esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Inner Motion Loops
### The Stiffness Matrix
#### 4.1.1 Motor Position  
Considering the robot dynamics, Equations (1) and (2) in a steady-state and an input control in the motor side, we have  
\[K(q-\theta)=-J^{T}\!f, \tag{15}\]  
\[K(\theta-q)=\tau=K_{p\theta}(\theta_{d}-\theta), \tag{16}\]  
where \(\theta_{d}\) is the desired motor position, and \(K_{p\theta}\) is the proportional gain matrix of the motor position control. Solving for \(\theta\) from Equation (16),  
\[\theta=\theta_{d}-K_{p\theta}^{-1}\tau=\theta_{d}-K_{p\theta}^{-1}J^{T}\!f. \tag{17}\]  
Replacing (17) in (15) and solving for \(q\), we have  
\[q=\theta-K^{-1}J^{T}\!f=\theta_{d}-\big{(}K_{p\theta}^{-1}+K^{-1}\big{)}J^{T}\!f, \tag{18}\]  
Therefore, the link positions will be affected by an equivalent stiffness that is influenced by the passive stiffness, \(K^{-1}\), and the active stiffness of the motion control, \(K_{p\theta}^{-1}\). Since the motor and link positions are equivalent and assuming the reduction factor \(n=1\), we can consider that \(\theta_{d}=q_{d}\). For small deformations, \(\Delta x\) and \(\Delta q\) can be related,  
\[\Delta x\approx J(q)\Delta q=J(q)(\theta_{d}-q). \tag{19}\]  
Then, the cartesian stiffness matrix is  
\[K_{x}=\frac{f}{\Delta x}=J^{-T}\big{(}K_{p\theta}^{-1}+K^{-1}\big{)}^{-1}J^{ -1}. \tag{20}\]  
Hence, in this case, the active and passive stiffnesses are equivalent.
## 3 Experimental Content and Methodology
### Monitoring Points
#### 3.1.2 Strain Measuring Points  
FBG strain sensors are embedded in the track slab, the CA mortar layer and the track bed at different sections of the tested beam, namely, at 0 L, 1/4 L, 1/2 L, 3/4 L and L. The measuring points are all located on the longitudinal central axis of the track system. The Moi-sm130 FBG demodulator is used for data acquisition and processing, and the data were collected every 30 min at a sampling frequency of 2 Hz. The specific layout of the FBG strain sensor is shown in Figure 4.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix A Two-Stage Detection Algorithm
### Results of the Synchronization Sequence Detection Algorithm  
In this section, we present some results when the synchronization sequence detection algorithm is employed. Figure 14 presents a comparison between the expected \(P_{FA}\) and the actual \(P_{FA}\) value. The expected \(P_{FA}\) was varied from \(10^{-5}\) to \(10^{-4}\). The number of Monte Carlo trials was set to \(10^{6}\) iterations, and an AWGN channel was assumed. As can be seen, the actual \(P_{FA}\) value stayed very close to the expected one.  
Figure 15 depicts \(P_{FA}\) versus the probability of correct detection, \(P_{CD}\). The \(P_{FA}\) was varied from \(10^{-5}\) to \(10^{-4}\); the SNR was set to \(0\) dB; and the number of Monte Carlo trials was equal to \(10^{5}\) iterations. The figure shows that the presence of a synchronization sequence was always detected independent of the \(P_{FA}\) value set.  
Next, we present some results comparing the detection performance of the proposed two-stage detection algorithm against the single-stage one. The number of Monte Carlo trials here was set to \(10^{5}\) iterations, and the AWGN channel was used. For both results shown in Figure 11, the probability of false alarm \(P_{FA}\) was set to \(10^{-4}\), and the probability of false disposal \(P_{FD}\) was set to \(10^{-3}\). Figure 5a shows the correct and misdetection probabilities for the SNR varying from \(-12\) dB up to \(0\) dB. As can be noticed, the proposed two-stage detection scheme presented a probability of correct detection of one for SNR values greater than \(-4\) dB, i.e., the presence of the synchronization sequence was always detected, while the single scheme only achieved a probability of one for SNR values greater than \(2\) dB. Figure 5b shows the correct miss and false detection probabilities when the noise power varied from \(-30\) dBW up to \(0\) dB. As can be seen, both schemes presented the correct miss probability equal to one and the false detection probability equal to zero for all noise power values considered in this experiment. It is important to be mentioned that the combined working of the two detection stages decreased the probability of false detection (also known as false alarm) of the two-stage algorithm when compared to the probability of false detection of the second stage alone (see Figure 12).
## 2 Materials and Methods  
### Site Description  
The study was conducted in north-central Florida, about 15 km northeast of Gainesville, at the Austin Cary Forest (ACF), managed by the University of Florida (Figure 1). The study site included four 50 \(\times\) 50 m permanent plots inside the forest that were established in 1999 and were randomly located within an eddy-covariance tower's footprint [33]. The study area was a mixed stand of naturally regenerated longleaf pine (~70% of trees) and slash pine (~30% of trees). In 2018, the research area had a density of 185 trees per ha\({}^{-1}\) with an average tree diameter of 30.31 \(\pm\) 6.66 cm, basal area of 13.61 \(\pm\) 0.03 m\({}^{2}\) ha\({}^{-1}\), and height of 22.47 \(\pm\) 6.33 m. Because the overstory canopy was relatively open, the understory was dense and mostly dominated by saw palmetto (_Serenoa repens_ ([PERSON]) Small), gallberry (_Ilex coriacea_ ([PERSON]) [PERSON]), and wax myrtle (_Myrica cerifera_ ([PERSON]) [PERSON]) [34]. The trees in the plots were approximately 80-year-old, where the approximate age of longleaf pine was 90 years, and that of slash pine was 70 years. The soil was mostly acidic, Ultic Alaquods (sandy, siliceous, thermic), poorly drained, and low in organic matter. Two of the four measurement areas were located in a wetter area of the site, while the other two were located in a drier area (Figure 1).  
The region had seasonal precipitation with long-term mean annual precipitation of 1219 \(\pm\) 218 mm (1988-2018) [35] and a long-term mean annual temperature of 20.77 \({}^{\circ}\)C (1988-2018) [35]. The average minimum and maximum monthly air temperature ranged from 12.8 \({}^{\circ}\)C in January to 27.5 \({}^{\circ}\)C in July [35].  
The ongoing management objective for this forest is to restore and maintain an unevenaged, old-growth savanna-like pine forest with a widely spaced overstory and a diverse understory species composition. Thinning and a three-year prescribed fire cycle have been used to achieve these management objectives. Selective thinning was done in May 2016, where approximately 30% of the trees per ha were harvested, of which ~70% were longleaf pine, and ~30% were slash pine. The first prescribed burn was done at the time of the plot establishment in 2000. Since then, the area was burned every three years (2003, 2006, 2009, 2012, 2015, and 2018). The most recent prescribed burn was conducted in June 2018. These burns, on average, were of moderate intensities with flame heights reaching no more than 1.5 m, with burns carefully monitored by fire crews ([PERSON], Austin Cary Forest, Gainesville, FL, USA, personal communication).  
Figure 1: Images depicting (**A**) map of the state of Florida, USA extracted from world imagery basemap in ArcGIS; (**B**) location of Austin Cary Forest within Florida, and (**C**) the four 50 \(\times\) 50 m permanent plots (white squares) that were used to monitor overstory dynamics and where the five understory samplings per plot were placed (locations not shown).
## 2 Materials and Methods
### Clustering Analysis
#### 2.4.3 Hierarchical Clustering  
Hierarchical clustering is a method of forming clusters by repeating a process of grouping the most similar entities. Results are usually given in the form of dendrograms, with each individual belonging to only one cluster. Various definitions of similarity or distance between individuals are possible, and the results of clustering are derived differently according to the connection method between clusters. Generally, the Euclidean distance is used, where one has to make sure that all attributes have the same scale. This is a special case of the Minkowski distance with p. An agglomerative clustering algorithm starts with N groups, each initially containing one training instance and, merging similar groups to form larger groups, until there is a single one. A divisive clustering goes in the other direction, starting with a single group and dividing large groups into smaller groups, until each group contains a single instance. Hierarchical clusters have the advantage of being able to easily identify the structural relationships between clusters in the form of dendrograms. Through this, the distance between items and the distance between clusters can be known and the robustness of the cluster can be analyzed by checking the similarity between variables in the clusters.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 6 Conclusions  
We extended and improved our previous study [8] which predicted the trajectories of drifters using evolutionary computation, and we also predicted the trajectories of drifters using various machine learning techniques [44; 45; 46; 47; 48; 49]. To the best of the authors' knowledge, this was the first trial in which machine learning has been applied to the prediction of drifter trajectories, and it significantly improved upon the representative numerical model, MOHID.  
In terms of MAE, RBFN using the WEKA library showed the best performance, an improvement of 35.20% over the numerical model MOHID. LSTM using PyTorch showed the best performance regarding NCLS, an improvement of 6.24% over MOHID. These neural network-based methods did not take a long time to construct a model. In the future, we plan to experiment with other representative variants of RNNs such as gated recurrent units [50], and we will design more models that increase the performance of DNNs or basic RNNs by adding more training data.  
Conceptualization, D.-Y.K. and Y.-H.K.; methodology, Y.-W.N. and H.-Y.C.; software, Y.-W.N. and H.-Y.C.; validation, Y.-W.N., H.-Y.C. and D.-Y.K; formal analysis, Y.-W.N. and Y.-H.K.; investigation, Y.-H.K.; resources, D.-Y.K.; data curation, D.-Y.K.; writing--original draft preparation, Y.-W.N., H.-Y.C., S.-H.M. and Y.-H.K.; writing--review and editing, S.-H.M. and Y.-H.K.; visualization, H.-Y.C.; supervision, Y.-H.K.; project administration, Y.-H.K.; funding acquisition, D.-Y.K. and Y.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research was a part of the project titled 'Marine Oil Spill Risk Assessment and Development of Response Support System through Big Data Analysis', funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare that there is no conflict of interest regarding the publication of this article.
## 5 Summary, Conclusions, and Future Work
### Conclusions and Future Work  
AE signals captured during a fatigue crack growth experiment were analyzed and found to match closely with the FEM simulations. AE energy generated at one crack tip travels to the other tip and establishes a standing-wave pattern that has a characteristic dominant frequency, which depends on the crack length. A novel approach which utilized the robust image recognition capabilities of the AlexNet convolutional neural network and the Choi-Williams transform of experimental AE signals was presented. This novel AI  
\begin{table}
\begin{tabular}{c c} \hline
**Iteration (\(i\))** & **Validation Accuracy** \\ \hline
1 st & 94\% \\
2 nd & 100\% \\
3 rd & 100\% \\
4 th & 100\% \\
5 th & 98\% \\ Average & 98.4\% \\ \hline \end{tabular}
\end{table}
Table 3: Network accuracy using stratified k-fold cross validation (\(k=5\)).  
system proved to be effective at predicting the crack length of an AE signal in a binary scenario at an accuracy of 98.4%. The proposed approach can be used to obtain the crack-length estimation from every AE signal without recourse to the AE-signal history. An invention disclosure [43] covering the developed methodology has been filed.  
In future, this concept could be extrapolated to similar applications that go beyond a binary crack length prediction. Regression models could be combined with this more discretized application mode to increase the overall AI capabilities of such a system. As previously mentioned, the approach presented herein is not limited to the AlexNet architecture; further improvements could be explored using other CNN architectures. To expand upon the classification capabilities, further experimentation to capture sufficient datasets in other crack lengths will be valuable to constructing a comprehensive crack monitoring AI system.  
In this article, the formal analysis was done by [PERSON] and [PERSON]. The detailed methodology was provided by [PERSON] and [PERSON]. [PERSON] prepared the original draft; review and editing were done by [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Office of Naval Research (ONR), grant numbers N00014-17-1-2829 and N00014-21-1-2212.  
Not applicable.  
The authors declare no conflict of interest.
# The Solid-Liquid Phase Interface Dynamics in an Undercooled Melt with a Solid Wall
## 1 Introduction  
Directional and volumetric crystallization are the basis of many technological solidification processes and are often found in nature (e.g., freezing of water and various solutions, solidification of magma) [1, 2, 3, 4, 5, 6, 7]. Mathematical models of such processes are based on the description of heat and mass transfer in solid and liquid phases separated by a moving crystallization front. This interphase boundary is generally curvilinear and moves according to a certain time-dependent law, which is determined by solving the problem. The mathematical model of a purely thermal problem was first formulated by [PERSON] [8, 9, 10, 11], and all problems with a moving boundary of phase transformation now bear his name. Note that, in general, the unsteady Stefan problem has no exact analytical solution, and the solution of each individual model strongly depends on the geometry of the crystallization domain and boundary conditions. The solution to such problems is usually constructed using approximate analytical (e.g., the method of differential series [12, 13]) and numerical (e.g., the method of fixed boundaries [14]) methods.  
One of the fruitful approaches to solving the Stefan problem is the method of boundary integral equation (BIE), first proposed by [PERSON] and [PERSON] [15, 16]. The idea of this method is to derive an integro-differential equation for the interface function determining the position and velocity of the crystallization front. Note that such an equation was derived in [15, 16] for a purely thermal problem. The BIE that describes the thermal concentration problem was derived by [PERSON] and [PERSON] [17] in the case of parabolic mass transfer in the melt/solution. Further, they considered the scenario of local non-equilibrium (fast) crystallization described by a hyperbolic impurity diffusion equation and derived the corresponding BIE [18]. Note that the high-speed BIE has a limiting transition to the low-speed BIE [18]. An important step in the BIE theory was the case of convective fluid flows considered in [19, 20]. Note that the BIE allows us to study the morphological stability of interfacial boundaries of a certain shape [21, 22, 23], derive the selection criterion of stablegrowth mode for dendritic crystals [24; 25; 26], as well as study the shape and evolution of various patterns [27].  
In this paper, we derive a new BIE for the case when a single-component melt or solution crystallizes under the significant influence of a solid wall in liquid. Examples of such processes are (i) dendritic growth in a mold, (ii) freezing of water in lakes of shallow depth, (iii) and solidification of lava in magma chambers. The new BIE has a limiting transition to the previously developed theory in the absence of a solid wall and is found below for 2D and 3D cases.
## 5 Results, and Discussion
### Results of Training and Validation
#### 5.1.3 Analysis of Augmented Model Performance with Different Architectures by Class  
As shown in Table 4, the class-specific results under augmented conditions reveal that Swin Transformer consistently achieved the highest average AP50 (89.77), AP50:95 (67.27), and IOU (84.77), outperforming both ViT (88.33, 66.12, 83.36) and PVT (88.43, 65.44, 83.14) by roughly one to two percentage points. In terms of individual classes, Excavator emerged as one of the more straightforward categories, with all three backbones attaining AP50 values above 90--Swin Transformer led at 92.44, followed closely by ViT (91.03) and PVT (90.16). Meanwhile, Remicon appeared notably more challenging, as reflected by lower AP50 scores (ViT: 84.72, Swin: 86.14, and PVT: 85.91) and reduced IOU metrics. This discrepancy suggests that Remicon's visual features might have been less distinct or more prone to occlusion within cluttered construction settings, underscoring the need for specialized data or tailored augmentation strategies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**ViT**} & \multicolumn{3}{c}{**Swin Transformer**} & \multicolumn{3}{c}{**PVT**} \\  & **Raw** & **Aug** & **Gain** & **Raw** & **Aug** & **Gain** & **Raw** & **Aug** & **Gain** \\ \hline AP50 & 85.37 & 88.33 & 2.96 & 86.71 & 89.77 & 3.06 & 85.63 & 88.43 & 2.8 \\ AP50:95 & 61.37 & 66.12 & 4.75 & 62.53 & 67.27 & 4.74 & 61.42 & 65.44 & 4.02 \\ IOU & 78.71 & 83.  
Moreover, analyzing \(\mathit{AP_{S}}\), \(\mathit{AP_{M}}\), and \(\mathit{AP_{L}}\) highlights that larger machinery (e.g., excavators and dump trucks) consistently achieved higher detection rates, whereas smaller or less visually salient equipment posed a greater challenge. For instance, Swin Transformer attained \(\mathit{AP_{S}}\) scores as high as 67.01 for Excavator, suggesting that its hierarchical attention mechanism effectively captured fine-grained details of partially obscured objects.  
In addition, as shown in Table 5, PVT achieved the highest average and peak FPS, making it the fastest among the three. Swin Transformer showed a moderate speed advantage over ViT while still providing competitive real-time performance. Although all models shared the same minimum FPS (34), PVT stood out by consistently maintaining higher speeds, as reflected by its upper-range metrics.
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure
#### 2.2.2 Lyapunov Function-Based MPC for DFIG  
Since one of the drawbacks of MPC is the absence of stability guarantees in nonlinear systems, incorporating a Lyapunov function into the controller's structure can enhance the controller's performance while addressing this limitation. The following steps outline the design of a Lyapunov-based nonlinear controller for the modeled DFIG system. The initial step in designing a Lyapunov-based MPC involves selecting the desired cost function. Based on the dynamics of the DFIG system, the following structure for the cost function was chosen [16]:  
\[J=\int\limits_{t_{k}}^{t_{k}+N_{p}}\bigg{[}\widetilde{x}(\tau)^{T}\ Q_{c}\ \widetilde{x}(\tau)+u_{k}(\tau)^{T}\ R_{c}\ u_{k}(\tau)\bigg{]}d\tau \tag{25}\]  
where \(\widetilde{x}(\tau)\) represents the predictive state variable of the system, \(N_{p}\) is the predictive horizon, and \(\mathbf{Q}_{c}\) and \(\mathbf{R}_{c}\) are the weighted matrices of the cost function. The purpose of the control strategy in this paper is defined as follows:  
\[u_{k}^{*}(t)=\underset{u_{k}\in S(N_{c})}{min}J \tag{26}\]  
According to Equation (26), the objective is to find a control law in the form of \(u_{k}^{*}(\mathbf{t})\) that minimizes the cost function defined in Equation (25). In this equation, \(N_{c}\) is the control horizon, and \(S\) is a family of fixed continuous piecewise functions with a sampling period of \(N_{c}\).
## 2 Materials and Methods
### Microsatellite Analysis  
The following microsatellite _loci_ were analyzed: A113, A7, Ap43, Ap55, and B124 [24]. The PCR reactions were performed (35 cycles, 56 \({}^{\circ}\)C annealing) using DreamTaq Polymerase (ThermoFisher Scientific). Each marker was amplified individually. A total of 1 uL (A7, Ap43) or 0.5 uL (B124, Ap55, A113) of the amplification products were combined in two separate samples (A7, B124 and Ap43, Ap55, A113) and denatured in the presence of Hi-Di formamide and 0.25 uL of 350 bp Rox standard (Life Technologies) in a total volume of 10 uL and subjected to capillary electrophoresis on ABI Prism 310 apparatus. The following oligonucleotide primers were used with fluorescent marks positioned on the forward primer: A113-F: (6-FAM) CTCGAATCGTGGCGCTC, A113-R: CCTGTATTTTGCAAC-CTGC, A7-F: (HEX) GTTAGTGCCCTCTCTTTC, A7-R: CCCTTCTTCATTTCC, AP43-F: (HEX) GGCGTGCACAGCTTTTCC, AP43-R: CGAAGGTGTTCAG GCC, AP55-F: (6-FAM) GATCACTTCGTTTTCAACCGT, AP55-R: CATTCGGATGTGACGACT, B124-F: (6-FAM) GCAACAGGTCGGGTTAGAG, B124-R: CAGGATAGGGTAGGTAAGCAG.