esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrafast Mid-IR Laser Pulses Generation via Chirp Manipulated Optical Parametric Amplification
## 3 Theory on Broadband Pumped OPA
### Simulations on BBP-OPA
It is observed that the signal experiences gain-narrowing effect during the process, which results in ~100-nm narrower bandwidth than the initial seed. On the contrary, owing to the opposite GDDs of pump and seed pulses, the idler pulses are obtained with a much broader bandwidth compared to the signal. Not only do the idler bandwidth increase with the pump bandwidth in Figure 8b, the one from chirp-manipulated system is also much broader than the unchirped case, indicating the validity of the BBP-OPA in broadening the idler bandwidth. Moreover, the tunability of the BBP-OPA is investigated and the results are plotted in Figure 9. The phase-matching efficiency in the BBP-OPA scheme is insensitive to the center wavelengths of the signal and idler pulses. As a result, idler pulses tunable from the NIR to Mid-IR regions can be obtained with over 600-nm bandwidth. The conversion efficiencies and TL durations of the tunable idler pulses are plotted in Figure 9b. All idler spectra can support TL durations of less than 9 fs, corresponding to <1.5 optical cycles, meanwhile a conversion efficiency of >14% can be achieved. Note that the nonlinear crystal selected here is BBO, which has strong absorption in the Mid-IR range. However, the presented concept of chirp manipulation in OPA is not limited by the crystal, therefore the BBP-OPA scheme shows great potential for the generation of few-cycle Mid-IR pulses in non-degenerate regions.
# Research on the High Speed of Piston Pumps Based on Rapid Erecting of Launch Vehicles
## Methodology
### High-Speed Design of Piston Pumps  
The extremely fast flow rate of hydraulic oil in the axial piston pump aggravates the cavitation phenomenon under high-speed situations. Because the pressure in the oil discharge area (the piston chamber, valve plate discharge port and oil discharge port in the oil discharge state) is extremely high, much higher than the saturation vapour pressure and air separation pressure of hydraulic oil, nearly no cavitation occurs. Cavitation occurs mainly in the suction state of the piston chamber due to low pressure in the suction area. Two factors contribute to the pressure drop in the piston chamber. The first is the loss of pressure throughout the suction process in the piston chamber, and the second is the pressure loss near the centre of rotation caused by the centrifugal force of rotation around the shaft [32; 33].  
The overflow surface of the window area of the traditional axial piston pump cylinder block is waist-shaped (Figure 6a,c), and the flow area is tiny, preventing the oil from flowing freely and effectively, resulting in a significant increase in pressure loss along the oil. The pressure loss is insignificant at low speeds, but at high operating speeds, the traditional structure's along-range pressure loss increases dramatically, resulting in particularly severe cavitation in the piston chamber. In order to address this problem, the overflow surface of the window area has been improved to significantly increase the area of the overflow surface while maintaining the pack angle of the window area equal before and after the change (\(\angle a_{1}=\angle a_{2}\)) (Figure 6). The structure of the valve plate has been modified accordingly to match the modified window overflow surface, and the before and after views are not redundantly represented in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Value** & **Description** & **Value** \\ \hline Inlet pressure & 0.101325 MPa & Air dissolved volume & 13\% \\ Outlet pressure & 25 MPa & Saturation vapour pressure & 400 Pa \\ Swash-plate angle & 15\({}^{\circ}\) & Hydraulic oil density & 800 kg/m\({}^{3}\) \\ Air molar mass & 28.97 kg/kmol & Modulus of elasticity of & 1.5 \(\times\) 10\({}^{9}\) Pa \\ Number of pistons & 11 & Viscosity & 7 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s \\ Outer radius of piston & 10 mm & Hydraulic oil temperature & 25 \({}^{\circ}\)C \\ Radius of piston distribution circle & 48 mm & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions and structural parameters.  
Figure 5: Fluid domains Meshes of an axial piston pump.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 4 Simulation Results and Analysis
### Network Performance with Different Adjustment Factors  
As shown in Figures 2 and 3 under different network sizes with a step size of 50, the network performance index changes with the adjustment factor. It can be seen that in the process of selecting the next hop, the situation that some nodes are overused is somewhat relieved due to the influence of the cache queue index of high-cache-usage nodes. Thus, the adjustment factor \(\alpha\) has more noticeable effects on NL and PLR, while impacts on AE and AH are relatively limited. When \(\alpha\) is in \((0,1)\), NL and PLR are improved with its increase. In contrast, when it is in \([1,20]\), both of them will increase \(\alpha\). Here, it needs to be explained in combination with the analysis in Section 3.3.1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Note** \\ \hline Network coverage & \(100\) m \(\times\)\(100\) m & \(S\) \\ Node number & \(100\)–\(300\) nodes & \(N\) \\ Battery initial energy & \(0.5\) J & \(Eo\) \\ Maximum communication radius & \(30\) m & \(R\) \\ Packet generation rate & One packet/round & \(r\) \\ Packet size & \(1024\) bits & \(dm\) \\ Buffer size & \(20\) packets & \(Qm\) \\ Adjustment factor & \(0\)–\(20\) & \(\alpha\) \\ Inequality aversion index & \(2.5\) & \(\varepsilon\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters Values.  
When \(\alpha\geq 1\), as \(\alpha\) increases, NL gradually decreases, and the packet loss ratio also increases. Its reason is that with the rise in \(\alpha\), the \"punishment intensity\" increases at first, while it is just the opposite in the later period. Therefore, when \(\alpha\) increases to a certain extent, the routing interferes with node congestion during its early and middle stages, thus failing to prevent those nodes in high forwarding task state from being repeatedly selected on time; therefore, the network performance starts to decline.  
From the above analysis, different \(\alpha\) values will bring about fluctuations in network performance. If the network size is small, \(\alpha<1\) will have little impact. If the network size is greater than 250, \(\alpha<1\) will cause the number of AHs to increase. If focusing on lower AH and AE, \(\alpha\) should take a larger value, but \(\alpha>5\) will increase the PLR and decrease the NL. If concerned with packet loss and lifetime, \(\alpha\) should take values between 0 and 1. Considering all things, in the subsequent simulation, we take the adjustment factor \(\alpha=1.3\).
## 3 Results and Discussion  
### Comparing to CenterNet  
In this section, the results of the two-step license plate identification algorithm are presented. In the first step, we performed CenterNet test training with a small amount of data to check whether all the elements related to training were correct. Therefore, 500 training images and 500 testing images in TFRecord form were generated without any augmentation (see Figure 7, labeled \"No_Augment1\"). During training, the \"Adam\" optimizer with standard parameters was used. The results showed that the original model converged, although the results were not satisfactory. As a second step, training and validation data were generated from all training and validation data to determine the starting point. The trained network demonstrated better results (see Figure 7, labeled \"No_Augment_2\").  
Next, data was generated by following the augmentation and training protocols described in the previous section. The resulting images are labeled as \"Augment_1\" in Figure 7, and they show a positive improvement in the model's performance. \"Augment_2\" shows even better results with a larger amount of data provided. The model was now capable of detecting both very small and very large objects, and the number of true positive objects had increased. However, the precision of the model still needed improvement, as it was detecting different numbers that are not necessarily related to license plates. Furthermore, the low recall indicates that the model was still unable to find a lot of small objects, which may be due to issues with the model's structure, input size, or coding principles. To achieve high accuracy with this specific model, modifications to the coding and learning methods were necessary.  
Figure 6: License plate encoding: (**a**) original image is converted to 256 \(\times\) 256 image; (**b**) object center point is encoded using Gaussian distribution; (**c**) distances from the current pixel center point to the object’s upper left x point; (**d**) distances matrix from the object’s current pixel point to the object’s upper left y point; (**e**) distances from the current point of the center point to the lower left x point of the object; (**f**) distances matrix from the current pixel center point to the lower left y point of the object.  
The proposed algorithm read and processed all generated data in TFRecord. The last four output matrices were modified to a new format. Then, the proposed network weights were reinitialized, and the network was retrained based on the CenterNet training protocol. The error function for calculating corner distances was changed from \"L2\" to \"Wing Loss\". Training the network with all databases resulted in better performance compared to using the CenterNet method, as shown in Figure 7, labeled \"2 Point_wing\". The proposed model demonstrated an improvement in detecting smaller objects and errors. Therefore, the Hourglass type network with modified inner blocks, as shown previously, proved to be superior to the CenterNet method.
## 3 Results  
Of the 13 subjects, two were excluded from the analysis since activities were not reported with sufficient accuracy. Finally, after cropping to the desired timestamps, the collected data set contained \(n=11\) usable recordings for a total duration of approximately 35 h.  
Figure 3 illustrates, as an example, the computed time series \(\overline{E}\) for subject 3 and the estimated thresholds.  
For each subject \(s\), the error assessment \(Err_{s,a}=Tref_{a}-Tdet_{a}\) was performed by comparing the reference total time spent in each activity type \(Tref_{a}\) and the corresponding detected time \(Tdet_{a}\). A coefficient of variation (\(CV_{s}\)) is also computed per subject using the following equation:  
\[CV_{s}= \frac{\sum_{a=1}^{4}|Er_{s,a}|}{T_{s}}\times 100 \tag{3}\]  
with the duration of the measurement \(T_{s}\) in min. This metric provides the global error ratio with respect to the total duration of the measurement in percent.  
From the Rest and Low intensity columns of Table 3, one can observe that the algorithm had difficulties with very low amplitude motions since both intensities have similar ENMEG values. However, if amplitudes are similar, the dynamic ranges are different as resting usually means close to no motion at all. To improve the results, a moving standard deviation of \(\overline{E}\) was computed over a 1 min sliding window, this signal will be noted \(\sigma_{\overline{E}}\). An empirically defined threshold was then applied, which value was \(8\times 10^{-4}\) g. Finally, to be fully considered as Rest, a timestamp must have a corresponding \(\overline{E}<\tau_{2}\) and \(\sigma_{\overline{E}}<8\times 10^{-4}\). The new results are presented in Table 4.  
Figure 3: Raw accelerometers data from subject 3 along axis x, y and z and the corresponding averaged ENMEG signal \(\overline{E}\) plotted on a logarithmic scale for better reading in blue, the regular ENMO (without gravity estimation) is displayed as reference in dashed green, the doted lines are the activity thresholds. The involved activities were, in order: resting, desk working, walking, running, resting, walking and desk working.  
The overall accuracy is improved as the average \(CV\) decreased from 16.2% to 10.6%. However, significantly more reliable results were achieved by merging the two lowest intensity activity types, as illustrated in Table 5 the average \(CV\) dropped to 3.7%.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 1 Introduction
Specifically, DGAN constructs dynamic node relationships by allowing additional nodes beyond the original adjacency matrix, thereby enabling flexible modeling of long-distance dependencies. During the attention computation, nodes exhibiting high correlation with the target node are identified based on attention scores; nodes that exceed the minimum attention score with respect to the target node and its neighbors are dynamically added into the adjacency matrix, thereby forming a dynamic adjacency matrix. As illustrated in Figure 1, blue nodes indicate original adjacency connections, whereas red nodes denote newly added nodes through dynamic updates. Subsequently, a mask matrix is generated from the dynamic adjacency matrix to achieve a dynamic balance between local and global attentions via a masked attention mechanism. Furthermore, to effectively model cooperative interactions among multiple joints in human actions, a node-partition bias is introduced in the attention module, enabling the network to more effectively extract localized features from relevant body regions. Extensive evaluations on the NTU RGB+D datasets validate that the proposed DGAN, through its dynamic modeling of node dependencies, surpasses state-of-the-art methods in terms of accuracy. The main contributions of this paper include the following:  
1. We propose a Dynamic Graph Attention Network, which leverages dynamic node selection and a masked attention mechanism to effectively model both local and global node interactions in a flexible manner;
2. We introduce a novel node-partitioning strategy, which enables the network to efficiently extract relational dependencies from correlated nodes during the attention computation process;
3. Experimental results demonstrate that the proposed approach consistently outperforms current state-of-the-art methods across multiple key metrics.
# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 2 Literature Review
### AI and Machine Learning in Misinformation Detection  
The rapid advancement of artificial intelligence (AI) and machine learning (ML) has opened new avenues for detecting and mitigating climate-related misinformation on social media platforms. Natural language processing (NLP) techniques, particularly those utilizing transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), have demonstrated significant efficacy in analyzing and classifying the credibility of online content. These models can process vast amounts of textual data to identify patterns indicative of deceptive narratives, thereby enabling the automated detection of misinformation. Recent studies have underscored the potential of hierarchical ML models in discerning climate misinformation. For instance, research published in Communications Earth and Environment [15] highlights that such models can effectively identify stimuli contributing to the spread of climate misinformation, emphasizing the importance of advanced computational techniques in understanding and countering deceptive content. Additional recent work [16] introduced a dual-layer attention mechanism for fake news detection that improved F1-score performance by 9% on benchmark datasets such as LIAR and Climate-Fact. Similarly, ref. [17] used sentiment-aware convolutional neural networks to enhance credibility classification of environmental tweets, with improved accuracy compared to traditional classifiers. Moreover, the proliferation of AI-generated content has introduced both opportunities and challenges in misinformation detection. A synthesis brief presented at the Nobel Prize Summit 2023 discusses how the combination of opaque social media algorithms, polarizing social bots, and new generative AI tools can create a \"perfect storm\" for climate misinformation, necessitating collaborative efforts among policymakers, researchers, and the public to address these emerging threats [18]. Despite these advancements, the dynamic nature of misinformation necessitates continuous refinement of AI and ML models to adapt to evolving deceptive strategies. Integrating these technologies with human expertise, encouraging interdisciplinary collaborations, and promoting digital literacy are crucial for developing robust systems to effectively combat climate-related misinformation on social media platforms.
## 1 Introduction
### Cross-Condition Fault Diagnosis Using Transfer Learning
Regarding aerospace topics, examples also exist for cross-condition fault diagnosis using TL. [PERSON] et al. [14] designed a TL-based algorithm for the fault diagnosis of aero-engine gas path data at different operating points. By unilaterally aligning the target domain to the source domain, the distribution discrepancy between the engine data at different operating points was minimised. [PERSON] et al. [15] examined the cross-condition fault diagnosis of industrial gas turbines operating at different rotational speeds. Transfer of knowledge is achieved by reusing the structure and weight of the CNN trained by labelled source domain data in the target domain unlabelled data. Although there have been numerous examples of applying TL in cross-condition fault diagnosis, most applications focus on component-level fault diagnosis rather than system-level problems. Hence, this work aims to discuss system-level cross-condition fault diagnosis using TL. Taking the aircraft environmental control system (ECS) as the system of interest, this system-level cross-condition fault diagnosis is generally more complicated than component-level studies, because more factors would affect the distribution discrepancy when the operating conditions change. For instance, when the operating conditions of a bearing change, only the speed, load, and wear condition affect the data, whereas, in an ECS, different operating conditions mean that a wide array of parameters from the ambient, bleed, and target conditions could all be different and contribute to the distribution discrepancy. In addition to more parameters, different interactions between the components are also expected under different operating conditions. Therefore, this work on how TL is applied to ECS cross-condition fault diagnosis would deepen the understanding of how TL facilitates cross-condition fault diagnosis at the system level.
## 3 Results  
Tables 7 and 8 show the experts' answers to the qualitative response survey's questions. In those cases in which the experts confirmed that the activity permitted the performance of the functional ROMs identified for each action, the answer was \"The activity is adequate\". \begin{table}
\begin{tabular}{c c c} \hline \hline
**Action** & **Upper Limb Movement** & **ROM** \\ \hline \multirow{4}{*}{Grab vegetable} & Shoulder flexion & 0\({}^{\circ}\)–30\({}^{\circ}\) \\ \cline{2-3}  & Elbow neutral position & 0\({}^{\circ}\) \\ \cline{2-3}  & Wrist flexion & 10\({}^{\circ}\) \\ \cline{2-3}  & Wrist ulnar deviation & 15\({}^{\circ}\) \\ \cline{2-3}  & Neck flexion & 10 \({}^{\circ}\) \\ \hline \multirow{4}{*}{Put vegetables into the basket} & Shoulder flexion & 30\({}^{\circ}\) \\ \cline{2-3}  & Shoulder abduction & 20\({}^{\circ}\) \\ \cline{2-3}  & Elbow neutral position & 0\({}^{\circ}\) \\ \cline{2-3}  & Wrist flexion & 10\({}^{\circ}\) \\ \cline{2-3}  & Wrist ulnar deviation & 15\({}^{\circ}\) \\ \cline{2-3}  & Neck flexion & 10\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sequence of actions and the upper limb movements involved in the picking vegetables up off the ground activity. To interpret the SUS score, it must be converted to a percentile rank. The outcomes from the experts' evaluations are shown in Table 9. Based on the distribution of all scores, a raw SUS score of 86.25 converts to a percentile rank of 86.25%, which objectively means that the evaluated system has a higher perceived usability than 86.25% of all products tested. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Activity** & **Answer FLM1** & **Answer FLM2** \\ \hline Hand-washing & No. Grabbing the gloves is an uncomfortable movement. It could be better if the gloves were already attached to the controllers. & No. Grabbing the gloves is an uncomfortable movement. It could be better if the gloves were already attached to the controllers. & No. Grabbing the gloves is an uncomfortable movement. It could be do not involve finger movements. \\ \hline Pouring a drink & No. Key-pressing action does not involve finger movements. Drums will be a better alternative. & No. Key-pressing action does not involve finger movements. \\ \hline Animal caring & Yes. The activity is adequate. & Yes. The activity is adequate. \\ \hline Picking apples & Yes. The activity is adequate. & Yes. The activity is adequate. \\ \hline Picking vegetables & Yes. The activity is adequate. & Yes. The activity is adequate. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of activities’ evaluation by FLM experts.