esa-sceva/satcom-chunk-collection huggingface dataset:
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 4 Substring Search Algorithms
### Suffix Tree  
Suffix tree [28; 30; 31] is a tree-like representation of text supporting a wide range of applications on strings. The suffix tree is pre-processed data structure that enables a substring search on the stored string. We now give the definition of the suffix tree:  
Definition 6: (Suffix tree).: A suffix tree for string \(t=t_{1}\ldots t_{n}\) is a rooted, directed tree with the following properties:  
* Each edge is labeled with a non-empty substring of \(t\), named the edge label.
* Every internal node has at least two children.
* No two edges out of a node have edge labels starting with the same character.
* The tree has \(n\) leaves, labeled from \(1\) to \(n\).  
Definition 7: (Path label).: The path label of a node is the concatenation of the edge labels on the path from the root to that node.  
Definition 8: (Suffix tree search).: A string \(\chi\) is a substring of \(t\) if and only if it is a prefix of some suffix of \(t\).  
Figure 2a shows an example of a suffix tree constructed from the text \"coconut\". To check if a string \(\chi\) is a substring, the algorithm searches for a path from the root whose labels match \(\chi\). For instance, searching for a string \"coconut\", we begin at the root node and start checking the neighbor edge labels, down to the matching node, i.e., Node 1 is the matching one, and it corresponds to the occurrence in the text. Similarly, the search of \"co\" leads us to the intermediate node whose leaf nodes \((1,3)\) are the positions in the text.  
The suffix tree can be constructed in \(O(n)\) time for a string of length \(n\)[31]. Furthermore, it can be shown that a suffix tree has at most \(2n\) nodes, and storing edge label for all edges would require \(O(n^{2})\) in the worst case. (Consider a suffix tree for the strings \(t_{1}t_{2}\ldots t_{n}\), where \(t_{i}\) is unique. The suffix tree would contain a distinct edge for each of the \(n\) suffixes \(t_{1}\ldots t_{n}\), \(t_{2}\ldots t_{n}\), \(\ldots,t_{n}\). These suffixes have a total length of \(O(n^{2})\).) Searching for a substring \(\chi\) of length \(m\) takes \(O(m+occ)\) time to find all occurrences of \(\chi\), where \(occ\) is the number of occurrences.  
Figure 2: An example of the data structure constructed from the text “coconut”. (**a**) A suffix tree; (**b**) a suffix array; (**c**) a position heap tree.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### World Size and Simulation Time
When considering a motorway (freeway or interstate highway), the world space is generally a few lanes wide (typically 2-4 in the United Kingdom (UK)) but may be considered hundreds of km long. To reiterate, if VANETs are used to manage traffic information, the relevant area needs to be long enough to allow users to change routes should an incident occur. As an example, when there are incidents on the M5 southbound motorway in Birmingham, England frequently uses M6 as a backup plan, because it is the best alternative route, which channels traffic approximately 20 km north or 25 km south of the M5 junction. Therefore, to provide a realistic model for this scenario, the world needs to be approaching 50 km long. [PERSON] et al. (2007) [PERSON] et al. (2007) discuss a Freeway model with a world size of 20,000 \(\times\) 2000, without units, although it is assumed to be meter. This is because there are not many 2-km wide roads, and even without any unit, a road that is only 10 times as long as it is wide would be unusual. As previously stated, maritime shipping presents an alternative VANET scenario. When discussing ships at sea, areas of open ocean are unlikely to provide sufficient node density to form viable VANETs. However, areas near ports or stretches of water between land masses, such as the Irish Sea and the English Channel,present higher node densities and sufficient traffic to make VANETs viable. In such cases, the world model could be the scope of the port authority, such as Liverpool, or the entire body of water under consideration, such as the north of the Irish Sea. Search and rescue operations are also considered appropriate scenarios for mobile ad hoc networks, and as with previous examples, the size of search and rescue operations can vary considerably. In some cases, the location is known, and the \"search\" part of the operation may only cover a few hundred square meters. However, the search area of major incidents may cover many km\({}^{2}\). Where people are missing on either land or sea, lines of searchers will progress over an area, and at sea, the entire line will move together, track forward together and then move together in the opposite direction to cover large areas. The size of the search area will be governed by the actual situation, prevailing conditions, number of casualties, and number of searching vessels available.
## 2 State of the Art
### Consensus Protocols for Ethereum and Energy Efficiency
In fact, PoA sacrifices some security and fault tolerance to achieve simpler, faster consensus with known validators, while full Byzantine Fault Tolerance (BFT) algorithms provide a stronger guarantee against malicious participants. This is why PoA is considered less robust in adversarial environments, where collusion, and therefore centralization, is more likely to arise [48]. To reduce the probability of collusion, further mechanisms need to be added. For example, in [48], the authors propose to add a Verifiable Random Function to the authority election to introduce randomness in the authority selection. As it was mentioned, the risks of PoA greatly depend on the type of network. For example, for IoT networks, the authors of [52] propose an implementation of Clique that enables to substitute dormant or misbehaving validator nodes by redundant ones to avoid centralization and slow block production. In [53], the authors propose a permissioned blockchain system for clustered Wireless Sensor Networks (WSNs) using PoA. It introduces two main innovations: (1) an energy-aware PoA mechanism that rotates block proposers to extend network lifetime, and (2) a Multi-Level Blockchain Model (MLBM) that organizes WSN clusters into local and master blockchains. Each cluster maintains its own blockchain and periodically submits block headers to the main chain, improving data integrity and block production. The obtained results show that the proposed approach outperforms traditional PoA in both network lifetime and block production. After reviewing the state of the art, it can be concluded that, in general, vanilla algorithms of PoA are currently not sufficiently robust to be implemented \"as is\" in production systems, and that countermeasures adapted to the specific domain need to be implemented. For example, a cross-domain approach would typically imply a dynamic validator selection based on specific parameters (e.g., reputation, security score, online aging, performance [54, 55]).
## 2 Materials and Methods
### Software  
In this section, the software capable of managing M-Bus communication, reading the counters at a certain frequency and finally storing this information for later analysis is described. The software implemented on the Raspberry Pi, was developed on the Raspbian operating system that comes to be a Raspberry implementation of the most used Linux distributions \"Debian\". In addition to being one of the most widely used operating systems in the world, it is free software and has a multitude of open source support that will facilitate the installation and even compilation of all the software necessary for our monitoring process.  
In this operating system, drivers for the serial port and a library to be able to access to communicate through the M-Bus protocol were incorporated. For this purpose, the free code library \"libmbus\" developed and maintained by the Swedish company Raditex Control AB was used. The library is programmed with source code in C language. This library was used to communicate with M-bus slaves (meters) through the wired M-bus protocol incorporating simple applications for network scanning, meter reading and presentation of the data of each meter in XML format, simplifying the future treatment of the information. The library allows us, in addition to the use of the serial port, communication through the TCP IP Internet protocol, being able to access any Mbus Master equipment on the market that we find in the facilities to be monitored.  
Thus, our prototype already has tools to communicate, to access a scan or meter reading, although it is necessary to have a place to store such data as a database. Following the line of free software, it was found the option to use the MySQL database [29]. MySQL is a relational database management system developed under two types of licenses: general public license and commercial license by Oracle Corporation. It is considered the most popular open source database in the world and one of the most popular in general with Oracle and Microsoft SQL Server, especially for web development environments. For its implementation in raspberry pi, the public license version is already available in the repository of the \"Raspbian [30]\" operating system itself, so it was selected to be used as a pillar of the storage of reading data.  
Finally, the prototype must have a small web interface to consult the stored data, so a small web application is designed capable of communicating with the database and executing shell commands in Linux. It was selected to program this application in PHP language (hypertext preprocessor), since it is an open source programming language widely used in web development that can be embedded in HTML code.  
Figure 5: Hardware developed for reading different energy counters.
# COMS Visible Channel Calibration Using Moon Observation Data
## 2 Data and Methods
### Methodology  
To use the Moon image to compute sensor degradation, it is necessary to obtain at least one full Moon disk image per month. The Ephemerides provides Moon positions and associated dates to obtain a full Moon image using the observation schedule in LA mode. In addition, the Moon image can be observed on the edge in FD mode. The observed Moon image size in the LA mode is 2080 (EW) \(\times\) 1200 (NS) pixels. Figure 1 shows Moon images from the COMS visible channel. COMS MI normally observes the Moon as shown in Figure 1a; however, it also observes the Moon on the Earth on the edge of LA image between one and two times annually, as shown in Figure 1b.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **Time Coverage** & \begin{tabular}{c} **Moon Observations** \\ **(Times)** \\ \end{tabular} & \begin{tabular}{c} **Channel** \\ **(Degree)** \\ \end{tabular} & \begin{tabular}{c} **Absolute Phase Angle Range** \\ **(Degree)** \\ \end{tabular} & \
\begin{tabular}{c} **Useful Observation** \\ **for Calibration** \\ \end{tabular} \\ \hline COMS MI & April 2011–December 2017 & 152 &
\begin{tabular}{c} Visible \\ (0.6 \(\upmu\)m) \\ \end{tabular} & 0–90 & 31–60 & 146 & 59 \\  & & & 61–90 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of COMS MI Moon Observation Data.  
Lunar calibration of the visible channel was performed via a comparison of disk-integrated lunar irradiance derived from satellite observations with a reference lunar irradiance retrieved by an empirical irradiance model, such as the Robotic Lunar Observatory (ROLO) model [22]. This study aims to define unified calibration references in the framework of GSICS activities; therefore, it used the lunar calibration tool known as the GSICS Implementation of the ROLO model (GIRO) [24]. The ROLO model in GIRO can cover a spectral range from the visible (0.35 \(\upmu\)m) to near infrared (2.35 \(\upmu\)m) with 1% relative accuracy and has been widely used as a reference for instrument monitoring on GEO [12; 26] and LEO [27] satellites.
## 4 Experimental Results and Discussion
### Initial Bed Height Rise Ratio and Average Bed Fluctuations  
In the experiments of fluidization, instantaneous initial bed height rise is found to be more prominent for 45\({}^{\circ}\) distributor plate for shallow beds; however, for deep beds, the initial bed height rise ratio is reduced a bit due to diffusion and collapse of larger bubbles within the deep beds during their travel up the gasifier. The significant initial bed height rise ratio for the 45\({}^{\circ}\) slotted plate is owing to the larger bubbles first rupturing at the surface, causing more expansion of the bed surface. However, the instantaneous initial bed height rise for the perforated distributor plate is less bloated due to smaller bubbles rupturing at the bed surface. Furthermore, the average bed fluctuations are more dominant for perforated plate distributors due to numerous smaller-sized bubbles traveling straight up the column having negligible lateral velocities due to the 90\({}^{\circ}\) emerging angle to the bed. However, for a 45\({}^{\circ}\) slotted plate for deeper beds, bubbles rise along a 45\({}^{\circ}\) path, such as the angular path of the inlet airflow, causing the flow's radial and tangential velocity component to be more prominent in the lower region of the bed. There is a trend of particles and emulsion phases to shift more towards the periphery of the gasifier, so on average, the fluctuations at the surface of the beds have damped. Hence, losing most of the total fluidizing energy while striking more toward the gasifier wall due to centripetal force due to the flow's tangential velocity component.  
On the other hand, the vertically straight holes in the perforated plate distributor cause several smaller bubbles to travel upward, having a maximum axial velocity component resulting in the eruption of bubbles at the bed surface with a prominent average bed fluctuations ratio. However, a hybrid plate causes a mixture of smaller and bigger bubbles due to the blend of openings of larger slots and smaller holes. Thus, intermediate bed height rise was recorded in the experiments, as shown in the graph in Figure 11.  
Figure 12 below describes the Initial Bed height rise fluidization phenomena for different distributor plates at 3.0*Umf and initial bed height, L/D = 1.16. The figure depicts the prominent bed height rise for a 45\({}^{\circ}\) slotted plate and less for a perforated distributor plate owing to the different bubble sizes approaching the bed surfaces.  
Figure 10: Bubble rise dynamics with time for a hybrid plate at U = Umf.
## 1 Introduction
Similar to the transition from pixel-wise classification to fully convolutional CNNs, 3D-CNNs are better able to use the context of the whole image and provide faster speeds in comparison with a per-slice 2D segmentation. In the field of prostate segmentation in MR imaging, many different CNN architectures building on top of the V-net or the U-net have been proposed. For instance, ref. [15] proposed the addition of deep supervision, ref. [16] used a more recent densenet-resenet architecture [13; 17; 18] introduced a boundary-aware cost function. Prostate segmentation in 3D Ultrasound (US) imaging, although much less prevalent, has experienced a similar development, with a recent paper employing the attention mechanism to exploit the information from several layers [19]. Some of these architectural choices, and several others, will be further elaborated in Section 2. Despite the high performances reported by many of the aforementioned papers, it could be argued that they all incur in a common pitfall: they are designed to perform well on one single prostate dataset. Therefore, it is unknown how robust the model would be when applied to any other dataset. This kind of robustness is paramount if the model is to be applied in a real-life scenario, where the images may come from many different scanners, and may be analyzed by many different experts. Furthermore, robustness is also desirable in the sense that the produced segmentations should be accepted by different experts, despite their possibly varying criteria for segmentation; in other words, the produced segmentations should ideally behave like an average prediction from several experts. In this paper, a robust algorithm based on CNNs for MR and US prostate image segmentation is proposed. It leverages both common and not-so-common design choices, such as a hybrid densenet-resnet architecture (Section 2.3), deep supervision (Section 2.4), 3D data augmentation (Section 2.5), a cyclic learning rate (Section 2.7), checkpoint ensembling (Section 2.8) and a simple yet effective post-processing technique to increase the resolution of the segmentations known as Neural Resolution Enhancement (Section 2.9). This technique, besides improving the segmentation performance, allows the CNN to produce segmentations with resolutions beyond that of the original image. Furthermore, the model is trained on five different datasets simultaneously (Section 2.1), achieving an excellent performance on all of them. Finally, the weights obtained from this model are used to train (through transfer learning) an US segmentation model on two different datasets, achieving also an excellent performance on them both. Results are presented both quantitatively (Section 3.1), by presenting the metrics (Section 2.10) achieved on every dataset, and qualitatively (Section 3.2), by showing images of the predicted and Ground Truth (GT) segmentations on several patients. The paper is closed by a discussion, (Section4), about the clinical impact of the proposed model, and a brief conclusion (Section 5).
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 6 Experimental Results
### mSSA-Based Anomalous Behavior Detection  
The proposed mSSA based anomaly detection algorithm is validated using two data sets. One is the video streams that are collected locally using drones as the end layer devices, another is a public data set from the Next Generation Simulation (NGSIM) public Lankershim Boulevard Dataset. In the NGSIM dataset, the vehicle trajectory data were collected on the Lankershim Boulevard in the Universal City neighborhood of Los Angeles on 16 June 2005. A total of 30 min of data are available from 8:30 am to 9:00 a.m. Detailed characteristics of different channels of vehicles trajectory data include spatial information of trajectories in terms of local_x and local_y, vehicle length, vehicle velocity, acceleration, etc. The time interval of each data point is 0.1 s.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### SG Integration in Planning
#### 2.7.1 Economic Impact in SG Implementation  
SG technology demonstrates economic benefits through DSM, energy efficiency, and DG integration. For example, in Oman, SGs have reduced the peak energy demand, showcasing the value of SG maturity models in strategic planning [136]. Additionally, they provide innovative solutions for forecasting and enhancing operational reliability, which has significantly improved the economic performance and system reliability in Boao Town, Hainan [26].
## 3 Emerging and State-of-the-Art Technologies
### Wireless Power Transfer and Energy Harvesting  
Energy efficiency is a critical consideration in 6G communication systems due to the large number of user devices and base stations. Specifically, in mMTC, millions of IoT devices will be deployed across metropolitan and rural areas, serving various purposes, such as environmental monitoring (e.g., temperature, humidity sensors) or operational control (e.g., lighting systems, sprinkler systems) [18; 133; 134; 135]. These devices will be connected wirelessly, either with each other or with nearby base stations, necessitating energy-efficient operation and communication. A major challenge arises from the reliance on batteries, which have a limited lifespan and require periodic replacement, leading to increased maintenance efforts and costs. To overcome this limitation and achieve energy independence, two key techniques have been proposed, either as standalone solutions or in combination to support battery life extension and efficient energy harvesting [136; 137; 138; 139; 140; 141].  
Energy harvesting involves collecting ambient energy (e.g., electromagnetic, thermal, acoustic, solar) [138] from the environment and converting it into usable electrical energy. This energy is then utilized for the device operation and communication with other devices. This method is particularly crucial for devices that are not connected to the electrical grid. However, energy harvesting does not need to function as a standalone solution; it can be used in conjunction with either a wireless energy transfer unit or batteries to enhance system efficiency. By integrating these methods, battery life can be extended, reducing the energy footprint of the device. Wireless power transfer, on the other hand, refers to the deliberate transfer of electromagnetic energy from a power station to the device. This approach often leverages nearby radio towers operating in specific frequency bands (e.g., 5G) to supply energy [142]. Another innovative technique is to use a centralized radio tower to transmit electromagnetic waves to passive devices, utilizing the phenomenon of backscatter communication [143; 144]. Backscatter devices, which resemble RFID tags, enable sensors to operate without batteries by reflecting ambient RF signals using various modulation schemes. This method eliminates the need for power-hungry components, such as oscillators, mixers, and amplifiers. Instead, it relies on low-cost, low-complexity devices that consume minimal power, often harvested from the surrounding environment. Notably, backscatter communication is particularly appealing as it enables batteryless operation and reduces overall system complexity and energy consumption.