esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Data Collection  
### Multi-Camera Injection Analysis System  
The system configuration is illustrated in Figure 1a, which is comprised of a timing device, a multi-camera system, a VICON verification system (a 3D motion analysis system), an arm model, and a reverse blood detection device. The precise parameters are enumerated in Table 1 as follows.  
The camera configuration of the VICON verification system is depicted in Figure 1b, which includes 11 Vero cameras and one Vue HD full-synchronous high-speed camera. In this experiment, the Vero cameras were sampled at 100 Hz. The multi-camera system, as shown in Figure 1c, comprises four CMOS cameras that are directly connected to a personal computer (PC) and the VICON system to construct the operator's hand injection model separately. Figure 1d displays the general camera for puncture detection, which is employed to monitor the puncture time during the injection. The timing device and the reverse blood detection device are shown in Figure 1e. The PC is linked to the timing device via a type C interface. Moreover, the photo sensor in the reverse blood detection device is linked to the Arduino main board of the timing device. The PC is responsible for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Camera Type** & **Model** & **Resolution** & **Frame Rate** & **Company** \\ \hline VICON & High-speed cameras & Vero & 2048 \(\times\) 1088 & 330 FPS & VICON Company \\ Verification System & HD & & & & \\  & full-synchronous high-speed camera & Vue & 1920 \(\times\) 1080 & 30 FPS & VICON Company \\ Multi-Camera System & CMOS cameras & DFK 33 UX290 & 1920 \(\times\) 1080 & 40 FPS & IMAGINGSOURCE \\  & General camera & - & 1920 \(\times\) 1080 & 30 FPS & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: VICON system and Multi-camera system specifications.  
power supply and data transmission, while the arm model is powered independently. The data acquisition software system was developed using Microsoft Visual C# 2022.
# Lightweight Stacked Hourglass Network for Human Pose Estimation
## 3 Proposed Method
### Residual Block Design  
#### 3.2.1 Dilated Convolution  
In human pose estimation, it is important to increase the receptive field so that the network can learn to recognize the features of the full human body. However, if the kernel size is increased to widen the receptive field, the computational cost also increases. Since our goal was to design an efficient hourglass network, we constructed a residual block using dilated convolution [33]. The number of parameters in the standard convolution is shown in Equation (1), where the kernel size is \(K\), the input channel size is \(C\), and the output channel size is \(M\). If the size of the input and output is the same as \(H\times W\), the required computational cost is shown by Equation (2):  
\[\#\ param=K^{2}CM \tag{1}\]  
\[Computational\ Cost=K^{2}CMHW \tag{2}\]  
In the case of the dilated convolution, if the kernel size remains the same, the number of parameters and the computational cost remain the same as the standard convolution; however, the receptive field is wider, depending on the dilation size \(D\). For dilated convolutions with a \(3\times 3\) kernel size when \(D_{1}=2\), the kernel size and computational cost are the same as for the \(3\times 3\) standard convolution, but the receptive field is the same as for the \(5\times 5\) standard convolution. Additionally, as shown when \(D_{1}=2\) and \(D_{2}=3\) in Figure 3, dilated convolution has zero padding inside the kernel; thus, the computational cost is slightly lower than it is for standard convolution with the same kernel size.  
Figure 3: Standard convolution with \(3\times 3\) kernel size and dilated convolution. \(D=1\) is the same as the standard convolution. \(D=2,\ 3\) are calculated by placing zero padding inside the kernel, as shown in the figure.  
Figure 2: Proposed hourglass architecture, which utilizes an additional skip connection (red dashed arrow in the figure) from the previous stack (\(n-1\)) to the current stack (\(n\)).
## 4 Numerical Procedure  
The proposed numerical scheme can be applied to solve first-order differential equations. Since Equations (39)-(41) are more than first-order ODEs, these ODEs Equations (39)-(41) will be converted into first-order ODEs as follows:  
\[f^{\prime}=f_{1},\;f(0)=0 \tag{43}\]  
\[f_{1}^{\prime}=f_{2},f_{1}(0)=1 \tag{44}\]  
\[f_{2}^{\prime}=\frac{1}{1+\frac{1}{\beta}}\Big{[}f_{1}^{2}-ff_{2}-M(E_{1}-f_{1 })+\lambda f_{1}+F_{f}f_{1}^{2}\Big{]},f_{2}(0)=x_{1} \tag{45}\]  
\[\theta^{\prime}=\theta_{1},\;\theta(0)=1 \tag{46}\]  
\[\theta_{1,i}=\theta_{1,i-1}+hH_{i}+h^{2}H_{1,i-1}\theta_{1}(0)=x_{2} \tag{61}\]  
\[\phi_{i}=\phi_{i-1}+h\phi_{1,i}+h^{2}G_{i-1},\ \phi(0)=1 \tag{62}\]  
\[\phi_{1,i}=\phi_{1,i-1}+hG_{i}+h^{2}G_{1,i-1}\phi_{1}(0)=x_{3} \tag{63}\]  
where \(F=\frac{1}{1+\frac{1}{B}}\{f_{1}^{2}-ff_{2}-M(E_{1}-f_{1})+\lambda f_{1}+F_{r} f_{1}^{2}\}\)  
\[F_{1}=\frac{1}{1+\frac{1}{B}}\{f_{1}f_{2}-fF+Mf_{2}+\lambda f_{2}+2F_{r}f_{1}f_ {2}\}\]  
\[H=\frac{P_{r}}{\left(1+\frac{3}{B}R_{d}\right)}\Big{\{}-f\theta_{1}-E_{c}f_{2} ^{2}-ME_{c}(f_{1}-E_{1})^{2}-N_{b}\theta_{1}\phi_{1}+N_{t}\theta_{1}^{2}\Big{\}}\]  
\[H_{1}=\frac{P_{r}}{\left(1+\frac{3}{B}R_{d}\right)}\{-f\theta_{1}-fH-2E_{c}f_{ 2}F-2 ME_{c}(f_{1}-E_{1})f_{2}-N_{b}\phi_{1}H-N_{b}\theta_{1}G+2N_{t}\theta_{1 }H\}\]  
\[G=S_{c}\Big{\{}-f\phi_{1}-\frac{N_{t}}{N_{b}}H+\gamma\phi\Big{\}}\]  
\[G_{1}=S_{c}\Big{\{}-f_{1}\phi_{1}-fG-\frac{N_{t}}{N_{b}}H_{1}+\gamma\phi_{1} \Big{\}}\]  
The skin friction coefficient, local Nusselt number, and local Sherwood number are defined as  
\[C_{f}=\frac{\tau_{w}}{\rho U_{w}^{2}} \tag{64}\]  
\[Nu_{x}=\frac{xq_{w}}{k(T_{w}-T_{\infty})} \tag{65}\]  
\[Sh_{x}=\frac{xj_{w}}{D(C_{w}-C_{\infty})} \tag{66}\]  
where  
\[\tau_{w}=-\mu\Big{(}1+\frac{1}{B}\Big{)}\Big{(}\frac{\partial u}{\partial y} \Big{)}_{y=0}\]  
\[q_{w}=-\Big{(}1+\frac{16e^{+}\tau_{w}^{2}}{3e^{x}}\Big{)}\Big{(}\frac{ \partial T}{\partial y}\Big{)}_{y=0}\]  
\[j_{w}=-\Big{(}\frac{\partial u}{\partial y}\Big{)}_{y=0}\]  
By applying transformations (38), dimensionless forms of skin friction coefficient, local Nusselt, and local Sherwood numbers are expressed as  
\[R_{e}^{1/2}C_{f}=-\bigg{(}1+\frac{1}{\beta}\bigg{)}f^{\prime\prime}(0) \tag{67}\]  
\[R_{e}^{-1/2}Nu_{x}=-\bigg{(}1+\frac{4}{3}R_{d}\bigg{)}\theta^{\prime}(0) \tag{68}\]  
\[R_{e}^{-1/2}Sh_{x}=-\phi^{\prime}(0) \tag{69}\]  
where \(R_{e}=\frac{U_{w}x}{v}\) denotes the Reynolds number.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 2 Circuit Structure and Operation Analysis  
### Circuit Structure  
Evolved from an LLC resonant converter, rectification diodes of the transformer secondary side are replaced by active power switches, and an inductor is introduced into the secondary side to form a resonant tank along with the resonant capacitor. Figure 1 shows the realized topology of the isolated bidirectional symmetric resonant converter with a dual active full bridge. In definition, forward charging mode is the power transfer from power grid \(V_{DC\_Grid}\) to battery port \(V_{Bat}\), and reverse discharging mode marks the power transfer reversely. With the usage of the symmetric resonant scheme, the power switches can achieve soft switching in either direction of power transfer and thus reduce the switching losses and EMI. Figure 2 illustrates the voltage gain curves of the traditional LLC resonant converter; the reverse-mode voltage gain is limited to be less than 1.  
Figure 1: The proposed isolated bidirectional symmetric resonant converter with dual active full bridge.
## 1 Introduction
We provide a detailed assessment of the relationship between DEM quality and image texture in our study area and use our findings to optimize the output from available algorithms and parameters contained in the ASP. With this, we aim to reduce errors and artifacts in stereo-derived DEMs which may significantly impact subsequent topographic analyses. We demonstrate the effect of DEM quality issues with a geomorphic application of the generated DEMs by assessing landscape curvature. Our work shows that identifying optimal stereo-processing techniques will reduce noise and enhance sharp ridge and valley features.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 2 Microbial Chassis as Cell Factories for Edible Terpenoids
### Prokaryotic Microorganisms
#### 2.1.2 Corynebacterium glutamic  
_C. glutamicum_, well-known for rapid growth, broad substrate spectrum, and resistance to bacteriophage infections, and can be cultivated on a large scale, is generally regarded as a safe (GRAS) organism [43; 44]. Therefore, it is often used in the food industry, particularlyin the large-scale production of amino acids [45]. With the improvement of genetic manipulation techniques, its production spectrum has expanded, and it is often developed into a cellular factory for terpenoids.  
_C. glutamicum_ contains glycosylated C\({}_{50}\) carotenoids decapreoxanthin, which is a yellow pigment. It indicated that _C. glutamicum_ has an abundance of FPP and GGPP precursors. In order to extend the ability to synthesize other carotenoids, [PERSON] et al. integrated their newly identified gene cluster _crtNaNcM_ from _Planococcus martimus_ into the _C. glutamicum_ genome. Various novel C\({}_{35}\) carotenoids were detected in the cells. It indicates that _C. glutamicum_ can serve as a promising carotenoid cell factory [32]. [PERSON] et al. knocked out the key genes involved in the native C\({}_{50}\) carotenoid synthesis, ranging from _crtE_ to _crtYe_, and strengthened the expression of CrtE/B/I to increase the lycopene level. Additionally, they discovered a bifunctional enzyme, OluLCY, exhibiting the highest \(\varepsilon\)-cyclase activity and the lowest \(\beta\)-cyclase activity, and performed multi-copy integration of _oluLCY_ to the host. By further strengthening the MVA pathway to increase the supply of IPP, they obtained a strain with an \(\alpha\)-carotene titer of 1.054 g/L. It was the first report of \(\alpha\)-carotene synthesis in _C. glutamicum_[33]. [PERSON] and co-workers assembled lycopene \(\beta\)-cyclase, \(\beta\)-carotene hydroxylase, and \(\beta\)-carotene ketolase in a lycopene-producing _C. glutamicum_ platform to produce astaxanthin via \(\beta\)-carotene. By optimizing and balancing the expression of CrtZ and CrtW, an astaxanthin titer of 103 mg/L was achieved in a 2 L fed-batch bioprocess. Furthermore, the pathway was expanded towards astaxanthin-\(\beta\)-d-diglucoside biosynthesis by additional expression of a glycosyltransferase CrtX [34].  
_C. glutamicum_ is a non-ubiquinone-containing bacterium. To produce Coenzyme Q\({}_{10}\), [PERSON] et al. increased the supply of precursor FPP and decaprenyl diphosphate (DPP) by expressing FPP synthase (IspA) and DPP synthase (DdsA). Then, they knocked out the side-product synthesis pathway and replaced the key synthetic enzyme with a mutant that eliminates feedback inhibition, thereby increasing the flux into the shikimic acid pathway to synthesize pHBA. After they enhanced the precursor supply pathway, the 10 HB condensation pathway, and subsequent modification pathways into _C. glutamicum_, the resultant strain UBI413 was shown to synthesize CoQ\({}_{10}\)[35].
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 9 Conclusions
### Comparison with Other Works  
We outline a comparison between other works in the domain and our proposed approach. As compared to [11] where two separate gateways are used (one for UAVs fleet and one for USVs fleet), our architecture uses a simpler approach with only one gateway for both UAVs and USVs. Moreover, their analysis focuses on the routing protocols and network capacity, not on channel propagation. In contrast to [44] where a path planning algorithm based on environmental parameters (wind speed and direction) for UAVs is presented, we propose an architecture and a positioning algorithm based on the communication parameters related to path loss that allows the positioning of UVs to deploy an entire network infrastructure based on UAVs and USVs. With respect to [45; 46], our algorithm uses all types of UAV channels (A2A, A2G equivalent to G2A) and USV channels (OW, S2S, A2S). In addition, our algorithm does not employ only one UAV as in [45]. In comparison with [46] where the air-to-air channel models are investigated, we used UAV propagation models adapted to a wider range of applications. For example, concerning UAV-related channels, besides the air-to-air channel, we analysed an air-to-ground channel (having the same properties as the ground-to-air channel). We drew the conclusion that the 3 GPP model for Rural Macro-cell is very suitable for isolated areas, with poor coverage as those covered by the current paper (e.g., the Danube Delta). With respect to [47; 48; 49; 50; 51], our proposed algorithm is not intended for USV path planning but for finding the optimum final positions of the UV nodes that ensure the formation of a USV-UAV network in conditions of communication reliability.  
To conclude this subsection, according to our knowledge, our proposed approach is unique. It can be the starting point for many collaborations between specialists in the different layers of Internet of Things architecture to build a strong Internet of Unmanned Vehicles ecosystem.
## 3 Results
### Application of the SSM Estimation Model  
The model was used to estimate regional SSM from the S1 SAR and S2 MSI images in the vegetation-covered area under clear-sky conditions. Figure 7a,b shows the SSM (20 m \(\times\) 20 m) maps in Daxing on 28 April 2019 and 29 April 2021, respectively. The spatial distribution of the estimated SSM has large variability with a range of 0-0.49 cm\({}^{3}\)/cm\({}^{3}\), mainly distributed between 0.12 and 0.28 cm\({}^{3}\)/cm\({}^{3}\) (Figure 7a). There were no significant drought events or other extreme weather events. As seen in Figure 7b, the fields are slightly wetter than in Figure 7a. The estimated SSM is below 0.44 cm\({}^{3}\)/cm\({}^{3}\), and the eastern part is wet. This can provide valuable reference information for various types of applications, especially for water resource management.  
Figure 6: SSM validation under six models.  
Figure 8a,b shows the SSM maps in Gu'an on 28 April 2019 and 29 April 2021, respectively. As seen in Figure 8a, SSM is distributed from 0.09 to 0.46 cm\({}^{3}\)/cm\({}^{3}\), the western part of Gu'an is relatively dry, while the southern part is relatively dry. As seen in Figure 8b, the fields are slightly droughter in most areas. SSM is below 0.40 cm\({}^{3}\)/cm\({}^{3}\), but in most areas, it is between 0.06 and 0.27 cm\({}^{3}\)/cm\({}^{3}\), and the northern area is arid. This can provide valuable reference information for irrigation management. Thus, it can be concluded that SSM estimated by the model was highly consistent with in situ measured SSM. The model developed in our research is applicable for SSM estimation of farmland.
## 4 Social Mechanism
### Global Trustworthiness  
The purpose of computing the global trustworthiness is to determine the overall trustworthiness of a peer within the social network. At this stage, peer b, regardless of its adjacency to peer a, can iteratively visit other peers to calculate its trustworthiness within the entire network using the density connectivity relationship established by DBSCAN. The specific calculation process is outlined in Algorithm 2.  
```
Input: Algorithm's access object \(n\), computation peer \(b\), successful transaction count \(S_{sc}\), iteration upper limit \(I_{ul}\); Output: Global trust value \(GT_{ab}\); Initialization:\(sc\gets 0\), \(ul\gets 0\), \(GT_{ab}\gets 0\), \(n\gets a\), weight \(W_{tr}\gets 1\); Begin while\(sc\leq S_{sc}\) && \(ul<I_{ul}\)do \(NT\gets 0\), \(count\gets 0\) for\(i\in\) neighbors of \(n\)do if\(PT_{ib}>0\)then \(NT\gets NT+PT_{ib}\) \(sc\gets sc+1\), \(count\gets count+1\)  invQueue.push(pair(\(1\), \(W_{tr}/2\))) endif endfor \(NT\gets NT*(W_{tr}/count)\) \(GT_{ab}\gets GT_{ab}+NT\) \(ul\gets ul+1\) if\(sc\leq S_{sc}\) && \(ul<I_{ul}\)then \(n=\textit{invQueue}[0].first\) \(W_{tr}=\textit{invQueue}[0].second\) \(invokeQueue.\textit{erase}(begin)\) endif endwhile return\(GT_{ab}\)
```  
**Algorithm 2** Global Trustworthiness Calculation  
In addition to enhancing the fairness of the trust value mechanism, computing global trust plays another crucial role in facilitating the recovery mechanism, as discussed in the upcoming section. In order to ensure the integrity of the block content obtained through the recovery mechanism, DC-SoC typically initiates requests to peers from other organizations. Given that distinct organizations within the channel lack direct interactions, except for communication facilitated through anchor peers, they maintain identical copies of the ledger. Hence, it is crucial to filter suitable peers, based on the computation of global trust, in order to initiate the recovery request. The final trustworthiness can be obtained by combining the local and global trusts using the calculation method specified in (3):  
\[T_{ab}=\lambda\times PT_{ab}+(1-\lambda)GT_{ab} \tag{3}\]  
The confidence factor \(\lambda\) represents the weighting factor for local trustworthiness, allowing adjustment of the relative proportion of local trustworthiness and global trustworthiness. Typically, \(\lambda\) is assigned a value between 0.5 and 1. In practical applications, the value of \(\lambda\) can be adjusted based on specific requirements and needs.
# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 3 Experiments and Results  
### Experimental Setup and Evaluation Metrics  
The experiments were carried out on ex-vivo pig eyes. A micro-manipulator was used to grip the needle. The CALLISTO eye assistance computer system was established to show the microscopic image and display preview of OCT scans (see Figure 5). A foot pedal connected to the RESCAN700 was settled to relocate the scan area. We captured the needle with different poses and positions in OCT scan area on ex-vivo pig eyes. The OCT cube with resolution of \(512\times 128\times 1024\) voxels and a corresponding imaging spatial area of \(3\times 3\times 2\) millimeters, 60 OCT cubes with 7680 B-scan images were manually segmented and marked the needle pixels as the ground truth data set.  
Both of the aforementioned methods were implemented on a Intel(R) Xeon(R) CPU E5-2620 v3 2.40 GHz with a GeForce GTX 980 Ti and memory of 64 GB running Ubuntu 16.04 operating system. The MF based method was implemented with OpenCV 2.4 in C++. The FCN based method used Caffe [26] framework to design network with python and the rest image processing parts were implemented also with OpenCV 2.4 in C++. We used four metrics to evaluate the performance of two  
Figure 5: The experimental setup of ophthalmic microsurgery on ex-vivo pig eyes. The micro-manipulator is designed to grasp the syringe and place the needle close to the eye tissue. The CALLISTO eye assistance computer system is set up to display the en-face microscopic image and cross sectional images of OCT cube.  
methods. Let \(n_{p}\) be the number of needle pixels in prediction, and \(n_{p}^{\prime}\) be the number of needle pixels in prediction correctly. The details of the four metrics are described as follow: (1) pixel error number can be false positive (FP) needle pixels and false negative (FN) needle pixels in predicted result; (2) pixel accuracy rate equals \(n_{p}^{\prime}/n_{p}\); (3) The average bounding box absence rate indicates the degree of missing needle segment in B-scan image; (4) The width error of bounding box influences the further needle dimension analysis.
# SMS: A Secure Healthcare Model for Smart Cities
## 6 Conclusions and Future Scope  
This paper proposes a unique framework for the continuous health monitoring of patients. The proposed framework combines the three modern-day technologies IoT, cloud computing, and edge computing to facilitate efficient medical services. This is particularly helpful in critical cases that require \(24\times 7\) monitoring of the patients that is otherwise difficult with manual monitoring (nurses or ward boys). The system has the provision to capture any change in the condition of the patients in real time and send alerts to the doctors and care providers. In addition, this information can be used to construct prediction and analytics models using deep-learning techniques that can predict the incoming situation of the patient on the basis of the fluctuations in the vitals of the patients. Furthermore, the patients can be monitored regarding dietary and self-care instructions after they are discharged from the hospital with the help of the wearable devices worn by the patients. The amalgamation of big data and IoT in the proposed model also facilitates the storing of genomic data in a cost-effective and reliable manner and can open new horizons for genomic data analytics for treatments of various chronic and hereditary diseases. In addition, the data collected from the patients can serve as a repository for future medical references, prescriptions, and case studies. As a future scope, we can design a deep-learning-based analytical model for predicting the conditions of the patients in the near future and come up with state-of-the-art methodologies for diagnosis and treatments. As an extension of this work, the proposed model shall be converted into a prototype and will be evaluated with the help of simulation and real datasets.  
**Author Contributions:** Conceptualization, M.A.A. and G.T.; methodology, M.A.A. and S.P.; formal analysis, M.A.A., S.P. and G.T.; investigation, M.A.A. and S.P.; resources, G.T. and S.P.; data curation, M.A.A., G.T., S.P. writing-original draft preparation, G.T. and M.A.A.; writing-review and editing, M.A.A. and S.P.; visualization, G.T. and S.P.; supervision, G.T. and S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Amid global decarbonization mandates, urban distribution networks (UDNs) face escalating voltage volatility due to proliferating distributed energy resources (DERs) and emerging loads (e.g., 5G base stations and data centers). While virtual power plants (VPPs) and network reconfiguration mitigate operational risks, extant methods inadequately model load flexibility and suffer from algorithmic stagnation in non-convex optimization. This study proposes a proactive voltage control framework addressing these gaps through three innovations. First, a dynamic cyber-physical load model quantifies 5G/data centers' demand elasticity as schedulable VPP resources. Second, an Improved Termite Life Cycle Optimizer (ITLCO) integrates chaotic initialization and quantum tunneling to evade local optima, enhancing convergence in high-dimensional spaces. Third, a hierarchical control architecture coordinates the VPP reactive dispatch and topology adaptation via mixed-integer programming. The effectiveness and economic viability of the proposed strategy are validated through multi-scenario simulations of the modified IEEE 33-bus system (represented by 12.66 kV, it is actually oriented to a broader voltage scene). These advancements establish a scalable paradigm for UDNs to harness DERs and next-gen loads while maintaining grid stability under net-zero transitions. The methodology bridges theoretical gaps in flexibility modeling and metaheuristic optimization, offering utilities a computationally efficient tool for real-world implementation.  
urban distribution networks; virtual power plants; voltage control strategy; networks reconfiguration; termite life cycle optimizer +
Footnote †: journal: _Algorithms_