esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Centralized Scheduling Algorithm for the IEEE802.15.4e TSCH Network
### Network Traffic and the Duty Cycle  
This research assumes that in each duration of an S-sized slotframe, the n\({}_{i}\) node in graph G generates a constant number of packets, that is, \(\widetilde{\qq}_{i}\). The network supports multi-point to point traffic, in which all packets are aimed at the root node, n\({}_{0}\), so that each link (n\({}_{i}\), p\({}_{i}\)) is only used for the uplink. Each of these nodes forwards the data packet to the parent p\({}_{i}\) node, which eventually will lead all the packets to the master node (PAN coordinator). Assuming that all packets in the node are waiting in the queue at the beginning of the slotframe, this research uses the parameter \(\widetilde{Q}\) as a symbol of the total packets that must be sent to the PAN coordinator (n\({}_{0}\)) in one slotframe, which can be expressed by Equation (2):  
\[\widetilde{Q}=\sum_{i=1}^{N-1}\widetilde{\q}_{i}. \tag{2}\]  
It is stated in Section 3.2 that each node on the network cannot transmit and receive simultaneously and receive data from many nodes at once. Therefore, to collect all data in the master node, the transmit and receive processes are needed in stages. All network nodes that have been scheduled to be active by the master node, send/receive the data simultaneously. The simultaneous transmit/receive process will be done in several cycles, until all data in the network reach the master node (n\({}_{0}\)). The state that all data have arrived at the master node is marked by the fulfillment of q\({}_{0}\) = \(\widetilde{Q}\) condition, where q\({}_{0}\) is the queue size of node 0 (n\({}_{0}\)).  
To meet the stringent requirements related to packet delay and power consumption, the scheduling algorithm in this study strived for a minimum duty cycle value for each node in the network. To send  
Figure 8: Two types of links on TSCH networks: (**a**) duplex conflict links (DCLs) and (**b**) duplex conflict free links (DCFLs).  
network traffic of \(\widetilde{\mathsf{Q}}\) to master node (n\({}_{0}\)), the scheduling algorithm will arrange to enable the entire transmission to be carried out in the first \(\lambda\) slot of each slotframe. Since \(\lambda\leq\mathsf{S}\), the remaining slots of \((\mathsf{S}-\lambda)\) will be left empty, thus leading to the duty cycle of \(\lambda\)/\(\mathsf{S}\). If the nodes are evenly distributed on the network, the minimum number of active timeslots (\(\lambda\)) in one slotframe is equal to \(\widetilde{\mathsf{Q}}\).
## 3 Experiments and Results
### Experiments Setup
#### 3.1.2 Training and Implementation  
In terms of hardware environment, this experiment is carried out under the pytorch deep learning framework of the Windows10 system, whose Intel (R) Core (TM) i5-11400F [EMAIL_ADDRESS] GHz GPU NVIDIA 3060 12G memory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Train Set (70\%)** & **Validation Set (20\%)** & **Test Set (10\%)** \\ \hline No. Of Original ZY-3 Images & 3351 & 957 & 479 \\ No. Of Added ZY-3 Images & 4163 & 1189 & 595 \\
38-cloud of Landsat OLI 8 dataset & 27 & 8 & 3 \\ GF-1 WFV dataset & 76 & 22 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of datasets.  
Figure 5: Augmented example images in complex scenarios (with snow and clouds).  
In the training phase, we use AdamW optimizer [35] to train on 256 \(\times\) 256 images with some hyper-parameters: learning rate (lr) = 0.001, \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999 and \(\varepsilon\) = 1 \(\times\) 10\({}^{-8}\). On top of our network, we set the Sigmoid function to tidy up the results. The loss function adopts the Dice loss function. Training optimization indicators include overall accuracy, recall, precision, F1, and IoU. It took our network about 200 epochs to converge.  
As mentioned above, we use Pytorch framework to build and train our model. All training is performed with a batch size of 16 and a validation set is used to evaluate performance during training. The main software packages used include python 3.7, CUDA 11.6, cuDNN 8.5, pytorch1.12, etc. The experimental loss function is shown in Figure 6.
## 4 Results and Discussion
### Ablation Study Results of Transformer Models
#### 4.1.1 Ablation Study of BERT  
The ablation study of each component of the BERT model in the three benchmark datasets and their importance are presented in Tables 2 and 3. Based on the results of the ablation study, the attention mechanism emerges as the most critical component of the BERT architecture for FND, with its removal causing the highest average performance drop of 0.1504 and a standard deviation of 0.2303. In contrast, residual connections appear to be the least impactful, with an average performance degradation of just 0.0014 and minimal variance (standard deviation of 0.0016), suggesting limited importance in this context. Dataset-specific analysis reveals that the best-performing configuration for FakeNewsNet is the model without attention (accuracy: 0.5250), which, despite the general trend, may indicate overfitting or noise in the dataset. The ISOT dataset achieves peak accuracy (0.9997) with the baseline model intact, emphasizing its sensitivity to architectural changes. Interestingly, the LIAR dataset performs best when the feedforward network is removed (accuracy: 0.5955), highlighting the varied contribution of components depending on the characteristics of the dataset. In general, the average baseline performance for all datasets is 0.6871, with a standard deviation of 0.2257, demonstrating both the robustness and variability of BERT's architecture in handling various FND tasks.  
\begin{table}
\begin{tabular}{l l c c} \hline
**Dataset** & **Configuration** & **Test Accuracy** & **Test F1** \\ \hline \multirow{8}{*}{FakeNewsNet} & baseline & 0.4750 & 0.3059 \\  & no\_positional\_encoding & 0.4750 & 0.3059 \\  & no\_attention & 0.5250 & 0.3615 \\  & no\_feedforward & 0.4000 & 0.3520 \\  & no\_residual\_connections & 0.4750 & 0.3059 \\  & no\_layer\_normalization & 0.3500 & 0.2750 \\  & no\_dropout & 0.3750 & 0.3738 \\  & no\_cls\_token & 0.4750 & 0.3059 \\ \hline \multirow{8}{*}{ISOT} & baseline & 0.9997 & 0.9997 \\  & no\_positional\_encoding & 0.9990 & 0.9990 \\  & no\_attention & 0.5267 & 0.3634 \\  & no\_feedforward & 0.9997 & 0.9997 \\  & no\_residual\_connections & 0.9990 & 0.9990 \\  & no\_layer\_normalization & 0.9990 & 0.9990 \\  & no\_dropout & 0.9997 & 0.9997 \\  & no\_cls\_token & 0.9987 & 0.9987 \\ \hline \multirow{8}{*}{LIAR} & baseline & 0.5866 & 0.5878 \\  & no\_positional\_encoding & 0.5412 & 0.5154 \\  & no\_attention & 0.5585 & 0.4002 \\  & no\_feedforward & 0.5955 & 0.5635 \\  & no\_residual\_connections & 0.5830 & 0.5415 \\  & no\_layer\_normalization & 0.5585 & 0.4002 \\  & no\_dropout & 0.5934 & 0.5753 \\  & no\_cls\_token & 0.5799 & 0.5809 \\ \hline \end{tabular}
\end{table}
Table 2: BERT Ablation Study: Performance Across All Datasets.
# Porcine Parvovirus 7: Evolutionary Dynamics and Identification of Epitopes toward Vaccine Design
###### Abstract  
Porcine parvovirus 7 (PPV7) belonging to the genus _Chapparvovirus_ in the family _Parvoviridae_, has been identified in the USA, Sweden, Poland, China, South Korea and Brazil. Our objective was to determine the phylogeny, estimate the time of origin and evolutionary dynamics of PPV7, and use computer-based immune-informatics to assess potential epitopes of its Cap, the main antigenic viral protein, for vaccines or serology. Regarding evolutionary dynamics, PPV7 had 2 major clades, both of which possibly had a common ancestor in 2004. Furthermore, PPV7 strains from China were the most likely ancestral strains. The nucleotide substitution rates of \(NS1\) and \(Cap\) genes were \(8.01\times 10^{-4}\) and \(2.19\times 10^{-3}\) per site per year, respectively, which were higher than those reported for PPV1-4. The antigenic profiles of PPV7 Cap were revealed and there were indications that PPV7 used antigenic shift to escape from the host's immune surveillance. Linear B cell epitopes and CD8 T cell epitopes of Cap with good antigenic potential were identified in silico; these conserved B cell epitopes may be candidates for the PPV7 vaccine or for the development of serological diagnostic methods.  
PPV7; Cap; B cell and T cell epitopes; vaccine; evolution Article
## 4 Experimental Results
### Influence of Ambient Temperature and Threaded Groove on the Gas Sealing Performance of Alloy Plug  
#### 4.1.1 The Influence of Ambient Temperature on Gas Sealing Performance  
When the ambient temperature at which the samples in the experiment were located was raised from 30 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, respectively, the gas breakthrough pressure values at the time of gas sealing performance failure of all alloy plug samples increased by about 10% on average, and when the ambient temperature was raised from 60 \({}^{\circ}\)C to 90 \({}^{\circ}\)C, the gas breakthrough pressure values increased by about 15% on average, as shown in Figure 12. This indicates that as the temperature increases, the expansion of the alloy plug is greater than the casing bore, and the alloy plug generates greater radial force, which in turn increases the gas sealing performance of the alloy plug. This trend is more pronounced when the temperature exceeds 60 \({}^{\circ}\)C.
# Rapid Tracking Satellite Servo Control for Three-Axis Satcom-on-the-Move Antenna
## 1 Introduction
Both scenarios may have an impact on the tracking performance of the SOTM antenna on the satellite, potentially leading to the SOTM antenna losing track of the satellite. The goal of this paper is to establish a three-axis SOTM antenna kinematic model that is simple to calculate and easy to implement and to design a servo controller that overcomes the contradiction between the rapidity and overshoot of the control system. The main contributions of this paper are: For the three-axis SOTM mechanical structure, a three-axis SOTM antenna kinematic model based on a new modified Denavit-Hartenberg (NMDH) method is introduced; a modified ADRC is introduced to improve the accuracy of the SOTM antenna pointing to the target satellite; a piecewise linear method is introduced to improve the engineering feasibility of the modified ADRC. The remainder of this paper is organized as follows: Section 2 introduces the three-axis SOTM mechanical structure, introduces a NMDH method and thus obtains the kinematic model of the three-axis SOTM antenna and its inverse kinematic solution; Section 3 introduces an ADRC based on a smooth and continuous fal function as the controller of the SOTM servo control system and designs a fal function piecewise linear engineering implementation method; Section 4 is the experimental verification, which mainly verifies the scheme designed in this paper through experiments; Section 5 is the summary of the entire paper.
## 4 Discussion
Based on the numerical simulation results, the impact diameter of the jet obtained under working condition 1 is significantly smaller than that under other conditions, aligning with the clustering results. Working condition 3 exhibits a greater distance from the other conditions, ranging between 7.96 and 14.75. The impact diameter of the jet derived from working condition 3 is 54.8 cm, which is much larger than all other conditions. Looking at the overall clustering results, two categories can be identified: working condition 3 and the rest when the distance between clusters ranges from 7.47 to 11.37. Three categories emerge: operating conditions 3, 8, 9, and 10, and the remaining conditions when the distance between clusters falls between 5.97 and 7.47. When the distance between clusters is less than 5.97, further classification becomes overly detailed and lacks practical significance, thus avoiding excessive segmentation. Combining Table 3 with the clustering results suggests that changes in the pressure and nozzle diameter have far less impact on the jet compared to the cone angle. The optimal nozzle parameters for jet effectiveness correspond to working condition 3 according to the clustering results. In cases where the pressure supplied by the jetting device is insufficient, efforts should be made to maintain a nozzle diameter of 2 mm and a cone angle of 115\({}^{\circ}\).
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 2 Spacecraft Dynamics
### Coordinate Systems
#### 2.2.3 Body Reference Frame  
The body reference frame is centered at the CoM of the spacecraft, its associated coordinate system has origin at the spacecraft CoM and is denoted by the basis vectors \(\{\mathbf{\hat{b}_{1}},\mathbf{\hat{b}_{2}},\mathbf{\hat{b}_{3}}\}\), where the unit vector \(\mathbf{\hat{b}_{1}}\in\mathbb{R}^{3}\) is aligned with the longitudinal axis of the spacecraft, the \(\mathbf{\hat{b}_{2}}\in\mathbb{R}^{3}\) unit vector on the nadir facing side of the spacecraft, and the \(\mathbf{\hat{b}_{3}}\in\mathbb{R}^{3}\) unit vector is selected to complete the right-handed Cartesian coordinate system, as depicted in Figure 2.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Experimental Setup
#### 4.1.2 PSO Segmentation Parameters
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Description** \\ \hline \(k\) & Number of segments (set to 2 in experiments): determines image partitioning. \\ _CostFunction_ & Objective function guiding PSO: minimizes image partitioning quality. \\ _VarSize_ & Decision variable matrix size, \([k,size(X,2)]\): indicates the matrix for decision variables. \\ _nVar_ & Number of decision variables: calculated from _VarSize_ and represents total variables. \\ _VarMin_, _VarMax_ & Lower and upper bounds of decision variables based on image data. \\ _MaxIt_ & Maximum iterations (50): controls optimization duration. \\ _nPop_ & Population size (5): aligned with PSO swarm size. \\ \(w\) & Inertia weight (initially 1): balances particle velocity and best-known position. \\ _wdamp_ & Inertia weight damping (0.99): regulates inertia weight decrease. \\ _c\({}_{1},{}_{2}\)_ & Personal and global learning coefficients (1.5 and 2.5): influence particle movement. \\ _VelMax_, _VelMin_ & Velocity limits: preserve particle velocities within bounds. \\ \hline \end{tabular}
\end{table} 
Table 1: PSO segmentation parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{8}{c}{**Experiments**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Weight (_w_) & 1 & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 \\ Best Cost & 93.3202 & 93.3203 & 93.3203 & 93.3204 & 93.3205 & 93.3984 & 93.3725 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiments for finding parameters of weight (_w_) based on the best-cost values.
## 3 Results
### Experiment 1  
In this experiment, a regression model is built to estimate the target _wtrm_avg_TmTmp_Bpg1_ from the variables indicated in Table 1. The experiment consists of taking half of the available data to train the model, then testing it with the remaining half of the data. The particularity is that the models are trained and tested using different temporal aggregation. It starts with the default 5-min data provided by the SCADA system and increases to 10, 15, 30, and 60 min. In all cases, the performance of the network is evaluated through the root mean square error(RMSE) and the performance of the model is provided both in the training and test phases. In all cases, a SLFN network of 40 hidden nodes is used. For each data aggregation and for each individual WT, 25 models have been trained and the average result is provided.  
Numerical values for this experiment are presented in Table 4, in which the temporal aggregation providing the lower RMSE for each WT is highlighted in bold.  
Figure 7: Internal view of a wind turbine. The main parts of the transmission chain are visible: Main bearing, gearbox, brake, generator, power control, and ambient sensors. The actual physical position where the sensors are located, for the variables listed in Table 1, are indicated by arrows.  
The results presented in Table 4 have been obtained using the same network model in all the cases (SLFN of 40 hidden nodes), to fairly compare the results between WTs and the different temporal aggregations. We have already studied SLFNs and shown that they work well for this purpose regardless of how they are trained, but there is always an optimal value for the network size (number of nodes) that achieves the best performance [4].
# New Advances in Audio Signal Processing
## 3 Conclusions  
New technologies, the advancement of AI and its applications, and the availability of higher computational power are driving the evolution of audio processing and analysis techniques. Conversely, these fast-paced advancements make it possible to discover the informative power of audio signals, which are being ever-more utilized for automatic analysis.  
New advancements in audio processing techniques include the deep learning-based analysis of sound signals, most notably speech and sound events, leading to the rise in CNN-based approaches. State-of-the-art results have been achieved and constantly revised for applications like COVID-19 monitoring and marine and construction sound event detection, and novel ways of enhancing CNNs have been explored, such as the usage of scalograms for challenging the current paradigm of using spectrograms or data augmentation on the test set.  
On the other hand, pre-existing solutions such as pitch detection or sound field modeling are pushed to their limits with faster, more efficient or accurate algorithms, whose development is made possible by modern equipment and whose application is driven by current digital and real-time industrial needs.
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.3 Water Absorption
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Cube** & **Weight in** & **Oven-Dried** & **Water** & **Water** \\
**Number** & **Sample** & **Water (\(W_{1}\))** & **Weight (\(W_{3}\))** & **Absorption** & **Absorption (\%)** \\ \hline Pi1 & Pi1 (a) & 232.27 & 368.75 & 1.83 & 0.50 \\ \hline \multirow{3}{*}{Pi2} & Pi2 (a) & 569.24 & 903.95 & 3.38 & 0.37 \\ \cline{2-6}  & Pi2 (b) & 604.15 & 960.21 & 2.58 & 0.27 \\ \cline{2-6}  & Pi2 (c) & 585.99 & 929.89 & 3.09 & 0.33 \\ \hline \multirow{3}{*}{Pi3} & Pi3 (a) & 592.35 & 941.24 & 1.94 & 0.21 \\ \cline{2-6}  & Pi3 (b) & 603.47 & 957.89 & 1.93 & 0.20 \\ \cline{2-6}  & Pi3 (c) & 605.55 & 961.88 & 1.69 & 0.18 \\ \hline \multirow{3}{*}{Pi4} & Pi4 (a) & 605.09 & 960.67 & 0.99 & 0.10 \\ \cline{2-6}  & Pi4 (b) & 604.79 & 959.72 & 1.93 & 0.20 \\ \cline{2-6}  & Pi4 (c) & 593.34 & 943.13 & 2.67 & 0.28 \\ \hline \multirow{3}{*}{Pi5} & Pi5 (a) & 247.94 & 394.24 & 1.28 & 0.32 \\ \cline{2-6}  & Pi5 (b) & 590.23 & 938.52 & 3.12 & 0.33 \\ \cline{2-6}  & Pi5 (c) & 580.71 & 921.59 & 3.19 & 0.35 \\ \hline \multirow{3}{*}{Pi6} & Pi6 (a) & 585.34 & 938.19 & 3.31 & 0.35 \\ \cline{2-6}  & Pi6 (b) & 567.51 & 909.56 & 3.18 & 0.35 \\ \cline{2-6}  & Pi6 (c) & 593.15 & 922.78 & 1.82 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Water absorption values for the studied sample.
# Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development--A Case Study
###### Abstract  
High-performance computing (HPC) enables both academia and industry to accelerate simulation-driven product development processes by providing a massively parallel computing infrastructure. In particular, the automation of high-fidelity computational fluid dynamics (CFD) analyses aided by HPC systems can be beneficial since computing time decreases while the number of significant design iterations increases. However, no studies have quantified these effects from a product development point of view yet. This article evaluates the impact of HPC and automation on product development by studying a formula student racing team as a representative example of a small or medium-sized company. Over several seasons, we accompanied the team, and provided HPC infrastructure and methods to automate their CFD simulation processes. By comparing the team's key performance indicators (KPIs) before and after the HPC implementation, we were able to quantify a significant increase in development efficiency in both qualitative and quantitative aspects. The major aerodynamic KPI increased up to 115%. Simultaneously, the number of expedient design iterations within one season increased by 600% while utilizing HPC. These results prove the substantial benefits of HPC and automation of numerical-intensive simulation processes for product development.  
HPC; CFD; automation; benefits; costs; product development Article
# DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection
## 4 Experiments and Discussions
### Incremental FSOD Protocol
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Novel Categories**} & \multicolumn{4}{c}{**Base Categories**} & \multirow{2}{*}{**Parameters**} \\ \cline{2-2} \cline{4-9}  & **AP** & & **AP\({}_{50}\)** & & **AP\({}_{75}\)** & & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** \\ \hline Given Supports & 1 shot & 5 shot & 1 shot & 5 shot & 1 shot & 5 shot & 1 shot & 5 shot & 1 shot & 5 shot \\ \hline Meta R-CNN [25] & 8.7 & 11.2 & 19.9 & 25.9 & 6.8 & 8.6 & 27.3 & 28.5 & 50.4 & 52.3 & 27.3 & 28.2 \\ FGN [57] & 8.0 & 10.9 & 17.3 & 24.0 & 6.9 & 9.0 & 24.7 & 26.9 & 44.3 & 47.6 & 25.0 & 27.4 \\ Attention RPN [32] & 8.7 & 10.6 & 19.8 & 24.4 & 7.0 & 8.3 & 20.6 & 23.0 & 37.2 & 42.0 & 20.5 & 22.4 \\ DAnA-FasterRCNN [35] & 11.9 & 14.4 & 25.6 & 30.4 & 10.4 & 13.0 & 27.8 & 32.0 & 46.3 & 54.1 & 27.7 & 32.9 \\ DMA-Net & 12.7 & 14.0 & 26.6 & 29.4 & 11.3 & 12.5 & 28.0 & 30.2 & 49.8 & 53.3 & 29.3 & 31.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Incremental results for novel and base classes of COCO with different numbers of support images.