esa-sceva/satcom-chunk-collection huggingface dataset:
# SPCL: A Smart Access Control System That Supports Blockchain
## 5 Implementation and Evaluation
### Experiment II  
In Experiment II, the frequency of the mobile client Raspberry Pi was changed to 0.6 GHz, 0.7 GHz, 0.8 GHz, 0.9 GHz, 1.0 GHz, and 1.1 GHz, and then each protocol was tested in different elliptic curves. The average values are calculated after ten running times and calculation times. The experimental results on elliptic curve P-256 are shown in Figure 4 and show that SPCL calculation time is the shortest in different curves and different frequencies. SPCL has the shortest running time under all curves when the Raspberry Pi frequency is set to 0.8/1.2 GHz, 0.9/1.2 GHz, 1.0/1.2 GHz, and 1.1/1.2 GHz, respectively. Under all curves, the Raspberry Pi frequency is 0.7/1.2 GHz and 0.6/1.2 GHz, and the running time of SPCL is second only to PSLA.  
Figure 3: Comparison of average computational and running time under balanced computing power.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Schemes** & **Mobile Client (MC)** & **Smart Lock (SL)** & **Running Time** \\ \hline
2 PAKEP & \(3T_{SM}+T_{KDF}+6T_{H}+2T_{X}\) & \(3T_{SM}+T_{KDF}+4T_{H}+3T_{X}\) & \(6T_{SM}+2T_{KDF}+10T_{H}+5T_{X}\) \\ [32] & \(\approx 107.01\) ms & \(\approx 108.46\) ms & \(\approx 254.91\) ms \\ \hline LIakep & \(2T_{SM}+T_{KDF}+6T_{H}+2T_{X}\) & \(4T_{SM}+T_{KDF}+4T_{H}+3T_{X}\) & \(6T_{SM}+2T_{KDF}+10T_{H}+5T_{X}\) \\ [28] & \(\approx 73.88\) ms & \(\approx 141.07\) ms & \(\approx 240.38\) ms \\ \hline ESEAP & \(3T_{SM}+2T_{E/D}+12T_{H}+5T_{X}\) & \(3T_{SM}+2T_{E/D}+9T_{H}+4T_{X}\) & \(6T_{SM}+4T_{E/D}+21T_{H}+9T_{X}\) \\ [29] & \(\approx 110.49\) ms & \(\approx 103.47\) ms & \(\approx 229.08\) ms \\ \hline Xie & \(3T_{SM}+6T_{H}+2T_{X}\) & \(3T_{SM}+2T_{E/D}+5T_{H}+T_{X}\) & \(6T_{SM}+2T_{E/D}+11T_{H}+3T_{X}\) \\ [30] & \(\approx 97.71\) ms & \(\approx 101.13\) ms & \(\approx 219.84\) ms \\ \hline SM2 & \(2.5T_{SM}+T_{KDF}+2T_{H}\) & \(2.5T_{SM}+T_{KDF}+2T_{H}\) & \(5T_{SM}+2T_{KDF}+4T_{H}\) \\ [31] & \(\approx 82.80\) ms & \(\approx 84.28\) ms & \(\approx 192.84\) ms \\ \hline PSLA & \(1.5T_{SM}+T_{KDF}+5T_{H}\) & \(3.58M+T_{KDF}+5T_{H}\) & \(5 SM+2T_{KDF}+10T_{H}\) \\ [27] & \(\approx 51.03\) ms & \(\approx 116.61\) ms & \(\approx 185.58\) ms \\ \hline
**SPCL** & \(\mathbf{2T_{SM}+T_{LID}+2T_{H}}\) & \(\mathbf{2T_{SM}+T_{LID}+2T_{H}}\) & \(\mathbf{4T_{SM}+2T_{LID}+4T_{H}}\) \\  & \(\approx\mathbf{66.59}\) ms & \(\approx\mathbf{67.33}\) ms & \(\approx\mathbf{148.23}\) ms \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of computational costs and running time.
# Development of a Robotic Arm Based Hydrogel Additive Manufacturing System for In-Situ Printing
## 2 Robotic Arm Printing Platform
### Nozzle  
A double-light-source inkjet light-curing nozzle system is custom designed and consists of a micro-valve, two ultraviolet (UV) light sources (symmetrical arrangement) and a nozzle holder (as shown in Figure 1b,e). The micro-valve and two light sources are mounted onto the nozzle holder; the distance between nozzle and substrate is 15 mm (_lt_). Material is pressed into the nozzle from a nozzle tail pipe with a pressure of 0.05 MPa. The nozzle is 0.1 mm in diameter and 10 nanoliters of material can be ejected per drop.  
In Figure 1b, the wavelength of UV light is fixed at 365 nm, the focal length is 20 mm (_f_), the light spot diameter (_D_) is 6 mm and the incident angle is 45\({}^{\circ}\). The energy power of the double light source is 267 mW and the average energy density is 940 mW/cm\({}^{2}\).  
As shown in Figure 1c, the droplets are ejected from the nozzle at the frequency \(F\) to form a continuous line with the movement of the nozzle. The UV light spot is just below the nozzle to cure the ejected material. It is assumed that the time required for the ejected material to be cured completely is \(t\). The velocity of the nozzle (_v_) is denoted as Equation (1). Where \(D\) is the light spot diameter, \(d\) is the droplet diameter:  
\[v=\frac{D-d}{2t} \tag{1}\]  
Nozzle frequency (_F_) is related to the nozzle velocity (_v_) and the droplet spacing (\(\Delta x\)), as shown in Equation (2).  
\[F=\frac{v}{\Delta x} \tag{2}\]  
The droplet diameter (_d_), curing time (_t_), droplet spacing (\(\Delta x\)), line spacing (\(\Delta L\)) should be determined before printing.
## 3 Proposed Method
### 3D Skeleton Estimation  
Dissimilar from Microsoft Kinect, the 2D skeleton is estimated from the RGB image and then fused with corresponding depth information to form a 3D skeleton. This two-stage method is preferable for RGB-D cameras other than Microsoft Kinect which did not provide a toolbox for skeleton estimation, and, thus, it has wider applications. This was contributed by recent developments on deep-learning neural networks which make the estimation of 2D/3D human skeleton joints from complex backgrounds a reality [32].  
We use the OpenPose [25] tool, which is advantageous in speed performance and robustness to joint occlusions. OpenPose is not restricted to the detection of a full skeleton, but all visible or perhaps some occluded joints can be detected. However, only four joints (nose, neck, left shoulder, right shoulder) will be used for posture classification in the proposed system. Figure 3 illustrates for OpenPose (a) the complete skeleton model and (b) an example of 2D skeleton extracted for a sleeping person covered with blanket.  
Figure 3: (**a**) A complete skeleton model used by OpenPose which contains 18 joints. (**b**) An example of 2D skeleton extracted for sleeping person (with blanket) by using OpenPose.  
Figure 2: (**a**) The system setup; (**b**) the camera tilting angle.  
After aligning the RGB and depth images via parameters provided by the manufacturer, depth information for joints detected by OpenPose can be retrieved to form a 3D skeleton. To stably obtain the depths for the four joints (especially the left and right shoulders), trilateral filtering [33] is performed to the depth values near the detected joints. A trilateral filter consists of three Gaussian kernels as adaptive weights considering depth similarity, luminance similarity, and the distance between the neighboring pixels and the center pixel. It is used to smooth out the variations of depths in a 33 \(\times\) 33 window centered at the selected joints. In this way, we obtain the coordinates (\(u\), \(v\)) of the 2D joints and their corresponding depths \(z_{c}\), which is one kind of the feature input used in our proposed classifier and is called \"2D skeleton plus depth (\(u,v,z_{c}\))\".  
With the aid of a calibrated camera, we convert the \"2D skeleton plus depth\" into a \"3D skeleton\" via the aid of camera intrinsic parameters, i.e., each joint is represented as the \((x_{c},y_{c},z_{c})\) coordinates in the camera coordinate system with a known \((u,v,z_{c})\).
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 2 Methodology
### LSPP Model Design  
#### 2.2.1 Basic Structure  
The LSPP model is based on the Informer algorithm, and the model framework is shown in Figure 2, which consists of three modules: data preparation, encoding-decoding, and performance prediction. Firstly, the time series data of shield tunnelling are subjected to preprocessing and feature extraction, and the data are position-encoded and input into the encoder to obtain the time series data features. Then, the decoder outputs the predicted time series feature representation in the form of multidimensional vectors based on the feature representation of the historical sequence given by the encoder and a part of the target sequence. Finally, the prediction module converts the feature vector from the decoder output into the final predicted values of the model.  
Figure 1: Informer model topology.
## 2 Methodology
### Mission Profile  
A mission profile was created with the purpose of observing the performance of the electrical propulsion system in the created UAV model. To achieve this, two mission profiles were simulated. The first one (named Mission Profile 1) was only for cruise performance at 3000 m (9842 ft), and the second one had ascending and descending phases of flight. For the second one (named Mission Profile 2), the altitude was 3000 m (9842 ft) to 3500 m (11,482 ft) and was then shifted to 1500 m (4921 ft). Altitude selection was triggered by time. Yaw and roll movements were not simulated in this study. The cruise speed was set to 50 m/s based on the UAV's specifications. Both mission profiles ended when the SoC reached 20%. It should be noted that only one battery was used to simulate related mission profiles.
## 6 Simulation and Comparison
### Performance of the RC IR-HARQ Transmission Scheme
Furthermore, as the average number of transmissions is less than the RCP scheme, the encoder and decoder pairs are also less in the sender and receiver, which is important in practical wireless communication systems. Figure 6: The comparison of the average number of transmissions of 100 data blocks in our RC IR-HARQ scheme and the RCP scheme in Case 1 with different initial channel capacity \(\boldsymbol{I^{(1)}(W)}\): (a) \(\boldsymbol{I^{(1)}(W)=0.3}\); (b) \(\boldsymbol{I^{(1)}(W)=0.4}\); (c) \(\boldsymbol{I^{(1)}(W)=0.6}\); (d) \(\boldsymbol{I^{(1)}(W)=0.7}\).
## 5 Conclusions  
In this paper, a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems is proposed, which can achieve a higher target range and velocity estimation accuracy and lower communication BER under the same spectral efficiency. According to the combination of MIMO-OFDM and IM technology, the waveform design based on the MIMO-OFDM-IM integrated system is realized. For radar, after conventional processing and information compensation, a segmented coherent accumulation method based on sparse signal reconstruction is used to realize parameter estimation. The numerical results show that the proposed method can achieve a better PSLR performance and higher accuracy of range and velocity estimation. For communication, we use the subcarriers corresponding to the OFDM symbols of each subblock as a benchmark; the information samples are detected iteratively through an antenna index; and the information symbols are obtained through a series of processes such as weight updating, screening, and sorting. The simulation experiment proved that the proposed scheme has a high information transmission efficiency and good communication information detection performance. In the future, sn MIMO-OFDM waveform design based on adaptive, robust, and PAPR reduction will be our research focus. The optimized waveform design can further solve the problems of radar-communication performance trade-off and algorithm practicability.  
[PERSON] and [PERSON]. ([PERSON]) developed the theory and signal model. [PERSON] and [PERSON]. ([PERSON]) performed and analyzed the numerical simulations. [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] wrote and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Nature Science Foundation of China under grant 61771367.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to restrictions, e.g., privacy or ethics.  
The authors declare no conflicts of interest.  
Figure 11: The variation in radar–communication trade-off curve with the number of activated subcarriers under different SNRs (\(N_{r}=N_{t}=4\)). (**a**) SNR = 6 dB; (**b**) SNR = 12 dB.
## 4 Performance Analysis and Discussion
### Reliability of Delivery  
The reliability of delivery falls with the increase in operating time due to the energy commitments to data processing, remaining sensor energy and hop count. The reliability falls more slowly in the proposed RRM mechanism than in the other tested mechanisms, as revealed in Figure 16, indicating that the routing path is more reliable, and that the energy consumption is more stable, in RRM than in the other mechanisms. Conversely, DSR and AODV consume more energy and incur greater packet loss than RRM. The flooding of routing overhead rises with the network scale, significantly affecting the performance of the entire network.  
Figure 15: Packet delivery ratio.  
The proposed RRM mechanism includes intra-cluster routing and inter-cluster routing functions. The nodes periodically exchange routing information in intra-cluster routing, revealing the relationships among clusters from the local routing table in inter-cluster routing, which includes the next hop nodes and the edge of cluster. Accordingly the RRM mechanism has more routing paths available when compared to the DSR and AODV mechanism. The reliability of the proposed RRM mechanism is higher than that of the DSR and AODV mechanism.  
The simulation results indicate that the average reliability of data delivery for RRM, AODV and DSR are 0.625, 0.498 and 0.434 respectively. The reliability of data delivery for RRM improved between 25% and 43%, which is higher than that of AODV and DSR mechanism. In addition, the reliability of the former is about 25% higher than that of the later.
# Induced Effect of Gold Nanoparticles (AuNPs) and Halide Ions on Pyridoxine Molecule Stability
## 4 Conclusions  
Halogen ions are assimilated in the human body from many food sources, and they play many metabolic roles. The administration of gold nanoparticles as a tonic and/or for antibacterial purposes can lead to unknown interactions with consequences that are difficult to understand. By the action of halide ions, gold nanoparticles can dissolve; chemical interactions between them can be spontaneous leading to the formation of potentially toxic chemical species.  
This study demonstrates both the chemical and electrochemical interaction between pyridoxine molecules, halide ions and gold nanoparticles.  
In the presence of inert support electrolytes, such as fluoride ions, gold nanoparticles catalyze the electrochemical degradation of pyridoxine molecules. At the same time, pyridoxine decreases the electrooxidative dissolution of gold nanoparticles.  
Active electrolytes, chloride and bromide, lead to active electrogenerated species, so the electrochemical degradation of pyridoxine and nanoparticles takes place more easily. As a general conclusion, gold nanoparticles most easily degrade electrochemically in the presence of bromide ions and pyridoxine molecules in the presence of chloride ions.  
Regarding the current trend in consuming foods and food supplements without rigorous information, it should be noted that there can be biological conditions conducive to adverse interactions having a negative impact. The novelty of this study is attributed to experimental results that introduce new evidence of spontaneous or practically induced interactions, which can occur in binary and ternary systems consisting of pyridoxine, AuNPs and halide ions.  
This study should be continued with clinical trials to identify the benefits or, conversely, the adverse effects of the simultaneous administration of these compounds.  
**Author Contributions:** All authors had equal contribution to the article along with the first author. Conceptualization, A.S. and B.T.; methodology, A.S., C.-I.S. and B.T.; software, B.T. and B.O.; validation, B.O.; investigation, B.T.; resources, B.O. and C.T.; writing--original draft preparation, A.S. and B.T.; writing--review and editing, B.O.; supervision, A.S.; funding acquisition, B.T., C.-I.S., C.T. and B.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The funding of this work was supported by the research grants awarded by the University of Craiova, Romania, in the competition \"The Awards of Research Results--ISI Articles\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 2 Connectivity in Tactical MANETs
### Nodes Mobility  
The nodes mobile patterns and parameters have a significant impact on network connectivity including average speed, prevailing direction, pausing time distribution, and initial locations [9]. In many cases, real-world mobility traces are not available, so we have to resort to mathematical models to reproduce realistic nodes trajectories. If the mobility model is not carefully selected and configured, evaluation of network protocols may lead to misleading conclusions, either overestimating or underestimating the performance in tactical scenarios. For example, it is generally accepted to assume group mobility as the most suitable model for tactical MANETs. Such model is constrained by assuming permanent group affiliations, homogeneous group velocities or acceleration which is not always satisfied in real-world situations during tactical missions [10]. Thus, there are still many opportunities for developing realistic mobility models for tactical MANETs.
# Antenna-on-Chip for Millimeter Wave Applications Using CMOS Process Technology
###### Abstract  
In this paper, a monopole patch antenna is designed, and the structure of the antenna is analyzed. The manufacturing process adopts TSMC 0.18 \(\upmu\) CMOS process technology. An artificial magnetic conductor (AMC) on the M1 layer is proposed in this paper to increase the radiation gain and reduce the reflection coefficient (S11) magnitude for impedance matching and antenna performance. This method can make up for the radiation efficiency and benefits of the antenna-on-chip that are affected by the high dielectric constant and low resistivity of the silicon substrate of the CMOS process. The antenna designed in this paper obtains a simulated bandwidth of 37.5 GHz to 69.5 GHz using the Electromagnetic Simulation Software, and the fractional bandwidth of the design is 60%. Among them, 62 GHz shows a maximum gain value of \(-\)2.64 dBi. Actual measurements have confirmed that the reflection coefficient of the antenna on the chip proposed in this paper is the same as the simulation trend, and a wider bandwidth is obtained from 20.9 GHz to 67 GHz, with a fractional bandwidth of 104.89%. This bandwidth covers millimeter wave 28 GHz, 38 GHz, and 60 GHz application frequencies.  
millimeter wave; antenna-on-chip; CMOS process +
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## 3 Results
### Measurements and Model Validation
#### 3.2.1 Phantom Dielectric Material Characterization  
The results of the characterization of dielectric properties of the muscle homogeneous phantom are shown in this subsection. A study on the influence of sugar concentration in water's dielectric constant and conductivity has been carried out to fabricate a liquid that mimics muscle tissue. In Figure 7, the obtained values for different sugar concentrations are shown. Once the target real permittivity was achieved, mostly between 40% and 50% concentrations at low frequencies, it could be observed that beyond 1.6 GHz, the conductivity was higher than the one of the muscle. The opposite happened below this limit.  
In this manner, liquid sugar-based phantoms are conducted for each of the studied frequency bands, where real permittivity matching is prioritized (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & **Target Properties**[25; 26; 27] & **Measured Phantom Properties** & **Phantom Material** \\ \cline{2-5}  & \(\varepsilon\mathbf{r}\) & \(\sigma\) & \(\varepsilon\mathbf{r}\) & \(\sigma\) & **Sugar (\%)** \\ \hline
0.402 & 57.1 & 0.79 & 57 & 0.4 & 51 \\
0.868 & 55.1 & 0.93 & 54 & 0.75 & 51 \\
2.45 & 52.7 & 1.74 & 52.5 & 2.5 & 41 \\
5.8 & 48.5 & 4.96 & 48.8 & 7.5 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fabricated muscle phantom properties for the frequencies of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Freq [GHz]**} & \multicolumn{3}{c}{**Size [mm]**} \\ \cline{2-4}  & x & y & z \\ \hline
0.402 & 355 & 160 & 255 \\
0.868 & 355 & 160 & 255 \\
2.45 & 100 & 100 & 100 \\
5.8 & 50 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum phantom size that reliably represents an entire body for implant communication and EM attenuation for subcutaneous applications (0–10 mm insertion depth). The average absorption rate is the same as for deep implants.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Freq [GHz]**} & **Case Subcutaneous** & **dm3** & **Volume Reduction with Respect to** \\  & **(S) Deep Implant (D)** & **References**[34; 35] in. **[\%]** \\ \hline
0.402 & S & 14.484 & 77.7 \\
0.402 & D & 32.589 & 49.8 \\
0.868 & S & 14.484 & 77.7 \\
0.868 & D & 23.536 & 63.8 \\
2.45 & S & 1 & 98.5 \\
2.45 & D & 8 & 87.7 \\
5.8 & S & 0.125 & 99.8 \\
5.8 & D &As expected, higher/lower conductivity values were obtained above/below the aforementioned limit of 1.6 GHz. However, for simplicity, they were considered as references for computer model validation as long as the numerical model imported these consistent properties for comparison. Therefore, emphasis was given to the coherence of the numerical-experimental validation. Once the results are verified, theoretical permittivity values corresponding to muscle properties can be adopted without loss of generality.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### Addressing Privacy and Security Concerns  
With the increasing use of localization technologies in healthcare and military applications, ensuring data privacy and system security is essential. Future research should explore advanced encryption techniques, tamper-proof hardware, and blockchain-based solutions to protect sensitive location data [23; 30]. Compliance with privacy regulations for healthcare environments should be a core consideration in designing secure RTLSs.
## 4 Discussion
### Limitations and Future Work  
The YRDUA, the focus of this study, possesses unique geographical and climatic characteristics [85]. Consequently, the identified driving mechanisms and model results are specific to this region and may vary significantly from those in tropical or arid urban environments. Due to the relatively high climate consistency within the study area, this paper adopts coarse-resolution reanalysis data to characterize regional climate change. In future studies on a larger scale, more detailed data are required to describe the influence of climate factors on CS. Additionally, our analysis utilizes static cross-sectional measurements of soil nitrogen and phosphorus, which constrains dynamic causal interpretation of nutrient-vegetation relationships; future work should incorporate temporal soil sampling to address this limitation. Another limitation of our study is the omission of the effects of urban atmospheric, soil, and water pollutants on vegetation CS. This aspect, particularly the health and growth status of vegetation under various stressors, should be incorporated in subsequent studies [86]. In addition, this study primarily examines the independent effects of various factors on CS. However, the synergistic effects (enhance or diminish CS) among these factors are also worthy of in-depth study.