esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Collection
### Geometric-Functional Road Characteristics  
Data on the geometric and functional characteristics of the 20 km section of existing road were collected. In particular, the examined road has one carriageway with one lane per direction. The procedure for recording data on existing road characteristics is based on the adoption of specific software to collect geo-referenced information on the geometric-functional characteristics of each element. In the absence of a complete database of infrastructure characteristics, these were determined and entered into the software pseudo-manually.  
As a first step, the road was divided into intersections and segments, which were defined between the center of successive intersections. In addition, each segment was subdivided into smaller homogeneous sections. Segments were classified into curves and straight lines, while intersections were classified according to the regulation method and the number of legs. Figure 1a represents the location of the analyzed section of the existing road.  
Road redesign was planned to improve traffic flow and safety on the road concerned. In particular, the construction of five tunnel bypasses and the rehabilitation of the existing route was planned. The five bypasses had lengths, respectively of 2200 m, 1050 m, 320 m, 1155 m, and 1800 m and were developed in the five sections shown in Figure 1b. The construction of these bypasses will create alternative paths to the existing route and divert traffic flow away from residential areas. This solution will reduce vehicle conflicts associated with the numerous intersections and driveways on the existing route. In fact, there are no crossings or junctions along the entire length of the tunnel bypasses.  
At the same time, the rehabilitation of the existing route improves safety by reducing planimetric curvature and reorganizing the road platform and accesses, thereby enhancing the intrinsic safety characteristics of the road.  
The necessary information on geometric-functional road characteristics is collected both for the existing road path and for the redesigned layout. The gathered data are divided between segments and intersections, as summarized in Table 2. These data are needed for the subsequent calculation of the Crash Modification Factors (CMFs), which will be discussed in Section 5.4.  
Figure 1: Location of analyzed road: (**a**) existing road and traffic sections; (**b**) redesigned road.
## 2 Methods
### OCT Image Analysis  
Cross-sectional OCT images were analysed at 1-mm intervals and OCT analysis was performed by an independent investigator blinded to patient and procedural information at the intravascular image core laboratory in Chung-Ang University Hospital. OCT measurements and definitions have been based on previous studies [20; 21]. Stent and luminal areas in every image were measured and mean values are reported in this study. Intra-stent thrombus was defined as a protruding mass in-between or over stent struts (Figure 1A). A malapposed strut was defined as a strut that has detached more than 120 \(\upmu\)m from the vessel wall, as the strut thickness of BP-BES is 120 \(\upmu\)m (Figure 1B,C). Neointimal hyperplasia (NIH) area was calculated as the stent area minus the luminal area (Figure 1D). Percent NIH area was calculated as NIH area \(\times\) 100/stent area. The NIH thickness was defined as the distance between the luminal surface of the covering tissue and the luminal surface of the strut. An uncovered strut was defined as having the NIH thickness of 0 \(\upmu\)m (Figure 1E,F). The percentage of malapposed or uncovered struts was calculated as the (malapposed or uncovered struts/total number of struts in each stent) \(\times\) 100, respectively.  
Figure 1: Representative images regarding the OCT analysis method. (**A**) Cross-sectional postimplantation OCT image showing intrastent white thrombus (white circle) and tissue prolapse. (**B**) OCT image showing the apposed and malapposed struts after stent implantation. (**C**) Zoomed-in image of the white box in Figure 1B demonstrating an apposed strut (a) and malapposed struts (b and c). (**D**) Follow-up OCT image demonstrating NIH area of 0.94 mm\({}^{2}\), defined as the stent area (13.50 mm\({}^{2}\)) minus the luminal area (12.56 mm\({}^{2}\)). (**E**) Follow-up OCT image showing the covered and uncovered struts. (**F**) Zoomed-in image of the white box in Figure 1E demonstrating an uncovered strut (a) and covered struts (b, c, and d). Abbreviations: OCT, optical coherence tomography; NIH, neointimal hyperplasia.
## 4 Performance Evaluation
### Impact of Caching Time  
Figure 5 studies the impact of the proposed multiuser access control algorithm on the user access probability and the delay violation probability for the accessed users under different video caching time \(D_{1}\) settings. The simulation results in the figure align with  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Default Value** \\ \hline \hline Tile number in the vertical and horizontal directions in a & (8, 4) \\ projected video plane & 1.5 \\ Tile number in the vertical or horizontal direction in the FoV & 1.5 \\ Frame number per second of the video chunk & 1.5 \\ UDP packet size & 1.5 \\ Data size per tile per frame & 1.5 \\ Data size processed per second by the MEC server & 1.5 \\ Data size processed per second by the user & 1.5 \\ Data size changing ratio after computing offloading & 1.5 \\ Compression ratio of the video chunk & 1.5 \\ Typical actual caching time & 0.5 s \\ Preset E2E delay threshold & 0.02 s \\ Delay violation probability threshold & 0.02 \\ Transmission rate from the cloud server to the MEC server & [0,160] Mbps, \\ Transmit power of the MEC server-connected BS & 0.5 W \\ Bandwidth of the MEC server-connected BS & 100 MHz \\ Distance between the users and the BS & [15,20] m, uniform distribution \\ Path loss exponent & 3.5 \\ Noise power spectrum density & \(-\)174 dBm/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
the numerical results, indicating that the performance analysis provided in the scheme design is accurate. As can be seen from the figure, when the caching time \(D_{1}\) increases from 0.5 s to 0.65 s, the amount of data that need to be transmitted and computed within each period increases, leading to a slight increase in the delay violation probability. However, the proposed multiuser access control algorithm strictly ensures that the delay violation probability for the accessed users is kept within the required E2E delay threshold. Similarly, as \(D_{1}\) increases, the system's user access probability also decreases.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### Existing ML-Based approaches
#### 8.2.4 Federated Learning
The different approaches mentioned above share a common challenge related to the high number of transmissions required for the BS or the aggregator to broadcast the parameters of the developed model with the rest of the nodes in the WSN, which introduces a different communication overhead that requires high energy consumption [84; 85].
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.4 Sensor Node Platform for Agriculture Monitoring  
The node proposed by [23] was explicitly designed for agriculture-monitoring applications. The module is built around an ATmega328P-PU microcontroller with 2 kB RAM, 1 kB EEPROM and 32 kB in-system self-programmable flash memory. It can achieve 16 MIPS at 16 MHz clock speed. Communication by the proposed node was realized using the CC1101 transceiver from Texas Instruments operating in the 300-348 MHz, 387-464 MHz and 779-928 MHz frequency bands. In the 868/915 MHz band, it is capable of transmittingat a maximum power of 12 dBm. The data rate is adjustable from 0.6 to 600 kbps. Communication ranges of over 400 m are achievable by the node. The hardware was developed around a panStamp NRG2 module.  
An on-board Bosch Sensortec BME280 environmental sensor is utilized for measuring temperature, humidity and air pressure. The proposed mote can accommodate additional sensors for agriculture-monitoring purposes. The node is powered by a series connection of two standard AAA alkaline batteries that supply 3 V. Energy harvesting systems can be easily incorporated to the mote. Regarding power consumption, the sensor mote consumes 1.1 uA during sleep mode. The average current consumption during transmission is approximately 17.5 mA at 0 dBm and around 30.1 mA at 12 dBm.
## 1 Introduction
It is worth noting that machine learning has been increasingly utilized across various disciplines for its ability to enhance predictive performance [20]. Spectroscopy combined with linear discriminant analysis is applied in the authentication of leafy greens, as LDA has demonstrated satisfactory classification outcomes [12, 16]. Given the extensive range of the VIS-NIR spectrum, it potentially elevates computational complexity and may influence the outcomes of LDA classification. To tackle this concern, genetic algorithms are utilized to meticulously select the most informative wavelengths from the vast pool of 2101 available wavelengths of the VIS-NIR spectrum [21]. Nonetheless, when the number of selected wavelengths is set limited, GA is easily influenced by individual wavelengths and may obtain a local solution, and the selected wavelengths are redundant when the number is set excessively large, which is not what we wanted [22]. Considering these issues, the number is set to 30 in this study, and then stability selection, random forest algorithm, and analysis of variance methods are used to evaluate the importance of each wavelength selected by GA [23, 24]. By analyzing the GA-selected wavelengths, their corresponding importance values, and their specific locations,we can identify a subset of suitable bands for classification purposes, thus obviating the need for utilizing the entire VIS-NIR spectrum. In this study, we present a viable solution to the issue of cost associated with VIS-NIR spectrometry in leafy vegetables authentication and achieve rapid, non-destructive, and precise discrimination between various types of organic and non-organic leafy vegetables. The selected 550-910 nm and 1380-1500 nm wavelengths used in the classification of the leaf spectra and the selected 750-900 nm and 1700-1820 nm wavelengths used in the classification of the stem spectra achieved accuracies and f1 scores of over 98%.
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 6 Conclusions  
Today, high performance and energy efficiency are required in many scenarios from consumer electronics devices to datacenters. This trend has led to the development of emerging storage devices, called ZNS SSDs. In this paper, we briefly explored the internal architectures and behaviors of ZNS SSDs. We also proposed an energy and performance-optimal mapping mechanism for ZNS SSDs, called ReZNS. ReZNS adopts a renewable concept into ZNS SSDs by reusing the remaining capacity inside each zone. In addition, it was designed to have application-level transparency; it requires no change to application codes, and applications never know how ReZNS works. We built a prototype of ReZNS on the NVMeVirt [28] emulator and extensively evaluated ReZNS via different benchmarks from both performance and energy perspectives. Experimental results with eight different workloads showed that ReZNS improves overall performance by up to 60% compared with the baseline. Moreover, it can save up to 64% of the power consumed by zone-reset commands inside ZNS SSDs. Finally, we believe that ReZNS will be widely accepted in a variety of use cases because it provides transparency, low energy consumption, and high performance. Especially, since various consumer electronics adopt NAND flash-based storage devices, such as eMMC and UFS, ReZNS creates new opportunities to prolong their lifespans. In future work, we seek to explore the possibility that data can be scattered across multiple zones. We also want to explore explicit scenarios to minimize energy consumption.  
**Author Contributions:** Conceptualization, C.L. and D.K.; methodology, C.L.; software, C.L.; validation, C.L. and D.K.; formal analysis, C.L. and D.K.; investigation, C.L. and D.K.; resources, C.L., H.K. and D.K.; data curation, C.L.; writing--original draft preparation, C.L. and D.K.; writing--review and editing, D.A. and D.K.; visualization, C.L., S.L., G.M. and D.K.; supervision, D.A and D.K.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Gachon University research fund of 2022 (GCU-202300690001) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2023-00251730).
## 2 Quality Assessment of COSMIC-2 GPS Observations
### Cycle Slip Ratio  
The cycle slip ratio is the ratio of the total number of actual observations to the number of cycle slips, which is used to explain the cycle slip of observation data. The satellite receiver is moving at high-speed. Due to the fast speed of satellite movement, the poor space environment of the satellite receiver, the influence of the multipath effect and other reasons, it will cause the discontinuity of observation data or the loss of lock of GPS signal, resulting in a cycle slip of observation data [26]. The larger the cycle slip ratio is, the less the cycle slip occurs in the data, and vice versa. The statistical information of the cycle slip ratio of the observation of six COSMIC-2 satellites is listed in Table 3.  
Table 3 indicates that the average cycle slip ratio of DOY 138-142 is 8 or 9, and the cycle slip ratio of six COSMIC-2 satellites is generally lower than that of the receiver on the surface of the Earth. Sat-2 and Sat-3 own the highest cycle slip ratio among the six COSMIC-2 satellites, with a ratio of 10, and Sat-6 owns the lowest one, with a ratio of 7. The above analysis shows that the frequency of cycle slips of COSMIC-2 during the observation period is large, and the number of cycle slips is large.
## 2 System Modeling  
### Experimental Setup  
The experimental setup consists of a PLC, a 1 kW brushed synchronous generator (SG), a frequency converter (FC), a 4 kW asynchronous motor, an encoder, an analog DC converter, two DC power supplies (24 V and 72 V), three current transformers (CTs), and 6 contactors. Figure 1 shows the experimental setup used in the study.  
SG is driven by an asynchronous (induction) motor that is mechanically coupled to its shaft. The asynchronous motor is powered by the frequency converter (drive), which controls its speed and, consequently, the frequency of the SG. The control device used in the system is an S7-1215C DC/DC/DC PLC. The system can be monitored and controlled through software and SCADA within the TIA Portal interface.  
An AI/AQ module (SM1234) and an Energy Meter module (SM1238) are integrated into the PLC. The AQ module regulates the frequency and voltage of SG via the drive and DC converter, while the Energy Meter module measures all electrical parameters of the SG, including phase-to-phase voltages, phase-to-neutral voltages, power factors, active power, apparent power, reactive power, etc.  
The PLC and encoder operate on a 24 V DC power supply, while the rotor (field) windings of the SG use a separate 0-85 V DC supply. Moreover, the system includes an incremental rotary encoder with a PPR value of 1024, mechanically coupled to the SG shaft, which measures the speed of the generator to regulate its frequency. Current transformers facilitate the electrical connection between the SG and the energy meter, while contractors enable the connection and disconnection of various load groups. In the experimental setup, the nominal values of the SGs are as follows:  
* Power: 1 kW;
* Voltage: 380 V AC (220 V phase-neutral);
* Frequency: 50 Hz;
* Speed: 1500 rpm;
* Excitation current: 2.1 A DC
* Excitation voltage: 72 V DC;
* Current: 2.3 A AC;  
The system components are shown in Figure 2.  
Figure 1: Experimental setup.
# A Comparative Analysis of Arabic Text Steganography
## 3 Text Steganography
### Random and Statistical Generation  
This class generates a cover item based on statistical properties by considering word and character sequences. Sometimes, the created stego text attracts a person who intercepts the message by appearing as a random sequence of words/characters.  
As an example in this category, the structure of the Omega network integrated with part-of-speech (POS) in [36] by substituting \"verb from cover\" with \"verb from secret\" and \"noun of cover\" by \"noun of secret.\" Besides, letter frequency and word length were two of the statistical properties used by the authors of [37] to create a stego word using the actual dictionary items and a codebook of mappings between bit sequences and lexical items. Table 2 shows an example of Random and Statistical Generation. The stego words consist of the repetitions of the three letters 'a,' 'r,' and 'd' in an indecipherable way. As a result, the generated file in this approach is incomprehensible and raise suspicions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Secret Message** & **Cover Text** & **Stego Text** \\ \hline \multirow{3}{*}{**A**} & \multirow{3}{*}{“abaca”} & “aard
## 5 Evaluations
### Performance Evaluation  
The performance of the MPFRS-FC and APCRS-FC algorithms are evaluated in comparison with SPRF-EtoED, LBF-EtoED routing, and scheduling techniques. These techniques are adapted to support no-waiting scheduling. The comparison is conducted in terms of the success rate, execution time, and the sum of end-to-end delays. In the following, 12 experiments consisting of 100 test cases are presented.  
Figure 5: The overall workflow of simulation.  
All success rate results are shown in Figure 6. The orange part of the bars indicates optimized solution, the blue part indicates timeout terminations, and the green part indicates infeasible. In general, when the flow scale is small and the network scale is small, the four technologies can achieve a high scheduling success rate. As the flow scale increases, the flow correlation decreases, the percentage of blue (timeout) cases for the LBF-EtoED algorithm gradually increases. The MPFRS-FC algorithm has the least number of timeouts, i.e., it always gives a clear scheduling result within the range of time limit. In particular, it demonstrates better scheduling performance than the APCRS-FC, SPRF-EtoED, and LBF-EtoED algorithms in highly connected network topologies shown in Figure 6d. Nevertheless, in a relatively simple network topology scenario, the scheduling performance of the APCRS-FC algorithm is optimal. For a more detailed discussion on the algorithm performances, we cover the following points in detail: the impact of traffic load, traffic type, network load, and network type on the performance, respectively.
## 4 Data-Driven CIM Construction
### Example of Data-Driven CIM Construction
#### 4.3.3 Smart City Brain  
The Smart City Brain represents the convergence of internet brain architecture and smart city development, creating a city-level, intelligent, and highly complex system. Built upon the Internet as its foundational infrastructure, it leverages vast urban data resources to conduct real-time, holistic analyses of citywide operations. By optimizing the allocation of public resources and continuously refining social governance, the Smart City Brain plays a key role in advancing sustainable urban development [70; 71; 72]. For example, in Hangzhou, China, the city's Smart City Brain integrates diverse data sources such as traffic surveillance, environmental monitoring, and public security systems. Through advanced AI and data analytics, the city can optimize the traffic flow by dynamically adjusting signal timings, reducing congestion, and improving the air quality. The system also supports real-time crime analysis, helping law enforcement respond swiftly to incidents. By integrating these functionalities into one platform, Hangzhou enhances its urban management efficiency, while improving the daily lives of its residents.  
To achieve intelligent urban operations and optimize urban management systems, the Smart City Brain harnesses cutting-edge information technologies, artificial intelligence, and data analytics. It processes data from sensors, surveillance cameras, social media, and other sources, performing comprehensive analyses across critical domains like transportation, energy management, environmental monitoring, and public security [29; 73]. The system empowers urban managers with data-driven decision-making tools and improved management strategies. For instance, in Singapore, the Smart City Brain uses data analytics to monitor the energy consumption across the city, identifying inefficiencies and suggesting targeted improvements to reduce waste. It also integrates environmental data to forecast pollution levels, helping the authorities take preventive actions.  
In summary, the Smart City Brain provides a framework for intelligent, coordinated urban management by enhancing the operational efficiency, optimizing resource allocation, and improving residents' quality of life. The integration of diverse data sources allows cities to operate more sustainably and efficiently, offering a holistic approach to urban governance.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 5 Results
### Hardware Implementation Challenges and Comparison with Related Work
On the other hand, the proposed hardware implementation of the FFT excision filtering algorithm works with high-power CW interference while maintaining the desired \((\ C/No)\cong 40\) dB. The proposed HDDM uses multiple frequency bands with different windowing functions; hence, this results in the use of more hardware resources. This appears to be reflected in their resource allocation, using 13,625 LUTs compared to 1600 in our 22-bits (fixed-point) design and 159 DSPs compared to 40 in our proposed design.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 2 Methodology
### Spatiotemporal Graph Attention (ST-GAT) Model  
The constructed ST-GAT model is composed of two key parts. The first is the graph attention layer, which is responsible for capturing spatial relationships among monitoring points. The second is the temporal layer, which models time-dependent patterns and sequences in the data, tracking changes over time.