esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Supporting Communication Protocols
### The Virtualization Layer: VaNetLayer  
The VaNetLayer defines procedures for several vehicles (the physical nodes, PNs) to collaborate in the emulation of stationary virtual nodes (VNs) that can be used to store persistent state information (PSI). As shown in Figure 2, the VNs cover non-overlapping regions, delimited so that there is one VN at each crossroads, and the road segments in between are covered with as many VNs as needed ensure that every PN can communicate directly with all the other PNs in its current region and the adjacent ones.  
The main task in the operation of a VN is the distribution of roles of leaders and backups among the PNs. The leader of a region takes charge of packet reception, buffering and forwarding in the communication with other VNs, whereas the backups maintain replicas of the PSI from the virtualization layer and the protocols above. This way, the virtual node can work even when the supporting vehicles fail or leave the region, if there remains at least one inside.  
Figure 1: The stack of protocols of our approach.  
Figure 2: The intersection-based layout of the VaNetLayer (the circles represent vehicles).  
The PSI managed at the virtualization layer (which is the cornerstone of VN emulation) includes counters of nodes and messages from the current region and the neighboring ones, as well as the MAC addresses of their leaders. An exponentially weighted moving average of the number of PNs is computed, too, that provides a basis for QoS estimations.  
The interface offered by the VaNetLayer to the protocols above exposes the notion of regions, the role (leader or backup) played by a PN at each moment, and functions to send/receive messages and to get/set/check the PSI. Since the PSI may be crucial for some protocols to run properly, the VaNetLayer implements mechanisms to try to preserve it, even if a region becomes empty for some time (see [12]).
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 2 Data and Methods
### Methods
#### 2.3.7 GSE Change and GMB Estimation  
After offset correction of the DEMs, the GSE change values can be calculated. Firstly, the glacier boundary in the study area was determined based on the RGIG.0. The GSE changes from 2000 to 2009, 2009 to 2022, and 2000 to 2022 were estimated by calculating the difference between the NASA DEM, ALOS DEM, and InSAR DEM. Based on the elevation changes and glacier area, the glacier volume was calculated [68], and the GMB in the study area was estimated by using Equation (2).  
\[MB=\frac{\sum_{i=1}^{n}\Delta h_{i}S_{i}\rho_{ glacier}}{S_{total}\rho_{ water}} \tag{2}\]  
Equation (2), \(MB\) represents the GMB, \(i\) denotes each altitude band, \(h_{i}\) is the average GSE change values, \(S_{i}\) is the glacier area corresponding to each altitude band, \(S_{total}\) is the total glacier area, \(\rho_{water}\) is the density of water, and \(\rho_{glactic}\) is the density for conversion from glacier volume to mass balance. The paper adopts the research by [PERSON] et al. [18], wherein 850 \(\pm\) 60 kg\(\cdot\)m\({}^{-3}\) is used as the glacier volume-mass conversion parameter, and \(\sigma_{\rho}=\pm\)60 kg\(\cdot\)m\({}^{-3}\) is the estimated error margin for GMB calculation.  
The accuracy of the DEM data, DEM co-registration and correction, and assumptions and measurement dates of glacier surface ice and snow density can all affect the precision of GMB estimation. This study uses Equation (3) to evaluate the error of the mass balance [69].  
\[\sigma_{\text{geo}}=\sqrt{\Delta h^{2}\sigma_{\rho}^{2}+\rho_{glactic}^{2} \sigma_{\Delta h}^{2}} \tag{3}\]  
Equation (3), \(\sigma_{\rho}=\pm\)60 kg\(\cdot\)m\({}^{-3}\); \(\Delta h\) represents the annual average GSE change; \(\rho_{glactic}\) is the density for conversion from glacier volume to mass balance; and \(\sigma_{\Delta h}\) is the error in GSE change, and it primarily depends on the accuracy of the DEM data. The standard deviation of elevation change in non-glacier areas can be used as an essential reference for evaluating the error in elevation change [70]. According to the theory of error propagation, the error in long-term scale elevation change is described by Equation (4).  
\[\sigma_{\Delta h}^{2}=\frac{\sigma_{\Delta h_{i}}^{2}}{N_{eff}} \tag{4}\]  
Equation (4), \(\sigma_{\Delta h_{i}}\) represents the annual average elevation change of different height bands in non-glacier areas, and \(N_{eff}\) represents the effective number of observed pixels.
## 4 Evaluations of Prediction Results Using Accuracy Metrics
### Evaluations of Performance of Different Training Algorithms
The evaluation values of MSE, RMSE and MAE for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **R** & **R\({}^{2}\)** & **MSE (cm\({}^{2}\))** & **RMSE (cm)** & **MAE (cm)** \\ \hline TRAINBFG & 0.9449 & 0.8760 & 552.03 & 23.49 & 19.38 \\ TRAINCGB & 0.9480 & 0.8784 & 526.19 & 22.94 & 19.01 \\ TRAINCGP & _**0.9314**_ & _0.8499_ & _685.03_ & _26.17_ & _21.84_ \\ TRAINGLM & _**0.9690**_ & _0.9318_ & _315.45_ & _17.76_ & _13.62_ \\ TRAINRP & 0.9531 & 0.8909 & 475.50 & 21.81 & 17.79 \\ TRAINSCG & 0.9458 & 0.8709 & 548.59 & 23.42 & 19.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of six training algorithms using scale-dependent metrics. Figure 6: Evaluation results of six training algorithms using scale-dependent metrics. (**a**) Evaluation results using MSE. (**b**) Evaluation results using RMSE. (**c**) Evaluation results using MAE. The evaluation results of “TRAINGLM” are minimum, and the evaluation results of “TRAINCGP” are maximum. Abbreviations: (1) BFG: TRAINBFG, (2) CGB: TRAINCGB, (3) CGP: TRAINCGP, (4) GLM: TRAINGLM, (5) RP: TRAINRP, (6) SSG: TRAINSCG. the \"TRAINCGP\" algorithm were 685.03 cm\({}^{2}\), 26.17 cm and 21.84 cm, respectively, and they were the maxima of the evaluation values of the six training algorithms. It can be inferred from the evaluation results of correlation metrics and scale-dependent metrics that the \"TRAINGLM\" algorithm shows the best performance for training the backpropagation ANN prediction model, and the \"TRAINCGP\" algorithm has the worst performance for training the backpropagation ANN prediction model of damage locations in deep piles.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 4 Digital Control Flow Chart
### System Operation  
In Figure 13a, the output voltage and the input current are sensed by the V_sample module and the I_sample module, respectively. The sensed output voltage is sent to the digital controller and then to the DPWM generator to create a suitable gate driving signal for the switch \(S_{1}\), so as to keep the output voltage constant at a desired value. The sensed current is sent to the look-up table to generate a suitable gate driving signal for the switch \(S_{2}\), so that the cut-off time point of the auxiliary switch \(S_{2}\) can be determined.
## 4 AE Signal Analysis and Application
### AE Energy Release in the Process of Stress Change  
Figure 7 shows the AE activity curves corresponding to the two loading activities in the plastic damage stage. It can be found that the generation of the AE signal in the loading process of the full-scale pipe is closely related to the loading state of the pipe, and whether the pipe is stressed or not is a necessary condition for the generation of the AE signal; however, the change of the loading state of the pipe is the direct cause of the generation of the AE signal. In the two-load loading process shown by the black frame line, the cumulative acoustic emission energy curve also climbs while the load gradually increases. When the load stopped increasing at t\({}_{2}\) and t\({}_{4}\), the cumulative AE energy curve also tended to be stable synchronously. When the load is in the holding stage, the pipeline is still under pressure, but the acoustic emission signal disappears, and the cumulative acoustic emission energy curve tends to be stable.  
Figure 6: Characteristics of AE energy response during pipe loading.  
The pipe is subjected to a load level of 426 kN at the t \(>\) t\({}_{\rm d}\) stage and 454 kN at the t\({}_{\rm 2}\)-t\({}_{\rm 4}\) stage, and the load level is increased by 6.5%; the raw AE activity is in the calm period at both stages, and the AE signal count and energy are both 0, which further indicates that the generation of the AE signal is not related to the load level to which the specimen is subjected. The change of load (stress) is the direct cause of AE signal generation.  
In order to quantitatively study the evolutionary characteristics of the AE signal and the amount of load variation in the pipe specimen, statistical and correlation analyses were performed on the amount of load variation \(\Delta\)F, the accumulated AE energy \(\Sigma\)E, and the maximum value of single AE energy E\({}_{\rm max}\) during the 18 loading activities in the plastic damage stage. The statistical characteristics are shown in Figure 8. The Figure 9 shows the correlation coefficients between the two indicators; the direction of the oval shape represents positive and negative, and the color is the corresponding coefficient correlation.  
Figure 8: The evolution of AE parameters in the process of stress change line graph.  
Figure 7: Stress–AE response curve during loading.  
The above phenomenon proves that the AE energy index and cumulative AE energy index can effectively characterize the loading stage and damage degree of the full-scale pipe, and the AE energy and cumulative AE energy can directly reflect the change of stress in the pipe specimen, which can monitor the damage process of the pipe.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 2 Sources/Types of BBN
### Blade-Wake Interaction Noise  
Blade-wake interaction or BWI noise is a mid-frequency broadband noise source that is due to the blade interaction with turbulent (nondeterministic) portions of the wakes of preceding blades, as shown in Figure 4. BWI noise was first introduced as a prominent broadband noise source by [PERSON] et al. [8]; the presence of this noise source was only postulated for forward flight conditions. Since the disturbances associated with BWI originate at the leading edge, it can also be referred to as leading-edge noise [9].  
Figure 3: Illustration of turbulence ingestion.  
Figure 2: Illustration of broadband noise sources.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
###### Abstract
This capability is a crucial step toward the larger goal of robust template-to-real-world instance alignment, which, in turn, enables a range of critical downstream analysis tasks in infrastructure monitoring and analogous industrial applications, such as transferring segmentation masks or performing condition assessments, by analyzing discrepancies between the aligned template and the real-world instance. While our method demonstrates significant potential and succeeds in learning robust keypoints for most of the tested classes, it is essential to note that it was not successful with every class, highlighting areas for future improvement. The main contributions of this paper are as follows:  
1. A self-supervised deep-learning technique for detecting robust, geometrically meaningful keypoints on synthetic templates of visual element classes (demonstrated on road-marking templates) and trained without manual keypoint annotations by enforcing equivariance to geometric transformations applied to the template data;
2. The successful application and evaluation of a multi-head architecture for class-specific keypoint prediction within this self-supervised framework, tailoring feature learning to different template geometries;
3. The demonstration and quantitative evaluation of a robust template-to-transformed-template alignment pipeline leveraging the learned keypoints on synthetic data, highlighting its effectiveness compared to the failure of classical feature-based methods (like ORB) on these templates, thereby showcasing the method's potential for future template-to-real-world instance alignment. This paper is structured as follows: Section 2 reviews related work. Section 3 describes the proposed GeoTemplateKPRNet methodology, covering the model architecture, self-supervised loss functions, training strategy, and datasets. Section 4 outlines the experimental setup and results, with comparisons and ablation studies. Section 5 offers a discussion. Lastly, Section 6 wraps up the paper and proposes directions for future research.
## 5 Discussion
### Paleenvironment Reconstruction
#### 5.1.4 Terrigenous Debris Influx and Primary Productivity  
In lacustrine sedimentation, the influx of terrigenous debris significantly influences both organic matter enrichment and mineral composition. Aluminum (Al) primarily accumulates in clay, feldspar, and other aluminosilicate minerals. Al is highly stable and remains unaffected by diagenesis [52], making it a reliable proxy for characterizing the input of terrigenous debris. In the study area, the Al content of the Da'anzhai Member shale ranges from 1.9 to 13.5% (average = 10.4%). There are notable differences in Al content among different lithofacies, with argillaceous shale exhibiting higher Al content (the average contents of ORA and OPA are 10.7% and 10.9%, respectively) compared to mixed shale (the average content of ORM is 9.2%), indicating a higher input of terrigenous debris in argillaceous shale.  
Primary productivity is a crucial factor determining organic matter enrichment and is also a critical aspect in reconstructing paleo-sedimentary environments. The basic nutrients of the biogeochemical cycle are linked to primary producers and production processes. The contents of related elements such as Cu, Ni, and Si are commonly used to assess the strength of primary productivity in lucustrine sediments [52; 53]. In this study, we chose to use (Ni + Cu + Zn) as an indicator of paleo-productivity. The (Ni + Cu + Zn) content in the Da'anzhai Member shale of the study area varies from 32.6 to 255.6 ppm, with an average of 206.2 ppm. From the perspective of different lithofacies, the (Ni + Cu + Zn) values exhibit a pattern similar to Al content, with argillaceous shale having higher (Ni + Cu + Zn) values than mixed shale. Generally, in transitional sedimentary systems from continental to marine, there is often a positive correlation between terrigenous debris input and paleo-productivity [52; 53]. Figure 8 delineates the association between terrigenous debris input and paleo-productivity within the Da'anzhai Member. The findings reveal a positive correlation between terrigenous debris input and proxies signifying paleo-productivity, indicating that terrestrial organic matter, accompanied by related nutrients, played a pivotal  
Figure 7: Bivalve biocasts among different lithofacies of the analyzed Da’anzhai Member shale (\(n=36\); \(n\)—number of experimental samples): (**a**) 2966.31 m, ORA; (**b**) 2969.37 m, ORA; (**c**) 2982.09 m, ORM; (**d**) 3000.3 m, OPA.  
role in fostering paleo-productivity. To mitigate the influence of terrigenous debris on paleo-productivity assessment, we adopted the (Ni + Cu + Zn)/Al ratio as a proxy to quantify the original primary productivity in the water column. The (Ni + Cu + Zn)/Al ratio in the Da'anzhai Member shale ranges from 0.0015 to 0.0024, with an average value of 0.0020. Remarkably, negligible variation exists in the (Ni + Cu + Zn)/Al ratio among shale samples from different lithofacies, suggesting that the original primary productivity in the water column exhibited minimal divergence during the formation of distinct lithofacies.
## 1 Introduction  
The development of new efficient methods to synthesize organic heterocycles that are both economical and eco-friendly presents a great challenge for the scientific community.  
Solvent-free reactions are highly significant from both economical and synthetic points of view. These kinds of reactions ensure an essential facet of green chemistry to reduce the risks to humans and the environment.  
Multicomponent reactions (MCRs) have gained importance because of their efficiency and effectiveness as a method for one-pot synthesis of a wide range of heterocycles [1; 2; 3; 4; 5; 6]. The optimal MCR is sufficiently flexible. Thus, it can be conducted to generate adducts with a variety of functional groups that may then be selectively paired to enable different cyclization manifolds, thereby leading to a diverse collection of products.  
Among the MCRs, the Bignelli reaction [7] is used for the direct synthesis of 3,4-dihydroxyrimidine derivatives. The 3,4-dihydroxypropylidine and its derivatives are highly significant because they generally show diverse medicinal properties, such as calcium channel blockers, antihypertensive and anti-inflammatory agents, and \(\alpha\) 1-a antagonists [8; 9; 10; 11]. Furthermore, 3,4-dihydroxypropylidine derivatives have emerged as important target molecules because of their pharmacological and therapeutic properties, such as antimitotic, antiviral, antitumor, anticarcinogenic, antibacterial, and fungicidal activities [12; 13; 14; 15; 16; 17; 18; 19].  
Several improvements were made towards good reaction conditions, and they involved the use of catalysts/reagents, transition metal-based reagents, ionic liquids, polymer-immobilized reagents, microwaves, and ultrasound irradiation; these improvements have been recently reported for the synthesis of the aforementioned compounds [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33].  
Despite all of the improved methods, organic synthesis methods still have many drawbacks, such as the use of organic solvents, long reaction times, high costs, low yields, nonsustainable catalysts, and purification issues [34; 35; 36; 37; 38; 39; 40].  
In this study, we propose a new and highly efficient approach to the one-pot synthesis of Biginelli 3,4-dihydropyrimidine derivatives under planetary ball milling solvent-free and catalyst-free conditions (Scheme 1).
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 5 Experiments
### Experimental Setups  
The experiments were performed on a server that was equipped with two NVIDIA Tesla P100 GPUs, each having 16 GB of memory, and had a storage capacity of 256 GB. The server was running on the Ubuntu 20.04 operating system. The DSTSAN model was implemented using Pytorch 1.13.0 and Python 3.10.6.  
To ensure the validity of the comparative experiments, this research adhered to the experimental settings commonly used in most baseline methods. The four datasets were divided into training, validation, and testing sets in a proportion of 6:2:2, respectively. For the prediction task, we utilised 12 historical time steps to forecast the subsequent 12 time steps of traffic flow conditions, denoted as \(T=T^{\prime}=12\). As this research only considered traffic data from the datasets, the traffic signal dimension, D, was set to 1. The model was trained using the AdamW optimiser [13], with a learning rate of 0.001. The batch size for the data was set to 32, and the training process was conducted for 200 epochs. Additionally, the early stopping strategy was applied to the validation set with a patience value of 15.  
The performance of model has been assessed using three metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The MAE measures the absolute difference between the predicted values and the actual values, providing an indication of the accuracy of the predictions. The RMSE quantifies the difference between the predicted value and the ground truth, primarily serving as a measure of how well the model fits the data, as well as being sensitive to outliers. The MAPE represents the percentage error between the predicted results and the actual observed values, providing insights into the relative degree of error between the predictions and the ground truth. The formulas for these metrics are as follows:  
\[\left\{\begin{array}{c}MAE(x,\hat{x})=\frac{1}{\Omega}\sum\limits_{i\in \Omega}\left|x_{i}-\hat{x}_{i}\right|\\ RMSE(x,\hat{x})=\sqrt{\frac{1}{\Omega}\sum\limits_{i\in\Omega}\left(x_{i}- \hat{x}_{i}\right)^{2}}\\ MAPE(x,\hat{x})=\frac{1}{\Omega}\sum\limits_{i\in\Omega}\frac{\left|x_{i}- \hat{x}_{i}\right|}{x_{i}}\end{array}\right. \tag{18}\]  
where \(x_{i}\) denotes the \(i\)th ground truth, \(\hat{x}_{i}\) represents the \(i\)th predicted value, and \(\Omega\) represents the number of samples, which in this experiment is set to 12. Moreover, this research performed data pre-processing by removing instances with missing values and filtering out data samples with a traffic volume below 10 to mitigate the impact of noise. To ensure robustness, all experiments are repeated 10 times and calculated the average values as the final results.
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.1 Hydrocarbons: Lineal, Branched and Cyclic
These results are in contrast with those showed in literature for foals finished with concentrates [25; 27],  
Figure 1: Volatile families of _longissimus thoracis et lumborum_ muscle from foals (displayed as percentage) influenced by the breed and finishing diet. recording higher values compared to ours. On the other hand, the outcomes for the D1 group are in agreement with those reported by other authors [54], who observed the highest total hydrocarbon contents in animals fattened with conventional concentrate. Considering the singular trends, most individual hydrocarbons were also affected by the finishing diet, apart from pentane, 2-methyl, cyclopentane-methyl and dodecane. Besides, D2 foals generally displayed lower amounts of hydrocarbons than the D1 group, except for undecane, 5,5-dimethyl- (\(p<0.001\)). Moreover, in both finishing diets, the lineal, branched and cyclic hydrocarbons with the highest amounts were the same, specifically nonane, pentane, 3-methyl and bicyclol[3.2.0]hepta-2,6-diene, respectively. However, the D2 group showed values of 1.55, 2.20 and 2457 AU \(\times\) 10\({}^{4}\)/g fresh meat for these singular compounds (respectively), which were significantly lower (\(p<0.05\)) than those from the D1 group, with 2.04, 2.83 and 2797 AU \(\times\) 10\({}^{4}\)/g fresh meat. Thus, D1 foals tended to report the greatest amounts in most of hydrocarbons, probably related to the high content of C18:1\(n\)-9 (Table 2). In fact, [PERSON] et al. [29] reported that degradation of this fatty acid generates hydrocarbons, among them octane and nonane. According to the finishing diet, also in this case, this family of volatile compounds represented the major percentage over the total volatile substances (Figure 1). Considering the four treatments, it is evident that BU-D1 had the highest values of total hydrocarbons (3015 AU \(\times\) 10\({}^{4}\)/g fresh meat), followed by BU-D2 (2832 AU \(\times\) 10\({}^{4}\)/g fresh meat), JN-D1 (2705 AU \(\times\) 10\({}^{4}\)/g fresh meat) and JN-D2 (2235 AU \(\times\) 10\({}^{4}\)/g fresh meat). Among the hydrocarbons, BU-D1 also showed the greatest amounts of octane and nonane, probably related to the high content of C18:1\(n\)-9 [29]. On the other hand, no significant interactions were detected among the main categories (breed \(\times\) finishing diet), except for cyclopentane, 1,1-dimethyl-, cyclopropane and undecane, 5,5-dimethyl-. However, although lineal, branched and cyclic hydrocarbons were found in a higher proportion in our samples, they have no relevance in the volatile pattern of meat, since previous studies showed that their contribution to the aroma of meat is minimal due to its high odor threshold value [29; 55].