esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 5 Conclusions  
This study developed and validated a multiple logistic regression model using HOMA-IR, gender, age, and Diabetes Mellitus status to predict metabolic syndrome (MetS) in a Korean population. Our findings indicate that Diabetes Mellitus status exerts the strongest impact on MetS risk, followed by HOMA-IR, gender, and age. The model demonstrated good overall predictive accuracy (AUC = 0.819), although its performance declined among older adults (\(\geq\)50 years), suggesting the need for age-specific refinements and caution when applying the model to this demographic. While the results suggest potential utility for earlier risk stratification and targeted interventions, further external validation in other cohorts is required before the model can be widely adopted, particularly given the cross-sectional design, which does not establish causality. Future research could also incorporate additional indicators (e.g., inflammatory markers, lifestyle factors) to enhance predictive power, especially in older populations, and longitudinal data would clarify whether insulin resistance actively drives MetS progression or simply correlates with it. Taken together, these findings provide valuable baseline data and lay a foundation for refining MetS prediction models in diverse populations.  
Conceptualization: A.-S.H.; methodology: A.-S.H.; data analysis and interpretation: A.-S.H.; writing--original draft preparation: A.-S.H.; writing--review and editing: J.-C.L.; supervision: J.-C.L.; project administration: A.-S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The KNHANES data were reviewed by the Institutional Review Board of the Korea Disease Control and Prevention Agency and approved by its Ethics Committee (2018-01-03-C-A).  
Patient consent was not applicable for an observational study where we analyzed pre-existing data. Given the nature of the study, there was no direct interaction with patients, and informed consent was deemed not required.  
The data used in this study are not publicly available due to privacy and ethical restrictions.  
The authors declare no conflict of interest.
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 4 Implementation Criteria
### Two Implemented Versions
#### 4.2.1 Version 1, Minimal Mismatch  
This version has been designed with non-minimal diode \(W\times L\). Its calculated optimal \(R_{in}\) at room temperature is roughly 10 k\(\Omega\). \(N=60\) has been chosen based on the trade-off between an \(NF\) value close to unity and manageable \(C_{in}\) and \(\tau\) values--see Section 3.1. The theoretical \(r_{DS}~{}=~{}1.2\) M\(\Omega\) for each diode stage results from the optimal \(R_{in}\) and the chosen \(N\). The actual value is \(r_{DS}~{}=~{}1.1\) M\(\Omega\). The dimensions of the diodes are \(W\) = 450 nm \(\times\) 2 and \(L\) = 450 nm. The values for the filtering and coupling capacitors are \(C_{gate}=C_{drain}=C_{gnd}\) = 36 fF implemented as 1.2-V poly-on-pplus capacitors. The simulated \(C^{\prime}_{in}\) is 1.68 pF. \(R_{bias}\) has been implemented as a diode-connected MOSFET with a null \(V_{GS}\). According to simulations, factor \(k\) is roughly 0.75. The dimensions of \(M4\) are \(W\) = 450 nm and \(L\) = 450 nm, whereas \(M3\) has the same dimensions as the diodes, \(R_{PTAT}~{}=~{}3.5\) M\(\Omega\). A 100 nA biasing current in the baseband amplifier following the ED, which translates into a \(3.3-\mu\)S \(g_{m}\) in the chosen technology, would yield \(NF~{}=~{}1.08\) in the worst case, namely, for \(R_{in}~{}=~{}2.2\) k\(\Omega\), i.e., at 120 \({}^{\circ}\)C when the PTAT block is not used. This confirms that \(NF\) can be considered roughly equal to unity when calculating \(P_{SEN}\). The PTAT block, start-up included, consumes 6 nW at 20 \({}^{\circ}\)C, 1.4 nW at \(-\)40 \({}^{\circ}\)C and 12.2 nW at 120 \({}^{\circ}\)C.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## Key Research Findings  
### Understanding the Energy Basics for SUHI  
The SUHI is essentially caused by alterations of the surface energy balance after replacing natural land with an artificial surface [1; 8; 9]. The energy balance of a typical urban surface can be expressed as: incident solar radiation (Q\({}_{\rm L}\)) + incoming longwave radiation (Q\({}_{\rm L}\),) + anthropogenic heat releases (Q\({}_{\rm F}\)) = reflected solar radiation (Q\({}_{\rm R}\)) + outgoing longwave radiation (Q\({}_{\rm L}\),) + latent heat (Q\({}_{\rm E}\)) + sensible heat (Q\({}_{\rm H}\)) + heat storage (Q\({}_{\rm S}\)) (Figure 4). Satellite thermal sensors measure LST indirectly by detecting Q\({}_{\rm L}\), from the Earth surface [29]. The urban-induced SUHI, which represents an increase in Q\({}_{\rm L}\), and, in general, is dominated by different energy fluxes during the day and night [1; 4; 6]. The daytime SUHI is widely attributed to a reduction in reflected solar radiation and latent heat flux, which increases the sensible heat and upwelling thermal radiation received by thermal sensors [9]. In contrast, the nighttime SUHI was mainly contributed by the larger storage heat during the day for a later release at night [2; 6]. The anthropogenic heat flux, as an urban-specific energy input, could increase the SUHI independent of the time-of-day by magnifying the upward thermal radiation and the heat storage [7]. Factors impacting any energy component of urban surface can influence the daytime and/or nighttime SUHI intensity and even convert the urban area from a source to a sink of heat.  
Figure 4: A diagram of the energy balance for a hypothetical urban area. Red and blue arrows designate energy input and partition in the urban system, respectively. The example values (units: W m\({}^{-2}\)) were from Oke [132]. The values in red represent the energy change in the urban region relative to rural areas.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 5 Experiment and Analysis
### Performance Evaluation
#### 5.2.2 Tracking Performance  
In the previous experiment, we evaluated the agents' localization performance at the selected test points. To further evaluate the performance of the localization system in a dynamic scenario, we let the agents move along a preset route in E2 (see Figure 8). In practice, we hold the UWB device and walk at a near-constant speed along the preset route. In addition, we make the agent and anchor close in height to better achieve two-dimensional localization. In the experiment, we employ three agents (agent 1, agent 2, and agent 3) as an example to evaluate the performance of our system. As shown in Figure 8, we let the three agents go straight along the three sides of the rectangle, respectively. The red circle represents the anchor, the semi-rectangular blue line is the preset route, the red cross sign is the tracking result of agent 1 starting from the left bottom, the magenta cross is the tracking result of agent 2 starting from the left top, and the green cross is the tracking result of agent 3 starting form the right top. The results show that the trajectory estimation of the three agents can well follow the preset route. Since it is not easy to relate the real-time position estimation to the ground truth, real-time localization errors cannot be counted. Note that the localization results are raw data, which means we do not use any filtering algorithms. The experimental results verify the potential of our system in practice application.
## 5 Semantic Understanding of Concept Layer
### Feature-Learning-Based Method  
Both the method based on visual features and object semantics rely mainly on artificial design in feature extraction, which is not only subjective, and it is not enough for more complex HSR remote sensing images. In recent years, feature learning, especially deep learning, has been introduced into the field of remote sensing for semantic understanding of geographic scenes due to its excellent performance in image classification [121]. The general flow of geographic scene classification based on feature learning is shown in Figure 5.  
Figure 5: Geographic scene classification diagram.  
Semantic understanding of the concept layer based on feature learning refers to the process of learning a potential scene classification feature through a series of mapping and transformation by using HSR remote sensing images as input of the model in machine learning tasks. Machine learning models can autonomously express and extract features from image data, abandoning the previous pattern of extracting features based on pre-designed rules [122, 123]. Therefore, in the face of a complex surface environment, better classification results of geographic scenes can be obtained. At present, the commonly used machine learning models include sparse coding [124], neural network [125], support vector machine [126] and deep learning [127] (Figure 6). As a new intelligent method of pattern recognition in recent years, a deep learning network composed of multilaminate nonlinear mapping layers has become an especially important development direction in the field of remote sensing image processing [128]. Deep Learning is a deep structure neural network, which can extract the features of remote sensing images better than shallow structure models such as artificial neural networks and support vector machine models. Moreover, deep learning models can learn more abstract and distinguishable semantic features autonomously. The deep learning approach converts the semantic understanding of the concept layer into an end-to-end problem. On the one hand, the pre-trained deep learning network structure can be directly used to learn the global features in the visual layer of images to understand the semantics of the concept layer [129]. On the other hand, the deep learning network can also be used as a local feature extraction operator to jointly complete the semantic understanding of the concept layer with the help of feature code technology. Common deep learning models include convolutional neural networks (CNN) [130, 131, 132], deep belief network (DBN) [133], recurrent neural network (RNN) [134], automatic encoders [135], graph convolutional networks (GCN) [136], generative adversarial networks (GANs) [137, 138] and so on. The deep learning method can be divided into three categories according to the supervision mode: (1) full supervision, (2) semi-supervised and (3) weak supervision.
# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Advantages** & **Disadvantages** \\ \hline
[8; 9; 10] & Electro Oculography & Non-invasive. & Low precision. \\  & & Insensitive to light. & Influenced by electrode position \\  & & Can be applied with eyes closed. & and physiologic electrical signals. \\
[11; 12; 13; 14] & Video Oculography & Non-contact. & Sensitive to light. \\
[11; 12; 13; 14] & (Visible Light Camera) & Real-time. & Influenced by eyelids and \\  & & Inexpensive. & eyebrows. \\
[15; 16; 17] & Infrared Oculography & Non-invasive. & Influenced by position, eyelids, \\  & & (Infrared Rays or Near & \\  & & & light conditions. & and eyebrows. \\
[18; 19; 20; 21] & & High precision. & Invasive. \\  & & Can be applied with eyes closed. & Complex equipment. \\  & & & Suitable for scientific research. & Costly. \\  & & Non-invasive. & Not applicable to daily life. \\
[22; 23; 24] & Magnetic Resonance & Observes both eye and brain & Subjects need to remain stationary \\  & Imaging & activity. & for long periods. \\  & & Suitable for medical research. & Difficult to apply in practice. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Eye-tracking studies and methods in recent years. * We constructed an eye image dataset with eyelid coverage. Its usability and reliability in practical applications were ensured by scientific labeling and segmentation. This dataset can be used to train deep learning-based eye-tracking models in the future;
* We developed a real-time tracking system based on the YOLO model to detect eye movements with high precision and introduced strict tracking evaluation metrics for accuracy and reliability.
## 3 Results  
Both PPA-SiBGC and LANDIS-II NECN showed strong performance for pools at the two model intercomparison sites, frequently achieving \(R^{2}\) values approaching unity. Yet, both models showed weak performance for fluxes. The models failed to accurately predict ANPP, while PPA-SiBGC showed stronger \(r_{soil}\) performance and LANDIS-II NECN showed stronger NEE performance. The \(R^{2}\) values for both models and sites are visualized in Figure 5.  
On average, PPA-SiBGC outperformed LANDIS-II NECN across the sites and metrics tested, showing higher correlations, lower error, and less bias overall (HF-EMS \(\overline{R^{2}}=0.73,+0.07\), \(\overline{RMSE}=4.68,-9.96\), \(\overline{ME}=-0.84,-5.96\); JERC-RD \(\overline{R^{2}}=0.73,+0.01\), \(\overline{RMSE}=2.18,-1.64\), \(\overline{ME}=1.33,+1.03\)). This result is based on calculating mean values for \(R^{2}\), RMSE, MAE, and ME in order to clearly translate the overall results. The two models produced the following mean values for each of the four statistical metrics and two sites:  
As shown in Table 6, PPA-SiBGC yielded higher \(R^{2}\) values and lower RMSE, MAE, and ME values in comparison to LANDIS-II, on average, across all sites and metrics tested. Below, we provide model intercomparison results individually for the two sites, HF-EMS and JERC-RD.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### ML Models
#### 2.4.2 Ordinal Logistic Regression  
The OLR is used when the response variables represent ordered phenomenon and have multiple classes. For the problem considered in this study, the classes or damage categories were ordered and, hence, OLR was suitable. In OLR, the event of interest is a particular damage state and the lesser ones. The probability of the \(i\)-th input vector, \(x_{i}\), classified as being in or lower than a damage state \(f\) is computed by Equation (9). Through the sigmoid transformation, Equation (6), the formulation is shown in Equation (10).  
\[P(y_{i}\leq f|x_{i})=h(\theta x_{i}), \tag{9}\]  
\[\ln\biggl{[}\frac{P(y_{i}\leq f|x_{i})}{P(y_{i}>f|x_{i})}\biggr{]}=\theta x_{i}. \tag{10}\]  
The optimization problem for OLR is the same as that for LR (Equation (8)). The OLR model finds the linear boundary between a damage class and the higher damage classes, i.e., boundary 1 separates category 0 (coefficient vector \(\theta_{0}\)) from 1 to 3, boundary 2 separates categories 0 and 1 (coefficient vector \(\theta_{1}\)) from 2 and 3, and boundary 3 separates categories 0, 1, and 2 (coefficient vector \(\theta_{2}\)) from 3. Consequently, damage probabilities of the \(j\)-th sample of the test case are evaluated using the following equations,  
\[P(y_{j}=0|x_{j})=P(y_{j}\leq 0|x_{j})=h(\theta_{0}x_{j}), \tag{11}\]  
\[P(y_{j}=1|x_{j})=P(y_{j}\leq 1|x_{j})-P(y_{j}=0|x_{j})=h(\theta_{1}x_{j})-h( \theta_{0}x_{j}), \tag{12}\]  
\[P(y_{j}=2|x_{j})=P(y_{j}\leq 2|x_{j})-P(y_{j}\leq 1|x_{j})=h(\theta_{2}x_{j})-h( \theta_{1}x_{j}), \tag{13}\]  
\[P(y_{j}=3|x_{j})=1.0-P(y_{j}\leq 2|x_{j})=1.0-h(\theta_{2}x_{j}). \tag{14}\]
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 5 Evaluations
### Implementation
#### 5.1.2 Experiment Setups and Plans  
The actual environmental noise in the wireless communication scenario is a random variable. Even if the transmit power is adjustable, it is hard to obtain the real SNR at the receiver. Therefore, we conduct a distance-driven evaluation for the QR\(-\)LoRa prototype system. Specifically, we fix the location of the transmitter and perform the test by moving the receiver over different distances. According to the free space path loss model [29], if the transmit power is fixed, the received SNR decreases with the increase in the propagation distance. Hence, we examine the BR and BER performance of QR\(-\)LoRa with different propagation distances instead of the SNR, which is more practical. The experiments are conducted in outdoor environments, involving the Light of Sight (LOS) propagation and Non-Light of Sight (NLOS) propagation. The floor plan is depicted in Figure 12. It is noted that Rx is not set behind or in the building, which is not the complete NLOS propagation, because the amplitude of the QR\(-\)LoRa chirp is sensitive to the noise and interference in the environment.
# Passively Q-Switched Operation of a Tm,Ho:LuVO\({}_{4}\) Laser with a Graphene Saturable Absorber
## 1 Introduction
Tm,Ho:YVO\({}_{4}\) and Tm,Ho:GdVO\({}_{4}\) have been used as the laser crystal in continuous wave (CW), PQS, and acousto-optically (AO) Q-switched mode operation, and PQS Tm,Ho:YVO\({}_{4}\) and Tm,Ho:GdVO\({}_{4}\) laser were demonstrated with an SA of a 2 mm-thick Cr:ZnS crystal with a pulse width of less than 100 ns and a pulse energy over 100 \(\upmu\)J [8; 28]. However, 2D materials were rarely used as an SA for Tm\({}^{3+}\) and Ho\({}^{3+}\) co-doped vanadate crystals in PQS laser operations. Compared with other vanadate crystals, the LuVO\({}_{4}\) crystal has larger absorption and emission cross-sections in the vicinity of 800 nm and 1.064 \(\upmu\)m, respectively [29]. In addition, various Ho:LuVO\({}_{4}\) lasers (e.g., CW, Q-switched, actively mode-locked and single-longitudinal mode Ho:LuVO\({}_{4}\) laser) have been demonstrated [21; 22; 23; 24; 14], which show that the LuVO\({}_{4}\) crystal is an attractive host material at 2 \(\upmu\)m wavelength range. We demonstrated an AO Q-switched Tm,Ho:LuVO\({}_{4}\) laser in 2018. An average output power of 3.77 W was achieved at a pulse repetition frequency of 10 kHz with an incident pump power of 14.7 W, and a pulse energy of 2.54 mJ was obtained at 1 kHz with a pulse duration of 69.9 ns [25]. However, the Tm,Ho:LuVO\({}_{4}\) crystal is a new laser crystal at 2 \(\upmu\)m wavelength range, and its use therein has rarely been reported, and in particular, it has never been reported in use in PQS-mode operation. In this paper, We demonstrated a PQS-mode operation of Tm,Ho:LuVO\({}_{4}\) laser with a graphene SA at 77 K. A Tm,Ho:LuVO\({}_{4}\) crystal was dual end-faces pumped by a laser diode (LD) with a center wavelength of 798.6 nm and the output power of 13 W. A pulse energy of 40.4 \(\upmu\)J and an average output power of 1034 mW were acquired at 2057.03 nm, and the narrowest pulse width was 300 ns.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 3 Marine Bioactive Compounds and the Food Industry
### Bioactive Marine Peptides  
Proteins and peptides derived from marine organisms display several unique functional properties, such as film-forming, gel-forming, and foaming capacities. For example, marine collagen and gelatin are widely used in the food industry as food additives mainly due to their gel-forming ability, film-forming, texture improvement, and water-holding capacity [144]. Furthermore, there has been much attention and considerable interest in the food industry about bioactive peptides due to their nutritional and therapeutic values. These biologically active peptides play crucial roles in metabolic regulation and modulation. Especially, proteins from fish, mollusks, and crustaceans are among the excellent sources of all essential amino acids. Fish protein powders and fish protein supplements, including fish protein hydrolyses (FPHs), are widely used in fish protein-incorporated food products, and they are available in the market. Fish protein powders are often produced by drying mechanically deboned fish flesh, which is washed with water or diluted in salt solutions (surimi). The amount of collagenous matter and myofibrillar proteins is relatively greater in surimi compared to fish flesh [31; 141]. Therefore, dietary proteins can be replaced by fish protein concentrates without any effect on metabolic balance. When considering the functionality of these FPHs, several studies have revealed their high solubility [111]. Due to their high solubility, FPHs could successfully be incorporated into drink formulations. Several research groups have focused their attention on enhancing water-holding capacity in raw and cooked foods by introducing FPHs [146]. Apart from interaction with water, FPHs' interfacial and surface properties are linked to their interactions with fats and oils. These interactions determined the emulsifying and foaming properties of FPHs. However, bitternes associated with FPHs may hinder their use in several food applications. Therefore, further studies are needed to explore the interfacial properties of FPHs, including their emulsifying and foaming properties in functional foods and nutraceuticalists. For example, salt, phosphate, and cyclodextrin were found to be important ingredients to mask the bitter taste of FPH [51]. Moreover, FPHs have the potential to be used as flavorings and as \"extracts\" to be added to food products like soups, bisques, frozen seafood, fillings, and snacks in order to enhance their flavor. When considering algal proteins, the class of phycobiliproteins (phycoerythrin, phycocyanin, and allophycoyanin) and lectins are two major groups of functionally active proteins, which show several beneficial functions, such as antioxidant and antidiabetic properties. Phycobiliproteins, water-soluble protein pigments, are found mainly in cyanobacteria and red algae (Spirulina) and are used as food dyes [52; 118].
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 7 Challenges
### Adversarial Attacks  
Diverse methods for generating adversarial attacks have been employed, yet a prominent observation is that a majority of these strategies (60%) rely on a white-box framework. However, this threat model is often unrealistic for potential adversaries. In real-world situations, black-box attacks hold greater practicality, underscoring the need to comprehensively tackle the challenges posed by these attacks and their corresponding defense strategies.  
When examining attack methodologies, numerous techniques for crafting adversarial attacks have been put forth. It becomes evident that FGSM holds the highest frequency of usage, with JSMA and C&W attacks following closely. However, FGSM's applicability in the context of IoT ML-based security systems could be an impractical option, given that it operates by perturbing each potential feature to create adversarial examples.
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 1 Introduction
However, improved readability and a simplified algorithm were required because online diagnosis must be possible. The difference between an ITSC and a turn-to-turn short needed to be examined by simplifying the change of magnetic flux for complex inductance in PVA terms. The PVA was researched based on the synchronous reference frame of the D-Q transformation. This was drawn as a circle with the sum of the scalar values of the D and Q-axes. When this is checked over time, a pattern of direct current components can be obtained in an ideal steady state [60]. Furthermore, it can be estimated that the amplitude of the pulsation varies greatly depending on the degree of fault. However, the pulsation width of the ITSC is not much different from the actual steady state because of the iron loss and copper loss. Nevertheless, when all the information about the magnetic flux and current are moved to the D-axis of the D-Q synchronous reference frame over time, and only the zero component remains always on the Q-axis, analysis becomes easy. Therefore, in this study, linear separation of the ITSC was performed, which was too difficult in previous studies. This was done by defining the relationship between turn-to-turn shorts and magnetic-flux linkage pulsation, as well as defining the change relation between the ITSC and the current.