esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Volatile Organic Compounds Identified in Sugar Belle Leaves  
We examined the influence of three rootstocks on the leaf volatile profile of the Sugar Belle mandarin. Table 1 identifies the hexane-extracted volatile leaf metabolites that were detected by GC-MS from Sugar Belle scion leaves that were grown on US-897, C-35, and SO rootstocks, and the groups into which we classified each VOC. A total of 44 volatile organic compounds were extracted from Sugar Belle leaves consisting of 14 monoterpenes; 14 terpen alcohols; 4 sesquiterpenenes; 4 fatty acids or their methyl esters (ME); 2 aldehydes; 2 esters, 1 ketone, and coumarin were grouped as \"other\"; and 2 were unknown compounds (Table 1). Terpene alcohols were the most concentrated, primarily due to linalool. We detected very few terpene aldehydes such as neral and gerani, which are typical of many sweet orange varieties. The overall VOC profile of Sugar Belle mandarins revealed a predominance of \(\beta\)-pinene, p-cymene, \(\beta\)-ocimene, linalool, \(\gamma\)-terpinene, \(\beta\)-elemene, and \(\beta\)-caryophyllene as the major VOCs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound No.** & **Leaf VOC** & **VOC Class** & **Compound No.** & **Leaf VOC** & **VOC Class** \\ \hline
**1** & \(a\)-Thujene & Monoterpene & **23** & Thymol, ME & Terpene alcohol \\
**2** & \(a\)-Pinene & Monoterpene & **24** & Thymolquinone & Other \\
**3** & Sabinene & Monoterpene & **25** & m-Thymol & Terpene alcohol \\
**4** & \(\beta\)-Pinene & Monoterpene & **26** & Unk -ol \(m/z\) 69/154A & Terpene alcohol \\
**5** & \(\beta\)-Myrcene & Monoterpene & **27** & Unk -ol \(m/z\) 69/154B & Terpene alcohol \\
**6** & \(a\)-Terpinene & Monoterpene & **28** & Unk -ol \(m/z\) 69/154C & Terpene alcohol \\
**7** & p-Cymene & Monoterpene & **29** & Chavicol & Terpene alcohol \\
**8** & D-limonene & Monoterpene & **30** & Geranyl acetate & Monoterpene ester \\
**9** & \(\beta\)-ocimene & Monoterpene & **31** & \(\beta\)-Elemene & Sesquiterpene \\
**10** & \(\gamma\)-Terpinene & Monoterpene & **32** & \(\beta\)-Caryophyllene & Sesquiterpene \\
**11** & Unk Terpene alc 1 & Terpene alcohol & **33** & \(\alpha\)-Humulene & Sesquiterpene \\
**12** & Unk Terpene alc 2 & Terpene alcohol & **34** & Unk \(m/z\) 165/180 & Unknown \\
**13** & \(\alpha\)-Terpinolene & Monoterpene & **35** & Coumarin & Other \\
**14** & p-Cymenene & Monoterpene & **36** & Ethyl 4-ethoxybenzoate & Other \\ \hline \hline \end{tabular}
\end{table}
Table 1: Volatile organic compounds (VOCs) that were detected by GC-MS in the hexane extracts of mature leaves from Sugar Belle mandarin hybrid. Compound numbers are given for their association with Figure 1 PCA loading plots.
## 4 Conclusions  
Tackifier DT was synthesized via free radical polymerization of DVB, AM, and NVF. The polymer's formation from these three monomers was confirmed through FTIR, 1H-NMR, and thermogravimetric analysis.  
Tests on the aging resistance, salt resistance, and temperature resistance of polymer DT concluded that it exhibits excellent aging and salt resistance at 15% NaCl and 200 \({}^{\circ}\)C. The viscosity retention rate remains 87.1% at 240 \({}^{\circ}\)C, demonstrating excellent high-temperature resistance. This study examined the effect of different NaCl concentrations on the apparent viscosity of the polymer solution, concluding that polymer DT exhibits excellent salt resistance.  
High-temperature and high-pressure rheological tests indicate that DT-containing drilling fluids exhibit consistent changes in viscosity and strength within the temperature range of 150 \({}^{\circ}\)C to 180 \({}^{\circ}\)C, which stabilizes these properties at elevated temperatures. Shear tests indicate that DT exhibits a shear-thinning effect, likely related to the formation of hydrophobic association microstructures with phenyl groups in the side chains, along with changes in the hydrodynamic volume of polymer molecules in solution and a loosening of the screw structure. The transition from a rotational to a network aggregate structure leads to either an increase in viscosity or a constant viscosity.  
In conclusion, tackifier DT demonstrates excellent salt resistance, high-temperature resistance, and even ultra-high-temperature resistance, making it suitable for meeting the technical requirements of drilling fluids in high-temperature and ultra-high-temperature environments. Furthermore, the preparation process and parameters for this polymer are straightforward and amenable to industrialization.  
Conceptualization, Y.G.; Methodology, Y.C. and H.Y.; Formal analysis, X.Q.; Investigation, X.W. All authors have read and agreed to the published version of the manuscript.  
The study was supported by the university-level team research project of Hainan Vocational University of Science and Technology in 2024: \"Research on Polyionic Liquid Materials and Their Catalytic Properties(HKKY2024-TD-17)\"; Hainan Vocational University of Science and Technology university-level Teaching Reform Project 2025: \"Research on Ideological and Political Teaching Model of 'Chemical Engineering Principles' Based on Goal and Problem Orientation (HIKJG2024-012); Research on Teaching Methods of Mechanical Manufacturing Courses under Informationization Conditions\" (HJKG2024-35).  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Henan Xintai Energy. The remaining 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 Methodology
### Implementation Details  
Having established the theoretical foundation and architectural design of our approach, we now turn to the practical aspects of implementing and training the model.  
We implemented our model using PyTorch 1.4 [29], with the following hyperparameters: hidden size of 128, latent dimension of 64, 2 LSTM layers, dropout rate of 0.2, learning rate of 0.001, and Lyapunov regularization weight of 0.1. The model was trained for 100 epochs using the Adam optimizer with a batch size of 32.  
Our input features include aircraft position coordinates (x, y, z) and wind components (wind_x, wind_y), with a sequence length of 20 time steps for input and a prediction horizon of 10 time steps. All features were standardized before training to ensure numerical stability.  
For trajectory sampling during inference, we generate multiple samples from the latent distribution to visualize the range of possible future trajectories, providing a comprehensive view of prediction uncertainty, as illustrated in Figure 4.  
The overall methodology presented integrates stochastic process modeling, deep learning, and stability theory to effectively address the challenges of trajectory prediction in general aviation. By combining these approaches, we create a system that not only produces accurate predictions but also quantifies uncertainty in a manner that supports safety-critical decision making in non-towered airport environments.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 6 Conclusions  
In the paper, several new alternatives of the simplest Active Disturbance Rejection Controllers were designed and experimentally investigated based on the approximation of the actual plant by the integrative time-delayed IPDT models. The presented new controllers expand the range of ADRC solutions suitable for time-delayed systems [12].  
The main design requirement was simplicity, especially considering that the IPDT models have only two parameters and are therefore less sensitive to the selection of appropriate optional tuning parameters. Another tuning criterion was sensitivity to the measurement noise, which allows to operate with low energy dissipation near the steady states. The obtained results show that the closed loop characteristics can be significantly improved by appropriate model, controller selection and tuning.  
In addition, it was shown how to implement the proposed method without complex simulations or experiments. Thereby, by a quasi-authentic illustrative example of an as fast as possible and simultaneously smooth temperature control, the dead-time compensation proposed in [33] did not prove to be the optimal solution.  
The presented performance measures and cost functions, chosen to express different requirements for the transients, were used to design a set of controllers when simple IPDT models are used.  
All the controllers developed may be verified by a web application and the TOM1A system used is also available for remote experimentation. It offers the possibility to verify all types of the described control algorithms. For more information, see the page [[http://apps.iolab.sk/advanced/appsc2020/](http://apps.iolab.sk/advanced/appsc2020/)]([http://apps.iolab.sk/advanced/appsc2020/](http://apps.iolab.sk/advanced/appsc2020/)) (accessed on 10 February 2021).  
**Author Contributions:** Writing-original draft preparation, M.H. Simulations, P.B. and M.H. Editing, D.V., P.M.O., P.B. and M.H. Project administration, P.B. Web application, K.Z. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grants APVV SK-IL-RD-18-0008 Platoon Modelling and Control for mixed autonomous and conventional vehicles: a laboratory experimental analysis, VEGA 1/0745/19 Control and modelling of mechatronic systems in embobility and within research program P2-0001, financed by the Slovenian Research Agency.  
Supported by Slovenska e-akademia, n. o.  
The authors declare no conflict of interest.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
###### Abstract  
5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.  
congestion control; SDN; routing feedback; in-network control; 5G cellular network +
Footnote †: journal: Sensors
## 5 Case Study of Developing Liver Cancer Diagnosis
### Customizing the Platform for HCC Diagnosis
#### 5.3.2 Training ML Models for HCC  
The six essential ML models of the platform were applicable to HCC diagnosis. We developed an additional ML model for analyzing medical features presented in tumors. The types of ML models implemented in this case study are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ML Models**} & **Organ** & **Lesion** & **Tumor** & **Hepatic** & **Image** & **HCC Type** & **HCC Stage** \\  & **Segmentation** & **Segmentation** & **Segmentation** & **Segmentation** & **Feature** & **Classifier** & **Classifier** \\  & **Model** & **Model** & **Model** & **Model** & **Classifier** & & \\ \hline ML Category & \multicolumn{6}{c}{Segmentation} & Classification \\ \cline{2-7} ML Algorithm & \multicolumn{6}{c}{U-Net} & CNN & LSTM & SVM \\ \hline Functional Components & \begin{tabular}{c} Organ \\ Segmentation \\ Manager \\ \end{tabular} & \begin{tabular}{c} Lesion \\ Segmentation \\ Manager \\ \end{tabular} & \begin{tabular}{c} Tumor \\ Segmentation \\ Manager \\ \end{tabular} & \begin{tabular}{c} Lesion/Tumor \\ Segmentation \\ Manager \\ \end{tabular} & \begin{tabular}{c} Medical \\ Segmentation \\ Manager \\ \end{tabular} & \begin{tabular}{c} Disease Type \\ Classifier \\ \end{tabular} & \begin{tabular}{c} Disease Type \\ Classifier \\ \end{tabular} \\ \hline Prediction & \begin{tabular}{c} Liver \\ in CT Slices \\ \end{tabular} & \begin{tabular}{c} Lesions \\ in Liver \\ \end{tabular} & \begin{tabular}{c} Tumors \\ in Lesions \\ \end{tabular} & \begin{tabular}{c} Hepatic \\ Segments \\ \end{tabular} & \begin{tabular}{c} Listed of \\ Image \\ Features \\ \end{tabular} & \
\begin{tabular}{c} HCC \\ Occurrence \\ \end{tabular} &
\begin{tabular}{c} Stage of \\ HCC \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Machine learning models for HCC diagnosis.  
Figure 11: Original CT slice and the mask of the liver organ.  
The selection of ML algorithms for segmentation and classification has been made through extensive experiments with training the models with other candidate models.
## 3 Results
### Comparison of CPU Resources for Different Orders
#### 3.5.1 Verification of CPU Resources on PC  
The current study looks at two important computing phases linked to gravity field order: numerical integration and filtering. The goal of comparison analysis is to discover the optimal balance of accuracy and efficiency, giving theoretical support for enhancing the orbit determination performance of spaceborne GNSS receivers. The validation results for the personal computer (PC) are shown in Figure 7 and Table 6:  
Figure 6: Orbit determination error accuracy for different orders of EIGEN6C (unit: meters).  
Figure 7: Computation time on PC: (a) numerical integration time; (b) square root information filter time.  
The receiver currently features a built-in gravity field model of order 50. After testing on a personal computer (PC), it was discovered that increasing the order from 40 to 60 resulted in a significant improvement in accuracy while incurring no notable increase in computing time. As a result, raising the gravitational field order is a realistic strategy. The next step is to move the code to the spaceborne GNSS receiver and ensure that it works properly.
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 3 Proposed Algorithms
### Setup for User-Specific Environment
#### 3.2.2 Crowd Formation Constraint: Forced Position Constraints for Agents  
The crowd formation constraint means that agents should form a certain shape as a group. In other words, the meaning of satisfying the formation constraint is that agents within a group place a specific position for formation. These shapes can be a circle, a square, or a certain closed polygon. While there are many ways to sample positions for formation, we use a 3D mesh projection method where the vertices of a mesh are projected onto the 2D plane on the ground, and those projected points become targets that agents must move to. This method has the advantage because it allows the user to easily configure various formations. Figure 5 shows the formation of a crowd in this way.  
After the initial positions of all agents in the group are set, the position of the group is set. The group position is set by averaging all the positions of the agents inside. Equation (2) shows the calculation of the position of the group \(P_{a}\):  
Figure 4: A group that is a subset of the entire crowd.  
\[P_{a}=\frac{1}{A}\sum_{i=1}^{A}p_{i}\ \ \ \text{where}\ \ x_{i}\in G_{a}\ (i=1,\,2,\,\ldots,\ A) \tag{2}\]  
where \(G_{a}\) is the \(a\)-th group, \(x_{i}\) is the agent belonging to \(G_{a}\), \(p_{i}\in\mathbb{R}^{3}\) is the current position of \(x_{i}\), \(P_{a}\in\mathbb{R}^{3}\) is the group position of \(G_{a}\), and \(A\) is the number of agents belonging to \(G_{a}\).
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 3 Bounds on Position Estimation
### Two-Dimensional CRLB on Position  
The probability density function of the ToF distribution summarized in Equation (7) is written explicitly as  
\[p_{\tau}(\tau;h)=\frac{1}{\sqrt{2\pi\sigma_{\tau}^{2}}}\exp\Bigg{\{}-\frac{1} {2\sigma_{\tau}^{2}}\sum_{i=1}^{N}\left(\tau-\frac{d_{i}}{c}\right)^{2}\Bigg{\}}, \tag{9}\]  
where \(d_{i}=\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}}\) is the Euclidean distance between antennae \(g_{i}=[x_{i}\,,y_{i}]\) and \(h=[x\,,y]\). The elements of the FIM \(\mathbf{I}(h)\) are computed using Equation (8) as  
\[[\mathbf{I}(h)]_{11} =\frac{1}{c^{2}\sigma_{\tau}^{2}}\sum_{i=1}^{N}\frac{(x-x_{i})^{2 }}{d_{i}^{2}},\] \[[\mathbf{I}(h)]_{12} =[\mathbf{I}(\theta)]_{21}=\frac{1}{c^{2}\sigma_{\tau}^{2}}\sum_{ i=1}^{N}\frac{(x-x_{i})(y-y_{i})}{d_{i}^{2}}, \tag{10}\] \[[\mathbf{I}(h)]_{22} =\frac{1}{c^{2}\sigma_{\tau}^{2}}\sum_{i=1}^{N}\frac{(y-y_{i})^{2 }}{d_{i}^{2}}.\]  
By definition, the CRLB on estimating the 2-D position of \(h\) is \(\sigma_{h}^{2}\geq\mathbf{I}^{-1}(h)=\mathrm{adj}\{\mathbf{I}(h)\}/|\mathbf{ I}(h)|\), where the adjugate \(\mathrm{adj}\{.\}\) and determinant \(|.|\) are written as  
\[\mathrm{adj}\{\mathbf{I}(h)\} =\frac{1}{c^{2}\sigma_{\tau}^{2}}\Bigg{[}\begin{matrix}\sum_{i=1 }^{N}\frac{(y-y_{i})^{2}}{d_{i}^{2}}&-\sum_{i=1}^{N}\frac{(x-x_{i})(y-y_{i})}{ d_{i}^{2}}\\ -\sum_{i=1}^{N}\frac{(x-x_{i})(y-y_{i})}{d_{i}^{2}}&\sum_{i=1}^{N}\frac{(x-x_{ i})^{2}}{d_{i}^{2}}\end{matrix}\Bigg{]}, \tag{11}\] \[|\mathbf{I}(h)|=\frac{2}{(c^{2}\sigma_{\tau}^{2})^{2}}\sum_{i=1} ^{N}\sum_{\begin{subarray}{c}j=1\\ i\
eq j\end{subarray}}^{N}\Bigg{(}\frac{\mathcal{D}_{2}}{2d_{i}d_{j}}\Bigg{)} ^{2}, \tag{12}\]  
where \(\mathcal{D}_{2}\) is the determinant of the distance matrix  
\[\mathcal{D}_{2}=\Big{|}\begin{matrix}x-x_{i}&y-y_{i}\\ x-x_{j}&y-y_{j}\end{matrix}\Bigg{|}. \tag{13}\]  
To make this result more accessible to a design engineer, we can loosely visualize the CRLB as a scalar value by defining an overall \"position variance\" as \(\sigma_{p}^{2}=\sigma_{x}^{2}+\sigma_{y}^{2}\geq\mathrm{tr}\{\mathbf{I}^{-1} (h)\}\), which can be written as  
\[\sigma_{p}^{2}\geq\frac{c^{2}\sigma_{\tau}^{2}N}{2}\Bigg{[}\sum_{i=1}^{N}\sum_ {\begin{subarray}{c}j=1\\ i\
eq j\end{subarray}}^{N}\Bigg{(}\frac{\mathcal{D}_{2}}{2\,d_{i}\,d_{j}} \Bigg{)}^{2}\Bigg{]}^{-1}. \tag{14}\]
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results  
### Antenna Design  
The main objective of the proposed antenna design process was to be as small as possible, which made the design quite complex as it depended on many parameters (geometrical shape and size of the antenna components). To achieve a better size reduction, we chose a very thin substrate of high-dielectric material [8] and installed a small pin which transforms this antenna into a PIFA (planar inverted F-antenna). The choice of the pin position was based on the observation that the maximum size reduction would be achieved by placing the pin away from the zero point of the electric field as well as at a suitable point so that it did not affect the tuning of the antenna.  
Furthermore, another requirement was to transmit in two frequency bands MedRadio and ISM for the transmission of vital data and its energy management. To achieve the function at both frequencies, two U-shaped slots were placed on the conductive surface which redefined the path of the surface currents. The basic geometry of the U-slotted antenna was first introduced in 1995 by [PERSON] and [PERSON] [17].  
The antenna was designed directly into the pancreas rectangle box displayed in Figure 1, and the shorting pin position and U-slots placement and dimensions were manually optimized to achieve optimum performance at the frequencies of interest, using HFSS Software (high frequency structure simulator) [15].  
Antenna Characteristics  
The patch surface and ground plane had dimensions of 18 mm \(\times\) 26 mm and 19 mm \(\times\) 27 mm, illustrated in Figures 2 and 3. The substrate was made of high-dielectric material (Rogers RO 6010 \(\varepsilon\)r = 10.2, tan \(\delta\) = 0.023) and a thickness of 0.635 mm. An identical superstrate layer covered the patch surface to insulate the metallic radiating patch from the surrounding human tissues. In the edge of the antenna, we inserted a shorting pin with radius of 0.2 mm, shown in Figure 2. Two U-shaped slots were added in the patch surface, with dimensions shown in Figure 1. A 50 \(\Omega\) coaxial cable of 5 mm length was used as feed, which consisted of an external conductor (radius = 0.48 mm) made of dielectric material Teflon (tm) (\(\varepsilon\)r = 2.2) and an internal one (radius 0.24 mm).  
The antenna was designed in the center of a simplified equivalent model, with the electrical properties of the pancreas and the shape of a rectangle box of dimensions 74 mm  
Figure 1: Proposed antenna design for pancreas implantation.  
Figure 3: Design of the proposed antenna.  
Figure 2: Proposed Antenna Characteristics.  
\(\times\) 127 mm \(\times\) 59 mm. The electrical properties of the pancreas that represent the pancreas tissue at the frequencies of interest are shown in Table 1 from IT'IS database [18].
## 3 Delay and Energy Consumption Analysis  
Based on the frame structure and protocol operation described earlier, in this section we use an AMC (Absorbing Markov Chain) model and formulate its transition matrix for the different FSA protocol variants, which allows to derive two important performance parameters:  
* The average number of frames in which the process keeps in the same transient state.
* The average number of frames until the process is absorbed.  
Ultimately, we use these two parameters to formulate the analytical expressions needed to compute the average delay and energy consumption of the FSA-ACK, FSA-FBP and DFSA protocols.