esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Signal Processing While Drilling
### Data Preprocessing
\(\left|X_{i}-\overline{X}\right|\leq 3\sigma\), \(X_{i}\) is normal data and should be retained. The \(3\sigma\) criterion is employed for the removal of gross errors, even though the probability of observing data with an error greater than \(3\sigma\) is less than 0.003. Consequently, when the Pauta criterion, also referred to as the \(3\sigma\) criterion, is applied for gross error removal, the likelihood of discarding valid data is minimal. As a result, some unreasonable outliers may still be retained.
# Robotic Coverage Path Planning for Ultrasonic Inspection
## 2 Related Work
In the present work, we focus on volumetric ultrasonic inspection. The respective coverage relates to the propagation of ultrasound waves through the investigated materials. We deploy ray tracing to approximate the process of ultrasonic inspection. The goal is to predict the volumetric coverage for a single sensor position. In order to model inspection via UT, it is necessary to have a closer look at the underlying physical process. For ultrasonic inspection, phased arrays are commonly used for signal generation and data acquisition. The total focusing method (TFM)--introduced in [19]--is therefore one of the most popular methods. The term refers to a post-processing of the full matrix capture, which accounts for all received signals of all receiver-sender pairs of the phased array. In order to calculate volumetric coverage for a single sensor position, we need to approximate how ultrasonic waves propagate from a specific sender to a specific receiver interacting with the specimen. Generally, strategies for numerically evaluating sound propagation can be grouped into wave-based and geometric-based approaches: The underlying wave equation can be solved by classical methods, e.g., the finite element method and boundary element method [20; 21]. This is especially useful to evaluate low frequency effects, such as diffraction. A geometric acoustic approach, on the other hand, is suitable for high-frequency waves, i.e., if the examined wave lengths are short in relation to the objects that the wave is interacting with. In order to additionally account for low frequency effects, geometric- and wave-based approaches can be combined [22]. For our purpose, we use a geometric approach, which already is a widely studied field, motivated by a variety of use cases, e.g., auralization for audio post processing and virtual environments, or simulation of classrooms helping the design process [20; 23; 24]. Recently, realistic ultrasound rendering was studied to enhance medical training [25; 26; 27; 28].
# Variable Selection Using Adaptive Band Clustering and Physarum Network
## 3 Experiment  
### Corn Data Set  
#### 3.1.1 Data Set  
This database is available at [28]. Three different Near Infrared (NIR) spectrometers were used to measure the corn samples. The whole spectrum was from 1100 to 2498 nm and the scanning resolution was 2 nm, namely, there were 700 wavelengths in total. There were 80 samples in the database. Each sample had four measured properties, i.e., water, oil, protein, and starch. The protein content was the property predicted in this paper. Numerical range of the protein content was from 7.6540 to 9.7110%. We used the hold-out method to select the training set and test set, i.e., we randomly selected the training and test sets. There were ten rounds of selection to generate the training set and test set. In each round, a training set and a test set were randomly produced. A training set consisted of 20 samples and a test set consisted of the remaining 60 samples. In addition, the performance of the model was assessed by the predicted root mean square error (RMSEP) and the correlation coefficient (R) between the predicted values and the real values in the test set. The RMSEP is defined in Formula (9)  
\[\text{RMSEP}=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(\hat{y}_{i}-y_{i}\right)^{ 2}}{N}} \tag{9}\]  
where \(\hat{y}_{i}\) are the predicted values, \(y_{i}\) are the measured values, and \(N\) represents the number of the test sets.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 3 Data Analysis, Results, and Discussion
### What Methods Are Being Employed to Enhance IMU-Based Adult Human BP Assessment?  
Various sensor fusion methods and data signal analysis techniques have been employed in studies focusing on inertial sensors for measuring BPs. These methods have contributed to improving the accuracy and reliability of detecting and quantifying BPs. Notably, [PERSON]'s algorithm was found to be superior in some studies, offering high accuracy and low computational load, while the Principal Component Analysis (PCA) fusion method demonstrated optimal performance in selecting relevant features. These findings suggest that the choice of sensor fusion method and data analysis technique can significantly impact the effectiveness of inertial sensors in assessing BPs. Researchers should carefully consider these factors when designing experiments and data processing pipelines.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### RF-EMF Exposure Levels  
This section summarize the levels of exposure reported by the different studies, divided by different types of indoor environments. For sake of homogeneity, all the exposure levels were reported also as a percentage of the reference level according to the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines [43] for the corresponding frequency band. When considering levels of exposure due to the wide RF-EMF frequency band, the values were compared to the lowest (i.e., the strictest) ICNIRP reference level of the whole band, in order to account for the worst case.
# Healing Effects of Photobiomodulation on Diabetic Wounds
## 3 Photobiomodulation (PBM)  
The use of light as a therapy dates back to ancient civilizations, with ancient Egyptians and Indians making use of sunlight (heliotherapy) for healing and promoting health. Professor [PERSON] reported the earliest application of PBM in 1967. He described how irradiation accelerated hair growth on the back of shaven mice [12]. This piqued his interest, and he went on to show how PBM could stimulate wound healing in mice [13] and in human patients [14; 15]. The use of light as a treatment hassubsequently evolved and is used to reduce inflammation and edema, to treat neurological disorders and pain, and to promote healing of various tissue types.  
PBM involves the use of nonionizing forms of light from sources including lasers, light-emitting diodes (LEDs), and broadband light in the visible and near-infrared (NIR) spectra to cause physiological changes and therapeutic benefits. It is a non-invasive phototherapy that can be used alone or in combination with other wound treatments. PBM produces photophysical and photochemical changes within cells without eliciting thermal damage. Despite the fact that PBM is applied to treat a wide variety of ailments, it remains underutilized and controversial. This is mainly due to the poor understanding of the underlying cellular and molecular mechanisms, so its use is largely experimental. Another contributing factor is the fact that a large number of settings and parameters, such as wavelength (nanometers, nm), fluence (joules per square centimeters, J/cm\({}^{2}\)), power density (watts per square centimeters, W/cm\({}^{2}\)), pulse structure (nanoseconds, ns), and timing (seconds, s) of the applied light must be carefully chosen for each treatment. A less than optimal choice of these parameters results in unsatisfactory findings or even a negative therapeutic outcome [16]. There is almost a complete lack of reports of side effects or adverse events associated with PBM [16], something which counts largely in its favor.  
Light that falls within the so called \"optical window\" at red and NIR wavelengths (600 to 1070 nm) are typically used for PBM [16]. The lower wavelengths in the red spectrum (600 to 700 nm) do not penetrate into tissue as deeply and are used to treat superficial tissue, while longer wavelengths (780 to 950 nm) penetrate much deeper and are used to treat deeper-seated tissues. The application of light in the blue/violet spectrum has been shown to suppress pathogenic bacterial growth. The power of light used typically lies in the range of 1 to 1000 mW and depends on the application. The dose (or fluence), which is a function of the combination of irradiance (medicine) and time (dose), is also very important, and also varies depending on the application [16].
## 2 Materials and Methods
### Geophysical Sensors  
Automatic Resistivity Profiling (ARP) and frequency domain electromagnetic induction (EMI) represent the most potentially useful methods for preliminary soil characterization. ARP equipment (developed by Geocarta SA, Paris, France) allows the measurement of apparent electrical resistivity (ERa) of soils resulting from all soil resistances in the investigated volume. A mobile multi-electrode system, consisting of several electrodes mounted on 8 gear wheels, was towed by a quad and progressively made to advance on the soil surface with a signal acquisition speed of 8 m/s. Three different distances between dipoles (0.5, 1.0 and 1.7 m) allowed the simultaneous survey of three depths in a vertical section.  
EMI methods in the frequency domain were used to measure the apparent electrical conductivity (ECa) of the subsurface (approximately from 1 to 10 m of depth). Two portable instruments (DUALEM 642-S and PROFILER EMP-400), transported by a sled pulled by a quad, acquired georeferenced (GSSI) data continuously along parallel trajectories. One day per technique was necessary for the survey. The use of DUALEM (Milton, ON, Canada) enabled measurements in two configurations (HCP and PRP), each characterized by three distances between coils, to obtain different investigated soil depths (1, 2, 3 m in PRP and 3.2, 6.4, 9.5 m in HCP). With the PROFILER, 3 frequencies (7 KHz, 10 KHz and 15 KHz) were measured in line VDM (vertical dipole mode) in a bandwidth extending from 1 kHz to 16 kHz in 1 kHz step, with a presumed investigated depth of approximately 1.8 m. Therefore, in the analyzed industrial site, ARP and DUALEM prospections were carried out before the main earthmoving and leveling works, when large heaps of waste were still  
Figure 1: Sketch map of the study site at national, regional, local and field scales.  
present in the site. Thus, the survey did not cover the entire soil surface. Furthermore, only the EMI survey was repeated after the leveling works, using a PROFILER EMP-400 (GSSI, Nashua, NH, USA).
## 6 Conclusions  
The HK model, a novel SNR-based weighting strategy proposed in this study, improves GPS positioning accuracy in multipath-prone environments such as city streets and urban canyons, outperforming conventional methods. By leveraging the standard deviation of SNR values, the model distinguishes between LOS and NLOS signals and assigns weights accordingly. Validation using observational datasets from three representative environments--open sky (OS), city street (CS), and urban canyon (UC)--demonstrated the superiority of the HK model over conventional elevation- or SNR-based weight models.  
In the UC environment (TEHE site), where multipath interference was most severe, the HK model reduced the horizontal RMSE from 13.0 m to 4.7 m and the vertical RMSE from 19.5 m to 6.9 m, yielding a 3D positioning improvement of up to 22.4 m. On typical city streets (SOND site), horizontal RMSE improved from 11.6 m to 3.8 m, while at the OS site (IHU4), horizontal and vertical errors decreased from 4.6 m and 5.3 m to 3.0 m and 4.0 m, respectively. Temporal analysis at the TEHE site over 24-hourly intervals further confirmed the model's reliability: 3D RMSE improved in all 24 cases, with horizontal  
Figure 13: Improvement of positioning accuracies based on the HK weight model for 24-hourly datasets at TEHE in addition to the number of observed satellites: (**a**) hourly horizontal RMS error; (**b**) hourly vertical RMS error; (**c**) hourly 3D RMS error; (**d**) hourly number of visible satellites.  
improvements observed in 22 of 24 cases. The HK model consistently outperformed five other weight models, particularly under degraded satellite visibility conditions. These results demonstrate the model's capability to enhance real-time GPS positioning accuracy by effectively mitigating multipath effects without the use of auxiliary sensors. The HK model includes several parameters that can be tailored empirically, allowing for optimization based on receiver manufacturer or hardware specifications. As a result, applying the model to different devices or environments may benefit from recalibrating these parameters to achieve optimal performance.  
Conceptualization, H.-I.K. and K.-D.P.; methodology, H.-I.K.; software, H.-I.K.; validation, H.-I.K.; investigation, H.-I.K.; resources, K.-D.P.; data curation, H.-I.K.; writing--original draft, H.-I.K.; writing--review and editing, K.-D.P.; supervision, K.-D.P.; funding acquisition, K.-D.P. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement grant funded by the Ministry of Land, Infrastructure and Transport (RS-2022-00141819, Development of Advanced Technology for Absolute, Relative, and Continuous Complex Positioning to Acquire Ultra-precise Digital Land Information).  
Not applicable.  
The data should be available upon request to the corresponding author.  
All authors were employed by the company PP-Solution Inc. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### Mechanical Properties
The springiness of pangasius gels did not show statistically significant differences in all treatments (\(p\geq 0.05\)). This finding is consistent with previous studies on the effects of high-pressure treatments on _Nemiptera japonicus_ gels [32] or barramundi gels [2]. In contrast, the gel strength of all HPP samples was significantly higher than that of HIGs (\(p<0.05\)), ranging from 346.21 N.mm to 464.92 N.mm (Table 3). Among the HPP samples, the gel strength was comparable in all treatments (\(p\geq 0.05\)), with the highest average value (464.92 \(\pm\) 13.56 N.mm) observed in samples with 2% added salt subjected to 500 MPa/10 \({}^{\circ}\)C/10 min, followed by cooking at 90 \({}^{\circ}\)C for 30 min. The comparable hardness and higher gel strength of high-pressure-induced pangasius gel with 1% added salt, compared to heat-induced gels (HIGs) with 2% salt, may be attributed to the ability of HPP to enhance interactions among myofibrillar proteins, promoting protein solubility at low salt concentrations [9]. Additionally, pressure causes the disruption of myofibrillar structures, accompanied by the depolymerization of protein polymers, resulting in higher gel strength of myofibrillar proteins under low salt conditions [11]. Furthermore, [PERSON] and [PERSON] [22] reported that under reduced salt conditions, pressure can promote the formation of high molecular weight polypeptides, potentially through disulfide bonding, thereby increasing gel strength. HPP enhances protein bonding reactions and induces the formation of dense gel network structures, contributing to increased gel strength under reduced salt conditions [4]. As a result, HPP has been extensively studied for reducing the amount of salt added to protein gels. Studies have shown that the presence of salt improves protein solubility, enhances bonding, and maintains the denaturation/aggregation pattern of major proteins under thermal conditions [37]. However, using high salt concentrations in protein gels is undesirable due to their negative impact on product quality and consumer health. Therefore, HPP can be beneficial for producing high-quality pangasius gel at low salt concentrations. In this experiment, high-pressure treatment resulted in pangasius gel at a lower salt concentration of 1% with comparable mechanical properties to HIGs.
# Balloon Design for Mars, Venus, and Titan Atmospheres
## 2 Theoretical Model
### Planetary Environment  
The atmospheric characteristics of the planetary body play an important role in the design of balloons. Examples of the effect of environment include: (a) the atmospheric density determines the size of the balloon; (b) the wind directions affect the trajectory; and (c) the infrared and solar radiation determines the requirements on the strength of the balloon envelope. Other factors that impact the balloon design are: surface albedo, surface thermal inertia, topography, time of year of the flight, atmospheric composition and temperature.  
For the design of balloons on different planets/moons, a standard atmospheric profile of the particular body has to be used along with the temperature and diurnal variation, gravity, irradiance, albedo, eccentricity, etc. For the design of a balloon on Mars and Venus, the atmospheric profile data are taken from [38], and the HASI Titan data model [39] is used for Titan. Each of these models reproduces the atmospheric profile of temperature, pressure and air density. Table 1 lists atmospheric parameters for Earth, Mars, Venus, and Titan.
## 3 Data and Analysis  
### Acquisition and Preprocessing of Data  
Monitoring data used here were collected from the long-term monitoring system of a No. 42 turnout near the Jin-Hu station of the Beijing-Shanghai high-speed railway. This area contains a track system of welded turnout, ballastless track, and continuous girder from top to bottom. Under the coupling actions of environmental factors and train loadings, premature degradation or damage emerges in the track system due to the complicated interaction between structures. Therefore, a long-term monitoring system of the track system was installed and operated in this area and its schematic is presented in Figure 1. One measuring point of air temperature, six measuring points of rail-track slab relative displacement, four measuring points of track slab-girder relative displacement and two measuring points of girder displacement were placed. All the measurements were monitored by the fiber Bragg grating sensors due to their advantages of lightweight, small size, high sensitivity, and so on [31]. The measurement accuracies of temperature and displacements are 0.1 \({}^{\circ}\)C and 0.1 mm, and the sampling interval is 15 min.  
The analyzed temperature and displacement data were collected from 1 May 2015 to 2 September 2018. In summer and winter, the monitoring system has a potential risk of failure due to extremely high temperature and low temperature. Moreover, short-term artificial maintenance, failure of acquisition system, and interruption of transmission process also lead to short-term or long-term loss of monitoring data. Therefore, the raw data collected by the monitoring system need to be preprocessed. Firstly, the data with abnormal amplitudes are eliminated, including data with very large or very small amplitudes and flat signal data generated by the malfunction of sensors [32]. After removing these data with abnormal amplitude, four measuring points were abandoned because of few residual data, and only eight measuring points were reserved. Then, the daily data of the eight measuring points were averaged to form a new time series for analysis.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 3 Results
### Determination and Distribution of the Initial RSSI Values of the Anchors in Off-Line Mode  
For the purpose of obtaining the RSSI map, it was necessary to measure all the RSSI values between all anchors and measurement positions.  
In the case of the industrial laboratory, the coordinates of the measurement positions were given in the form [segment, position]. The segment denotes the segment of the assembling line. The first segment was located between the anchors 1 and 2, the second segment was located between the anchors 2 and 3, etc. All positions were measured from the beginning of the segment in steps of 10 cm in length.  
After obtaining the RSSI map values in of-line mode, various analyses were possible regarding RSSI in the function of the position and the structure of the anchors. One of the possibilities to make a satisfactory representation of the RSSI values distributions was to use MATLAB. The distribution was obtained by defining a color contour for every measured position.  
The dimensions of the assembly line presented earlier determined 160 measurement points (the length of the line was 6 m, while the width was 1.5 m; therefore, along the length 2 \(\times\) 60 measurement points were defined, while along the width 2 \(\times\) 20 measurement points were defined). From Figure 7 one can conclude that the shorter sides of the assembly line had a shape of a half-circle, but for presentation purposes, the curved parts of the assembly line were approximated by straight lines. The industrial laboratory, as well as the assembling line itself, consisted of metal elements such as tools, holders, cables, etc.; therefore, in no circumstances could a homogeneous distribution be obtained.  
In Figure 8, the distribution of the off-line RSSI values regarding different anchors is presented.  
Results presented in Figure 8 are mainly used to check whether the anchors are positioned well. Based on the presented data, one can conclude that the anchors were positioned well, and that the number of the anchors was satisfactory for the required coverage of the assembling line.  
The characteristic values for each anchor are given in Table 1.  
Figure 7: The structure of the measurement position and anchors at the industrial laboratory.  
Figure 8: Distribution of the RSSI values for the Anchors: (**a**) Anchor 1; (**b**) Anchor 2; (**c**) Anchor 3; (**d**) Anchor 4; (**e**) Anchor 5.
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 2 Materials and Methods
### Mathematical Models
#### 2.2.1 Electromagnetic Field Analysis  
The electromagnetic wave propagation equations are calculated using [PERSON]'s set of equations and are reduced to a general form [20]. The equations are shown as follows:  
\[\
abla\times\left[(\varepsilon_{r}-\frac{j\sigma}{\omega\varepsilon_{0}})^{-1} \
abla\times H_{\varphi}\right]-\mu_{r}{k_{0}}^{2}H_{\varphi}=0 \tag{1}\]  
where \(\varepsilon_{0}=8.8542\times 10^{-12}\) F/m is the permittivity of free space, \(\varepsilon_{r}\) is the relative permittivity (\(\varepsilon_{r}=1\)), \(\sigma\) is the electric conductivity (S/m), \(\mu_{r}\) is the relative permeability and \(k_{0}\) is the free space wave number (m\({}^{-1}\)), \(H_{\varphi}\) is magnetic field strength (A/m).  
The microwave energy is emitted from the slot of the microwave antenna and propagated into the porous lung tissue. The boundary conditions for the analysis considering electromagnetic wave propagation are:  
1. In the microwave antenna dielectric inlet, specify an input microwave power.
2. Z-axis as the axis of symmetry.
3. The outer boundary along the lung tissue is considered the radiation boundary [21].  
\[\hat{n}\times\sqrt{\overset{\rightarrow}{E}}-\sqrt{\hat{u}}H_{\varphi}=-2 \sqrt{\hat{u}}\overset{\rightarrow}{H}_{\varphi 0} \tag{2}\]
4. The microwave antenna outer and inner conductors are regarded as ideal dielectric [21].  
\[\hat{n}\times\overset{\rightarrow}{E}=0 \tag{3}\]