esa-sceva/satcom-chunk-collection huggingface dataset:
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work  
As previously mentioned, network flooding is widely applied in WSNs, but it is not energy-efficient enough. Increasing energy efficiency in WSNs is critical because of the limited power supply of nodes. Hence, providing reliable and energy-efficient flooding in WSNs is an important research topic in the research community [4; 16; 17]. Aiming at reliable flooding in low duty-cycle WSNs with unreliable wireless links, [PERSON] et al. propose a dynamic switching-based reliable flooding (DSRF) framework [16]. In DSRF, a flooding tree structure is dynamically adjusted based on the packet reception results to save energy and reduce delay, when encountering a transmission failure. Similarly, authors in [17] focus investigation on minimum-delay and energy-efficient flooding tree construction considering the duty-cycle operation and unreliable wireless links in WSNs. By formulating the problem as an undetermined-delay-constrained minimum spanning tree (UDC-MST) problem, they then design a distributed Minimum-Delay Energy-efficient flooding Tree (MDET) algorithm to construct an energy optimal tree with flooding delay bounding.  
However, network flooding still suffers from broadcast storms which causes a large number of redundant messages and packet loss. Topology control aims to increase energy efficiency of network flooding and to reduce broadcast storms [8]. In this section, we review several methods of topology control. Topology control restricts the topology of a network with the goal to increase the network's lifetime. The topology of a network is defined by its set of active nodes and active links between any two nodes. The optimal solutions to topology control methods are unfortunately NP-hard to find [8]. Hence, we also review approximation algorithms.
## 4 Experimental Results
Figure 5: Wavefront coding system, including spatial light modulator (SLM) with size 15.36 mm \(\times\) 8.64 mm and resolution 1920 \(\times\) 1080; beam splitter (BS); polarizer (P); observation object (Object); lens (Lens) with focal length 200 mm; and camera (Camera). Since the PSFs are consistent for different defocus amounts, the wavefront encoding system has the same degree of blurring for different defocus objects. This makes it possible to use the same PSF for image recovery in later stages. To demonstrate the DoF extension effect of the wavefront encoding system, a conventional imaging system is built to image objects with a large DoF for comparison. The conventional imaging results for a tilted scale are shown in Figure 7a, where the object is clearly imaged at the focus (0\(\sim\)1 mm), but the object is gradually blurred at the front defocus (shown in the green box) and the back defocus (shown in the red box). The magnified images of the front defocus and back defocus parts are shown in Figures 8 and 9, respectively, which show that the objects are gradually blurred or even indistinguishable as the defocus amount increases. The imaging results of the wavefront coding system modulated by the cubic phase mask are shown in Figure 7b. Although there is some blurring at the focus, the blurring degree at the front and back defocus is improved compared with the conventional imaging system, and the blurring degree is thought to be consistent at different positions. The PSF shown in Figure 6 is used as the convolution kernel, and the Wiener filter [16] is used as the decoding method. The decoded image is shown in Figure 7c. Like Figure 7a, we zoomed in to show the front defocus (shown in the green box) and back defocus (shown in the red box) of the intermediate image shown in Figure 7b and the decoded image shown in Figure 7c. Figure 8a-c show the image of the conventional optical system, the intermediate image, and the decoded image of the wavefront coding system at the front defocus, respectively. Figure 9a-c show the image of the conventional optical system, the intermediate image, and the decoded image of the wavefront coding system at the back defocus, respectively. The wavefront coding system distinguishes details that cannot be resolved by the conventional imaging optical imaging system at the front and back defocus. The experimental results indicate that the phase mask optimized by the improved particle swarm algorithm achieved the DoF extension in the wavefront coding system.
# Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities
## 2 Materials and Methods
### Materials  
The substrate is a polyethylene naphthalate (PEN) (Teonex(r) Q65H from Teijin DuPont Films Japan Limited, Tokyo, Japan) with a thickness of 125 \(\upmu\)m. No pre-treatment of the surface is needed before manufacturing. The antenna and layout have been made of silver ink (DGP 40 LT-15C from ANP USA, Inc., Pleasanton, CA, USA) with about 30% of silver nanoparticles and triethylene glycol monoethyl ether (TMGE) as the solvent.  
The bulk heterojunction has been obtained dissolving regioregular poly(3-hexylthiophene-2,5-diyl) (Rieke Metals Inc., Lincoln, NE, USA) and [6,6]-phenyl C61 btwyric acid methyl ester (PCBM) (Solenne B.V.) in 1,2-Dichlorobenzene (o-DCB,Sigma-Aldrich) with a 1% wt:1% wt ratio and stirred for 2 h at 60 \({}^{\circ}\)C. The poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS, CLEVIOS P VP CH 8000) solution was sprayed in a dilution of 1:3 in Isopropyl alcohol (Sigma-Aldrich). The highly conductive PEDOT:PSS utilized for the anodes was a mixture of PEDOT:PSS (CLEVIOS PH 1000) and Ethylene glycol. Finally, the polyethyleneimine (PEI) solution was obtained dissolving the polymer with 0.1% wt in ethanol. The non-printed elements have been glued to the tag with conductive resin H20E (Epoxy Technology, Inc., Billerica, MA, USA) and a 50 \(\upmu\)m-thick dry adhesive, AR Clear 8932 (Adhesives Research, Inc. Glen Rock, Pennsylvania, USA) for the chip assembly.  
The non-printed elements included in the tag are the silicon RFID chip (SL900A RFID chip from AMS AG, Unterpremstaetten, Austria), an SMD inductor of 47 nH to match the antenna and a capacitor of 2.2 \(\upmu\)F to optimize the read range, both from TE Connectivity, Ltd. (Schaffhausen, Switzerland).
## 3 Important Indicator Screening Based on RF Algorithm
### Random Forest Algorithm  
Random forest (RF) is an ensemble learning algorithm based on decision trees. It is known as \"a method representing the level of ensemble learning technology [36; 37]\". The al gorithm is simple, easy to implement, and has low computational overhead, demonstrating strong performance in numerous real-world tasks. The random forest (RF) algorithm has a wide range of applications in prediction problems and is often used for feature selection. In large-scale learning, feature selection can eliminate uncorrelated or weakly correlated input variables and create a subset of the most relevant input variables to reduce data noise, modeling time, and improve the prediction accuracy of the model [38]. Importance screening is calculated based on the sum of the increase in homogeneity caused by the impact factors. After fitting the data, the RF model will provide a measure of the importance of the variables in the data attribute column. In sklearn, the feature_importances parameter represents the feature importance of the random forest model. This parameter returns a numpy array object, which corresponds to the importance of the training features as determined by the RF model. In the feature importance array, the larger the value, the more important the attribute column is for prediction accuracy [39]. The basic principle can be summarized in three steps: random sampling, random feature selection, and aggregated voting. The specific steps of the RF algorithm are shown in Figure 4.  
1. Randomly select \(n\) samples from the sample set to form a new sample set.
2. A decision tree is created from the sample set obtained through sampling. At each node of the spanning tree, the following steps are taken: (a) Randomly select \(d\) feature attributes without repetition. (b) Use these features to divide the sample set and find the best feature for division.
3. Repeat steps 1-2 \(b\) times to establish \(b\) decision trees and form a random forest.
4. The trained random forest is used to predict the test samples by comprehensively considering the output of each tree and voting on the results.  
Among them, \"\(b\)\" learners are independent of each other, which enables the parallelization of decision methods.
## 2 Forward Equations
### Preliminaries
For example, applying this operator to \(\mathbf{F}_{u}^{+}\) yields  
Figure 1: Example of a layered elastic medium with (**a**) \(P\)-wave velocity \(c^{\varepsilon}\) (in m\(\cdot\)s\({}^{-1}\)), (**b**) \(S\)-wave velocity \(c^{\varepsilon}\) (in m\(\cdot\)s\({}^{-1}\)) and (**c**) density \(\rho\) (in kg\(\cdot\)m\({}^{-3}\)) as a function of depth \(z\) (in m). Above \(z_{u}\) = 0 m and below \(z_{l}\) = 1 m, the medium is homogeneous. The dashed magenta line indicates the focusing depth \(z_{l}\) = 0.5 m, where a virtual source is to be constructed. \[\mathcal{Z}\mathbf{F}_{U}^{+}=\mathcal{Z} \begin{pmatrix}F_{U,P}^{+}(p,z,\tau)&F_{U, PS}^{+}(p,z,\tau)\\ F_{U,\text{\tiny{US}}}^{+}(p,z,\tau)&F_{U,\text{\tiny{US}}}^{+}(p,z,\tau)\end{pmatrix} = \begin{pmatrix}F_{U,P}^{+}(p,-p,z,-\tau)&F_{U,PS}^{+}(-p,z,-\tau)\\ F_{U,\text{\tiny{US}}}^{+}(-p,z,-\tau)&F_{U,\text{\tiny{US}}}^{+}(-p,z,-\tau) \end{pmatrix} . \tag{4}\]  
Our objective is to retrieve the focusing functions and Green's functions from reflection and transmission data, to be acquired at \(z_{l}\) and \(z_{l}\). Let \(R_{U,P}\), \(R_{U,PS}\), \(R_{U,SP}\) and \(R_{U,SS}\) be the \(PP\)-, \(PS\)-, \(SP\)- and \(SS\)-reflection responses at \(z_{l}\) (for their definitions, see Appendix B.1). Based on these recordings, we can construct an operator \(\mathcal{R}_{U}\) that convolves a wavefield with the reflection response. When applied to \(\mathbf{F}_{U}^{+}\), this multidimensional convolution is defined as  
\[\mathcal{R}_{U}\mathbf{F}_{U}^{+}=\int_{0}^{\tau} \begin{pmatrix}R_{U,PP}(p, \tau-\tau^{\prime})&R_{U,PS}(p,\tau-\tau^{\prime})\\ R_{U,SP}(p,\tau-\tau^{\prime})&R_{U,SS}(p,\tau-\tau^{\prime})\end{pmatrix} \begin{pmatrix}F_{U,PP}^{+}(p,z_{l},\tau^{\prime})&F_{U,PS}^{+}(p,z_{l},\tau^{ \prime})\\ F_{U,SP}^{+}(p,z_{l},\tau^{\prime})&F_{U,SS}^{+}(p,z_{l},\tau^{\prime})\end{pmatrix}  \mathrm{d}\tau^{\prime}. \tag{5}\]  
Similar operators \(\mathcal{R}_{L}\), \(\mathcal{T}_{LU}\) and \(\mathcal{T}_{LU}\) can be constructed for convolution with the reflection response at \(z_{L}\), the transmission response from \(z_{l}\) to \(z_{l}\) and the transmission response from \(z_{l}\) to \(z_{u}\), respectively. Apart from \(\mathcal{R}_{U}\), \(\mathcal{R}_{L}\), \(\mathcal{T}_{LU}\), \(\mathcal{T}_{LU}\) and \(\mathcal{Z}\), we make use of two window operators that will be defined in the following section.
# An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression
## 5 Discussion of Results
The main purpose of developing a new encoding algorithm is to provide an alternative solution to the algorithms where long characters are represented by long symbols, such as Huffman encoding or arithmetic coding. The developed encoding algorithm uses all the advantages of the Huffman encoding algorithm. In addition, it offers solutions to address the gaps in the Huffman encoding and arithmetic coding algorithms. Thus, the proposed algorithm bridges the gap in the literature.
## 5 Conclusions  
In this paper, spectrum sharing between the GEO satellite system and the UD-LSC network is considered. To address the interference among different systems and different  
Figure 5: The sum of satisfaction performance comparison against different average traffic requests of LBCs.  
Figure 6: Provided capacity of the proposed MGBA-SPA scheme of each ground LBC.  
beams, by exploiting the dense topology of LEO satellites, we propose a multi-satellite cooperative beam association and power allocation scheme. A multiple LEO satellite to multiple ground beam cell association problem is first formulated, and a many-to-many match game-based beam association (MGBA) algorithm is proposed to obtain a stable matching. Then, with the matched beam association results, a multi-satellite power allocation optimization problem is formulated with non-convex interference constraints and a successive convex approximation (SCA)-based power allocation (SPA) algorithm is proposed to maximize the satisfaction of beams. Simulation results show that the proposed MGBA-SPA scheme outperforms the MGBA-UPA, MGBA-TPA, RBA-UPA, and RBA-TPA schemes.  
Conceptualization, G.C., W.W. and J.T.; software, J.T. and X.Z.; validation, J.T. and X.Z.; data curation, J.T., C.W. and X.Z.; writing--original draft preparation, J.T.; writing--review and editing, C.W. and J.T.; project administration and funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under the Grant No. 61801033, No. 62171052 and No. 61971054. This work was also supported by the BUPT Excellent Ph.D. Students Foundation, Grant/Award Number: CX2020215.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 6 Conclusions  
This paper successfully introduced a CNN-RNN-based ensemble model enhanced with XAI to predict key battery metrics using two different drone Li-ion batteries numbered 92 and 129 during drone flight. The ensemble model in Drone 92 showed the best performance with 0.00032 of MAE, 0.00067 of RMSE, and 0.98665 of R\({}^{2}\) scores. Similarly, in Drone 129, the best performance was with 0.00030 of MAE, 0.00044 of RMSE, and 0.98094 of R\({}^{2}\) performance metrics. The results have shown that a minimally lower error rate was achieved. The R\({}^{2}\) value of Drone 92 is higher in terms of explaining the data than Drone 129. However, it is seen that Drone 129 has lower error rates (MAE and RMSE). Moreover, the Partial Dependence Plots were interpreted with the decision tree algorithm, and the effect of the Current (A) value on the model estimations in both drone flights is quite good. When the current value is around \(-4\), the model performs the task more sensitively and shows more changes. The dataset can be trained better with more data. Additionally, better performance can be achieved by using other artificial intelligence techniques and algorithms. It is expected that this study aims to establish benchmarks for future research, fostering advancements in drone and battery technologies. It also aids in enhancing battery performance via continuous, data-driven monitoring, enhancing drone safety, and optimizing operations.  
Conceptualization, B.E. and S.O.; methodology, B.E. and S.O.; software, B.E.; validation, B.E., S.O., A.A., E.B. and S.S.; formal analysis, B.E., S.O., A.A., E.B. and S.S.; investigation, S.O., A.A. and E.B. resources, S.O., A.A. and E.B.; data curation, B.E.; writing--original draft preparation, B.E. and S.O.; writing--review and editing, E.B. and S.S.; visualization, B.E.; supervision, E.B. and S.S.; project administration, S.O., A.A. and E.B.; funding acquisition, E.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The research was conducted collaboratively by the Battery Research Group at Sivas University of Science and Technology and MOBILERS team at Sivas Cumhuriyet University. The authors declare no conflicts of interest.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 3 Structure Introduction and Ground Motion Input  
### Structure Introduction  
Three FE models of underground structures were employed with three kinds of joint distribution forms and were simulated using the fabricated station structure under construction or already built. The first fully prefabricated subway station, namely, Yuanjidian Station [28], Changchun city, Jilin province, China, is a single-arch double-layer horseshoe structure with a cover depth of 3.5 m, a total width of 20.5 m, and a total height of 17.45 m. The standard ring width of the station structure is 2 m and was assembled using seven precast components. The physical and mechanical parameters of the structures and soil are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Structural Component**} & **Strength** & **Elastic Modulus** & **Poisson’s** & **Density** \\  & **Grade** & **(GPa)** & **Ratio** & **(kg\(\cdot\)m\({}^{-3}\))** \\ \hline Prefabricated component & C50 & 34.5 & 0.25 & 2450 \\ Middle plate & C40 & 32.5 & 0.25 & 2450 \\ Middle column & C40 & 32.5 & 0.25 & 2450 \\ Steel & HRB400 & 210 & 0.30 & 7850 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical and mechanical parameters of the structures.  
Figure 2: Comparison of the displacement time history at point A and point B.  
Qingdao metro station, China, was optimized and adjusted based on the prefabricated scheme of Changchun metro, as shown in Figure 2(a). The mainlining structure was composed of five precast components, among which, the bottom plate was adjusted from three to one, as shown in Figure 2(b). The design of Longxing Station of Shenzhen Metro, China, made use of further optimization based on the assembled scheme of Qingdao metro. The two components of the vault were adjusted to one, and the single ring structure was assembled using only four large prefabricated components, as shown in Figure 2(c). The construction process [28] of Yuanjidian Station is shown in Figure 4. The sizes of the three models are not exactly the same. However, in order to focus on the impact of the connecting joints of the vault and the bottom plate on the seismic response of the fabricated station structure, the size of Changchun subway station was taken as the prototype to model Qingdao Subway Station and Shenzhen Subway Station. The fabricated station structure is composed of single ring structures assembled using reinforcement bundles. This study only focused on the lateral seismic response of the single-ring structure and did not consider the longitudinal seismic response of the station structure.
# Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO
## 2 Related Work
### Abbreviations Smart Grids Based on Traffic Management Techniques  
A global scheduling optimization problem was addressed in order to optimize power and minimize the total cost of charging electric vehicles during the day. However, the solution was not practical, so the authors formulated an alternative local scheduling optimization method. The findings demonstrated that the local scheduling method could achieve a performance that was comparable to that of the global method. Several algorithms have been proposed [10; 11; 12]. A queueing model with priority discipline was proposed, which allowed the network to effectively manage coordination between charging and discharging processes by favoring the discharge of electric vehicles during the peak period [13]. In addition, the model balanced the number of electric vehicles between charging stations to reduce their occupancy states and minimize the waiting time of electric vehicles. Two other algorithms for decentralized power generation and decentralized charging station management of D-EVSS were proposed to manage the interactions between EVs and D-EVSSs by maximizing driver satisfaction in terms of reduced waiting time and minimum load for D-EVSSs [10] as well, as described in ref. [11]. A proposed scheduling strategy in the form of a moving-window optimization scheme that considered load and price forecasts as well as power demand generally exhibited a faster convergence characteristic than the global optimization scheme. In this manner, a trustworthy assessment of the ideal low-cost charging strategy was accomplished. A previous study [12] presented a sequence of charging-related events using an approach based on the max-plus method, illustrating the platform with the electric vehicle as an autonomous entity and the charging station as the energy-supplying entity. Each task in the charging process had its occurrence date assessed using the created max-plus model. To estimate the distribution of electric vehicles and their direction to charging stations, they also developed a predictive charging strategy.  
Most current works rely on cellular or Wi-Fi technologies to collect real-time vehicle information. The real-time intelligent coordination system (STRCS), a new online coordination system for plug-in electric car charging, is presented in ref. [14]. A prediction unit that can estimate the future energy needs of PEVs and an optimization unit that offers effective charging coordination make up this system. The outcomes revealed STRTCS's strong performance in terms of its quick management of numerous parking lots. A solution to the energy-constrained vehicle routing issue using a sparse station network was presented in ref. [15]. This study's goal was to determine the routes and quantity of recharging needed so that the total time of cars on the road was as short as possible.