esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Free Methods
#### 4.2.6 Summary of Trimap-Free Methods  
The trimap-free methods utilize the powerful feature extraction ability of deep neural networks to generate alpha mattes without the need for a trimap. Researchers have continuously improved the quality of trimap generation using semantic information, attention mechanisms, or user interaction. As these models do not require the production of a trimap, the user's expertise requirement is greatly reduced, and the application scenarios for image matting models are becoming more widespread. However, it is important to note that without the guidance of a trimap, these algorithms may be less robust in certain scenarios. For instance, in images with multiple objects or moving objects, the models may struggle to accurately separate the foreground from the background. This limitation arises because the absence of a trimap makes it challenging for the algorithm to differentiate between different objects or track their movements effectively.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
###### Abstract  
Much attention has been focused lately on the Opportunistic Routing technique (OR) that can overcome the restrictions of the harsh underwater environment and the unique structures of the Underwater Sensor Networks (UWSNs). OR enhances the performance of the UWSNs in both packet delivery ratio and energy saving. In our work; we propose a new routing protocol; called Energy Efficient Depth-based Opportunistic Routing with Void Avoidance for UWSNs (EEDOR-VA), to address the void area problem. EEDOR-VA is a reactive OR protocol that uses a hop count discovery procedure to update the hop count of the intermediate nodes between the source and the destination to form forwarding sets. EEDOR-VA forwarding sets can be selected with less or greater depth than the packet holder (i.e., source or intermediate node). It efficiently prevents all void/trapped nodes from being part of the forwarding sets and data transmission procedure; thereby saving network resources and delivering data packets at the lowest possible cost. The results of our extensive simulation study indicate that the EEDOR-VA protocol outperforms other protocols in terms of packet delivery ratio and energy consumption.  
Underwater Sensor Networks (UWSNs); Opportunistic Routing (OR); void node; hop count; energy consumption; Packet Delivery Ratio (PDR) 2021 21, 1942. [[https://doi.org/10.3390/s21061942](https://doi.org/10.3390/s21061942)]([https://doi.org/10.3390/s21061942](https://doi.org/10.3390/s21061942))  
1
## 3 Outage Performance Analysis
### Outage Probability of \(\mathrm{D_{1}}\)
\begin{split}& A_{23}=\sum_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{I\Omega_{SR}}{\Omega_{SR}+\Omega_{SP}\delta k }\exp\left(-\frac{I\Omega_{SR}+\Omega_{SP}\delta k}{P\Omega_{SP}\Omega_{SR}} \right)\\ &-\frac{1}{\Omega_{SP}}\sum_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\int_{\frac{\delta}{P}}^{\infty}\left(2\sqrt{ \frac{k}{\eta\Omega_{RP}\Omega_{SR}}}yK_{1}\left(2\sqrt{\frac{k}{\eta\Omega_ {RP}\Omega_{SR}}}y\right)\exp\left(-\left(\frac{I\Omega_{SR}+\Omega_{SP}\delta k }{I\Omega_{SP}\Omega_{SR}}\right)y\right)\right.\\ &+2\sqrt{\frac{\delta k}{\eta I\Omega_{RD_{1}}\Omega_{SR}}}yK_{1}\left(2 \sqrt{\frac{\delta ky}{\eta I\Omega_{SR}\Omega_{RD_{1}}}}y\right)\exp\left(- \left(\frac{I\Omega_{SR}+\Omega_{SP}\delta k}{I\Omega_{SP}\Omega_{SR}}y \right)\right)\right.\\ &\left.-2\sqrt{\frac{\left(\delta\Omega_{RP}+I\Omega_{RD_{1}}\right)ky}{ \eta I\Omega_{RD_{1}}\Omega_{RP}\Omega_{SR}}}K_{1}\left(2\sqrt{\frac{\left( \delta\Omega_{RP}+I\Omega_{RD_{1}}\right)ky}{\eta I\Omega_{SR}\Omega_{RD_{1}} \Omega_{RP}}}\right)\exp\left(-\left(\frac{I\Omega_{SR}+\Omega_{SP}\delta k}{I \Omega_{SP}\Omega_{SR}}\right)y\right)\right)dy\end{split}  \tag{19}\]  
Performing similar computation, \(A_{24}\) is given by  
\[ \begin{split}& A_{24}=\frac{2}{\Omega_{SP}}\sum_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\int\limits_{\frac{1}{P}}^{\infty}\left(\sqrt{ \frac{ky}{\Omega_{RP}\Omega_{SR}\eta}}K_{1}\left(2\sqrt{\frac{ky}{\Omega_{ RP}\Omega_{SR}\eta}}\right)e^{-\left(\frac{I\Omega_{SR}+\Omega_{SP} \delta k}{I\Omega_{SR}\Omega_{SP}}\right)y}\right.\\ &\left.-\frac{I\Omega_{RD_{1}}}{\Omega_{RP}\delta+I\Omega_{RD_{1}} }\sqrt{\frac{\left(\Omega_{RP}\delta+I\Omega_{RD_{1}}\right)ky}{\Omega_{RD_{1}} \Omega_{RP}\Omega_{SR}\eta I}}K_{1}\left(2\sqrt{\frac{\left(\Omega_{RP} \delta+I\Omega_{RD_{1}}\right)ky}{\Omega_{RD_{1}}\Omega_{RP}\Omega_{SR}\eta I}} \right)e^{-\left(\frac{I\Omega_{SR}+\Omega_{SP}\delta k}{I\Omega_{SR}\Omega_{SP} }\right)y}\right)dy\end{split}  \tag{20}\]  
It is noted that the probability of channel between the primary network and the secondary network is given as  
\[\Pr\left\{\left|h_{SP}\right|^{2}<\frac{I}{P}\right\}=1-\exp\left(-\frac{I}{P \Omega_{SP}}\right) \tag{21}\]Substituting Equations (17)-(20) into (16), \(A_{2}\) can be calculated by  
\[A_{2}=(A_{21}+A_{22})\left(1-\exp\left(-\frac{I}{P\Omega_{SP}}\right)\right)+A_{ 23}+A_{24}. \tag{22}\]  
Then, \(P_{out\_D1}\) can be computed based on \(A_{1}\), \(A_{2}\).
## 5 Conclusions  
This paper proposed UEP-based FTN signal processing to compensate for performance degradation due to symbol interference. The UEP method divided encoded bits into groups according to priority, and FTN interference ratios were differentially applied to each group. In deciding which group had the higher priority, the HSS-LDPC decoding algorithm was applied. By employing the LDPC coding method and FTN interference ratios of 20%, 30%, and 40%, we confirmed that the performance of the proposed UEP-FTN method, when applying the larger interference ratio to each group, improved by about 0.2 to 0.3 dB at the BER of \(\mathbf{10^{-4}}\), compared with the conventional FTN method.  
In addition, we applied the UEP-based FTN algorithm to OFDM applications in a multipath channel, with an interference ratio of 30%. It was confirmed that the data could be decoded in the multipath channel, and the performance could be improved by 0.1 to 0.3 dB, as the number of subcarriers of the OFDM-FTN method increased.  
In the paper, we confirmed that the UEP-FTN method has both a performance improvement and high transmission efficiency, and it is useful for next-satellite and/or wireless mobile communication.  
[PERSON] and [PERSON] conceived and designed the simulations; [PERSON] performed the numerical simulations and analyzed the data; [PERSON] wrote the paper; and [PERSON] revised the paper and offered useful writing suggestions.  
The research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (**NRF-2017R1D1A1A09082161**).  
The authors declare no conflict of interest.  
The data used to support the findings of this study are available from the corresponding author upon request.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 3 Range of Compounds to Which SPME Has Been Applied
### Environmental Analysis  
There is a great deal of research showing detrimental impacts of environmental organic pollutants on both humans and animals, making human exposure to them a serious concern. Understanding the bioaccumulation and toxicity of these contaminants in environmental toxicology and clinical studies requires the monitoring of these pollutants and endogenous metabolites in organisms [34].  
SPME is a well-established, green process for extracting and preconcentrating analytes across a wide range of matrices. Since its inception in the early 1990s, SPME has swiftly progressed in terms of coating materials, geometries, and uses. SPME was originally developed for environmental investigations, but it has now become a major method for analyzing volatile and semi-volatile organic compounds (VOCs and SVOCs). SPME's versatility, particularly in its headspace version (HS-SPME), has made it widely used for pollutants with diverse physicochemical features, including medicines, insecticides, and metal-organic compounds. This adaptability addresses issues in traditional sample preparation, indicating a significant impact on future analytical procedures [35; 36].  
In their work on the procedure for the control of butyl and phenyl tin pollution in the environment by GC-FPD, [PERSON] et al. found that NaBEt\({}_{4}\) ethylation-SPME extraction-GC-FPD (gas chromatography-flame photometric detection), supplemented with acidic extraction for solid matrices, is a promising method for identifying organotin chemicals in water and complicated environmental samples. They discovered that mechanical stirring greatly enhanced analytical performance during adsorption, with detection limits that are 10-700 times lower than classical liquid-liquid extraction and 2-20 times lower than SPME with magnetic stirring. Their optimized SPME approach produces results comparable to isooctane extraction and detects organotins at concentrations lower than 1 ng/L as Sn in aqueous samples. While co-extracted organic matter in complex matrices can influence triphenyl tin (TPhT) responses, recent research on adsorption-desorptionbehavior suggests that this approach is a useful tool for routine organotin detection in water [37].  
Table 1 contains information about the different types of compounds that have been detected by SPME, the types of nanoparticles used in SPME fiber coating, and their analytical applications.
# The CYGNSS Mission: On-Going Science Team Investigations
###### Abstract  
In 2012, the National Aeronautics and Space Administration (NASA) selected the CYclone Global Navigation Satellite System (CYGNSS) mission coordinated by the University of Michigan (UM) as a low-cost and high-science Earth Venture Mission. The CYGNSS mission was originally proposed for ocean surface wind speed estimation over Tropical Cyclones (TCs) using Earth-reflected Global Positioning System (GPS) signals, as signals of opportunity. The orbital configuration of each CYGNSS satellite is a circular Low Earth Orbit (LEO) with an altitude -520 km and an inclination angle of -35\({}^{\circ}\). Each single Delay Doppler Mapping Instrument (DDMI) aboard the eight CYGNSS microsatellites collects forward scattered signals along four specular directions (incidence angle of the incident wave equals incidence angle of the reflected wave) corresponding to four different transmitting GPS spacecrafts, simultaneously. As such, CYGNSS allows one to sample the Earth's surface along 32 tracks simultaneously, within a wide range of the satellites' elevation angles over tropical latitudes. Following the Earth Science Division 2020 Senior Review, NASA announced recently it is extending the CYGNSS mission through 30 September 2023. The extended CYGNSS mission phase is focused on both ocean and land surface scientific investigations. In addition to ocean surface wind speed estimation, CYGNSS has also shown a significant ability to retrieve several geophysical parameters over land surfaces, such as Soil Moisture Content (SMC), Above Ground Biomass (AGB), and surface water extent. The on-going science team investigations are presented in this article.  
CYGNSS; GNSS-R; science investigations; ocean; land; atmosphere +
Footnote †: journal: _Fronte sensing_  
The CYGNSS Mission: On-Going Science Team Investigations  
[PERSON]  
1 Climate and Space Sciences and Engineering Department, University of Michigan (UM), Ann Arbor, MI 48104, USA; [EMAIL_ADDRESS]  
2 Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA 91125, USA; [EMAIL_ADDRESS] (J.A.C.); [EMAIL_ADDRESS] (M.M.)  
3 Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; [EMAIL_ADDRESS]  
4 Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA; [EMAIL_ADDRESS]  
5 SRI International, Ann Arbor, MI 48105, USA; [EMAIL_ADDRESS]
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 3 Principle of Multi-Block Local Binary Pattern  
### Principle of Local Binary Pattern  
Due to the high ambient temperature and complex image background, the detection of surface defects for steel plates becomes a puzzling problem. The change of illumination will cause a linear change in the gray level of the image. The local binary pattern (LBP) is a method to describe the texture of the image that can eliminate linear illumination by comparing the gray values of pixels. LBP was introduced by [PERSON] [13; 14] and has been widely applied in many fields [15] such as metal surface quality detection [16], paper quality detection [17], image texture analysis [18], and target detection [19].  
The LBP descriptor was initially proposed as an effective grayscale-invariant texture descriptor based on image gray levels. The basic LBP descriptor is defined in a \(3\times 3\) pixel area. The gray value of the center pixel is s \(g_{c}\). The gray values of its 8 neighborhood pixels are \(g_{0}...g_{7}\), respectively. The texture description T of the center pixel can be expressed as Equation (1):  
\[\mathrm{T}\sim(g_{0}-g_{c},\cdots,g_{7}-g_{c}) \tag{1}\]  
Then, compare the gray values of the eight neighborhood pixels with \(g_{c}\). If the value is greater than \(g_{c}\), the binarization value of the pixel is 1, otherwise the binarization value of the pixel is 0. Then, the texture description T after binarization can be expressed as Equation (2).  
\[\mathrm{T}\sim(s(g_{0}-g_{c}),\cdots,s(g_{7}-g_{c})) \tag{2}\]  
s(x) is calculated by Equation (3):  
\[\mathrm{s(x)}=\left\{\begin{array}{c}1,x>0\\ 0,x\ll 0\end{array}\right. \tag{3}\]  
After binarization, randomly choose one of eight neighborhood pixels as a starting point. It is worth noting that the choice of starting point is random, but the choice of starting point for the entire image should be consistent, for convenience, this paper consistently selected the top left point as the starting point. Then, encode all binarization values clockwise to a binary number. Therefore, the LBP value of pixel \((x_{c},y_{c})\) is calculated in Equation (4).  
\[\mathrm{LBP}(x_{c},y_{c})=\sum_{i=0}^{7}s(g_{i}-g_{c})2^{i} \tag{4}\]  
Figure 6 shows the complete process of the local binary pattern (LBP).  
The LBP value for each pixel could be calculated by the above calculation process. In this way, an LBP image can be obtained [20].  
Figure 5: Scales.
## 3 Results
### Results of Performance and Usability Test Using Phantom Model  
The performance and usability of the system were assessed using a laparoscopy phantom model. The actual response time between the controller's action and the camera's response was observed to be 3.02 \(\pm\) 1.04 ms, on average, over 20 trials. Moreover, depending on the controller, camera movement was measured ten times for the up, down, left,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Test Item** & \multicolumn{2}{c}{**Acceptance Criteria**} & **Result** \\ \hline \multirow{5}{*}{Leakage Current} & Earth & NC & 0.5 mA & 0.02 mA \\ \cline{2-5}  & Leakage & \multirow{2}{*}{SFC} & 1 mA & 0.007 mA \\ \cline{2-5}  & Current & & 0.1 mA & 0.28 mA \\ \cline{2-5}  & Current & & 0.5 mA & 0.029 mA \\ \cline{2-5}  & Patient & NC & 0.1 mA & 0.001 mA \\ \cline{2-5}  & Leakage & \multirow{2}{*}{SFC} & 0.5 mA & 0.001 mA \\ \cline{2-5}  & Grounding Resistance & & 0.1 \(\Omega\) & 0.052 \(\Omega\) \\ \hline \multirow{2}{*}{Power Input} & \multirow{2}{*}{Input Current \(\leq\)10\%} & Rated Value & 1.3 A \\ \cline{3-5}  & & & & Measured Value & 0.15 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Safety tests, including leakage current, grounding resistance, and power input, were performed in the IEC 60601-1 series by KTL. The leakage current test, conducted at a voltage of 264 Vac and a frequency of 60 Hz, differentiated normal conditions (NCs) and single fault conditions (SFCs). It passed tests within tolerance for the leakage current, grounding resistance, and power input.  
and right directions with averages of 90.05\({}^{\circ}\)\(\pm\) 1.33\({}^{\circ}\), 90.64\({}^{\circ}\)\(\pm\) 1.31\({}^{\circ}\), 89.87\({}^{\circ}\)\(\pm\) 1.39\({}^{\circ}\), and 90.41\({}^{\circ}\)\(\pm\) 1.45\({}^{\circ}\), respectively, and an accuracy of approximately 99.4%. Usability evaluations conducted by four clinical experts yielded the following results:  
1. The average usability score for the controller was 90 points, with consensus among four surgeons;
2. The foot switch operation in the ICT laparoscopy system received an average score of 77.5 \(\pm\) 3.75;
3. The average score for the operation and delay between the controller and camera was 81.25 \(\pm\) 4.82;
4. The average score for the driving speed and camera range was 85 \(\pm\) 5.  
This evaluative feedback was crucially utilized for product enhancement purposes. The dimensions and mass of the hairband controller were deemed suitable, and its operation method was also considered appropriate. The camera's rotation angle received a positive evaluation for its adequacy. However, there were instances where the movement perception was critiqued as overly broad, coarse, or rapid. This feedback indicates a need to enhance the smoothness and refinement of the camera movement. Users could benefit from customizable movement speeds to accommodate individual sensitivity variations, allowing them to adjust the speed according to their preferences.
## 4 Conclusions  
In this paper, a novel approach to creating a tunable dual-passband MPF based on the influence of wavelength-selective amplification and the P1 oscillation state under an optically injected DFB semiconductor laser was proposed and experimentally demonstrated. The experimental results show that the central response frequency of the passband in the MPF could be widely tuned up to 36.91 GHz by adjusting the detuning frequency \(\Delta f\) and the injection ratio \(R\). At the same time, the center frequencies of the two passbands were only limited by the bandwidths of the modulator and photodetector, and the tunable range of the passbands could be further adjusted by using a DOMZM and BPD with larger bandwidths. The experimental results show that the out-of-band suppression of the MPF was more than 20 dB. Furthermore, adjusting the injected ratio \(R\) and detuning frequency \(\Delta f\) to balance the optical power of two single-passband MPFs produced a dual-passband MPF with a maximum out-of-band suppression ratio increased by 23.2 dB. Meanwhile, the insertion loss was only about 15 dB, which is much lower than many other dual-passband MPFs.  
[PERSON] designed and set up the experiment; [PERSON], [PERSON] and [PERSON] carried out the measurements under the supervision of [PERSON], [PERSON] and [PERSON] provided the theoretical support for modelling the experiment. [PERSON] wrote the manuscript and all authors suggested improvements. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key Research and Development Project (2019 YFB2203201), National Natural Science Foundation of China (61605023, 61805032, 61875028, 61520106013, and 61727816), Dalian Science and Technology Innovation Foundation (2018J11 CY006), and Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.
# Using IoT for Cistern and Water Tank Level Monitoring
## 4 Experimental Results  
### Physical Scale Model  
To demonstrate the feasibility of monitoring water tanks and cisterns using the IoT, we will describe with pictures the installation of the water level monitoring system for the cistern, water tank, water flow, water quality, and water pump. We used a physical scale model to prove the proposed system's operation.  
In Figure 12a, we see a circuit connected to a water level sensor (ultrasonic sensor JSN-SR04T-2.0), which is responsible for measuring the water level in the tank. A WeMos D1 mini ESP8266 WiFi controls this sensor. Figure 12b shows a second circuit; we see another water level sensor (ultrasonic sensor JSN-SR04T-2.0), which measures the water level in the tank. The ultrasonic sensor is controlled by a NodeMCU v2 ESP8266 WiFi.  
We placed a bucket on top of a table to emulate a water tank because this tank should go on top. Above it hangs the ultrasonic sensor measuring the water level in the tank. See Figure 13a. Similar to the water tank, we placed a bucket below the ground level since this water container must be placed below. Above it hangs the ultrasonic sensor for measuring the cistern's water level. Furthermore, inside that bucket is the hose that supplies tap water along with the flow sensor; the pump to bring water up to the tank is also submerged in the water, and the sensor that measures the water quality is placed right there. See Figure 13b.  
Figure 12: (**a**) Water tank level sensor circuit. (**b**) Cistern level sensor, water quality, and submersible pump circuit.
# Sustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits
## 3 Biological Sources of the Compounds Used in Films, Coatings, and Packaging
### Agricultural Processing Byproducts
#### 3.2.1 Materials from Vegetables and Fruits
Recent studies have focused on improving the functionality of grape seed protein films through various methods: (i) blending with other biopolymers like chitosan or cellulose to enhance mechanical and barrier properties [51]; (ii) incorporating active compounds such as essential oils or nanoparticles to impart additional functionality like antimicrobial activity [57]; (iii) optimizing extraction and film-forming conditions to maximize protein yield and film performance [51]. Lipid-based coatings are crucial in improving the water vapor resistance of edible films. Grape seed oil, a by-product of wine production, could be incorporated into coating formulations to enhance moisture barrier properties [49]. Natural waves such as bees wax or carnauba wax are often blended with other biopolymers to improve film properties, particularly hydrophobicity [42]. The combination of these various components allows for the creation of tailored edible films and coatings to address the specific preservation needs of small fruits and grapevine by-products. By utilizing these sustainable materials, the food industry can work towards reducing environmental impact while maintaining product quality and extending shelf life.