esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Bench Introduction  
### Experimental Equipment  
To investigate the effect of solar radiation on the performance of the direct-expansion solar PVT heat pump system, the experimental platform was set up in Fuzhou City, China, and the research configuration contained 12 PVT panels and four standard PV panels, in addition to key equipment such as batteries, inverters, heat pump components, heating tanks and heat storage tanks, as shown in Figure 2.  
The experiment utilized T-type solar PVT panels manufactured by Zhongneng Electric Appliances Co., Ltd., Fuzhou, China (shown in Figure 3) and standard PV panels. Both were mounted on stainless-steel frames facing due south to optimize solar energy absorption efficiency. The parameters related to the PVT collectors are listed in Table 1.  
Figure 1: Direct-expansion PVT Heat Pump System.  
Figure 2: Schematic Diagram of the Experimental Setup.  
Compared to traditional PV panels (see Table 1 for relevant parameters), the system used in this study incorporates PVT panels with a T-type evaporator on the backside, cooled by the refrigerant R410a flowing through the backplate. All PVT panels are connected to the heat pump unit in parallel via copper pipes, with black insulation wrapped around the pipes to minimize heat loss.  
The heat pump unit used in the experiment is the KSD95 EPA dual heat source unit developed by Zhongneng Electrical Appliances Co., Ltd. (Wenzhou, China). It consists of components such as return pipe, suction pipe, compressor, discharge pipe, condenser, and expansion valve. These components are connected to the heating water tank via copper pipes to facilitate water circulation. The water circulation is powered by an A80/180 M  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline PV/T Panel Place of origin & Fuzhou City, China \\ PV/T Panel Brand name & SOLAR MODULE \\ PV/T Panel Model number & T-type model \\ PV/T Panel Material & single crystal silicon \\ PV/T Panel Size/mm & 1755 \(\times\) 1038 \\ PV/T Panels Peak power/pmax (W) & 360 \\ PV/T Panel Rated voltage (V) & 34 \\ PV/T Panel Rated current (A) & 10.6 \\ PV/T Panel Open circuit voltage (V) & 40.8 \\ PV/T Panel Short-circuit current (A) & 12.7 \\ PV Panel Place of origin & Fuzhou City, China \\ PV Panel Brand name & SOLAR MODULE \\ PV Panel Material & single crystal silicon \\ PV Panel Size/mm & 1755 \(\times\) 1038 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of PV/T Panels and PV Panels.  
Figure 3: Front View and Cross-Sectional Diagram of the PVT Components.  
type circulating water pump, located between the outlet of the heating water tank and the inlet of the heat pump unit.
## 3 Simulated Results and Analysis
### Effects of the Installing Position and the Square Patches Length  
In general, the effect of the installation position is varied for the position of the rectangular cavity \({}^{\prime}\!d_{y}\)' whereas other parameter values were kept constant. The following parameter values were constrained throughout the experiment: the square patch length \({}^{\prime}\!l_{p}\)', the rectangular slot length \({}^{\prime}\!l_{s}\)', the rectangular slot width \({}^{\prime}\!w_{s}\)', the cavity length '\(c_{x}\)', the cavity width '\(c_{y}\)', the cavity height '\(c_{z}\)', the exciting probe length '\(l_{f}\)', and the gap between parasitic patches '\(a\)' and '\(b\)'. Figure 3 shows six cases of the measured variation of the rectangular cavity positions '\(d_{y}\)', ranging from \(15.0\) to \(20.0\) mm versus the frequency (GHz).  
Figure 1: The configuration of the proposed antenna: (**a**) top view; (**b**) perspective view; (**c**) photograph of the fabricated antenna design.  
It could be seen from Figure 3 when the values of '\(d_{y}\)' shifted from the center of the slot along the y-axis, the return loss curve went under the \(-10\) dB line, and the minimal return loss appeared at \(d_{y}=18.0\) mm, as expected in Table 1.  
Similarly, the effect of the installation position and the effects of the square patch length varied according to the lengths of the square patch '\(l_{p}\)' whereas other parameter values were kept constant. The following parameter values were constrained throughout the experiment: the position of the rectangular cavity '\(d_{y}\)', the rectangular slot length '\(l_{s}\)', the rectangular slot width '\(w_{s}\)', the cavity length '\(c_{x}\)', the cavity width '\(c_{y}\)', the cavity height '\(c_{z}\)', the exciting probe length '\(l_{f}\)', and the gap between parasitic patches '\(a\)' and '\(b\)'. Figure 4 showed five cases of the measured variation of the lengths of the square patch from \(l_{p}=9.8\) to \(13.8\) mm versus the frequency (GHz). It could be seen from Figure 4 that the minimal  
Figure 3: Simulated return loss for different values of ‘\(d_{y}\)’.  
Figure 2: Simulated return loss and gain pattern of the proposed antenna with the parameters’ value in Table 1: (**a**) single patch; (**b**) \(5\times 5\) patches array.  
return loss appeared at \(l_{p}=11.8\) mm, as expected in Table 1. Nonetheless, when the square patch lengths '\(l_{p}\)' deviated from 11.8 mm, the return loss values were likely to increase.
# Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles
## 2 Modeling and Energy Consumption Analysis on an Electric Sanitation Vehicle
### The Systematic Modeling
#### 2.2.2 Modeling the Electric Motors and Battery Pack  
Since the dynamics of the electric motor were not what this study concerned, the demand power of the two motors and the battery can be calculated by  
\[P_{m}=\frac{T_{m}\cdot n_{m}}{9550} \tag{3}\]  
\[P_{bat}=\frac{P_{m}^{+}}{\eta_{dis}\eta_{m}}+P_{m}^{-}\eta_{cha}\eta_{m} \tag{4}\]  
where \(T_{m}\) represents the torque of the motor, Nm; \(n_{m}\) represents rotating speed, rpm; \(P_{m}\) and \(P_{bat}\) represent the demand power (kW) of the motor and the battery, respectively; being positive (\(P_{m}^{+}\)) means the motors consumed electricity from the battery, and being negative (\(P_{m}^{-}\)) means the motors regenerated electricity to store in the battery; \(\eta_{dis}\) and \(\eta_{cha}\) represents the discharging and charging efficiency of the battery, respectively; and \(\eta_{m}\) stands for the motor efficiency, which could be obtained by the look-up tables of the motors shown in Figure 7.  
Figure 7: Efficiency map of the two motors: (**a**) the efficiency map of the driving motor and (**b**) the efficiency map of the working motor.  
Some important characteristic curves of the battery, including the open-circuit voltage and discharging resistance, are shown in Figure 8. Considering the battery health protection, the characteristics of the battery should have changed steadily [24]. Hence, the battery SoC (state of charge) was set to vary from 0.3 to 0.8.  
Assuming that the effects of temperature on the battery are not involved, a simplified battery model is adopted with appropriate precision, which is described as [25]  
\[\frac{\text{d}SoC}{\text{d}t}=-\frac{V_{bat}-\sqrt{V_{bat}^{2}-4R_{bat}P_{bat}}}{2 R_{bat}Q_{bat}} \tag{5}\]  
where \(V_{bat}\), \(R_{bat}\), and \(Q_{bat}\) represent the open-circuit voltage (V), the internal resistance (\(\Omega\)), and the capacity of the battery (\(\text{Ah}\)), respectively. Specifically, the increment of the SoC signifies the variation of vehicle electricity consumption.
## 5 Conclusions  
In this study, the performances of different shielded technologies in reducing power line interference were comprehensively compared for effective biopotential acquisition. By investigating the performance of the proposed Active-Shield circuit with respect to ECG, EOG, and EMG signals acquired under normal laboratory settings, experimental results revealed that the proposed shielded drive method could effectively suppress power line interference including its harmonic components, and greatly improve the signal quality compared to conventional hardware-shielded and software filtering methods. The results also showed that the shielded drive circuit could significantly eliminate the influence of electrode lead jitter interference on the acquired signals, so it could be a great candidate to take care of the noises introduced by body movements of the subjects. In conclusion, the findings of the present study could be quite useful for providing a simple and effective solution to reduce power line interference and movement artifacts to obtain high-quality biopotential signals.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 1 Introduction
### Contributions  
The main contributions of this paper are as follows:  
* An efficient and energy-aware secure key management system is proposed, using two specialized nodes labeled Calculator key (CK) and Distribution key (DK);
* These two nodes, i.e., CK and DK, are responsible for the generation, verification, and distribution of secret keys;* The energy and trust factors of MANET nodes are utilized for the selection of these nodes, i.e., CK and DK;
* Extensive experiments are performed for the verification and validation of the proposed secure key management system.  
The remaining paper is organized as follows: Section 2 presents the proposed methodology. The selection of the Calculator Key (CK)- and the Distribution Key (DK)-based secure communication framework is presented in Section 3. Section 4 presents the simulation environment and performance metrics used for comparative analysis. Experimental results and a comparative analysis are presented in Section 5. Concluding remarks are discussed in Section 6.
## 3 Results and Discussion
### Single- and Multi-Block Varietal Classification
in some articles, multi-block analysis has already been applied for the discrimination of wines. In particular, in some works, low- or mid-level data-fusion (DF) approaches were exploited. An example is the work by [PERSON] et al. [18], where Argentine white wines were classified using low-, mid-, and high-level data-fusion approaches. All the classification outcomes were extremely satisfying, but the most accurate solutions were provided by mid- or high-level DF strategies. Variable Projection Indices (VIPs) can be computed to find the variables of each block that contribute the most to the varietal discrimination [39]; VIP \(>\) 1 is the criterion usually adopted to establish whether a predictor significantly contributes to the model or not. This kind of analysis identified tartaric and malic acid from the UHPLC data block, as well as Cu, Na, Ni, Sr, Ca, and Fe from the ICP-OES block as discriminant variables, whereas 3-methyl-1-butanol, 2-methyl-1-butanol, ethyl hexanoate, and 2-phenylethyl acetate were found to be the volatile components (GC-MS data block) relevant in the varietal discrimination of the three wines. The significant role of tartaric acid--which is the main organic acid in wines--in contributing to the chemical stability, color, and taste of the wine [40] is related to the differing acidity of _Passerina_, _Trebbiano_, and _Pecorino_ varieties, as highlighted by sensory analysis (Appendix A). On the other hand, the discriminant volatiles are associated with specific pleasant sensorial attributes--such as fruity (ethyl hexanoate) and floral (2-phenylethyl acetate)--or unpleasant odors, namely solvent or pungent notes (3-methyl-1-butanol) [41]. Nevertheless, the difference in the concentration of the above odor-active components did not result in distinct and systematic differences in the attributes evaluated by the panelists, which reveals the greater efficiency of the combined chemical analysis and chemometrics compared to sensory analysis in the varietal discrimination of the three wines here investigated. Concerning the discriminant ability of the major (Na and Ca) or minor (Cu, Ni, Sr, and Fe) mineral elements, a possible difference in the soil composition cannot be invoked as the reason for the observed effect because, as discussed before, the samples of the three wine varieties here-analyzed are not cultivated in specific and different areas and, regardless of the grape variety, the prevalent cultivation environment is the hilly zone between the Adriatic Sea and Apennine Mountains. Instead, as documented in the literature for other wine varieties [4, 7, 42], the different content of the above elements in the three wines can be attributed to a selective ability of the different grape plants to absorb the mineral elements from the cultivation soil, or a different maturity of the grapes subjected to the winemaking process.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 3 Study of the Receiver Criticalities and Workflow for the Upgrading Feasibility Study
### Logistical Changes  
The L-P receiver was installed on the primary focus positioner of SRT, as mentioned in the Introduction and shown in Figure 1. This optical position of the telescope represents a critical aspect when talking about receiver maintenance operations. In fact, it is possible to reach the primary focus of the telescope only using lifting platform, with all the attendant limitations (i.e., discomfort for disassembly of receiver components, use only in good weather conditions, etc.). For this reason, in this feasibility study, we consider maintaining, on the receiver block installed on the primary focus of SRT, only the essential components, such as the feed, the Dewar block with its cooling system, the linear-to-circular polarizer, the noise calibration unit and the electronic control system (see these blocks in Figure 3). The filter selector block will be transferred in a dedicated rack installed on the APEX room (see Figure 1). The APEX room is accessible thanks to a ladder installed on one of the four quadruped legs. In this way, in case of receiver malfunction, it is possible to act with maintenance operation analyzing all components of the filter selector block that work at ambient temperature, and only secondarily moving on to analyze the Dewar block with its components. Furthermore, it is possible to install new microwave filters easily, which  
Figure 12: (**a**) Block diagram of the Dewar L-P receiver; (**b**) Photo of the Dewar block.  
Figure 13: Results of the measurement campaign for surveying the signals generated by the Agilent 8761B switches.  
could be necessary to reject new possible RFIs or to experience new temporary observation modes. In this manner, basically, it becomes a more resilient and practical system.
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 4 Experiment and Result Analysis  
### Experimental Preparation  
We implemented the Rev-riscv processor using Verilog code. Building on this foundation, we developed the Rev-riscv-II processor platform with an optimized branch prediction module and added data dependency judgment. For running smaller-scale programs to save time, we used ModelSim SE-64 10.5 (Mentor Graphics, Wilsonville, OR, USA) for simulation. The EDA development tool Vivado HLS 2018.3 (Xilinx, San Jose, CA, USA) was utilized to verify and conduct practical testing of both the pre-optimized and optimized processor platforms, as well as to evaluate resource consumption before and after design optimization. Both processors were successfully run on the ARTIX-7 series FPGA (XC7A100T, EmbedFire, Dongguan, China). We used the officially provided RISC-V compilation tool Eclipse (Eclipse Foundation, Ottawa, ON, Canada) to generate assembly files corresponding to C-language test programs (in RISC-V assembly language) and then used the Opcode tool (Windows Power Shell, Microsoft, Redmond, WA, USA) to download the compiled files to the FPGA.  
Since the CoreMark benchmark program is a widely accepted industry standard for processor benchmarking, we use CoreMark to test the overall real-time performance of the processor. Additionally, because the division tests in CoreMark account for only a small proportion, while conditional branch prediction instructions have a normal presence, we have supplemented with a self-test case for division to evaluate the real-time performance of the processor when running high-density division instructions. (Branch prediction is sufficiently tested within the CoreMark tests, so no additional test cases were added specifically for this purpose).  
The distribution of this subsection is as follows:  
First, phased experiments are conducted to verify the optimization results of branch prediction and division data dependency.  
Then, the CoreMark test cases and division test cases are used to validate the overall improvement in the real-time performance of the processor.  
Finally, a summary and analysis of the real-time test results are provided.
###### Abstract  
In this paper, dual-branch pre-distorted enhanced asymmetrically clipped direct current (DC) biased optical orthogonal frequency division multiplexing (PEADO-OFDM) for underwater optical wireless communication (UOWC) is firstly proposed and simulated. The performances of PEADO-OFDM on the underwater optical channel model (UOCM) are analyzed and further compared with the typical ADO-OFDM. Using the Monte Carlo method for the modeling of UOCM, we adopt a double-gamma function to represent three different water qualities including clear, coastal and harbor waters. The full-duplex architecture enables the removal of Hermitian symmetry (HS) from conventional optical OFDM and can increase the spectral efficiency at the cost of hardware complexity. A new PEADO-OFDM transmitter is also proposed to reduce the complexity of the transmitter. The simulation results exhibit that our proposed dual-branch PEADO-OFDM scheme outperforms the typical ADO-OFDM scheme in spectral efficiency, bit error rate (BER) and stability over the underwater channels of three different water qualities.  
pre-distorted enhanced; underwater optical wireless communication (UOWC); ADO-OFDM; gamma-gamma function; full-duplex +
Footnote †: journal: _Photonics_  
**Clitton:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Dual-Branch Pre-Distorted Enhanced ADO-OFDM for Full-Duplex Underwater Optical Wireless Communication System.  
_Photonics_  
**2021**, \(8\), 368. [[https://doi.org/10.3390/photonics8090368](https://doi.org/10.3390/photonics8090368)]([https://doi.org/10.3390/photonics8090368](https://doi.org/10.3390/photonics8090368))
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 4 Analysis of Numerical and Experimental Results
### Modal Analysis  
Based on the Timoshenko beam theory and the transfer matrix method, the first three vertical vibrational elastic modes are numerically obtained; see Figure 11. Based on the conclusions of [35; 36], in the present research we do not give the experimental elastic vertical modes. A hammering test of the experimental model was carried out in calm water to validate the numerical result. The natural frequencies can be obtained by Fourier transform of the decayed time series of the transient structural response following the hammering impact. Table 4 presents the comparison of elastic vibrational wetted natural frequencies between calculation and measurement. The discrepancy between the experimental and numerical results for the wet natural frequency of the first order is 3.439%, which satisfied quite well the requirement of the principle of similarity. Although the errors for the second and third orders of wet natural frequencies are larger than 10%, they have little influence on the overall results, because the effect of the higher order deformation modes on the loads is rather small compared with the first order vibrational mode, which can be verified by Figure 12. In Figure 12, the vertical bending moments obtained based on different number of elastic vibrational modes are presented; it can be concluded that the first order deformation mode is most important, and the contributions from orders of elastic modes greater than three can be safely discarded. A similar conclusion for a linear case was also reported in [37].  
Figure 11: First three elastic modes.  
Figure 10: Retardation functions. (**a**) \(K_{33}\); (**b**) \(K_{55}\)
# Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
## 5 Conclusions  
We have proposed a method based on ANN for measuring the IV curves in a diluted square array of antidots on an Nb film at different applied fields and temperatures. Because of their exceptional approximation capability, ANNs have recently been recommended for the prediction of IV curves in superconducting films. Their increasing role in this field motivated us to present a thorough analysis of three different architectures--namely, feedforward, cascaded, and layer-recurrent networks--which were trained using six different learning algorithms. Each algorithm was executed for ten different configurations of number of neurons in the hidden layers, resulting in a total of sixty ANN models for each architecture. Our results, based on MATLAB simulations, suggested that feedforward networks trained with \(BR\) manage to achieve the lowest MSE, but take a lot of time to converge, while those converging faster (in terms of number of iterations and training time) yield larger MSEs.  
Since we pinpoint the pros and cons of various architectures with various possible configurations, our proposed framework may be used as a benchmark in all relevant works utilizing ANN in the prediction of IV curves. It is widely known that each geometry of arrays of antidots exhibits different current-voltage curves, leading to vastly varying critical currents and critical temperatures.  
Figure 8: Training time taken by each model to converge.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Network** & **No. of Neurons** & **Algo.** & **MSE** & **Epochs** & **Training Time (s)** \\ \hline Minimum MSE & Feedforward & [12 10] & BR & \(4.55\times 10^{-8}\) & 126 & 26.037 \\ Minimum Epochs & Cascaded & [5 2] & CGF & \(2.80\times 10^{-7}\) & 9 & 0.297 \\ Minimum Time & Feedforward & [8 4] & NR & \(2.27\times 10^{-6}\) & 17 & 0.109 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the best results.  
Figure 7: Number of iterations (epochs) taken by each model to converge.  
We sincerely believe it is prudent to study the effectiveness of our approach for each of those geometries, and this we have left as our prospective followup work.  
[PERSON] performed the transport measurements on the Physical Properties Measurement System. [PERSON] was responsible for the research work related to Artifical Neural Networks; he performed all the simulations. [PERSON] is an expert of algorithms and formal methods; in this work he was responsible to make a choice of the algorithms and architectures, and reviewed the simulation results. [PERSON] conceived the entire concept, analyzed the obtained data from the Physical Properties Measurement System, and did all the write-up.  
The authors declare no conflict of interest.
## Nomenclature  
\(\mathbf{X},\mathbf{X_{\mathrm{N}}}\): raw PMU measurement matrix, normalized PMU measurement matrix \(N\): number of PMU measurement samples \(\mathbf{H},\underline{h}\): hidden state matrix, hidden state vector \(T,\underline{t}\): number of PMU measurement time steps, single time step \(Q,P,K\): number of hidden dimensions, number of features, number of measurements \(\alpha,s\): attention weight and score value \(\underline{x}_{\mathrm{F}},\mathbf{X_{\mathrm{F}}}\): feature vector, feature matrix \(\underline{x}_{\mathrm{P}}\): class probability vector \(\underline{z}\): z-score vector \(\mu,\sigma\): sampled mean, sampled standard deviation \(y,y_{\mathrm{Loc}},y_{\mathrm{Type}}\): disturbance event, event location, event type \(\underline{\theta}_{\mathrm{F}},\underline{\theta}_{\mathrm{E}},\underline{ \theta}_{C}\): parameter for feature extraction, embedding, classification