esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Simulation Experiment and Result Analysis
#### 3.3.2 Performance Comparison in Different Dimensions  
The AFS-MMSBAS algorithm is compared with the MDBAS, AFS, and AFS-MDBAS algorithms under the same parameters. The performance of the algorithms is tested with respect to the dimensions D = 10, D = 100, and D = 200 of the basic test functions. In order to prevent the experimental results from being affected by randomness, each test function is independently run 50 times. The other parameters are the same as those in 3.2.1. Finally, the average value, standard deviation, and running time of the optimization results are obtained. The experimental results are shown in Table 5, and the optimal results are shown in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline population & 30 & antenna number & 10 \\ visual & 6 & antenna length & step/5 \\ attempt number & 20 & \(\delta\) & 0.95 \\ crowding factor & 0.618 & variation rate & 0.05 \\ step of fish & 0.6 & p & 40 \\ iterations & 800 & q & 0.65 \(\times\) iterations \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial parameters of AFS-MMSBAS algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Calculation Complexity** & **Algorithm** & **Calculation Complexity** \\ \hline AFS & O(DN\({}^{2}\)) T & AFS-DBAS & O(DN\({}^{2}\))t\({}_{1}\) + O(c\({}_{1}\)D)t\({}_{2}\) \\ BAS & O(D) T & AFS-MMBAS & O(DN\({}^{2}\))t\({}_{1}\) + O(c\({}_{2}\)D)t\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm complexity. By analyzing Table 2, it can be seen that in low dimensions (D = 10), the MDBAS, AFS, AFS-MDBAS, and AFS-MMSBAS algorithms have good optimization ability and stability for most test functions. Among them, the MDBAS algorithm obtains the optimal mean and standard deviation for \(f_{5}\), the AFS algorithm obtains the optimal mean and standard deviation for \(f_{2}\) and \(f_{11}\), the AFS-MDBAS algorithm obtains the optimal mean and standard deviation for \(f_{3}\) and \(f_{10}\), and the AFS-MMSBAS algorithm obtains the optimal mean and standard deviation for \(f_{1}\), \(f_{4}\), \(f_{8}\), and \(f_{9}\). At low dimensions, each algorithm has certain advantages over different functions, and the gap is not obvious. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Func.
# RF Exposure Assessment for Various Poses of Patient Assistant in Open MRI Environment
## 2 Materials and Methods  
### Numerical Modeling of RF Coils for Open MRI Systems  
Because the majority of the main magnetic field (B\({}_{0}\)) of open MRI systems forms vertically (z-direction), the excitation magnetic field (B\({}_{1}\)) of the imaging RF coils needs to be orthogonally formed (in the xy-plane) toward the B\({}_{0}\) direction. These conditions affect the driving method of the imaging RF coils. By contrast, circularly polarized (CP) B\({}_{1}\) fields enhance the signal-to-noise ratio (SNR), particularly in low-field MRI systems, and thus the configuration of two independent RF coil elements is widely used in commercial low-field open MRI systems [20]. A commercially available low-field (0.3 Tesla) open MRI system was used as a reference for the numerical electromagnetic (EM) field simulations of the overall dimensions of the magnet and the types of imaging RF coils (www.hitachi-medical-systems.eu/products/mri/airis-vento-03t.html (Accessed on 28 May 2021)). Commercial EM field simulation software, Sim4 Life (Zurich Med Tech AG, Switzerland), with a finite difference time domain solver was employed to assess the RF energy exposure of the patient assistant for two different RF coils (i.e., the head and body coils).
## 4 Discussion
An intriguing finding was the inversion of the dose profile between IRT and VMAT. Despite IRT delivering a higher overall dose, the deep tissue dose was lower compared to VMAT. This inversion means that while IRT effectively targets the surface and immediatesubsurface regions, it spares deeper tissues and adjacent organs at risk, which is a significant advantage in reducing potential side effects and collateral damage to non-target tissues. The overall findings from these analyses are instrumental in underlining the advantages and limitations of each modality. They advocate for a tailored approach in choosing between IRT and VMAT based on specific patient geometries and clinical conditions. This study thereby contributes to the nuanced understanding of therapeutic radiotherapy, paving the way for optimized, patient-centric cancer treatment protocols. The insights gained emphasize the importance of personalized treatment planning and the potential of IRT in enhancing therapeutic outcomes while minimizing adverse effects. The study has a few limitations that should be noted. The relatively small cohort size may affect the statistical significance of some findings, but the dosimetric comparisons still offer valuable insights. While VMAT and electron beam therapy were simulated rather than used in actual clinical treatments, the simulations were rigorously designed to reflect clinical practice. Additionally, the absence of clinical evaluation or patient follow-up means that the study did not assess treatment-related side effects, leaving open the question of how the dosimetric advantages translate into clinical outcomes such as local control or toxicity. Future studies incorporating these aspects would help to further validate the findings.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 2 Background and Related Work
### Generating Synthetic EHR Notes  
The generation of synthetic EHR text for use in medical NLP is still at an early stage [3]. Most studies focus on the creation of English EHR text, using hospital discharge summaries from the MIMIC-III database [7; 8; 13; 14]. In addition, a corpus of English Mental Health Records was explored [15]. Unlike the mixed healthcare data used in this study, these EHR notes have a more consistent, template-like structure and contain medical jargon, lending itself to clinical/biomedical downstream tasks found in related work [8; 13; 14; 15]. Most of these studies focused on classification downstream tasks. To the best of our knowledge, we are the first study that attempts to generate synthetic data for sequence labeling (NER).  
Decoding from language models is the predominant approach in related work to generate synthetic text. Approaches include unigram-language models and LSTMs [7], as well as transformer-based methods such as GPT-2 [13; 14; 15]. In particular, [PERSON] et al. [8] concluded that GPT-2 was suitable for text generation in a low-resource scenario. In this research, we compare a standard LSTM-based model with a transformer-based model (GPT-2). At the time this research was conducted, the only pre-trained Dutch transformer models available were BERT-based [16; 17]. Since no pre-trained Dutch GPT-2 model existed, we chose to fine-tune an openly available English GPT-2 [6] on our data for this purpose.  
Prior studies also consider different ways to generate EHR notes with a pre-defined topic. These approaches include conditional generation on clinical context [8; 13] and guiding by keyphrases extracted from an original note [14; 15; 18]. As a result, the synthetic notes inherently have one-to-one relations with the original data. In this study, we do not use the conditional text generation approaches for two reasons. First, the NER use-case does not require strong guarantees on the topic of synthetic training examples. This is different from downstream tasks like classification. Second, we do not want that synthetic notes have a one-to-one link to real data. We assume that this benefits privacy protection. Instead of the conditional generation mentioned above, we use short prompts to generate whole EHR notes without a pre-defined topic.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### Computational Complexity  
Advanced localization systems that integrate deep learning algorithms, such as LSTM networks for UWB, face challenges related to computational load and real-time processing [60]. In resource-constrained environments, such as emergency response operations, ensuring real-time performance without overburdening the system is crucial. Optimization techniques and efficient hardware implementations are needed to address these issues [65].
## 1 Introduction
Currently, the main research on UAV communication can be divided into two categories: (1) cellular-connected UAVs, where a UAV connects to mobile communication networks and performs flight tasks, and (2) UAV-assisted cellular networks, where a UAV acts as a network relay, providing network services to other devices [7]. UAV-assisted SAGINs provide a feasible solution for eliminating coverage holes. A UAV, characterized by high mobility and freedom from terrain limitations, can quickly respond and be deployed in remote areas or regions affected by natural disasters, where TNs are unavailable or interrupted, thereby restoring network communication. Therefore, the dynamic and mobile nature of UAVs makes them highly suitable for SAGINs and positions them as the primary contributors to aerial networks. The development of UAV communication and SAGINs are closely intertwined, with UAV-assisted SAGINs becoming one of the key technologies to achieve ubiquitous coverage, auxiliary relaying, and information collection in SAGINs [8]. With the advancement of 5G, TNs have made significant progress in terms of scale and performance. However, the coverage of TNs heavily depends on well-developed infrastructure, making it difficult for remote areas and regions with underdeveloped infrastructure to access 5G networks. According to data from the Global Digital Economy Conference, as of now, the global terrestrial population coverage of 5G is about 45%. For maritime regions, due to the lack of sufficient infrastructure, relying solely on terrestrial or constellation networks is challenging for providing stable and wide-ranging network services. As shown in Figure 1, SAGINs connect terrestrial, aerial, and space networks through unified standards. Unlike traditional fixed terrestrial base stations, LAPs and HAPs act as aerial base stations and relay nodes, featuring advantages such as redeployability, high flexibility, and lower costs. Additionally, UAVs demonstrate significant application value in fields such as rescue, transportation, and military operations. In the long run, SAGINs benefit from both terrestrial and satellite networks and are expected to achieve effective cost control.
## 2 Methodology
### ELM-Based Prediction Model  
The ELM model is chosen as the core model for DD prediction in this paper, and its structure is shown in Figure 3. It is a new type of feedforward neural network [22]. Compared with the traditional single hidden layer feedforward neural networks (SLFNs), ELM has some significant advantages, such as fast training speed, good generalization ability, and few adjustable parameters. Its specific principles are as follows  
Given a \(Q\)-group sample \((x_{n},t_{m})\), where \(x_{n}=\left[x_{n1},x_{n2},\ldots,x_{mQ}\right]^{T}\) and \(t_{m}=\left[t_{m1},t_{m2},\ldots,t_{mQ}\right]^{T}\). Assume that the activation function of the hidden layer neurons is \(g(x)\), the output vector of the network is as follows  
\[T=\left[t_{1},t_{2},\ldots,t_{Q}\right]_{m,Q} \tag{13}\]  
\[t_{j}=\left[t_{1j},t_{2j},\ldots,t_{mj}\right]^{T}=\sum_{i=1}^{l}\beta_{im}g( \omega_{i}x_{i}+b_{i})\ \ \ (j=1,2,\ldots,Q) \tag{14}\]  
Figure 3: Extreme learning machine (ELM) structure.  
where \(b_{i}\) denotes the threshold of the \(i\)th neural node of the hidden layer, \(\omega_{i}=\left[\omega_{1i},\omega_{2i},\ldots,\omega_{mi}\right]^{T}\) is the weight of the \(i\)th neural node, and \(\beta_{i}=\left[\beta_{1i},\beta,\ldots,\beta_{mi}\right]^{T}\) is the weight of the \(i\)th neural node. Equation (13) can be described as Equation (15)  
\[H\beta=T^{\prime} \tag{15}\]  
where \(H\) is the hidden layer output matrix, which can be represented by Equation (16)  
\[H\!\left(\omega_{1},\omega_{2},\ldots,\omega_{l},b_{1},b_{2},\ldots,b_{l},x_{ 1},x_{2},\ldots,x_{Q}\right)=\left[\begin{array}{ccc}g(\omega_{1}\!\cdot\!x_ {1}+b_{1})&\cdots&g(\omega_{l}\!\cdot\!x_{1}+b_{l})\\ \vdots&\cdots&\vdots\\ g(\omega_{1}\!\cdot\!x_{Q}+b_{1})&\cdots&g\!\left(\omega_{l}\!\cdot\!x_{Q}+b_{ l}\right)\end{array}\right] \tag{16}\]  
According to the ELM theorem, if \(l=Q\), for arbitrary \(\omega\) and \(b\), Equation (17) can be obtained  
\[\sum_{j=1}^{Q}\left\|\!\left|t_{j}-y_{j}\right\|\!\right|=0 \tag{17}\]  
where \(y_{j}=\left[y_{1j},y_{2j},\cdots,y_{mj}\right]^{T}(j=1,2,\cdots,Q)\). When \(Q\) is large, \(l\) is usually less than \(Q\) in order to reduce the amount of computation, which means that the ELM training error can be approximated to an arbitrary number \(\varepsilon>0\).  
\[\sum_{j=1}^{Q}\left\|\!\left|t_{j}-y_{j}\right\|\!\right|<\varepsilon \tag{18}\]  
The connection weight \(\beta\) can be obtained as follows  
\[\min_{\beta}\left\|\!\left|H\beta-T^{\prime}\right\|\!\right| \tag{19}\]  
Its solution can be expressed as Equation (20)  
\[\hat{\beta}=H^{+}T^{\prime} \tag{20}\]  
where \(H^{+}\) is the generalized inverse Moore-Penrose matrix of the \(H\) matrix.  
In summary, the ELM model does not require iterative corrections to weights and thresholds, and it outperforms conventional SLFNs.
## 2 Method  
### Numerical Method  
The airfoil DU 91-W2-250 was developed by TU Delft, which has a relative thickness of 25% with a trailing edge gap of 0.65% chord [32]. The present study is based on a 2D simulation with ceiling and bottom boundary similarity in a wind tunnel. With this kind of set, the numerical method is easily validated by the authors' previous measurements [33]. In order to simulate the dynamic stall phenomenon, a rotational zone whose radius is three-times that of the model chord is placed at the pitching center, as shown in Figure 1. The rotational zone and airfoil model oscillate like a rigid body, and the movement is defined with a user-defined function (UDF) in ANSYS Fluent [22]. The rest of grid is connected to the rotational grid by interfaces. The block interfaces between the dynamic and static zones are non-conformal because, as the airfoil moves, the O-grid attached to the airfoil rotates rigidly (as a solid body) together with the airfoil.  
The Reynolds number is \(3\times 10^{5}\), which confirmed the turbulence that appeared in the previous study. Flows under such a relatively low Reynolds number are highly non-linear [34]. Therefore, turbulence should be considered in the simulation. There are three main turbulence simulation methods, i.e., direct numerical simulation (DNS), Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES). A turbulence model should present a satisfactory representation of an important phenomenon while introducing the least amount of complexity [35]. In the present study, considering numerical simulation cost and acceptable accuracy, the unsteady Reynolds-averaged Navier-Stokes (URANS) method is seem to be the most suitable one to simulate the dynamic stall phenomenon [15]. Consequently, in the CFD software ANSYS Fluent, the transition SST \(k-\omega\) model, which is capable of capturing the transition process, was chosen. Some researchers have also discussed that the accuracy of this model is satisfactory [15; 17; 36; 37; 38; 39; 40].  
Figure 1: (a) Mesh topology of the whole domain; (b) mesh details near the leading edge; (c) mesh details near the trailing edge.
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 4 Lateral Stability
### Lateral Overturn Stability  
Rollower will occur when the uphill side tires lose contact with the ground (the contact force falls to zero). Equation (17) becomes:  
\[\frac{Gcos(\theta_{l})*L_{d}-Gsin(\theta_{l})*H_{d}}{n}=0, \tag{18}\]  
where \(\theta_{lim}\) is the limit instability angle of the tractor,  
\[\theta_{l}=\theta_{lim}-\psi_{l}, \tag{19}\]  
Therefore, substituting Equations (19) and (15) into Equation (18), Equation (18) can be rearranged to obtain the limit instability angle.  
* Initial state \[\theta_{lim0}=\arctan(L_{d}/H_{d}), \tag{20}\]  
In the initial state, the tractor does not adjust its attitude, and the adjustable attitude angle of the tractor (\(\psi_{l}\)) is 0. Thus, in the initial state, the instability angle of this tractor is equal to the ordinary one.  
* Attitude adjustment state \[\theta_{lim}=\arctan(L_{d}/H_{d})+\arctan(\frac{H_{d}-H_{u}}{B}),\] (21) where \(L_{d}\) and \(B\) are constant values, and \(H_{d}\) and \(H_{u}\) vary with the input of the downhill-side and uphill-side adjustment mechanisms, respectively.  
Therefore, the tractor can increase the limit instability angle by actively adjusting the values of \(H_{d}\) and \(H_{u}\) (the height of the COG on both sides) together.  
Substituting Equation (20) into Equation (21) yields:  
\[\theta_{lim}=\theta_{lim0}+\arctan(\frac{H_{d}-H_{u}}{B}). \tag{22}\]  
Based on the above equation, by actively adjusting the attitude of an attitude adjustment tractor, the limit instability angle \(\theta_{lim}\) can be larger than its initial value \(\theta_{lim0}\). As shown in Equation (22), when \(H_{d}\) is constant, the limit instability angle (\(\theta_{lim}\)) of a reconfigurable tractor is composed of two parts: the former part is the limit instability angle at its initial state, the latter one is the adjustable attitude angle (\(\psi_{l}\)). In other words, a tractor can enhance its lateral overturn stability by actively adjusting its posture.  
As referenced in Section 3, we denote \(\alpha_{u}\) and \(\alpha_{d}\) as the inputs of the active adjustment mechanisms in the uphill and downhill sides, respectively. Putting \(\alpha_{u}\) and \(\alpha_{d}\) into Equation (3), we obtain:  
\[\left\{\begin{aligned} & Downhill\ side:H_{d}=f(\alpha_{d})\\ & Uphill\ side:H_{u}=f(\alpha_{u})\end{aligned},\right. \tag{23}\]  
By substituting Equation (23) into (21), the limit instability angle \(\theta_{lim}\) is:  
\[\theta_{lim}=\arctan(L_{d}/f(\alpha_{d}))+\arctan(\frac{f(\alpha_{d})-f( \alpha_{u})}{B})=h(\alpha_{u},\alpha_{d}), \tag{24}\]  
where \(\alpha_{u}\) and \(\alpha_{d}\) as the inputs of the active adjustment mechanisms on the uphill and downhill sides, respectively.  
Therefore, the limit instability angle, \(\theta_{lim}\), can be obtained by employing the inputs \(\alpha_{u}\) and \(\alpha_{d}\) as variables.
### Meeting Emerging Healthcare Technology Needs  
Within the development of smart, interconnected healthcare-supporting devices, several different approaches are ongoing. Whereas the 802.15.6 WBAN standard is expired, approaches like the ETSI TC SmartBAN, one2M2, or the Alliance for IoT and Edge Computing Innovation (AIOTI) are working on fitting solutions to bring together such technologies.  
However, as the frontiers of digital health are pushed, several key areas within a WBAN framework call for enhancement to meet evolving requirements and must be part of the mentioned synchronization processes:  
Data Rate Capabilities: The burgeoning integration of Artificial Intelligence (AI) in healthcare necessitates data rates exceeding the current WBAN standard limit of 10 Mbps. AI-driven applications, particularly those demanding real-time processing and instantaneous decision-making, require bandwidths that can accommodate the increased data throughput. Adaptive Security: The landscape of cyber threats is continuously evolving, rendering static security measures insufficient. WBANs must adopt adaptive security protocols that can dynamically respond to new threats, integrating AI to underpin advanced threat detection and adaptive response mechanisms.  
Energy Efficiency: Although the WBAN standard prioritizes low power consumption, there is a pressing need to push beyond the current benchmarks. AI can be pivotal in optimizing power management and tailoring energy usage to user behavior and environmental conditions, thereby extending the wearables' battery life and operational reliability.  
Encryption and Privacy: With the proliferation of wearables for health monitoring, safeguarding patient data privacy is of the utmost importance. The adoption of advanced encryption techniques and AI-enhanced privacy measures is essential to bolster defenses against unauthorized access and data breaches.  
Interoperability and Personalization: The heterogeneity of devices and systems in the healthcare ecosystem necessitates robust interoperability standards. AI's potential to personalize device functionality to individual user patterns necessitates the WBAN standard to be flexible enough to accommodate diverse AI models and communication protocols.
# The Microverse: A Task-Oriented Edge-Scale Metaverese
## 3 The Microverse: Rationale and Architecture
### Microrained IoT Networks  
Microhained IoT networks are an innovative paradigm within the Internet of Things (IoT) connections, which introduces a novel age characterized by enhanced efficiency, security, and scalability [49; 41]. In contrast to conventional IoT topologies, which frequently depend on centralized servers for data processing and management, microchannel networks spread these functionalities over a decentralized network of microcontrollers or microprocessors integrated into IoT devices.  
Figure 1: Microverse: a hierarchical view.  
The fundamental principle underlying microchannel IoT networks revolves around utilizing blockchain technology, which offers a robust and transparent infrastructure for managing data transactions [50]. Every IoT device in the network is equipped with its microcontroller, establishing a node inside the blockchain system. These nodes collectively contribute to the maintenance and operation of the distributed ledger. The decentralization process minimizes the presence of singular points of failure, bolstering the network's ability to withstand disruptions and diminishing its susceptibility to cyber-attacks.  
The term _microchained_ highlights the incorporation of blockchain technology at a smaller scale, explicitly emphasizing its deployment at the device level. The micro-blockchain methodology facilitates effective and tamper-proof data storage and retrieval, as each data transaction is cryptographically interconnected and chronologically recorded inside the blockchain. Implementing security measures in IoT devices plays a crucial role in upholding the reliability and authenticity of the generated data, hence cultivating a sense of confidence and trust among the many parties involved. In addition, microchannel IoT networks effectively tackle issues of scalability. The decentralized structure of the design facilitates the smooth integration of supplementary devices without imposing excessive load on a central server. The importance of scalability becomes increasingly significant as the size and complexity of IoT networks continue to grow.  
Security issues hold significant importance in the IoT environment since they frequently involve the interaction of devices with sensitive data. Microchained Internet of Things (IoT) networks offer a resilient security architecture utilizing blockchain's immutability. Illicit modifications to data pose significant difficulties, and the blockchain's inherent transparency guarantees responsibility and the capacity to track any security violation.