esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, the autonomous UAV navigation perspective of UAV-enabled wireless communication for IoVs was studied. It was indicated that, for autonomous navigation, real-time UAV trajectory optimization is required, which must be computationally tractable. However, satisfying problem objectives, such as obstacle-free kinodynamic-aware navigation in dense urban areas and urban canyons, imposes a computational burden on the overhead controller, where channel modeling and wireless communication performance are extra concerns. Therefore, in this paper, only kinodynamic-aware trajectory generation  
Figure 8: Simulation results of the RRT method in 153.546 s: (**a**) 3D view; (**b**) 2D view.  
was considered. We first studied the motion principles and dynamics of quadrotors, the most popular UAVs for dense environments. Then, linear state-space kinodynamic models were developed to be used in the convex optimization for real-time trajectory generation. A systematic trajectory optimization problem was introduced with some convex relaxation techniques. The optimization variables are motion primitives that satisfy motion constraints, such as speed, acceleration, and heading change, which can be directly used without a path-tracking controller. To make the trajectory dynamically feasible, to satisfy quadrotor rotational rates, a trajectory smoothing technique was also proposed. The smoothing technique was based on state feedback, and thus motion primitives produced by the trajectory optimization can still be used without an extra controller.  
Future works will study appropriate channel modeling for UAV-enabled RIS-assisted communication for IoVs. The developed model will be used to modify the trajectory optimization problem and discuss its computational complexities with possible solutions and research topics. Nevertheless, uncertainties in IoVs and users' positions, channel state information in dynamic environments, and randomly blocked LoS paths make UAV path planning a probabilistic problem, which can be used to extend the research.  
Conceptualization, M.E. and A.V.S.; methodology, M.E.; software, M.E.; validation, M.E., A.V.S. and M.D.; formal analysis, M.E.; investigation, M.E.; resources, M.E. and M.D; data curation, M.E.; writing--original draft preparation, M.E.; writing--review and editing, M.D.; A.V.S.; visualization, M.E.; supervision, A.V.S.; project administration, A.V.S.; funding acquisition, A.V.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Australian Research Council. Also, this work received funding from the Australian Government via grant AUSMURIB000001, associated with ONR MURI grant N00014-19-1-2571.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
## 2 Related Work and Methods
### Proposed Method
#### 2.4.1 Data Reconstruction Based on Autoencoder  
According to Section 2.1, we reconstruct the data utilizing a single-layer autoencoder aiming to minimize the reconstruction error:  
\[\min\!/(X;\hat{X})=\min\|X-\hat{X}\|_{2}^{2}=\min\|X-\sigma(Y+b_{2})\|_{2}^{2}\]  
\[=\min\|X-\sigma(\sigma(W_{1}+b_{1})+b_{2})\|_{2}^{2} \tag{12}\]  
Figure 2: Contour plots of three different sparse matrices.  
where the activation function used is the sigmoid function: \(\sigma(z)=1/(1+exp(-z))\).  
In the coding process, \(X\) is projected onto the latent space subspace, resulting in a low-dimensional representation of \(Y\). In the coding layer, the weight matrix \(W_{1}\) directly operates on the original data and serves as the projection matrix that maps the input data back into the subspace. Each column of the weight matrix \(W_{1}\) quantifies the significance of its corresponding feature, thereby enabling feature selection to be performed using the weight matrix \(W_{1}\). This is the main idea behind most current autoencoder-based feature selection approaches.  
The loss function of existing autoencoders mostly adopts the sum of squares or F-norm, which is susceptible to outliers and noise. This sensitivity leads to high fluctuations in the results. It can be seen from Section 2.3 that the problem of noise and outliers can be solved by applying \(\ell_{2,1/2}\)-norm to the loss function. Many people have applied it to the NMF, and we introduce it to autoencoders here.  
At the same time, since autoencoder-based feature selection primarily measures the importance of features using the weight \(W_{1}\) of the coding layer, enhancing the sparsity of the matrix is a critical method for selecting the most discriminative features. For instance, [PERSON] et al. [19] used \(\ell_{2,1}\) constraint on the weight matrix \(W_{1}\). However, it is known that the solution obtained by \(\ell_{2,1/2}\)-norm is sparser, so we apply \(\ell_{2,1/2}\)-norm for \(W_{1}\) to achieve a sparser solution. At the same time, [PERSON] et al. [20] utilized \(\ell_{2,1}\) regularization constraints on the weight matrix to address issues such as poor generalization ability resulting from overfitting and excessive training time caused by an excessive number of hidden nodes in neural networks. In summary, the objective function is formulated as follows:  
\[\min\|X-\hat{X}\|_{2,1/2}^{1/2}+\lambda\left(\|W_{1}\|_{2,1/2}^{1/2}+\|W_{2}\|_ {2,1}\right) \tag{13}\]  
where \(\lambda\) represents a regularization parameter.
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 3 Methods  
Previously developed indoor temperature and heat demand models for buildings were utilized to forecast the indoor temperature and heat demand in individual buildings. Two different optimization strategies were utilized in the predictive optimization of the heat demand in 201 buildings. First, the parameters of the forecast models were estimated for each building utilizing hourly recorded measurement data. Then, the heat demand and indoor temperature in individual buildings were forecasted for the next 48 h. Finally, two different optimization strategies, minimum variance optimization and peak load cutting, were utilized to optimize the hourly heat demand in each building. In minimum variance optimization, the variance of the heat demand of individual buildings were minimized. In peak load cutting, the hourly heat demands of individual buildings were cut during the defined hours. In both cases, the indoor temperatures were kept between defined limits and the total heat demands were not allowed to increase. The forecast models, parameter estimation and predictive optimization strategies are further discussed in the next sections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Building Group** & **Number of Buildings** & **Range of \(V\) [m\({}^{3}\)]** & **Median \(V\) [m\({}^{3}\)]** & **Range of \(P\) [kW]** & **Median \(P\) [kW]** \\ \hline Apartment buildings & 82 & 9791–87,916 & 21,615 & 0–960 & 170 \\ Schools & 35 & 8380–250,830 & 29,750 & 0–3720 & 270 \\ Commercial, public and office buildings & 84 & 9969–169,372 & 35,247 & 0–2200 & 245 \\ \hline \end{tabular}
\end{table}
Table 1: Measurement data from January 2013 for 201 buildings in the city of Jyvaskyla, Finland.
## 3 Design Equations of Key Components in the Presented LED Tube Lamp Driver
### Design of Series Resonant Tank \(L_{r}\) and \(C_{r}\)  
Figure 6 depicts the equivalent circuit during the design of the series resonant tank; \(R_{o}\) is the equivalent resistance of the T8-type LED tube lamp, and can be represented by \(R_{o}=V_{o}/I_{o}\). By using the fundamental approximation theory, the fundamental components of the switch voltage \(v_{DS2}\) can be expressed by [23; 24]  
\[v_{DS2-fund}=\frac{2}{\pi}V_{DC}\sin 2\pi f_{S}t. \tag{5}\]  
Figure 5: The utilized control circuit for the presented LED tube lamp driver.  
Referring to Figure 6, the equivalent load resistance \(R_{eq}\) can be expressed as [24]  
\[R_{eq}=\frac{8V_{o}}{\pi^{2}I_{o}}=\frac{8}{\pi^{2}}R_{o}. \tag{6}\]  
With a \(V_{o}\) of 60 V and an \(I_{o}\) of 0.3 A, the equivalent load resistance \(R_{eq}\) is given by  
\[R_{eq}=\frac{8}{\pi^{2}}\bigg{(}\frac{60}{0.3}\bigg{)}=162.1\ \Omega.\]  
Referring to Figure 6, the series resonant tank is composed of a resonant inductor \(L_{r}\) in series connection with a resonant capacitor \(C_{r}\); and the resonant frequency \(f_{o}\) can be expressed by  
\[f_{o}=\frac{1}{2\pi\sqrt{L_{r}C_{r}}}. \tag{7}\]  
The loaded quality factor \(Q_{L}\) is expressed by  
\[Q_{L}=\frac{\sqrt{\frac{L_{r}}{C_{r}}}}{R_{eq}}. \tag{8}\]  
In order to obtain ZVS for the two active switches, the switching frequency \(f_{S}\) is designed to be larger than the resonant frequency \(f_{o}\) so that the resonant tank resembles an inductive network [24].  
Therefore, the relationship between switching frequency \(f_{S}\) and resonant frequency \(f_{o}\) is selected as  
\[f_{S}=4f_{o}. \tag{9}\]  
Combining (7) and (8) with (9), the resonant capacitor \(C_{r}\) is given by  
\[C_{r}=\frac{2}{\pi f_{S}R_{eq}Q_{L}}. \tag{10}\]  
By selecting an \(f_{S}\) of 55 kHz and a \(Q_{L}\) of 0.9, the resonant capacitor \(C_{r}\) is computed as  
\[C_{r}=\frac{2}{\pi\times 55k\times 162.1\times 0.9}=79.3\ \mathrm{nF}.\]  
In addition, \(C_{r}\) is selected to be 82 nF.  
Combining (7) with (9), the resonant inductor \(L_{r}\) is given by  
\[L_{r}=\frac{4}{\pi^{2}{f_{S}}^{2}C_{r}}. \tag{11}\]  
Figure 6: Equivalent circuit during the design of the series resonant tank.  
With an \(f_{S}\) of 55 kHz and a \(C_{r}\) of 82 nF, the resonant inductor \(L_{r}\) is computed as  
\[L_{r}=\frac{4}{\pi^{2}\times\left(55k\right)^{2}\times 82n}=1.63\ \mathrm{mH}.\]
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 4 Proposed Protocol  
This section introduces the proposed protocol comprising five phases: initialization, registration, login and authentication, password update, and user revocation and re-registration. Table 1 lists the notations for the proposed protocol.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(V_{i}\) & Vehicle user \\ \(FN_{j}\) & Fog node \\ \(CS^{{}^{\prime}}\) & Cloud server \\ \(ID_{i}\) & Identity of vehicle user \\ \(ID_{j}\) & Identity of fog node \\ \(PID_{i}\) & Pseudo identity of vehicle user \\ \(RID_{i}\) & Secret pseudo identity of vehicle user \\ \(PSW_{i}\) & Password of vehicle user \\ \(N_{i}\) & Secret key of vehicle user \\ \(N_{j}\) & Secret key of fog node \\ \(s_{r}x_{i}y_{j}\) & Secret keys of cloud server \\ \(r_{i}\) & Set of random numbers \\ \(R_{i}\) & Set of public keys \\ \(SK_{i-j-s}\) & Session keys of \(V_{i}\), \(FN_{j}\), and \(CS\) \\ \(Auth_{i}\), \(Auth_{j}\) & A secret value needed for authentication \\ \(h(\cdot)\) & Cryptographic hash function \\ \(\oplus\) & Exclusive OR operation \\ \(\parallel\) & Concatenation operation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations of the proposed protocol.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 3 Results
### Yield Estimation
In contrast, the lower-yielding Field 1 (374 kg/stremma) shows comparatively reduced LAI values during these stages, highlighting their central role in influencing productivity. Additionally, variations in yield among the top-performing fields appear to be influenced by the LAI dynamics from Booting to Grain Filling, indicating that maintaining robust LAI values during these growth phases is crucial for maximizing yield potential.
## 2 Short-Term and Long-Term Collision Probability Calculation Models
### Short-Term Collision Probability Calculation Model
#### 2.1.2 Time Window Filtering  
The space targets may collide with the constellation satellite only when they travel into the shell of the constellation. Thus, the orbital altitude of the space target position determines whether it may collide with a constellation satellite. When the height of the space target position is outside the range of the shell height, the target will not collide with the constellation satellite. The constellation satellite may collide with the target only when the space target is within the shell range.  
Assuming that the position and velocity of the space target at moment \(t\) is \(\rho_{s}(\stackrel{{\rightarrow}}{{r}}(t),\stackrel{{ \rightarrow}}{{\overline{v}}}(t))\), the radius of the space target at the moment is:  
\[r(t)=\sqrt{\left(\stackrel{{\rightarrow}}{{r_{x}}}(t)\right)^{2 }+\left(\stackrel{{\rightarrow}}{{r_{y}}}(t)\right)^{2}+\left( \stackrel{{\rightarrow}}{{r_{z}}}(t)\right)^{2}}} \tag{2}\]  
Given that the height interval of the constellation shell is \([a-D/2,a+D/2]\), the time window for the possible collision between the space target and the constellation satellite can be expressed as  
\[a-D/2<r(t)<a+D/2 \tag{3}\]  
Different LEO megaconstellations have different orbital inclinations, and the latitude ranges that the constellations can cover are also different. Space targets may be higher than the latitudes that the constellation can cover when passing through the shell. Therefore, whether the latitude is within the latitude range covered by the constellation when the satellite is in the constellation shell should be continuously analyzed.  
In accordance with the integral formula of orbital area, the moment vector of momentum per unit mass \(\stackrel{{\rightarrow}}{{h}}\) is expressed as  
\[\stackrel{{\rightarrow}}{{h}}=\stackrel{{ \rightarrow}}{{r}}(t)\times\stackrel{{\rightarrow}}{{ \overline{v}}}(t)=h_{x}i+h_{y}j+h_{z}k \tag{4}\]  
where \(h_{x},h_{y},h_{z}\) are the projected  
target will not collide with the constellation satellite and should be eliminated. The filtering is expressed as  
\[\delta_{s}>i+\Delta i \tag{7}\]
###### Abstract  
This paper proposes an approach to investigating the effect of different temperatures and flow velocities on the acoustic performance of silencers in a more accurate and meticulous fashion, based on steady computational results of the flow field inside the silencer using computational fluid dynamics (CFD). This approach can transfer the CFD results--including temperature and flow velocity distribution--to acoustic meshes by mesh mapping. A numerical simulation on the sound field inside the silencer is then performed, using the CFD results as a boundary condition. This approach facilitates the analysis of complex silencer designs such as perforated tube silencers, and the numerical predictions are verified by a comparison with available experimental data. In the case of the three-pass perforated tube silencer of a car, the proposed approach is implemented to calculate the transmission loss (\(TL\)) of the silencer at different temperatures and flow velocities. We found that increasing the air temperature shifts the TL curve to a higher frequency and reduces the acoustic attenuation at most frequencies. As the air flow increases, the curve moves to a slightly lower frequency and the acoustic attenuation increases slightly. Additionally, the pressure loss of perforated tube silencers could be calculated according to the total pressure distribution of their inlet and outlet from the steady computational results using CFD.  
Keywords:perforate tube silencer; transmission loss (\(TL\)); pressure loss; computational fluid dynamics (CFD); temperature; air flow velocity +
Footnote †: journal: Applied Mechanics and Physics Letters
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.2.2 Influence of the Infiltration Module  
The sensitivity analysis of the seepage module primarily focuses on evaluating the impact of the seepage effect while disregarding its influence. As shown in Figure 15, the seepage effect has a minimal impact on the bottom elevation of the breach, but it has a substantial impact on the breach width and outflow discharge. The seepage module has  
Figure 13: Simulated and measured discharge results of typical section Beichuan and Tongkou.  
an impact on various aspects of the dam breach. It results in a wider ultimate breach width, a faster breach development rate, an increase in the breach peaking discharge, an earlier occurrence of the breach peaking discharge, and a shorter duration of the dam break. The seepage effect reduces the critical beginning velocity of the dam particles, increases the lateral extension rate of the breach, and makes it easier to fulfill the critical collapse condition.
## 1 Introduction  
In recent years, with the development of electronic information technology and the massive use of plastic electronics, the materials waste problem has become increasingly serious, and plastic consumption and wastes have already been one of the major concerns in the world nowadays, especially in developing countries [1; 2; 3]. Therefore, it is essential to develop and investigate potential bio-degradable, bio-compatible, bioresorbable, or even metabolizable devices, which serve as the components of various electronic and photonic products. Environmentally-friendly electronics based on the natural materials such as polypeptide [4], albumen [5], and nucleic bases [6] are potential approaches that promise to meet these needs.  
DNA (deoxyribonucleic acid), one of the most famous biopolymers, is a unique three-dimensional molecule with the sequence of four base pairs and carriers the genetic information for all living organisms. DNA exists in three different conformations including A, B, and Z forms. The most commonly known DNA structure is the B-form DNA and it possesses the right-handed helical sense, with diameter, rise per base pair of 2 nm, 0.34 nm, respectively [7]. DNA is negatively charged polyelectrolyte, and always are compensated by the positive charges inorganic cations, e.g., sodium ions [8]. Since purified DNA can usually only be soluble in water, which has been restricted in many fields for its further applications, so many efforts have been made to improve its solubility in organic solvents. For example, [PERSON] et al. [9] developed cationic surfactants such as cetyltrimethylammonium chloride (CTMA) in the preparation of DNA based complexes. Many other researchers have also utilized the DNA surfactant complex for the application in electronic devices.  
Organic-soluble DNA-lipid complexes prepared with DNA and various cationic surfactants, including octadecyltrimethylammonium chloride (OTMA) and lauroylcholine (Lau), have gained considerable attention for their ease of fabrication process such as spin coating, dip-casting, or even printing techniques. Moreover, they show excellent physicochemical properties including high transparency, good thermal stability, and large bandgaps (4.7 eV). Among these cationic surfactants, CTMA is one of the most popular reported materials. Figure 1 illustrates the chemical structure of DNA, the surfactants, and the electrostatic interactions between them. The film of the DNA-CTMA complex has been employed in wave guides [10; 11], lasers [12; 13; 14], and as a nonlinear optical material for second harmonic generation [15].  
In this mini-review, after a general introduction to the physicochemical performance of DNA-lipid complex, we summarized the recent research progress of DNA-lipid complexes in various electronic and photonic devices. In particular, we explain in details of the organic field effect transistors, in which the DNA-lipid complex functions as the gate dielectric or interlayer. At last, the future prospects of DNA in other electronic applications are presented, and the key issues for future development are discussed.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 6 Conclusions and Future Research  
This research investigated the potential benefits of integrating aerial and ground-based sensing approaches into a HSP. The field experiment results prove that the HSP can considerably improve the aerial data quality through the data validation and calibration process. The improvement in aerial data quality makes HSPs a potential solution to address some of the existing challenges of PA--in particular, the high cost and effort required in the ground-based sensing approach and low data quality of UAV-based sensors due to UAV movement and vibration. From a ground-based sensing perspective, a HSP enables us to reduce the number of required ground-based nodes by using an aerial node as a secondary source of data collection. A lower number of ground-based nodes results in a lower cost of procurement, deployment, and maintenance, which is one of the main deterrents of PA adoption. From an aerial sensing perspective, the UAV data quality can be constantly validated and calibrated using ground-based data as ground truth. Such a process ensures that UAV-based sensors constantly provide high-quality data. Therefore, it can be concluded that the integration of aerial and ground-based approaches into the HSP can improve the efficiency and effectiveness of the farm management process by reducing the cost and improving the data quality.  
In our future research, we plan to take into account additional variables in sensor data calibration, including the soil composition, irrigation factors (such as irrigation frequency, volume, and ground slope), and fertilisers used. We also plan to devise machine learning-based algorithms for automatic sensor data calibration by using data sets collected from multiple farms, across regions, states, and countries. Such research will further improve the data quality, and lower the cost and effort required for PA, while at the same time improving decision making and automation processes. These advancements in IoT-based PA will help to increase food production and our ability to deal with drought and climate change.  
**Author Contributions:** Conceptualisation, H.B., A.Y. and D.G.; Formal analysis, H.B.; Methodology, H.B.; Software, H.B. and A.Y.; Supervision, D.G. and A.Y.; Writing--original draft, H.B.; Writing--review and editing, D.G. and A.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.