esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Remote Sensing Data
#### 3.2.2 Sentinel Radar and Optical Imagery  
Additional radar imagery was collected by the Sentinel-1 satellite constellation, operated by the European Space Agency (ESA). Sentinel-1 (C-band, \(\sim\)5.5 cm wavelength) images were obtained in dual-polarization mode, meaning that signals were transmitted vertically and received both vertically (VV) and horizontally (VH). These data were collected in high-resolution mode (\(10\times 10\) m).  
Owing to the seasonal variation in the herbivore community composition, herbivore density, and rainfall, the Reserve is highly dynamic and surface features such as soil inundation and vegetative cover often change rapidly within a year. Therefore, it was critical to obtain satellite imagery and field data that were collected during the same time period. This guided our selection of Sentinel-1 data as the complement to the PALSAR-2 imagery. Sentinel-1 imagery was captured on two dates, 7 June 2018 and 19 June 2018. The imagery was collected in ascending orbit at an incident angle of \(\sim\)33\({}^{\circ}\). One frame was sufficient to cover the entire study area. Images were calibrated to sigma-naught and filtered using a \(3\times 3\) median filter.  
Optical imagery was collected by the Sentinel-2B satellite, also operated by the ESA. Sentinel-2 data are collected in 13 spectral bands, ranging from \(\sim\)443 nm - \(\sim\)2190 nm. One frame of Sentinel-2 Level-1C top-of-atmosphere reflectance imagery was acquired for a single date, 11 June 2018. We planned to use imagery captured in July 2018 in order to include optical data coincident with the 13 July PALSAR-2 data, but an image collected early in the month was obscured by cloud cover, and subsequent image captures were collected while prescribed burns were occurring within the Reserve. We did not include the burned optical imagery in our analysis as our SAR imagery was collected before the burns, and our field verified sites did not include any already burned areas. Burned grasslands can experience enhanced regrowth and typically recover very rapidly. The visible and near-infrared bands (collected at 10 m resolution) along with vegetation red edge and shortwave infrared bands (collected at 20 m resolution) were used in this study. The bands collected at 20 m resolution were resampled to 10 m resolution using a nearest neighbor technique.
## 3 Results and Discussion
### Proximate Composition and pH Values of Coated Chicken Liver Samples  
The effects of spices and cooking methods on the proximate composition and pH values of the coated chicken liver samples are presented in Table 2. The incorporation of thyme and turmeric into the formulation of the samples did not demonstrate a significant impact on the moisture content among the deep-fried samples (\(p>0.05\)). However, the addition of turmeric caused an increase in the moisture content of the oven-cooked samples (\(p<0.05\)). Studies have reported that turmeric powder protects membrane integrity, increases the water retention of muscle tissue, and reduces cooking loss due to its strong antioxidant activity [43]. The moisture content of the oven-cooked control sample was found to be lower than that of the deep-fried samples (\(p<0.05\)). Similarly, in a study, it was reported that deep-fried chicken nuggets had the highest moisture content as compared to those cooked in a conventional oven and microwave oven [44]. In the present study, during oven cooking, contact of the sample surfaces with air and a longer cooking time compared to deep-fat frying resulted in dry crust formation and a lower moisture content in the samples. As reported by [PERSON] et al. [44], the moisture loss was more pronounced in the outer layers and crust formation is influenced by a combination of water migration and other physical and chemical phenomena that occur during the cooking process. The addition of thyme and turmeric to the sample formulations did not cause a significant difference in protein, fat, and ash contents of the samples (\(p<0.05\)) (Table 2). Moreover, the cooking method was detected as significant on the protein and ash contents of the samples. it was observed that while the protein content of oven-cooked samples was found to be statistically higher than those of the deep-fried samples, the fat content of the oven-cooked samples was detected as lower than that of the deep-fried samples (\(p<0.05\)). One of the reasons for this situation is that the oven-cooked samples lost more moisture than the deep-fried samples; therefore, the proportional protein value of the oven-cooked samples is higher. The second reason is attributed to the absorption of oil by the samples during the process of deep-fat frying. [PERSON] et al. [45] and [PERSON] et al. [46] stated that due to simultaneous heat and mass transfer, some of the oil used during frying is absorbed and diffused by the product. Additionally, [PERSON] et al. [47] reported that moisture loss resulted in increased fat absorption during the deep-frying of chicken nugget samples. In consideration of the elevated level of oil absorption, the resultant cooking loss value is not numerically high. In the present study, higher oxidation levels were detected in the deep-fried samples compared to the oven-cooked samples due to the higher fat content of the deep-fried samples. Coated chicken liver samples had a pH value ranging from 6.53 to 6.76. Similarly, in a study, the initial pH of cooked minced chicken liver was reported as 6.13 [39]. It was
## 8 Conclusions  
The main issues that influence the design of underwater communication protocols are the battery life of underwater sensors and the dependability of low-rate acoustic communications. In this work, we used a stochastic model for piezoelectric energy harvesting and took into account the dynamic nature of underwater ambient resources to optimize the transmission schedule of the nodes. Our method considers spatio-temporal uncertainty in the available energy of the piezoelectric energy harvesting method. Our simulation results demonstrate that the proposed adaptive scheme, based on the decision of time slots to perform transmission, extends the network's lifespan. An approach, distributed cooperative multi-agent reinforcement learning is proposed, considering the realistic channel and environmental specifications. Nodes interact with the environment to learn energy harvesting opportunities and select their transmission times with an adaptive strategy. Our main focus is exploiting the available ambient energy while alleviating the load on the cluster head. Performance evaluations reveal that our proposed approach improves the lifetime of the network by 15%, while the incorporation of energy harvesting only contributes 10% to the lifetime. Our study aims to provide valuable insights for researchers investigating optimal time slot allocation for the sustainable operation of UASNs. Towards this aim, we show the contribution of energy harvesting to the traditional contention-free medium access methods.
## 2 The Influence of Different Factors on Oral Microbiome
### Degree of Dysplasia and Disease Management  
The severity of dysplasia and the management strategies employed significantly influence the oral microbiome. Advanced dysplasia often correlates with more profound microbial changes, reflecting the disease's progression and the increasingly dysbiotic environment [145; 146; 147; 148]. Different treatments, including surgical intervention, radiation, and pharmacological therapies, can further modulate microbial communities [149; 150; 151; 152; 153; 154; 155; 156]. Advanced dysplasia, characterized by significant cellular atypia and architectural disruption, is associated with distinct shifts in the oral microbiome. Studies have shown that severe dysplasia is often accompanied by a decrease in microbial diversity and an increase in pathogenic bacteria, such as _Fusobacterium nucleatum_ and _Porphyomonas gingivalis_[145; 146; 147]. These bacteria are known to promote inflammation and may contribute to the progression from dysplasia to carcinoma by creating a pro-carcinogenic microenvironment [148]. Disease management strategies, including surgical resection, radiation therapy, and chemotherapy, also impact the oral microbiome. Surgical interventions, while removing dysplastic lesions, can disrupt the local microbiome and promote the colonization of opportunistic pathogens during the healing process. Post-surgical microbial imbalances can lead to complications such as infections and delayed healing [149; 150]. Radiation therapy, commonly used in the treatment of OSCC, can have profound effects on the oral microbiome. Radiation can damage the salivary glands, reducing salivary flow and altering the oral environment. This reduction in saliva, which has antimicrobial properties, can lead to a decrease in microbial diversity and an increase in opportunistic pathogens, such as _Candida_ species, contributing to conditions like oral candidiasis [151; 152]. Additionally, radiation-inducedchanges in the oral microbiome can exacerbate inflammation and increase the risk of secondary infections [153]. In the study by [PERSON] et al. [154], oral microbiome dysbiosis was observed in HNSCC patients, with notable shifts during radiotherapy. By the end of treatment, the dysbiosis lessened; however, elevated levels of _Fusobacterium nucleation_ and _Porphyomonas gingivalis_ were associated with poorer clinical outcomes. Chemotherapy, which targets rapidly dividing cells, can also affect the oral microbiome by disrupting the balance of microbial communities. Chemotherapeutic agents can reduce the abundance of beneficial bacteria and allow for the overgrowth of resistant and pathogenic species. This dysbiosis can result in oral complications such as mucositis, candidiasis, and periodontal disease, highlighting the need for careful microbial management in patients undergoing chemotherapy [155,156]. Figure 1 illustrates a concise overview of all the previously discussed factors influencing the oral microbiome.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 4 System Design  
To verify the method proposed in the paper, a PD monitoring system based on microcontroller and convolutional neural network was designed, and the overall framework is shown in Figure 12. The system used a pulse current sensor to sense the PD signal and then amplifier and detector circuits to condition the PD signal, and, finally, the data was acquired by the microcontroller with its own ADC. The laboratory test environment is shown in Figure 13.  
The data acquisition section used an STM32F769 microcontroller, triggered by an industrial frequency signal with a sampling frequency of 5.4 MHz. The acquired data was subjected to feature construction after Section 3.1, and, after superimposing 15 cycles of data to generate PNG grayscale images, it was transferred to the data analysis section through the serial port.  
The data analysis section used an STM32H743 microcontroller ported with the convolutional neural network described in Section 2.2. This microcontroller used the serial port to receive the grayscale image data and then performed a neural network calculation to identify and obtain the discharge type and return the identification result to the data acquisition section via the serial port for display.  
Figure 11: CNN memory allocation diagram.
# An Optical Encoder Chip with Area Compensation
###### Abstract  
A photodiode area-compensation method based on light intensity distribution characteristics is introduced to solve the problem of the hybrid optical encoder's inconsistent absolute code output signals. This method performs area compensation of different degrees according to the irradiance received by the photodiodes at different positions, thus achieving the consistency of output signals and reducing the bit error rate of absolute code signals. Based on the 0.35 \(\upmu\)m CMOS process, a four-channel photodiode array chip for a reflective hybrid optical encoder was designed. Moreover, the absolute code photodiode arrays were designed with area compensation. The test results show that the square wave duty cycle error of the output signals is less than 2% when the LED light source works normally. When the LED working current changes by \(\pm\)2.85 mA, the output signal's square wave duty cycle error is less than 3.1%. In each case, the square wave duty cycle error of the output signals is small, so it can be seen that the area compensation method based on light intensity distribution can achieve good consistency of the output signal. The chip has been taped and packaged, and the chip area is 21.45 mm\({}^{2}\).  
photodiode; hybrid optical encoder; reflection; area compensation; chip +
Footnote †: journal: _electronics_  
0
Footnote 0: Copyright** © 2022 by the authors. Licensee 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/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)))
## 2 Experiment  
The LEDs were grown by metal-organic chemical vapor deposition on (0001)-sapphire substrate using a 1.0 \(\upmu\)m AlN buffer layer followed by a 0.5 \(\upmu\)m thick undoped Al0\({}_{0.6}\)Ga0\({}_{0.4}\)N and a 1.0 \(\upmu\)m thick n-Al0\({}_{0.6}\)Ga0\({}_{0.4}\)N template. Then, Al0\({}_{0.49}\)Ga0\({}_{0.51}\)N/Al0\({}_{0.55}\)Ga0\({}_{0.45}\)N MQWs were grown as active regions. The thicknesses of wells and barriers measured by transmission electron microscopy are 2.8 nm and 5.9 nm, respectively. p-Al0\({}_{0.3}\)Ga0\({}_{0.7}\)N (25 nm)/Al0\({}_{0.6}\)Ga0\({}_{0.4}\)N (25 nm) were used as p-type layers. Finally, a 25 nm p-GaN contact layer was deposited. Based on the above structure, four samples were grown, and the different characteristics are listed in Table 1. LEDs A, B and C have 5, 10 and 40 period MQWs, respectively. LED D has a similar structure to LED A, except that it has a 1 nm thick i-AlN interlayer between the active region and the p-type layers. A 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m square geometry p-n junction device was fabricated using standard lithographic techniques to define the feature and reactive ion etching (RIE) was used to expose the n-Al0\({}_{0.6}\)Ga0\({}_{0.4}\)N Ohmic contact layer. n-type Ohmic contacts of Ti/Al/Ni/Au (15 nm/80 nm/12 nm/60 nm) were deposited by electron beam evaporation and annealed with a rapid thermal annealing system at 900 \({}^{\circ}\)C for 30 s in nitrogen ambience. For transparent p-contacts, Ni/Au (6 nm/12 nm) layers were electron beam deposited and annealed in air ambience at 600 \({}^{\circ}\)C for 3 min. The device was completed with the deposition of a Ni/Au (5 nm/60 nm) p contact. Figure 1a-p shows the flow chart of the device process in order, in which Figure 1a,e,i,m show the spinning coating, Figure 1b,f,j,n show the lithographic process,Figure 1c shows the RIE; Figure 1d,h,l,p show the glue cleaning; and Figure 1g,k,o show the electron beam evaporation of metals. The electroluminescent (EL) spectrum was measured at room temperature using a HORIBA Jobin Yvon's Symphony UV-enhanced liquid nitrogen-cooled charge-coupled device detector. Agilent 4156 C was used as the current source.  
\begin{table}
\begin{tabular}{c c c} \hline \hline LEDs & EBL & QW Number \\ \hline A & no & 5 \\ B & no & 10 \\ C & no & 40 \\ D & 1 nm AlN & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The quantum-well (QW) numbers and electron blocking layer (EBL) structures for all the deep UV light-emitting diodes (DUV-LEDs) used in this work.  
Figure 1: The flow chart of the device process. (**a**,**e**,**i**,**m**) show the spinning coating. (**b**,**f**,**j**,**n**) show the lithographic process. (**c**) shows the RIE. (**d**,**h**,**l**,**p**) show the glue cleaning. (**g**,**k**,**o**) show the electron beam evaporation of metals.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 2 Materials and Methods
### Development of the Predictor Model
For an input activation vector \(\mathbf{h}\) of size \(t\) and a mask vector \(m\), where mask elements are either 0 or 1, the Dropout is expressed as  
\[\mathbf{h}_{dropout}=\mathbf{h}\odot\mathbf{m}, \tag{15}\]  
where \"\(\odot\)\" denotes element-wise multiplication, and \(m\) is the mask vector created randomly with probability \(p\) (the deactivated neuron proportion):  
\[m_{i}=\left\{ \begin{array}{rl}\frac{1}{1-p}&\text{with probability}\ \ 1-p\\ 0&\text{with probability}\ \ p\end{array} ,\right. \tag{16}\]  
where \(p\) is the probability that the element will be deactivated, and \(\frac{1}{1-p}\) is the scaling factor for maintaining the activation level during training. After applying Dropout during training, the output vector \(\mathbf{h}_{dropout}\) is scaled by \(\frac{1}{1-p}\) to preserve the expected output activation:  
\[\mathbf{h}_{dropout}=\frac{\mathbf{h}\odot\mathbf{m}}{1-p}. \tag{17}\]  
The Dense layer (or fully connected layer) transforms the input data through a linear combination followed by an activation function. An input activation vector \(\mathbf{h}_{dropout}\) of size \(n\) is transformed into an output activation vector \(\mathbf{h}_{dense}\) of size \(m\) using a weight matrix \(\mathbf{W}\) and a bias \(\mathbf{b}\). The linear transformation is expressed as follows:  
\[\mathbf{z}=\mathbf{W}\cdot\mathbf{h}_{dropout}+\mathbf{b}, \tag{18}\]  
where \(\mathbf{W}\) is an \(m\)\(\times\)\(n\) weight matrix, \(\mathbf{b}\) is a displacement vector of size \(m\), and \(\mathbf{z}\) is an intermediate output vector of size \(m\). The modified (special) ReLU activation function is applied to the intermediate output \(\mathbf{z}\) to obtain the final output vector \(\mathbf{h}_{dense}\):  
\[\mathbf{h}_{dense}=\text{ReLU}(\mathbf{z}), \tag{19}\]  
that is  
\[\mathbf{h}_{dense}=\text{ReLU}(z_{1}),\,\text{ReLU}(z_{2}),\,\ldots,\,\text{ ReLU}(z_{m}), \tag{20}\]  
and  
\[\text{ReLU}(z_{i})=\max(0,\,z_{i}). \tag{21}\]  
Thus, for each element \(z_{i}\) in vector \(\mathbf{z}\), if \(z_{i}>0\), it remains unchanged, and if \(z_{i}\leq 0\), then it is replaced by 0.
## 2 Materials and Methods
### Quality Control Tools  
Radiation exposure was measured using the RaySafe Xi multimeter (Unfors RaySafe AB, Billdal, Sweden); Star test pattern for focal spot size; Leeds University image quality test tools (FG18) for low- and high-contrast measurement and TO.10 for minimum contrast detail detectability measurements; and Radcal 1800 cc ionization chamber for scatter radiation measurements. Moreover, the variable thicknesses of Al, Cu, and PMMA patient equivalent phantoms were measured.
###### Abstract  
The performance of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Horizontal, shallow-water channels are characterised by extremely disadvantageous transmission properties, due to strong multipath propagation and refraction phenomena. The paper presents the results of communication tests performed during a shallow, inland-water experiment with the use of a laboratory model of a UAC system implementing the Orthogonal Frequency-Division Multiplexing (OFDM) technique. The physical layer of data transmission is partially configurable, enabling adaptation of the modulation and channel coding parameters to the specific propagation conditions. The communication tests were preceded by measurement of the UAC channel transmission properties. Based on the estimated transmission parameters, four configurations of OFDM modulation parameters were selected, and for each of them, communication tests were performed with the use of two Error-Correction Coding (ECC) techniques. In each case, the minimum coding rate was determined for which reliable data transmission with a Bit Error Rate (BER) of less than \(10^{-4}\) is possible.  
underwater acoustic communications; UAC; orthogonal frequency-division multiplexing; OFDM; error-correction coding +
Footnote †: journal: Applied Sciences
# Enhancing Chatter Stability for Milling Thin-Walled Blades by Designing Non-Uniform Allowance
###### Abstract  
During the milling of thin-walled blades, the removal of material exhibits strong time-varying dynamics, leading to chatter and a decrease in surface quality. To address the issue of milling vibrations in the machining of complex thin-walled blades used in aerospace applications, this work proposes a process optimization approach involving non-uniform allowances. The objective is to enhance of he stiffness of the thin-walled parts during the milling process by establishing a non-uniform allowance distribution for the finishing process of thin-walled blades. By applying the theory of sensitive process stiffness and conducting finite element simulations, two processing strategies, namely uniform allowances and non-uniform allowances, are evaluated through cutting experiments. The experimental results demonstrate that the non-uniform allowance processing strategy leads to a more evenly distributed acceleration spectrum and a 50% reduction in amplitude. Moreover, the surface exhibits no discernible vibration pattern, resulting in a 35% decrease in roughness. The non-uniform allowance-processing strategy proves to be effective in significantly improving the rigidity of the thin-walled blade processing system, thereby enhancing the stability of the cutting process. These findings hold significant relevance in guiding the machining of typical complex thin-walled aerospace components.  
thin-walled parts; vibration; sensitivity; stiffness; allowance +
Footnote †: journal: Applied Sciences
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 2 Coupled CFD-DEM Mathematical Equations
### Dust Particle Trajectory Equation  
In this paper, the motion differential equation of the dust particles in air flow can be expressed as:  
\[m_{i}\frac{d\overset{\rightarrow}{v}_{i}}{dt}=\overset{\rightarrow}{f}_{ drag,i}+m_{i}\overset{\rightarrow}{g} \tag{4}\]  
where \(\overset{\rightarrow}{f}_{drag,i}\) is calculated with the free-flow traction model, \(\overset{\rightarrow}{f}_{drag,i}=0.5C_{d}\rho_{g}A_{b}\bigg{|}\overset{ \rightarrow}{v}_{p-a}\bigg{|}\overset{\rightarrow}{v}_{p-a}\), in which \(A_{b}\) is the projected area of dust particles, \(\overset{\rightarrow}{v}_{p-a}\) is the relative velocity between the particles and the air flow and \(C_{d}\) is the drag coefficient, which is calculated from the following formula [26],  
\[C_{d}=\begin{cases}24/Re&Re\leq 0.5\\ 24(1.0+0.15 Re^{\partial_{1}687})/Re,0.5<Re\leq 1000\\ 0.44&Re>1000\end{cases} \tag{5}\]  
where \(Re\) is the Reynolds number, \(Re=\rho_{a}d_{p}\bigg{|}\overset{\rightarrow}{v}_{p-a}\bigg{|}/\mu_{a}\) and \(\rho_{a},\mu_{a}\) is the air density and dynamic viscosity coefficient. When particles collide with other particles or solar panels, the contact force between the particles and the other particles or solar panels must be considered. The motion equation of particles can be rewritten as:  
\[m_{i}\frac{d\overset{\rightarrow}{v}_{i}}{dt}=\overset{\rightarrow}{f}_{ drag,i}+m_{i}\overset{\rightarrow}{g}+\overset{\rightarrow}{F}_{n}+ \overset{\rightarrow}{F}_{nd}-\overset{\rightarrow}{F}_{\tau} \tag{6}\]  
where \(\overset{\rightarrow}{F}_{n},\overset{\rightarrow}{F}_{nd},\overset{ \rightarrow}{F}_{\tau}\) are the normal force, normal damping force and tangent damping force, respectively, which were all calculated by the soft sphere model.