esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This work presented an optimization energy management framework for an AC microgrid using the MVO. The master-slave strategy combined MVO in the master stage with a Successive Approximations (SAs) power flow approach in the slave stage, enabling a robust search for optimal battery energy storage system scheduling. Two operating conditions were addressed: (i) grid-dependent and (ii) self-powered. The key conclusions are as follows:  
* Technical and environmental performance: Numerical tests on a 33-node microgrid revealed that the proposed Parallel MVO consistently led to the best performance in both energy losses and CO\({}_{2}\) emissions reduction. In the grid-dependent scenario, energy losses were reduced to approximately 2374.85 kWh from a base of 2484.57 kWh, while CO\({}_{2}\) emissions were cut to near 9.8693 TonCO\({}_{2}\) from 9.8874 TonCO\({}_{2}\). For the self-powered mode, losses reached about 2373.53 kWh (compared to 2484.57 kWh in the base case), and emissions were held at 16.0364 TonCO\({}_{2}\) (down from 16.0659 TonCO\({}_{2}\)). Compared to other metaheuristics, the PMVO exhibited lower standard deviation values, reflecting high consistency and reliability.
* Voltage regulation and loadability: The PMVO-based dispatch ensured that all voltage magnitudes and line currents remained within prescribed limits. Even under the self-powered mode, where grid support was absent, voltage regulation stayed below the 10% threshold, and no line exceeded 100% of its thermal rating.
* BESS scheduling: The daily charge and discharge cycles for each BESS unit indicated efficient usage of storage capacity. No state-of-charge violations were observed, and batteries were charged in periods of available surplus generation and discharged mainly during higher load intervals. This helped reduce technical losses and emissions without compromising power quality.  
In addition to the primary tests on the 33-node system, the proposed methodology was further validated on a large-scale 136-bus microgrid to demonstrate scalability and robustness. The results confirmed that the PMVO algorithm maintains its superior performance even under significantly more complex topologies and operating scenarios. Specifically, the algorithm successfully minimized both energy losses and emissions while adhering to operational constraints, such as voltage profiles and thermal line limits, across a higher-dimensional decision space. This extended validation highlights the algorithm's adaptability and positions it as a viable solution for real-world deployment in medium- to large-scale microgrid applications.
# Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet
## 4 Experiment and Analysis
### Ablation Experiments  
To balance all aspects of the model's evaluation metrics as well as the detection speed and parameters. We conducted a sequence of trials not only to prove the efficacy of the modules but also to consider the lightweight model and the high accuracy detection required by the actual edge device deployment process. We validated some major modules inside the model, including the Transformer, the attention module, and the modified PANet. We performed experiments on these three main components separately and then in two-by-two combinations. The findings of these tests are outlined in Table 3. Here, P stands for precision, R represents recall, mAP denotes mean average precision, Para. refers to the number of model parameters, and GFlop indicates the number of gigaflops (billions of floating-point operations per second). These parameters are also applicable in Table 4.  
We can conclude from the ablation experiments that the accuracy value of the lightweight MobileNetV3 network in crack detection did not meet the detection requirements, but the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Models** & \(\mathbf{p}\) & **R** & **mAP** & **Para.** & **GFlop** & **Weight** \\ \hline A & YOLOv3 & 0.69 & 0.738 & 0.792 & 8666692 & 12.9 & 17035 \\ B & YOLOv4 & 0.802 & 0.81 & 0.821 & 6056606 & 16.5 & 47435 \\ C & YOLOv5s & 0.838 & 0.876 & 0.893 & 7012822 & 15.8 & 14115 \\ D & YOLOv7 & 0.833 & 0.85 & 0.914 & 6014988 & 13.2 & 12001 \\ E & SSD & 0.784 & 0.723 & 0.736 & 11136374 & 13 & 92782 \\ F & RT-DETR & 0.903 & 0.842 & 0.921 & 19873044 & 56.9 & 39518 \\ G & Ours & 0.874 & 0.831 & 0.908 & 3122326 & 4.0 & 6515 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison experimental results.  
Figure 8: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Models** & \(\mathbf{p}\) & **R** & **mAP** & **Para.** & **GFlop** \\ \hline A & M3 + PANet & 0.752 & 0.763 & 0.809 & 1630732 & 2.8 \\ B & A + TF (backbone) & 0.774 & 0.8 & 0.842 & 2310220 & 3.3 \\ C & B + SE (backbone) & 0.816 & 0.814 & 0.853 & 2834508 & 3.7 \\ D & C + TF (PANet) & 0.828 & 0.818 & 0.862 & 3099084 & 4.0 \\ E & (Neck) & 0.874 & 0.831 & 0.908 & 3122326 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments.  
combination of the Transformer for feature extraction of cracks significantly improved the performance in all aspects. The improvement of PANet using BoTNet also made the feature fusion better. The introduction of two attention mechanisms also enhanced the network's focus on the characteristics of cracks, and although SENet brought some parameters, the computational burden brought by these parameters was worthwhile after the integrated improvement of the detection effect.
## 2 Materials and Methods
### Particle Swarm Optimization-Based Human Detection and Initialization for Tracking  
#### 2.2.1 Particle Swarm Optimization  
The particle swarm optimization (PSO) method was designed for observing and simulating collective behaviors, such as those of a flock of birds or school of fish [35]. For every iteration, PSO allows the particles to progressively seek the optimal value. In other words, for each iteration we calculate the fitness value for a group of variables in each of the particles and move towards the nearest optimal value and continue this tightening pattern to progressively bring the particles closer to the optimal value.  
\[\begin{split} V_{i}(t+1)&=\omega V_{i}(t)+c_{1}r_{1 }(P_{IB}(t)-p_{i}(t))+c_{2}r_{2}(P_{GB}(t)-P_{i}(t))\\ P_{i}(t+1)&=P_{i}(t)+V_{i}(t+1)\end{split} \tag{3}\]  
where \(c_{1}>0\) and \(c_{2}>0\) represent the learning rate and are often set to the same value. \(r_{1}\in[0,1]\) and \(r_{2}\in[0,1]\) are random values related to learning rate components and \(\omega\) is the inertia value. \(V_{i}(t+1)\) is the velocity of particle \(i\), which indicates the difference between the optimal value and the fitness value of each particle, hence the greater the difference from the optimal value, the higher the velocity is and \(P_{i}(t+1)\) is the position of particle \(i\) at time \(t+1\). \(P_{GB}\) is the global best and represents the value that is closest to the optimal value among all the particles. \(P_{IB}\) is the individual best of each particle and represents, historically, the closest position of the \(i\)th particle. Thus, \(P_{GB}\) is updated when the \(P_{IB}\) of a particle is closer to the optimal value than \(P_{GB}\) is. Consequently, \(P_{GB}\) gets closer to the optimal value with every iteration.  
Figure 3: The extracted depth maps: (**a**) the person in the image is about 5 m away from the camera and (**b**) the person in the image is about 2 m away from the camera.
## 5 Results and Discussion
### Comparison and Discussion
#### 5.3.2 Comparison between Load Models/Effects  
The load value of the optimized model is compared with the specification model and the extreme load effect scenario model, as shown in Table 7. It can be seen from the table that, compared with the specification model, the value of the uniformly distributed load in the optimized model of the three representative suspenders is reduced, and the decrease is from 1.6 kN/m to 4.1 kN/m. The value of concentrated force is significantly increased, which can reach four times that of the value of the specification model. Compared with the extreme load effect scenario model, in terms of the uniformly distributed load, the load value of the optimized model has increased to a different extent. In terms of the concentrated force, the load value has been relatively close to that of the extreme load effect scenario model, indicating that the effect of the concentrated force has been reasonably considered in the extreme load effect scenario model. To explain the results further, the increase in the value of the uniformly distributed load in the optimized model is mainly an adjustment to ensure that the optimized model has a reasonable safety margin for the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Suspender** & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) \\ \hline HMM & 0.98 & 1.02 & 0.90 & 0.61 & 3.98 \\ HSL & 0.50 & 0.60 & 0.90 & 0.85 & 3.65 \\ HSS & 0.90 & 1.30 & 0.90 & 0.73 & 3.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimized values of optimization variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Suspender** & **Calculation Scheme** & **Uniformly Distributed Load (kN/m)** & **Concentrated Force (kN)** \\ \hline HMM & 6.4 & 1433 \\ HSL & 8.9 & 1314 \\ HSS & 7.7 & 1426 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimization results of traffic load model for suspenders.  
extreme load effect of the suspender after considering the role of the adjustment coefficient of effect comprehensively.  
The effect under the action of the optimized model is calculated and compared with the extreme load effect (\(E_{\text{EV}}\)) obtained by the extrapolation method, and the growth rate of effect corresponding to each optimal solution is shown in Figure 9. As can be seen from the figure, the growth rate is positive, indicating that the optimized model can well contain the adverse effects that may occur during the design reference period of the suspenders. Among them, the growth rate of the HMM is between 6.4% and 27.4%, the growth rate of the HSS is between 8.8% and 17.0%, and the growth rate of the HSL is no more than 5.9%. The load effect level and safety margin for the extreme load effect of the optimized model are in accordance with the mechanical characteristics of each representative suspender, which is a result with a reasonable optimization effect and practical applicability.
## 3 Results
### Cephalometric Analysis  
Descriptive data of cephalometric measurements before and after treatment are shown in Table 2.  
Paired \(t\)-test results (Table 3) showed that there is a statistically significant difference towards an increase between the first and the second measurement of the SNB angle (\(p\) = 0.005), the CoGo (\(p\) < 0.001), the Ls:E (\(p\) = 0.040), and the pro \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**T0**} & \multicolumn{2}{c}{**T1**} \\ \hline  & **M** & **SD** & **M** & **SD** \\ \hline SNA (\({}^{\circ}\)) & 82.17 & 4.30 & 81.99 & 4.30 \\ SNB (\({}^{\circ}\)) & 76.97 & 3.54 & 78.19 & 3.97 \\ ANB (\({}^{\circ}\)) & 5.21 & 2.11 & 3.81 & 1.67 \\ Wits (mm) & 3.28 & 3.19 & 0.79 & 2.49 \\ ANPC (\({}^{\circ}\)) & 8.35 & 5.65 & 5.35 & 4.80 \\ SN:NL (\({}^{\circ}\)) & 8.43 & 3.10 & 8.12 & 3.16 \\ MaxMandAngle (\({}^{\circ}\)) & 20.77 & 5.93 & 19.98 & 6.53 \\ NSA (\({}^{\circ}\)) & 124.33 & 4.07 & 124.30 & 4.53 \\ SArGo (\({}^{\circ}\)) & 142.66 & 5.97 & 142.52 & 6.08 \\ MeGoAr (\({}^{\circ}\)) & 122.18 & 7.20 & 121.28 & 5.59 \\ NSGn (\({}^{\circ}\)) & 65.68 & 4.89 & 65.62 & 5.12 \\ SN:CoGo (\({}^{\circ}\)) & 26.77 & 7.30 & 26.00 & 6.86 \\ CoGoMe (\({}^{\circ}\)) & 119.57 & 6.92 & 119.79 & 6.27 \\ U:Max (\({}^{\circ}\)) & 113.77 & 8.90 & 111.33 & 4.44 \\ L1:Mand (\({}^{\circ}\)) & 97.18 & 7.03 & 99.65 & 5.81 \\ U:L1 (\({}^{\circ}\)) & 128.29 & 10.38 & 129.05 & 5.79 \\ U1 MPlane (mm) & 6.23 & 2.83 & 5.28 & 1.81 \\ LIMPlane (mm) & 4.39 & 1.93 & 5.16 & 2.10 \\ OJ (mm) & 3.87 & 3.49 & 3.57 & 2.84 \\ OB (mm) & 7.58 & 3.79 & 3.32 & 2.80 \\ Ls:E line (mm) & 1.67 & 1.37 & 2.90 & 2.00 \\ Li:E line (mm) & 2.09 & 1.80 & 2.63 & 1.84 \\ CoGn (mm) & 104.70 & 4.58 & 109.94 & 5.06 \\ \hline \end{tabular}  
*—mean; SD—standard deviation.  
\end{table} 
Table 2: Descriptive statistics of the measured variables before (T0) and after (T1) therapy (N = 18). of the lower incisors (\(p\) = 0.021 and \(p\)! 0.001, respectively). The decrease was observed for the ANB angle (\(p\) = 0.003), Wits (\(p\)! 0.001), ANPG (\(p\)! 0.001) and overbite (\(p\)! 0.001) values and the protrusion of the upper incisors (\(p\) = 0.022) (Tables 3 and 4). The results of the paired \(t\)-test are shown in Table 3, and the results of the Wilcoxon test for the protrusion of upper incisors to the apical base of maxilla are shown in Table 4.  
Figure 3 shows a typical example of a case before and after treatment with Twin Block from our study. Superimposed analyses before and after treatment show the downwards and forwards displacement of the lower facial third, but no change in vertical relations in terms of the change in the mandibular plane angle (\(p\) = 0.283, visible in results from Table 3). \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Related-Samples Wilcoxon Signed Rank Test Summary**} \\ \hline Total N & 18 \\ Test Statistic & 33.000 \\ Standard Error & 22.962 \\ Standardized Test Statistic & \(-\)2.286 \\ Asymptotic Sig. (2-sided test) & **0.022** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Wilcoxon test for protrusion of upper incisors to the apical base of the maxilla.
## 2 Related Works
AOMDV performs well, but it does not have intelligent decision capabilities to select immediate routes by their source without discovering new routes or selecting alternate routes via intermediate nodes during failure, which diminishes performance and enhances end-to-end delay. To conquer this dilemma, Ref. [25] proposes an AOMDV intelligent decision protocol (AOMDV-ID). AOMDV-ID acts as hybrid protocol (proactive and reactive). Ref. [26] proposes an improved AODV routing protocol based on restricted broadcasting by communication zones for transmitting data in a largescale VANET. Following the maximum grey correlation degree principle, a series of key communication zones is selected to construct a restricted broadcasting area for route discovery in AODV. Simulation results show that, compared with the traditional AODV, the proposed improved protocol has advantages in packet delivery fraction, overhead, and route setup time.
## 2 Health Condition Monitoring
### Data Processing
#### 2.2.2 Feature Processing
These feature extraction methods can effectively solve the dimension disaster caused by high-dimensional data and reduce the impact of data noise on the analysis results. However, when analyzing the MSAE data affected by many factors, the combined methods have greater advantages. [PERSON] et.al found that it could not meet the needs of fault diagnosis using the Fourier transform method to analyze the instantaneous angular velocity of marine diesel engines affected by multiple excitations. The combined method of EMD and KICA could be used to extract the feature, which effectively improves the efficiency and accuracy of main engine fault diagnosis [44]. The combination of multiple methods performs better in obtaining the data characteristics of MSAE and has advantages in condition monitoring.
## 5 Simulations and Discussion
### Simulation Results and Discussion
The noise reductions in different frequency ranges are calculated in Table 5. The noise reduction of the new leaky FxLMS algorithm between 50 Hz and 65 Hz was 33.3 dB, while that of the leaky FxLMS algorithm with a constant leakage factor (\(\gamma=0.8\)) was 31.7 dB. The noise reduction of the new leaky FxLMS algorithm between 40 Hz and 80 Hz was 29.5 dB, while that of the leaky FxLMS algorithm with a constant leakage factor (\(\gamma=0.8\)) was 27.8 dB. The noise reduction of the new leaky FxLMS algorithm between 20 Hz and 100 Hz was 24.7 dB, while that of the leaky FxLMS algorithm with a constant leakage factor (\(\gamma=0.8\)) was 24.6 dB. The noise reduction of the new leaky FxLMS algorithm between 10 Hz and 100 Hz was 22.1 dB, while that of the leaky FxLMS algorithm with a constant leakage factor (\(\gamma=0.8\)) was 22.0 dB. It can be observed easily that the noise reductions of the two leaky FxLMS algorithms diminished with increasing bandwidth, and the new leaky FxLMS algorithm had a higher noise reduction. In other words, the leaky FxLMS algorithm had the better performance of noise reduction in a narrow relative frequency band where the center frequency for sound quality control was contained. In addition, upon increasing the length of the control filter and the sampling frequency, the two leaky FxLMS algorithms had a wider frequency band for noise reduction. The step size of the leakage term in the new leaky FxLMS algorithm could be distinguished from that of the total control algorithm. The value range of the step size in the leakage term could be from 0.00005 to 0.05. Figure 9: Waveforms of the output signals for the feedback control filter in the simplified structure using the new leaky FxLMS algorithm, and (**a**) using the FxLMS algorithm, (**b**) using the leaky FxLMS algorithm (\(\gamma=0.6\)), (**c**) using the leaky FxLMS algorithm(\(\gamma=0.8\)), and (**d**) using the leaky FxLMS algorithm (\(\gamma=0.99\)).
## 4 Algorithms for Tree Compatibility and Incomplete Directed Perfect Phylogeny
### Idpp  
By Lemma 1 (Section 3.4), we can solve any instance \(A\) of IDPP by converting it to a profile \(\mathcal{P}_{A}\) and then using BuildNT to determine if there exists a tree \(\mathcal{T}\) that displays \(\mathcal{P}_{A}\). If \(\mathcal{T}\) exists, then it must be a perfect phylogeny for \(A\). Otherwise, no perfect phylogeny for \(A\) exists.  
In [13], [PERSON] et al. gave a specialized algorithm for IDPP; we refer to their algorithm as PPSS. PPSS can be viewed as a variation of BuildNT, with two key differences, which we explain next. Let \(A\) be an instance of IDPP, and let \(\mathcal{P}=\mathcal{P}_{A}\).  
1. PPSS works with \(H_{\mathcal{P}}\setminus U_{\text{root}}\) instead of \(H_{\mathcal{P}}\). This is correct, since every vertex in \(U_{\text{root}}\) is semi-universal. Let \(m\) denote the number of ones in \(A\). Then, \(H_{\mathcal{P}}\setminus U_{\text{root}}\) has \(n+k\) nodes and \(m\) edges (see Figure 4). Note that \(m\) can be considerably smaller than \(M_{\mathcal{P}}\), since \(H_{\mathcal{P}}\) contains edges for both the zeroes and the ones of \(A\). Since the number of edge deletions is \(\mathcal{O}(m)\), the total work to maintain the connected components throughout the entire execution of PPSS is \(\mathcal{O}(m\log^{2}(n+k))\), if we use HDT, and \(\mathcal{O}((m+s)\cdot\log(n+k))\), if we use HDT(0).
2. PPSS updates the set of semi-universal nodes _after_ it computes a successor position. It does so by traversing each of the resulting connected components. Each such traversal takes \(O(n+k)\) time, assuming the connected components are represented by a spanning forest. The number of times a successor position is computed is bounded by the number of edges in the final phylogeny, which is \(\mathcal{O}(\min\{n,k\})\). Thus, the total work performed by PPSS in updating semi-universal nodes is \(\mathcal{O}(nk)\). In contrast, BuildNT updates the set of semi-universal nodes _while_ computing a successor position; i.e., after each tree edge deletion.  
The running time of PPSS is therefore \(\mathcal{O}(nk+m\log^{2}(n+k))\), if PPSS is implemented using HDT, and \(\mathcal{O}(nk+(m+s)\cdot\log(n+k))\), if we use HDT(0) (we note that a somewhat better running time can be achieved if \(H_{\mathcal{P}}\) is extremely dense [13]).
## 2 Circular Fringe Pattern and Their Applications
proposed a new method to extract the displacement information from fringe pattern in 2-D fields based on the property of FT or Hilbert transforms [21]. A method using the Simulated Annealing technique to determine the phase term from a single fringe pattern was proposed by [PERSON] et al. [22]. In this method, the authors automatically partition the interferogram using a recursive method that stores in a quad tree data structure with a limit of fringes in each partition. Finally, [PERSON] and [PERSON] proposed a chirp FT-based method to estimate the parameters of [PERSON]'s rings [23]. In addition, the proposed method can also be used to measure the curvature radius of a spherical surface. Recently, the applications of the methods for separating complex fringe patterns have been received a lot of attention. In addition to demodulating the phase of multiple fringe sets superimposed in a single image [11], for example, [PERSON] and [PERSON] proposed the method based on the 2-D CWT to filter out parasitic fringes or to extract two separate superimposed fringe families with information encoded in their phase [24]. [PERSON] and [PERSON] proposed an enhanced method for two-shot fringe pattern phase-amplitude demodulation using Gram-Schmidt orthonormalization with Hilbert-Huang transform by filtering out the spurious noise and background illumination and performing fringe normalization [25]. Another application is the method for nondestructive material evaluation by using two moire interferometry fringe patterns with encoded orthogonal in-plane displacement information [26]. Therefore, aiming towards similar requirements, as shown in the above studies, we propose a method for automatically separating complex fringe patterns.