esa-sceva/satcom-chunk-collection huggingface dataset:
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 7 Discussion
We have also found that both approaches, CNN and Gabor, do not perform when presented with completely novel speakers, and so there is still a fundamental limitation with regard to generalisation. In addition, compared to other existing studies in multi-speaker speech reconstruction, such as [PERSON] et al. 's exemplar-based approach [26], [PERSON] et al. 's GAN-based approach [23], [PERSON] et al. 's GAN-based approach [19], and [PERSON] et al. 's sequence-to-sequence system [16] adapted from Tacotron 2, which necessitate the inclusion of additional speaker characteristic features as auxiliary conditions for speaker identification, our Gabor-based model eliminates the need for such additional features, allowing for direct application in multi-speaker speech reconstruction. Finally, it should also be noted that although our results are an improvement over CNN-based approaches, the resulting speech quality is still very limited, with significant distortion present. The work in this field is still relatively young, and there is still a lot of improvement to be found to achieve higher speech quality and improve intelligibility, especially with multiple speakers. However, fundamentally, while improved results have been reported in this paper, there will always be limitations with a purely front-facing lipreading approach, due to tongue and vocal cord movement that is not visible. While this paper reports a number of interesting findings and demonstrates the viability of using low-dimensional Gabor features for speech reconstruction, there are a number of future research directions that will be explored. Firstly, in this paper, the focus was on comparing our proposed method using the same datasets and metrics as in other research, so future work could consider additional datasets and, in particular, data also recorded from 30 degrees and side on, rather than simply full-face cameras only. This would require additional datasets, and also additional experimentation with the Gabor feature extraction. While focusing on lightweight features, future work will also consider additional Gabor features, as well as integrating other feature extraction methods and modalities. Finally, after improving system performance, future work would require the improved system and additional feature extraction method to be tested with both objective metrics and subjective listening tests, as well as in environments with acoustic and visual noise.
## 5 Simulation Method Uncertainties
### Frequency Error  
An additional issue for the simulations as compared with the measurements is that the simulations are performed at single frequencies in the frequency domain, whereas the real measurements are spread across the entire measurement bandwidth (and, indeed, at lower power levels beyond the nominal measurement bandwidth). To somewhat compensate for this, we simulate the RCS at five discrete frequencies across the measurement bandwidth and average the result. However, it is important to understand how well this improves the estimate over a single frequency and the residual uncertainty remaining. To address this, we performed SBR+ simulations at the 13 full MoM simulation points with a much finer frequency resolution (101 points across the band). In Figure 19 we show the variation in the RCS across the band, along with the RCS values obtained from just the central frequency and from averaging the five frequencies used in our other simulations.  
At these 13 points, the maximum error arising from simulating only 5 frequencies rather than 101 is 0.48 dB, and the mean error is 0.16 dB. If we were to simulate only a single frequency instead (and, in doing so, substantially reduce simulation time), the maximum error is 0.73 dB and the mean 0.29 dB. In these cases, using five frequencies rather than the single center frequency approximately halves the error relative to the full sweep.  
However, this is not the key issue addressed by the five-frequency sweep. The larger concern is that, as can be seen in points one and nine, there are some places where there is a null at a certain frequency. In these cases, if the single frequency point were very near the null, the error would be huge. For example, in the case of point one, the RCS value across this small frequency range varies by 31 dB. These nulls result from near-perfect destructive interference of the scattered signal in one particular direction at one particular frequency. Experience with simulations of other objects shows that these nulls are generally less deep when a MoM solver is used than with the SBR solver used here, but they occur with both. Simulating at more than one frequency significantly reduces the error in such a case. It is principally for this reason that we perform all of our simulations at five frequencies rather than just one.  
Figure 18: Angular error in position as a function of integration time.
## 1 Introduction
### Biodiesel and Biolubricant Production: Advantages and Challenges  
As in the case of other vegetable oils, several research works have focused on biodiesel and biolubricant production from vegetable oils, presenting an interesting chemical route to produce different intermediate and final products, with great potential for energy purposes or other usages [11; 12]. Normally, biodiesel production is carried out through transesterification of triglycerides with methanol (or ethanol), yielding fatty acid methyl esters (FAMEs, that is, biodiesel) and glycerol. FAMEs can be converted to biolubricants through a second transesterification with more complex alcohols such as neopentylglycol (NPG), trimethyl-lolpropane (TMP) [13], or pentaerythnitol (PE), normally under vacuum conditions [14]. In this process, methanol is evolved, which could be reused in the first transesterification. Consequently, this combined process shows different advantages: (1) A high atom economy is possible, as high yields are normally obtained in both transesterification processes, and the intermediate products are reused for further steps. Finally, some by-products like methanol can be reused in the first transesterification. Consequently, low amounts of pollutants can be evolved in the global process. (2) Many of the products obtained are biodegradable compared to petroleum-based products, reducing the environmental impact in hypothetical spills. (3) A wide range of feedstocks (vegetable oils, WCO, or animal fats) can be used, proving their versatility and adaptability to different contexts.  
However, biodiesel and biolubricants obtained through this method have different challenges, such as their low oxidation stability and improvable cold properties [14; 15]. The former is mainly due to the presence of double bonds in ester structures (derived from fatty acids), and using vegetable oil with low levels of poly-unsaturated compounds such as linolenic acid is recommended. Otherwise, auto-oxidation processes can be accelerated, implying quality loss in the final biodiesel or biolubricant, especially regarding increased viscosity and acidity due to polymerization and free fatty acid generation, respectively [14]. Nevertheless, there are alternatives such as antioxidant addition, explained in the following subsection.
## 5 MPPT Algorithms
### Deep Q-Network (DQN)
#### 5.2.1 Agent Architecture and Reward Function  
The Deep Q-Network (DQN) agent used in this work was designed to operate based on three core elements: the state space, the action space, and a multi-objective reward function. These are defined as follows:  
State Space (\(S\)).  
The agent observes the system state at each timestep through the following vector:  
\[S=\left\{V_{pv},I_{pv},D,\Delta D\right\}\]  
where \(V_{pv}\) and \(I_{pv}\) are the PV panel voltage and current, \(D\) is the duty cycle of the DC/DC converter, and \(\Delta D\) is the perturbation applied to \(D\).  
Action Space (\(A\)).  
The agent can select from a discrete set of actions that modify the duty cycle:  
\[A=\left\{-0.03,-0.01,-0.005,-0.001,0,0.001,0.005,0.01,0.03\right\}\]  
These values allow the agent to fine-tune the converter operation in small or large steps, or maintain the current setting.  
Reward Function (\(R\)).  
The total reward at each timestep is composed of seven distinct components:  
\[R=r_{1}+r_{2}+r_{3}+r_{4}+r_{5}+r_{6}+r_{7}\]  
The components are:  
* \(r_{1}\)**: Relative Power (base reward)** \[r_{1}=\frac{P_{t+1}}{P_{MPP,STC}}\] This ensures higher rewards when operating near the GMPP.
* \(r_{2}\)**: Reward/Penalty for Power Change** \[r_{2}=\begin{cases}\left(\frac{P_{t+1}}{P_{MPP,STC}}\right)^{2},&\Delta P>- \delta_{1}\\ -\left(\frac{P_{t+1}}{P_{MPP,STC}}\right)^{2},&\Delta P<-\delta_{1}\end{cases} \text{ with }\delta_{1}=1\]
* \(r_{3}\)**: Duty Cycle Limit Penalty** \[r_{3}=\begin{cases}0,&0\leq D\leq 1\\ -1,&\text{otherwise}\end{cases}\]
* \(r_{4}\)**: Proportional Penalty for Power Drop** \[r_{4}=\begin{cases}\Delta P\cdot\left(\frac{5}{P_{MPP,STC}}\right),&\Delta P<- 2\\ 0,&\text{otherwise}\end{cases}\]\(r_{5}\)**: Significant Power Gain Reward**  
\[r_{5}=\begin{cases}\frac{P_{t+1}}{P_{MMP,STC}},&\Delta P>3\\ 0,&\text{otherwise}\end{cases}\]
* \(r_{6}\)**: Current Stability Reward**  
\[r_{6}=\begin{cases}+0.5,&|\Delta I|<0.001\\ -1.5\cdot|\Delta I|,&\text{otherwise}\end{cases}\]
* \(r_{7}\)**: Exponential Reward for Power Stability**  
\[r_{7}=\begin{cases}\alpha\cdot\exp\Bigl{(}-\frac{|\Delta P|}{\beta}\Bigr{)},& |\Delta P|<\delta_{2}\\ 0,&\text{otherwise}\end{cases}\quad\text{with }\alpha=1.5,\ \beta=0.05,\ \delta_{2}=0.005\]  
This multi-component reward function was carefully designed to guide the agent towards fast convergence, stable operation, and global maximum power tracking while penalizing unnecessary oscillations or control saturation.
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 2 Image Denoising Method Based on Probability Statistics
### Noise Screening and Processing
#### 2.4.2 Noise Processing  
Noisy pixel blocks and effective pixel blocks are separated by the above steps. The noisy pixel blocks are processed separately and the valid pixel blocks are retained. The noise processing method adopts the comprehensive processing method, and the setting method selects the threshold. If noise frequency is lower than the threshold selected by the method, the neighborhood mean method is used, that is, the processing value of noise is changed to the average value of the four adjacent pixel blocks in the original image and then output. See Figure 9 for details.  
If noise frequency is higher than the threshold selected by the method, median processing is adopted, that is, the noise processing value is changed to the median value of the adjacent eight pixel blocks in the original image and then output. See Figure 10 for details.  
The processing of low-density random value impulse noise is shown in Figure 11a. The median algorithm is likely to output the noise value as the median value after processing,  
Figure 8: The process of screening.  
Figure 10: Median processing.  
Figure 9: Neighborhood average.  
and noise processing has a low fault tolerance rate and poor denoising effect. However, if mean filtering is selected, the image continuity and denoising effect can be ensured to the greatest extent by taking advantage of the fact that the overall frequency of noise points with low density is lower and the probability of adjacent points to noise points is lower. The processing of random value impulse noise with high density is shown in Figure 11b. The noise points are seriously connected, and the use of mean filtering will lead to a mutual influence between the noise points, which will affect the denoising effect. If the median filter is used, the mutual influence of adjacent noise points can be avoided and the denoising effect can be improved.  
Finally, to maximize the preservation of image details and reduce smear, the processing value of effective pixel blocks will be directly output without processing. The specific noise processing process is shown in Figure 12.
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 2 Milk Macro-Constituents
### Sugars  
The main sugar in milk is lactose, an O-\(\beta\)-d-galactopyranosyl-(1-4)-d-glucopyranose, which makes up 4.2-5% of the total mass content of milk. Lactose is a disaccharide including \(\alpha\)-d-glucose (or \(\beta\)-d-glucose) and \(\beta\)-d-galactose [26]. The C1 atom of glucose can easily change its configuration via mutarotation giving the \(\alpha\) and \(\beta\) forms of lactose. The two forms have different specific optical rotations, i.e., +89.4\({}^{\circ}\) and +35.0\({}^{\circ}\) at 20 \({}^{\circ}\)C, respectively. In milk, they are found in a \(\beta/\alpha\) ratio of approximately 1.68. The \(\alpha\)-lactose monohydrate is obtained from milk through a process of crystallization at moderate rates and temperatures below 93.5 \({}^{\circ}\)C [27]. This method is commonly used in dairy processing and for the production of lactose-based products, such as in the pharmaceutical industry where lactose is often used as a carrier for drugs. Lactose digestion and absorption in the small intestine involves its hydrolysis by the lactase enzyme in glucose and galactose [28]. Hence, lactose represents the principal source of long-term energy in milk [29; 30; 31]. Recently, the attention to lactose-free products has increased due to the widespread prevalence of lactose intolerance, which results from a deficiency of the lactase enzyme in the jejunum [32].
## 2 Network Model  
### Topology Model  
The network logical topology formed into data collection of wireless sensor networks is modeled on reverse trees, \(T=(V,L)\), shown in Figure 1. In which, \(V\) represents a collection of nodes, \(L\) represents a collection of links which connect the nodes. Root nodes of \(T\), named \(s\), represent sink nodes. \(n_{v}\) represents the number of nodes, \(n_{v}=|V|\). \(n_{e}\) Represents the number of logic links, \(n_{e}=|L|\). Orderly nodes pair (\(i_{j}\)) represents the link between the node \(i\) and the node \(j\), (\(i_{j}\)) \(\in\)\(V\)\(\times\)\(V\), that means the node \(i\) is the next hop node of the node \(j\), \(i\) is the child node of \(j\) in topology \(T\). Link (\(i_{j}\)) \(\in\)\(L\) is shorthanded for \(l_{i}\). Any node \(i\) has the only father node \(f(i)\), except root node \(s\), (\(ij\)) \(\in\)\(L\), that is \(j=\)\(f(i)\). Assume that there is a positive integer, \(n\), which establishes the formula \(k=f^{n}(i)\), then node \(k\) is calledthe ancestor node of node \(i\), node \(i\) is the descendant node of node \(k\). A collection, \(d(i)=\{k\in\textit{V}|\exists\textit{n}>0\), \(i=f^{n}(k)\}\), represents a descendant nodes collection of the node \(i\). In \(T=(\textit{V,L})\), the path form the node \(i\) to the node \(s\) is \(p_{i}\). Set \(p\) represents all paths to sink nodes, \(n_{p}=|\textit{P}|\) is the number of paths. \(M_{i}\) represents a set of all links which compose path \(p_{i}\).  
Setting \(T=(\textit{V,L})\) and \(P\), the routing matrix, \(\textit{A=}(a_{i})_{n,n_{i}}\) can be calculated: line \(i\) of \(A\) corresponds to path \(p_{i}\), row \(j\) corresponds to link \(l_{j}\), in which, \(a_{\bar{y}}=1\), represents that path \(p_{i}\) includes link \(l_{j}\), that is, \(l_{j}\in M_{i}\). This paper assumes that any link is covered by at least one path.  
If the routing between the internal node \(i\) and the gather node \(s\) in wireless sensor networks has not changed into the data collection periods, topological relations are considered to be stable between the sensor node \(i\) and the gather node \(s\). When inferring lossy links, we assume that network topology is known. Network topology can be inferred using end-to-end measurement data if it is not known.
## 2 Numerical Methodology
### Contact Point Handling  
The geometrical distortion near the contact point between pebbles will cause difficulties in the meshing process and reductions in grid quality; the contact points need to be processed to obtain suitable meshes and ensure simulation accuracy [36]. [PERSON] et al. [37] investigated the influence of reduction method on pressure drop in beds. Results indicate that the error of the simulation results is within 0.5% when the contact points are treated by the reduction method and the particle diameters are reduced to 98%d and 99%d, which have a very small effect on the simulated gas flow characteristics. In addition, [PERSON] et al. [14] and [PERSON] et al. [20] used CFD to simulate the purge gas flow characteristics in pebble beds by reducing the particle diameter to 98%d and obtained reasonable simulation results. Therefore, in this study, the pebble diameters were reduced to 98%d. The processed pebble bed model will be used in CFD simulation. The influence of the reduction in pebble diameter on packing structures will be discussed in Section 3.1.  
Figure 4: Pebble bed models with different walls and boundaries: (**a**) model and (**a**) boundaries with 4 fixed walls, (**b**) model and (**b**) boundaries with 2 fixed walls and 2 periodic boundaries, and (**c**) model and (**c**) boundaries with 4 periodic boundaries.
# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 3 Results
### Algorithm for Optimal Capturing of Dermoscopy Images in Melanoma Diagnostics  
An algorithm for image capture, assessment and display using automatic or manual mode is presented in Figure 13. After preparing a setup that consists of of a digital camera or smartphone coupled with a handheld dermoscope, the region of interest (ROI) on a skin surface is determined. In the next the step, the user could select between automatic or manual camera mode.  
In the automatic mode, all camera parameters are adjusted automatically; in the case of too low quality in the assessment step, the user has to recapture a photo with a correction of the ROI on the skin.  
Figure 12: Skin mole dermoscopy images captured on (**a**) Fotofinder Medicam 1000s (factory settings), (**b**) Fotofinder Medicam 1000s with colour correction, (**c**) Canon EOS 10M in auto mode, (**d**) Canon EOS M10 manual colour white balance (CWB) setting, (**e**) iPhone 7 in auto mode, and (**f**) iPhone 7 manual CWB setting.  
Figure 13: Algorithm for image capturing, assessing and displaying in automatic or manual camera mode.  
In the manual mode, a camera must be calibrated first. The calibration requires the adjustments of two camera parameters, colour white balance (CWB) and shutter speed. The most effective way to perform colour calibration is adjusting the CWB in the camera settings to match the illumination light profile. To avoid under- or overexposed images, the exposure time is locked on a middle grey colour patch value (neutral 8) of a ColorChecker Classic target. The calibration takes a few minutes of user time, and remains valid while the light condition remains unchanged. In the next step, images are captured and evaluated on other distortions, such as motion or defocus blur. A successfully captured image is saved in JPG or in RAW format. Images can be displayed directly on the camera's or smartphone's screen, or on a large computer monitor.
## 4 Discussion
### Improvement of Prediction Method Based on Neural Network  
This study focused on the prediction of noise in the short term. The noise levels of the monitoring points were predicted in seconds, minutes, 10 min, and 30 min, and contrasted with the measured values. The results revealed that there is a certain relationship between time interval and model performance. One possibility is that when the time interval increases, the randomness of the noise time series data set is weakened by the averaging operation, so the model can suitably fit the variation law of the noise level. Besides, previous studies have asserted that when the spatial and temporal diversity of noise sources is high, the prediction accuracy of the noise prediction model is reduced [59]. Poor performance was achieved by the model in 30-min interval data sets, probably because the LSTM network was not adequately trained and also few data sets were recorded. [PERSON] et al. employed LSTM to forecast the reliability of the server system and found that the prediction accuracy of one month's data set is higher than that of the 24-h data set, indicating that more training data has a positive impact on the prediction accuracy of the model [60]. Previous studies have developed many classic models of noise prediction, which attained good performance. However, with the development of Internet of Things technology and the promotion of big data, many classic models are no longer applicable in the case of large data volume. At this time, a new model capable of processing large data samples is needed to realize adequate management.
# Energy-Efficient AC Electothermal Microfluidic Pumping via Localized External Heating
## 3 Simulations and Results
### Voltage and Flow Rate  
Figure 11 shows a graph of voltage (V) vs. flow rate (\(\upmu\)m\({}^{3}\)/s). Each simulation was run with a channel height of 100 \(\upmu\)m. At 1 V, we observe a vortices lobed patter similar to that seen in several other surface graphics from this study. This is located centrally over the electrode array, with the higher velocities located on the interior of these lobes, the highest velocities located in the lower two lobes. The flow rate recorded in this simulation was 0.0005 \(\upmu\)m\({}^{3}\)/s. The application of 2 V causes notable change in the movement pattern, as the single pattern in the previous simulation for 1 V seems to have spread horizontally along the channel, the high-velocity lobes separating from one another. A vortices shape mimicking that seen in the previous simulation seems to begin to emerge between these separated lobes, but at much lower speeds. The flow rate in this simulation was recorded to be 0.0001 \(\upmu\)m\({}^{3}\)/s. The flow rate recorded in the fluid from these simulations was 0.004 \(\upmu\)m\({}^{3}\)/s at 4 V. The flow rate was measured 0.011 \(\upmu\)m\({}^{3}\)/s when applied voltage was 6 V. The graph in Figure 11 offers a quantitative visualization of how flow rate \(\upmu\)m\({}^{3}\) (/s) increased with voltage (V) in our study. With flow rate (\(\upmu\)m\({}^{3}\)/s) on the \(y\)-axis and voltage (V) on the \(x\)-axis, we observe higher voltage exhibits a super-linear relationship with velocity.
## 4 Results And Discussion
### Simulation Results for RFI Classification  
Figure 4 presents the confusion matrix of RFI classification using four different pretrained CNNs. As is vivid, the classification accuracy is above 90% for all the techniques, but the ResNet18 has a more accurate result with a precision of 98.3% comparatively.  
Figure 5 illustrates a comparative result of the elapsed running time using each pretrained CNN architecture. The consumed time has been computed using the \"tic-toc\" function of MATLAB. It is clear that AlexNet is comparatively less time-consuming and more efficient.  
Figure 4: RFI classification phase results using (**a**) AlexNet (97.1%), (**b**) VGG16 (97.5%), (**c**) GoogleNet (96.9%), and (**d**) ResNet18 (98.3%).