esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Developing Optogenetic Control of PMI Localization  
As the function of PMI is dependent on its nuclear localization, we reasoned that, if we could control this latter with LINuS, we could effectively control endogenous p53 levels with blue light. To this aim, we cloned three constructs differing only in the LINuS variant used, as different NLSs photocaged in the J\(\alpha\) helix of the _As_LOV2 domain have different properties such as background in the dark or extent of nuclear accumulation after light induction [33] (Figure 2a). Albeit two of the three constructs showed statistically significant nuclear accumulation after light induction (Figure 2b,e), we were not satisfied with their performance, as the fold change in nuclear localization before and after light was less than two.  
Figure 2: **Engineering optogenetic control of PMI localization.** (**a,c**) Schematic representation of the PMI_LINuS and PMI_LEXY constructs. The NES is the PKIt NES. GGS is a flexible linker. (**b,d**) Representative images of HCT116 cells transiently transfected with the indicated constructs before and after 40 min of blue light illumination. (**b,d**) Scale bar, 20 \(\upmu\)m. (**e**) Bar plot showing the nuclear intensity of the indicated constructs in the dark or after illumination with blue light normalized to  
the values for the PMI_LINuS_1 construct in the dark (for the LINuS constructs) and to PMI_LEXY construct in the dark (for the LEXY construct). NS p \(\geq\) 0.05, *** p \(<\) 0.001, _t_-test. (**f**) Plot showing the nuclear intensity of the mCherry fluorescence measured in HCT116 cells transiently transfected with the PMI_LEXY construct shown in (**c**) in cells illuminated with blue light twice with a 40 min-long activation phase. Graph represents mean plus 95% confidence interval. N = 23 cells.  
Therefore, we turned to LEXY, which is similar to LINuS in that it is also based on _A_sLOV2, however, in this case, the PMI peptide would be nuclear unless light is applied to the cells. LEXY has proven to be easily applicable to various proteins of interest without the need of optimization [35; 43; 44]. PMI fused to mCherry and LEXY (PMI-NLS-mCherry-LEXY, for simplicity referred to as PMI_LEXY from now on) could be robustly accumulated into the cytoplasm after blue light illumination of HCT116 cells (Figure 2c-e), showing a fold change close to three. We further confirmed the reversibility of this accumulation (Figure 2f).
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 2 Materials and Methods
### Unit of AC Current Source
#### 2.1.1 Voltage Generator  
The AC voltage generator is built based on the AD9850 module, as shown in Figure 2b. The module is widely available in the commercial market with price around $12. The AD9850 is a highly integrated device that uses advanced DDS technology, digitally programmable frequency synthesizer, and clock generator function. It has capabilities to produce sine and square waves signals 1-volt peak to peak (\(V_{\text{PP}}\)) in amplitude and frequency range from DC to 40 MHz. The frequency tuning, control, and phase modulation words are loaded into the AD9850 via a parallel byte or serial loading format. The parallel load format consists of five iterative loads of an 8-bit control word, while serial loading is accomplished via a 40-bit serial data stream on a single pin [34].  
Figure 2: AD9850-based voltage generator module with frequency range of 10 Hz to 10 MHz.  
In this project, the AD9850 module is used to generate sine-wave voltage signals. The determining frequency value is controlled by software that installed on the microcontroller ATMega823 via serial loading format. Figure 2a shows pins connectivity between the AD9850 and the ATMega823. W_CLK is a clock signal for 40-bits serial data streaming, FQ_UD is frequency update signal for new data streaming, D7-Data is 5-bytes data streaming, and RESET is signal to reset the AD9850 to the original state. In here, simple op-amp circuit as shown in Figure 2c is perform a band pass filter (BPF), is used to conditioning the output of AD9850 in 'pure' sine wave signal within frequency range of 10 Hz to 10 MHz and amplitude of 0.5 volt.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 5 Conclusions  
This study presents a novel multi-party controlled semi-quantum dialogue protocol based on hyperentangled Bell states. Within the protocol, [PERSON] and [PERSON] first apply classical data compression techniques to reduce redundancy. Subsequently, they employ quantum state encoding methods to map the compressed information onto hyperentangled Bell states within the polarization-spatial mode DOF. The multi-party controllers implement hierarchical authorization, permitting communication between participants only when all controllers concur on authorization. The introduction of Hoffman compression encoding, especially for the device-constrained classical party, can greatly save the physical storage space of the participants. Security analysis demonstrates that the protocol is robust against both common external and internal attacks posed by dishonest participants or controllers.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Protocols** & **The Quantum State Used** & **c** & **q** & **\(b\)** & \(\eta\)** \\ \hline [PERSON] [21] & Bell States & \(2n\) & \(3n\) & \(3n\) & \(50\%\) \\ [PERSON] [36] & GHZ State & \(3n\) & \(5n\) & \(0\) & \(60\%\) \\ Pan [37] & Bell States & \(2n\) & \(4n\) & \(2n\) & \(33.3\%\) \\ [PERSON] [23] & Hyperentangled Bell States & \(2n\) & \(3n\) & \(n\) & \(50\%\) \\ Shi [38] & Bell States & \(2n\) & \(4n\) & \(n\) & \(50\%\) \\ MCSQD & Hyperentangled Bell States & \(4n\) & \(4n\) & \(2n\) & \(66.7\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Efficiency comparison.  
Compared to existing SQD protocols, the proposed MCSQD protocol exhibits significant advantages in terms of resource conservation and transmission efficiency. Although the generation of hyperentangled states presents some challenges, the protocol remains feasible with current technology, thereby offering promising prospects for practical applications.  
Methodology, M.-N.Z.; Software, Y.-H.F.; Validation, R.-G.Z. and Y.-H.F.; Formal analysis, M.-N.Z.; Investigation, M.-N.Z.; Resources, Y.-H.F.; Data curation, Y.-H.F.; Writing--original draft, M.-N.Z.; Writing--review & editing, R.-G.Z.; Visualization, M.-N.Z. and Y.-H.F.; Supervision, R.-G.Z.; Project administration, R.-G.Z.; Funding acquisition, R.-G.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant Nos. 62172268 and 62302289, and the Shanghai Science and Technology Project under Grant No. 23 YF1416200.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Convergence Check Phase-Field Scheme for Modelling of Brittle and Ductile Fractures
## 5 Numerical Examples
### High Cyclic Fatigue
Comparison of the displacement at the top pin for both values of the relative error parameter is presented in Figure 21.  
From Figure 21, it can be concluded that the increase in error parameter \(q\) and greater extrapolation of fatigue variable \(\overline{\psi}\) leads to faster development of the fatigue crack and earlier fatigue fracture. However, differences in the number of cycles until the final fracture are small and acceptable considering the contribution of computational time saving. For error parameter value \(q=5\), the fatigue fracture occurs after 195,700 loading cycles, while for \(q=25\), the final fracture occurs after 191,600 loading cycles. This shows a difference of 2% on the result. Importantly, the simulation for error parameter value \(q=5\) was finished in 23.4 days, while for \(q=25\), simulation was over in 6.5 days, which was 72% faster than the reference simulation. To gain an insight, for \(F=200\) N and \(q=25\), a fatigue fracture emerges after 765,800 loading cycles, which is computed in 21 days. Additionally, the referent simulation is calculated with slight cycle skipping. The complete cycle-by-cycle simulation requires even more computational time, which brings a reduced computational time by controlled extrapolation to an even greater extent. With further increase in the error parameter \(q\), shorter computational time can be achieved but at the expense of accuracy of the results. The propagation of the fatigue crack in several stages is displayed on the CT specimen in Figure 22.  
The crack path observable in Figure 22 is in accordance with the expectation. From Figure 22a it can be noticed that the fatigue crack initiates after \(\approx\)26,000 loading cycles, which is also the moment when number of allowed extrapolated cycles experiences significant collapse (Figure 20). The crack propagates slowly until approximately 140,000 loading cycles, when the crack propagation still stable but with a higher crack growth rate, which  
Figure 21: Comparison of the displacement amplitude of the top pin for two values of the relative error parameter \(q\). is demonstrated by Figure 22c,d. Then, after 195,000 loading cycles, unstable crack growth emerges, which is observable in Figure 22d. Based on the results of the example, it can be concluded that the CCPF scheme provides correct solutions for HCF problems, and application of the cycle-skipping methodology provides significantly shorter computational time compared to cycle-by-cycle simulations.
## 5 Future Directions
### Historical and Forward-Looking Context  
Figure 7 extends the evidence base backward and forward in time to address reviewer requests for historical grounding and justified projections. The timeline aggregates: canonical textbooks and early real-time architectures establishing PID/PLC servo control in the 1980s [142; 143; 144; 145]; adaptive and fuzzy control advances of the 1990s that broadened manipulator autonomy [146; 147; 148; 149]; foundational model predictive control (MPC) stability/optimality treatments and engineering texts that diffused MPC into multi-input industrial motion in the 2000s [150; 151; 152]; surveys and monographs capturing the rise of neural networks, deep learning, and reinforcement learning in robotics across the 2010s [153; 154; 155; 156]; and the current 2020s wave of digital twins, human-robot collaboration, and collaborative robot safety standards that are emblematic of Industry 4.0 [100; 112; 113; 137; 141].  
The 2023-2025 band highlights the emergence of 5G/URLLC non-public industrial networks, edge-deployed DRL, and the first quantum-inspired robotic controllers reported in the recent literature [133; 134; 135; 157]. Looking toward -2030, Industry 5.0 policy documents, quantum-industrial roadmaps, and simulation/edge-AI outlooks project hybrid AI + Edge + Quantum stacks supporting sustainable, human-centric production ecosystems [136; 138; 139; 140; 158; 159; 160].
## 3 Materials and Methods
### Measurement Tool and Statistical Analysis  
To address our research questions, the questionnaire consisted of two parts: demographic profile and construct items. To develop scales for measuring constructs, the measures used, adapted from past research, were modified to suit the context of IoT. Prior to the main study, to test the validity of our study instruments, a pre-test and a pilot study were conducted. The first was on 15 students from International  
Figure 1: Proposed Internet of Things acceptance model (IoTAM).  
Hellenic University (Greece) and the second on 10 participants from MTurk. Pre-test respondents were told to provide comments for the constructs, including the wording of the scales and the length of the survey instrument. Results of both tests show acceptable instrument reliability and validity of the measurements.  
The survey questionnaire comprised of both previously validated and created for the context items. Participants were asked to provide their demographic information and respond to 57 statements on the constructs of this study namely: \"User character\" (created for the context), dichotomic question placing themselves on the first or second user mode category; \"Cyber resilience\" (created for the context), 10 items; \"Social influence\" 8 items; \"Cognitive instrumentals\", 6 items; \"Trust\", 5 items; \"Long-term orientation\", 8 items; \"Flexibility\", 5 items; \"Perceived Usefulness\", 4 items; \"Perceived Ease Of Use\", 3 items; \"Attitude\", 3 items; \"Behavioral intention\", 5 items. Each statement, except \"User character\", was measured on a seven-point Likert scale with 1 meaning \"strongly disagree\" to 7 meaning \"strongly agree\". The items and their sources are listed in Appendix B and questionnaire is presented in Appendix C.  
Descriptive statistics, using IBM SPSS version 19, were made to summarize respondent characteristics and key study variables. Correlations between the key constructs were assessed using Pearson's correlation coefficients. All statistical structural equation modeling (SEM) analyses were conducted with maximum likelihood estimation routines available from IBM SPSS Amos 16. The criteria used to appraise the structural model were model fit indices, as well as the magnitude and direction of path estimates. The standardized path coefficients were assessed to examine the statistical significance and directions of path estimates that exist between the variables in the model. Fit indices used to evaluate the model fit included normed chi square--(\(\chi^{2}\)/\(df\)), goodness of fit (GFI), adjusted goodness of fit index (AGFI) and root mean square error of approximation (RMSEA).
## 4 The Results and Discussion of Experiment
### The Experiments on CEC2014 and CEC2020  
In this section, the CEC2014 tests are utilized to prove the performance of the MGTO. The results of the MGTO in complicated CEC2014 functions are compared with nine Metaheuristic Algorithms which are frequently quoted in the literature. The comparison results of CEC2014 benchmark functions are represented in Table 2. It can be seen from Table 2 that the MGTO provides the best results in 12 out of 30 cases for Avg and 15 out for 30 cases for Std. For other cases, it can be summarized as follows: SSA provides the best results in F24 and F25 for Avg; ROLGWO provides the best results in F6, F9 and F11 for Avg and F13 for Std; GTO provides the best results in F12 for Std; AOA provides the best results in F5 for Std; DSCA provides the best results in F6 for Std; and HSCAHS provides the best results in F9, F10, F11 and F16 for Std. In these cases, other algorithms are better than the MGTO, with a slightly different result. It can be proved that the MGTO has better efficiency and stability in solving global optimization problems. The MGTO can search the whole space consistently through subsequent iterations, and this can improve the convergence.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **MGTO** & **GTO**  
For CEC2020 benchmark functions, the functions (F1-F4) are as follows: translational rotation function, translational rotation schwefel function, translational rotation luminacek bi-rastrigin function and expansion rosenbrock's plus wiewangk function. F5-F7 functions are mixed functions. F8-F10 functions are composite functions. The results of CEC2020 calculated by MGTO, GTO, AOA, SSA, WOA, GWO, PSO, ROLGWO, DSCA and HACAHA are displayed in Table 3. According to Table 3, MGTO provides the best results in 6 out of 10 cases for Avg and 7 out for 10 cases for Std. For other cases, it can be summarized as follows: ROLGWO provides the best results in F3 for Avg, RSA provides the best results in F3 for Std and SSA provides the best results in F9 for Std. In function F4, all algorithms achieve the same Avg of 1.90E+03. Therefore, it can be concluded that the proposed algorithm can enhance the performance of GTO and solve CEC2020 functions better. Generally speaking, the MGTO has a better performance in solving optimization problems.
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 6 Discussion  
### On the Join Operation  
Unlike the leave and key refresh operations, the join operation is not conducive to the creation and installation of a new key, but it simply distributes the current one to the new group members. This allows us to make it computationally less burdensome than the other two operations.  
Before successfully completing, a leave/key refresh operation has to make certain that all intended inverters receive the new group key. Otherwise, inverters could end up using different group keys, the old and the new one, so partitioning the group into two logically disconnected subgroups. If during the execution of a leave/refresh operation, an inverter becomes temporarily unavailable (e.g., because of a reduced sun exposure), the execution is aborted. Later, GMS will inform KDS when the inverter recovers. Then, GMS triggers a new execution of the operation. Of course, more complex solutions could be conceived whereby the execution is not aborted and the unavailable inverter gets updated later upon recovery. For the sake of system simplicity, we chose not to pursue this way, as intermittent  
Figure 8: Memory usage of the prototype.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Cryptosystem** & \begin{tabular}{c} **RSA-Keys** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} **Security** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **Group** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Memory** \\ **Overhead** \\ **(VMRSS)** \\ \end{tabular} &
\begin{tabular}{c} **Inverter Main** \\ **Memory** \\ \end{tabular} & **Ratio** & **Efficiency** \\ \hline ECDHE-RSA- & \multirow{3}{*}{3072-bit} & \multirow{3}{*}{128-bit} & 50 & \multirow{3}{*}{5 MB} & \multirow{3}{*}{512 MB} & 5 MB/512 MB = 1\% & \multirow{3}{*}{99\%} \\ AEs128-GCM- & & & & & & & MB = 1\% \\ SHA256 & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Memory overhead: main results.  
availability events are infrequent and mostly brief (of course the KDS wisely avoids triggering a rekeying/refreshing operation in a cloudy day or when the dark is approaching).  
In contrast, the join operation does not require the execution to abort. Actually, as soon as a joining inverter has performed step 2a and 2b, it may join the group and start sending multicast messages using the current group key.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 4 Conclusions  
In this research article, the effectiveness of an improved compact mm-wave eight-port MIMO antenna in terms of gain, bandwidth, ECC and isolation was comprehensively investigated. The proposed eight-port ring-shaped antenna has compact dimensions of 70 \(\times\) 44 \(\times\) 0.8 mm\({}^{3}\), achieving a bandwidth of 18 GHz varying from 28 to 46 GHz. The antenna achieved a gain of 6.8 to 8.5 dBi and radiation efficiency of 91 to 97%. The compact size and good diversity show the effectiveness of the tested antenna. The simple circuitry, compact size, enhanced bandwidth, reduced mutual coupling and excellent diversity support that the proposed antenna is a potential candidate in mm-wave MIMO communication for 5G applications.  
Author Contributions: Conceptualization, M.K.K.; Methodology, M.K.K.; Software, M.K.K.; Validation, S.L. and M.I.K.; Investigation, S.L.; Resources, S.L.; Data curation, M.K.K. and M.I.K.; Writing--original draft, M.K.K.; Visualization, M.K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref \#** & **Size (mm)** & **Elements** & \begin{tabular}{c} **Bandwidth** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Mutual** \\ **Coupling (dB)** \\ \end{tabular} & \
\begin{tabular}{c} **Gain (dBi)** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency** \\ **(\%)** \\ \end{tabular} & **ECC** \\ \hline
[20] & 60 \(\times\) 120 & 8 & 1.4 & 13 & 3.2–5 & \(\ldots\) & \(\leq\)0.03 \\ \hline
[21] & 50 \(\times\) 50 & 8 & 8 & 15 & \(\ldots\) & \(\ldots\) & 0.5 \\ \hline
[22] & 150 \(\times\) 75 & 8 & 2.7 & \textgreater{}12.6 & \(\ldots\) & 38–83 & 0.31 \\ \hline
[25] & 35 \(\times\) 30 & 4 & 8.9 & 20 & 10.24 & \(\ldots\) & \textless{}0.0002 \\ \hline
[26] & 56 \(\times\) 56 & 4 & 5.19 & 20 & 1.8–3.2 & 80 & \textless{}0.13 \\ \hline
[27] & 60 \(\times\) 60 & 4 & 3.05 \& 2.41 & \textgreater{}30 & 8.14 \& 8.04 & 99.5 \& 70 & \textless{}0.0035 \\ \hline
[30] & 25 \(\times\) 15 & 4 & 4 & 17 & 7.8 & 95 & 0.0001 \\ \hline
[34] & 15 \(\times\) 25 & 2 & 3.89 & 30 & 5.83 & 88 & \textless{}0.005 \\ \hline
[35] & 30 \(\times\) 15 & 2 & 6.4 & 35.8 & 5.42 & 83 & \textless{}0.005 \\ \hline Proposed work & 44 \(\times\) 70 & 8 & 18 & 30 & 6.8–8.5 & 91–97 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the antenna.  
Figure 20: ECC and DG of the presented antenna.
## 2 GNSS/IMU Sensors Data Characteristics Analysis  
This study uses a dual-frequency smartphone Huawei Mate 30 with the Kirin 990 chip developed by Huawei Technologies Co., Ltd., (Shenzhen, China), as the main experimental object, and the integrated Hi1103 chip developed by Hisilicon corporation supports L1 and L5 dual bands of GPS, B1 band of BeiDou (BDS), G1 band of GLONASS, and E1 and E5a dual bands of Galileo. For comparison, the single-frequency smartphone Huawei Mate 10 with BCM47531 chip developed by Broadcom corporation, Analog Devices triaxial inertial sensor ADIS16405, and South geodetic receiver are employed in these experiments, as shown in Figure 1. The smartphone's Huawei Mate 30, Mate 10, triaxial inertial sensor ADIS16405, and South geodetic receiver are, respectively, fixed on the board, which can be fixed with support on the vehicle during the experiment. In addition, it should be noted that the distance between the two smartphones and the ADIS16405 is about 10 cm, while the distance between the Mate 30 and the geodetic receiver is about 15 cm.  
The experiments were conducted in the east campus of Sun Yat-sen University in Guangzhou City, Guangdong. Considering that the experimental areas include a playground and some narrow roads, the devices were fixed on a bicycle for testing. The first two experiments were conducted on the playground and sidewalk, the third experiment was conducted through the tunnel, and the final experiment is a long trajectory. The length of the dynamic trajectories is approximate 6 km, with an average velocity of around 3.5 m/s and a maximum velocity of around 5.2 m/s, and the whole trajectories are showed in Figure 2.  
The GNSS observation data of the Mate 30 and Mate 10 were collected by the Android app GEO++ RINEX Logger V2.1.6 with RINEX version 3.03, while the IMU data of the Mate 30 and Mate 10 were collected by the app named Ubi-Collector developed by our group. The gyros and accelerometers data of triaxial inertial sensor ADIS16405 were collected by the IMU Evaluation software. The coordinate value of the reference station was calculated on the international terrestrial reference frame (ITRF) 14 in 2022 with an accuracy of cm, and the position of the geodetic receiver with differential positioning was taken as the reference to compare in subsequent experiments.  
Figure 1: View of the experiment devices. Left: Huawei Mate 10, triaxial inertial sensor ADIS16405, Huawei Mate 30, and South geodetic receiver. Right: the reference station is placed in the open area on the roof.  
Figure 2: Dynamic trajectories of the whole experiments. The pictures named A1, A2, A3, and A4 show some realistic GNSS-degraded environments for each experiment, and the yellow box shows the location of the reference station. A denotes the area.