esa-sceva/satcom-chunk-collection huggingface dataset:
# Highway Microgrid Project Evaluation under Energy Transportation Integration
## 5 Conclusions  
This paper proposes an evaluation index system and comprehensive evaluation method suitable for highway microgrid construction, and takes a practical highway microgrid project as an example to obtain the following conclusions.  
1. In view of the lack of comprehensive evaluation of the \"Source-Network-Load-Storage\" microgrid scheme, the index system of a highway microgrid is developed in this paper. The index system suitable for the expressway microgrid is constructed from three aspects. The results yield an evaluation value of the planning scheme adopted in this paper of 0.9127, and the highway microgrid has good economic, technical, and environmental benefits, which provides a new idea for the construction of a highway microgrid under the integration of energy and transportation.
2. The use of the AHP-entropy weight method can not only make use of subjective experience, but also make use of objective facts, so as to avoid the evaluation deviation caused by the imperfect index system. The grey comprehensive evaluation method based on the AHP-entropy weight method is obtained and used to solve the problem that the fuzzy evaluation method can only evaluate the system containing model factors, but not the evaluation indexes with correlation.  
A highway microgrid is a new type of microgrid. The evaluation of an expressway microgrid is an evaluation problem involving multiple angles. In terms of indexes, this paper builds an index system from three dimensions: economy, technology, and environment. With the development and operation of this kind of microgrid, more operational data will be accumulated, and the evaluation indicators can be expanded in terms of the impact of the microgrid on traffic, so as to improve the evaluation system.  
Conceptualization, L.Y. and H.F.; methodology, Z.Y.; software, L.Y.; validation, H.F., M.S. and Z.Y.; formal analysis, Z.Y.; investigation, M.S.; resources, L.Y.; data curation, H.F.; writing--original draft preparation, M.S.; writing--review and editing, Z.Y.; visualization, M.S.; supervision, L.Y.; project administration, H.F.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Science and Technology Projects of Transportation and Energy Integration in China Energy Construction Co., Ltd., CEEC2021-KJZX-08-1.  
Not applicable.  
Data Availability Statement: The numerical data used to support the findings of this study are included within the article.  
Authors [PERSON] and [PERSON] were employed by the company Gezhouba Group Transportation Investment Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from China Energy Construction Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 3 Results
### Classifier Training Time  
In order to evaluate the training time of the investigated classifiers, the cross-validation scheme was run four times for each subject, each time with an increasing number of EEG channels retained in the analysis, to explore the scalability of each classifier for analyses with higher spatial resolutions. The temporal resolutions were not varied, but we expect that increasing the temporal resolution has a similar effect on training time, since the training times for the STBF-based classifiers are primarily dependent on the number of parameters in their respective covariance matrix estimators, as evidenced by the complexity  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**2 Trials**} & \multicolumn{8}{c}{**Nb. of Training Blocks**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline stbf-struct \(>\) stbf-shrunk & **0.014** & **0.006** & **0.040** & **0.040** & **0.004** & 0.846 & 0.888 & – & – \\ stbf-struct \(>\) stbf-emp & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** \\ stbf-struct \(>\) xdawn+rg & 0.103 & **0.004** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** \\ \hline stbf-shrunk \(>\) stbf-emp & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** \\ stbf-shrunk \(>\) xdawn+rg & – & – & 0.163 & **0.001** & **0.001** & **<0.001** & **<0.001** & **<0.001** & **<0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(p\)-values as in Table 1, averaging over two testing trials.  
Figure 3: Accuracy of the different classifiers for all 21 subjects relative to the number of blocks available for training. One block consists of 135 epochs and corresponds to 27 seconds of stimulation. Accuracies are shown for the evaluation settings averaging over 1, 2, and 3 trials of testing stimuli. Figure A1 contains results for all numbers of trials. Although stbf-emp is unstable when few training data are available, regularization of the covariance matrix (stbf-shrunk and stbf-struct) drastically improves performance.  
calculations in Section 4.2. Figure 4 shows the measured training times. These results were obtained using a laptop with an Intel(r) Core(tm) i7-8750H CPU (Intel Corporation, Santa Clara, CA, USA) and 16 GB of RAM.  
Figure 4 shows that the training time of stbf-struct increased less steeply than that of stbf-shrunk and stbf-emp. The training time of all three STBF-based classifiers was much lower than that of xdawn+rg, which appears nearly constant when using 4, 8, 16, or 32 channels.
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 3 Results
### Sprayed LM Transmission Line  
Table 1 shows the dimensions and parameters of the sprayed LM transmission line (TL). The TL was designed for a 50-\(\Omega\) impedance. First, the TL was implemented and measured using a standard Cu trace, then using a sprayed LM trace made with a polyimide mask. Figure 14a shows a schematic image for the sideview of the TL. Figure 14b shows an image for the sprayed LM TL. Figure 14c shows the measured insertion loss and return loss of the Cu and LM TLs. For both of the LM and Cu TLs, the insertion loss (\(S_{21}\)) is less than 5 dB up to 20 GHz, and the return loss (\(S_{11}\)) is greater than 10 dB up to 10 GHz. Therefore, it is concluded that there is an acceptable match between LM and Cu TL performances. Bending the LM transmission line at a radius of curvature of 8 cm did not significantly affect the insertion loss or the reflection coefficient. The bending radius was limited by the characterization setup and connectors rather than the performance of the sprayed LM.  
Figure 14: Sprayed-LM transmission line: (**a**) cross-section, (**b**) photo of the implemented sprayed-LM TL, and (**c**) measured insertion loss (\(S_{21}\)) and return loss (\(S_{11}\)) of a straight Cu TL and a straight and curved sprayed LM TL.  
Figure 13: Reflection coefficient of Cu and LM rectangular patch antennas.  
The Cu TL was fabricated using copper tape; the material cost for the TL is estimated to be $0.046. The LM transmission line was fabricated using a polyimide mask (material cost of approximately $0.01) and the amount of LM used to create the transmission line was approximately 0.05 mL (material cost of approximately $0.062). Although the materials cost of the LM TL is more expensive than the Cu TL, it is still inexpensive. The sprayed LM has the additional advantage of compatibility with flexible and stretchable substrates.
## 3 Results and Discussion
### Optimization of the Conditions  
For online, rapid, and fast detection, the chronoamperometry method was selected. After analyzing the CV, UN-MNPs were selected due to their high electrocatalytic effect, resulting in higher sensitivity towards the H\({}_{2}\)O\({}_{2}\) reduction compared to other modified MNPs. The reduction peak, as shown (Figure 2D), ranges from \(-\)0.1 V to \(-\)0.4 V. Hence, the applied potential in chronoamperometry varied in the range of \(-\)0.1 V to \(-\)0.4 V. The chronoamperometry was run with the stepwise addition of H\({}_{2}\)O\({}_{2}\). By increasing the magnitude of the applied cathodic potential, the sensitivity of the calibration curve increased in Figure 3. To avoid the reduction in redox-active compounds, \(-\)0.3 V was selected as the applied potential, even though \(-\)0.4 V shows better sensitivity. It must be taken into account that a lower potential magnitude (\(-\)0.3 V) is favorable as it causes fewer interfering effects by other electroactive compounds present in the solution.  
The number of UN-MNPs drop-cast on the MWCNT/GCE can improve the sensitivity of the proposed biosensor. GCE/MWCNT/UN-MNPs were prepared where the amount of drop-cast UN-MNPs was varied from 10 \(\upmu\)L to 30 \(\upmu\)L. The highest sensitivity was observed with only 10 \(\upmu\)L of the UN-MNPs and a larger volume did not cause a higher sensitivity.  
Figure 2: Cyclic voltammetry (CV) recorded using different developed electrodes in the presence and absence of H\({}_{2}\)O\({}_{2}\): (**A**) CV of the GCE/MWCNT; (**B**) GCE/MWCNT/MNPs-CH. (**C**) GCE/MWCNT/MNPs-TEOS; (**D**) GCE/MWCNT/UN-MNPs.
# Self-Learning Power Control in Wireless Sensor Networks
## 4 Method
### Q-Learning for Single-Agent Systems  
MDP problems can be solved through 3 methodologies: Dynamic Programming (DP), Monte Carlo (MC) and Temporal-Difference (TD) [38]. We have selected the TD solution because it does not need a model of the environment (contrary to dynamic programming) and it is completely incremental, unlike Monte Carlo methods. TD methods provide returns online, at every step, while the value function is estimated using past learned estimates. They are distinguished in off-policy and on-policy. The difference resides in the dependency to a policy. In particular, although the off-policy methods are independent from the policy selected, the value function approximates to the optimum. Instead, on-policy methods are influenced by the policy chosen. We employ Q-learning, a well-known off-policy TD algorithm, which is integrated in a TPC protocol. The action-value function is renamed as Q-value, \(Q\), and defined as follows:  
\[\begin{split} Q_{k+1}(s_{k},a_{k})=(1-\alpha_{k}(s_{k},a_{k})) \cdot Q_{k}(s_{k},a_{k})+\\ \alpha_{k}(s_{k},a_{k})\cdot[r_{k+1}+\gamma\cdot\max_{a}\ Q_{k}(s _{k+1},a)],\end{split} \tag{5}\]  
where \(\alpha_{k}\in[0,1]\) is the learning factor at the \(k\)-th episode and \(\gamma\in[0,1]\) is the discount factor. \(\alpha\) indicates the weight that the system gives to new rewards by updating the long-term Q-value. \(\gamma\) dims the contribution of the expected maximum Q-value in the new state. Q-learning is a lightweight algorithm that, in the single agent system, provides the convergence of the estimated action-value to the approximated optimal value with probability 1, considering the following assumptions [38]:  
\[\sum_{k=1}^{\infty}\alpha_{k}=\infty \tag{6}\] \[\sum_{k=1}^{\infty}\alpha_{k}^{2}<\infty \tag{7}\]  
In addition to the assumptions in Equations (6) and (7), the system reaches convergence if all the actions are continuously explored [38].
## 8 Conclusions  
There are four primary disaster management strategies: mitigation, preparedness, response, and recovery. UAVs are most commonly utilized in the response strategy. UAVs have emerged as one of the most promising and effective new technologies for disaster assistance SAR. UAV computing offers several advantages, including being simple to deploy, quick, safe, and efficient and effective, making it ideal for disaster recovery. Consequently, during a disaster, UAV computing intelligence assists SAR by providing SAR with better situational awareness, detecting survivors, operating computer modeling of the disaster, bringing required equipment and supplies, and minimizing loss of life, among many other applications. The findings showed that using UAV computing for assisted SAR missions in the disaster area can significantly improve situational awareness and help to locate areas with greatest risk based on captured images and computed information. We found that the network performance was efficient by observing the overall parametric evaluation of the effective throughput, delay, load, traffic sent and received, and path loss with different SAR distances. The path loss increases with the SAR distance. UAVs might utilize federated learning techniques to evaluate captured data locally instead of sending it to a disaster center. The transmission and processing of the picture to the UAV fog might be included in future research. Furthermore, resource allocation and energy consumption are the main challenges in UAV computing networks, which need to be addressed in the future. Tethered UAV computing may solve UAV computing battery life, but it may not be suitable for disaster management. Moreover, trajectory planning is one of the major UAV computing challenges in disasters and harsh environments that needs to be solved to avoid obstacles.  
Conceptualization, S.H.A., A.V.S., N.S.R. and A.H.; methodology, S.H.A., A.V.S. and S.VS; software, S.H.A., A.S., M.A.A. and S.K; validation, S.H.A., A.V.S.; writing--original draft preparation, S.H.A., A.V.S., A.H., A.S. and S.K; writing--review and editing, N.S.R., S.H.A., S.S., S.K., V.O.N. and M.A.A.; visualization, S.H.A., S.V.S., V.O.N., M.A.A., S.S., S.V.S., A.S. and N.S.R.; supervision, A.H., S.K. and N.S.R.; project administration, A.V.S., S.V.S.; M.A.A. and S.S. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Path losses from different SAR distance.
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 5 Simulation Results  
Before conducting simulation validation, it is necessary to study the choice of gain values in the sliding mode controller. The specific selection of gain values for a sliding mode controller is influenced by factors such as system characteristics, control requirements, and operating conditions. In generally, the range of sliding mode gain values should be adjusted appropriately. Increasing the sliding mode gain can improve the system's sensitivity and response speed but may lead to chattering and instability. Therefore, within the range of gain values, it is important to ensure that it does not exceed the system's tolerance for chattering and prevent the occurrence of instability. Reducing the sliding mode gain can enhance system stability and reduce chattering but may sacrifice dynamic performance and response speed. Thus, when determining the range of gain values, a balance between stability and performance needs to be found.  
The specific range of sliding mode gain values is typically determined based on the specific system and can be adjusted and optimized through methods such as experimentation, simulation, and empirical analysis. It is important to select gain values that meet the control requirements of the system while maintaining stability and performance. In comparison to the commonly used trail-and-error method and parameter optimization methods, this paper adopts the trail-and-error method to obtain a suitable range of sliding mode gains, as shown in Table 1.  
When the sliding mode gain parameter is within the reference range mentioned in Table 1, the system exhibits good dynamic and steady-state characteristics. If the value is lower than the reference range, the dynamic response speed of the system will decrease. Similarly, if the value exceeds the reference range, the steady-state characteristics of the system weaken, leading to oscillations.  
To verify the applicability of the proposed AFSMFTC-based autonomous landing system, various simulations and comparative experiments are presented below.  
An F/A-18A CBA is selected as the object in this study, the model parameters of which are listed in reference [42]. The initial position of the CBA and the ideal touchdown point on the carrier deck are (0, 5, 210) m and (0, 2500, 10.8) m, respectively, and the initial state parameters are \(\phi_{0}=0^{\lx@math@degree}\), \(\theta_{0}=4.6^{\lx@math@degree}\), \(\alpha_{0}=8.1^{\lx@math@degree}\), \(\beta_{0}=0^{\lx@math@degree}\), \(V_{a}=70\) m/s and \(V_{c}=12.86\) m/s. The settings of the controller parameters include the sliding-mode surface parameters \(c_{1}=diag(8,8,10)\) and \(c_{2}=diag(12,10,12)\), the adaptive-law parameters \(\kappa=0.8\), \(\lambda=1.5\) and \(\gamma=10\) and the fuzzy-controller membership functions \(\mu_{N}(s)=\frac{1}{1+\exp(5(s+3))}\), \(\mu_{Z}(s)=\exp(-s^{2})\), \(\mu_{P}(s)=\frac{1}{1+\exp(5(s-3))}\) and \(\hat{\vartheta}_{h}^{T}=[0.1,0.1,0.1]^{T}\).
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### Channel Estimation
#### 2.2.2 Non-Imaging LOS Channel Gain  
A generalized Lambertian radiation pattern can model the LOS irradiance of LED as shown in Figure 3. The vector \(v_{\text{RS}}\) can be written as \(v_{\text{RS}}\) = [a, b, c] = [X\({}_{\text{R}}\), Y\({}_{\text{R}}\), Z\({}_{\text{R}}\)]-[X\({}_{\text{S}}\)\(\rm Y_{S}\), \(\rm Z_{S}\)], where [\(\rm X_{S}\), \(\rm Y_{S}\), \(\rm Z_{S}\)] and [\(\rm X_{R}\), \(\rm Y_{R}\), \(\rm Z_{R}\)] are the positions of transmitter and receiver, respectively [28]. The optical LOS channel gain between the tth LED lamp and the rth PD is proved by:  
\[\rm h_{rt}(0)=\left\{\begin{array}{cc}\frac{(m+1)\rm A_{PD}}{2\pi d_{rt}^{2}} \upmu\ \upmu\ \cos^{m}(\varnothing\rm rt)\cos(\theta\rm rt),&0\leq\theta\leq\phi\\ 0,&\theta>\phi\end{array}\right.. \tag{9}\]  
To conclude, \(\rm m=-ln2/ln\) (\(\rm cos\ \Psi_{1/2}\)) is the emitted lighting order with \(\rm\Psi_{1/2}\) is the semi-angle of the transmitter at half power, \(\rm A_{PD}\) is the PD active area, \(\rm d_{rt}\) is the distance between the tth LED lamp and the rth PD, \(\upmu\) and \(\upeta\) are the optical filter gain likewise the optical lens, respectively, \(\rm\phi_{rt}\) is the emission angle, and \(\rm\theta_{rt}\) is the incident angle. Moreover, if \(\rm\theta_{rt}\) is outside the Field of View (FOV) of the receiver, the optical gain \(\rm h_{rt}\) is zero and when an imaging non-diversity receiver (ImADR) is used, \(\rm\theta_{rt}\) becomes zero.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 6 Proof of Concept
### Simulated Scenario  
Firstly, the optimization of the QoE perceived by the user of a real-time video streaming service based on the adjustment of the RLC UM transmission buffer size was performed.  
Secondly, the optimization of the QoE perceived by the user of a file transfer service based on the adjustment of some of the RLC AM parameters was tested. The AM RLC parameters used were the t-PollRetransmit and the t-StatusProhibit timers. The selection of these parameters was made based on the performance of the RLC AM and following the study carried out in [17].  
To perform the tests in the simulated environment the LTE module of the ns-3 simulator, called LENA [27], was used. LENA is an open-source LTE/evolved packet core (EPC) network simulator. The Evalvid tool [28] was used to simulate the real-time video streaming service and to evaluate its performance.
## 3 Graphene and Graphene-Related Materials for Biological Applications
### Interaction between Graphene and Related Materials and Biological Systems  
Graphene-like materials' viability for biological applications is strongly related to the interactions between graphene and cellular and tissue structures.  
Despite the astonishing properties, the use of graphene flakes and rGO as drug carriers without any modification is quite challenging in biological environments due to their high hydrophobicity [78]. The use of GO overcomes this issue due to its high hydrophilicity. This property leads to a cellular uptake through both endocytotic and macro-pinocytotic mechanisms [79; 80].  
Nonetheless, the use of graphene flakes and rGO in biological environments could be exploited by performing covalent and non-covalent modifications [81]. These less invasive and controlled modifications preserve the high conductivity of graphene flakes and rGO allowing their use in neuronal repairing [82], in vivo cellular imaging [83], and stimuli-mediated drug delivery [84; 85].  
The extended sp\({}^{2}\) system of graphene and rGO represents a strong advantage with respect to other platforms used in the biological environment such as inorganic nanoparticles [86] due to their facile modifications through cycloadditions [87], e.g., 1,3-dipolar cycloadditions [88], intrene addition [89], and amide condensation [90]. The intrinsic reactivity of graphene and related materials allows to easily build up compound libraries filled with plenty of materials with the basic properties of graphene and tuned biological activity.
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 1 Introduction
As a result, there are no methodologies or guidelines to design quantized DL models where the designer can trade-off both main optimization objectives, the models' performance (e.g., accuracy) against the model complexity (e.g., model size). This trade-off is fundamental since different computational environments will provide different computing capabilities, and it is up to the designer to find a balance between both objectives. In this paper, we propose a methodology based on statistical analysis and its related algorithms to determine the degree of quantization that is required to achieve a given trade-off between model performance and model complexity and, without loss of generality, we validate the proposed approach in a well-known DNN architecture that has been used for AMR. To the best of the authors' knowledge, this is the first work that proposes a methodology that allows the design of quantized DL models for AMR with a given trade-off between accuracy and model complexity. The main contributions of this paper are twofold:  
1. We determine the strength of the relationship between the trainable parameters of a DNN architecture and objectives such as model performance (i.e., accuracy) and inference cost (related to model size). 2. We provide a method of analysis to determine the degree of quantification that is required in each layer to achieve the desired trade-off between both objectives. The rest of the paper is organized as follows. Section 2 introduces some terminology and background related to quantization in DNN. Section 3 presents the related work on quantized and non-quantized DNN for AMR. Section 4 shows the experimental methodology used to determine the relationship between the parameters quantified in the DL model and the optimization objectives to trade-off. Section 5 presents the experimental analysis of the effect of inputs, activation, and quantified weights on the performance and size of a well-known DL model (VGG10 1D-CNN) that has been used for AMR using the methodology proposed in the previous section. In addition, we present the related algorithms that support the methodology and can find model architectures that trade-off the optimization objectives. Finally, Section 6 concludes the paper and outlines future work in this research.