esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Future Research Scopes
### Dynamic Offloading  
To overcome the limitations of computation, storage, and energy of IoT or terminal devices, both academia and industries are considering data and computation offloading as a promising solution [10]. One of the primary objectives of an edge cloud network is to minimize service delay as we mentioned before. The service delay combines both execution delay and communication delay. Offloading is a technique that involves transferring computation and storage from resource-constrained end devices to resource-full edge nodes in order to enhance the performance of execution. On the other hand, offloading the computation also reduces energy consumption of end devices, which ultimately reduces the CO\({}_{2}\) gas emissions in the environment [10; 57]. Several research works proposed offloading algorithms to improve execution performance and reduce energy consumption. The workload of an application can be offloaded to either a single edge server or multiple servers for concurrent execution [3]. However, dynamically allocating workload from a mobile device to the edge clouds remains a significant challenge. Researchers from academia and industry are working on deep reinforcement learning (DRL) for dynamic offloading. The research work presented by [PERSON] et al. [79] proposed a distributed DRL approach to optimize task offloading decisions to maximize quality-of-experience (QoE) in edge cloud computing. [PERSON] et al. [80] presents an energy-efficient distributed task offloading framework for edge cloud networks. They combine a Long Short-Term Memory network (LSTM) with RL for robust decision making. Hence, AI-integrated controllers for dynamic offloading have become another key research topic in the area of SDN-enabled edge cloud networks, aiming to enhance application performance and user experience.
# Train Scheduling with Deep Q-Network: A Feasibility Test
## 4 The Proposed Method
### Deep Q-Learning  
Each instance of subproblem is defined by specifying the number of local trains preceding the express train and the target schedule of trains. In this paper, instead of trying to solve each instance whenever it is necessary, we try to learn an agent or a policy which can make near-optimal decisions for all instances. There has been several methods to learn such an agent (see, e.g., in [19]). Among them, we use deep neural networks, i.e., deep Q-network (_DQN_) [18], which we estimate _Q-values_ defined as  
\[Q^{*}(s,a)=\max_{\pi}\mathbb{E}\left[r_{t}+\gamma r_{t+1}+\gamma^{2}r_{t+2}+ \cdots|s_{t}=s,a_{t}=a,\pi\right]\]  
which is the maximum sum of rewards \(r_{t}\) discounted by \(\gamma\) at each time step \(t\) after making an observation \(s\) and action \(a\) is chosen a policy \(\pi=P(a|s)\) is used. The idea is to represent Q-value function as a recursive equations, known as Bellman equation, and iteratively update Q-values with these equations, i.e.,  
\[Q_{i+1}(s,a)=\mathbb{E}\left[r+\gamma\max_{a^{\prime}}Q_{i}(s^{\prime},a^{ \prime})\right].\]  
\(Q(s,a)\) is parameterized by \(\theta\) in the form of the deep neural network. The loss function which guides the update of \(\theta\) is  
\[L(\theta)=\mathbb{E}\left[\left(r+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{ \prime};\theta)-Q(s,a;\theta)\right)^{2}\right]. \tag{16}\]  
To train the neural network, random scenarios, i.e., instances of the subproblem are generated. For each scenario, the agent choose an action following the \(\epsilon\)-greedy policy in which most of the time the agent choose the action of the large \(Q\)-value but there exists a small chance, \(\epsilon\), of choosing a random action. Then, the obtained experiences, \((s_{t}^{i},a_{t}^{i},r_{t}^{i},s_{t+1}^{i})\) for scenario \(i\) and time \(t\), are used for defining the loss function \(L(\theta)\), which is then minimized with respect to \(\theta\). The learned Q-network can be used to make a decision by choosing the action which attains the largest \(Q\)-value.  
We also adopt two strategies proposed in [18] to prevent a possible divergence of learning. The first strategy is using the experience replay memory which stores experiences \((s,a,r,s^{\prime})\) during the training and whenever updates of \(\theta\) are performed several experiences are randomly chosen in the memory to form the loss function. In the second strategy, we maintain two neural networks, called the target network and the behavior network. The target network, parameterized by \(\theta^{-}\), is used to compute the target value in (16) and the behavior network is used to explore the scenarios. Thus, at each iteration \(i\), the loss function is defined as  
\[L_{i}(\theta)=\mathbb{E}\left[\left(r+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{ \prime};\theta_{i}^{-})-Q(s,a;\theta_{i})\right)^{2}\right]\]  
where the expectation is taken over the random experience in the memory. The parameters \(\theta^{-}\) of the target network are updated to the parameter \(\theta\) of the behavior network according to the predefined schedule.
## 3 Numerical Modeling and Setup
### Computational Fluid Dynamics Setup  
Computational fluid dynamics (CFD) method was used for flow simulation. The impeller domain shown in Figure 3a was discretized by tetrahedral unstructured mesh elements. The mesh number was checked to balance the simulation accuracy and the time-cost. The residual of the hydraulic efficiency was checked for determining the final mesh scheme. The mesh schemes with about 0.16, 0.32, 0.60, 1.5, and 3.1 million nodes (different leading-edge shape has similar but different mesh node number) are checked by monitoring the residual of the hydraulic efficiency for less than 1%. As an example, the mesh check detail of the initial leading-edge impeller is shown in Figure 3b and Table 1. After checking, we see the meshes for different leading-edge impellers have about 1.5 million nodes and 6.0 million elements. Figure 3 shows the schematic map of mesh.  
Figure 3: The schematic map of the mesh for computational fluid dynamics (CFD) simulation: (**a**) detailed view of mesh distributions; (**b**) variation of residual in the independence check.  
Based on the domain, three types of boundaries are given:  
1. [label=()]
2. Velocity inlet was given on the inlet boundary. The velocity value depends on the flow rate \(Q=0.25\ Q_{d}\). On this boundary, pressure followed the Neumann condition;
3. Pressure outlet was given on the outlet boundary. The average static pressure was 0 Pa, based on the environment pressure of 1 Atm. On this boundary, velocity followed the Neumann condition;
4. No-slip walls were set on the solid walls, including the blade, hub, and shroud.  
The fluid medium is set as water at 20 \({}^{\circ}\)C. Steady state simulation was conducted before transient simulation. The maximum iteration number was 1000 for steady state, with convergence criteria of RMS (root mean square) value less than \(1\times 10^{-5}\). The transient simulation was conducted because the flow was continually changing during rotation. A total of 360 time-steps were set for each revolution. The maximum iteration number for each time-step was 10 for transient state with convergence criteria of RMS value less than \(1\times 10^{-5}\).
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 6 Simulation Results
### Effect of Exploratory Factor \(\varepsilon\)  
In the \(\varepsilon\)-greedy policy, \(\varepsilon\) is a very important parameter. Therefore, the effect of \(\varepsilon\) on convergence is measured. Figure 8 shows the average reward and cumulative rewards under three different policies, where \(a_{\text{max}}=0.05\), \(\rho=0.3\), and \(a_{\text{mim}}=1.0~{}\times~{}10^{-5}\). These policies are defined as: (1) Dynamic \(\varepsilon\): \(\varepsilon=\varepsilon_{\text{init}}\times 0.99^{episode}\), where \(\varepsilon_{\text{init}}\) is the initial value of \(\varepsilon\) and \(\varepsilon_{\text{init}}=1\); (2) Fixed \(\varepsilon\), where the \(\varepsilon\) is fixed at 0.5; (3) Fixed \(\varepsilon\), where \(\varepsilon\) is fixed at 0.9.  
Figure 8a shows the average reward of agent with different policies. As shown in Figure 8a, the average reward does not vary much with the number of episodes when \(\varepsilon\) is fixed. Additionally, when \(\varepsilon\) is fixed, the average reward and cumulative rewards of \(\varepsilon=0.5\) are higher than that of \(\varepsilon=0.9\). While the average reward increases slowly with the number of episodes when \(\varepsilon\) is dynamic, where \(\varepsilon=\varepsilon_{\text{init}}\times 0.99^{episode}\). It converges when the number of episodes reaches about 10,000. In addition, convergence of the average reward with dynamic \(\varepsilon\) is more than the fixed \(\varepsilon\). The average reward is the lowest when  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Default Value** \\ \hline Number of episodes & 20,000 \\ \hline Number of time slots & 100 \\ \hline Learning rate & OneCycleLR policy \\ \hline Initial exploration factor & 1 \\ \hline Discount factor & 0.99 \\ \hline \(R_{1}\), \(R_{2}\) & 1, 0.7 \\ \hline Replay buffer size & 10,000 \\ \hline Minimum batch size & 32 \\ \hline Optimizer & Adam \\ \hline Activation function & Relu \\ \hline Loss function & Equation (17) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters.  
\(\varepsilon=0.9\). Figure 8b shows the cumulative rewards of agent with different policies, it shows the advantages of dynamic \(\varepsilon\) more intuitively compared with Figure 8a.
# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## Materials
### Burned Area (MODIS MCD64A1) and Land Cover (MODIS MCD12)  
Since a SEVIRI-detected active fire may fill only a very small fraction of the SEVIRI pixel (e.g., down to 10\({}^{-4}\) of the \(\approx\)10 km\({}^{2}\) pixel area; reference [41]) we used the 500 m MCD64A1 MODIS burned area product [54] to identify the actual area burned by each discrete fire event. The MCD64A1 product maps burned area using time-series of burn-sensitive vegetation indices, along with active fire (AF) detections to seed the algorithm and help identify the date of burning [54]. The MCD64A1 product performs well in Africa [55], is used within GFED, and has shown reasonable agreement with far higher spatial resolution burned area estimates derived from 30 m Landsat data [56]. It does however show a tendency to underestimate burned area, typically by between 5 and 41% depending on tree canopy cover [54].  
To identify the land cover affected by each fire we used the 500 m spatial resolution MODIS land cover product (MCD12Q1, reference [48]), which was updated annually until 2012. Five different classification schemes are available, and we selected the 16 class International Geosphere-Biosphere Programme (IGBP) land cover scheme [57]. Similar to reference [32], we simplified the number of classes into five broad fire-relevant types (closed canopy forest, shrubland, woodland savanna, grassland savannah and cropland/managed lands). The majority land cover type present within each SEVIRI active fire pixel was identified using these data.
## 3 Materials and Methods
### Data Forwarding Method
#### 3.3.1 Data Forwarding Procedure  
The procedure of data forwarding mainly contains the following three stages, as can be seen in Algorithm 1.  
1. In the beginning of each time slot, each sensor node and sink node broadcasts its beacon signal, e.g., the identifier, orientation and residual energy. Therefore, each sensor node knows its neighbors.
2. When \(v_{i}\) hears the beacon signal from \(s_{m}\), it adds \(s_{m}\) to the set of its available sink nodes \(S(i,t)\). Similarly, if \(v_{i}\) can hear the beacon signal of sensor node \(v_{j}\), \(v_{i}\) will add \(v_{j}\) to the set of its neighbors \(H(i,t)\). Additionally, the distance and orientation of each neighbor or reachable sink node can be acquired locally via the Received Signal Strength (RSS) and Arrival of Angle (AoA) of the beacon signal, respectively. If \(v_{i}\) cannot hear from any sink nodes or sensor nodes, \(v_{i}\) will wait until the next time slot coming.
3. Sensor node \(v_{i}\) selects the reachable sink node or next relay node by the algorithm RelaySelect which performs a learned choice of a relay node. The RelaySelect algorithm will be introduced in detail in the third subsection.
## 3 Results and Discussion
### In Silico Drug-Likeness Prediction  
As can be seen from Table 4, the calculations from the SwissADME website allow for the analysis of which synthesized compounds have better pharmacokinetics and drug-like properties. All of them had a TPSA between the limits suggested for good bioavailability (20-130 A\({}^{2}\)). The vast majority are inhibitors to cytochrome enzymes, which could affect the metabolism and present drug-drug interactions [27], **13** being the least CYP inhibitor, followed by **11**, **12**, and **9**. Although their water solubility was moderate, all of them are predicted to have a high gastrointestinal (GI) absorption (although this can be partially limited for **1-10**, being P-gp substrates). The exception to this is compound **13**, which is poorly soluble and has low GI absorption. The great majority seem to be able to permeate the blood-brain barrier (BBB), although this was not the case for compounds **9**, **11**, **12**, and **13**. However, as all the BBB permeant compounds are also P-gp substrates, they would be pumped out from the brain and we would expect no interactions with the central nervous system due to this. Due to these enlisted data, we could expect the synthesized compounds to be, in general, suitable for oral administration. [PERSON]'s rule of five [57] can be applied as a first filter, which accounts for the physicochemical properties related to the drug-likeness of a molecule. The molecular weight, number of H-bond donors and acceptors, and lipophilicity are in general accordance to the Lipinski rule. Only compounds **9** and **13** presented a violation, in both cases related to their very high lipophilicity. Considering the predicted pharmacokinetics and drug-likeness, compounds **11** and **12** with nitro substitution can be considered as promising lead compounds for further studies, which can be additionally supported by the fact they were amongst the most active in vitro compounds, both as AChE inhibitors, **12** as an XO inhibitor, and **11** as part of the antiproliferative imidazoles in cancer cell lines. Figure 4: Compound **11** (orange) depicted with both of the analyzed receptors. Highlighted are the residues at distances <5.0 Å from **11**, with some surfaces active to show the cavity in which the docking analysis put the best docked pose. (**a**) EGFR, with the near residues in purple. (**b**) HER2, with the near residues in cyan.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 3 Detailed Description of the Proposed Method
### Point Cloud Tracking and Pose Optimization
#### 3.3.2 Frame-to Frame Point Cloud Registration  
The number of point clouds collected by the TOF camera is large at close range, which affects the registration speed of the point clouds at the front and back points. It is necessary to downsample the point clouds. The ICP algorithm is usually used to solve the transformation relationship between two sets of point clouds. The ICP algorithm has the disadvantage of being easily trapped in a local minimum. It is sensitive to the initial pose guess, otherwise the algorithm cannot converge on the correct result.  
The standard ICP registration algorithm was used in this study. The estimated pose in stereo vision is regarded as the initial value input to improve the accuracy of the initial pose. Therefore, the initial guess of the pose variation between two datasets is provided by the binocular-based method. The feature points in stereo vision are mapped to the point cloud data and clustered to obtain several SURF-3D key points for the point cloud registration. As shown in Figure 8, the small, white squares represent the key points of SURF-3D after K-means clustering.  
The steps of the frame-to-frame point cloud registration algorithm are shown in Figure 9.  
Figure 8: Schematic Pdiagram of the point cloud with the SURF-3D key points.  
Figure 9: Flow chart of the frame-to-frame point cloud registration algorithm.  
1. Down-sampling the source point cloud using a voxel filter; 2. Using the initial pose obtained by vision to perform the initial transformation on the source point cloud and accomplishing a rough registration of the point cloud; 3. The K-dimensional tree is used to accelerate the search for point pairs between the source point cloud and the target point cloud when using the nearest neighbor to search for the corresponding points; 4. It is judged whether the points belong to the same SURF-3D key point area when using the minimum value of the Euclidean distance to determine a point pair; the false matching phenomenon is rejected. 5. The convergence condition is that the sum of the distance between the matched points is less than a given threshold or greater than the preset maximum number of iterations.
# Electrically Equivalent Head Tissue Materials for Electroencephalogram Study on Head Surrogates
###### Abstract  
With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and MEG capabilities. However, to achieve translational results, such phantom heads should have matching geometric features and electrical properties. Here, we have designed and fabricated multilayer chopped carbon fiber-PDMS reinforced composites to represent phantom head tissues. Two types of phantom layers are fabricated, namely seven-layer and four-layer systems with a goal to achieve matching electrical conductivities in each layer. Desired electrical conductivities are obtained by varying the weight fraction of the carbon fibers in PDMS. Then, the prototype system was calibrated and tested with a 32-electrode EEG cap. The test results demonstrated that the phantom effectively generates a variety of scalp potential patterns, achieved through a finite number of internal dipole generators within the phantom sample. This innovative design holds potential as a valuable test platform for assessing wearable EEG technology as well as developing an EEG analysis process.  
Electroencephalogram (EEG); Electrical conductivity; Composite material; PDMS; carbon fiber +
Footnote †: journal: Medical and Aerospace Engineering
## 6 Discussion  
Sci-tech journals currently play an important role in the innovation systems of each country as a carrier of academic content, and effective journal evaluation and prediction models will play a crucial role in the development of sci-tech journals, so the purpose of this study was to dynamically evaluate the possible future development of the journal through journal historical metrics, which will enable us to allocate the right resources and develop appropriate strategies for journal development. In this chapter, we will discuss the methods and results of this paper and analyze the theoretical and practical implications based on the above content.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject Class** & **MSE** & **MAE** & **RMSE** & **MAPE** & **NRMSE** & **80\%** & **85\%** & **90\%** \\ \hline Medicine & 0.00040 & 0.01372 & 0.01990 & **14.032\%** & 0.03096 & **82.656\%** & **72.847\%** & **55.622\%** \\ \hline Engineering & 0.00034 & 0.01020 & 0.01844 & 20.840\% & 0.04779 & 69\% & 57\% & 44\% \\ \hline Pedagogy & 0.00045 & 0.01311 & 0.02123 & 27.958\% & 0.03149 & 71.622\% & 59.459\% & 44.595\% \\ \hline Technical Science & 0.00018 & 0.00902 & 0.01330 & 19.659\% & 0.03335 & 69.412\% & 58.235\% & 44.706\% \\ \hline Economic & 0.00086 & 0.01806 & 0.02924 & 35.588\% & 0.04092 & 53.947\% & 43.421\% & 31.579\% \\ \hline Transportation & 0.00015 & 0.00969 & 0.01237 & 17.177\% & 0.07558 & 61.538\% & 50\% & 30.769\% \\ \hline Social Science & 0.00030 & 0.01213 & 0.01743 & 17.112\% & 0.02764 & 68.868\% & 53.774\% & 36.792\% \\ \hline Natural Science & 0.00036 & 0.01278 & 0.01887 & 22.791\% & 0.06288 & 61.667\% & 52.5\% & 38.333\% \\ \hline Materials Science & 0.00026 & 0.01225 & 0.01599 & 43.718\% & **0.12383** & 55.263\% & 47.368\% & 34.211\% \\ \hline Agronomy & **0.00014** & **0.00869** & **0.01180** & 14.211\% & 0.05090 & 79.310\% & 63.793\% & 48.276\% \\ \hline Basic Science & 0.00022 & 0.00993 & 0.01477 & 27.944\% & 0.05378 & 59.483\% & 48.276\% & 34.914\% \\ \hline Others & 0.00077 & 0.01635 & 0.02775 & 27.026\% & 0.03310 & 62.745\% & 53.922\% & 37.255\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: LSTM results’ evaluation metrics of different classes of objects. The best data in each column are displayed in bold.
# Low Carbon Optimization of Deep Foundation Pit Support in Undulating Strata
## 3 Foundation Pit Support Scheme Design
### Theoretical Calculation Design
#### 3.1.2 Calculation Working Conditions  
Excavation of the foundation pit is carried out by the open excavation method, with simultaneous excavation and support. The design calculation is based on the parameters of the soil layer on site, including calculating the magnitude of the active soil pressure and determining the maximum depth at which the soil layer can remain stable after excavation. This is used to determine the depth of excavation for each layer. According to the engineering situation of reasonable engineering excavation layered calculation, finally achieve support completion. The working conditions are divided as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Number** & **Support Type** & **Horizontal Spacing (m)** & **Vertical Spacing (m)** \\ \hline
1 & Reinforced concrete internal support & 9 & 1.5 \\
2 & Steel pipe supports & 3 & 7 \\
3 & Steel pipe supports & 3 & 4 \\ \hline \end{tabular}
\end{table}
Table 4: Information on internal support.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Number** & **Support** & **Horizontal** & **Vertical** & **Angle of** & **Overall** & **Length of Anchorage** \\  & **Anchor Type** & **Spacing (m)** & **Spacing (m)** & **Incidence (\%)** & **Length (m)** & **Section (m)** \\ \hline
1 & internal bracing & 9.000 & 1.500 & -  
The computer-aided design method is employed to calculate the supporting structure of the deep foundation pit, which involves the calculation of earth pressure, the design of equal section reinforcement of piles, the calculation of internal force and deformation, the calculation of anchor pile and bolt, the checking calculation of uplift resistance of the supporting pile, the checking calculation of overall stability, and the checking calculation of anti-overturning.