esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 2 Core Technologies for Smart IoHT Applications
### Wireless Body Area Networks (WBANs)  
In smart healthcare, a WBAN is one of the most important core technologies. A WBAN is a set of intelligent IoT devices mounted on the human body to collect patient vitals and transmit this information to a data center for decision making. A WBAN works via wireless technology consisting of light and smart sensors attached to or implanted in the human body, measuring parameters such as heart rate, body temperature, blood glucose level, etc. These data are then visually provided to medical staff or caregivers to monitor and analyze the patient's condition and devise appropriate treatment schemes [30].  
The WBAN architecture consists of hardware, software, and communication technology. The hardware includes all devices and sensors used to collect patient data; the software consists of a man-machine interface, network protocols, and an operating system. In addition, communication technology in a WBAN plays a significant role in transmitting medical data between devices and software. Figure 3 illustrates the architecture of a typical WBAN system. Depending on the goals of each WBAN network, different communication technologies will be used. Table 3 compares the characteristics of some communication technologies of WBAN systems [31].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Characteristics** & **Cloud** & **Fog** & **Edge** \\ \hline Latency & High & Low & Low \\ Bandwidth & High & Low & Very low \\ Storage & High & Low & Low \\ Server Overhead & Very high & Low & Very low \\ Network Congestion & High & Low & Low \\ Energy Consumption & High & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between advanced computing technologies [28].  
\begin{table}
\begin{tabular}{l l l r r r} \hline \hline
**Wireless Technology** & **Standard** & **Network Topology** & **Transmission Range** & **Frequency** & **Bit Rate** \\ \hline ZigBee & 802.15.4 & Star, cluster tree & 10–20 m & 2.4 GHZ & 250 Kbps \\ Bluetooth & 802.15.1 & Piconet, scatternet & 10–30 m & 13.56 MHz & 2.1 Mbps \\ Low-Power & 802.15.1 & Star & \(\sim\)50 m & 2.4–2.5 GHz & 1 Mbps \\ Bluetooth & & & & 75.9 Kbps \\ IEEE 802.15.6 & 802.15.1 & Star & \(<\)100 m & NB, UWB, HBC & \(-\)15.6 Mbps \\ UWB & 802.15.4a & Piconet, & 10 m & 3.1–10.6 GHz & 480 Mbps \\ Wi-Fi & 802.11 & Mesh & 100 m & 2.4 GHz & 54 Mbps \\ Low-Power Wi-Fi & 802.11 ah & One-hop & 100–1000 m & 780–950 MHz & 150 Kbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison of communication technologies for IoHT systems.  
In recent years, to respond to more increasing requirements of humans in the smart healthcare domain. Besides existing core medical technologies, some emerging technologies are being researched and are expected to provide booming results, including the Metaverse, digital twins, and AI. In the next section, we will discuss detail these issues.
## 3 Candidate Selection and Localization
### Image Pyramid Search Technique  
The image pyramid search framework allows this type of target search. The image pyramid hierarchically splits the candidates using information from different image pyramid levels to disjointed subsets. This process allows many candidates to be discarded early in the search process because their similarity coefficients are lower than the pre-defined threshold for similarity. One case involving three image pyramid levels of the search process is shown in Figure 10.  
The search process starts at the highest pyramid level (level 2) of the candidate. The candidate on the highest pyramid level is identified by computing the similarity using hybrid-projection values, \(H_{p,2}(r)\) and \(H_{S,2}(x,y,r)\), at each position \((x,y)\). The candidate, \(obj_{2,1}\), on level 2, which is indicated by a green circle in Figure 10, is also identified. The candidate with a similarity score that exceeds the similarity threshold \(T_{\text{min,2}}\) is stored in the candidate list. The value of \(T_{\text{min,2}}\) is also dependent on the application and must be set to the minimum expected object visibility. On the next lower pyramid levels (level 1), the search process uses the candidate that is located on the previous pyramid level. This includes candidates \(obj_{1,(1,2,3,4)}\), which are denoted by a yellow circle and are inherited from their parent candidate \(obj_{2,1}\) using a \(2\times\ 2\) searching area on image pyramid level 1. Only candidate \(obj_{1,2}\), which is denoted by a red square, has a similarity score that exceeds the similarity threshold \(T_{\text{min,1}}\). This process is repeated until all matching candidates have been tracked to the lowest pyramid level,  
Figure 9: Hybrid projection features of texture-less searching images.  
which, in this case is level 0. Finally, the best matching result with a similarity score that exceeds the similarity threshold \(T_{\text{min},0}\) is denoted by the red fill square in Figure 10.  
In the search process, the similarity threshold \(T_{\text{min},l}\) is set in a different image pyramid level. The most intuitive way for the user to determine the value is to choose it between 0 and 1. If the threshold is set to be a low value, more candidates will be selected in the searching process. It also means the computation burden will increase. Furthermore, the similarity score of the candidates should be decreased on the higher pyramid level because the details are missed. Therefore, the similarity threshold \(T_{\text{min},l}\) must be slightly reduced on higher pyramid levels in order to avoid a candidate that is missed. For example, the similarity threshold of each higher image pyramid level is decreased by 0.1 by rule of thumb.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 1 Introduction
Each vote cluster consists of multiple votes, with each vote potentially originating from a distinct capsule in the previous layer. The proximity of votes within a cluster signifies the information extracted from the same part of an object from capsules in previous layers. Consequently, the variance within a vote cluster can serve as an indicator of the confidence in the encoded information that the vote clusters represent. This suggests that vote clusters with lower variance are more reliable in encoding information related to a specific part of an object. Thus, greater weights are assigned to centroids originating from clusters with lower variance. We offer a comprehensive explanation of the definitions and terminologies presented in the existing literature [7]. Our objective is to minimize confusion and prevent misinterpretations surrounding the cluster routing technique, thereby enhancing its utilization across different scenarios. Moreover, we carry out extensive experiments to investigate the impact of agreement indicators and hyperparameters. Our goal is to determine whether variance can serve as the sole dependable agreement indicator, or if increasing the number of votes per cluster invariably results in better performance. Expanding upon the non-iterative cluster routing concept [7], we propose two variations of cluster routing: one features a streamlined routing process with the potential to reduce the number of parameters while maintaining performance levels comparable to those of existing methods, and the other is designed to effectively handle an increase in the number of votes per cluster, thus improving the overall efficiency of cluster routing. Empirical evaluations across several data sets show that our proposed methods utilizing cluster routing consistently yield higher accuracy, when compared to existing capsule networks. Moreover, our capsule network maintains the beneficial attributes of previous CapsNet versions, including strong generalization to images taken from new viewpoints. The contributions of our work are outlined in the following:  
* The definitions and examining the implementations within the cluster routing literature are clarified to support the effective deployment of cluster routing techniques. * Comprehensive experiments are performed to investigate the impacts of agreement indicators. * Two novel variations of cluster routing are proposed [7] based on the routing principle of achieving consensus among votes of clusters.
## 1 Introduction  
A smart grid is a technologically advanced and modernized electrical grid that integrates digital communication, advanced technologies, and intelligent devices to improve the efficiency, reliability, sustainability, and security of power generation, distribution, and consumption [1, 2]. Unlike conventional electrical grids, which are predominantly one-way systems in which power flows from centralized generation facilities to consumers, smart grids facilitate two-way communication and real-time data exchange between the grid's components [2]. While smart grids offer numerous advantages, such as increased efficiency and dependability, they are also susceptible to numerous cyber threats that can compromise their security and functionality. There can be different types of cyberattacks on smart grids; one such typical scenario is shown in Figure 1, which shows that an attacker from a remote location can easily send malware to the communication network [3]. Cyberattacks are developed to obtain unauthorized access to damage and disrupt or steal an IT asset, other sensitive data, etc. [1]; in the case of power data, cyberattacks are primarily focused on compromising the data's integrity and the availability of the service. Cyberattacks can originate from trusted individuals within an organization or from unknown parties located remotely.  
This paper aims to show the effect of false data injection attacks on the power dataset collected from an IoT server of a 10 KV solar PV system in a laboratory environment and to detect such attacks using machine learning approaches. To demonstrate the effects of a false data injection attack (FDIA), a simulated FDIA was conducted on a portion of the power dataset using Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)), accessed on 10 May 2024). This allowed for the voltage curve to be visualized before and after the attack. Different machine learning methods were utilized to efficiently detect the attack. Among these algorithms, decision tree and logistic regression showed superior performance. These approaches were employed to construct an ensemble approach, aiming to enhance the overall detection of the FDIA. The techniques used in this paper are commonly utilized in several fields, yet there are limited research resources on power datasets. We attempted to integrate the ensemble methodology, which relies on the individual performances of the algorithms employed. There is no definitive formula for determining which approaches to use in an ensemble. In our research, we selected the top two algorithms based on their evaluation metrics and combined them, resulting in improved performance. This comparison analysis will give researchers insights for choosing machine learning approaches in anomaly detection. This work focuses exclusively on FDIA. However, it is possible to include additional types of attacks to evaluate the performance of these algorithms and develop an intelligent anomaly detection system.
# Evaluating the Efficacy of Segment Anything Model for
## 1 Introduction
This model exhibits the capacity to deliver highly precise image segmentation, offering variable degrees of segmentation masks based on user inputs, as well as autonomous recognition and segmentation [23]. SAM's remarkable image segmentation capabilities stem from its robust architectural design, underpinned by the training of over 100 million annotations. The central concept of the model is to provide an intuitive and interactive approach for users to segment the specific elements they require [24]. Currently, SAM is undergoing extensive testing across a broad spectrum of applications within the medical field, with the aim of assessing its potential to supersede various previous algorithmic models [24; 25; 26]. Simultaneously, numerous SAM-based extensions are gaining traction as researchers strive to address the potential limitations of SAM through various means, capitalizing on its formidable generalized segmentation capabilities to the fullest extent [23; 27]. At present, there exists a dearth of research focused on utilizing the SAM model for remote sensing image segmentation. It appears that the distinctive advantages of this model remain largely unrecognized, particularly within the realm of agricultural and UGS remote sensing. The well-defined and regular distribution patterns of agricultural fields and UGSs present a significant opportunity to harness the unique segmentation capabilities of SAM. Considering the focus of our research, i.e., the applicability of SAM for regular-shaped vegetation feature characterization, we acknowledge that certain agricultural land use areas are in close proximity to urban regions, as can also be seen in various satellite images presented in this paper. Through numerous runs of SAM on such images, we have seen that urban cover types often exhibit considerable diversity,posing challenges for SAM segmentation. Moreover, UGSs are commonly found at the periphery and near cultivated land, sharing similar characteristic attributes. Therefore, we aimed to further verify SAM's capability to adequately segment and distinguish between ARSI and UGS land use types. Given that semantic segmentation is crucial for addressing the issue of numerous coverage types in remote sensing images, it is useful and more informative to conduct tests involving multiple types of peri-urban/urban human-maintained vegetation covers, i.e., farmlands and parks in this instance. As a result, our novel study delves into the evaluation and testing of SAM's performance for remote sensing image segmentation in agricultural and UGS contexts, particularly utilizing very high spatial resolution aerial images. We aim to offer distinctive insights that can facilitate the wider adoption of SAM in the field of ARSI and UGS segmentation. Additionally, we conduct an examination of the present constraints encountered by SAM within the realm of ARSI processing, yielding valuable insights into the process.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 2 Design Constraints and Security Issues in WSNs  
### Physical Characteristics and Constraints  
Sensors in most of wireless sensor networks are greatly limited in terms of device size, battery capacity, computing capacity, communication capacity, and storage capacity, which make the development of applications a challenge. A feasible and efficient security protocol should minimize the number of operations needed for calculation, communication, and storage. Therefore, the following characteristics of a WSN should be taken into consideration during protocol design [25; 26; 27; 28]:  
* Limited battery capacity--Sensor networks are usually deployed in outdoor environments. Due to size limitation, each sensor is usually equipped with a small battery. As a result, a sensor is unable to calculate and communicate when the battery runs out.
* Limited memory--the cache size of a sensor is usually measured in tens of megabytes, which puts forward higher requirements for the length and number of keys stored.
* Limited bandwidth--due to power limitation, most sensors use narrowband signal transmission, and the transmission rate generally does not exceed 10 KB/s.  
* Limited calculation power--In order to reduce the power consumption of CPU, most sensor nodes only use 8-bit 4-megahertz microcontrollers.
* Good scalability--Wireless sensor networks must allow new legal nodes to join the existing network at any time. At the same time, the failure of any node will not affect the normal operation of the network.
* Variability in network topology--Since sensors are often installed on mobile devices, the topology of wireless sensor networks often change. Thus, network stability and nodes connectivity should be ensured in all protocol designs.
* Environment--Some wireless sensor networks are expected to be used for remote control and reconnaissance, and they are deployed in insecure and unstable environments, which makes them subject to many attacks, such as spoofing attacks, physical damage, and any other mechanical failures associated with environmental factors.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 4 Simulation Results
Beyond \(4\) dB, the gap decreased, and the proposed pattern achieved performance close to the optimal pattern. At a velocity of \(30\) km/h, the performance gap between the proposed pattern and the optimal pattern was relatively small across all SNR ranges. In contrast, at a velocity of \(150\) km/h, the temporal variability of the channel increased, causing a slight degradation in the performance of the proposed pattern. Nevertheless, it consistently outperformed the conventional pattern, demonstrating high reliability even in high-mobility scenarios. For a delay spread of \(100\) ns, all patterns demonstrated stable MSE performance across all SNR ranges, with the proposed pattern maintaining consistently lower MSE compared to the conventional pattern. On the other hand, when the delay spread increased to \(300\) ns, all patterns experienced performance degradation due to more irregular channel variations. Nonetheless, the proposed pattern still outperformed the conventional pattern, proving its high reliability. The conventional pattern showed a rapid performance degradation as the delay spread increased or the velocity rose, with its performance degradation particularly evident in high-mobility scenarios. Additionally, when the SNR increased beyond 2 dB, the conventional pattern tended to converge to a fixed MSE threshold. This is attributed to the fact that the conventional pattern estimates all received signals processed by the clustering algorithm as the same channel, failing to effectively reflect subtle channel state variations in high-SNR conditions.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 2 Antenna Design and Analysis
### Design Process  
Figure 2 presents the detailed process for the proposed antenna to study the performance of the 5G dual-band monopole antenna. Because two antenna elements are placed symmetrically to share the ground plane, S\({}_{11}\) and S\({}_{21}\) are the same as S\({}_{22}\) and S\({}_{12}\), respectively. For simplicity, we only discuss S\({}_{11}\) and S\({}_{21}\). The simulated S-parameters are plotted in Figure 3. In Antenna 1, a three-quarters circular radiation patch with the circular slotted ground plane is designed. Antenna 1 can achieve a \(-10\) dB impedance bandwidth of 500 MHz (2-2.5 GHz) and 1880 MHz (4.05-5.93 GHz). However, the isolation between the radiating elements is non-ideal over the operation bands, which is illustrated in Figure 3b. In Antenna 2, an L-shaped branch is designed to realize a lower frequency shift (right) and a higher frequency shift (left), which can be seen in Figure 3a. By adding an inverted U-shaped slot and an I-shaped slot on the back side, it can reduce the mutual coupling between the antenna elements. In addition, as shown in Antenna 3, introducing the step-shaped radiating elements can realize useful impedance bandwidth. The result indicates that the impedance bandwidth covers 2.33-2.68 GHz and 3.93-5.13 GHz, and the value of S\({}_{21}\) adjacent to the defected ground structure decreases to below \(-24\) dB and \(-17\) dB in the 2.5 GHz and 4.5 GHz bands, respectively. Finally, a T-shaped slot and a G-shaped slot are incorporated at the bottom plane to reduce the mutual coupling to below \(-15\) dB for the entire frequency band.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Parameter & W\({}_{1}\) & W\({}_{2}\) & W\({}_{3}\) & W\({}_{4}\) & W\({}_{5}\) & W\({}_{6}\) \\ Value & 2 & 6.75 & 3.5 & 4.75 & 3.4 & 3.9 \\ Parameter & W\({}_{7}\) & W\({}_{8}\) & H\({}_{1}\) & H\({}_{2}\) & H\({}_{3}\) & H\({}_{4}\) \\ Value & 1.5 & 4.34 & 2.5 & 12.05 & 18 & 1.5 \\ Parameter & H\({}_{5}\) & H\({}_{6}\) & D\({}_{1}\) & D\({}_{2}\) & D\({}_{3}\) & D\({}_{4}\) \\ Value & 6.5 & 4 & 0.5 & 1 & 0.5 & 0.4 \\ Parameter & D\({}_{5}\) & R\({}_{1}\) & R\({}_{2}\) & W & L & \\ Value & 0.5 & 6.2 & 9 & 41 & 30 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the presented antenna (unit mm).  
Figure 1: The geometry and prototype of the proposed design: (**a**) perspective view, (**b**) top view, and (**c**) bottom view.
## 3 Results and Discussion  
### Degradation of the Et-PABA Using Different Oxidation Processes  
The removal efficiencies of Et-PABA using UV alone, the PDS alone and the UV/PDS combined process, respectively, were evaluated. As shown in Figure 2A, the removal efficiency of Et-PABA using a single UV process was only 23.7%, because the main ultraviolet radiation wavelength of 254 nm lies just within the absorption band of Et-PABA [32]. The UV process may cause photolysis of a part of Et-PABA because a small amount of active radicals such as \({}^{\bullet}\)OH may be generated in the water to indirectly oxidize the contaminants [41]; the degradation efficiency of Et-PABA using the PDS process alone was only 21.2%, because PDS can theoretically oxidize and degrade parts of the organic compounds. By contrast, under the same reaction conditions, the removal efficiency of Et-PABA using the UV/PDS combined process increased rapidly, and the degradation efficiency can reach 98.7% in 30 min. The synergy value was calculated according to Reference [42]. It was a positive value (0.918), which stands for a synergistic effect that may greatly improve the removal efficiency of Et-PABA. SO\({}_{4}\)\({}^{\bullet}\)-generated from the PDS activated by the ultraviolet light has a higher oxidation-reduction potential of E\({}_{0}\) = 2.60 V, and its strong oxidizing ability can transform organic matter into an excited state and thus, the organic contaminants can be removed from water efficiently via an oxidation reaction [43]. Moreover, Figure 2B shows that the UV/PDS process can also efficiently remove PABA, indicating the universality of a UV/PDS process for the removal of PABA-type UV filters in water. Particularly, the degradation efficiencies of Et-PABA and PABA using the UV process were slightly higher than that using the PDS process after a 20 min reaction. This may be due to the fact that if UV radiation is increased or the reaction time is extended, UV alone can also remove contaminants from the water,  
Figure 1: The apparatus used for the ethyl 4-aminobenzzoate (Et-PABA) degradation.  
while PDS does not. Therefore, as time goes on, the individual UV process exhibits a better removal efficiency than the PDS alone. These results were consistent with the results of the previous study [32].  
Subsequently, we also studied the removal efficiency of Et-PABA using PDS activated by heat energy. As shown in Figure 2C, the degradation rate did not increase significantly with the increase in the reaction temperature, mainly due to the relatively stable properties of PDS, thus PDS could not be activated by heat energy at low temperatures (25-45 \({}^{\circ}\)C) [44]. [PERSON] et al. [32] also found the UV/PDS process was a time efficient process, compared to the heat-activated PDS oxidation of methyl paraben.
## 4 Experimental Results
### Optimal K  
To understand the optimal \(K\), we have pre-defined different thresholds when applying the AIC rule. Figure 13 presents a plot of the average matching accuracy of Trajectory 6 with \(K\) values of 6-30 based on 25% training data; each \(K\) value was run 50 times. Choosing a larger \(K\) will increase the accuracy to some extent with the cost of adding computation complexity. Thus, we set \(K=7\) for computation simplicity purposes while maintaining reasonable localisation accuracy.