esa-sceva/satcom-chunk-collection huggingface dataset:
# Altering the Supply of Shielding Gases to Fabricate Distinct Geometry in GMA Additive Manufacturing
They also observed a reduction in distortion by altering the shielding gases [12]. Researchers also observed that adding helium to argon changed the arc characteristics, droplet formation, metal transfer, and weld bead profile [13]. [PERSON] et al. [14] showed that adding increasing amounts of helium to Ar-CO\({}_{2}\) leads to a higher heat flux and current density on the arc axis. However, there is a dearth of literature showing the effects of ternary blends on WAAM processes. There have been significant efforts made by researchers to simulate the WAAM process. Fully coupled models for cold-metal transfer WAAM and GMAW were presented by [PERSON] et al. [15] and [PERSON] et al. [16], respectively. However, the simulation timeline is not feasible. It is also worth considering that the surface active elements (often sulfur, but also oxygen) have significant impacts on the surface tension of the molten pool, widely known as the Marangoni effect, as shown in the equation below [17]. \[\gamma=\gamma_{m}^{o}-A(T-T_{m})-RTT_{s}ln[1+k_{1}a_{i}e^{-(\frac{\Delta H^{o} }{RT})}] \tag{1}\]  
where, \(\gamma_{m}^{o}\). is the surface tension of the pure metal at the melting temperature, A is the negative of the surface tension gradient for the pure metal, \(T_{m}\) is the melting point of the material, \(R\) is the universal gas constant, \(\Gamma_{s}\) is the surface excess at saturation, \(k_{1}\) is a constant related to the entropy of segregation, \(a_{i}\) is the thermodynamic activity of component i in the solution, and \(\Delta H^{o}\) is the standard heat of adsorption. It was recently shown that the surface tension-driven flow is the dominant driving force in fluid flow [18]. In this study, two-bead wall geometries were fabricated with different shielding gas mixtures but otherwise identical AM parameters. One key objective of the present study was to reveal whether the changes seen in the melt pool fluid flow in the binary and ternary shielding gas conditions were achieved by a change in arc characteristics or by a variation in the surface tension of the molten pool. A detailed investigation of arc characteristics and droplet frequency was thus conducted, followed by computational fluid dynamics modeling incorporating the volume of fluid (VOF) method. The thermophysical properties of the gas mixture were incorporated to estimate the heat flux and process efficiency in the model. The model was applied to understand the effects of the surface tension changes due to the concentration of surface active elements.
# Streamlining Visual UI Design: Mining UI Design Patterns for Top App Bars
## 5 Evaluation  
### RQ1: What Is the Performance of Our Representation Learning Model?  
We evaluated the performance of our representation learning model by assessing its top-1 prediction accuracy on the entire test set, as detailed in Table 2. The top-1 accuracy was determined by considering the token with the highest prediction probability as the model's output, as defined by the Formula (7). Overall, our model demonstrates proficiency in autoregressive learning within the sequence representation of TABs. This suggests that the designed representation effectively captures the TAB design patterns.  
To delve deeper into understanding the performance dynamics of our model, we analyzed prediction accuracies across sequences of varying lengths. Notably, our model exhibits strong predictive capability for short sequences of length 2, achieving an accuracy of 68.71%. However, there is a noticeable decline in performance for sequences of length 3, indicating potentially less significant regularity in TAB sequences of this length.  
In contrast, as sequences extend to medium lengths (4-8), the model demonstrates an improvement trend. Particularly noteworthy is the model's peak accuracy of 92.31% when handling sequences of length 6, highlighting its robust predictive ability for medium-length sequences.  
Remarkably, when confronted with longer sequences, notably those of lengths 9 and 10, the model showcases exceptionally high accuracy. This aligns with the notion that as contextual information accumulates, the task of predicting subsequent tokens becomes increasingly manageable.
# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 2 Materials and Methods  
The techniques used to carry out the proposed methodology and its implementation in a digital system based on FPGA are presented below. A general review of robot theory, the DWT time-frequency analysis technique, statistical indicators, and a quick look at neural networks are conducted. On the other hand, a general description of the FPGA device used for this work is made.
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 4 Results
### Comparison with Other Methods
The calculation time for the sequential calculation on previously prepared image fragments of the YUV420p color model with a resolution corresponding to the size of the encoder input is 0.74 s. By paralleling the calculations performed by the ANN, this time can theoretically be reduced below 0.1 s.  
The resolution of the test image to unify the comparison was 416 \(\times\) 240, for which the compared methods have a watermark capacity of 100 bits. The encoder of the proposed method operates natively at a resolution of 128 \(\times\) 128, so it needs to perform eight encode operations to embed the watermark. The native image resolution at which the encoder operates, which is 128 \(\times\) 128, embeds 96 watermark bits. For comparison purposes, the same watermark was embedded over the entire area of the 416 \(\times\) 240 video frames. However, other bit strings can be encoded in each of the \(128\times 128\) image areas, which together will make up the larger watermark. For example, by embedding a watermark on a similar resolution of 256 \(\times\) 256, a watermark of 384 bits can be embedded. The proposed method has a higher capacity than the compared methods and has the potential to increase it further.
## 6 Result and Discuss
### Transmission Efficiency Analysis
#### 6.3.1 Transmission Accuracy Analysis
The simulation shows that RL-ADTA (0.1, 0.2, 0.01) can meet the requirements of different levels of monitoring application data transmission accuracy. The priority of efficiency and accuracy of underwater network data transmission can be adjusted through parameter configuration.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Monitoring Fermentation Variables
#### 4.1.2 Sugar Level Monitoring  
Monitoring sugar levels during fermentation is essential for determining the progress and completion of the process. AI systems utilize spectroscopy, refracometry, and other sensor technologies to continuously measure sugar concentrations. Machine learning algorithms process this data to provide accurate predictions of sugar level changes, enabling timely interventions to maintain desired fermentation trajectories. Accurate sugar level monitoring ensures that the fermentation process is proceeding as expected and helps winemakers decide when to make necessary adjustments, such as adding nutrients or adjusting temperature. This continuous monitoring also helps in detecting and correcting issues such as stuck or sluggish fermentations, which can significantly impact the quality of the wine [77].  
A study by [PERSON] et al. demonstrated the application of a bienzymatic biosensor for monitoring glucose and ethanol content during wine fermentation. The biosensor, based on glucose oxidase (GOx) and alcohol dehydrogenase (ADH), was effective in real-time monitoring and provided results consistent with conventional methods, highlighting its potential utility in winemaking [78].  
[PERSON] et al. developed an online glucose analysis system with high precision and a wide detection range for fermentation monitoring. This system, based on a homemade screen-printed enzymatic biosensor chip, showed excellent detection performance during the fermentation of sophorolipid and ethanol, making it a valuable tool for precise process control [79].  
Furthermore, [PERSON] et al. successfully used Raman spectroscopy combined with machine learning algorithms to predict total sugar concentrations, ethanol levels, and pH during wine fermentation. This approach proved to be a viable tool for rapid and accurate fermentation monitoring [80].
## 3 Analysis of the DQL Method
### Convergence Analysis
#### 3.1.2 Convergence of the Directional Direct Search Method  
The convergence theorem of the directional direct search method is cited from ([9] p. 122). The convergence of the directional direct search method uses the following assumptions.  
**Assumption 1**: _The level set \(L(x_{0})=\left\{x\in\mathbb{R}^{n}:f(x)\leq f(x_{0})\right\}\) is compact._  
**Assumption 2**: _If there exists an \(\alpha>0\) such that \(\alpha_{k}>\alpha\), for all \(k\), then the algorithm visits only a finite number of points._  
**Assumption 3**: _Let \(\xi_{1}\), \(\xi_{2}>0\) be some fixed positive constants. The positive bases \(D_{k}\) used in the algorithm are chosen from the set_  
\[D=\left\{\bar{D}\text{ positive basis}:cm(\bar{D})>\xi_{1},\left\|\bar{d} \right\|\leq\xi_{2},\bar{d}\in\bar{D}\right\}. \tag{23}\]  
**Assumption 4**: _The gradient \(\
abla f\) is Lipschitz continuous in an open set containing \(L(x_{0})\) (with Lipschitz constant \(v>0\))._  
Notice that Assumption 2 holds if the directional direct search method uses a finite set of positive bases. However, as we desired the ability to use an infinite set of positive basis, we implemented a sufficient decrease rule to ensure Assumption 2 held.  
**Theorem 1**: _Suppose the directional direct search method only accepts new iterates if \(f(x^{k+1})<f(x^{k})-(\delta^{k})^{2}\) holds. Let Assumption 1 hold. If there exists an \(\alpha>0\) such that \(\delta^{k}>\alpha\), for all \(k\), then the DQL method visits only a finite number of points, i.e., Assumption 2 holds._  
See Theorem 7.11 of [9].  
We have the following convergence theorem.  
**Theorem 2**: _Let Assumptions 1-4 hold. Then,_  
\[\liminf_{k\rightarrow+\infty}\lVert\
abla f(x_{k})\rVert=0, \tag{24}\]  
_and the sequence of iterates \(\{x_{k}\}\) has a limit point \(x_{\star}\) for which_  
\[\
abla f(x_{\star})=0. \tag{25}\]  
See Theorem 7.3 of [9].
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### Case Study 2--Unscrewing Nuts and Battery Cell Disassembly
#### 4.5.2 Design and Use of End-Effectors  
Figure 15a,b show the structural design and application of end-effectors for unscrewing nuts and removing battery cells, respectively. The end-effector designed for unscrewing nuts is a multi-purpose adaptor, comprising a series of steel cylinders and a spring system, as depicted in Figure 15a. During the unscrewing process, the steel cylinders adaptively enclose the nuts, allowing for their removal through rotation and friction induced by the multi-purpose adaptor. The end-effector for battery cell removal is a vacuum chuck. It consists of four chucks and pipelines, as shown in Figure 15b. To remove a battery cell, two chucks secure it, as highlighted in the red rectangular box in Figure 15b. Due to safety and payload considerations, only one battery cell should be removed at a time.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 2 Methods and Materials
### Trunk Detection
#### 2.1.2 Improved DBSCAN Algorithm Flow  
DBSCAN is a density-based clustering algorithm. Its clustering results can be determined by the tightness of sample distribution [14]. It defines the cluster as the largest set of points connected according to the specified density and can divide the area with high enough density into clusters [24].  
The improved DBSCAN algorithm is derived from the traditional DBSCAN algorithm combined with the above automatic parameter generation method. The following is detailed description of our improved DBSCAN algorithm: (1) By calculating the distance between two points, obtain the \(\varepsilon\) neighborhood sample set of all points in the point cloud slice whose distance is less than or equal to \(\varepsilon\). (2) Obtain the core points set satisfying the condition that the number of points in \(\varepsilon\) neighborhood is greater than or equal to \(MinPts\). (3) Initialize the cluster number to 0. (4) The points in the core point set are recursively processed to expand the cluster until all the points in the core point set are visited. See Appendix A for more details.  
Figure 1 shows the above clustering process of our improved DBSCAN algorithm. \(MinPts\) is set to 5 and \(\varepsilon\) is the radius of the circle in the figure. Red points are core point objects because their \(\varepsilon\)-neighborhood contains at least five points. Black points are not core point objects. The collection of all the points in the core object \(\varepsilon\)-neighborhood forms a cluster. In Figure 1, all points in the circle with the blue arrow on the left form one cluster, and all points in the circle with the blue arrow on the right form another cluster. Other points are noise points.
###### Abstract  
Transmission latency minimization and energy efficiency improvement are two main challenges in multi-hop Cognitive Radio Networks (CRN), where the knowledge of topology and spectrum statistics are hard to obtain. For this reason, a cross-layer routing protocol based on quasi-cooperative multi-agent learning is proposed in this study. Firstly, to jointly consider the end-to-end delay and power efficiency, a comprehensive utility function is designed to form a reasonable tradeoff between the two measures. Then the joint design problem is modeled as a Stochastic Game (SG), and a quasi-cooperative multi-agent learning scheme is presented to solve the SG, which only needs information exchange with previous nodes. To further enhance performance, experience replay is applied to the update of conjecture belief to break the correlations and reduce the variance of updates. Simulation results demonstrate that the proposed scheme is superior to traditional algorithms leading to a shorter delay, lower packet loss ratio and higher energy efficiency, which is close to the performance of an optimum scheme.  
c pacs: 2019
## 5 Prompt Structure Ablation Study
### Full Prompt with Both  
Combining one-shot examples and table data samples in the prompt resulted in the highest accuracy improvements across most models, as shown in Figure 6. However, this configuration also incurred the highest token costs, with cost increases ranging from 88.4% (_Claude-3.5_) to 159.0% (_Qwen-2.5-72b_). Despite the significant cost overhead, this approach yielded substantial performance gains in high-capacity models, particularly _GPT-4o-mini_ (115.4%), _Qwen-2.5-72b_ (109.5%), and _Llama3.3-70b_ (90.8%).  
Figure 5: Cost and accuracy increase by model when using a prompt with a one-shot example.  
Interestingly, more minor improvements were observed in models such as _Gemini-1.5-pro_ (26.3%) and _Mixtral-8x22b_ (29.0%), which also experienced disproportionately high-cost increases of 109.1% and 149.3%, respectively.  
**Recommendation:** The complete prompt configuration should be reserved for tasks where accuracy is paramount and token cost is not a constraint. High-capacity models like _GPT-to-mini_, _Qwen-2.5-72b_, and _Llama3.3-70b_ benefit the most from this approach and can justify the increased token cost. For budget-sensitive applications or models with limited capacity, using only one of the two components, either table data samples or one-shot examples, provides a more cost-effective alternative.
## 4 Discussion
### Clinical Implications of the Composite Utility Metric  
In order to bridge the gap between traditional performance metrics and the nuanced requirements of clinical decision making, a series of composite measures have been proposed to evaluate ML models in healthcare. Table 4 summarizes four key metrics--the overall model score (Equation (20)), clinical utility score (Equation (25)), Weighted Endpoint Accuracy Score (WEAS; Equation (30)), and the composite utility metric (Equation (32)). These metrics not only capture a model's global discriminative capacity (via AUC, sensitivity, and specificity) but also incorporate predictive values and a misclassification penalty,thereby aligning model evaluation with clinical priorities. Such an integrated approach is essential to ensure that the models are both statistically robust and clinically actionable. The resulting composite utility \(U\) is of significant clinical relevance as it combines the model's performance on critical endpoints with an explicit penalty for misclassification errors. While the WEAS of approximately 81.4% demonstrates that the model reliably distinguishes between the healthy (class 0) and severely pathological cases (class 4), the composite metric \(U\) further addresses the impact of misclassifications across all classes. Specifically, the penalty component in \(U\) quantifies the cost of errors based on their distance from the true class, reflecting the clinical intuition that misclassifying a healthy patient as severely ill (or vice versa) is more consequential than errors involving intermediate classes. For example, with a balancing parameter \(\lambda=0.5\), the computed utility \(U\approx 0.2669\) indicates that, despite robust performance at the endpoints, the model incurs a non-negligible cost when it strays from accurate classification in a clinically meaningful way. This approach aligns with clinical decision protocols where endpoint accuracy remains paramount but intermediate class errors still require mitigation. The integration of macro-averaged sensitivity (77.69%) and specificity (94.78%) in the composite model score provides critical insights into real-world functionality. These metrics reveal how well models balance the detection of positive cases (minimizing false negatives) against the ability to correctly identify negative cases (minimizing false positives); both fundamental requirements for clinical adoption. Similarly, the clinical utility score (83.9%) emphasizes predictive values that directly correlate with patient management outcomes, as clinicians prioritize minimizing diagnostic ambiguity through high positive and negative predictive values. In a healthcare setting, such as grading knee osteoarthritis severity [43; 48], this composite measure provides a more holistic evaluation of the model's clinical usefulness. It ensures that models selected for clinical use not only exhibit high accuracy in discriminating between the most critical cases but also maintain an acceptable level of performance across
## 3 Results and Discussions
### Selection of the Time of Irradiation  
The delayed luminescence of the yeast samples when responding to various durations of irradiation and acquired at the same length of the camera exposure is presented in Figure 6. This set of measurements was necessary to determine the length of irradiation, or the relative dose of irradiation that would give an adequate signal-to-noise ratio but not saturate the sample to complicate the time trace of the delayed luminescence. We note that the post-operating time of 10 s between two consecutive irradiation-acquisition cycles unavoidably complicated the time trace of delayed luminescence. The duration of the irradiation, which was controllable with the fiber-switching of the coupling of the delivery fiber to the LSLS light source, was set at the following numbers: 10 ms to 90 ms at an interval of 10 ms, 100 ms to 900 ms at an interval of 100 ms, 1 s to 9 s at an interval of 1 s, and 10 s to 100 s at an interval of 10 s. The time of integration was fixed at 100 s. Therefore, the intensity acquired represented the dose response of the samples. The sample under the irradiation had a solid scattering phantom surrounded by yeasts, and the two types of samples were set at the same surface level. The trace of the red circles in Figure 6A represents the delayed luminescence averaged over the region-of-interest (ROI) corresponding to yeast (the white circle in Figure 6B), whereas the trace of black diamonds in Figure 6A represents the delayed luminescence averaged over the ROI corresponding to the solid scattering phantom (the black circle in Figure 6B). Based on the red trace that corresponds to the luminescence of yeasts when responding to different lengths of irradiation, it was determined that 1 s of the duration of irradiation produced an adequate yield of delayed luminescence without saturating the yeast. Therefore, 1 s was used as the fixed time of irradiation in the subsequent assessment of the delayed luminescence that was acquired at different lengths of the exposure time of the CCD to deduce the time-integrated trace of the luminescence.  
Figure 6: Delayed luminescence acquired from yeast over 100 s of exposure time after switching off the irradiation of various durations. The trace of red circles represents the delayed luminescence averaged over the region-of-interest (ROI) corresponding to yeast, whereas the trace of black diamonds represents the delayed luminescence averaged over the ROI corresponding to the solid scattering phantom. The small red elliptical spot at approximately 1 o’clock position represents the residual luminescence (afterglow) of the illumination-delivery fiber.
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
## 3 Proposed Dynamic Window Approach
### The Candidate Local Goal
#### 3.2.3 Distance-to-the-Obstacle Term  
This evaluation term is used to select a local goal point far away from obstacles.  
\[dist2=\frac{dist(\mathbf{P}_{\textit{gaallocal}}\rightarrow\mathbf{P}_{\textit{ obstacle}})}{\sum_{i=1}^{m}dist(\mathbf{P}_{\textit{gaallocal}}\rightarrow\mathbf{P}_{\textit{ obstacle}})} \tag{16}\]  
where \(\mathbf{P}_{\textit{obstacle}}\) is the position of obstacles detected by the agent, \(m\) is the number of obstacles.  
Figure 5: The selection of the candidate local goals.  
The optimal local goal is selected by the evaluation function Equation (17):  
\[g_{end}=\mu_{dist1}\bullet dist1+\mu_{head1}\bullet heading_{local}+\mu_{dist2} \bullet dist2 \tag{17}\]  
where \(\mu_{dist1}\), \(\mu_{dist2}\) and \(\mu_{head1}\) are the weights of the functions. The \(goal_{local}\) (_i_) point corresponding to the larger set of results in \(g_{end}\) is used as the local goal.