esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### GNSS RTK Corrections and Field Data Acquisition
The field observations were conducted on 12 September 2019, with the starting times of the repetitions shown in Table 1. The mission was planned with at least a two-hour difference between repetitions using the respective GNSS corrections to reduce the effect of a particular constellation on the positioning accuracy [30]. Geometric Dilution of Precision (GDOP) values were under a maximum tolerance of six during the entire study [31]. Mission planning was conducted using the Trimble GNSS Planning Online website ([[https://www.gnssplanning.com/](https://www.gnssplanning.com/)]([https://www.gnssplanning.com/](https://www.gnssplanning.com/)), accessed on 12 October 2021). During the fieldwork, the number of visible GPS + GLONASS satellites ranged from 13 to 16, while four SBAS satellites were visible during the same time. An elevation mask of 10\({}^{\circ}\) was applied for GNSS observations. The total electron content (TEC) ranged from 10.19 to 11.21 during this study, with minimal variation during the individual repetitions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Repetition**} & \multirow{2}{*}{**GNSS Corrections**} & \multirow{2}{*}{
\begin{tabular}{c} **Starting Time** \\ **(UTC + 01:00)** \\ \end{tabular} } & \multicolumn{4}{c}{**Number of Satellites**} & \multirow{2}{*}{**GDOP**} & \multirow{2}{*}{**TEC**} \\ \cline{3-3} \cline{5-8}  & & & & & & & \\ \hline \multirow{3}{*}{\(1\)st} & CROPOS & 10:00 & 8 & 5 & 13 & 2.14 & 10.19 \\  & Base station & 10:15 & 8 & 6 & 14 & 1.83 & 10.54 \\  & SBAS & 10:30 & 10 & 6 & 16 & 1.71 & 10.65 \\  & Mobile device & 10:45 & 9 & 6 & 15 & 1.61 & 10.75 \\ \hline \multirow{3}{*}{\(2\)nd} & CROPOS & 13:00 & 12 & 4 & 16 & 2.30 & 10.81 \\  & Base station & 13:15 & 11 & 5 & 16 & 1.84 & 10.97 \\ \cline{1-1}  & SBAS & 13:30 & 11 & 5 & 16 & 1.81 & 10.99 \\ \cline{1-1}  & Mobile device & 13:45 & 11 & 5 & 16 & 1.68 & 11.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Start time periods and GDOP for the used RTK corrections in two repetitions according to Trimble GNSS Planning Online.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 4 Design and Deployment
### Weather Proofing  
The casing design for the animal tracking nodes was complicated by the fact that they needed to be both weather-proof and physically suitable to the animals without causing irritation. To provide a very close fitting enclosure for our electronics, we 3-D printed the casing and then designed a harness to secure the device to livestock (in this case, sheep). After several trials, we settled on a harness made of soft but durable material which secured the sensor node on the top of the animal's back. Figure 4 shows the soil node and the animal tracker in their respective enclosures.
## 4 Bayesian Analysis of Weibull Mixture Distribution Using NG-RJMCMC Algorithm  
### Change the Weibull Distribution into a Member of the Location-Scale Family  
#### 4.1.1 Form of Transformation and Location-Scale Parameter of Weibull Distribution  
If the random variable \(T\) follows the Weibull distribution with the shape parameter \(\eta>0\) and the scale parameter \(\lambda>0\), \(T\sim\mathrm{Weibull}(\eta,\lambda)\), then the p.d.f. is given by Equation (10) [63]:  
\[f_{T}(t|\eta,\lambda)=\left\{\begin{array}{cc}\frac{\eta}{\lambda}\big{(} \frac{t}{\lambda}\big{)}^{\eta-1}e^{-(\frac{t}{\lambda})^{\eta}}&,t\geq 0\\ 0&,t<0\end{array}\right. \tag{10}\]  
and CDF is given by Equation (11) [64]:  
\[F_{T}(t|\eta,\lambda)=\left\{\begin{array}{cc}1-e^{-(\frac{t}{\lambda})^{ \eta}}&,t\geq 0\\ 0&,t<0\.\end{array}\right. \tag{11}\]  
Equation (11) can be rewritten as Equation (12):  
\[F_{T}(t|\eta,\lambda) = \left\{\begin{array}{cc}1-e^{-(\frac{t-0}{\lambda})^{\eta}}&,t \geq 0\\ 0&,t<0\end{array}\right.\] \[= F_{T}\big{(}\frac{t-\mu}{\lambda}\big{)}_{\mu=0}.\]  
Based on Equation (12) and the explanation in Section 3.1, it can be concluded that the Weibull distribution is a member of the scale family. To facilitate the analysis of the Weibull distribution, it is necessary to transform it into a location-scale distribution.  
If the random variable \(T\) is transformed into a new variable, \(Y=\ln T\), then \(Y\) has an extreme value (EV) type 1 (Gumbel-type for minima) distribution with the location parameter \(\mu=\ln\lambda\) and the scale parameter \(\sigma=\frac{1}{\eta}\)[64; 65] (this explanation can be seen in Appendix A). Therefore, the p.d.f. and CDF for \(Y\sim\mathrm{EV}-\mathrm{I}\big{(}\mu=\ln\lambda,\sigma=\frac{1}{\eta}\big{)}\) are given by Equations (13) and (14), respectively [66; 67; 56]:  
\[f_{Y}(y|\mu,\sigma)=\frac{1}{\sigma}\exp\biggl{[}\biggl{(}\frac{y-\mu}{\sigma }\biggr{)}-\exp\biggl{(}\frac{y-\mu}{\sigma}\biggr{)}\biggr{]} \tag{13}\]  
\[F_{Y}(y|\mu,\sigma)=1-\exp\biggl{[}-\exp\biggl{(}\frac{y-\mu}{\sigma}\biggr{)} \biggr{]}, \tag{14}\]  
where \(-\infty<y<\infty\), \(-\infty<\mu<\infty\), and \(0<\sigma<\infty\).
# A Novel Connected-Components Algorithm for 2D Binarized Images
## 1 Introduction  
Connected-component labeling (CCL) is a fundamental operation in image processing, computer vision, and pattern recognition, serving as a building brick for object detection, shape analysis, and segmentation tasks [1]. The notion of CCL is fundamental to various image analysis tasks, including document processing, biomedical imaging, and industrial defect detection [2, 3, 4].  
The problem, simply put, involves identifying and labeling distinct groups of connected pixels in a binarized image. Given an input image consisting of only two pixel values, black (foreground) and white (background), the goal is to assign unique labels to clusters of connected foreground pixels while ensuring that all pixels within the same component share the same label.  
Mathematically, the connected-component problem can be described as follows:  
Given a binary image I defined on a 2D lattice \(\Omega\subseteq Z^{2}\), let \(P\) be the set of all black pixels, \(\mathrm{I}(\mathrm{x},\mathrm{y})=1\). The task is to partition \(P\) into maximally connected subsets \(\mathrm{C}_{i},\mathrm{i}=\overline{1,n}\) such that each subset \(\mathrm{C}_{i}\) satisfies the connectivity condition: \(\forall(\mathrm{p},\mathrm{q})\in\mathrm{C}_{i}\), there exists a sequence of pixels \(\{\mathrm{p}_{0},\mathrm{p}_{1},\,\ldots,\mathrm{p}_{k}\}\) where \(\mathrm{p}_{0}=\mathrm{p},\mathrm{p}_{k}=\mathrm{q}\), and each consecutive pair \((\mathrm{p}_{i},\mathrm{p}_{i+1})\) is connected under a given neighborhood definition.  
The two most common definitions of pixel connectivity in digital images are as follows:* 4-way connectivity: A pixel is connected to its immediate vertical and horizontal neighbors.
* 8-way connectivity: A pixel is also connected to its diagonal neighbors.  
Classical algorithms such as two-pass labeling [5], disjoint sets [6], and contour tracing [7] despite their simplicity and effectiveness often struggle with scalability in real-time, high-resolution applications, or on more specialized architectures due to factors such as memory overhead, inefficient cache usage, and the need for repeated image passes. Modern approaches, such as those based on dynamic data structures [8; 9; 10], parallelization [5; 8; 11], or optimized access patterns [12], have been introduced in order to address some of these issues and achieve better overall results.
## 5 Event Detection
### Knowledge-Driven Event Detection  
A second detector uses a knowledge-driven approach that employs expert knowledge and therefore detects events for which descriptions are available. Techniques for collecting expert knowledge have been mostly applied for describing risks and failures, e.g., Fault Tree Analysis (FTA) [18] or Failure Mode and Effects Analysis (FMEA) [19], but a similar methodology can be followed for non-fault based events. Once collected, expert knowledge can then be expressed semantically, in the form of ontologies and rules [20; 21] such as the FOLO ontology [4], whose purpose is to describe anomalies and root causes thereof. This acquired expert knowledge can be combined together with semantic expressions of the sensor observations and system context into a Knowledge Graph (KG). When the sensor observations and context in this KG trigger a rule created by a domain expert, an event is detected.  
The second detector on the ventilation unit monitoring use case applies this knowledge-driven approach. It maintains, for every sensor and observed property, a window of the ten last semantic observations (5 min of data) and enriches them with semantic descriptions of the operating context (observed property, sensor and the type of the monitored room). For example, one expert rule states that a large enough increase over time of the humidity in a bathroom is indicative of a shower. If instead the humidity increase takes place in a bedroom, the detector will consider the pattern as a humid weather event. Two dedicated works further detail the expert knowledge collection [21] and the semantic event detector (applied on a train bogie monitoring use case) [4].
# Decision System Based on Markov Chains for Sizing the Rebalancing Fleet of Bike Sharing Stations
## 3 Methodology
### Theoretical Evaluation for the Proposed Model  
As mentioned in Section 3.4, \(\Delta t_{REF}\) is a daily time slot repeating each day at the same hourly interval. If for the subsystem identification a few months from the lifetime are used (\(T_{I}\subset T\)) then the same daily time slot \(\Delta t_{REF}\) for another period of the same length (\(T_{E}\subset T\), \(|T_{I}|=|T_{E}|\)) is used for evaluation.  
The model identification on the same subsystem is performed again for \(T_{E}\), obtaining a second model \(\Psi_{E}\), which is equivalent to the previously identified model (\(\Psi_{E}\equiv\Psi_{I}\)) if and only if the maximum error \(e\) between the associated probabilistic functions (\(F^{I}\mapsto\lambda^{I}\mapsto\Psi_{I}\) and \(F^{E}\mapsto\lambda^{E}\mapsto\Psi_{E}\)) for each model is below a tolerance \(th=3\%\):  
\[\Psi_{I}\equiv\Psi_{E}\iff e:=max_{i}\left(\frac{2|\lambda_{i}^{I}-\lambda_{i} ^{E}|}{|\lambda_{i}^{I}|+|\lambda_{i}^{E}|}\right)<th \tag{30}\]  
The model equivalence via accepted probabilistic function deviation can also be visualized in Figure 11:
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 3 Experiments and Results
### Dataset  
The experiments are performed on the benchmark LUNA16 (LUng Nodule Analysis 2016) dataset [42]. It is derived from the publicly available LIDC/IDRI database. This data collection comprises labeled data from 888 patients. The data for each patient are composed of CT scan data and labeling (0 for non-cancer and 1 for cancer). Each image is in 3D MHD format with 2D slices of 512 \(\times\) 512 dimensions. For each pixel within a 2D slice, the range of gray levels is from \(-1000\) to 1000. The lung region varies in 2D slices of an image. MHD is a Metalmage medical format that contains a description file of an image. Each \(.MHD\) file comes with a \(.RAW\) file that contains explicit data of that image. The dataset characteristics are summarized in Table 1. A 3D representation of a lung CT scan is shown in Figure 9. The dataset can be downloaded from (accessed on 5 June 2023) [[https://luna16.grand-challenge.org/Download/](https://luna16.grand-challenge.org/Download/)]([https://luna16.grand-challenge.org/Download/](https://luna16.grand-challenge.org/Download/)).
## 1 Introduction
The acquisition performance of the low received power GPS signal could be enhanced with the aid of the proposed SEV process, and the number of acquired GPS satellites could therefore be increased to continue the navigation service for users in urban or indoor environments. In addition, most of the applications of the time-frequency analysis in GPS field focused on the interference/jamming detection [11, 12, 13] or structural monitoring [14, 15], and this work further extends the application of the time-frequency analysis on GPS signal detection. Accordingly, the remainder of this paper is organized as follows. Section 2 presents the concept of the time-frequency analysis, and it includes the procedure of generating a spectrogram with small error. Section 3 reports the time-frequency applications on GPS low received power signal detection and verification. The concept of the signal existence verification (SEV) for low received power signals is explained in detail. In Section 4, several experiments are conducted to validate the performance of the proposed method. Finally, Section 5 presents the summary and concluding remarks.
## 3 Results
### Environmental Effects on RE Spatiotemporal Variation
#### 3.3.2 Environment Effects on RE Interannual Dynamics  
With respect to each grassland type, we analyzed the interannual dynamic relationship between RE and environmental variables (Figure 8, Table 5). In the four alpine grassland subtypes, the R\({}^{2}\) between the annual RE and annual average Ta was 0.73-0.91 (Figure 8a). The R\({}^{2}\) between the annual RE and annual average EVI, excluding the alpine meadow steppe, was 0.44-0.63 (Figure 8c). There was no significant correlation between the annual RE and the annual total PRE or PAR (Figure 8b,d). In the three temperate grassland subtypes, the R\({}^{2}\) between the annual RE and the annual total PRE was 0.2-0.64 (Figure 8b). The R\({}^{2}\) between the annual RE and the annual average EVI was 0.28-0.63 (Figure 8c), but there was no significant correlation with the annual average Ta or the annual PAR (Figure 8a,d). In contrast to the positive promotion effect of PAR on RE at the seasonal scale (Figure 7d), the effect of annual total PAR on annual RE was not significant (Figure 8d).  
Figure 8: Interannual dynamic correlation between annual RE and (**a**) annual average Ta, (**b**) annual total PRE, (**c**) annual average EVI, and (**d**) annual total PAR from 2001–2015 in alpine and temperate grasslands. The dark cyan fitted lines refer to the fit lines for the alpine grassland subtypes, and the orange lines refer to the fit lines for the temperate grassland subtypes. The red lines refer to the fit lines across all seven grassland subtypes.  
We found that when the mean annual PRE exceeded 400 mm, which was the case in all the alpine grasslands and in MS among the temperate grasslands (Table 5), annual average Ta always provided a positive stimulus for annual RE (Figure 8a). This suggests that in grasslands with adequate moisture availability, the interannual dynamics of RE were controlled by factors other than PRE. Compared with the significant positive correlation between annual average RE and annual total PRE in temperate grasslands, the correlation for these in alpine grasslands was negative or close to zero (Figure 8b). When temperature was restricted, the annual total PRE had almost no stimulating effect on the annual RE in the same grassland type. In summary, the interannual change in RE in alpine grassland is mainly attributed to the annual average Ta and annual average EVI, and the interannual change in RE in temperate grassland is attributed to the annual total PRE and annual average EVI.
## 2 Materials and Methods
### Metabolites Identification and Multivariate Data Analyses  
Identification of volatile and non-volatile silylated components was performed by comparing their retention indices (RI) in relation to n-alkanes (C6-C20), mass matching to NIST, WILEY library database and with standards if available. Peaks were first deconvoluted using AMDIS software (www.amdis.net, accessed on 23 April 2022) before mass spectral matching. Peak abundance data were exported for multivariate data analysis by extraction using MET-IDEA software ([PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], 2006). Data were then normalized to the amount of spiked internal standard (Z)-3-hexenyl acetate, pareto scaled and then subjected to principal component analysis (PCA), hierarchical clustering analysis (HCA) and partial least squares discriminant analysis (OPLS-DA) using SIMCA-P version 13.0 software package (Umetrics, Umea, Sweden). All variables were mean-centered and scaled to Pareto variance.
## 3 Results
### Frequency-Domain Analysis of Vibration Velocity Response  
In order to study the frequency composition of traffic load excitation decay on the road, the time-domain data are converted into frequency-domain data by FFT (fast Fourier transform). Figures 8-10 depict the frequency spectrum of each measurement point under varied working conditions.  
Figure 8 depicts the spectrum of several measurement sites within the scheme 1 and scheme 2 test sections. The frequency range of the test vehicle causes a little amount of road vibration. The overall frequency is low, mainly within 30 Hz. As the driving speed of the test vehicle decreases, the influence frequency is gradually reduced to 0-15 Hz. The maximum peak value within the range of influence is only within 0-5 Hz. The frequency distribution of the vibrational energy is relatively concentrated, with a prominent peak value [37, 38].  
Figure 9 shows the spectrum of the measurement points at various depths while the test vehicle goes through the two test sections at 120 km/h. As shown in Figure 9, the longitudinal decay of vibration velocity is due to the decay of the corresponding frequency. Furthermore, the frequency composition varies a little between 0-15 Hz. The roadbed and the superstructure layer have little effect on the composition of vibration frequencies. As depth increases, the corresponding frequency amplitude drops [39].  
Figure 10 depicts the spectrum of measurement points at various horizontal distances as the test vehicle goes through the two test sections at 120 km/h. Similar to the longitudinal direction results, the transverse attenuation of vibration velocity is caused by the attenuation of the corresponding frequency. The frequency range remains rather constant, between 0-15 Hz. Combined with Figure 9, it is possible to deduce that the spatial position of vibration velocity diffusion has no effect on the frequency range. As the distance from the excitation point increases, the frequency of the corresponding point decreases [40].  
It can be seen that Figures 8-10 show the apparent frequency band division in the excitation spectrum generated by the test vehicle. The distribution of vibration frequencies in scheme 2 is smooth, and the frequency attenuation is systematic. On one side of Option 1, the gravelly soil is relatively scattered and disordered. Different damping at different positions and directions means the vibration cannot be uniformly diffused [41, 42].
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 1 Introduction
* _Advancements in Mobile Network Positioning:_ The automotive sector is currently probing the potential of 5G NR precise positioning to bolster position accuracy, especially in regions with compromised GNSS coverage. This initiative is pivotal for autonomous vehicular operations and V2X safety protocols. Certain autonomous vehicular applications necessitate stringent position accuracy metrics. In such scenarios, 5G NR is being assessed as a potential component of sensor fusion systems, supplementing GNSS and its corrective algorithms to enhance positional accuracy. This survey and comprehensive analysis explore the significant advancements introduced by 6G technology, comparing them to preceding 5G technologies. It assesses crucial aspects like data rate speed, system capacity, latency, and additional enhancements such as optimal coverage for very high-mobility, mobile edge computing, augmented reality (AR) facilitation, and enhanced connection reliability. Additionally, the paper delves into the pivotal enabling technologies essential for 6G support development environments based on V2X, evaluating feasibility, advantages, challenges and, consequently, implementation solutions. This paper is organized as follows: Section 1 provides a concise overview of 6G technology, emphasizing key features and the V2X roadmap. Additionally, Section 2 reviews the cellular systems evolution from 1G to 5G, while Section 3 presents the background and ongoing evolution of 6G. Section 4 examines the requirements and enabling technologies of 6G, and Section 5 explores the broad applications of 6G, detailing its impact on healthcare, high-performance precision agriculture, intelligent transportation systems, and logistics and supply chains. In Section 6, the focus shifts to the impact of 6G communications infrastructure and technology on supporting the V2X applications addressing critical evaluation, challenges for the future and implementation solutions. Finally, Section 7 offers a comprehensive conclusion.
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 3 Results and Discussion
### RTDs Readout System Measurements  
The validation of the 24 windings RTDs readout electronics was performed using a Peltier module as heating source and a reference temperature sensor (commercial PT-100) to monitor the temperature in the sensor area. The Peltier module was used due to their quickly and easier temperature change and control. The sensing area of the RTD (only the meander area) was placed over the Peltier module in order to eliminate the influence of the heating in the leading wires. An increase of temperature between 15 to 50 \({}^{\circ}\)C was applied and the output signal, after amplification, was read by a microcontroller (STM32). Figure 12a shows the different values of output voltage (measured in the output node, Out of Figure 8) and the corresponding temperature when the Peltier module is powered with a constant voltage. A dynamic test was also performed powering the Peltier module with a voltage square wave (on/off pulses) for seeking the transient RTD electronic system  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Windings in the** & **Simulated Sensor’s** & **Experimental Sensor’s** \\
**Meander-Shaped Geometry** & **Sensitivity (mV/\({}^{\circ}\)C)** & **Sensitivity (mV/\({}^{\circ}\)C)** \\ \hline
5 & 0.1022 & 0.0719 \\
8 & 0.1486 & 0.1126 \\
12 & 0.2108 & 0.1683 \\
16 & 0.2730 & 0.2258 \\
20 & 0.3345 & 0.3052 \\
24 & 0.3966 & 0.3542 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensor’s sensitivity of each sensor.  
Figure 11: Comparative study of the sensors sensitivities obtained in the experimental results.  
Figure 10: Experimental sensitivity of the sensors for the different fabricated RTDs according to the windings number. Orange line represents an increase temperature from 35 to 45 \({}^{\circ}\)C, and black line a decrease temperature from 45 to 35 \({}^{\circ}\)C.  
response (Figure 12b). The results of Figure 12 show the proper operation of the developed system. In future, a graphical user-friendly interface will be developed to automatically display the temperature data of multiple sensors, as well as for enabling the sensor selection and providing warnings if a temperature exceeds a pre-defined value.