esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Vehicle Detection and Tracking System
### Pre-Trained Offline Classifier  
Based on the study of [PERSON] [10], which outperforms other methods, in this section, a machine-learning-based method is proposed to classify both headlights and taillights in nighttime images. In the previous study, a learning headlight detector is created to discriminate headlights from non-headlights by fusing the Adaboost classifier and Haar features. However, we cannot use the previous work directly in our context, because the Adaboost classifier is a binary classifier and it has limitations in classifying multiclass data. Thus, we present a new method that combines the multiclass Adaboost classifier [15], Haar features, and a color feature extracted from the L*a*b* color space to discriminate head and tail lights. The proposed learning method is described as follows, according to the flowchart illustrated in Figure 2. First, a training dataset consisting of the headlight, taillight, and negative images is annotated from the captured images. Negative samples that do not contain any headlights or taillights are randomly taken. These include reflections on vehicle bodies, road surfaces, and road signs. Before extracting features, all the training images must be resized to the same dimensions, which are selected from the experiment. Next, each training image is converted from RGB to grayscale and L*a*b* color space.  
For the grayscale image, we use two-rectangle and three-rectangle features to extract Haar features. The process for extracting these features is not mentioned in this paper, as they are described in particular detail in [16]. For the L*a*b* image, only the a* channel representing the green-red color is considered. This channel is chosen statistically in the experiment and the mean value of the a  
Figure 1: Overview of the proposed system.  
channel is calculated as the Lab color feature. The statistics of the mean values of the a* channel from 100 headlight images and 100 taillight images are depicted in Figure 3. It is clearly observable that the mean values of the a* channel of headlight images are lower than 140, whereas thoses of the a* channel of taillight images are higher than 140. Moreover, when the mean values of the a* channel from 300 headlight images and 300 taillight images are calculated, they are still at the same distribution as in the Figure 3. Therefore, the Lab color feature might be a promising feature to discriminate headlights and taillights. Finally, the combination of Haar features and the Lab color feature is input into a multiclass Adaboost classifier to train the learned classifier.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Short-Range Communication Technologies
#### 5.1.3 Ultra-Wide Band (UWB) Technology  
UWB is considered as the short-range wireless technology using short pulses of bandwidth 3.1-10.6 GHz [22]. Using UWB, the devices can operate at very low power and also support multiple users with data rates greater than 480 Mbps [152; 153; 154]. With UWB, it is possible to communicate with a unique randomising code by transmitting Gigabits of information per second. These signals have low amplitude that makes them more covert and harder to degrade in the noisy environment [152; 153; 154]. This allows the UWB to be more secure in transmission with low probability of detection and interception. Devices using the UWB technology are generally designed to have a large processing gain [152; 153; 154].  
Several UWB nodes were deployed in the PRoPART project for obtaining an accurate and robust positioning technique. The proposed solution utilised the inertial and odometry data of the vehicle along with the UWB ranging estimates to estimate the vehicle position. The estimates obtained were accurate and 95% of the computed positions were shown to have a positioning distance error of less than 27.1 cm [155]. In [156], machine learning was employed to improve the RMSE of UWB based ranging estimates, and the positioning distance error was reduced to less than 10 cm. The UWB transmitter employs low power signals which are below \(-41.3\) dBm/MHz emission limit. This low emission with time hopping (TH) code, direct sequence (DS) code or a combination of DS/TH codes protect the data from eavesdropping attack.  
A comparison of these four technologies is given in Table 5.
## 1 Introduction  
### Road Safety and Pavement Markings' Retroreflectivity  
During the last decades, traffic crashes have become a worldwide concern. The last UN General Assembly established the Second Decade of Action for Road Safety 2021-2030 with a target to reduce death and injuries caused by traffic by a minimum of 50% by the year 2030 [1]. The report recommends ensuring safe road use by guaranteeing that road infrastructure considers the needs of all road users and is designed to facilitate safe behaviors, including the use of clear and intuitive pavement markings [1].  
Pavement markings are one of the most important features for roads due to their contribution to road safety improvement. Due to their relatively low cost and broad availability, pavement markings are a low-cost solution to reduce traffic crashes, especially in developing countries [2]. However, adequate pavement markings must present visibility during the day by the contrast of the marking material with the pavement surface, and their nighttime visibility depends on the retroreflectivity provided by the glass beads in the pavement markings.  
Retoreflectivity is an engineering measure of the efficiency of the pavement markings' ability to reflect the light from vehicle's headlights back to the light source. Pavement markings' retroreflectivity is measured by the coefficient of retroreflected luminance (\(\rm{R_{L}}\), \(\rm{mcd/m^{2}/lx}\)) given by the ratio of the luminance (brightness to the driver from the markings surface, \(\rm{mcd/m^{2}}\)) and the illuminance, in lux (lx), of the vehicles' headlight on the marking [3; 4]. Retroreflectivity (\(\rm{R_{L}}\)) is most required in low-light and nighttime conditions to improve the readability and perception of the information provided by pavement markings.  
The improvement and maintenance of pavement markings' retroreflectivity correlate to a reduction in traffic crash rates [3; 5]. Higher values of retroreflectivity reduce the detection distance of pavement markings, especially for elderly drivers [6], which improves their reaction time. In addition, studies have shown that pavement marking retroreflectivity values higher than 200 \(\rm{mcd/lx/m^{2}}\) are related to a lower number of traffic crashes. Moreover, the maintenance of pavement markings' quality presents a positive effect on road safety [7].
## 7 Open Research Issues
### Future Direction  
To obtain a representable empirical model with high performance in path loss prediction, an extensive measurement campaign and experimental data across similar buildings under consideration will be required. The result should be modeled with multiple parameters and examined in a commercial environment with more obstructions to accurately determine the stability of the selected empirical models.  
In the context of using machine learning models for path loss prediction at mid-band and high-band frequencies in distinct scenarios, the lack of interpretability of machine learning models can limit their practical usability and acceptance. Addressing this research gap by developing a transparent and explainable model is essential to establishing trust and facilitating effective deployment in real-world scenarios.  
In future research, an ensemble supervised machine learning-based path loss model that can predict path loss in distinct urban environments that comprise modern and old buildings that have fewer structure layouts and infrastructure will be used to enhance a trusted empirical model at the mid-band frequency spectrum to develop a hybrid model. This unique urban environment is essential for the path loss model because the construction materials in old buildings present different attenuations as compared to modern buildings, as it was reported in [96] and adapted in [97] that the attenuation in modern buildings is approximately 20-25 dB higher than the attenuation in older structures. This proposed model can be used to support the design of wireless communication networks in any urban environment.  
In this case, all the relevant features that describe the environment under consideration, with the adoption of the feature selection technique to further prioritize the most relevant features, will be considered to generate optimal datasets for the training and testing of the machine learning model. An illustration of some of the relevant features is presented in Figure 9.  
Relevant RF planning software tools, such as PL5 version 5.1, will be used to carry out the 3D ray tracing technique and path profile simulation to generate additional information, as well as that from a measurement campaign for a comprehensive dataset.  
Figure 9: Illustration of some of the training features.  
The proposed model is anticipated to aid RF engineers in designing effective mid-band and high-band channel modeling for distinct scenarios, and it will be very helpful for 5G/6G research and system implementation.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 2 Preliminaries
### Knowledge Graph Embedding
#### 2.1.3 Neural Network Based Models  
Neural network based models assume that entities and relations are represented as vectors. They exploit a multi-layer neural network with nonlinear features to measure the plausibility of facts. For a triple \((h,r,t)\), neural network models take its vector representations \(\mathbf{h}\), \(\mathbf{t}\), and \(\mathbf{r}\) as input and then output the probability of the triple to be true after the feed-forward process.  
ConvE: ConvE [19] is a neural network based model where the latent semantics of input entities and relations are modeled by convolutional and fully connected layers. Entities and relations are represented as vectors in space \(\mathbb{R}^{d}\). The score function is defined as follows:  
\[f(h,r,t)=g(\text{vec}(g(\text{concat}(\mathbf{\hat{h}},\mathbf{\hat{r}})* \omega))\mathbf{W})\mathbf{t}, \tag{6}\]  
where \(\mathbf{\hat{h}}\) and \(\mathbf{\hat{r}}\) denote a 2D reshaping of \(\mathbf{h}\) and \(\mathbf{r}\), respectively; the \(\text{concat}(\cdot)\) function concatenates two matrices \(\mathbf{\hat{h}}\) and \(\mathbf{\hat{r}}\); \(\omega\) denotes a set of filters; \(*\) denotes a convolution operator; the \(\text{vec}(\cdot)\) function reshapes the tensor produced by the convolution layer into a vector; \(g(\cdot)\) denotes a nonlinear function. ConvE applies the sigmoid function \(\sigma(\cdot)\) as the last layer activation to the score, which is \(p_{htt}=\sigma(f(h,r,t))\), and learns the embeddings by minimizing the following binary cross-entropy loss function:  
\[\sum_{(h,r,t)\in\mathcal{T}\cup\mathcal{T}^{\prime}}-(y_{htt}\cdot\log(p_{htt })+(1-y_{htt})\cdot\log(1-p_{htt})), \tag{7}\]  
where the definitions of \(\mathcal{T},\mathcal{T}^{\prime}\), and \(y_{htt}\) are the same as the above Equation (5).
## 2 Materials and Methods
### ANN-Particle Swarm Optimization (ANN-PSO) Model  
PSO is a computational population-based method inspired by the social behavior of swarms in their search space built on their own and neighbors' past successful experiments. In PSO terminology, swarm members are called \"particles\", each one indicating a possible solution [49]. The immediate aim  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Dataset** & **Mean** & **Maximum** & **Minimum** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Coefficient** \\ **of Variation** \\ \end{tabular} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Inputs \\ \(\left(\frac{q}{gH_{1}^{3}}\right)\) \\ \end{tabular} } & Total & 0.1669 & 4.4699 & 0.0040 & 0.5208 & 3.1208 \\  & Training & 0.1869 & 4.4699 & 0.0040 & 0.5700 & 3.0496 \\  & Testing & 0.0696 & 0.1850 & 0.0088 & 0.0562 & 0.8073 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Output \\ (SD) \\ \end{tabular} } & Total & 0.7146 & 6.3500 & 0.0572 & 0.8318 & 1.1641 \\  & Training & 0.7315 & 6.3500 & 0.0572 & 0.8986 & 1.2285 \\ \cline{1-1}  & Testing & 0.6323 & 1.2936 & 0.1687 & 0.3747 & 0.5925 \\ \hline \hline \end{tabular} Note: \(g\) acceleration due to gravity (m/sec\({}^{2}\)).  
\end{table}
Table 2: Statistical parameters of data.  
Figure 2: Structure of the artificial neural network (ANN) model.  
of particles in this algorithm is to find a better position in the search space based on past experiences of flights they and their neighbors have had, to change their location and update their velocity.  
In this algorithm, \(X_{i}~{}=~{}(x_{i1},x_{i2},\ldots,x_{iD})\) represent particles, and the situation of these particles with best-fitting function is shown as the best current position, which is \(P_{i}~{}=~{}(p_{i1},p_{i2},\ldots,p_{iD})\), and the corresponding fitting function is _Pbest_, and _Gbest_ is known as the best general position of records \(P_{g}~{}=~{}\left(p_{g1},p_{g2},\ldots,p_{gD}\right)\). Velocities of particles are denoted by \(V_{i}~{}=~{}(v_{i1},v_{i2},\ldots,v_{iD})\). Below equations demonstrate how velocity and position of particles are updated in each iteration:  
\[V_{id}(t+1)~{}=~{}\omega(V_{id}(t)+c_{1}rand(0,\varphi_{1}).(P_{id}(t)-X_{id}(t ))+c_{2}rand(0,\varphi_{2}).\left(\left(P_{gd}(t)-X_{id}(t)\right)\right) \tag{4}\]  
\[X_{id}(t+1)~{}=~{}X_{id}(t)+V_{id}(t+1)\ \ \ \ \ d~{}=~{}1,2,\ldots,D \tag{5}\]  
in which \(\varphi_{1}\) and \(\varphi_{2}\) are constant acceleration values. In Equation (4), _rand_ generates a random number through the distance of its inputs, \(\omega\), \(c_{1}\) and \(c_{2}\) respectively denote inertia weight, cognitive parameter, and social parameter. PSO is employed in an algorithm depicted in Figure 3a to explore the optimum weight values for the designed ANN network.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 2 OCDM Basics  
### Principle of OCDM  
Originating from the Fresnel transform [15], a set of orthogonal chirp signals has been proposed in [14] as the kernel of OCDM. Let \(\psi_{n}\left(t\right)\) be a set of waveforms with chirp wave-number \(n\in\left[1,N\right]\) as follows:  
\[\psi_{n}\left(t\right)=e^{j\frac{\pi}{4}}e^{-jn\frac{N}{T^{2}}\left(t-n\frac{T }{N}\right)^{2}},t\in\left[0,T\right] \tag{1}\]  
As an example, a 16-chirped OCDM system is illustrated in Figure 1, where the real part of chirped waveforms is depicted in a solid line, whereas the dashed line stands for the imaginary part. It it obvious that these chirp waveforms are mutually orthogonal in the chirp dimension:  
\[\int_{0}^{T}\psi_{m}^{*}\left(t\right)\psi_{n}\left(t\right)dt=\int_{0}^{T}e^ {j\pi\frac{N}{T^{2}}\left(t-n\frac{T}{N}\right)^{2}}e^{-jn\frac{N}{T^{2}} \left(t-n\frac{T}{N}\right)^{2}}dt=\begin{cases}1&\text{if }m=n\\ 0&\text{instead}\end{cases} \tag{2}\]  
In the time domain, the useful baseband transmission signal for the OCDM block \(k\) labeled as \(s_{k}(t)\) can be obtained by multiplexing these \(N\) waveforms over the duration \(T\) where each waveform is multiplied by a QAM cell \(x_{k}^{n}\) carrying the useful information:  
\[s_{k}\left(t\right)=\sum_{n=0}^{N-1}x_{n}^{k}\psi_{n}\left(t\right),t\in\left[ 0,T\right] \tag{3}\]  
Similar to OFDM [13], the orthogonality property of (2) can be exploited to recover data in the receiving block \(r_{k}\left(t\right)\):  
\[\hat{x}_{n}^{k}=\int_{0}^{T}r_{k}\left(t\right)\psi_{n}^{*}\left(t\right)dt \tag{4}\]
## 3 Results
### Analytical Results
#### 3.1.2 Total Extractible Compounds and Total Phenolics in Extracts  
Table 1 presents total extractible compounds (dry matter) and total phenolics (expressed as gallic acid derivates) in the aqueous extracts from leaves of _Plantago lancecolata_, the non-irradiated extract (PlaC) versus the six irradiated extracts (Plal488, Plal514, Plal532, Plal552, Plal650, Plal785) at 2-h extraction; numerical values and rate (%) in comparison with the control extract, for each laser extract.  
Figure 4: (**a**) Plantamajoside; (**b**) Acteoside.  
Figure 3: Polyphenols profile in the aqueous extracts from _Plantago lanccolata_ L. _Folium_. Track T1, laser extract at 785 nm (PlalS785); Track T2, laser extract at 660 nm (PlalS660); Track T3, laser extract at 552 nm (PlalS552); Track T4, laser extract at 532 nm (PlalS532); Track T5, laser extract at 514 nm laser (PlalS514); Track T6, laser extract at 488 nm (PlalS488); Track T7, control, non-irradiated extract (PlalSC); Track T8, reference compounds series (ref.): rutin (Rf\(\sim\)0.42), chlorogenic acid (Rf\(\sim\)0.49), hyperoside (Rf\(\sim\)0.64), cynasoside (Rf\(\sim\)0.69), vitexin (Rf\(\sim\)0.75), caffeic acid (Rf\(\sim\)0.93), and umbeliferone Rf–(0.98).  
The comparison of the total extractible values in the samples indicates the extracts at 488, 514, 532, and 552 nm (Plal488, Plal532, Plal532, Plal552) with some small acquisitions of compounds in the extracts, up to 6.52% respectively, while the laser-assisted extracts at 660 and 785 nm (Plal 660 and Plal785) show lower values, till 2.14% respectively.  
More visible variations were observed in the case of total phenolics content in the test extracts; for example, compared to the control non-irradiated sample, there were measured decreased total phenolics content in the case of extracts at 488 nm (\(-\)3.47%) and 514 nm (\(-\)15.50%), similar quantities in the case of extract at 532 nm (+0.31%), and significant increases, that exceed the potential variation of the batches, in the case of total phenolics in extracts at 552 nm (+12.5%), 660 nm (+15.50%), and 785 nm (+15.50).
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 1 Introduction
### Historical Background--Evolution of Code Obfuscation  
Code obfuscation is a crucial practice in cybersecurity, serving various purposes such as protecting software intellectual property, preventing unauthorized access, and deterring reverse engineering. As cyber threats continue to evolve, traditional security methods often fall short, making obfuscation an essential tool in the software security arsenal. This paper offers a comprehensive overview of code obfuscation, covering techniques, benefits, challenges, and ethical concerns. The evolution of code obfuscation techniques highlights the growing need for software security and the protection of intellectual property. Initially, obfuscation techniques were simplistic and focused on basic and simple transformations such as renaming variables and functions. However, over time, they have advanced to include more sophisticated techniques that aimed to significantly increase the difficulty of reverse engineering efforts [1]. Early code obfuscation techniques emerged in the 1980s. The International Obfuscated C Code Contest back in 1984 [4] showcased creative ways to write intentionally obscure C code, marking one of the first public recognitions of code obfuscation practices. The techniques used at that time mainly involved lexical transformations, such as renaming variables with meaningless and nonsensical identifiers and syntactic transformations, which altered the code structure to reduce readability while preserving functionality [3,5]. By the late 1990s, researchers began formalizing the concept of code obfuscation, categorizing various techniques and developing taxonomies to better understand their applications and limitations. These studies emphasized the use of control flow obfuscation, which involves altering the execution path of a program by adding redundant or misleading control statements, making it harder to deduce the original program logic [3,5]. In the early 2000s, code obfuscation advanced significantly with the introduction of theoretical frameworks [6, 7, 8]. Researchers initiated the first comprehensive theoretical studies on obfuscation, introducing the concept of virtual black-box property and investigating the extent to which semantic information could be concealed. This period also saw the development of model-oriented obfuscation techniques, which aimed to obscure not only the code but also the underlying computational models, such as circuits or Turing machines [1,5]. The historical progression of code obfuscation techniques reflects a continuous, persistent effort to outpace reverse engineering tactics. What began as simple lexical changes has evolved into complex transformations designed to thwart both automated and manual analysis efforts. This evolution highlights the ongoing tension between software developers aiming to protect their code and attackers attempting to expose it [1,5].
## 4 Results  
### Model Performance  
The performance of the two main architectures, CNN-SE-FFM-BiLSTM and CNN-FFM-SE-BiLSTM, was evaluated on the tool presence classification task. The models were trained and validated on the Cholec80 dataset, and their performance was assessed using average precision metrics for each tool. Figure 4 shows the performance of both models. As demonstrated in Figure 4, the CNN-SE-FFM-BiLSTM demonstrated better overall performance compared to the CNN-FFM-SE-BiLSTM model. The boost in performance is clearly pronounced in tools with more complex patterns of usage, especially the scissors and irrigator which are less represented in the data set compared to other tools and are held near the grasper, which led to them being confused with it in some instances or even occluded by it in some other cases. Table 2 presents accuracy, precision, recall and F1 score calculated at a threshold of 0.5 for the CNN-SE-FFM-BiLSTM model.  
Figure 4: Average precision results of each tool and mean average precision (mAP) for all evaluated architectures: CNN-FM SE, CNN-FM-SE-BiLSTM, and CNN-SE-FFM-BiLSTM.  
The single-label model achieved an outstanding performance with only one SE block placed after the feature fusion module and without the need for additional temporal features extracted by the BiLSTM network in the multi-label setting. Figure 4 demonstrates the performance for the CNN-FFM-SE single-label model evaluated on single tool images of the Choice80 data set. Figure 4 also presents the performance of the CNN-SE-FFM-BiLSTM model to allow comparison of the performance of single-label and multi-label classification approaches.