esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Analysis of AI Language Models Based on Scholarly Literature
### Evaluation Criteria and Methodology  
Table 3 presents a structured analysis based on the synthesis of existing research, which highlights the distinctive strengths, opportunities, and limitations of the AI models ChatGPT, Bard, Llama, Ernie, and Grob. This synthesis is grounded in the following selected criteria: Strengths and Opportunities, Weaknesses and Challenges, Performance Metrics, Scope of Knowledge, Use Cases Excelling In, Use Cases Limited By, Knowledge Update Frequency, Computational Efficiency, and Domain Expertise Rating. The categories of Strengths and Opportunities, Weaknesses and Challenges, Scope of Knowledge, and Use Cases have been directly extracted from an extensive array of academic papers analysed in this study. These dimensions represent the empirical findings and scholarly assessments articulated within the published literature. The judgements on Performance Metrics are not derived from our empirical experimentation but instead reflect a subjective synthesis based on the performance and behaviours of the models as reported in the literature. Similarly, the Scope of Knowledge is an interpretive assessment of the range of topics and the depth of discourse that each model demonstrates across various domains, as depicted in the scholarly works reviewed. The categories of Use Cases Excelling In and Use Cases Limited By encapsulate instances of proficiency and deficiency as reported in the papers reviewed within their respective applications and evaluations. Knowledge Update Frequency has been estimated from the update and versioning information presented within these studies, which is a critical factor for the models' ongoing accuracy and relevance. Computational Efficiency represents a qualitative aggregate of how the models balance their functional prowess with the computational overhead required, which is crucial for assessing their real-world applicability. Lastly, the Domain Expertise Rating has been deduced from the literature's consensus on each model's command over specialised knowledge areas. This evaluative framework, entirely based on a scholarly review of published research, does not include any new experimental data generated by the authors of this paper. It aims to offer an academic perspective rather than definitive quantitative results. It is an attempt to synthesise the reported observations and findings to compare and contrast the current capabilities and future potential of these prominent AI language models. Large-scale and longitudinal studies, as well as cross-disciplinary experimentation, are needed to scientifically validate these results. We hope these insights are viewed as part of the ongoing effort in this direction rather than a conclusive comparison between LLMs.
## 3 Results and Discussion
### Attachment and Morphology of Macrophages  
The attachment and activation of macrophages on a biomaterial's surface could lead to inflammatory response. Therefore, the attachment and activation of macrophages on Cu-doped TiO\({}_{2}\) thin films was performed to evaluate the anti-inflammation function of the film. Figure 9 shows the fluorescence images and counting results of the macrophages on Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces. Obviously, the macrophages on the Cu3 and Cu4 were significantly less than the cells on Cu0, Cu1 and Cu2, and the morphology of macrophages showed typical shrinkage, suggesting a non-activated phenotype. SEM micrographs (Figure 10) displayed further results of the macrophages' morphology. The macrophages on the samples of Cu0, Cu1 and Cu2 showed a multilateral and spreading morphology, with many pseudopods stretching out and aggregating, indicating an activated phenotype, while the macrophages on the Cu3 and Cu4 samples showed a round and shrinking morphology, suggesting a non-activated phenotype. This result demonstrated that the more Cu doped in the TiO\({}_{2}\) films, the fewer macrophages adherent to and activated on the surfaces. The result also suggested that the Cu doped TiO\({}_{2}\) thin film surfaces may be toxic while the human body contains 0.1 g of Cu per 75 kg of body weight and it is widely distributed in the blood, bone and muscle [39].  
Figure 8: Migration of SMCs on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces after culture of one day. The migration distance was calculated from at least 12 images.  
The change of macrophage morphology often accompanied by their immune functions, such as: phage function and secretion function changes [48]. Activated macrophages can release specific chemical factors, which induce other inflammatory cells and cytokines to flock to the trauma location and cause inflammation. Thus, the attachment and activation of macrophages on the biomaterials is often detected as an evaluation of their anti-inflammation properties. Therefore, the above results showed that the samples Cu3 and Cu4 had good anti-inflammatory properties.  
Figure 9: **(A)** The fluorescence photographs and (**B**) cell counting results of macrophages on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces after 24 h incubation (mean \(\pm\) SD, * \(p<0.05\) compared with Cu0, Cu1 and Cu2, \(n=3\)).
## 3 Results and Discussions
### Transmission Path Characteristics
A comprehensive macroscopic analysis reveals that Chenzhou City experiences significant influence from short-distance atmospheric transport throughout the four seasons. Notably, the prevalence of such short-distance transport peaks during summer, eclipsing that of autumn and winter. Furthermore, discernible variations in transport pathways emerge during autumn and winter. Predominantly, the pivotal transport routes in spring, summer, autumn, and winter converge upon regions in Guangdong, northwest and southwestern Henan, Hubei, southern Anhui, as well as select areas of southwestern Jiangxi. This pattern underscores their paramount role in contributing to the ambient air pollution levels in Chenzhou City.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 4 Experimental Results
### Qualitative Comparison of Human Pose Estimation  
Figure 7 presents a qualitative evaluation of pose estimation, comparing skeleton extraction results obtained using ground truth, HigherHRNet with HRNet-W32 backbone, and the proposed method. In the skeleton extraction analysis of the proposed method, GT annotations for the nose position were excluded from evaluation, as previously explained in the occlusion experiments. This exclusion was necessary because the GT nose annotations were centered on the head rather than precisely located at the nose. Unlike HigherHRNet, which incorrectly produced extra skeletons, the proposed method successfully extracted exactly two skeletons when two individuals were present in the image. However, self-occlusion led to the left arm of the left person being undetected, as most of its joints were not visible. For the visible joints, detection accuracy was high, and no joint-switching problem occurred.  
Figure 8 visualizes the confidence maps for joints in occluded scenes. In Figure 8, The red and green circles represent the same joint on different individuals. The traditional OpenPose often fails to accurately localize body parts within occluded areas. In contrast, our proposed method can more precisely detect the corresponding joints by leveraging additional 3D topological information from depth images.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Neck** & **Shoulder** & **Elbow** & **Wrist** & **Hip** & **Knee** & **Ankle** & **mAP** \\ \hline \multicolumn{8}{c}{Occlusion Subset of 383 images as in NTU RGB+D 120 dataset} \\ \hline HigherHRNet [18] & - & 0.47 & 0.47 & 0.43 & 0.63 & 0.68 & 0.55 & 0.54 \\ OpenPose [27] & 0.62 & 0.49 & 0.44 & 0.33 & 0.65 & 0.69 & 0.63 & 0.54 \\ Ours (\(R=3\)) & 0.67 & 0.78 & 0.54 & 0.38 & 0.86 & 0.74 & 0.70 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results on evaluating the accuracy of pose estimation in the occlusion test set.  
Figure 7: Comparison of pose estimation results of HigherHRNet and proposed method: (**a**) ground truth; (**b**) HigherHRNet [18]; (**c**) ours (\(R=3\)).
## 4 Results and Discussion
It is interesting to note that the accuracy was consistent for the first 9 or 10 layers for each of the material systems, but for the engineered polymer without fiber fill, the signal-to-noise ratio was sufficiently deeper into the part, allowing for the analysis up to the 14 th layer (type B and C) and the 17 th layer (type A). This additional penetration is likely caused by the highly elastic nature of the viscoelastic HTN material, relative to PCTG, at room temperature, allowing for less attenuation of the signal. The PET-CF sample, with the carbon fiber, will tend to cause considerable scatter; thus, it is not surprising that the penetration of the acoustic signal would be diminished for the fiber-filled system. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Frequency** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
10 MHz & 0 * & 90 & 8 & 99 & 19 & 110 & 29 & 119 & 38 & 129 \\
15 MHz & 0 & 90 & 9 * & 100 & 19 & 110 & 29 & 120 & 39 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Raster orientation up to 10 th layer of PCTG part A scanned with a mean transducer frequency of 10 and 15 MHz. Figure 4: Errors against the number of layers for all parts with sample type A scanned with 10 MHz transducer. Figure 5: Errors against the number of layers for all parts with (**a**) sample type B and (**b**) sample type C scanned with 10 MHz transducer.
# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 5 Results
### Simulation Results and Analysis
#### 5.1.1 The Accuracy Comparison of Different Algorithms
Table 3 shows the ARMSE and average state estimation errors of different SLAM algorithms in colored noise. In Figure 10, we observe that there is a certain error between the estimated path obtained by the EKF-SLAM, UKF-SALM, and SRUKF-SLAM algorithms in colored noise and the true path of the robot, indicating that the three algorithms have poor resistance to non-Gaussian noise interference. The simulation results of the MCUKF-SLAM algorithm are slightly better than the above three algorithms. The proposed MCSRUKF-SLAM algorithm is optimal, and the estimated path obtained is also closer to the true path of the robot. Therefore, the proposed MCSRUKF-SLAM algorithm has better anti-interference ability and robustness in colored noise. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **ARMSE (m)** & **Aerror\({}_{\mathbf{x}}\) (m)** & **Aerror\({}_{\mathbf{y}}\) (m)** & **Aerror\({}_{\mathbf{\phi}}\) (rad)** \\ \hline EKF-SLAM [13] & 1.0039 & 0.4128 & 0.6310 & 0.0168 \\ UKF-SLAM [20] & 0.8756 & 0.2956 & 0.4927 & 0.0138 \\ SRUKF-SLAM [23] & 0.8348 & 0.2549 & 0.4776 & 0.0136 \\ MCUKF-SLAM [39] & 0.8023 & 0.2335 & 0.4511 & 0.0129 \\ The proposed MCSRUKF-SLAM & **0.4737** & **0.1905** & **0.2708** & **0.0079** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of average RMAE and average state estimation error for some SLAM algorithms in Gaussian mixture noise. Figure 11: RMSE of five algorithms in colored noise. Figure 12: State estimation errors of five algorithms in colored noise. Figure 10: Comparison of simulation results of five algorithms in colored noise. In Figures 11 and 12, and Table 3, we observe that UKF-SLAM and SRUKF-SLAM have better accuracy performance than the EKF-SLAM algorithm. UKF-SLAM and SRUKF-SLAM have almost the same accuracy performance, and MCUKF-SLAM has better accuracy than the EKF-SLAM, UKF-SLAM, and SRUKF-SLAM algorithms. The proposed MCSRUKF-SLAM algorithm is significantly better than other algorithms in pose estimation accuracy. Furthermore, the accuracy of the proposed MCSRUKF-SLAM algorithm improves by 50.3%, 39.9%, 38.2%, and 36.3% compared to EKF-SLAM, UKF-SLAM, SRUKF-SLAM, and MCUKF-SLAM, respectively. In summary, the experimental results show that the proposed MCSRUKF-SLAM algorithm has better pose estimation accuracy compared to other algorithms in Gaussian mixture noise and colored noise. The simulation results confirm the proposed MCSRUKF-SLAM algorithm's robustness and effectiveness under non-Gaussian noise conditions.
## 5 Optimized Design of the Cooling Channel and Its Cooling Performance  
### Structural Improvement of the Cooling Channel  
In order to improve the active cooling performance and save the cooling water, another cooling channel design has been carried out, as shown in Figure 15. The improvement of the new cooling structure mainly includes three aspects. Firstly, two annular water storage channels were added internally at the junction of the rudder shaft and lower flange. These channels enhance boiling heat transfer in this region, which is characterized by the highest heat flux density. This modification helps to lower the temperature and alleviate the boiling heat transfer burden on the microchannels at the top of the rudder shaft and the liquid collection ring. Then, the volume of the liquid collection sleeve at the upper constriction of the rudder shaft was increased. This change allows cooling water to flow beyond the microchannels, thereby expanding the heat transfer area and increasing water storage capacity. Finally, the microchannels at the constriction of the rudder shaft were deepened, further increasing the heat transfer area. The results show that the improved  
Figure 14: (**a**) Surface temperature distribution of the rudder shaft at \(t=1250\) s with an initial inlet flow rate of \(0.5\) m/s. (**b**) Axial temperature distribution on the surface of the rudder shaft at \(t=1250\) s. (**c**) Variation in maximum temperature of the rudder shaft over time.  
structures can provide better active cooling effect and reduce the risks of boiling dry out. There may be a better design available. In future work, we can conduct simulations for other cooling channels and evaluate their performances.
###### Abstract  
Background: Monitoring the vital signs of delirious patients in an intensive care unit (ICU) is challenging, as they might (un-)intentionally remove devices attached to their bodies. In mock-up scenarios, we systematically assessed whether a motion detector (MD) attached to the bed may help in identifying emergencies. Methods: We recruited 15 employees of the ICU and equipped an ICU bed with an MD (IRON Software GmbH, Grunwald, Germany). Participants were asked to replay 22 mock-up scenes of one-minute duration each: 12 scenes with movements and 10 without movements, of which 5 were emergency scenes (\"lying dead-still, with no or very shallow breathing\"). Blinded recordings were presented to an evaluation panel consisting of an experienced ICU nurse and a physician, who was asked to assess and rate the presence of motions. Results: Fifteen participants (nine women; 173 \(\pm\) 7.0 cm; 78 \(\pm\) 19 kg) joined the study. In total, 286 out of 330 scenes (86.7%) were rated correctly. Ratings were false negative (FN: \"no movements detected, but recorded\") in 7 out of 180 motion scenes (3.9%). Ratings were false positive (FP: \"movements detected, but not recorded\") in 37 out of 150 scenes (24.7%), more often in men than women (26 out of 60 vs. 11 out of 90, respectively; \(p<0.001\)). Of note, in 16 of these 37 FP-rated scenes, a vibrating mobile phone was identified as a potential confounder. The emergency scenes were correctly rated in 64 of the 75 runs (85.3%); 10 of the 11 FP-rated scenes occurred in male subjects.  
Conclusions: The MD allowed for identifying motions of test subjects with high sensitivity (96%) and acceptable specificity (75%). Accuracy might increase further if activities are recorded continuously under real-world conditions.  
motion detector; noncontact monitoring; Internet of Things devices Article
## 3 Results and Discussion
### Nanoparticle Characterization
#### 3.2.3 XRD  
USeNPs phase composition and crystal structure were identified using the XRD techniques depicted in Figure 4. The sample's XRD pattern indicates that it is nanocrystalline and closely resembles typical selenium powder, implying that selenium particles were formed. The formed NPs are selenium because at the 20 angle in the range of 14-52.4\({}^{\circ}\) there is a specific diffraction peak, which is essentially consistent with the diffraction peak of JCPDS card number 65-1290. The pattern's diffraction peak is very narrow, which is an indication that the physcosynthesized USeNPs are very small, crystalline, and have a poor amorphous character. The obtained XRD patterns showed the main peaks characteristic of crystalline SeNPs at the values of 19.01\({}^{\circ}\), 30.8\({}^{\circ}\), 43.5\({}^{\circ}\), 28.5\({}^{\circ}\), 30\({}^{\circ}\), 39.42\({}^{\circ}\), 50.069\({}^{\circ}\), and 58.923\({}^{\circ}\) (Figure 4), respectively, corresponding to the crystal planes (121), (013), (147), (061), and (530). [PERSON]'s equation calculated the average crystalline size of biogenic selenium nanostructures to be approximately 13.26 nm. Background noise was produced by the biological approach of SeNPs, which could result from the presence of additional bioactive compounds within the _U. lactuca_ extract (Figure 4). The XRD pattern of the powder shows that post-annealing is not required to achieve the desired crystalline phase [30].  
Figure 4: XRD spectrum of UseNPs.  
Figure 3: The UV-Visible spectrum of _Uliva lactuca_ mediated selenium nanoparticles.
# Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network
## 4 Experimental Results and Analysis
### Network Attack Simulation Experiment
#### 4.2.2 Attack Simulation to the Technology Routes Network  
To verify the generality of MCNDI, we performed a similar simulation for the technology routing network. The technology routing network is shown as an example of a large and complex network, showing the actual distribution architecture of a routing level network. The network contains 2113 nodes and 6632 links, and its topology is shown in Figure 6b. According to the above equation, we can obtain the weight distribution of the H-index, closeness centrality, k-shell indicator, and network constraint coefficients under the CRITIC method as:  
\[[H,COC,KS,NCC]=[0.15093,0.18123,0.19675,0.47109] \tag{20}\]  
Then, we can obtain the 2113 nodes' importance composite scores' MCNDI.  
The cost of simulating the attack in a large network by proportion is huge, while deleting a large number of nodes in a single stage will cause the set of network nodes to change drastically, which is not conducive to identifying the most critical set of nodes. Therefore, we calculated every five nodes after deleting them as the evolutionary phase, which again does not change the trend of the network. In Figure 7b, the horizontal axis represents the evolutionary stage (every five nodes are a basic part) and the vertical axis is the network efficiency ratio. The simulation showed that after the start of evolution, when about 2.5 percent of nodes were removed, it already caused the \(NEP\) to drop to 60%, reflecting the vulnerability of real networks with scale-free characteristics to deliberate attacks, and the network efficiency of MCNDI converged faster than other methods during this process, demonstrating the better efficacy of the method in targeting critical node rankings in large real networks.
## 2 Materials and Methods
### Geo-Positional Transformer-CNN  
To address the limitations of traditional models in capturing the spatial heterogeneity of SOM and modeling geographic positional information, this study proposes a dual-branch SOM prediction model named Geo-Positional Transformer-CNN (GPTransCNN). The model consists of two branches: the Geo-Positional Transformer (GPTransformer) and a one-dimensional convolutional neural network (1D-CNN). GPTransCNN is a supervised regression model designed to predict continuous SOM values based on geographic positional information and environmental covariates.
## 4 Speech Emotion Recognition Datasets
### English Language Datasets
#### 4.1.5 Sayee  
SAVEE (Surrey Audio-Visual Expressed Emotion) [29] is a dataset designed for research on emotion recognition in speech, including both audio and visual components. The data comes from recordings made by four male native speakers of English. During the recordings, the actors expressed seven emotions: anger, disgust, fear, happiness, sadness, surprise, and a neutral emotional state, allowing for the study of different forms of emotional expression. The recordings were made in controlled conditions, ensuring high quality of both audio and visual signals. The SAVEE dataset consists of 480 files, including audio recordings and audio-visual materials. The sentences spoken by the actors were carefully selected to correspond to the emotions expressed, allowing for precise analysis of both acoustic and facial expressions. Due to its structure and diverse emotions, the dataset is suitable for the development of artificial intelligence technologies, speech recognition systems, and applications that analyze emotions in real time.