esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The key to improving the efficiency of a charging station is to optimize the allocation of resources and establish a sound charging network. As an engineering system, the charging station network involves many elements, and some scholars have also conducted related research on the network itself. Using complex network theory, [PERSON] and [PERSON] first constructed the charging station network structure of Shanghai under ideal conditions, and then constructed the electric vehicle charging station network of Shanghai, Xi'an, Hefei, and Dalian under actual conditions. Thus, it is verified that the network structure of the EV charging station has a significant impact on its utilization rate and stability [9]. [PERSON] et al. defined the network connection strategy of the new charging station from two dimensions of network centrality and the number of charging piles at network nodes, built a dynamic simulation model of charging network, simulated the promotion of charging station construction under different strategies, and revealed the evolution law of urban charging network structure characteristics and usage efficiency [10]. Based on the \"input-output\" critical infrastructure interdependence analysis method, [PERSON] et al., taking energy, communication, water supply, and transportation systems as objects, combined with the hierarchical structure of each system, built an interdependent network model, and found that energy nodes have the highest influence [11]. [PERSON] et al. analyzed the distribution characteristics of new energy vehicle charging piles from May 2016 to April 2019 and the temporal and spatial changes among provinces from the perspective of complex networks, and then analyzed the time distribution of new energy vehicle charging piles in combination with national policies and new energy vehicles, so as to promote the sustainable development of electric vehicles and green energy industry [7]. Based on the perspective of policy incentives and consumers' preference for electric vehicles, [PERSON] et al. used complex networks to build an evolutionary game model and proved that balanced dynamic subsidies and tax policies have advantages for the promotion of charging stations [12]. By summarizing and analyzing the previous literature on the layout of charging stations for new energy vehicles, it is found that there are few studies on the spatial layout of charging stations by the network analysis method in the academic circle. The main purpose of this paper is to build a new energy vehicle charging station network to analyze its structural characteristics, so as to provide an idea and reference for the construction and development of new energy vehicle charging stations.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 5 Case Studies
### Results of the EMO  
The adjustment of energy consumption in local MGs directly affects the total energy sharing and total energy demand in the energy market, as shown in Figure 10. It can be seen that as the internal selling prices were lower than the grid selling prices during the periods of 11:00-12:00 and 13:00-16:00, the total energy demand in the energy market increased significantly. The total energy sharing in the energy market also increased as internal buying prices rose higher than the grid buying prices  
Figure 8: Adjustment of load demands.  
Figure 9: Increased utility of MGs based on internal prices.  
during the period of 7:00-18:00. The results show that MGs will actively participate in energy market transactions under the incentive of internal electricity prices.  
Energy demand and sharing in the energy market is the premise for the EMO to make profits. In the periods of 0:00-7:00 and 18:00-24:00, MGs were in the buying state thus the EMO could only purchase energy from the power grids to meet the demands of MGs and could not obtain profits. Figure 11 compares the profits of the EMO under the internal price strategy and the grid price strategy during the period of 7:00 and 18:00. Under the incentive of the internal price strategy, the profit of the EMO increased significantly in these periods. The daily profit of the EMO increased from CNY 171.6 to CNY 277.7 and the rate of increase is 61.82%.  
To further illustrate the advantages of the proposed method, the net energy and PV utilization of the MMG were analyzed. The comparison of net energy curves is shown in Figure 12. During the period of 0:00-6:00, the charging of the ESS led to an increase in net energy. During the period of 18:00-24:00, the discharging of the ESS led to a decline in net energy. During the period of 6:00-18:00, the change in net energy was affected by ESS scheduling and the load demand response under internal prices. Obviously, the proposed method produced smaller fluctuations of the net load than  
Figure 11: Comparison of the utility of the EMO.  
Figure 10: Comparison of energy trading in the energy market.  
the original method, and the PV energy reversal in the MMG during the period of 10:00-15:00 was significantly suppressed.  
Moreover, the comparison of peak-to-average ratio (PAR) and PV utilization ratio are shown in Table 2. Through comparisons, it is found that the proposed method can effectively reduce the peak-to-average ratio of the net load and improve the utilization ratio of PV energy.
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 3 Proposed Method  
We introduce the 3D object detection method MS3D. Initially, we delineate the fundamental architecture of the network. Subsequently, we present the pivotal technique for voxelizing point cloud data. Following this, we elaborate on the SecondFPN feature pyramid network. Finally, we introduce the loss function.  
\begin{table}
\begin{tabular}{c c} \hline
**Abbreviation** & **Full Name** \\ \hline
3D-SSD [16] & 3D Single Shot MultiBox Detector \\ IA-SSD [17] & Improved Anchor-based Single Shot MultiBox Detector \\ VoxelNet [1] & VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection \\ SECOND [2] & Sparsely Embedded Convolutional Detection \\ PointPillars [18] & PointPillars: Fast Encoders for Object Detection from Point Clouds \\ CIA-SSD [13] & Contextualized Intermediate-level Anchoring for Single Shot 3D Object Detection \\ SA-SSD [4] & Scale-Aware Single Shot MultiBox Detector for 3D Object Detection \\ Diswot [19] & Discrete Wasserstein Objectives for Learning with Limited Data \\ LargeKernel3D [20] & Large Kernel Matters—Improving Semantic Segmentation by Global Convolutional Network \\ OcTr [21] & Occlusion-aware Transformer for 3D Object Detection \\ VoxelNeXt [22] & VoxelNeXt: Voxels for the Next Generation of 3D Object Detection \\ CasA [23] & Cascaded Alignment for 3D Object Detection \\ BtDet [24] & Bird’s Eye View Object Detection with Localization Refinement \\ MSF [25] & Multi-Scale Feature Aggregation for 3D Object Detection \\ ConQueR [26] & ConQueR: Monocular 3D Object Detection by Construction and Query \\ MV3D [27] & Multi-View 3D Object Detection Network for Autonomous Driving \\ EPNet [28] & Efficient PointNet for 3D Object Detection in Point Clouds \\ PointPainting [29] & PointPainting: Sequential Fusion for 3D Object Detection \\ MVP [30] & Multi-View Prediction for 3D Object Detection from Monocular Images \\ LoGoNet [31] & Local and Global Network for 3D Object Detection from Point Cloud \\ SFD [32] & Sparse Feature Detection for Point Cloud Based 3D Object Detection \\ Virtual Conv [33] & Virtual Convolution for Efficient Point Cloud Processing \\ \hline \end{tabular}
\end{table}
Table 1: List of 3D object detection algorithms.
## 1 Introduction
### Paper Motivation and Contributions  
This survey focuses on reviewing the main characteristics, benefits and challenges of IEC in 5G based on previous research that took place from 2014 to 2021. Many of the previous works focused on different aspects of IEC in 5G and some of these works are quite old. The expectations of IEC in 5G are high as they provide a promising transformative platform (an enhanced effective end-user experience) and spreading in new fields such as IoT, new services and models, rapidly exceeding gigabits speeds, and enhancement in network performance with better reliability. Networks and services in 5G technology are expected to deliver enormous economic benefits because they rely on successful IEC mobile edge communication. However, due to the emerging service models which most often use a large number of terminal devices, the increased computing and data needs may overshadow the computing and storage infrastructure installed in the IEC. In addition, higher IEC installation and storage capacities can be decreased by cost pressure in the telecoms industry [17]. Based on the effectiveness and benefits of IEC in 5G, the contributions offered by our review could be summarized as follows:  
1. We conducted an overview of IEC in 6G including characteristics, benefits, challenges, new and open cases and applications and recent market drivers.
2. We summarized integration of IEC in 5G networks related works from 2014 to 2021 including surveys, state of the art and future research.
3. Furthermore, we discussed the key factors for network 6G including momentum, architecture and market.
4. Finally, open research challenges and issues and new future direction in IEC with 6G network will be provided.
# 3D-Printed Concrete Bridges: Material, Design, Construction, and Reinforcement
## 5 Reinforcement in 3D-Printed Concrete Bridges
[35] employed 2% steel fibers by volume in their mix to increase the strength and ductility of the printed layers, demonstrating the potential of fiber-reinforced concrete as a viable alternative to traditional reinforcement methods. Additionally, [PERSON] et al. [25] utilized 13 mm polyvinyl alcohol (PVA) fibers in their fiber-reinforced high-strength cementitious mortar to further enhance the structural properties of the printed elements. These examples underscore the versatility and effectiveness of fiber reinforcement in addressing the mechanical challenges associated with 3D-printed concrete structures. Recent studies have demonstrated the feasibility of embedding FRP grids within printed concrete layers, improving structural integrity while maintaining printability [45; 46; 47]. This method has not yet been applied in 3D-printed concrete bridges, but it has potential for future applications, particularly in lightweight, corrosion-resistant structures where conventional reinforcement may be impractical. While FRP-based reinforcement in 3 DPC offers numerous benefits, challenges remain, particularly in ensuring adequate bonding between FRP elements and the printed concrete matrix, optimizing grid spacing for uniform load distribution, and addressing long-term durability concerns. Future research should explore hybrid reinforcement techniques, where FRP is combined with steel or discrete fiber reinforcement to maximize performance while maintaining ease of printing. Hybrid approaches that combine innovative and traditional techniques have also been explored. For example, the Striatus bridge is a 3D-printed masonry arch bridge that eliminates internal reinforcement in the printed blocks by relying on compression-only principles [41; 48]. The structural stability of the arch is achieved through its geometry, while the foundation employs reinforced concrete pads to anchor steel footings and ground screws, demonstrating a blend of traditional and modern construction methods. In conclusion, post-tensioning was the most used reinforcing approach, combining steel cables, rebars, and optimized designs to enhance structural capacity and stability. However, fiber-reinforced polymer (FRP) reinforcement can be a promising alternative, offering a lightweight and corrosion-resistant solution that can be directly integrated into the printing process. Additionally, post-printing methods, such as installing reinforcement cages in printed formwork, provide flexibility for incorporating advanced materials like Fe-SMA rebars. Hybrid approaches, such as the compression-only Striatus bridge,highlight the potential for integrating traditional and modern techniques in 3D-printed bridge construction.
# The BCL Method for DSSC: Basis and Applications +
## 5 The \(\mathbf{I_{T}}\) Parameter: A Measure of the Electronic Density on the Anchor Group
### Theoretical Background
#### 6.1.2 Available Energy for Injection (F1)  
The F1 parameter showed on Figure 23 is obtained by calculating the available energy through the \(I_{T}\) parameter previously defined by a set of equations that enable the calculation of the absorbed energy (\(E_{A}\)), the delivered energy (\(E_{D}\)) on the anchor ligand (\(LAn\)), and the available energy for injection (F1):  
\[E_{A}=\sum_{\
u}E_{\
u}f_{osc} \tag{26}\]  
\[E_{D}=E_{A}f(LAn) \tag{27}\]  
\[\text{F1}=E_{D}\phi_{T}(An) \tag{28}\]  
These parameters are determined with data obtained from TDDFT calculations that provide the energy and the oscillator strength at which an excitation occurs, the distribution function value for the anchor ligand, \(f(LAn)\), and the participation of the anchor group on the triplet state, \(\phi_{T}(An)\).
## 1 Introduction
The choice of the Ga\({}_{2}\)O\({}_{3}\)-shell is motivated by the following advantages: (i) Ga\({}_{2}\)O\({}_{3}\) presents high thermal stability, especially the \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) crystalline phase (the most stable phase), which is especially suitable for CO sensing in high temperature environments; (ii) the resistivity of Ga\({}_{2}\)O\({}_{3}\) is very sensitive to CO at high temperature [41]; and (iii) the presence of Ga\({}_{2}\)O\({}_{3}\) layer covering the GaN surface increases the response of GaN sensors to CO [19]. The mechanism of gas sensors integrating GaN/Ga\({}_{2}\)O\({}_{3}\) core-shell NWs is based on the NW resistance modulation before and after exposition to gases, such as CO. The core-shell NW heterostructures are characterized by the existence of two depletion zones: the first one at the air/shell interface, and the second one at the shell/core interface [40]. The adsorption/desorption of the gas molecules on the heterostructure surface modifies the depth of the first depletion layer, which in turn alters the depth of the second one, and thus induces a modulation of the corresponding potential barrier heights. Carrier transport in the core is thus affected, resulting in a large change in resistance and thus in an enhanced response of the NW-based sensor system. Because the performance of sensors primarily depends on the material properties composing the active layer of the device, it is of crucial importance to control these properties and achieve material synthesis in the first time. In this paper, we investigate the synthesis of GaN/Ga\({}_{2}\)O\({}_{3}\) core-shell NWs with a special focus on the formation of the Ga\({}_{2}\)O\({}_{3}\) shell. The GaN NWs grown by plasma-assisted molecular beam epitaxy (PA-MBE), are post-treated following thermal oxidation to form Ga\({}_{2}\)O\({}_{3}\)-shell surrounding the GaN-core. We establish that the thickness of the Ga\({}_{2}\)O\({}_{3}\)-shell can be modulated by changing the oxidation conditions (temperature, O\({}_{2}\) flux and time of oxidation), from 1 to 14 nm. By investigating the thickening of the shell, we establish that the oxidation process follows the classical model with an initial rapid oxide-shell growth, followed by a second stage of reduced but continuous oxide growth. We also discuss the influence of the phase environment on the oxidation rate. Finally, the structural properties of the shell are investigated by combining XRD, TEM-STEM and EDX analyses. Then, we demonstrate that the oxide shell is crystalline, presents the \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) phase, the most stable one, and is synthesized in epitaxial relationship with the GaN-core.
## 4 Results and Analysis
### Network Parameter Evaluation
#### 4.2.1 Study of Transmission Time in SDN Environment
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Value** \\ \hline Number of Packets & 31 \\ Mean & 24.979 ms \\ Standard Deviation & 0.898 ms \\ Minimum (Min) & 23.571 ms \\
25 th Percentile (25\%) & 24.452 ms \\ Median (50\%) & 24.869 ms \\
75 th Percentile (75\%) & 25.517 ms \\ Maximum (Max) & 26.783 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics of transmission time for non-IP Packets. tion. This has more computation steps and requires time for convergence to a solution. For comparison with the transmission delays as shown in Tables 2 and 3, it can be observed that the bulk of the transmission delay is contributed by encryption and decryption overhead. Therefore, to reduce the transmission delay, the proposed method needs to be fine-tuned with approaches for minimization in delay to meet the demand for specific delay-critical applications.
## 3 Experiments and Results
### Network Parameters and Experiment Configurations
#### 3.2.2 Experiment Configurations  
In the process of training the model, SI-SNR is employed as the loss function, and the batch size is 1. AdamW is applied as the optimizer of the algorithm, and the weight decay is \(1\times 10^{-4}\). The initial value of the learning rate is \(2.5\times 10^{-4}\), and if the loss value of the model verification set does not decrease compared with the best loss value in two consecutive iteration cycles, the learning rate is attenuated to half of the original learning rate. If the performance of the model cannot be improved for 10 consecutive iteration cycles, the training will be stopped. At the same time, in order to prevent over fitting, L2 regularization processing is added, which is 5 in the initial stage, 20 in the middle stage and 100 in the later stage of training, and the dropout gradually increases--that is, 0, 0.1 and 0.5.
## 3 Results and Discussion
### Adsorption Studies of Cu\({}^{2+}\) Metal Ion
#### 3.2.3 Effect of the WPET-SiAMPy Composite Nanofibres Dosage on Cu\({}^{2+}\) Binding Capacity  
The effect of mass loading on the adsorption of Cu\({}^{2+}\) was carried out using different doses of chelating adsorbents WPET-SiAMPy nanofibres (0.001, 0.005, 0.01 and 0.025 g). The effect of chelating adsorbent dosages were investigated in order to determine the optimum dosage of the modified nanofibres. The effect of PET-SiAMPy as the adsorbent was not investigated because studies in Sections 3.2.1 and 3.2.2 already proved that WPET-SiAMPy was more effective than PET-SiAMPy. The adsorption capacity (q\({}_{\text{e}}\)) of the resultant solution are shown in Figure 7.  
The adsorption pattern of the Cu\({}^{2+}\) adsorption against the dosage conducted over a range of masses for WPET-SiAMPy nanofibres of 0.001-0.025 g is illustrated in Figure 7. The  
Figure 6: Average adsorption capacity of Cu\({}^{2+}\) from aqueous solution using PET, WPET, PET-SiAMPy, and WPET-SiAMPy nanofibres (Cu\({}^{2+}\) concentration 100 mgL\({}^{-1}\), pH 5, dosage 0.01 g, 120 min).  
Figure 7: Adsorption capacity of Cu\({}^{2+}\) from aqueous solution using different WPET-SiAMPy nanofibres dosages (Cu\({}^{2+}\) concentration 100 mgL\({}^{-1}\), 1 min, pH 5).  
adsorption capacity and the percentage of Cu\({}^{2+}\) adsorbed on the WPET-SiAMPy composite nanofibres at different doses shows that the adsorption capacity decreased with an increase in dosages. The adsorption capacity decreased from 1.29 to 0.06 mmol/g by increasing the WPET-SiAMPy nanofibres dosage from 0.001 to 0.025 g per adsorption process of 10 mL (100 mgL\({}^{-1}\) Cu\({}^{2+}\)). The decrease in Cu\({}^{2+}\) adsorption capacity may be due to the aggregation of WPET-SiAMPy nanofibres or more likely due to the increase in excess adsorption sites as the dosage increased [31]. Thus, the active sites of WPET-SiAMPy nanofibres were fully exposed to Cu\({}^{2+}\) ions in aqueous solution even at the lowest dosage, causing the rapid saturation of the surface and yielding high adsorption capacity, as seen in Figure 7. The increase in WPET-SiAMPy composite nanofibres dosage increased the availability of adsorption sites resulting in a high WPET-SiAMPy nanofibres active sites-to-Cu\({}^{2+}\) ions ratio; thus, the availability of Cu\({}^{2+}\) ions was insufficient to occupy all adsorbent active sites, hence the decrease in adsorption capacity. Increasing the WPET-SiAMPy nanofibres dosage enhanced the active adsorption sites' availability. As seen in Figure 7, the high Cu\({}^{2+}\) binding capacity was achieved using 0.001 g dosage of the chelating adsorbents (WPET-SiAMPy nanofibres) if all fixed parameters (100 mgL\({}^{-1}\) Cu\({}^{2+}\) (10 mL) at pH 5 for 1 min) are maintained.
## 4 Privacy-Preserving Incentive Mechanism
### Route Validation Scheme
A witness vehicle \(\mathcal{T}^{V}\) generates its signature as follows: 1. \(\mathcal{T}^{V}\) constructs a polynomial \(f\) with \(n_{p}-n_{w}\) degrees defined on Galois field \(GF(2^{n_{l}})\) (\(A_{i}[x]\in GF(2^{n_{l}})\)) using Lagrange interpolation, such that \(f(0)=H_{4}(des)\) and \(f(\kappa_{i})=E_{k}(m_{i}),\ i=1,\ 2,\ \ldots,\ n_{p}-n_{w}\), where \(k=H_{5}(n_{p}\big{|}|n_{w})\). 2. \(\mathcal{T}^{V}\) chooses a random index \(\kappa\
otin\big{\{}\kappa_{1},\ \kappa_{2},\ldots,\ \kappa_{n_{p}-n_{w}}\big{\}}\) and generates \(m=E_{k}^{-1}(f(\kappa))\). 3. \(\mathcal{T}^{v}\) randomly selects \(c\in\mathbb{Z}_{q}\) and generates the EC Elgamal signature \((A,\ \beta)\) of \(m\), where \(A=c\cdot P,\ \beta=(m-sk\cdot A[x])c^{-1}\). 4. \(\mathcal{T}^{v}\) replies to the route validation request by sending a message, \(\{ID,\ m,\ \kappa,\ (A,\ \beta)\}\), to \(\mathcal{L}^{V}\). Once \(\mathcal{L}^{V}\) receives more than \(n_{w}\) responses from the nearby vehicles, it aggregates the fake and collected signatures to generate a position proof \(\mathcal{P}\) and sends it to the verifiers \(\mathcal{V}\): \[\mathcal{P}=\Big{\{}(ID_{i},\ m_{i},\ \kappa_{i},\ (A_{i},\ \beta_{i}))_{i=1,\ 2,\ \ldots,\ n_{p}},\ pos,\ n_{p},n_{w}\Big{\}}.\] Each \(\mathcal{V}\) then verifies the position proof \(\mathcal{P}\) as follows: 1. \(\mathcal{V}\) generates the public keys \(pk_{ID_{j}}=\sum_{i=1}^{m}h_{i}(ID_{j})y_{i},\forall j\in\big{\{}1,2,\ldots,n_ {p}\big{\}}\). 2. \(\mathcal{V}\) verifies whether the signatures \((A_{i},\beta_{i})\) are valid by checking whether \(m_{i}\cdot P=A_{i}[x]\cdot pk_{ID_{i}}+\beta_{i}\cdot A_{i}\) holds for \(i\in\big{\{}1,2,\ldots,n_{p}\big{\}}\). 3. \(\mathcal{V}\) randomly selects \(n_{p}-n_{w}\) tuples from \((ID_{i},m_{i},\kappa_{i},(A_{i},\beta_{i}))_{i=1,2,\ldots,n_{p}}\), reconstructs the polynomial \(f\) using Lagrange interpolation, such that \(f(0)=H_{4}(des)\) and \(f\big{(}\kappa_{j}\big{)}=E_{k}\big{(}m_{j}\big{)}\), where \(k=H_{5}\big{(}n_{p}\big{|}|n_{w})\), and verifies whether \(f(k_{i})=E_{k}(m_{i})\) holds for all \(i\
eq j\). 4. \(\mathcal{V}\) accepts the position proof if it passes all verifications. To prevent malicious behavior by a single \(\mathcal{V}\), the position proof is sent to all \(\mathcal{V}\) in the RSU network, and the PBFT algorithm [38] is used to generate a consensus mechanism. The presence of malicious nodes will not impact the final consensus when the number of malicious nodes is less than one-third of the participating nodes. When consensus is achieved that the position proof is valid, the digest of the position proof, \(H_{6}(\mathcal{P})\), instead of the plaintext \(\mathcal{P}\), is written to the latest block of the blockchain. \(\mathcal{S}_{\mathcal{C}P}\) will sign on the unfreezing transaction after verifying that the position is in the blockchain (which indicates that the position proof has been verified by the majority of the verifiers).