esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Degrees-of-Freedom (2 DOF) Magnetic Spring-Based Electromagnetic Energy Harvester
## 4 Coefficient Analysis of the 2 DOF Oscillator System  
The proposed 2 DOF system consists of two floating and two fixed ring magnets (all axially magnetized). The magnetic spring restoring forces were calibrated for the 2 DOF system based on the calculation of the restoring forces. To determine the magnetic restoring force for the first moving magnet, the second moving magnet is considered a fixed magnet. In the same way as the second moving magnet, the first moving magnet is assumed to be a fixed magnet. The linear and nonlinear coefficients of the magnetic restoring forces are determined in the graphs in Figures 4a and 5a (magnetic restoring force vs. position of the floating magnet) by using curve fitting tools.  
Subsequently, the linear and nonlinear stiffness of these curves were determined. For the 2 DOF oscillator system, the analytical measurements of the restoring forces were used to measure the linear and nonlinear stiffness of the system. The third-order polynomials were used to determine the linear and nonlinear stiffness of both floating magnets' restoring forces. Figures 4a and 5a show the analytical measurement of the magnetic restoring forces versus deflections of the floating magnets within the maximum deflection of 30 mm. Figures 4b and 5b provide the relative error (%) in comparing the experimental data with analytical or predicted values over a range of displacement values. The sharp spike near zero displacement (around 0 m) in Figure 4b suggests that this region's experimental force is very small or near zero. Since the denominator of the relative error formula is  
Figure 3: Magnetic restoring force of (**a**) 1 st floating magnet and (**b**) 2 nd floating magnet.  
the experimental force, dividing by values near zero causes the relative error to explode, producing very large values. The symmetric \"X\" shape in Figure 4b around the origin indicates that this issue occurs for small positive and negative displacement values, which is typical in symmetric magnetic restoring force systems. In Figure 5b, around \(-0.012\) m, the experimental force is nearly zero, causing the denominator in the formula to be very small. As a result, small absolute differences between the prediction and experiment produce huge percentage errors, hence the spike. The prediction model and experimental results align well across most displacement ranges. But at \(-0.012\) m, there is a significant loss of reliability due to the near-zero experimental force, possible high sensitivity, noise in measurement, or the model not capturing certain behaviours accurately at that point.  
Figure 4: (**a**) Magnetic restoring force of the 1 st floating magnet. (**b**) Relative errors of the fitting curves versus the analytical force.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
###### Abstract  
With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.  
medical image methods; liver cancer diagnosis and treatment; image fusion 2020
## 4 Design Procedures and Simulation Results
### Design of Virtual Synchronous Generator Topology  
For implementing the VSG topology, the main parameters to be designed are the inertia constant \(K_{I}\) and the damping constant \(K_{D}\). The parameter \(K_{D}\) can be calculated using:  
\[K_{D}=\frac{\Delta P}{\omega_{g}\Delta\omega} \tag{23}\]  
Once \(K_{D}\) was calculated, the inertia constant \(K_{I}\) was computed using the desired time constant for the system, \(\tau_{f}\):  
\[\tau_{f}=\frac{K_{I}}{K_{D}} \tag{24}\]  
In this case, the damping constant, \(K_{D}\), was calculated to be 14.07, assuming \(\Delta P\) of 100% (10 kW) for 0.5% change in the angular frequency (1.885 rad/s). Then, for a time-constant of 0.01 s, the \(K_{I}\) value was calculated to be 0.14. The inertia constant from the VSG is:  
\[H=\frac{K_{I}\omega_{g}^{2}}{2P_{rated}}=1s \tag{25}\]  
The frequency and ROCOF of the system after a step-increase of 2 kW on the load, with and without the VSG, is presented in Figure 14a,b, respectively. The dip in frequency and the ROCOF of the system was reduced with addition of the VSG, as expected. As with the previous cases, the additional inertia from the VSG slowed the system down, and the settling time for the frequency was increased compared to the case without virtual inertia. The peak-power injected was slightly higher than that of the synchronverter and Ise lab's topology. However, the time taken for the power to return to the  
Figure 13: Simulation results from ISE lab’s topology: (**a**) system frequency after a step-increase of 2 kW load; (**b**) ROCOF after a step-increase of 2 kW load; (**c**) increase in inverter power as a response to system frequency decrease [(40)].  
steady value of 1000 W was much longer than for the synchronverter leading to a larger energy usage from the DC side.
# Parameter Analysis on Seismic Response of Long Lined Tunnel by 2.5D Substructure Method
## 4 Method Verification  
### Free Field Response of Elastic Half Space  
According to the stiffness matrix method, the 1D site response is obtained. The half-space site response under the oblique incidence of seismic wave is calculated in the frequency domain. Through time delay, the 1D site response can be expanded to the 2.5D finite element response of each node.  
To verify the accuracy of 2.5D finite element free field response, select a uniform half-space with density \(\rho\) of 2500 kg/m\({}^{3}\), [PERSON]'s modulus \(E\) of 1.323 Gpa and [PERSON]'s ratio of 0.25. The reference point A taken is located in the middle of the site surface. The size of the site model is 800 m in length and 400 m in height.  
As shown in Figure 3, the velocity-time history expression of the input bidirectional pulse is as follows  
\[v(t)=A_{max}\Big{[}1-2\big{(}\pi f_{p}(t-t_{0})\big{)}^{2}\Big{]}e^{-(\pi f_{p }(t-t_{0}))^{2}} \tag{26}\]  
where the maximum amplitude \(A_{\max}\) = 0.001 m/s; the predominant frequency of the Fourier spectrum\(f_{p}\) = 2.0 Hz; time is expressed in \(t\); the time of pulse displacement reaching peak value is \(t_{0}\) and the calculation time step is 0.005 s.  
According to the numerical simulation requirements, the mesh size of plane finite element needs to be discretized as \(\Delta y\times\Delta z=12.5\) m \(\times\) 12.5 m.  
It can be seen from Figure 4 that when the incident pulse is P wave, the site response of 2.5D finite element at observation point A is consistent with the analytical solution under the oblique incidence of seismic wave. When the SV wave is inclined to incident, the 2.5D finite element site response and analytical solution are in good agreement at observation point A in Figure 5. The above numerical examples show that the accuracy of site response in the plane 2.5D finite element is verified when the seismic wave is obliquely incident.
## 2 Materials and Methods
### Multivariate Analysis
#### 2.4.2 Optimal Wavelength Selection  
Colinearity and redundancy are general risks to spectral data analysis. Optimal wavelength selection aims to select a subset of wavelengths which have the calibration ability with reduced colinearity and redundancy. Optimal wavelength selection can reduce the computation cost and simplify the models while keeping the prediction ability. In this study, successive projections algorithm (SPA) was used to select optimal wavelengths.  
SPA is a forward variable selection method, and it has been widely used in spectral data analysis [18]. The details of SPA are summarized as follows:1. Manually define range of the number of variables to be selected.
2. Randomly select a variable and calculate the projection of this variable on the other variables.
3. Select the variable with the largest projections into the candidate subset, then the corresponding variable for projection is used for projecting on the residual variables.
4. Repeat steps (2) and (3) until the number of variables in the candidate subset is equal to the maximum number.
5. Build multiple linear regression (MLR) models using different numbers of variables in the subset, and the variables corresponding to the model with the minimum RMSE are selected as optimal variables.
# Silver Isotopes in Silver Suggest Phoenician Innovation in Metal Production
## 1 Introduction
On the other hand, the supergene deposits underwent some secondary reconstitution of precipitating silver that occurred at lower temperatures and might have involved redox reactions. Hence, Ag derived from these reactions contains a wider range of Ag isotopic composition in comparison to the source deposit (\(-12\)\(\varepsilon^{109}\)Ag to 20 \(\varepsilon^{109}\)Ag; see [36]). Silver isotopes, therefore, can be used in archaeology for geological classifications of the exploited ores. Thus far, all Ag isotope analyses in archaeology have been applied to silver coins. The results match the range of variation in hypogene silver minerals and not those of supergene secondary deposits. The \(\varepsilon^{109}\)Ag of Hellensitic and Early Roman (600-170 BCE) silver coins range between \(-1\) and \(+2\)\(\varepsilon^{109}\)Ag [39; 40]. Within this time period, coins dating to 220-211 BCE all range between 0 and \(+0.5\)\(\varepsilon^{109}\)Ag [39]. Medieval and modern coins range between \(\pm 2\)\(\varepsilon^{109}\)Ag [37; 38; 41]. In this study, we analyzed silver isotopic ratios of hoarded silver in the Southern Levant from the Bronze and Iron Ages and compared the results to the Pb isotopic results obtained previously. As the silver hoards predate the invention of coinage, this dataset comprises the earliest reported Ag isotopes of archaeological silver items. The preservation of Ag isotopes in ancient silver was also approached in this study since many of the items were sealed in bundles of cloth and/or cached in sealed ceramic vessels [25]. [PERSON] et al. [40] showed that for ancient silver coins, silver in the patina is isotopically lighter due to the formation of Ag\({}_{2}\)S, while the layer below the patina is isotopically heavier. This was explained by secondary, post-depositional variations, due mainly to underground interactions with soil-water during the prolonged burial in the ground. This is because Ag isotopic ratio is sensitive to low-temperature redox reactions [34; 42]. [PERSON] et al. [40] suggest that Ag isotopes of the core of the coins were not altered, yet this was based on buried (unsealed) coins only. Here, we had a unique opportunity to compare Ag isotopes of sealed and unsealed silver (see below). Although limited by the small size of the sample sets, we approached the question of whether oxidizing burial conditions also affected the cores of unsealed silver items.
## 2 Mathematical Formulation
Under the assumptions and notations, the mathematical model for the problem is defined as follows:  
**Objective Function:**:  
\[min\left[\underset{i\in I}{max}(f_{i})-\underset{i\in I}{min}(r_{i})\right] \tag{1}\]
**Constraint Conditions:**:  
\[S_{ij}\geq r_{i}\;\forall i\in I,j\in J_{i} \tag{2}\]  
\[S_{ij}<d_{i}\;\forall i\in I,j\in J_{i} \tag{3}\]\[S_{ij}+\sum_{m=1}^{M_{ij}}y_{ijm}\cdot d_{ijm}\leq D_{i}\;\forall i\in I,j\in J_{i} \tag{4}\]  
\[w_{ijmin}\leq\sum_{k=1}^{K}y_{ijm}\cdot z_{mk}\leq w_{ijmax}\;\forall i\in I,j \in J_{i},m\in M_{ij} \tag{5}\]  
\[\sum_{i=1}^{I}\sum_{j=1}^{I}\sum_{m=1}^{M_{ij}}x_{ijt}\cdot y_{ijm}\cdot z_{mk} \leq w_{k}\;\forall t\in 0,T,\forall k\in K \tag{6}\]  
\[S_{ijt}+\sum_{m=1}^{M_{ij}}y_{ijt}\cdot d_{ijt}\leq S_{ij}\;\forall i\in I,j \in J_{i},j^{\prime}\in pred(j) \tag{7}\]  
\[\sum_{j=1}^{J}x_{ijt}\leq q_{i}\;\forall i\in I,\;\forall t\in 0,T \tag{8}\]  
\[\sum_{m=1}^{M_{ij}}y_{ijm}=1\;\forall i\in I,j\in J_{i} \tag{9}\]  
\[dur_{ij}=\sum_{m=1}^{M_{ij}}y_{ijm}\cdot d_{ijm}\;\forall i\in I,j\in J_{i},m \in M_{ij} \tag{10}\]  
\[S_{ij}\geq r_{i}\;\forall i\in I,j\in J_{i} \tag{2}\]0  
where the objective function is the minimization of the total makespan, the duration of the whole multi-project schedule. Constraint (2) ensures that the start time of the task for each project should be greater than or equal to the release date of project. Constraint (3) ensures that the start time of the task for each project should be less than the due date of project. Constraint (4) ensures that each project needs to be completed before the due date. Constraint (5) implies that the number of workers assigned to each task must be within certain limits due to the work-space constraint. Constraint (6) implies that at any time, the number of type-\(k\) workers assigned to the tasks should be lower than or equal to the total number of type-\(k\) workers. Constraint (7) ensures that the precedence relationships between tasks of the same project cannot be violated, and the starting time of the task should be greater than or equal to the finishing time of its preceding set of tasks. Constraint (8) implies that the number of tasks that can be executed concurrently for each project should be limited due to floor space constraints. Constraint (9) ensures that each task is being performed only in one mode. Constraint (10) denotes that the processing time of a task is equal to the processing time of the assigned execution mode for that task. Constraint (11) implies that the end time of the task is equal to the start time plus the processing time, which means the task is non-preemptive.
## 3 Description of the Method
### Definition of the Chine Curves
#### 3.3.2 Definition of the Outer Chine  
The outer chine curve is established with the inner chine curve as the reference (Figure 12). Let us denote \(\boldsymbol{n}_{i}\) as the unit normal vector at the position of the chine curve in the horizontal plane. To start from the discrete point \(\mathbf{Q}_{ci}\), these points on the outer chine curve can be evaluated as follows:  
\[\mathbf{Q}_{ri}=\mathbf{Q}_{ci}+W_{r}(x)\cdot\boldsymbol{n}_{i} \tag{28}\]The longitudinal function, denoted as \(W_{r}(x)\), can be utilized to calculate the variation of the width of the splash guard along the ship's length. The spray rail can be designed with a uniform width in the mid-aft section of the hull while gradually tapering and narrowing towards the bow. To construct this function, the multi-segment function composed of straight lines and quadratic functions can be utilized as follows:  
\[W_{r}(x)=\left\{\begin{array}{ll}W_{\text{rm}}&x\leq L_{0}\\ W_{\text{rm}}\times(1-(\frac{x-L_{0}}{L_{c}-L_{0}})^{2})&x>L_{0}\end{array}\right. \tag{29}\]  
The discrete points \(\mathbf{Q}_{ri}\) on the outer chine curve can be appropriately calculated based on Equation (28). As a result, the control equation for the outer chine curve of the spray rail can be then constructed via the curve interpolation method (as explained in Section 3.3.1) in the following form:  
\[\mathbf{S}_{r}(u)=\sum_{i=0}^{n}N_{i,3}(u)\mathbf{P}_{ri},\ \ 0\leq u\leq 1 \tag{30}\]  
where \(\mathbf{P}_{ri}\) represents the control points of the outer chine curve. Due to the similarity between the inner and the outer chine curves, the node vectors of the inner chine curves are employed for interpolating the outer chine curve. This approach is capable of effectively facilitating the subsequent construction of the spray rail utilizing a ruled surface. For the design of a planing hull with spray rails, the additional outer chine curve is needed. If there are no spray rails, only the inner chine is needed.
## 4 Result Analysis of Detection Model
### Training Phase  
Figure 7 illustrates various loss curves and precision and recall metrics to evaluate the effectiveness of the proposed object-detection models. The three-loss curve, which consists of 'box loss', 'class loss' and distributive focal loss, also known as 'dfl loss', refers to the model's loss in terms of bounding box, class and the data imbalance problem. The model is run for 100 epochs. At the beginning of the epoch, the model had the highest loss value. The maximum box loss was greater than 1.2 at the outset of the epoch and it began to decrease as it progressed. After 100 epochs, the model possessed a minimum loss value of 0.1. After 100 epochs, class loss and dfl loss tended to decrease to 0.1 from their initial maximums of 2.5 and 1.8, respectively. The mean average precision (mAP) method evaluates the efficacy of object-identification models, whereas the recall curve is used to classify object detection as a true positive [39]. During the early epochs, the mAP precision and recall curves may have a minimum value of 0. As the training process advances and the model's parameters endure improvements, the model's capacity to accurately identify objects is enhanced, resulting in an increase in mean average precision (mAP). The initial precision and recall curve values are 0 and 0.4, respectively, and increase to 1 after 100 epochs.  
The precision-recall and loss function curves are fully depicted in Figure 8's visual representation of the model's validation phases. The curves display similar trends in the validation phase as in the training phase. In order to assess how well the model generalizes to new data, it is essential to consider the validation loss. The loss curve revealed a recurrent pattern where the model first showed the highest loss and increased performance by gradually lowering the loss throughout 100 epochs. The maximum box loss value observed was 1.5 at the beginning of the period, which then decreased to 0.6 upon completion of all epochs. At the beginning, the maximum class loss and dfl loss values were 3.5 and 2, respectively. After all 100 epochs were complete, these values gradually decreased to 0.5 and below 1.2. The validation loss shows a decreasing trend over time, supporting the effectiveness of our approach. The precision and recall curve exhibits an initial value of 0, which progressively increases to 1 after 100 epochs during the validation phase. This observation indicates that the object-detection model has shown substantial progress in validating the unseen data.  
Figure 6: Example of database logs.  
Figure 7: Box loss, class Loss, DFL loss, map during training phase during model training.
###### Abstract  
In order to investigate the fatigue cracking resistance of engineered cementitious composites (ECC) used in in total life pavement, the semi-circular bending (SCB) test and improved three-point bending fatigue test (ITBF) were utilized in this study. The digital image correlation (DIC) method was also utilized to track the surface strain fields of specimens during the SCB test. X-ray computed tomography (CT) and digital image processing (DIP) technologies were applied to measure the internal-crack distribution of the ITBF specimen. The results of the SCB test showed that the fatigue cracking damage process of ECC can be divided into three stages and that the cracking stable propagating stages occupied the main part, which indicates that ECC has excellent ductility and toughness and could work very well with existing cracks. The ITBF results showed that the fatigue cracking resistance of ECC was better than epoxy asphalt concrete (EAC). In addition, the internal-crack distribution along the depth direction of the ITBF specimen could be presented well by the image pixel statistical (IPS) method based on CT scanning of image slices. It could be found that multiple cracks propagate simultaneously in ECC, instead of a single crack, under the OSBD pavement working condition.  
engineered cementitious composites; orthotropic steel bridge decks pavement; fatigue cracking resistance; X-ray computed tomography; image pixel statistical method +
Footnote †: journal: Applied  
1  
Fatique cracking resistance of engineered cementitious composites under working condition of orthotropic steel bridge decks pavement.
## 4 Simulation Experiments and Discussions
### Comparative Analysis with Traditional ACO  
In order to evaluate the performance of the proposed OMACO algorithm, this section compares the optimization ability of OMACO and the traditional ACO. Figure 6 shows the monitoring path solved by the traditional ACO with the same parameters to Section 3.1. Different from Figure 4, the path sequences (node \(6\to 7\to 9\)) repeat up to eight times in Figure 6, and the UAV3 trajectory (blue) between steps 450 and 500 can be seen following by UAV1 (red). This leads to the same monitoring period and is very harmful to the monitoring privacy protection. However, the UAV path in Figure 4 has no obvious repetitive path or circular trajectory, and there is no UAV following the other. Therefore, compared  
Figure 4: The persistent monitoring path obtained by the OMACO algorithm.  
Figure 5: The actual monitoring period of task nodes obtained by the OMACO algorithm.  
with the traditional ACO, the proposed OMACO algorithm can obtain better privacy protection performance.  
Figure 7 shows the actual monitoring period obtained by using the traditional ACO. There exist many nodes that have been monitored overdue many times, resulting in the waiting time of the task node frequently exceeding the expected period. Therefore, the proposed OMACO algorithm is superior to the traditional ACO in improving the monitoring frequency.  
Table 3 shows a detailed comparison between OMACO and ACO on each task node monitoring data. Based on the proposed OMACO algorithm, most task nodes have been visited more times than that of ACO. Therefore, the average visit number is higher than the traditional ACO. Correspondingly, the average actual period will decrease and be less than ACO. Also, it is found that the ACO algorithm is not appropriate for our problem because the node No.10 exceeds its upper bound.  
Figure 8 shows the iterative curves of the objective functions obtained by OMACO and ACO, and the related data are shown in Table 4. In the first iteration, the algorithm designed in this research has a lower value of objective function than ACO. This is because the waiting time of the task node has already been considered by OMACO when calculating the transition probability based on the greedy strategy. In fact, the node selection strategy has been optimized before the initial ant path. The traditional ACO only relies on the heuristic function and pheromone to decide the node transition probability. Consequently, the pheromone is equal on all path segments in the initial iteration which leads to a randomly path generated.  
Figure 6: The persistent monitoring path obtained by ACO algorithm.  
Figure 7: The actual monitoring period of each node obtained by ACO algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **OMACO** & **ACO** \\ \hline Iterations & 4 & 28 \\ Minimum Cost & 0.433 & 0.814 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Solution comparison between OMACO and ACO.  
Figure 8: The objective function iteration curves of the two algorithms.