esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Effective Number of Pulses on CVD Diamond Coating Ablation
That is to say, with the increase of the ablation depth, a defocus of the irradiation region occurs and finally renders the MRR descending. In order to investigate the influence of fs pulsed laser ablation on the carbon phase transformation of the diamond coatings, a Lorentzian-Gaussian fit is applied to make deconvolution of the obtained Raman spectra of the samples. The original Raman spectra and the integrated deconvolution peaks are all shown in Figure 4, where Raman fit stands for the original data after the Lorentzian-Gaussian fit, TP is the fitted peak of trans-polyacetylene, AS is amorphous sp3 carbon, DIA is the diamond phase, D stands for D-band, and G stands for G-band. During the CVD process, amorphous carbon and diamond particles are always co-deposited on the substrate surface, and the grain size of the deposited diamond coating is so small (see Figure 1b) that a large amount of graphite and amorphous carbon phase adhere to diamond grain boundaries. That non-diamond phase makes the Raman spectrum of nano CVD diamond coating always present a typical characteristic of a sharp diamond peak accompanied by wide D peaks. Meanwhile, compared with the microcrystalline diamond, the Raman spectra of the as-grown nano CVD diamond, also has a large G-band range of 1500 cm\({}^{-1}\) to 1600 cm\({}^{-1}\), as can be seen from Figure 4a. With the decrease of the scanning speed, the intensity of diamond peaks reduces gradually due to the laser-induced graphitization. The diamond peak disappears completely when the scanning speed decreases to 1.2 mm/s, and the Raman spectrum transforms to a typical graphite Raman line, suggesting the graphitization can be enhanced with the rising effective number of pulses. Moreover, the fitted diamond peaks have a shift  
Figure 2: SEM photographs of fs laser-irradiated regions under different scanning speeds. (**a**) As-grown; (**b**) 2.0 mm/s; (**c**) 1.8 mm/s; (**d**) 1.6 mm/s; (**e**) 1.4 mm/s; and (**f**) 1.2 mm/s. The red arrow indicates the direction of laser polarization. toward lower wavenumbers (from the original 1335.24 cm\({}^{-1}\) to 1332.52 cm\({}^{-1}\) at 1.4 mm/s). This shift pattern indicates that the compressive stress in diamond coating introduced during deposition can be released by fs laser irradiation.
## 1 Introduction
Meanwhile, we also notice that some scholars aim at reducing delay for transit or emergency vehicles at signalized intersections by temporarily adjusting the traffic signal timing to benefit transit vehicles [32; 33]. This paper proposes a new methodology to design a control model for the TSP systems based on Hierarchical-Colored PNs (HCPN) and adopts the state space analysis to verify the correctness and reliability of the control model before the deployment stage. The resulting system is based on the network of the on-board unit and roadside unit (RSU) for TSP applications that provide transit vehicles with safety and convenience. Once an approaching transit vehicle is detected by an RSU, signal control systems determine whether the current state meets the priority control conditions. If it is satisfied, the transit vehicle and its predecessor or successor vehicles with the same direction are allowed to pass through the intersection. If it is not satisfied, we might prefer to maintain the original control strategy without conducting priority control ones. As a result, one can infer that the proposed priority strategies are able to reduce the travel time of public transit vehicles moderately and economically. The remainder of this paper is organized as follows. Section 2 demonstrates the model of the TSP intersection. Section 3 develops the HCPN-based TSP control models. Then, the analysis of the results is presented by using full reachable states in Section 4. Concluding remarks are given in Section 5.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 3 Quadrotor Attitude Control System Design
### ESO Parameter Tuning with CMA-ES Algorithm  
Although there are some rules about parameter tuning in ESO, it is still repetitive work, even for designers with considerable experience. The parameters to tune are a list of \(\left[\beta_{01},\beta_{02}\right]\) in Algorithm 1 for the roll and pitch axes. In this section, only the ESO in the roll axis is optimized.  
In the simulation environment, the disturbance can be directly obtained, so we optimize the process during flight, and the controller is the actor network trained in Section 3.3, along with the disturbance feedback that is calculated by the ESO. A task is designed to test and quantify the performance of the ESO. The system starts on the zero point state, and the controller tries to hold the system on the zero point state for a period of time, with the same time-varying square wave disturbance added to the system in each test. The costs in each task are computed with the error of ESO output \(\zeta_{0}\) and disturbance \(\xi\) in every time step during the task.  
In the real quadrotor platform, we designed a specific test: the quadrotor is placed on the ground with no external disturbance, and control signal \(a_{x}\) is sent to the controller; but, the motors are not actuated and remain still during the test. It is worth noting that the control signal \(a_{x}\) here is not computed by the controller in Section 3.3; we set \(a_{x}\) as a square wave. In this situation, from the perspective of the controller, a disturbance compensates for the control signals to keep the quadrotor still, so the value of disturbance \(\zeta\) is just the negative of current control signal \(a_{x}\) multiplied by the CGP \(b_{0}\). Using this test, we avoid problems, such as safety risks, computational costs, and battery capacity limitation, etc. The task for real quadrotor optimization is that the system starts on the zero point state and holds still for a period of time, and the same time-varying control signal \(a_{x}\) is added to the system in each test. The costs in each task are also computed with the error of \(\zeta_{0}\) and \(\zeta=-a_{x}*b_{0}\) in every time step during the task.  
We present ESO parameter tuning with the CMA-ES algorithm in this section. The CMA-ES algorithm is a stochastic or randomized method for real-parameter (continuous domain) optimization of nonlinear, nonconvex functions. The CMA-ES algorithm consists of five parts: sampling, calculating costs, updating the mean value, adapting the covariance matrix, and step-size control [24].
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 5 Performance Evaluation
### Clustering Scheme Comparison
#### 5.3.3 Time Complexity Analysis  
The time complexity of Algorithm 1 K-means++ Clustering in the proposed IWLC scheme is \(O(KNI)\), where \(N\) is the number of UAVs, \(K\) is the number of clusters, and \(I\) is the number of iterations which is set to 50 in the simulation. The time complexity of Algorithm 2 Cluster Head Selection is \(O\Big{(}K*\frac{N}{K}\Big{)}=O(N)\). The time complexity of Algorithm 3 Cluster Maintenance is dominated by Algorithm 2.  
Table 5 summarizes the time complexity of the proposed IWLC scheme and the two benchmark methods, namely WSCA and ACO. It can be observed that the proposed IWLC scheme enhances the performance of the number of clusters, PDR, network lifetime, cluster head changing ratio, and energy consumption with equivalent time complexity.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 3 Lp-Norm Gravity Inversion Based on Adaptive Differential Evolution
### Adaptive Adjustment of Regularization Factor  
The regularization factor \(\lambda\) determines the main fitting object in the inversion process. While \(\lambda\to 0\), the observation data is mainly fitted; on the contrary, the model regularization term is mainly fitted. [PERSON] et al. [45] and [PERSON] [46] have presented some adaptive techniques to adjust the regularization factor, but these methods are only suitable for conventional gradient-based iterative search algorithm, and are not suitable for global nonlinear DE algorithms. In order to fit DE algorithms, [PERSON] et al. [37] combined the ideas of [PERSON] [46] and [PERSON] [47], then proposed the following update method:  
\[\begin{array}{c}\lambda^{G+1}=\left\{\begin{array}{l}\xi\lambda^{G}\ \text{if}\ \Phi^{G}_{d,\ mean}>\Phi^{G-1}_{d,\ mean}\\ \lambda^{G}\ \ \text{otherwise}\end{array}\right.,\\ \Phi^{G}_{d,\ mean}=\frac{1}{NP}\sum\Phi^{G}_{d,\ i},\end{array} \tag{25}\]  
where \(\Phi^{G}_{d,\ mean}\) denotes the average misfit function value, \(\xi\in(0,1)\) is attenuation coefficient, and equal to 0.65. At the 0-th iteration, the regularization factor is defined as:  
\[\lambda^{0}=10\frac{\sum\Phi^{0}_{d,i}}{\sum\Phi^{0}_{m,i}},i\in[1,NP] \tag{26}\]  
The above method is successfully used for the inversion of the magnetic method, but this updated method ignores the phenomenon that the ratio of \(\Phi_{d}\) and \(\Phi_{m}\) is constantly changing during the search process. Therefore, in order to enable \(\lambda\) to balance the effects of \(\Phi_{d}\) and \(\Phi_{m}\) throughout the inversion process, the following updated method is proposed:  
\[\begin{array}{c}\lambda^{G+1}=\left\{\begin{array}{l}\xi\lambda^{G}\ \text{if}\ \Phi^{G}_{d,\ mean}\geq\Phi^{G-1}_{d,\ mean}\\ (1-c_{\lambda})\lambda^{G}+c_{\lambda}\text{max}\big{(}\lambda^{G},\lambda^{ t}\big{)},\ \text{elseif}\ \Phi^{G}_{d,\ mean}\leq\delta_{\lambda}\\ \lambda^{G}\ \ \text{otherwise}\end{array}\right.,\\ \lambda_{t}=\frac{\sum\Phi^{G}_{d,i}}{\sum\Phi^{G}_{m,i}},\end{array} \tag{27}\]  
In which, \(c_{\lambda}\in(0,1)\) denotes the learning rate, and is set at 0.8 based on the experimental results. \(\lambda_{t}\) is the ratio of \(\Phi^{G}_{d,\ mean}\) and \(\Phi^{G}_{m,\ mean}\) in the current population, \(\delta_{\lambda}\) is a given threshold and is defined as follows:  
\[\delta_{\lambda}=\frac{1}{2 NP}\sum\Phi^{0}_{d,i} \tag{28}\]  
Compared with Equation (25), it can be found that the new update method of regularization factor is more complicated. At the same time, the new adjustment method is more reasonable due to the influence of \(\lambda_{t}\).
## 2 Structures and Principle  
A slot waveguide is a special type of waveguide where a layer made of a low-refractive-index material is sandwiched between two layers made of high-refractive-index materials. Moreover, the slot-waveguide structure exhibits a refractive index difference between the materials with electric field discontinuity [17]. When transmitting optical energy, the transverse magnetic (TM) signals have an optical field concentrated in the low refractive index region. In contrast, transverse electric (TE) signals are concentrated in the high refractive index region. This special structure induces a dual reflection phenomenon that facilitates low-loss transmission.  
A simple multimode interference coupler consists of the input waveguide, the output waveguide, and the coupling area. The input and output waveguides must support single-mode propagation. The MMI coupler is based on the self-imaging effect [19]. Self-imaging is a property of multimode waveguides by which an input field profile is reproduced in single or multiple images at periodic intervals along the propagation direction of the waveguide. Thus, the self-imaging effect is a consequence of the mutual interference between excitation modes in an optical waveguide [20]. The length of the MMI can be determined through the self-imaging effect.  
Under conditions of symmetric interference, the length of the multimode interference coupling region, with dimensions of \(1\times\mathrm{N}\), can be expressed as follows:  
\[L_{\mathrm{MMI}}=\frac{3L_{\pi}}{4\mathrm{N}}, \tag{1}\]  
where \(L_{\mathrm{MMI}}\) is the length of the coupling region. When N is equal to 3, the length that corresponds to the triple image point in the MMI waveguide can be determined. \(L_{\pi}\) is the beat length of the fundamental mode, and the first-order mode is defined as follows:  
\[L_{\pi}=\frac{\pi}{\beta_{0}-\beta_{1}}\ \approx\ \frac{4n_{\mathrm{r}}W_{ \mathrm{MMI}}^{2}}{3\lambda_{0}}, \tag{2}\]  
where \(\beta_{0}\) and \(\beta_{1}\) are the propagation constants of the fundamental and first-order modes, respectively; \(W_{\mathrm{MMI}}\) is the width of the coupling region; \(\lambda_{0}\) is the wavelength; and \(n_{\mathrm{r}}\) is the effective refractive index of the waveguide. The most important performance parameters for MMI couplers are the excess loss and the imbalance [21]. The excess loss of the coupler is described by the following expression:  
\[\mathrm{Excess\ Loss}=-\ 10\log_{10}\ \left(\frac{P_{\mathrm{out}}}{P_{\mathrm{in}} }\right), \tag{3}\]  
where \(P_{\mathrm{out}}\) is the output optical power and \(P_{\mathrm{in}}\) is the input optical power. Imbalance is used to measure the spectral uniformity of a device and is given by the following expression:  
\[\mathrm{Imbalance}=-\ 10\log_{10}\ \left(\frac{P_{2}}{P_{1}}\right), \tag{4}\]  
where \(P_{1}\) and \(P_{2}\) are the maximum and minimum optical power at the individual outputs, respectively.
# On-Demand Waveguide-Integrated Microlaser-on-Silicon
## 2 Results and Discussion
### Device Fabrication
Inset: high-magnification SEM image showing the Si-post with a diameter of approximately 1.6 \(\upmu\)m. The post is located in the longitudinal center of the Si WG with a width of approximately 500 nm and length of approximately 105.8 \(\upmu\)m. The gap size between them was approximately 1.1 \(\upmu\)m. Scale bar: 10 \(\upmu\)m and 1 \(\upmu\)m (inset). **(b)** Fabrication of InGaAsP microdisks. (i) Epitaxial InGaAsP/InP wafer design. (ii) Low-magnification SEM image showing an array of microdisks after the CAIBE and selective chemical etching. (iii) High-magnification tilt-view SEM image showing single microdisk with submicron-sized InP-post structure. The diameter and thickness of the InGaAsP microdisk were approximately 4.5 \(\upmu\)m and 260 nm. Scale bar: 1 \(\upmu\)m. **(c)** The PDMS micro-transfer-printing process. (i) Pick-up and separation of single microdisk from the InP substrate. (ii) Locating and coarse alignment with a target Si-post on SOI device wafer. (iii) Center-to-center align-transfer. (**d**) Si WG-integrated microlaser-on-Si. (i) Low-magnification top-view SEM image of fabricated device. Scale bar: 10 \(\upmu\)m. (ii) High-magnification top- (ii), and tilt-view. (iii) SEM images showing the integration region (white dotted box of (i)). (ii) Transferred microdisk with misalignment of approximately 700 nm. Gray dotted circle indicates the Si-post. Scale bar: 2 \(\upmu\)m (iii). Evanescently coupled microdisk and WG with an air gap size of approximately 40 nm. Scale bar: 1 \(\upmu\)m.  
A PDMS stamp with a 20 \(\times\) 20 \(\times\) 20 \(\upmu\)m\({}^{3}\) cubical microtip was surface-treated under the UV radiation with an illumination power of 10.4 mW/cm for 60 s to control the adhesive energy [37]. The low- and high-magnification SEM images depicted in Figure 2d show the top and tilt views of the Si WG-integrated microlaser-on-Si device. The transferred microdisk laser was slightly off-centered at approximately 700 nm with respect to the center of the Si-post. Consequently, the upper boundary of the microdisk, which was originally designed to be aligned with the underlying Si WG, was shifted upward by the same misalignment ((ii), Figure 2d). It should be noted that this misalignment inevitably occurs during the micro transfer printing owing to the limited imaging resolution of the optical microscope system and the finite precision of manual manipulation. An extensive study on misalignment, including the statistical results for different sizes of Si-posts, can be found in Ref. [30]. The high-magnification tilt-view SEM image reveals the details of the coupling geometry ((iii), Figure 2d). As anticipated, the boundary of the microdisk was air-suspended, and a nanoscale air gap between the microdisk and the Si WG, typically a few tens of nanometers, was clearly observed. Notably, this air gap can be controllably increased or decreased by selectively depositing materials on top of the Si-post (e.g., SiO\({}_{2}\), resist polymers) or additionally dry-etching the Si WG in the coupling region.
Ensuring stable and reliable connectivity requires advanced mobility management and transmission techniques to overcome these limitations. A comprehensive survey of data transmission management for UAV-connected networks in next-generation mobile ecosystems is provided in [13]. This paper contributes to the ongoing discourse by presenting a structured survey of existing approaches to handover management and load balancing in ultra-dense 5G/6G environments. Unlike prior works that focus on narrow aspects, this review synthesizes methodologies across multiple domains, identifies limitations, and outlines future research directions. The goal is to inform the design of more resilient, AI-assisted network architectures in the context of increasingly dense and dynamic wireless ecosystems. UDNs [16] refer to wireless network architectures characterized by the deployment of a very high number of small cells (femtocells, picocells) within a given geographical area, resulting in a cell density that far exceeds that of traditional network designs. The primary goal of UDNs is to significantly improve network capacity, spectral efficiency, and user experience by reducing the distance between users and access points, enabling higher frequency reuse and supporting massive device connectivity, which is essential for 5G and 6G cellular networks, as well as any future advancements. The remainder of this paper is organized as follows: Section 2 provides essential background information on 5G and 6G cellular networks, their architectures, enabling technologies, and small-cell types. Section 3 presents a comprehensive review of recent research related to handover management, load balancing, and AI integration. Section 4 discusses current challenges and outlines future research directions for UDNs. Section 5 concludes the paper by summarizing the key insights and emphasizing the impact of AI-driven solutions in next-generation wireless systems.
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Studies by [PERSON] et al. [1]  
[PERSON] _et al._[1] carried out intense study of the jamming attack detection mechanism with experiments using the MICA2 Note platform. Firstly, they collected data about various percentages of the PSR and PDR (measured at the transmitter end) for constant, deceptive, random, and reactive jammers for BMAC and 1.1.1 MAC protocols for varying distances between the transmitting-node and the jammer. They considered additional jammer parameters like on-off periods for the random jammer and different packet sizes for the reactive jammer. The results show that although the PSR and the PDR vary for different jammers under different conditions, it is difficult to conclude about jamming and its type by these parameters alone. They then studied the levels of carrier sensing time, energy consumption, and the received signal strength as well as the received signal spectrum under normal and jamming conditions for two application layer protocols: Constant Bit Rate (CBR) and Maximum Traffic and tried to identify the jammer type through spectral discrimination using the Higher Order Crossing (HOC) method [14]. They conclude through these experiments that it is not always possible to use simple statistics, such as average signal strength, energy, or carrier sensing time to discriminate jamming condition from the normal traffic, because it is difficult to devise thresholds. They also conclude that the HOC method can distinguish the constant and deceptive jamming from the normal traffic, but cannot distinguish the random and reactive jamming from the normal traffic. Finally, they conclude that if PDR is used with consistency checks like, checking own PDR and signal strength and comparing the same with those of the neighbors, and/or ascertaining own distances from the neighbors, then the combination can very effectively detect and discriminate various forms of jamming.  
The study is rigorous and the suggested methodology is sound. Its limitations are: (1) the complete process has to be done by the WSN node which is taxing, and (2) that the node may not be able to communicate with its neighbors during jamming to get the required statistics for comparison, as required in the method.
## 4 Feature Extraction of RC Valve Fault
### Fault Characteristics Analysis of Response Signal
#### 4.2.2 Extraction of Response CIs  
The characteristic information of valve leakage is included in the rotation frequency and its frequency doubling components of RC. The high multiple frequency component is produced by rotor unbalance, which is less affected by the rotation frequency features and it contains fewer effective fault information. Therefore, it is difficult to extract fault features information from the high multiple frequency component. Thus, this paper only uses the one-fold, two-fold, and three-fold frequency components to extract fault feature information, and it ignores the high multiple frequency components.  
The energy spectrum amplitude of the one-fold, two-fold, and three-fold frequency components of the rotation frequency are used as the response CIs. Taking one million data from each group and divided into 50 parts to conduct spectral analysis on each part. Additionally, the comb filter is used to obtain the response CIs from the spectrum of the response signal. Table 2 shows the response CIs.  
In Table 2, the number of each CI is 50. ERF, EDRF, and ETDF denote the energy spectrum value corresponding to one-fold, two-fold, and three-fold rotation frequency, respectively.