esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Rural roads constitute an integral part of the roadway network providing vital access to rural towns and communities (including farms and ranches). According to the fatality facts 2021, around 40% of traffic fatalities took place in rural areas, although only 20% of people in the U.S. live there, and 32% of all vehicle miles traveled (VMT) occurred in rural areas [1]. These statistics demonstrate the need for improving rural road safety and the requirement to effectively include it in the ongoing state safety improvement programs.  
Network screening is the first step of highway safety improvement programs (HSIPs) which involve the identification of sites with potential for safety improvement on the roadway network. The resulting list of identified sites are given priority for detailed engineering studies to identify crash patterns, contributing factors, and potential countermeasures [2, 3]. Errors in ranking the sites may produce a large number of false negatives (i.e., sites needing safety improvement mistakenly considered as safe) and a large number of false positives (i.e., truly safe sites identified as needing safety improvement). These errors result in an inefficient use of the resources dedicated to safety improvements and eventually reduce the overall effectiveness of the safety management programs. Therefore, the correct identification of safety improvement sites is essential for the successful implementation of any highway safety program.  
Most traditional network screening methods rely on the crash frequency, crash rate, crash severity, or any combination of these measures [4; 5]. Most of the existing ranking techniques for identifying safety improvement sites are reactive in nature [6], as they rely on crash data only [7]. Thus, these techniques require crashes to occur before identifying the potential sites for safety improvement. On the other hand, several proactive approaches have been proposed and used that require detailed data on roadway geometry and roadside characteristics, besides crash and traffic data [8]. A major challenge for network screening on rural roads is the lack of access to the required crash, roadway, and traffic data at the network level especially for roadways that are owned and operated by local agencies.  
This research seeks to evaluate the performance of a new, yet simplistic approach for network screening that was tailored for use on rural roadways [9]. The proposed method requires minimal data for implementation, which could be valuable for agencies having limited access to detailed databases or extensive technical expertise.
## 7 Conclusions and Future Work
### Supporting Multipath Mechanism in the D-MSR  
To provide reliable communication between the endpoints, multipath routing is considered in the routing layer. In this case if a node fails or an edge is broken, an alternative path can be used for delivering the packets. This approach is followed in several industrial wireless standards such as WirelessHART and ISA100.11a. We intend to consider this capability in the routing layer and in D-MSR.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 3 Experiment
### Semantic Segmentation Models  
We selected UNet [39], SegNet [40], and DeepLabV3+ [41] for our land cover segmentation due to their proven performance and distinct architectural strengths. By incorporating skip connections, UNet's encoder-decoder structure effectively captures both local and global features, which are essential for precise boundary delineation [39]. SegNet strikes a balance between memory efficiency and high-resolution feature preservation, making it suitable for handling complex satellite imagery [40]. DeepLabV3+, which utilizes atrous convolution and atrous spatial pyramid pooling (ASPP) modules, excels in capturing multi-scale contextual information, which is crucial for intricate segmentation tasks [41].
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 4 Results
### Watermarking Research Results  
The results of the proposed watermarking method will be discussed in this subsection. Firstly, the impact of watermarking will be discussed in terms of the accuracy of the reproduction of the watermarked carrier to its original form (Table 8). Next, the importance of changing the compression value and image resolution in the context of watermark survival will be discussed (Tables 9-11 and Figure 7). Characteristics showing the training process are included at the end (Figures 8-10). Figure 7: Preview of the results of watermark embedding and extraction under CRF = 20 compression conditions for: 1, 15, and 30 frame of test video for a 480 \(\times\) 640 video fragment encoded with a \(128\times 128\) encoder (viewed from the left, the columns represent consecutive video frames, viewed from the top, the consecutive rows represent the original image, the watermark image, the watermarked image, and the recovered watermark). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Frame \#** & **CRF 0** & **CRF 7** & **CRF 12** & **CRF 16** & **CRF 20** & **CRF 22** & **CRF 23** & **CRF 24** & **CRF 25** \\ \hline
1 & CHAR 1: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 15 & CHAR: 13 \\
5 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 15 & CHAR: 15 & CHAR: 14 \\
10 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 14 \\
20 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 15 & CHAR: 15 & CHAR: 12 \\
25 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 15 & CHAR: 15 & CHAR: 13 \\
30 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 16 & CHAR: 15 & CHAR: 14 & CHAR: 13 \\ \hline \end{tabular}  
* \({}^{1}\) Bit error rate of the mean, \({}^{2}\) Bit error rate of the most frequently occurring element (value), \({}^{3}\) Bit error rate of the median.  
\end{table} 
Table 10: Algorithm performance results for a 480 \(\times\) 640 video fragment encoded with a 128 \(\times\) 128 encoder trained for CRF = 24 for changes in CRF coefficient in the form of a number of correctly decoded characters.
## 5 Discussion
### Analysis of Fuzzy Uncertainties  
In the real world, energy price and energy demands often exhibit vagueness and ambiguity because of the subjectivity of human judgment [25]. According to FPP theory, the minimum, medium, and maximum values of these parameters are sufficient for expressing a triangular fuzzy parameter. And the proposed DRSFO-EES model can be used to effectively address the uncertainties expressed as triangular fuzzy parameter of the objectives and constraints. For FPP, the confidence level \(\alpha\) is an indication of the manager's violation risk attitude towards imprecise information [56; 57]. In the present application of the DRSFO-EES model, four confidence levels (\(\alpha\) = 0.5, 0.6, 0.8, and 0.9) were used to examine the impacts of different confidence levels on the EES. Generally, a higher confidence level implies a higher likelihood of satisfying the fuzzy confidence constraints, resulting in less uncertainty about the imprecise constraints. For instance, a confidence level of 0.8 indicates that the credibility of the constraint (e.g., \(Cr\{M\widetilde{A}L_{gt}\Big{|}MA_{gt}\geq M\widetilde{A}L_{gt}\Big{\}}\geq\alpha\)) is greater than or equal to 0.8. However, a higher confidence level increases the system costs. Contrarily, a lower confidence level implies a more aggressive attitude of the decision maker regarding the expected total system costs, and increases the uncertainty of the fuzzy constraints, resulting in a higher risk of violating the energy demand. Table 5 gives the optimized solution under different confidence levels \(\alpha\) during the planning periods. As can be seen, a higher \(\alpha\) increases the coefficients of the right-side constraints, further necessitating electricity import and increasing pollutant emission and the system costs. Vehicle ownership, heat processing, and coke processing also increase with \(\alpha\), all accompanied by pollutant emissions.
## 8 A Look-Ahead, Missing Functionality and Research Challenges
### Missing Sub-Aspects
#### 8.2.2 Support for Application-Level Multi-Tenancy  
SaaS providers continuously aim to optimize the cost-efficiency, scalability and trustworthiness of their offerings. Traditionally, these concerns have been addressed by a _shared-everything multi-tenant architecture_ where multiple tenants are hosted by the same application instance.  
Common requirements in multi-tenant SaaS applications are support for performance isolation between tenants and exploitation of hybrid clouds. Existing platform-level multi-tenancy support, as offered by existing CO frameworks in the form of resource quota management for different user groups and tenant-aware access control to API objects, is a great first start for accommodating these requirements but is not enough for accommodating multi-tenant SaaS applications. For example, it is possible to host multiple tenants in separate application containers, thus providing performance isolation and security isolation but at a higher operational cost. On the other hand, hosting multiple tenants in the same container is more cost-efficient but CO frameworks cannot offer security isolation and protection against aggressive tenants who flood the application container with a high request rate.  
A key missing component is, therefore, a network admission component for throttling incoming network traffic from aggressive tenants before this traffic can hit the Layer 4 load balancing tier of container orchestration frameworks. Mesos' framework rate limiting [375] and so-called service meshes such as Istio [391] for Kubernetes already exist for protecting the Master API of the CO framework against aggressive tenants, but this should be extended for network traffic to any application deployed. Providing a single service mesh that can host different tenants with some level of isolation and security between the tenants is however planned future work in Istio [392].
## 3 System Model and QoT Estimation
### Physical-Layer Impairments Estimation
The nonlinear refractive index of the fiber is denoted as \(n_{2}\). The effective areas of the fiber at wavelengths \(\lambda_{r}\) and \(\lambda_{k}\) are designated as \(A_{eff}(\lambda_{r})\) and \(A_{eff}(\lambda_{k})\), respectively. \(\beta_{2}\) corresponds to the parameter pertaining to group velocity dispersion (GVD), while \(\beta_{3}\) represents the linear slope of GVD. These parameters play a crucial role in accurately characterizing the nonlinear effects and dispersion properties of the optical fiber within the system.
# The Evolution of Volatile Memory Forensics
## 3 Memory Acquisition
### Acquisition Techniques
#### 3.2.4 System Management Level  
The system management level is an operating mode present on a systems' architecture that is independent of all normal system operations. The system management level only handles low-level operations such as Basic Input/Output System and Unified Extensible Firmware Interface (BIOS/UEFI), not the operating system or user applications, and it has higher privileges than a hypervisor. Because of the independence from the operating system and virtual machines, memory acquisition at this level is an attractive option. However, there are few implementations of acquisition tools at this level. A potential explanation for the dearth of BIOS-level memory capture tools is that BIOS tooling development typically has a slower iteration speed and is less portable than the development for higher-level tools. _SmmBackdoor_[27] is a rare example of such a tool, but its installation is complex and quite specific to computer model. To use SmmBackdoor, a user provisions the software directly to the UEFI system in order to \"infect\" the System Management Module and capture memory. The author of this tool intended it as an example of an exploit rather than a typical, blue-team forensic aid. Additionally, it appears that SmmBackdoor is limited in memory scope to the System Management Module's System Management RAM.
_applied_  
_sciences_  
_Article_  
**Influence of the Main Blown Film Extrusion Process Parameters on the Mechanical Properties of a High-Density Polyethylene Hexene Copolymer and Linear Low-Density Polyethylene Butene Copolymer Blend Used for Plastic Bags**  
[PERSON] \({}^{\copyright}\), [PERSON] \({}^{\copyright}\) and [PERSON] \({}^{\copyright}\)  
Department of Manufacturing Engineering, Universidad Nacional de Educacion a Distancia (UNED), 28040 Madrid, Spain  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel: +34-913-988-660  
**Featured Application: This work is of particular interest in the design of products widely used in daily life, such as HDPE and LLDPE blend plastic bags. The aim of this study is to analyze how the main process parameters (BUR, TUR, and TR) affect the mechanical properties of this type of product, helping designers to adapt the production to customers' new requirements while maintaining the product properties. All the samples were manufactured on an industrial scale. The results are beneficial so that the highest tensile and impact properties can be obtained through minimal changes in the already mentioned process parameters, thus also reducing the amount of waste due to products manufactured outside the specification limits.**  
Polyethylene plastic bags manufactured via blown film extrusion have different quality specifications depending on their intended use. It is known that the mechanical properties of a film depend on the process parameters established, but little is known concerning how they affect one another, even more so due to the variety of polyethylene materials and processing techniques. This study focuses on establishing a proper correspondence of important mechanical properties like the dart impact, tensile strength at break, and elongation at break with commonly used process parameters like the blow-up ratio, take-up ratio, thickness reduction, and neck height, for a high-density polyethylene hexene copolymer and a linear low-density polyethylene butene copolymer blend film. Because this polyethylene mixture is an anisotropic material, interesting R\({}^{2}\) values equal to or higher than 0.90 were found: a BUR with elongation at break and tensile strength at break in the MD and TD, a TUR with elongation at break in the MD and tensile strength at break in the MD and TD, and a TR with elongation at break and tensile strength at break in the MD. Also, a relationship between the dart impact and both the neck height and thickness were found.  
**Keywords: blown film extrusion; HDPE; LLDPE; mechanical properties; process parameters; plastic film**
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning  
### Definition of Federated Learning  
Federated learning is the process of distributed machine learning training deployed across multiple clients. To ensure data privacy, federated learning only allows clients to exchange model gradients with a central server. In this process, each client trains its own model using local data and then uploads the local model to the central server. After aggregating all received models, the server returns the new global model to each client, as shown in Figure 1.  
In practice, it is assumed that each \(n\) client \(\{C_{1},C_{2},\ldots,C_{n}\}\) holds a private dataset \(\{D_{1},D_{2},\ldots,D_{n}\}\) and has no direct access to the data of other clients. A typical training flow consists of the following three steps [8]:  
1. Initialization: At the \(t\)-th round of communication, the client downloads the latest model, \(W_{t}\), from the server for initialization.
2. Local training: Each client, \(C_{k}\), iteratively trains on its own local dataset, \(D_{k}\), and hyperparameters, \(\eta\). The local model, \(W_{k}^{t}\), is updated according to \(W_{k}^{t+1}\gets W_{k}^{t}(\eta,D_{k})\) and sent to the server.
3. Model aggregation: The server aggregates the collected local models \(\{W_{1},W_{2},\ldots,W_{n}\}\) into a global model for the global model update.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Light  
Light-driven soft robots are designed based on the light-responsive effect of polymers, such as liquid crystal elastomers, liquid crystal gels, and some ionic polymers, to deform and move their bodies [156; 157; 158]. This technology, which is to convert light into mechanical energy, is found in possession of some distinct functions, from cleanliness, directional motility, and sensing, to wirelessly controlled interactions between humans and environments [159]. It is foreseeable in the future that the technology will provide novel opportunities for the development of soft robots in space.  
[PERSON] et al. fabricated a series of transparent actuators using local surface plasmon resonance semiconductor nanocrystals that respond to infrared light [160]. The actuators have the advantages of wireless and remote control on camouflage soft robots, and significant deformation (curvature up to 0.66 cm\({}^{-1}\)) under near-infrared light irradiation (200 mW\(\cdot\)cm\({}^{-2}\)). It is realised by preparing three semiconductor nanocrystals as photothermal conversion agents to construct a photo-actuator. [PERSON] et al. demonstrated a smart thin-film composite with perception and motility in the field of light-driven robots [161]. The robot is capable of coordinated actuation, proprio-exteroception, and communication. Under visible light with the irradiation of 100 mW\(\cdot\)cm\({}^{-2}\), the maximum driving deformations of the light-driven film on tempered glass, printing paper, and sandpaper are 0.49, 0.472, and 0.443. In addition, the temperature and light are controllable by electric signals, driving the robot uninterruptedly. Using the light-driven film, kirigami soft robots with multitasking and information feedback functions, such as walkers, robotic hands, and centipedes, can be designed.  
As a new actuation mechanism being developed in recent years, light-driven robots have some aspects for further optimisation, including: (1) this kind of actuator requires a high-intensity laser to make the photosensitive part of the robot heat up and move around, which consumes a lot of electric energy during this period, so the energy conversion efficiency needs to be improved; and (2) due to the light nature and soft material, light-driven robots can be miniaturised, but they can also result in a small output force, small load bearing, and limited application scenarios.
# Sketch-Guided Topology Optimization with Enhanced Diversity for Innovative Structural Design
## 4 Experiments
### Comparative Analysis with Existing Methods  
The proposed TO framework is compared with the sketch-guided TO algorithm (baseline 1) by [PERSON] et al. [26] and the interactive TO algorithm (baseline 2) by [PERSON] et al. [12]. Baseline 1 directly utilizes the entire reference sketch image and a similarity constraint between the TO solution and the sketch. In order to avoid using existing design datasets, to ensure comparability between methods, load condition parameter perturbation is adopted for baseline 1, replacing its data-driven approach of enhancing diversity. Baseline 2 combines the free-hand sketch and human scoring to achieve controllability, and achieves solution diversity by perturbing the sensitivity numbers of the soft-skilled BESO algorithm according to human input. To avoid the uncertainty of human input, the random perturbation of density values is employed as an approximate strategy, with three different perturbation magnitudes for analysis. The proposed TO framework without load condition parameter perturbation is also compared. While traditional TO algorithms, such as SIMP or BESO, are unable to offer design diversity, existing multi-solution TO algorithms achieve this with three typical methods: simply perturbing the design parameters like load force magnitudes, data-driven feature learning, and adjusting the optimizable variables based on interactive human input. Data-driven methods offer strong design diversity, but both the diversity and quality of the solutions depend on the size and quality of the training dataset. As such, quantitative comparison between these two types of methods is unfeasible. Due to the uncertain effect of the subjectivity of human input, quantitative comparison between these types of methods and parameter perturbation-based ones is also unfeasible. Therefore, this paper only conducts quantitative comparative experiments using the two latest parameter perturbation-based methods, with the same optimizable parameters. For each compared method, sketch example case 1 is used as the human input, and the perturbation range of the load force parameter \(F_{x}\) is set as [0.3, 1.5]. The evaluation criteria for the methods are the same as above, with the average of 20 repeated runs for each method being the evaluation metric. Table 3 presents the results of the comparative experiment. In terms of solution quality, the proposed TO framework, along with baseline 1, outperforms baseline 2 by a large margin, verifying that perturbations in the load condition or Fourier sampling frequencies have a very similar impact on the solution quality, but perturbing the density values significantly reduces the quality. The proposed method achieves the best performances, as indicated by the metrics in bold. In terms of diversity, the proposed framework outperforms
# Investigation of Cavitation Noise in Cavitating Flows around an NACA0015 Hydrofoil
## 4 Results and Discussion
### Cloud Cavitation
#### 4.2.3 Influences of Different Cloud Cavitation Number  
In the complex flow condition of cloud cavitation, three different cavitation numbers were investigated and their similarities and disparities are discussed in this part.  
Figures 17 and 18 show the acoustic pressure level at different locations at different times, and three different cavitation numbers are considered. It is noteworthy that point U10, located in the tail of the hydrofoil, presented a relatively higher acoustic pressure during the collapse of either the front bubbles or trailing vortex. Besides this, Figure 19 indicates that the pressure fluctuation at point U10 was significantly higher than other points nearby, for instance, \(T_{2}\). Therefore, higher pressure fluctuation was presumably the cause for the peak acoustic pressure value at U10.  
Figure 16: Acoustic pressure pulsation at different locations for \(\sigma\) = 0.65.  
Figure 15: Acoustic power spectral density at U5 and U6 for \(\sigma\) = 0.65.  
Figures of acoustic power spectral density (PSD) of three different cavitation numbers are presented. Note that the same position was selected in different conditions. When \(\sigma=0.55\), the frequency of the second peak, which represents the growth and collapsing of front bubbles, was about twice as much as that of the first peak, which represents the growth and collapse of the trailing vortex. To be specific, the growth and collapsing of the trailing vortex occurred once a cycle, but that of the front bubbles happened twice in one cycle.  
Figure 17: Acoustic pressure at different flow times for \(\sigma=0.55\).  
Figure 18: Acoustic pressure at different flow times for \(\sigma=0.75\).  
When \(\sigma=0.65\), the frequency and magnitude of the first and second peak were almost the same as that of \(\sigma=0.55\).  
When \(\sigma=0.75\), a difference can be observed that the frequency of the second peak was three times higher than that of the first peak, and that means the growth and collapsing of leading-edge cavitation occurred three times a cycle. Additionally, the first peak was relatively higher than that of \(\sigma=0.55\) and \(\sigma=0.65\), which are in good correlation with the phenomenon that the collapsing of trailing vortex and front bubbles occurs at the same time, as shown in Figure 20.