esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The main contributions are made as follows:  
* We adopted a dense optical flow estimation algorithm that combines the HS pyramid large displacement optical flow method with the LK local optical flow method to improve the robustness of the algorithm and introduced the DF-\(\beta\) based on an edge-preserving strategy as the smoothing term of the optical flow energy equation to ensure edge integrity. * We adopted the non-quadratic penalty function and Delaunay triangulation occlusion determination to reduce the inaccuracy of optical flow estimation caused by the 'ghost' problem and to improve the accuracy of moving object detection. * We proposed a moving object detection algorithm based on optical flow thresholding of Harris robust feature points. According to the MSAC algorithm, the background model is calculated, and the background robust feature points are screened. The optical flow value of the Harris robust feature points is used as the threshold and can be applied to both a static or moving camera.
# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 3 _Ekf_ Based Model
### Toggle Mechanism Model  
We have shown the model in [13] and herein simply introduced it below.  
From the geometry of Figure 4, we can obtain equations as follows:  
\[\Phi(\theta)=\left[\begin{array}{c}r_{1}\sin_{\theta_{1}}+r_{3}\sin(\theta_{ 2})-f\\ r_{5}\sin_{\theta_{5}}+r_{4}\sin(\theta_{1}+\phi)-h-f\end{array}\right]=0. \tag{1}\]  
Figure 3: Damage distance and detected distance.  
\(\Phi(\theta)\) describes the parameter relationships in the \(TM\) model. Then, we can obtain  
\[x_{B}=r_{1}\cos(\theta_{1})+[r_{3}^{2}-(f-r_{1}\sin(\theta_{1}))^{2}]^{\frac{1}{2 }}. \tag{2}\]  
Using [PERSON]'s principle, we can obtain  
\[\begin{array}{ll}0&=\int_{t_{1}}^{t_{2}}[\delta T_{t}+\delta W^{A}+\delta W^ {C}]dt\\ &=\int_{t_{1}}^{t_{2}}\delta\theta^{T}\left[\frac{\partial T_{t}}{\partial \theta}+\frac{d}{dt}\frac{\partial T_{t}}{\partial\theta}-Q^{A}-Q^{C}\right]dt+ \left.\frac{\partial T_{t}}{\partial\theta}\delta\theta\right|_{t_{1}}^{t_{2}},\end{array} \tag{3}\]  
where \(\delta W^{A}\) is the virtual work, and \(\delta W^{C}=-\delta\theta^{T}Q^{C},Q^{C}\) are the generalized constraint reaction forces.  
We use energy method to gain dynamic motion equation. The total kinetic energy of the system is described below.  
\[T_{t}=T_{2}+T_{3}+T_{5}+T_{B}+T_{C}, \tag{4}\]  
where \(T_{2},T_{3},T_{5},T_{B},T_{C}\) represent the kinetic energies of link two, three, five, and sliders B and C, respectively.  
\(Q^{A},Q^{C}\) are calculated as follows:  
\[Q_{A}=\left[\begin{array}{c}F_{C}r_{5}\sin_{\theta_{5}}\\ F_{B}r_{3}\sin_{\theta_{2}}\\ F_{B}r_{1}\sin_{\theta_{1}}+F_{C}r_{4}\sin_{\theta_{1}}+\phi\end{array}\right], \tag{5}\]  
where \(\lambda\) is the Lagrange multiplier.  
\[Q_{C}=-\lambda\begin{bmatrix}0&r_{5}\cos(\theta_{5})\\ r_{3}\cos(\theta_{1})&0\\ r_{1}\cos(\theta_{1})&r_{4}\cos(\theta_{1})+\phi\end{bmatrix}. \tag{6}\]  
Figure 4: The physical model of \(TM\). Establish a Cartesian coordinate system and set the origin at link one. \(F_{C}\) and \(F_{B}\) are the force on sliders \(B\) and \(C\), respectively. \(m_{2}\), \(m_{3}\), \(m_{5}\), \(m_{B}\), and \(m_{C}\) indicate the mass of links two, three, five, and sliders \(B\) and \(C\), respectively. \(r_{1}\), \(r_{2}\), and \(r_{4}\) indicate the three-side lengths of two, and \(r_{3}\) and \(r_{5}\) indicate the lengths of links three and five respectively; \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{5}\) are the angular positions of link two, three, and five; \(h\) is the height between the two horizontal guides where sliders \(B\) and \(C\) travel; and \(f\) is an offset between links two and three [13].  
Thus, the equation of motion for the \(TM\) is  
\[M(\ddot{\theta})+N(\theta,\dot{\theta})+\lambda\Phi_{\theta}^{T}=Q_{A}. \tag{7}\]  
Using the Equation (6) and trajectory \(x_{B}\), we can obtain \(\dot{x}_{B},\ddot{x}_{B},x_{C},\dot{x}_{C},\ddot{x}_{C}\), and \(F_{C}\); for details see the previous paper [13].
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
###### Abstract  
Smart nanoparticles for medical applications have gathered considerable attention due to an improved biocompatibility and multifunctional properties useful in several applications, including advanced drug delivery systems, nanotheranostics and in vivo imaging. Among nanomaterials, zinc oxide nanoparticles (ZnO NPs) were deeply investigated due to their peculiar physical and chemical properties. The large surface to volume ratio, coupled with a reduced size, antimicrobial activity, photocatalytic and semiconducting properties, allowed the use of ZnO NPs as anticancer drugs in new generation physical therapies, nanoantibiotics and osteoinductive agents for bone tissue regeneration. However, ZnO NPs also show a limited stability in biological environments and unpredictable cytotoxic effects thereof. To overcome the abovementioned limitations and further extend the use of ZnO NPs in nanomedicine, doping seems to represent a promising solution. This review covers the main achievements in the use of doped ZnO NPs for nanomedicine applications. Sol-gel, as well as hydrothermal and combustion methods are largely employed to prepare ZnO NPs doped with rare earth and transition metal elements. For both dopant typologies, biomedical applications were demonstrated, such as enhanced antimicrobial activities and contrast imaging properties, along with an improved biocompatibility and stability of the colloidal ZnO NPs in biological media. The obtained results confirm that the doping of ZnO NPs represents a valuable tool to improve the corresponding biomedical properties with respect to the undoped counterpart, and also suggest that a new application of ZnO NPs in nanomedicine can be envisioned.  
zinc oxide nanoparticles; doped ZnO; rare earth; transition metals; bioimaging; drug delivery; theranostics; antibacterial properties +
Footnote †: journal: Applied Physics Letters
# Experimental Evaluation of Thermal Performance and Durability of Thermally-Enhanced Concretes
## 3 Results and Discussion
### Resistance to Carbonation  
The average carbonation rates of all specimens measured for eight weeks after casting were summarized in Table 8 and presented in Figure 8. These showed that the carbonation rates of NWAC were similar with those of LWAC, although LWAC was generally known to have higher carbonation rates. However, this mainly depended on the types of lightweight aggregate (LWA) [38]. Since CO\({}_{2}\) reactivity is determined by cement microstructures, and permeability influences carbonation depth, some types of LWA having higher water absorption ratios or leading to pozzolanic reactions by sintering can increase the rates. However, the raw material of LWA used in this study is slate and it has a low water absorption ratio (below 6%) and no pozzolanic reaction. Thus, carbonation depths of LWAC were similar to NWAC.  
Figure 8: Average carbonation rates for normalweight aggregate concrete (NWAC) and lightweight aggregate concrete (LWAC).  
Carbonation rates of NWAC and LWAC were almost two times greater when insulation materials were added regardless of aggregate types, the compressive strength class, and density. This is because that added insulation materials result in the higher water-filled pores and permeability (to gas). Thus, thermally-enhanced concretes with insulation materials used in this study are weak to carbonation attack and additional protection methods are needed, such as an increase in the cover depth of the concrete.
# Dispatchable Substation for Operation and Control of Renewable Energy Resources
## 2 Requirement for Dispatch-Able Substation
### Emergency Grid Support and AGC  
To operate a power system without adequate support from synchronous generators, a TSO needs to prepare sufficient number of frequency support devices to withstand contingency situations such as an \"N-1 scenario\". A sudden drop in the number of power supply devices leads to imbalance and frequency deviation. Failure to balance out immediately after an event can trigger other generators to trip out for de-synchronization protection. In a power system with high renewable energy penetration, the frequency stability of the system can be severely exacerbated. A spinning reserve or hydro-pumped generation can be a direct solution for such situations, but they are not cost-effective and flexible. Recent studies have focused on the role of the ESS and renewable generators in the frequency support action. Because of its fast response, an ESS can act as a synchronous generator by creating a synthetic frequency response for grid support in the case of a contingency situation. The same control strategy can be adopted for converter-based generators, such as PV and a special type of wind turbine [21] (Types 3 and 4). The difference between ESS and renewable generators lies in the amount of time the  
Figure 2: Operation of an energy storage system (ESS) in a dispatchable substation.  
frequency response can be supplied. For prolonged frequency support from renewable generators, their operating efficiency can be intentionally curtailed from the maximum power harnessing status.  
The DSS can be a suitable control center for interconnected renewable sources to arbitrate frequency support requirements for grid interconnection. Based on the forecast information and capacity received from the dispatchable substation, the TSO can calculate the operation reserve for the DSS. Then, the DSS manages reserve assets behind substations, such as the ESS and headroom of curtailed RES generation.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 2 Background
### Standards
Thereafter, the network controller and the new device perform an Elliptic Curve Diffie-Hellman (ECDH) key exchange. Using the session key resulting from the ECDH key exchange, the network controller securely sends the network key used for protecting traffic among nodes. The development of the S2 protocol shows the natural need for having individual device identities and keys. The Zigbee IP [1] application profile uses a significantly different bootstrapping method when compared to other standards discussed above. Authentication in the network is based on using the Extensible Authentication Protocol (EAP). In particular, Zigbee IP uses certificates with Transport Layer Security (TLS), i.e., EAP-TLS as the authentication method. The standard requires manufacturers to setup a Public Key Infrastructure (PKI) and install device certificates at the time of manufacturing. Certificates, together with EAP-TLS, allow for device authentication without any user action. Once EAP-TLS authentication is successfully completed, then the resulting keys are used to protect the configuration information sent by the controller to the new device. Zigbee Light Link and Zigbee IP both rely on some form of manufacture provisioned credentials. While these credentials can be used for verifying that devices belong to a certain manufacturer, they provide no information about the device owner deploying them. Therefore, it is possible that a Zigbee light bulb may connect to the controller of a neighbor who also owns devices from the same manufacturer. The Zigbee IP specification realizes this potential issue of joining the wrong network. Several mechanisms for overcoming the challenge of correct network selection are mentioned in section 6.4 of the Zigbee IP specification [1]. One of the suggestions states that joining nodes could scan for beacons and discover all Zigbee IP networks in its radio range. Joining nodes would then display information about all of the networks and allow users to select the correct network which the node should join.
## 4 Experiment
### Ablative Experiments on MaSTr1325  
We fixed the Backbone as LDMNet and reorganized the modules at the Neck position for retraining. The Recombine Attention Module (RAM) focuses on better fusion of feature maps with high differences, while the combination of Mamba and Hilbert Curve Scan (HCS) enables effective extraction of contextual information from deep features. The experimental results presented in Table 9 show that incrementally adding RAM and Mamba-2 (with HCS) can improve the model's mIoU by 0.87% and 1.22%, respectively. Moreover, using HCS with Mamba can enhance the model's mIoU by an additional 0.26%. Simultaneously  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{1}{c}{\multirow{2}{*}{**mIoU (\%) \(\uparrow\)**}} & \multicolumn{2}{c}{**mPA (\%) \(\uparrow\)**} & \multicolumn{2}{c}{\(\delta\)} \\ \hline \multicolumn{2}{c}{**DDRNet + Mamba**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multicolumn{2}{c}{+SS} & & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \multirow{5}{*}{+SS2D [53]} & +AFF & 92.53 & 94.97 & - & - \\  & +RAM & 93.87 (+1.34) & 95.56 (+0.59) & +1.34 & +0.59 \\  & +AFF & 93.09 & 94.68 & \(-\)0.56 & \(-\)0.29 \\  & +RAM & 94.23 (+1.14) & 95.34 (+0.66) & +0.36 & \(-\)0.22 \\ \multirow{5}{*}{+OSSM [57]} & +AFF & 93.04 & 94.37 & +0.51 & \(-\)0.60 \\  & +RAM & 94.42 (+1.38) & 95.48 (+1.11) & +0.55 & \(-\)0.08 \\ \multirow{5}{*}{+HCS} & +AFF & 94.68 & 97.02 & +2.15 & +2.05 \\  & +RAM & 95.96 (+1.28) & 97.68 (+0.66) & +2.09 & +2.12 \\ \hline \multicolumn{2}{c}{LDMNet + Mamba} & & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \multirow{5}{*}{+SS} & +AFF & 93.43 & 95.42 & +0.90 & +0.45 \\  & +RAM & 94.12 (+0.69) & 95.80 (+0.38) & +0.25 & +0.24 \\ \multirow{5}{*}{+SS2D [53]} & +AFF & 94.17 & 95.94 & +1.64 & +0.97 \\  & +RAM & 95.34 (+1.17) & 96.84 (+0.90) & +1.47 & +1.28 \\ \multirow{5}{*}{+OSSM [57]} & +AFF & 94.47 & 96.02 & +1.94 & +1.05 \\  & +RAM & 95.06 (+0.59) & 96.51 (+0.49) & +1.19 & +0.95 \\ \multirow{5}{*}{+HCS} & +AFF & 95.32 & 97.38 & +2.79 & +2.41 \\  & +RAM & 96.16 (+0.84) & 97.92 (+0.54) & +2.29 & +2.36 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the enhancement effects of RAM and AFF [28] on different Backbones on MaSTr1325, where SS represents the Sequential Scan Module, SS2D [53] denotes the 2D Selective Scan Module, and OSSM [57] signifies the Omnidirectional Selective Scan Module. \(\delta\) is the change obtained by subtracting the corresponding items of AFF from RAM, with DDRNet + Mamba (SS) as the comparison object. “-” indicates that the current row is used as the baseline.  
incorporating all three modules results in an improvement of 1.6% in mIoU and 1.15% in mPA, validating the effectiveness of these modules.
## 3 Results and Discussion
### Fourier-Transformed Infrared Spectroscopy (FTIR)  
FTIR spectra of both raw and treated biomasses are displayed in Figure 3. A fairly wide band can be observed around 3350 cm\({}^{-1}\), often associated with aliphatic and aromatic O-H stretching vibration found in hydroxyl and carboxylic groups or traces of water in the solids analyzed [67]. Bands in the interval 2800-3000 cm\({}^{-1}\) are due to stretching and bending vibrations of CH [68] for methyl and methylene groups.  
In addition, visible bands at 1625 cm\({}^{-1}\) and 1745 cm\({}^{-1}\) appear in both original biomass and GVL treated material, attributed to vibrational stretching of the C=O [68] from acetoxy groups of xylans. These bands are less noticeable in the case of ethanol-treated solids and practically non-existent when acetone is used in the process. Apart from this, the peak corresponding to 1240-1250 cm\({}^{-1}\) related to the stretching of the C-O bond in hemicellulose acetyl esters [69; 70] is only significant in the case of starting biomass and GLV-treated solids. Therefore, this would confirm the data given in Figure 1, where there is no hemicellulose in olive-tree pruning biomass treated under optimal conditions with ethanol and, especially, with acetone.  
Figure 2: X-ray diffraction (XRD) pattern for both raw material and solid from organosolv treatments at optimal conditions.  
Additionally, the signals located in the range from 1245 cm\({}^{-1}\) to 1700 cm\({}^{-1}\) are related to the lignin moiety [71; 72], due to aromatic ring vibrations and surroundings (guaia-cyl propane units, syringyl ring breathing, C-H deformations, and C=O stretching in conjugation to aromatic rings), suggesting a lower lignin content for acetone-treated solids.  
Finally, apart from 1160-1163 cm\({}^{-1}\) band, there is a peak at 1035 cm\({}^{-1}\) with an in-plane ring stretching at 1114 cm\({}^{-1}\), which is frequently associated with cellulose (stretching of the CO-(C/H) in alcohols and ethers) [73; 74]. Their clear presence in the spectral representations of solids pretreated with the three solvents (GVL, acetone, and ethanol) under optimized conditions encourages the idea that cellulose is the major structural polymer in all solids after microwave-assisted organosolv treatments.
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 5 Experiment and Results
### Results
As the environment changes from easy to difficult, the coverage radius also starts decreasing, leading to less user coverage as can be seen from Figures 7-10. Among all the environments, high-rise urban has had the least improvement in user coverage. In Figure 10a-d, there is little to no change in user coverage, and it can be considered that given the constraints and environment, there can be no better solution that can be found in that particular scenario. Dense urban, while still being a challenging environment, has had some improvement in coverage radius in all scenarios. Figure 9: User coverage for dense urban environments. Among all the implemented algorithms, PSO implementation has benefited the most from parameter tuning and a phased restart. RS values remain consistent throughout the experiments, although the placement is random; after averaging a thousand placements, the results achieved have reached a stable value. Optimized results for PSO, DE and DFO have reached nearly the same user coverage in all environments, and hence, the results obtained can be perceived as the \"feasible peak\" in that scenario, which is an improvement over the default algorithm configuration. Finally, a Friedman test was conducted for all environments; the results are presented in Table 6.  
The Friedman test shows that there are no significant differences between the implemented algorithms with the introduction of a phased restart and parameter tuning except with RS, which is to be expected. Highlighted in red are the scenarios where there is a significant difference in performance between the implemented algorithms. The rest are cases where all the algorithms have reached a similar level of performance. With the addition of more constraints or more complicated methods such as power efficiency or resource allocation, dynamic users, etc., there is expected to be a much greater difference between the implemented algorithms. Figure 10: User coverage for high-rise urban environments. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{Suburban 90} & \multicolumn{13}{c}{Ufhn 90} & \multicolumn{13}{c}{Darse urban 90} & \multicolumn{13}{c}{High-rise urban 90} \\ \hline \multicolumn{13}{c}{\(p\)-values} & \multicolumn{13}{c}{23.10} & \multicolumn{13}{c}{26.31} & \multicolumn{13}{c}{27.00} & \multicolumn{13}{c}{30.00} \\ \cline{2-13} \multicolumn{13}{c}{\(p\)-values} & \multicolumn{13}{c}{38.40 \(\pm\)10\(\%\)} & \multicolumn{13}{c}{82.82 \(\pm\)10\(\%\)} & \multicolumn{13}{c}{58.98 \(\pm\)10\(\%\)} & \multicolumn{13}{c}{138.80 \(\pm\)10\(\%\)} \\ \hline AgeWithin & DFO \(\mathrm{PSO}\)
## 2 Methodology
### Multi-Objective Constrained Optimization
#### 2.1.2 The Steps of NSGA-II in the Current Work  
The implementation of NSGA-II follows the steps mentioned below. Its pseudo-code is mentioned in Algorithm 1.  
1. Parent Initialization: Initialize \(\mu\) parent vectors randomly.
2. Evaluation: Evaluate the objective functions and constraints for all parent vectors.
3. Constraint Handling: Discard infeasible individuals as needed.
4. Offspring Generation: Apply recombination and mutation to generate \(\lambda\) offspring.
5. Evaluation: Evaluate the objective functions and constraints for offspring.
6. Non-dominated Sorting: Rank all individuals (parents and offspring) based on Pareto dominance.
7. Crowding Distance Calculation: Maintain diversity by calculating the crowding distance for each individual.
8. Parent Selection: Select the top \(\mu\) individuals based on non-dominance and the crowding distance for the next generation.
9. Convergence Check: Continue until the convergence criteria, such as a stable Pareto front, are met or the maximum number of generations is created.  
The optimization stops when the improvement in the Pareto front becomes negligible over several generations, indicating convergence, or when the maximum number of generations is created.  
By incorporating NSGA-II, the proposed method efficiently handles multiple objectives while maintaining a diverse set of feasible solutions. This approach is particularly well suited to problems where trade-offs between conflicting design goals are necessary. The optimization is implemented in Python 3.8.3 using a Python-based library: _pymoo_ Multi-objective Optimization in Python [22]. It is a framework that offers state-of-the-art single- and multi-objective optimization algorithms in Python.  
```
Input: Population size (\(\mu\)), Offspring size (\(\lambda\)), Max generations (\(N\))  Initialize population \(p\) with size \(\mu\) for each individual in \(p\)do  Randomly initialize decision variables  Evaluate objective functions and constraints endfor whilegeneration \(<N\)do  Offspring generation:  Generate \(\lambda\) offspring using recombination and mutation  Evaluate objective functions and constraints for offspring  Combine populations:  Create a combined population \(q=p\cup\) Offspring  Non-dominated sorting:  Sort individuals in \(q\) based on Pareto dominance into different fronts \(F_{1},F_{2},...,F_{n}\)  Crowding distance calculation:  Calculate crowding distance for each individual in each front  Selection:  Select top \(\mu\) individuals from \(q\) based on non-dominance (starting from \(F_{1}\)) and crowding distance  Update population:  Set the new population \(p\) to the selected \(\mu\) individuals  Check for convergence: if convergence criteria (e.g., stable Pareto front or max generations reached) are met then  stop endif  Increment generation by 1 endwhile Output: Pareto-optimal solutions
```  
**Algorithm 1** NSGA-II