esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
###### Abstract  
We present an algorithm selection framework based on machine learning for the exact computation of _treewidth_, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-art exact treewidth algorithms on a wide array of graphs and use this information to predict which of the algorithms, on a graph by graph basis, will compute the treewidth the quickest. Experimental results show that the proposed meta-algorithm outperforms existing methods on benchmark instances on all three performance metrics we use: in a nutshell, it computes treewidth faster than any single algorithm in isolation. We analyse our results to derive insights about graph feature importance and the strengths and weaknesses of the algorithms we used. Our results are further evidence of the advantages to be gained by strategically blending machine learning and combinatorial optimisation approaches within a hybrid algorithmic framework. The machine learning model we use is intentionally simple to emphasise that speedup can already be obtained without having to engage in the full complexities of machine learning engineering. We reflect on how future work could extend this simple but effective, proof-of-concept by deploying more sophisticated machine learning models.  
treewidth; tree decomposition; algorithm selection; machine learning; combinatorial optimisation +
Footnote †: journal:  
_Algorithms_
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 2 Materials and Methods
### Dataset
#### 2.2.2 Extraction of ARC-Reconstructed Multicoil Raw k-Space Data  
An in-house pipeline was developed replicating all post-processing steps done on an MR scanner to go from raw k-space data to DICOM images viewed by clinicians for diagnostic decisions. To the best of the authors' knowledge, no centralized resource is available describing all these steps, which can make it difficult for those interested in reconstruction to familiarize themselves with the process before model development. The authors thus saw value in describing these steps, shown schematically in Figure 2, with examples of pipeline intermediates at several steps in Figure 3. Unless otherwise specified, all post-processing steps were implemented using functions in GE Orchestra 1.10.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**MR Acquisition Information**} \\ \hline Scanner: GE Discovery MR750 3T Scanner (GE Healthcare, Milwaukee, WI) & Gradient System Max Strength: 50 mT/m \\ Max Slew Rate: 200 mT/m/ms & \\ \hline Coil: 18-channel knee transmit/receive coil (Quality Electrodynamics (QED), Mayfield Village, OH) & \\ \hline TR/TE: 1002/29 ms & FOV: 150 mm & Slice Thickness: 0.6 mm (0.6 mm spacing between slices) \\ \hline Flip Angle: 90 & SAR: 0.0939 & Echo Train Length: 36 \\ \hline Frequency: 128 & Bandwidth: 244 & ARC [45]: 4 (R = 2 in k\({}_{y}\), k\({}_{z}\)) \\ \hline Acquisition Matrix: & Image Dimensions: 512 \(\times\) 512 \(\times\) 200 \\ \(256\times 256\times 200\) & Resolution: 0.586 mm \(\times\) 0.6 mm \\  & Voxel Size: 0.293 mm \(\times\) 0.293 mm \(\times\) 0.6 mm \\ \hline \end{tabular}
\end{table}
Table 1: Acquisition parameters for 3D fat-suppressed CUBE sequence used in K2S dataset, and for this challenge.  
Some sequences may leverage PI techniques (such as ARC or GeneRalized Auto-calibrating Partial Parallel Acquisition (GRAPPA)) to acquire fewer lines within k-space, instead exploiting already acquired data across multiple coils to mitigate aliasing artifacts at the expense of SNR [17; 45]. This was the case for our sequence; consequently, the first step in post-processing raw multicoil k-space data was applying ARC to impute unacquired k-space lines. Subsequently, Fermi filtration was applied: given MR images are often zero-padded in k-space, ringing artifacts can emerge from the sharp boundary in k-space between nonzero and zero points [46]. A Fermi filter smooths this boundary, reducing ringing artifacts at the expense of sharpness in the reconstructed image. A custom Fermi filtration function was used, using the Fermi filtration radius and width parameters extracted from raw sequence metadata. After Fermi filtration, k-space was zero-padded to the intended image dimensions (in our case, from 256 \(\times\) 256 \(\times\) 200 to 512 \(\times\) 512 \(\times\) 200), completing k-space post-processing. All k-space post-processing was on multicoil data.
## 5 Numerical and Simulation Results
### The Performance of the Sampling RRT Technique  
We used an RRT-like technique to generate random paths for a time interval of interest of 25 s with five time slots, each lasting 5 s, and the results are shown in Figure 8. We took three random samples for the first step (sampling) time. The number of random samples increased exponentially with \(3^{k}\) for the coming sampling times. The problem objectives and constraints should be checked for all samples and validated. It takes more than 150 s for RRT to generate random samples with valid obstacle-free paths that satisfy motion constraints as well as calculate problem objectives, i.e., the energy-efficient path with the minimum distance between the UAV and vehicles. The running time indicates that RRT is computationally prohibitive (153.546 s) in such a complex environment. Specifically, since the navigation time interval of interest is 25 s, the running time of RRT is significantly higher, implying that it cannot be used for real-time applications, i.e., less than 25 s.  
Figure 6: Simulation results for the second scenario, in which the UAV maximum altitude is limited to be less than the average height of a tall building.  
Figure 7: Simulation results illustrate the performance of the smoothing technique.
## 2 Materials and Methods
### Method  
#### 2.2.1 Preparation of Cellulose Nanocrystals (CNCs) via Sulfuric Acid Hydrolysis  
The synthesis of cellulose nanocrystals (CNCs) through acid hydrolysis is controlled by the chemical hydrolysis reaction, as illustrated in Figure 1, as described by [PERSON] and [PERSON] [27]. The acid hydrolysis method was selected to produce CNCs from commercially sourced microcrystalline cellulose (MCC) due to the anticipated high yield with a stable aqueous suspension and high crystallinity index. In this study, a 14 mmol quantity of MCC was dispersed in 100 mL of distilled water and stirred at room temperature for 1 h. In a separate container, 64% (\(v/v\)) sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) was dissolved in 36 mL of distilled water and stirred for 10 min at room temperature. Then, 50 mL of the acid solution was gradually added to the MCC suspension to achieve a pH of approximately 0.82 and a final concentration of sulfuric acid of 21.3% (\(v/v\)). The mixed solution was mechanical stirred and heated at a constant of 45 \({}^{\circ}\)C for an hour, after which 200 mL of cold distilled water was added to stop the hydrolysis reaction. A white product, CNCs, was formed and collected by centrifugation at 4020 rpm for 20 min. The product was washed by vortexing in distilled water at 2200 rpm for 2 min and was then centrifuged at 4020 rpm for 20 min to eliminate unreacted precursors, i.e., sulfate ions. This washing step was repeated several times until the pH level of the product reached \(\sim\)6-7. The final CNC product was freeze-dried at 45 \({}^{\circ}\)C for 24 h and crushed into powder using a crucible. [PERSON] et al. [28] proposed Equation (1) to estimate the yield percentage (%\(\gamma\)) of the prepared sample (Table A1). Synthesis of cellulose nanocrystals (CNCs) is shown in Figure 2.  
\[\%\gamma=\frac{\textit{Weight after hydrolysis}}{\textit{Weight initial}}\times 100 \tag{1}\]
## 4 Proposed Framework
The length of our incident description token was less than 512 tokens; therefore, we utilized padding (_PAD_) to fill the unused token slots (further details below). Our BERT model output an embedding vector of 768 in each of the tokens and had 340M trainable parameters. * **ERNIE 2.0**[16]: Enhanced Representation through knowledge Integration (ERNIE) is a pre-trained framework that performs the training of new sequences with historically trained tasks. For text classification, ERNIE 2.0 has outperformed BERT with highly accurate results [16]. ERNIE 2.0 captures the contextual information with a series of shared text encoding layers, customized with recurrent neural networks or deep transformers with a stacked self-attention layer. Its multi-task learning encodes lexical, syntactic, and semantic information across tasks. When a new task arrives, this framework can incrementally train the distributed representations without forgetting the previously trained parameters. In our framework, we used 12 layers, 12 self-attention heads, and 768 dimensions in the hidden layer, resulting in 94M trainable parameters. * **RoBERTa**[15]: Robustly optimized BERT emphasizes data being used for pre-training and the number of passes for training. The RoBERTa architecture was proposed to overcome the drawback of the original BERT model by increasing _batch size_ from 256 to 8k, providing better speed for performance metrics [15]. We used a pre-trained transformer with built-in (vocab) 160 GB in size for RoBERTa to conserve our computational resources. It reduced the perplexity of the masked language model by providing the provision to train with larger batches and longer sequences. It also provided the dynamic masking pattern over the training data during data preprocessing. In our framework, we implemented a large RoBERTa model with 12 encoder layers, 12 attention heads, and 768 dimensions in the hidden layer, resulting in 110M trainable parameters. Finally, if an incident was classified as NMIR, it was assigned to the normal priority queue. If an incident was classified as MIR, it was directed to the high-priority queue and assigned immediately to the incident processing acceleration teams in order to resolve the issue as soon as possible.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 2 Analysis of the Electrical Connector Intermittent Failure Mechanism
### The Intermitt Failure Mechanism of the Electrical Connector  
The intermittent failure mechanism of electrical connectors is complicated and affected by many factors, including the degradation, environmental stress and so on. In particular, the intermittent failure mechanism under the comprehensive effects of thermal cycle and vibration is more difficult to analyze. In this paper, the vibration stress is taken as typical stress for investing the intermittent failure mechanism. Under the action of vibration stress, the two contact pairs of the electrical connector will move away from each other. Considering that the electrical connector may be subjected to vibration stress from various directions during operation, the direction of relative displacement is random. The relative motion of two contact pairs is considered to be normal motion and tangential motion in this paper, as shown in Figures 4a and 4b, respectively.  
In Figure 4, the asperity of solid line indicates the initial position, and the asperity of dotted line indicates the displacement boundary. The circular area within the solid line circle and dotted line circle represents the change range of contact area during movement.  
It can be considered that the area outside the solid circular is completely covered with the insulating film layer. The dashed area is metallic contact. According to [PERSON] contact theory [32], the relationship between the contact radius \(a\) and the contact depth \(d\) is \(d=a^{2}/2A\), where \(A\) represents the radius of curvature of the asperity. Let \(\Delta d\) be the normal displacement; then, the radius of the dotted circle in Figure 4a is  
\[a_{f}=\sqrt{a^{2}-2A\Delta d} \tag{3}\]  
Combining Equations (2) and (3), the maximum value of contact resistance \(R_{\rm s}\) caused by relative displacement is  
\[R_{s}=\left(\rho_{1}+\rho_{2}\right)/4\sqrt{a^{2}-2A\Delta d} \tag{4}\]  
According to Equation (4), it can be seen that the contact resistance will increase with the increasing of the displacement \(\Delta d\). If the relative displacement is large enough, the contact resistance will exceed the failure threshold, leading to failure. Further, once the external stress disappears, the asperity will return to its initial position due to the restoring force of the elastic part of the electrical connector. Then, the failure disappears and the whole process manifests as an intermittent failure.  
Figure 4: The movement of asperity for (**a**) the normal relative movement and (**b**) the tangential relative movement.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 6 Engineering Optimization Recommendations
### Analysis Method  
The establishment of a system model is imperative for conducting a parameter sensitivity analysis [30], that is, the determination of the functional relationship between system characteristics and factor \(P=f(x_{1},\cdots,x_{k}\cdots x_{n})\). When analyzing the influence of parameters on the characteristic \(P\), the rest of the parameters \((x_{1},x_{2}\cdots x_{n})\) can be made to take the base value and fixed value, and the parameters \(x_{k}\) can be made to change within their possible range, then the system characteristic \(P\) can be defined as:  
\[P=f\Big{(}x_{1}^{*},\cdots,x_{k}\cdots x_{n}^{*}\Big{)}=\varphi\Big{(}x_{k} \Big{)} \tag{27}\]  
It can thus be determined that the variation curve of maximum settlement of the extant tunnel is the \(P-x_{k}\) characteristic curve. Combined with the results of the analysis of the influencing factors, the sensitivity of the system characteristics P to the perturbation of the parameters \(x_{k}\) can be given. According to the \(P-x_{k}\) curve, we can understand the sensitivity of the system characteristics to a single factor. When a small change in the parameter \(x_{k}\) causes a large change in \(P\), the parameter is highly sensitive, and vice versa for low sensitivity.  
Figure 14: Existing tunnel settlement under varying strata loss rates: Settlement overall (**a**); settlement maximum (**b**).  
To better compare the sensitivity of the three factors, the depth of the new tunnel, its diameter, and the rate of ground loss, to the existing tunnel, a dimensionless sensitivity function is introduced:  
\[S_{k}(x_{k})=\left(\frac{|\Delta P|}{P}\right)/\left(\frac{|\Delta x_{k}|}{x_{k} }\right)\,(k=1,2,\cdots,n) \tag{28}\]  
where: \(S_{k}\) is the sensitivity of the parameter \(x_{k}\); \(|\Delta P|/P\) is the relative rate of change of the system characteristic \(P\); \(|\Delta x_{k}|/x_{k}\) is the relative rate of change of the parameter \(x_{k}\); the larger the parameter \(S_{k}\) change, the larger the effect on the system. In the smaller case of \(|\Delta x_{k}|/x_{k}\), after derivation, \(S_{k}(x_{k})\) can be approximated as follows:  
\[S_{k}(x_{k})=\left|\frac{d\varphi(x_{k})}{dx_{k}}\right|\frac{x_{k}}{\varphi(x _{k})}\,(k=1,2,\cdots,n) \tag{29}\]  
The sensitivity factor \(S_{k}^{*}\) of the parameter \(x_{k}\) can be obtained by substituting the parameter \(x_{k}=x_{k}^{*}\) into the above equation, as shown in Table 2. \(x_{k}^{*}\) is the parameter base value, determined by the initial calculation parameters set.  
It can be seen that the diameter has the greatest influence on the existing tunnel, with a sensitivity factor of 1.87, followed by the rate of stratum loss, with a sensitivity factor of 1, and finally the depth, with a sensitivity factor of 0.53.
# The Effects of L-Citrulline and Malic Acid on Substrate Utilisation and Lactate Elimination
## 3 Results
### RER and Lactate Levels during Active Recovery  
Malic acid and citruline supplementation had no effect on lactate levels at maximum power (Figure 5).  
Neither malic acid nor citruline supplementation had a significant effect on RER, RPE and lactate values during the 9 min active recovery protocol (Figure 6). Time points of lactate, RER and RPE measurements during active recovery are shown on the x-axis. RER (left y-axis) and lactate (right y-axis) values are displayed accordingly.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Rest End** & **0 W** & **35 W** & **70 W** & **105 W** & **140 W** & **175 W** & **210 W** & **Peak Load** \\ \hline Placebo & \(75.5\pm 12.5\) & \(76.9\pm 11.4\) & \(85.5\pm 11.4\) & \(100.3\pm 12.3\) & \(117.2\pm 16.2\) & \(130.1\pm 16.5\) & \(142.0\pm 17.7\) & \(154.3\pm 19.4\) & \(160.0\pm 12.0\) \\ Malic acid & \(70.4\pm 7.5\) & \(75.1\pm 9.2\) & \(84.2\pm 8.9\) & \(98.2\pm 9.7\) & \(113.3\pm 12.6\) & \(126.7\pm 13.7\) & \(141.4\pm 17.2\) & \(151.7\pm 18.4\) & \(158.3\pm 12.4\) \\ citruline & \(70.9\pm 9.6\) & \(74.5\pm 9.9\) & \(84.4\pm 11.4\) & \(98.5\pm 10.0\) & \(114.0\pm 14.9\) & \(128.1\pm 16.1\) & \(141.6\pm 16.9\) & \(152.4\pm 19.9\) & \(161.6\pm 16.9\) \\ \hline \end{tabular}
\end{table}
Table 4: The average HR values of three different supplementation groups during exercise.  
Figure 5: The lactate values of three different supplementation groups at RER 1.1 (pairwise Wilcoxon signed-rank test) The boxplot shows the median (line), lower and upper quartile (column), minimal and maximal values (whiskers) and outliers (circle).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Placebo RPExHR** & **Malic Acid RPExHR** \\ \hline \(175\) W & \(1825.1\pm 511.48\) & \(1633.5\pm 497.8\) \\ \(210\) W & \(2082.3\pm 638.71\) & \(1953.4\pm 687\) \\ \hline \end{tabular}
\end{table}
Table 3: Mean and standard deviation (\(\pm\)SD) of RPExHR values at different loads during exercise (malic acid–placebo).
## 3 Statistical Methodology
### Surrogate Models
#### 3.3.2 Radial Basis Function Models (RBF)  
A so-called radial function employs the Euclidean distance between the input parameter and a fixed point, known as the center. A Radial Basis Function (RBF) model is constructed using a linear superposition of different radial basis functions, which is presented in Equation (5):  
\[g_{s}=\sum_{t=1}^{N_{tra}}w_{t}\psi(\|x-x_{t}\|) \tag{5}\]  
To use the combination of \(N_{tra}\) radial functions as a RBF surrogate, each point \(x_{t}\) corresponding to the training subset (\(X_{tra}=\{x_{t}\}\)) is employed as the center, \(w_{t}\) are weights associated with each center \(x_{t}\) and \(\psi\) is a basis function that acts over the aforementioned euclidean distance. In this work, the library PySMO developed by the Institute for the Design of Advanced Energy Systems based on the work of [PERSON] and [PERSON] et al. [10; 51] is employed on Python to construct RBF surrogates. Depending on the variable to be metamodeled, the best function \(\psi\) amongst those available in the library (linear, cubic, thin-plane spline, gaussian, and multi-quadratic) is selected to obtain the greatest fitting.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 3 Results  
### Usability Assessment of Imagery Processing  
To ensure the imagery quality for accurate classification, the median value of NDVI was chosen to assess the accuracy of the processed images. Next, the standard deviation was calculated to assess the effect of cloud interference. It was expected that a larger standard deviation would indicate more severe cloud interference.  
The results showed that the median of NDVI showed good variation in phenological characteristics, with no abrupt changes in standard deviation produced, indicating good results from the processed image (Figure 4). These statistical results demonstrated that the image quality was significantly improved after cloud interference removal, linear interpolation, and SG filtering.
# Modeling and Simulation of Sky Survey
## 4 Design of the Model Solving Algorithm
### Observation Efficiency Statistics  
Through the constraint analysis in Section 3.6, we are able to find that the main factors affecting the observation efficiency are sunlight and lunar light, so these two constraints are mainly considered in the preliminary statistics of the observation efficiency. We know that the threshold value for judging the inefficient time period is set differently, and the final optimized results differ. The threshold should be set in combination with the orbital parameters of the satellite and also the observation needs. A detailed analysis of the effect of different parameters on the optimization results will be given later in the experimental section. The steps to calculate the inefficient time period are shown in Algorithm 1. For inefficient observation time periods, we perform prearranged observation tasks shown in Figure 2.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### The Role of UAVs in Wireless Communications
#### 2.2.3 Backhaul Network  
Also known as backhaul, in telecommunications, this term refers to the infrastructure that connects the core network with the subnetworks. In cellular networks, this network is used to connect the BSs and the core network. Typically, this connection is established using optical fiber and microwave links, among other methods. However, when this network has been damaged, temporary connections can be established using UAVs. For this, several techniques or methods are available; one alternative is to create multi-hop networks using multiple UAVs in a specific topology (e.g., a linear topology).
## 3 Challenges in Big Data for Machine Learning
### Model Interpretability  
Model interpretability is a cornerstone of ML in high-stakes applications like healthcare, finance, and criminal justice, where decisions carry significant consequences. While complex models such as deep neural networks (DNNs) and ensemble methods offer high predictive accuracy, their \"black-box\" nature often obscures the reasoning behind their decisions. This lack of transparency can erode trust, impede accountability, and lead to ethical concerns when decisions adversely affect individuals or society [219; 220].  
Interpretability techniques such as SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and Integrated Gradients (IGs) have emerged to address these challenges. These methods reveal how input features contribute to a model's output, allowing stakeholders to validate and understand predictions. For instance, in healthcare, interpretable models can explain why certain symptoms or tests lead to a diagnosis, enabling doctors to corroborate or challenge the recommendations. In finance, interpretability ensures that loan approvals or fraud detection decisions can be justified to regulators and affected parties [221; 222].  
High-stakes applications also demand actionable insights to guide decision making. Counterfactual explanations, which suggest minimal changes to inputs required to alter a prediction, provide clear and user-friendly feedback. For example, a counterfactual explanation might indicate that increasing an applicant's credit score by a specific amount would lead to loan approval, empowering users to take corrective actions. Similarly, Anchors offer localized, rule-based explanations that enhance the clarity and reliability of predictions in domains such as fraud detection [223; 224; 225].  
Moreover, inherently interpretable models, such as generalized additive models (GAMs) with pairwise interactions, offer transparency by design, enabling stakeholders to understand how features influence outcomes without relying on post hoc explanations. Attention mechanisms in DL, particularly in transformers, further enhance interpretability by highlighting specific input elements, such as words or image regions, that drive predictions [226; 227].  
The importance of interpretability in high-stakes applications extends beyond technical validation; it fosters trust, supports compliance with regulatory requirements, and ensures ethical accountability. As ML systems become more pervasive in critical domains, interpretability remains a crucial factor in balancing model complexity with societal and operational demands [228; 229].