esa-sceva/satcom-chunk-collection huggingface dataset:
# A New String Edit Distance and Applications
## Appendix A
### Complexities  
Allowing for the possibility of both single- and multiple-character gains and losses in interaction makes the algorithm exponentially more complex. The insert costs are computed via the weighted Levenshtein distance as if the edit path was first stuttered forward, then edited from the motif (with no stutters) to the string. The delete costs are computed by forcing the string to edit back into a motif, then adding the cost for dropping a motif.  
In a case where the motif is AAAGA and all edit costs are equal to 1, evidently the cost of inserting or deleting a single A or G would be 1; however, in the constructed dictionary the cost of these operations are set to 5 each, resulting from a forward stutter (1) and the removal of the remaining 4 letters (\(4\times 1\)). The cost of inserting or deleting a letter not part of the main motif, e.g., T, is set to 6 in the dictionary, as it is calculated with the same steps as before (5) and an additional change of the last remaining letter to T (1). Strings of all lengths up to \(2k-1\) are also included in the dictionary, i.e., costs of insertion or deletion are set for strings AAA to 3, AAAGAT to 2 and AAACGATC to 4, and so forth.  
Cost can be set asymmetrically for insertions and deletions. If insertion costs were changed to 2 but deletions, substitutions, and stutters remained at a cost of 1, the dictionary entries would change. The string AAA in this dictionary would have an insert cost of 3 (blank \(\rightarrow\) AAAGA (+1) \(\rightarrow\) AAA (+2)), but a delete cost of 5 (AAA \(\rightarrow\) AAAGA (+4 for two inserts) \(\rightarrow\) blank (+1 for stutter)).  
It gets more complicated when one considers adding two or three motifs at once, as shown in Example A1.  
**Example A1**.: _In this example the motif is AAAGA. Forward stutter is followed by many individual letters inserted in between, as shown:_  
\[\textbf{A(TTTTAAAGATTTT)AAGA}.\]  
_To get to the same string, another route would be to stutter once and then insert another motif in between the letters of the motif that is already there, along with other insertions, as follows:_  
\[\textbf{A(TTTT)AAAGA}\textbf{(TTTT)AAGA}.\]  
_The RFL distance does not address stutter-within-stutter, in either the forward or backward direction. Beginning an attempt to do so would require allowing the second stutter to take place at any point in the first motif, e.g., for the motif AAGG, consider A(AAGG)AGG, AA(AAGG)GG, and AAGA(AAGG)GG, in addition to accounting for all the single-character operations that could take place on either motif, and once again bounding above by some maximum number of possible single-character insertions._  
Additionally, the RFL distance does not include cases where more than \(k-1\) characters are inserted or deleted between an outer motif's characters, where \(k\) is the longest motif, since the maximum length of strings included in the lookup dictionary is \(2k-1\).
## 4 Case II: Active Eavesdropper
### Semi-Blind Feedback Scheme  
In the case of a passive eavesdropper, it is shown that the performance is better when using the blind feedback scheme. However, in the case of an active eavesdropper, the  
Figure 13: Wiretap Full-Duplex transmission with self-jamming in case of an active eavesdropper.  
presence of a jamming signal from user E leads to significant destruction of the reception behavior of user A. Therefore, small sharing of known symbols or pilot symbols between user B and user A should be established to guarantee the reliability and security of transmissions.  
The processing flowchart of the semi-blind algorithm is presented in Figure 14. In general, it is nearly similar to the joint iterative blind feedback algorithm in Section 3.2, except that the temporary decoding and encoding processes are skipped. Instead, the known pilot symbols, which are added to the information sequence on the transmitter side, are used to form the intended signal and the feedback loop. In particular, for \(i=1\) (first iteration of the iterative algorithm), a first SI cancellation and intended channel estimation are performed for all symbols in order to overcome a larger number of errors and achieve a sufficient level of convergence. When \(i\in[2,i_{max}]\), the known pilot symbols \(\mathbf{x}_{pilot}=4\) (symbols) are used to form the feedback loop. When it reaches the maximum number of iterations (\(i=i_{max}\)), the algorithm is stopped, the SI component can be canceled, and the equalized signal can be fully achieved by the estimation versions of the SI channel and the intended channel. After that, the known pilot symbols are suppressed, and the equalized signal will undergo the demodulation, de-interleaver, and decoding processes to obtain the final decoded message. Here, it is noticed that the SPA decoding algorithm also performs only one iteration (\(j_{max}=1\)) in the decoding step when the system achieves the best channel estimation (\(i=i_{max}\)).  
The proposed semi-blind algorithm can be summarized in three steps:  
**Step 1:** The received signal \(\mathbf{y}_{A}\) is used to estimate the SI channel \(\hat{\mathbf{h}}_{AA}\) and to cancel the SI component based on the reference transmitted signal \(\mathbf{x}_{A}\);  
**Step 2:** The residual signal \(\tilde{\mathbf{y}}_{A}\) after step 1 will go to an equalizer to estimate the intended channel \(\hat{\mathbf{h}}_{BA}\);  
**Step 3:** Using pilot symbols \(\mathbf{x}_{pilot}\) that are added to the information sequence on the transmitting side, a feedback loop is created with the estimation version of the intended channel \(\hat{\mathbf{h}}_{BA}\) to form \(\tilde{\mathbf{y}}_{BA}\). This signal is passed to the subtraction process from the received signal and performs the next joint iterations.  
Next, we will introduce the performance in terms of the MSE, BER, and security gap \(S_{g}\) in the case of an active eavesdropper.  
Figure 14: Joint iterative semi-blind algorithm flow chart.
# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 2 Preliminaries
### Hierarchical Text Classification  
The task of _text classification_ consists of categorizing texts into a predefined set of categories that do not have any hierarchical structure. Text classification is crucial in NLP [22], which is a subfield of linguistics, computer science, and artificial intelligence that is concerned with how computers can be programmed to process and analyze natural language data.  
On the other hand, HTC addresses the task of classifying a text document into a _hierarchy of classes and sub-classes_. Text classification can therefore be regarded as a special case of HTC with only one hierarchy level and no sub-categories. Hierarchical classification problems can be categorized on the basis of their _hierarchy structure_, _label type_ and _label depth_. In this work, we shall consider _tree_ hierarchies with _single partial-depth_ labels, which means that every text shall be assigned to a single label that can be any node in a tree hierarchy.  
In many circumstances, hierarchical classification may be desirable as categorizing documents into varying levels of abstraction better fits the nature of certain applications, e.g., product categorization or support-ticket classification. In addition, as it has been consistently shown by numerous cognitive studies [23], people tend to favor categorization at different levels of abstraction.  
Formally, HTC comprises a collection of text documents \(x_{1},\ldots,x_{j}\in\mathbb{X}\), where \(\mathbb{X}\) is a document space; and a fixed set of classes \(\mathbb{Y}=\{y_{1},y_{2},\ldots,y_{k}\}\) belonging to some hierarchy. Given a training set of labeled documents \((x_{1},y_{1}),\ldots,(x_{n},y_{n})\) on a hierarchy, where \((x_{i},y_{i})\in\mathbb{X}\times\mathbb{Y}\), we wish to learn a classifier or classification function \(\gamma\) that maps documents to classes,  
\[\gamma:\mathbb{X}\rightarrow\mathbb{Y},\]  
so that each text document is only assigned to a single label, and a certain utility metric (see Section 4.2) is maximized.
## 4 Advanced Positioning Methodologies Using UWB and Wi-Fi RTT Signals  
### Integrated Positioning of Data Fusion  
Positioning methods that rely on a single measurement method have difficulty achieving optimal accuracy, reliability, and continuity in different scenarios. Factors such as signal blocking, attenuation, interference, and reflection may degrade positioning performance or even cause complete failure. Fusion positioning provides an effective solution to improve positioning performance by integrating multiple data types, usually from different sensors. Data fusion methods can be divided into two types: loosely coupled and tightly coupled. Loosely coupled methods calculate positioning results based on each sensor independently and integrate these results into an optimal solution through weighted fusion techniques. Although loosely coupled methods are easy to implement and highly adaptable, they usually result in low accuracy and insufficient information utilization. Tightly coupled methods fuse information directly in the measurement domain to maximize information utilization and thus improve accuracy. However, this method is relatively complex and may be less robust, especially in complex NLOS environments. Several studies have explored fusion positioning solutions that combine UWB or Wi-Fi RTT with other data sources. Fusion methods include Kalman filtering, Bayesian filtering, particle filtering, least squares,maximum likelihood estimation, factor graph optimization, etc. These methods process data from different signal sources by weighting, optimization, estimation, and calibration to improve positioning accuracy and robustness. Tables 3 and 4 list the research on the fusion of Wi-Fi RTT with other sensors and the research on the fusion of UWB with other sensors in recent years, respectively. IMU is robust in the relative positioning of vehicle odometers or pedestrian odometers, and can effectively supplement the absolute positioning of UWB or Wi-Fi RTT signals. In addition, vision-based odometer technology can also be combined with UWB or Wi-Fi RTT, which is particularly suitable for indoor applications of unmanned aerial vehicles (UAVs) and robots.
## 2 Novel Frameless Slotted-ALOHA Protocol  
The main objective of the slotted-ALOHA protocol is to enable active users to have the opportunity to send their packets without conflict. A typical example is seen in the slotted-ALOHA protocol that is used to handle the transmission from users to a common receiver, e.g., a base station, via a shared wireless medium. Moreover, the more users there are in a cell, the higher the probability of packet conflict. Although in random-access protocols, the slotted-ALOHA protocol can achieve high throughput and low complexity.
## 7 Numerical Examples  
In this section, we introduce two models of IVPs with varying step sizes \(h\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{x}_{i}\) & **Ralston’s Scheme** & **RK3 Scheme** & **ARK3 Scheme** & **Heun’s Scheme** & **VRK3 Scheme** \\ \hline zero & zero & zero & zero & zero & zero \\
0.1 & \(7.9184\times 10^{-7}\) & \(7.9184\times 10^{-7}\) & \(1.1480\times 10^{-6}\) & \(4.3572\times 10^{-7}\) & \(7.9594\times 10^{-8}\) \\
0.2 & \(1.6379\times 10^{-6}\) & \(1.6379\times 10^{-6}\) & \(2.3876\times 10^{-6}\) & \(8.8818\times 10^{-7}\) & \(1.3848\times 10^{-7}\) \\
0.3 & \(2.5408\times 10^{-6}\) & \(2.5408\times 10^{-6}\) & \(3.7254\times 10^{-6}\) & \(1.3561\times 10^{-6}\) & \(1.7141\times 10^{-7}\) \\
0.4 & \(3.5031\times 10^{-6}\) & \(3.5031\times 10^{-6}\) & \(5.1684\times 10^{-6}\) & \(1.8377\times 10^{-6}\) & \(1.7229\times 10^{-7}\) \\
0.5 & \(4.5273\times 10^{-6}\) & \(4.5273\times 10^{-6}\) & \(6.7240\times 10^{-6}\) & \(2.3307\times 10^{-6}\) & \(1.3400\times 10^{-7}\) \\
0.6 & \(5.6159\times 10^{-6}\) & \(5.6159\times 10^{-6}\) & \(8.3997\times 10^{-6}\) & \(2.8321\times 10^{-6}\) & \(4.8292\times 10^{-8}\) \\
0.7 & \(6.7710\times 10^{-6}\) & \(6.7710\times 10^{-6}\) & \(1.0204\times 10^{-5}\) & \(3.3383\times 10^{-6}\) & \(9.4375\times 10^{-8}\) \\
0.8 & \(7.9947\times 10^{-6}\) & \(7.9947\times 10^{-6}\) & \(1.2144\times 10^{-5}\) & \(3.8448\times 10^{-6}\) & \(3.0504\times 10^{-7}\) \\
0.9 & \(9.2884\times 10^{-6}\) & \(9.2884\times 10^{-6}\) & \(1.4231\times 10^{-5}\) & \(4.3460\times 10^{-6}\) & \(5.9642\times 10^{-7}\) \\
1 & \(1.0653\times 10^{-5}\) & \(1.0653\times 10^{-5}\) & \(1.6472\times 10^{-5}\) & \(4.8351\times 10^{-6}\) & \(9.8318\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the absolute errors among third-order schemes in Problem 1, for \(h=5\times 10^{-2}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{x}_{i}\) & **Ralston’s Scheme** & **RK3 Scheme** & **ARK3 Scheme** & **Heun’s Scheme** & **VRK3 Scheme** \\ \hline zero & zero & zero & zero & zero & zero \\
0.1 & \(7.9184\times 10^{-7}\) & \(7.9184\times 10^{-7}\) & \(1.1480\times 10^{-6}\) & \(4.3572\times 10^{-7}\) & \(7.9594\times 10^{-8}\) \\
0.2 & \(1.6379\times 10^{-6}\) & \(1.6379\times 10^{-6}\) & \(2.3876
# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
## 1 Introduction
To search the large scenario space and identify critical scenarios, this paper proposes an innovative way of utilising a genetic algorithm (GA). The selected critical scenarios are then tested and validated on a MATLAB simulated driving environment. As the majority of the proposed algorithms developed to safely manoeuvre a NLOS scenario utilise vehicular communication to detect and avoid obstacles, the implementation platform presented in this paper incorporates vehicular communication as a means for allowing the testing of these algorithms in the future. Thus, the primary contribution of the paper is:  
* The development of a new and effective methodology to search for critical NLOS scenarios from a large scenario space. This involved defining a critical scenario as used in this paper and included the development of a methodology to evaluate critical scenarios for NLOS cases. This has been performed by combining several indicators, for example, time to collision (TTC) and total stopping time (TST), as presented in Section 3. This is followed by searching for critical scenarios within the scenario search space through the unique use of a genetic algorithm. Figure 1: Scenario Development Process. * The subsequent implementation framework that simulates the scenarios selected by the GA. This platform incorporates vehicular communication. We envisage this contribution to be utilised for testing AV algorithms developed for NLOS road scenarios and these algorithms include built-for-purpose communication protocols. The paper is organised as follows: Section 2 discusses the related works on AV testing methods, critical scenario selection and the use of evolutionary algorithms to search the scenario space. Section 3 discusses the methodology for determining the metric for identifying NLOS critical scenarios (adopted as a fitness function for the GA); details of the GA and simulation environment were used to validate GA output. The experiments and results are presented in Section 4, followed by discussion in Section 5 and conclusion in Section 6.
## 4 Experimental Results and Discussion
### Influence of the Key Parameters of the VFPEH on Its Output Performance
#### 4.1.1 Diameter of Cylindrical Bluff Body
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Harvester Type** & **VFPEH-20** & **VFPEH-25** & **VFPEH-30** & **VFPEH-35** \\ \hline Diameter (mm) & 20 & 25 & 30 & 35 \\ Maximum output voltage U\({}_{\text{max}}\) (V) & 54.9 & 56.2 & 60.2 & 58.8 \\ maximum output power P\({}_{\text{max}}\) (mW) & 5.40 & 5.60 & 6.47 & 6.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Output characteristics of the VFPEH with different diameters. effect is the best, and the VFPEH output power maximum value is 6.47 mW. It can be seen from the position of the dotted line in Figure 3b that the VFPEH with a cylindrical bluff body with a diameter of 30 mm has a wider range of output power and wind velocity, the VFPEH-30 has a higher output for the same wind velocity and a greater range of wind velocities for the same output.
## 2 Methods
### The Design of the Hardware
#### 2.1.3 Power Control Circuit  
The Beidou transparent transmission terminal requires a wide voltage input of 9 V to 32 V. Specifically, the main control chip of the Beidou transparent transmission terminal requires a 3.3 V input power supply, which also powers the TTL to 232 conversion circuit. The BDM910 Beidou module requires power inputs of 12 V and 5 V. Thus, the LM5175 PWPR for stabilizing to 12 V, the TPS5430 DDAR for narrowing down to 5 V, and the HE2210M533R for the 3.3 V supply essential to the main control chip and the TTL to 232 conversion circuits.  
The main control chip GPIO interface connects to the enable pins of the LM5175 PWPR and TPS5430 DDAR chips, controlling the on and off states of the 12 V and 5 V power supplies by adjusting the output level. This strategic power management affords considerable energy savings and extends the operational lifespan of the buoy's battery charge.
# Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
## 4 Discussion
The accuracy in the heart visualization and 3D model realization is fundamental. Furthermore, the MR visualization requires high resolution to enable surgeons to examine even the slightest imperfections in anatomical features. In this context, HL2 presents some technical limitations regarding the resolution of the hologram it can generate and on the stand-alone functionality for a more complex MR experience. Indeed, using Holographic Remoting settings with a wired connection may be necessary for projects demanding high device performance. The MR experience enables an interactive exploration of individual parts of the heart and sectioning in every direction. Additionally, the original DICOM images can be viewed simultaneously across all three-dimensional planes. Other functionalities can be added and integrated, such as measurements, marker positioning, and visualization from different perspectives. In particular, to further optimize the application, several future developments are planned, as follows:  
* Adding the capability to measure the aortic valve annulus point by point;
* Incorporating blood flow dynamics into the hologram;
* Creating a 3D model of the mitral valve using echocardiography;
* Utilizing the'sharing experience' feature, which allows multiple users, each with their own device, to view and interact collectively with the same hologram. Although only one patient's result is reported in this study, the potential and limitations of the proposed methodology were clear to the surgical team. The MR experience, completed with different activities and interactions, speeds up the recognition of structures and decision making. It provides additional information to traditional 2D images and it is also more manageable and interactive than the 3D printed model. At the same time, the segmentation process is currently flawed. Indeed, the procedure to achieve accurate segmentation of even intracardiac structures still requires manual intervention, although deep learning procedures are increasingly demonstrating high results.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 4 Our Presented TRP-MC Algorithm
### Hexagon Division  
We consider the sensor field is covered by a fixed number of SPs and one of the efficient methods has been discussed in reference [40] for sensor deployment. The authors use a regular hexagon to divide the sensor field and the length of the regular hexagon is set to the transmission range. Therefore, in each regular hexagon, only one node needs to keep active and the other nodes can stay in sleep mode momentarily. This method makes full use of the perceived range of sensor so that the active nodes in the network are decreased. In the same way, we can introduce a regular hexagon to select the SPs for a mobile collector. Figure 7 shows the selection of SPs using a regular hexagon. The side length of the regular hexagon is the transmission range of sensors and each sensor in the covered area can upload its data to its corresponding SP. This approach decreases the number of SPs and the travel route length is reduced. However, the areas covered by two adjacent SPs still have few overlaps.
## 2 Literature Review
### Path Planning in Agricultural Applications
In [59], the authors showed the simulation results of  
Figure 1: 3D path planning algorithms taxonomy [5; 47]. an algorithm designed to autonomously perform the path planning process for UAVs in agricultural lands. The purpose of this study was to provide the appropriate conditions to automate the process and carry out further audit activities. The algorithm considers photogrammetric parameters such as ground sample distance (GSD) and overlap between photos. For this, image processing techniques were used. In [62], the authors used autonomously acting ground robots for various agricultural applications. They researched different applications for path planning techniques to various agricultural contexts and applied land coverage and point-to-point navigation techniques. They used the D\({}^{*}\) to find the optimal path in a partial environment. However, this method is not very efficient since it uses a node-based algorithm (D\({}^{*}\) algorithm), and it is also designed for 2D areas [63]. As mentioned before, among the 3D path planning methods, metaheuristics may be the most efficient method. In [64], the authors proposed a custom model to navigate semi-autonomous agricultural robots with trailer. However, the geometry features were considered in 2D. In addition, the authors did not focus on finding the optimal path. Therefore, mobile devices moving on the non-optimal path map may not be successful in using their resources efficiently. In [65], the authors proposed a path planning method inspired by the Ant Colony Optimization (ACO) algorithm to multipoint measurements in potato ridge cultivation. However, the related method did not perform successfully in finding optimal paths and is also useful for 2D areas. It may be unlikely to be implemented on real robots due to the fact that they did not focus on the recognition of obstacles and the avoidance of mechanisms of them. In [57], three local search metaheuristic algorithms, which were simulated by annealing and tabu search references, were used to calculate annual crop planning with a new irrigation mechanism. The objective function of this study was to maximize the gross benefits associated with the allocation of crops. The authors claimed that the tabu search method gave the best results in comparisons. In [66], an evolutionary algorithm was used for a complex strategic land use problem based on the management of a farming system. This study aimed to pursue a multi-purpose strategy that fulfilled spatial constraints in the 50-year planning management of the farm. Although the study is comprehensive, the metaheuristic method used and proposed may not be a very performant and efficient solution.