esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM
#### 4.2.1 Random Forest1 Experimental Results  
The optimal hyper-parameters of the Random Forest1 are determined by random search, as shown in Table 8. Figure 17 is the confusion matrixes of the classifier on the testing set. The model tends to confuse passenger ships with fishing boats, and confuse tankers with cargo ships.  
To explain the confusion matrixes, we use t-SNE [32] to visualize the static features. t-SNE is a method of data visualization which can map data from high-dimensional space to low-dimensional space. If two samples are similar in high-dimensional space, the distance between their maps in low-dimensional space will be close. Figure 18 shows the visual results of static features. There are partial overlaps between the reduced dimensional distributions of passenger ships and fishing vessels, as well as that of tankers and cargo ships, which implies that the static information of the misclassified ships is similar.  
Figure 19 shows the importance of the static information features. It can be seen that the ship's dimensional characteristics, such as A, length, and length-width ratio, can better describe the ships' features than draught. In addition, although the features C and D contribute little to the classifier, these two parameters have been reflected in the length-width ratio, girth, area, and width, which proves that the static features constructed in this paper are effective in the ship classification task.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Items**} & \multicolumn{6}{c}{**Evaluated on Samples**} & \multicolumn{6}{c}{**Evaluated on Ships**} \\ \cline{3-11}  & & **Random** & **Forest** & **1D-CNN** & **Bi-GRU** & **XGBoost** & **MFLCM** & **Random** & **Forest** & **1D-CNN** & **Bi-GRU** & **XGBoost** & **MFLCM** \\ \hline \multirow{3}{*}{**T-CNN**} & class0 & 0.9931 & 0.9217 & 0.7191 & 0.8548 & _0.9985_ & 0.8889 & 0.6667 & 0.6667 & 0.7778 & _0.8889_ \\  & class1 & 0.9320 & 0.7861 & 0.6052 & 0.5467 & _0.9344_ & 0.9237 & 0.6271 & 0.6864 & 0.6102 & _0.9322_ \\  & class2 & 0.9340 & 0.8879 & 0.7690 & 0.8117 & _0.9332_ & 0.9032 & 0.7903 & 0.7419 & 0.8065 & _0.9355_ \\  & class3 & 0.8244 & 0.6173 & 0.5972 & 0.6839 & _0.8797_ & 0.8287 & 0.7315 & 0.6296 & 0.7546 & _0.8750_ \\  & Total & & 0.9205 & 0.7914 & 0.6566 & 0.7008 & _0.9362_ & 0.8696 & 0.7077 & 0.6643 & 0.7222 & _0.9010_ \\  & Accuracy & & & & & & & & & & & \\  & F1 score & 0.9196 & 0.7873 & 0.6629 & 0.6991 & _0.9359_ & 0.8719 & 0.7127 & 0.6681 & 0.7239 & _0.9019_ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance evaluation of base classifiers and MFLCM.  
Figure 17: (a) Confusion matrix of samples (Random Forest1); (b) Confusion matrix of ships (Random Forest1).
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 4 Conclusions  
Due to the fact that audio applications have high demand in different sectors of information technology, such as the IoT and streaming, it is mandatory to provide good audio quality to the users. In this scenario, ASIC implementations of digital filters are a resourceful option for audio systems with standard configuration protocols such as SPI and I2C. For this reason, the ASIC implemented in this work provides an important alternative to audio signal processing in hardware instead of the typical software approach for noise canceling.  
Because of the high cost due to ASIC manufacturing, the FPGA prototype implementation of the proposed equalizer was a very useful stage in the ASIC implementation as a proof of concept and a reference model for the ASIC's functionality validation. With this design flow, the importance of prototyping before ASIC manufacturing was confirmed.  
It is worth mentioning that the TMSC 180 nm testing technology used to manufacture the ASIC can be changed for commercial leading-edge nanometric technologies, but taking advantage of the same digital system design.  
During the FPGA implementation phase of the proposal, we found that the adaptive filter was able to reduce or eliminate static noise (crackling sound), but not white noise at low SNR values; to avoid this problem, the word length of the filter coefficients was increased, and an overflow-underflow condition was added to the design. Consequently, the system performance had an audible quality improvement. These changes imply that the format and the word length of the filter coefficients improve the signal-to-noise quantization ratio upon further developments. Following the input word convention of matching the word length with the filter order and as this ASIC is intended to be used in conjunction with an I2S bus, the highest audio sampling standard to be used is a 24-bit stereo word.  
Conceptualization, L.P.-E.; Software, C.A.-G.; Investigation, R.D.-V. and R.R.-C.; Project administration, O.L.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 3 PM2M Overlay Network over PRACH
### Priority Mapping  
IEEE 802.15.6 provides priorities through the Contention Window (CW) and Access Categories (AC). There are eight different ACs with different minimum and maximum CW values, which define User Priorities (UP) to access the medium, as shown in Table 1.
## 4 Results  
### Performance Evaluation  
To avoid skew results caused by the random sampling of training and testing datasets, we performed 20 random repetitions of the five ML regression models (87 samples for training and 30 for testing). The performance of five ML models with respect to \(R^{2}\), RMSE and MAE is displayed in Figure 5. GBRT surpasses the other models on the average (\(\overline{R^{2}}\) = 0.854, \(\overline{RMSE}\) = 0.674 and \(\overline{MAE}\) = 0.456), however, GBRT acts less robust to the training/testing random split according to the distribution of \(R^{2}\), RMSE and MAE. RF delivers the worst accuracy on the average (\(\overline{R^{2}}\) = 0.807, \(\overline{RMSE}\) = 0.781 and \(\overline{MAE}\) = 0.545), and it also acts less robust to the training/testing random split. Nonetheless, SVR achieves a desirable result while it also acts reasonably robust to the training/testing random split (\(\overline{R^{2}}\) = 0.835, \(\overline{RMSE}\) = 0.730 and \(\overline{MAE}\) = 0.550), which indicates that SVR is highly stable to the random sampling processes. Overall, all models achieved satisfactory performances, which indicates that ML algorithms are appealing methods for cotton LAI estimation.
## 3 Beamforming Techniques
### Robust Beamforming with NVL Technique  
The NVL technique is automatic and the loading level is dependent on the signal power, noise power, the norm of the steering vector both with and without look directionerror consideration, and the steering vector distortion bound. Using the NVL technique, the new array correlation matrix due to the look direction error can be calculated using Equation (8),  
\[R_{nnew}=R+(R-1\times\mu\times I) \tag{8}\]  
here \(\mu\), is shown in the following equation,  
\[\mu=1+\beta \tag{9}\]  
and,  
\[\beta=min\frac{\phi*\rho}{(\|R_{ac}-\phi*I\|)} \tag{10}\]  
where,  
\[\rho=R-R_{ac} \tag{11}\]  
\[\theta=\frac{trace(R_{ac})}{L} \tag{12}\]  
where \(R_{ac}\) and \(R\) denote the array correlation matrix in the presence of mismatch and without any mismatch, respectively. NVL provides a close form expression for the loading factor with some approximations.
# Backlight Unit Excessive Dimming Based on Perceptual Image Analysis
## 1 Introduction
Most of the existing dimming studies have used an optimization method based on a mathematical image processing or image quality measures, such as using an algorithm based on the pixel value of an image [14] or using Gaussian distributions [15]. However, since image quality is judged by human eyes, it is necessary to consider the human visual system (HVS) additionally. There was a study that considered part of HVS [16], but there are various theories on the human perceived image qualities. Therefore, methodologies are presented to maximize dimming based on brightness sensitivity and contrast response function, which is the most important in perceived brightness and contrast respectively, which are important in image quality degradation by the excessive dimming. In addition to global dimming, local dimming is efficient and red, green, and blue (RGB) dimming is the most efficient. Local dimming is good for power efficiency because dimming can be applied to each divided image block of the backlight, and it is advantageous in improving the contrast by reducing light leakage [9]. RGB dimming can improve contrast and further minimize power consumption by separately adjusting R, G, and B backlight, but has a disadvantage of increasing implementation cost. In this paper, methods that can optimize global, local and RGB dimming altogether are proposed. The proposed methods optimize the excessive dimming by maximizing dimming without significant perceived image quality deterioration with a certain amount of tolerance according to the application areas. The methods analyze the degree of perception of image quality deterioration caused by pixel saturation due to the compensation for excessive dimming based on image characteristics and HVS for reduction of power consumption. The structure of this paper is as follows. In Section 2, the motivations of dimming optimization through image characteristic and human perception are described. Section 2 also describes how HVS theories can be applied to the excessive dimming. In Section 3, the proposed methods of maximizing dimming are presented. In Section 4, the performance is verified based on actual images using the dimming simulator, and Section 5 concludes the paper.
## 3 Computational Implementation
### Validation of the CLBM Code  
The program is verified using a well-known typical design [38; 39], which consists of an air-filled square cavity with constant Prandtl number (\(Pr=0.71\)), adiabatic horizontal walls, and constant temperature (\(T_{h}\) and \(T_{c}\)) on the vertical walls. With a maximum difference of 2%, the results in Table 2, which include speeds, stream functions, and Nusselt number values, show a strong match with the reference results. Table 2 below provides a quantitative comparison between the present findings and those reported in the literature.  
Figure 3, given below, compares the flow configurations with a Rayleigh number (\(Ra=10^{7}\)). For qualitative validation, we looked at a cavity setup where an isothermal heater sticks out from the bottom wall. There's also an isothermal partition, with height \(h\) and width \(w\), placed at a distance d from the right wall, kept at a constant temperature \(T_{h}\). The two vertical walls are kept at a cooler temperature \(T_{c}\), while the top and bottom horizontal walls are insulated.  
The quantitative validation results are summarized in Table 3, where a comparison of the maximum values of the stream function (\(\Psi_{max}\)) and velocity components (\(U_{max}\), \(V_{max}\)) was performed, showing a quantitative agreement within a margin of error of approximately 5% with the results of [PERSON] et al. [40; 41]. The streamlines are presented on the right, while the isotherms appear on the left. The streamlines are on the right, while  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Present Study** & **[PERSON] et al. [38]** & **Deviation (\%)** & **[PERSON] [39]** & **Deviation (\%)** \\ \hline \(Nu\) & 8.90 & 8.81 & 1 & 8.80 & 1 \\ \(U_{max}\) & 63.14 & 64.73 & 2.40 & 64.63 & 2 \\ \(V_{max}\) & 220.71 & 220.40 & 0.14 & 219.26 & 1 \\ \(\Psi_{max}\) & 16.75 & 16.72 & 0.20 & 16.75 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the present results with those reported in the literature for \(Ra=10^{6}\).  
Figure 3: Isotherms: Comparison of the present results (**a**) with those of [PERSON] (**b**) [38; 39].  
the isotherms are on the left. When it comes to streaming, the isothermal structures that are achieved numerically and those that are acquired empirically exhibit extremely good agreement and have similar shapes. The highly developed nature of mixed convective fluxes is evident. In the two structures being compared, the closed cells show natural convection, while the open lines of the stream function show forced convection. The value of \(\Psi_{max}\) represents the maximum of the stream function. As shown in Figures 3 and 4, the findings demonstrate a high degree of agreement with the experiment.
## 4 Implementation Results and Comparisons
### Results  
Using the Vivado IDE tool, our dedicated and scalable architecture is modeled in a Verilog HDL. The implementation results on the Virtex-7 FPGA device are presented in Table 1. The underlying field \(m\) is given in column one. The FPGA slices and look-up tables (LUTs) are listed in order to investigate the utilized hardware resources in columns two and three, respectively. The required clock cycles (CCs) and provided time period (PP in \(ns\)) for logic synthesis are provided in columns four and five, respectively. The operational frequency (MHz) of the multiplier circuits for one polynomial multiplication computation is presented in column six. Column seven shows the latency (in \(\upmu\)s) and is the time to perform one finite field multiplication. The total power consumption of our multiplier circuits is given in column eight. Finally, to provide a reasonable comparison, we have defined a performance metric concerning slices times latency, shown in the last column. The lower the value of slices \(\times\) latency, the higher will be the performance metric. It is important to note that the slices, LUTs, operational frequency and power parameters in Table 1 are obtained from the Vivado IDE tool. The values for latency are calculated using Equation (3). The performance metric is calculated using Equation (4).  
\[Latency\ (in\ \upmu s)=\frac{Clock\ cycles}{Clock\ Frequency\ (in\ MHz)} \tag{3}\]  
\[Performance\ metric=FPGA\ slices\times Latency(in\ \mu s) \tag{4}\]  
For dedicated designs, Table 1 reveals that the increase in the length of the underlying field increases in slices and LUTs. All the dedicated multipliers require one clock cycle for one finite field polynomial multiplication. The dedicated multiplier can operate at a clock frequency of 500, 476, 465, 451 and 443 MHz for \(m=163,233,283,409\) and 571. The increase in the underlying field or operand length increases the computational time, i.e., latency (see column seven). Similar to latency, power values show an increase with the increase in the length of binary fields. Moreover, due to the parallel multiplication architecture style, our multiplication circuits consume more power. Although our dedicated multiplication architectures are power-consuming, they can operate starting from 440 MHz for a larger 571-bit binary field length. There is always a tradeoff. In short, with a clock cycle overhead, the power consumption of our architectures can be reduced by employing a digit-serial multiplication style instead of the use of a fully parallel approach. The product of slices \(\times\) latency, given in the last column of Table 1, increases with the increase in the length of the utilized binary field.  
For all the NIST-recommended binary fields (163, 233, 283 409 and 571), our scalable architecture utilizes 3753 slices and 7461 LUTs. It takes four clock cycles and requires 0.013 \(\upmu\)s for one polynomial multiplication computation. It can operate at a maximum clock frequency of 305 MHz. The power consumption of our scalable architecture is 3.905 W.
## 4 Smart Home Control System Prototype and Performance Evaluation
### Verification of Directional Angle Data Assisted 3D Indoor VLP Algorithm  
We verified the proposed smart home control system based on the direction-angle-supported 3D visible light positioning algorithm through an experiment. The experiment setup was the same as that shown in Figure 8, and the horizontal area was 1.5 \(\times\) 1.5 m. The VLC lightbulb was mounted at a height of 1.9 m with a diffuser diameter of 20 cm. The camera resolution of the smartphone used in this experiment was 1920 \(\times\) 1080 and the exposure time was 1/3000 s with ISO 100. The smartphone was placed in the delineated area, which was divided into a 20 cm \(\times\) 50 cm grid. The origin position in this experimental area was the projected point of the lightbulb at the horizontal plane. For the direction angle, the pitch angle \(\varphi_{\mathrm{x}}\) and roll angle \(\varphi_{\mathrm{y}}\) were limited from \(-40^{\circ}\) to \(40^{\circ}\), and the azimuth angle \(\varphi_{\mathrm{z}}\) varied from \(-60^{\circ}\) to \(60^{\circ}\).  
The smartphone was placed at four different positions with different direction angles, and the captured images at these four positions with rolling shutter patterns are shown in Figure 11. It is obvious that when the phone rotates and moves, the captured image changes from a circle to an ellipse and varies in area, which is quantized and applied to Equation (6) to calculate the 3D world coordinates of the camera. The measured directional angles and coordinates of the camera are presented in Table 3. To evaluate the proposed direction angle data supported 3D positioning algorithm, we calculate the positioning error rate (PER) using the function adopted in [37]:  
\[\mathrm{PER}(\%)=\frac{\sqrt{\mathrm{Ax_{0}}^{2}+\mathrm{\Delta y_{0}}^{2}+ \mathrm{\Delta z_{0}}^{2}}}{\sqrt{\mathrm{x_{0}}^{2}+\mathrm{y_{0}}^{2}+ \mathrm{z_{0}}^{2}}}\times 100\%, \tag{9}\]  
where (x\({}_{0}\), y\({}_{0}\), z\({}_{0}\)) are the measured world coordinates of the camera and (\(\mathrm{\Delta x_{0}}\), \(\mathrm{\Delta y_{0}}\), \(\mathrm{\Delta z_{0}}\)) are the calculated absolute values of the positioning error. From Table 4, it is obvious that the 3D world coordinates of the user can be obtained using the VLC lightbulb with a PER of approximately 7%. In terms of precision, this PER translates into a positioning error of less than 15 cm, which is sufficient for accurate and precise control of most smart home devices in close proximity.  
Figure 11: Measured 3D world coordinates of camera with images captured at four positions.
## 3 Results  
### Molecular Docking of TMA, TMAO and Human Serum Albumin  
The 3D human serum albumin (HSA) structure can be divided into three homologous domains, each one is known to be made up of two separate helical subdomains (named A and B), connected by random coil. This modular structural organization of HSA provides a variety of ligand binding sites [20,21]. From the docking of TMA targeting HSA, we detected the existence of three main cluster poses (Figure 1, Energy reported in Table S1, Supplementary Material), whilst from the TMAO and HSA docking, we found two cluster poses both located quite externally at the proximity of the HSA surface (Figure 1, Energy reported in Table S1, Supplementary Material). Furthermore, for TMA, the lowest energy pose lies in the opposite side of HSA with respect to the TMAO's one (Figure 1). In addition, the external localization of the TMAO binding poses, despite being easily reached, could result in less stable binding interactions in time; the opposite is instead observed for TMA, whose two lowest energy poses are buried inside the HSA helices and with the third positioned close to the S-S bond between Cys514-Cys559. No binding sites were found at the dimeric interface, suggesting the accessibility of the found clefts.  
Figure 1: HAS in complex with TMA (blue coloured) and TMAO (green coloured) in their respective lowest energy binding poses; the ligands are reported in a stick model with a VdW transparent surface.  
Analysing, in detail, TMA interactions with HAS, we observed that the two most stable TMA poses are located in the proximity of the disulphide bridges (Figure 2): in the most stable binding pose, Cys316-Cys361 and Cys360-Cys369 lie close to TMA (Figure 2A,B), whilst in the second pose, even if the cysteine residues are not directly involved in binding interactions with the ligand, TMA's binding site is buried within a cleft whose conformation is directly affected by the presence of S-S bonds involving Cys289-Cys278 and Cys279-Cys265 (Figure 2C,D). In order to evaluate if the oxidation state of HSA can affect TMA binding, we reduced the state of Cys316, Cys 361, Cys360 and Cys369, converting the S-S bond in the corresponding thiol moieties S-H, and redocked the ligand. Prior to proceeding with the docking experiments, a full relaxation and minimization of the reduced protein was carried out to solve any local strains. As a result, we obtained a significantly higher binding energy (\(-10.8\) vs. \(-14.1\) kJ/mol), thus associated with a corresponding much lower binding affinity.
## 3 Results
### Pig Behaviors from Direct Visual Observation
#### 3.2.2 Behavioral Differences in Pigs Under Heat Stress and Cooling Treatments  
Upon analyzing the observation data, distinct differences were observed in the behaviors of sows subjected to heat stress and cooling treatments. There were substantial variations in circadian activity patterns in sow and piglet behaviors.  
The sows under heat stress spent significantly more time lying down (\(p<0.01\)), with an additional 1.43 h or 49.3% time (Figure 5, left), predominantly from 06:00 to 18:00 (Figure 4).  
Figure 4: Visually observed number of pens where sows were lying, nursing, and sleeping (**left-hand charts**) and the number of pens where piglets were walking, nursing, and sleeping (**right-hand charts**) at different times of the day under heat stress (charts with red curves) and cooling (charts with blue curves).  
While not statistically significant (\(p>0.05\)), these sows were observed to spend 0.12 h (27.4% time) more on drinking (Figure 5, left), especially during daylight hours from 06:00 to 18:00 (Figure 3). The frequency of sows sitting in the morning (6:00-12:00) was similar across treatments, yet heat-stressed sows sat for an additional 0.13 h (36.1% more) over the entire 24-h period (Figure 5, left).  
Conversely, these sows slept 1.34 h less (a 10.7% decrease, \(p<0.05\), Figure 5, left), mainly before 09:00 (Figure 4), and spent 0.11 h less time (5.8%) eating than those under the cooling treatment (Figure 5, left), particularly during the afternoon hours of 13:00-18:00 (Figure 3). They also spent slightly less time on standing and nursing.  
Piglets raised by sows under heat stress and cooling treatments also displayed distinct behaviors (Figure 5, right). Those with heat-stressed sows engaged in nursing for 1.05 h less (a decrease of 17.3%) and slept for 1.09 h more (an increase of 9.0%) compared with piglets of sows under cooling conditions. These differences in piglet nursing and sleeping, however, were not statistically significant (\(p>0.05\)). Although both groups of piglets walked for approximately the same duration (-5 h), a \(t\)-test revealed a significant difference (\(p<0.05\)) between them. This significance likely resulted from the varied distributions of walking times throughout the 24 h period observed in each group.