esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 4 Results
### Stacking Model  
The LR, DT, and NBC algorithms have relatively low accuracy compared to the XGB algorithm. However, by using high-accuracy XGB to stack the three relatively low-accuracy algorithms of LR, DT, and NBC, the model performance of these three machine-learning algorithms is improved. Table 5 shows that the accuracy of the stacked model for these classifiers with the new dataset increases by approximately 10%.  
Table 6 shows that the stacking model accuracy of the new dataset increases by around 1% compared to the original dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Time(S)**} \\ \cline{2-10}  & **ODS** & **NDS** & **ODS** & **NDS** & **ODS** & **NDS** & **ODS** & **NDS** & **ODS** & **NDS** \\ \hline LR & 0.8482 & 0.8586 & 0.7978 & 0.8412 & 0.8576 & 0.8745 & 0.8054 & 0.8412 & 0.0327 & 0.0606 \\ DT & 0.8513 & 0.8605 & 0.8176 & 0.8487 & 0.8029 & 0.8078 & 0.8173 & 0.8287 & 0.0423 & 0.0463 \\ NBC & 0.8514 & 0.8575 & 0.8445 & 0.8773 & 0.8573 & 0.8631 & 0.8545 & 0.8664 & 0.0048 & 0.0053 \\ XGB & 0.9543 & 0.9554 & 0.9467 & 0.9547 & 0.9528 & 0.9548 & 0.9523 & 0.9554 & 0.5853 & 0.7207 \\ \hline \end{tabular} (ODS: Original dataset, NDS: New dataset).  
\end{table}
Table 4: Model performance comparison on different datasets.  
Figure 6: The new dataset after feature construction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **Before Stacking** & **After Stacking** \\ \hline LR & 0.8586 & 0.9585 \\ DT & 0.8605 & 0.9560 \\ NBC & 0.8575 & 0.9535 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy after stacking for three classifiers.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.1 Estimating the Location of the Destination  
Without determining the exact location of a UAV, we intend to determine the cell ID where the UAV is hovering. We also consider that a UAV has a communication range that is twice the length of a cell. The UAV knows the other UAVs' IP addresses and highest speed ranges. Moreover, UAVs share the global pheromone map and location information from the beginning of a mission. Thus, the pheromone map and location tables are populated from the beginning of a mission. The details on how the pheromone map is populated are in [26]. When a sender intends to send a data packet to the destination, the sender obtains the location of the destination from its location table. However, if we consider a  
Figure 9: Flowchart with the update mechanism of the one- and two-hop neighbor data tables.  
scenario where the destination is far from the sender, we expect considerably old location information. In such a scenario, the sender uses its pheromone map and path-planning mechanism to estimate the destination's current location/cell ID.  
The sender calculates the number of waypoints (\(n\)) that the destination might have flown through after the last known location to estimate the current location of the destination, as follows:  
\[n=\frac{t_{passed}}{t_{s}}+1, \tag{2}\]  
where \(t_{passed}\) and \(t_{s}\) denote the time passed after the update time of the last known location and the required time for a UAV to fly over a cell at its highest speed. Following its pheromone map and path-planning mechanism, the sender UAV estimates the flight path and current location/cell ID of the destination UAV.
## 4 Results and Analysis
### Habitat Quality Spatial Distribution Characteristics
Consequently, the habitat quality in these areas ranges between 0.25 and 0.5, classifying them as medium-grade areas. The low-grade areas are primarily composed of constructed and unused land, featuring a high population density. Large expanses of construction land have encroached upon bare land and farmland, fragmenting the continuous natural habitat and leading to significant habitat fragmentation. Construction activities introduce various forms of pollution, including air, water, and noise pollution. The intense human activities in these regions have markedly disrupted the local ecological structure, damaging the integrity of the habitat and reducing the regional habitat quality.
Ascorbic acid is essential for human health, intaking from edible plants due to humans lack of the last step ascorbate synthetic enzyme [28]. Four de novo biosynthesis pathways of ascorbic acid: the L-galactose, L-glucose, myo-inositol, and D-galacturonate pathways have been proposed in plants [29]. The L-galactose pathway of ascorbate biosynthesis starting from glucose has been extensively studied in higher plants [30]. Mannose-6-phosphate isomerase (PMI), phosphomannomutase (PMM), mannose-1-phosphate guanyllyltransferase (GMP), GDP-mannose 3,5-epimerase (GME), GDP-L-galactose phosphorylase (GGP), L-galactose-1-phosphate phosphatase (GPP), L-galactose dehydrogenase (GDH), and L-galactono-1,4-lactone dehydrogenase (GLDH) related to the ascorbate biosynthesis while L-ascorbate oxidase (AO), L-ascorbate peroxidase (APX), dehydrorosarcoma reductase (DHAR), and monodehydrorosarcoma reductase (MDAR) control the regeneration of ascorbate [5; 30]. Elucidation of key metabolites and candidate genes responsible for fruit quality is beneficial for breeding new pitaya cultivars and improving their nutritional values. To date, candidate genes related to betalain biosynthesis were isolated from transcriptome data of red-peel and yellow-peel pitayas [31; 32]. Besides, several candidate genes involved in sugar biosynthesis were also obtained through RNA-Seq [16]. However, compared with red-peel pitayas, genes related to sugars and organic acids in fruit development and maturation of yellow-peel pitaya are rarely studied. In this study, nine fruit development stages of 'WCHL' and 'YCHL' pitaya pulps were collected to investigate the changes in the total phenol, total flavonoid, ascorbate, starch, soluble sugar, major sugar, and organic acid components. Subsequently, the transcription abundance of candidate genes related to ascorbate synthesis and regeneration, starch synthesis and degradation, major sugar synthesis and TCA cycle were analyzed in the three fruit developmental stages of 'WCHL' and 'YCHL' pitaya pulps.
# Three-Dimensional Convolutional Vehicle Black Smoke Detection Model with Fused Temporal Features
## 5 Experiments
### Ablation Study  
Recall that our model in general is based on ResNet50. We first introduce a two-branch structure to obtain spatial and temporal features at different resolutions. Then we design a cross-scale feature fusion module to mix the features of the target image at different depths in the model. In addition, an attention module is designed to find the important frames for black smoke detection among multiple inputted frames. These modules collectively compose our model. To further validate the efficacy for each designs, five ablated models were trained and evaluated.  
* \(3D_{R}\) denotes the two-branch structural model of branch 1 and branch 2
* \(3D_{RC}\) denotes the introduction of the CSFF module on top of \(3D_{R}\)
* \(3D_{RCS}\) is the addition of the SAM module on top of \(3D_{R}\)
* \(3D_{RCSF}\) is the FULL version of our model, which indicates that the FF module is added to \(3D_{R}\). The FF module includes the CSFF module and the SAM module.  
These five models were evaluated individually under identical experimental conditions on the test set. The comparative results in terms of accuracy, recall, F1-score, and inference time are presented in Table 3.  
The experimental results demonstrate that the two-branch structure with 3D convolution (\(3D_{R}\)) enhances the network's capability to extract the temporal motion features of black smoke, resulting in a 3.31% increase in accuracy and a 2.52% improvement in recall compared to the original model. The inclusion of the CSFF module in \(3D_{RC}\) facilitates comprehensive integration of multi-segment features of black smoke across temporal and semantic dimensions, yielding a slight improvement in both accuracy and recall over \(3D_{R}\). The \(3D_{RCS}\) model, which incorporates the SAM module, addresses the limitations of relying solely on short-term temporal cues by enhancing attention to global information and mitigating background noise, thus showing a marginal improvement in performance compared to \(3D_{RC}\). The fully integrated \(3D_{RCSF}\) model proposed in this section achieves significant improvements, with increases of 7.4% in accuracy and 6.6% in recall relative to the original model.
# Federated Learning for Surface Roughness
## 2 Related Technologies  
### Federated Learning  
Federated learning [23] is a distributed machine learning technique that enables the collaborative training of a shared model across multiple devices or nodes without centralizing data. Instead of sharing raw data, only model gradients are exchanged to ensure data privacy and security. In the federated learning framework, each node independently trains the model using its local data and uploads the computed gradients or model updates to a central server. The server then aggregates these updates and distributes the updated global model back to the nodes for the next round of training. While federated learning effectively preserves data privacy, it faces practical challenges such as non-identically distributed data and model update aggregation. Techniques like Federated Averaging are commonly used to address these issues by performing weighted averaging of model updates, ensuring that data heterogeneity among devices does not compromise the model's generalization capability. In this study, the experimental setup involves two industrial PCs (IPCs) functioning as agent nodes. Each IPC computes model gradients based on its local data and uploads the results to a central server for model weight updates. The updated model is then sent back to the IPCs to continue the subsequent training process.
# Numerical Simulation of the Kelvin Wake Patterns
## 5 Discussions of Kelvin Wake Angle
### The Effect of Source Type on Wake Angle  
The two free surface patterns for flow past the Kelvin source and Rankine source, respectively, are computed for the Froude number \(F^{\prime}=0.8\). The amplitudes of ship waves, generated by two source types, should be adjusted to equalize. A wave profile computed for Kelvin source with \(e^{\prime}=0.9\) is present in Figure 11a and the wave profile computed for the Rankine source with \(\mu^{\prime}=0.5\) is given in Figure 11b. After measurement, the wake angles in the two free surface patterns are approximately equal, which means that source type does not affect the variation in wake angle. Furthermore, the wake angle has the same trend, no matter whether there is a Kelvin source or Rankine source as a disturbance. In Figure 12, there are trends of wake angle with the effect of Froude number for Kelvin source and Rankine source; both downtrends have two stages, but the first interval has a longer range in Figure 12a. The wake angle sharply decreases at Froude number 2.8 when the flow passes the Kelvin source. However, the wake angle sharply decreases at Froude number 2.0 when the flow passes the Rankine source. Therefore, the source type has an effect on the threshold about Froude number.  
Figure 11: Perspectives and plans view of the free surface computed for \(F^{\prime}=0.8\): (**a**) Kelvin source with \(e^{\prime}=0.9\); (**b**) Rankine source with \(\mu^{\prime}=0.5\). The top two free surface patterns are the perspectives and the bottom two free surface patterns are the plans. The red line marks the edge of the wake angle in each plan view.  
Figure 10: A plot of apparent wake angle \(\theta\) against Froude number \(e^{\prime}\) for a flow past the Kelvin source, with the source strength \(F^{\prime}=0.8\) (the red circles) and \(F^{\prime}=1.2\) (the blue circles).  
On the other hand, the Rankine source possesses a better capacity to avoid introducing very small spurious waves throughout the domain. Focusing on the upstream domain in Figure 11a, the spurious waves can be found, so that the whole free surface elevation is touched by spurious waves. Nevertheless, if we want to simulate the ship wave accompanied with sea clutters, introducing small spurious waves is a reasonable method.
## 3 Understanding Biofilms
### Biofilm Formation
#### 3.3.5 Influence of External Factors on Biofilm Formation  
The conversion of planktonic microbes into biofilm-forming, adhering states is a meticulously controlled process affected by several external influences. These environmental stimuli serve as signals, prompting bacteria to form biofilm as a survival mechanism. Critical parameters like pH, temperature, and nutrient availability significantly influence this transition, affecting the behavior and resilience of biofilm-forming microbes.  
pH and Biofilm Formation  
The pH levels in their surroundings significantly impact the ability of bacteria to produce biofilms. Depending on the pH levels, various types of bacteria display different capacities for creating biofilms. According to [PERSON] et al. (2018), _E. coli_ strains produce more biofilms when the medium pH is 7.4 [92]. Similarly, a study found that _Salmonella enterica_ serotype Kentucky forms more biofilms at higher pH levels (7.0 and 8.0) than lower ones (4.0 to 6.0) [93].  
Temperature Effects  
Temperature is a significant component influencing biofilm development. The optimal temperatures for biofilm formation differ across species. Studies have shown that different types of bacteria respond differently to temperature when it comes to the formation and growth of biofilms. For instance, _E. coli_ was found to form biofilms most effectively at 25 \({}^{\circ}\)C, while it struggled at 37 \({}^{\circ}\)C [92]. Additionally, research by [PERSON] et al. revealed that exposure to 26 \({}^{\circ}\)C for three days led to increased production of polysaccharides compared to 10 \({}^{\circ}\)C [94]. Furthermore, [PERSON] and [PERSON]'s research suggested that the optimal temperature for _Klebsiella Pneumonia_ biofilm growth is around 40 \({}^{\circ}\)C, as this was when the highest levels of biofilm formation were observed [95]. Increased or decreased temperatures may induce stress in microbial cells, promoting biofilm formation as a defensive mechanism against thermal stressors.  
Nutrient availability serves as a primary catalyst for biofilm formation. Bacteria often demonstrate increased metabolic activity in nutrient-dense settings, promoting fast biofilm development. Nutrient deprivation may trigger biofilm development as a survival strategy, enabling bacteria to preserve resources and endure unfavorable circumstances [96; 97]. _Salmonella_ biofilms are more prone to formation in nutrient-deficient conditions, where adherence confers a competitive edge in resource acquisition [98]. Another study demonstrated that _P. aeruginosa_ exhibited a 4.6-fold increase in biofilm formation under nutrient-limited conditions compared to nutrient-rich environments [97].
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Self-Supervised Learning of ResNet-50 Backbone  
We chose the MoCo architecture [24] for the self-supervised pretraining of the ResNet-50 backbone. In this approach, a dictionary of embeddings from previous versions of the feature extractor are cached to provide, without additional computation, a large amount of negative examples to a contrastive loss at each iteration.  
The main advantage of MoCo is that it does not require large batch sizes [6] and, thus, can be trained on a single desktop GPU. In this approach, only few embeddings of negatives are computed in each training iteration using the current version of the encoder. Many other embeddings, computed with previous versions of the encoder, are cached and, thus, reused. The encoder is updated using a momentum rule based on the encoder weights, thus converging more slowly towards the encoder.  
The Moco algorithm is described in Figure 2. Here \(x^{q}\) and \(x^{k}\) are two \(64\times 64\) pixel patches. We designate \(x^{q}\) as the query patch and \(x^{k-}\) as \"negatives\". At each training iteration a new query patch is considered and a \"positive\" patch \(k_{+}\) is generated by the pretext task. A number of random negative patches are sampled and passed through the momentum encoder to produce \"negative\" embeddings. The similarity, \(q\cdot k\) is computed between the query patch and the embedding of the positive patch and of the negatives. The embeddings of the negatives are the union of those computed from the negatives in the current batch and those taken from a FIFO queue of negative embeddings. We use the NTXent loss on the similarity measure:  
\[L_{q}=-log\frac{exp(q\cdot k_{+}/\tau)}{\sum_{i=0}^{K}exp(q\cdot k_{-}^{i}/\tau)} \tag{1}\]  
The encoder and momentum encoder are both ResNet-50 networks, as described in the backbone block of Figure 1 but with an additional average pooling and fully connected layers on top. The fully connected layer has 512 output neurons and produces the embedding. The encoder's parameters \(\theta_{q}\) are updated with SGD while the momentum encoder's parameters \(\theta_{k}\) are updated using Equation (2) where \(m=0.99\) is the momentum value:  
\[\theta_{k}\gets m\theta_{k}+(1-m)\theta_{q} \tag{2}\]  
Patches in SSL are pre-processed by first clipping to the 3 rd and 97 th percentile computed, for each band, over the pre-training dataset. Then the patches are standardized  
Figure 1: ResNet50-UNET architecture (s2 = stride 2 px). The FC layer of ResNet50 has been removed. Numbers show output channels of each block.  
by subtracting the mean and dividing by the standard deviation computed on a part of the EuroSAT dataset [25].
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### SEU and SEDNUI Robustness Comparison  
To evaluate a circuit's resilience to radiation-induced errors such as SEUs and DNUs, transient fault injection simulations are typically performed. This involves injecting a brief current pulse at sensitive nodes in the circuit model to simulate the charge deposited by a high-energy particle hit [20]. The minimum amount of this injected charge needed to flip the cell's state is the critical charge (Qc), with higher values indicating greater robustness. Specifically, Qc represents the minimum value of \(Q_{inj}\) found through simulation that successfully induces an upset. The injected current pulse shape, \(I_{inj}(t)\), is commonly modeled using a double-exponential function, as shown in Equation (1):  
\[I_{inj}(t)=\frac{Q_{inj}}{\tau_{\alpha}-\tau_{\beta}}\bigg{(}e^{-\frac{t}{ \tau_{\alpha}}}-e^{-\frac{t}{\tau_{\beta}}}\bigg{)} \tag{1}\]  
Figure 10: Hold power access time comparison between the proposed RMLP-18T cell and previous works.  
Here, \(Q_{inj}\) is the total charge injected in a single event simulation; \(\tau_{\alpha}\) is the charge collection time constant; and \(\tau_{\beta}\) relates to the ion track formation time. Based on the relevant literature for this technology node, \(\tau_{\alpha}\) and \(\tau_{\beta}\) were set to 50 ps and 200 ps, respectively [21]. Figure 11 illustrates example transient waveforms resulting from such charge injections at various sensitive nodes. Additionally, the time at which the current pulse reaches its maximum, \(t_{max}\) can be determined. When \(\tau_{\alpha}\) is 50 ps and \(\tau_{\beta}\) is 200 ps, \(t_{max}\) is 93 ps, as shown in Equation (2).  
\[t_{max}=\frac{\tau_{\alpha}\tau_{\beta}}{\tau_{\beta}-\tau_{\alpha}}\text{ln} \bigg{(}\frac{\tau_{\beta}}{\tau_{\alpha}}\bigg{)} \tag{2}\]  
Proposed RMLP-18T cell achieves a critical charge (Qc) of 37.83 fC (Figure 12). While this is not the absolute highest value reported--NRHC14T, SEA14T, and SIMR18T boast Qc > 50 fC--it is considerably better than many other cells, such as HPHS12T (9.29 fC), QCCS12T (11.6 fC), and RHBD14T (17.4 fC). This demonstrates a solid, competitive level of tolerance against SEUs. As mentioned back in Section 2.2, the cell's defense against certain SEDNUs relies on careful layout design rather than inherent circuit recovery.  
Figure 11: Different amounts of charge (10 to 35 fC) are injected into (**a**) node Q (**b**) node QB (**c**) node S1, and (**d**) 10 to 25 fC is injected into nodes Q and QB simultaneously.
## Appendix A Prompt Template
### Few-Shot Examples  
In this section, we present the few-shot examples selected for our study and justify their inclusion. These examples were chosen to provide a comprehensive and representative set of rare cardiovascular diseases, ensuring the robustness and reliability of our findings.  
1. Chronic ischemic heart disease. Old anterior infarction, a single-vessel disease with complete revascularization. Severe systolic dysfunction of the left ventricle. No current angina.
2. Complete atrioventricular block paroxysmal. Definitive DDDR pacemaker implant. Post-procedure Takotsubo syndrome. Psychological stress due to unforeseen hospital admission and administration of isoproterenol as possible inducers. Transient moderate systolic dysfunction of the left ventricle at the expense of akinesia of the middle and apical segments. Coronary arteries without significant lesions.
3. Neuroendocrine tumor of intermediate grade stage IV. Carcinoid heart disease. Cardiac insufficiency. Critical tricuspid stenosis. Severe tricuspid insufficiency. Severe pulmonary stenosis. Moderate pulmonary insufficiency. Surgical replacement of tricuspid and pulmonary valves. Complicated postoperative period with carcinoid crisis. Death.
4. Marfan syndrome: carrier of a pathogenic mutation in FBN1. Aneurysm of the ascending aorta and severe aortic insufficiency intervened. [PERSON]'s procedure and aortic tube. Dilation of the left ventricle and secondary systolic dysfunction, severe at discharge, currently moderate (LVEF 44%) with NYHA class I.  
The selection process for these examples involved several key steps to ensure their representativeness and relevance to our study objectives. First, we consulted with a panel of medical experts to identify a diverse set of rare cardiovascular diseases that are representative of the broader spectrum of such conditions. The experts provided insights into the clinical significance, prevalence, and diagnostic challenges associated with each disease.  
Next, the examples were selected based on specific criteria aimed at ensuring diversity, complexity, and relevance. The selected cases cover a range of rare cardiovascular diseases, including ischemic heart disease, stress-induced cardiomyopathy, carcinoid heart disease, and genetic syndromes like Marfan syndrome. These cases exhibit complex clinical presentations and diagnostic challenges, ensuring that the LLMs are tested on a variety of difficult scenarios. Additionally, the examples are relevant to the study's objectives of evaluating the LLMs' ability to handle rare and complex cardiovascular diseases.\(\backslash\)  
By following this rigorous selection process, we ensured that the few-shot examples are representative of the diversity and complexity of rare cardiovascular diseases. This approach enables a comprehensive evaluation of the LLMs' ability to handle such cases, providing robust and reliable insights into their potential to assist medical professionals.