esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Verification by Comparison with Previous Experimental Results
### Validity of a Perfect Plastic Collision
#### 3.1.2 Comparison Between the Estimated and Measured Maximum Deflections  
The maximum deflection \(\delta_{\mathrm{u}}\), assuming a perfect plastic collision, was calculated as the satisfying deflection Equation (16) based on the energy conservation concept. For comparison, the maximum deflection \(\delta_{\mathrm{uk}}\), assuming no energy loss, was also calculated using Equation (17). Figure 5 shows a conceptual diagram of \(\delta_{\mathrm{u}}\) and \(\delta_{\mathrm{uk}}\) obtained with \(E_{\mathrm{t}}\) and \(E_{\mathrm{k}}\), respectively, using the calculated \(P\)-\(\delta\) curve.  
From Figure 5, it can be seen that \(\delta_{\mathrm{u}}\) is smaller than \(\delta_{\mathrm{uk}}\). This means that in the case of considering a perfect plastic collision, energy is lost when a weight collides.  
\[E_{\mathrm{t}}\ =\ \left(M_{\mathrm{w}}/\left(M_{\mathrm{be}}\ +\ M_{\mathrm{w}} \right)\right)\ E_{\mathrm{k}}\ =\ \int_{0}^{\delta_{u}}P(\delta)d\delta \tag{16}\]  
\[E_{\mathrm{k}}\ =\ \int_{0}^{\delta_{\mathrm{uk}}}P(\delta)d\delta \tag{17}\]  
Figure 6 compares the estimated maximum deflections \(\delta_{\mathrm{uk}}\) and \(\delta_{\mathrm{u}}\) based on \(E_{\mathrm{k}}\) and \(E_{\mathrm{t}}\), respectively, with the experimental results of \(\delta_{\mathrm{u.exp}}\). The figure shows that the estimated maximum deflection \(\delta_{\mathrm{uk}}\) based on \(E_{\mathrm{k}}\) significantly exceeds the experimental value and that this difference increases when the deflection is larger. In contrast, the estimated maximum deflection \(\delta_{\mathrm{u}}\) based on the transmitted energy \(E_{\mathrm{t}}\) is generally larger than the experimental value; however, it is closer to the experimental value than in the case of \(\delta_{\mathrm{uk}}\). This indicates that the maximum deflection \(\delta_{\mathrm{u.exp}}\) can be determined accurately and conservatively by using the transmitted energy \(E_{\mathrm{t}}\).  
Consequently, \(\delta_{\text{u,exp}}\) can be predicted considering the energy loss, due to the complete plastic collision between the weight and RC beam. This is because the target RC beam is a bending-failure type and is plastically deformed; therefore, the collision with the weight behaves like a nearly perfect plastic collision. On the other hand, in some cases, \(\delta_{\text{u}}\) greatly exceeds \(\delta_{\text{u,exp}}\). This is especially evident when the amount of deformation is large. It is assumed that this occurs because the RC beam has reached a region, in which the assumption of plane conservation does not hold. In the next section, based on this investigation, the scope of application of the plane-holding assumption is examined.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 1 Introduction
This paper comprehensively considers the factors that affect the quality of WSN monitoring, such as data collection time, sensor association and cluster heads (CHs) selection. A joint acquisition time and sensor association optimization algorithm (ATSAO) is proposed to prolong the lifetime of the WSN and enhance the stability of monitoring. The optimal topology and collection time control strategy are obtained. 2. The joint optimization problem is formulated as a multi-constrained mixed integer programming problem, and an effective iterative algorithm is proposed based on block coordinate descent (BCD) technology to obtain its sub-optimal solution to achieve WSN energy consumption minimization and maximize the satisfaction of collecting data to extend the lifetime of the sensor network and ensure the accuracy and reliability of monitoring. 3. The sensor association is modeled as a 0-1 multi-knapsack optimization problem. The methods with different complexity are proposed to address the problem, and their performance differences are compared by simulation so that they can be selected according to the actual needs in the project. The rest of this paper is organized as follows: Section 2 summarizes the literature review on WSN. In Section 3, we introduce the WSN system model for microgrid status monitoring. Section 4 constructs a multi-constrained mixed-integer optimization problem and propose an efficient multi-iterative algorithm to solve it. In Section 5, the simulation results of the algorithms in this paper are presented. Compared with the existing work, the uniqueness of this paper is discussed in Section 6. Finally, we conclude the paper in Section 7.
## 3 Coherent \(\pi\)-Electron Angular Momentum and Current of a Molecule Having Two Aromatic Rings
### Two Ring Components of \(\pi\)-Electron Angular Momentum and Current
#### 3.1.6 Bond Currents and Time Evolution of Coherent Ring Current  
Figure 11 shows the magnitudes of bond current, \(J_{ij}\), which were calculated using Equation (42) at the maximum coherence time together with the directions of bond currents for the three types of electronic coherence in the first period of oscillation. The arrow along each bond of (\(P\))-2,2'-biphenol denotes the initial direction of the bond current. Magnitudes of the averaged ring current at the maximum coherence time are of an order of tens of \(\mu\)A: \(\overline{J}=\) 161, 86.5 and 63.4 \(\mu\)A for (\(b_{1}+b_{2}\)), (\(a+b_{1}\)),  
Figure 10: Time-dependent coherent angular momentum of (\(P\))-2,2′-biphenol for three types of electronic coherence. Modified from [87] with permission of the American Institute of Physics. The pulse form for (a + b\({}_{2}\)) electronic coherence in [87] should be replaced by that in the figure.  
and \((a+b_{2})\) coherences, respectively [87]. If the phenol ring is assumed to be a ring with radius \(r\) and the magnitude of the angular momentum \(l=rp\) with linear momentum of electrons, \(p\), is given, the ring current \(J\) can be estimated by using the relation \(2\pi rJ=ep/m_{e}\). The magnitudes of the ring currents for the \((b_{1}+b_{2})\), \((a+b_{1})\), and \((a+b_{2})\) coherences are 216, 163.5 and 25.5 \(\upmu\)A, respectively. The magnitude of each ring current is large compared with that of the corresponding averaged ring current except for \((a+b_{2})\) coherence. The difference in the magnitudes between the averaged bond current at the maximum coherence time and the ring current calculated within a perfect ring model mainly originates from the fact that phenol ring has a functional OH group. [PERSON] et al. reported an electronic current of 84.5 \(\upmu\)A for Mg porphyrin excited by a circularly polarized UV intense laser pulse to yield population inversion [51]. It is interesting that \((P)\)-2,2\({}^{\prime}\)-biphenol and Mg porphyrin have the same order of magnitude in ring currents, though they have different numbers of electrons and different radii of their rotation.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 5 Results and Validation
### YOLOv12 vs. YOLOv11: Performance Evaluation on the Proposed Dataset  
To analyze the performance improvement from the previous version (YOLOv11) to the latest one (YOLOv12), a YOLOv11 model was also trained. The performance comparison between the two versions was carried out based on the results obtained during the final training epoch (epoch 20) (see Table 5), as well as through evaluation on the test set (see Table 6). This approach ensures an objective and relevant comparison under real-world conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Conf** & **IoU** & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline
0.30 & 0.50 & 0.996 & 0.961 & 0.978 & 0.980 & 0.774 \\
0.50 & 0.50 & 0.996 & 0.961 & 0.978 & 0.980 & 0.776 \\
0.70 & 0.50 & 1.000 & 0.792 & 0.884 & 0.896 & 0.730 \\ \hline \hline \end{tabular}  
* Metrics are computed on the test set using the specified confidence and IoU thresholds.  
\end{table}
Table 4: Evaluation results of the model on the test set.  
Figure 11: R_curve and F1_curve for YOLOv12s: (**a**) R_curve. (**b**) F1_curve.  
Comparing the final training epoch of the two models, YOLOv12 demonstrated better overall performance than YOLOv11. Both models achieved very high precision, but YOLOv12 showed a higher recall and a better F1 score, indicating a more balanced detection capability. Additionally, YOLOv12 attained a higher [EMAIL_ADDRESS]. Although YOLOv11 had a slight edge in [EMAIL_ADDRESS]:0.95, YOLOv12 proved to be more stable and efficient overall.  
The final evaluation on the test set confirms that YOLOv12 achieved superior performance compared to YOLOv11 for the analyzed task. YOLOv12 outperformed YOLOv11 in four out of five evaluation metrics (precision, F1 score, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95), while matching it in recall. Notably, YOLOv12 achieved a 2.3% increase in precision (from 97.4% to 99.6%) and a 1.1% improvement in F1 score (from 96.7% to 97.8%), demonstrating a reduction in false positive detections. Both YOLOv11 and YOLOv12 were trained and evaluated using the same proprietary dataset of 744 primary images, with on-the-fly augmentation during training resulting in approximately 14880 virtual samples (virtual images) over 20 considered training epochs.  
Both models exhibited the same recall, indicating a similar capability to detect all relevant objects in the images.  
The F1 score, which balances precision and recall, was 0.978 for YOLOv12s compared to 0.967 for YOLOv11s, confirming an advantage of the newer version.  
YOLOv12s also had a higher [EMAIL_ADDRESS] and a better [EMAIL_ADDRESS]:0.95, indicating improved localization accuracy.  
In conclusion, YOLOv12 offers higher accuracy and more reliable detection than YOLOv11, making it more suitable for applications where minimizing classification errors is critical.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results
### Signal Classification Accuracy
#### 3.2.1 Efficiency of the \"Bump\" Wavelet in Feature Extraction  
The bump wavelet was chosen due to its structural affinity with the underwater acoustic signals of interest. Its ability to effectively decompose signals and extract salient features was paramount in the acoustically challenging underwater environment. Iterative testing and optimization of the \"voices per octave\" parameter within the MATLAB Wavelet Toolset underscored the importance of high-resolution analysis in effective feature extraction. The term voices per octave (VPOs) refers to the number of divisions within each octave of frequency when analyzing a signal. An octave represents a doubling in frequency, and more voices within an octave imply a finer resolution, which allows for a more detailed capture of signal nuances that are especially important in the varied and noise-prone underwater environment.  
* **Iterative testing and parameter optimization**: A pivotal aspect of the methodological approach involved iterative testing and optimization of the \"voices per octave\" parameter within the MATLAB Wavelet Toolset. This parameter fine tuning was instrumental in enhancing the frequency resolution of the analysis, allowing for a nuanced examination of the signal characteristics at various scales.
* **Resolution impact and signal analysis performance**: Optimizing the VPO facilitated a high-resolution exploration of the signals, enabling the detection of intricate features that are often masked by noise or the signal transient nature. The bump wavelet performance, bolstered by the precise calibration of the VPO, underscored its effectiveness in the feature extraction process, thereby affirming its selection for the analysis carried out.
# FEM-Based Compression Fracture Risk Assessment in Osteoporotic Lumbar Vertebra L1
## 2 Methods and Materials  
### Problem Formulation  
In order to model bone tissue as elastoplastic three-dimensional solid, the Ramberg-Osgood stress-strain equation was applied [25]:  
\[\varepsilon=\frac{\sigma}{E_{0}\varepsilon^{d}}+\alpha\left(\frac{\sigma}{K} \right)^{n}\varepsilon^{b} \tag{1}\]  
where \(E_{0}\) is the modulus of elasticity, \(K\) is the strain hardening coefficient, \(n\) is the strain-hardening exponent, and \(\alpha\) is the yield offset, where \(\varepsilon=1\) equality was used. According to research [25], the loading rate of cortical bone influences the behaviour of the stress-strain relation. Therefore, our model of risk evaluation should be treated as the upper risk limit for load rates, which do not exceed rates of \(\varepsilon\leq 1\).  
In this work, the von Mises-Hencky criterion is chosen to predict the fracture of the model. It is defined in Equation (2): There, \(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\) are the maximum, intermediate and minimum principal stresses. \(\sigma_{Y}\) presents yield stress (40 MPa) [26].  
\[\sqrt{\frac{(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3})^{2}+(\sigma_{ 3}-\sigma_{1})^{2}}{2}}=\sigma_{Y} \tag{2}\]  
In the case of nonlinear analysis, the equilibrium equations are presented at time step \(t+\Delta t\):  
\[\mathbf{M}^{t+\Delta t}\mathbf{U}^{\prime\prime(i)}+\mathbf{C}^{t+\Delta t} \mathbf{U}^{\prime(i)}+{}^{t+\Delta t}\mathbf{K}^{(i)t+\Delta t}\Delta\mathbf{ U}^{(i)}={}^{t+\Delta t}\mathbf{R}-{}^{t+\Delta t}\mathbf{F}^{(i-1)} \tag{3}\]  
where \(\mathbf{M}\) is the mass matrix, \(\mathbf{C}\) is the damping matrix, \({}^{t+\Delta t}\mathbf{K}^{(i)}\) is the stiffness matrix, \({}^{t+\Delta t}\mathbf{R}\) is the vector of nodal loads, \({}^{t+|detlat}\mathbf{F}^{(i-1)}\) is the vector of nodal forces in case of iteration iteration \((i-1)\), \({}^{t+\Delta t}\Delta\mathbf{U}^{(i)}\) is the vector nodal displacements while the iteration is \((i)\), \({}^{t+\Delta t}\mathbf{U}^{\prime(i)}\) is the vector of total velocities while the iteration is \((i)\), and \(\mathbf{M}^{t+\Delta t}\mathbf{U}^{\prime\prime(i)}\) is the vector of total accelerations while the iterations is \((i)\). Using an implicit time integration Newmark-Beta scheme and employing [PERSON]'s iterative method, the presented equations are cast in the following form:  
\[{}^{t+\Delta t}\mathbf{K}^{(i)t+\Delta t}\Delta\mathbf{U}^{(i)}={}^{t+\Delta t }\mathbf{R} \tag{4}\]  
where \({}^{t+\Delta t}\mathbf{R}^{(i)}\) is the effective load vector and \({}^{t+\Delta t}\mathbf{K}^{(i)}\) is the effective stiffness matrix. The three-dimensional static and dynamic analyses were performed using Abaqus (c) [27] software.
# Complex Battery Storage Fire Propagation Translational Forensic Study Using Cellular Automata
## 3 Results
### Fire Propagation Probabilistic Risk Analysis  
The primary objective of this dynamic modeling simulation is to establish the probability and rate of fire spread (in a relative time context) of the battery module. Assigning a probability range between 0 and 1 for fire propagation enables the analysis to calculate both the burning timesteps and the proportion of the battery module affected. However, this probabilistic analysis approximates the likelihood of directional spread originating from a randomly chosen fire source within a densely packed 2-D battery module. While each cell maintains a constant burning duration, the rate of propagation depends on the pathway of spread and the batteries that remain unburnt. It is noteworthy that this study involved 100,000 randomly generated simulations. This extensive number of simulations not only refines prior research but also significantly improves the accuracy in assessing the probability of fire spread.  
Figure 10: 18650 multi-cell test setups.  
The main simulation function is invoked to implement the updated parameters. The desired outcomes include a fire damage map alongside the maximum percentage burnt, minimum percentage burnt, average percentage burnt, maximum timesteps, minimum timesteps, and average timesteps for each step. Figure 11 shows an example of the fire map where the fire's origin is marked with an \"*\". The batteries that are already burnt are represented by a \".\", while the batteries that remain unburnt are indicated by an \"X\".  
Figure 12 illustrates the average percentage of burnt batteries for different probabilities. At a probability of approximately 0.6 of a single cell igniting, nearly the entire battery module is burnt (-100 percent). Conversely, if the propagation probability remains at or below 0.4, a fire starting in a single cell will not extend throughout the entire module.  
Furthermore, the timesteps required for the battery pack to be fully burnt exhibit substantial variability, as demonstrated in Figure 13. This variance appears to be directly linked to the potential fire scenarios and aligns with the percentage of the batteries consumed by fire. On average, approximately 42 timesteps are necessary for the battery pack to be fully burnt.  
Figure 11: Example of map of fire damage in simulation: Fire started at “*\"; burnt batteries are indicated by “.”; and unburnt batteries are indicated by “X”.  
Figure 12: Impact of cell ignition probability on battery burns.
# A Heuristic Approach for a Real-World Electric Vehicle Routing Problem
## 3 Problem Description  
The problem can be defined on a complete directed graph \(G=(V,\,A)\) with a set of nodes \(V=\{0\}\cup N\cup F\) and a set of arcs \(A=\{(i,\,j)\mid i,\,j\in V,\,i\
eq j\}\). Node 0 is the distribution center (also called the depot), the set \(N=\{1,\,2,\,\ldots,\,1N\,|\}\) is the set of customers, and the set \(F=\{\,|\,N\,|\,+1,\,\ldots,\,\,|\,N\,|\,+\,|\,F\,|\}\) is the set of charging stations. The travel time and distance on arc \((i,\,j)\) are denoted by \(t_{ij}\) and \(d_{ij}\), respectively. Each customer \(i\in N\) has a load \(q_{i}\), which is positive for a pickup request and negative for a delivery request, a service time \(t_{i}\), and a time window \([a_{i},\,b_{i}]\), where \(a_{i}\) and \(b_{i}\) are the earliest and latest allowable start service times, respectively. Each customer should be visited exactly once. A fleet of heterogeneous electric vehicles is available at Node 0. The fleet composes \(K\) different types of vehicles. Each vehicle type \(k=\{1,\,2,\,\ldots,\,|\,K\,|\,\}\) has a maximal load capacity \(Q^{k}\), a maximal driving range \(D^{k}\) (due to limited battery capacity), a charging time \(t_{c}^{k}\), a charging cost \(c_{c}^{k}\) per unit of time, an acquisition cost \(c_{f}^{k}\) if it visits any customer, and a travel cost \(c_{v}^{k}\) per unit of distance. Each vehicle departs from Node 0 after a given time \(a_{0}\), serves some customers, and finally ends at Node 0 before a specified time \(b_{0}\). The vehicle capacity \(Q^{k}\) must be respected at customer \(i\). While in transit, the vehicle can return to Node 0 to charge, reload parcels or unload parcels (i.e., multi-trips) if necessary. The total travel distance of a vehicle after the last charge should not exceed its maximal driving range \(D^{k}\). Each vehicle should start the service of a customer within the given time windows. If the vehicle arrives earlier than \(a_{i}\), a waiting cost proportional to the waiting time is incurred. Meanwhile, when the vehicle returns to Node 0 to prepare for the next trip (including charging), a given waiting time \(t_{w}\) is spent, which also incurs a waiting cost proportional to the waiting time. The parameter \(c_{w}\) is the cost per unit of waiting time. Each vehicle should go to one of the charging stations before it runs out of electricity and charge to full capacity in its predefined charging time \(t_{c}^{k}\)[24].  
The problem consists of determining a route plan for satisfying customers' demands while minimizing the sum of the acquisition costs, travel costs, charging costs, and waiting costs at customers and the distribution center. It contains four aspects: (1) the vehicle type assigned to each customer; (2) the visiting sequence of customers; (3) the charging station or distribution center and time chosen by the vehicle if it needs recharging; and (4) the departure time when the vehicle should first leave the distribution center.  
For a detailed description and mathematical formulation of the proposed problem, interested readers are referred to similar papers [6; 19].
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## Appendix A Channel Models
### Air-to-Air (A2A) Communication Channel Model  
The free-space channel model is used for UAV-to-UAV communication. When \(U_{i}\) transmits signals to \(U_{j}\), the received power at \(U_{j}\) is expressed as  
\[P_{U_{L},U_{j}}(t)=P_{tx,U_{i}}+G_{tx,U_{i}}+G_{rx,U_{i}}-PL_{FS,U_{L},U_{j}}(t) \tag{11}\]  
where  
\[PL_{FS,U_{i},U_{j}}(t)=10 log(4\pi d_{U_{i},U_{j}}(t)f/c)^{\rho} \tag{12}\]
## 2 Advances and Achievements in Seed Biotechnologies in the Decade 2014-2024
### Seed Quality Improvement Through Biotechnological Innovations
#### 2.2.1 Synthetic Seed (Artificial Seed) Technologies  
One of the advanced approaches in plant biotechnology, synthetic seeds are produced from plant embryos and have gained widespread attention. In plant cell cultures, encapsulation and micropropagation are co-integrated to develop synthetic or artificial plant seeds [76]. Mostly used as a seed substitute, a synthetic seed comprises encapsulated vegetative buds, somatic embryos, or vegetative plant materials; germinates into plantlets under in vitro or in vivo conditions; and maintains regeneration potential after cryostorage [76]. This is an ideal method for the germplasm conservation of elite plant varieties and transportation and is successfully applied to multiple plant species of socio-economic value. Synthetic seed production includes multiple steps, namely the cultivation of plant material in tissue culture, somatic embryogenesis, the encapsulation of plant embryos in a protective coating of gelling agents, the cultivation and maturation of the encapsulated embryos on growth medium, and the storage of synthetic seeds in a dry and cool space, followed by germination [77]. The plant clones produced as artificial seeds are used in place of expensive F1 hybrids for many crop species and improve ex vitro acclimatization and scaling up in vitro plant cultures [77]. In recent times, artificial seed production has employed different plant materials, such as shoot tips, somatic embryos, nodal segments, auxiliary buds, protocorm-like bodies (PLBs), and embryogenic callus [78]. The advantages of artificial seed production include long-term storage, easily manageable, genetic homogeneity, cost-effectiveness, and improved germplasm conservation [79]. In plants of socio-economic importance, artificial seed production has been investigated, namely medicinal plants, ornamentals, vegetable crops, fruits, orchids, etc. [80; 81; 82; 83; 84; 85; 86; 87]. The method has been pivotal in seedless watermelon and seedless grapes, plants that cannot produce seeds, and essential for plant proliferation [79]. Moreover, artificial seed production has also been achieved in plants namely pineapple (_Anantas comosus_ ([PERSON]) [PERSON]), breadruit (_Artocarpus altilis_ [PERSON][PERSON]), citrus plants, _Raurolfia tetraphylla_ L., etc. Artificial seed production holds immense potential to benefit crops and increase productivity; however, further research is required on the cryopreservation capabilities of artificial seeds and cultivation on commercial levels.