esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Palatine Future Measurements  
In Figure 4, the results are presented as mean and confidence intervals for the four points that are given in millimeters (mm). At P1 and P4, no statistical differences were observed between the evaluated treatments (groups HG, CT and SVT). At P2, the CT group (0.32; CI: 0.22-0.43) showed greater expansion than the HG (0.16; CI: 0.15-0.17; \(p=0.0008\)) and SVT (0.21; CI: 0.18-0.24; \(p=0.0127\)).  
At P3, the CT group (0.26; CI: 0.24-0.29) showed greater expansion than the HG (0.14; CI: 0.13-0.15; \(p<0.0001\)) and SVT (0.23; CI: 0.21-0.24; \(p=0.0076\)). Furthermore, the HG group showed less expansion than the SVT group (\(p<0.0001\)).  
Figure 4: Linear measurement of points (**A**) P1; (**B**) P2; (**C**) P3; (**D**) P4. The mean palatal suture expansion (mm) at 4 points (P1, P2, P3 and P4). The results are presented as mean \(\pm\) confidence interval (n = 5). The P1 and P4 values did not pass the Shapiro-Wilk Normality test and were evaluated in the Log of Y format. The horizontal bars represent statistical difference between the groups (One-Way ANOVA and Tukey post-test; \(p<0.05\)).  
In the DI variable (Figure 5B), the HG group (0.68; CI: 0.65-0.71) had a shorter distance (mm) than the CT (1.48; CI 0.97-1.99; \(p\) = 0.0006) and SVT groups (1.39; CI: 1.26-1.51; \(p\) = 0.0016). The same difference between groups was observed in the SD distance: the HG group (0.22; CI: 0.19-0.25) had a smaller distance (mm) than the CT (0.75; CI: 0.30-1.22; \(p\) = 0.0005) and SVT (0.73; CI: 0.48-0.98; \(p\) = 0.0004) (Figure 5A).
###### Abstract  
Reproduction of the exact interocclusal relationship using digital workflow is crucial for precise fabrication of accurate prostheses. Intraoral scanner is known to be valid for the measurement of quadrants, however, the role of missing area in the quadrant scan on the virtual interocclusal record (VIR) is uncertain. This study aimed to evaluate the accuracy of VIR in quadrant scans using an intraoral scanner (IOS) under four different edentulous conditions. Eight scans per group were obtained using a laboratory scanner and three IOSs (Trios3, CS3600, i500). Based on trueness and precision, Trios3 had the best results, followed by CS3600 and i500. The trueness and precision were affected by edentulous conditions. The three IOSs showed deviation in the posterior region during assessment of VIR for the missing area with posterior support. CS3600 and i500 showed deviation in the short-span edentulous area without support. In extended edentulous condition without support, Trios3 showed overclosure, while i500 showed an angular deviation. In some groups scanned with Trios3 and i500, the tilting effect was observed. Based on the edentulous condition and type of IOS used, local or general deviations in occlusion were seen. The accuracy of VIR was dependent on accurate scan data. Thus, registration of the occlusal relationship in an edentulous area with more than two missing teeth using IOSs may be clinically more inaccurate than that with a laboratory scanner.  
intraoral scanners; virtual interocclusal record; digital dentistry; quadrant scan; CAD/CAM
# Artificial Intelligence Algorithms for Treatment of Diabetes
## 1 Introduction
This can only be achieved by a multivariable approach where physiological data from wearable devices such as wristbands are integrated with CGM and insulin infusion information to capture the metabolic state of the individual and compute the optimal insulin dose to be infused by the pump [62]. The model predictive control (MPC) approach has been demonstrated as the most effective type of control algorithm for AID systems [65; 66; 26]. It necessitates the estimation of the future BGC to determine the optimal insulin dose to be infused by the pump. The accuracy of future BGC estimates increases when future disturbances are estimated and included in predicting future BGC excursions [19]. ML algorithms can analyze glucose and insulin data to determine the insulin dose requirements and analyze historical data to identify trends and patterns that can be used to anticipate events and proactively make insulin dose adjustments [19]. Unfortunately, both interpersonal variations in BGC dynamics among people with T1D and intrapersonal variations in BGC dynamics of an individual throughout a day, reduce the accuracy of fixed models developed based on historical data. Consequently, recursive models to predict future BGC trends and adaptive control techniques become more appealing for use in AID systems [62; 27; 66]. Their performance improves further by providing information from ML algorithms to detect or predict disturbances that affect BGC dynamics. AI algorithms are gaining prominence in automating complex tasks in the management of diabetes. The paper is structured to introduce various methods to address the challenges in improving detection, classification, and prediction accuracy for better treatment of T2D and T1D in the Methods section. ML for better analysis of electronic health records (EHR) of people with diabetes will be introduced in Section 2.1. Section 2.2 focuses on ML techniques integrated with data preprocessing, imputation of missing data, and data reconciliation to predict the occurrence of food intake and exercise, and their concurrent incidence. Section 2.3 presents the integration of ML with automatic control to improve BGC regulation with AID. Section 3 presents the results of implementing the ML algorithms  
Figure 1: Devices and information flow in multivariable automated insulin delivery systems. Historical data set depository and cloud/server for AI algorithms are used to improve glucose control by incorporating information on identified historical patterns. presented in Section 2.2 to data collected in free-living, and simulations to illustrate the effects of ML algorithms outlined in Section 2.3. Section 4 provides the discussion of results, and Conclusions are given in Section 5.
## 2 Materials and Methods
### Regression Models  
The relationship between the L8 SR reflectance and insitu measurements was developed by exploiting a simple linear backward elimination method. The backward elimination method begins with all the variables observed in the model. At each step, the least significant variable is removed. This process continues until there are no more insignificant variables. The user defines the level of significance at which the variables can be removed from the model [49]. In this study, IBM SPSS programming statistics v. 23.0 (Armonk, NY, USA), was used. Figure 5 shows the outline of the methodology applied in the present study. A regression model between the measured turbidity and the surface reflectance was applied. The output model has been validated, and the final results werethematatic maps. For March, the regression was determined between the insitu turbidity on 13 March 2014 and the surface reflectance on 6 March 2014, while for November, the regression was between the insitu turbidity on 21 November 2014 and the surface reflectance on 17 November 2014.  
Water quality indicators, such as turbidity, chlorophyll, and temperature, as well as suspended matter, have been retrieved from remote sensing, according to [22]. The following four types of expressions have been used to show the general forms of these experimental equations:  
\[Y=A+BX\]  
\[Y=ABX\]  
\[Y=A+B\ln X\]  
\[\ln Y=A+BX\]  
where \(X\) is the measurement from remote sensing (i.e., radiance, reflectance, and energy); \(Y\) representswater quality parameters; \(A\) and \(B\) are empirically derived factors; and \(X\) could be energy, reflectance, orradiance in a single or two-band ratio. This concept has been adopted by many researchers in the past to retrieve the parameters of water quality; therefore in the present study,we followed the same concept, constructing an algorithm for turbidity retrieval that is dependent on the relationship between LSSR and insitu observation.
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 4 Proposed Architecture
### Sensor Node Monitoring Agent Overview
#### 4.2.3 Transport of Collected Data
In the case of WIA-PA, networks may operate in two distinct modes: a hierarchical network topology that combines star and mesh, or a star-only topology. The star-only topology is a special case of the hierarchical network. For this reason, this topology uses the same services available in the hierarchical topology for data transport. In WIA-PA, the Virtual Communication Relationship (VCR) is the main standard block to access the objects specified in the User Application Objects (UAOs). VCRs distinguish the routing and communication resources allocated to each UAO. Each VCR has a VCR identifier, a source UAO ID, a destination UAO ID, address of source device/destination device and the VCR type. Similar to WirelessHART services and ISA100.11a contracts, in WIA-PA, VCRs can be classified according to the application scope: publish/subscriber (P/S) VCRs, used for publishing periodic data; report source/sink (R/S) VCRs, used for transferring aperiodic events and trend reports (alarms); and client/server (C/S) VCRs, used for transferring aperiodic and dynamic paired unicast messages (for getting and setting operations in UAO). Additionally, VCRs also provide aggregation methods. In this architecture, the sensor node monitoring agent data are UAOs capable of representing log messages. Thus, sensor node monitoring agents, available in each field device, as well as routing devices, need to select the appropriate VCR. P/S VCRs are appropriate for real-time operations, using exclusive timeslots in intra and inter-cluster communication. For this reason, this type of VCR should not be used for sending the monitoring data. On the other hand, R/S and C/S VCRs use the CAP period of the IEEE802.15.4 slot inside clusters, and use shared timeslots in inter-cluster operations. Thus, C/S and R/S VCRs are the most appropriate for transporting sensor node monitoring data in WIA-PA networks.
## 4 A Case Study in Milan--Italy
### Introduction
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{3}{c}{**Technical Data**} \\ \hline Floors above ground [n\({}^{\circ}\)] & 3182 \\ \hline Underground floors [n\({}^{\circ}\)] & 204 \\ \hline Total staircase [n\({}^{\circ}\)] & 1376 \\ \hline Total landings [n\({}^{\circ}\)] & 9415 \\ \hline Elevators [n\({}^{\circ}\)] & 768 \\ \hline Apartments [n\({}^{\circ}\)] & 22,172 \\ \hline Basements [n\({}^{\circ}\)] & 16,209 \\ \hline Lofts [n\({}^{\circ}\)] & 1509 \\ \hline Technical rooms [n\({}^{\circ}\)] & 321 \\ \hline \end{tabular}
\end{table} 
Table 1: The table shows the total area to be surveyed within the Milan project. To enable the collection of local three-dimensional measurements and the subsequent association of assets within the buildings with each area. While global centimeter-level accuracy is not necessary for surveying entire complexes and buildings, the focus is on cost containment. Additional requirements include the ability to navigate items topologically and unambiguously, as well as ensuring measurement accuracy and geolocation. * To acquire photographic and geometric documentation that allows for the identification of assets and items within the buildings, facilitating the integration and updating of data already present in the company's information system. * To enable the storage of photographic and three-dimensional documentation in formats not restricted by proprietary software, ensuring compatibility for future visualization. * To enable the acquisition of a three-dimensional point cloud model of the buildings, which will support the future implementation of automatic self-location applications, and to define an online platform for the exchange of three-dimensional and photographic data.
# Time Scale Control Using Dynamic GMDH Neural Network Forecasting Based on Real Measurement Data
## 5 Conclusions  
The presented research results can be used as convincing arguments for National Metrological Institutes (NMIs) to opt for a cheaper and effective alternative to caesium fountains to control local time scales. The research presented in this article could be a breakthrough for NMIs, especially those that do not have expensive caesium fountains. The UTC(PL) control method proposed in this publication, using the GMDH neural network and the developed algorithm, is based on real measurement data and offers a much more advantageous alternative. The main advantage of this innovative method is its radically lower costs. The purchase of the GMDH Shell tool, together with the implementation of the entire solution, is priced at around USD 500. This is a symbolic amount compared to the purchase of a caesium fountain, the cost of which oscillates around USD 1,500,000. Moreover, the research conducted by the author confirms that the method is not only economical, but also characterized by low complexity and low computational costs. Both the method and the GMDH Shell tool are extremely accessible in terms of hardware requirements, a standard PC is enough to run them.  
It is worth emphasizing that the capabilities of GMDH neural networks go far beyond metrology. The discussed GMDH neural networks have a very wide range of applications. The author successfully applied his forecasting algorithm based on a GMDH neural network to estimate the power generated by photovoltaic panels. This is a key element in the algorithm for flexible renewable energy management in households, which is described in publication [25]. In addition, the author's previous studies have shown that GMDH neural networks can be successfully used to model the power generated by wind turbines [26]. These examples clearly demonstrate the great potential and wide possibilities of using GMDH neural networks in many different fields of engineering.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author has reviewed and edited the output and takes full responsibility for the content of this publication.  
The author declares that there are no conflicts of interest.  
The following abbreviations are used in this manuscript:  
NMI National Metrology Institute  
BIP Bureau International des Poids et Mesures  
UTC Universal Time Coordinated  
MJD Modified Julian Date  
PFS Primary Frequency Standard  
SFS Secondary Frequency Standard  
GUM Polish for Glowny Urzad Mair (Central Office of Measures)  
IMEI Polish for Instytut Metrologii, Elektroniki i Informatyki (Institute of Metrology, Electronics and Co. GMDH Group method of data handling
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 5 Discussion
### Comparison of MNOs  
To evaluate the similarity of individual MNOs in terms of the obtained PCC results, we calculated PCCs for each pair of MNOs, using the heatmaps from Figures 4 and 5 as input, as shown in Figure 25. In this case, the heatmap shows PCCs for TCP and UDP illustrated in the upper-right and lower-left edges, respectively. Please note that the different colormap scale ranges from 0.87 to 1.  
Figure 23: Measured RSRQ-5G versus SINR-5G for four MNOs, UDP: (**a**) UL or (**b**) DL.  
Figure 24: Regression lines of RSRQ-5G versus SINR-5G for four MNOs, UDP: (**a**) UL or (**b**) DL.  
Heatmaps are more similar for TCP than for UDP. Generally, the smallest PCCs occur for the entire data set (i.e., all MNOs), thus confirming its large variation. The PCCs are most similar for Orange and T-Mobile. It may result from the fact that these MNOs are using the same base stations or mast locations. Already, during the development of the LTE network, these MNOs commissioned the management of RAN, using the RAN sharing technique, for the Net-WorkS! Company (Daytona Beach, FL, USA). Hence, similar values of the relevant QoS metrics and signal parameters were obtained during drive tests for these MNOs. The most significant differences in the analyzed heatmap occur for Plus and Play for TCP and UDP, respectively.
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
###### Abstract  
In the rapidly advancing domain of vehicular traffic management and autonomous driving, accurate lane change predictions are paramount for ensuring safety and optimizing traffic flow. This study introduces a comprehensive two-stage prediction model that harnesses the capabilities of long short-term memory (LSTM) for anticipating vehicular lane changes. Initially, we employed a variety of models, such as regression methods, SVMs, and a multilayer perceptron, to categorize lane change behaviors. The dataset was then segmented based on vehicle trajectories and lane change patterns. In the subsequent phase, we utilized the superior classification outcomes from LinearSVC to curate our training data. We developed two dedicated LSTM networks tailored to specific datasets: the lane-keeping LSTM (LK-LSTM) and the lane-changing LSTM (LC-LSTM). By integrating insights from both models, we achieved a comprehensive prediction of vehicular lane changes. Our results indicate that the unified prediction model markedly enhances prediction precision. Accurate lane change predictions offer valuable contributions to advanced driver-assistance systems (ADAS), with the potential to minimize traffic mishaps and enhance traffic fluidity. As we transition to a more autonomous automotive era, refining these predictions becomes essential in seamlessly merging human and automated driving experiences.  
LSTM; lane change prediction; LinearSVC
###### Abstract  
The communication in quarantined areas, e.g., due to the new COVID-19 pandemic, between isolated areas and in areas with technical damage has resulted in a great deal of interest concerning the safety of the population. A new method for ensuring communication between different areas, using unmanned aerial vehicle (UAV) networks with a well-established mobility schedule is proposed. UAVs fly based on a mission plan using regular polygons covering an area from a map. The area is considered to be equidistantly covered with points, grouped in triangles which are further grouped into hexagons. In this paper, UAVs, including battery charging or battery swapping stations and light weight Wi-Fi boards, are used for the data transfer among drones and stations using delivery protocols. UAV network analysis and evaluation (lengths of the arcs in seconds) based on experimental preliminary flight tests are proposed. Multiple simulations are performed based on six DTN algorithms, single-copy, and multiple-copies algorithms, and the efficiency of data transmission (delivery rate and latency) is analyzed. A very good delivery rate of 0.973 is obtained using the newly introduced TD-UAV Dijkstra algorithm.  
unmanned aerial vehicle (UAV) network; drone; delay tolerant networks (DTN); optimization algorithms; mobility schedule; quarantined areas; experimental flight +
Footnote †: journal: Sensors
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 2 Star Pattern and Catalog  
Star identification algorithms are mainly divided into two parts: offline establishment of a star pattern database and online algorithm application. This section primarily focuses on the offline process, which includes the selection of a navigation star pattern and the construction of a star catalog.
## 5 Workspace Analysis  
The 2D workspace layer was extracted from the 3D workspace, as shown in Figures 7a and 8a. The 2D workspace (XZ-plane) at X = 0 and (YZ-plane) at Y = 0 are presented, respectively, in Figures 7b and 8b, while the difference between the KUKA serial manipulator, with sign (+) in red, and the hybrid manipulator, with sign (\(\times\)) with black, can be noticed in the inner and outer workspaces. However, Figure 7c shows that the hybrid manipulator workspace area covered 132.61% compared to the KUKA manipulator. Figure 8c shows that the hybrid manipulator workspace covered 128.33% compared to the KUKA manipulator. Furthermore, Figures 7d and 8d show the range of motion for the hybrid manipulator compared to the KUKA manipulator. In addition, Tables 3 and 4 present the dimension of the 2D workspace range at X = 0 and Y = 0, respectively.  
Figure 6: 3D workspace for KUKA KR6 R900 with (+) sign and hybrid manipulator with (\(\times\)) sign.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \(\mathbf{Y_{Min}}\) & \(\mathbf{Y_{Max}}\) & \(\mathbf{Z_{Min}}\) & \(\mathbf{Z_{Max}}\) \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline KUKA & \(-981.2\) & \(981.2\) & \(-422.9\) & \(1356\) \\ HYBRID & \(-1094\) & \(1094\) & \(-536\) & \(1469\) \\ \hline \end{tabular}
\end{table}
Table 4: 2D Workspace Range at Y = 0 (Front).  
Figure 7: Workspace analysis at X = 0 : **(a)** extract slice from the 3D workspace at X = 0; **(b)** 2D workspace; (c) 2D workspace area; **(d)** limitation of the 2D workspace range.
# From 5G to 6G--Challenges, Technologies, and Applications
## 4 Current Research
### Numerical Discussion
The technical report in [186] discussed the role of humans in the communication loop for tactful future networking and providing a better quality of experience to the users. Another report discussed the use of smart poles in urban areas for big data collection to enable smart city applications in Australia [187]. Beside this, the years 2019 and 2020 experienced growth in the number of conferences and journal issues oriented toward topics relating to future networks, such as _IEEE Antennas and Wireless Propagation Letters_ Special Cluster 2020 on \"5G/6G Enabling Antenna Systems and Associated Testing Technologies\", _IEEE Vehicular Technology Magazine_ Special Issue: \"6G: What Is Next?\", _China Communications Journal_ feature topic on \"6G Mobile Networks: Emerging Technologies and Applications\" and \"Edge AI in 6G Systems: Theory, Key Techniques, and Applications\", and _Journal of Communications and Networks_ Special Issue on \"6G Wireless Systems\", to name a few.
## 1 Introduction
[39] proposed an improved underdetermined blind source separation algorithm based on sparse component analysis (SCA) and applied on the sampled pressure pulsation signal to obtain separated signals. [PERSON] et al. [40] proposed a universal single-channel blind source separation method based on a combination of ensemble empirical mode decomposition and independent component analysis, which is suitable for autonomous underwater vehicles in time-varying ocean currents. These algorithms usually show good separation performance when separating noiseless mixed signals, but when separating strong noiseless mixed signals, there could be a large error--especially in the case of low signal to noise ratio (SNR), there will be a completely wrong conclusion, because these algorithms are based on the noiseless interference model. In rotating machinery operation, the signals measured by sensors inevitably contain noise. If the above blind source separation algorithm is directly used to separate the mixed vibration signal, many errors may be produced, or the wrong conclusions may be drawn. Therefore, a fault diagnosis method for rotating machinery is developed based on a combination of improved median filter (MF) and second-order blind identification (SOBI). Firstly, the window width of the median filter is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Secondly, the improved SOBI algorithm is used to separate the signal after noise reduction. Finally, the method is tested on strong pulse background noise and the rub impact dataset, and the fault feature signal is effectively separated.