esa-sceva/satcom-chunk-collection huggingface dataset:
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
## 1 Introduction
The system architecture is divided into 6 parts; The OFDM transmitter system, RF to optical up-conversion (RTO), mobile fronthaul link (MFL), Optical to RF down-conversion (OTR), DSP compensation scheme and the OFDM receiver system. However, the system architecture can further be divided into 3 main parts where the BBU is made up of the OFDM transmitter system and RTO. The MFL is made up of an optical fiber link and the RRH is made up of the OTR, DSP compensation unit and OFDM receiver system. The rest of the paper is organized as follows. In Section 1, we discuss the problem and review some related works. The simulation setup of the BBU i.e., OFDM transmitter system and the RTO conversion, are illustrated in Section 2. Sections 3 and 4 also illustrate the simulation setup of the MFL and RRH systems, respectively. The simulation parameters and results are provided and explained in Section 5 and finally in Section 6 we conclude.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### The Proposed Antenna Designs  
Various modifications at Figures 1, 2 and 4 were made to achieve the proposed antenna suitable for wireless networks, satellite systems, and/or IoT applications. The final dimension of the proposed design was the same as the initial antenna for characterization except for the reduction in thickness of the feed line from 4 mm to 3 mm. The proposed antenna geometric layout is depicted in Figure 6, featuring a simpler stepped-fed patch and L-slot inserted at the center region of the patch, and a major semi-circle is embedded adjacent to the bottom of the L-slot along the feed line on the ground plane. The area and dimensions of the DGS slots are optimized by parametric analysis.  
The antenna's overall dimension is 34 \(\times\) 28 \(\times\) 1.524 mm\({}^{3}\). The size of the optimized feedline is 3 \(\times\) 12 mm\({}^{2}\). Table 2 contains all the optimized design parameters of the proposed antenna. The 2022 high-frequency structure simulation (HFSS) was used to achieve optimal bandwidth, gain, and performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{\begin{tabular}{c} **Dimension** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{\begin{tabular}{c} **Dimension** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{\
\begin{tabular}{c} **Dimension** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{
\begin{tabular}{c} **Dimension** \\ **(mm)** \\ \end{tabular} } \\ \hline Ls & 34 & d & 9 & Lt & 4 & T & 11 \\ \hline Ws & 28 & a & 1 & L\({}_{\text{VS}}\) & 24 & S & 17.13 \\ \hline f\({}_{1}\) & 3 & b & 1 & c & 1 & U & 13 \\ \hline L\({}_{\text{f1}}\) & 10 & K & 6 & e & 6 & W\({}_{\text{VS}}\) & 8.87 \\ \hline f\({}_{2}\) & 4.5 & P\({}_{\text{v}}\) & 20 & Q & 8 & H\({}_{\text{p}}\) & 23 \\ \hline f\({}_{3}\) & 6 & V\({}_{\text{W}}\) & 6 & R\({}_{\text{S}}\) & 2.6 & m & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the proposed antenna.  
Figure 5: Reflection coefficient (S\({}_{11}\)) profiles of evolution antennas.  
Figure 6: Schematic of proposed antenna structures: (**a**) 3D view; (**b**) front view; and (**c**) back view.
## Materials and Methods
### Hg(II) Adsorption Test  
The Hg(II) adsorption capacity of sorbents was examined using the following procedure. Typically, 1 mg of the sorbent was dispersed into 100 mL of aqueous 1000 ppb Hg(II) (synthetic wastewater) in a polypropylene bottle. The suspension was shaken with a thermostatic shaker at 25 \({}^{\circ}\)C and aliquots were removed at regular time intervals, with dispersed sorbent removed from the aliquot using a magnet. The liquid sample was then stabilized by addition of 5% (w/v) KMnO\({}_{4}\) solution, then mixing with 1% (w/v) HCl solution. Prior to determination of the Hg(II) content, addition of sufficient amount of 12% (w/v) hydroxylamine aqueous solution is required to remove the excess KMnO\({}_{4}\) and to acquire clear solutions. Atomic absorption spectroscopy (Perkin Elmer, AAn analyst 200 equipped with FIAS 100 system) was utilized to determine the concentration of Hg(II) in the aliquot sample, through coupling with flow injection analysis system-hydride generation (FIAS-HG) (reducant 0.2% (w/v) NaBH4 in 0.05% (w/v) NaOH, carrier solution 3% (v/v) HCl). The Hg(II) removal efficiency (%) [10; 12] or breakthrough (BT, %) [45] was calculated according to the formula  
\[\text{Removal efficiency (\%)}=(C_{0}-C_{\text{t}}/C_{0})\times 100\%, \tag{1}\]  
where C\({}_{0}\) (mg L\({}^{-1}\)) is the initial concentration of Hg(II) and C\({}_{\text{t}}\) (mg L\({}^{-1}\)) is the concentration of Hg(II) ions after adsorption  
The mercury uptake (q\({}_{\text{e}}\), mg g\({}^{-1}\)), and removal efficiency, was calculated according to the equation:  
\[\text{q}_{\text{e}}=((C_{0}-C_{\text{e}})\times\text{V})/\text{m}, \tag{2}\]  
where C\({}_{0}\) is the initial mercury concentration in solution (mg L\({}^{-1}\)), C\({}_{\text{e}}\) is the equilibrium concentration of mercury (mg L\({}^{-1}\)) after adsorption, V is the volume of the solution (0.1 L; in this study), and m is the weight of the sorbent used (g). The 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) sample was employed to study pH dependence of Hg(II) removal, while HCl and NaOH were added for pH adjustment.  
In addition, partition coefficients (PC, mg g\({}^{-1}\) uM\({}^{-1}\)) were evaluated using the adsorption capacity or the mercury uptake (q\({}_{\text{e}}\), mg g\({}^{-1}\)) divided by the equilibrium concentration of mercury (uM) after adsorption [4; 46].  
\[\text{PC}=(\text{q}_{\text{e}}/\text{C}_{\text{e}}). \tag{3}\]
###### Abstract  
Given the existence of techniques that disrupt conventional RF communication channels, the demand for innovative alternatives to electromagnetic-based communications is clear. Covert communication, which claims to conceals the communication channel, has been explored using bio-inspired sounds in aquatic environments, but its application in terrestrial areas is largely unexplored. This work develops a mathematical analysis of a wireless sensor network that operates stealthily in outdoor environments by using birdsong audio signals from local birds for covert communication. Stored bird sounds are modified to insert sensor data while altering the sound minimally, both in characteristics and random silence/song patterns. This paper introduces a technique that modifies a fourth-level coefficient detail with a wavelet transform, then applies an inverse transform to achieve imperceptible audio modifications. The mathematical analysis includes a statistical study of the _On/Off_ periods of different birds' songs and a Markov chain capturing the system's main dynamics. We derive the system throughput to highlight the potential of using birdsong as a covert communication medium in terrestrial environments. Additionally, we compare the performance of the sound-based network to the RF-based network to identify the proposed system's capabilities.  
covert communications; sound-based steganography; performance analysis Article
# Seismic Isolation Effect of Non-Water Reacted Two-Component Polymeric Material Coating on Tunnels
###### Abstract  
An isolation layer is one of the countermeasures to promote the anti-seismic performance of tunnels. A newly invented polymeric material, non-water reacted two-component polymeric material (NRTCPM), is superior in impermeability and construction efficiency. In this study, covering a tunnel with NRTCPM coating to mitigate the damage caused by an earthquake is discussed, and an Impact Resonance Test (IRT) is firstly used to obtain the damping ratios and dynamic elastic modulus of NRTCPM. By using infinite element boundary, eight dynamic numerical modelsare made to study the isolation effects based on different density, Poisson's ratio, dynamic elastic modulus and thickness of isolation layer values. Three different conditions are explored in this paper, namely (1) no NRTCPM layer coating around tunnel; (2) different densities, Poisson's ratios and dynamic elastic moduli of a polymeric layer; and (3) various thicknesses of polymeric isolation layers around the lining. Tensile and compressive stresses are compared under these different conditions. The results show that retrofitting tunnel lining with this material has a good effect on seismic isolation. An optimum density and thickness of the NRTCPM layer is suggested considering cost and strength.  
seismic isolation; polymeric coating; tunnel; infinite element Article
## 2 Materials and Methods
### PS-InSAR Processing
Time-Series Analysis: Finally, displacement time series were computed relative to a network of ground control points (GCPs), ensuring temporal consistency throughout the analysis [19]. Commercial PS-InSAR processing software was used to conduct the analysis. The processing parameters were optimized based on the characteristics of the SAR dataset (pre-processed with SNAP 6.0), which consists of 29 high-resolution X-band images (24 TerraSAR-X images and 5 TanDEM-X images) acquired between August 2016 and  
Figure 1: PS-InSAR flow chart. September 2018. All scenes were acquired in HH polarization with varying baselines, temporal intervals, and Doppler centroid values. The reference (master) image, acquired on 23 October 2017, was used to generate interferometric pairs for the time-series analysis. These processing configurations were carefully selected to reflect both the specific properties of the imagery and the geotechnical conditions of the Honam High-Speed Railway corridor, enabling precise and reliable deformation monitoring.
# Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions
###### Abstract  
The rising frequency of heavy truck accidents in China poses a significant public safety risk, endanpering lives and property. However, current research based on data from heavy truck accidents in China remains limited, making it challenging to support the formulation of traffic management measures. To mitigate the severity of these accidents, this study analyzed five years of heavy truck accident data from a specific region in China and developed logistic regression models for different road conditions. The aim was to identify the key factors influencing accident severity and understand the underlying mechanisms. The findings revealed that, under urban road conditions, the severity of heavy truck accidents is significantly impacted by factors such as lighting conditions, road safety attributes, driver age, and vehicle driving status. On highways, accident severity is largely influenced by visibility, roadside protection measures, intersection and section types, vehicle driving status, inter-vehicle accident types, and road safety features. On expressways, critical factors include inter-vehicle accident types, driver violations, visibility, and road alignment. In conclusion, the factors contributing to the severity of heavy truck accidents vary according to road conditions, which necessitates tailored traffic management strategies. The study's findings offer theoretical support for more targeted approaches to preventing and controlling heavy truck traffic accident severity under different road conditions in China.  
traffic safety; heavy truck; road conditions; traffic accident severity; cause analysis; logistic regression model +
Footnote †: journal: Applied sciences
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 2 Materials and Methods
### Data Preprocessing and Entry  
Two investigators, who were general dental practitioners, were trained and calibrated to perform labeling under the supervision of an experienced Oral and Maxillofacial Radiologist.  
The selected BW radiographs were reviewed and had the rotation and contrast manually adjusted, if needed, to an acceptable level of differentiation between enamel, dentin, pulp, and air; to aid the model in the machine learning process, then exported as TIFF format with dimensions of 1920 \(\times\) 1440 pixels.  
For each patient, two categories of data were collected: demographic data and radiographic data. The demographic data included: patient's file number, date of birth, nationality, ethnicity, and number of bitewings taken.  
The radiographic data included the following datasets:  
1. Radiographic ID: Each BW image was renamed with ten digits. The last seven digits referred to the patient's file number on the R4 Clinical+ software, while the first three numbers represented the BW radiograph number for each specific patient.
2. Acquisition date: the date when the BW radiograph was taken in MM/DD/YYYY format.
3. Machine type: the type of machine used to acquire the BW radiographs, either sensor or digitalized plates.
4. Radiograph view side: which side of the dentition was included in the BW radiograph (right or left).
5. Score: the scoring system for each proximal surface and contact area between two teeth was established based on tooth structure, amount of contact overlap, and the field of the taken radiograph. The scores ranged from 0 to 11.
## 4 Discussion
### Sensitivity Analysis of MNDVI  
In the current study, \(f\)PAR in the light use efficiency model is often calculated based on NDVI, for example in EC-LUE [35] where \(f\text{PAR}=1.24\times\text{NDVI}-0.168\). This study believes that MNDVI may have greater advantages; therefore, we compared the two vegetation indices and analyzed the response differences in winter wheat growth during the winter wheat growing season. Therefore, we compared it with NDVI to analyze the differences between the two vegetation indices during the growth process of winter wheat. The current study indicates that NDVI shows saturation in estimating \(f\)PAR during the peak growth period of crops [36], making it difficult to estimate accurately. Therefore, based on the MODIS daily reflectance data product (MCD43A4), we compared the variations of pure winter wheat pixels in the North China Plain from 1 January to 30 June to analyze the saturation status of the two vegetation indices. Figure 10 shows the time series curves of NDVI and MNDVI after being filtered by a moving average window of size 5, as well as the absolute values of their slopes during different periods.  
Figure 9: The impact of considering differences in water stress and variety on the accuracy of \(f\)PAR estimation. (**a**) Establish relevant models based on measurement data from T3; (**b**) Validate the estimation accuracy of the models using measurement data from T11; (**c**) Establish relevant models based on measurement data from IS-A; (**d**) Validate the estimation accuracy of the models using measurement data from IS-G.  
From Figure 10, it can be seen that the range of MNDVI is approximately \(-0.1\) to \(0.75\) with a range of \(0.85\), while NDVI ranges from \(0.25\) to \(0.85\) with a range of \(0.6\). The absolute values of the slopes of MNDVI are also greater than those of NDVI throughout the growth cycle. Even after winter wheat has reached the jointing stage, when NDVI changes less and the absolute value of the slope is smaller, MNDVI still has a larger slope and continues to show a changing trend. The above two points indicate that, compared to NDVI, firstly, MNDVI is more sensitive to vegetation growth changes, and the differences in vegetation growth changes are more pronounced for MNDVI. Secondly, MNDVI is less prone to saturation and even after the jointing stage of winter wheat, there are still significant changes in MNDVI. Therefore, MNDVI has an advantage in estimating _f_PAR.
## 4 Parameter Sensitivity Analysis
### Radial and Longitudinal Grout Diffusion Phase
#### 4.2.1 Porosity \(\varphi\) and Permeability Coefficient \(K_{w}\)  
The influence of sandy soil porosity on grout diffusion has been studied extensively. Based on the existing studies, the sandy soil porosity varied from 0.3 to 0.57 [23; 27; 38; 49; 50]. When the porosity \(\varphi\) is 0.3, the corresponding permeability coefficient \(K_{w}\) is 5.00 \(\times\) 10\({}^{-4}\) m/s [38; 41].  
Figure 12: The effect of thickness of the tunnel radius \(R\) on grout pressure \(P\) ((**a**) tunnel section, (**b**) rectangular coordinate system).  
The porosity \(\varphi\) at the bottom of the tunnel is set from 0.30 to 0.36. The permeability coefficient can be obtained according to the linear relationship between the porosity and permeability coefficient. The permeability coefficient increases from 5.00 \(\times\)\(10^{-4}\) m/s to 6.00 \(\times\)\(10^{-4}\) m/s. The variation of the grout pressure \(P\) with the porosity \(\varphi\) and permeability coefficient \(K_{w}\) is shown in Figure 13.  
With the increase in porosity and permeability coefficient, the diffusion distance increases, which facilitates the loss of the grout pressure. Therefore, the grout pressure decreases gradually. In addition, compared to the grout pressure near the top area of the tunnel, more grout pressure dissipates near the bottom area. As \(K_{w}\) increases from 5.00 \(\times\)\(10^{-4}\) m/s to 6.00 \(\times\)\(10^{-4}\) m/s, the grout pressure at the top and bottom of the tunnel decreases from 167.9 kPa and 320.8 kPa to 159.3 kPa and 300.3 kPa, respectively. The grout pressure decreases by 5.6% and 6.4%. Similarly, [PERSON] et al. (2021) and [PERSON] et al. (2020) showed that the lower permeability coefficient hindered grout diffusion, which caused the grout pressure to dissipate faster [33; 38]. According to [PERSON] et al. (2022), the grout diffusion distance has a non-linear positive relationship with the injection pressure and permeability coefficient [35]. The diffusion distance increases with the increase in the grout pressure and permeability coefficient. Therefore, to make uniform the grout distribution and overcome the adverse impact of soil depth on the grout diffusion, the initial grout injection pressure near the bottom should be higher than the pressure at other locations.  
Although the increases in porosity and permeability coefficient are beneficial to the diffusion of the grout, large porosity and permeability coefficient are not conducive to improving the grouting effect [51]. Large porosity and permeability coefficient result in a faster flow of the grout in the soil, which leads to cavity formation inside the shield tail void. The reinforcement effect of the grout is weakened, which is detrimental to controlling the ground settlement. Therefore, the stratum with large porosity and permeability coefficient needs to be improved before tunnel engineering.
# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
## 4 Experiments
### Fine-Tuning Datasets
#### 4.1.1 Sentiment Datasets  
In order to cover different domains or sources, we chose the five sentiment datasets from different domains, including Twitter and reviews. The SS2030 [75], 40k-Tweets [76], and Twitter-AB [77] datasets were sourced from Twitter. In addition, ArSentiment [78], and MASC [79] were reviews datasets.  
* SS2030 dataset [75]: sentiment dataset that has been gathered from Twitter includes 4252 tweets focusing on a variety of social issues in Saudi Arabia. The data set was manually annotated, and it consists of two classes (2436 positive, 1816 negative).
* Twitter-AB [77]: This dataset consists of 2000 tweets that were gathered from Twitter and have been classified into 1k positive, and 1k negative. The dataset was manually labelled and included both MSA and the Jordanian dialect, encompassing diverse topics related to politics and the arts.
* 40k-Tweets [76]: There are 40,000 tweets in this dataset, 20,000 of which are positive and 20,000 of which are negative. These tweets are written in both MSA and an Egyptian dialect. Furthermore, the gathered tweets are manually labelled and span a wide range of topics such as politics, sports, health, and social problems.
* The ArSentiment [78] is a large and multi-domain reviews dataset consisting of over 45k reviews on the 5-rating scale, for movies, hotels, restaurants, and products. We used a rating scale to assign labels to data, 1 and 2 stars have been considered negative, 3 stars have been considered neutral, and 4 and 5 stars have been considered positive.
* Multi-domain Arabic Sentiment Corpus (MASC) [79]: a review dataset that was scraped from a variety of websites including Google Play, Twitter, Facebook, and Qaym. The dataset, which included several different domains, was manually annotated into two classes: positive, and negative.  
We selected datasets of varying sizes, some of which contained 40,000 sentences such as 40k-Tweets, and ArSentiment. Others, such as SS2030, Twitter-AB, and MASC had sizes of less than 7000 sentences. The selection of sentiment datasets with diverse domains and sizes was motivated by a desire to examine the impact of adaptation approaches from multiple perspectives. The statistics and classes distribution of the sentiment datasets are shown in Table 6. In addition, Table 7 provides the number of train, development, and test samples for each dataset.