esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Linear or Linear-like Formulations of Nonlinear Systems
### Feedback Linearization
#### 2.2.1 Input-State FL  
One considers single-input nonlinear systems for input-state FL. To ensure that a linearization exists, both controllability and involutivity conditions should be satisfied. The linearization is performed based on a new state, where the first state can be determined by solving the equations [23]:  
\[\
abla z_{1}\text{ad}_{\mathbf{f}}^{i}\mathbf{g}=0\ (i=0,\ldots,n-2),\ \
abla z_{1} \text{ad}_{\mathbf{f}}^{n-1}\mathbf{g}\
eq 0 \tag{4}\]  
where \(\text{ad}_{\mathbf{f}}^{i}\mathbf{g}\) is the adjoint representation of Lie algebra, and \(n\) is the number of states.  
Then, the new state \(\mathbf{z}\) can be defined as  
\[\mathbf{z}=\left[z_{1}\ L_{\mathbf{f}}z_{1}\ \ldots\ L_{\mathbf{f}}^{n-1}z_{1} \right]^{T} \tag{5}\]  
where \(L_{\mathbf{f}}z_{1}\) and \(L_{\mathbf{f}}^{n-1}z_{1}\) are Lie derivatives.  
Based on the new defined state \(\mathbf{z}\), a linear system can be formulated as Equation (2), where:  
\[\mathbf{A}=\left[\begin{array}{ccccc}0&1&0&\cdots&0\\ 0&0&1&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&1\\ 0&0&0&\cdots&0\end{array}\right],\ \mathbf{B}=\left[\begin{array}{c}0\\ 0\\ \vdots\\ 0\\ 1\end{array}\right],\ \upsilon=\beta^{-1}(u-\alpha) \tag{6}\]  
in which  
\[\alpha=-\frac{L_{\mathbf{f}}^{n}z_{1}}{L_{\mathbf{g}}L_{\mathbf{f}}^{n-1}z_{1 }},\ \beta=\frac{1}{L_{\mathbf{g}}L_{\mathbf{f}}^{n-1}z_{1}} \tag{7}\]  
The input-state FL only transforms the state equation, so a linear output equation is not available. Besides, the obtained equation in Equation (2) is linear. For controller design, all linear control design schemes can be applied to have the control \(\upsilon\), which can be transformed to the control \(u\) by Equations (6) and (7), where \(\beta\) cannot be zero.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 1 Introduction
* **U-T-U communication:** all UAVs in the swarm establish efficient communication which allows the information to be obtained and exchanged through sensors or radar. Two UAVs can either communicate with each other directly, or indirectly by construct multi-hop communication paths with other UAVs. * **U-T-I communication:** UAVs communicate with the fixed central control center, such as a ground station, to obtain real-time mission information. U-T-I communication is usually a direct communication. In the rest of this article, we use infrastructure to represent the central control center. The U-T-U communication and the U-T-I communication are combined through a communication architecture. In other words, the UAV swarm communication architecture defines how information \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Single-UAV System** & **Multi-UAV System** \\ \hline Survivability & Poor & High \\ Scalability & Limited & High \\ Speed of mission & Slow & Fast \\ Autonomy & Low & High \\ Cost & High & Low \\ Communication needs & High & Low \\ Radar cross-sections & Large & Small \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key performance comparisons of a single-Unmanned Aerial Vehicles (UAV) system and multi-UAV system. exchanged between the UAVs or between the UAVs and the infrastructure. Further considering the U-T-U communication, the routing protocol plays an important role in achieving reliable end-to-end data transmission [35]. These are also why the swarm communication architecture and routing protocol become important research topics in the field of UAV swarm. In this article, we focus on UAV swarm communication architectures and routing protocols from both academic and engineering perspectives. We compare the advantages and disadvantages of different architectures and routing protocols, and discuss suitable application scenarios on this basis. We hope that the comparative analysis in our literature review can be useful for deploying effective communication architectures and reliable routing protocols for UAV swarms. The rest of this article is organized as follows: systematic introduction and impersonal comparison of UAV swarm communication architectures are presented in Section 2. Section 3 gives an extensive overview of existing routing protocols, according to an easily accepted classification system. Finally, the article is concluded in Section 4.
## 3 Results and Discussion
### Ash Composition  
In Table 2, the results obtained for ash composition are included. In general, the compounds obtained are common in agricultural biomass, according to studies carried out by [PERSON] et al. [37; 38].  
In the ash analysis of biofuels, it is important to analyze K\({}_{2}\)O and SiO\({}_{2}\) content. [PERSON] et al. [10] pointed out that both compounds affected the melting point of ash, and the subsequent possibility to produce blockages in combustion systems. In addition, [PERSON] et al. [39] concluded that high SiO\({}_{2}\) concentrations could increase the risk of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & \\ \hline \multicolumn{12}{c}{**Ash Composition (\% db)**} \\ \hline  & **K** & **Cl** & **Si** & **P** & **Mg** & **Ca** & **S** & **K\({}_{2}\)O** & **SiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **ZnO** \\ \hline Ma 1 & 48.20 & 9.53 & 6.27 & 3.59 & 2.48 & 1.73 & 1.23 & 58.11 & 13.40 & 8.23 & 2.42 & 4.12 & 3.08 & 0.12 & 0.32 & 0.24 \\ Ma 2 & 46.45 & 9.65 & 7.94 & 2.83 & 2.35 & 2.13 & 1.12 & 55.96 & 17.00 & 6.48 & 2.98 & 3.90 & 2.80 & 0.20 & 0.40 & 0.13 \\ Ma 3 & 29.07 & 7.33 & 13.42 & 2.67 & 4.87 & 6.68 & 1.26 & 35.02 & 28.72 & 6.12 & 9.35 & 8.07 & 3.14 & 0.78 & 0.59 & 0.07 \\ Ma 4 & 18.93 & 5.98 & 17.57 & 1.55 & 7.05 & 8.04 & 1.24 & 23.40 & 37.58 & 3.56 & 11.26 & 11.70 & 3.09 & 1.51 & 1.06 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ash composition after combustion.  
slagging. Regarding K\({}_{2}\)O content, its value ranged from 23.4% to 58.11%, being higher in residues with corncob waste, whereas SiO\({}_{2}\) ranged from 13.4% to 37.58%, being higher for wastes containing corn stalk, especially in the case of corn stover where there might be a considerable number of impurities from the soil.  
When considering pelletizing of these wastes, the content in these compounds should be taken into account, as it could adversely affect combustion systems, due to slag generation and other elements generated, possibly because of ash melting.
# Kinetics of PTSA-Catalysed Polycondensation of Citric Acid with 1,3-Propanediol
## 2 Materials and Methods  
### Synthesis Procedure  
Poly(1,3-propanediol citrate) syntheses were carried out by using the IKA RCT basic magnetic stirrer with a heating function and equipped with the IKA ETS-D5 temperature sensor and four IKA 28.5 heating mantles. Anhydrorous citric acid (5.75 g; \(\geq\)99.5%, ACS reagents, ACROS ORGANICS) and 1,3-propanediol (3.43 g; \(\geq\)99.7%, CHEMSOLUTE) were used as reactants in solvent-free polycondensation in the presence of p-toluenesulforic acid (PTSA) catalyst (0.06 g; \(\geq\)98.5%, ACS reagents, SIGMA-ALDRICH). Each time, the molar ratio of the functional groups (COOH:OH) was 1:1, not including the hydroxyl group of citric acid. Mantles were heated and thermostated to set the temperatures (120, 135, 150 \({}^{\circ}\)C) before the syntheses. Syntheses were carried out in flat-bottom test tubes with magnetic stirring and water removal. Scheme 1 presents a simplified version of the reaction.  
For this work, the following nomenclature is established: (1) A carbonyl group bonded with an \(\alpha\) carbon is abbreviated as \(\alpha\)-C(O)O-, and (2) a carbonyl group bonded with a \(\beta\) carbon is abbreviated as \(\beta\)-C(O)O-; Scheme 1 shows citric acid with the \(\alpha\) and \(\beta\) carbons marked.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 3 WANs: Time Data Measurement and Analysis
### Network Data Capture Method  
A client/server-based software application and two computers as depicted in Figure 2 to capture time-related network data were used.  
The software tool running on the static host or client computer sends periodic timing requests containing the start time \(T_{1}\), to the static server computer via a communication network. The server acknowledges the request at the local time \(T_{2}\). The server then sends a reply at the time \(T_{3}\) using the same timing packet with timestamps \(T_{2}\) and \(T_{3}\). The host receives the timing packet back at time \(T_{4}\). Thus, the host machine accumulates \(T_{1}\), \(T_{2}\), \(T_{3}\), and \(T_{4}\) timestamps corresponding to all periodic timing requests and responses over the same network for a particular period in a log file. The timestamps were used to calculate  
Figure 2: Network data capture setup.  
uncorrected time offset and round trip delays (RTD) between host and server machines using the formulas below.  
\[\text{Time offset}=\frac{T_{4}-T_{3}}{2}+\frac{T_{1}-T_{2}}{2}, \tag{1}\]  
\[\text{RTD}=(T_{4}-T_{3})-(T_{1}-T_{2}). \tag{2}\]  
This study did not consider the effect of coding languages and operating systems in software tool implementation on measurements as these factors are estimated to introduce a negligible difference in measurements.
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 2 Description of Problem
### Control Objective  
The aim of this work is to design a proposed controller for trajectory tracking that is robust, will guarantee that the tracking error converges to a small region in finite time, and that will ensure all closed-loop signals have limited values. The controller will work effectively when Assumptions 1-5 are satisfied. Due to the assumptions made, it is designed for vehicles moving slowly (less than 1 m/s).
# IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison
## 2 Theoretical Background
### IpDFTc Algorithm
Similarly, the IpDFTc synchrophasor phase estimator at time \(t_{r}\) is \[\hat{\phi}_{1,r}=\hat{\phi}_{10,r}-\Delta_{c}\phi_{1,r},\] (16) where \(\Delta_{c}\phi_{1,r}=-\frac{W\big{(}\hat{x}_{10,r}-\hat{s}_{10,r} \big{)}}{W\big{(}\hat{s}_{10,r}\big{)}}\sin\big{(}2\hat{\phi}_{10,r}\big{)}+ \frac{\hat{a}_{20,r}}{\hat{a}_{10,r}}\frac{W\big{(}\hat{x}_{20,r}-\hat{s}_{10,r}+\hat{s}_{10,r}\big{)}}{W\big{(}\hat{s}_{10,r}\big{)}}\sin\big{(}\hat{\phi} _{20,r}-\hat{\phi}_{10,r}\big{)}\) and \(\hat{\phi}_{10,r}\) results from (10). Finally, the fundamental frequencies and the ROCOF are estimated respectively as:  
\[\hat{f}_{1,r}=f_{n}+\frac{f_{s}}{M}\hat{s}_{1,r}. \tag{17}\]  
and  
\[RO\hat{CO}F_{1,r}=\Big{|}\hat{f}_{1,r}-\hat{f}_{1,r-1}\Big{|}\cdot\frac{f_{s} }{L}, \tag{18}\]  
in which if \(\hat{f}_{1,r}\) and \(\hat{f}_{1,r-1}\) are the fundamental frequency estimates in two subsequent data records and \(L/f_{s}\) represents the time interval between the corresponding UTC reference times. Summarizing, the steps of the IpDFTc estimation algorithm are orderly reported in Table 1.
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 4 Experiment and Result Analysis
### Process of Experiment
#### 4.4.2 Comparative Experiments  
To illustrate the superiority of the proposed improved algorithm, DGBL-YOLOv8s, comparative experiments were conducted on the VisDrone dataset against two-stage algo  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Baseline** & **C2f\_DWR** & **GLSA-BiFPN** & **LSCDH** & **P/\%** & **R/\%** & **mAP50/\%** & **Para/M** & **Model Size/MB** \\ \hline v8s-0 & & & & 47.0 & 35.7 & 36.3 & 11.1 & 21.5 \\ v8s-1 & \(\surd\) & & & 48.9 & 36.4 & 37.2 & 10.9 & 21.0 \\ v8s-2 & & \(\surd\) & & 49.0 & 37.5 & 38.0 & 7.9 & 15.5 \\ v8s-3 & & & \(\surd\) & 52.0 & 41.6 & 42.6 & 9.6 & 18.7 \\ v8s-4 & \(\surd\) & \(\surd\) & & 49.3 & 37.2 & 37.9 & 7.7 & 15.0 \\ v8s-5 & \(\surd\) & & \(\surd\) & 50.6 & 41.7 & 42.8 & 9.4 & 18.3 \\ v8s-6 & & \(\surd\) & \(\surd\) & **53.9** & 42.9 & 44.7 & 7.4 & 14.7 \\ v8s-7 & \(\surd\) & \(\surd\) & \(\surd\) & 53.6 & **42.6** & **44.8** & **7.2** & **14.2** \\ \hline \hline \end{tabular} Note: Bold letters indicate the best experimental results.  
\end{table}
Table 4: Ablation experiments.  
rithms (Faster-RCNN, SSD) and single-stage algorithms (YOLOv3-Tiny, YOLOv4, YOLOv5). The experimental results are shown in Table 5.  
In the above table, the proposed DGBL-YOLOv8s outperforms several popular baseline models in key performance metrics, demonstrating significant improvements in both detection accuracy and model efficiency. Specifically, DGBL-YOLOv8s achieves an impressive mAP50 of 44.8%, which is a substantial improvement over YOLOv8s (36.3%) and other models such as YOLOv5 (32.9%) and YOLOv4 (15.7%). Additionally, it shows remarkable improvements in precision (P) and recall (R), reaching 53.6% and 42.6%, respectively, surpassing YOLOv8s (47.0% and 35.7%) and YOLOv5 (43.4% and 34.4%). In terms of computational efficiency, DGBL-YOLOv8s maintains a small model size (14.2 MB) and a low parameter count (7.2 M), making it more lightweight than many alternatives, like Faster-RCNN (260.8 MB, 136.7 M parameters) and YOLOv4 (118.2 MB, 61.9 M parameters), while still achieving superior performance.
## 4 Conclusions  
In this study, we investigated the interactions of six PDQLs with HSA via fluorescence spectroscopy, CD and FTIR and found that the PDQLs could bind to HSA at site II (subdomain IIIA), induce conformational and secondary structural changes of HSA and quench the intrinsic fluorescence of HSA in solution through a static quenching mechanism. The quenching of HSA fluorescence occurred with the formation of a 1:1 complex between PDQL and albumin. Furthermore, during the formation of the complex between HSA and PDQL, hydrophobic forces played a significant role. The steric effects of these groups play a major role in enhancing the PDQL-HSA interaction, whereas the electrostatic properties and lipid solubility of a group play only a minor role, according to comparative studies. The studies of the PDQL-HSA interactions showed that substitution with these familiar substituents on the benzene ring could enhance the binding affinity through van der Waals or hydrogen bonding forces, with the isopropyl substitution providing the largest enhancement of the binding affinity. This study provides useful information for further research in the rational design of this series of compounds.  
Supplementary materials are available online at [[http://www.mdpi.com/2076-3417/7/2/200/s1](http://www.mdpi.com/2076-3417/7/2/200/s1)]([http://www.mdpi.com/2076-3417/7/2/200/s1](http://www.mdpi.com/2076-3417/7/2/200/s1)).  
**Acknowledgments:** The authors are grateful to the National Natural Science Foundation of China (Nos. 31601657, 31272062, 31572033 and 21602043), the National Key Research and Development Program of China (2016 YED0200205 and 2016 YED0200201-1) and the Anhui Agricultural University Youth Fund Project (2014 zr003 and yj2015-25).  
**Author Contributions:** This research was carried on by all authors. [PERSON], [PERSON] and [PERSON] designed the theme of the study, as well as manuscript preparation. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] participated in the synthesis of compounds and fluorescence experiments. [PERSON] contributed to data interpretation and manuscript revision.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Fundamental OBR Principles and the Traditional Method to Analyze the Data
* (Strain) measurement: Mechanical strains along the length of the fiber calculated from the differences of the amplitude patterns along the fiber stored in two (or more) records  
It is important to mention that while optical fibers are quite robust, measuring strain with those has some limits. It is well known that so-called microbending of the fiber on the scale of just some micrometers leads to attenuation of the light inside the fiber [48; 49]. It can be reasonably assumed that the properties of a hardening material on the micrometer scale may slightly differ; e.g., due to small compositional or temperature changes. This may lead to small, slightly softer or harder \"grains\", which press differently on the fiber, thus inducing microbending. It is also known that higher loads lead to higher intensity losses [50]. An increasing load on the fiber and thus a decreasing signal amplitude are to be expected during the hardening process due to the permanently shrinking volume of the material (cf. Figure 5). The proprietary OBR-software usually used to perform the strain analysis uses a cross-correlation algorithm to determine the strain from the Rayleigh backscattered light. In the traditional method, each record will be compared to the reference record from before the experiment started. However, the more advanced the curing process, the higher the attenuation of the signal due to increased microbending and load on the fiber. We assume that the cross-correlation algorithm cannot function properly, due to the evermore diminishing signal amplitude compared to the very first record. As is shown below, the diminishing signal leads to the generation of random strain values when using the traditional analysis method (cf. Figure 2). Since this randomness has nothing to do with the regular shot noise, but is rather caused by the above-described failing of the analysis procedure, it is denominated \"procedural noise\". This is justified since the different analysis procedures described below actually do yield information.
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 4 Detection of Anomaly Zones by Visual Observation
### Areas of Ground Softening  
Figure 8a shows the 2D ER contour map for Section 2, which included an area of ground softening simulated by a mix of sands and polystyrene particles. The pocket of softened ground, which was 2 (L) \(\times\) 1.8 (W) \(\times\) 0.5 m (T), was located at position 1.5 m at a depth of 0.55 m. The ER in the area was significantly larger than that of the surrounding natural ground. The higher ER of the softened ground was distributed over its actual size, as shown in Figure 8b, where the ER is presented on the graph according to the measurement position by depth. The higher ER of the softened ground was attributed to the fact that water could easily percolate through the sands and polystyrene particles, resulting in a much lower water content than the surrounding natural ground. Consequently, the ER of the softened ground was higher than that of the surrounding natural ground over its actual area, whereas the ER of the cavities simulated by the polystyrene hemispheres was larger only at the top of the cavities.  
Figure 8: Test result for ground softening (Section No. 2) (**a**) Distribution of the ER. (**b**) ER according the position at each depth.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Discussion and Limitations  
The proposed method reduces the noise data from the training dataset and derives an efficient small-scale training dataset through purification. This method filters only the data that has a significant influence on forming the decision boundary of the ensemble model. These results can fundamentally improve the efficiency and performance of anomaly detection techniques using machine learning. However, some blind spots and limitations must be considered in deriving these results.  
The first is the absence of a method for determining the size of an appropriate training dataset. Although the derived dataset from the proposed method makes the clear decision boundary, the size of the training dataset itself is directly related to the sensitivity to changes in the network. Therefore, when the training data is too large, a behavior different from previous patterns may be classified as noise and eliminated even if it is normal behavior. On the other hand, slow but progressive attacks, and noise may not be detected using a too-small training dataset. Therefore, it is necessary to consider how to determine the size of the training dataset by observing the number of changes in the network.  
The second is a case in which meaningful data is gradually removed while the training dataset continuously removes noise data; thus, the dataset converges in a meaningless direction. For example, in the case of malicious behavior that is difficult to detect, hard voting (or soft voting) of the ensemble model may not be appropriate for attack detection. This aspect can converge in the direction where a specific label is continuously reduced in the training dataset, or the training dataset itself is over-fitting so that only a specific label is selected. This problem should be solved by considering the degree and ratio for acceptance and reflection to the new data and patterns.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 5 Material and Methods
### Traffic Generated by Control Message Flow  
During the user authentication and registration procedure, a new ID is generated and attributed by the server to the UE. The ID size can be represented in 8 characters (8 bytes). Two message types are used for network control and topology map management, i.e., update and notification messages:  
* Update message: this message is sent by UE to the Application Server when direct links are added or deleted. The message contains the updated list of directly connected users having direct links with the UE. The message size is variable and depends on the UE-managed links.
* Notification message: this message is sent in broadcast mode to all connected users and contains the topology map of the entire network. The IDs of the N active UEs are included, making the message size around N \(\times\) 8 bytes.  
Algorithm 4 illustrates the usage scenarios of update and notification messages.  
```
// User side For each UE do ifnew link is added or link removed then Send update message to server with containing connected IDs end if // Server side ifnew user is connected or update message received then Send notification to all UEs with updated topology map end
```  
**Algorithm 4** Usage scenarios of update and notification messages.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic
### Comparison between ITO and Mesh Technique  
The comparison of the transparency and sheet resistance of the ITO film and mesh structure is given in Table 1. According to this table, the transparency of the mesh structure is 15.82% higher than that of the ITO film, while its sheet resistance is more than 20-times lower than that of the ITO. Therefore, the mesh structure obtains better performance compared to that of the ITO film. In this paper, the mesh structure was chosen to design the transparent antenna integrated into the OLED light source.  
Figure 4: Comparison between the optical transparency for the ITO film and mesh structure in the visible spectrum.