esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Relative Motion Dynamics Model
### Coordinate System Definition  
The principal coordinate systems for formation flight dynamics involve the Earth Centered Inertial (ECI) frame and the Hill frame as shown in Figure 1. In the ECI frame, the X-axis follows the center of the earth with the vernal equinox, the Z-axis is normal to the equator plane and points to the geographic north pole, and the Y-axis is determined by the right-hand rule. In the Hill frame, the center is at the formation mass center. The x-axis is along the orbital radial direction, the y-axis is along the velocity vector of the mass center, and the z-axis is along the orbital angular momentum vector of the mass center.  
Electromagnetic formation flight is characterized by the orchestration of multiple satellites, each subject to electromagnetic forces produced by the collective ensemble of the formation. In our exploration of relative kinematics, we utilized two electromagnetic satellite, designated SatA and SatB, to derive the preliminary equations of relative motion in the port-Hamiltonian framework. The formation center of mass is invariant to electromagnetic interactions, as they do not modify the total momentum of the system. In the scenario where no other forces act on the formation, the center of mass would follow a trajectory consistent with Keplerian motion. It is critical to acknowledge, especially in low Earth orbit scenarios, the significance of the interplay between the magnetic dipoles and Earth's geomagnetic field, which introduces complexities in attitude stabilization and momentum regulation. Substantial efforts have been invested in the study of this issue, with numerous approaches being proposed for the control of attitude and the management of angular momentum [4]. Our study focuses on the translational reconfiguration of the EMFF, involving a control system with three degrees of freedom (3-DOF), primarily under the assumption of ideal attitude dynamics.
## 5 Conclusions  
The finding from both the reproducibility test and the subject groups allows the researcher to conclude that the _Periodest_ device is a reproducible tool that can be trusted to carry out the tooth mobility readings accurately.  
1. Reproducibility of the _Periodest_ device recordings can be achieved for patients of all age groups, with or without diagnosed periodontal disease.
2. Reproducibility of the _Periodest_ device readings over a period of time depends on the correct manipulation of the handpiece and the location of the teeth in the arches.
3. Younger populations present positive Periotest values, which translate into hard bone tissue surrounding the tapped teeth, while negative values are more usually observed in older populations, which translates to a greater capacity to absorb the impact force, softer tissue around the tapped teeth and, as a consequence, tooth mobility.
4. All subsequent _Periodest_ readings to the first, in the same session, were found to be different, with the majority of those being lower than the first.  
Conceptualization, O.G.; methodology, O.G., T.F.W. and B.J.M.; validation, O.G., T.F.W. and B.J.M.; formal analysis, O.G.; investigation, O.G.; resources, O.G., T.F.W. and B.J.M.; writing--original draft preparation, O.G.; writing--review and editing, T.F.W. and B.J.M.; visualization, O.G.; supervision, T.F.W. and B.J.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by five separate Institutional Ethics Committees, as stated in Section 2 (Methodology) of this paper; details are listed in Table 2, page 5.  
Written informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.
###### Abstract  
Precise knowledge of the phase center location of the global navigation satellite system (GNSS) antenna is a prerequisite for precise orbit determination (POD) of the low Earth orbit (LEO) satellite. The phase center offset (PCO) and phase center variation (PCV) values for the LEO antenna obtained from ground calibration cannot reflect the error sources encountered in the actual spacecraft environment. PCV corrections are estimated by ionosphere free (IF) carrier phase post-fit residuals of reduced dynamic orbit determination. Ambiguity resolution (AR) plays a crucial role in achieving the best orbit accuracy. The single receiver AR concept is realized using wide-lane (WL) and narrow-lane (NL) bias products. Single difference (SD) observations between satellites are applied to remove the receiver dependent phase bias. SD AR and traditional double difference (DD) AR methods are applied to fix the ambiguities. The recovered SD and DD IF ambiguities are taken as pseudo-observations to constrain the undifferenced IF ambiguity parameters in the POD process. The LEO orbits based on float ambiguity (FA), SD, AR, and DD AR are investigated. One year's data collected by the Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) mission and GPS precise products provided by the Center for Orbit Determination in Europe (CODE) were analyzed. Precise orbit generated by the Jet Propulsion Laboratory (JPL), independent satellite laser ranging (SLR), and K-band ranging (KBR) measurements were utilized to assess the orbit accuracy. More than 98% of SD WL and 95% of SD NL ambiguities are fixed, which confirms the good quality of the bias products and correctness of the SD AR method. With PCV corrections, the average phase residuals of DD and SD AR solutions are 0.13 and 0.41 mm, which indicates improved consistency between applied models and observations. Compared with JPL's orbit, the SD AR orbits achieve the accuracy of 6.0, 6.2, and 5.1 mm in along-track, cross-track, and radial directions. The SD AR solutions show an average improvement of 18.3% related to the FA orbits while 6.3% is gained by the DD AR approach. The root mean squares (RMSs) of SLR residuals for FA, DD AR, and SD AR solutions are 11.5, 10.2, and 9.6 mm, which validate the positive effect of AR on POD. Standard deviation (STD) of KBR residuals for SD AR orbits is 1.8 mm while 0.9 mm is achieved by the DD AR method. The explanation is that the phase bias products used for SD AR are not free of errors and the errors may degrade the KBR validation. In-flight PCV calibration and ambiguity resolution improve the LEO orbit accuracy effectively.  
single receiver ambiguity resolution; phase center variation (PCV) calibration; precise orbit determination; GRACE-FO satellites 2021134204[[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
# Generating Datasets for Real-Time Scheduling on 5G New Radio
###### Abstract  
A 5G system is an advanced solution for industrial wireless motion control. However, because the scheduling model of 5G new radio (NR) is more complicated than those of other wireless networks, existing real-time scheduling algorithms cannot be used to improve the 5G performance. This results in NR resources not being fully available for industrial systems. Supervised learning has been widely used to solve complicated problems, and its advantages have been demonstrated in multiprocessor scheduling. One of the main reasons why supervised learning has not been used for 5G NR scheduling is the lack of training datasets. Therefore, in this paper, we propose two methods based on optimization modulo theories (OMT) and satisfiability modulo theories (SMT) to generate training datasets for 5G NR scheduling. Our OMT-based method contains fewer variables than existing work so that the Z3 solver can find optimal solutions quickly. To further reduce the solution time, we transform the OMT-based method into an SMT-based method and tighten the search space of SMT based on three theorems and an algorithm. Finally, we evaluate the solution time of our proposed methods and use the generated dataset to train a supervised learning model to solve the 5G NR scheduling problem. The evaluation results indicate that our SMT-based method reduces the solution time by 74.7% compared to existing ones, and the supervised learning algorithm achieves better scheduling performance than other polynomial-time algorithms.  
5G new radio; real-time scheduling; dataset; optimization modulo theories; satisfiability modulus theories +
Footnote †: journal: _entropy_  
0000-0002-2300-3300][PERSON]  
1 Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China; [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (C.X.); [EMAIL_ADDRESS] (C.X.)
###### Abstract  
This article aims to examine the real-time precise point positioning (PPP) solution's accuracy utilizing the low-cost dual-frequency multi-constellation U-blox ZED-P9P module and real-time GNSS orbit and clock products from five analysis centers, including Bundesamt fur Kartographie und Geodasie (BKG), Centre National d'Etudes Spatiales (CNES), International GNSS Service (IGS), Geo Forschungs Zentrum (GFZ), and GNSS research center of Wuhan University (WHU). Three-hour static quad-constellation GNSS measurements are collected from ZED-F9P modules and geodetic grade Trimble R4s receivers over a reference station in Aswan City, Egypt, for a period of three consecutive days. Since a multi-GNSS PPP processing model is applied in the majority of the previous studies, this study employs the single-constellation GNSS PPP solution to process the acquired datasets. Different single-constellation GNSS PPP scenarios are adopted, namely, GPS PPP, GLONASS PPP, Galileo PPP, and BeiDou PPP models. The obtained PPP solutions from the low-cost module are validated for the positioning and precipitable water vapor (PWV) domains. To provide a reference positioning solution, the post-processed dual-frequency geodetic-grade GNSS PPP solution is applied; additionally, as the station under investigation is not a part of the IGS reference station network, a new technique is proposed to estimate reference PWV values. The findings reveal that the GPS and Galileo 3D position's accuracy is within the decimeter level, while it is within the meter level for both the GLONASS and BeiDou models. Additionally, millimeter-level PWV precision is obtained from the four PPP models.  
climate monitoring; urban microclimate; atmospheric water vapor monitoring; precipitation; land use; low-cost GNSS positioning; ZED-P9P +
Footnote †: journal: _articles  
0000-0001-5000-5000][PERSON] 1,2,*
## 4 Experiments
### Performance Comparison for TIS Prediction  
We compare the performance of our proposed method, NeuroTIS+, with existing state-of-the-art methods such as TITER, TISRover, and NeuroTIS. To conduct a fair comparison, all the methods are trained and tested on the same dataset. In order to avoid the side effect of imbalanced dataset, we also limit the number of negative samples in training set like kmer+TCN, but, in the test set, all the negative samples are selected. The other comparable methods are implemented with reference to their original papers. Note that, for all the neural network-based methods, the network size is set large enough to ensure that the model training will not be underfitted. Also, two regularization methods (i.e., Dropout and early stopping) are used to ensure that the model will not be overfitted.  
As shown in Tables 1 and 2, NeuroTIS+ performed the best among all the existing state-of-the-art methods and achieved the highest scores in all the evaluation metrics, especially in PRE, F1-score, and MCC, an average improvement of 41.26%/0.2803/0.0332/0.2508 over the second best method, NeuroTIS, on human and mouse datasets. This improvement indicates that NeuroTIS+ can effectively reduce false positives, which verifies the significance of promoting the CDS prediction and adaptive grouping strategy. Figure 6 also illustrates the ROC and PRC curve comparison of all the methods on the human and mouse datasets. It can be seen that NeuroTIS+ is consistently higher than other state-of-the-art methods on both curves. Given a false positive rate of 0.5%, NeuroTIS+ achieved 99.2% and 99.56% sensitivity on human and mouse genome sequences, respectively, which is 5.45% and 7.04% higher than the second best method, NeuroTIS. For the PRC curve, given a recall rate of 95%, NeuroTIS+ achieved 98.8% and 99.15% precision on human and mouse genome sequences, respectively, 24.33% and 30.12% higher than NeuroTIS, respectively.  
Figure 6: Comparison of ROC and PRC results of different methods. (**A**) ROC performance on human data; (**B**) ROC performance on mouse data; (**C**) PRC performance on human data; (**D**) PRC performance on mouse data.  
From the above analysis, we can conclude that accurate prediction of CDSs and homogenous feature building by adaptive grouping strategy play an important role in predicting TISs. All the experimental results demonstrate that NeuroTIS+ is a high-precision TIS prediction method.
## 2 Materials and Methods
### NMR (PFG and Relaxometry) Spectroscopy  
NMR measurements were performed using a Bruker NMR spectrometer AVANCE 300 Wide Bore working at 300 MHz on \({}^{1}\)H and equipped with a Diff30 Z-diffusion 30 G/cm/A multinuclear probe with substitutable RF inserts. Water self-diffusion coefficient (D) measurements were performed using the pulsed field gradient stimulated-echo (PFG-STE) method [35]. This sequence is opportune when the transverse relaxation time (T\({}_{2}\)) is very low and is considerably shorter than the longitudinal relaxation time (T\({}_{1}\)), and foresees three 90\({}^{\circ}\) RF pulses (\(\pi/2-\tau_{1}-\pi/2-\tau_{\rm m}-\pi/2\)) with two gradient pulses applied after the first and the third RF pulses, respectively. The echo is found at time of \(\tau=2\tau_{1}+\tau_{\rm m}\). The FT echo decays were analyzed by means of the relevant Stejskal-Tanner expression using Equation (5):  
\[I=I_{0}e^{-\beta D} \tag{5}\]  
with \(I\) and \(I_{0}\) representing the intensity/area of a selected resonance peak with and without gradients, respectively; \(D\) being the self-diffusion coefficient; and \(\beta\) being the field gradient parameter. This latter is defined by Equation (6):  
\[\beta=[(\gamma g\delta)^{2}(\Delta-\frac{\delta}{3})] \tag{6}\]  
where \(g\), \(\delta\), and \(\Delta\) are the amplitude, duration, and time delay of the gradient field, respectively. For the measurements, \(\delta\) and \(\Delta\) were kept at 0.8 and 8 ms, respectively, while \(g\) ranged between 50 up to 800 G cm\({}^{-1}\). For the self-diffusion measurements, an uncertainty of \(\sim\)3% was calculated.  
The spin-lattice relaxation times (T\({}_{1}\)) was measured by the inversion recovery sequence (\(\pi-\tau-\pi/2\)).  
The detailed experimental procedure to prepare the NMR sample is described elsewhere [31]. In short, before the NMR measurements, the membranes were dried in an oven, and were weighed and then immersed in distilled water at room temperature. After being removed from the water, they were quickly blotted dry with tissue paper (to eliminate most of the free surface liquid). Then, the water uptake was measured and the membranes were loaded into a 5 mm NMR Pyrex tube and closed with a normal cap.  
Both D and T\({}_{1}\) were investigated in the range 20-130 \({}^{\circ}\)C, with steps of 20 \({}^{\circ}\)C and 15 min of equilibration time for each temperature.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology
### Multi-Agent Modeling Approach
#### 3.1.2 Base Station Agent  
In a WSN, the base station is responsible for collecting and summarizing data from the sensor nodes, as well as performing data fusion, analysis, storage, and forwarding. To enable distributed sensor nodes without global information to have the function of ad hoc networking and transfer data to the base station, a routing protocol was designed (see  
Figure 1: A typical sensor node architecture.  
Section 3.2). The base station performs two functions: firstly, it performs depth broadcast (see Figure 2) during network initialization and assigns initial values to the depth of all sensor nodes, with the help of node-by-node forwarding from the sensor node; secondly, it performs depth broadcast again during global maintenance and updates depth assignments for all sensor nodes using sensor node-by-node forwarding.  
As depicted in Figure 2, the central large solid blue circle portrays the base station, while the small black hollow circle represents the sensor node. The dotted circle denotes the respective communication ranges of the base station and the sensor node, and the blue arrow indicates the propagation direction of depth information. It is evident from Figure 2 that the communication range of the base station exceeds that of the regular sensor nodes. Consequently, sensor nodes within the communication range of the base station can directly communicate with the base station, and their depth value is set to 1. Subsequently, nodes with a depth value of 1 relay the depth broadcasting, assigning a depth of 2 to nodes that have not received a depth value within their communication range. This process continues, and the depth broadcast emanates from the base station, spreading outwards until all sensor nodes have been assigned a depth value. The depth value of the base station is 0, signifying the shallowest depth. As the distance from the base station increases, the depth value increases, indicating greater depth.  
Considering that the base station has stronger computing power and a larger antenna area than ordinary sensor nodes, its information transmission range should also be larger. Sensor nodes farther away from the base station can also directly establish a connection with the base station. Furthermore, while the jammer cannot disable the communication function of the base station, it can still disrupt data collection by destroying the communication function of the sensor nodes around the base station and reducing the number of relay nodes.
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 3 BILSTM neural network
### Pre-Processing of Data  
The AIS data selected for the experiments in this paper were collected on 2 August 2020, and the ship with MMSI number 636017973 was selected from the Huangze Yang sea area, with a total of 2753 ship track points. AIS data processing first involved data cleaning and pre-processing to ensure the quality and relevance of the data. Key features were then extracted from the data, including the vessel speed, heading, and trajectory characteristics such as dwell time, as well as vessel behavior patterns in specific environments, such as obstacle avoidance and steering. These features were then used to train a ship trajectory prediction model using deep learning techniques. The goal of the model is to accurately predict a ship's position in the short term to improve the safety and efficiency of maritime navigation.
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA)  
In this study, HCA was carried out using the abundances of bioactive compounds (carotenoids, polyphenols, and ascorbic acid) of different rosehip flesh (Figure 4a) and seed (Figure 4b) species.  
Figure 4: Hierarchical cluster analysis (HCA) of different organic rosehip flesh (**a**) and seed (**b**) samples based on 19 variables. Investigated samples are clustered using Euclidean distance and Ward’s method. Abbreviations: RV-F: _Rosa villosa_-flesh; RRR-F: _Rosa rugosa ’_Rubra’-flesh; RRA-F: _Rosa rugosa ’_Alba’-flesh; RC-F: _Rosa canina_-flesh; RR-F: _Rosa rugosa_-flesh; RV-S: _Rosa villosa_-seeds; RRR-S: _Rosa rugosa ’_Rubra’-seeds; RRA-S: _Rosa rugosa ’_Alba’-seeds; RC-S: _Rosa canina_-seeds; RR-S: _Rosa rugosa_-seeds.  
Figure 3: Ascorbic acid content (mg 100 g\({}^{-1}\) fresh weight (FW)) of different organic rosehip flesh (**a**) and seed (**b**) samples species. Different small letters represent significant differences between flesh samples and seed samples species, respectively (\(p<0.05\)). RRA: _Rosa rugosa ’_Alba’; RC: _Rosa canina_; RR: _Rosa rugosa_; RRR: Rosa rugosa ’_Rubra’; RV: _Rosa villosa._  
Based on the HCA, flesh samples were grouped into three clusters (C1, C2, and C3) (Figure 4a). The first cluster (C1) was formed by RRA-F (_Rosa rugosa_ 'Alba'-flesh), RC-F (_Rosa canina-flesh_), and RR-F (_Rosa rugosa_-flesh) (flesh samples with highest contents of \(p\)-coumaric acid, ferulic acid, luteolin, and quercetin); the second cluster (C2) by RRR-F (_Rosa rugosa_ 'Rubra'-flesh) (sample with high contents of chlorogenic acid, caffe acid, and quercetin-3-\(O\)-glucose). The RV-F (_Rosa villosa_-flesh) was grouped separately as the third cluster (C3) (sample with highest contents of carotenoids (sum), individual carotenoids, flavonoids (sum), and rutin).  
As shown in Figure 4b, the seed samples were also grouped into three clusters (C1, C2, and C3). The first cluster (C1) was formed by RR-S (_Rosa rugosa_-seeds) and RRA-S (_Rosa rugosa_ 'Alba'-seeds) (seed samples with highest contents of ascorbic, gallic, and ferulic acids); the second (C2) cluster by RC-S (_Rosa canina_-seeds) (sample with highest contents of polyphenols (sum), chlorogenic, and caffe acids, flavonoids (sum) and individual flavonoids such as rutin, kaempfer-3-\(O\)-glucose and quercetin) and the third cluster (C3) by RRR-S (_Rosa rugosa_ 'Rubra'-seeds) and RV-S (_Rosa villosa_-seeds) (seed samples with high contents of carotenoids (sum) and individual carotenoids, such as \(\alpha\)-carotene and lutein).  
HCA results suggest that RV flesh and RC seeds can be considered as promising raw material for future researches and food and/or pharmaceutical industries.
## 4 Parameter Sensitivity Analysis  
Based on previous research results [27], this article conducted sensitivity analysis on various parameters, wherein each model parameter was changed while keeping the other parameters constant. The sensitivity levels were divided into three levels: high,  
Figure 23: Effect of conservation methods on top–bottom humidity difference. Figure 22: Effect of curing method on the moisture content of concrete panels. medium, and low, according to the influence of each factor on the humidity calculated by the program, respectively (high: h \(\geq\) 2%, medium: 1 \(\leq\) h \(<\) 2, low: h \(<\) 1%) and the sensitivity levels of each parameter are summarized in Table 3.  
The sensitivity analysis results showed that the influence of different parameters on the early age moisture field of concrete varied within a given range of values. The sensitivity of the cement type is only 0, and the sensitivity of water cement ratio is all less than 0.4. The sensitivity level of concrete material factors is low; concrete material factors had little influence on the moisture field of concrete panels. Environmental factors, including temperature, humidity, and solar radiation, significantly influenced panel moisture, while wind speed had a smaller effect. Construction parameters yielded a greater effect on the concrete road panel, while the thickness of the road panel had a smaller influence on the humidity field. Each parameter had a significant influence on the humidity field of the panel through two aspects, namely indirect and direct influence. (1) Indirect influence was achieved by affecting the temperature of the panel, which, in turn, raised or lowered the saturated vapor pressure inside the panel, resulting in changes in the relative humidity of the panel. For example, the water-cement ratio, ambient temperature, and solar radiation mainly affected the humidity indirectly through the