esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements in Research on Duck Tembusu Virus Infections
In 2012, TMUV strain TMUV-SDHS was isolated from sparrows around poultry farms in Shandong Province, northern China [14]. In 2013, TMUV-like flaviviruses (named CJD05) were isolated from broiler breeders with severe egg drop and fever in Fujian Province, China [24]. In 2012, a Malaysian DTMUV strain named Perak Virus was isolated from several duck farms in Malaysia, showing moderate similarity in genomic RNA sequences to DTMUV found in China [25]. In 2015, DTMUV variant strains were isolated from layer and broiler chicken farms in Thailand [26]. In 2019, a new strain of TMUV, TMUV-TP1906, was identified and isolated from mosquito pools collected in northern Taiwan. Its similarities to STWV and TMUV prototype strain MM1775 were 93.71% and 91.27%, respectively, marking the first isolation of TMUV from mosquitoes [27]. In 2022, the TMUV strain TMUV HQ-22 was isolated from infected geese in Anhui, China. Compared to other typical TMUV strains, it exhibited mutations in the envelope (E) protein and showed significant pathogenicity in goslings and ICR mice [15] (Figure 1). Figure 1: Timeline of the history of TMUV. The timeline illustrates the spread history of TMUV, the infected hosts, and typical strains isolated since 1955. In recent years, researchers have conducted extensive studies on DTMUV, making progress in understanding various aspects from the virus's genome structure to its pathogenic mechanisms, transmission routes, and host immune responses [11; 28]. The analysis of the virus's genome characteristics and protein structures has helped deepen our understanding of its lifecycle and pathogenic mechanisms, providing important clues for vaccine development and drug therapy. Although vaccines and detection methods have been developed, there are still many unresolved issues that require further research, such as the virus's long-term transmission dynamics and host immune mechanisms against the virus [29]. Vaccine development not only provides preventative protection to alleviate the impact of the disease on poultry farming but also helps to curb the spread of the virus. However, the role of innate immune responses as the body's first line of defense against DTMUV infection is also of great interest. This paper, through reviewing epidemiology, genomic characteristics, vaccines, and the interplay with host immune responses, contributes to a better understanding of the virus's infection mechanisms. It provides important scientific support for epidemic prevention and control, offers a comprehensive perspective for understanding and addressing DTMUV control strategies, and serves as a valuable reference for research and practices in related fields.
## 4 Result and Discussion
### Results for Delay with Circular-Shaped UAV Movement  
Figure 6 shows the data transmission delay for communication using bandwidth allocation estimated using the BRFFNN and LSGB models. It can be observed that there are some differences in the delays for the two models.  
The ML-based method yields an average delay of 0.00773 ms for LSGB and 0.00647 ms for the BRFFNN. These results indicate that LSGB incurs a slightly higher delay than the BRFFNN. Overall, the lower delay with the BRFFNN, even in complex data conditions, highlights its efficient performance and suitability for real-time applications in UAV communication scenarios.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 6 Edge Resource Optimization  
### Problem Statement  
The scenario considered in this work consists of a distributed 5G edge infrastructure composed of a set of physical servers, one per edge location, each server with a specific computing capacity (measured in the number of available cores) and a certain memory capacity (measured in MB of available RAM). Our objective is to deploy a set of virtual resources (VMs or containers) in this infrastructure, where each virtual resource has its own computing requirements (number of assigned cores), memory requirements (amount of assigned RAM in MB), and an estimated (forecasted) percentage of CPU usage for each time slot. Furthermore, to model latency in our system, we assume that each virtual resource is designated with a preferred edge location based on proximity criteria that consider the distance between the edge location and the end users it serves. If the virtual resource is not assigned to the preferred edge location, this is considered a latency violation. In our proposed model, we do not measure the exact time penalty caused by these violations but instead focus on counting the total number of virtual resources affected by latency violations.  
The problem at hand involves finding the optimal mapping of virtual resources (VMs or containers) to edge servers for a given time period (in our case, the mapping is performed once a day). As outlined in Section 2, deploying edge computing infrastructures on 5 Gnetworks presents new challenges related to reducing latency, minimizing energy consumption, and distributing workloads across geo-distributed edge nodes in various 5G locations (such as 5G base stations). This study tackles these challenges by considering different objective functions, such as minimizing the total number of cores utilized in the physical infrastructure in order to reduce energy consumption, optimizing load balancing among the different servers to improve workload distribution, and minimizing the number of virtual resources affected by latency violations to reduce the observed latency for end users and devices. Furthermore, we must account for some mapping constraints. For instance, it is essential to ensure that the aggregate CPU usage of all virtual resources assigned to a physical server should never exceed the server's capacity (number of available cores) in any given time slot. Additionally, there may exist optional constraints, such as imposing an upper limit on the utilization of resources (e.g., cores) within the edge infrastructure or establishing a limit on the number of latency violations.
## 1 Introduction
### Related Work
#### 1.1.3 Adaptability of Neural Networks for Fault Detection in Shipboard Systems  
The application of neural networks (NNs) for fault detection in pipelines has been extensively studied in various domains, including water distribution, crude oil transportation, and steam plant systems [17; 18; 19; 20; 21; 22; 23; 24]. These studies demonstrate the effectiveness of NNs in analyzing pressure and flow data, enabling accurate identification of anomalies and faults. Unlike traditional analytical methods, such as Takagi-Sugeno observers, NNs do not rely on explicit mathematical models, making them particularly suitable for complex and variable environments like shipboard firefighting systems.  
Ship firefighting systems operate under dynamic and challenging conditions that include the use of seawater, which can corrode or degrade certain hardware-based sensors, which can hinder effective data acquisition and nonlinear interactions among components such as pumps, valves, and piping networks, which are difficult to represent using analytical models.  
Methods based on mathematical observers, such as the Takagi-Sugeno descriptor systems, require precise modeling of system dynamics [26; 27]. While effective in well-defined systems, these methods face challenges when dealing with unmeasurable premise variables or highly variable operational conditions, such as those encountered in maritime applications.  
NNs provide a viable alternative by leveraging historical operational data to identify patterns and correlations. This data-driven approach enables NNs to detect faults robustly, even in the presence of noise or missing data. Additionally, NNs can be adapted to different types of ships with varying system configurations by retraining the models with data specific to each application, thereby enabling both flexibility and scalability.  
Although analytical methods and NNs represent different paradigms, they can be integrated to complement each other. Combining the analytical strengths of observer-based methods with the adaptive capabilities of NNs may facilitate the development of hybrid models that enhance fault detection and localization in shipboard systems, offering greater reliability across diverse maritime platforms.
# Automatic Identification of Landslides Based on Deep Learning
## 3 Materials and Methods
### DeepLab v3+  
DeepLabv3+ was proposed by the Google team in 2018 and is the DeepLab series model [34]. DeepLabv3+ is based on DeepLabv3 and improves it. It uses Deeplabv3 as the encoder, introduces atrous convolution in the encoder for downsampling and uses the spatial pyramid pooling module to extract multiscale information, which improves the accuracy by fusing low-level and high-level features.  
Its specific structure is shown in Figure 2. The encoder extracts image features through a deep convolutional neural network (DCNN), and the extracted feature layers are input to the decoder for \(1\times 1\) convolution. Meanwhile, the feature layers extracted by DCNN use \(1\times 1\), \(3\times 3\), \(3\times 3\), and \(3\times 3\) atrous convolutions for downsampling and pooling, where the expansion rates of atrous convolution are 1, 6, 12 and 18, respectively. Then, the obtained  
Figure 1: U-Net architecture. The blue box represents the multichannel feature layer. The channel number is shown at the top of the box. The white boxes represent the replicated feature maps. The arrows represent operations on the feature layers.  
new feature layer is concatted, its channel is changed to 1/5 of the original through 1 \(\times\) 1 convolution, and the decoder is entered for upsampling. The unsampled feature layer is concatted with the feature layer in the decoder, and then the 3 \(\times\) 3 convolution and upsampling are gone through to obtain the final prediction map.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 4 Keyword and Correlation Analysis  
To investigate the impact of multimodal interaction technology on teaching methods and instructional quality, this study selected \"Multimodal Image Recognition\", \"Application\", and \"Feedback\" as core keywords. A total of 25 research articles published in the past 5-10 years were manually screened to ensure relevance while maintaining broad coverage across different educational levels, specific disciplines, and target populations. Although these studies do not necessarily represent the most cutting-edge research in the field, they provide a representative sample of recent developments in the application of multimodal interaction technology in education. The selected articles were then analyzed using the VOSviewer tool to identify key trends, applications, and challenges.
## 5 Discussion
### Mixed Pixels  
The potential and advantages of using high spatial and temporal remote sensing data to describe the spatiotemporal variability in crop biophysical variables have already been demonstrated [53; 54]. Although the spatial resolution of the PROBA-V 100-m data improved when compared with the MODIS and SPOT-VGT data, mixed pixels still seem inevitable. For example, the effect of mixed pixels is a main reason that most of the NDVI profiles in Figure 5 follow a similar trend. In our study, this effect was not as serious because only larger and relatively homogeneous fields were chosen to evaluate the classification and estimation results. However, in regions with high farmland fragmentation, less accurate estimation results may be obtained because each pixel of PROBA-V reflectance data represents the averaged values of different crops. Fortunately, new satellites that combine higher spatial resolution with much shorter revisit cycles (such as the recently launched Sentinel-2A satellite and subsequent Sentinels), will weaken the influence of mixed pixels and, thus provide improved opportunities for crop classifications and yield estimations at field to sub-field scales [55; 56]. Currently, the unmanned aerial vehicle (UAV) data is also an important source of high spatial resolution data and it will be tested to fuse with satellite data to gain a better monitoring of agriculture in the further studies [57].
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 3 The Smart Collaborative Routing Protocol for IoT
### Clustering Behavior  
The proposed clustering behavior is conducted by two rounds, i.e., the election of CH and cluster formation. A reliable CH selection mechanism can improve the security in WSN [47]. In the first round, if a node wants to be a CH, it will send a request to its one-hop neighbors. Then, the neighbors respond based on previous behaviors of the node. If the number of supporting replies exceed a specific threshold value, the node will be regarded as a CH node. As shown in Figure 5.  
In the second round. After the CH node is selected, the remaining nodes will automatically establish the connection with the CH. Finally, the residual nodes serve as the MNs of the CH nodes and the cluster is formed.  
Figure 5: The process of clusters establishment.
# The Usability Testing of VR Interface
## 4 Results and Discussion
### Usability Analysis: Sus  
To realize ease of use of the three interfaces, the questionnaire was used for a survey and was measured by the SUS scale, which is a five-score scale. SUS consists of negatively and positively worded items. For statistical calculation, the negative items were reversed. A selection closer to 5 means that it is more positive, whereas a selection closer to 1 is more negative.  
In this paper, a SUS scale was used to test and therefore understand the indexed ease of use of the three interfaces. The ANOVA statistical analysis results are shown in Table 6 and Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Google Street View** & **VeeR VR** & **Sites in VR** \\ \hline SUS & 76.92 (18.77) & 54.83 (22.86) & 76.58 (19.53) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean value and standard deviation of System Usability Scale (SUS) in three forms of interface (Standard deviation listed in brackets, the full score is 100).  
Figure 3: Comparison among the three interfaces in SUS scale (full score is 100).  
Google Street View and Sites in VR scored both higher than 70 in the interface, which implies that they had a better usability than VeeR VR, as shown in Figure 3. Meanwhile, the VeeR VR interface had a lower mean value (around 50), and its interface design needs to be improved. The results of ANOVA show the VeeR VR differed significantly from Google Street View and Sites in VR, and no significant difference could be observed between Google Street View and Sites in VR. This finding indicates that both Google Street View and Sites in VR have similar usability, and both are better than VeeR VR in terms of interface usability. The results are illustrated in Table 7.  
The data above indicate that Google Street View and Sites in VR are better than VeeR VR in SUS scores. This shows that subjects considered Google Street View and Sites in VR to function better than VeeR VR with respect to usability. In addition, the interface of VeeR VR is not straightforward and causes confusion for first-time users to operate. By contrast, Google Street View is considered more user-friendly and easier to use. Its arrow icons serve well for users to easily understand that the arrow icon represents the direction of moving.  
As for Sites in VR, while it and VeeR VR share the same operational mode, the former is easier to use. The interface icons of VeeR VR are not straightforward enough; moreover, its selection speed is regarded as too fast, which thus makes it difficult to find the menu.
## 3 Parametric Analysis and Results
### Structural Characteristics and Peak Responses
The correlation of the wind forces are indicated by the color of the boxplot: blue [\(\rho_{FxM}=0\), \(\rho_{FyM}=1\)], red [\(\rho_{FxM}=0\), \(\rho_{FyM}=0.25\)], yellow [\(\rho_{FxM}=0.25\), \(\rho_{FyM}=0\)], cyan [\(\rho_{FxM}=0.5\), \(\rho_{FyM}=0.5\)], green [\(\rho_{FxM}=0.75\), \(\rho_{FyM}=0.5\)]. that even the extreme data, as well as the outliers, oscillate around one. The latter indicates that the A-\(\Delta\) effect does not have an important impact on the wind-induced displacement of the models considered. Similar to the results presented in Figure 5, the results presented in Figure 6 show that the correlation coefficient between the wind forces (i.e., \(\rho_{FxM}\) and \(\rho_{FyM}\)) has the most important impact on the \(R_{X}\) ratios. Similar observations are drawn for the \(R_{\tilde{X}}\) ratios, except that greater scatter is observed than those for the \(R_{X}\) values; however, the variation of \(R_{\tilde{X}}\) does not have an important impact on the wind-induced acceleration, indicating that the A-\(\Delta\) effect is negligible. Similar results to those presented in Figure 6 were obtained for the \(Y\)-direction (i.e., \(R_{Y}\) and \(R_{\tilde{Y}}\)), and for this reason they are not included. It is noted that the \(R_{\tilde{X}}\) values shown in Figure 6 include the effect of the wind-induced angular acceleration (\(\tilde{\theta}(t)\)), which is related to rotational velocity (\(\dot{\theta}(t)\)). Since \(\dot{\theta}(t)\) could be of concern for certain cases [14], to further evaluate the impact of the A-\(\Delta\) effect on \(\dot{\theta}(t)\), Figure 7 presents a comparison of \(\dot{\theta}(t)\) without and with the A-\(\Delta\) effect for all the structural models considered for selected values of \(\Omega_{\theta}\) and (\(\rho_{FxM}\), \(\rho_{FyM}\)). It can be observed in Figure 7 that the rotational velocity with and without the A-\(\Delta\) effect is practically the same, indicating that the A-\(\Delta\) effect has a marginal impact on the wind-induced rotational velocity. Similar observations were drawn for different values of \(\Omega_{\theta}\) and (\(\rho_{FxM}\), \(\rho_{FyM}\)), and for this reason they are not presented.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 4 Results and Experiment
### Class Imbalance and Model Performance
#### 4.3.2 Support Vector Machine  
As depicted in Figure 4, the SVM model exhibits the following strengths and weaknesses:  
* **Strengths:** SVM improves predictions for the \"Very High\" class with 7166 correct predictions. It also demonstrates marginal improvement over LR in identifying \"Low\" (16 correct predictions) and \"Medium\" (6 correct predictions) classes.
* **Weaknesses:** Despite improvements, the SVM model misclassifies the majority of \"Medium\" and \"High\" instances as \"Very Low\", highlighting its difficulty with under-represented classes.
###### Abstract  
Sidewalk autonomous delivery robots (SADRs) share sidewalks with pedestrians and may affect their perceived safety. In outdoor nighttime environments, the color of the lights on SADRs serves as a noticeable form of communication that can influence human emotions. Therefore, this study investigated whether the perceived safety of SADRs varied with the colors of their lighting. In this study, an experiment (\(n=30\)) was conducted where participants walked towards a robot from opposite directions to interact. The perceived safety of five different lighting colors (red, yellow, blue, green, and white) on SADRs was measured before and after the interaction using two perceived safety scales. The results showed significant differences in participants' perceived safety for the robot's red, green, blue, and yellow lighting before and after the interaction. Red lighting was rated the least perceived safe, while white and yellow lighting were rated the most perceived safe. Additionally, gender significantly influenced the perceived safety assessments, with females reporting lower perceived safety than males. These findings are valuable when designing SADRs that enhance pedestrians' perceived safety, thereby facilitating their integration into broader environments in the future.  
s +
Footnote †: journal: Medical  
can subtly influence human perception and psychology, even affecting human behavior [12]. For robots that cannot make physical gestures and lack display screens [13], the lighting color serves as an essential means of communication. Given that SADRs undertake delivery tasks at night, with increased opportunities for interactions between pedestrians and SADRs following wider commercialization, the further exploration of SADRs' lighting colors is crucial.  
Environmental and human factors also merit consideration in this study. Due to the nature of SADRs' delivery tasks, both pedestrians and SADRs are mobile during interactions. In outdoor environments and dark conditions, an individual's perceived safety may differ from that in confined indoor spaces. Therefore, it is necessary to investigate interactions between pedestrians and SADRs in walking scenarios and under dark outdoor conditions. Additionally, [PERSON] et al. [14] confirmed that male participants generally exhibit a more positive attitude towards robot interactions than female participants, which this study also aims to explore.  
This study aims to evaluate pedestrians' perceived safety regarding SADRs with different lighting colors in dark outdoor environments. Specifically, this study seeks to answer three research questions: (1) Is a perceived safety evaluation related to pedestrians' interactive behavior such as walking? (2) Are there differences in perceived safety among pedestrians for different lighting colors? (3) Does gender influence perceived safety assessments?
## 3 Linear Streaking Measurements at Free-Electron Lasers
### Experimental implementation
#### 3.1.2 Laser-Driven Terahertz Streaking for Complete Temporal Characterization
For FEL pulses with a broad initial bandwidth and large initial energy variation across the pulse, it may not be possible to neglect the initial chirp in these streaking measurements. Moreover, it may be crucial for experimental applications to characterize the spectral chirp in the FEL pulse. For linearly chirped FEL pulses, at least two measurements of the streaked photoelectron spectrum at opposite slopes of the vector potential are required to infer the temporal duration and shape of the ionizing pulse [104]. At FELs, different experimental approaches can be taken to achieve this on a single shot basis. For example, two time-of-flight detectors, both mounted in the plane of the streaking field polarization but in opposite directions could be used to record two spectra with opposite slope. Alternatively, it has been proposed that different phases of the streaking pulse can be realized by taking advantage of the Gouy phase shift, which occurs when a Gaussian beam propagates through a focus [105]. If single-shot information cannot be obtained, information regarding the average chirp of the FEL pulses could be obtained by making two sets of measurements with THz pulses generated with two distinctly different phases. This can be realized easily for certain THz generation methods. In particular, for THz pulses generated using collinear rectification in ZnTe or organic crystals with higher electro-optic coefficients.