esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 5 Discussion
### Interface Strategies While Waiting with Page Hierarchy Transitions  
This study found that the type of loading icon has a significant impact on users' emotional experience and satisfaction, while its effect on time perception is relatively weaker. This suggests that loading icons primarily influences the waiting experience through the emotional pathway. Among the three types of icons, hedonic-style icons significantly outperformed both the regular icon and no icon in terms of satisfaction and emotional valence. This is consistent with existing research that indicates \"fun designs can alleviate user anxiety and enhance emotions\" [31; 45]. Hedonic-style icons, due to their novelty and visual appeal, help divert users' attention during the waiting process, improve their emotional state, and thereby indirectly enhance the loading experience.  
In contrast, regular icons, while providing feedback on loading status, lack the same level of entertainment value. No icon, on the other hand, lacked any visual cues, making the waiting process feel monotonous, which significantly reduced both emotional experience and satisfaction ratings.  
Interestingly, in the half-screen page scenario, the no-icon strategy outperformed the icon strategies in the time reproduction dimension. Since the popup itself already provides some feedback, the role of the icon was somewhat diminished. Users lacking explicit references ended up underestimating the waiting time. This result aligns with the mechanism that \"blank pages can compress time perception,\" but it is important to note that its satisfaction performance was still the worst. This further corroborates the research conclusion that \"low time perception does not equal high satisfaction\" [46].  
Regarding text strategies, although there were no significant differences between humanized and regular text across all dimensions, both were significantly better than no text, indicating that text prompts are indispensable elements in the loading process. Humanized text slightly outperforms regular text across most dimensions, so designers may flexibly choose between them depending on the context to enhance interface friendliness. In the half-screen page scenario, no-text strategies scored significantly lower for both emotional valence and satisfaction dimensions, further validating the important role of text feedback in micro-interactions. Since the humanized text style used in this experiment was relatively mild, future studies should explore its potential impact on users' waiting experience by strengthening its stylistic features.
## 5 Conclusions  
Aiming at the problems, such as the influence of sensor nodes on marine mammals in UASNs and the large interference between underwater network nodes, a channel power allocation algorithm, based on game theory and mammal-friendliness in an underwater acoustic network, is proposed. The algorithm aims to avoid the communication interference of sensor nodes in marine mammals, and further avoid the direct mutual interference of sensor nodes, so as to improve the communication quality of nodes while ensuring mammal-friendliness.  
Firstly, with marine mammals as primary users and sensor nodes as secondary users, the system capacity of secondary users is maximized by a non-cooperative game without interfering with the communication of primary users. Secondly, the network interference level is introduced into the utility function, and then the transmitting power of the node is adjusted according to the income of the game. Simulation analysis shows that the proposed algorithm improves the communication quality and energy efficiency of UASNs while improving the capacity of the UASN system.  
Conceptualization, L.X.; methodology, L.X.; software, L.X. and C.C.; validation, L.X. and C.C.; formal analysis, L.X.; investigation, L.X. and C.C.; data curation, L.X.; writing--original draft preparation, L.X. and C.C.; writing--review and editing, L.X. and C.C.; supervision, C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Major Science and Technology Plan of the Hainan Province, No. Grant ZDKJ2021052.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Hybrid Graph Models for Traffic Prediction
## 6 Experimental Results
### Time Cost  
Some experiments are conducted to explore the training time and inference time of the proposed method and compared methods. The experiments run on the computer with Nvidia_V100 GPU, 16 G RAM, Intel I9700 CPU. Table 4 records the experimental results of different methods in the next 60 min on the METR-LA dataset. According to the experimental results in Table 4, traditional statistics-based methods and machine learning-based methods require less training time. Since FC-LSTM, DCRNN, STGCN, and Graph WaveNet use a single topology to predict road conditions, the training time is less than the proposed method. The proposed method uses a graph neural network, convolutional neural network, and attention mechanism to capture complex spatial-temporal information of the traffic network, and designs a learnable adjacency matrix to represent the potential topology of the network. Although the proposed method requires more training time than most of the compared methods, it achieves the best prediction performance due to the superiority of the proposed method.
## 2 A General Overview on SSNs
### Challenges behind Design of SSNs
Additionally, the service provider needs to provide an access control mechanism to its resources to ensure user's privacy and security. Resource sharing is mentioned before (it described in the fundamental requirements here it described as one of the challenges). * Network Management: it is concerned with managing a virtualized environment is a complex issue, as the virtual network may span over multiple physical networks from different infrastructure providers. Or, it may participate in one physical network from one infrastructure provider. The infrastructure provider may offer an infrastructure for multiple service providers, or multiple infrastructure providers may offer an infrastructure for one service provider, also the aggregation or dissolution of virtualized networks between different network entities and service providers may need to change resources requests according to the user's requirements. Moreover, it has to manage the collected information from diverse devices of different partners, while saving its privacy and removing conflicts and redundancy from it, on another level it has to manage user mobility from one physical location to another. All of that and other many cases may be in concern. Thus, there will be a real need for a scalable and flexible management system. * Mobility Management: providing mobility to the hosted application in a virtualized environment is a complex issue for several issues; the user location management to provide network communication to users by tracking their locations, the service handoff management to provide the service delivery to the user, who may also need location updates in a different VSN by being provided virtual mobility between VSNs.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 3 Results
### Gust Factor  
Figure 6 shows the relationship between the longitudinal and lateral gust factors and the mean wind speed at the two measuring points. The results show that the curves between the longitudinal and lateral gust factors and the mean wind speed at the two measuring points exhibit a close trend. When the wind speed is less than 8 m/s, the distribution of the gust factors is relatively scattered. The gust factors tend to cluster together when the wind speed surpasses 8 m/s. The distribution of lateral gust factors is generally more scattered compared to that of longitudinal gust factors, and the longitudinal gust factor is greater than the lateral gust factor. At the east and the west measuring points, the longitudinal and lateral gust factors have mean values of 1.94/2.00 and 0.88/0.97, respectively.  
The change in the gust factor is affected by the length of the time interval. A short time interval to affects \(\max(\overline{u(t_{g})})\) and \(\max(\overline{v(t_{g})})\), and a long time interval \(T\) affects \(U\). This paper uses \(T=3600\) s based on the research findings of [PERSON] [43] and [PERSON] et al. [44] to study the impact of short time intervals on the gust factor. Figure 7 depicts the variation in the longitudinal and lateral gust factors with time intervals under different wind speeds. The results reveal that the gust factor decreases with the increase in the time interval. The longitudinal and lateral gust factors eventually approach 1.5 and 0, respectively. Under the same wind speed, the longitudinal gust factor is usually much bigger than the lateral gust factor.  
Figure 5: Variation in turbulence intensity with time interval: (**a**) Variation in longitudinal turbulence intensity with time interval; (**b**) Variation in lateral turbulent intensity with time interval.  
Figure 6: Variation in gust factor with mean wind speed: (**a**) Longitudinal; (**b**) Lateral.
## 2 Related Work
### Image Classification
#### 2.2.2 Contrastive Language-Image Pre-Training  
Although deep learning techniques, such as convolutional neural networks (CNNs), have achieved remarkable success in image classification, their performance often relies on large amounts of labeled data, and their scalability to unseen categories is limited. To address this issue, OpenAI proposed the Contrastive Language-Image Pre-Training (CLIP) model [5], which enables zero-shot learning by learning the correspondence between images and text.  
The core innovation of CLIP lies in mapping images and text into the same space. This model uses different encoders to process images and text separately, mapping them into the same vector space. The image encoder uses ResNet [18] or Vision Transformer (ViT) [21] as the base architecture, converting input images into fixed-length feature vectors through convolutional layers or multi-head self-attention mechanisms. The text encoder uses a Transformer [22] model to process text inputs, converting them into embedding vectors with the exact dimensions as the image feature vectors, as shown in Figure 1.  
During training, CLIP learns the correspondence between image-text pairs in different batches through contrastive learning [23; 24; 25; 26]. For each image-text pair, the model maximizes the similarity of positive matches while minimizing the similarity of negative matches. This ensures that matching images and texts are mapped to the exact location in the embedding space while non-matching pairs are pushed to different places. This feature enables CLIP to accurately classify images based on textual descriptions in zero-shot learning scenarios, achieving high generalizability.
## 3 Higher-Layer Protocols: MAC & Routing
### Csma  
The Carrier Sense Multiple Access (CSMA) [19] is an evolution of ALOHA that includes a channel sensing mechanism, so before sending a data packet, the CSMA protocol checks if the shared channel is free. If not, it defers data packet transmission until the channel is freed. This protocol reduces considerably the channel collisions when compared with the ALOHA protocol, without requiring extra signaling.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Interfaces and Sub-Planes Connected with the Controller
#### 5.2.6 Network Operating System  
A network operating system should provide abstractions to access and interact with lower-level devices, provide the ability to access resources such as hard drives, network adapters, CPUs, etc. concurrently, provide services, and provide security mechanisms. The common services offered by a network operating system are providing network state and network topology information, device discovery, and the distribution of network configuration. Thus, the core functions provided by a network operating system are topology management, device management, notification management, statistics management, routing, and security mechanisms in conventional SDN architecture. However, in KDN architecture, a network operating system will additionally have the functions of knowledge creation and knowledge management. As a result, in the KDN design, the network service abstraction layer requires all of these services to be provided by either the control plane, management plane, or knowledge plane. As shown in Figure 6, a network operating system offers a platform for the implementation of the knowledge plane, the management plane, the control plane, and the network service abstraction layer in the KDN architecture. Keep in mind that, in the KDN design, the management abstraction layer is responsible for all management-related activities, while the controller is in charge of other duties such as routing and security, and the knowledge plane is in charge of knowledge creation and administration. However, in the original SDN architecture, all tasks of the network operating system were assigned to the controller. Before the advent of SDN, networks were managed using proprietary network operating systems and device-specific instruction sets. The purpose of a network operating system is to abstract device specific characteristics and provide common functionality. Due to network operating systems, application developers do not need to know about the low-level details of data distribution among forwarding elements; they only need to specify policies, which will be converted into low-level details by the network operating system and southbound APIs. The network operating system resides inside the combined control plane, which works according to the policies defined by the applications to provide knowledge-driven control and management operations to the data plane in the KDN paradigm.  
Network operating systems are discussed in detail with respect to the distribution of the control plane in Section 5.3.1.
## 4 Experiment Analysis
### Influence of Ambient Temperature and Air Content on the Support Height of Support Cylinder
(i) During the experiment,  
Figure 7:0 Pressure error of upper and lower chambers when injecting filtered air. the error of the support cylinder parts (such as machining error, nonlinear deformation caused by wire cutting of the beryllium bronze Feed, and fatigue damage of the rolling diaphragm) led to inconsistent parameters such as the internal resistance of the support cylinder and the volume of the upper and lower chambers; (ii) during the installation of the support cylinder, the length and bending angle of the hydraulic pipes in the upper and lower chambers were not kept the same, which also caused errors in the FSS; (iii) during the experimental data collection process, due to the uneven heat dissipation source in the laboratory, the collected laboratory temperature was inconsistent with the hydraulic oil temperature; (iv) the FSS had leakage in the experiment and the leakage rate was unstable. These factors will cause errors in support stiffness, but the size of the error can be controlled within the range of \(-100\) to \(190\) N/mm, which verifies the accuracy of the established mathematical model within the allowable range of error. Figure 7:1 Support stiffness error with hydraulic oil with filtered air. Figure 7:2 Support stiffness error with hydraulic oil with unfiltered air. Through the above experimental analysis, it can be seen that the support pressure error of the large-scale optical mirror FSS during the experiment was from \(-0.0045\) MPa to \(0.005\) MPa, the support height error was from \(-0.0045\) MPa to \(0.005\) MPa, and the support stiffness error ranged from \(-100\) N/mm to \(190\) N/mm. These errors can be controlled within a certain range to meet the requirements of large-scale optical mirror processing. Applying this technology to the processing for large-scale optical mirrors can solve the shortcomings of the existing support system of large and non-adjustable support stiffness in the processing of optical mirrors. It can effectively unload the impact of robot motion accuracy and tool vibration during processing while protecting the optical mirror surface and more accurately controlling the amount of mirror removal.
## 4 Conclusions  
Due to its relatively low cost, high ion-exchange potential, and environmentally benign structure, TTAPA resin represents a promising adsorbent for the elimination of heavy metal ions from industrial effluent. Fe, Cu, Zn, Pb, and Cd heavy metals were able to be effectively removed from industrial effluent. The prepared H-TTAPA resin was found to show a characteristic effect, with variations in the pH of the solution leading to variations in the adsorption. TTAPA acts as a flocculant and metal ion exchanger that helps in the sequestration of toxic and hazardous metal ions from industrial effluent. The adsorption strength of the prepared H-TTAPA resin for the tested metal ions was found to follow the order Fe(II) > Cu(II) > Zn(II) > Pb(II) > Cd(II). Other methods are not feasible due to their technical complexity and expense but, when the concentration of pollutants is low, the proposed process is very efficient. It was thus confirmed that TTAPA resin can be successfully used for the effective removal of various metal ions from industrial wastewater.  
Conceptualization, methodology, formal analysis, investigation, and writing--original draft preparation, K.M., M.A.A., S.A.A.-H. and S.T.; software, S.T. and S.P.; validation, S.T. and M.B.R.; resources, writing--review and editing, supervision, and project administration, S.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through (Prospective green technology innovation project, funded by Korea Ministry of Environment (MOE) (2022003160004).  
All the data and materials related to the manuscript are published with the paper and available from the corresponding author (S.T. and M.B.R.) upon request.  
The authors declare no conflict of interest.
## 3 Analysis of Deformation Control
### Analysis of Deformation Control during Construction
#### 3.1.2 Reinforcement Scope  
Figure 8 shows the soil displacement of the base and side wall. Refer to the research method of [PERSON] et al. (2011) for qualitative analysis of replacement rate and pile length [PERSON] et al. (2011). It can be seen that increasing the replacement rate can have a certain control effect on the base displacement. When the replacement rate is 10.3%, the uplift displacement of the basement is less than 68 mm; when the replacement rate in the original scheme is 22.3%, the displacement of the base during construction is less than 64 mm; when the replacement rate continues to increase to 40.3%, the base displacement is less than 60.43 mm, indicating that the degree of control effect is reduced. In addition, when the base displacement rate increases to a certain extent, the control effect of base displacement will be reduced. Therefore, in the actual design and construction of tunnel composite foundations, the replacement rate cannot be increased blindly, and the control of foundation deformation  
Figure 5: The opportunity of jet grouting pile reinforcement: (**a**) Embedded type; (**b**) Twice application; (**c**) Once application; (**d**) Layout after excavation.  
during the construction process should be taken as the primary goal, and the optimal replacement rate should be found. Based on the analysis of multiple factors, it is suggested that the optimal replacement rate of composite foundations in the Fujayao shallow buried section is 30.3%.  
In addition to the optimization of the horizontal reinforcement range, the optimization of the vertical reinforcement range also needs to be considered, that is, the optimization of the pile length. Figure 9 shows the soil displacement of the base and side wall under different pile lengths. It can be seen that when the control replacement rate is certain, the foundation displacement can be better controlled by increasing the pile length; when the pile length increases from 3 m to 15 m, the maximum uplift displacement of the base decreases from 73 mm to 43 mm. Compared with the increase in the replacement rate, the increase in the pile length has a better control effect on the foundation displacement. This is mainly because the increase in pile length increases the range of tunnel bottom reinforcement area to a certain extent, which reduces the thickness of soft soil in the underlying layer, thus effectively controlling the displacement of the basement uplift. The conclusion is similar to the research result of [PERSON] et al. (2011) [PERSON] et al. (2011). In addition, the deformation of the jet grouting pile itself in the reinforcement area is small, and the soil deformation is the main factor of the base uplift. Therefore, the optimal design scheme of the composite foundation of a collapsible loess tunnel can be achieved by appropriately increasing the pile length.  
Figure 6: Location of displacement monitoring points.  
Figure 7: Radial displacement of base and side wall (different reinforcement times).
## 3 Data Processing & Methodology
### Receiver Clock Offset w.r.t GPS Time  
The time difference between a receiver clock and the satellites for a particular GPS receiver is documented in the Common GPS GLONASS Time Transfer Standard (CGGTTS) files. The Consultative Committee for Time and Frequency (CCTF) has published a set of guidelines based on which the CGGTTS files are created. These guidelines have been compiled with the aim of achieving a system that can transfer time with an accuracy of \(1\) ns or better. They can be applied to all available global navigation satellite system constellations, such as GPS, GLONASS, Beidou, QZSS, and Galileo. For this paper, we process these files to get information on the receiver clock performance w.r.t the satellite time.  
The CGGTTS files record different important data types [24; 25]. One of them is Refsys. This is the time difference between the receiver clock and the global navigation satellite system (GNSS) time. If the GPS constellation is considered then Refsys is the difference between the receiver clock and GPS time (GPST). The data available in CGGTTS files are generally 16 min apart and the values are reported in units of 0.1 ns.  
We have used data from two different receivers with different grades of clocks; namely Ublox (Thalwil, Switzerland) and Sepentrio (Leuven, Belgium) receivers. The Ublox that has been used for this paper is the Ublox from the M8T series [26]. It is a low-cost receiver steered by TCXO (temperature compensated crystal oscillator). However, it is very small in size and thus is portable and can be easily mounted in many locations. The Sepentrio receiver, on the other hand, is expensive compared to Ublox. The Sepentrio receiver model PolaRx5 TR has been used [27]. It is steered by Rubidium clocks and has the option to be steered by external clocks too.  
The same Ublox was deployed at two different sites for data collection. The two sites were at Nanyang Technological University (NTU) and at Stirling Road. Figure 4 shows these two locations. The locations were chosen as per the convenience of the students to be able to record the data without intervention for as long as possible. The Septentrio receiver is located at the backup Time and Frequency Laboratory of the National Metrology Centre (NMC), Singapore (also shown in the Figure 4).  
Table 1 shows the common days that have been used for processing the results. The data from the NMC lab at Fusionopolis 2 for the corresponding date and time at both the Ublox locations (at NTU and Stirling Road) are used for the analysis.