esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 1 Introduction
In this paper, we design a novel chaotic pulse-shaping filter to generate an ISI-free chaotic baseband waveform. Taking RRC as a starting point, an unequal division method is proposed, which can not only retain the ISI-free property of RC but also produce asymmetric pulses that provide the oscillatory nature of the chaotic signal. Firstly, the RRC pulse is divided into three parts, with the time when the amplitude first decays to zero as a division point. The first two parts are combined to construct an asymmetric pulse. The overall response of those pulses can still hit zero at symbol sampling instants, which meets the Nyquist ISI criteria, such as RC. Then, taking the proposed pulse as an analytical solution of differential equations, a new hybrid dynamical system is constructed with an infinitely long and exponentially increasing precursor. Its chaotic property is proved by the positive maximal Lyapunov exponent. Implementing the proposed pulse as a shaping filter, a novel chaotic baseband wireless communication system is constructed. At the receiver, a mirrored copy of the transmitted pulse shaping as a matched filter is introduced, which avoids the complicated chaos synchronization between the transmitter and receiver. A corresponding threshold is calculated to relieve the ISI caused by multi-path propagation. Simulation results show that the bit error rate (BER) performance of the proposed method is very close to the lower bound of BER in a single-path AWGN channel. Under a multi-path channel, for BER =10\({}^{-3}\), the proposed method outperforms CBWCS by more than 2 dB when the threshold is zero. By setting optimal and suboptimal thresholds, the proposed method maintains a satisfactory BER, while the computational complexity of the threshold calculation is reduced by 52.5%. The rest of this paper is organized as follows. Section 2 briefly reviews the RC and RRC filter. The division method and new dynamical system are presented in Section 3. In Section 4, a novel chaotic wireless communication system is constructed, and its performance is analyzed. Finally, conclusions are drawn in Section 5.
## 2 Related Work
Localization is a critical challenge in V2V communication, and the relationship between beamforming and beam alignment is examined in study [19]. This study incorporates realistic vehicle dynamics and highlights the limitations of the Kalman filter for non-linear parameters. Hardware limitations on antenna elements and phase shifters are also considered. Directional antenna designs and beam steering play a crucial role in providing reliable and secure V2V communication, as studied in [27]. This study also addresses the Doppler effect and introduces an NLOS mode of operation for specific services. In another study [28], a customized beamwidth optimization problem is formulated to prevent beam misalignment in V2V communication. The authors propose a Monte Carlo-based beamwidth optimization (MCBO) method to address the issue statistically. While the aforementioned research studies provide valuable insights, there is a need for practical simulations and algorithm testing in various environments, including highways and suburban and urban areas. It remains unclear how V2V communication would perform in different scenarios, and different conditions can affect the reliability of algorithms and mathematical models. Furthermore, although beam steering and beamforming techniques have been extensively studied, their implementation in V2V scenarios requires further exploration. Therefore, this research aims to design a new algorithm and mathematical model for beam steering with a tracking ability, which will be tested in three environments (highways and urban and suburban areas) to evaluate the reliability and computational complexity of the algorithm for V2V links.
## 4 Antenna Performance Measurements
### MIMO Performance Analysis  
This section presents the MIMO performance analysis of the proposed dual-band MIMO DRA. MIMO performance in terms of data throughput has been measured. For the throughput measurements of the proposed design, the setup shown in Figure 18 has been used. The procedure presented in [19] has been used in this paper for the throughput measurements. Throughput, which is measured in bits per second (bps) in a communication network, is the amount of data transferred per unit time through a communication link from source to destination. The aggregate or system throughput is the overall data rates transferred to all the destinations in a communication network. The term throughput is often used for maximum throughput. The maximum throughput (bits/sec or bps) of a communication link or a node is also termed as its capacity. Figure 18 shows the MIMO throughput measurement setup. The figure shows the proposed dual-band MIMO antenna along with two monopoles (separated by 0.5\(\lambda\) spacing). The separation between the proposed DRA and monopole is 30 cm. Both the antennas are connected to Wideband Radio Communication Tester R&S(r)CMW500 (LTE node B (eNodeB)) along with a Huawai E398 USB modem dongle (which acts as the LTE user equipment (UE)). By using dedicated Physical Downlink Shared Channel sub frames the eNodeB (CMW500), the emulator transmits downlink data to the UE. The throughput is calculated using the Block Error Rate (BLER). The positive and negative acknowledgments returned by the UE are used to determine BLER. The throughput measurements of the given MIMO antenna were performed for a SISO and 2 \(\times\) 2 MIMO systems. Three types of modulations including QPSK, 16 QAM (quadrature amplitude modulation), and 64 QAM have been used to measure the throughput for the SISO and MIMO system of the presented antenna. Tables 2 and 3 depict the measured throughput for the SISO and MIMO antenna systems for the three modulation schemes along with the specified channel quality indicator (CQI) at 1.8 GHz and 2.6 GHz, respectively. The parameters used in the measurement setup include: bandwidth of 20 MHz, 100 number of resource blocks, \(-\)20 dBm of transmitted power, frequency bands of 1.8 GHz (band 3), and 2.6 GHz (band 7).  
All the results analyzed in this work clearly indicate that the proposed antenna is suitable for LTE applications and it is able to deliver an excellent throughput performance. The 2 \(\times\) 2 MIMO performance also justifies that the proposed antenna has a good level of isolation between ports to support the MIMO spatial multiplexing operation.
## 2 Related Works  
The effects of the environment on path loss motivated the incorporation of environment details in the form of 2D satellite images into path loss modeling. A number of works have emerged in which images are processed using CNN. Various image types are being processed that include satellite images, segmented images based on some parameters, pseudoimages, and combinations of various images.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
###### Abstract  
Physically unclonable functions (PUFs) have been touted for their inherent resistance to invasive attacks and low cost in providing a hardware root of trust for various security applications. SRAM PUFs in particular are popular in industry for key/ID generation. Due to intrinsic process variations, SRAM cells, ideally, tend to have the same start-up behavior. SRAM PUFs exploit this start-up behavior. Unfortunately, not all SRAM cells exhibit reliable start-up behavior due to noise susceptibility. Hence, design enhancements are needed for improving reliability. Some of the proposed enhancements in literature include fuzzy extraction, error-correcting codes and voting mechanisms. All enhancements involve a trade-off between area/power/performance overhead and PUF reliability. This paper presents a design enhancement technique for reliability that improves upon previous solutions. We present simulation results to quantify improvement in SRAM PUF reliability and efficiency. The proposed technique is shown to generate a 128-bit key in \(\leq\)0.2 us at an area estimate of 4538 \(\upmu\)m\({}^{2}\) with error rate as low as \(10^{-6}\) for intrinsic error probability of 15%.  
physically unclonable function; SRAM PUF; reliability +
Footnote †: journal: Low Power Electronics and Applications
## 2 Materials and Methods
### Measurement Protocols and Signal Processing  
Quantitative variables were measured in the following way. In this study, the menstrual cycle was separated into four distinct phases. The early proliferative (EP) phase, which corresponds to days 1-7, the late proliferative (LP) phase, which corresponds to days 8-14, the early luteal (EL) phase, which corresponds to days 15-21, and the late luteal (LL) phase, which corresponds to days 22-28 of the menstrual cycle. Attention and signal processing were focused on the electrical activity derived by the monopolar signal of the electrode 1 cm from the fundal region, as a recent study found that placing the embryo within one or two centimeters of the fundus significantly increased the pregnancy rate [12]. Recordings were separated into 5 min segments, with a maximum of 2 segments per recording. The electrical signal was lowpass-filtered, at 0.1 Hz, which cuts off noise signals corresponding to more than 6 cycles per minute, thereby limiting signals to frequency bands corresponding to the previously observed frequency of uterine contractions across the menstrual cycle [4; 13]. The following outcomes were extracted and measured from the filtered recordings: the Root-Mean-Square (RMS) Voltage Amplitude (VJZE-RMS) and the Mean Frequency (fJZE-mean) of the electrical activity recorded from within the endometrium, as direct indicators of the intensity and frequency changes in the uterine contractions.
## 4 Conclusions  
In this paper, a future 5G/6G solution integrating FSO, O-RAN, and mmW beamforming has been proposed. The FSO medium not only achieves transmission performance similar to that of physical fiber but also has the mobility to facilitate fast setup and overcome physical obstacles on the ground, thus helping to extend the end-use applications of O-RAN. In the aspect of mmW beamforming, the use of RL in the beamforming system can optimize the EVM by dynamically adjusting the beamforming phase according to the location of the communicating user. Nevertheless, beamforming at frequencies above 24 GHz has a maximum transmission distance of several hundred meters, leading to com  
Figure 11: O-RU utilizes a 10 km fronthaul direct or via CPE for cell phone network performance measurement. The box-and-whisker plot uses 25% fractions and 75% fractions, and coef is 1.5.  
munication blind spots at the edges of the sector's operating range of beamforming supply. O-RAN's O-RU antenna can be placed at the edge of mmW beamforming sectors, such as mmW beamforming blind spots, by fiber-optic fronthaul. Moreover, the frequency used for O-RAN can be staggered with the frequency used for mmW beamforming to avoid signal interference. In addition, the O-RU antennas can be relayed by fiber-optic fronthaul up to 10 km to extend from urban areas to less populated suburban or rural areas instead of installing mmW beamforming in less populated areas, which is not cost-effective. According to the measurement results, with 200 m FSO backhaul and 10 km fiber fronthaul, the cellular network performance at 200 m from the O-RU antenna of O-RAN is comparable to that without a 200 m FSO link backhaul and 10 km fiber fronthaul. As a result, the adaptive and open architecture of O-RAN improves the cost-effectiveness of integration with FSOs and enables communication services to reach remote rural and suburban areas, as well as urban areas. In summary, the integration of FSOs with RL-based mmW beamforming and O-RAN communication configurations can bring scalability and cost-effectiveness to current and future communication infrastructures in urban, suburban, and rural areas.  
Not applicable.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
## 1 Introduction
However, the changes that foods can undergo with this method make it necessary to search for other preservation techniques that maintain the quality and bioactive compounds and extend the shelf life of fresh mushrooms [15; 16]. Hot-air drying (HD) and freeze drying (FD) are two of the most used methods for drying mushrooms [21; 22; 23]. HD involves exposing the mushrooms to circulating hot air, which causes moisture evaporation and subsequent drying. This method is relatively simple and cost-effective; nevertheless, HD can lead to losses in bioactivity and potentially affects the texture and color of mushrooms [7; 13; 14; 20; 23]. On the other hand, FD is a more advanced and specialized method. It involves freezing the mushrooms and then subjecting them to a vacuum environment, where the frozen water undergoes sublimation (directly changing from solid to a gas). FD is known for its ability to retain the original color, texture, and nutritional properties of mushrooms. FD is suitable for heat-sensitive compounds (such as phenols, tocopherols, carotenoids, and ascorbic acid, which have antioxidant properties) and results in high-quality dried mushrooms; however, the FDis a more expensive and time-consuming process, requiring specialized equipment and expertise [22; 24; 25]. The selection of an ideal drying method for mushrooms is driven by the objectives of minimizing drying time and energy consumption, thereby reducing the cost, while ensuring the desired quality of the final product [17]. However, the choice between methods depends on various factors including available resources and cost considerations. Thus, to select the most appropriate drying process and to analyze the cost/benefit relationship in the technological re-adaptation of traditional practices, it is necessary to analyze the bioactivity losses linked to dehydration methods and to different dehydration temperatures. By using suitable techniques, it is possible to maintain the integrity of these bioactive compounds and secondary metabolites, thereby increasing the shelf life of mushrooms [10]. Therefore, the aim of this study was to evaluate the impact of air-drying temperature and freeze drying on the total phenolic content (TPC) as well as antioxidant activity of _F. antarctica_ and _R. patagonica_. Additionally, these research efforts are associated with initiatives to establish mycotourism and mycogastronomy as regional development alternatives. To achieve this, it is necessary to have solid information on different postharvest conservation techniques and storage methods that allow the sale of quality products with bioactive properties that are as similar as possible to those of fresh products.
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Algorithm  
Algorithm 1 shows the logical operation of the realized spectrum sharing access techniques, including dedicated access, co-primary shared access, LSA, and LAA. Other than static spectrum renting and LAA techniques, the degree of shared spectrum allocated to SBSs per building depends directly on the ABS pattern per APP for all techniques. The dedicated spectrum access technique needs only the transceiver 1 of each SBS. Since all other techniques work on both transceiver 1 and transceiver 2, implicitly, the dedicated spectrum access technique is an integral part of CoPSA, LSA, and LAA techniques.  
Figure 7: LSA techniques.  
Figure 8: A small cell configuration for LAA.
## 3 Preconditioned Precise Integration Time-Sweeping Method
### Preconditioned Precise Integration Algorithm  
The relationship between the TEM diffusion field and the virtual wavefield is as follows [20; 27]:  
\[\mathbf{f}(\mathbf{r},t)=\frac{1}{2\sqrt{\pi t^{3}}}\int_{0}^{\infty}\tau\exp\left(- \frac{\tau^{2}}{4t}\right)\mathbf{u}(\mathbf{r},\tau)d\tau, \tag{8}\]  
In Equation (8), \(\mathbf{f}\) and \(\mathbf{u}\) represent the diffusion field and the virtual wavefield, respectively, while t and \(\tau\) represent the diffusion field time and virtual time, respectively. The exp denotes the exponential function. Equation (8) is a Fredholm integral equation of the  
Figure 4: The results of differential pulses: (**a**) \(b_{z}\); (**b**) relative error curve.  
first kind, which exhibits typical ill-posedness. Its linearization and discretization can yield the following:  
\[\mathbf{f}(t_{i})=\sum_{j=1}^{n}k_{i,j}\mathbf{u}\big{(}\tau_{j}\big{)}, \tag{9}\]  
where \(k_{i,j}=-1/\sqrt{\pi t_{i}}\text{exp}\big{(}-\tau^{2}/4t_{i}\big{)}\big{|}_{ \tau_{j-1}}^{\tau_{j}}\), and Equation (9) can be expressed as the following linear equation system:  
\[\mathbf{K}\mathbf{u}=\mathbf{f}. \tag{10}\]  
The system of linear Equation (10) exhibits severe ill-conditioning. In this paper, we solve the virtual wavefield \(\mathbf{u}\) using the preconditioned precise integration method. Since the precise integration method requires the coefficient matrix \(\mathbf{K}\) to be symmetric and positive definite, Equation (10) is rewritten in the following form:  
\[\mathbf{K}^{T}\mathbf{K}\mathbf{u}=\mathbf{K}^{T}\mathbf{f}, \tag{11}\]  
Let the matrix \(\mathbf{A}=\mathbf{K}^{T}\mathbf{K}\). By left-multiplying both sides of Equation (11) by the preconditioned matrix \(\mathbf{Q}\) and setting \(\mathbf{u}=\mathbf{y}\), then  
\[\mathbf{Q}\mathbf{A}\mathbf{P}\mathbf{y}=\mathbf{Q}\mathbf{K}^{T}\mathbf{f}, \tag{12}\]  
Equation (12) can be simplified into the following form:  
\[\mathbf{M}\mathbf{y}=\mathbf{b}, \tag{13}\]  
where \(\mathbf{M}=\mathbf{Q}\mathbf{A}\mathbf{P}\), \(\mathbf{b}=\mathbf{Q}\mathbf{K}^{T}\mathbf{f}\). By applying the preconditioned precise integration method, the solution of linear Equation (13) can be expressed in the following iterative form [23]:  
\[\begin{cases}\mathbf{y}_{k}=\prod\limits_{i=0}^{k-1}\Big{(}\mathbf{I}+\text{exp}\Big{(} -2^{k-i-1}\zeta\mathbf{M}\Big{)}\Big{)}F(\zeta)b\\ \mathbf{y}_{k+1}=\Big{(}\mathbf{I}+\text{exp}\Big{(}-2^{k}\zeta\mathbf{M}\Big{)}\Big{)} \mathbf{y}_{k},k=0,1,2,\ldots\end{cases}, \tag{14}\]  
the \(\zeta\) represents the integration step size, and \(\mathbf{F}(\zeta)\) is expressed as follows:  
\[\mathbf{F}(\zeta)=\zeta\Big{[}\mathbf{I}+(-\mathbf{M}\zeta)/2+\Big{(}-\mathbf{M} \zeta)^{2}/6+\Big{(}-\mathbf{M}\zeta\Big{)}^{3}/24\ \Big{]}. \tag{15}\]  
After solving for \(\mathbf{y}\) using the iterative Formula (14), the virtual wavefield can be obtained by \(\mathbf{u}=\mathbf{P}\mathbf{y}\).
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 2 Materials and Methods
### Design of the Experimental Conditions  
#### 2.2.1 Levels of Automation (LOA) Modes  
The automation design focuses on the _decision_ and _action_ aspects of the overall process taken either by the robot or the user. This specifies the degree of control the user or robot in the decision of action(s) to be taken and the execution of the actions. It is conditioned in two levels for this study:  
1. Low LOA--the user has autonomy to select the type and order of cubes. The robot supports the user by bringing the type of cube the user selected via the user interface.
2. High LOA--the robot has autonomy to bring the specific type of cube and in the order preprogrammed in its operation. The user simply demands for a cube through the user interface and the robot brings the type of cube suitable for the specific configuration assembled.
## 3 Results  
### Simultaneous Quantification of the Nine Compounds in JGT  
The HPLC technique was improved for the simultaneous measurement of the nine compounds contained in JGT water extract (Figure 2A,B). The wavelengths used for quantitative analysis were 230 nm (albiflorin, benzoic acid, benzoylpaeoniflorin, and paeoniflorin); 250 nm (glycyrrhizin); and 275 nm (gallic acid, liquiritin apioside, liquiritinen, and liquiritin). By using a mobile phase of 0.1% (\(v/v\)) aqueous FA and acetonitrile, we effectively separated these nine compounds in 35 min. The retention times were 6.44 min (gallic acid), 14.28 min (albiflorin), 15.12 min (paeoniflorin), 16.71 min (liquiritin apioside), 17.21 min (liquiritin), 21.51 min (benzoic acid), 24.58 min (liquiritinen), 26.20 min (benzylozoeniflorin), and 34.62 min (glycyrrhizin). The calibration of each reference standard was calculated by the correlation between peak area (y) and concentration (x, \(\upmu\)g/mL) of the standard mixture, and determined from a linear regression equation (y = ax + b; Table 2). The linear ranges were 12.5 to 400 \(\upmu\)g/mL (paeoniflorin); 3.125 to 100 \(\upmu\)g/mL (gallic acid, liquiritin apioside, liquiritin, and glycyrrhizin); 1.5625 to 50 \(\upmu\)g/mL (albiflorin, benzoic acid, and benzoylpaeoniflorin); and 0.39 to 12.5 \(\upmu\)g/mL (liquiritinetin). The correlation coefficient for each compound indicated good linearity (r\({}^{2}\geq\) 0.9999). The limit of detection (LOD) and limit of quantification (LOQ) ranged from 0.014 to 0.587 \(\upmu\)g/mL and 0.041 to \begin{table}
\begin{tabular}{l l} \hline \hline
**Gene** & **Primer** \\ \hline _IL-1\(\beta\)_ & F: 5\({}^{\prime}\)-AAAAAAGCCCTGCTG-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-GTCGTTGCTTGTTCTCTTG-3\({}^{\prime}\) \\ \hline _IL-6_ & F: 5\({}^{\prime}\)-TTCCACAGTTGCCTTCTG-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-TTCCACAGATTTCCCAGAGAC-3\({}^{\prime}\) \\ \hline _TNF-\(\alpha\)_ & F: 5\({}^{\prime}\)-AAGCCTTGACACCACCACCTGGTGA-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-AGTTACAACCCATCGCTTGGCTGA-3\({}^{\prime}\) \\ \hline _MCP-1_ & F: 5\({}^{\prime}\)-GCATCCAGCTTGGCTCA-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-TTCCACGCCTACATTTGATCA-3\({}^{\prime}\) \\ \hline _TGF-\(\beta\)_ & F: 5\({}^{\prime}\)-TTGCTCAGTCCACAGAG-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-TGGTGTGTAGGGCAAGGAC-3\({}^{\prime}\) \\ \hline _\(\alpha\)-SMA_ & F: 5\({}^{\prime}\)-TAGGCACGTGTGTGACTCACA-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-CGACACTGCCTGACAGGGCCAC-3\({}^{\prime}\) \\ \hline _MMP-9_ & F: 5\({}^{\prime}\)-GTTTTGATGCATTGCTGAGATCCA-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-CCCACATTTGACCGCAGAGAA-3\({}^{\prime}\) \\ \hline _GAPDH_ & F: 5\({}^{\prime}\)-TGTGTGAGCTGCGCCTG-3\({}^{\prime}\) \\  & R: 5\({}^{\prime}\)-CACCACCCTGCTGCTGACCGTAT-3\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Primers used to amplify target genes. 1.780 \(\upmu\)g/mL, respectively. The amount of each compound in the water extract of JGT ranged from 0.222 mg/g (liquidirtigenin) to 55.292 mg/g (paeoniflorin).
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 2 Structure of the Portable Smart Spectrometer
### Data Transmission Module  
To make full use of the measured spectral data, it is uploaded to a smart device and the network for sharing and further use. The most commonly used data transmission methods are Bluetooth and USB; however, Bluetooth is more convenient for portable design than USB. Bluetooth 5.0 has low power consumption and a data transmission speed of up to 2 Mbps, which is sufficient to transmit spectral data [16]. Figure 6 shows the data transmission module of the PSS-1.0. To extend the functionality of the PSS-1.0, the PSS-1.0 is connected to the smart device via Bluetooth 5.0, then uploads the spectral data to a cloud network through the smart device. The result is displayed on the smart device.