esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 7 Conclusions  
This work studied an online mining railway optimisation problem. We first formulated the problem as a mixed integer programming problem and developed a novel online solution to address it. The solution was then evaluated through various test cases, which showed its effectiveness in solving the problem. It is worth mentioning that this model serves as an initial integrated optimisation model for the scheduling of mine railways and the allocation of station tracks. However, it still has limitations and room for further improvement. One limitation is that the scheduling window size needs to be carefully chosen, as choosing too large a window can result in computational complexity, while choosing too small a window can limit the ability to incorporate long-term scheduling decisions. Another limitation is that the proposed solution may not be suitable for very fast-paced real-time scenarios in which there is a need to make scheduling decisions on a second-to-second basis, as the time required to generate a schedule may be too long for the required response time.  
Given the promising results of this study, further research will focus on the following several aspects:  
* to consider more practical constraints, such as ensuring that the first-come-first-serve policy is upheld near load-out areas, which will increase the real-world applicability of the solution and make it more useful for mining companies; and
* to develop an effective heuristic algorithm to increase the efficiency of the solution. By improving the computational efficiency of the algorithm, this will make the solution more suitable for large-scale, real-world applications.  
Figure 11: Case 4: Train schedule.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time-Train** & **Mtest01** & **Mtest02** & **Mtest03** \\ \hline
0 & A & & \\
5 & E & & A \\
10 & H & G & E \\
15 & F & H & D \\
20 & G & F & E \\
25 & & & H \\
30 & & D & F \\
35 & & C & G \\
40 & & B & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Case 4: Train Schedule.  
**Author Contributions:** Conceptualization, X.R.; Methodology, X.R., K.S. and G.M.; Software, X.R.; Validation, X.R. and H.G.; Investigation, X.R. and H.G.; Data curation, H.G.; Writing--original draft, X.R.; Writing--review and editing, X.R. and H.G.; Supervision, K.S. and G.M.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant U21A20446; Guangzhou Applied Basic Research Program under Grant 202201011786.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are grateful to anonymous reviewers for their thorough and most helpful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Studies
### 3 Dpi  
3D image conversion refers to rendering a 2D image captured by a camera into an image of a new view. It can be used to record and reproduce visual perceptions from various angles. Classical image-based rendering technology requires sophisticated image capturing technology using specific hardware. Recently, 3D image conversion methods using RGB-D (red, green, and blue-depth) images obtained from a small digital camera or mobile phones equipped with a dual lens instead of expensive customized equipment have been investigated.  
In 3 DPI, parallax is separated using an RGB-D image and the information lost when an image of a new rendered view is restored. The 3 DPI technique can be classified into image-based and learning-based rendering. In image-based rendering, an image of a new view is synthesized using a set of posed images. It achieves good performance when the multiview stereo algorithm functions well or the image is captured using a depth sensor. Recently, many learning-based rendering techniques based on CNNs have been investigated.  
The learning-based rendering technique does not require expensive equipment because it can synthesize a new view image using a single image and a stereo pair image. [PERSON] et al. (2015) used a layered depth image (LDI) to reduce the complexity of arbitrary depth information. The input LDI image is classified into several local regions while considering the connectivity between pixels, and the image synthesized by applying the inpainting algorithm is merged  
Figure 1: Architecture of YOLOv4.  
into one LDI image. Subsequently, the depth information is changed and applied repeatedly. [PERSON]'s algorithm comprises three subnetworks: color inpainting, depth inpainting, and edge inpainting. The edge inpainting network preferentially restores the edge of the region requiring restoration. After edge restoration is performed, the color and depth information of the image boundary region is restored from the color and depth inpainting network.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 3 Parameters to Be Considered for the Design and Analysis of a Flexible Antenna  
The design and analysis of the flexible antenna need a wide range of parameters to be considered, rather than the conventional rigid antenna counterparts. Apart from the design considerations of choosing proper conductive and substrate materials for the flexible antenna as discussed in Session 2, certain parameters must be emphasized for the design, as well as the analysis of the flexible antenna which are discussed as follows:
## 2 Experimental Design and Methodology
### Characterization Processes
In order to ascertain the dielectric properties of the specimens, measurements of the dielectric constant and loss factor were undertaken. The measurements were conducted using a Fytronix device at a frequency of 1 kHz in accordance with the ASTM D150-20 standard [36]. The tests were conducted using disk-shaped specimens with a diameter of 20 mm and a thickness of 1 mm. Prior to commencing the measurement, the surfaces of the specimens were meticulously cleaned, and the device probe was carefully positioned in optimal contact with the specimens. The mean value was calculated from three measurements taken for each specimen in order to determine the dielectric constant. This approach was designed to guarantee the repeatability and precision of the measurement outcomes. In this study, Fourier Transform Infrared (FT-IR) spectroscopy was used to analyze the chemical compositions of the biocomposite specimens. The FT-IR spectra were obtained using a Shimadzu QATR-S spectrophotometer in the range of 400-4000 cm\({}^{-1}\) in attenuated total reflection (ATR) mode. For each specimen, 32 scans were performed at a spectral resolution of 4 cm\({}^{-1}\)[37]. The measurements were conducted at room temperature, with air employed as the reference to adjust the spectra. These analyses enabled the identification of characteristic functional groups in the chemical structures of the biocomposites. To examine the surface properties, a Dcorn-brand digital microscope was used. The specimens were observed under the microscope at 250\(\times\) magnification to analyze surface morphology in detail. The optical microscope was used to evaluate the surface homogeneity, roughness, and structural deformations of the composites.
## 3 Indoor Positioning Systems
### Systems Based on Sound
#### 3.3.1 Audible Sound  
It is possible to use audible beeps to encode information for location systems. The idea of making an artificial audible sound has many disadvantages, especially considering that sound signals tend to irritate humans closer to the sound source [4]. However, other, more sophisticated schemes allow the inclusion of encodings that act as watermarks in the sound. The sound can be from a beep to a song played in a shopping mall or any other public place, with this encoding inserted in the audio being undetectable by the human ear.  
For example, [PERSON] and co-authors presented an implementation of a user location system using digital audio signal watermarks, where a pseudo-random sequence was used to amplify several frequency bands of the host signal (which was music only) [98]. This model allows the insertion of different pseudo-random sequences to follow different targets in an indoor space. The authors claim that they obtained an accuracy of 1.30 m.
# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Datasets  
The IDRID dataset [20] was launched at the 2018 Biomedical Retinal Image Challenge International Conference for segmentation and rating. For segmentation of EX, HE, MA, and SE, there are 81 color fundus images with size \(4288\times 2848\), with 54 as the training dataset and 27 as the testing dataset. In total, there are 81 EX annotations, 81 MA annotations, 80 HE annotations, and 40 SE annotations. In this paper, these images undergo cropping, zero-padding, and resizing to \(640\times 640\). Considering the limited number of images in this dataset, to prevent overfitting, data augmentation methods such as horizontal flipping, vertical flipping, random rotation, and contrast limited adaptive histogram equalization (CLAHE) are involved. Throughout the experiments, these images are randomly cropped into patches of size \(256\times 256\) to better extract local information.  
The DDR dataset [21] contains 757 Chinese DR images with sizes from \(1380\times 1382\) to \(2736\times 1824\), with 383 images for training, 225 for testing, and 149 for validation. These fundus images are provided with pixel-level annotation for EX, HE, MA, and SE if the image has this type of lesion. In total, there are 486 EX annotations, 570 MA annotations, 601 HE annotations, and 239 SE annotations. In this paper, all images are cropped and resized to \(512\times 512\), and CLAHE and random cropping into patches of size \(112\times 112\) are performed to enhance the data.  
The ultra-wide-field fundus images dataset is a local dataset collected and organized by the Xinhua Hospital affiliated with Shanghai Jiao Tong University School of Medicine and captured under a 200-degree field of view (FOV) with 261 fundus images of \(3900\times 3072\) and pixel-level annotations of EX and HE. In the experiments, all images are resized to \(1300\times 1024\). A total of 165 images are used for training and 96 for testing. During training and testing, these images are randomly cropped into patches of size \(256\times 256\), and 20% of the patches from the training set are randomly selected to serve as the validation set in each iteration.
## 1 Introduction
#### Notations and Conventions  
We indicate with \(\lambda\) image voxel activity values, with \(y\) sinogram counts, and with \(c_{i,j}\) the probability that a photon pair emitted from pixel \(j\) is recorded in the sinogram bin \(i\). We use \(k\) to indicate the iteration number, using it as a superscript when referring to the estimate of a parameter at the \(k\)-th iteration. To avoid confusion with exponentiation, we indicate the latter with parenthesis; e.g., \(\lambda^{k}\) is the estimate of \(\lambda\) at the \(k\)-th iteration, while \(\left(\alpha\right)^{k}\) is the \(k\)-th power of \(\alpha\). The forward projection operator is indicated in component notation as \(y_{i}=\sum_{j}c_{i,j}\lambda_{j}\), and in matrix notation as \(y=H[\lambda]\), while the backprojector is indicated with \(H^{T}\).  
We define the normalization factor \(\eta_{j}=\sum_{i}c_{i,j}\). Matrix multiplications will be highlighted by the use of square brackets (e.g., \(y=H[\lambda]\)), while element-wise multiplications will be identified by the Hadamard product \"\(\circ\)\". Therefore, the forward projection of the element-wise multiplication between \(\lambda\) and a matrix \(\delta\) will be written as \(H[\lambda\circ\delta]\). Element-wise divisions are written as standard fractions. Throughout the paper, we will not use resolution modelling within the \(H\) operators. Point spread function modelling makes unconstrained MLEM reconstruction undetermined at high frequencies [9]; therefore, studying the convergence of both signal and noise at those frequencies is not possible, as it is dependent on the specific implementation.
## 2 Study Area and Data Preparation  
### Study Area  
Two counties located in Xinjiang Province, China, were selected as the study areas (Figure 1). The first county, Kuche, Aksu City, Xinjiang Province, China, is located at 40\({}^{\circ}\)46'-42\({}^{\circ}\)35' N, 82\({}^{\circ}\)35'-84\({}^{\circ}\)17' E. The second county, Luntai, Bayinguoleng Mongolian Autonomous Prefecture, Xinjiang Province, China, is located at 41\({}^{\circ}\)05'-42\({}^{\circ}\)32' N, 83\({}^{\circ}\)38'-85\({}^{\circ}\)25' E. Both counties are located in the south of the middle Tianshan Mountain and the north of Tarim Basin and belong to a warm temperate zone, with a continental arid climate. The north of both counties is mountainous, while the central and southern regions consist of plains. The main landforms of both counties are plains, which are mainly devoted to agriculture, cities and deserts.
## 3 PBCS Structure
### Receiver Structure and ANN Design
The constructed signal pattern (waveform) P(t), is sampled by the unit time delay and S(t) is obtained with the sample and hold circuit. Each sample corresponds to the neurons of the input layer. The number of neurons in the hidden layer is determined by the cross-validation in an offline process. The outputs \(y_{1}\), \(y_{2}\),..., \(y_{q}\) indicates recognition levels between the glossary and the applied signal in the q-tap frame. where I represents the number of neuron at input layer and \(\eta\) indicates learning-rate. Designer can design it as constant or adaptive. In case the network does not converge, [PERSON] _et al._[18] has generalized the formula. It uses to determine weight coefficients including \(\alpha\) momentum parameter. This formula is given in Equation (13):  
\[\Delta w_{{}_{ji}}(n)=\alpha\Delta w_{{}_{ji}}(n-1)+\eta\delta_{{}_{j}}(n)y_{{}_ {i}}(n) \tag{13}\]  
The activation function is effective on the sensitivity of the output layer. There are many activation functions in the literature. Three main functions are called sigmoid, linear and tangent hyperbolic and are shown in Figure 5. Due to the appropriation of upper and lower limit and its transition function, the sigmoid function has been selected as the activation function in our case. The problem in this system is to determine which pattern has been entered by the input layer. Therefore, the output corresponding to the input pattern should be one, and the rest should be zero. The most proper activation function for this purpose is the sigmoid as given in Equation (14):  
\[\Psi(\
u)=1/(1+\exp(-\lambda\times\
u)) \tag{14}\]  
However, this function carries the problem of determining a constant (\"\(\lambda\)\") at the formula, which affects the performance of the ANN directly. This value is determined by test experiments. The parameters used in this system are the constant user defined momentum coefficient and the adaptive learning rate coefficient. The hidden layer neuron quantity of ANN is determined by the cross-validation algorithm. The Sigmoid activation function was selected in the output layer. The ANN design obeying the above constraints is represented in Figure 6.  
Both the transmitter and receiver sides of PBCS are formed in line with unique algorithms in order to enable a more dynamic utilization for the communication medium. The following section will cover and analyze the simulation results regarding the design process of PBCS.
## 6 Discussion
### Limitations  
This research has a few limitations. First, we characterised the social media content related to the COVID-19 pandemic by only taking into account Twitter. However, a large amount of information is present in media which were not covered by this study. For example, Facebook is not included because its policies do not allow data scraping and analysis. Additionally, individuals are also exposed to COVID-19-related information through online news portals and traditional sources. Therefore, to obtain a more realistic picture of media content related to the pandemic, it would be advisable to extend the analysis to all the available sources. Hence, in future work, we plan to extend this study by integrating heterogeneous data sources, such as other social media platforms, online news portals and all the other sources of textual data in social media such as user comments on online news media. The second limitation arises from the fact that automatic classifiersnever have one hundred percent accuracy. In the case of sentiment classifier that we have trained, the accuracy is 0.79. That means that around 20% of tweets are not correctly annotated. This is a common drawback of all studies in the domain of NLP. However, the achieved accuracy is sufficient to give an overview of public opinions and attitudes. The third limitation is that this study analysed only texts in the Croatian language and, thus, the results are interesting to a smaller community. As mentioned in previous sections, these results can be of interest for further comparison of COVID-19-related communication on Twitter in different countries. In addition, a similar approach could be applied to any other language and/or country since the entire methodology is portable and only dependent on the available data sources and the maturity of the NLP methods per selected language.
## 3 Results
### Proactive System Based on Predictions
#### 3.2.1 Prediction Models Applying Classical Machine Learning Methods
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Bayes network (K2) & 69.45 & 0.39 & 0.45 & 0.66 \\ Bayes network (hill climber) & 66.14 & 0.44 & 0.49 & 0.49 \\ Bayes network (TAN) & 65.80 & 0.41 & 0.47 & 0.61 \\ \hline
**Naïve Bayes** & **70.55** & **0.41** & **0.46** & **0.62** \\ \hline Artificial neural network MLP & 67.32 & 0.42 & 0.48 & 0.56 \\ \hline Support vector machines (polynomial kernel) & 68.46 & 0.32 & 0.56 & 0.50 \\ Support vector machines (normalized polynomial kernel) & 66.93 & 0.33 & 0.57 & 0.49 \\ Support vector machines (Pearson VII) & 64.04 & 0.36 & 0.59 & 0.51 \\ Support vector machines (radial basis function) & 68.75 & 0.31 & 0.56 & 0.50 \\ \hline Voted perceptron & 66.80 & 0.33 & 0.56 & 0.61 \\ \hline K-nearest neighbors (K = 1) & 68.68 & 0.43 & 0.48 & 0.54 \\ K-nearest neighbors (K = 2) & 68.68 & 0.43 & 0.48 & 0.55 \\ K-nearest neighbors (K = 3) & 68.68 & 0.43 & 0.48 & 0.55 \\ K-nearest neighbors (K = 4) & 68.68 & 0.42 & 0.47 & 0.59 \\ K-nearest neighbors (K = 5) & 68.68 & 0.42 & 0.47 & 0.59 \\ K-nearest neighbors (K = 6) & 68.68 & 0.42 & 0.47 & 0.60 \\ \hline
**C4.5** & 66.91 & 0.44 & 0.47 & 0.49 \\ Random forest (num. iterations = 50) & 67.04 & 0.43 & 0.48 & 0.55 \\ Random forest (num. iterations = 100) & 67.05 & 0.42 & 0.48 & 0.55 \\ Random forest (num. iterations = 150) & 67.29 & 0.42 & 0.48 & 0.54 \\ Random forest (num. iterations = 200) & 67.04 & 0.42 & 0.48 & 0.54 \\ Random forest (num. iterations = 250) & 66.77 & 0.42 & 0.48 & 0.54 \\ Random forest (num. iterations = 300) & 67.05 & 0.42 & 0.48 & 0.54 \\ Random forest (num. iterations = 350) & 66.64 & 0.42 & 0.48 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results predicting porosity apparition when we applied classical machine learning methods using data extracted from both primary and secondary coating areas in addition to melting data. The best classifier, marked in gray, was naïve Bayes giving an accuracy level (percentage of correctly classified instances) of close to 70%. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline K-nearest neighbors (K = 4) & 79.67 & 0.36 & 0.42 & 0.82 \\ K-nearest neighbors (K = 5) & 79.24 & 0.35 & 0.41 & 0.83 \\ \hline
**K-nearest neighbors (K = 6)** & 80.29 & 0.35 & 0.41 & 0.84 \\ \hline C4.5 & 65.43 & 0.41 & 0.49 & 0.68 \\ Random forest (num. iterations = 50) & 72.62 & 0.35 & 0.43 & 0.81 \\ Random forest (num. iterations = 100) & 72.90 & 0.34 & 0.43 & 0.81 \\ Random forest (num. iterations = 150) & 72.79 & 0.34 & 0.43 & 0.81 \\ Random forest (num. iterations = 200) & 72.33 & 0.34 & 0.43 & 0.81 \\ Random forest (num. iterations = 250) & 72.19 & 0.34 & 0.43 & 0.81 \\ Random forest (num. iterations = 300) & 72.19 & 0.35 & 0.43 & 0.81 \\ Random forest (num. iterations = 350) & 72.19 & 0.35 & 0.43 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
# RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analog Indicators
## 3 Materials and Methods
### Experiment and Conceptual Test  
The general idea was to design a bench test based on covering the RFID tags with different metal plates. Circular openings with various radiuses were manufactured in metal plates that allow electromagnetic waves to pass through. The RFID tag was slowly moved towards or backward of the isolation area by rotating the metallic sheet, thus examining the effect of changing the amount of EM isolation in RFID tags. The operating frequency of UHF RFID was in the range of 860 to 930 Mhz. The size of the smallest RFID tag was 44 \(\times\) 44 mm, therefore, the smallest size of the circular window was set to 40 mm, while the largest size of the circular window to 80 mm, which corresponds to about \(\lambda\)/4 at 900 MHz. The EM waves emitted from the RFID reader antenna charge the tag antenna, and the absorbed energy turn on the tag chip by moving the cover. A stepper motor drives the shielding cover with an accuracy of 3\({}^{\circ}\) degree of rotation per step. The stepper motor was driven in half-step mode by a 4 ZeroBox (a microcontroller unit suitable for industrial applications) and controls the metallic cover [27]. The measurements were performed by a CAEN UHF RFID reader model R4300, and the data was sent to the computer through the serial port (Figure 2a). The reader was connected to a 9 dB linearly polarized antenna by SMA adapters and a 20 foot RG-58 cable with 1 db per 10 feet loss in the 900 MHz frequency range. According to Figure 2b, the metal cover moved from position 1 in steps of 3\({}^{\circ}\) to position 2, so that half of the tag was exposed to EM waves, then moving from position 2 to 3 caused the entire surface of the tag antenna to be uncovered. The same pattern was repeated in positions 4 and 5, and the whole surface of the tag antenna was to pass from covered to uncovered and vice versa.  
RSSI measurements were performed in 1080 samples for three different RFID tags and three different metal covers. The passive tags specifications are given in Table 1. The  
Figure 2: Test bench design and covering process.  
chips used in the RFID tags belong to the same family, but the structure and design of their antennas were entirely different and designed for various purposes.