esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Internet of Medical Things  
Wireless communication technology, driven by advancements in connectivity and mobility, has become an essential enabler in the digital era. This technology facilitates the seamless transmission of data and has gained significant attention from major carriers around the world. As wireless communication continues to evolve, the IoT emerges as a prominent paradigm, revolutionizing the way devices, objects, and systems interact and exchange information. With its ability to connect and interconnect countless smart devices, the IoT is poised to shape the future of networks, ushering in a new era of interconnectedness and intelligent communication. Initially, the concept of IoT was primarily focused on business and industry, but it has now expanded its scope to include homes and offices. IoT has become an integral component of modern living, and it is expected to be a crucial element of the future 6G network [42; 43; 44]. By 2025, healthcare is projected to account for one third of IoT devices, contributing approximately 40% of the total value of IoT technology [45].  
The term \"m-health\" is used to describe healthcare services that utilize mobile computing, medical sensors, and communication technologies. This emerging field has the potential to greatly improve access to healthcare for both patients and providers. By leveraging mobile devices, m-health enables the efficient and cost-effective delivery of healthcare services remotely. Telemedicine, telemonitoring, and patient education are examples of how m-health is utilized to provide healthcare services beyond traditional clinical settings. With m-health, healthcare professionals are able to remotely monitor patients, diagnose and treat them, and offer them information and support. This approach not only enhances convenience but also has the potential to improve the quality of care, reduce healthcare costs, and increase healthcare accessibility for underserved populations. The integration of 6 LowPAN with evolving 4G networks enables the development of future internet-based m-health services, which are referred to as m-IoT. It is important to note that m-IoT exhibits certain characteristics inherent to the global mobility of participants. This gives rise to the conceptualization of mobile IoT services. In a study [46], the use of m-IoT architecture and implementation was investigated for noninvasively measuring glucose levels. Wireless Sensor Networks, Wireless Body Area Networks, and Radio Frequency Identification (RFID) allow monitoring of patient position and status [47; 48; 49; 50]. It is possibleto track chronic diseases with these wearable devices. Wearables powered by artificial intelligence can track steps, heart rate, blood pressure, calories, and more. The Wearable Internet of Things (WiIoT) [51], the Internet of Health Things (IoHT) [52], the Internet of Medical Things (IoMT) [53], the Internet of Nano Things (IoNT) [54], and the Internet of Mobile Health Things (m-IoT) [46] are some of the variants derived from the core IoT concept in the healthcare fields depicted in Figure 6.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
###### Abstract  
The sprawling nature of Internet of Things (IoT) sensors require the comprehensive management and reliability of the entire network. Modern Internet Protocol (IP) networks demand specific qualitative and quantitative parameters that need to be met. One of these requirements is the minimal packet loss in the network. After a node or link failure within the network, the process of network convergence will begin. This process may take an unpredictable time, mostly depending on the size and the structure of the affected network segment and the routing protocol used within the network. The categories of proposed solutions for these problems are known as Fast ReRoute (FRR) mechanisms. The majority of current Fast ReRoute mechanisms use precomputation of alternative backup paths in advance. This paper presents an Enhanced Multicast Repair (EM-REP) FRR mechanism that uses multicast technology to create an alternate backup path and does not require pre-calculation. This principle creates a unique reactive behavior in the Fast ReRoute area. The enhanced M-REP FRR mechanism can find an alternative path in the event of multiple links or nodes failing at different times and places in the network. This unique behavior can be applied in the IoT sensors area, especially in network architecture that guarantees reliability of data transfer.  
Internet of Things (IoT); ReRoute; Multicast Repair (M-REP) Article
###### Abstract  
This study proposes a novel approach to input data configuration for the fault diagnosis of three-phase induction motors. Conventional neural network (CNN)-based diagnostic methods often employ three-phase current signals and apply various image transformation techniques, such as RGB mapping, wavelet transforms, and short-time Fourier transform (STFT), to construct multi-channel input data. While such approaches outperform 1D-CNNs or grayscale-based 2D-CNNs due to their rich informational content, they require multi-channel data and involve an increased computational complexity. Accordingly, this study transforms the three-phase currents into the D-Q synchronous reference frame and utilizes the D-axis current (Id) for image transformation. The Id is used to generate input data using the same image processing techniques, allowing for a direct performance comparison under identical CNN architectures. Experiments were conducted under consistent conditions using both three-phase-based and Id-based methods, each applied to RGB mapping, DWT, and STFT. The classification accuracy was evaluated using a ResNet50-based CNN. Results showed that the Id-STFT achieved the highest performance, with a validation accuracy of 99.6% and a test accuracy of 99.0%. While the RGB representation of three-phase signals has traditionally been favored for its information richness and diagnostic performance, this study demonstrates that a high-performance CNN-based fault diagnosis is achievable even with grayscale representations of a single current.  
three-phase induction motor; CNN; fault diagnosis; D-Q synchronous reference frame; input data configuration Article
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 4 Environmental Ground Surface Vibration
### Vibration Propagation in Whole Ground Surface
#### 4.3.2 Vibration Propagation 45 Degrees from Maglev Line  
Similar to those at 30 degrees from the maglev line, test points were set in the 45-degree direction. The distances to the foundation center were 0 m, 7.5 m, and 12.5 m (points G, Q, and R in Figure 4) in sequence. The acceleration time history and frequency spectra of each test point are shown in Figure 11 (two repetitions are shown, listed in order of distance).  
In the acceleration records presented above, a clear trend of attenuation can be observed: when the distance to the foundation center is no more than 7.5 m, the ground vibration magnitude attenuates quickly, like in the perpendicular direction. When the distance reaches 12.5 m, the magnitude becomes larger than before, and the peak acceleration  
Figure 11: Acceleration time history and frequency spectra of each test point in 45-degree direction.  
value can be over 0.04 m/s\({}^{2}\), which can be easily perceived by most people. During several repetitions, although the magnitude of the ground vibration varies, this trend of vibration amplification or so-called rebound is quite stable, indicating that the superposition of the traveling wave is significant.  
The graphs of the frequency spectra above show that, with the increase in the distance, the high-frequency components of the ground vibration first decrease and then increase slightly, so due to the superposition of the high-frequency components, the vibration-amplifying zone appears.
# Bricks Using Clay Mixed with Powder and Ashes from Lignocellulosic Biomass: A Review
## 3 Different Types of Ordinary Bricks: Preparation and Characteristics
### Possibilities to Improve the Brick's Characteristics  
For many years, the brick industry has been mobilizing to minimize its environmental impact. Using waste ash instead of soil is seen as a viable and ecological option for preserving natural resources and reducing ash toxicity and landfill problems. Researchers have incorporated various ashes in fired and unfired bricks as a replacement for clay such as fly ash [43; 44; 45], bottom ash [46; 47], round blast furnace slag [48], and incineration ash [49; 50]. Compared to other industrial ashes, lignocellulosic biomass combustion ash has not been thoroughly investigated in the production of bricks. For fired bricks, the role of biomass ash changes in the clay matrix during the drying and firing stage depending on the origin and nature of these used co-products. The main roles of these ashes found in the literature are:  
* Pore-forming agent: Since biomass ashes are composed of a percentage of organic matter, degradation during the firing stage occurs while creating pores within the clay matrix. In addition, this type of ash is used to decrease the density and thermal conductivity of the bricks for more thermally insulating building materials [6; 23; 51].
* Fluidizing agent: during firing, the melting oxides in the ash react with the silica and alumina in the clay to form a liquid phase and lower the melting point of the bricks. This contributes to the rapid densification and consolidation of the clay material [52; 53; 54].
* Clay substitute or sand: the biomass ashes of inorganic co-products have strong plasticity. They can then play the role of degreaser within the clay matrix, producing a glassy phase during firing [55; 56].
* Biomass ashes are waste products rich in organic matter and a percentage of unburned carbon. Their role is to act as a biofuel during the firing stage, reducing the temperature and energy consumption of the drying and firing process of the bricks in the tunnel kilns.  
For unfired bricks, research is moving toward using ash-based geopolymer bricks instead of cementitious binders as an ecologically sound, economically sensible, and sustainable binder that uses less energy and exhibits strong properties such as good mechanical properties, low permeability to liquids, resistance to high temperatures and acid attacks, and a significant reduction in CO\({}_{2}\) emissions. Indeed, any powder containing high silica and alumina contents and having an amorphous phase can be considered a geopolymer precursor. It is worth noting to recall that the geo-polymerization process is divided into four main steps: (1) The Dissolution of the oxidized minerals (silica and alumina) present in the raw material in an alkaline solution. (2) The diffusion of the dissolved oxidized minerals from the surface to the inner particular space and followed by coagulation and gel formation. (3) The Polycondensation forming a three-dimensional alumina-silicate network and (4) the hardening of the matrix.
## 6 Discussion  
### Ideal Proportioning System  
The applicability and performance of a proportioning valve on a bicycle are dependent on the conditions of operation. When available traction is high, there may not be enough bias to the front brake; when available traction is low, there may not be enough bias to the rear brake. Ideally, the bias to the rear brake would be relative to the rear tire's normal force. This ideal scenario was simulated with the bicycle model, and the results are shown in Figure 15.  
The ideal proportioning valve would operate by keeping the rear brake force equal to the friction coefficient times the rear tire's normal force. In most scenarios, the rear wheel locks before the front tire traction limit is reached; however, with an ideal proportioning valve, the rear wheel never locks. This means that the limiting factor is either front-wheel lock or tip-over. In the lower plot of Figure 15 for the center of mass ratios above 0.9, the stopping distance is limited by the available front tire traction, whereas, for the center of mass ratios below 0.9, the stopping distance is limited by imminent tip-over.  
The difference between the stopping distances of the prototype proportioning and ideal proportioning valves is shown in Figure 16. The ideal proportioning valve might not be necessary when the center of mass ratio is above 1.4, which corresponds to either a long wheelbase and/or a low center of mass. The advantage of the ideal valve increases and decreases relative to the longitudinal load transfer. The advantage is greatest when the risk of tip-over is highest; for this reason, this system would be best paired with a device that decreases front brake force when the rear tire's normal force reaches zero. However, this would not solve the front wheel lock scenario. Therefore, the ideal system for minimum stopping distances would include a weight-sensing proportioning valve, an anti-tip-over valve, and a front wheel anti-lock-braking system.  
Figure 14: Percent decrease in stopping distance for the prototype brake system showing an increased advantage for high centers of mass and when riding on steeper downslopes.  
Figure 16: Percent decrease in stopping distances for an ideal proportioning valve with rear-wheel normal force sensing versus the prototype system showing the greatest advantage shown on steep downslopes and lower C.O.M. ratios.  
Figure 15: Stopping distances for the prototype proportioning system and an ideal proportioning system with rear tire normal force sensing. For values of \(a_{cm}/h_{cm}\) below 0.9, the stopping distance becomes limited by tip-over.
## 2 Material and Methods
### Measurement of Salivary Oxidative Stress  
A total of five samples were excluded due to contamination. The operator analyzing the saliva was blinded as to which study group the patient belonged. Of the different tests performed, those recording plasma ferric reducing antioxidant power (FRAP), Trolox equivalent antioxidant capacity (TEAC), and cupric reducing antioxidant capacity (CUPRAC) are spectrophotometric techniques based on single-electron transfer methods [33; 34].  
FRAP test: The method of [PERSON] [35] was used, with some modifications. In this test, the antioxidants of the sample reduce the ferric tropyridtriazine (Fe\({}^{3+}\)-TPTZ) complex to ferrous tropyridtriazine (Fe\({}^{2+}\)-TPTZ), which has a bluish color. The intensity of this color is directly proportional to the reducing activity of the antioxidants.  
CUPRAC test: This test was carried out based on the method of [PERSON] et al. [36]. It involves the reduction of Cu\({}^{2+}\) to Cu\({}^{1+}\) mediated by the non-enzymatic antioxidants of the sample. The oxidizing compound is Cu\({}^{2+}\) bathocuproinedisulfonic acid (Cu\({}^{2+}\)-BCS), which reacts with the antioxidants of the sample and reduces to Cu\({}^{1+}\) bathocuproinedisulfonic acid (Cu\({}^{1+}\)-BCS)--a stable compound with maximum absorbance at a wavelength of 490 nm; the antioxidant activity is proportional to the formation of the Cu\({}^{1+}\)-BCS complex. The test  
Figure 4: Unstimulated whole saliva.  
was performed using 0.25 mmol/L of the disodium salt of bathocuproinedisulfonic acid (reagent 1) and 0.5 mmol/L of CuSO\({}_{4}\) (reagent 2).  
TEAC test: This test was carried out based on the procedure described by [PERSON] et al. [37]. ABTS (2.2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid) is a peroxidase compound that generates a cation radical when oxidized in the presence of hydrogen peroxide (peroxidation). This radical produces a blue-green color, and the antioxidants in the sample suppress this color in proportion to their own concentrations.  
Advanced oxidation protein products (AOPP) test: This test was used to evaluate salivary protein oxidation status based on the method of [PERSON] et al. [38].  
Total protein (TP) test: Total protein was determined using the procedure of [PERSON] [39] (Beckman Coulter OSR6132; Beckman Coulter Inc. 250 S. Kraemer Blvd, Brea, CA 92821, USA). The Cu\({}^{2+}\) ions in an alkaline solution react with the proteins and polypeptides that contain at least two peptide bonds, generating a violet color.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 5 Future Research Directions
### Integrating Blockchain with Other Technologies  
Combining blockchain with AI and cloud computing is poised to dramatically transform the management of agriculture and livestock sectors. Blockchain's robust framework for data management, characterized by its security, transparency, and immutability, is pivotal in ensuring the reliable storage and tracking of critical information such as production data, supply chain details, quality control, and sales data in these sectors. Whenintegrated with big data analytics, this repository of information can be processed into valuable insights, facilitating informed decision-making.  
Furthermore, utilizing AI and IoT technologies enables continuous monitoring of variables like crop growth conditions, soil quality, and livestock health, allowing for adaptation to climatic changes. Leveraging cloud computing's powerful computational abilities, vast quantities of data collected can be swiftly analyzed to forecast trends, anticipate yields, and preempt disease outbreaks.  
Blockchain enhances transparency and traceability in supply chain management, which is vital to food safety and quality. Combining AI with big data analytics further optimizes logistics, predicts market demands, and reduces waste. Additionally, smart contracts automate the execution of contract terms, streamline transactions like payments and insurance claims, and diminish the risks associated with human error and fraud.  
This holistic approach, integrating multiple technologies, not only elevates production efficiency and product quality in agriculture and livestock but also fosters sustainable development. It contributes to reinforcing global food security and elevating the transparency and reliability of supply chains. As technology evolves, the future landscape of agriculture and livestock is set to become increasingly smart, efficient, and eco-friendly.
## 2 Background and Related Work
### Visual Privacy Protection Techniques  
To address these privacy concerns, several visual privacy protection techniques have been developed and implemented in AR systems. These techniques offer different approaches to evalute privacy protection with user experience and functionality.  
Each of these privacy protection techniques offer distinct advantages and limitations, making them suitable for different use cases and privacy requirements. The choice between these methods often depends on factors such as the sensitivity of the information being protected, the required level of privacy, and the desired user experience. The effectiveness of these techniques in AR environments is particularly important as the technology continues to evolve and become more integrated into our daily life.  
To illustrate the impact of different obfuscation techniques, Figure 1 compares the effects of masking (Figure 1b), pixelation (Figure 1c), and blurring (Figure 1d) when applied to an AR system. These techniques are used to conceal sensitive information displayed on the screen, demonstrating various methods of privacy protection in AR environments.  
**Masking:** Figure 1b illustrates how this technique involves completely covering sensitive information with solid shapes or patterns, effectively creating a visual barrier between the protected content and any observers. While masking provides possibly better privacy protection and is computationally efficient to implement, it can significantly impact the user experience by removing all visual context from the protected area [24; 25].  
**Pixelization:** In Figure 1c, the pixelization technique, also known as the mosaic effect, offers a different approach to privacy protection. This technique reduces image resolution in specific areas by averaging pixel values within defined blocks. The result maintains the visual structure of the protected content while making specific details unclear [26].  
**Blurring:** Figure 1d illustrates how this technique applies a smoothing effect that preserves general shapes and colors while obscuring specific details that might be sensitive or private. Blurring can be implemented with varying intensity levels, offering adaptable privacy protection based on specific needs [27].
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters
### Communication Decision-Making Parameter  
After obtaining the anti-jamming strategies according to the knowledge graph search results, it is necessary to assign proper parameters for each strategy. The anti-jamming strategies considered in this paper mainly include: adaptive frequency change, adaptive frequency hopping, power adaptive and adaptive time hopping.  
In the time-frequency domain feature extraction process, the time-frequency parameters of the jamming are obtained, while in order to realize anti-jamming communication, the system needs to decide the optimized communication parameters under the jamming influence to acquire optimized communication parameters, so that the jamming signals will have minimum influences on the transmission of communication signals. Therefore, the purpose of anti-jamming parameter decision-making is to acquire the optimized communication parameters according to the time-frequency parameters of the jamming, to ensure the optimal communication signal transmission performance.
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
###### Abstract  
Iris detection and tracking plays a vital role in human-computer interaction and has become an emerging field for researchers in the last two decades. Typical applications such as virtual reality, augmented reality, gaze detection for customer behavior, controlling computers, and handheld embedded devices need accurate and precise detection of iris landmarks. A significant improvement has been made so far in iris detection and tracking. However, iris landmarks detection in real-time with high accuracy is still a challenge and a computationally expensive task. This is also accompanied with the lack of a publicly available dataset of annotated iris landmarks. This article presents a benchmark dataset and a robust framework for the localization of key landmark points to extract the iris with better accuracy. A number of training sessions have been conducted for MobileNetV2, ResNet50, VGG16, and VGG19 over an iris landmarks dataset, and ImageNet weights are used for model initialization. The Mean Absolute Error (MAE), model loss, and model size are measured to evaluate and validate the proposed model. Results analyses show that the proposed model outperforms other methods on selected parameters. The MAEs of MobileNetV2, ResNet50, VGG16, and VGG19 are 0.60, 0.33, 0.35, and 0.34; the average decrease in size is 60%, and the average reduction in response time is 75% compared to the other models. We collected the images of eyes and annotated them with the help of the proposed algorithm. The generated dataset has been made publicly available for research purposes. The contribution of this research is a model with a more diminutive size and the real-time and accurate prediction of iris landmarks, along with the provided dataset of iris landmark annotations.  
Keywords: iris; landmarks; deep learning; human-computer interaction +
Footnote †: journal: Medical Imaging