esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 4 Results and Discussion
### Implementation of the Proposed Method  
In order to evaluate the proposed design, the hardware description language Verilog was adopted to program the 2D DCT-IDCT image compression architecture. The image compression algorithm processes a 256 \(\times\) 256 image with an 8-bit greyscale resolution. Figure 5 depicts the step-by-step procedure demonstrating the image compression technique and the underlying architectural model. The 256 \(\times\) 256 digital input image is segregated into about 1024 \(8\times 8\) image segments.  
Two-dimensional DCT is performed on each \(8\times 8\) image block. This process involves applying 1D DCT to the rows, subsequently obtaining the transpose of the resultant image matrix and then performing 1D DCT to the resultant image matrix's columns. The cosine terms involved are stored as constants, as all of the \(8\times 8\) image segments use the same values. The proposed error-tolerant adders are used to accomplish the additions and subtractions involved in the butterfly diagram. The multiplications are carried out using the proposed fixed-point multipliers and shifters.  
The next step that must be applied is the reverse procedure required to recover the original image. One-dimensional IDCT is applied to the rows of the compressed image. Then, the transpose of the resultant matrix is taken, after which 1D IDCT is performed on the columns of the resultant matrix. The reconstructed image is recovered by merging all the \(8\times 8\) image segments.  
The computational complexity of the proposed architecture and existing architectures is listed in Table 5. The addition, multiplication, and shift operation counts are shown for the reference and proposed algorithms. The proposed 2D DCT-IDCT image compression architecture and a few existing architectures were synthesized with gpdk-45 and 90 nm technology libraries for the ASIC implementation. The Genus tool of Cadence was used to generate the power, delay, area, and power delay product (PDP) of the 2D DCT-IDCT image compression architectures, as given in Tables 6 and 7. A reduction of 31.01% in power, 12.17% in area, and 21.11% in PDP was observed with respect to the architecture in [25] when the synthesis was conducted using gpdk-90 nm technology libraries. When the synthesis was performed using gpdk-45 nm technology libraries, there was a reduction in power, area, and PDP of 28.21%, 5.95%, and 19.62%, respectively, with respect to the architecture in [25].
## 3 Results
### Neuropsychological Test Scores Analysis  
Twenty-four cognitive parameters across various cognitive domains were analysed for association with APOE carriage. GEE models assessing relationships between neuropsychological test scores and APOE genotype status, adjusted for age, sex and predicted IQ showed no relationships for most of the tests except logical memory (immediate) (\(p=0.014\) *), where the score was higher in participants who were carriers (see Table 3). \begin{table}
\begin{tabular}{c c c c} \hline  & **APOE \(\epsilon\)4\(-\)** & **APOE \(\epsilon\)4\(+\)** & \(p\)**-Value** \\ \hline \multicolumn{4}{c}{Retinal layer thickness, (\(\mu\)m) \(\pm\) SD} \\ \hline RNFL G & 96.5 (9.7) & 96.0 (7.6) & 0.698 \\ \hline RNFL T & 71.7 (12.3) & 66.7 (8.3) & **0.01** * \\ \hline RNFL TS & 130.8 (20.9) & 130 (21.4) & 0.758 \\ \hline RNFL TI & 145.2 (20.6) & 145.6 (20.4) & 0.904 \\ \hline RNFL N & 77.3 (12.1) & 76.6 (12.5) & 0.857 \\ \hline RNFL NS & 108.1 (21.4) & 105.3 (22.1) & 0.641 \\ \hline RNFL NI & 110.9 (22.3) & 117.1 (19.5) & 0.229 \\ \hline RNFL S & 119.5 (14.8) & 118.9 (13.5) & 0.787 \\ \hline RNFL I & 128.1 (18.4) & 131.8 (16.8) & 0.462 \\ \hline P Pole T & 66.7 (5.2) & 66.3 (6.0) & 0.823 \\ \hline P Pole TS & 67.5 (5.5) & 65.6 (6.1) & 0.227 \\ \hline P Pole TI & 67.1 (5.8) & 65.4 (5.8) & 0.202 \\ \hline \end{tabular} Abbreviations: Abbreviations: RNFL = retinal nerve fibre layer; G = global; T = temporal; S = superior; TS = temporal superior; TI = temporal inferior; N = nasal; NS = nasal superior; NI = nasal inferior; I = inferior; P Pole = posterior pole; FAZ = foveal avascular zone; SCP = superior capillary plexus; DCP = deep capillary plexus; VD = vessel density; peri = perimetry; cir = circularity. * \(p<0.05\).  
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline  & \
\begin{tabular}{c} **APOE \(\epsilon\)4\(-\)** \\ **(n = 85)** \\ \end{tabular} &
\begin{tabular}{c} **APOE \(\epsilon\)4\(+\)** \\ **(n = 24)** \\ \end{tabular} & \multirow{2}{*}{**B-Value**} & \multirow{2}{*}{**95\% CI**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{2-2} \cline{5-6}  & **Mean** & & & \\ \hline \multicolumn{4}{c}{Cognitive Test (Score)} \\ \hline Predicted & \(112\) & \(113.3\) & \(-1.47\) & \(-4.906\) to \(1.967\) & 0.402 \\ WAIS-IV\_PSIQ & \(12.6\) & \(14.8\) & \(-2.246\) & \(-4.030\) to \(-0.461\) & **0.014** * \\ \hline \end{tabular}
\end{table} 
Table 3: Differences between APOE non carriers and carriers for various domains (cognitive, mfVEP and MRI brain volumes).
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 3 Method: LEACH-MTC Based on Moving Target State Prediction  
In WSNs, when some network nodes cooperatively track a target, although the network monitoring performance is improved, the addition of a large number of working nodes will cause redundant measurement information of the target, and also cause waste of the node resources. In order to improve the utilization of nodes and reduce the energy consumption of the network, this section proposes an improved algorithm called LEACH-MTC based on the prediction information of moving targets, and its flow chart is shown in Figure 1.  
Based on the LEACH protocol, the LEACH-MTC algorithm uses EKF to obtain the state prediction of moving targets in a nonlinear system. Based on the current position state and the position state prediction of the moving target, an elliptical coverage area which is related to the state prediction is designed to determine the working nodes. Besides, the improved node sleep strategy and the improved cluster head selection strategy are designed under the constraints of moving targets, respectively. Subsequently, three improvements--elliptic coverage construction, node sleep strategy, and cluster selection strategy--are introduced under some constraints of moving targets.  
Figure 1: LEACH-MTC algorithm flow chart.
## 1 Introduction
Spectral parameters such as peaks' intensity ratio in emission (_em_)/excitation (_ex_) spectra of honey samples were also used to quantify the total protein/total phenolic contents [35; 37], as well as to detect eventual adulterations with syrups and cane sugar [23; 24; 36]. Sometimes, to reduce spectral overlapping problems due to the presence of many fluorophores, three-dimensional plots reporting both excitation and emission spectra, the so-called emission-excitation matrices (EEM), are recorded and further analyzed in combination with multivariate statistical methods, to characterize honey samples of different geographic and botanic origins [25; 35; 36; 37; 38; 39; 40; 41]. In the present work, an original investigation based on front-face fluorescence (FFF) of honey produced in Tuscany (Italy) from twelve different floral plants (acacia (_Robinia pseudoaccacia_ L.) also known as \"false acacia\" or \"black locust\", sulla (_Hedysarum coronarium_ L.), clover (_Trifolium_ L.), marruca (_Paliurus spina-christi_ Mill. ), sunflower (_Helianthus annuus_ L., 1753), chestnut (_Castanea sativa_ [PERSON], 1768), heather (_Erica scoparia_ L., 1753), lavender (_Lavandula_ L. 1753), arbutus (or strawberry tree) (_Arbutus unedo_ [PERSON], 1753), insula (_Inula viscosa_ (L.) [PERSON]), forest honeydew and alfalfa (_Medicago sativa_ L.)) is reported. In particular, the fluorescence spectral profiles of sixteen monolforal honey samples are analyzed in terms of different fluorescence contributions due to main fluorophores, such as phenolic acids, flavonoids, amino acids, and vitamins. An original \"semi-quantitative\" spectral analysis was proposed in order to identify specific bioactive compounds from the fluorescence spectra. Results reached from this spectral analysis are compared with those obtained by means of various analytical and chemical-physical methods on Italian honey samples (i.e., acacia, chestnut, arbutus, sulla, and heather) [20; 22; 42; 43; 44; 45] and on some of the Tuscan samples investigated here (i.e., heather, marruca, sulla, acacia, clover, chestnut, and sunflower) [17; 46]. The approach presented here opens to future perspectives for a quantitative analysis of fluorescence in terms of main fluorophores' content. Moreover, in the discussion part of the paper, the results obtained for the twelve different monolforal honey types are discussed in view of possible identification of groups of minor compounds whose presence, and relative concentrations, may be correlated to honey of specific botanic origins.
# A Framework for Real-Time 3D Freeform Manipulation of Facial Video
###### Abstract  
This paper proposes a framework that allows 3D freeform manipulation of a face in live video. Unlike existing approaches, the proposed framework provides natural 3D manipulation of a face without background distortion and interactive face editing by a user's input, which leads to freeform manipulation without any limitation of range or shape. To achieve these features, a 3D morphable face model is fitted to a face region in a video frame and is deformed by the user's input. The video frame is then mapped as a texture to the deformed model, and the model is rendered on the video frame. Because of the high computational cost, parallelization and acceleration schemes are also adopted for real-time performance. Performance evaluation and comparison results show that the proposed framework is promising for 3D face editing in live video.  
3D image/video editing; face editing/manipulation; augmented reality; parallel processing Article
## 3 Risk Assessment Model
### Vulnerability Assessment  
Vulnerability refers to the social and environmental impacts, loss of life, and economic losses caused by hazards. The relationship among the factors of vulnerability assessments and their classifications are listed in Table 4, and the values of the vulnerability factors are in Table 5 [35].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Vulnerability Factors**} & \multicolumn{5}{c}{**Vulnerability Classification**} \\ \hline
**Type** & **Symbol** & **Factor** & **I** & **II** & **III** & **IV** \\ \hline Loss of life & \(RP\) & Risk population & \(\leq\)10\({}^{4}\) & 10\({}^{4}\)–10\({}^{5}\) & 10\({}^{5}\)–10\({}^{6}\) & \(\geq\)10\({}^{6}\) \\ \hline \multirow{2}{*}{Economic loss} & \multirow{2}{*}{\(PG\)} & GDP per capita & \multirow{2}{*}{\(\leq\)10,000} & \multirow{2}{*}{10,000–40,000} & \multirow{2}{*}{40,000–70,000} & \multirow{2}{*}{\(\geq\)70,000} \\  & & & & & & \\ \hline \multirow{3}{*}{Social and environmental impacts} & \multirow{2}{*}{\(UH\)} & Urban & \multirow{2}{*}{Village} & \multirow{2}{*}{Township} & \multirow{2}{*}{Town} & County and \\  & & hierarchy & & & & & \\ \cline{1-1} \cline{5-5}  & \multirow{2}{*}{\(FL\)} & Facility level & \multirow{2}{*}{Common} & \multirow{2}{*}{Municipal} & \multirow{2}{*}{Provincial level} & \multirow{2}{*}{National level} \\ \hline \end{tabular} Note: The economic loss was classified according to China’s GDP per capita in 2021. This indicator could also be revised on the basis of the economic development of different countries.  
\end{table}
Table 4: The factors of vulnerability and their classifications.  
According to the research of [PERSON] et al. [35], the vulnerability assessment formula is expressed as follows:  
\[V=0.5 RP+0.1 PG+0.2 UH+0.2 FL \tag{24}\]  
The weight coefficients of the vulnerability assessment are determined according to the degree of influence of each factor. Losses of life are the most serious losses in all kinds of geological hazards and have the highest weight. The recovery of economic losses is faster than the recovery from social and environmental impacts, so it has the lowest weight. Therefore, the weight coefficients were adjusted on the basis of the research of [PERSON] et al. [35].
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 3 Finite-Element Simulation
### Nonlinear Restoring Force of the Linear-Arc Beam  
In order to obtain the nonlinear restoring force of a linear-arc beam, we used the COMSOL software to perform a steady-state study, apply a force in the z-direction on the mass, and obtain the displacement-restoring force curve of the curve-shaped beam. The results are shown in Figure 6. The relationship between the restoring force and transverse displacements was then fit to a polynomial, as follows:  
\[F_{r}=k_{1}u^{3}(L,t)+k_{2}u^{2}(L,t)+k_{3}u(L,t) \tag{18}\]  
where \(k_{1}\), \(k_{2}\), and \(k_{3}\) are constant coefficients on the third-, second-, and first-order terms, respectively.  
The result shows that when the curvature is 20 m\({}^{-1}\), the nonlinear restoring force of the linear-arc beam can be expressed as:  
\[F_{r}=27.23u^{3}(L,t)-0.15u^{2}(L,t)+0.02u(L,t) \tag{19}\]  
Figure 5: Voltage-acceleration diagram.  
Figure 6: Displacement-restoring force curve of the curve-shaped beam.  
The result shows that when the curvature is 40 m\({}^{-1}\), the nonlinear restoring force of the linear-arc beam can be expressed as:  
\[F_{r}=33.26u^{3}(L,t)-0.35u^{2}(L,t)+0.04u(L,t) \tag{20}\]  
Substituting Equation (19) or (20) into Equation (11), the governing equations of the PEH-C system with curvatures of 20 m\({}^{-1}\)  and 40 m\({}^{-1}\) were obtained, respectively. The equations for other curvatures could also be deduced by analogy. The corresponding coupled governing equations were derived by using the generalized Hamilton principle, laying the foundation for subsequent in-depth research.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods
### Local Statistical-Based Filter  
#### 2.2.1 Gaussian Laplace Filter  
GLF is a kind of filter which both considers de-noising and edge protecting of an image. As most edge detail locate in the high frequency region, high pass Laplace filter can be regarded as an effective and simple example in images to preserve edge detail [36]. Nevertheless, filtering performance is highly limited because of high sensitivity to noise and natural contradiction between de-noising and retaining details of edge. To this end, it is essential for Gaussian smoothing to be used in cooperation with a Laplace filter for BUS images [37]. GLF [32] can be shown as  
\[I_{\underset{f}{J}}(x,y)=I_{g}(x,y)\times h(x,y)+\
abla^{2}(I_{g}(x,y)\times h (x,y)) \tag{2}\]  
where \(\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y ^{2}}\), \(h(x,y)=\frac{1}{\sqrt{2\pi}\sigma}\exp(-\frac{x^{2}+y^{2}}{2\sigma^{2}})\) is a Gaussian function, and \(\sigma\) is the standard deviation. As illustrated above, GLF is obviously composed of two parts. One is for de-noising, and the other is for edge enhancement.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## Methodology
### Neural Networks Models Considered
#### 4.1.4 Bi-RNN  
A traditional RNN processes the time-steps in order, whereas Bi-RNN [31] exploits the order sensitivity present in RNN and the input sequence can be processed in forward and reverse directions. It may have overfitting issues as it has twice the number of parameters of a traditional RNN, however, overfitting problem can be controlled by employing good regularization techniques [26]. We employed RNN variants GRU and LSTM layers in our experiments. The forward and backward hidden sequences (i.e., \(\overrightarrow{h}\) and \(\overleftarrow{h}\)) for Bi-RNNs are given by:  
\[\overrightarrow{h}_{t} =\mathcal{H}(W_{x_{h}}x_{t}+W_{\overrightarrow{h}\
# Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control
## 6 Experiment
### Results  
#### 6.2.1 Overall Performance  
As shown in the Tables 1 and 2, the performance of the proposed method is ahead of the other methods at all junctions. Consistent with the expectation, the reinforcement learning method has stronger performance compared to the traditional methods. Moreover, among the reinforcement learning methods, the proposed approach outperforms Ape-X and A3C in terms of data efficiency and performance. In the proposed method, agents are trained by the traffic inference model to obtain larger and more diverse training data and derive better strategies based on these data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **1** & **2** & **3** & **4** & **5** \\ \hline Fixedtime & 234.08 & 274.47 & 264.68 & 222.28 & 249.98 \\ SOTL & 112.05 & 114.94 & 125.56 & 115.93 & 106.39 \\ \hline DQN & 93.43 & 92.12 & 104.66 & 107.30 & 92.62 \\ A3C & 92.47 & 90.60 & 105.50 & 102.01 & 89.38 \\ Ape-X & 76.30 & 78.50 & 86.87 & 84.33 & 81.90 \\ \hline Traffic & & & & & \\ Inference & **71.46** & **61.57** & **70.70** & **67.41** & **65.36** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average travel time for the five intersections in Suzhou, in seconds. The bolded result is the optimal result.  
Specifically, as shown in Figure 5, in the early training period, the proposed method is less efficient than Ape-X and A3C in terms of policy improvement. This is due to the fact that in the early period, the loss of the traffic inference model is still not converged, and its inference ability is still under training. Moreover, as the number of interactions with the environment increases, the traffic inference model starts to gradually converge. At this point, the efficiency of the proposed method for policy improvement starts to improve and gradually overtakes A3C and Ape-X, finally surpassing all other methods in terms of performance. Comparing the number of interactions between the agent and the environment, it is observed that a large number of interactions is spared using the proposed method compared with Ape-X and A3C while achieving the same performance.
## 3 Simulation Implementation
### Implementation of the Communication System
#### 3.2.1 Analog-to-Digital Conversion  
The first step is to measure the load voltage (rms), scale it down to a suitable value and convert the resulting analog signal into an equivalent digital signal. The ADC is implemented first by sampling the analog signal with sampling frequency of 50 kHz. After that, the sampled signal is quantized with the quantization level of 0.4 V. The quantized signal is converted or approximated to the nearest integer value and converted to bits by using the integer to bit converter block available in SIMULINK, as shown in Figure 5b.  
Figure 4: Circuit model of the inverter (parasities elements are outlined in red).  
Figure 5: SIMULINK implementation of the (**a**) RPWM control system; and (**b**) the analog-to-digital converter.
## 3 Experimental Results and Analysis  
### LT-NMR Results  
Low-field NMR tests were performed on the samples to investigate the effect of water content on the microstructure at low temperatures. In the NMR study, the intensity of the magnetic resonance signal (MR) represents the amount of hydrogen-containing liquid in the sample, while the change rate of the MR signal over time (relaxation time \(T_{2}\)) is related to the state of the molecules in the liquid. Therefore, analyzing the relaxation time of the hydrogen-containing liquid could provide a comprehensive view of the pore space distribution in the rock [27]. For a hydrogen-containing fluid (water) in the rock, the transverse relaxation time \(T_{2}\) is:  
\[\frac{1}{T_{2}}=\rho(\frac{S}{V})_{\text{pore}} \tag{2}\]  
where \(T_{2}\) is the transverse relaxation time (ms); \(\rho\) indicates the surface relaxation strength, which is related to the surface of the rock particles and the nature of the cement [13]; and \(S/V\) refers to the specific surface of the pores in the rock, which is related to the shape of the pore, e.g., if the pore is spherical, it is taken as 3/R. Therefore, observing the characteristics of the \(T_{2}\) spectrum of the sample can indirectly obtain the distribution pattern of different pore sizes.  
The distribution curves of the \(T_{2}\) spectrum of the sandstone samples for 0%, 30%, 50%, 80%, and 100% are shown in Figure 7. The \(T_{2}\) spectrum distribution mainly has three peaks, indicating that three sizes of pore structures were mainly formed in the sample. Moreover, the area under the peaks is mainly between 0.1\(\sim\)10 ms. According to [PERSON] et al. [28], there is a correlation between \(T_{2}\), pore diameter, and water type. For example, when \(T_{2}\) is 10\(\sim\)100 ms, the pore size is 0.1\(\sim\)1 \(\upmu\)m and is called mesopore, which is mainly composed of capillary water [27]. As shown in Figure 7, for the dried sample, the area of the \(T_{2}\) spectrum per size decreases compared to other saturated samples. As saturation increases, the area occupied by the second peak gradually increases from 2.19% (dry) to 9.32% (100%), showing that the increase in saturation has a significant effect on the development of mesopores in the sample. When the saturation exceeds 80%, the area of the third peak  
Figure 6: Verification of test stress uniformity hypothesis.  
increases by 3.57%, indicating that the macropores of the sample have developed further. Thus, saturation has a strong influence on the pore development of the sample at low temperatures. When the saturation is less than 80%, the micropores and mesopores in the sample spread predominantly, while the macropores of the sample gradually develop when the saturation is greater than 80%. In other words, the higher the water content, the more significant the increase in porosity of the sample after freezing. This indicates that the increasing moisture accelerates the expansion of the pores inside the sample and increases the sensitivity of the sample to the freeze heave forces.
## 4 System Overview
### Analog Front-Ends  
The RF front end of the receive-mode beamformer was constructed using a 32-element Uniform Linear Array (ULA) operating at 5.7 GHz, paired with a 32 direct conversion RF receiver chains on printed circuit boards (PCBs). The array elements are spaced at 0.6\(\lambda\), which equates to approximately 32 mm at 5.7 GHz. Each antenna element is designed as a vertical subarray consisting of \(4\times 1\) patch antennas, facilitating passive beamforming in the vertical plane. This analog beamforming operates independently of the spatial FFT-based beamforming algorithms discussed in this work.  
The outputs from the antenna elements are directly connected to 32 heterodyne receivers. These receivers utilize a centralized local oscillator (LO) system, which includes a low-phase-noise oscillator linked to a 32-output power divider network. Each receiver's initial stage features a low-noise amplifier (LNA) that delivers 16 dB gain at 5.8 GHz with a noise figure of 2.4 dB. Following amplification, the signals are filtered through a bandpass filter spanning 4.7 GHz to 6 GHz to eliminate out-of-band noise and interference. The filtered signals are then mixed and passed through low-pass filters to produce downconverted low intermediate frequency (IF) inputs.  
Subsequently, these 32 low-IF signals are amplified by approximately 30 dB and digitized using two ADC16x250-8 analog-to-digital converter (ADC) cards. Each ADC card supports 16 single-ended input channels, provides 8-bit resolution, and operates at sampling rates up to 250 Msample/s per channel. This setup ensures the high-fidelity digitization of the received signals for further processing [97].