esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Drones Challenges with 4G and 5G Networks  
The 4G and 5G-NR systems can be utilized to provide communication for ultra-low-altitude drone networks using U2U and U2I modes. However, they may have coverage issues, whereas drones travel in 3D and at much higher altitudes, i.e., from more than 150 m to 2 km, further overcoming mobility challenges. Drones, by comparison, are able to move randomly and discontinuously in any 3D direction in space at very high speed. Although 5G can handle the 2D mode, it may have obstruction issues that make the 3D mode difficult to handle. Due to the use of directional antennas in the BS, 5G has limited connectivity and necessitates frequent handovers for high-mobility drones. To cover high-density drones in the sky, additional antennas must be installed throughout the BS, which may be costly. Fifth Generation connectivity is incapable of handling dynamic handover management or providing seamless connectivity with path planning in a high-mobility scenario, such as cellular V2X, both of which are essential for autonomous drones flying in the airspace [86]. In high-density and city air mobility scenarios, latency, collision detection, and navigation are crucial, necessitating energy-aware deployment, ultra-High Speed with Low-Latency Communications (uHSLLC), and effective channel models for drones' communication. Considering the opportunities of developing technologies and services for the next decade, there is a significant need to move beyond 2D infrastructure coverage to fully 3D native services.
## 2 Related Concepts
### ACDE Algorithm
Generally speaking, the chaotic variable is first mapped to the value interval of the optimization variable and then the chaotic variable is used for optimization. Here, we use the logistic mapping commonly used by chaotic systems, with the basic expression as follows:  
\[x_{t+1}=\eta x_{t}(1-x_{t}), \tag{15}\]  
where \(x_{t}\in[0,1]\), \(t\) is a natural number and \(\eta\in[0,4]\); when \(\eta\)= 4, the system is completely chaotic. Step 3: Mutation. Different from the genetic algorithm, the DE algorithm realizes individual mutation by differential means. That is, two different individuals in the population are selected and the vector difference is scaled to perform vector synthesis with the individuals to be mutated [35]. The \(k\)-generation population generated by \(k\) iterations is denoted as:  
\[\Big{\{}\mathbf{x}_{i}(k)\Big{|}x_{j,i}(k)\in\Big{[}L_{jj},U_{jj}\Big{]},i=1,2,3,...,D;j=1,2,3,...,N\Big{\}}. \tag{16}\]  
Three individuals \(x_{i1}(k),x_{i2}(k)\) and \(x_{i3}(k)\) are randomly selected from the population and the mutation operations are performed according to the following equations:  
\[v_{i}(k+1)=x_{i1}(k)+F(k)\cdot(x_{i2}(k)-x_{i3}(k)), \tag{17}\]  
\[F(k)=F_{\min}+(F_{\max}-F_{\min})\frac{F_{i1}(k)-F_{i2}(k)}{F_{i1}(k)-F_{i3}(k )}, \tag{18}\]  
where \(F(k)\) is the adaptive scaling factor, which controls the influence of the difference vector and changes as the number of iterations \(k\) changes; \(i1,i2,i3\in[1,D]\) and \(i1\
eq i2\
eq i3\
eq i\); \(F_{\min}\) and \(F_{\max}\) are the upper and lower limits of the scaling factor, respectively; and \(F_{i1}(k)\), \(F_{i2}(k)\) and \(F_{i3}(k)\) are the optimal, suboptimal and worst-case fitness, respectively, with random selection for the current \(k\)-th generation sub-population. Through the mutation operations, an intermediate individual is created:  
\[\Big{\{}\mathbf{v}_{i}(k+1)\Big{|}\mathbf{v}_{j,i}(k+1)\in\Big{[}\mathbf{v}_{j,i}^{L},\mathbf{ v}_{j,i}^{U}\Big{]},i=1,2,3,...,D;j=1,2,3,...,N\Big{\}}. \tag{19}\]  
In the process of evolution, in order to ensure the validity of the solution, it is necessary to judge whether the \"genes\" in the \"chromosome\" satisfy the boundary conditions. If the boundary condition is not met, the \"gene\" is generated in the same way as the initial population, that is, it is regenerated by a random method. Step 4: Crossover. The inter-individual crossover operation is performed on the \(g\)-generation population [\(\mathbf{x}_{i}(k)\)] and the intermediate individual [\(\mathbf{v}_{i}(k+1)\)] produced by the mutation operations [36]. The specific method is as follows:  
\[\mathbf{u}_{i}(k+1)=\left\{\begin{array}{ll}\mathbf{v}_{i}(k+1),\text{ if }rand(0,1)\leq cr \text{ or }j=j_{rand}\\ \mathbf{x}_{i}(k),\text{ \
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 5 Discussion
Incorporating semantic priors such as airway labels or fissure segmentations may improve the regional interpretability and clinical usability further. Additionally, injecting controlled perturbations into the training correspondences would help quantify the model's sensitivity and reinforce its reliability. Expanding this framework to handle full 4D CT sequences or to quantify the registration uncertainty also presents promising future directions. In conclusion, we present a novel attention-based, bi-directional point cloud registration framework specifically designed for deformable lung CT alignment. Through architectural innovations and rigorous evaluations on a challenging large-scale dataset, we show that point-based learning, when tailored to the characteristics of respiratory deformation, can offer an accurate, efficient, and anatomically consistent registration performance. Beyond algorithmic improvements, our framework also provides interpretable and efficient deformation estimates that align with clinically meaningful structures, making it a strong candidate for deployment in medical image analysis workflows. We believe this work provides an important step toward more robust geometric modeling in medical image analysis. [PERSON] and [PERSON] participated in all phases and contributed equally to this work. Their contributions to this paper are as follows. [PERSON] contributed to the methodology, software, formal analysis, investigation, resources, data curation, original draft preparation, and visualization. The contributions of [PERSON] were reviewing and editing of the writing, supervision, and project administration. Also, both contributed to the conceptualization and validation of the paper. All authors have read and agreed to the published version of the manuscript. This research was supported by the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare (MOHW), Republic of Korea (No. HI22C041600). Not applicable. The Lung250M-4B dataset [16] used in this study is publicly available and can be accessed at [[https://github.com/multimodallearning/Lung250M-4B](https://github.com/multimodallearning/Lung250M-4B)]([https://github.com/multimodallearning/Lung250M-4B](https://github.com/multimodallearning/Lung250M-4B)), accessed on 3 February 2025. We gratefully acknowledge the insightful feedback and support provided by the Lung Vision AI team at VUNO Inc. Author [PERSON] was employed by the company VUNO Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest
## 5 Results and Discussion
### Spatial Interaction Between HDT Behaviors and Clustered Freight Zones  
Building upon the clustering of freight zones and their functional positioning, this section further analyzes the spatial interaction between different HDT behaviors and clustered grid cells to establish a two-way verification mechanism between regional functional labels and transport actor characteristics. On one hand, by identifying the structural characteristics of truck fleets within each clustered zone, we systematically validate the practical explanatory power of the pre-defined functional positioning. On the other hand, this analysis deepens our understanding of the intrinsic coordination mechanisms of freight activities, providing mechanistic insights into the micro-level operational logic of urban freight systems.  
Concurrently, the Herfindahl-Hirschman Index (HHI) is employed to measure the concentration of HDT trips. Originating from market concentration analysis in industrial economics, HHI ranges from [0, 1], where values closer to one indicate a higher concentration, and values approaching zero signify greater dispersion. Specifically, two dimensions are analyzed. Trip end concentration (\(HHI_{E}\)) measures the agglomeration intensity of freight activities at spatial nodes (e.g., the dominance of core nodes like port hubs and logistics parks). Trip route concentration (\(HHI_{R}\)) evaluates the spatial structuring of freight routes (e.g., the concentration of high-frequency round-trip corridors or fixed node pairs in transportation). We also introduce cosine similarity analysis to quantitatively validate the consistency of daily freight trip distributions across functional clusters, presented in a Sankey diagram. The cosine similarity (\(Sim_{cos}\)), a metric ranging from zero to one, reflects the consistency of distribution patterns: values closer to one indicate a greater stability in the distribution of trips across clusters. The results show strong stability with all \(Sim_{cos}\) values exceeding 0.95, validating that trip distributions across clusters are structurally consistent.
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 3 Results and Discussion
### Comparison of Model Accuracies Based on Data Generation
The large dataset size suited the CNN model, which required extensive training data and computational resources. Figure 5: ROC curves of four models for classification of EPG waveforms: (a) KNN, (b) SVM, (c) CNN, and (d) DT.
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 4 System Architecture and Security Model
### Security Goals  
According to the security model, the security goals are given as follows.  
Users' actual location information should be kept secret from \(SNS\) and unauthorized users. In addition, users' location information also can not be revealed to friends or strangers who do not match their pre-set access control policies.  
The social network server (\(SNS\)) should be prevented from obtaining users' actual location information though it can collude with malicious insiders to get users' approximate regions.  
The location server (\(LS\)) is prevented from getting users' real social network information, location information, and identity information.
## 2 Materials and Methods
### RNA Extraction  
A total of seven samples of biomass from cultures used in the first and second assays were collected for RNA extraction. Four samples from biomass of strain L5B5 cultured with NB supplemented with the carbon sources glycerol, glucose, mannose, and fructose, in the stationary phase of growth, and one from NB with glycerol in death phase. Additionally, two samples were collected from solid cultures of NA with glycerol in exponential and stationary phase. RNA was extracted using TRIsure and Direct-zol RNA Miniprep kit (Zymo Research, Irvine, CA, USA). Concentration was measured in a Qubit 2.0 fluorometer reaching \(>\)200 ng/uL for every sample. RNA Integration Number (RIN) was assessed using an Agilent Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA), rating values between 5.5 and 10.
## 2 Proposed Thermal Model
### Mathematical Model of Proposed Thermal Model  
Similar to [PERSON] current law, the main equations can be obtained as follows:  
\[\left\{\begin{array}{l}\frac{T_{e}-T_{F}}{R_{EF}}-\frac{T_{e}-T_{S}}{R_{FS}}- \frac{T_{e}-T_{W}}{R_{WF}}=0\\ \frac{C_{S}}{\mathrm{d}T_{S}}=P_{S}+\frac{T_{e}-T_{S}}{R_{FS}}+\frac{T_{e}-T_{ S}}{R_{S}}+\frac{T_{e}-T_{S}}{R_{S}}\\ \frac{C_{E}}{\mathrm{d}T_{R}}=P_{R}+\frac{T_{e}-T_{e}}{R_{E}}-\frac{T_{e}-T_{S} }{R_{SR}}\\ \frac{C_{E}}{\mathrm{d}T_{E}}=-\frac{T_{e}-T_{F}}{R_{EF}}-\frac{T_{e}-T_{B}}{R _{ER}}\\ \end{array}\right.. \tag{1}\]  
The equations contain a linear equation and it can be solved as follows:  
\[T_{F}=\frac{R_{EF}\ R_{FS}\ T_{W}+R_{FS}\ R_{WF}\ T_{E}+R_{EF}\ R_{WF}\ T_{S}}{R_ {EF}\ R_{FS}+R_{EF}\ R_{WF}+R_{FS}\ R_{WF}}. \tag{2}\]  
Substituting Equation (2) into the differential Equation (1), the third-order state equation for the simplified thermal model can be described as:  
\[\left[\begin{array}{c}\frac{\mathrm{d}T_{e}}{\mathrm{d}t}\\ \frac{\mathrm{d}T_{e}}{\mathrm{d}t}\\ \frac{\mathrm{d}T_{e}}{\mathrm{d}t}\\ \end{array}\right]=\left[\begin{array}{ccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\\ \end{array}\right]\left[\begin{array}{c}T_{s}\\ T_{R}\\ T_{E}\\ \end{array}\right]+\left[\begin{array}{ccc}b_{11}&b_{12}&0\\ 0&0&b_{23}\\ b_{31}&0&0\\ \end{array}\right]\left[\begin{array}{c}T_{w}\\ P_{s}\\ P_{R}\\ \end{array}\right], \tag{3}\]  
where:  
\(a_{11}=\frac{R_{EF}\ R_{FS}-R_{EF}\ R_{RS}\ R_{RS}\ R_{RS}\ R_{RS}\ R_{WF}-R_{ SF}\ R_{WF}\ R_{WF}}{C_{S}\ R_{SR}\ R_{WF}}\)\(a_{12}=\frac{1}{C_{S}R_{SR}}\)\(b_{11}=\frac{R_{EF}}{C_{S}\ R_{SF}\ R_{RF}+R_{IF}\ R_{WF}+R_{FS}\ R_{WF}}\)\(b_{12}=\frac{1}{C_{S}R_{SR}}\)\(a_{21}=\frac{1}{C_{R}R_{SR}}\)\(a_{22}=\frac{R_{EF}}{C_{R}R_{SR}}\)\(a_{31}=\frac{R_{EF}}{C_{E}\ R_{ER}\ R_{FSR}+R_{EF}\ R_{WF}+R_{FS}\ R_{WF}}\)\(b_{31}=\frac{R_{FS}}{C_{E}\ R_{FR}\ R_{WF}+R_{FS}\ R_{WF}}\).  
It can be concluded from Equation (3) that the stator temperature, rotor temperature, and end cap temperature are the state variables of the system, while the cooling water temperature, stator loss, and rotor loss are system inputs. In order to predict the motor stator temperature, rotor temperature, and end cap temperature, the parameters in the state equation should be identified.
## 3 Network Architecture  
### Network Model  
The suggested manner is applied to the medium scale system of the standard IEEE 33-bus, which is exceedingly utilized as a network model which is shown in. The network includes 33 buses and 37 branches ranging from _s1_ to s37. It includes 32 branches with normally closed SB pairs, as shown in Figure 2 with solid black lines, and 5 branches with normally opened SB pairs that are shown with the hidden black line. The pairs of breakers shown as red circles denote the beginning and ending of each branch.  
Figure 1: The block diagram of REF 542_plus_.  
The network architecture of the proposed approach between the CC and the SBs under a fault via wireless network of the SG is shown in Figure 3. The real physical fault in DN may occur in any branch due to several causes such as: Lightning, tree contact, birds, squirrels, and vandalism. The fault type may be Line to Ground (LG), Line to Line (LL), Line to Line to Ground (LLG) or Line to Line to Line to Line (LLL). The most fault type is LG which constitutes 80% from the total number of faults. The protection system will manage the fault by physical response to open the SB. The SB is designed to send the information (data) about its status and several measurements such voltage, current, frequency, time and date. The CC is designed to send actions (commands) to open or close the SBs. The wireless communication is widely used where the cyber-attack may be taken place to made a faked fault or disconnect the electricity on a pivotal area.  
where, \(L_{i}\) represents the load at the node \(i\), \(SB_{j}\) represents the smart breaker of the \(j\)th branches.
## 4 Current Status of the Application of Cucumber Genetic Transformation
### Genes Associated with Fruit Development  
#### 4.3.1 Fruit Length  
Fruit size and shape are important agronomic traits that are associated with quality. In _Arabidopsis_, the histidine deacetylase complex gene HDC1 regulates ABA sensitivity and promotes increases in organ size [71]. A recent study identified recessive allelic variation in a cucumber _HDC1_ homolog gene _Short Fruit2_ (_SF2_). This mutation significantly repressed fruit elongation [71]. Homozygous complemented plants were constructed by transferring the vector carrying the SF2 coding region together with 995 bp of the promoter into the _sf2_ mutant, and these plants displayed increased cell numbers and fruit length. Moreover, _SF2_ directly targets cytokin and polyamine biosynthesis to regulate cell proliferation [71]. Apart from that, two alleles of the _FRUITFULL_ (_FUL_)-like MADS-box gene _CsFUL1_ with a Q-K single nucleotide polymorphism associated with variation in fruit length were identified among 150 cucumber lines [72]. Overexpression of _CsFUL1A_ resulted in reduced fruit length compared with controls, whereas knockdown of _CsFUL1A_ by RNAi increased fruit length. Further transcriptome analysis and biochemical data revealed that _CsFUL1A_ inhibits _CsSUP_-mediated cell division and expansion as well as _CsPIN_-mediated auxin transport by binding to their promoter regions [72]. Ethylene plays an essential role in cucurbit fruit development. [PERSON] et al. [73] reported the ethylene regulation mechanism of cucumber fruit development; knockout of _SF1_--encoding a cucurbit-specific RING-type E3 ligase that regulates ethylene dosage--resulted in overproduction of ethylene, whereas knockdown of _ACS2_--encoding a rate-limiting enzyme for ethylene biosynthesis--led to reduced ethylene production. However, both mutants produced shortened fruits, suggesting a possible dose-dependent effect of ethylene on cucumber fruit length regulation. Further biochemical analyses indicated that SF1 specifically interacts with ACS2 to achieve precise control of ethylene dosage in cucumber fruits [73].
## 5 Example Analysis
### Overview of the Simulation System
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Equipment**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Value**} & \multirow{2}{*}{**Equipment**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Value**} \\ \hline  & Maximum generated power/(\(\delta\)W-\(\mathrm{one}^{-1}\)) & 100 & & Maximum input power/\(\mathrm{kW}\) & 200 \\ \cline{2-7}  & Rated efficiency & 0.26 & & Rated efficiency & 0.8 \\ \cline{2-7}  & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.03 & & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.02 \\ \cline{2-7} Microturbine/(four) & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 500 & & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 2500 \\ \cline{2-7}  & Litespan/a & 10 & & Lifespan/a & 10 \\ \cline{2-7}  & Ramp speed/(\(\delta\)W/\(\mathrm{min}\)) & Upper 30 & & Ramp speed/(\(\delta\)W/\(\mathrm{min}\)) & Upper 30 \\ \hline \hline \multirow{7}{*}{Heat-recovery boiler} & Maximum input power/\(\mathrm{kW}\) & 480 & & Maximum input power/\(\mathrm{kW}\) & 600 \\ \cline{2-7}  & Rated efficiency & 0.9 & & Heating and cooling efficiency parameter & 3.7 \\ \cline{2-7}  & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.02 & & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.02 \\ \cline{2-7}  & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 300 & & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 3900 \\ \cline{2-7}  & Litespan/a & 10 & & Lifespan/a & 10 \\ \hline \hline \multirow{7}{*}{Photovoltaic (PV) system} & Maximum generated power/\(\mathrm{kW}\) & 300 & & Maximum generated power/\(\mathrm{kW}\) & 100 \\ \cline{2-7}  & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.03 & & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.03 \\ \cline{2-7}  & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 6500 & & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 6000 \\ \cline{2-7}  & Litespan/a & 20 & & Lifespan/a & 20 \\ \hline \hline \end{tabular} \begin{tabular}{c c} \hline \hline \multirow{2}{*}{Exchanged power between the micro energy grid and the distribution network} & \multirow{2}{*}{Maximum exchanged power/\(\mathrm{kW}\)} & Lithium-bromide absorption-type refrigerator & \multirow{2}{*}{500} & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{2-7}  & Maintenance cost/(RMB/\(\mathrm{kW}\)-\(\mathrm{h}\)) & 0.025 \\ \cline{2-7}  & Initial investment cost/(RMB/\(\mathrm{kW}\)) & 3000 \\ \cline{2-7}  & Litespan/a & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Supply equipment parameters of the micro energy grid.
## 4 The Numerical Case Studies
### High Aspect Ratio Flexible Wing  
The second case study concerns the spar of a prototype wing, the linear and nonlinear dynamics of which have been investigated in recent studies [61; 62]. Importantly, the prototype skin is supposed to completely transfer the aerodynamic loads to the spar, making the structural behaviour of the latter a good approximation of the dynamical response of the whole spar-skin ensemble. The HAR, highly flexible wing is cantilevered, its geometry narrowing toward the tip with taper ratio changing at \(l=258\) mm (hereinafter, the mid-length section). Table 4 reports other geometrical and mechanical properties. The wing planform presents a tapered swept configuration, with trapezoidal planform, swept leading edge, and inward-kinked trailing edge. In this case, the fatigue damage that is induced by subsequent load cycles has been modelled as a reduction of the Young's modulus at the two most probable locations, i.e., at the clamped end and at the mid-length section, as portrayed in Figure 7 and enlisted in Table 5. The input, a perpendicular acceleration applied on the wingtip, was defined as a white Gaussian noise of peak value 1.5 g, sampled at \(f_{s,num\ 2}=256\) Hz for 5 s. 50 realisations were numerically simulated on the undamaged case for training; 10 others per case were used for testing the trained model. Five output channels were considered, mimicking the actual sensors array (plus the point of application of the laser Doppler velocimeter) actually used in the previous experimental studies and depicted in Figure 7.  
ECO- and MO-MMFCCs, firstly defined in [22] for the specific case of the Santa Maria and San Giovenale Cathedral bell tower, were adapted to this second numerical case. For the ECO-MMFCCs, 90% of the energy content was found at 43.76 Hz, by taking the mean over the five output channels. Therefore, this value has been set as the new cut-off frequency. The centre frequencies for MO-MMFCCs were defined from the modal analysis of the wing spar and benchmarked and validated against experimental data obtained in [61; 62]; they correspond, in order of increasing natural frequency, to the 1 st, 2 nd, 3 rd, and 4 th flapwise bending mode; the 1 st torsional mode; the 5 th flapwise flexural; the 1 st chordwise bending mode; the 2 nd torsional; and, the 6 th flexing modes. The tenth mode (the 7 th flapwise) fell over the maximum investigable frequency at 333.88 Hz and was therefore replaced by adding '1' to the begin of the array, thus resulting in  
\[f_{c}=[1\ \ 5.49\ \ 23.16\ \ 55.80\ \ 103.99\ \ 125.11\ \ 172.09\ \ 189.15\ \ 219.90\ \ 255.62]\]  
To conclude, the low frequency range of LO-MMFCCs was not changed and it remains defined between 0 and \(f_{s}/2\), which here becomes 128 Hz.  
For this second case, the major challenge comes from even large reductions in \(E\) resulting in minimal differences in terms of frequency shift, as can be seen in the last column of Table 5.