esa-sceva/satcom-chunk-collection huggingface dataset:
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### [PERSON] (RSA)  
The RSA is a cryptographic algorithm which belongs to the asymmetric (or public key) algorithm. Public key cryptography is an algorithmic, cryptographic system that is based on the use of two keys, a public and a private key used to cipher and decipher data. Particularly, if we use one of the keys to cipher the data, for example the public one, we must use the other, the private one, in order to decipher the data and get the right plaintext. Despite the slight relationship that exists between these two keys, all the public key cryptography security is based on the assumption that it must be impossible to retrieve any kind of information about one of the two keys starting from the other. These kinds of algorithms are based on mathematical relationships that have no efficient solution. These relationships make it extremely difficult for anyone to get the private key based only on the public key, which can be, for example, sniffed with a man-in-the-middle attack during the communication between the two legacy entities; and this is the foundation of the security of the public key cryptography. The security strength behind the RSA algorithm, indeed, is built upon two specific mathematical problems: the problem of doing factorization of large numbers, which is computationally infeasible when the involved numbers are too large; and the RSA problem which states that computing the private key when given only the public key must be an operation which cannot be performed in polynomial time, so it is a computationally infeasible operation too. Using RSA, in order to guarantee an adequate security, it is necessary to use a key with a size of, at least, 2048 bits. Keys of size 512 bits are breakable in a few hours. While, keys with a size of 1024 bits are widely used yet but it is preferable not to use them. Indeed, nowadays, due to the increasing power capability of dedicated hardware, it is possible to factorize an integer of 1024 bits in just one year but with a cost of one million of dollars. So, when using such a key length it is preferable to change the keys over time to get a higher degree of security. RSA is able to manage  
Figure 3: Confidentiality, Integrity and Authentication in AES.  
Figure 2: AES sent packet.  
message of limited size. Using keys with a higher size make it possible to cipher messages with higher size.
## 4 Simulation Analysis
### Estimation Performance Comparison
(**a**) vibration amplitude \(a_{1}\); (**b**) vibration frequency \(f_{1}\); (**c**) initial phase \(\phi_{1}\); (**d**) vibration amplitude \(a_{2}\); (**e**) vibration frequency \(f_{2}\); and (**f**) initial phase \(\phi_{2}\). To sum up, from the perspective of parameter estimation, the proposed improved STFT-based fine estimation method showed better performance than that of the rough estimation method for both single-component and multi-component high-frequency vibration signals.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 6 Future Work  
The proposed DTS-Net shows promising results for dense prediction tasks, such as semantic segmentation and depth estimation. In the future, this method can be extended to more challenging dense prediction tasks, such as instance segmentation, through combination with an object detection technique. This approach will allow for discrimination between instances of the same class and can mask those instances with different masks. The feature extractor component could also be replaced with a considerably smaller and simple architecture equivalent to the opposite layer (the space-to-depth), which could then be followed by a small number of convolutional filters. This would reduce the complexity of the feature extractor CNNs. The nearest label filtration (NLF) technique can also be improved using a separate CNN trained on small patches of the training images instead of the integrated branch for classification in current implementation for whole image classification, and the local prediction may attain better filtration accuracy as the current NLF considers the global labels of the image, and not all of those labels exist in every patch of the image.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 2 Methodology  
To construct the process of ship collision avoidance behavior recognition under different encounter scenarios, it is essential to identify collision avoidance turning points from ship AIS trajectories. In this paper, AIS data are preprocessed using cubic spline interpolation with a 20 s interval. Based on the curvature of ship trajectory points, the sliding window algorithm is applied, and collision avoidance turning points are identified by introducing factors like the recovering factor and the turning factor. Subsequently, the DCPA and the TCPA are used as indicators to detect encounter pairs of ships. Finally, the encounter scenarios of the ship pairs are assessed based on the COLREGs. The proposed model is applied to maritime traffic data from the area east of the Yangtze River Estuary, followed by a model comparative experiment to evaluate the model's effectiveness and superiority. Statistical analysis is then conducted to describe the collision avoidance behavior of ships in the study area. A flowchart of the process is shown in Figure 2.
## 3 Research Methodology
### Models Selection
#### 3.3.2 Selected Deep Learning Models
For specific tasks, the model undergoes fine-tuning on relevant datasets. Upon receiving text, BERT outputs category probabilities, selecting the most likely classification based on the highest probability. Many research works have recently used BERT to detect phishing emails [23; 33], fake news, and spam [66]. In our work, we have utilized the base BERT model, which has the following architecture: a BERT encoder that utilizes pre-trained BERT Transformer layers to encode input sequences into dense vector representations. It captures contextual and semantic relationships within the text, producing 768-dimensional embeddings for each input sequence. A dense layer with 64 neurons processes these embeddings using the ReLU activation function. Finally, the output layer consists of a single neuron with a sigmoid activation function designed for binary classification: phishing and safe emails. DistilBERT is a compact version of BERT that was introduced in 2019 [67]. It reduces BERT's size by 40% while retaining 97% of its performance. This smaller design makes DistilBERT up to 60% faster, offering a more efficient alternative to BERT. The model architecture consists of an encoder, similar to the BERT encoder, that consumes the input text to generate embeddings. A fully connected layer then processes these embeddings to produce predictions. Finally, the output layer uses a softmax activation function for classification, generating probabilities for each class. Robustly optimized BERT approach (RoBERTa) is an improved variant of BERT [30]. It enhances training using more data, larger mini-batches, and higher learning rates. RoBERTa is pre-trained on five diverse English corpora, including BookCorpus, CC-News, and OpenWebText [68]. These modifications improve its robustness and generalization for other tasks. We have used the base model, which retains BERT's core architecture but adjusts key hyperparameters for better performance. Recent research utilized RoBERTa for phishing email detection [66; 69]. Graph convolutional network (GCN) is a multilayer neural network that acts directly on a graph and generates embedding vectors for nodes based on the features of their neighbors [70]. GCN can achieve end-to-end node and structure feature learning by considering the network as a spectral graph. Furthermore, GCN applies to any graph with arbitrary topology [71]. The GCN architecture employed in our framework has the following components: an input layer that accepts a graph representation of the email data, where the node features are represented as TF-IDF vectors, and the edge index defines the connections between nodes. The architecture includes two GCN layers. The first GCN layer takes input dimensions equal to the size of the TF-IDF vector and outputs 128 hidden features using the ReLU activation function. The second GCN layer processes the 128-dimensional and produces output dimensions corresponding to the number of classes with a log-softmax activation function. Finally, the output layer generates class probabilities for binary classification.
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Testbed Description  
We investigate an application model similar to the one described in [33], in which sensors send notifications (small data packets) continuously and periodically to a middleware server. Such a solution may be applicable to many use cases where each device collects data which is gathered and processed by a centralized server. This scenario may be found in a grid of sensors for data collection e.g., weather stations or water quality measurement. The same middleware as proposed in our scenario was successfully used in a smart metering electrical power system as shown in [34] as well as in its further continuation presented in [35]. In Figure 1 we can see an overview of the proposed model.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 5 Conclusions  
In this paper, in order to develop an online-processing light field platform based on the lab-designed camera, we present a depth estimation framework through hierarchical baseline stereo-matching from a pair of light field raw images. Due to limitations in generalizability, applying deep learning models without specific training is particularly challenging for real-world scenes. By fusing macro-pixel-wise coarse stereo-matching and pixel-wise fine stereo-matching, along with a post-refinement pre-trained network, the proposed method has been evaluated as significantly more effective, showing a range of accuracy improvements in handling real-world small objects at a distance.  
In the future, a lot of work is needed to produce an exceptionally accurate, robust, and efficient depth estimation framework for objects with challenging textures under various lighting conditions using light field cameras. Also, a large dataset will be constructed to train deep learning models.  
Methodology, F.L.; validation, G.H.; writing--original draft preparation, F.L.; software, G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the first or corresponding author. The data are not publicly available due to privacy.  
Figure 6: Experimental results for the hand. **Top**: center sub-aperture image. **Middle**: depth map. **Bottom**: zoom-in point cloud outputs.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 2 Study Area and Data Sources  
The present study focused on the capital Port-au-Prince area, Haiti, which was severely damaged by the 2010 Haiti earthquake. More than 100,000 houses were heavily destroyed. One of the main reasons that led to more than 100,000 buildings being heavily destroyed is that these buildings had no or little consideration for seismic design. The post- and pre-earthquake satellite images were acquired after the 2010 Haiti earthquake through WorldView-2 on 9 January 2010 and QuickBird on 15 January 2010, respectively. The damaged level of buildings can be divided into five grades based on the European Macroseismic Scale 1988 (EMS-98) [21]. The building damage inventory was provided by UNITAR/UNOSAT [22] in which only four building damage levels existed (G 1, 3, 4, and 5). Figure 1 shows the comparison of pre- and post-event remote sensing imagery for each damage level. There is no difference of G1 buildings between pre- and post-event imagery. Even though G3 buildings have significant change on the roofs and its surroundings between pre- and post-event, the change is still difficult to be identified by remote sensing imagery. The debris around G4 can be identified, however, it is difficult to identify the difference of the G4 roofs. On the contrary, the roofs of G5 buildings are completely destroyed which indicated that it is feasible to identify the collapsed buildings from the images. Thus, it is difficult to find any edges and house boundaries using remote sensing imagery even with VHR 0.5 m. As mentioned above, heavy damage grades such as collapsed buildings are generally detected, and it remains challenges to identify low damage grades using the overhead imagery [23]. Thus, several low damage grades are usually aggregated as one grade. In this study, buildings with the damage grade from G1 to G4 were grouped as the non-collapsed category, and buildings with the damage grade of G5 were labeled as collapsed ones. There are 1789 buildings in the study area, and 610 buildings were labeled as collapsed ones. Figure 2 displays the distribution of collapsed and non-collapsed buildings in the study area.
## 3 Multi-Searcher Optimization
### Random Walk Rule  
As shown in Figure 4, after searching using the LS, each GS begins to move to a new position by a random motion technique called \"random walk\". Movement in the random walk is done from a point toward the target position, which leads to the GS being located in the area around the target position.  
Figure 4: Random walk rule of the global searcher (GS).  
Figure 3: Double-layer searcher search area.  
To randomly move from one position to another, the searcher should consider two variables, including the distance and direction angle. The distance is determined by a coefficient of the distance between two points, and the direction angle is a random value. The distance between the global searcher and the global best (known as d) is found by using Equation (37). \(\lambda\) is a random value with a distribution of uniform probability in a range from 0 to 1, and \(\theta\) is a random value with a distribution of uniform probability in a range from -\(\pi\)/4 to \(\pi\)/4. When all global searchers move according to the random walk rule, the new position can be obtained, and the obtained solution will be compared with the current global optimal solution. If the solution quality is higher, the global optimal solution will be replaced.
## 3 Materials and Methods
### Inspection
Inaccuracy on AE events localization at the cracking state was acknowledged since at that moment, the wave velocity significantly dropped, as illustrated by the UPV measurements as well; however, for reasons of consistency, the velocity was assumed constant throughout testing. At the damage state (crack width up to 0.3 mm), this loss of localization accuracy was approximately 20 mm/m as reported in [19]. Figure 3: The water permeability test setup: (**a**) before and (**b**) during testing. **Digital image correlation**: Prior to loading, the front side of the concrete beams were painted white and random black speckle patterns with a diameter up to 1 mm were marked by ink-rolling on the white surface. The speckle pattern covered the bending zone in an area of 100 \(\times\) 500 mm\({}^{2}\), as illustrated in Figure 2. A pair of high-resolution cameras were fixed on a tripod at a distance of 0.8 m from the beam, facing the front side in a stereoscopic setup (angle 30\({}^{\circ}\)). Lenses of 12 mm focal length were mounted on the cameras and the aperture was set at 1/5.6. Correlated Solutions provided the Vic-Snap software for the synchronous triggering of images captured each 2 s during loading. The load and displacement of the test bench were also recorded. Stereoscopic calibration was performed prior to testing. The captured images were post-processed using Vic-3D computational software to extract the deformation and strain maps. The analysis subset was fixed at 21 pixels and the step at 7 pixels, whereas the strain window size was 15 pixels. A spatial resolution of 0.08 mm/px was obtained. **Testing procedure**: The testing procedure is summarized in Figure 4. Before and after loading, inspection of the cracks was performed combining the UPV, microscopy scan and water permeability tests. It should be noted that the UPV measurements were conducted on dry concrete beams and earlier to permeability tests. After the latter, the samples were left to dry for at least 6 h prior to loading. Cyclic loading occurred as long as the cracks were healed and the healing network was clear, therefore the agent could be cleaned out after loading. Up to three loading cycles were performed at each beam.
# A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
## 1 Introduction
For this reason, MoS\({}_{2}\) has received a lot of attention because its bandgap is adjustable. MoS\({}_{2}\) is connected by a relatively weak van der Waals force. This means that the gas molecules can freely penetrate and diffuse through the layers. MoS\({}_{2}\)-based sensors are being actively studied in the field of gas detection. An MoS\({}_{2}\) -based sensor has excellent potential for gas detection but has low selectivity and a high operating temperature for hydrogen [7]. To overcome these limitations, Pd with good reactivity and selectivity to hydrogen was proposed as a catalyst [8]. We proposed a Pd-deposited MoS\({}_{2}\)-based hydrogen sensor operating at room temperature.
## 6 Experimental Test-Bed Implementation  
Our implemented test-bed consisted of the IBN tool, OSM platform, FlexRAN, OAI EPC core network functions, and OAI eNB for achieving E2E network slicing. The other module is the integration of machine learning model GAN which predicts resource utilization--CPU, RAM, etc. This system aims to automate the network slice configuration and management process and provide the clients with an easy way to create network slice according to their requirements. For testing purposes, we created three types of network slices eMBB, IoT, and URLLC by inserting higher-level configurations in the form of user intents/contracts from the IBN tool. The IBN tool dispatcheed those configurations to the OSM network orchestrator and SDN-RAN controller FlexRAN for further processing. Moreover, IBN provided higher-level contract configurations in the JSON file format to OSM and FlexRAN.  
Afterward, the OSM orchestrator deploys the generated JSON string slice template configurations to the physical layer with the help of the VIM manager OpenStack. On the other hand, FlexRAN enforces the slice template to underlying eNBs to allocate specific spectrum and resource blocks with the help of the master controller and local agents. The slice template generated through the RAN slicing configurator also provides the configurations (IP addresses of EPC functions) for the connectivity of dedicated EPC with selected eNB. In this way, a specific slice is created and  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **System Specifications** \\ \hline \multirow{4}{*}{FlexRAN} & OS: UBUNTU 16.04 \\  & RAM: 16 GB \\  & CPU: CORE-i5 3.0 GHZ \\  & SSD: 500 GB \\ \hline \multirow{4}{*}{OSM} & OS: UBUNTU 18 LTS \\  & RAM: 252 GB \\  & CPU: 32 Cores 2.10 GHZ \\  & H/D: 2 TB \\  & OSM Version: 7 \\  & Openstack Version: stein \\ \hline \multirow{4}{*}{IBN tool} & OS: Window 10 \\  & RAM: 16 GB \\  & CPU: Core I5 3.0 GHZ \\ \cline{1-1}  & H/D: 1 TB \\ \cline{1-1}  & Programming languages: PHP, JAVA \\ \cline{1-1}  & Database: MYSQL \\ \hline \multirow{2}{*}{SDR USRP B210} & Frequency Range: 70 MHz-6 GHz \\ \cline{1-1}  & Channels: 2 TX \({}^{*}\) 2 RX \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test-bed components configurations and specifications.  
stitches together with core and access network automatically. The whole slice creation process is automated where the user just used IBN GUI and defined his contract for the slice and IBN automatically deployed those configurations to the physical layer with the help of OSM and FlexRAN. Furthermore, we have used the EPC eNodeB provided by the well-known open source community OpenAirInterface (OAI) [45]. Further, two eNodeBs were deployed with the help of SDR USRP B210. Three core networks have been deployed on different VMs by using VIM OpenStack for the creation of three slices. Smartphones and OAI simulated UEs were also used for testing and validation purposes. The test-bed components with their configurations and specifications are illustrated in Table 2.
# Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon
## 3 The Position Change Problem and the Algorithms
### Formulation of the Position Change Problem
Theoretically, when the initial amounts of fuels are equal for all the vehicles, by applying \"TTH\", the difference among the fuels at the any time slot is bounded by \(r\times\delta\times s\times(g(n)-g(1))\). The derivation of this amount is given at Lemma 1. The proof is given at the Appendix A. ```
Input:\(V\), \(F(0)\), \(\delta\) Output:\(P\) \(V\): the set of vehicles \(F(0)\): Initial fuel amounts at \(t=0\), i.e., \(F(0)=\{F_{1}(0),F_{2}(0),...,F_{n}(0)\}\) \(\delta\): Size of each time slot \(P\): Positions of the vehicles at each time slot \(t\gets 0\) \(k\gets 0\) while\(F_{i}(t)>0\) for all \(i\)do  Sort the vehicles by their remaining fuel \(F_{i}(t)\) in decreasing order \(c(i)\): the position of the vehicle \(i\) in the sorted list for each vehicle \(i\in V\)do \(P(i,k)\gets c(i)\) /* calculate fuel amount at the next time slot */ \(F_{i}(t+\delta)\gets F_{i}(t)-r\times(1-g(P(i,k)))\times\delta\times s\) \(t\gets t+\delta\) \(k\gets k+1\) return\(P\)
```  
**Algorithm 1**Traveling Time Heuristic. **Lemma 1**. : _When the initial amounts of fuels are equal for all the vehicles, by applying \"TTH\", the difference among the fuels at time \(t_{k}\) is bounded by \(r\times\delta\times s\times(g(n)-g(1))\) for \(k\geq 0\)._  
It should be noted that the largest difference occurs after the time slot that starts with the equal amount of fuels for all the cars. For example, suppose that at \(t_{0}\), all the fuel amounts are equal to \(x\). Then, at time \(t_{1}\), the first truck of the time slot \(0\) has fuel amount of \(a_{1}^{\prime}=x-r\times\delta\times s\times(1-g(1))\) and the \(n\)th truck of the time slot \(0\) has fuel amount of \(a_{n}^{\prime}=x-r\times\delta\times s\times(1-g(n))\). So the largest fuel difference at time \(t_{1}\) is \((a_{n}^{\prime}-a_{1}^{\prime})=r\times\delta\times s\times(g(n)-g(1))\).
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
###### Abstract  
Indoor positioning systems (IPSs) are increasingly vital for various applications on the Internet of Things (IoT), including home automation, navigation in large buildings, AR, and smart city development. These systems rely on techniques such as time of arrival (ToA), angle of arrival (AoA), and received signal strength indicator (RSSI), and Bluetooth low energy (BLE). Despite advancements, challenges such as signal fluctuations, multipath effects, and high infrastructure costs limit the accuracy and adoption of these systems. This paper proposes a deep neural network-based approach to enhance angle estimation by leveraging phase drift values, an underutilized aspect in current models. By employing the phase drift-dependent lightweight angle estimation (PLAE) model, we aim to improve angle prediction accuracy, particularly in complex indoor environments. Experimental results demonstrate that our model achieves higher accuracy compared to traditional methods. The integration of time series data handling capabilities in our approach highlights its potential to provide more reliable indoor positioning solutions. This research contributes to the development of specialized models for precise AoA estimation, addressing the gaps in existing methodologies.  
Keywords: indoor positioning SYSTEMS (IPS); angle of arrival (AoA); deep learning (DL)