esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 2 Generic Dispersion Analysis  
Let \(A(t,z)\) represent the envelope of an electromagnetic (EM) pulse, which propagates in the z-direction in a dispersive medium with the dispersion coefficient \(\beta_{2}\), and thus \(A(t,z)\) obeys the following equation [14]:  
\[i\frac{\partial A(t,z)}{\partial z}=\frac{\beta_{2}}{2}\frac{\partial^{2}A(t,z )}{\partial t^{2}} \tag{1}\]where \(t=t^{\prime}-z/v\) is the time in a frame of reference that travels at the velocity of light in the medium (\(v=c/n\), where \(c\) is the vacuum light's velocity, and \(n\) is the mean refractive index), i.e., \(t^{\prime}\) is the real time. This equation is valid provided all spectral components of the pulse are governed by the following dispersion relation:  
\[\frac{n}{c}\omega+\frac{\beta_{2}}{2}\omega^{2}=k \tag{2}\]  
If \(A(t,0)\) is the initial pulse profile, then the pulse profile at the end of the medium \(A(t,z)\) can be evaluated by the following convolution:  
\[A(t,z)=\int\limits_{-\infty}^{\infty}K\big{(}t-t^{\prime},z\big{)}\,A\big{(}t ^{\prime},0\big{)}dt^{\prime} \tag{3}\]  
where the dispersion Kernel (see Appendix A) is  
\[K\big{(}t-t^{\prime},z\big{)}=(-2\pi i\beta_{2}z)^{-1/2}\exp\left(-i\frac{(t- t^{\prime})^{2}}{2\beta_{2}z}\right) \tag{4}\]
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 4 Experiments
### Ablation Experiments
#### 4.3.2 Analysis of the Effectiveness of MNet Backbone Network  
In order to verify the effect of the MNet backbone network proposed in this article, we conduct lightweight backbone networks SqueezeNet [46], EfficientNet [47], and MobileNetV2 [48] as comparison models. Four sets of comparative experiments were designed: (a) SqueezeNet; (b) EfficientNet; (c) MobileNetV2; and (d) MNet. In these comparative experiments, only the backbone network was modified, and other structures remained consistent. It can be seen from Figure 9 that using the MNet backbone network proposed in this article can better capture smaller ball objects.
## 2 Results and Discussion
### X-ray Diffraction  
X-ray powder diffraction patterns of the bare Fe\({}_{3}\)O\({}_{4}\) particles (a) MPMs (b) and EDA-MPMs (c) are illustrated in Figure 3, respectively. In Figure 3a, the XRD pattern of Fe\({}_{3}\)O\({}_{4}\) particles shows main peaks at 20 of 31\({}^{\circ}\), 36\({}^{\circ}\), 43\({}^{\circ}\), 53\({}^{\circ}\), 57\({}^{\circ}\) and 63\({}^{\circ}\), corresponding to (220), (311), (400), (422), (511) and (440) Bragg reflection, respectively. It can be seen that the crystalline pattern coincides very well with the standard pattern of Fe\({}_{3}\)O\({}_{4}\). Such a crystal system is important for the magnetic properties to be maintained. It can be found in Figure 3b that the polymer encapsulation can not change the typical spectra of Fe\({}_{3}\)O\({}_{4}\) nanoparticles. The diffraction peaks at 31\({}^{\circ}\), 36\({}^{\circ}\), 43\({}^{\circ}\), 53\({}^{\circ}\), 57\({}^{\circ}\) and 63\({}^{\circ}\) for the EDA-MPMs are consistent with that of Fe\({}_{3}\)O\({}_{4}\) nanoparticles. From the XRD pattern of EDA-MPMs (c), it was observed that the intensity of diffraction peaks was weakened but did not disappear. Additionally, the new diffraction peaks at 20 = 25\({}^{\circ}\), 33\({}^{\circ}\), 39\({}^{\circ}\), 41\({}^{\circ}\), 49\({}^{\circ}\), 54\({}^{\circ}\) and 73\({}^{\circ}\) suggested the presence of Fe\({}_{2}\)O\({}_{3}\).
## 6 Conclusions  
This paper has addressed the challenge of interference in UAV self-organizing networks operating in complex emergency rescue scenarios. To improve network organization and management under such conditions, we proposed a capacity-constrained weighted clustering algorithm. The algorithm begins with a K-means++-based partitioning algorithm to form initial node partitions, then selects both the cluster head and backup cluster head by integrating the external interference factor into a weighted evaluation. A dynamic maintenance mechanism was also introduced to preserve cluster stability and robustness over time.  
Simulation results demonstrate that, under interference conditions, our algorithm achieves more balanced cluster loads, extends average cluster head maintenance duration, and reduces recovery time following cluster head failures. Moreover, the network can rapidly recover from node disconnected, further enhancing its suitability for deployment in emergency rescue environments.  
In this study, we focused on the design of clustering algorithms in interference environments. However, the study is subject to several limitations. In future work, we will conduct a more in-depth investigation of the weight coefficients employed in the CH and BCH selection process, optimizing these coefficients for each task scenario to enhance the clustering algorithm's adaptability. Furthermore, we will build a hardware-in-the-loop simulation platform that integrates targeted routing protocol designs and various network optimization strategies, and we will explore FPGA and GPU acceleration schemes. Using this platform, we will conduct comprehensive evaluations of our algorithms' real-time performance and energy efficiency to further improve system responsiveness and energy utilization. At the same time, we will carry out small-scale real-world flight tests to validate the reliability of the simulation results.  
Conceptualization, S.L. and P.G.; methodology, S.L. and X.G.; software, S.L. and J.L.; validation, S.L. and Z.F.; formal analysis, S.L. and P.G.; investigation, W.W. and X.G.; resources, W.W.; data curation, J.L. and Z.F.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and Z.F.; visualization, S.L. and P.G.; supervision, W.W. and X.G.; project administration, P.G. and X.G.; funding acquisition, P.G. and X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Natural Science Foundation of China (Grant No. 62472037) and the National Natural Science Foundation of China Youth Fund (Grant No. 62203048).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Genus, Species, and Subspecies Classification of _Salmonella_ Isolates by Proteomics
## 2 Materials and Methods
### Database Search and Criteria for Peptide Identifications  
Initial peptide identification by a database search and subsequent processing follows the previously described workflow [11]. In brief, tandem mass spectra were searched against the larger protein sequence database described above using the MASCOT search engine (Matrix Science, Boston, MA, USA), with a precursor ion mass tolerance of 50 ppm for data collected in Orbitrap Elite and 10 ppm for Orbitrap Lumos. Fragment ion mass tolerance was set as 0.8 Da. Trypsin was selected as the digestion enzyme with up to two miscleavages. Carbamidomethylation of cysteine residues was set as a fixed modification and oxidation of methionine residues was set as a variable modification. Data from each set of six replicate injections were merged using MassSieve v 1.13 to create a single list of peptide identifications for each isolate [33]. Peptide identifications were accepted with an ion score equal to or greater than the identity score threshold (\(p\geq 0.05\)). Indeterminate peptide identifications were removed. Peptides from proteins with less than two identified peptides were discarded.
## 2 Results and Discussion
### Chemical and Morphological Characterization of Bz-DVB-AN Copolymer  
The following bands can be seen in the SV3 pristine copolymer's FTIR spectra (Figure 1): N-H groups were allocated to the primary amine at 1448 cm\({}^{-1}\) and 708 cm\({}^{-1}\); 3434 cm\({}^{-1}\) and 1647 cm\({}^{-1}\) were ascribed to the -NH\({}_{2}\) and -CH\({}_{2}\) bonds at 2931 cm\({}^{-1}\). The FTIR spectrum of the Bz-DVB-AN sample showed adsorption bands in the following areas: 3510-3350 cm\({}^{-1}\) attributable to (aliphatic-NH, and -OH), 1680-1570 cm\({}^{-1}\) attributable to (-C=C, aromatic ring, NH in secondary and primary amines, respectively), 1360-1210 cm\({}^{-1}\) attributed to (\(\gamma\)N-Caliphatic, \(\gamma\)P=O bond), 1169-950 cm\({}^{-1}\) attributed to (\(\gamma\)P=O) and 880-709 cm\({}^{-1}\) assigned to (N-H groups in the amine primary and P-OR) [18; 19].  
SEM images of the pristine SV3 copolymer and of the aminophosphate Bz-DVB-AN copolymer are presented in Figure 2. It can be observed that both before and after the K-F reaction, the copolymer has a uniform surface morphology.  
The EDX spectrum of Bz-DVB-AN copolymer, presented in Figure 3b, indicates that the functionalization of SV3 copolymer with 3-pyridine  
Figure 1: FTIR spectra of SV3 copolymer and Bz-DVB-AN sample.  
evidenced by the decrease in N\(\%\) from 21.7\(\%\) for SV3 to 18\(\%\) for the final product and an increase in O\(\%\) from 10\(\%\) for SV3 to 13.7\(\%\) for Bz-DVB-AN.  
The findings of the TG-DTG-DTA analysis of SV3 in nitrogen are displayed in Figure 4a. The mass loss occurred in three stages: the first stage, which was 6.7\(\%\) (-59.9 \({}^{\circ}\)C) as a result of physically adsorbed water evaporating; the second stage, which was 4.7\(\%\) (-223.2 \({}^{\circ}\)C) as a result of pendant group degradation; and the third stage, which was 64.8\(\%\) (\(\sim\)433.4 \({}^{\circ}\)C) as a result of DVB-AN degradation. A total of 19.8\(\%\) of the residue remained after 80.2\(\%\) of the total mass was eliminated at 800 \({}^{\circ}\)C. Figure 4b shows the TG-DTG-DTA analysis of Bz-DVB-AN in nitrogen. It is evident that the mass losses occurred in three phases. The evaporation of physically adsorbed water was responsible for the first stage (25-140 \({}^{\circ}\)C) with--3.56\(\%\) (-58.6 \({}^{\circ}\)C); the pendant group degradation for the second stage (140-360 \({}^{\circ}\)C) with--10.21\(\%\) (-193 \({}^{\circ}\)C), and the DVB-AN degradation for the third stage (360-660 \({}^{\circ}\)C) with--61.12\(\%\) (-432.1 \({}^{\circ}\)C). A total of 74.89\(\%\) of the whole mass was lost, leaving 25.11\(\%\) of residue at 800 \({}^{\circ}\)C. Good thermal stability is demonstrated by the functionalized copolymer with the aminophosphate group (Bz-DVB-AN).
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 3 Stimuli-Responsive Polymeric Nanosystems
### Endogenous Stimuli-Responsive Polymeric Nanosystems
#### 3.1.1 Enzyme-Responsive Polymeric Nanosystems
conjugated the drug to poly(\(N\)-(2-hydroxyethyl) methacrylamide(PHPMA) with Gly-Phe-Leu-Gly peptide substrate in the polymer backbone and side-chains to enable tumor-selective drug release and improve the therapeutic efficacy [118; 119]. This strategy was extended to a two-drug (gemcitabine and paclitaxel) combination system linked to PHPMA copolymers to achieve a synergistic antitumor effect (Figure 1) [118]. Most recently, [PERSON] et al. reported a semiconducting polymer nano-PROTAC (SPNpro) system that is composed of a semiconducting polymer core conjugated with proteolysis targeting chimera (PROTAC) segments via peptide fragment to synergize phototherapy with CatB-triggered protein degradation for photo-immunometabolic cancer therapy (Figure 2) [120]. The other enzymes that have been widely used as promising stimuli for developing activatable drug delivery systems include oxidoreductase and phospholipides [121; 122; 123]. For example, [PERSON] et al. employed glucose oxidase as gate keeper to design nanosystems that are sensitive to glucose levels in blood for controlled release of insulin encapsulated in the core [124; 125]. This glucose-activatable nanoformulation allowed for diabetes management in the self-regulated manner. Owing to their frequently upregulation in tumors, the phospholipase A2 (PLA2) has drawn great interest as a target for developing activatable drug delivery nanosystems. Capitalizing on this, [PERSON] et al. developed the secretory phospholipase A2 (sPLA2)-responsive liposome drug-delivery nanosystems (Figure 3) [126]. In this case, the antitumor ether lipids (AELs) were masked as prodrugs. The prodrugs of AELs (proAELs) were synthesized for liposome preparation and the loaded prodrugs could be triggered by sPLA2 at tumor microenvironment selectively, which not only minimized the undesired side effects but also maximized the antitumor efficacy. Figure 1: Structure of cathepsin-responsive HPMA copolymer drug conjugates: left, P(GFLG)-DOX; right, ST-PHPMA-DOX-COOH. Reproduced from ref. [118] with permission from the American Chemical Society. Figure 2: Schematic illustration of SPNpro-mediated IDO degradation for cancer photommunometabolic therapy. (**a**) Structure and CatB-specific activation mechanism of SPNpro. (**b**) SPNpro-mediated activatable photo-immunometabolic therapy with two processes: (i) a series of cancer immune responses including immunogenic cell death (ICD), tumor-associated antigen release, DC maturation, and effector T (Teff) cell activation upon NIR photoirradiation; (ii) SPNpro-mediated immunometabolic intervention processes including CatB-specific activation of IPP, IDO and VHL targeting, proteasome recruitment, IDO degradation, Trp upregulation and Kyn depletion, and Teff cell activation. Reproduced from ref. [120] with permission from the Nature Publishing Group.
# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 3 Materials and Methods
### ML Models
Random forest (RF): To classify data, random forest uses a collection of decision trees that are generated at random. If there is use of bagging, random forests will introduce even more unpredictability. In random forests, each tree is built from a different bootstrap sample of the data, and the tree construction process is altered to improve accuracy. The optimum split across all variables is used to divide each node in a typical tree. Each node in a random forest selects a subset of predictors at random and uses the best of them to make predictions. This seemingly contradictory approach outperforms a wide variety of classifiers and support vector machines, including discriminant analysis and neural networks, and is highly resistant to overfitting [29]. RF can be described as:  
\[\hat{f}=\frac{1}{B}\sum_{b=1}^{B}f_{b}\left(x^{\prime}\right) \tag{8}\]  
Support vector machine (SVM): SVM-based regression models are helpful for modeling complicated relations that cannot be well captured by lower-order polynomial equations. Figure 4: Process of the machine learning algorithm and evaluation. Because of its high power and great generalization capacity, SVM is widely used for tackling issues involving pattern recognition, classification, regression, and prediction. SVM can be described with the following equation [30]:  
\[g(x)=w^{T}\Phi(x)+a \tag{9}\]  
where \(w\) is the weight factor, \(\Phi\) represents the mapping function, \(x\) is the input vector, and a is the bias. Additive model (AM): One type of nonparametric regression technique is the additive model (AM). The AM constructs a subset of nonparametric regression models with the help of a one-dimensional smoother [31]. Here is a simplified representation of the training procedure for additive models:  
\[F_{m}(x)=F_{m-1}(x)+ah_{m}(x) \tag{10}\]  
Here, \(F_{m}(x)\) is a model ensemble that improves upon the \(F_{m-1}(x)\) model by combining m weak learning models \(h_{m}(x)\) to find the true solution. A scaling factor, which can range from 0 to 1, is added to reduce the contribution of each iteration in an effort to prevent the model from overfitting.
## 2 Principle
### Methods
#### 2.2.3 Modal Phase Measuring Deflectometry (MPMD)  
In MPMD, the height and slopes of the SUT are represented by the well-established mathematical model and updated by optimizing the model coefficient \(\mathbf{c}\) to minimize the discrepancy between the re-projection in ray tracing and the actual measurement [7]. As shown in Figure 2, the incident ray \(\overrightarrow{p}\) is reflected off the SUT to the screen, and the reflected ray \(\overrightarrow{r}\) can be obtained with Equation (11):  
\[\overrightarrow{r}=\overrightarrow{p}-2\left\langle\overrightarrow{p}, \overrightarrow{n}\right\rangle\overrightarrow{n}, \tag{11}\]  
where \(\overrightarrow{n},\overrightarrow{p},\) and \(\overrightarrow{r}\) are the unit vector of the normal, the incident ray and the reflected ray, respectively, and \(<<\) represents the dot product.  
The position of the screen relative to the camera (rotation matrix \(R_{m}\), translation vector \(T_{m}\)) can be obtained through a pre-calibration procedure, which includes camera calibration, screen calibration, and geometric calibration. Once the pose of the screen is initialized via pre-calibration, the corresponding intersection \(S^{\tau}\) of the screen is calculated. The coefficient vector \(\mathbf{c}\) is initialized using the mathematical model of the SUT.  
The coefficient vector \(\mathbf{c}\) and the pose of the screen (\(R_{m}\), \(T_{m}\)) are optimized by minimizing the differences between the re-projection \(S^{\tau}\) and the measurement \(S\) on the screen among the \(N\) pairs of correspondence in the least squares sense. The nonlinear least squares problem in Equation (12) can be solved using the Levenberg-Marquardt algorithm.  
\[[R_{n}^{\tau},T_{n}^{\tau},c^{\tau}]=\arg\min_{R_{m},T_{m}\in T}\sum_{n=1}^{N} \|S_{n}^{\tau}(R_{m},T_{m},c)-S_{n}\|. \tag{12}\]
## 2 Preliminaries and Problem Formulation
### Problem Formulation  
As shown in Figure 1, \(\rho_{ij}\) is defined as the relative distance between the \(i\)th USV and the \(j\)th USV; its equation and differential equation are given as follows:  
\[\rho_{ij}=\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}, \tag{16}\]  
\[\dot{\rho}_{ij} =-u_{i}\cos(\psi_{i}-\lambda_{ij})+u_{j}\cos(\psi_{j}-\lambda_{ij})\] \[+v_{i}\sin(\psi_{i}-\lambda_{ij})-v_{j}\sin(\psi_{j}-\lambda_{ij}). \tag{17}\]  
\(\lambda_{ij}\) is defined as the relative angle between the \(i\)th USV and the \(j\)th USV; its equation and differential equation are given below:  
\[\lambda_{ij} =\arctan\frac{y_{j}-y_{i}}{x_{j}-x_{i}}, \tag{18}\] \[\lambda_{ij} =\frac{1}{\rho_{ij}}\{-u_{i}\sin(\psi_{i}-\lambda_{ij})+u_{j}\sin (\psi_{j}-\lambda_{ij})\] \[-v_{i}\cos(\psi_{i}-\lambda_{ij})+v_{j}\cos(\psi_{j}-\lambda_{ij} )\}. \tag{19}\]  
Figure 1: Formation model.  
The relative information \(\rho_{ij}\) and \(\lambda_{ij}\) can be measured by using local sensors, such as lidar and the gimbaled camera. If the \(i\)th USV and the \(j\)th USV are said to be neighbors, the \(i\)th USV is able to obtain the data \(\eta_{j}\) and \(v_{j}\) directly.  
The control objective is to design a distributed controller for the \(i\)th USV to track the leader with model uncertainties, input saturation and limited communication ranges. Specifically, it is to achieve the following two objectives.  
1. \(\underline{R}<\rho_{ij}<\underline{L}\) and \(\underline{\theta}<\lambda_{ij}<\overline{\theta}\). where \(\underline{R}=\max\{R_{i},R_{j}\}\), \(R_{i}\) and \(R_{j}\) are minimum avoidance ranges of the \(i\)th USV and the \(j\)th USV, respectively. \(\underline{L}=\min\{L_{i},L_{j}\}\), \(L_{i}\) and \(L_{j}\) are maximum communication ranges of the \(i\)th USV and the \(j\)th USV, respectively. \(\underline{\theta}\) and \(\overline{\theta}\) are the minimum and maximum bearing angle of the \(i\)th USV.
2. \(\lim_{t\rightarrow+\infty}\lvert\rho_{ij}-\rho_{ij,d}\rvert\leq c_{1}\) and \(\lim_{t\rightarrow+\infty}\lvert\lambda_{ij}-\lambda_{ij,d}\rvert\leq c_{2}\). where \(c_{1}\) and \(c_{2}\) are positive constants that can be made small enough.  
**Remark 2**.: _Objective (1) means that the connectivity preservation and the collision avoidance are considered if the \(i\)th USV is a neighbor of the \(j\)th USV. Objective (2) represents the formation tracking problem._
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 2 Related Work  
Previous research has documented that eye-tracking technology has been widely used across many areas with regard to customer behaviour studies and production evaluation. Also, the advancement of Human-Computer Interaction (HCI) has developed considerably as we attempt to understand not only how humans interact with computer screens but why we make certain choices or our attention is drawn to specific areas of the screen rather than others. If this can be understood, then it can be manipulated to improve customer engagement with an IPTV service. Moreover, Markov chains have been extensively incorporated into studies on HCI and other sorts of behavioural data, such as eye gaze movement, which is also known as sequential or time series data.
## 5 Nominal Simulation and Monte Carlo Shooting Simulation
### Simulation with 5 s Switching Mode  
In the nominal simulation, it was observed that the UAV basically reached a steady state within 5 s after entering the switching mode, as shown in Figure 15. Therefore, the soft transition design of the 5 s switching mode was introduced for comparison.  
Similarly, MC shooting simulation with 5 s switching mode uses the same random seeds with the same main parameter perturbations, and the simulation results are shown in Figure 16.  
It can be observed that in the 100 MC shooting simulations, the mode switching task was completed well under most conditions, but there were also a few cases with perturbation of main parameters, resulting in poor altitude holding, throttle, and rotor speed saturation. As shown in Figure 17, details of the simulation results in Figure 16 are provided, and it was observed that, at the moment of switching to the rotary-wing mode (at the 5 th second), the altitude channel had not yet entered a steady state. It was preliminarily judged that more time was needed to complete the altitude tracking, so it was considered to extend the duration of the mode switching for comparison.  
Figure 16: MC shooting simulation of transition phase with 5 s switching mode.  
Figure 14: MC shooting simulation of transition phase without switching mode.  
Figure 15: Nominal simulation of transition phase with 5 s switching mode.