esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Input Characteristics  
Figure 4a illustrates the transfer characteristics of HJLTFET and DMGE-HJLTFET. As can be seen clearly from this figure that the DMGE-HJLTFET can produce higher ON-state current than HJLTFET, and the ON-state current of the DMGE-HJLTFET was \(5.46\times 10^{-4}\) A/\(\upmu\)m at Vgs = 1.2 V, however, the corresponding ON-state current of the HJLTFET was only \(1.05\times 10^{-4}\) A/\(\upmu\)m. Meanwhile, the OFF-state current of DMGE-HJLTFET was \(8.4\times 10^{-18}\) A/\(\upmu\)m while the OFF-state current of HJLTFET was \(3.1\times 10^{-17}\) A/\(\upmu\)m. Consequently, compared with the conventional HJLTFET, the Ion/Ioff ratio of the DMGE-HJLTFET was increased by one order of magnitude. In addition, the DMGE-HJLTFET has a smaller average subthreshold swing (SSave) because of structural innovation, the SSave (which is extracted from Vmin to Vt; Vmin is the gate voltage where drain current equals to Ioff, and the gate voltage where the drain current becomes \(1\times 10^{8}\) A/\(\upmu\)m is taken as the threshold voltage Vt) values of the DMGE-HJLTFET and HJLTFET were 15.4 mV/Dec and 24.5 mV/Dec, respectively.  
The transconductance (g\({}_{\text{m}}\)) is an important parameter to evaluate the analog performance of devices, which is defined as the first derivative of drain current (Ids) with respect to VGS, the formula of g\({}_{\text{m}}\) is given by Equation (1):  
\[\text{g}_{\text{m}}=\frac{\text{d}\text{I}_{\text{ds}}}{\text{dV}_{\text{gs}}}. \tag{1}\]  
As shown in Figure 4b, the g\({}_{\text{m}}\) of the DMGE-HJLTFET increased observably compared with HJLTFET. The maximum g\({}_{\text{m}}\) value of the DMGE-HJLTFET was \(1.51\times 10^{-3}\) S/\(\upmu\)m at Vgs = 1.14 V, while the maximum g\({}_{\text{m}}\) value of the HJLTFET was \(6.02\times 10^{-4}\) S/\(\upmu\)m. Therefore, the maximum g\({}_{\text{m}}\) of the DMGE-HJLTFET was almost 26 times more than the HJLTFET.  
Figure 3: **(a)** The ON-state energy band diagram of the HJLTFET and DMGE-HJLTFET; **(b)** the OFF-state energy band diagram of the HJLTFET and DMGE-HJLTFET.
## 1 Introduction
The previous works cited above primarily focus on the case of an FGM plate subjected to in-plane loading in the middle surface of the plate, and not much work can be found for the analysis of the case of out-of-plane loads. [PERSON] [25; 26] studied the SCF and stress-gradients due to a circular hole in a radial FGM plate subjected to anti-plane shear loading and obtained the closed expressions for the stresses and displacements. A novel definition is introduced for the SCF in FGM plates with geometrical discontinuities in general. [PERSON] [27] presented an analytical solution of the elastic stress fields around a circular elastic inclusion in a radial FGM plate under a uniform anti-plane shear loading at infinity. [PERSON] and [PERSON] [28] analyzed the stress concentration around an arbitrarily shaped hole reinforced with an FGM layer in an infinite plate under anti-plane shear and performed the optimized analysis of the SCF. The above works are mainly for anti-plane problems. In fact, in addition to anti-plane shear loading, structures subjected to out-of-plane bending moments are also very common in engineering. Thus, it is also very significant to study the bending problems of FGM plates. Recently, some works have been made on the out-of-plane bending of FGM plates through thickness material property variation. For example, [PERSON] et al. [29; 30] investigated the resultant force concentration around holes in a transversely isotropic FGM plate with the material properties varying along the thickness direction under concentrated loads and moments. [PERSON] and [PERSON] [31] provided a solution of stresses and moments around circular and elliptical holes in FGM infinite plates with material property variation along the thickness using a complex variable approach. However, to the best of the author's knowledge, the bending of FGM plates with radially varying material properties has not yet been addressed. In this paper, the bending stress concentration around a circular hole in an FGM thin plate, which has arbitrary variations of elastic properties along the radial direction, is investigated based on the theory of complex variable functions. Following the introduction, the general solutions of the stresses and moments are presented for the plate subjected to out-of-plane bending moments by means of the method of piece-wise homogeneous layers. Then, numerical examples are given to discuss the effect of different radial variations of elastic parameters on the distribution of the moments for two different cases: a whole FGM plate with a circular hole and an FGM ring reinforced in a homogeneous perforated plate. Finally, conclusions of the present work are summarized.
## 3 Methodology
### Tuning Algorithms and Machine Learning Models  
The purpose of this study is to evaluate the predictive performance of a series of support vector machine (SVM)-based composite models. SVM was adopted as the basic model. Sparrow Search Algorithm (SSA), Improved Sparrow Search Algorithm (ISSA) and Kernel Principal Component Analysis are gradually integrated, and the Improved Sparrow Search Algorithm (KPCA-ISSA) is used to construct composite models of different complexity.  
The SSA-SVM model introduces the SSA algorithm on the basis of the traditional SVM to perform the task of feature selection and parameter optimization. The complexity of the model is enhanced by introducing additional parameters in the training process, and the optimal solution is determined by an iterative search process.  
The ISA-SVM model is optimized on the basis of SSA-SVM, and the search strategy and parameter adjustment mechanism of the composite model are improved by introducing adjustment parameters. This improvement effectively reduces the time and computing resource consumption of model training.  
KPCA-ISA-SVM model further integrates KPCA as a means of feature extraction, thus enhancing the ability of the model to deal with nonlinear problems. The introduction of KPCA as a feature extraction step significantly improves the complexity of the model and correspondingly increases the operation time. Especially when dealing with large-scale datasets, the feature extraction of KPCA and the training process of SVM require more computing resources, which may lead to a significant increase in computing costs. Despite the complexity of the KPCA-ISA-SVM model, feature extraction through KPCA is expected to improve the generalization performance of the model.  
In summary, with the gradual integration of algorithms in the composite model, the complexity of the model gradually increases, which is not only reflected in the model structure, but also reflected in the training process and computing requirements. However, this increase in complexity is intended to improve the overall predictive performance of the model through finer feature selection and parameter tuning, as well as more powerful feature extraction capabilities.  
The following offers an introduction to each component.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 3 Interference Mitigation Challenges  
When comparing UAVs to traditional ground UEs, it is important to note that UAVs typically operate at higher altitudes. This presents a unique challenge for gNBs as they must provide 3D communication coverage instead of the usual 2D coverage for ground UEs. While conventional gNBs are angled downward to serve ground users and minimise inter-cell interference, UAVs must be integrated into this 3D communication network. This introduces new challenges that must be addressed in order to achieve effective and seamless communications. One of the most critical challenges in UAV communication within a cellular network is interference management. Interference in aerial users is exacerbated by LoS-dominated UAV-gNB channels, which is largely due to the high altitude of UAVs. As a result, this phenomenon represents a significant obstacle to the proper functioning of UAVs in the network. In summary, the integration of UAVs into cellular networks requires careful consideration of 3D coverage, interference management, and other challenges unique to aerial communication.  
Figure 2 illustrates aerial interference in a 5G network. Specifically, it highlights the impact of height on interference for two UAVs, namely UAV1 and UAV2, which fly at altitudes \(h_{1}\) and \(h_{2}\), respectively. UAV1 experiences severe interference from a neighbouring gNB, whereas UAV2, flying at a greater altitude (\(h_{2}>h_{1}\)), faces interference from three neighbouring gNBs due to the presence of favorable LoS links. This observation suggests that higher altitude leads to interference from all neighbouring gNBs due to the existence of favorable LoS links. During downlink communication, each UAV experiences interference from multiple neighbouring gNBs that are not associated with it due to strong LoS channels, resulting in poor downlink performance. In uplink communication, the UAV creates significant interference for several adjacent non-associated gNBs, thereby causing a new gNB interference problem. Although resource block (RB) allocation is a common approach for addressing this issue, it is ineffective for severe air-ground interference. This is because of the limited number of RBs available to UAVs and the high frequency reuse for terrestrial UEs in cellular networks. Thus, developing an interference mitigation technique that accounts for the unique channel and interference characteristics of cellular-connected UAVs is critical. Although current terrestrial mitigation strategies can partially mitigate air-ground interference, severe interference cannot be entirely eliminated. This paper presents an interference mitigation algorithm that is designed exclusively for downlink scenarios involving 5G-connected UAVs.  
The objective of this research paper is to devise a new approach to power control that can mitigate interference in 5G networks, specifically addressing the challenges associated with the use of UAVs. To accomplish this, we put forward the utilisation of the DQL algorithm as a means of mitigating interference in power control.
# Self-Organization When Pedestrians Move in Opposite Directions. Multi-Lane Circular Track Model
## 2 The Model
This limit is taken because one of the individuals will change lanes at time \(t\) and thus, either \(\theta_{i}^{(+)}\) or \(\theta_{j}^{(-)}\) will not be defined at exactly that time. Note also that the angles of the colliding individuals do not have to be equal. It is enough that they differ by an integer multiple of \(2\pi\), which includes the case of them being equal. Adding or subtracting \(2\pi\) to an angle does not change the position. For all pairs \(i,j\) such that \(1\leq i,j\leq N\), we define  
\[\tau_{ij}=\left\{ \begin{array}{cl}\frac{\theta_{j}^{(-)}(0)-\theta_{i}^{(+)}(0)} {2\omega}&\text{if }\theta_{j}^{(-)}(0)>\theta_{i}^{(+)}(0)\\ \frac{\theta_{j}^{(-)}(0)-\theta_{i}^{(+)}(0)}{2\omega}+\frac{\pi}{\omega}& \text{if }\theta_{j}^{(-)}(0)<\theta_{i}^{(+)}(0).\end{array} \right. \tag{1}\]  
We assume that \(\tau_{i_{1}j_{1}}\
eq\tau_{i_{2}j_{2}}\) if \(i_{1}\
eq i_{2}\) or \(j_{1}\
eq j_{2}\). Equation (1) will be further discussed in the next section. The discussion in that section should make the reader clear that the assumption \(\tau_{i_{1}j_{1}}\
eq\tau_{i_{2}j_{2}}\) if \(i_{1}\
eq i_{2}\) or \(j_{1}\
eq j_{2}\) implies that two different collisions never occur at the same time. 10. If two individuals, the individual \(i\) moving counterclockwise and the individual \(j\) moving clockwise, collide at time \(t\), exactly one of them changes lanes at that time. The probability that the individual \(i\) changes lanes is \(1/2\) and thus, \(1/2\) is also the probability that \(j\) changes lanes. Assume the collision occurs in lane \(\ell\). If \(1<\ell<L\), the individual changing lanes moves to lane \(\ell-1\) with probability \(1/2\) or to lane \(\ell+1\), also with probability \(1/2\). If \(\ell=1\), the individual changing lanes moves to lane \(2\), and if \(\ell=L\), it moves to lane \(L-1\). 11. An individual can only change lanes when it collides with an other individual. We note that the assumptions in points 6 and 9 above are not restrictive at all. For example, if the initial angles were to be selected randomly with a uniform probability distribution around the track, assumptions in points 6 and 9 would be satisfied with probability \(1\). Those assumptions are made to simplify the analysis but are not fundamental. Our model is very simple and is based on local responses only. The selection of the individual that changes lanes, and the lane it moves to, are independent of the location of all the other individuals. The individuals do not make smart decisions attempting to prevent future collisions.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 4 Application Direction of Dynamic Measurement
### Navigation
The system can also suppress laser phase noise and common system errors [68]. The use of quantum inertial devices to realize the drift-free inertial navigation system not only requires the device to have high precision but also needs to further increase its sampling bandwidth, which is the weak link of atom interference devices due to the existence of dead time [44]. Therefore, there is still a long way to go before we realize the true benefits of high-precision and drift-free inertial navigation. At present, further research can be carried out on aspects of device configuration and combination with traditional devices.
# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 4 Conclusions  
In this paper, we have compared the detection performance of 11 well-known CNN models as the YOLO_v2 feature extractors and identified an excellent feature extractor based on the precision and computational cost. Then, the parametric studies were carried out: (1) Influence of the training epochs on detection results. (2) The influence of different feature extraction layers on the detection results was compared. (3) The size of the testing images also affects the detection results. The associated parameters indeed have an impact on the detection results. Finally, the working mechanism of the feature extractor was explained by visualizing the features obtained by the feature extractor.  
Based on the above research results, this paper draws the following conclusions:  
1. The comparison of 11 well-known network models indicated that the'resnet18' has a high precision (\(AP\) = 0.89) and fast computing speed.
2. Influence of relevant parameters on detection results: 1. Once the detection precision is stable, there is no need to increase the epoch number, which will increase the computing cost. 2. An appropriate selection of the feature extraction layer can help to improve the detection results. Too shallow or too deep layers can also lead to unsatisfactory detection results. 3. The detection precision increases with the resolution of the images used; but once it reaches the optimal value, it is meaningless to further increase the image resolution, as it means more detection time.
3. In the process of feature visualization, the feature extractor can extract effective crack features and confirms the conclusion of 2 (b).  
[PERSON] contributed to the paper in conceptualization, methodology, investigation, formal analysis, original draft preparation, software, visualization, and data curation. [PERSON] contributed to the paper in conceptualization, methodology, investigation, formal analysis, original draft preparation, and supervision. [PERSON] and [PERSON] contributed to the paper in investigation, formal analysis methodology, investigation, and review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Results of crack feature extraction.  
This research received no external funding.
## 3 Experimental Results and Analysis
### Dynamic Strength Criterion
#### 3.4.2 Dynamic Mohr-Coulomb Strength Criterion
Through trigonometric transformation of the Mohr-Coulomb criterion,  
\[\sigma_{1}=\sigma_{3}\cdot\tan^{2}\theta+2c\cdot\tan\theta \tag{13}\]  
\[\theta=\frac{\pi}{4}+\varphi \tag{14}\]  
When subjected to varying strain rates, the dynamic compressive strength of rock can be expressed as  
\[\sigma_{\mathrm{dc}}=\sigma_{3}\cdot\tan^{2}\theta+2c_{\mathrm{d}}\cdot\tan\theta \tag{15}\]  
The relationship between dynamic cohesion and static cohesion can be expressed as  
\[c_{d}=c_{s}+\Delta c_{d} \tag{16}\]  
where \(c_{s}\) denotes the static cohesion under reference strain rate conditions, specifically defined as the cohesion at \(10^{-5}\) s\({}^{-1}\) in this study. By synthesizing Equations (12), (15), and (16), the dynamic compressive strength of granite under quasi-static loading conditions can be expressed as  
Figure 16.0  
From Equation (15), the uniaxial static compressive strength (\(\sigma_{\mathrm{us}}\)) at zero confining pressure and a static strain rate is derived as  
Figure 16.1  
Thus, the dynamic compressive strength can be simplified to  
Figure 16.2  
As delineated by Equation (19), the dynamic compressive strength of granite under triaxial quasi-static loading conditions can be characterized as a linear superposition of three components: uniaxial static strength, confining pressure enhancement, and strain rate effect amplification. This constitutive formulation explicitly elucidates the synergistic strengthening mechanisms of confining pressure and strain rate effects on the dynamic strength of granite.
## 2 Configuration of the Proposed Wireless Battery Charging System for a Quadcopter
### Control Part  
The control part of the proposed system performs specific tasks such as generating the clock signal for driving transmitting coils, monitoring each coil's terminal voltage, and controlling the four-way \(XY\) table to align the centroids of the transmitting and receiving coils.  
Generally, it is not easy to land a drone at a specific point on the drone station precisely. In most cases, the centroids of the transmitting and receiving coil are misaligned, as shown in Figure 5.  
When the misalignment between the coils happens, the efficiency of the wireless power transmission deteriorates. In order to solve this problem, an intelligent automatic alignment algorithm based on the hill climbing algorithm is proposed and implemented.  
The automatic alignment algorithm was implemented inside a microcontroller. The microcontroller keeps measuring the terminal voltages of each transmitting coil simultaneously, and finds out which transmitting coil has the lowest voltage. Generally, when the receiving coil of the drone is near to a certain transmitting coil, the corresponding transmitting coil's terminal voltage tends to decrease. Therefore, the microcontroller can detect the transmitting coil with a voltage difference and, from this moment on, the microcontroller tries to align the centroid of the transmitting coil with the receiving coil by moving the four-way directional \(XY\) table.  
Figure 4: Four-way directional \(XY\) table.
## 6 ABT-RRMAC Comparison with DA-MAC
### DA-MAC Implementation and Motivations  
We decided to focus our attention on the DA-MAC protocol proposed in [26] for two main reasons:  
1. Although the DA-MAC does not deal with a sectorized approach as well as our proposal, the work is extremely related with the deafness problem but does not consider the energy consumption of nodes.
2. Unlike the most of the cited related works that use the traditional directional antennas, DA-MAC establishes that the mobile nodes are equipped with a switched beam SAS technology. This feature is getting very close to our implementation model, which employs an SAS adaptive array.  
The first step consists in the implementation of the DA-MAC on Omnet++. Basically, we slightly modify the _Dcf.cc_ and _Contention.cc_ modules provided by Omnet++ by inserting the DA-MAC algorithm designed in [26]. The _Dcf.cc_ module controls the directional RTS/CTS exchange process and allows for channel access, while the _Contention.cc_ module is essentially an FSM (finite state machine), which defines three possible node states: _Idle, Defer, IfsAndBackoff_; note that the Defer state coincides with the deaf state defined in the DA-MAC algorithm. The _Dcf_ and the _Contention_ modules communicate with each other through signals realizing together the MAC layer in the simulator. The DA-MAC algorithm provides for a partitioning of the single MAC channel into two channels (control channel and data channel); however, the IEEE802.11 standard does not cover the possibility of separating channels inside the same MAC; for this reason, we decide to use the _Multimac_ code provided by Omnet++: in this regard, the two independent channels are emulated by using two different MAC interfaces: one for data traffic and one for control packets.  
Figure 11: Energy consumption vs. node mobility speed.
###### Abstract  
The dynamic modeling, motion control and flexible vibration active suppression of space robot under the influence of flexible base, flexible link and flexible joint are explored, and motion and vibration integrated fixed-time sliding mode control of fully flexible system is designed. The flexibility of the base and joints are equivalent to the vibration effect of linear springs and torsion springs. The flexible links are regarded as Euler-Bernoulli simply supported beams, which are analyzed by the hypothetical mode method, and the dynamic model of the fully flexible space robot is established by using the Lagrange equation. Then, the singular perturbation theory is used to decompose the model into slow subsystem including rigid motion and the link flexible vibrations, and fast subsystems including the base and the joint flexible vibrations. A fixed time sliding mode control based on hybrid trajectory is designed for the slow subsystem to ensure that the base and joints track the desired trajectory in a limited time while achieving vibration suppression on the flexible links. For the fast subsystem, linear quadratic optimal control is used to suppress the flexible vibration of the base and joints. The simulation results show that the controller proposed in the paper can make the system state converge within a fixed time, is robust to model uncertainty and external interference, and can effectively suppress the flexible vibration of the base, links, and joints.  
fully flexible base-link-joint space robot; singular perturbation theory; motion and vibration; fixed time sliding mode control; flexible vibration suppression control; integrated control