esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 4 Related Works
### Surveys on Digital Twins  
As can be seen in the survey conducted by [PERSON] et al. [30], there is abundant material on DTs, as well as surveys on the subject. More general surveys on technologies and applications can be found in the works of, for example, [PERSON] et al. [3] or [PERSON] et al. [2]. Surveys with a more general discussion of (H)DTs can be found in [31; 32; 33; 34; 35; 36]. In these reviews, the focus lies more on the history of HDTs, the architecture and components that make up an HDT, and the ethical questions surrounding the use of HDTs.  
The work of [PERSON] et al. on DTs in coaching [37], and the study by [PERSON] et al. on the IOT context of DTs [38], are also worth mentioning. In the first report, an overview of the algorithms, sensors, and platforms most used in DT applications for coaching can be found. In the second one, all the aspects that should be considered in setting up a DT architecture are discussed. Although the focus lies on industry, one of the scenarios examined in depth is that of a digital patient.  
In our study, we focus on the healthcare applications of HDTs and the implementation of machine learning in this context. Several surveys related to this area have been found. In the work of [PERSON] et al. [5], a section is dedicated to the different machine learning algorithms that are used in DT implementations. They find that the use cases and services implemented generally determine the choice of model, but they also state, _However, ML models, and in particular DL (Deep Learning) methods, are generally perceived as black boxes_.  
When looking at surveys on Human Digital Twins, a general overview can be found in the article by [PERSON] et al. [12]. Five different types of human data are defined, and HDTs are grouped based on two criteria. The first is the type of modeling, which includes human body/organ modeling and human behavior modeling, with subdivisions such as activity, social interaction, and lifestyle. The second is the application domain, healthcare, industry, or daily life. Two surveys that look at the architecture of HDTs in healthcare are [14; 23]. Both articles end with a list of open challenges. In both lists, the need for a better understanding of ML frameworks is mentioned. Furthermore, [23] underscores the challenges that arise due to the complexity of data collection, preparation, and processing, and [14] states that _The deployment of twins for healthcare is mainly affected by two factors, available computing power and latency_.  
Although these surveys provide a clear overview of the architectures used in HDTs, they do not investigate the selection and application of ML algorithms in detail, which is required to answer the last three research questions we formulate.
## 4 Results and Discussion  
In order to perform qualitative and quantitative assessments of the efficiency of the proposed framework, ground truth information was determined by a medical expert, who manually marked the tumor region external boundaries on each fluorescent image sample of the dataset. The overall region included in the ground-truth-determined margins is considered as the ground truth binary mask, which was used to calculate the segmentation performance metrics to be depicted in the following subsections. As true positive (TP) pixels we consider the pixels of the binary mask extracted via the proposed segmentation pipeline that are active in the ground truth tumor margin region (intersection of estimated tumor mask with ground-truth one), as true negatives (TN) the pixels of the calculated binary mask that have been successfully classified as non-belonging to the tumorous area (background area that does not intersect with either of the two masks), as false positives (FP) the image positions that are active in the estimated tumor margin but are not present in the medical expert's evaluation mask (non-intersection of estimated tumor mask with ground-truth mask), and as false negatives (FN) we consider the pixels of the ground truth tumor mask that have not been included in the estimated tumor region. The overall consideration is more clearly illustrated in Figure 3.  
The results of Section 4 are articulated into four subsections: (a) Methodological Efficiency at Various Depths of Observation; (b) Efficiency of Image Processing Tools; (c) Evaluation of Detection Results; and, (d) Qualitative and Quantitative Comparisons with Other Segmentation Techniques.
## 1 Introduction
Although substantial improvements have been made in the above approaches, there are still some drawbacks, such as: (i) some audio event information that may be critical may not be captured; (ii) in terms of classification performance and model generalization ability, the performance of traditional recognition algorithms is very restricted; (iii) hybrid models have the disadvantage of neglecting the trend that leads to recognition errors of individual models; and (iv) end-to-end approaches cannot exceed 2D CNN in terms of model generalization and recognition accuracy with feature learning. To overcome the mentioned limitations, in this paper, we propose a new one-dimensional end-to-end CNN integrated with Bayesian optimization (BO) [51] and ensemble learning [23; 52] methods that learn directly from the audio signal. It also offers a consolidated design to reduce the computational complexity and the needed data for model training, aiming to improve model prediction, generalization, and robustness over a single classifier. Comprehensive and comparative experiments are conducted using UrbanSound8k [53] dataset to evaluate the proposed method performance. The experiment results indicated that our model recognition achieves state-of-the-art performance, outperforms traditional methods, and achieved higher accuracy up to 94.46% which is 5% higher than existing end-to-end approaches. The proposed approach has few trainable parameters compared to 2D CNNs, which have millions of parameters [32; 45; 54].
## 3 Numerical Simulation Models and Methods
### CFD Modeling  
The commercial software ANSYS Fluent 18.2 was used to calculate and solve the pressure drop characteristics of the soybean packed bed. The inlet and outlet of the model were set as velocity inlet and pressure outlet boundaries, respectively, and the cylindrical wall surface and grain particle surface were set as nonslip velocity boundaries. [PERSON] et al. [41] provided an accurate definition of the flow in a fixed bed and showed that the flow in the bed is laminar when the particle Reynolds number \(\mathrm{Rep}\) is less than 300. In this simulation, the inlet velocity was set to 0.02-0.6 m/s and the \(\mathrm{Rep}\) was 10-297, so the laminar flow model was used for calculation. When solving the computational model, the energy equation is not considered, only the continuity equation (Equation (2)) and the momentum conservation equation (Equation (3)) of the fluid are solved.  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\rho\overset{\rightarrow}{v} \right)\ =\ 0 \tag{2}\]  
\[\frac{\rho\partial(\overset{\rightarrow}{v})}{\partial t}+\rho\overset{ \rightarrow}{v}(\
abla\cdot\overset{\rightarrow}{v})\ =\ -\
abla p+\
abla\cdot(\overline{\tau})+\rho\overset{ \rightarrow}{g} \tag{3}\]  
where \(\rho\) is the density of the fluid, \(\mathrm{kg/m^{3}}\); \(\overset{\rightarrow}{v}\) is the velocity vector, \(\mathrm{m/s}\); \(p\) is the static pressure, \(\mathrm{Pa}\); \(\overline{\tau}\) is the stress tensor; \(\overset{\rightarrow}{g}\) is the gravitational acceleration, \(\mathrm{m/s^{2}}\). \(\overline{\tau}\) is described as,  
\[\overline{\overline{\tau}}\ =\ \mu\left[\left(\
abla\overset{\rightarrow}{v}+ \
abla\overset{\rightarrow}{v}^{T}\right)\right]-\frac{2}{3}\delta\
abla \cdot\overset{\rightarrow}{v} \tag{4}\]  
where \(\mu\) is the dynamic viscosity of the air, \(\mathrm{kg/(m\cdot s)}\); \(\delta\) is the unit tensor.  
The flow process was solved by the steady-state method. The coupled pressure-velocity solver used the SIMPLE algorithm, and the pressure terms were discretized using the PRESTOI format. The other equations were discretized in the second-order windward format, and the convergence residuals of all variables were set to \(10^{-6}\) to obtain more accurate results.
## 2 Methodology  
We collected vibration signals from vibration sensors mounted on the axial boxes and on-board monitoring system of a train. Root mean square (RMS) of original vibration signals is selected as the health index since it effectively describes the signal strength and reveals the impact of failure development on vibration energy. A careful review of RMS data states that there are two degradation phases, i.e., normal operation phase I and rapid degradation phase II, as shown in Figure 1. In phase I, the RMS fluctuates trivially, indicating a relatively healthy state. In phase II, the degradation speed grows abruptly until the occurrence of failure. The demarcation point between the two phases is defined as phase changing time (PCT).  
Precise detection of PCT is important since it influences the lifetime prediction accuracy. We introduce a 3\(\sigma\)-interval criterion for screening of PCT. Assume that RMS in phase I follows a normal distribution with mean \(\mu\) and variance \(\sigma^{2}\). With both \(\mu\) and \(\sigma\) obtained from historical data, the 3\(\sigma\)-interval of the extracted indicator can be constructed as \([\mu-3\sigma,\mu+3\sigma]\). We used this interval to monitor the state of the wheel tread by a comparison with latest RMS data. The wheel tread was treated normal if the RMS value fell within the interval, and abnormal otherwise. The abnormal state might be caused by either a fault or random noises. However, when multiple consecutive data were detected as abnormal, there was a high probability that a fault had occurred. In light of this, the following procedure was proposed for PCT detection (Algorithm 1).  
```
(1) Let \(w=0\), find the first time \(\tau_{0}\) when the extracted RMS value exceeds 3\(\sigma\)-interval; (2) Let \(w=w+1\), find the time \(\tau_{w}\) when the consecutive \(w+1\) RMS values \(\left\{y_{f+k}\right\}_{k=0:w}\) exceed 3\(\sigma\)-interval, i.e., \(\left|\left|y_{f+k}-\mu\right|>3\sigma\right|_{k=0:w}\); (3) Compare \(\tau_{w}\) with \(\tau_{w-1}\). If \(\tau_{w}\
eq\tau_{w-1}\), return to Step (2); else, output \(\tau_{w}\) as the PCT value.
```  
**Algorithm 1** PCT detection procedure  
According to the detected PCT, we could divide the whole degradation process of the wheel tread into two phases and the RMS values in phase II were further used for RUL prediction.
## 4 Monitoring and Analysis of Ground Deformation Based on SBAS-InSAR Technology
### Data Processing Flow  
N + 1 SAR images covering the study area at different times were processed, and one of the landscape images was selected as the super main image. By setting appropriate time and space thresholds, the above SAR data were generated into an M amplitude differential interferogram combination. The technical flow of ground deformation monitoring by the SBAS-InSAR method is shown in Figure 7, and the spatial and temporal baseline distributions obtained during data processing are shown in Figure 8a,b.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Data Type** & **Date** & **Polarization Mode** & **Track Configuration** & **Spatial Baseline (m)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Sentinel-IA image parameters table.
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 3 Problem Formalization  
Let a knowledge graph be represented as \(KG=(E,\ V,\ A,\ T)\), where \(E\) represents the collection of entities, \(V\) represents the collection of attribute values, \(A\) represents the collection of attributes, and \(T\) represents the collection of attribute triples. Each triple, \(t=(h,\ a,\ v)\) in \(T\), is represented as \(T=\{(h,\ a,\ v)|h\in E,\ a\in A,\ v\in V\}\). Assume that two heterogeneous knowledge graphs are represented as \(KG_{1}=(E_{1},\ V_{1},\ A_{1},\ T_{1})\) and \(KG_{2}=(E_{2},\ V_{2},\ A_{2},\ T_{2})\). The goal of the entity alignment task is to infer the equivalent relationship \(S=\{(e_{i1},\ e_{i2})|e_{i1}\in E_{1},\ e_{i2}\in E_{2},\ e_{i1}\equiv e_{i2}\}\), where \(\equiv\) indicates equivalence and \(i\) represents the \(i\)-th aligned entity pair. For example, in heterogeneous knowledge graphs \(KG_{1}\) and \(KG_{2}\), the entity alignment problem is to infer whether the equivalent relationship between \(e_{i1}\) and \(e_{i2}\) exists or not, as shown in Figure 1. In the recipe field, the relationships between dishes are missing; therefore, we indirectly establish the associations of the dishes through the attribute values of the dishes. The green nodes represent entities and the white nodes represent attribute values.
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Visualization Improvement
#### 2.6.3 Minimum Noise Fraction Transformation  
Minimum noise fraction transformation (MNF) was developed to increase the PCA's inability to reliably separate signal and noise components in multispectral images. The MNF is a transformation which, instead of maximizing the variance of the data, maximizes the noise content of each component to obtain the maximum signal-to-noise ratio of each component when is reversed.  
The MNF transformation allows the removal of noise from the image [31], thus determining the actual size of the image, and reducing computational requirements. It is a linear transformation consisting of the following separate principal component analysis rotations:  
* The first rotation uses the main components of the noise covariance matrix to decorrelate and rescale the noise in the data resulting in transformed data in which there is no band-to-band correlation.
* The second rotation uses the main components derived from the original image data after the noise of the first rotation has been whitened and rescaled by the standard deviation of the noise. Since there will be more spectral processing later, the inherent dimensionality of the data will be determined by examining the final eigenvalues and associated images.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 2 Research Status of Tunnel Lighting System
### Evolution of Tunnel Lights  
The efficiency of the tunnel depends on achieving a reliable lighting source. Since the first artificial tunnel in the world, namely the \"Faint incline Road\" in Qin Dynasty [6], tunnel lighting has included diverse and relatively simple lighting sources such as torches, kerosene and gas lamps, and so on.  
Traditional tunnel lighting has consistently made use of incandescent lamps, fluorescent lamps, and high-pressure sodium lamps (HPSL) as light sources, however, with the rapid development of modern vehicle, transportation, logistics and other industries, there is excessive demand for superior optical properties of light sources (appropriate color temperature and excellent color rendering, lighting efficiency, luminous flux, light attenuation, service life, and visibility in the smoke of vehicle emissions). At the same time, with the progress in modern optical technology and manufacturing processes, LED lamps have comparatively more obvious comprehensive advantages in terms of technical parameters such as service life, color rendering, color temperature range, light flux utilization, and regulation convenience, as shown in Table 2.  
At present, incandescent and fluorescent lamps are essentially no longer used in tunnel lighting, HPSL is being replaced by LED light sources on a large scale. The advantages of LED lights in the aspects of visual performance, luminous efficiency and production cost make them the ideal light source for the highway tunnel lighting environment [10].  
Along with the evolution of lighting sources, the control mode of tunnel light sources is also evolving. This can be roughly categorized into artificial control, timing control and intelligent control. Intelligent control is the mainstream control mode of tunnel lighting sources at present. Moreover, the characteristics of 0-100% step-less dimming of LED light source has made step-less dimming the mainstream control method that is replacing the traditional logic switch method.  
* Note 1: Compared with same criterion of light attenuation (70%) and rate of lamp replacement.  
\end{table}
Table 2: The characteristics and advantages of LED tunnel lights.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 2 Study Area and Use Data Sets  
The study area is located in Antalya City, which is one of the most susceptible regions in Turkiye to landslides, described as active deep-seated rotational landslides [19; 20; 21]. The area is characterized by a combination of mountainous and semi- mountainous topography, with a Mediterranean climate that is evident in the local weather patterns.  
The geological structure surrounding the Buyukalan neighborhood in Antalya is defined by the complex interaction of karstic rocks, sedimentary layers, and volcanic formations. The area is predominantly covered by Antalya tuff, a type of karstic rock that lacks a well-developed joint system, and exhibits variable porosity and mechanical behavior. This tuff plays a crucial role in determining the geotechnical properties of the region and constitutes a major component of its geological foundation [22].  
According to the 1:100,000 scale geological map [23], the study area comprises ten different lithological units, including alluvialion, slope debris, sandstone-mudstone-limestone, sandstone, melange, olistostrome, limestone, spilitic basalt, cherty-limestone, and a complex of radiolarite-chert-shale (Figure 1). These formations range in age from the Quaternary to the Middle Triassic (Ladinian). The dominant geological formations in the study area are Quaternary-aged slope debris, Upper Senonian-aged Kirkdirek Formation (ophiolitic melange), Jurassic-Cretaceous-aged Derekoy Radiolarite (radiolarite-chert-shale), and Lower Cretaceous-Malm-aged Beydaglar Formation (neritic limestone). The large landslide that occurred on 14 March 2017, was located within the Jurassic-Cretaceous Derekoy Radiolarite formation, characterized by chert, radiolarite, and shale lithologies.  
This study was conducted in the Buyukalan region, approximately 65 km west of Antalya city center, covering a total area of 243 km\({}^{2}\). A total of 128 deep-seated rotational landslides were identified within the area, one of which occurred on 14 March 2017, causing significant structural damage [24; 25; 26].
## 2 System Principle
### System Structure Composition
As GNSS RF signals propagate along the tunnel direction within the LCX cables, they are consistently and uniformly radiated outward, providing GNSS signal coverage throughout the tunnel. Compared to direct signal transmission via antennas, using LCX cables, which impose geometric constraints on the radiation path, significantly reduces multipath effects during signal propagation within the tunnel. Since this study focuses on two-dimensional positioning within tunnels, it assumes that all receivers are positioned at the same height. To prevent the target receiver from being obstructed and unable to receive GNSS signals, two LCX cables are installed at a certain height above the ground, as shown in Figure 3. However, this configuration may introduce errors in the positioning results. In Figure 3, the algorithm proposed in this study treats the GNSS signal propagation distance, EF, as the y-coordinate of the receiver. The positioning errors can be partially corrected by applying differential correction values, which are transmitted in real-time by the reference receiver.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods  
#### 2.3.1 Multivariate Scattering Correction  
Multivariate scattering correction (MSC) is a common data processing method for multi-wavelength calibration modeling. It can effectively eliminate the near-infrared diffuse reflection spectrum of sample mirror reflection and uneven noise and eliminate the spectral baseline drift phenomenon and spectral non-repeatability to enhance the component content-related spectral absorption information [23; 24; 25].
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion
### Sensitivity to the Sampling Frequency  
For this analysis, the functions \(P_{E}(t)\) and \(P_{C}(t)\) have been used as in Equations (29) and (30), but the sampling time is swept between 0.1 ms and 5 ms. As a reference, both \(P_{E}(t)\) and \(P_{C}(t)\) have a period equal to 43.1 ms.  
Figure 16 represents the functions \(P_{E}(t)\) and \(P_{C}(t)\) obtained using Equations (32) and (33) with different sampling periods from 0.5 ms to 5 ms. In the latter, the piecewise aspect can easily be observed.  
Table 6 shows how the sampling time impacts in the average power calculated using the proposed methods.  
These results show that the estimation error observed when using ideal synthesis signals of \(P_{E}(t)\) and \(P_{C}(t)\) (see Table 2) is partially due to the sampling frequency. In fact, a noticeable degradation of the results (more than 10%) has been remarked when the number of measurements falls under 15 samples per cycle of the signal (i.e., with sampling time is longer than 3 ms).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Average Power (mW) for Different Sampling Periods Ts**} \\ \cline{2-7}  & **0.1 ms** & **0.5 ms** & **1 ms** & **2 ms** & **3 ms** & **4 ms** & **5 ms** \\ \hline Reference method & 118.050 & 118.050 & 118.050 & 118.050 & 118.050 & 118.050 & 118.050 \\ Instantaneous power (method 1) & 118.025 & 117.530 & 115.983 & 109.957 & 100.417 & 88.3360 & 74.2730 \\ One-period average (method 2) & 118.551 & 118.097 & 116.691 & 111.200 & 102.506 & 91.3430 & 77.9310 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the results obtained with different sampling times.  
Figure 16: AC component of \(P_{E}(k)\) and \(P_{C}(k)\), filtered and non-filtered, with different sampling periods.
## Materials and Methods  
### Data  
Data used for testing and evaluating the features were collected at the hospital Ziekenhuis Oost-Limburg (Genk, Belgium) during the period of 2015-2017. In detail, data were collected from patients hospitalised at the ICU and coronary care unit who were at these wards for at least ten days. Data consisted of vital parameters which were recorded continuously by Philips Intellivue monitors (Philips Electronics Nederland B.V., Amsterdam, The Netherlands), that recorded continuously and was annotated on average hourly to bi-hourly by the nursing staff. The recorded data was extracted from the electronic medical record for a total of 447 different patients, three of them readmitted to the unit again, hence, in total 450 recorded admissions annotated with either mortality or survival by discharge. The age of the patients was 65 (\(\pm\)16) years old, 305 of patients were males and 142 were females. The average duration of stay at the ICU is 20.96 days with a minimum of 10 days, maximum of 97 days, median of 30 days and IQR of 20-53 days of ICU stay. The vital parameter data consisted of the heart rate, the respiration rate, oxygen saturation, arterial blood pressure (ABP), non-invasive blood pressure (if ABP was not measured) and body temperature (not frequently). The patient population of the study has different reasons for ICU admission as shown in Figure 1. The local Ethical Committee was notified and approval was obtained (19/0023R).