esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 2 Experimental Investigation
### Mixture Conditioning and Sample Preparation for AMPT Testing  
Laboratory compacted specimens were prepared from loose mixtures. Before compacting, the cardboard box filled with the mixture was placed in an oven at the compaction temperature (e.g., 144 \({}^{\circ}\)C) for 1.5 h for ease of handling and compaction. There is always a concern of aging the mixture during this extended period of heating and any unwanted change in mixture properties. Hence, the oven heating time of the boxed mixtures was kept low enough to just break the materials and spread them on a tray to achieve a uniform compaction temperature. Furthermore, to counter the concern, binders were recovered from both mixtures before and after 1.5 h of heating and tested in a Brookfield viscometer (discussed later). After heating the loose mixtures, specimens were compacted to 7% \(\pm\) 0.5% air voids using a Servopac gyratory compact (SGC). The SGC compacted specimens were 150 mm in diameter and 178 mm in height. The specimens were then cored and cut to 100 mm diameter by 150 mm height and tested in the AMPT in order to obtain the mechanical properties of the mixtures.  
Simultaneously, the virgin aggregate and binders were also collected, and laboratory mixtures were prepared with exactly the same mix design as that of the loose mixtures at the site (i.e., mixtures 1-3 as shown in Figures 2-4). Then, these lab mixtures were exposed to different aging conditions to simulate the field/plant-produced mixtures. Specimens with similar dimensions and air void levels were prepared and tested in the AMPT. The testing program details of the laboratory-prepared mixtures for short-term aging evaluation are given in Table 2. Note that the specimen ID presents information about aging time and duration; for example, M1_S135T2 refers to: M1 means mixture 1, S135 means short-term aging temperature of 135 \({}^{\circ}\)C, and T2 means short-term aging duration (time) of 2 h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lab Mixture** & **Specimen ID** & \begin{tabular}{c} **Short-Term Aging** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Short-Term Aging** \\ **Durations (hour)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Mixture 1 \\ (Base mix from site) \\ \end{tabular} } & M1-S135T2 & 135 & 2 \\  & M1-S135T3 & 135 & 3 \\  & M1-S135T4 & 135 & 4 \\  & M1-S145T2 & 145 & 2 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Mixture 2 \\ (Wearing mix from plant) \\ \end{tabular} } & M2-S135T2 & 135 & 2 \\  & M2-S135T3 & 135 & 3 \\  & M2-S135T4 & 135 & 4 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Mixture 3 \\ (Wearing mix from site) \\ \end{tabular} } & M3-S135T4 & 135 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing program details for laboratory specimens.  
Figure 4: Gradation of wearing coarse field loose mixture (mixture 3).
## 4 Results and Findings
### Top Ten Most-Cited Publications  
The field's influential contributions are exhibited in the most-cited publications in cybersecurity and other publications that are most cited in ML/DL research. The article \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Source/Journal** & **Subject Area** & **Type** & **Documents** & **\%** \\ \hline
1 & _IEEE Access_ & Engineering, Technology and Innovation & Journal & 352 & 9.48\% \\
2 & _Sensors_ & Sensors Technology, Applications, Artificial Intelligence \& & & Journal & 157 & 4.23\% \\  & _Applied Sciences_ & Engineering \& Technology, Materials and Environmental Sciences, Biomedical Sciences & Journal & 107 & 2.88\% \\
3 & _Computers and Security_ & Cybersecurity, System Security, Network and Data security & Journal & 107 & 2.88\% \\
4 & _Electronics (Switzerland)_ & Communication, Robotics, Automation & Journal & 95 & 2.56\% \\
5 & _Computers, Materials and Continua_ & Computer Science, Materials, Engineering and Mathematics & Journal & 85 & 2.29\% \\
6 & _Mathematical Journal of_ & Artificial Intelligence, Machine Learning, Data Science, Cybersecurity \& Robotics & Journal & 81 & 2.18\% \\
7 & _IEEE Internet of Things_ & IoT Architecture, IoT Protocol, IoT Applications, Interoperability and Standards & Journal & 55 & 1.48\% \\
8 & _Future Internet_ & Communications, Cybersecurity, Artificial Intelligence \& Machine Learning & Journal & 40 & 1.08\% \\
9 & _Computers and Electrical Engineering_ & Computer Science, Electric and Electronics Engineering, Control and Systems & Journal & 38 & 1.02\% \\
10 & _Cluster Computing_ & Computer Networks \& Communications, Cybersecurity, Software Security & Journal & 35 & 0.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top ten productive publication sources. \"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions\" leads with 3744 citations, followed by \"Machine Learning: Algorithms, Real-World Applications and Research Directions\" with 3026, which summarises the concepts, CNN architectures and challenges as well as applications and future directions of deep learning techniques. \"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions\" lists 1205 citations of the type of applications and threats associated with DL systems. Other works mentioned in this document are highly cited and cover adversarial machine learning, blockchain with AI, ChatGPT, and healthcare. From that, it is clear that this research spans widely and is highly impactful. Table 6 details the search results' top ten most-cited research publications. In summary, the data portray the exponential expansion of cybersecurity and ML/DL research, which results from many authors' contributions in every organisation and almost all the journals worldwide. As such, this vibrant research landscape is essential to tackling contemporary cybersecurity challenges and thinking ahead about next-generation systems.
## 4 Results
### Tests on Fresh Composite
#### 4.2.4 Electrical and Sensing Tests  
Electrical tests carried out on unloaded samples allowed the evaluation of the evolution of the conductivity properties of the materials with increasing percentages of carbon microfibers. Due to their weak mechanical strength, making them unsuitable for construction applications, composites with amounts over 0.25% were not considered. The outputs clearly showed a drop in electrical resistance for doping levels higher than 0.1%, identifying the percolation threshold and confirming that composites with slightly lower inclusion levels are the most suitable for smart monitoring (see Figure 9--line). Those types of samples were then tested for sensing evaluation. The obtained Gauge factors show the higher performance of doped composites, up to a GF of 435, strongly higher than values of traditional strain gauges that are around 2.
## 2 Materials and Methods
### Kinetics of Acetamiprid Photo-Oxidation Using the Photoactive Microparticles  
The photo-oxidation rate of Acetamiprid was investigated using photosensizers P2-P4 in aqueous solutions at a pH of 10. In each experiment, 25 mg of the photosensitizer was added to 10 mL of a 0.1 ppm Acetamiprid solution in a test tube. The heterogeneous mixture was stirred and equilibrated with air. The test tubes were irradiated at room temperature using a 125 W medium-pressure Hg vapor lamp, surrounded by an aqueous FeCl\({}_{3}\) solution (0.1 M) acting as a filter for wavelengths below 450 nm. The tubes were positioned 2 cm from the lamp. Control experiments were carried out in the dark to assess potential adsorption of Acetamiprid on the polymeric photocatalyst. Additional control tests were also conducted under the same conditions without any photosensitizer, and Rose Bengal (5 uM) was used as the photosensitizer. For kinetic studies at different pH values, each sample's pH was pre-adjusted accordingly by adding small quantities of 0.01 M HCl or 0.01 M NaOH. All experiments were performed in quintuplicate.  
The method used to monitor Acetamiprid degradation involved pausing the reaction by turning off the lamp at each time interval, followed by collecting a 20 uL aliquot of the reaction mixture. The reaction was then resumed by switching the lamp back on to continue the process. The photo-oxidation reaction of Acetamiprid was determined by analyzing each aliquot by the HPLC chromatography ZORBAX column Reversed-Phase Agilent 1260 (Agilent Technologies, Inc., Santa Clara, CA, USA), using acetonitrile and water (1:2 volume ratio) as mobile phase, the Acetamiprid absorption was monitored at 243 nm with a flow rate of 1 mL\(\cdot\)min\({}^{-1}\) and a column temperature maintained at 30 \({}^{\circ}\)C.
## 2 Materials and Methods
### Adsorption Experiments  
Adsorption experiments were carried out by agitating the SSS powder in an Erlenmeyer flask (500 mL) that contained 250 mL of the MC solution of desired concentration and pH in a magnetic stirrer at 300 rpm. The experiments were carried out with varying adsorbent dosage (1-3 g/L), initial MC concentration (20 -150 mg/L), temperature (25-40 \({}^{\circ}\)C) and pH (2-12). The samples were withdrawn from the Erlenmeyer at predetermined time intervals. The MC solution was separated from the adsorbent using a Hettich brand centrifuge at 5000 rpm for 3 min and analyzed for their MC content spectrophotometrically at the appropriate wavelength (\(\lambda_{\text{max}}=264\) nm) using a UV spectrophotometer (Shimadzu). The effect of pH was studied by adjusting the pH of the MC solutions using HCl andNaOH. All experiments were performed in duplicate, all data was calculated and average values are taken to represent the results.  
The uptake of MC adsorbed onto SSS per unit mass was calculated using the following equation:  
\[q_{t}=\frac{(C_{0}-C_{t})V}{m} \tag{5}\]  
where \(q_{t}\) is the amount of MC adsorbed onto SSS (mg/g) at time t, \(C_{0}\) and \(C_{t}\) are the liquid phase concentrations of MC at initial and time t, respectively. \(V\) (L) is the volume of MC solution and m (g) is the mass of the SSS adsorbent used. The MC removal efficiency was calculated as follows:  
\[Y^{\prime\prime}_{0}=\frac{(C_{0}-C_{t})}{C_{0}}\times 100 \tag{6}\]  
and the average relative error (ARE) was calculated as follows [25]:  
\[ARE=\sum_{i=1}^{N}\left|\frac{q_{e},\exp-q_{e},\text{ model}}{q_{e},\text{ exp}}\right|_{i} \tag{7}\]  
where \(q_{e\text{ exp}}\) and \(q_{e\text{ model}}\) (mg/g) are the uptake at equilibrium measured (exp) and calculated by the isotherm model (model), respectively. We also used the same relationship to determine the mean relative errors for the kinetic models.
# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 2 Related Works
### Arabic Lexicon Construction
Five popular machine learning classifiers are used, NB, KNN, SVM, LR, and neural networks are used as base classifiers for five different types of feature sets. The experimental results indicate that SVM produces the greatest results with Part Of Speech (POS) features. For the Saudi dialect, [PERSON] et al. [5] created an Arabic lexicon for the Saudi dialect SA. There are three main steps. In the first step, they utilized a learning algorithm that uses seed words and punctuation to extend the dictionary. In the second step, they employed a dictionary produced by [PERSON] et al. [17] and translated itusing the Buckwalter translation. This dictionary includes more than 150,000 words and associated punctuation. Finally, in the third step, they manually introduced new terms. It has more than 14,000 sentiment terms. The authors provided four rules for dealing with negation. The implementation of these rules improved performance by 3%. To the best of our knowledge there are no research studies that have attempted to build a lexicon for Arabic SA that is devoted to the COVID-19 pandemic. Thus, the ArSentiCOVID lexicon is the first lexicon constructed for COVID-19. The developed lexicon handles both negation and emojis. The lexicon is large, containing 39,428 polarity words. Furthermore, it is taken advantages of both construction methods, manual and automatic.
## 3 Controller Design
### The Design of SC-AFNN  
To solve the problem of active adaption to variational desired trajectories, SC-AFNN is introduced to entirely replace the CE-SOSMC designed in Section 3.1. Firstly, the hybrid robot is operated under the control of CE-SOSMC, and the ideal training data samples are generated. Then, the fuzzy rule base is built automatically by subtractive clustering algorithm. Finally, the SC-AFNN is constructed from the fuzzy rule base to entirely replace the CE-SOSMC. The input and output of the SC-AFNN controller are, respectively, the desired pose \([\mathbf{x}_{d},\dot{\mathbf{x}}_{d},\dot{\mathbf{x}}_{d}]\), the actual pose \([\mathbf{x},\dot{\mathbf{x}}]\), and the control torque \(\mathbf{\tau}\).
## 3 Rotation Matrix for Transition Dipole Moment to the Optical Frame  
The objective of this contribution is to compute the linear and non-linear population transfer following birefringence decomposition of an arbitrary k-vector, with an emphasis on showing its application to the more challenging biaxial symmetries. The combination of crystal optics that evaluate birefringence decomposition in the explicit molecular frame and non-linear excitation theory is the key technique for analysis and is explicitly shown for biaxial crystals for the first time here. The X-ray crystallographic coordinates from the structure solution are needed as the first step in the analysis. Typically, quantum chemical calculations of transition dipole moments are conducted in the molecular frame using quantum chemistry methods. Electronic transitions are often accurately found at the TD-DFT level using an appropriate density functional, such as CAM-B3 LYP in the case of light atoms [22]. With Cartesian coordinates in the crystallographic directions, an Euler rotation matrix then transforms this direction to the optical frame [14] (Figure 2). In the following, all optical calculations are performed in the coordinate frame of the principal refractive indices.  
Figure 2: For the example of a triclinic unit cell (**left**), there is no relation between the crystallographic Cartesian axes (\(a\), \(b\), \(c\); green) and the principal optical directions (\(n_{1}\), \(n_{2}\), \(n_{3}\), red). The indicatrix is not shown. A transition dipole moment vector \(\mu\) in the crystallographic coordinate frame is transformed to the optical frame by Euler rotation (Euler angles \(\kappa\), \(\lambda\), \(\
u\); **right**).
## 4 Simulations and Results
### Results and Discussions
The main aim of this comparison is to evaluate the performance of algorithms and to identify which encryption algorithm is suitable for our proposed system. The encryption mechanism is performed out of the blockchain. Average execution time is considered as a performance parameter through which the performance of an algorithm is evaluated. Encryption time is the total time taken for converting plain text into cipher text and depends on two main factors: size of data (to be converted) and size of key (to be generated). In our proposed system, each vehicle communicates with other vehicle in an encrypted form. From comparison of different hashing algorithms, we conclude that AES128 is better in terms of average execution time. Though, AES256 is based on the same mechanism on which AES128 works; still, the key size of AES256 is greater than AES128, that is why its execution time is high. With the utilization of encryption algorithms, system security is achieved; while, user comfort is compromised. DES algorithm converts message into block of 64 bits and divides key into three different parts. Due to this, the performance of algorithm becomes worst. Affine cipher algorithm performs worst than all other algorithms because its key is composed of two parts. During encryption process, first part of key is multiplied with data in plain text and then second part is added. We conclude that both AES128 and AES256 perform better than other algorithms. However, their execution time varies with the key size. Figure 8: Comparison of Hashing Algorithms. In Figure 11, a trust value is calculated by first dividing the number of successful transactions by the total number of transactions and then multiplying the result by 100 (Equation (1)). The total number of successful transactions is calculated by subtracting invalid/unsuccessful transactions from the total transactions which occurred. In our proposed system, trust management is considered between ordinary vehicles and edge node vehicles. From the simulation results, we conclude that as the total number of successful transactions increases, trust rate also increases. The higher the trust rate, the higher the reliability of the system. Due to this, network communication also increases with minimum chances of malicious activities. The number of malicious activities are also reduced. \[Avg.\;Trust\;Rate=\frac{No.\;of\;Successful\;Transactions}{Total\;no.\;of\;Transactions }*100 \tag{1}\]  
Figure 10: Comparison of Encryption/Decryption Algorithms. Figure 9: Performance of System with and without Cache Server.
# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
### Multi-Sensor
#### 3.2.1 Classification by Modality
Regarding the DL-based methods, CenterFusion [67] associates RADAR point clouds with image detections, concatenates features from both the camera and RADAR, and re-calculates the depth, rotation, and velocity of the objects. CRAFT [68] introduced the Soft-Polar Association and Spatio-Contextual Fusion Transformer to facilitate information sharing between the camera and RADAR. In recent years, inspired by BEV-based 3D detection methods with cameras, BEV-based feature maps have also been generated in camera and RADAR fusion, leveraging RADAR's precise occupancy information. RCBEV4d [69] extracted spatial-temporal features from RADAR and applied point fusion and ROI fusion with transformed BEV image features. CRN [70] achieved comparable performance to LiDAR detectors on the nuScenes dataset, outperforming them at long distances. RCBEVDet [71] introduced a dual-stream RADAR backbone to capture both local RADAR features and global information, along with a RADAR Cross-Section (RCS)-aware BEV encoder that scatters point features across multiple BEV pixels rather than just one. This method outperformed previous approaches on the nuScenes dataset. In general, RADAR serves as a complementary information source to camera-based 3D detection methods, providing more accurate depth information, especially in adverse weather conditions. Others: Another camera combination involves the fusion of RGB cameras and infrared (IR) cameras, which can significantly improve detection performance, particularly under poor visibility conditions such as adverse weather or nighttime [124; 125]. [PERSON] et al. [126] also proposed a multi-spectral pedestrian detector that combines visible-optical (VIS) and infrared (IR) images. This method achieves state-of-the-art (SOTA) performance on the KAIST dataset [127]. When the RGB and infrared cameras share the same field of view (FOV), an image fusion technique can be employed to create a single combined image, making it easier to distinguish VRUs from the background [128]. MMPedestron [129] is a novel multi-modal model for pedestrian detection that consists of a unified multi-modal encoder and a detection head. It can fuse data from five modalities (RGB, IR, Depth, LiDAR, and Event) and is extendable to include additional modalities. The data sources can also span multiple domains, such as traffic surveillance, autonomous driving, and robotics. Trained on a unified dataset composed of five public datasets with different modalities, MMPedestron surpasses the existing SOTA models for each individual dataset. This demonstrates the potential of a unified generalist model for multi-sensor perception in pedestrian detection, outperforming sensor fusion models tailored to specific sensor modalities.
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 4 Proposed Method
### Feature Extraction
#### 4.2.6 Word's Orientation (WOr)  
One of the salient features of the Diwani style is the words written in a slanted format, as Figure 10 shows. WOr descriptor mainly specifies how, on average, words in text images are oriented. The flood-fill algorithm is used to detect words from no diacritics images that are generated in the processing step. After calculating each word's orientation, the mean orientation along with the number of detected words are combined to form the final WOr descriptor.  
WOr algorithm was used to distinguish Diwani from other styles. Diwani style yield an orientation average of about 45 degrees compared to 0 degrees by other styles.  
Figure 8: Vertical straight lines detection.  
Figure 9: Thuluth and Mohakik diacritics.
## 6 Evaluation Performances
### Implementation
It generates important files;.xml describes the simulation environment with a GUI, and.csv contains the simulation parameters and calculated metrics. #### 6.2.1 Simulation Scenario

We experimented different simulations with different densities of heterogeneous nodes moving randomly within a 2000 m \(\times\) 2000 m area for 400 s. The simulation parameters are summarized in Table 2. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Routing protocol & OLSR, MHAR-OLSR \\ \hline Traffic model & UDP \\ \hline PHY/MAC layer & IEEE 802.11p \\ \hline Range of MANET & \(\approx\)100 m \\ \hline Range of vehicules & \(\approx\)300 m \\ \hline Range of UAV & R1 \(\approx\) 400 m and R2 \(\approx\) 1000 m \\ \hline Area size & 2000 \(\times\) 2000 m\({}^{2}\) \\ \hline Simulation time & 400 s \\ \hline Mobility generator & BonnMotion \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. #### 6.2.2 Studied Metrics

Packet Delivery Ratio (PDR): allows revealing in percentage whether a protocol can send all outgoing data. Its calculation of the lost packets is the ratio between received ones by the destination node \(D_{p}\) and those sent by the source node \(S_{p}\) (Equation (5)). Higher values of PDR illustrate better performance. So PDR of 100% signifies that the network has high reliability and availability. \[PDR=\frac{D_{p}}{S_{p}}\times 100 \tag{5}\]

End-to-End delay (E2E): describes the transfer time of the packet from the source to destination. It must be respected, otherwise the contained data in the transmitted packet becomes useless for the application. Equation (6) allows us to determine the E2E value:

\[E2E=\frac{t_{R}^{p}}{\sum D_{p}} \tag{6}\]

where:

\(t_{R}^{p}\): Time of receiving the packet by the destination node

Energy cost per packet (\(E_{cpp}\)): gives the ratio between the total consumed energy over the number of successfully received packets at the destinations (Equation (7)). \[E_{cpp}=\frac{E_{total}}{D_{p}} \tag{7}\]

where

\[E_{total}=E_{trans}+E_{rec}+E_{idle} \tag{8}\]

### Results and Discussion

To study the impact of density, we vary the number of nodes from 20 (low density) to 200 (important density). Figure 11 shows the percentage of data packets received at the destination as a function of node numbers. We remark that both protocols have the same shape with a minor difference in value within the interval of 20-60 nodes. In fact, MHAR-OLSR does not provide a considerable improvement to the communication since it can not detect more than two distinct paths, including a VANET or FANET device.