esa-sceva/satcom-chunk-collection huggingface dataset:
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 3 Results and Discussion
### Evolution of Melt Pool Characteristics
#### 3.1.1 Melt Pool Morphology and Area  
Figure 3a,c illustrate the evolution of melt pool morphology under different SED settings. Under low-SED experimental conditions, the melt pool observed took on a smaller, spherical droplet shape. As the laser power increased and the scanning speed correspondingly decreased, leading to a gradual rise in the SED; the melt pool underwent significant morphological changes, transitioning from a regular hemispherical droplet to a teardrop shape with a longer tail. Concurrently, the contour of the melt pool became sharper, and the area of the melted region noticeably expanded. Under higher-SED conditions, the melt pool during solidification exhibited a phenomenon of tail separation, forming separate molten spheres. These series of changes in melt pool morphology indicate that the laser power and scanning speed had a significant impact on the formation process of the melt pool.  
To clarify the influence of melt pool morphology characteristics on process parameters, Figure 3d,e statistically presents the trends in melt pool area under different laser powers and scanning speeds. As shown in Figure 3d, the melt pool area increased with the increase in laser power. The increase in laser power enhanced the intensity of energy input, leading to higher energy density, which transferred heat to farther powder and melted it into molten droplets [26]. At the same laser power, the effect of different scanning speeds on the melt pool area was similar. In Figure 3e, for points with similar SEDs on the two plotted lines, the melt pool area at high scanning speeds is much larger than that at low scanning speeds. This is because under high-scanning-speed conditions, greater laser power was required to achieve the same energy density as at low scanning speeds. These comparative  
Figure 3: Melt pool morphology under different process parameters: (**a**) SED = 62.5 Jmm\({}^{-2}\) (Supplementary Video ESM_1); (**b**) SED = 75 Jmm\({}^{-2}\) (Supplementary Video ESM_2); (**c**) SED = 116.67 Jmm\({}^{-2}\) (Supplementary Video ESM_3, 4); and (**d,e**) area changes.  
results indicate that laser power had a more significant impact on the melt pool area. It directly affected the amount of energy input during the process, while scanning speed only affected the interaction time between the laser and the powder [26].
# A Review on Methods and Systems for Remote Collaboration
## 5 Communication Methods and Cues
### Behavior Detection
[120], in their study, focused on how gaze cues support remote collaboration when using the ThirdEye display. ThirdEye is a hemispherical display that helps the local user to determine the gaze direction of the remote helper, which is then displayed on a mobile terminal. The experiment showed that the local user's attention is driven to the objects of interest when using ThirdEye faster than when not using it. Omnigaze [121], another telepresence system, uses an omnidirectional video camera that has a spherical display on top of it. An eye tracker positioned at the remote user's location captures their gaze direction, which is then represented as information on the spherical display. This system enhanced the remote collaboration to some extent; however, it had many disadvantages, one being that the remote user could see the local user but not vice versa, which caused discomfort. Wearable RemoteFusion [122] shares eye gaze and hand gestures in an MR environment. The local user can see in AR the hand gestures of the remote user guiding him, while the remote user has a view of the local worker's eye gaze in VR. The study showed that users had an above-average feeling of co-presence, while the remote user was under less psychical load and concentration. [PERSON] et al. [123] investigated whether sharing the eye gaze or the head pointer improved performance when resolving a task between two remote collaborators. The results showed that there were no significant differences between the two methods. Thus, the head pointer method, which is less expensive, could be used instead of the eye tracking one. ZoomTouch [124] is a system that allows multiple users to control a robot using a hand-tracking module remotely. Recognized hand gestures are used to control the robot, which has embedded tactile sensors. [PERSON] et al. [125] reviewed eye-tracking prototypes categorized by their functionalities, subject used, and physical task types.
## 3 Results
### Soybean Yield Estimation with ML  
#### 3.3.1 Spectral Feature Assessment and Variable Selection  
Based on PCA biplots assessments, two major principal components for 24 VIs and five REFs accounted for yield variability of 65.88% (PC-1) and 21.42% (PC-2, Figure 4a), respectively. Eigenvectors for the REFs in green, red, and blue wavebands tended towards the top of the biplot (Figure 4a), showing strong influence on PC-2. While REFs in RE and NIR wavebands, and derived VIs, densely formed a cluster towards the top-left, extreme left, or lower-left region, showing stronger influence on PC-1. Eigenvectors for GNDVI and GRVI tended toward the lower-left region while VARI in the top-left region. Six VIs, including GCI, NDRE, GOSAVI, GSAVI, GDVI, and VARI, had strong correlations with SY when evaluated using the Pearson correlation (r). Following these observations, a total of five REF and six VI features were identified as inputs for the SY estimation (Figure 5).
# A New Method for 2D-Adapted Wavelet Construction:
## 3 Results
### Detection of 2D Mass-like Patterns in Digital Mammography Images
#### 3.2.1 Design of the Simulations Using the Proposed 2D Strategy
To measure the level of agreement between the actual location of the inserted mass-like pattern and the estimated location, the Intersection over Union (IoU) measure was used. The IoU metric in object detection evaluates the degree of overlap between the region where a mass is actually located in an image and the prediction of such a region given by a certain algorithm [50]. It is defined as the intersection area of both regions divided by their union area, and ranges from 0 to 1, where 0 indicates no overlap and 1 means perfect overlap. In this contribution, it is considered that the detection of the mass-like pattern is achieved for an IoU \(\geq\) 0.30. In addition to the estimated _shapelets_ for each pattern, another 61 classic wavelets, such as Daubechies 1-32, Symlets 2-19, and Coiflet 2-12, were used in the detection, available in their 2D version in PyWavelets [51]. Thus, the confusion matrix and the ROC curve of the detection were computed using the R language [52].
# Frequency-Separated Attention Network for Image Super-Resolution
## 2 Related Work
### Image Super-Resolution Based on Deep Learning  
Compared to traditional mathematical methods, neural networks possess superior non-linear matching capabilities. SR methods based on deep learning include different usage scenarios, such as those based on classic blind image SR [3], non-blind image SR [19], real image SR [20], text-focused scene image SR [21], lightweight image SR [22], hyperspectral image SR [23], video SR [24], attenuation correction SR [25; 26], etc. [PERSON] et al. first introduced SRCNN [3], which contains three convolutional layers, to address super-resolution issues. To regularize the solution, CSCN [4] and SCN [5] integrate traditional sparse coding with deep learning, demonstrating the value of conventional methods in deep neural networks. Unlike networks that upscale LR images to an HR size before input, ESPCN [10] and FSRCNN [9] directly learn the mapping from LR to HR, effectively increasing the network's responsiveness. At the end of the network, the sub-pixel convolution and deconvolution layers are used to obtain SR images. This network structure significantly influenced the design of subsequent SR networks.  
Vgg-net [27] indicates that deeper networks can perform more complex non-linear mappings, thereby enhancing the restoration of details in SR images. Since normalization in super-resolution networks can lead to artifacts and slow and unstable training, SR networks employ alternative methods to address the vanishing gradient problem, facilitating easier network training. Since LR and HR images share most fundamental features, VDSR [6] employs the residual learning of the differences between LR and HR images, not only increasing the network layers to 20 but also enhancing the network's convergence speed. MemNet [7] uses memory blocks containing recursive and gate units to address the issue of weakened information flow due to increased network depth. Unlike increasing the number of convolutional layers to deepen the network, DRCN [8] employs a very deep recursive layer to enhance the network's feature abstraction and parameter sharing, while recursive supervision and skip connections are used to mitigate the effects of network depth. Residual blocks [28] are used in networks [11; 12] to address the vanishing gradient problem, further increasing network depth and representational capacity. To further increase network depth, [PERSON] et al. [13] and [PERSON] et al. [16] directly connected the current layer with all subsequent layers using dense skip connections. This forms a contiguous memory mechanism, adaptively integrating information from multiple levels. Deep learning-based methods have shown exceptional performance in super-resolution tasks. The proposed method is based on deep learning.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Adaptive-DecayRank Algorithm with Bayesian Updating
#### 4.2.3 Adaptive and Dynamic Scoring Function  
The Adaptive-DecayRank formula with Bayesian updating is defined in Equation (7). The Gamma distribution parameters \(\alpha_{u}\) and \(\beta_{u}\) in Equation (6) represent our prior beliefs about the decay factors. For each iteration, the algorithm observes and adjusts node scores based on the recent changes. The scoring function is given by  
\[v_{u}^{(k+1)}=c\hat{P}v_{u}^{(k)}+(1-c)h_{u}e^{-\delta_{u}(k-A_{u}^{(T)})}, \tag{7}\]  
where \(v_{u}^{(k+1)}\) is the updated score for node \(u\) at iteration \(k+1\), \(c\) is the damping factor controlling the influence of neighboring nodes, \(\hat{P}\) is the transition probability matrix, \(v_{u}^{(k)}\) is the node score for \(u\) at iteration \(k\), \(h_{u}\) is the personalization vector capturing the inherent importance of node \(u\), \(\delta_{u}\) is the decay factor for node \(u\), dynamically updated using Bayesian inference, and \((k-A_{u}^{(T)})\) is the time elapsed since the last update of node \(u\).  
In Equation (7), the term \(c\hat{P}v_{u}^{(k)}\) represents dynamic PageRank propagation, and the adaptive temporal decay is represented with the expression \((1-c)h_{u}e^{-\delta_{u}(k-A_{u}^{(T)})}\), emphasizing recent interactions and diminishing the influence of older data.  
**Posterior updates:** Equation (6) shows the prior distribution for \(\delta_{u}\); that is, the conjugate prior, ensuring efficient computation. The _adaptive decay factor_, \(\delta_{u}\), is computed as the mean of the posterior Gamma distribution and is updated using \(\alpha_{u}\) and \(\beta_{u}\):  
\[\delta_{u}=\frac{\alpha_{u}}{\beta_{u}}. \tag{8}\]  
The _shape parameter_ (\(\alpha_{u}\)) is adjusted to reflect the magnitude of observed changes, with larger changes resulting in fast updates.  
\[\alpha_{u}^{(l+1)}=\alpha_{u}^{(l)}+\Big{|}v_{u}^{(k+1)}-v_{u}^{(k)}\Big{|}. \tag{9}\]  
The _rate parameter_ (\(\beta_{u}\)) is updated to maintain stability, preventing drastic shifts when changes are minimal:  
\[\beta_{u}^{(l+1)}=\beta_{u}^{(l)}+\Big{(}1-\Big{|}v_{u}^{(k+1)}-v_{u}^{(k)} \Big{|}\Big{)}. \tag{10}\]  
By leveraging Bayesian inference, the Adaptive-DecayRank algorithm dynamically adjusts \(\delta_{u}\) in real time, ensuring sensitivity to significant changes in graph structure while mitigating noise. The conjugacy of the Gamma distribution guarantees computational efficiency, enabling scalability for large-scale dynamic graphs. This approach addresses concerns about innovation, clarity, and robustness in anomaly detection for dynamic graphs.
## 2 Mathematical Modeling
### Linearization of Active Power Losses  
The active power flow in a line connecting buses \(i\) and \(j\), denoted as \(P_{ij}\), can be represented by (1), whereas the power flow in the opposite direction (\(P_{ij}\)) is given by (2). \(V_{i}\) and \(V_{j}\) represent the voltage magnitudes at nodes \(i\) and \(j\), respectively, while \(g_{ij}\) and \(b_{ij}\) are the conductance and susceptance of line \(ij\), respectively. Finally, \(\theta_{ij}\) is the angular difference between nodes \(i\) and \(j\).  
\[P_{ij} =V_{i}^{2}\mathrm{g}_{ij}-V_{i}V_{j}\mathrm{g}_{ij}\cos\theta_{ij}-V_ {i}V_{j}\mathrm{b}_{ij}\sin\theta_{ij} \tag{1}\] \[P_{ji} =V_{j}^{2}\mathrm{g}_{ij}-V_{i}V_{j}\mathrm{g}_{ij}\cos\theta_{ij}+ V_{i}V_{j}\mathrm{b}_{ij}\sin\theta_{ij} \tag{2}\]  
Active power losses, denoted as \(\varphi_{ij}\), can be expressed as the sum of the power flows in both directions, as indicated in (3).  
\[\varphi_{ij}=P_{ij}+P_{ji}=\mathrm{g}_{ij}\Big{(}V_{i}^{2}+V_{j}^{2}-2V_{i}V_ {j}\cos\theta_{ij}\Big{)} \tag{3}\]  
Assuming the following approximations:  
\[V_{i}\approx V_{j}\approx 1\ \ p.u. \tag{4}\] \[\cos\theta_{ij}\approx 1-\frac{\theta_{ij}^{2}}{2} \tag{5}\]  
Replacing (4) and (5) in (3) and performing some algebraic operations, the power losses can be expressed as follows:  
\[\varphi_{ij}=\mathrm{g}_{ij}\theta_{ij}^{2} \tag{6}\]  
where \(g_{ij}\) and \(\theta_{ij}\) represent the conductance of circuit \(i\)-\(j\), and the angular difference between buses \(i\) and \(j\), respectively. Note that Equation (6) is non-linear; nonetheless, it can be linearized through piecewise linear functions as follows:  
\[\theta_{ij}^{2}\approx\sum_{y=1}^{Y}m_{ij,y}^{s}\cdot\Delta\theta _{ij,y}\dot{\gamma}(i,j)\in\Omega_{l};\ \ y=1,\cdots,Y \tag{7}\] \[\theta_{ij}=\theta_{ij}^{+}-\theta_{ij}^{-};\ \ \forall(i,j)\in\Omega_{l}\] (8) \[|\theta_{ij}|=\sum_{y=1}^{Y}\Delta\theta_{ij,y}=\theta_{ij}^{+}+ \theta_{ij}^{-};\ \ \forall(i,j)\in\Omega_{l}\ \ y=1,\cdots,Y\] (9) \[0\leq\Delta\theta_{ij,y}\leq\frac{\Delta\theta_{ij}^{max}}{Y};\ \ \forall (i,j)\in\Omega_{l}\] (10) \[m_{ij,y}^{s}=(2y-1)\frac{\Delta\theta_{ij}^{max}}{Y};\ \ \forall(ij)\in\Omega_{l};\ \ y=1,\cdots,Y\] (11) \[\theta_{ij}^{+}\geq 0;\ \ \text{and}\ \ \theta_{ij}^{-}\geq 0; \tag{12}\]  
Note that \(\Delta\theta_{ij,y}\) is a continuous voltage angle variable associated with the _Y-th_ number of piecewise linear functions, which is used to represent active power losses. Equation (7) represents the linearized losses calculated by adding the linear functions. In (8), two slack variables are used to substitute \(\theta_{ij}\). In (9) the sum of the slack variables is used to represent the absolute value of \(\theta_{ij}\). Equation (10) ensures that the linearization variables do not exceed the pre-established value. Finally, (11) indicates the slope of each linearized line in the Y-th number of linear functions. Figure 1 depicts the linearization of \(\theta_{ij}^{2}\) with Y piece-wise linear functions, where the slope \(m_{ij,y}^{s}\) represents the square of the voltage angle to linearize \(\theta_{ij}\).
## 2 Materials and Methods
### Model Calibration and Validation  
The SWAT-CUP (Soil and Water Assessment Tool-Calibration and Uncertainty Program) software was developed to calibrate and validate hydrological processes by the SWAT model [23]. The Sequential Uncertainty Fitting Algorithm (SUFI-2), which is capable of analyzing a large number of parameters and measuring data from multi-gauge stations simultaneously, was used to obtain a better correlation between simulated and observed data, following [46]. Calibration is one of the processes that adjusts the model input parameter values and their boundary conditions to match the simulated values with the observed values [47]. Calibration can be carried out through uncertainty analysis (UA) or sensitivity analysis (SA), such that the model predictions lie within in a certain confidence range [48; 49]. The applicability of the SWAT model can be checked through careful calibration using one of the following algorithms: generalized likelihood uncertainty estimation (GLUE), sequential uncertainty fitting (SUFI-2), parameter solution (ParaSol), and Markov chain Monte Carlo (MCMC) [46; 50; 51; 52].  
Modeled streamflow data were calibrated (1991-1997) and validated (1998-2001) with observed climatic data and five LULC scenarios for various time periods. Five performance indices were analyzed, and the results were satisfactory. Model calibration on a sub-stream scale was carried out. Finally, the calibrated parameters were used for the analysis in the Arc-SWAT project.  
The objective functions, p-factor, and r-factor values for calibration (1991-1997) and validation (1998-2001) are presented in Tables 2 and 3 with the observed precipitation and using various LULCs at Qianjiang and Tianr hydrological stations, respectively. The performance indicators of the model for Qianjiang station had more satisfactory results, compared to that for Tianr station. However, the performance indicator values for Tianr station, as shown in Table 3, still had acceptable values. These values confirmed the good performance of the model using observed climatic data and all five LULC scenarios.
###### Abstract
Supporting this, researchers such as [13; 14; 15] agree that increased water flow reduces the resistance of solid particles, while also raising shear stress and lowering shear strength, ultimately leading to slope failure. Studies like those of [12; 16] have focused on how water drainage impacts slope stability. Research has shown that increased rainfall amplifies Drago forces, alters the solid phase, and can result in failures such as sliding and rockfalls. Similarly, advanced numerical simulations conducted by [17] reached conclusions consistent with [18]. These findings highlight the critical role water drainage plays in slope stability, making it an essential factor in slope stability assessments. In South Africa, and particularly in Limpopo Province, most roads consist of engineered slopes in rugged terrains. Slope failure susceptibility due to influential factors such as rainfall, rock mechanical integrity, the shear strength of geological attributes, and geotechnical design has been articulated in various roads within Limpopo Province [12; 19; 20; 21]. Nonetheless, knowledge of the impact or influence of strata orientation on the subject of slope instability within Limpopo is rarely documented. Likewise, the R37 regional road was developed through a rugged topography, which resulted in the exposure of artificial slopes consisting of several geological structures. In addition, certain localities along the R37 cut slope exhibit undocumented minor occurrences of failure, a geo-hazard that cannot be undermined. The R37 road passes through the eastern lobe of the Bushveld Igneous Complex, an economically viable geological province, making it to act as a vital component in the transportation network of agricultural and mining goods and other services. As such, an investigation of this nature is crucial to understand the dynamics involved in the R37 slopes and how best the problem can be dealt with. This paper aims to correlate the orientation of strata to slope instability through empirical evidence, numerical simulation, and structural mapping to develop a failure susceptibility chart.
## 2 Materials and Methods  
### The Bone-Plate Configurations  
#### 2.1.1 Patient-Adapted Plate Geometry Generation  
The starting point of the geometry generation was a physical SQBP plate (Pro supraprecineal Stryker plate--Stryker, Kalamazoo, MI, US), from which both the SQBP and SPP geometries were obtained. The two plates, indeed, share the same supraprecineal portion, intended for placement on the arcuate line of the pelvic brim, but the former has an additional portion lying on the infraprecineal side of the pelvis, intended to fit the quadrilateral surface of the ilium.  
Following the principles of reverse engineering and studying the SQBP plate characteristic shapes, the following steps have been followed to reproduce every feature of the undeformed plate in the CAD environment (Rhinoceros 3D, Associates, Seattle, WA, USA):  
* The whole plate profile was drawn lying on a plane, and extruded (thickness equal to 2 mm) thus obtaining a solid (Figure 1a).
* The supraprecineal portion of the plate was bent in order to recreate an anatomic curvature which follows the arcuate line of the ilium. The bending has been performed in the x-y plane along the z axis so that, at the end of this step, the plate still lies on the x-y plane (Figure 1b)
* The infraprecineal portion of the plate was bent with a 90\({}^{\circ}\) curvature with respect to the x-y plane and, afterward, was inflected along the z axis to fit to the quadrilateral surface of the ilium (Figure 1c).  
The so obtained undeformed plate geometry was then morphed on the hemipelvis CAD geometry (a standard Sawbones right hemipelvis--SKU: 3415-1--Sawbones(r), Europe AB, Malmoe, Sweden) in the Hyperworks environment (Altair Engineering, Inc., Troy MI, United States) to obtain a patient-adapted geometry. In detail, the supraprecineal side of the already meshed plate (see Section 2.2.1) was adapted to the bone surface exploiting the _freehand_ command of Hypermorph. Through the _freehand_ command some nodes of the plate were projected on the surface using the _move node to surface_ option and setting command parameters exploiting a trial and error procedure. After morphing, the plate hole countersinks were obtained by Boolean differences between the plate and spheres of 3.5 mm radius after having mathematically defined their center locations.  
Starting from the patient-adapted SQBP plate design, the SPP geometry was derived by deleting the infraprecineal portion of the plate. The two obtained geometries are shown in Figure 2.
## 6 Results
### Baseline Algorithms and Evaluation Metrics  
To demonstrate the convergence performance and reliability of the proposed algorithm, this paper compares the proposed multi-modal-MAPPO with two baseline algorithms: MAPPO and MAAC. MAPPO is a collaborative decision-making algorithm based on distributed reinforcement learning. By decoupling the policy optimization of agents from the global state value evaluation, it ensures training stability while achieving efficient adaptive solutions for multi-node collaborative tasks. MAAC is a multi-agent reinforcement learning algorithm based on the actor-critic framework, incorporating mechanisms from MADDPG, COMA, VDN, and attention mechanisms. In addition, to quantitatively evaluate the performance of the proposed algorithm, the following metrics are employed:  
* Hit ratio: Due to the limited user cache capacity, proactively pushed content may be evicted by cache replacement strategies before the user issues a request, leading to a cache miss. To measure the continuous effectiveness of the push strategy, the hit ratio is defined as follows: \[HR=\frac{1}{T}\sum_{t=1}^{T}\frac{h\_counts(t)}{H\_counts(t)}\] (33) where \(h\_counts(t)\) represents the number of content requests that are still accessible in the user's cache during the \(t\)-th time slot, and \(H\_counts(t)\) represents the total number of content requests issued by the user node during the \(t\)-th time slot.
* Average delivery delay \(\mathcal{D}\): The average latency for all content requests over the period \(T\).