esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
## 2 Materials and Method
### Motivation and Contribution of Our Work  
Distinguishing between the symptoms of normal aging and the onset of dementia is not a straightforward task. The question of when and how healthy aging transitions into the early stages of Alzheimer's disease (AD) remains a topic of limited understanding and active research [22; 23; 24].  
The NHATS offers valuable insights, going beyond simply identifying the presence or absence of disability. It provides us with nuanced measures of disability, shedding light on how older individuals and their families adapt, including the utilization of assistive devices, environmental modifications, and assistance from others.  
The absence of a clear demarcation between the symptoms of normal aging and dementia, combined with the availability of a comprehensive dataset such as the NHATS, presents us with a unique opportunity to address this challenge.  
As such, our contribution involves the development of a machine learning framework capable of unravelling the intricate relationships between various parameters and measures. This framework enables us to classify individuals into high-risk and low-risk categories using minimal input parameters derived from the NHATS dataset.  
Our study is motivated by the need for data-driven dementia prognosis, the potential of machine learning techniques, and the accessibility of NHATS data.  
Given the challenges associated with mobility in old age, a non-intrusive method that relies on lifestyle parameters without subjecting patients to invasive tests becomes increasingly important. In this paper, we introduce an approach that leverages machine learning techniques to fulfil this need.  
The contribution of our work is as follows:  
* Review of previous work done on the detection of dementia using machine learning techniques.
* Studying potential opportunities in applying machine learning techniques to NHATS data and gleaning important parameters that have a significant impact on the onset of dementia.
* Models designed based on this work can aid medical professionals as the first screening system that communicates important trends. Hence, they automate the human-intensive activity of preliminary filtering. We used artificial neural networks and random forest techniques for ML modelling.
* Discovery of several important parameters based on statistical methods and domain wisdom that have an impact on dementia such as lifestyle, economics, social circle, and factors that may not be part of the conventional framework of dementia detection, such as the use of technology (discussed in Section 2.3.3).
* Discovery, using temporal analysis, of lifestyle parameters that degrade with time.  
We propose to design a machine learning model that can predict the onset of dementia based on the above parameters. Our research work focuses on finding the key parameters that have a significant impact on the onset of dementia and creating a model to detect the risk of the onset of dementia using information about the lifestyle of an individual.
## 4 Discussion
### Comparison with Previous Studies
#### 4.1.3 Response Time  
In this study, the response times of ChatGPT-4o, DeepSeek, Gemini Advanced, and Claude 3.7 were compared in the context of questions concerning TDIs in the primary dentition, and statistically significant differences were observed among the models. Gemini Advanced and ChatGPT-4o delivered the fastest responses, whereas DeepSeek exhibited the slowest--potentially limiting its applicability in time-sensitive clinical situations. Claude 3.7 showed intermediate performance in terms of response time. Notably, Spearman correlation analysis revealed no significant relationship between response time and accuracy. Although shorter response times may be advantageous in urgent clinical scenarios, they might compromise the depth and completeness of information. For example, despite its quicker response, Gemini Advanced scored significantly lower in completeness than DeepSeek, suggesting a potential trade-off between speed and informational richness. This finding is consistent with previous studies. A study conducted in Turkiye comparing ChatGPT-3.5, ChatGPT-4o, Google Bard, and Microsoft Copilot on multiple-choice questions from the Dental Specialty Examination reported ChatGPT-3.5 as the fastest, while ChatGPT-4o had the longest response times [33]. Similarly, another study evaluating ChatGPT-3.5 and ChatGPT-4 on open-ended periodontal surgery questions found that ChatGPT-4 generally required more time to formulate responses [34]. These results underscore the necessity of balancing response speed with content quality when incorporating AI chatbots into clinical dentistry. While rapid feedback is desirable--especially in emergency scenarios such as pediatric dental trauma--accuracy and completeness are ultimately more critical for ensuring optimal patient outcomes. Moreover, in pediatric dentistry, fast response times may help alleviate parental anxiety or support early triage decisions in acute situations. However, whether quicker responses truly enhance patient education or clinical workflow remains underexplored and should be investigated in future research. Therefore, model selection for clinical use should be based on a comprehensive evaluation that considers not only speed but also the clarity, depth, and reliability of the information provided. To the best of our knowledge, this is the first study to report on the response times of the DeepSeek and Claude 3.7 models in the context of pediatric dental trauma, offering novel insights into their clinical utility.
###### Abstract
The probability density function of the delay distribution is modeled in [11] using a log-skew-normal function, but the study is based on the assumption that current is exponentially related to the process parameter, which is not generic when analyzing the variability of the delay with respect to underlying process parameters. Delays and random variables are modeled using second-order Taylor expansions in [12]. However, as the results presented in this paper revealed, second-order Taylor expansions are not accurate enough to model nonlinearities for some underlying process parameters, such as front gate work function in FDSOI. Therefore, the third key point is how to accurately model the nonlinearity of circuit performance to process variation. This paper first presents a compact, physics-based current model composed of an explicit approximation of surface potential and an accurate charge density model. A non-iterative method is applied to improve the computational efficiency of the surface potential calculation. With the introduction of an ultra-compact delay model [10], the sensitivity and nonlinearity of delay with respect to process parameters can be analyzed. Furthermore, this paper proposes an improved methodology to determine the variations in delays from physical parameter (\(L\), \(T_{Si}\), etc.) variations. The details regarding how to establish the connection between delays and process parameter variances are presented in Section 2. Section 3 presents the accuracy and computational efficiency of the proposed model. Section 4 summarizes the work.
# Bounded-Error Parameter Estimation Using Integro-Differential Equations for Hindmarsh-Rose Model
## 1 Introduction
For example, it presents a bifurcation with respect to its slow-fast parameter [13]. In order to recover the behaviors of a neuron from the HR model, several parameter-estimation procedures were proposed in the literature. In [14], nonlinear optimization is used and exploits the particular structure of the relevant cost function. In [12], the authors propose two approaches. The first one deals with a synchronization-based parameter estimation and a least squares problem, subject to constraints. The second one is based on adaptive observers as in [15]. This method aims to find a dynamical system, so that it synchronizes with the measured voltages from a real neuron. However, none of the cited papers considered the noise on the data and the impact of the size of the measurement noise domain on the estimated intervals. The first work evoking this question can be found in paper [5]. It deals with a method based on ID relations, to estimate, first, the HR model parameters, then the probability that the results permit to reproduce the correct behavior of the model output near an equilibrium point. The probability is calculated from the evaluation, \(M\) times, of the model and a classical floating-point method, to create stochastically disturbed measurements. To complete this study, the bounded-error framework is considered in the proposed paper. In contrast to [5], this article does not use a probabilistic interpretation of measurement noise. Instead, the system outputs are assumed to be disturbed by bounded uncertainty, with unknown probability distributions in their interior. To handle this task, the concept of interval arithmetic is used. It is employed to analyze the accuracy of the parameter identification algorithm, relying on the same integration relations as in [5]. The results of the proposed new approach are interval bounds for all parameters to be identified, in which the true parameters are located with 100% certainty, if the measurement noise is assumed to be bounded. This kind of result is impossible to be obtained, with the algorithm published in [5]. Moreover, these results allow for detecting the structural changes of the system dynamics with certainty, by checking whether the parameter values corresponding to the bifurcation point are included or not in the estimated parameter ranges. This paper is organized as follows. In Section 2, the parameter estimation procedure based on the ID-IO polynomials in the bounded-error framework is explained, and Section 3 presents both the HR Model and the numerical results. Section 4 concludes the paper.
## 3 Results and Discussion
### Spatial and Temporal Variation of Drought in NEC Based on CDI
The average drought impact scopes were 30.5%, 29.3%, 21.4%, 16.8% and 19.5% from May to September, respectively, which indicated that the farmland and grassland in NEC was most severely affected by spring drought, and it was the main factor limiting crop sowing and the cultivation of strong seedlings. The year 2000 had the highest average drought impact scope (67.4%) during the growing season, which had abnormally high temperatures and  
Figure 8: Spatial pattern of different drought level frequencies based on the CDI in the growing season of NEC from 1982 to 2020.  
little rain around June and July. The drought lasted for a long time with high intensity, which exacerbated the adverse effects of drought on crops in the absence of sufficient water [6]. This caused serious consequences, and the grain yield was reduced by more than 30% in the majority of areas. Therefore, based on the CDI, drought hazard evaluation is important for the government and farmers to formulate relevant disaster prevention and mitigation strategies and reduce drought losses.
# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 1 Introduction
The indicators for identifying the key conflict points are mainly centered on the probability of conflict, and seldom take into account the inherent complex network characteristics of the field taxi system and its impact on potential conflicts; airport hotspots are mainly identified as static hotspots based on historical trajectory data, and these hotspot areas are prone to conflict events only in some specific cases. The hotspots at airports are mainly identified as static hotspots based on historical trajectory data, and these hotspots are prone to conflicts only under certain specific circumstances, without considering the complexity of the dynamic traffic flow on the airport surface and the spatial and temporal distribution of hotspots. Therefore, this paper is oriented to the operation of large busy airports in China, considering the complex network characteristics of the airport surface running and taxiing system, the complexity of the traffic of the mixed operation of the aircraft vehicle, and the possible changes in the hotspots in the airport surface with space and time, combined with the complex network theory and the simulation of the traffic operation of the airport surface, to explore the potential hotspots of conflicts in the airport surface, and analyze and predict the temporal and spatial distribution of the potential hotspots of conflicts, identifying dynamics. The key conflict hotspots are identified, and the taxiing paths of aircraft are optimized for the key hotspot areas to reduce the conflict risk of the airport surface.
## 3 A Short Overview of Selected Examples of Measuring and Monitoring Systems on Sea Ships
### Temperature Measurements in the Ship's Hazardous Areas
#### 3.3.3 Real Measurement Line with Transmitter as Second-Order Inertial Oscillation Element
Figure 12: Laboratory experimental circuit of temperature measurement line with the possibility of taking account capacities of the Pt-100 sensor and connecting cables, where: R/I—second-order inertial transmitter, mA—DC ammeter, \(U_{ps}\)—source of voltage, Ro—equivalent of all resistances in the 4–20 mA current loop, \(C_{s1,2}\)—equivalents of own capacities of Pt-100 sensor, \(C_{n1,2}\)—equivalents of own capacities of cables, K- switch. transmitter with a span \(\Delta\)T = T\({}_{\rm max}-\)T\({}_{\rm min}\) = 60 - (-30) = 90 \({}^{\circ}\)C at measured temperature T\({}_{\rm o}\) = 50 \({}^{\circ}\)C for equivalent load resistance R\({}_{\rm o}\) = 600 \(\Omega\). In Figure 13 an unexpected alternating current i(t) disturbed a correct temperature measurement procedure was presented. Actually, the shape of the alternating current depends on the measured temperatures. From that, a mean value of measured current I\({}_{\rm m1}\) by DC ammeter differs from measured value I\({}_{\rm m2}\) when only DC current appears while the capacitive feedback is off. To explain the nature of unexpected additional error while measuring temperature in the discussed cases, a collected set of alternating currents observed for different measured temperatures was presented in Figure 14. For legibility of the picture for a few selected temperatures, only a significant portion of the current waveforms has been included. For each graph, mean values for bath cases were pointed out. It is worth emphasizing that mean value of current I\({}_{\rm m2}\) depends on shape of current, which can be higher, lower or equal to the mean value of DC current in the case of lack of capacities feedback. Actually, that is observed by DC indicator as additional relative error \(\delta_{\rm a}\) of measured temperature and can be calculated according to the following formula:  
\[\delta_{\rm a}=\frac{(\rm I_{m2}-\rm I_{m1})[mA]}{16\ [mA]}\cdot 100[\%] \tag{5}\]  
where value of 16 mA results from the 4-20 mA standard current transmitter. To calculate the absolute value, simply multiply the relative error by the measuring range, which was mentioned above, in the discussed case is \(\Delta\)T = 90 \({}^{\circ}\)C. The adequate values of additional absolute errors were presented in Figure 14[27]. As a result, a new error appeared, and what is worse, that error was not observed during commissioning procedures while using only resistance simulator sensor. To avoid such situations, in GMU there was patented an impedance simulator of Pt-100 sensors [28] introducing a new concept. According to this, during commissioning it is possible to take into account also the sensor's own capacities. This solution improves significantly the quality and reliability of commissioning, and in consequence accuracy of evaluation of the quantity of transported liquefied gas.
# Advancements and Research Trends in Microgrids Cybersecurity
## 3 The Action of IEC 62351
However, its implementation in actual operation scenarios, such as overcurrent relay coordination or DER management systems, is open to interpretation. For example, IEC 62351-6 standard stipulates the use of digital signatures to ensure integrity in IEC 61850 message exchanges, but the digital signature requires a high computational time with consequent problems for practical implementation in GOOSE messages. For these reasons, IEC 62351 cannot offer a strict procedure for the implementation of cryptography techniques. Reference [17] provides an assessment of the security of IEC 62351 and concludes that, although the standard contains some inaccuracies and unconventional choices, and does not consider new cryptographic algorithms that could provide the same security guarantees at a lower performance cost, the standard provides a significant security improvement, by assuring authenticity, integrity and confidentiality of data. Some recent papers address the issue of IEC 62351 implementation. A complete evaluation of security mechanisms for IEC 61850 message exchanges, including GOOSE, SV, routable-GOOSE (R-GOOSE), routable-SV (R-SV), MMS is presented in Reference [18]. The implementations of IEC 62351-4 Security for IEC 61850 MMS Messages has been discussed in Reference [19]. An analysis of the implementation of Message Authentication Code (MAC) Algorithms for GOOSE Message Security according to IEC 62351 has been presented in Reference [20]. An analysis and performance evaluation of the implementation of IEC 62351-6 probabilistic signature scheme to secure GOOSE Messages is contained in Reference [21]. For these reasons, the implementation of IEC 62351-based techniques to secure common microgrid protocols will represent a significant improvement. Still, some effort has to be invested in order to apply the standard in different contexts.
## 2 Dynamic Optimization Strategies for Public Transportation Operations
### Optimization Model Formulation
#### 2.4.4 Constraints  
In order to be close to the actual operation scenario and ensure the normal and efficient operation of the line, the constraints are set as follows:  
1. Bus skipping constraints For any bus \(i\), it is not permitted to jump two bus stops consecutively during a trip, and the total number of stops skipped during a single trip shall not exceed five. \[y_{i,j}+y_{i,j+1}\leq 1,\forall i,j\] (12) \[\sum_{j=1}^{M}y_{i,j}\leq 5,\forall i\] (13)
2. Stop skipping protection constraint For any bus stop \(j\), it is not allowed to be skipped by two consecutive arriving buses. \[y_{i,j}+y_{i+1,j}\leq 1,\forall i,j\] (14)
3. Bus operating speed constraint The bus travel speed needs to meet the roadway speed limit requirements. \[v_{min}\leq v_{i,j}\leq v_{max},\forall i,j\] (15)(4) Bus capacity constraint  
Buses should not be overloaded, i.e., at any given moment, the number of passengers on a bus should not exceed the authorized number.  
\[N_{i,j}\leq N_{max},\forall i,j \tag{16}\]  
(5) Departure interval constraint  
There is a limited number of buses and drivers, so the departure intervals for buses should not be too small to be unavailable. Meanwhile, in order to reflect the attributes of public services, the departure interval should not be set too large, making passengers wait for a long time during peak periods.  
\[x_{min}\leq x_{k}\leq x_{max} \tag{17}\]  
(6) Service rate constraint  
The percentage of passengers unable to embark/disembark properly as a result of the stop-skipping strategy should not exceed the established maximum.  
\[\frac{\sum_{j=1}^{m}y_{ij}N_{u}^{ij}}{\sum_{j=1}^{m}N_{u}^{ij}}<5\% \tag{18}\]
# Influence of Mix Proportions on Rheological Properties, Air Content of Wet Shotcrete--A Case Study
## 3 Results and Discussion
### Influence of Various Factors on Rheology and Air Content of Fresh Wet Shotcrete
#### 3.1.3 Effect of Fiber  
Figure 8 shows the relationship between fiber length and rheological properties, and air content. The air content, relative yield stress, and viscosity showed an increasing trend while slump decreases with the increase of fiber length. The results show that the fiber has an adverse effect on the fluidity with high yield stress and high viscosity, which is consistent with the results confirmed by [PERSON] et al. [60]. The reason may be that the  
Figure 7: Schematic of the effect of sand ratio (SR) on the flowability.  
specific surface area of long fiber is large, which can consume a part of free cement paste, and the friction between particles and aggregate increases with the increase of fiber length. [PERSON] et al. [20] indicated the existence of fiber hinders the flow of cement paste. In the same way, increasing the total surface of the solid after mixing the long fibers will cause more bubbles to be entrained in, resulting in a higher air content in the \"FI29 mm\" mixing ratio. The schematic diagram of FI affecting fresh shotcrete characteristics is shown in Figure 9.  
Combined with the experimental results of pumpability and shootability in Table 3, longer fibers help to reduce pressure drop and increase build-up thickness. In addition, it is determined that the addition of fibers can improve the shootability by increasing the stacking thickness [35]. In report [32], the authors point out that low slump does not mean low pumpability. Adding fiber into fresh concrete can improve the bond strength of fresh concrete and reduce the segregation during pumping. However, [PERSON] et al. [35] considered that the mixed fiber has an adverse effect on the pumpability of fresh wet  
Figure 8: Effect of fiber on slump, air content, relative yield stress, and viscosity.  
Figure 9: Schematic of fiber (FI) affecting the flowability, shootability, and air content.  
shotcrete (reducing slump and increasing yield stress). Different results may be related to the lubrication layer in the pipeline. If the lubricating layer has been formed, the low slump concrete will flow in the form of \"plug\", and the friction resistance is small.  
According to the linear fitting results in Figure 8, the fitting effect of fiber and slump is the best, R\({}^{2}\) = 0.9998, and the fitting effect of yield stress is the worst, R\({}^{2}\) = 0.8777. The prediction results of the influence of fiber on rheological properties and air content may be used in practice.
# Additive Manufacturing of PH 13-8 Mo Family: A Review
## 3 DED-Arc of PH 13-8 Mo Family
### Material Properties
#### 3.3.2 Corrosion Resistance  
The corrosion resistance of the as-built and aged PH 13-8 Mo manufactured using DED-Arc was investigated by [PERSON] et al. [69]. The corrosion resistance of as-built, aged at 400, 500, and 600 \({}^{\circ}\)C samples was compared. The as-built microstructure had the lowest corrosion resistance due to the existence of the \(\delta\)-ferrite phase. The Cr-rich \(\delta\)-ferrite phase caused a depletion in Cr in its surrounding area, which contributed to sensitization. The specimen aged at 500 \({}^{\circ}\)C had a relatively higher corrosion resistance than the specimen aged at 400 \({}^{\circ}\)C because of the decreased dislocation density and residual stresses of the sample aged at 500 \({}^{\circ}\)C. Secondary phases (M\({}_{25}\)C\({}_{6}\) carbides) were formed during the highest temperature (600 \({}^{\circ}\)C) aging process. These secondary phases are well known to be deleterious to corrosion resistance. It was unfolded that a suitable corrosion resistance can be maintained by eliminating the unwanted \(\delta\)-ferrite and Cr-enriched carbides.