esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Concept of IoV
### Challenges Facing IoV  
In recent years, various technologies have been integrated for the sole purpose of designing and deploying an IoV architecture. Some of these technologies are IEEE 802.11p, Microwave Access, Dedicated Short-Range Communication, Long-Term Evolution, and cloud computing [89]. While IoV is a promising field, it presents myriad challenges. Some of the bottlenecks IoV developers must grapple with include issues associated with data protection, liability, architecture, resource allocation, and diverse interconnected nodes [90; 91; 92].  
The most common IoV network challenges are as follows:  
* Complex and diversified networks;
* The security and reliability of services, incompatibility and accuracy of services, and limited storage capacity;
* Data storage and management;
* Scalability;
* Internet provision;
* Poor reception and weak signals that hinder the availability of satellite-based GPS systems;
* Disruptive tolerant communication;
* High mobility of dynamic topologies and vehicle nodes;
* Localization issues;
* Addressing and tracking network fragmentation.
# UAV Detection Using Thrust Engine Electromagnetic Spectra
### Types of Engines for UAV's Propulsion
A DC motor can be run on DC current or under pulse width modulation control without the usage of complex electronic speed controller circuitry. Modern electric motors, known as brushless direct current (BLDC) motors, are frequently utilized in small UAVs that are powered by electricity for all purposes. The key benefits of the motor are:  
* The design is more sturdy and compact because brushes, commutators, and slip rings are not used. * Low rotor inertia results in rapid dynamic reaction; simple, lightweight, and lossless rotor construction yields low inertia and great efficiency. * High efficiency enables the machine's structure to be smaller. * High life cycle occasionally exceeding 30,000 h.
* Due to the fact that the motor windings are a component of the permanent stator, there are no moving electrical components and mechanical commutation. * High reliability because the only component that is subject to friction force is the bearing. Permanent magnets spinning around a fixed stator with windings make up a BLDC motor. The electronic speed regulator circuit, which also detects the rotor's rotational speed and direction, controls the flow of power to the stator windings. High-current pulse width modulation is utilized to regulate motor speed. As a result, the stator windings are activated at varying times while maintaining a consistent frequency. Internal combustion engines are not used on copter-type UAVs due to the weight, momentum, and lack of mechanical thrust control possibilities of the motor. A mass-produced solution for the use of internal combustion engines in recreational-type copter UAVs has not yet been available. Most recreational copter-type UAVs use electrically driven thrust power plants for propulsion. The only internal combustion engines that could be utilized in copter-type UAVs alongside BLDC electric thrust motors as electric energy generators are probably hybrid internal combustion engines.
## 1 Introduction  
Robot arm manipulation is a crucial aspect of modern robotics, with applications ranging from industrial automation to advanced research tasks. As the field of robotics evolves, the challenge shifts toward developing efficient and adaptable techniques capable of learning or implementing robust solutions for complex tasks. While certain manipulations might be inherently difficult, creating applications that achieve high performance across diverse tasks in continuous state spaces remains a significant hurdle [1]. This difficulty is often rooted in the need for precise control, real-time adaptation, and the integration of learning algorithms [2].  
Reinforcement Learning (RL) [3] has demonstrated significant success in robotic control applications [4]. However, a critical aspect of applying RL is the design of the reward function, which not only defines the goal of the learning task but also dictates how the agent learns to achieve it [5]. Crafting an appropriate reward function to accurately capture the nuances of complex tasks can be challenging and time-consuming. This process often requires substantial domain expertise and involves a considerable amount of trial and error.  
To address the challenges associated with reward function design, Inverse Reinforcement Learning (IRL) was introduced as a promising solution [6]. Rather than manually crafting a reward function, IRL seeks to infer the underlying reward structure by observing expert demonstrations. This approach is particularly valuable in robotics, where human experts can provide demonstrations or guide the robot along expert trajectories that follow desired behaviors more intuitively than by explicitly defining reward functions.  
One key advantage of IRL is its ability to capture subtle aspects of tasks that might be overlooked in hand-crafted reward functions. By learning directly from expert behavior, IRL can lead to more natural and efficient robotic movements, mirroring the precision and flexibility of human demonstrations [7]. Furthermore, IRL can adapt to complex environments and nuanced behaviors, where defining a reward function explicitly would be not only time-consuming but also error-prone. This flexibility makes IRL particularly well suited for dynamic and unstructured environments, common in real-world robotic applications, where behavior might need to be adapted on the fly.  
This problem is still under research and is being further explored theoretically [8] in order to evaluate the accuracy of the obtained reward and to establish convergence bounds that lead to more feasible reward estimation methods. However, many methods and applications have been proposed, such as Apprenticeship Learning via Inverse Reinforcement Learning (AL-IRL), which serves as the foundational method and motivation for this work. The following section outlines the methods proposed in this work, along with the underlying motivation and contributions of our approach.
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### 3D Reconstruction Algorithms
#### 2.1.4 Algorithms with Proposed Modifications  
In the previous sections, we discussed the requirements for 3D head reconstruction algorithms from selfie videos from the usability viewpoint (Section 2.1.1). Later, the methodology for the improvement of 3D head reconstruction was proposed (Section 2.1.2). The methodology consists of key modifications of the general-purpose 3D reconstruction algorithm to improve 3D head reconstruction from selfie videos. Here, we introduce implementations of algorithm improvements according to the presented methodology. The first branch of the baseline algorithm is created by adding image background elimination and is labeled as _Pipeline_ 2 with sub-branches [a! b] (Figure 1). The sub-branches differ in one change of a parameter value: the value of Describer Density parameter in the FeatureExtraction node in the case of variant (a) is _normal_, and in case of variant (b) is _high_. Background elimination is implemented as _7. Initial feature point selection_ following the _6. Feature point detection_ step of the generalized 3D reconstruction pipeline. The initial feature point selection (or elimination of unnecessary points) process requires information about the bounds of the main object, i.e., the head. This information is provided by the _3. Head detection_ step of the generalized 3D reconstruction pipeline. The background elimination is implemented through feature point selection--it was a more reasonable way to integrate this step with the Meshroom pipeline. Simple masking of the background in the initial images would lead to spurious feature points on the edge of the background cutting. A convolutional neural network (CNN) single-shot detector (SSD) [80] is used for head detection [81] in the images (Figure 2). The model adopted in this research was developed by the authors of LAEO-Net [82]. The model's suitability for the task was evaluated by manually revising the head detection results on the collected dataset of 19 videos. The bounding box (BBox) that indicates the boundaries of the head in the image will be used to remove those feature points that are behind the boundary of the head. During the head detection, not only data on the location of the head in the image are collected, but additionally intermediate results from the intermediate convolutional layers of the CNN (Figure 3). Data from the feature layers are used as features to describe the image patch containing the head. By using these features, the frames can be grouped according to similarity. This grouping will be exploited later in redundant frame dropping.
# Generation of Stereo Images Based on a View Synthesis Network
## 3 The Proposed Approach
### Depth Estimation  
First, we estimated a depth map from an input image. We used the network and pretrained model proposed by [PERSON] et al. [9] because of its simple and efficient architecture. It consists of three parts that represent different scales. In the first scale, [PERSON] et al. trained two sizes of models. One model is based on an ImageNet-trained AlexNet [4] and the other is initialized by the VGG network [5]. We choose the VGG-initialized model for our model because of its higher performance.  
The network uses three scales to complete the prediction task. First, it predicts a coarse but spatially varying set of features in scale 1. Then, it predicts a more detailed view at a midlevel resolution in scale 2. In scale 3, it refines the predictions at a higher resolution. Note that although the network can predict depth maps, surface normals and semantic labels from one image, we used it only for depth estimation because the semantic labels predicted by this network were not sufficiently accurate to fit our model. By contrast, the pyramid scene parsing network (PSPNet) [23] can deliver a higher performance.  
Because the network is trained for the NYUDepth [24] indoor data set, the output size is only \(147\times 109\), which is smaller than the input image (\(558\times 501\)). Therefore, we could not use the network to compute disparities for all pixels directly. Also, the depth map predicted by the network does not cover the entire input image. The width of missing regions along the four borders was 5 pixels. Thus, to fit the input image, we extended the borders of the depth map and then upscaled it. To extend the depth map, we added five pixels to each border by copying the colors from the nearest pixels. This simple strategy was used because in human vision, information along the edges of a scene is often ignored and considered less important. To upscale the depth map, we used bicubic interpolation [25], which generates a smoother result than bilinear interpolation and nearest-neighbor interpolation. The result is presented in Figure 2. We use grayscales to facilitate visualizing the upscaled depth map.
## 5 Laboratory Tests and Real-Time Validation
### FPGA Design and BEE-Cube SDR
As it can be seen in Table 2, transmitted signal power is equal to \(-40\) dBm and in Figure 19c, AWGN power is varying from \(-56\) to \(-44\) dBm. Thus, SNR changes from \(16\) to \(4\) dB. It can be seen that, by filtering, the system can obtain its performance near to a normal situation even under the MCWI attack.
## 2 Materials and Methods
### Data  
#### 2.2.1 Cropping Intensity Data  
The cropping intensity data were calculated from remote sensing enhanced vegetation index (EVI) data. Seasonal time-series EVI data can clearly reflect key crop phenology, including planting data, emergence data, and maturity data [42]. A rule-based algorithm was developed to identify and map the spatial distribution of cropping intensity in China using phenological data from in situ field observations and MODIS EVI time-series data [18]. The algorithm contained three procedures: smoothing the EVI time series, detecting the potential cropping cycle, and determining the appropriate peak values as the number of cropping cycles. The above algorithm was used to identify and generate the annual cropping intensity of China between 2000 and 2015 [20].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline ID & Region & Abbreviations &
\begin{tabular}{c} **Percentage of the Whole** \\ **Cropland Area (\%)** \\ \end{tabular} \\ \hline
1 & Middle and Lower Reaches of Yangtze River region & MLY & 19.2 \\
2 & Huang-Huai-Hai region & HHH & 19.0 \\
3 & Northeast region & NE & 17.7 \\
4 & Southwest region & SW & 14.8 \\
5 & Loess Plateau region & LP & 9.0 \\
6 & Inner Mongolia and Great Wall region & IM & 8.4 \\
7 & South China region & SC & 6.2 \\
8 & Gan-xin region & GX & 5.0 \\
9 & Qinghai-Tibet region & QT & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regions of the Chinese cropland ecosystem.  
Figure 1: Spatial distribution of the cropland area in China. The base map is the spatial distribution of cropping intensity. The agricultural division is from the comprehensive agricultural zoning map, and agricultural regions include the Northeast region (NE), Inner Mongolia and Great Wall region (IM), Gan-xin region (GX), Huang-Huai-Hai region (HHH), Loess Plateau region (LP), Qinghai-Tibet region (QT), Middle and Lower Reaches of Yangtze River region (MLY), Southwest region (SW), and South China (SC).
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT on the Ground
#### 3.2.1 Extensions with Applications in Medicine
A handful of optimization protocols are put in place to optimize the throughput of information while maintaining lower power costs. The methods used are max-min rate, QoS sufficient operational regimes, and max-sum rate. The proposed scheme is tested sufficiently and proven to not degrade performance with larger networks. In our survey, we have not come across any paper reporting significant work on the security of this extension, except a few ones briefly discussing confidentiality in the physical layer [106; 107]. The Internet of Bio-Nano Things is a recent extension of the IoT [108]. It is used in molecular biology [109], disease diagnosis [110], and related areas.To the best of our knowledge, the security of this extension has not been studied in any existing research report.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 3 Improved Bilateral Filtering Function  
### Traditional Bilateral Filtering Functions  
In a traditional Retinex algorithm, the central-surround filter function is typically a Gaussian function. Hence, once operation of the algorithm is complete, the edges of the image are blurred, and details in the image disappear. To address this, in our algorithm we have adopted a bilateral filter [22] as an \"edge maintenance-noise reduction\" filter, which is defined mathematically as  
\[f^{\prime}(i,j)=\frac{\sum\limits_{(m,n)\in\Omega_{p,i,j}}w_{d}(m,n)w_{r}(m,n) f(m,n)}{\sum\limits_{(m,n)\in\Omega_{p,i,j}}w_{d}(m,n)w_{r}(m,n)}, \tag{6}\]  
where  
\[w_{d}(m,n)=\exp(-\frac{\left(i-m\right)^{2}+\left(j-n\right)^{2}}{2\sigma_{d}^ {2}}) \tag{7}\]  
\[w_{r}(m,n)=\exp(-\frac{\left(f(i,j)-f(m,n)\right)^{2}}{2\sigma_{r}^{2}}). \tag{8}\]  
\(f(m,n)\) and \(f^{\prime}(i,j)\) are respectively the input and filtered images, \(w_{d}\) and \(w_{s}\) are kernel functions in the space and range domains, and \(\sigma_{d}\) and \(\sigma_{r}\) are spatial distance and pixel difference scale parameters, respectively. In addition, \(\Omega_{p,i,j}\) is the set of pixel points in the input image, with \((i,j)\) defined as the center and \(2p+1\) as the radius, and \(p\) is the radius of the filter. With this algorithm, a larger filter radius corresponds to a larger filter interval. However, calculation is made correspondingly more complex.  
From (6), (7), and (8), it can be noted that the efficiency of a bilateral filter is determined by \(\sigma_{d}\) and \(\sigma_{r}\). Experimentation has revealed [23] that increasing the value of \(\sigma_{d}\) leads to a blurrier image after filtering. Similarly, increasing the value of \(\sigma_{r}\) increases the size of the \(2p+1\) pixel interval and reduces the correlation between pixels. In typical situations, the values of these two parameters are manually adjusted according to the requirements of the particular application. Hence, in this paper, we propose an adaptive adjustment method for selecting the values of \(\sigma_{d}\) and \(\sigma_{r}\).
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 3 Results
### Evaluation Metrics and Performance Analysis in MANETs
#### 3.2.3 End-to-End Delay in Network Transmission
From this, it can be concluded that an effective data transmission path topology for covering a larger area would be a network where some nodes have two one-hop neighbors, while others have three one-hop neighbors.
## 5 Conclusions
The combination of these two approaches will facilitate the identification of defects in a more expeditious and precise manner while also leveraging the predictive capacity of machine learning to anticipate potential quality issues before they materialise. This approach will result in a reduction in the number of defective products and an improvement in overall production efficiency. 2. It is evident that key parameters such as temperature, cooling speed, and tool wear have a significant impact on the occurrence of defects. It is imperative that these variables are monitored and controlled in real time to reduce defects and improve production efficiency. By focussing on optimising these parameters, the company can reduce the incidence of defects such as cracks and surface irregularities, thereby improving product quality. 3. Although machine learning models such as neural networks, bagged trees, and support vector machines have demonstrated satisfactory performance, there is scope for further optimisation. It would be beneficial for the company to investigate the potential of deeper neural networks and advanced ensemble learning techniques to enhance prediction accuracy and reduce processing time. This will facilitate more precise detection and prevention of defects. 4. The successful implementation of modern technologies such as machine learning (ML) requires ongoing training of employees. The organisation should facilitate regular workshops on the utilisation of predictive systems and the analysis of data, ensuring that the production team is fully equipped to employ these tools effectively. Training will facilitate a more rapid response to data-driven insights, thus further enhancing production processes. 5. It would be beneficial for the company to implement automated quality control systems based on machine learning and visual inspection technologies. Such systems will facilitate the real-time detection of defects, thus reducing the likelihood of halting production, mitigating the potential for production losses, and enhancing overall efficiency. In addition, automation will guarantee consistent quality while reducing operational costs. Implementing these improvement actions will significantly improve production quality, reduce costs, and increase competitiveness in the marketplace. The integration of traditional methods with advanced ML techniques, along with the optimisation of production parameters, employee training, and the use of automation, will create a robust quality management framework. This framework will enable the company to meet the demands of modern production environments, ensuring high quality product while maintaining efficiency and adaptability.
# An Integrated Approach to Biomedical Term Identification Systems
## 5 Evaluation
### The MANTRA Corpus  
The MANTRA corpus was developed as part of the MANTRA project, aimed at providing multilingual terminologies and semantically annotated multilingual documents in English, French, German, Spanish, and Dutch. The MANTRA corpus consists of three parallel corpora: Medline titles, sentences from drug labels provided by the European Medicines Agency (EMEA), and sentences from patents made available by the European Patent Office. The Medline and EMEA corpora include parallel texts in English, French, Dutch, German and Spanish, while the patents corpus is available for English, French and German. In the case of the English-Spanish pairs, both Medline and EMEA corpora include  
Figure 11: Example of semantic graph generated from five concepts identified in a sample text.  
100 textual parallel units (titles or sentences) annotated with a subset of UMLS concepts from MeSH, SNOMED CT and MedDRA.  
To test our system, we have used the Spanish subset in MANTRA Medline. We have taken into account the first 2000 documents. Analyzing these 2000 documents of Medline, we have been able to verify that the average number of tokens is of 12 words per document.  
The primary evaluation metrics consisted of standard measures from the NLP community, namely micro-averaged precision, recall, and balanced F-score, the last one being the official evaluation measure:  
\[Precision(P)=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall(R)=\frac{TP}{TP+FN} \tag{2}\]  
\[F1 score=2*\frac{P*R}{P+R} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
where \(TP\) = true positives, \(FP\) = false positive and \(FN\) = false negative.