or left unchanged if the adjusted result delivers a lower distortion.
## 3 Optimal Power Allocation (PA), Cooperative Relaying and Channel Models in PD-NOMA
### Fuzzy Inference System for Optimal PA
#### 3.1.2 Fuzzy Rule Matrix (FRM) for Optimal PA  
The FRM is the decision-taking stage of the FIS system. At this stage, the output of the fuzzification stage is passed through the FRM for execution. The FRM depends on the input parameters and their respective MFs for each fuzzy value. It uses IF-THEN logical statements for the appropriate execution of fuzzy inputs. The proposed system gives more weight to the input variable \(SNR\) for each user signal. It means if \(SNR\) is low, comparatively more power will be allocated to that user. In other words, the impact of  
Figure 5: Membership function (MF) plot for input variable foliage depth (\(d_{f}\)).  
Figure 6: Membership function (MF) plot for output variable power levels.  
input variable \(SNR\) on PA is comparatively greater than the other inputs, such as distance and foliage depth. The FRM is made up of 27 possible rule combinations based on the input variables and their respective MFs: \(d=\{d_{L},d_{M},d_{H}\}\), \(SNR=\{SNR_{L},SNR_{M},SNR_{H}\}\), and \(d_{f}=\{df_{L},df_{M},df_{H}\}\). The FRM observes the fuzzy inputs and takes decisions, which are then used as inputs to the defuzzification stage. Table 1, depicts the fuzzy rule-based system for three inputs. To better understand the Table 1, we have three input variables, \(d\), \(SNR\), and \(d_{f}\) each consisting of three different triangular MFs: L, M, and H. So a combination of 27 different rules are implemented using these input variables. The outcome of the FIS-based system has five MFs (VL, L, M, H, and VH), that are completely dependent on the arrangements of input parameters: \(d\), \(SNR\), and \(d_{f}\).
# Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study
###### Abstract  
This study compared the performance of near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) for the prediction of moisture, protein, total phenolic content (TPC), ferric reducing antioxidant potential (FRAP) and total monomeric anthocyanin (TMA) content in 65 samples of Australian wheat flour. Models were constructed on 50 of the wheat samples, with the 15 remaining samples used as a dependent test set. NIRS showed excellent results for the prediction of protein content (R\({}^{2}\)\({}_{\text{test}}\) = 0.991; RMSEP = 0.22% \(w/v\)) and acceptable to good results for TPC (R\({}^{2}\)\({}_{\text{test}}\) = 0.83; RMSEP = 3.9 mg GAE/100 g), FRAP (R\({}^{2}\)\({}_{\text{test}}\) = 0.92; RMSEP = 5.4 mg TE/100 g) and moisture content (R\({}^{2}\)\({}_{\text{test}}\) = 0.76, RMSEP = 0.62% \(w/v\)). Similarly, MIRS showed the best results for protein prediction (R\({}^{2}\)\({}_{\text{test}}\) = 0.93, RMSEP = 0.62% \(w/v\)) and acceptable results for moisture content (R\({}^{2}\)\({}_{\text{test}}\) = 0.83, RMSEP = 0.65% \(w/v\)), FRAP (R\({}^{2}\)\({}_{\text{test}}\) = 0.83, RMSEP = 7.0 mg TE/100 g) and TPC (R\({}^{2}\)\({}_{\text{test}}\) = 0.73, RMSEP = 5.6 mg GAE/100 g). However, the TMA content could not be predicted. Finally, moving window analysis was conducted to determine the optimum wavelength ranges for predicting selected analytes. On average, this improved RMSECV values by an average of 18-20% compared to the corresponding full wavelength models, when using the same component selection method. The results confirm that infrared spectroscopy may be useful for the real-time quantitation and/or screening of key quality parameters in wheat, such as protein, TPC and antioxidant capacity.  
a _Applied Sciences_  
1  
Footnote 1: email: [EMAIL_ADDRESS]  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
near-infrared spectroscopy (NIRS); mid-infrared spectroscopy (MIRS); Fourier transformed infrared (FTIR) spectroscopy; total phenolic content; antioxidant capacity; _Triticum aestivum_
# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 5 Use Case and Experimental Detail
### Use Case Detail and Semantic Data Applications Ontologies
#### 5.1.2. Teens' Mood Care (TMC) Semantic Data Application  
The TMC is also a WoO based semantic data application that collects data from teens through physiological sensors and provides various services in order to care of teens' mood. The data has been collected from teens while they are at home or school. Then the data has been transported to the cloud platform for the analytics and provisioning of services. Teens have various mood swings based on their situation. When they face a different type of situation in the school or home, their body generates various types of electrodermal activity signals. Based on this activity, the current state of teens' mood can be obtained. This application contains various VOs and CVOs with different contextual data quality requirements. In this application, CVOs include Mood_monitoring_CVO, Sleep_monitoring_CVO, etc., and relevant VOs and other semantic concepts to support application features. We developed the VOs and CVOs ontology model for TMC as shown in Figures 19 and 20.  
Figure 19: TMC application ontology model classes hierarchy.  
Figure 18: ASC CVO ontology OWL/XML code (excerpt view).  
The TMC CVOs ontology model contains various types of VOs that harmonize the physiological sensor data. The TMC application VOs and CVOs have data quality threshold classes with respect to the required accuracy level, missing data range level, etc. for example the Mood_monitoring_CVO has various data quality threshold values and rules in order to process aggregated data from the VOs. These mood state rules include _hasMoodState some Grief, hasMoodState some Boring, hasMoodState some Scary_, _hasMoodState some Happy_, and _hasMoodState some Hate_. The sample of measurement rules include _(measureBy some Plus_VO) and _(SomeValue some xsd: float [>1.01f))_, _(measureBy some Blood_pressure_VO)_ and _(SomeValue some xsd: float [>2.01f)_). The data quality requirement rule includes _(hasAggregatedQualityRequirement some DataQuality)_ and _(thresholdvalue some xsd: float [>90.0%1f)_).
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 3 The Fed-HT Algorithm
**Corollary 1**. : _If all the conditions in Theorem 1 hold, for a given precision \(\epsilon>0\), we need at most \(T\leq C_{1}\log(\frac{\|x_{0}-x^{*}\|}{\epsilon})\) rounds to obtain_  
\[E[\|x_{T}-x^{*}\|^{2}]\leq\epsilon+g_{1}(x^{*}),\]  
_where \(C_{1}=-(\log(\theta_{1}))^{-1}\), \(\theta_{1}=(1+2\alpha)(1-\frac{1}{12\kappa_{d}})^{K}\in(0,1)\), and \(g_{1}(x^{*})=\frac{\zeta_{1}B^{2}}{1-\overline{\varphi}_{1}}\|\
abla f(x^{*}) \|^{2}\)._  
**Remark 2**. : _Corollary 1 indicates that under proper conditions and with sufficient rounds, the estimation error of the Fed-HT is determined by the second term--the statistical bias term--which we denote as \(g_{1}(x^{*})\). The term \(g_{1}(x^{*})\) can become small if \(x^{*}\) is sufficiently close to an unconstrained minimizer of \(f(x)\), so it represents the sparsity-induced bias to the solution of the unconstrained optimization problem. The upper bound result guarantees that the Fed-HT can closely approach \(x^{*}\) arbitrarily under a sparsity-induced bias, and the speed of approaching the biased solution is linear (or geometric) and determined by \(\theta_{1}\). In Theorem 1 and Corollary 1, \(\theta_{1}\) is closely related to the number of local updates \(K\). The condition number \(\kappa_{d}>1\), so \((1-\frac{1}{12\kappa_{d}})<1\). When \(K\) is larger, \(\theta_{1}\) is smaller, so is the number of rounds \(T\) required for reaching a target \(\epsilon\). In other words, the Fed-HT converges faster with fewer communication rounds. However, the bias term \(g_{1}(x^{*})\) will increase when \(K\) increases. Therefore, \(K\) should be chosen to balance the convergence rate and statistical bias._  
We further investigate how the objective function \(f(x)\) approaches the optimal \(f(x^{*})\). **Corollary 2**. : _If all the conditions in Theorem 1 hold, let \(\Delta_{1}=l_{d}\|x_{0}-x^{*}\|^{2}\), and \(g_{2}(x^{*})=O(\|\
abla f(x^{*})\|^{2})\), we have_  
\[E[f(x_{T})-f(x^{*})]\leq\theta_{1}^{T}\Delta_{1}+g_{2}(x^{*}).\]  
The proof details can be found in Appendix A.5. Because the local updates on each device are based on SGD with dense parameters, without the HT operator, \(l_{d}\)-smoothness and \(\rho_{d}\)-strongly convexity are required, which depend on dimension \(d\) and are stronger requirements for \(f\). Furthermore, \(\|\
abla f(x^{*})\|\leq d\|f(x^{*})\|_{\infty}\), i.e., \(g_{1}(x^{*})\) and \(g_{2}(x^{*})\) are \(O(d^{2}\|f(x^{*})\|_{\infty}^{2})\), which are suboptimal compared with the results for traditional IHT methods in terms of dimension \(d\). In order to solve such drawbacks, we develop a new algorithm in the next section.
# Development and Validation of a System for Game Control Based on a Force Plate
## 4 Mechatronic System Design
### Electronic Design  
The electronic architecture of the system for game control is shown in Figure 8. It consists of an ATmega32U4 microcontroller with necessary periphery components, four analog-digital (AD) HX711 transformers, four load cells, and a PC with an application for data acquisition, system calibration, and game control.  
The electronic board for measuring and PC communication is shown in Figure 9. The ATmega32U4 microcontroller is connected to the PC via a USB connection. The power supply is secured through a USB connection with the PC. Force measurements are conducted by using the load cells (part number L6P-C3-45 kg), which generate a voltage proportional to the measured force [21]. The generated voltage is sampled with a 10 Hz frequency using an HX711 AD converter and is then amplified 128 times and converted into a digital word.  
The HX711 AD converters are connected to the ATmega32U4 microcontroller with two digital pins. For a total of four load cells, eight ATmega32U4 microcontroller digital pins are used. Force measurement is carried out with all four load cells simultaneously. Each of the four load cells are calibrated prior to use by employing known mass calibration  
Figure 8: Electronic architecture of the system for game control.  
Figure 7: Parts of force plate: bottom view.  
weights of 200 g, 500 g, 700 g, and 1000 g. A load cell is a linear measurement element, and thus the dependency of the known measured force on the result of the AD conversion will be linear. The force measured by the load cell \(i\) can be calculated according to the following relation [22]:  
\[F_{i}=k_{i}ADC_{LCi}-TARE_{i} \tag{14}\]  
where the following are true:  
* \(k_{i}\) is the calibration coefficient of the \(i\)th load cell;
* \(ADC_{LCi}\) is the conversion value of the HX/11 AD converter, which is connected to the \(i\)th load cell;
* \(TARE_{i}\) is the value of the force measured by the unladen \(i\)th load cell.  
The calibration coefficients \(k_{i}\) are calculated through the procedure described in [23] and shown in Table 3.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Multi-Robot Formation Control Simulation Experiment
#### 4.1.5 Dynamic Formation Simulation and Results Analysis under Random Obstacle Scenarios  
When there are obstacles in the scene, by reducing the density of the obstacle area the Voronoi centroid can be divided into obstacle-free areas, so that the robot can avoid obstacles in the process of tracking the centroid. The density distribution is shown in Figure 9a. Figure 9b-e shows the multi-robot formation control under the obstacle map at different moments; when the number of iterations \(k\) is between 10 and 125, the position of the generating point must be constantly changed to avoid obstacles in the path. The error also changes accordingly. When the multi-robot system passes through the obstacle area, that is, when \(k=130\), the robots quickly restore the initial formation. Under the action of the PID controller, the error fluctuation gradually tends to zero. The basis for judging whether the formation is stable is whether the sum of the distances between the robots and the formation center tends to \(\sqrt{2}*l\), as shown in Figure 9g. When the number of iterations \(k=130\), the formation-maintaining function value remains at 8.4, that is, the formation returns to a stable state.  
Figure 8: Dynamic formation simulation under the obstacle-free scenario: (**a**) \(k=28\); (**b**) \(k=285\); (**c**) position error; (**d**) formation preservation.
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.10 GIT for Vision and Language  
The Generative Image-to-text Transformer (GIT) [111] focuses on simplicity. The architecture entails just one multi-modal encoder and one text decoder. The image encoder employed is a vision transformer [99], which therefore eliminates the need for an object detector. The encoder is pre-trained using a contrastive objective on a large-scale dataset consisting of 0.8 billion image-text pairs. The text decoder is a standard transformer; it is trained using the language modeling objective, which aligns the input image and the corresponding textual caption. It also generates text in an autoregressive manner. Their approach separates the pre-training based on the contrastive language modeling objectives, contrary to the approach used in CoCa [110], which unifies the training using these objectives. A key feature of their model is that they use a self-attention mechanism for the decoder that attends to both the image and text representations, i.e., they are concatenated. This is as opposed to using self attention separately on the text tokens and then incorporating the visual information via cross-attention in the transformer stack. They show that in a large-scale pre-training setting, their approach achieves superior performance. However, in a smaller-scale setting, the cross-attention approach outperforms the pure self-attention approach.  
GIT is simple but achieves state-of-the-art performance. This could largely be attributed to the scaling up of the training data (0.8 B image-text pairs) and the model size (0.7 B parameters). They evaluate their model on challenging benchmarks in image and video captioning, visual question answering, image classification and scent text recognition. As of 22 August 2022, a variant of GIT topped the publicly available MS COCO leaderboard [69].
###### Abstract  
We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for \(1<t<T/4\), where \(t\) is the number of milliseconds in a simulated exposure frame and \(T\) is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.  
Keywords:edge detection; spiking neural networks; bio-inspired image processing; computational photography; machine learning and classification +
Footnote †: journal:
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 3 The Scheme Design
### Establishment of Session Keys
#### 3.3.4 New Nodes to Join In  
The new node is preloaded with things like a unique identifier \(ID_{Ni}\), a pair of public and private key (\(S_{Ni}\),\(P_{Ni}\)), a digital signature (\(W_{Ni}\),\(w_{Ni}\)), the hash function \(H\), and the binary symmetric function \(F\)A new node sends its joining request to a base station, and after the base station receives the request, it will validate its legality by the signature (\(W_{Ni}\),\(w_{Ni}\)). In the new round of clustering, the new node will be added to the associated cluster and establish the session keys with the cluster head or base station.
## 3 Characterization of Mechanical Properties of the Material
### Plastic Yielding and Plastic Flow  
To experimentally evaluate the plastic yielding and plastic flow of the material, we conducted a tensile test. The test parameters were as follows: rolling direction (RD), 45 degrees to the RD (45D) and transversal direction (TD) to the RD. To evaluate the width strain and longitudinal strain simultaneously, a digital image correlation GOM-Aramis 5M system was used. The through-thickness strain was calculated based on the width strain and longitudinal strain values and assuming a constant volume, and the anisotropy coefficient or \(r\)-value was calculated. Using linear regression, the values were calculated between 10% and 20% of plastic strain. Table 4 shows the mean values of the three samples. These values were considered representative of the plastic flow of the material.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **RD** & **45D** & **TD** \\ \hline AR (-) & 0.616 & 1.043 & 1.085 \\ PSTT (-) & 0.766 & 0.983 & 0.981 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Anisotropy coefficients of the Inco718 material (mean value, where the deviation was less than 0.03 in all cases).  
Figure 4: Chord modulus evolution in the AR material and PSTT material.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Pre-Strain** & **0\%** & **1\%** & **1.5\%** & **2\%** & **3\%** & **4.5\%** & **7\%** & **9\%** & **10\%** \\ \hline AR (GPa) & 176 & 160 & 156 & 155 & 152 & 149 & 149 & 149 & 149 \\ PSTT (GPa) & 177 & 157 & 155 & 153 & 151 & 148 & 148 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chord modulus evolution of the Inco718 material.  
As shown in Table 4, there was no significant difference between the AR and PSTT behaviour of the samples. The largest difference between both states was found at RD (0.15 of difference). However, the difference was not remarkable from a practical point of view.  
To evaluate the role of plastic yielding in triggering a stress state, the R\({}_{\text{P02}}\) was calculated in each direction (RD, 45D and TD). In addition, to analyse the potential non-symmetry of the plastic contour, plastic yielding triggering stresses were calculated under compression loads using an anti-buckling device [17]. Table 5 shows the tension and compression values in each direction.  
Differences under 3% were found between the values of AR and PSTT. In terms of non-symmetry of the plastic contour, the difference was less than 5%.  
Similar to the plastic flow and elastic behaviour, no appreciable difference was observed between the AR and PSTT states. Therefore, the use of a symmetric surface will not introduce too much error, as reviewed by [PERSON] et al. [18].