esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Trajectory Prediction  
In the problem of trajectory prediction, the future prediction is usually based on past experience or the history of previous trajectories. Thus, LSTMs, designed to process time sequence data, can likely perform well on such a task. [PERSON] et al. [14] proposed a novel LSTM model in predicting human trajectories in the crowded space and [PERSON] et al. [15] incorporated more information including occupancy map and camera image to build a robust model in predicting pedestrian trajectories. Such ideas are also effective in predicting cars' trajectories as cars and pedestrians share common motion patterns to some degree. Inspired by their work, we will consider using LSTM-based approaches to process the time series data in the Waymo dataset and learn the driving policy underlying it. In addition, a non-LSTM-based method can be effective among the trajectory prediction problems. [PERSON] [16; 17] has introduced a non-LSTM-based method using Fuzzy controller to predict trajectory for nonholonomic wheeled mobile robots.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 1 Introduction
The behavior of the antenna was analyzed when it was placed on the surface of the pancreas of two anatomical models (male and female) with all the electric properties and geometrical characteristics of biological tissues for the analysis of signal behavior as it propagated through the human organs. Radiation performance, electric field distributions, SAR values, and maximum allowable power values as well as electric field fluctuations when the electromagnetic wave was away from the antenna were calculated and evaluated in each anatomical model, and a comparative study was performed between the two models.
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
###### Abstract  
Sea fog is a weather phenomenon suspended in the ocean-atmosphere boundary layer. This phenomenon makes the horizontal visibility of the sea atmosphere less than 1 km. Sea fog reduces sea surface visibility. Moreover, the inhomogeneous sea fog particles in the transmission channel result in the absorption and scattering of photons, which seriously affect the performance of optical detection instruments. Polarization imaging detection can solve this problem. However, the evolution law of transmission characteristics between polarized light and inhomogeneous sea fog particles remains unclear. Therefore, we use the equivalent analysis method to improve Monte Carlo, and finally construct the inhomogeneous particle scattering model. The influence of wavelength and relative humidity on DOP (Degree of Polarization) was calculated by the model. The simulated sea fog was created using brine with a preset concentration, and then established an experimental system close to the actual sea fog environment. Indoor polarized light transmission experiments verified the inhomogeneous particle scattering model. Results showed that the accuracy of the inhomogeneous particle scattering model can reach more than 75%. In the visible band, the DOP decreases with the wavelength increase. DOP\({}_{450}\) (Degree of Polarization at 450 nm wavelength) is approximately 3-10% higher than DOP\({}_{532}\), and DOP\({}_{532}\) is approximately 5% higher than DOP\({}_{671}\). The relative humidity increases from 45% to 85%, and DOP increases by 10-15%. Therefore, in the visible band, the wavelength and relative humidity are inversely proportional to DOP.  
inhomogeneous sea fog particles; equivalent analysis method; relative humidity; Monte Carlo; degree of polarization +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment. _Appl. Sci._**2023**, _13_, 905. [[https://doi.org/10.3390/appl3020905](https://doi.org/10.3390/appl3020905)]([https://doi.org/10.3390/appl3020905](https://doi.org/10.3390/appl3020905))
## 3 Effect of Error Sources on Antenna Gain  
In the process of antenna holographic measurement, both feed offset error and scanning error will affect the far-field pattern of the antenna, resulting in a decrease in antenna gain and a reduction in antenna receiving efficiency. This section will use the different error models established in Section 2 to analyze the influence of different error sources on the far field of the antenna and establish the influence mechanism of different error sources on the antenna gain loss.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 8 Challenges and Future Trends
### Arabic OCR Models Challenges  
* Models are still in their infancy stage regarding the investigation of the effect of applying different Arabic text embeddings, such as BERT and FastText. This is a promising direction for researchers to follow;
* Transformer-based architectures showed promised results. However, it is still a challenge for practitioners to develop solutions using these architectures unless ways to deal with the lack of Arabic datasets and computational resources are developed;
* Experiments have been conducted with approaches that use N-shot in complex languages that feature strokes, such as Chinese. Nevertheless, these approaches have not yet been explored in Arabic. It is postulated that these approaches may show effectiveness if transferred to a rich language, such as Arabic [72];
* It remains an open challenge for researchers to develop models that are able to recognize handwritten Arabic text in cross-lingual and multi-font environments [73].
# TiO2 Modified Geopolymers for the Photocatalytic Dye Decomposition
## 2 Materials and Methods
### Photocatalytic Activity  
The photocatalytic activity of the industrial TiO\({}_{2}\) photocatalysts incorporated into the geopolymer matrix and their pure counterparts under UV light illumination was evaluated based on dye decomposition. Before photocatalytic testing, 0.15 g of the photocatalyst was mixed with 60 ml of a solution of RhB dye and distilled water (in the concentration of 1 \(\times\) 10\({}^{-5}\) mol\(\cdot\)dm\({}^{-3}\), pH = 7.0 measured by pocked pH meter ad 110 ph) and stirred at 500 rpm for 5 min to obtain a uniform dispersion. After this, the dispersion was illuminated by a 250 nm enhanced Mercury Xenon lamp (200 W), housed in a LC-08 (Hamamatsu). The UV photons were generated in three wavelength regions (photon energy, E\({}_{\mathrm{ph}}\), in parentheses), of \(\lambda\) = 360 nm (E\({}_{\mathrm{ph}}\) = 3.44 eV), \(\lambda\) = 310 nm (E\({}_{\mathrm{ph}}\) = 4 eV) and \(\lambda\) = 250 nm (E\({}_{\mathrm{ph}}\) = 4.96 eV), which cover almost 80% of the light energy output of the lamp. We also used bandpassfilters with transmissions in the range of 280-400 or 320-410 nm. For more details as well as for the spectral dependence of the normalized intensity of the light source, see our previous work [53]. The samples were obtained at specific reaction times; before UV-Vis spectroscopy measurements, they were further centrifuged at 6000 rpm for 2 min in order to eliminate possible light scattering on photocatalyst particles. The photocatalytic activity of the photocatalysts was evaluated based on RhB dye decomposition. The absorbance spectra of the RhB dye were measured using a PerkinElmer Lambda 20 spectrophotometer in the spectral region of 400-750 nm, where the main broad band of the RhB dye was found with the absorption maxima at 555 nm. The photocatalytic activity (X) was calculated according to the following equation: X (%) = \(\text{(A}_{0}-\text{A})/\text{A}_{0}\)\(\times\) 100, where X is the dye conversion in %, A\({}_{0}\) is the initial dye absorbance at the absorption maxima and A is the dye absorbance at the absorption maxima at the specific reaction time of photocatalysis [54]. The error in the determination of X was estimated to be \(\pm\) 1%. Furthermore, in order to exclude the possible absorption of dye by the photocatalysts themselves, identical dispersions were analyzed in the same manner under the same experimental conditions, but without illumination.  
It should also be emphasized that we have studied the possible decomposition of RhB dye caused by the illumination alone in the absence of a photocatalyst under the same testing conditions.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 3 Potential Applications
### Trust Management  
A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can by shared with other vehicles for improving their route planning and driving decisions. To avoid making inappropriate decisions, it is essential to ensure the trustworthiness of other vehicles which may behave maliciously by injecting false information in the network. An efficient trust management system should accurately identify the trustworthiness of individual vehicles and detect malicious drivers. Existing trust management solutions in this context follow either the centralized cloud-based approach, or the fully decentralized P2P approach [35]. Each of these approaches has its own drawbacks that eliminate addressing the requirements and challenges of vehicular networking applications and services.  
An edge computing architecture can overcome the limitations of both approaches, by providing low-latency, high scalability and efficient data access services. Despite its essential role, trust management research in the context of edge computing is quite limited, basically because it is a very recent paradigm, and its infrastructure is not standardized yet [36].
# MCCM: An Approach for Connectivity and Coverage Maximization
## 5 Validation, Simulation and Comparison with Existing Algorithms  
We consider terrain where nodes can move without blockage in their ways and have enough energy to reach the final destination. Our goal is to understand how the coverage maximization algorithm behaves. We run different simulations to measure the performance of MCCM algorithm. The number of devices in each scenario varied between 25 and 150 devices (\(N=\{25,50,75,100,125,150\}\)). They were distributed randomly in the area of interest. We tested the algorithm using homogeneous and heterogeneous distributions. We mean by homogeneous, the presence of sensors with the same characteristics and technical properties. In heterogeneous distributions, sensors are different in sensing ranges and characteristics. We choose the sensing range of the devices in the homogeneous distributions to be 5 m. It varies between 3 m and 8 m for heterogeneous distributions. For a better study, we followed the statistical approach by taking a sample of 10 runs for each deployment. We chose the area of interest to be a rectangular area of  
Figure 5: Distance between boundaries.  
250 m \(\times\) 250 m. The simulation parameters are presented in Table 1. The device characteristics are picked similarly to [33].  
Figure 6 shows the simulation results for 150 devices in both heterogeneous and homogeneous environments. The blue colored figures represent the deployment before applying our algorithm, the red colored figures represent the result after applying our algorithm and the green circles represent drones.  
Figure 6a shows a random deployment of 150 devices in a homogeneous environment and Figure 6b shows the result after applying our algorithm. The coverage gain is 90%. The average device displacement is 18.21 m. Each device \(D_{i}\) has at least three devices directly connected to it. Figure 6c shows a random deployment of 150 devices in a heterogeneous environment, and Figure 6d shows the result after applying our algorithm. The coverage gain reached 87%. The average device displacement is 22.13 m. Each device \(D_{i}\) has at least four devices directly connected to it. In the next section we present a comparative study with other related works.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Area size & 250 m \(\times\) 250 m \\ Number of nodes & 25, 50, 75, 100, 125, 150 \\ Sensing range (meter) & 3, 5, 8 \\ Battery voltage & 11.1 V \\ Horizontal Movement \(H_{e}\) & 2.8 J/m \\ Energy Capacity C & 4900 mAh \\ Battery Energy & 195,804 J \\ Speed & 2 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 6: Coverage gain by MCCM: the blue (resp. red circles) represent the devices before (resp. after) running our algorithm; The green nodes are the drones that bridge the isolated islands.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## 2 Motivation
### A Synthetic Test Case
This is due to the fact that the focused incident fields in this data collection configuration mainly see the OI, and not the undesired scatterers, and therefore, the measured scattered data on the circular sector measurement domain are mainly due to the OI. From a mathematical point of view, the use of the focused incident field has made the scattered field data less sensitive to the unknown undesired scatterers, and therefore the inversion algorithm can work with less informative data. Figure 3: (**a**) represents the true relative permittivity profile. The two light blue circles are assumed to be the OI, and the other two side objects (red and yellow) are the undesired scatterers. (**b,c**) represent the non-focused (omnidirectional) and focused incident fields, respectively, which are used to irradiate the imaging domain. (**d,e**) represent two type of data collection schemes with antennas located either on a full circle or on a two circular sectors, respectively. To investigate this further, we make the imaging domain smaller by shrinking it into a rectangular imaging domain of the size \(16\times 8\) cm\({}^{2}\) discretized into \(61\times 61\) cells so as to only enclose the OL, and not the undesired scatterers. This will then completely ignore the presence of the undesired scatterers in the inversion algorithm. The inversion of the data collected on the circular sectors for this smaller imaging domain for the two incident field types are shown in Figure 4e,f. As can be seen, the inversion of the data due to the omnidirectional incident field is now even poorer since  
Figure 4: (**a,b**) represent the inversion of the data collected on the full circular measurement domain for two cases: omnidirectional and focused incident fields, respectively; (**c,d**) represent the inversion of the data collected on the circular sector measurement domain for two cases: omnidirectional and focused incident fields, respectively; (**e,f**) represent the inversion of the data collected on the circular sector measurement domain for the smaller imaging domain  
ignoring the presence of the undesired scatterers has resulted in significant modelling error in the inversion algorithm. However, as expected, the inversion of the data collected on the circular sectors due to the focused incident field still shows the overall geometry of the OI due to the data being less sensitive to the undesired scatterers. Finally, we note that, in this example, the beams of the focused incident fields are always toward the centre of the imaging domain (see the definition for \(\cos^{m}\psi\) above). If, for example, the focused beams of the transmitting antennas were all parallel to the \(x\)-axis, some incident fields (i.e., those corresponding to the antennas at the top and bottom ends of the circular sectors) might not even interact with the OI, thus, reducing the information content of the data. (Of course, this is not an issue with omnidirectional incident fields.)
# Progressive Teaching Improvement For Small Scale Learning: A Case Study in China
## 4 Experiments and Discussions
### Learning Performance Prediction
#### 4.2.2 Comparison with Different ANN Configurations  
To demonstrate the necessity of concatenation and Leaky Relu activation function, Figure 9 compares the F1 score on different ANN architecture configurations. The proposed configuration achieves the best F1 score 0.7372 across four selected lessons. ANN that does not use concatenation performs worst on this task. The dropout rate of 0.3 was applied after each layer except the last one, indicating some units were temporally ignored in the training procedure. The concatenation was used to concatenate layer output without dropout, enabling the output layer to fully used all features extracted in previous layers. Concatenation helps the model to fully use uncondensed low-level features, which may contain important information ignored by high-level layers or dropout function. Leaky Relu can also preserve information in initial layers in this task.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Cellular
#### 3.2.1 Global System for Mobile (GSM) Communications  
The GSM is a standard developed by the European Telecommunication Standards Institute (ETSI), which describes the protocols for the different digital cellular networks generations, i.e., 2G, 3G, 4G, 5G, and 6G. Different regions utilize different frequencies, such as the 850 MHz, 900 MHz, 1800 MHz, and 1900 MHz bands, with a channel separation of 200 kHz, as well as constant envelope modulation and carrying traffic channels, according to the time division multiple access (TDMA) principle [77]. It operates in the licensed bands, so interference is prevented with other devices. GSM can be used with three positioning techniques, i.e., the GSM fingerprinting, range-based, and angle-based positioning [20].
## 2 Datasets and Methods
### Processing of Elevation Data and Retrieval Landsat 8-Derived Indices  
A 30 m resolution Digital Elevation Model (DEM) in raster format from an Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) corresponding to Harare city was downloaded from the United States Geological Survey's (USGS) earth explorer website. The clip function in ArcGIS was used to confine the DEM to the boundary of Harare to reduce demand for storage and facilitate analysis. The DEM was used to derive Aspect at 30 m resolution. Landsat data were used to derive MNDWI, NDBaI, NDBI, and UI using Equations (7)-(10) [42; 48; 50; 61; 63; 64]:  
\[MNDWI=\frac{\rho Green-\rho SWIR2}{\rho\rho Green+\rho SWIR2} \tag{7}\]  
\[NDBI=\frac{\rho SWIR2-\rho NIR}{\rho SWIR2-\rho NIR} \tag{8}\]  
\[NDBaI=\frac{NIR-MIR}{NIR+MIR} \tag{9}\]  
\[UI=\frac{SWIR2-NIR}{SWIR2+NIR} \tag{10}\]  
Visual inspection was used to analyze the variations of the physical properties represented by these indices in the city.
# Delay Bounded Multi-Source Multicast in Software-Defined Networking
## 6 Performance Evaluations
### The Running Time of Involved Algorithms  
The time complexity of the algorithms depends not only on the network scale \(|\)V\(|\), but also on the number of sources and destinations of a given multi-source multicast. In general, the number of sources and destinations is far fewer than the network scale \(|\)V\(|\). In this section, we further evaluate the running time of our DBMS algorithm and the other involved algorithms in both small scale random networks and large synthetic networks. The network scale varies from 10 to 30 and from 40,000 to 10,000 for both small scale networks and large synthetic networks, respectively. The number of destinations of evaluated multi-source multicasts ranges from 5 to 13 on the small scale Biznet. The default number of sources is 5. For the large synthetic networks, we kept the number of destination nodes constant at 2000, and varied the number of source nodes from 20 to 60. We collected the running time of the algorithms under varied settings of evaluations, and report the average results in Tables 6 and 7 respectively. It is clear from Table 6 that as the number of the destination nodes increase the running time for all the concerned algorithms increases slightly as well. From Table 7 we can deduce that as the number of source nodes increases, the execution time reduces for all the concerned algorithms. Our DBMS scheme performs much better than the other algorithms in this regard.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(|\)D\(|\) = 5, \(|\)V\(|\) = 10 & \(|\)D\(|\) = 7, \(|\)V\(|\) = 15 & \(|\)D\(|\) = 9, \(|\)V\(|\) = 20 & \(|\)D\(|\) = 11, \(|\)V\(|\) = 25 & \(|\)D\(|\) = 13, \(|\)V\(|\) = 30 \\ \hline CPLEX & 0.0029 s & 0.00403 s & 0.00582 s & 0.00652 s & 0.00948 s \\ DBMS & 0.00712 s & 0.00989 s & 0.02113 s & 0.03632 s & 0.04714 s \\ mRAERA & 0.01727 s & 0.02981 s & 0.04174 s & 0.05352 s & 0.07023 s \\ ST & 0.04326 s & 0.05532 s & 0.06831 s & 0.07982 s & 0.09831 s \\ SPT & 0.06523 s & 0.08121 s & 0.15735 s & 0.18437 s & 0.28571 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: The execution time of the algorithms under varied settings on Biznet.  
Figure 7: Impact of (**a**) the number of source nodes \(|S|\), and (**b**) the delay bound \(\delta\) on Number of Links.
## 2 Materials and Methods
### Light-Emitting Diode (LED) Wavebands Selection  
In this work, the LEDs of different wavelengths for the developed multispectral system were selected based on the hyperspectral imaging (HSI). For this purpose, hyperspectral imaging data of all three categories of meat samples were collected using a laboratory-based hyperspectral imaging system in the reflectance mode in a wavelength range of 400 to 1000 nm. The system comprised an electron multiplying charge-coupled device (EMCCD) camera, line-scan imaging spectrograph (Headwall Photonics, USA), objective lens of 28 mm focal length, and detector size of 1004 \(\times\) 1002 pixels. A more detailed description of the used HSI system can be found in [14,21]. The collected images were corrected, and a partial least squares (PLS) regression model for each parameter (fat, moisture, and protein) was developed with extracted spectral data and Y vector of reference (measured) values. Regression coefficient (beta coefficient) was used to select the wavelength containing essential information. The regression coefficient method selects the optimal wavelengths that have the highest absolute value of the regression coefficient from the PLS regression model.
## 2 Hierarchical Control for Frequency Regulation  
### Primary Level  
The primary level maintains local control stability and manages power converter drive signals. Regarding autonomous power distribution, as mentioned earlier, droop control is a common method for parallel AC interface DGs. To achieve the desired AC frequency amplitude \(\omega\), the system compares the measured active power value \(P\) with the reference values \(P^{*}\) through the droop equation below [24]:  
\[\omega=\omega^{*}-m(P-P^{*}) \tag{1}\]  
where \(\omega^{*}\) represents the rated value of \(\omega\) and \(m\) is the droop slope.  
The calculation of \(m\) is determined by  
\[m=\Delta\omega/P_{max} \tag{2}\]  
where \(\Delta\omega\) represents the maximum frequency deviation limit, while \(P_{max}\) denotes the maximum inverter output active power.  
In much of the existing literature, this power reference value \(P^{*}\) is typically set to zero to simplify control logic and adapt to various operating modes. The droop control is simplified to use frequency as a feedback signal to automatically adjust the power output of the DG, thereby ensuring system stability.