esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 5 Measurements Results
### Packets Loss and Jitter  
Figure 18 shows that the PER rate is clearly worse when we choose a higher bandwidth (20 Mbps) in each transmitter thread. PER average, when considering the V2I scenario, is higher, as the bandwidth required increases. This is due to the higher number of collisions observed. The PER average increases with distance. The graphs corresponding to the same side show lower PER rates than those of Infront, where vehicles are closer.  
Figure 17: Average effective throughput received.  
Figure 19 shows the jitter (deviation on the time) observed. Jitter values are higher when transceivers are located on the same side and increase as the transmission bandwidth does. If the communication channel becomes saturated, and that is something that can happen at 10 Mbps bandwidth, the increase of the transmission bandwidth should not improve the speed, but it could harm the jitter. Therefore, lowering the transfer rate itself would mean an improvement in the effective throughput.  
Figure 18: Average packet error rate (PER) observed.  
Thus, we can conclude from the observation of the measured data that communication between vehicles (V2V) located in opposite sides (called Infront in the figures) transmitting with rates not too high (10 Mbps) is preferable. V2V-Infront-10 is the operation configuration that offers better results, and therefore, the one that should be considered preferable.
# Normalized Weighting Schemes for Image Interpolation Algorithms
###### Abstract  
Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end-user applications. Therefore, in this work, the author introduced four weighting schemes based on some geometric shapes for digital image interpolation operations. Moreover, the quantity used to express the extent of each shape's weight was the normalized area, especially when the sums of areas exceeded a unit square size. The introduced four weighting schemes are based on the minimum side-based diameter (MD) of a regular tetragon, hypotenuse-based radius (HR), the virtual pixel length-based height for the area of the triangle (AT), and the virtual pixel length for hypotenuse-based radius for the area of the circle (AC). At the smaller scaling ratio, the image interpolation algorithm based on the HR scheme scored the highest at 66.6% among non-traditional image interpolation algorithms presented. However, at the higher scaling ratio, the AC scheme-based image interpolation algorithm scored the highest at 66.6% among non-traditional algorithms presented, and, here, its image interpolation quality was generally superior or comparable to the quality of images interpolated by both non-traditional and traditional algorithms.  
circle; triangle; normalization; digital zoom; image; interpolation; weighting Article
## 3 Results  
This study aimed to explore the differences in eye movement patterns (AFD, TFD, and scanpath length) across age groups, reading conditions (AR and R), and text types (fictional and non-fictional). Furthermore, predictive models were developed to identify the eye movement variables that predict reading comprehension, both literal and inferential, and to investigate whether these predictors varied according to reading conditions and text types while controlling for age. Table 2 displays the reading comprehension performance and eye movement variables based on age, reading conditions, and text types.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Grade**} & **Reading** & \multirow{2}{*}{**Text Type**} & **Literal** & **Inferential** & \multirow{2}{*}{**AFD**} & **TFD** & **Scanpath** \\  & **Condition** & & **Comprehension** & & & & **Length** \\ \hline \multirow{4}{*}{1} & \multirow{2}{*}{R} & Fiction & \(-1.15\) (1.31) & \(-1.21\) (1.00) & \(0.70\) (1.36) & \(0.66\) (1.87) & \(0.20\) (1.03) \\  & & Non-fiction & \(-1.07\) (1.36) & \(-1.30\) (0.89) & \(0.32\) (0.76) & \(0.29\) (1.04) & \(0.40\) (1.41) \\ \cline{2-7}  & \multirow{2}{*}{AR} & Fiction & \(-0.64\) (1.04) & \(-0.62\) (1.07) & \(0.82\) (1.25) & \(-0.31\) (0.81) & \(-0.81\) (1.20) \\  & & Non-fiction & \(-1.03\) (0.90) & \(-1.24\) (0.81) & \(0.61\) (1.13) & \(-0.13\) (1.14) & \(-0.79\) (0.66) \\ \hline \multirow{4}{*}{2} & \multirow{2}{*}{R} & Fiction & \(0.23\) (0.50) & \(-0.20\) (0.95) & \(0.37\) (1.27) & \(0.25\) (0.80) & \(0.18\) (0.83) \\  & & Non-fiction & \(-0.75\) (0.85) & \(-0.69\) (0.90) & \(0.36\) (1.30) & \(0.30\) (1.08) & \(-0.06\) (0.97) \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{AR} & Fiction & \(-0.34\) (0.94) & \(-0.38\) (0.97) & \(0.13\) (1.01) & \(0.52\) (1.01) & \(-0.38\) (0.96) \\ \cline{1-1}  & & Non-fiction & \(-0.35\) (0.82) & \(-0.42\) (0.76) & \(0.13\) (0.95) & \(-0.01\) (0.92) & \(-0.36\) (0.79) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of literal and inferential comprehension and eye tracking variables. Values are presented as mean (SD). All scores are reported in Z-scores. R: reading-only; AR: audio-assisted reading; AFD: average fixation duration; TFD: total fixation duration.
# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 5 Discussion
### Analysis of Testing Results  
To ensure the rationality of the evaluation methods, the verification of the evaluation results is a crucial step. In the studies conducted by [26; 27], a method was employed to quantify the complexity of scenarios based on the location and number of surroundingvehicles. The evaluation results were verified through on-road tests. However, it should be noted that the vehicles used in these studies were not equipped with intelligence and were different from AVs. Therefore, obtaining an accurate validation of the evaluation results was not possible. In this study, one of the state-of-the-art ADS, Baidu Apollo, was utilized for validation to ensure the credibility of the simulation results. The 700 scenarios were derived from real-world crashes; the risk values obtained ranged from 0.41 to 0.71, and the scenario complexity values ranged from 0.32 to 0.69.  
According to the results, the AV avoided a crash in 546 out of the 700 scenarios, which accounts for 78% of the total. Among the 546 avoidable scenarios, both the complexity and risk distributions fell within the lower range. Complexity was predominantly concentrated in the range of 0.40-0.50, while risk spanned from 0.45 to 0.53. On the other hand, the 154 unavoidable scenarios exhibited a broader range of complexity and risk compared to the avoidable scenarios. Complexity is mainly concentrated in the range of 0.37-0.48, and risk ranges from 0.50 to 0.58.  
Delta V is used to characterize the level of danger. In the avoidable scenarios [49], complexity and risk are positively correlated with Delta V, meaning that as complexity and risk increase, the Delta V gradually becomes larger. On the other hand, the \(GTTC_{min}\) is used to quantify the level of danger during the operation of the vehicle in the avoidable scenarios. As complexity and risk increase, the \(GTTC_{min}\) tends to decrease. As the complexity and risk level of scenarios increase, AVs face greater challenges, raising the likelihood of crashes. Conversely, AVs can more effectively manage scenarios with lower complexity and risk.
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 2 Materials and Methods  
In ecology, HDMs link data on the distribution of habitat to abiotic or biotic conditions [57] that are also derived from remote sensing data. Typically, the starting point of HDM is location data on the occurrence of a habitat as response variable, and mapped environmental or satellite data as predictors [58]. Statistical methods, such as Random Forests, are used to estimate the presence of habitat as a function of the predictor variables.
## 4 A Distributed Mapping Service
### An Algorithm for Distributed Map Construction
#### 4.1.3 The Sink Computes Complete Network Map  
The sink fuses the partial map received from all cluster heads into a complete map of the network. By applying the same interpolation step performed at the local cluster heads, the sink generates a complete map of the network. When requested by the user, this map can be refined by querying a more detailed map from the cluster head(s) managing the area of interest.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 2 Problem Formulation
### Setup  
Consider a monostatic UAV-based SAR imaging system that transmits frequency modulated signals \(s_{\mathrm{t}}(\tau)\) and receives backscattered echoes of a similar waveform \(s_{\mathrm{r}}(\xi,\tau)\) at the same aperture position, where \(\tau\) denotes the range time and \(\xi\) the azimuth. The SAR system operates at THz frequencies and is mounted on a quadcopter, which is one of the desirable platforms for such systems under development; see the problem setup in Figure 1.  
Assume that the THz SAR system follows the straight path that agrees with the azimuth axis, and the object under test is of an arbitrary shape and located in the center of the scene under illumination at the reference range distance \(R_{0}\). The range distance between the platform antenna and the point of the scene under illumination at each aperture position can be defined as  
\[R=\sqrt{(\xi-\xi^{\prime})^{2}+(\rho^{\prime})^{2}}, \tag{1}\]  
where \((\xi,0)\) and \((\xi^{\prime},\rho^{\prime})\) are the coordinates of the actual aperture position and the point of the scene under illumination, respectively.
###### Abstract  
This study was conducted to investigate the effect of ageing on bitumen, before and after modification. Nano-clay modified bitumen (NCMB) was produced by adding nano-clay (NC) to 60/70 penetration grade bitumen; then, the binder was tested using conventional tests for properties such as penetration, softening point and viscosity. These tests were carried out on the modified binder before and after ageing. A rolling thin film oven (RTFO) was used to simulate short-term ageing (STA), and a pressure ageing vessel (PAV) was used to simulate long-term ageing (LTA) for the modified binder. After initial results showed an improvement for the modified bitumen regarding the effect of ageing, the investigation continued using Fourier transform infrared spectroscopy (FTIR), where the microstructure distribution of the modified binder before and after ageing was observed. Finally, there was no doubt that the effect of ageing on mixtures should be investigated. For this step, the indirect tensile strength (ITS) test, which highlights the strength changes that occur for the mixtures after ageing, was selected. The results indicated that the tensile strength of mixtures made with modified bitumen showed better resistance against ageing when NC was added, which is in good agreement with the results of previous binder tests. The results of this study show that the modification of bitumen using nano-clay as an additive improves the ageing resistance of the binder, which is consequently reflected in the strength of the asphalt mixture.  
bibliography, nano-clay; ageing; consistency test; Fourier transform infrared spectroscopy (FTIR); indirect tensile strength test (ITS)
## 1 Introduction
The literature shows us different types of attacks that can appear while using the RPL protocol, and these attacks and their consequences or impact are summarized in Table 1.  
Figure 1: Internet of things (IoT) architecture [7,8,10]. This paper presents an investigation about the attacks present in RPL and the mitigation mechanisms used in the literature. This research is exposed through a structured literature review (SLR) according to Massaro protocol presented in [23], and following these reviews [24; 25; 26]. The contribution of this research is to establish a starting point for new investigations regarding the attacks mitigated in the RPL protocol and the strategies used, that is: What attacks have been presented? How many contributions exist? If it exists or not, is there persistence in the authors' research line, and what is the future trend? To date, there is no review of state of the art using the SLR methodology that can guide researchers regarding the development and contributions of existing publications. This situation becomes the contribution of this manuscript, to contribute to the literature an SLR review covering the proposals that mitigate attacks in RPL and their mitigation strategies. The main object of this paper is to show the progress of security aspects in RPL-protocol, and answer the following research questions. RQ1: What are the development issues in IoT in RPL-security? RQ2: What are the trends to solve the security problems of the RPL-protocol? RQ3: What is the trend of future research?
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
#### 2.4.3 Multi-Headed CNN  
The data collected from the Mel spectrogram and the Mel frequency cepptal coefficients are combined as a single input to learn global features of the sounds in this architecture. Each head of the multi-headed 2D network has several feature learning blocks that consist of one convolutional layer and one batch normalization layer to improve training speed and the stability of the network. This architecture is composed of 3 feature learning blocks at each branch, followed by a max pooling layer that allows the network to be robust against noise and distortions. After this, a DropOut layer is used in order to regularize the network and prevent it from overfitting. The data of the two networks are then flattened and concatenated, providing the input of a three phase neural network. At last, a neural layer with activation softmax is connected to make the final predictions. A simplified diagram of this architecture can be seen in Figure 6.  
Figure 5: Flow charts of the distress signal recognition approaches adopted in this paper from top to bottom: Classifier using 1D CNN with features extracted from raw data; Classifier using 2D CNN with features extracted from Spectrogram or MFCCs; Classifier using Double 2D CNN with features extracted from Mel spectrogram and MFCCs.
# Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs
## 2 Related Research
However, it still has the disadvantages of a delay in data transmission and void hole problems. Many routing protocols based on data forwarding [30] are used to deliver data to the destination. An evidence-theory-based opportunistic routing (EBOR) [31] protocol was proposed by [PERSON] et al. They chose the best next forwarder according to the residual energy of the node and packet transmission probability. However, this causes high data overheads. There are also some dynamic routing protocols. In [32], the authors propose the self-organized ad hoc mobile (SOAM) routing protocol to solve the routing problem of mobile nodes, but the broadcast packets need to traverse the whole network to achieve connectivity, which leads to higher energy consumption. Although traditional routing protocols have performed well in UASNs, they are limited by multiple constraints and a high computational complexity [33]. With the advance of artificial intelligence (AI), many intelligent algorithms have been developed for the routing protocol of UASNs, for example, the artificial fish swim algorithm (AFSA), simulated annealing algorithm (SAA), an colony algorithm (ACA) [34], and Q-learning algorithm. Although these intelligent algorithms can determine the optimal solution through iteration, they still have the shortcoming of falling into the local optimum because of the long search time. QELAR [35] is an energy-efficient and lifetime-aware routing protocol based on Q-learning. In QELAR, the reward function is designed using energy consumption and residual energy to calculate the Q-value; then, the nodes are selected to forward data with a high Q-value. Although QELAR is more energy efficient, it cannot optimize the routing process without depth and latency information. Thus, in [36], [PERSON] et al. propose an energy-efficient depth-based opportunistic routing protocol that performed well in terms of power consumption and PDR. However, the end-to-end delay of the network increased due to the advance detection for the void hole. The protocols above have their advantages in terms of energy consumption, PDR, network lifetime, etc. However, most of them do not consider the routing void problem caused by the movement of underwater nodes in a real environment. Therefore, we propose OCMR to reduce this problem to avoid data retransmission, reduce the end-to-end delay and improve PDR.
## 1 Introduction
[14] applies artificial intelligence for experimental modeling of hexapod robots, demonstrating the integration of advanced computational techniques in robotic control. In [15], a path-tracking controller is designed for omnidirectional gait in hexapod robots, and [16] focuses on leg design, which is essential for ensuring efficient and adaptable movement. Motion control is explored in [17] using model-based design, emphasizing the importance of precise control algorithms. Furthermore, ref. [18] introduces a model-based real-time motion tracking approach using dynamical inverse kinematics, relevant for improving the control and accuracy of hexapod robots. Additionally, the textbook [19] provides comprehensive insights into inverse kinematics, modeling, motion planning, and control of manipulators and mobile robots, offering valuable theoretical background and practical applications. The DAR method introduced in this work stands out as an innovative solution to the key challenges identified in these studies. By offering a more accurate approach to inverse kinematics in scenarios with uniform terrain, the DAR method has the potential to significantly advance the field of robotics. Its scalability also makes it a promising candidate for future exploration in dynamic analyses, where precise control and adaptability are essential. The structure of this paper is as follows: Section 2 begins with an introduction to the Directed Angular Restitution (DAR) method, laying the foundation for the subsequent analyses. Section 3 then provides a comparative analysis of inverse kinematics methods, demonstrating the performance of the DAR method in comparison to traditional approaches. Building on this, Section 4 presents the mathematical model of the closed-loop trajectory, which is essential for understanding the kinematic behavior of the system. Section 5 offers a detailed kinematic analysis of the hexapod robot, covering both direct and inverse kinematics. The discussion continues in Section 6 with the results and simulations, where the effectiveness of the DAR method is evaluated. In Section 7, the focus shifts to stability and support polygons, exploring their critical role in maintaining the robot's balance. Finally, Section 8 concludes the paper, summarizing the key findings and proposing directions for future research.
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 1 Introduction
Moreover, feature selection based on NRS does not require any prior knowledge of the feature space structure, and hence it is an ideal tool for online stream feature selection. This paper proposes a dynamic online label distribution feature selection model based on label correlation and NRS, and proposes a dynamic label distribution feature selection algorithm for processing stream features. Mutual information is widely used to measure the degree of dependence between random variables and is an effective indicator for evaluating the discriminative ability of candidate features in the feature selection process. The proposed method uses the mutual information method to process the label space and obtain the correlation between labels through mutual information and graph characteristics. In addition, a new label space neighborhood relationship is proposed, in which the neighborhood class of the instance is constructed in the label space, replacing the calculation of the traditional logical label equivalence class. Simultaneously, the nearest neighbor distribution of the surrounding instances is used to calculate the neighborhood of the instance in the feature space, avoiding the problem of neighborhood granularity selection faced by the traditional NRS model. On this basis, the NRS model is extended to fit the label distribution data, and the corresponding feature dependency is redefined. Combining label correlation and NRS, a dynamic feature selection algorithm framework is proposed. The main contributions of this paper are as follows:  
1. The average nearest neighbor method is used to calculate a new form of neighborhood granularity, and the mutual information method is used to calculate the label weight to obtain the label correlation. By combining the neighborhood granularity and label correlation, a new NRS relationship and feature importance model is constructed. 2. The traditional NRS model is generalized to adapt it to LDL. 3. Using the above model, a new label distribution stream feature selection algorithm is proposed that combines the new NRS model with the stream feature online importance update framework. As a result, it can better handle the label distribution stream feature problem. The rest of this paper is organized as follows. In Section 2, we introduce the related concepts, including LDL, feature correlation, and NRS theory. Section 3 introduces label correlation and the NRS models. In Section 4, we propose a dynamic feature selection algorithm based on label correlation and NRS. We report our experimental results in Section 5. Finally, Section 6 summarizes this paper and discusses future work.
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 4 Discussion and Directions
### DDPG-MPCC Action Is Optimal  
DDPG-MPCC is designed to enhance the performance of MPCC, particularly in the context of time-varying network scenarios. Recall that the proposed DDPG agent is composed of three main components: (i) State space, (ii) Action space, and (iii) Reward. For tractability [7], we use the MPCC utility as a reward after each Mutual Information (MI). In Figure 6, we illustrate the comparison of the evolution of the average sending rates (actions) between MPCC and DDPG-MPCC over the number of MIs. DDPG is specifically tailored for continuous action spaces, providing an opportunity to enhance the MPCC.  
In our DDPG-MPCC design, we seamlessly integrate a deterministic policy network (Actor) and a Q-network (Critic). The core concept involves capturing network dynamics through a policy iteration mechanism that alternates between policy improvement (actor) and policy evaluation (critic). This process is depicted in Figure 7, showcasing the evolution of average utilities (shaping optimal policy) for MPCC [5] and DDPG-MPCC over the number of MIs.  
The actor's role is to enhance the policy using a policy gradient, while the critic evaluates the policy based on current parameters. We establish an actor and critic model by representing the policy function and Q-network parameters. The policy objective function, \(J(\theta_{\mu})\), for the actor-network is calculated as a weighted cumulative expected reward. Additionally, Figure 8 illustrates the evolution of the average reward (\(R_{t}\)) of the trained DDPG, that is, the optimal policy over the number of MIs, while DDPG-MPCC and MPCC aim at maximizing utility.