esa-sceva/satcom-chunk-collection huggingface dataset:
# Antenna Designs for 5G/IoT and Space Applications
###### Abstract  
Antenna design has received renewed attention in the last few years. This is thanks to an explosion of interest in a range of applications, from the Internet of Things, low frequency long-range applications to high frequency mmWave 5G mobile technologies. There has also been renewed interest in wearable antennas that form body area networks. These include wearable garments, as well as materials that directly attach themselves to skin, such as e-skin. In addition to this, a renewed interest in space and space exploration has renewed interest in satellite technologies and applications, such as CubeSats, intersatellite communications and deep space exploration. All these emerging applications bring a renewed interest in looking at special materials and new designs for antenna systems. This will bring new challenges in designing such antennas.  
For this purpose, this Special Issue is intended to shed some light on recent advances in antenna design for these new emerging applications and identify further research areas in this exciting field of communications technologies. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to:  
Antenna design for Internet of Things;  
Beamforming and smart antennas for 5G;  
Antenna design for wearable applications;  
Antenna design for body area networks;  
Antenna design for chipless RFID;  
Metamaterial-based antennas;  
Smart antennas, beamforming and MIMO;  
Aeronautical and space applications;  
Antenna design for CubeSat;  
Antenna design for deep space communication  
Antenna design for biomedical systems and applications;  
Implanted antennas;  
UWB and multispectral technologies and systems;  
MM-wave and THz antennas.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 5 Performance Evaluation
### Latency  
The main factors affecting the end-to-end delay in tree routing are the number of hops to the sink node and the queuing delay. The traditional tree routing chooses a parent node based on the number of hops to the sink node. This shows the best performance when the traffic bit rate is low. However, as the traffic bit rate increases, congestion occurs at a node with a small number of hops to the sink node, resulting in a sharp increase in end-to-end delay. To solve this problem, the linear weighted sum-based scheme considers the congestion metric with the number of hops in the decision-making process. As shown in Figure 6, since the weights between the metrics are the same in the linear weighted sum-based scheme, load distribution is not effectively performed as the traffic bit rate increases. This increases the queuing delay and thus it shows the worst performance. In addition, when the bit error rate increases, a node having a better link quality rather than having a lower number of hops can be selected as a parent node; hence, the end-to-end delay is slightly increased. In the MCMD-based scheme, load balancing is performed properly because the decision maker pre-determines the weight of the congestion metric by considering the network environment. However, when the bit error rate increases, a node with a better link quality rather than with a lower number of hops is selected as a parent node; hence, the end-to-end delay is slightly increased.  
As described above, in the MCMD-based scheme, the decision maker pre-determines the weight of each metric based on prior knowledge of the network environment. Thus, it performs well for pre-configured network scenarios, but cannot cope with changes in network conditions that occur at runtime. In contrast, in the proposed scheme, each node learns the optimal action according to the network environment through Q-learning. The  
Figure 5: Simulation results: packet delivery ratio. (**a**) Packet delivery ratio according to traffic bit rate; (**b**) Packet delivery ratio according to bit error rate.  
Figure 6: Simulation results: end-to-end delay. (**a**) End-to-end delay according to traffic bit rate; (**b**) End-to-end delay according to bit error rate.  
proposed scheme shows the best performance because it can adaptively cope with changes in network conditions.
## 3 SATis5 Testbed Architecture
Initially, the existing non-3 GPP based physical layer (e.g., DVB-S2x/RCS2 based) is used over the satellite, which allows faster satellite integration into 5G. However, work is currently ongoing in 3 GPP to investigate support for 5G New Radio (NR) air-interface over satellite [26; 27]. This would allow the SatRAN function to be fully 3 GPP compliant. With reference to Figures 4 and 7, note that the core network residing in the central node is used to operate the cellular access network at the remote site and is different from the SatCore, which is integrated within the satellite hub platform site and is used to operate the satellite backhaul network. The satellite network functions are virtualized by transferring their execution environment from a dedicated server to a Virtual Machine using the OpenStack Pike Virtualized Infrastructure Manager (VIM). Satellite Virtual Network Functions (VNF) includes the SatRAN software element, the satellite 3 GPP SatCore, the satellite Network Management System as well as additional auxiliary VNFs deployed on the same system using the OpenStack VIM. In terms of Management and Orchestration, the satellite hub platform system is integrated with the Open Source platform. With reference to ETSI TR 103 611 [28] and Figure 8 below, the integration scenario of satellite networks in the 5G system architecture, which is of interest here is the so-called \"Indirect Mixed 3 GPP Non Terrestrial Network Access\". In this integration scenario, 5G UEs are served by an access point. This access point is served by a trusted mixed 3 GPP Non-Terrestrial Networks (NTN) access network. UE management applies to the NTN enabled UE. The NTN enabled UE endorses a multiplexer node role. Another wording for this scenario could be \"Indirect 3 GPP NTN access with non-3 GPP L2, non-3 GPP L1\". In this case, there is indirect connection through a 5G satellite access network (bent-pipe satellite case) but with a mixed 3 GPP NTN access network instead of a 5G satellite access network (i.e., instead of a 3 GPP defined NTN 5G access). Figure 7: Satellite integration into 3 GPP architecture (Setup 1).
## 2 Material and Methods
### Feature Extraction and Selection
#### 2.2.2 Principal Component Analysis (PCA)  
Principal component analysis (PCA) is a statistical method that is generally used to reduce the dimensionality of high-dimensional data [41]. PCA projects the multidimensional data into a new reduced linear coordinate system using Singular Value Decomposition (SVD). The coordinates of this system represent the largest aggregate of variance within a time-series dataset and are useful for better visualization and interpretation of complex multivariable data. The dimensionality reduction helps to observe trends, clusters, and outliers within data [42; 43]. In the proposed study, principal components were selected based on the explained variance of the features. All features that had explained the proportion of variance exceeding 85% were selected for further analysis.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 3 Design of _uw_-Cellular Network
### Protocol Design
#### 3.3.2 DataLink Layer Protocol Design  
In this work, an on-demand scheduling based on reservation MAC protocol for hierarchical networks is designed. Channel resources can be reasonably scheduled according to the size of the data volume to be sent by each node. The protocol utilizes spatio-temporal characteristics to improve channel utilization while avoiding data conflicts. The protocol also incorporates an automatic retransmission mechanism to ensure the reliability of the data. The basic principle of protocol design is shown in the Figure 4.  
First, the base-station node sends a trigger (TR) packet to notify the terminal nodes within its transmission range to reserve the channel with RTS packets, and then waits for these RTS packets. Second, each terminal node which receives the TR packet as well as has DATA packets to send fills the RTS packet with the size of these packets and then sends the RTS packet. Next, the terminal node whose RTS packet is received by base-station node will be assigned some time slots for sending DATA packets. The CTS packet sent by the base-station node requests each terminal nodes who reserved the channel successfully to send DATA packets at the specified time. The base-station node will wait for the DATA packets until the time expired. Afterwards, the base-station node sends ACK packet as feedback to notify each node whether the data packets were received successfully or not. At last, if base-station node has DATA or CTRL packets to send, the packets would be sent  
Figure 4: Scheduling diagram of SDDA.  
Figure 3: OFDM data frame structure.  
following the ACK packet. And then the base-station sends TR packet to start a new round of data transmission.  
In _uw_-Cellular networks, which are designed as hierarchical network, the lower-level network BS nodes are not only responsible for collecting data from the terminal nodes within their coverage area, but also for transmitting data to the designated higher-level network BS nodes. Under the condition that all nodes can communicate with each other using only hydroacoustic, the low-level BS node needs to act as a terminal node of the high-level BS node and obey the scheduling of it. In the event that in some specific applications where the BS nodes of different levels are interconnected by cable, the scheduling between the lower-level and higher-level base station nodes does not need to be considered.
## 1 Introduction
The efforts in the online phase required many RSS time samples to be measured in the same position to reduce the effect of the multipath phenomenon. This kind of solution causes IPS to be slow and as such, is not appropriate for the tracking process. The main contributions of this article include: (1) Study the characteristics of Multiple SSID signals in 2.4 GHz, which can be configured on the same AP. (2) Propose a new algorithm based on MSSIDs to reduce the effect of multi-path issues by fusing MSSIDs signals, and computational costs by selecting informative APs. (3) Study the effect of informative AP numbers in K-means and Fuzzy C-means clustering technique. (4) Compare the proposed system performance with state-of-the-art methods. The rest of this article is arranged as follows: the related works are reviewed in Section 2. The fingerprinting technique is illustrated in Section 3. MSSID and the characteristics of MSSID signals are described in Section 4. The proposed system and measurement setup technique are illustrated in Sections 5 and 6, respectively. Section 7 shows the results and the discussion. In Section 8, the conclusion and future work are discussed.
## 3 Experiments and Results
### Case 1: Various Walking Speed Experiment
#### 3.2.2 Results  
The control performance of the proposed method is displayed. Figure 2(a),b shows the tracking trajectories of the leg joints. In the first 20 s, the walking gait cycle was approximately 2 s and the walking speed was approximately 0.5 m/s. The gait cycle was changed as 1.5 s for 18 s. Finally, the gait cycle was maintained for 1 s. Figure 2(c) shows the tracking errors of the leg joints. Despite the change in the walking speed, the trackingerrors of the joints do not changed significantly. Figure 3d shows the change in the control torque of the leg joints. Figure 3e shows the change in \(\hat{Y}\). Figure 3f shows the change in \(\hat{\tau}_{e}\).  
In Table 2, the tracking errors are statistically calculated for the two subjects. The mean errors (MEANs) of the tracking performance were 3.6\({}^{\circ}\) and 3.3\({}^{\circ}\), respectively. And the minimized mean square errors (MSEs) of the tracking performance were 2.3\({}^{\circ}\) and 2.8\({}^{\circ}\), respectively.
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 5 Discussion
### Architecture of an Energy-Efficient IoT Air Quality Monitoring System
Critical components are supported by Mini UPS devices, which allow operation during power outages. Additionally, the system can be powered by solar panels, especially in remote outdoor settings. Energy-saving algorithms are used to manage sensor and gateway behaviors dynamically, placing the devices into low-power or sleep modes when appropriate. The proposed system's low annual energy consumption (26.28 kW-h) contributes to reaching large-scale energy efficiency objectives by reducing the operational footprint compared to traditional systems (e.g., 100-150 W-h/day for LoRaWAN-based systems). Assuming the use of fossil fuel-based electricity (0.5 kg CO2/kW-h), this could yield an estimated CO2 reduction of ~13 kg/year per deployment, supporting smart city sustainability goals. Future research will quantify additional metrics, such as impacts on ozone-depleting substances, through life-cycle assessments. In summary, the proposed architecture enables the development of scalable and sustainable IoT systems for air quality monitoring. Through a combination of energy-aware hardware choices, communication protocols, and data handling strategies, the suggested architecture ensures low power consumption while maintaining high accuracy and responsiveness. The successful implementation of a distributed IoT air quality monitoring network requires not only the selection of the right sensors but also the following characteristics (Table 4):  
* Redundancy at all levels (power, communication, and storage);
* An adaptive network structure (mesh or clustering);
* Proactive diagnostics and calibration; and
* Flexible data processing that takes errors into account. The basis of our proposed energy-efficient indoor air quality monitoring system is the Smart Air sensor (Figure 3), which is an intelligent device used for the remote monitoring of the air environment. It provides accurate data on air pollution levels, temperature, and humidity, as well as the concentration of fine particles of PM2,5, PM10, NO2, and CO2. An example using a typical Smart Air sensor that is equipped with a low-energy microcontroller, PM2,5, PM10, NO2, CO2 sensors, a temperature and humidity sensor, and a wireless data transmission module can be found in Ref. [56]. A Zigbee gateway enables communication between different devices such as temperature sensors, lamps, and switches. This gateway collects information from the sensors, processes it, and transmits it to a server or cloud over a Wi-Fi network. Its role is critical for ensuring uninterrupted communication between the system components [57].
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Dataset Preprocessing and Augmentation
#### 3.2.1. Dataset Annotation  
In the context of financial data processing, dataset annotation is an important step, particularly in applications such as financial attack detection and stock price prediction. For financial transaction data, the focus is primarily on labeling transactions as normal or anomalous (fraudulent). Stock price data, on the other hand, needs to be labeled according to price trends within a specific time window, such as rising, falling, or stable.  
1. **Annotation of financial transaction data**: Based on historical records and background information of transactions, each transaction is labeled as \"normal\" or \"fraudulent\".
2. **Annotation of stock price data**: For stock price data, annotation is typically based on historical trends of price changes. For example, if the stock price in a given time window rises beyond a certain threshold, it might be labeled as \"rising\"; conversely, if the price falls below the threshold, it is labeled as \"falling\".
# Basic Approaches for Reducing Power Consumption in Finite State Machine Circuits--A Review
###### Abstract  
Methods for reducing power consumption in circuits of finite state machines (FSMs) are discussed in this review. The review outlines the main approaches to solving this problem that have been developed over the last 40 years. The main sources of power dissipation in CMOS circuits are shown; the static and dynamic components of this phenomenon are analyzed. The power consumption saving can be achieved by using coarse-grained methods common to all digital systems. These methods are based on voltage or/and clock frequency scaling. The review shows the main structural diagrams generated by the use of these methods when optimizing the power characteristics of FSM circuits. Also, there are various known fine-grained methods taking into account the specifics of both FSMs and logic elements used. Three groups of the fine-grained methods targeting FPGA-based FSM circuits are analyzed. These groups include clock gating, state assignment, and replacing look-up table (LUT) elements by embedded memory blocks (EMBs). The clock gating involves a separate or joint use of such approaches as the (1) decomposition of FSM inputs and (2) disabling FSM inputs. The aim of the power-saving state assignment is to reduce the switching activity of a resulting FSM circuit. The replacement of LUTs by EMBs allows a reduction in the power consumption due to a decrease in the number of FSM circuit elements and their interconnections. We hope that the review will help experts to use known methods and develop new ones for reducing power consumption. We think that a good knowledge and understanding of existing methods of reducing power consumption is a prerequisite for the development of new, more effective methods to solve this very important problem. Although the methods considered are mainly aimed at FPGA-based FSMs, they can be modified, if necessary, and used for the power consumption optimization of FSM circuits implemented with other logic elements.  
FPGAs; LUTs; EMBs; clock gating; decomposition; state assignment; coarse-grained methods; fine-grained methods +
Footnote †: journal: Applied sciences
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 4 Experiments and Analysis  
### Sun Glare Dataset  
The sun glare dataset [33] includes images captured by different cameras, compiled by gathering sample images from the World Wide Web and annotating the glare areas in the input images. The terms \"glare\" and \"sun glare\" were used to search for images online. A labeling team then identified and annotated these images, marking the glare regions in the ground truth binary images. Another group verified these masks to ensure the accuracy of the labels. In total, 200 images were collected and labeled using this method. The public dataset was accessed on 12 April 2024 at [[https://github.com/maesfahani/glaredetection](https://github.com/maesfahani/glaredetection)]([https://github.com/maesfahani/glaredetection](https://github.com/maesfahani/glaredetection)).  
After downloading this dataset, we performed some preprocessing steps. First, we resized all images and masks into \(256\times 256\) dimensions to respect specific input size requirements in deep learning models. Then, we augmented the number of images and masks from 200 to 1200 using various transformations such as random rotation, horizontal flip, and vertical flip.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 2 System Architecture
### Key Parameters Analysis
#### 2.1.4 The Average Number of Working Switch Elements  
The number of switch elements in the structure only reflects the control complexity in static. The number of working switch elements provides a more visual and dynamic representation of the control complexity--the number of working switch elements changes when the working mode changes. The average number of working switch elements is more comparable and calculated by dividing the sum of the number of switch elements working in each mode by the total number of working modes. As mentioned before, the only difference between the structure DP and EP is the combiner-switch parts. Thus, the total number of the working switch in combiner-switch parts can be compared directly. The number of working switches \(S\) in the structure DP can be calculated by the following equation:  
\[\begin{array}{l}S=\log_{2}(1+\log_{2}(M))\cdot(1+\log_{2}(M))\\ +\sum\limits_{m=1}^{\log_{2}M-1}2^{m-1}\cdot\log_{2}(\log_{2}(M)-m+1)\cdot(\log _{2}(M)-m+1)\end{array} \tag{1}\]  
meanwhile, the number in the structure EP is \(2M-2  
switch elements, the structure EP will be focused on in the following. The analyses and simulations were performed based on it.
## 3 Results
### Vegetation Bias
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline  & **Mean** & **STD\({}^{\star}\)** & **MAD** & **Q1** & **Q2(MED)** & **Q3** & **Points with Absolute Difference (\%)** \\ \cline{2-10}  & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & \(\leq\)5 m** & \(\leq\)**10 m** & \(\leq\)**15 m** & \(\leq\)**20 m** \\ \hline \multicolumn{10}{c}{LVIS2019: 5,195,224 points} \\ \hline NASADEM & –5.6 & 9.3 & 5.4 & –11.7 & –3.6 & 0.7 & 50\% & 69\% & 82\% & 91\% \\ NASADEM\_VB1 & 5.6 & 8.9 & 4.7 & 0.1 & 3.6 & 10.0 & 48\% & 72\% & 86\% & 93\% \\ NASADEM\_VB2 & 6.9 & 9.1 & 5.0 & 0.7 & 5.2 & 11.7 & 42\% & 67\% & 83\% & 92\% \\ NASADEM\_VB3 & 0.9 & 7.9 & 3.7 & –3.2 & 0.8 & 4.3 & 59\% & 83\% & 93\% & 97\% \\ NASADEM\_VB4 & 1.7 & 8.0 & 3.9 & –2.6 & 1.4 & 5.3 & 57\% & 82\% & 92\% & 97\% \\ \hline \multicolumn{10}{c}{LVIS2005: 9,280,657 points} \\ \hline NASADEM & –7.2 & 9.5 & 4.9 & –12.4 & –4.5 & –0.3 & 49\% & 68\% & 80\% & 89\% \\ NASADEM\_VB1 & 4.2 & 8.7 & 4.6 & –0.8 & 2.7 & 9.1 & 51\% & 74\% & 88\% & 94\% \\ NASADEM\_VB2 & 5.7 & 8.9 & 5.0 & 0.2 & 4.5 & 10.8 & 45\% & 70\% & 85\% & 93\% \\ NASADEM\_VB3 & –0.5 & 7.9 & 3.7 & –4.0 & 0.1 & 3.4 & 60\% & 84\% & 93\% & 97\% \\ NASADEM\_VB4 & 0.3 & 8.0 & 3.8 & –3.2 & 0.4 & 4.5 & 59\% & 83\% & 93\% & 97\% \\ \hline \multicolumn{10}{c}{LVIS1998: 2,570,730 points} \\ \hline NASADEM & –13.6 & 9.9 & 7.3 & –19.7 & –12.8 & –5.2 & 24\% & 39\% & 58\% & 76\% \\ NASADEM\_VB1 & 4.4 & 9.0 & 5.3 & –0.5 & 4.5 & 10.1 & 41\% & 69\% & 87\% & 95\% \\ NASADEM\_VB2 & 5.4 & 9.1 & 5.4 & 0.4 & 5.8 & 11.2 & 36\% & 65\% & 85\% & 94\% \\ NASADEM\_VB3 & –3.1 & 8.5 & 4.7 & –7.4 & –1.7 & 2.3 & 53\% & 79\% & 91\% & 96\% \\ NASADEM\_VB4 & –2.5 & 8.7 & 4.9 & –7.0 & –1.1 & 3.2 & 51\% & 79\% & 91\% & 96\% \\ \hline \end{tabular}
\end{table} 
Table 3: Statistics of the differences between LVIS data from 2019, 2005, and 1998 and heights interpolated for original NASADEM and NASADEM corrected by four vegetable bias models. The statistics is provided in terms of the mean difference, Standard Deviation (STD\({}^{\star}\)) for differences without outliers larger than \(\pm\)50 m, Median Absolute Difference (MAD), 25 th empirical quartile Q1, the Median (MED) or the 50 th empirical quartile Q2, the 75 th empirical quartile Q3, and percentages of points having absolute differences below certain thresholds.