esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics
#### 4.2.6 Optimal Configurations for Mean Average Precision (mAP)  
Table 8 shows the configurations optimizing mAP, a detection metric that averages precision across recall levels, for the seven models. ResNet101 leads with a mAP of 0.992104 using SGD (learning rate 0.01), StepLR, and CrossEntropy, its depth capturing intricate features for detection, stabilized by momentum and periodic learning rate drops. ResNet50 follows at 0.991379 with SGD (learning rate 0.01), CosineAnnealingWarmRestarts, and CrossEntropy, where cyclical learning enhances generalization. DenseNet121 achieves 0.989826 with SGD (learning rate 0.01), CosineAnnealingWarmRestarts, and FocalLoss, leveraging FocalLoss to address hard examples.  
EfficientNet-B2 (0.989524) uses AdamW (learning rate 0.0001), StepLR, and LabelSmoothing, combining adaptive optimization with regularization. ConvNeXt (0.986675) and ViT-B-16 (0.985686) employ SGD (learning rate 0.001) with CosineAnnealingLR and ReduceLROnPlateau, respectively, and LabelSmoothing, suggesting regularization aids modern architectures. MobileNetV3 (0.98276) uses SGD (learning rate 0.01), StepLR, and CrossEntropy, performing competitively despite its lightweight design.  
mAP scores cluster tightly between 0.98276 and 0.992104, indicating robust detection capabilities across models, with convolutional models slightly ahead of transformers.
# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 2 System Overview
### NFTs Image Dataset  
In this work, a publicly available NFT-Classifier dataset from Kaggle was used [25]. The dataset includes three of the most popular NFTs collections available, known as art categories BAYC (Bored Ape Yacht Club), Crypto Punks, and Azuki. A total of 3000 photos in various sizes and aspect ratios are included. The 24 \(\times\) 24 pixel size and 2000 \(\times\) 2000 pixel size, respectively, make up the smallest and largest images. There are an equal number of images in each collection category in the dataset utilized in this study. This harmony facilitates model training by preventing the model from becoming biased toward a certain class [26]. Furthermore, the dataset that NFT-inspired works of art in an image file format (.png). Only the image files were used in this work. The dataset was divided into 70% training and 30% validation portions, with all images resized to a size of 224 by 224. Figure 2 shows sample images of the NFT dataset used in this paper.  
Figure 1: Proposed solution scheme of NFT plagiarism checker.  
To train the model using NFT images, we first collect a dataset of NFT images from the above open-access resources along with their associated metadata, including file size, format, and structure. Next, the images are preprocessed by resizing them to a fixed size of 224 by 224 and standardizing their pixel values. For the model architecture, we employ four different models: ResNet50, DenseNet, MobileNetV2, and EfficientNetB0. Each model is trained separately using the preprocessed images and their associated metadata. During training, we use a triplet semi-hard loss function for all models, which encourages the model to differentiate between similar and dissimilar NFT images. The compared models are trained using the Adam optimizer with a learning rate of 0.001 and a batch size of 64 over 100 epochs, along with other hyperparameters mentioned in Table 1.
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods
### MHAM Algorithm  
#### 2.3.1 Algorithm Description  
The design of the MHAM algorithm builds upon the spectral characteristics of different pixels, for example, the reflectance of the cloud pixels in the visible band is greater than that of the clear sky pixels, while the brightness temperature in the infrared channel is usually lower than that of the clear sky and haze pixels, and the brightness temperature difference is also higher than that of the clear sky and haze pixels. The reflectivity ratio of the near-infrared channel and the visible channel (such as the ratio of R\({}_{0.865}\) and R\({}_{0.65}\)) has different values in different underlying surfaces (Zhou, 2022; Yang, 2022). The above characteristics were taken as the prior knowledge of this algorithm, and the adaptive improvements to the threshold selection were carried out for the MERSI instrument. Based on the selected MERSI bands, the tests were divided into two groups to distinguish the cloud, clear and haze pixels from each other. The flowchart of the algorithm is shown in Figure 2.  
Firstly, the DN (data number) values at different bands were extracted from the MERSI-II L1 data and converted to the apparent reflectance of the top of the atmosphere (TOA) at visible wavelengths or the brightness temperature at the infrared bands. The geolocation information and observed angles were extracted from the dataset. It must be noted that the MERSI Level 1 data at 1 km instead of those at 250 m were used in this analysis. Secondly, the ice/snow pixels were filtered out using the normalized difference snow index (NDSI), which was calculated using the apparent reflectance values at 0.55 \(\upmu\)m (R\({}_{0.55}\)) and 1.64 \(\upmu\)m (R\({}_{1.64}\)) for Landsat or R\({}_{0.55}\) and R\({}_{2.12}\) for MYD35 or R\({}_{0.865}\) and R\({}_{1.24}\) for MXD04 DT. Here, the ratio of the difference between R\({}_{0.55}\) and R\({}_{1.64}\) to the sum of R\({}_{0.55}\) and R\({}_{1.64}\) was taken as the NDSI. When both NDSI \(>\) 0.4 and R\({}_{0.865}\) \(>\) 0.1 were true, the pixel was  
Figure 2: Flowchart of the MHAM algorithm using FY3D/MERSI-II observations.  
treated as it was ice/snow. The inland water was identified by the combined conditions of the NDVI and R\({}_{2.13}\). For the remaining pixels, R\({}_{0.65}\), BT\({}_{11}\) and the standard derivation of R\({}_{0.47}\) were used together for distinguishing the cloud pixels. Based on the binary tree method, the cloud and non-cloud pixels were, respectively, determined. Combined with the actual characteristics of the MERSI data, clear pixels over brighter surfaces can be capture accurately by introducing the variation between R\({}_{0.865}\) and R\({}_{1.64}\). In addition to the lower reflectance and higher brightness temperature values of the clear areas, the clear pixels and the remaining haze regions can be separated. More details about the conditions are summarized in Table 2.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
###### Abstract  
Along with the rapid development of the Global Navigation Satellite System (GNSS), satellite navigation signals have become more diversified, complex, and agile in adapting to increasing market demands. Various techniques have been developed for processing multiple navigation signals to achieve better performance in terms of accuracy, sensitivity, and robustness. This paper focuses on a technique for processing two signals with separate but adjacent center frequencies, such as B1I and B1C signals in the BeiDou global system. The two signals may differ in modulation scheme, power, and initial phase relation and can be processed independently by user receivers; however, the propagation delays of the two signals from a satellite are nearly identical as they are modulated on adjacent frequencies, share the same reference clock, and undergo nearly identical propagation paths to the receiver, resulting in strong coherence between the two signals. Joint processing of these signals can achieve optimal measurement performance due to the increased Gabor bandwidth and power. In this paper, we propose a universal scheme of asymmetric dual-band tracking (ASYM-DBT) to take advantage of the strong coherence, the increased Gabor bandwidth, and power of the two signals in achieving much-reduced thermal noise and more accurate ranging results when compared with the traditional single-band algorithm.  
Keywords:BeiDou Navigation Satellite System; ASYM Dual-Band Tracking; tracking channel architecture; Gabor Bandwidth +
Footnote †: journal: Sensors
# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 3 Techniques
### Recommender Systems  
Recommender systems collect information on the users' preferences for a set of elements (e.g., books, applications, websites, travel destinations, e-learning material, etc.). In the context of students' performance, the information can be acquired explicitly (by collecting users' scores) or implicitly (by monitoring users' behaviour, such as visits to teaching materials, documents downloaded, etc) [69]. RS consider different sources of information to provide predictions and recommendations. They try to balance factors such as precision, novelty, dispersion and stability in recommendations.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
###### Abstract  
The Fifth Generation of Mobile Communications (5G) will lead to the growth of use cases demanding higher capacity and a enhanced data rate, a lower latency, and a more flexible and scalable network able to offer better user Quality of Experience (QoE). The Internet of Things (IoT) is one of these use cases. It has been spreading in the recent past few years, and it covers a wider range of possible application scenarios, such as smart city, smart factory, and smart agriculture, among many others. However, the limitations of the terrestrial network hinder the deployment of IoT devices and services. Besides, the existence of a plethora of different solutions (short vs. long range, commercialized vs. standardized, etc.), each of them based on different communication protocols and, in some cases, on different access infrastructures, makes the integration among them and with the upcoming 5G infrastructure more difficult. This paper discusses the huge set of IoT solutions available or still under standardization that will need to be integrated in the 5G framework. UAVs and satellites will be proposed as possible solutions to ease this integration, overcoming the limitations of the terrestrial infrastructure, such as the limited covered areas and the densification of the number of IoT devices per square kilometer.  
IoT; 5G; UAV; satellite communications +
Footnote †: journal: Sensors
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 2 Structural Principle and Design Requirements  
### Structure of Machine and Seed Metering Device  
As shown in Figure 1a, the self-propelled industrial hemp seeder was mainly composed of a Dynamic chassis system, Seeding and fertilizing system, Ditch seeding system,and control systems. The matching power of the seeder is 6.3 kW, the weight of the whole machine is 350 kg, the breadth is 1.1 m, it can sow 3-6 rows, the seed depth is 30-80 mm, it can open the trench 3-6 rows, the working speed is 2-5 km/h, the total of seeding quantity is 57.6-62.4 kg/hm\({}^{2}\), Fertilizer application rate is 50-125 kg/hm\({}^{2}\), rear-wheel drive seeding operation, compact machine structure, light and compact weight, small turning radius, can complete the furrowing, sowing, fertilization, and other processes.  
Figure 1b shows the metering and fertilization system of the self-propelled industrial hemp seeder, mainly composed of a fertilizer box, fertilizer applicator, seed applicator, fertilizer applicator shaft, and seed applicator wheel. The power is input into the metering and fertilization system through the power chassis system to drive the seed applicator and fertilizer applicator to work and realize the seeding function. According to the agronomic requirements of the hemp fiber industry in mountainous areas, the metering mechanism adopts the hole wheel metering device, which can install 3-6 metering devices. Each metering device is installed parallel through the metering shaft, convenient for adjusting the row spacing. The seed metering shaft and fertilizer metering shaft are chain transmissions to ensure synchronous seed metering and fertilization operation.  
As the core component of the metering and fertilization system, the working performance of the metering device directly determines the working quality of the seeder. The type hole wheel seed metering device mainly comprises a box cover, seed box, flexible baffle, inoculation cup, seed cleaning brush, seed protector, and seed metering wheel, as shown in Figure 2. There are several shaped holes in the radial direction of the metering wheel that are concentrically coordinated with the metering shaft. The brush and seed protection plate connect with the shell thread through the nut.  
Figure 1: Self-propelled industrial hemp seeder. (**a**) Whole structure (**b**) Seeding and fertilizing system.  
Figure 2: Type hole wheel seed metering device.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 1 Introduction
The properties and applications of the obtained nanostructured thin films will be discussed. \begin{table}
\begin{tabular}{c c c} \hline
**Deposition Method** & **Benefits** & **Disadvantages** \\ \hline Spray pyrolysis & Low-cost deposition method, high growth rate & Very complicated process; low yield \\ \hline Chemical bath deposition & Cheaper and simple method; deposition process & Wastage of solution after the deposition process \\  & requires substrate and solution in the container & \\ \hline Electron beam evaporation & Multiple thin films can be produced & The non-uniform evaporating rate could be seen \\  & & due to filament degradation \\ \hline Electrodeposition method & Cheaper deposition method; high deposition rate & Not suitable for large-scale production \\ \hline Ion beam deposition & Uniform morphology of the obtained films & Deposition rate very low; expensive \\ \hline Atomic layer epitaxy & Can synthesize high-quality films & High energy waste rate observed during the \\  & & deposition process \\ \hline Chemical vapor deposition & Can synthesis thick films under a high deposition rate & High temperature is high-temperature deposition \\  & & during the deposition \\ \hline Molecular beam epitaxy & High purity and epitaxial materials could be produced & Very expensive \\ \hline Magnetron sputtering & High adhesion and uniform morphology of the & Very expensive; low deposition rate \\  & & obtained films \\ \hline Pulsed laser deposition & Dense and porous morphology observed in the & Very expensive \\  & & obtained samples \\ \hline Spin coating & Very thin, fine, and uniform morphology of the & High-speed spinning becomes very difficult when \\  & & the size of the substrate increases \\ \hline Thermal evaporation & High deposition rate during the deposition process & Very poor coverage due to low vacuum \\ \hline \end{tabular}
\end{table} 
Table 1: The advantages and disadvantages of various deposition techniques.
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 5 Using The 5G Physical Layer Signal for TSC Synchronization
### Improving Synchronization Precision with Slot Indication in 30 kHz Subcarrier Bandwidth  
To provide synchronization with the reference time, the SC transmits the slot indication such that it arrives at an even timing interval of the reference time. For example, when the subcarrier spacing is 30 kHz, the slot duration is 500 \(\upmu\)s and a new slot indicator is transmitted every 500 \(\upmu\)s. In the illustrated example of Figure 8, the time format is hours:minutes:seconds:milliseconds microseconds nanoseconds. The slot indicators are transmitted such that they arrive at the UE when the reference time is an integer multiple of 500 \(\upmu\)s. Slot indication signal [SL#0], for instance, arrives at the UE when the reference time is 16:30:23:000 000 000; slot indication signal [SL#1] arrives at the UE when the reference time is 16:30:23:000 500 000, 500 microseconds later, and so on.  
The enforcement of the arrival time of the indication signals enables the UE to maintain precise synchronization with the reference clock between slot indication signals by using the precise internal clock, used to maintain frequency and phase synchronization. Here, the UE may use every slot indication signal to check and/or adjust the synchronization of the UE clock using one or more slot indication signals per radio frame. The degree of drift of the UE clock relative to the reference time, the distance from the UE to the SC, the velocity of the UE, and the particular process being executed by the UE may factor into determining how frequently to check and/or adjust the synchronization of the UE clock using one or more indication signals.  
When a 60 kHz, 120 kHz, or 240 kHz subcarrier spacing is used, the respective subframes will have 4, 8, or 16 slots. Thus, the regularity of the slot indicator signal depends on the subcarrier spacing. For example, when the subcarrier spacing of 15 kHz is used, the slot indication signal is sent every 1 ms. In contrast, when we use 240 kHz, the slot indication signal is sent every 62.5 \(\upmu\)s, which can provide higher precision. The level of precision attainable with this synchronization method depends on the subcarrier spacing utilized, making it a versatile and adaptable solution for 5G network synchronization needs.  
Figure 8: Precise synchronization and Slot Indication for subcarrier bandwidth of 30 kHz in radio frame structure.
## 1 Introduction
The impacts of latency in blockchains negatively influence systems' efficiency, performance, real-time processing, and quality of services. To the best of our knowledge, this is the first work that modifies the blockchain model to cope with e-healthcare authority levels and real-time specifications. This work proposes a modified model of a blockchain that is used to store, process, and manage data in the field of e-healthcare. Firstly, this work studies and classifies healthcare data according to their nature and sensitivity. It investigates and analyzes the roles and authorities that are interwaven with data access and processing. Secondly, understanding the nature of transaction is an important step at the beginning of this work. Unfortunately, many works apply blockchain technology without studying the implications of using transactions, which obviously shows a lack of understanding of transaction specifications. Therefore, the proposed model modifies the shape of data within the blocks, since the regular form of transaction is not required for all data, operations, and processes. Thirdly, it also introduces a modified form of PoS that implies the fairness of the proof-of-queue protocol (PoQ). The proposed protocol is called fair-proof-of-stake (FPoS). Actually, in PoS, the chance of creating and validating blocks is given according to the amount of stake the miner puts in, which causes difficulty for new miners. The PoQ, on the other hand, queues miners and gives a fair chance to every miner. The proposed FPoS uses the block creation and correctness procedure of PoS for a specific number of cycles; then, it gives chances to the miners waiting in the queue (but they cannot compete in the PoS manner). Fourthly, it presents a modified procedure for block verification that relaxes the verification for some trusted transactions and blocks. Obviously, all proposed modifications help to reduce costs, support processing speed-up, and enhance security and privacy. The experimental results show that the total execution time using the modified blockchain was reduced by about 49% comparing to traditional blockchain model. Additionally, the number of messages using modified blockchain was reduced by about 53% compared to the traditional blockchain model. The rest of this paper is organized as follows: Related work is described in Section 2. Section 3 shows the analysis of healthcare specifications. Section 4 introduces the modified blockchain model that suits e-healthcare specifications. Section 5 illustrates the numerical analysis and the experimental results. Section 6 discusses the advantages of the proposed model, while Section 7 concludes the paper.
The developed waveform generator was measured to verify the wideband generation method and decrement of phase error varying time. The remainder of this study is organized as follows. The study area and proposed method are discussed in Section 2. The simulation and the experimental results are presented in Section 3. The discussion for this study is outlined in Section 4. Finally, our conclusions are presented in Section 5.
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 8 Conclusions and Future Work  
Internet-enabled embedded devices connect real-life objects called IoT devices to the Internet. The security of IoT devices is very important, as IoT vulnerability can profoundly affect human life. Available security solutions in the literature related to embedded platforms were surveyed, and it was inferred that typical security solutions are not suitable for embedded platforms. Virtualization technologies in embedded platforms using the MIPS architecture were surveyed, as virtualization technology is considered as a security solution to some of the security issues in IoT devices. Although the virtualization technology solves a portion of the presently existing security issues in IoT gadgets, it adds some new issues to the IoT systems. We identified the requirements for the enhancements of virtual machine security and performance in embedded hypervisors.  
The feasibility of a security solution named ASMI on a MIPS-embedded platform was analyzed. The ASMI architecture was found suitable for embedded devices, as the requirements of virtualization in embedded devices match the features of the ASMI architecture. The required modifications to the MIPS architecture to successfully implement the ASMI architecture were also identified. The detailed operation of the ASMI architecture on the MIPS embedded platform was described. Simulation and synthesis of memory access operations in the ASMI architecture using _ModelSim_ and _Xilinx__ISE_ software revealed that the ASMI architecture has the potential to improve the performance and security of virtual machines.  
In the future, implementing ASMI on a proper simulation platform and analyzing the security and performance of the virtual machines running on the ASMI-enhanced hypervisor would clearly establish the practicality of the architecture.  
[PERSON] wrote the paper and the works related to the article. [PERSON] has given the complete guidance for the work, and she corrected the logical and grammatical mistakes in the paper.  
**Funding:** This research received no external funding.  
Figure 7: Time taken by a single memory access in different address translation mechanisms.