esa-sceva/satcom-chunk-collection huggingface dataset:
Hence, we can save communication rounds. **Our contribution. ** In this paper, we first design two novel protocols, i.e., HeReLU and HeRefresh, to address the drawbacks of existing LHE-based SNNI frameworks. Then, we leverage HeReLU and HeRefresh to build a new framework for SNNI called HeFUN. The idea of HeFUN is to use HeReLU to evaluate the ReLU activation function and use HeRefresh to refresh intermediate neural network layers. The benefits of our proposed method are twofold. Firstly, compared to current HE-based frameworks, our approach significantly accelerates inference time while achieving superior accuracy. Secondly, compared to current 2 PC-based frameworks, our methodology reduces communication rounds and safeguards circuit privacy for service providers. We highlight the superiority of our approach compared to current approaches in Section 2. A comparison summary is shown in Table 1. The contribution of this paper is as follows:  
* We proposed a novel iLHE-based protocol, HeReLU, that can exactly evaluate the ReLU function _solely_ based on HE, which achieves better communication rounds than current HE-MPC hybrid frameworks. The analysis is presented in Section 5.1. * We proposed a novel iLHE-based protocol, HeRefresh, that refreshes noise in ciphertexts to enable arbitrary depth of the circuit. HeRefresh also reduces HE parameters' size, hence significantly reducing computation time. Considering the ciphertextrefreshing purpose, HeRefresh outperforms bootstrapping 300\(\times\) in relation to computation time (we detail this in Section 5.2). Furthermore, our protocol deviates from [PERSON]'s bootstrapping technique, so we bypass the circular security requirement. * We build a new iLHE-based framework for SNNI, named HeFUN, which uses HeReLU to evaluate the ReLU activation function and use HeRefresh to refresh intermediate neural network layers. * We provide security proofs for the proposed protocols. All proposed protocols, i.e., HeReLU and HeRefresh, are proven to be secure in the semi-honest model using the simulation paradigm [21]. Our framework, HeFUN, is created by composing sequential protocols. The security proof of the HeFUN framework is provided based on modular sequential composition [22]. * Experiments show that HeFUN outperform previous HE-based SNNI frameworks. Particularly, we achieve higher accuracy and better inference time. The rest of the paper is organized as follows. Section 2 reviews current SNNI frameworks, Section 3 provides the necessary cryptographic background, Section 4 presents our proposed approach, Section 5 presents our implementation and evaluation results, Section 6 provides some discussions and potential future works, and Section 7 concludes our work.
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
###### Abstract  
Quadrature phase shift keying (QPSK) is a digital modulation technique that transmits data at a constant frequency whilst varying the phases of the carrier signal. QPSK is one of the fundamental modulation schemes for orthogonal frequency division multiplexing systems (OFDM). It is a stable modulation technique with good spectral efficiency. However, during transmission, the carrier signal can undergo numerous phase changes. This creates phase ambiguity problems at the receiver end. This results in inter-symbol interference (ISI) and a high bit error rate (BER). In this paper, the wind-driven optimization was incorporated into the genetic algorithm (GA) as its population selection function. This hybrid algorithm was used to determine the phase assignments for the QPSK. The developed QPSK was implemented on an OFDM network and the message signal was recovered at more than 92% accuracy in a noisy Rayleigh fading channel and 100% accuracy in a noiseless channel. The enhancements greatly mitigated phase ambiguity and bit errors.  
bit error rate; carrier offset drift; genetic algorithm; orthogonal frequency division multiplexing; phase ambiguity; quadrature phase-shift keying; wind-driven optimization 2022  
222226174[[https://doi.org/10.3390/s22166174](https://doi.org/10.3390/s22166174)]([https://doi.org/10.3390/s22166174](https://doi.org/10.3390/s22166174))
## 3 Proposed Fusion Algorithm
### ISMV-CNMF Algorithm via AO  
To reconstruct the high-spatial-resolution HSIs by calculating \(\mathbf{A}\) and \(\mathbf{S}\), the JSMV-CNMF algorithm is proposed via AO, as shown in Algorithm 1. The JSMV-CNMF iteratively updates the following two convex subproblems until convergence,  
\[\mathbf{S}^{k+1}\in\arg\min_{\mathbf{S}\succeq\mathbf{0}_{M\times k }}\ \frac{1}{2}\ \mathbf{C}(\mathbf{A}^{k},\mathbf{S})+\lambda_{h}\phi_{h}(\mathbf{S})+ \lambda_{s}\phi_{\mathrm{s}}(\mathbf{S}), \tag{11}\] \[\mathbf{A}^{k+1}\in\arg\min_{\mathbf{A}\succeq\mathbf{0}_{M \times N}}\frac{1}{2}\ \mathbf{C}(\mathbf{A},\mathbf{S}^{k})+\lambda_{a}\phi_{a}(\mathbf{A})+ \lambda_{e}\phi_{e}(\mathbf{A}), \tag{12}\]  
where \(k\) is the iteration number of the outer loop. Owing to the iterative order of \(\mathbf{S}\) prior to \(\mathbf{A}\), an initial value of \(\mathbf{A}^{0}\) can be determined by successive projection algorithm [43; 44]. To solve these subproblems (11) and (12), the alternating direction method of multipliers (ADMM) [45] is utilized to design an efficient solver in the following subsections.  
```
1:Input:\(\mathbf{Y}_{h}\), \(\mathbf{Y}_{m}\), \(\mathbf{F}\) and \(\mathbf{G}\).
2:Output\(\mathbf{Z}=\mathbf{A}^{\mathrm{s}}\mathbf{S}^{k}\).
3:Initialize\(\mathbf{A}^{0}\).
4:\(k=0\).
5:while the stopping rule of the outer loop is not met do
6: update \(\mathbf{S}^{k+1}\) by (11);
7: update \(\mathbf{A}^{k+1}\) by (12);
8:\(k:=k+1\);
9:endwhile.
```  
**Algorithm 1** JSMV-CNMF algorithm for solving (6).
# Risk-Sensitive Rear-Wheel Steering Control Method Based on the Risk Potential Field
## 4 Experimental Study Using a Driving Simulator
### Results  
Herein, the results of the experiment with a frequent driver (subject S1) and an occasional driver (subject S6) are presented as representative examples. Figure 14a,b illustrates the time histories of the front steering angle, rear steering angle, yaw rate, and sideslip angle when subject S1 drove three times at 60 km/h and 80 km/h, respectively, under each condition. Figure 15a,b shows the trajectories of subject S1 driving at 60 km/h and 80 km/h, respectively. Figure 16a,b depicts the Lissajous diagrams of the steering wheel angle and yaw rate when subject S1 drove at 60 km/h and 80 km/h, respectively, using the data obtained during the third drive in each condition. Similarly, the respective experimental results of subject S6 are illustrated in Figures 17-19.  
We observed that the application of the risk potential field 4 WS actively steered the rear wheels in order to make the vehicle follow the reference yaw rate. Additionally, the trajectory was equivalent to that of the reference driver. In the case of the 2 WS and zero-sideslip angle 4 WS, the yaw rate and trajectory exhibited considerable variations. However, in the case of the risk potential field 4 WS, nearly no variation was observed in the results of the three experiments for the yaw rate and trajectory. The Lissajous diagrams indicate that the amount of corrective steering was reduced because smaller and fewer whirls around the origin were generated by the proposed method. Furthermore, the comparison of vehicle trajectories in Figure 18a,b indicates that the difference from the reference driver's trajectory was larger than that observed in the proposed method. Moreover, the variation was higher when the vehicle velocity was 80 km/h than when it was 60 km/h. Therefore, the proposed rear-wheel steering control system provided a more significant control effect at higher velocity regions.  
Figure 13: Overview of the experimental equipment.  
Figure 14: Comparison of the vehicle behavior for subject S1 at (**a**) \(V=60\) km/h and (**b**) \(V=80\) km/h.  
Figure 15: Comparison of the vehicle trajectory for subject S1 at (**a**) \(V=60\) km/h and (**b**) \(V=80\) km/h.  
Figure 16: Comparison of Lissajous diagrams of the steering angle and yaw rate for subject S1 using the third trial data in each condition at (**a**) \(V\) = 60 km/h and (**b**) \(V\) = 80 km/h.  
Figure 17: Comparison of the vehicle behavior for subject S6 at (**a**) \(V\) = 60 km/h and (**b**) \(V\) = 80 km/h.
# Portable Prototype of Hydrogen Fuel Cells for Educational Training
## 4 Experimental Setup
### Seventth Experiment: Faraday's First Law (Consumption)  
Before describing the fuel cell consumption experiment, learners should be familiar with its operation principle and components (Figure 12). Figure 12, fuel cell stack, shows that the cell consists of five parts. On the far left and right are the bipolar plate, then two electrodes, the anode and the cathode, and in the middle, there is a membrane. From the left is the entry of hydrogen (H\({}_{2}\)), while oxygen (O\({}_{2}\)) comes from the right. When the hydrogen molecules pass to the anode, their bond is broken because the electrons start moving through it. Now the hydrogen molecules become positively charged ions. On the other hand, oxygen arrives at the cathode and waits for the electrons that have left the hydrogen. The directed movement of electrons from the anode to the cathode represents an electric current. When electrons reach oxygen, their atoms absorb electrons, becoming negatively charged ions. Therefore, hydrogen ions can pass through the membrane and combine with oxygen ions to form water (H\({}_{2}\)O). The ions cancel each other out because the electrons lost by the hydrogen get to the oxygen, and when these ions come into contact, they cancel each other out. It follows from this that the main product is electricity. Electricity and the byproducts are ordinary water and heat [51]. The experiment aims to measure hydrogen consumption dependent on time and electrolysis current. The hydrogen tank is filled to 60 mL in the first part. Furthermore, switch off the electrolyzer and set the resistance so that a current of 600 mA flows through the circuit. Then the results are recorded, and Table 4 is obtained. The results show that the consumption increases with time, as does the gas that is leaked.  
Figure 11: Consumed H\({}_{2}\).  
In the experiment's second part, the tank was filled with 60 mL of hydrogen after the electrolyzer was turned off. Then, the current passing through the resistor was measured with an ammeter. The resistance is set so that the current is of a certain amount, according to the table. Each measurement lasted 2 min, and the results are recorded in Table 5.  
Table 5 shows that consumption is proportional to the amount of current. However, the gas leaked is always the same because it depends on time, and from the previous experiment, a gas loss constant of 0.8 mL/min was determined, which must be taken into account.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 4 Discussion  
### Influence of Alternating Temperature on the Calcium Carbonate Content  
The process of T1 increased the rate of calcium carbonate production in the initial reaction period, the reaction duration was relatively shorter, and the final calcium carbonate content was the lowest. In contrast, the process of T2 decreased the rate of calcium carbonate production in the initial reaction period but allowed for a longer reaction duration, and the final calcium carbonate content was the highest. The process of T3 produces results that are between those of T1 and T2.  
The increase in temperature increased the urease activity, which also accelerated the rate of calcium carbonate production [31,32]. Similar observations were found in [PERSON] et al., where the urease activity in sand consolidation by EICP gradually increased with increasing temperature (from 25 to 45 \({}^{\circ}\)C) [33]. This is also evidenced by the result of calcium carbonate production (T2 \(>\) T3 \(>\) T1) at 12 h, as shown in Figure 5. However, the increase in urease activity was limited. Urease is a protein, and its activity will decay over time [34, 35, 36]. [PERSON] et al. found that when crude soybean extract samples were exposed to laboratory air (temperature 25 \({}^{\circ}\)C and relative humidity 40%), urease was largely inactivated after 72 h and showed a decrease in urease activity within the first few hours [26]. Based on the measurement of the urease activity of crude extracts from  
Figure 9: SEM images of the cemented aeolian sand specimens. (**a,b**) The connectivity of the sand particles treated by EICP; (**c,d**) spherical material on the surface of sand particles and the pores between the sand particles; and (**e,f**) calcium carbonate was generated around sand particles.  
watermelon seeds at various temperatures (from 25 to 70 \({}^{\circ}\)C), [PERSON] et al. claimed that the urease activity decreased with time and that the decay rate was more pronounced at high temperatures [12]. Similar observations regarding the effect of time on urease activity can also be found in [PERSON] et al. [37]. Therefore, the survival duration of urease will also directly affect the final calcium carbonate production.  
Only the calcium carbonate content of T2 maintained a significant increasing trend after 60 h, while that of T1 and T3 remained almost constant. The calcium carbonate precipitation rate reached approximately 75% at 60 h; the reactants were not yet completely consumed, but the reaction stopped. This indicates that the urease in the process of T1 and T3 was mostly inactivated, while the urease in T2 maintained a certain activity after 60 h, and the reaction continued until 72 h, which eventually produced the highest calcium carbonate content.  
The process of T2 corresponds to the process from sunset to sunrise in natural desert environments. From the above results, it is recommended the construction step of mixing the cementing solution into the aeolian sand be scheduled at sunset when using the EICP and mixing methods for curing aeolian sand.
## 5 Conclusions
### Future Work  
For more complicated networks in which many sensor nodes and network switches exist, the network delay will increase, causing the synchronization performance to be degraded, and the delay may exceed the sampling time. Thus, future work will include the evaluation of the limited numbers of network sensor nodes and switches. Subsequently, we will develop a high-speed sensor network system based on a hierarchical structure for achieving a large-scale sensor network system with real-time and data-acquisition synchronization to solve this problem.  
Figure 9: Experimental results with various sensors at 1 kHz: (**a**) shows overall result and (**b**) shows enlarged result.  
For concrete applications described in Section 1.5, our proposed sensor network system with high sampling rate based on the accurate simultaneous synchronization of clock and data acquisition will be applied to robot control with real-time sensory feedback, ITS using vision and other sensor networks, and security and surveillance based on the sensor network system.  
Moreover, we plan to extend our sensor network system to a wireless sensor network system using RT-WiFi [27; 28]. RT-WiFi technology was proposed for wireless cyber-physical control systems, and the effectiveness of RT-WiFi technology was also confirmed. Because high-speed data communication in wireless network systems can be performed with RT-WiFi technology, we consider that the proposed method (clock synchronization and data-acquisition synchronization) can be applied to RT-WiFi technology.  
All authors made substantial contributions to this research. [PERSON] proposed the research concept and the basic idea, and wrote the paper; [PERSON] implemented the proposed method for evaluating the effectiveness and performed the experiments; [PERSON] constructed the basic sensor network system; [PERSON] and [PERSON] developed the high-speed vision system and the high-speed tactile sensor. All authors participated in the critical discussions of the experiments, edited the paper, and gave the final approval of the paper.  
This research was supported in part by the JST ACCEL JPMJAC1601.  
The authors declare no conflict of interest.
# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 6 Evaluation
### SIR Infection Diffusion in Population  
To show the spread of a disease that follows SIR model, a simulation was run over a population network of 100,000 agents. The disease transmissibility was set at 0.036. At the start of the simulation, 500 people were randomly marked as infected. The simulations are run without additional interventions and have an attack rate (total number of people infected in the simulation) of about 70%. Figure 6 shows that as the simulation progresses through steps, more people get infected, reducing the number of susceptible persons. The epidemic peaks around the 40 th iteration and then starts falling as more people enter the recovered state. During the course of a simulation, 70% of people got infected. The reason for the high number of total infections is the higher transmissibility of disease and longer times in the infectious state (set at 11 simulation days).  
It would be interesting to compare the numbers from SIM-D against the actual spread of infections. However, unfortunately, daily stats about disease spread during the season (Nov-Feb) are not available for Peshawar. A rough comparison can be done against the number of flu influenza cases reported in Khyber Pakhtunkhwa (KP) [44; 45] as Peshawar is part of KP, therefore naturally possessing similar demographics. SIM-D was able to report comparative numbers (70,000 in comparison to 55,000 actual cases). One major reason for the difference is that the cases are often not reported and hence do not get recorded.
# A Compact Rectifier Design Method Utilizing Harmonics
## 2 Design and Simulation
### Simulation
When the length of the stub \(l_{10}\) varies between 3\(\mathrm{\SIUnitSymbolMicro g}\)/20-\(\mathrm{\SIUnitSymbolMicro g}\)/5(5-7 mm), the amplitude of the 2 nd harmonic is higher. Since the propagation loss of the 3 rd harmonic is greater than that of the 2 nd one in free space, the 3 rd harmonic should be higher than the 2 nd one when utilizing both of them simultaneously. Therefore, the length of the stub \(l_{10}\) is selected as 2\(\mathrm{\SIUnitSymbolMicro g}\)/15(4.8 mm), and the input impedance of the DC-pass filter at the 2 nd harmonic is \(\mathrm{\SIUnitSymbolMicro g}\)\(\Omega\). In this case, the 2 nd and 3 rd harmonics amplitudes are \(-1.2\) dBm and \(1.3\) dBm respectively. Figure 5 analyzes the rectifying efficiency and harmonic amplitude of different rectifier at different input power. Figure 5a-c have different circuit layouts and the main difference between them is the input impedances of the DC-pass filter at harmonics. The layouts of them are all realized through \(\pi\)-type stub and the DC-pass filter composed of five open stubs, but their sizes are different to realize different harmonic utilize states. Figure 5d is the convention harmonic suppression rectifier, and the simulation results are described here for comparison with Figure 5a-c. Figure 5a shows the performances of the rectifier utilizing both the 2 nd and 3 rd harmonics. When the input power is 18 dBm, the 2 nd and 3 rd harmonic amplitudes are \(-1.2\) dBm and \(1.3\) dBm, respectively, and the rectifying efficiency is 72%. Figure 4: Simulated harmonic amplitude with the length of the stub \(l_{10}\) at 18 dBm input power. Figure 5b shows the simulated result of the rectifier utilizing the 2 nd harmonic. When the input power is 18 dBm, the 2 nd and 3 rd harmonic amplitudes are 7.4 dBm and \(-\)35 dBm, respectively, and the rectifying efficiency is 71.2%. The 3 rd harmonic is almost totally reflected back into the diode, and only the 2 nd harmonic is utilized. Figure 5c demonstrates the simulated result of the rectifier utilizing the 3 rd harmonic. The 2 nd and 3 rd harmonic amplitudes are \(-\)28.3 dBm and 7.3 dBm, respectively, and the rectifying efficiency is 72%. Figure 5d presents the simulated result of the harmonic suppression rectifier. The 2 nd and 3 rd harmonic amplitudes are \(-\)41 dBm and \(-\)31 dBm, respectively, and the highest rectifying efficiency is 77%. The rectifying efficiencies of these four rectifiers are all higher than 70%. The specific circuits can be selected by the demands of the practical applications. Since the amplitude of the fourth harmonic is much smaller than that of the 2 nd and 3 rd harmonic, the fourth harmonic is not discussed here. The input power of the rectifiers (18 dBm) can be applied for long-distance high-power microwave power transmission systems, such as drones. The proposed design method is still applicable when the diode is selected for medium and low power.
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
## 3 Measurement Results  
### Multi-Mode SAR ADC  
A 12 bit multi-mode SAR ADC is implemented in the 0.18 \(\upmu\)m standard CMOS process to demonstrate the proposed idea. The chip microphotograph is shown in Figure 14, in which the core circuits have an area of 1.219 mm \(\times\) 0.938 mm. Figure 15a,b show the output power spectrum  
Figure 12: The processing flow of the proposed self-testing platform.  
Figure 13: The self-testing platform architecture.  
before/after calibration under a 3 kHz and 1 V\({}_{\text{p-p}}\) input signal with a 125 kHz sampling rate. The third harmonic distortions without and with calibration are \(-65.53\) dB and \(-78.95\) dB, respectively. The SFDR here is 65.39 dB before calibration. The SFDR of 78.80 dB can be achieved after calibration. However, the improvement in SNDR and ENOB is not that significant because the SAR ADC performances are dominated by system noise and tones caused by supply noise and cross-talk, which should be overcome by careful layout.
## 2 Materials and Methods
### Regression Models and Genetic Algorithm
#### 2.1.2 Support Vector Machine  
[PERSON] and [PERSON] [74] developed the support vector machine (SVM) to solve classification tasks by defining the hyperplanes that best separate data points. In high-dimensional spaces, these hyperplanes act as decision boundaries; maximizing the margin between points ensures optimal separation. [PERSON] et al. [75] extended this concept to support vector regression (SVR), which approximates nonlinear functions via linear splines. SVM and SVR models generalize well, handle both linear and nonlinear problems effectively, and use kernel functions for complex transformations. However, their performance is limited when dealing with large datasets due to computational complexity.