esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Aiming at the motion control problem of intelligent vehicle tracking the desired trajectory effectively, this paper proposes a trajectory planning and control strategy based on an improved terminal sliding mode. This method has the advantages of strong robustness and high tracking accuracy. The specific content of this article is summarized as follows:  
* The traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem, which reduces the sampling area and improves the planning efficiency of the algorithm;
* This paper designs an improved terminal sliding mode controller. The controller comprehensively considers the lateral error and heading error of intelligent vehicle trajectory tracking to improve the accuracy of trajectory tracking;
* In this paper, the simulation experiment of the improved RRT algorithm and the Matlab-Carsim HIL simulation platform experiment are carried out. The experimental results show that the designed controller has high precision and strong robustness. Therefore, the improved algorithm can be applied to the intelligent driving platform and has certain engineering application value.  
In future research, the controller needs to be further studied in the continuous steering state so that it can still maintain a high tracking accuracy. Experiments in complex environments will also be required in the future. Therefore, a nonlinear tire model is required.  
**Author Contributions:** Methodology and writing--original draft preparation, A.L.; software, C.N.; formal analysis and investigation, X.H.; data curation, G.L.; resources, C.X.; funding acquisition, A.L., S.L. and X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project is supported by National Natural Science Foundation of China (Grant No. 51505258, 51975276, 51405272 and 61601265), Shandong Provincial Natural Science Foundation, China (Grant No. ZR2015 EL019, ZR2020 ME126 and ZR2021 MF131), Shandong Province Higher Educational Youth Innovatio Science and Technology Program (Grant No. 2019 KJB019), Open project of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, China (Grant No. 1903), Open project of Hebei Traffic Safety and Control Key Laboratory, China (Grant No. JTKY2019002).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **2 s** & **16 s** & **20 s** \\ \hline Improved (degree) & 12.84 & 7.99 & \(-\)2.40 \\ \hline General (degree) & 21.98 & 12.45 & \(-\)2.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum front wheel angle error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **2 s** & **16 s** & **20 s** \\ \hline Improved (degree) & \(-\)2.373 & 2.335 & \(-\)1.087 \\ \hline General (degree) & \(-\)2.852 & 2.339 & \(-\)1.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum centroid side slip angle error.
# Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts
## 3 Methodology
### Assessment of Rear-End Collision Risk
For the velocity difference bidirectional control model, \(TTC_{n+1}(t+\Delta t)\), denoted as \(TTC_{n+1}^{\Delta v}(t+\Delta t)\), is expressed as follows:  
\[TTC_{n+1}^{\Delta v}(t+\Delta t)=\frac{\varphi_{n+1}(t+\Delta t)+s_{e}}{\mu_{ n+1}(t+\Delta t)-f_{\Delta v_{n+1}}\mu_{n+1}(t)\Delta t} \tag{11}\]  
By taking partial derivation of Equation (11) with respect to \(f_{n+1}^{\Delta v}\), we obtain the following equation:  
\[\frac{\partial TTC_{n+1}^{\Delta v}(t+\Delta t)}{\partial f_{\Delta v_{n+1}}} \bigg{|}_{(\overline{v},\ \overline{s})}=\frac{(\varphi_{n+1}(t+\Delta t)+s_{e})\mu_{n+1}(t)\Delta t}{ \left(\mu\,_{n+1}(t+\Delta t)-f_{\Delta v_{n+1}}\mu_{n+1}(t)\Delta t\right)^ {2}} \tag{12}\]  
Since \(\frac{\partial TTC_{n+1}^{\Delta v}(t+\Delta t)}{\partial f_{\Delta v_{n+1}}}>0\), it can be observed that when \(f_{\Delta v_{n+1}}>0\) (IP), \(TTC_{n+1}^{\Delta v}(t+\Delta t)\) increases, resulting in lower collision risk and improved safety. On the contrary, when \(f_{\Delta v_{n+1}}<0\) (OP), \(TTC_{n+1}^{\Delta v}(t+\Delta t)\) decreases, leading to higher collision risk. Based on the analysis presented above, we can conclude that for both the spacing and velocity difference bidirectional control models, the IP effect decreases rear-end collision risk, while the OP effect increases the risk.
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 4 Three-dimensional Imaging of Moving Targets with InISAR Technology
### ISAR Imaging with Combined Motion Compensation
The range profiles after combined motion compensation is expressed as:  
\[Y_{0}(t_{m},r)=\text{sinc}[\frac{2B}{c}(r-R_{p})]e^{-j\frac{4\pi}{\lambda}R_{ p}} \tag{15}\]  
\[Y_{ci}(t_{m},r)=\text{sinc}[\frac{2B}{c}(r-R_{p})]e^{-j\frac{4\pi}{\lambda}R_ {p}}e^{-j\phi_{Yci}} \tag{16}\]  
where:  
\[\phi_{Yci}=\frac{\pi}{\lambda R_{p}}[x_{Lci}^{2}-2x_{Lci}(x_{p}+V_{x}t_{m})+z_ {Lci}^{2}-2z_{Lci}(z_{p}+V_{z}t_{m})] \tag{17}\]  
Once the motion compensation is done, the azimuth compression is then accomplished by applying Fourier Transform (FT) to the aligned range profiles along the slow time, which produces the ISAR images in the range-Doppler domain:  
\[I_{0}(f_{a},r)=\text{sinc}[\frac{2B}{c}(r-R_{p})]\text{sinc}[T(f_{a}-f_{0})] e^{-j\frac{4\pi}{\lambda}R_{p}} \tag{18}\]  
\[I_{ci}(f_{a},r)= \begin{array}{l}\text{sinc}[\frac{2B}{c}(r-R_{p})]e^{-j\frac{4\pi}{ \lambda}R_{p}}e^{-j\phi_{Lci}}\\ \text{sinc}[T(f_{a}-f_{0}-\Delta f_{xci}-\Delta f_{zci})]\end{array}  \tag{19}\]  
where \(T\) denotes the integration time for imaging, \(f_{a}\) denotes the Doppler frequency. For the ISAR images of each channel, the Doppler frequency offsets caused by target motion along the baseline direction are denoted as \(\Delta f_{xci}\) and \(\Delta f_{zci}\), which are determined by:  
\[\Delta f_{zci}=\frac{x_{Lci}V_{x}}{\lambda R_{p}} \tag{20}\]  
\[\Delta f_{zci}=\frac{z_{Lci}V_{z}}{\lambda R_{p}} \tag{21}\]  
The phase component of the same scatterer in different images are expressed as:  
\[\phi_{lci}=\frac{\pi}{\lambda R_{p}}(x_{Lci}^{2}-2x_{Lci}x_{p}+z_{Lci}^{2}-2z_ {Lci}z_{p}) \tag{22}\]  
Equation (19) indicates that the ISAR images in different receiving channels are mismatched in Doppler spectrum, which is induced by target motion and displacement of antenna phase centers. Equations (20) and (21) reveal that the amount of mismatch is determined by the length of baseline and target velocity.
## 4 Results and Discussion
### Experimental Characterization of Self-Phase Modulation Broadened UV Pulses
Although in this regime the Fourier limit is well below 10 fs (Figure 4f), the beam undergoes beam breakup [14] accompanied with a strong divergence after the crystal. Furthermore, it shows enhanced fluctuations both in the spatial and spectral intensity distribution. As can be seen from Figure 4b, putting the self-focus just outside the crystal still delivers a sizable spectral broadening down to a Fourier limit of 13.4 fs (Figure 4e, 1.5 cm in front of the beam focus) without undesirable beam distortions and no loss in output energy. This regime delivers broadened pulses feasible for further applications and justifies the focusing assumptions made in our previous simulations. The resulting change in beam divergence due to the moderate self-focusing is then compensated for by the recollimation lens L2. We investigated the spectral broadening by SPM in Sapphire, CaF\({}_{2}\), LiF\({}_{2}\) and fused silica crystals of various thicknesses between 0.5-4 mm. For each crystal we work in the regime with optimum SPM broadening as discussed before. In accordance with our theoretical simulations we find that crystals with a higher _n\({}_{2}\)_ lead to broader pulses. With the smallest _n\({}_{2}\)_, LiF\({}_{2}\) delivers the smallest broadening. Although sapphire has a larger _n\({}_{2}\)_ than CaF\({}_{2}\), experimentally we observe slightly better broadening in CaF\({}_{2}\). Fused silica gives similar results to CaF\({}_{2}\). However impurities cause large beam scattering. When trying different crystal thicknesses, we achieve larger broadening with thinner crystals as predicted by the theory. Best results are obtained for a 0.5 mm CaF\({}_{2}\) plate with a compressibility factor of \(\sim\)2. However, the required tight focusing leads to unwanted multi-photon absorption and to beam distortions caused by the progressive deterioration of the crystal. Therefore we employ 1 and 2 mm thick CaF\({}_{2}\) disks for the compression experiments discussed below. The experimentally achieved spectral broadening is generally about 25% less than the calculated values in section 2. We believe that this is due to the chirp of the pulses neglected in the calculations as well as the deviations of the beam profile from a Gaussian like intensity distribution, which lead to significant spatial distortions well before the self-focus length reaches the crystal length.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 3 Applications of Smart Drug Delivery Devices
### Wearable Smart Drug Delivery Devices  
IoT-based wearable drug delivery devices are innovative solutions for healthcare, leveraging technology to improve delivery accuracy and efficiency. Worn on the body, like a patch or wristband, health devices track vital indicators and administer medication based on collected data. The devices aid medication adherence for patients with chronic conditions, reducing the risk of adverse events occurring.
## 3 System Description
### Smart PV Modules  
Each panel or group of panels includes a _Smart Module_ that measures some relevant magnitudes in the panels and sends this information to a _Central Node_ placed in the tower supporting the arrays of panels. These modules will therefore include some sensors to measure the magnitudes of interest and some circuitry in charge of the transmission. The power supply for these electronic circuits will be obtained from the panels themselves, while guaranteeing that the modules keep working even under low-radiation conditions--or during the night. This involves the use of some sort of energy storingdevice that manages to keep the circuit working when the panels are not providing enough energy; supercapacitors [25, 26, 27] have been identified as an optimum component to achieve this goal. Moreover, these devices can be found in the literature associated to both communication systems [28] and photovoltaic panels [29]. The most relevant features of these devices as compared to batteries, which are the traditional storage systems, are their capability to supply high current peaks and their almost non-existent maintenance within 500,000 charge/discharge cycles (typical batteries endure less than 500 charge/discharge cycles).
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 4 Training
### Multitask Discriminator Loss  
Our discriminator is a multitask discriminator that performs three tasks: (1) distinguishing between real and generated images, (2) classifying the content class of the input images, and (3) classifying the style class of input images. The loss functions can be expressed in the following manner:  
**Adversarial Loss (Real vs. Fake):** This remains as in the standard GAN setup, where the discriminator aims to correctly classify real and generated images.  
\[\mathcal{L}_{\text{adv}}=-\mathbb{E}_{x\sim p_{\text{real}}(x)}[\log D_{\text{ adv}}(x)]-\mathbb{E}_{x_{c},x_{k}}[\log(1-D_{\text{adv}}(G(x_{c},x_{s})))]. \tag{4}\]  
**Content Classification Loss:** This loss penalizes the discriminator for incorrect classification of the content class of the input fonts.  
\[\mathcal{L}_{\text{content}}=-\mathbb{E}_{x_{c}\sim p_{\text{data}}(x_{c})}[ \log D_{\text{content}}(c|x)] \tag{5}\]  
where \(D_{\text{content}}(c|\,x)\) represents the probability distribution over content classes produced by the discriminator given an input \(x\), and \(c\) is the true content class.  
**Style Classification Loss:** Similar to the content classification loss, this loss penalizes the discriminator for an incorrect classification of the style class of the input fonts.  
\[\mathcal{L}_{\text{style}}=-\mathbb{E}_{x,s\sim p_{\text{data}}(x_{s})}[ \log D_{\text{style}}(s|x)] \tag{6}\]  
where \(D_{\text{style}}(s|x)\) represents the probability distribution over style classes produced by the discriminator given an input \(x\), and \(s\) is the true content class.  
**Combined Multitask Discriminator Loss:** The total loss for the multitask discriminator is calculated by combining these three components, potentially with weighting factors to balance their contributions. The resulting loss is as follows:\[\mathcal{L}_{D_{\text{total}}}=\mathcal{L}_{\text{adv}}+\lambda_{\text{content}} \mathcal{L}_{\text{content}}+\lambda_{\text{style}}\mathcal{L}_{\text{style}} \tag{7}\]  
where \(\lambda_{\text{content}}\) and \(\lambda_{\text{style}}\) are hyper-parameters that control the relative importance of the content and style classification tasks compared to the adversarial task
## 1 Introduction  
Bone tissue engineering (BTE) is an interdisciplinary field in which physicians, biologists and engineers collaborate to develop and produce artificial bone tissues [1]. One common strategy for the treatment of bone defects are in vitro applications of cultured autologous cells or stem cells on scaffolds followed by the implantation into specific tissues and tissue regions. Thereby, scaffolds play key roles, serving to mimic physiological in situ environments [2]. For this reason, they should have high interconnectivity, high porosity, good biocompatibility, as well as sufficient mechanical strength and reliability [3]. Besides metals and ceramics [4], plastic polymers such as Polycaprolactone (PCL) are in use as materials for the production of these three-dimensional (3D) structures [5].  
PCL is a 3D-printable semi crystalline synthetic polymer that can be reversibly deformed in certain temperature ranges [6, 7, 8]. Thereby, the melting point reaches from 55 to 63 \({}^{\circ}\)C [9]. PCL shows viscoelastic behavior [10] and a good biocompatibility [11]. Pure PCL is hydrophobic, which can negatively affect cell adhesion and cell proliferation [12, 13]. Scaffolds can be manufactured using additive producing processes, which are also coined as 3D printing. Hereby, products are created by adding the material step by step. Thereby, the geometry of the scaffolds is modeled using computer-aided design (CAD) software [14]. Single layers can be produced by heated viscous materials that are pressed out of a needle by compressed air. The final construct geometry is created by stacking the PCL layers \"layer by layer\" [15]. A widely used method allowing the approximation of mechanical and structural behaviors via numerically solving partial differential equations in engineering and BTE is the finite element method (FEM). Hereby, in contrast to classical numerical approaches, the function is not defined over the entire functional area but is divided into a finite number of subareas. With a parametric FEM design, geometric and material properties can be varied [16; 17]. In this study, we aimed to develop a parametric model for layered, 3D-printed PCL scaffolds, consisting of interconnected compartments. This kind of scaffold allows the development of 3D networks of cells. As architecture plays a crucial role for the successful development and usage of these layered scaffolds, a suitable and flexible numerical in silico twin can be an important tool [18; 19; 20; 21]. Currently, there are no parametric numerical models available for such scaffolds.
## 2 Background
### Related Studies
The autonomous underwater vehicle (AUV)-aid method employed a large number of sensors and one AUV to balance the algorithm's performance and cost. The AUV was used for localization and carrying messages of disconnected sensors or time-critical information [19]. The anchor-based method relied on the TDOA locally measured by a sensor to detect range differences from the sensor to four anchors [20]. The AFLA method was a self-localization algorithm designed for anchor-free UASNs [8]. However, none of the schemes mentioned above provide sufficient accuracy. In order to improve the localization performance, we propose here two novel algorithms: PCFL and ACFL, and aim at projecting the locations of task anchors to the mobile node's plane which converts the UABL problem from a 3D to a 2D one.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 10 Open Challenges with Wi-Fi 6
### Challenges
#### 10.1.2 QoS Support  
Resource assignment represents a key component of QoS support, typically to control OFDMA and TWT. In addition, MU MIMO, spatial reuse, higher-order modulations, and the novel 6 GHz band increase the capacity.  
QoS guarantees and network slicing are crucial requirements in the industry. Flows may be prioritized to expedite them relative to other flows. This was already possible in Wi-Fi through EDCA. However, some wireline technologies, e.g., Time-Sensitive Networking (TSN), which is based on ethernet, propose resource reservation for flows. As a result, real-time guarantees can be granted, which is important for real-time applications. Zero-queueing delay is achievable if resources are accurately reserved [206].  
Network slicing has been proposed for 5G networks [207]. A slice is a logical network with guaranteed transmission resources, i.e., the nodes assigned to that slice can utilize a defined capacity. Wi-Fi 6 can support such slices to some extent by dedicating RUs or TWT agreements to a set of nodes. However, slice isolation is challenging when multiple accesspoints can orchestrate overlapping BSSs [208; 209]. Thus, when multiple access points compete for channel access, the guarantees may not be met. Here, a controller can help to coordinate all access points. However, in the presence of other non-controlled access points, granting guarantees remains a challenge.  
The synchronization of nodes is crucial in real-time networks such as TSN. The Wi-Fi standard already supports time synchronization through the IEEE 802.11v/mc amendments [208]. Researchers are designing Wi-Fi 7 to fulfill the resource reservation challenges mentioned above and to make Wi-Fi compatible with TSN [210].  
The \"Reliable and Available Wireless\" (RAW) working group in the IETF studies wireless technologies for use in real-time environments such as Industry 4.0. These use cases typically utilize current wireline technologies such as TSN that support soft and hard real-time guarantees [211]. For both purposes, the RAW group studies various wireless technologies and Wi-Fi 6 is among them.
## 4 Discussion
### Implications for Fisheries Management
Light purse seine fishing vessels operating in the Arabian Sea are in close proximity to the EEZs of numerous countries, with some vessels operating along these EEZs. While China's fisheries administration mandates that fishing vessels operating on the high seas keep AIS equipment operational, the absence of AIS information is a global reality for fishing vessels. The unrecorded fishing vessel activity contributed to an additional 19% (5500 h) of the activity by AIS-equipped fishing vessels between 2017 and 2022 in the Northeast Pacific [31]. Given the difficulty in identifying AIS information for short durations, it becomes imperative to enhance the supervision and regulation of fishing vessels by integrating other technical methods. Incorporating tools such as night-light remote sensing [32], synthetic aperture radar (SAR) image data, aerial surveillance (\"flyovers\") [33], and remote sensing radio information is essential for establishing a comprehensive supervision system for the operational behavior of fishing vessels. This integrated approach aims to achieve more effective management of fishing vessel operations.
## 5 Uncertainty Estimation
### Estimation of Model Parameters
Therefore, overestimations of AAE--e.g., classifying dust as HAF aerosols (gray box in the dust case in Figure 13a,b) or classifying NA aerosols as HAF or dust aerosols (gray or pink box in the NA case in Figure 13a,b)--result in an underestimation of AOD and an overestimation of SSA. In general, the retrieval error in AOD and SSA is high when NA particles are mistakenly classified as an absorbing type (blue box in Figure 13a,b) because of large differences in AAE. Misclassifying NA particles as absorbing results in an underestimation of AOD, as expected but the error does not vary significantly between a misclassification as dust or HAF. However, this misclassification results in a severe overestimation of SSA under low SSA conditions. Because absorbing aerosols absorb the upward Rayleigh scattering below the aerosol layer, an increase in ALH decreases the radiance at TOA and reduces spectral variations. The spectral dependence is also decreased with the decreasing AAE. Thus, it can be expected that an overestimation of the AAE results in an overestimation of the ALH, as shown by the gray and pink boxes for NA conditions in Figure 13c. However, the radiance decrease and change in spectral variation resulting from an ALH increase is smaller than the effects of AOD and SSA variation and thus the change in radiance from an incorrect AAE assumption can be offset by adjusting the AOD and SSA, rather than the ALH. Because of this, the general trend of retrieval error with varying ALH under different misclassification scenarios cannot be easily explained. Figure 13: Retrieval error in **(a)** AOD, **(b)** SSA and **(c)** ALH (km) due to aerosol misclassification for each aerosol type. Box colors denote misclassified aerosol type under the conditions shown on the \(x\)-axis. Gray: HAF, pink: Dust and blue: NA Errors are obtained by testing the algorithm with an aerosol type that is different from the true condition. Each box-and-whisker plot for a true condition shows the retrieval error distribution derived from the 108-member ensemble for AOD and SSA and 81-member ensemble for ALH.