esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Methods and the Use of Location and Activity Data in Forests
## 7 Conclusions  
We have provided a review of available PNT technologies in forested environments, and for several emerging technologies and concepts that are likely to impact natural resource management. Specifically, we reviewed PNT technologies including GNSS, Bluetooth, UWB, RFID, and QR codes. We also reviewed concepts that may influence natural resources in the future, including LBS, geofences, wearable technology, activity recognition, mesh networking, the IoT, and big data. It is clear from our review that new possibilities for monitoring, analyzing, and reporting the movements of people,equipment, wildlife and objects in remote areas are emerging quickly and will advance safety, work productivity, and research methods in ways that have not previously been feasible. Currently, there are relatively few examples of applying IoT, LBS, and big data to operational forestry and wildland fire, outside of the common use of remote sensing and fire behavior modeling. In the future, however, we will likely see a much wider range of big data and sensor-based topologies and concepts diffusing from computer science, marketing, and other fields. Solutions for sharing data in remote areas will generally include a mix of short-range (Bluetooth, BLE, ANT), radio, cellular, and satellite technologies, but will require some modification of currently available applications designed to function seamlessly in urban areas. For this reason, the off-the-grid approaches being developed for day-to-day data sharing in natural resources are also of interest and importance for supporting public safety in urban areas during environmental disasters (hurricanes, floods, and fires), military conflicts, and other situations when cellular and internet communication may be denied or degraded. Emerging, map-based positioning approaches such as SLAM and map matching, perhaps when combined with elements of inertial navigation or LBS, are likely to increase in importance. To best take advantage of the new, big data paradigm in natural resources, we have proposed a simple hierarchical model for location and data sharing in IoT applications off-the-grid. At lower tiers with many resources (e.g., hundreds of people and equipment), sensor data is processed and transmitted locally with low-cost, low-bandwidth devices. At higher tiers, aggregated data is transferred over longer distances via higher-cost, higher-bandwidth communications equipment, with the most critical data sent through global networks (e.g., satellites). This tiered structure corresponds closely with Incident Command on wildland fires, the general organization of the forest products supply chain in operational forestry, wildlife population monitoring, and many other federal, state, and industrial organizational structures in natural resources.
## 4 Conclusions  
Ripples have been extensively studied for various materials because of their promising applications. For all of the aforementioned applications, uniform rippled structures with well-defined large areas are required. However, the efficient fabrication of uniform rippled structures with a large area is a challenge, which limits its industrial applications. Not only scanning speed and intervals but also pulse number and irradiation modes are crucial for surface morphologies of uniform rippled structures with a large area. In multishot femtosecond induced ripples, the incubation effects are crucial for the formation of surface rippled structures. Therefore, in this study, the effects of the pulse number and irradiation modes on the surface morphologies of rippled structures, including ablation areas and periods of subwavelength ripples, are studied on the metal titanium surface. The experimental results indicate the following: (1) An increase in ablated areas with ripple structures is observed with the increasing number of pulses. The diameters of ablated areas significantly vary from 14.0 \(\upmu\)m to 18.1 \(\upmu\)m during the first 50 pulses. When the pulse number increases from 60 to 100, the diameters of the ablated areas are approximately the same, which varies from 18.4 \(\upmu\)m to 18.6 \(\upmu\)m. (2) The periods of ripple structures reduce with an increase in the number of pulses. The interference between the surface plasmon field and laser field plays an important role for the formation of subwavelength ripples. (3) Samples first irradiated using several shots and then the remaining shots have larger ablation areas and finer rippled structures because of different incubation effects. (4) When the pulse number in the first series is less than that in the second series, rippled structures on titanium are characterized by larger areas and periods. Therefore, uniform rippled structures with large areas can be modified by designing the laser pulse modes and pulse number.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] designed the experiments, performed the SEM characterization and analyzed the data. [PERSON] wrote the manuscript. [PERSON] and [PERSON] edited the manuscript. [PERSON] supervised the work.  
Figure 5: Dimensions and periods of a multiple-shot ablated region with subwavelength ripples at different femtosecond pulse irradiation modes.  
Funding: This research was financially supported by National Key R&D Program of China (Grant No. 2018 YFB1107401), National Natural Science Foundation of China (NSFC) (Grant Nos. 51805014 and 51705328), Beijing Natural Science Foundation (Grant Nos. 3174045 and 3194045), open project of Beijing Engineering Research Center of Laser Technology (Grant No. BG0046-2018-03), and Shanghai Sailing Program (Grant No. 17 YF1408500).
## 6 Challenges and Limitations
### Regulatory and Legal Challenges  
Navigating the regulatory landscape for AI in healthcare is complex. AI applications may be considered medical devices or software as a medical device (SaMD), subjecting them to regulatory oversight. Regulations vary by country and may not be fully established for AI technologies. Compliance requires understanding regulatory requirements, conducting thorough validation and documentation, and engaging with regulatory agencies. Legal considerations also include liability issues [64]. Determining responsibility in cases where AI contributes to an adverse event is a challenge. Clear guidelines and legal frameworks are needed to address liability and ensure accountability.
## 2 Representative Examples
### Rotundone  
Rotundone, a guaieene-derived sesquiterpenoid, is responsible for the distinctive peppery aroma in wine and spices. Its odor threshold is extraordinarily low (16 ng/L in wine, 8 ng/L in water) [27]. To put this odor threshold in perspective, 5 mL of rotundone would be enough to make all wine produced annually in Australia taste distinctly of pepper. Rotundone is biosynthesized in the grape berry, and becomes most prominent after veraison [27]. Rotundone concentration is also higher in fruit grown at high elevations with harsh, exposed growing conditions. Many Australian red wines are described as being peppery because of high rotundone concentrations. Rotundone is common in grapes across many regions: other peppery wines include California Zinfandel, many California and French Syrah-Granache blends, Bourdieu varietals, _etc_. Rotundone and the pepper flavor/aroma are only present in red wines. Rotundone has been synthesized by the aerial (_i.e._, non-enzymatic) oxidation of \(\alpha\)-guaiee [28], consistent with the observation that the peppery aroma often increases when wine is left in a glass over time to oxidize.  
Several questions about rotundone formation could be addressed using quantum chemical calculations. First, consider the putative carbocation cyclization/rearrangement mechanism shown in Scheme 4. In this mechanism, a deprotonation/reprotonation process, via a germacrene intermediate, is shown, but it is not clear whether this process could be replaced by a concerted intramolecular proton transfer [39]. This proton transfer process--be it concerted or stepwise--would result in a secondary carbocation (Scheme 4, top right). Secondary carbocations are often avoided as intermediates [1; 35; 36; 37], so it is possible that proton transfer is combined with cyclization. Whether or not formation of one particular diastereomer is energetically preferred upon cyclization is also not clear. Quantum chemical calculations of the sort described above could solve these puzzles. Once \(\alpha\)-guaieene is formed, non-enzymaticoxidation can convert it to rotundone [28]. A putative mechanism for this process, analogous to a mechanism for lipid peroxidation, is also shown in Scheme 4 (bottom). In this process it is not clear why abstraction of an allylic hydrogen atom to form the specific allylic radical shown predominates; abstraction from three other allylic positions are also possible. Once the allylic radical is formed, it is not clear why O\({}_{2}\) capture at the specific site shown is preferred, nor is it clear if there is diastereoselectivity for the capture step. Again, quantum chemical calculations could shed light on these issues.  
**Scheme 4.** Proposed biosynthetic formation of rotundone: \(\alpha\)-guaiene-forming carbocation cascade followed by radical-initiated oxidation.
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 4 Experimental Study
### Development of PSO-ELM Model  
Similarly to ELM, NumHLNs of PSO-ELM also needs to be investigated. So, 250 PSO-ELM models with NumHLNs from 1 to 250, with a number of particles in the swarm  
Figure 8: Prediction results of the ELM model with various HLNs.  
of 250 and a maximum iteration of 1000, were developed. Then, each model was run five times to predict the training subset and the validation subset (see Figure 9). The reason why models with more than 250 HLNs were not developed is that the computational load of the model increases sharply as NumHLNs increases. The prediction performance in Figure 9 shows that differences between \(\text{Acc}_{\text{train}}\) and \(\text{Acc}_{\text{validate}}\) in the 250 PSO-ELM models are all less than 5%, and when NumHLNs exceeds 100, the values of \(\text{Acc}_{\text{train}}\) and \(\text{Acc}_{\text{validate}}\) do not change much. To find the optimally performing PSO-ELM model among these 250 models, the ranking method presented by [PERSON] et al. (2019) was again adopted. It is obvious in Figure 9 that the model with 246 HLNs achieves the optimal performance, whose average \(\text{Acc}_{\text{train}}\) and average \(\text{Acc}_{\text{validate}}\) are 0.9915 and 0.9994, respectively. So, 246 was chosen as NumHLNs.  
After NumHLNs in PSO-ELM was determined, the number of particles in the swarm (NPS) and the maximum number of iterations also need to be investigated. Hence, 10 PSO-ELM models with the maximum number of iterations of 1000 and the number of particles from 20 to 350 were established and run five times to predict the training subset (see Figure 10). As shown in Figure 10, there are no significant changes in these accuracy curves after 800 iterations, and 200 is the optimal NPS with the highest prediction accuracy. Thus, 800 and 200 were chosen to establish the final PSO-ELM model for discriminating microseismic events and mine blasts.  
Figure 9: Prediction results of the PSO-ELM model with various HLNs.
###### Abstract  
To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters--baseline distance, focal length, and depth of field--affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R\({}^{2}\) = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications.  
micro-scale 3D reconstruction; monocular vision; laser-induced breakdown spectroscopy (LIBS); ablation morphology; plasma characteristics Article
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 1 Introduction
### A Single-Dimensional Attention-Based Method for Scene Text Recognition  
The majority of existing recognition systems employ Convolutional Neural Networks (CNNs) [10] to automatically extract various degrees of character feature information from  
Figure 1: Character recognition technique. (**a**) Character image recognition in one dimension; (**b**) character image recognition with sequence prediction in a 1D perspective; (**c**) a character image recognition method based on semantic segmentation.  
pictures [11; 12; 13]. In general, these approaches reduce character pictures to one-dimensional characteristics. For example, ref. [12] proposed convolutional recurrent neural network (CRNN) for sequence annotation by combining CNN and recurrent neural network (RNN), in which they deal with character sequences of variable lengths while reducing reliance on segmentation. [PERSON] et al. [13] suggested a technique to analyze character sequences that combines connected temporal classification (CTC) [14] with long short-term memory (LSTM) to address the issue of irregular spacing between characters as well as the difficulty of character alignment. Figure 1a,b demonstrates two instances of one-dimensional character and text recognition. Although these approaches are effective, they compress a character's attributes into a one-dimensional shape without taking into account the character's structural information, creating background noise interference [15].
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Group Mobility  
The group mobility model represents nodes that are moving around the logical center with weak or strong relationships between them. There are some mobility models and communication protocols for group mobility in WBAN. In addition, the group mobility model is largely dependent on the collaboration application; therefore, network architecture such as C-SPINE [5] needs to cooperate with the group mobility model.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
## 4 Proposed BM-GCN Model
### GCN for Inter-Node Graphs  
In this section, we model the phishing detection problem as a binary classification. The inputs of this model are the three feature graphs discussed earlier and the outputs of this model are the prediction labels of Ethereum nodes.  
In our model, we use the layer-wise propagation rule of [PERSON] et al. [24] to build a multilayer GCN. The rule is as follows:  
\[H^{(l+1)}=\sigma\Big{(}\tilde{M}^{-\frac{1}{2}}\tilde{A}\tilde{M}^{-\frac{1}{2}} H^{(l)}W^{(l)}\Big{)} \tag{9}\]  
In the above equation, \(\tilde{A}=A+I_{N}\) denotes the adjacency matrix of the graph \(G\) with self-connections added, \(I_{N}\) is the identity matrix, \(\tilde{M}_{ii}=\sum_{j}\tilde{A}_{ij}\) and \(W^{(l)}\) are the trainable weight matrices, \(\sigma(\cdot)\) is the activation function, and \(H^{(l)}\in\mathbb{R}^{N\times D}\) is the matrix of activations in the \(l\)-th layer, \(H^{(0)}=X\).  
Then, we use the propagation rule mentioned above to build a GCN. For each feature graph, we use graph convolution to generate the embedding of the feature, which is shown on the left side of Figure 4. The input graph \(G\) is denoted by \(G=\{n_{1},n_{2},...,n_{|V|}\}\), where \(n_{i}\) is the \(i\)-th node, and \(x_{i}\) is the representation of \(n_{i}\). For the three feature graphs \(\{G_{i},G_{v},G_{f}\}\) in our model, we denote the vector of the \(i\)-th node as \(\{x_{i}^{i},x_{i}^{v},x_{i}^{f}\}\).  
In order to predict the labels of nodes, we concatenate the outputs of three GCN models as \(X_{i}=(x_{i}^{i}:x_{i}^{v}:x_{i}^{f})\), and use a dense layer \(y=f(w\cdot X+b)\) and a softmax layer to obtain the predictions of node labels. The softmax function is as follows:  
\[p_{i}=\frac{\exp(y_{i})}{\sum_{k=1}^{n}\exp(y_{k})} \tag{10}\]
## 3 The Joint Relay Selection and Power Control Optimization Method via Stackelberg Game  
In this section, we formulate the anti-jamming joint optimization problem as a Stackelberg game firstly, which is an effective theoretical method to deal with the hierarchical confrontation relationship between the user and jammer. Then we prove the existence of Stackelberg equilibrium (SE). We propose a hierarchical joint optimization algorithm under the game frame in the last.
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 3 Methodology
### Superquadric Tracking  
As superquadric fitting is numerically unstable, we do not sequentially estimate parameters \(\Lambda\) based on the assumption that the shape of the objects does not change sequentially. We only update the pose parameters \(\Phi\) at consecutive frames. The naive approach to estimate the pose \(\Phi^{t}\) at frame \(t\) is  
\[\min_{\Phi^{t}_{i}}\sum_{k=0}^{K_{i}}((F(\mathrm{Tr}_{\Phi^{t}_{i}}(p^{t}_{k}); \Lambda_{i})-1))^{2}, \tag{6}\]  
where \(\Lambda_{i}\) is the estimated superquadric parameters at the initial frame.  
However, the outliers which are caused by miss-segmentation lead to the low accuracy of pose estimation. Therefore, we down-weigh the points that are far away from the center of objects during optimization by introducing a coefficient, \(\beta\).  
A naive approach to down-weighing distant points is employing threshold value based on the Euclidean distance from the centroid of the object and each point. However, there can be two possible problems here. First, as the distance is defined in absolute scale, the threshold value might differ between different scaled objects. Second, calculating the distance in Euclidean Space is not compatible for a superquadric parameter estimation, because the distance in superquadric shape is non-linear. Therefore, we introduce a scale-invariant non-linear threshold parameter to down-weigh miss-segmented points for robust pose parameter estimation.  
We introduce coefficient \(\beta\) to the minimization equation, Equation (6), to eliminate the points distant from the origin in an object-centered coordinate system. First, we transform the point cloud \(\mathbf{p}\) by the transformation matrix \(\Phi_{t-1}\) at the previous frame. For a point defined by a vector \(\mathbf{p}_{o}=(x_{o},y_{o},z_{o})\) in the transformed point cloud, there is a scalar value \(\beta\) and vector \(\mathbf{p}_{s}=(x_{s},y_{s},z_{s})\) on the superquadric surface, which satisfies \(\mathbf{p}_{s}=\beta\mathbf{p}_{\mathbf{o}}\). Thus, for vector \(\mathbf{p}_{s}\), the following equation holds:  
\[F(\beta x_{o},\beta y_{o},\beta z_{o})=\left\{\left(\frac{\beta x_{o}}{s_{x}} \right)^{\frac{2}{\beta_{2}}}+\left(\frac{\beta y_{o}}{s_{y}}\right)^{\frac{2}{ \beta_{2}}}\right\}^{\frac{z_{2}}{\beta_{2}}}+\left(\frac{\beta z_{o}}{s_{z}} \right)^{\frac{2}{\beta_{1}}}=1. \tag{7}\]  
From this equation, it follows that  
\[\beta=F^{-\frac{\zeta_{1}}{2}}(x_{o},y_{o},z_{o}). \tag{8}\]Instead of estimating the pose \(\Phi_{t}\) directly, we estimate the residual pose \(\Phi_{t-1:t}\). Therefore, the equation below is applied to estimate the residual pose \(\Phi_{t-1:t}\) (using \(x_{+}\equiv max(0,x)\)):  
\[\min_{\Phi_{t}^{-1:t}}\sum_{k=0}^{K_{t}}((\beta-\beta_{th})_{+}(F(\text{Tr}_{ \Phi_{t}^{t}}(\text{Tr}_{\Phi_{t}^{t-1:t}}(p_{k}^{t}));\Lambda_{i})-1))^{2}. \tag{9}\]  
We define the hyperparameter \(\beta_{th}\) as 0.75 throughout the experiment.
# A Soft, Biocompatible Magnetic Field Enabled Wireless Surgical Lighting Patty for Neurosurgery
## Materials and Methods  
### Fabrication for Surgical Lighting Patty  
The process began with a copper (Cu, thickness: 18 \(\upmu\)m)/polymide (PI, thickness: 12 \(\upmu\)m) bilayer film (AC181200R, DuPont(tm) Pyralux(r), Wilmington, DE, US) on a glass slide (2\({}^{\prime\prime}\times 3^{\prime\prime}\)). Next, we deposited photoresist (AZ(r)1518, AZ Electronic Materials, Somerville, NJ), US; spin-coating at 3000 r.p.m. for 30 s; baking on a hot plate at 100 \({}^{\circ}\)C for 4 min) on top of a bilayer film. The UV photo-lithography enabled us to expose the light (UV intensity for 265 mJ/cm\({}^{2}\)) to form patterns on the prepared film with a patterned mask. The solvent development (AZ(r)Developer 1:1, AZ Electronic Materials, Somerville, NJ, US; for 30 s) and wet etching (copper etchant, Alfa Aesar(tm), Tewksbury, MA, US; for 3 min) procedure define patterns, including copper traces and contact pads, on a flexible substrate. Next, we mounted components, including light emitting diodes (LED), which were being used for the light source (emitting color: white, material: InGaN, viewing angle: 140\({}^{\circ}\), and light intensity: 640 Lux (with sponge), 1420 Lux (without sponge) of the proposed patty, transistors, a reed switch, resistors, and capacitors on the patterns by soldering. Biocompatible polymers, PDMS (SylgardTM 184 silicone elastomer kit, Dow(r), Midland, MI, US, thickness: 50-100 \(\upmu\)m), encapsulated the central lighting layer by a simple coating process and were cured at a vacuum oven at 80 \({}^{\circ}\)C for 1 h. The top and bottom surgical sponge then sandwiched together the encapsulated layer. This resulted in a thin (1 mm thickness), flexible surgical lighting patty. For powering the device, thin enameled wires twisted with retrieval strings connected batteries to the light-emitting element. Collectively, our devices could be fabricated with 8 hours of effort in standard laboratory facilities using low cost commercially available components. The following Table 1 summarizes the steps required for tests and fabrication of the surgical lighting patty, and detailed information on the layout and components are found in Figure S1 and Video S1, S2.