esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Next, it makes corresponding routing policy, changes the routing paths, and generates optimal forwarding policy through real-time iterations. The intelligent control policies are then implemented at the sensor nodes to optimize the data forwarding process. The main contributions of the paper can be summarized as follows:  
Figure 1: Simplified data forwarding path for mesh-structured sensor networks. * We proposed a deep graph reinforcement learning (DGRL)-based framework for intelligent traffic control in SDWSN systems. By learning and optimizing the data forwarding policy, the SDN controller can provide adaptive and effective routing control for dynamic traffic patterns and network topologies. * We designed an actor-critic network architecture for the DRGL model, which takes into account both graph convolutional networks and deterministic policy gradient. Reward functions for the reinforcement learning and training method were developed for the DRGL model. * Compared with traditional routing protocols, the proposed DGRL traffic control mechanism can effectively reduce the probability of network congestion, especially in the case of high concurrent traffic intensity. Simulation experiments based on the Omnet++ platform show that, compared with existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase PDR (Packet Delivery Ratio), and reduce the probability of network congestion. The rest of the paper is organized as follows. Section 2 summarizes the related literature in the research direction. Section 3 describes the system structure of the proposed deep reinforcement learning model DGRL in detail. We also discuss the specific training method and updating process when the simulation network is running. Section 4 shows detailed experimental results and presents performance evaluation. Section 5 presents discussion and challenges associated with DGRL. Section 6 concludes the paper and discusses future work.
## 3 Sample Environments  
A sample environment can be a basic piece of equipment that is used to bring a sample to a steady state in order to measure the material properties and morphology or it can be used to bring the material out of equilibrium and in the experiment follow the structural evolution on the pathway to equilibrium. The latter can be of pure fundamental nature or a process mimicking an industrial process where several parameters like pressure, temperature, shear force, and tensile load are all varied simultaneously.  
While many hard-condensed matter neutron scattering experiments use cryogenics, vacuum, and high magnetic fields, soft matter research has its own specialized sample environment requirements. Cryogenic conditions convert soft matter in general to hard matter, which is only in rare circumstances of interest in soft matter research.  
Although neutron facilities historically were the first to develop sample environments for non-equilibrium studies, the advances in sample environments at synchrotron sources have in recent times taken place at a quicker pace than those at neutron sources. This does not imply that there is less demand for such developments, but it is more a reflection of the fact that it is more difficult to develop and implement novel sample environments at neutron sources, as a result of the size and intensities of the available neutron beams and the limited number of available instruments. While in the 1990s, X-ray beam sizes  
Figure 2: A flow-through cell used to study the liquid-liquid demixing process, which is the preliminary stage before casting the material to form a porous membrane. The degree of phase separation and the composition of the different phases determines the membrane structure to a large degree. The stirred reaction vessel is connected to the measuring cell, and the reacting mixture can be pumped through the neutron beam (reproduced with permission from [15], AIP Publishing, 2017).  
on the order of 300 \(\upmu\)m were quite common, specialized beamlines currently can produce sub-micron size beams. Neutron beam sizes usually vary between 1 and 10 mm.  
Traditionally, one key advantage that neutrons have over X-rays is their penetrating ability through different sample environment materials, making it possible for neutrons to study in a more facile manner materials under extreme conditions, such as elevated pressures, for example. However, the present generation of upgraded high energy storage rings generates high brilliance even in the photon energy range where the penetration is strongly enhanced and can be foreseen to encroach upon this neutron monopoly.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 2 Deployable Mesh Antenna Design
### Deployment Structure  
The stow volume dictated the type of mechanical deployment scheme to be used. The antenna is a wrap-rib type, and the mesh layer is attached to the radial ribs. The ribs are wrapped around the center  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline Dw & wire diameter & 4 mm \\ Dg & Diameter of the ground plane & 80 mm \\ Dh & Diameter of the helix & 68 mm \\ Hf & Height of the feed-point & 6.1 mm \\ N & Number of helix turns & 5.5 \\ \(\alpha_{f}\) & Pitch angle of the feed section & 11.6\({}^{\circ}\) \\ S & Feed turn spacing & 1.5 \\ Nf & number of feed turns & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design parameters of backfire helical antenna.  
Figure 5: Simulation and measurement results of backfire helical antenna within operational bandwidth: (**a**) VSWR; (**b**) axial ratio; (**c**) directivity in co-polarization; and (**d**) polar plot of co- and cross-polarization at E-plane center frequency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Co-pol (dBic)** & **HPBW** & **10 dBBW** & **AR (dB)** & **X-pol (dBic)** & **Z (\Omega)** \\ \hline
5.7 & 115.4\({}^{\circ}\) & 157\({}^{\circ}\) & 1.1 & \(-\)13.1 & 53-j3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of performance of backfire helix at center frequency.  
hub during storage time and at deployment time, as it reverts to its original shape due to the rigidity of the rib. The deployment mechanism steps can be seen in Figure 6a,b. It is a one-time sequence with duration less than 60 s where the antenna unfolds into a deployed state. For this operation, very flexible spring ribs were designed for adequate bending to tolerate tensions until deployment.  
Each rib is a parabolic strip cut out from a spring steel plate. To ascertain restoring forces, material properties such as flexibility and rigidity of strip cuts with various configurations were analyzed. Since the rigidity of a single plate is insufficient, making it difficult to wind it around the main hub, a \"sandwich\" structure is adopted, in which a honeycomb rubber core is disposed between two plates. Therefore, with rubber cores discretely arranged along the ribs, rigidity is improved so that the surface plate can be buckled when bent, so the parabolic shape of the reflector is retained after deployment, guaranteed by the extremely high rigidity of the ribs.
## 4 Security Threats
### Negotiation
#### 4.2.6 False Claims of Continuous Changing Demands or Environmental Conditions  
Spectrum sharing mechanisms are responsible for maintaining appropriate resource allocations, independently of the state of the network. Each time the state of a network is significantly changed, the mechanisms have to carefully reassign the available resources. This should take place whenever either the underlying topology changes (e.g., new nodes are attached, move considerably, or are disconnected), or the operational and environmental parameters are altered (e.g., propagation conditions, interference from foreign networks or devices--not controlled by this mechanism--_etc._). Although various approaches for resource reallocation triggering exist, including at regular time intervals (which are probably the more robust), the specific proposals are out of scope for this paper.  
Dynamic reallocation triggering raises an important problem in the spectrum sharing procedure. A non-conformant node may falsely claim that either its environment or its resource demands are constantly changing, in order to cause frequent spectrum reallocations. Given the cost in terms of signaling traffic, computation load on the network node(s) computing the allocations, handover delays for nodes possibly reassigned, _etc._, this could be a significant attack, with denial of service characteristics (considering the computation load on the re-allocator), but also introducing major disturbances to all nodes since their transmissions could be constantly interrupted and their transmission schedules changing.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 5 Evaluation  
### Simulation Assumptions  
This part of the research presents the metrics and parameters used in simulations. The topology employed in the simulations carried out is two parallel highways (\(2\times 4\) lanes) across the area and eNodeB, which is placed in the middle lane of both directions. The VANET vehicles are equipped with the DRSC, and the positions of vehicles are provided by the location server. Vehicles that move in similar directions in the proposed algorithm are considered to be neighbors. Also, a V2R communication scenario is considered for simulating our idea. The overview of our scenario is presented in Figure 4. The protocol is implemented on NS-2.29, and the vehicle mobility is generated with the usage of SUMO [51]. The WAVE-802.11p protocol is defined as allowing communication among vehicles of high-speed or between a road-side infrastructure network and a vehicle. The metrics of performance used in the assessment of simulation results were packet drop ratio, throughput, end-to-end delay, clusters number and packet delivery ratio versus the increasing number of vehicles in networks size of 4 km\({}^{2}\) in cases where the simulation times is 1000 s and is constant for all changes. The simulation is performed 10 times for all mentioned three algorithms (CACOIOV, ACO, AODV) and the average is used in the result. In both algorithms of CACOIOV and ACO, 150 rounds are considered for artificial ants to achieve the best tour in each of 20 iterations, which indicates criteria for the enhancement in quality of the best tour.
## 1 Introduction
### Motivation  
The headquarters has the decision-making and scheduling power in the SM system to integrate and manage each manufacturer. Since there are multiple manufacturers (sub-factories and plants), they communicate a lot to collaborate. The communication process may contain sensitive data information. Attackers can access the network and sensitive data through IIoT devices with relatively poor security performance and learn business entities' latest business policies or objectives [10]. In addition, redundant communication may cause manufacturing parts that are no longer available for service to be used by other manufacturers after one manufacturer is down for unavoidable reasons, resulting in the inability to complete the overall work [11]. Therefore, the headquarters is introduced into the SM system as the control center, which can integrate and manage the entire SM system. Each manufacturing plant only needs to obey the decision and perform the work. Even so, the amount of data in SM is not reduced. Still, some of their data flows are forwarded to headquarters, and direct communications with headquarters can be intercepted or tampered with security risks. Existing research results show that Blockchain is reliable for solving identity authentication and trust problems [12]. Blockchain can solve source reliability and result in verification problems through a non-repudiation consensus mechanism [13].  
This paper proposes a Blockchain and DT-based heuristic multi-cooperation scheduling framework. While ensuring the efficiency of the whole process of smart manufacturing, it can provide privacy and security protection to the maximum extent. This paper aims to develop an SM framework that protects device privacy and provides legal identity authentication in an IIoT environment while making the operating process of this framework more efficient and accurate. Our framework divides the smart manufacturing network into three layers: the device layer, the edge layer deploying Blockchain, and the cloud layer deploying DT. This will create a virtual platform simulating all possible operations in the physical world. All data interactions between manufacturers are completed in DT, the established manufacturing status is given after data processing in DT, and the result report is sent to the headquarters. The data between headquarters and virtual manufacturing is only the interaction of results and decisions, and there is no more valuable information. Moreover, the volume of these data is much smaller than the detailed data interaction of classical conditions, thus speeding up the decision making and SM progress. The decision to deploy the headquarters in the cloud (outside of the DT) will be stored in the Blockchain, and no one can tamper with the decision content.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
###### Abstract  
Video streaming services are one of the most resource-consuming applications on the Internet. Thus, minimizing the consumed resources at runtime in general and the server/network bandwidth in particular are still challenging for researchers. Currently, most streaming techniques used on the Internet open one stream per client request, which makes the consumed bandwidth increases linearly. Hence, many broadcasting/streaming protocols have been proposed in the literature to minimize the streaming bandwidth. These protocols can be divided into two main categories, namely, reactive and proactive broadcasting protocols. While the first category is recommended for streaming unpopular videos, the second category is recommended for streaming popular videos. In this context, in this paper we propose an enhanced version of the reactive protocol Slotted Stream Mapping (SST) called Share All SST (SAST), which we prove to further reduce the streaming bandwidth with regard to SST. We also propose a new proactive protocol named the New Optimal Proactive Protocol (NOPP) based on an optimal scheduling of video segments on streaming-channel. SASST and NOPP are to be used in cloud and CDN (content delivery network) networks where the IP multicast or multicast HTTP on QUIC could be enabled, as their key principle is to allow the sharing of ongoing streams among clients requesting the same video content. Thus, clients and servers are often services running on virtual machines or in containers belonging to the same cloud or CDN infrastructure.  
video streaming; cloud-based streaming; stream sharing; video-on-demand; bandwidth minimization Article
## 3 Case Studies
### Rocker Arm
#### 3.1.1 Topology Optimization Approach  
As depicted in Figure 1, the process started with the adoption of the CAD system that was adopted by the designer to model the initial design space. The functional surfaces were also identified and modelled, i.e., green volumes shown in Figure 5. The geometry was then imported within the topology optimization environment in which the material was assigned, the constraints and loading conditions were defined, and the two objective functions were to maximize stiffness and target mass \(\leq\) 30%. As depicted in the following figure (Figure 5), the result of the topological optimization analysis has a mass of 193 g. This tessellated model was then redesigned and the final feature-based geometry, validated with FEM analysis, showed a mass of 221 g with a maximum displacement of 0.28 mm and a minimum FoS of 2.4. The results of this TO analysis do not have a particularly complex geometry and the rocker can be manufactured both through conventional and AM technologies.  
The following figure (Figure 6) shows the results of the TO approach for the redesign of the rocker in the case in which titanium is used as a material. In this case, the objective functions were to maximize stiffness and target mass \(\leq\) 20%. The initial design space was the same as that defined in the previous analysis, while the TO results showed an 80% mass reduction compared to the initial design space of 1000 g. Nevertheless, the redesigned  
Figure 4: Rocker designed through a traditional design approach: loading conditions (**a**) and results of FEM analysis (**b**).  
Figure 5: Stages of the TO approach for the redesign of the rocker in Ergal.  
process performed to obtain a feature-based CAD model resulted in a slight increase in mass whose final value was 223 g.  
As abovementioned in Section 2.1, the redesign operations entailed the need to verify and validate the final geometry by means of FEM analysis (Figure 7), the results of which show a maximum displacement of 0.33 mm and a minimum FoS of 3.5. In this case, the redesigned model presents very complex geometry that can be manufactured through AM technologies only. EOSINT M 280, an industrial 3D printer made by EOS GmbH of Munich (Germany), is adequate for the fabrication of this component. It is based on the DMLS (Direct Metal Laser Sintering) process that uses a CO\({}_{2}\) laser with high power for sintering the metallic powder material according to the CAD file [68].
## 2 Description of Compressor and Computational Considerations
### The Scheme of Self-Induced Slot and Trailing Edge Blowing on Guide Vane  
According to the RSI by the guide vane, the previous work added the blowing at the guide vane trailing edge. Differently, this study focused on the self-induced slot at the guide vane trailing edge. Figure 4 depicts the trailing edge blowing and the self-induced slot distribution. In the trailing edge blowing model, due to the flow separation appearing on the suction side of the guide vanes, the exit location of blowing was set near the suction side, see Figure 4a. The self-induced slot was set inside the guide vanes, the length of which was half of the guide vane chord length, and the height was consistent with that of the guide vane. The width of the slot was half of the width of the guide vane trailing edge. The slot was parallel to the guide vane outlet angle.  
The difference between the self-induced slot and the trailing edge blowing is that the self-induced slot does not encounter any injection flow from the outside. Therefore, the main fluid flows in and out of the slot at different spans of the guide vanes through the self-induced slot, and this part flow affects the wake's style and further affects the aerodynamic load on the impeller. In addition, the primary numerical method and boundary condition  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Symbol** & **Value** \\ \hline The fine-grid convergence & \multirow{2}{*}{GC\(l^{21}\)} & \multirow{2}{*}{0.0025\%} \\ index between grids 1 and 2 & & \\ \hline The fine-grid convergence & \multirow{2}{*}{GC\(l^{32}\)} & \multirow{2}{*}{0.0028\%} \\ index between grids 2 and 3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: The (**a**) trailing edge blowing scheme and (**b**) self-induced slot scheme.  
were consistent for all three models. Meanwhile, the boundary condition at the outlet in the TEB model considered the additional mass flow caused by blowing flow.  
To analyze the wake style under the trailing edge blowing and the self-induced slot methods, the wake judgment criterion was according to non-dimensionalized momentum thicknesses \(\theta/d\), which are based on the velocity profile [23] and calculated by  
\[\frac{\theta}{d}=\int\limits_{-\infty}^{+\infty}\frac{U}{U_{\infty}}\left(1- \frac{U}{U_{\infty}}\right)\mathrm{d}\left(\frac{y}{d}\right) \tag{3}\]  
where \(\theta\) represents the momentum thicknesses, and \(U_{\infty}\) and \(U\) represent the main flow velocity and the local velocity, respectively. \(d\) and \(y\) represent the width of the guide vane trailing edge and the guide vane passage, respectively. Once \(\theta/d>0\), it is defined as the weak or pure wake state, which means there is insufficient blowing or no blowing. Once \(\theta/d<0\), it is defined as the jet state, the blowing flow of which is overmuch. The state is defined as the momentumless wake once \(\theta/d\approx 0\), and the blowing properly compensates for the wake deficit.
## 1 Introduction
However, when the EEG signals decompose into narrow overlapping frequency bands, the raw signals are severely degraded, which leads to a decrease in classification accuracy. It is possible to avoid the degradation of the EEG signal quality. Therefore, it is necessary to decompose EEG signals into fewer parts in the form of appropriately scaled frequency bands, which results in less damage to EEG signals. Based on several studies, MI can cause an increaseor decrease in the energy of frequency rhythms in related cortical areas, illustrating fixed frequency bands that contain discriminative information. In addition to the previously mentioned methods, other methods for solving these problems have been proposed but cannot solve the problem completely. In [21], [PERSON] et al. used 7 Hz to 30 Hz and 7-120 Hz frequency ranges with the same method for extracting features. They reported a 1% higher average accuracy in the 7-120 Hz frequency range than in the 7-30 Hz frequency range, which proves that most of the information related to distinctive activity patterns, such as the left and right hands, is in the 7-30 Hz frequency range. In [22], a 7-30 Hz pass-band filter and ten filter banks were used on four subjects. The filter bank method could increase the accuracy better than the 7-30 Hz pass-band filter. In [23], [PERSON] et al. argued that selecting different features for different subjects from filter banks is necessary to increase the average accuracy. Another study [23] measured the percentages of selecting features from each filter bank, where the subjects were selected from two to four filter banks. Most subjects used two filter banks, and the filter banks selected for each person were different. For example, for Subject 1, 33% of the selected features were from filter bank 3, and 67% were from filter bank 6. In the following, our paper briefly explains the background of research conducted in our research direction. Because most researchers used an SVM classifier for classification, we do not discuss the background of SVM classification. Our study has only used valid benchmarks and datasets for comparison with the results.