esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Conductivity and Infrared Spectroscopy of Elastic Polymer Composite Materials
## 2 Materials and Methods  
### The Base Material of Elastic Polymer Composite Materials  
Using the soft, insulating, and durable properties of elastic polymer materials, we design devices that fit closely and provide flexibility and resilience. However, these elastic polymers are typically non-conductive. Through research and experimentation, it was discovered that polysiloxy polymers, which are often silicones, exhibit strong adsorption properties.  
Polysiloxy polymers are characterized by their silicon-oxygen backbone, with the basic repeating unit being -Si-O-. To impart electrical conductivity, these polymers are doped with a high concentration of graphite particles. Silicones are known for their stable, non-toxic structure and rubber-like physical properties, making them an ideal base material when combined with conductive fillers like graphite. The structural unit of silicones is shown in Figure 1a.  
Silicon atoms in polysiloxy polymers are bonded with various organic functional groups, such as methyl, ethyl, and phenyl, making these polymers semi-organic and semi-inorganic. Oxygen plasma treatment can enhance the -SiO\({}_{\text{n}}\)(OH)\({}_{4-\text{n}}\) groups on the silicon surface, with the degree of modification being influenced by the intensity of the oxygen plasma and the treatment duration [9].  
To investigate the electrical properties related to carbon doping concentrations, different doping levels of the polymeric rubber-like material were produced. Highly adsorptive carbon was chosen for doping within silicon-based polymers and polyurethane rubber. The repetitive structural units of these materials are depicted in Figure 1b as thermoplastic rub  
Figure 1: Repetitive structural units of (**a**) polysiloxy polymers; (**b**) polyurethane.  
ber (TPR). This approach aimed to explore how varying concentrations of carbon influence the electrical properties of the composite material.
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation
### Results and Discussion
#### 5.3.4 Network Overhead  
Figure 7 shows the comparison between the network overhead of different methods and the number of different mobile data flows in different scenarios. We can find that as the number of mobile data flows increases, the network overhead of all methods becomes larger. This is because the increase in the number of mobile data flows requires that more packets during handovers need to be buffered. As can be seen from Figure 7a,b, under the same number of mobile data flows, our proposal performs better than ProPull and LPBMMS in terms of network overhead. In the cross-UPF handover scenario, when the number of mobile flows is 10, the overhead of BDMM is reduced by 7.1% and 49.183% compared with ProPull and LPBMMS, respectively. In the non-cross-UPF handover scenario, when the number of mobile flows is 10, the overhead of BDMM is reduced by 4.5% and 49% compared with ProPull and LPBMMS, respectively. The network overhead of LPBMMS is almost twice that of BDMM because LPBMMS buffers packets at both the source base station and the prediction base station, while BDMM only buffers data at the source access router. When the prediction success probability is 50%, the handover delay of LPBMMS is similar to that of BDMM. ProPull is the same as BDMM and only buffers data on the source access router. Since ProPull has higher handover latency than BDMM, ProPull needs more time to buffer mobile data flows. Therefore, when the mobile data flow rate is fixed, ProPull needs to occupy more buffer resources than BDMM. In summary, compared with ProPull and LPBMMS, BDMM is more efficient when network resources are limited.  
Figure 6: Comparison of average handover delay in different scenarios: (**a**) cross-UPF handover scenario; (**b**) non-cross-UPF handover scenario.  
Figure 7: Comparison of network overhead in different scenarios: (**a**) cross-UPF handover scenario; (**b**) non-cross-UPF handover scenario.
## 6 Limitations and Future Research Directions
### Future Work  
The exploratory nature of this study indicates a journey towards an AR that transcends traditional visual interfaces, suggesting a sensory-rich future in which AR experiences become more tangible and multi-dimensional.  
Voice control integration stands as a frontier for user interactions within AR, potentially simplifying the complexity of tasks and making the technology more accessible. This natural mode of communication could prove especially beneficial for users with motor disabilities or those in situations where manual interaction is hindered.  
Advancements in AI offer a promising trajectory for enhancing AR systems. Image recognition algorithms could allow the AR environment to be more responsive and context-aware, tailoring experiences that are both adaptive and personalized. These intelligent systems could assist in recognizing user intentions, environmental factors, and even emotional responses to create a more intuitive user interface.  
The iterative design and evaluation process is a critical step towards refining the inclusivity of AR technologies, particularly for users who have visual impairments. As we expand our understanding of diverse user experiences and preferences, the development of design guidelines for accessible AR applications becomes a foundational aspect of inclusive technology design.  
This study illuminates the need for a paradigm shift in AR development, one that prioritizes sensory diversity and inclusivity. The positive reception of the NAI by participants demonstrates a clear market for AR experiences that engage senses beyond sight. For individuals with visual impairments, such developments are not just an enhancement but a necessity, opening up new avenues for education, entertainment, and assistance.  
The implications of expanding AR into non-visual domains are profound. In education, it can democratize learning materials, making them more accessible for students with visual impairments and potentially enhancing the learning experience for all students through multisensory engagement. In the realm of gaming, it can introduce novel gameplay mechanics that offer unique and immersive experiences. For navigation, it can offer intuitive guidance to users, relying on audio cues and spatial awareness to aid those unable to depend on visual information.
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 2 Suspension Systems  
### Functions of the Suspension Systems  
Suspension is a critical component of modern automobiles. It typically comprises elastic elements, guiding mechanisms, dampers, buffer blocks, and lateral stabilizers, which elastically connect the body to the axle, as shown in Figure 1. The elastic elements bear the load, the guiding mechanisms ensure that the suspension components move along specific trajectories, and the dampers dissipate the vibrational energy transmitted by the spring mechanisms. Thus, the primary function of the suspension is to transmit all forces and moments between the wheels and the frame, mitigate the impact loads transmitted from the road to the frame, and attenuate the vibrations induced in the load-bearing system. Furthermore, it ensures that the vehicle maintains ideal dynamic characteristics when driving on uneven surfaces and under varying load conditions, ensuring handling stability and enabling high-speed driving capability. As consumer demands for enhanced vehicle performance continue to escalate, the importance of ride comfort and safety has become more pronounced; both factors are closely related to the suspension system. Consequently, suspension plays an increasingly important role in automotive design.  
The body acceleration, suspension dynamic deflection, and tire dynamic load are important metrics for evaluating the ride comfort and handling stability of automobiles. The suspension is tasked with mitigating and absorbing vibrations from the wheels, while also transmitting driving and braking forces between the wheels and the road surface during vehicle operation. During vehicle maneuvers, the suspension must withstand lateral forces from the body and effectively suppress pitching vibrations during acceleration and braking. These functions significantly enhance the vehicle's stability and safety.  
Figure 1: The structure of a typical suspension system.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 2 Materials  
### Study Site  
The study site is the Camargue region located in the south-east of France (centered on 4\({}^{\circ}\)35\({}^{\prime}\) E and 43\({}^{\circ}\)33\({}^{\prime}\) N) (Figure 1). The Camargue has a Mediterranean climate with a mild winter and a long dry summer. It has a variety of landscapes, such as agricultural areas, urban zones, water areas, and forests. Agriculture is the main economic sector of the Camargue region. However, among agricultural cropsrice is the most dominant crop type, having an important impact on the economy and ecology of the region. According to the INRA (National Institute of Agricultural Research) data of 2011, rice crop in the Camargue comprises 55% of the agricultural crops. Rice in Camargue is frequently grown in rotation with rain-fed crops, especially wheat, depending on the soil types. For deep soils, rice is frequently replaced by durum wheat and is cultivated for one to five years. For shallow soils, farmers usually prefer continuous rice cultivation ([PERSON], 2016).
## 2 Background
### IoT Devices  
#### 2.2.1 Roadside Units (RSUs)  
Roadside units (RSUs) are critical components in vehicle communication networks such as vehicular ad hoc networks (VANETs). They are permanently installed infrastructure devices that are usually located along roads or at intersections and enable communication between vehicles (vehicle-to-infrastructure, or V2I), as well as between vehicles and external networks such as the Internet or traffic management systems.
## 2 Relevant Literature
### Common Statistical and Technological Tools
The rise of the IoT, AI, and big data analytics made it possible to create more accurate and dynamic virtual representations of cities. Therefore, digital twins started to gain prominence in smart city studies as researchers recognized their potential to revolutionize urban planning and management [32,66]. This technology, originally developed for manufacturing and product design, found new applications in urban contexts due to the increasing complexity of city systems and the need for data-driven decision-making. Digital twins are virtual representations of physical objects or systems that integrate with machine learning for predictive analysis and the IoT for real-time data collection, increasingly recognized as pivotal in smart cities for enhanced urban management [67]. Digital twins in smart cities integrate vast amounts of real-time data from IoT sensors, leverage cloud computing for processing and storage, and utilize AI and machine learning algorithms for predictive analytics [68]. Scholars began adopting digital twins in their research to simulate urban environments, optimize resource allocation, enhance sustainability efforts, and improve citizen engagement. Blockchain technology is gaining widespread attention in smart city work as a tool for safeguarding data and transactions [69,70]. Utilizing a decentralized consensus system, the blockchain maintains records in an open and immutable ledger, discouraging unauthorized changes [71]. Blockchain's features are particularly well-suited in areas sensitive to concerns about identity, payments, and supplies, as the data being processed must remain accurate and secure. As smart cities implement various initiatives, data generated from various sources needs to be stored, managed, and analyzed effectively and efficiently. Cloud computing provides the infrastructure to store, manage, and analyze this information effectively. Thus, the emergence of cloud computing technology has made possible the widespread availability of the requisite computational resources and storage space for analyzing vast amounts of data from IoT devices and other sources [68,72]. Storing data in the cloud improves security and permits scalable real-time processing power for analysis when needed [73,74]. Scalability and flexibility are crucial to smart city initiatives. Collectively, these scientific tools serve as the foundation for smart city studies, improving cities in terms of intelligence, sustainability, and responsiveness to the expectations of new generations [75]. As these technologies advance, they are expected to play a more significant role in shaping future urban environments.
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 3 Methodology
### The Edge-to-Cloud System Model for Surveillance Camera-Based Applications  
There is a trend to forward computation from the network core to the edge where most of the data are generated. Edge computing has exhibited its potential in reducing the reaction time, minimizing bandwidth usage, and improving energy efficiency. Edge computing performs data processing at the \"edge\" of the network, close to the data source. For network cameras, audio, and other sensors, there is a need to balance both cloud computing and edge computing domains to deliver refined, reliable, and usable data. For edge computing of delay-sensitive video tasks, a camera source node can offload its video task to nearby edge nodes via local wireless/optical networks, and edge nodes are within the local communication range of the camera. The camera captures the video sequences and divides each of them into multiple video chunks, compresses these video chunks, and then, delivers them to edge nodes. Next, edge nodes implement video processing functions on the received video chunks and upload the results to a cloud server for video analysis (such as object/event detection). A delay-sensitive video assignment is supposed to be processed within a limit and will fail if the deadline is passed. In this study, as shown in Figure 5, we consider an edge computing network, which comprises three primary components, namely the camera source node, edge node, and cloud server.  
Figure 5: Overview of the proposed edge-to-cloud system model.  
* Camera source node: The camera node periodically generates video tasks, divides each video task into a number of video chunks, compresses the video chunks at certain compression ratios, and then, assigns the compressed video chunks among all edge nodes as per scheduling policies.
* Edge node: The edge has computational ability and storage capacity and helps preprocess video chunks. Moreover, edge nodes can form cooperative groups based on the specific group formation policy and receive compressed video chunks as per the video load assignment policy.
* Cloud server: The cloud server collects the preprocessing results from edge nodes, which has abundant computational abilities, and performs additional video analysis.  
During a sparse edge node deployment, an edge node will only connect to one of the available cameras at a certain location; however, in a dense deployment, an edge node may have multiple choices for selecting multiple cameras. In this study, we focus on reducing processing load on the cloud server by minimizing the video chunks that are fed from the camera sources. Therefore, an edge device runs preprocessing tasks to filter uninteresting video chunks. The term \"uninteresting video chunks\" is defined via scenario specification: for surveillance scenarios, it is static scenes without any objects and gradual changes.
# Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change +
## 5 Conclusions  
In this paper the authors proposed a template scheme for a cryptographically secure pseudo-random generator based on the chaos theory. Several configurations were proposed and tested with TestU01 and NIST batteries and performance-wise against the Mersenne Twister implementation in the GNU C++ standard library, an established CSPRNG and a state of the art chaos based pRNG. The tests showed that for certain configurations, most of the seeds tested passed the statistical analysis. The throughput depends on the entropy builder function and the parameter change interval and as in most cases, a trade-off must be made between speed and security. As proof of concept, the generator was used to implement a one-time pad cryptographic system. The system was used to encipher UHD images which were then analyzed by computing entropy, through histograms and correlation coefficients. Therefore, chaotic functions are suitable building blocks for pseudo-random number generators but a mechanism for breaking patterns is necessary for ensuring randomness for all initial states. The authors plan to include the one-time pad developed in this research in a real time high resolution video streaming application. In order for this to work, throughput must be increased with a factor of 10.  
**Author Contributions:** Conceptualization, O.D. and R.H.; methodology, O.D., R.H. and C.M.; software, R.H.; validation, C.M.; writing-original draft preparation, O.D.; writing-review and editing, O.D., C.M. and R.H. All authors have read and agreed to the published version of the manuscript  
**Funding:** The Open Access publication cost was funded by the PubArt program of the University Politehnica of Bucharest.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 13: Correlation for the high entropy level image (**top**) and the low entropy level image (**bottom**).
## 3 Problem Formulation  
### Side Slope Problems  
For a long time, the finite unit method has been applied in slope analysis. The calculation block diagram used in the finite element calculation is shown in Figure 1, and its boundary conditions are often of two types. The first type is displacement boundary conditions. The horizontal and bottom vertical displacements around the perimeter in Figure 1 are equal to zero; that is, when the displacement on the left (point _i_) is equal to zero, the displacement on the right (point _j_) must also be equal to zero. According to the definition of the displacement calculation:  
\[u_{i}-u_{j}=\int_{i}^{j}\varepsilon_{i\to j}dl \tag{1}\]  
where \(u_{i},u_{j}\) denotes the displacements at points \(i\) and \(j\) in the figure, \(\varepsilon_{i\to j}\) denotes the strain in the _ij_ segment, and dl denotes the integration along the \(i\) to \(j\) segments. From Equation (1), it can be seen that to make the _ij_ segment displacement integral equal to zero, only the integration of strain in the _ij_ segment is equal to zero; however, in the horizontal segment surface immediately below the bottom edge, the site should be dominated by compressive strain, and it is unlikely to produce tensile and compressive strain resulting in the sum of the _ij_ segment displacement integral that is equal to zero.  
The second type is the stress boundary condition. The boundary condition for the numerical analysis of the slope is shown in Figure 2. The far-field stress boundary condition is calculated according to elastic mechanics. At the peripheral stress boundary, the expression of the boundary condition is as follows.  
\[\left.\sigma_{xx}\right|_{x=0}=\sigma_{zz}\right|_{x=0}=\frac{v}{1-v}\gamma_{y }y;\left.\sigma_{yy}\right|_{x=0}=\left.\gamma_{y}y;\left.\tau_{xy}\right|_{x =0}=\left.\tau_{yz}\right|_{x=0}=\left.\tau_{xz}\right|_{x=0}=0 \tag{2}\]  
where \(v,\gamma_{y}\) are the Poisson ratio and specific gravity of the geological material and \(\sigma_{xx},\sigma_{yy},\sigma_{zz},\tau_{xy},\tau_{yz},\tau_{xz}\) are the positive and shear stresses, respectively.  
Figure 1: Displacement boundary condition chart of slope analysis.  
The physical meaning of Equation (2) is.  
\[\left.\sigma_{xx}\right|_{x=0}=\sigma_{zz}\right|_{x=0}=\sigma_{3};\left.\sigma_{ yy}\right|_{x=0}=\sigma_{1};\left.\tau_{xy}\right|_{x=0}=\left.\tau_{yz}\right|_{x=0}= \left.\tau_{xz}\right|_{x=0}=0 \tag{3}\]  
where \(\sigma_{i},i=1,2,3\) is the principal stress, and the corresponding \(y\)-value magnitude can be calculated by substituting Equation (3) into any current strength criterion. According to the inequality, when the numerical calculation results in the \(y\)-axis value greater than the \(y\)-value obtained according to the strength criterion, the damage has occurred at the site, i.e., with the increase in the calculated depth, the damaged area becomes increasingly large; however, the site becomes increasingly stable with increasing depth, which is not compatible with the site. A correct boundary condition corresponds to a correct solution; if the boundary condition is incorrect, the solution is incorrect.
## 7 Discussion and Challenges
### Future Usage: DT Interaction and Enhanced Maintenance Operations
#### 7.3.3 Challenges in Using XR Devices  
In recent years, with the improvements in and miniaturisation of the computer, the development of XR technologies has increased, and new challenges have arisen with it [5; 6; 75; 97]. A lot of research is underway to improve these technologies and speed up their implementation.  
Firstly, several issues concerning the usage of HMD have been found in the literature, mostly through feedbacks from users [25; 84]. The comfort of the device is a major concern for most users, especially during continuous use (e.g., the field of view's size and weight). _Cybersickness_ is also an important aspect that needs to be taken into account when using XR devices [84]. It is caused when visual, vestibular and proprioceptive systems conflict with each other, in which the eyes perceive a movement that is not synchronised with what the motionless user perceives. The main symptoms are headaches, disorientation and nausea. It can also be caused by ergonomic discomorts, which reinforces the importance of the right choice of the XR device.  
To correctly apply the augmentations in the virtual space, a good calibration and pose estimation is needed. Many authors have tried to resolve this issue by using the object recognition abilities offered by the camera of XR devices [66; 71; 81], but most of the literature uses specific markers to define what to display or which equipment the user is targeting [25; 26; 53; 71; 72; 77; 80]. Nevertheless, regardless of the initial calibration method used to display the augmentations, the precision of this calibration needs to be maintained throughout the use of the solution [72]. [PERSON] et al. propose an alternative by allowing the designer of the augmentations to fix their position near their related equipment directly in the virtual space [25]. Once the user has performed the initial calibration to detect in which room of a building he or she is, the augmentations is enabled through specific triggers and their position is fixed even if the users move in the calibrated room.