esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Mechanisms in Plasmon-Mediated Heterostructure  
### Formation of Plasmon-Mediated Heterostructure Based on Schottky Contact  
The plasmon-mediated heterostructure (PMH) is a kind of composite structure that is constructed with plasmonic metal (gold, silver, and copper) and a semiconductor (conventionally, TiO\({}_{2}\), ZnO, Fe\({}_{2}\)O\({}_{3}\) and WO\({}_{3}\), as well as new merged BiVO\({}_{4}\) and g-C\({}_{3}\)N\({}_{4}\)) in nanoscale [34; 35; 36]. In such a heterostructure, the distinctive feature is the formation of the Schokkty barrier at the interface, which is usually used for regulating the behaviors of HCs. We set an n-type semiconductor as an example and assume that the work function of metal is higher than that of the semiconductor, \(W_{\rm m}>W_{\rm s}\) (Figure 1a). After they come into contact, there is a tendency for the electrons of the semiconductor to flow into the metal due to the higher Fermi level of the semiconductor than the metal (Figure 1b). The flowing of electrons will not proceed until the formation of a new balance of the Fermi level, namely E\({}_{\rm F}\). In the band diagram, the band of the n-type-semiconductor will bend downwards, as seen in Figure 1c. At the moment, the space-charge region with no free electrons is formed and built-in the electric field, and points from the semiconductor to the metal. In this region, the strength of the electric field builds up a high resistance region so that the electrons must have enough energy to get across this region, or barriers in other words, which is defined as the Schottky barrier (\(\varphi_{B}\)) [37; 38].  
The height of the Schottky barrier determines the capacity of electrons to get across. It can be described as:  
\[\varphi_{B}=W_{\mathrm{m}}-\chi_{s} \tag{1}\]  
where \(\chi_{s}\) is the electron affinity of the semiconductor (equal to \(E_{0}-E_{\mathrm{CB}}\)), and it represents the semiconductor's ability of accumulating electrons. The height of the barrier can be adjusted in two ways: (1) choose the plasmonic metals with a relatively lower work function [39], such as Ag (4.5-4.7 eV), rather than Au (5.3-5.5 eV); and (2) a higher electron affinity of the semiconductor, which is TiO\({}_{2}\) in some works [40].
## 2 Related Work
Then, a pyramid CNN approach is proposed with different models trained for each identified consumer cluster. Finally, time horizon and the time granularity affect the accuracy of the energy predictions. The forecast horizon refers to the period for which the prediction is made, and the time granularity indicates the prediction step [2]. Most of the state-of-the-art solutions address the one-step-ahead energy prediction. Very few approaches in the literature address the multi-step energy prediction, such as the hourly day-ahead forecasting for different types of consumers or producers [42; 43]. They feature purer accuracy than the one-step energy forecasting. An ensemble learning framework is proposed in reference [44] for average day-ahead energy consumption forecasting, but only a single value is predicted for the next day. The solution was tested at the household level and obtained a MAPE between 11% and 16%. Different approaches for the prediction of hourly day-ahead photovoltaic energy generation are studied in reference [45]. Several learning models are compared with a second-order regressive method and an ensemble model combining all models which achieved the highest performance. In reference [46], a deep residual neural network-based solution is used for the prediction of electrical energy consumption of households. The model performance was evaluated by using a dataset containing energy-consumption data from several households in the same region with a time granularity of 1 min. The model predicts energy values for 24 h of the next day. In this context, a relevant problem is that the energy-prediction results are difficult to be compared because they use different or non-public datasets and the metrics used to quantify the performance of the system are different [47; 48]. When analyzing the above-presented state-of-the-art, we noticed that most of the literature is focused on one-step-ahead energy prediction and only several approaches to the day-ahead energy prediction for energy prosumers. Moreover, the data-driven deep-learning models focus on raw energy data. Few consider other types of features that could be related to improve the prediction accuracy. In this paper, we address the identified knowledge gap in the literature by proposing a hybrid deep-neural-network model for day-ahead energy prediction that ensembles classification with regression models. The classification model is implemented by using K-Means clustering and multilayer perceptron to predict the energy classes concerning the prosumer daily peaks, valleys, and baseline of each energy. The multilayer perceptron regression model is trained to fine-tune the energy-prediction results. We are not using only the raw monitored energy, but we derive several energy-based features useful in the prediction process, such as the prosumer energy profile peaks and valleys concerning the daily baseline, correlated with weather information. Moreover, the prediction model was tested not only on historical datasets, but it was integrated with two prosumers, and the real-time data streams of energy data were considered in the training and prediction process.
Electronics_**2014**, \(3\), 419-443; doi:10.3390/electronics3030419  
_Keywords_  
**ISSN 2079-9292**  
www.mdpi.com/journal/electronics  
Multicarrier Spread Spectrum Modulation Schemes and Efficient FFT Algorithms for Cognitive Radio Systems  
**[PERSON] * and [PERSON]**  
Electronics and Communication Engineering Department, PSG College of Technology,  
Peelamedu, Coimbatore-641 004, India; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +91-422-242-4265; Fax: +91-422-257-3833.  
_Received: 17 May 2014; in revised form: 2 July 2014 / Accepted: 4 July 2014 / Published: 17 July 2014_  
**Abstract:** Spread spectrum (SS) and multicarrier modulation (MCM) techniques are recognized as potential candidates for the design of underlay and interweave cognitive radio (CR) systems, respectively. Direct Sequence Code Division Multiple Access (DS-CDMA) is a spread spectrum technique generally used in underlay CR systems. Orthogonal Frequency Division Multiplexing (OFDM) is the basic MCM technique, primarily used in interweave CR systems. There are other MCM schemes derived from OFDM technique, like Non-Contiguous OFDM, Spread OFDM, and OFDM-OQAM, which are more suitable for CR systems. Multicarrier Spread Spectrum Modulation (MCSSM) schemes like MC-CDMA, MC-DS-CDMA and SS-MC-CDMA, combine DS-CDMA and OFDM techniques in order to improve the CR system performance and adaptability. This article gives a detailed survey of the various spread spectrum and multicarrier modulation schemes proposed in the literature. Fast Fourier Transform (FFT) plays a vital role in all the multicarrier modulation techniques. The FFT part of the modern can be used for spectrum sensing. The performance of the FFT operator plays a crucial role in the overall performance of the system. Since the cognitive radio is an adaptive system, the FFT operator must also be adaptive for various input/output values, in order to save energy and time taken for execution. This article also includes the various efficient FFT algorithms proposed in the literature, which are suitable for CR systems.  
**Keywords:** spread spectrum; multicarrier modulation; underlay cognitive radio; interweave cognitive radio; fast fourier transform
## 4 Formulating the Expression of the Total Number of Transmissions and Receptions
### The Formulation of \(N_{\textbf{Ttot}}\) and \(N_{\textbf{R}tot}\)  
**Theorem 1**.: _Assume that all \(N\) sensor nodes are deployed randomly and uniformly in a distributed WSNs with a boundary length of \(1\), and each node has a transmission range of \(r_{t}\). If we gather data based on CS-DDSG scheme, then \(N_{\textbf{Ttot}}\) and \(N_{\textbf{R}tot}\) are, respectively, expressed as follows:_  
\[N_{\textbf{Ttot}}=N_{s}+\sum_{q=1}^{N_{f}}N_{t}^{q}, \tag{61}\]  
\[\begin{array}{l}N_{\textbf{R}tot}=N_{s}N\pi r_{t}^{2}-C_{N_{s}}^{2}\pi^{2}Nr_ {t}^{4}+\sum_{q=1}^{N_{f}}N_{t}^{q}N\frac{3\sqrt{3}}{4}r_{t}^{2}-\sum_{q=1}^{N _{f}}\frac{C_{N_{t}^{q-1}}^{2}NN_{t}^{q}}{6}\Big{(}\frac{7\pi}{8}-\frac{3\sqrt {3}}{8}\Big{)}\\ -\sum_{q=1}^{N_{f}}\frac{C_{N_{t}^{q-1}}^{2}4r_{t}^{2}NN_{t}^{q}}{\pi}\Big{(} \frac{13}{24}-\frac{\sqrt{3}}{2\pi}\Big{)}\Big{(}\frac{\pi}{4}-\frac{3\sqrt{3} }{16}\Big{)}-\sum_{q=1}^{N_{f}}\frac{N_{t}^{q-1}\pi Nr_{t}^{q}\big{(}N_{t}^{q -1}\big{)}}{6}\Big{(}\frac{7\pi}{8}-\frac{3\sqrt{3}}{8}\Big{)}\\ -\sum_{q=1}^{N_{f}}4N_{t}^{q}N_{t}^{q-1}N\pi r_{t}^{4}\Big{(}N_{t}^{q}-1\Big{)} \Big{(}\frac{13}{24}-\frac{\sqrt{3}}{2\pi}\Big{)}\Big{(}\frac{\pi}{4}-\frac{3 \sqrt{3}}{16}\Big{)},\end{array} \tag{62}\]  
_where \(N_{r}^{0}=N_{r}^{P},N_{r}^{0}=N_{s}\), \(N_{r}^{q}=N_{r}^{q-1}\times p_{1}\) and \(N_{f}=q^{*}-1\), where \(q^{*}\) satisfies \(N_{t}^{q^{*}}=N_{r}^{q^{*}-1}\times p_{1}\leq 0\). The expression for \(N_{r}^{P}\) is given in Equation (34)._  
**Proof.** As presented in the above derivation, we can obviously obtain Equation (61) based on Equation (21) and the correlative description in Stage 2 of CS-DDSG. Furthermore, by combining Equations (21), (34) and (60), we can obtain the expression of Equation (62).
# Dosimetric Evaluation in Micro-CT Studies Used in Preclinical Molecular Imaging
## 3 Results  
### Analytical Results  
Figure 4 shows the analytically attenuated X-ray fluence spectra (at 35 and 45 kV) obtained with [PERSON]'s parameterization method. Both spectra show the attenuation peaks of the Boone's \(L\)-lines (at 8.4, 9.4, and 11.4 keV) by 0.5 mm Al.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **45 kV X-ray Beam** & \\ \hline
**Mouse ID** & **Modality** & **Current (mA)** \\ \hline
1 & _Standard_ & 0.2 \\
2 & _Good_ & 0.2 \\
3 & _Best_ & 0.2 \\
4 & _Standard_ & 0.4 \\
5 & _HR_ & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MicroCT image settings used for the experimental IDS measurements in mice.  
Figure 4: X-ray fluence spectra calculated with the polynomial interpolation coefficients reported by [PERSON] et al. for tungsten anode. Both spectra were analytically attenuated by an added filtration of 0.5 mm Al. The \(L\)-lines at 8.4 and 9.4 keV appear as a single line because of the limited energy resolution.  
Figure 3: The TLD-100 dosimeters’ locations under the rodent’s skin. The image corresponds to the three-dimensional reconstruction of one of the mice imaged in the microCT unit at 45 kV, 0.2 mA, in the _Best_ modality.  
Table 2 shows the values for the total air kerma per mAs (\(\varGamma(E)\)) and the total air dose per mAs (\(\varGamma^{D}(E)\)) performances of the Apogee tube at isocenter (290 mm away from the source) at 35 and 45 kV.  
The air-to-water dose conversion factor (\(\tau\)) for X-rays at 35 and 45 kV is presented in Table 3. This value allows the analytical estimation of the radiation-imparted dose at the surface (IDS) of experimental animals during a computed tomographic study with specific parameters of acquisition (kV, mAs, and modality).  
For this analytical model, a mouse was theoretically simulated as a water cylinder, with a diameter of 24 mm (\(R=12\)), which results in \(\varGamma_{SID}=(1/(SID-R))^{2}=12.9\). The resultant IDS is presented in Table 4 (dose contributions due to backscattered radiation were not considered in these calculations).
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 3 Methodology  
By analyzing related work and the current research status of the automotive cybersecurity field, we concluded that the research field and the automotive parts market are eager to achieve technological and product breakthroughs related to DBC files. In the current research, the reverse results obtained with existing methods were far from the contents of DBC files, except when our team's bit-level reverse method using multiple linear regression models was employed. However, our previous work focused on the reversal of CAN traces with messages related to car driving behaviors. Although the results were very similar to the information contained in the DBC files, the body behaviors defined in the DBC files were still blank, for example, the information on lights, wipers, and doors.  
In order to fill the gaps in the previous work, this paper proposes an innovative technique to reverse body-related CAN messages based on labeled bits. This technique first tags and segments the CAN traces collected in different scenarios and filters out the body-related candidate messages based on tags related to the body behavior. After that, the valid messages corresponding to the automotive body behaviors are recorded using fuzzing and packet analysis techniques. Finally, the valid messages corresponding to each body behavior are executed AND OPERATION bit-by-bit to get the valid bits describing the automotive body behavior in CAN messages. The overall flow of the method described in this paper is shown in Figure 2.
## 2 The Proposed Schemes: DAE-EDMR and T-DAE-EDMR
### Method I: DAE-EDMR  
In the conventional approaches, the denoising function was set as \(\mathcal{H}\): \(\mathbb{R}_{+}^{N\times N}\mapsto\mathbb{R}_{+}^{N\times N}\), more specifically \(\mathcal{H}\): \(\tilde{\mathbf{D}}\mapsto\mathbf{D}\), such as in semi-definite relaxation and nonlocal patch tensor-based methods. These techniques require high computational complexity, as they perform iterative matrix multiplication operations while solving high-order optimization problems. In order to overcome this problem, NN-based techniques have recently been proposed. However, the dimension of the input and output data is large, which are the entire elements of the EDM. As a result, they exhibit low performance and require a large amount of training data and a large-sized neural network.  
Considering the above problems, we propose a new method, namely DAE-EDMR, to denoise the noisy EDM in an efficient way.
## 2 Renewable Energy Source Trends for Micro-Grids
### Integration of RES into Hybrid Micro-Grid Systems  
A massive rollout of DERs, together with their real integration in the micro-grid management, is an expected requirement in achieving a real impact on technology. The cost of energy will be a critical factor for driving investments in renewable and alternative energy technologies, and a stable investment climate will be required to exploit new hydrocarbon reserves and develop alternative and renewable energy resources.  
A successful hybrid micro-grid deployment depends on a fully integrated system. The integration of RES in hybrid micro-grid systems has become increasingly attractive, mainly because of the minimal operating and maintenance costs. The cost of PV systems and WTs has dramatically declined in the last few years. PV and WT systems also become more efficient due to advancing technologies and communication systems. The hybrid micro-grids combine the benefits of RES with the advantages of conventional power generation, delivering lower, long-term operating costs of ownership.  
Three models have been proposed in relation to the energy prosumers that are integrated into the micro-grids: Peer-to-peer, prosumer-to-grid, and prosumer community groups [36]. The first model supports the ability of electricity producers and consumers to directly buy and sell electricity, while the next two methods are close to the centralized grid model concepts and architecture.  
Each component of a hybrid micro-grid brings advantages that strengthen the entire system, but energy storage systems are a key enabler, contributing to power stability and energy time-shifting. The deployment of long-term ESS can have an important effect on the optimal operation of micro-grids. In addition to balancing the demand-supply when power shortage or surplus are encountered, ESS can be used to maintain dispatchable DG units operating at their maximum efficiency and can prevent or reduce the use of expensive energy sources during peak hours. More details on ESS will be presented in the next section.
# A Photolysis-Assist Molecular Communication for Tumor Biosensing
## 5 Conclusions  
In this paper, we propose a synchronization-assist MC for tumor biosensing by mitigating the ISI for improved BER and channel capacity. The information molecules are released when the symbol \"1\" is transmitted. When the symbol \"0\" is transmitted, the receiver emits light to degrade the redundant molecules from the previous symbols to reduce the ISI. Because the photolysis reaction happens when the symbol \"0\" is transmitted, and the symbol \"1\" is already received, the signal strength of symbol \"1\" will not reduce. We obtain the optimal symbol duration to achieve the maximum channel capacity. We validate the improved performance of the system by simulations. Future work will consider the biological information molecules dynamic in complex vessels and investigate the optimal symbol duration by utilizing the convex optimization to the channel capacity and BER.  
Figure 8: (**a**) Bit Error Ratio (BER) versus signal-to-noise ratio (SNR) with different distances. dB denotes the unit of SNR. (**b**) Std and Se denote standard deviations and standard error, respectively.  
Figure 7: (**a**) Bit Error ratio (BER) versus signal-to-noise ratio (SNR) with the different symbol interval. dB denotes the unit of SNR. (**b**) Std and Se denote standard deviations and standard error, respectively.  
**Author Contributions:** Conceptualization, Y.S.; methodology, Y.S., H.B. and Y.C.; validation, H.B.; formal analysis, Y.S. and H.B.; investigation, Y.S. and H.B.; writing--original draft preparation, Y.S. and H.B.; writing--review and editing, Y.S., H.B. and Y.C.; visualization, Y.S. and H.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (62171106).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The experimental data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Spatio-Temporal Big Data Collaborative Storage Mechanism
### Aggregation Proof Protocol
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## 4 Experiments and Analysis  
### Energy Harvester Design of the S-Type Generator Beam in Different Environment  
Low frequency vibration energy exists widely in various environments. Because the S-type generator beam proposed in this paper can be used in the design of energy harvesting devices in different environments, it has a broad application prospect. Thus, two typical operating environments, the up-and-down vibration excitation and the wind-induced vibration excitation, are presented in this paper as energy harvester designs for the application of S-beam.  
To drive the beam, the energy harvester is designed to provide suitable torque to the middle of the S-beam. As shown in Figure 4a, a \"U\"-shaped torsional beam is designed to be bonded in the middle of the generator beam in the up and down vibration excitation environment. The torsional beam absorbs the vibration excitation in the environment and converts it into the torque applied in the middle of the generator beam, driving the generator beam to move and generate electric energy. Thus, in addition to the design of supporting parts (the base and columns), the energy harvester also has a limit device in the middle of the beam to ensure that the beam moves in the desired way. The base of the \"S\"-type power generation beam is made of 301 stainless steel with a thickness of 0.1 mm. The two ends of the power generation beam are pasted with PVDF piezoelectric plates to convert motion energy into electric energy.  
In a windy environment, an appropriate bluff body can be utilized to accomplish the energy transfer from fluid to solid. It is known that, within a certain speed range, the wind passing through a cylinder forms alternated vortices shedding at the backside of the cylinder and then the cylinder is affected by VIV (vortex-induced vibration). This principle has been utilized in the design of the energy harvester, as shown in Figure 4b. A rotation bearing is rigidly connected at the midpoint of the S-shaped power beam and one end of the rigid rod with a cylindrical (aerodynamic) shape is connected to the bearing. As such, the wind passing through the energy harvester will turn the vortex-induced forces received by the cylindrical rods into the torsion received in the mid part of the generator beam, thereby driving the beam's motion. Thus, the wind energy will be converted into  
Figure 3: The stress distribution of the generator beam during movement (inter-well motion).  
a torque received in the middle of the generating beam due to the vortex-induced forces applied by the cylindrical element, driving the generator beam's motion.