esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
### Contribution  
* First design of quantum gates for PRESENT and GIFT symmetric key cryptography. As far as we know, we firstly implemented PRESENT and GIFT block ciphers as quantum circuits. We present a method of implementing PRESENT and GIFT block ciphers as quantum circuits for application to the oracle of the Grover search algorithm.
* Quantum circuits with optimized qubits for PRESENT and GIFT block ciphers. One of the most important factors when evaluating quantum circuits is optimizing the number of qubits. When designing quantum circuits, new qubits are allocated to temporal storage or new values. However, we use an on-the-fly approach to recycle the initially allocated qubits until the encryption is finished. By using efficient Sbox quantum implementation, we did not allocate qubits except for the initial key and plaintext.
* Quantum gates and circuit depth analysis for PRESENT and GIFT block ciphers. Proposed PRESENT and GIFT implementations are evaluated using the IBM ProjectQ framework, a quantum computer emulator ([[https://github.com/ProjectQ-Framework/ProjectQ](https://github.com/ProjectQ-Framework/ProjectQ)]([https://github.com/ProjectQ-Framework/ProjectQ](https://github.com/ProjectQ-Framework/ProjectQ)), accessed on 10 May 2021) [14]. IBM ProjectQ uses a variety of quantum compilers that allow us to simulate quantum computers or draw quantum circuits. Among them, the resource counter compiler, which is a quantum resource estimator, measures quantum resources by analyzing qubits, quantum gates, and circuit depth. Compared with quantum implementation results of other block ciphers, we implemented low-cost PRESENT and GIFT quantum circuits.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods
### Software
#### 3.4.2 System Initialization and Parameter Verification  
Whenever the system starts, it must start a variable with a zero value, represented in Figure 3 as \"X = 0\", which has the function of a counter.  
Next, before being able to proceed, it is necessary to check whether there have been any changes in the stimulation values.  
These values are the parameters that control the stimulation pulse--one is the pulse amplitude and the pulse duration. These values are stored in the database, which, in this case, was a _Google Sheet_, as shown in Figure 5A.  
Figure 4: Code 3—checking the temperature and humidity values.  
Figure 5: **(A)** Google sheet containing the stimulation parameters and **(B)** code 4—comms setup.  
For the ESP32 microcontroller to access these data, first it needs a Wi-Fi connection, since the data are stored in the cloud. For this, the ESP32 needs two pieces of information: the network name (SSID) and the password, Figure 5B.  
After being connected, it needs to access the _Google Sheet_ to obtain the information about the stimulation parameters. However, for this iteration to happen, and for the ESP32 to interact on a read-only basis with the _Google Sheet_, a _Google Script_ is needed. This has the function of specifying and automating, through functions, the task that is intended to be performed; of being programmed to work only for a specific _Google Sheet_, not accessible to anyone else; and operating in three distinct modes, \"called\" in the ESP32 code, in order to perform the desired tasks.  
Through this feature, the values contained in the _Google Sheet_ are collected and added to its variables, as is visible in the code present in Figure 6.
## 2 Double-Layer Reinforcement Learning Algorithm Design  
### Basic Concepts of RL  
Reinforcement learning (RL) is a machine learning method that learns the optimal policy through interaction with the environment to maximize cumulative rewards. The core concepts of RL include state, action, reward, policy, value function, and environment. The state represents the specific situation of the environment at a certain moment, the action is the behavior that the agent can take in a specific state, and the reward is the immediate score that the environment feeds back after the agent takes an action. The policy determines the rule of the action taken by the agent in each state, and the value function estimates the value of a certain state or state-action pair.  
The RL problem is usually modeled as a Markov Decision Process (MDP), which includes a state space \(S\), an action space \(A\), a state transition probability \(P\), a reward function \(R\), and a discount factor \(\gamma\). At each time step, the agent starts from state \(s\in S\) and selects action \(a\in A\); the environment transitions to a new state \(s^{\prime}\in S\) based on the state transition probability \(P(s^{\prime}|s,a)\) and provides a reward \(r=R(s,a)\). The objective of the MDP is to find the optimal policy \(\pi^{*}\) that maximizes the expected cumulative reward, expressed as follows:  
\[R_{t}=\sum_{t=0}^{\infty}\gamma^{t}r_{t} \tag{1}\]  
where \(\gamma\in[0,1]\) is the discount factor, and \(r_{t}\) is the reward at time step \(t\).  
The value iteration method is a common policy in RL. Q-learning is a model-free value iteration method that finds the optimal policy by iteratively updating the state-action value function \(Q(s,a)\). DQN approximates the Q-value function by using a deep neural network to solve the problem of traditional Q-learning in high-dimensional state spaces, but DQN has the problem of overestimation of Q-values. Double Q-learning reduces the overestimation of Q-values. It improves learning stability and efficiency by separating action selection and Q-value updates.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### 5G QPSK at 868 MHz Measurement Descriptions  
Figure 6 shows the configuration for 5G technology where a 5G NR signal was generated using a Wideband Radio Communication Tester (Rohde & Schwarz CMW500) and demodulated with a SA (Arnitsu MS2090A). After programming the NR signal in MATLAB (5G Toolbox), the ARB Toolbox R&S was used to convert the.mat file to a.wv file that carries baseband signal digital IQ data. The CMW500's ARB Generator (using the General Purpose Radio Frequency Generator Application) can generate a modulated RF signal from a waveform file. The generated signal has a subcarrier spacing of 15 kHz, 20 MHz bandwidth, QPSK modulation, 100 subframes, a normal cyclic prefix of 4.7 \(\upmu\)s duration and hence 14 OFDM symbols (66.7 \(\upmu\)s duration) per slot. Frequency sweep is performed, and for each frequency a power sweep is carried out; this process is automated through MATLAB's scripts using SCPI commands.  
Through the Arnitsu MS2090A license 0888 NR SA application is enabled, offering several values related to signal quality measurement. The center frequency, channel bandwidth and sub-carrier spacing of the new radio signal were selected for each measurement case. SCPI commands were used to automate the measurement process and for obtaining Synchronization Signal Block (SSB) data such as the Synchronization Signal-Reference Signal Received Quality (SS-RSRQ) and Synchronization Signal-Reference Signal Received Power (SS-RSRP). The data are sent in the JSON file type.
# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
## 5 Simulation Results  
The simulation of monitoring the status of the EOGS is carried out according to the actual operating conditions of the space station on orbit. During orbiting, there are usually three astronauts in the space station. The overall cabin capacity is 150 m\({}^{3}\), the electrolytic temperature in the electrolytic cell is 35 \({}^{\circ}\)C, the temperature in the cabin is 25 \({}^{\circ}\)C, the electrolytic current is 20 A, and the orbital situation of Tiangong 2 is 2 people and 30 days. We set the simulation test as 3 people and 30 days (720 h), which is in line with the actual situation of the long-term stay of the space station.  
As shown in Figure 6, the estimated value of the total pressure in the cabin can track the real value well, almost coincident with the real value. The enlarged picture shows the comparison between the real value and the estimated value of the total pressure in the cabin on the 20 th day of the system operation.  
As shown in Figure 7, the estimation error of UKF based cabin total pressure is shown. The estimation error of the total pressure in the cabin is tiny, and the amplitude of the positive and negative values does not change great. The estimation error does not exceed 2% of the true value, which not only shows the high accuracy of the model, but also shows that the accuracy of the UKF estimation is high. which not only shows the high accuracy of the model, but also shows the high accuracy of UKF estimation.  
Figure 8 shows that the comparison between the true value of the oxygen partial pressure in the cabin and the estimated value based on UKF. As seen from Figure 8, there are periodic increases in estimation errors over the time, but these increases take place during the working condition switching of electrolysis current (5 A to 10 A), therefore there is the larger error between the true value and the estimation value during the working stage of 10A electrolysis current. During the working stage at 5 A electrolysis current within 30 days, the estimation error is small and the estimated value of the oxygen partial pressure in the cabin can also track the real value well. In Figure 8, the enlarged picture  
Figure 6: Comparison of true value and estimated value of total pressure in cabin.  
Figure 7: Estimated error of total pressure in cabin.  
also shows the comparison between the real value and the estimated value of the oxygen partial pressure in the cabin on the 20 th day of the system operation.  
As shown in Figure 9, the estimation error of oxygen partial pressure in the cabin based on UKF. The estimation error of the oxygen partial pressure in the cabin has little change in the range of positive and negative values, and it will increase periodically, but the estimation error is less than 2%.
## 2 Theoretical Background and Hypotheses Development
### Stimulus (S): Social Robot (MOCCA)'s Features
#### 2.3.2 Perceived Agency--Morality (PAM) and Dependency (PAD)  
Agency refers to an entity's capacity for self-motivation and independence, demonstrated through mechanisms such as autonomy, functional influence, animacy, and volition [43; 44; 45]. It reflects the ability to act in ways driven by internal thoughts and feelings rather than solely by external environmental factors [46; 47]. With advancements in AI and improvements in algorithms, social robots capable of interacting with humans have emerged. Some of these robots can even behave in human-like ways to a certain extent.  
An entity perceived as lacking sufficient agency is often seen as having low levels of motivation or intent for wrongdoing, as is commonly assumed for non-anthropomorphized robots [48]. In the case of MOCCA, an anthropomorphized social robot perceived to possess high agency, users might believe that it can direct its own words and that some of its dialogue is driven by internal cognitive or affective states. However, in the context of social robots, agency is typically defined as the ability to perform self-directed behaviors autonomously [18], enabled by human-designed decision-making algorithms and adaptive programming. Behaviors, whether performed by humans or social robots, are generally evaluated similarly, with good actions regarded as good and bad actions as bad, regardless of the acting entity [49]. This suggests that individuals may attribute similar moral capacities to humans and social robots, evaluating their behaviors within a comparable ethical framework [50].  
Morality refers to the principles or rules that distinguish between right and wrong or good and bad behavior [51]. While humans are capable of creating and following their own moral commands, robots are designed to follow commands embedded in their programming [52]. Social robots can therefore exhibit morally appropriate behavior by simulating the actions dictated by those moral rules. Although MOCCA is perceived as having high agency, its actions remain fundamentally dependent on human-designed programming and external inputs. This dependency reflects the inherent limitations of social robots. Dependency refers to an entity's reliance on external sources or inputs to perform actions, make decisions, or achieve objectives [53]. In other words, while MOCCA can simulate autonomous behavior through adaptive learning and episodic memory, it remains constrained by a pre-programmed framework. It can generate conversations and perform tasks only within the boundaries predefined by its programming and data inputs.
## 3 Results
### ANFIS-GA Model  
The surface roughness of the machined Al6061 aluminum blocks has been predicted using the hybrid ANFIS-GA algorithm. A total of 23 and 7 data have been, respectively, employed to train and test the model. The swarm population size, the acceleration factors C1 and C2, and the configuration \"triangular MF shape--grid partition FIS generator--hybrid optimization method\" obtained in 3.2 and 3.3 have been adopted for the actual model training. The results generated by different ANFIS-GA configurations are reported in Table 8 as the RMSE value and R2.  
It emerged that the ANFIS-GA hybrid model trained using the size of population POP = 25, the mutation rate Mu = 0.2, and the cross-over percentage 0.4 generated a better RMSE of 0.01097 and the regression values R2 of 0.9939 for training and 0.8102 for testing. In Figure 19, the prediction error and the training behavior of the ANFIS-GA training model are plotted against the sample number.  
\begin{table}
\begin{tabular}{c c c c c c
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
###### Abstract  
The successful return of Chang'E-5 (CE5) samples urges the hot topic of the study of the Moon in geochemistry. The elemental data of the analyzed moon samples reported in the literature were collected to determine the elemental abundances in moon samples. Based on 2365 analytical records of moon samples from ten missions of Apollo, Luna, and CE5, elemental abundances of 11 major oxides including Cr\({}_{2}\)O\({}_{3}\), 50 trace elements including Ti, P, Mn, Cr, and 15 rare earth elements (REEs) including Y are derived based on statistical distributions of normal, log-normal, and additive log-ratio transformation, respectively. According to the value of 13.5% CaO content, moon samples are classified into two types, as low-Ca and high-Ca samples, whose elemental abundances are also calculated respectively based on the methods used in the total moon samples. With respect to the mid-ocean ridge basalt (MORB) of the Earth, moon samples (including the Moon, low-Ca, and high-Ca samples) are rich in Cr, REEs, Th, U, Pb, Zr, Hf, Cs, Ba, W, and Be and poor in Na, V, Cu, and Zn in terms of their concentrations, and are enriched in Cr and depleted in Na, K, Rb, P, V, Cu, Zn in spider diagrams. The CE5 sample is a low-Ca type of moon sample and is clearly rich in Ti, Fe, Mn, P, Sc, REEs, Th, U, Nb, Ta, Zr, Hf, Sr, Ba, W, and Be and poor in Mg, Al, Cr, and Ni in terms of their concentrations relative to the moon or the low-Ca samples. If compared with the moon sample, the CE5 sample is also clearly rich in K, REE, and P.  
lunar samples; CE5; low-Ca; high-Ca; spider diagram +
Footnote †: journal: Appl. Sci. **2023**, _13_, 360. [[https://doi.org/10.3390/app13010360](https://doi.org/10.3390/app13010360)]([https://doi.org/10.3390/app13010360](https://doi.org/10.3390/app13010360))
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 6 Conclusions  
In this paper, we proposed a novel GAN-based speech enhancement technique utilizing the progressive generator and multi-scale discriminator. In order to reflect the speech characteristic, we introduced a progressive generator which can progressively estimate the wide frequency range of the speech by incorporating an up-sampling layer. Furthermore, for accelerating and stabilizing the training, we proposed a multi-scale discriminator which consists of a number of sub-discriminators operating at different sampling rates.  
For performance evaluation of the proposed methods, we conducted a set of speech enhancement experiments using the VoiceBank-DEMAND dataset. From the results, it was shown that the proposed technique provides a more stable GAN training while showing consistent performance improvement on objective and subjective measures for speech enhancement. We also checked the semi-real-time feasibility by observing a small increment of RTF between the baseline generator and the progressive generator.  
As the proposed network mainly focused on the multi-resolution attribute of speech in the time domain, one possible future study is to expand the proposed network to utilize the multi-scale attribute of speech in the frequency domain. Since the progressive generator and multi-scale discriminator can also be applied to the GAN-based speech reconstruction  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **STOI** \\ \hline Noisy & 1.97 & 3.35 & 2.44 & 2.63 & 0.91 \\ SEGAN [24] & 2.16 & 3.48 & 2.68 & 2.67 & 0.93 \\ AECNN [26] & 2.59 & 3.82 & **3.30** & 3.20 & 0.94 \\ SERGAN [26] & 2.59 & 3.82 & 3.28 & 3.20 & 0.94 \\ CP-GAN [38] & 2.64 & 3.93 & 3.29 & 3.28 & 0.94 \\ \hline The progressive generator without adversarial training & 2.65 & 3.90 & **3.30** & 3.27 & 0.94 \\ The progressive generator with the multi-scale discriminator & **2.71** & **3.97** & 3.26 & **3.33** & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of results between different GAN-based speech enhancement Techniques. The best result is highlighted in bold type.  
Figure 6: Illustration of \(L_{1}(G_{n})\) as a function of training steps.  
models such as neural vocoder for speech synthesis and codec, we will study the effects of the proposed methods.  
Conceptualization, H.Y.K. and N.S.K.; methodology, H.Y.K. and J.W.Y.; software, H.Y.K. and J.W.Y.; validation, H.Y.K. and N.S.K.; formal analysis, H.Y.K.; investigation, H.Y.K. and S.J.C.; resources, H.Y.K. and N.S.K.; data curation, H.Y.K. and W.H.K.; writing--original draft preparation, H.Y.K.; writing--review and editing, J.W.Y., W.H.K., S.J.C., and N.S.K.; visualization, H.Y.K.; supervision, N.S.K.; project administration, N.S.K.; funding acquisition, N.S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFCIT1701-04.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison of Forecast Results
#### 3.3.1 Discussion on the Long-Term Forecasting Results  
We discuss the method of long-term prediction part in the decision-making framework with SVR model based on decomposition strategy that we proposed. Then compare the results with the SVR and SARIMA on the forecast set. Their performance is shown in Figures 4 and 5. Table 6 gives the demand analysis results of those models.  
According to the results, the proposed long-term forecasting method is similar to the traditional forecasting methods in the test set. However, the extrapolation error of the proposed method in the prediction set is obviously smaller. Additionally, the monthly error ratios of the two models show that the proposed DS-SVR model is lower, which can be explained theoretically. The DS-SVR model limits the effects of various factors on the market to each subinterval in the process of feature analysis, thus reducing over-fitting errors that may occur in the actual training process. Moreover, the two methods are well in line with the general trend of the construction machinery market in the long-term forecasting process.  
Then, the forecasting results are compared with the seasonal method of the ARIMA model, and the final effects of long-term prediction are verified. The test is conducted with the medium-sized case, which is more complicated than the other two types. Figures 6 and 7 show the experiment results.  
It can be seen that both DS-SVR and SARIMA can follow the changing trends of the market in long-term forecasting. However, the overall performance of the SARIMA model on the prediction set is biased to one side, while the DS-SVR model is closer to the actual situation. The prediction RMSE of the SARIMA model on the prediction set is 1515, and the prediction RMSE of the DS-SVR model is 1209. For the long-term prediction, the DS-SVR model results are significantly better than SARIMA. On the other hand, the SVR method based on multi-source data analysis is theoretically more reliable, which is reflected in the face of abrupt changes in upstream and downstream industries and the impact of national financial policies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Long-Term Forecast Model** & **RMSE of** & **RMSE of** & **MAE of** & **MAPE of** & **Average** \\  & **Test Set** & **Forecast Set** & **Forecast Set** & **Forecast Set** & **Error Rate** \\ \hline SVR Model of Long-term & 2672 & 8189 & 6345 & 38.29 & 50.61 \\ DS-SVR Model & 2606 & 4306 & 3246 & 19.36 & 26.61 \\ SVR for type Decomposition (Large) & 259 & 535 & 427 & 23.05 & 30.42 \\ SVR for type Decomposition (Medium) & 1107 & 1209 & 970 & 23.56 & 25.94 \\ SVR for type Decomposition (Small) & 1769 & 3223 & 2418 & 23.95 & 33.02 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Forecast results of long-term forecast algorithm.  
Figure 5: A comparison of long-term prediction errors between the proposed DS-SVR model and the SVR model.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 3 Binary Quadratic Nets  
This section introduces a new Petri net class called _binary quadratic nets_ to represent the Ising and QUBO models. The Ising model is a mathematical model in statistical mechanics. The model is a graph in which the vertices correspond to spins, and the edgesinteract between spins. Petri nets are also suitable for expressing Ising models because their fundamental components, places, and transitions can naturally represent the states of the spins and their interactions. A token in a place can show the corresponding spin state when we introduce colors \(\{-1,+1\}\) to tokens. For QUBO models, the color type of tokens becomes \(\{0,1\}\).  
In our Petri net modeling-based Ising model formulation, Petri net models representing target optimization problems were converted into the corresponding binary quadratic nets. In other words, our proposed method is a net transformation from problem-domain Petri nets into binary quadratic nets. Binary quadratic nets are entirely equivalent to Ising or QUBO models.
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
## 1 Introduction
In this paper, we propose an approach that employs a back propagation artificial neural network (BPNN), which is one of the most popular machine learning tools. The theory of BPNN is described in detail, and parameter optimization on atmospheric channels is proposed. In parameter optimization, BPNN is mainly used to predict, rather than search, the optimized parameter. We employ BPNN to CVQKD, which could adjust the modulation variance to an optimal value to ensure the system security and make the system performance optimal. The numerical results show that the proposed scheme is equipped to improve the secret key rate efficiently. The structure of the paper is listed next. In Section 2, we briefly introduce the transmission models under different intensities of turbulence and the security analysis--whereafter, the Monte Carlo method is employed to estimate transmittance distribution of the atmospheric turbulence channel. In Section 3, we propose the BPNN-based CVQKD scheme. Section 4 verifies the performance improvement and security analysis of the CVQKD system with numerical simulations. In the end, the conclusions are drawn in Section 5.