esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 4 SDAG Model and HSDFG Scheduling
### HSDFG Scheduling
#### 4.3.1 Problem Definition
\(l(c_{i})\) denotes the load on processor \(c_{i}\), defined as follows:  
\[l(c_{i})=\sum_{\alpha\in U(c_{i})}et(\alpha) \tag{15}\]  
When instance \(\alpha_{i}\) is assigned to processor core \(c_{i}\), \(AFT(\alpha_{i})\) denotes the actual execution end time of instance \(\alpha_{i}\). \(\alpha_{exit}\) denotes the terminating instance of the HSDFG. If a given HSDFG has multiple terminating instances, then _makespan_ is defined as follows. \[makespan=max(AFT(\alpha_{exit})) \tag{16}\]  
Our goal is to make full use of the limited number of processor cores to minimize s. Therefore, the objective function is defined as follows:  
\[min\ makespan\]
## 2 Route Generation Methodology
### Higher-Level Path Generation: Genetic Algorithm (GA)
#### 2.3.4 Mutation Operation  
The mutation operators are meant to explore neighboring solutions by making small changes in the chromosomes. For this research, two mutation operators are proposed: vehicle and starting point mutation. The vehicle mutation occurs when a new vehicle is randomly selected for the corresponding chromosome block, and a starting point mutation is carried out when a new starting point is randomly selected for the chromosome block.  
The mutation can only happen once per chromosome block and the mutation operator used is randomly selected with an equal probability of being a vehicle or starting point mutation. This is to prevent over-searching in the local solution space. To perform the mutation, the chromosome blocks are iterated over, and a random number is generated. If it is less than a predefined mutation probability, mutation will occur. However, the mutation operator is randomly selected as either a vehicle or starting point mutation. The vehicle mutation has the potential to remove vehicles from the chromosome. This means that some vehicles can be excluded from operation in some solutions. This presents a unique challenge since all vehicles should be used in operation. To solve this issue, vehicle mutation is only performed if changing the current vehicle assignment does not completely remove a vehicle from operation. The mutation process can be seen in Figure 7.
# Morphological Foot Model for Temperature Pattern Analysis Proposed for Diabetic Foot Disorders
## 2 Materials and Methods  
### Image Acquisition  
IR images were acquired with the affordable thermal camera model TE-Q1 Plus from Thermal Expert(tm) (i3 system Inc., Daejeon, Korea) which has been previously described and was previously calibrated [10]. RGB-D images were acquired with an Intel(r) RealSense(tm) D415 camera (Intel Corporation, Santa Clara, CA, USA). The resolutions of the RGB-D images were conditioned by the maximum value achievable by the depth sensor (1280 \(\times\) 720 pixels), whereas for the IR images they are determined by the TE-Q1 Plus sensor (384 \(\times\) 288 pixels). IR images were stored in Tag Image File Format (TIFF) and the RGB images in Portable Network Graphic (PNG) format. In both cases, 32 depth bits were used. These cameras were assembled together in a customized support, manufactured in a 3D printer, that kept the cameras horizontally aligned. RGB-D images were translated and cropped to match the coordinate system and size of the IR images [25; 26]. Thus, each pixel was represented by five channels (R,G,B,D,IR).  
An image acquisition campaign was carried out in May 2021 among coworkers at our facilities. The group of volunteers consisted of 22 healthy subjects, nine women and thirteen men, whose average ages were 36.4 \(\pm\) 10.5 and 36.1 \(\pm\) 7.4 years old, respectively. The average female European foot size was 37.5 \(\pm\) 1.3, ranging between 36 and 40, whereas the male's was 42.8 \(\pm\) 1.0 in a range between 41 and 45. No subject was part of the acquisition more than once and none of these volunteers presented partial amputations or deformations of the feet. Informed consent was obtained from all subjects involved in the image acquisition. In addition, acquired data were codified and anonymized to ensure subject confidentiality and data protection.  
A non-constrained acquisition protocol was selected for the presented setup in which the working distance between the cameras and the subject's feet was set at approximately 80 cm. Furthermore, the acquisition was carried out in a room with controlled luminosity, humidity, and an average ambient temperature of 25 \({}^{\circ}\)C. Prior to image acquisition, the cameras were stabilized for approximately 15 min [10; 31]. Four sets of images were acquired per subject. The first one, at T0, was captured as soon as the person took their shoes off and sat with their legs extended forward or laid down in a supine position with their feet off the ground. Thus, this takes the minimum amount of time since the person took their shoes off and image acquisition occurred at T0. The subsequent images were taken every five minutes up to 15 (T5, T10 and T15); meanwhile, the subject was at the same resting position keeping their feet off the ground. Therefore, a total of 88 images were available. It must be clarified that, prior to image acquisition, the subjects were not exercising or performing strenuous physical activity, bathing, or using hot or cold compresses for cleaning purposes.
## 3 Proposed Method
### Segment Embedding  
Next, we convert the variable-length segments to fixed-length vectors. In a previous study [9], we compared the methods of linear interpolation, LSTM auto-encoders [26], and LSTM classifiers to convert variable-length segments to fixed-length vectors. According to [9], the fixed-length vectors obtained using linear interpolation do not accurately represent the acoustic differences of each phoneme. The fixed-length vectors, obtained from the LSTM auto-encoder architecture, were shown to cluster with frame length information as the frame length information is the most important information for restoring a latent vector to the original feature vectors of the segment. Finally, the fixed-length vectors obtained from the LSTM classifier architecture are more suitable for fixed-length vector extraction to derive subword units.  
Based on these previous results, we decided to convert the variable-length segments to fixed-length vectors by using the LSTM classifier structure [9], shown in Figure 2. The fixed-length vectors are extracted at the point between the LSTM encoder (blue) and the DNN decoder (red), where the acoustic properties of each segment are projected into the latent space.  
\(s_{i}^{l}\) represents the \(i\)-th segment with the label \(l\). Here, the label \(l\) consists of 40 phoneme symbols commonly used for Korean speech recognition. \(f_{k}\) represents the \(k\)-th 40-dimensional log-Mel filter-bank feature spliced in time with adjacent \(\pm\)1 frame for a segment of length \(n\). \(x_{i}^{l}\) represents the \(i\)-th fixed-length vector with the label \(l\). \(y^{l}\) represents the one-hot vector of label \(l\). Similar to the model structure, we used conventional LSTM consisting of an input gate, a forget gate, and an output gate, and the peephole connections were not used. As the model parameter, we used one hidden layer with 80 LSTM units with the tanh activation function and the Adam optimization algorithm with \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, \(\varepsilon\) = 10\({}^{-8}\), a batch size of 64, and 10 epochs with a learning rate of 0.01.  
Figure 1: Framework for building the proposed unit using segment embedding and clustering.  
Figure 2: LSTM structure for fixed-length vector extraction.  
Here, we did not optimize parameters such as the dimension size or the number of layers at the encoder and decoder stages because, whenever the model parameters change, we have to perform all of the following steps again: Segment embedding, segment clustering, and acoustic model training for a speech recognition experiment. However, in light of previous studies [26; 27; 28], the 80-dimensional latent vector used in this study is of an appropriate size to represent the segment. A previous study [27] argued that speech segments perform best when they are represented with 50-200 dimensions in nonlinear embedding using Laplacian eigenmaps. In addition, other studies [26; 28] also used 60-100 dimensional latent vectors to represent speech segments or acoustic features.
## 2 Path Planning Algorithm Based on the Markov Decision Process (MDP)
### Determination of Markov Action Sets  
In order to provide the state sets for path planning, the area for AUV deployment is mapped to discretized grids according to the bathymetry and coastline. Islands or obstacles, where the AUV cannot pass through, are marked on the gridded map. In the 2D case, the AUV moves only on a horizontal level. The grids, where the AUV can arrive at the successor step, are labeled by numbers 1 to 9 (Figure 1). Initially, the AUV is in the center grid 5. After the AUV takes an action \(i\) (\(i=1\), 2... 9), the AUV can only appear in its adjacent eight grids. Otherwise, it stays in grid 5 by taking no action. In the 3D case, the AUV can reach 27 cubes (Figure 2). Technically, it can only move to its adjacent 26 cubes by taking effective action, otherwise, staying in cube 15 by taking no action. Since the aspect ratio of the ocean--which is a ratio between the depth and horizontal length scale--is usually much less than 1, only vertically upward and downward movements are kept for the vertical actions, thus the action set can be significantly reduced through dumping the other 16 actions related to vertical movements. Now, the size of the state set \(s\) is reduced from 27 to 11. This division of actions is suitable for the practical movement of AUVs. Table 1 shows the partitioning of the 3D action set.
## 5 Simulating CMLs for Computational Experiments
### Key Simulation Factors  
Two key factors are to be determined when simulating CMLs. The first is the CMLs' mean length, which is a physical parameter, and the second is the spatial resolution, translating to number of pixels per area, which is not a physical feature but a computational one. Since a CML is represented as a numerical array, the spatial resolution dictates how many elements will be in that array. The higher the resolution, the more pixels the area is being partitioned to.  
It may be easy to understand why we do not proclaim the CMLs' orientation to be a key factor. Since we have established that the orientation is completely random and distributed uniformly, there is no distribution parameter that needs to be pre-set to model it. What is not so straightforward is the reason spatial density of CMLs is not declared as a key factor. The answer is computational modularity. Sets of CMLs are generated such that they allow for all relevant spatial densities to be utilized in the experiments they mean to serve. All sets are generated with a sufficiently high number of CMLs that allows for the highest spatial density of CMLs. This way, when lower spatial densities are desired, smaller randomly selected subsets of the CMLs can be utilized. For instance, for an area of 10 \(\times\) 10 (km\({}^{2}\)), a set of 1000 CMLs is generated. The experiment of interest requires a density of 2 CMLs per km\({}^{2}\), thus 200 CMLs are randomly selected, and the fact that there is a \"sufficiently high\" number of CMLs allows for many Monte-Carlo iterations with different subsets of CMLs. Here \"sufficiently high\" refers to a number so high that it allows for the maximal number of CMLs to be randomly selected out of the set, multiple times. The contrary would be to limit the set to having \"just enough\" CMLs, thus allowing only one manner for selecting the maximal number of CMLs by simply selecting the entire set. The latter would not allow for repetitions of the experiment in a Monte-Carlo setting.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 5 Conclusions  
In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in NsCT domain is proposed. This method was tested on different remote sensing images. After an image is decomposed by NsCT transform, there will be one low-frequency sub-band and several high-frequency sub-bands. Contrast stretching is applied to enhance the contrast of the low-frequency component, and the non-local means filter is used to suppress the noise contained in the first high-frequency sub-band. The processed coefficients are then reconstructed using the inverse nonsubsampled contourlet transform. Finally, the unsharp filter is adopted to enhance the details of the reconstructed image. The performance of the proposed method was evaluated and compared  
Figure 9: The histogram of experimental data in Table 4. (**a**) H; (**b**) MSE; (**c**) PSNR; (**d**) GMSD; (**e**) MAE; (**f**) MSSSIM.  
using the H, MSE, PSNR, GMSD, MAE, and MSSSIM metrics. Experimental results demonstrate that the proposed algorithm has better performance when compared with other existing techniques.  
This work was supported by the Key Scientific and Technological Research Project of Jilin Province under Grant Nos. 20150204039 GX and 20170414017 GH; the Key Scientific and Technological Project of Changchun Government under Grant No. 14 KG064; the Natural Science Foundation of Guangdong Province under Grant No. 2016A030313658; the Innovation and Strengthening School Project (provincial key platform and major scientific research project) supported by Guangdong Government under Grant No. 2015 KTSCX175; the Premier-Discipline Enhancement Scheme Supported by Zhuhai Government and Premier Key-Discipline Enhancement Scheme Supported by Guangdong Government Funds.  
The experimental measurements and data collection were carried out by [PERSON] and [PERSON]. The manuscript was written by [PERSON] with the assistance of [PERSON] and [PERSON]. All authors reviewed the manuscript.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### PCR Amplification and Sequencing  
Our research group resequenced the genome of 126 domestic donkeys, analyzed these data with reference genome (GenBank accession number: NC_052186.1) and generated a collection of 17.28 million SNVs and 1.5 million Indels [2], a total of 34 SNVs and 10 Indels were located on the _NR6A1_. Primer Premier 5.0 software was used to design 13 pairs of specific primers to amplify the total region on _NR6A1_ gene (Table S1). To detect the polymorphisms, we used the above primers to design PCR amplification reactions. Reaction system is 25 uL, including 12.5 uL 2\(\times\) Taq Master Mix (TIANGEN, without-dye, MF002, Beijing, China), 9.5 uL ddH\({}_{2}\)O, 50 ng/uL DNA template 1 uL, 10 umol/L upstream primer (Sangon Biotech, Shanghai, China) 1 uL, 10 umol/L downstream primer (Sangon Biotech, Shanghai, China) 1 uL. The PCR protocol was as follows: 95 \({}^{\circ}\)C for 5 min, 30 cycles of denaturing at 95 \({}^{\circ}\)C for 30 s, annealing at Tm (Table 1) for 30 s, and extension at 72 \({}^{\circ}\)C for 30 s, with a final extension at 72 \({}^{\circ}\)C for 10 min. After completing the whole reaction, PCR products were verified by electrophoresis in 1% agarose gel stained with ethidium bromide. The PCR product size was compared with the 2000 bp DNA ladder (MeiSbio,MF025, Beijing, China). Finally, the products were sent to the sequencing company (Beijing Genomics institution, Beijing, China) for sequencing, and results were analyzed using DNAstar software (Version 12.1, DNASTAR, Inc, USA). A total of 455 individuals were genotyped and used for the following correlation analysis. In this study, we showed 7 SNVs, which had a genotype frequency of one loci greater than 5% for each genotype and were associated with TLN, body size or carcass traits. The information of amplicon used for PCR of these seven SNVs are shown in Table 1.
# Effects of Agricultural Biomass Burning on Regional Haze in China: A Review
## 2 Biomass Burning and Regional Haze
### Agricultural Open Field Biomass Burning in China
The emissions of OC, EC and PM have two peaks, in May to June and October, after the harvesting of crops. Because the two periods are times of important agricultural activities, the increase of biomass burning, fertilizer and other agricultural activities during this time contributes a great amount of OC, EC and PM [26]. The sensitivity of gaseous pollutants and aerosol particle emissions from biomass burning to air quality was also simulated by [PERSON] et al. [27]. Their results suggest that the improvement of provincial-level emissions estimation of primary PM, SO\({}_{2}\) and NO\({}_{\text{X}}\) should be considered in future studies. Estimates of the effects of biomass burning on regional haze and air quality are based on emission inventories of agricultural straw burning. The emission inventory was calculated by multiplying the total amount of crop residue burnt by the corresponding emission factors. Methods for establishing an emission inventory include a bottom-up approach and a top-down approach [22]. The difference between the two approaches is the calculation of amount of crop residue burnt. In the bottom-up approach, the total amount of crop residue burnt was based on statistical data (e.g., product of crop production, residue-to-production ratio, dry matter-to-crop residue ratio, the percentage of dry matter burned in fields, etc. ), whereas the top-down approach was based on satellite data. There are also two approaches about satellite data, one is based on the burned area, and the other is based on FRP. However, each of the methods has some limitations. Statistical data acquisition requires more time to analyze the collection of agricultural straw products, burnt fraction and burnt efficiency data, while satellite data perform poorly for small fires, especially for burned-area-based approach. Indoor biofuel burning and open field burning with a smaller area was not detected by satellite. FRP-based approach was developed in the past few years; it can calculate biomass burning emission inventory with higher temporal and spatial resolution, which is more suitable for model users. Nonetheless, the emission inventory is important for estimating the effect of agricultural residue burning on air quality and climate change [11].
## 1 Introduction
Here, plasma-based processes have turned out to be promising alternative techniques [17]. Different types of cold atmospheric pressure plasmas were taken into account for the activation of this type of coated surface. When applied directly to the sample surface, filamentary DBDs in various configurations [18; 19] as well as sliding discharges [19] damage the functional layer even when they are operated at low voltages directly at the ignition limit. The coating contains a thin metal layer and thus favours the emergence of relatively high-current filaments that cause the destruction through all coating layers. This could also be observed for DBD-based jet plasmas. Moreover, particles were observed on the optical functional layers which originate from the ambient air and are carried onto the surface by the gas flow. Therefore, the filamentary DBDs and the DBD-based jet plasmas are unsuitable for the treatment of sensitive optical functional layers due to their discharge properties. However, DBDs can also be operated in diffuse [18] or also called quasi-homogenous mode depending on the duration of the exciting high-voltage pulses [20], the general configuration of the plasma source and the process gas. In this mode, gentle surface preparation comparable to treatments by glow discharges can be achieved. Moreover, treatment of glass surfaces in this mode is most probably highly effective [21] and was already performed successfully for glass surfaces [22; 23]. Furthermore, DBDs offer the advantage of easy handling since no vacuum equipment and periphery are required. In previous works, such plasmas have also been investigated for quite different applications in optics manufacturing. This includes the improvement in adhesiveness of classically cemented optics [24], surface cleaning [25], e.g., for increasing the laser-induced damage threshold [26], and polishing of optical glasses and media [27]. Against this background, demonstrating the exceptional high potential of DBDs at atmospheric pressure in the production of precision optics, the suitability of plasmas for the enhancement of optical contact bonding processes was investigated in the present work.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 3 Path-Tracking Controller
### Steer Compensation Controller  
The goal is to enhance steering precision and mitigate the effects of system execution delay; thus, compensation is made for the steering angle. The steering system in commercial vehicles exhibits an inherent execution delay. To enhance the precision of the steering and mitigate the effects of this delay, compensation is made for the steering angle. The difference between the sensor-measured steering angle and the steering angle calculated by the MPC controller is defined as the system's execution delay \(\epsilon_{\delta}\). The specific formula for steering angle compensation is provided as follows:  
\[u_{f}=K_{a}\epsilon_{\delta}+K_{b}(e(k)_{\delta}-e(k-1)_{\delta}) \tag{30}\]  
where \(K_{a}\) is the proportional term, which is used to reduce deviations. \(K_{b}\) is a differential term used to improve lag.  
For the front wheel angle at time \(k\), it is composed of three parts: \(u(k-1)\) is the front wheel angle from the previous moment, \(\Delta u(k)\) denotes the optimal angle adjustment increment at the present moment, and \(u_{f}\) is the compensation angle.  
\[\delta_{s}=u(k-1)+\Delta u(k)+u_{f} \tag{31}\]  
The ultimate angle of the vehicle's steering wheel input is expressed as follows:  
\[\delta_{f}=i_{s}*\delta_{s} \tag{32}\]  
In the equation, the variable \(i_{s}\) denotes the steering transmission ratio.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 3 System Model
### Computing Resource Forecasting Model  
The GRU combines parameter information such as historical offloading decisions, task attributes, and computational resources and utilizes historical information about the end device to predict the task that arrives at the next state of the end-device network. The GRU controls the flow of information through the introduction of update gates and reset gates, and it performs better in capturing long-range dependencies in a time series. Update gates help the model to decide how much previous information to keep in the current hidden state. Reset gates are used to decide which historical data to discard. The structure of these gates allows the GRU to update information more efficiently at each time step, so GRU-based models are trained faster. The complete GRU update process is denoted by Equations (8)-(11) [38]:  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]), \tag{8}\]\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]), \tag{9}\]  
\[\widetilde{h_{t}}=\tanh(W\cdot[r_{t}\times h_{t-1},x_{t}]), \tag{10}\]  
\[h_{t}=(1-z_{t})\times h_{t-1}+z_{t}\times\widetilde{h_{t}}, \tag{11}\]  
where \(x_{t}\) denotes the input vector, \(h_{t-1}\) denotes the state memory variable at the previous moment, \(h_{t}\) denotes the state memory variable at the current moment, \(z_{t}\) denotes the update gate state, \(r_{t}\) denotes the reset gate state, \(\widetilde{h_{t}}\) denotes the state of the current candidate set, \(\sigma\) denotes the sigmoid activation function, and \(W\) denotes the weight parameter for the multiplication of the connection matrix.  
In the MEC network scenario, this paper predicts information about future tasks through the GRU model to make decisions and allocate computing resources to tasks. When the actual task arrives, it is offloaded and recomputed if the error range between the actual task and the predicted task is within the allowed threshold. The accuracy of the prediction model is improved by training the GRU network model and updating the weights and biases of the gates in the network.