esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 5 Conclusions  
This article creatively explores the use of two AD2S1210 devices in a dual-channel round inductsyn by connecting them in parallel using the same clock crystal oscillator for synchronized dual-channel decoding. Based on the input/output signal characteristics of the JGX120/360 dual-channel round inductsyn developed by Changzhou Weinengda Precision Machinery Co., Ltd., a decoding circuit is designed. The feasibility of the design scheme is verified through Multisim simulation and practical circuit testing, demonstrating the effectiveness of the design approach. Through the decoding circuit designed based on AD2S1210 in this paper, complex analog signal processing in the input/output of the dual-channel round inductsyn can be avoided. This allows for the direct digital transmission of angular position information to the system microprocessor, greatly simplifying the operational process of the dual-channel round inductsyn. In the future, we will further simplify the decoding circuit, increase system reliability and integration, use AI technology to optimize software algorithms such as noise reduction filtering and signal processing, and improve decoding accuracy based on the special requirements of a dual-channel round inductsyn applied to high-orbit satellites.  
**Author Contributions:** Investigation, [PERSON], [PERSON] ([PERSON]) and [PERSON]; Writing--original draft, [PERSON] ([PERSON]); Writing--review & editing, [PERSON] ([PERSON]); Supervision, [PERSON]; Funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology
### Search of Candidate Extreme Points  
Based on the wavelet detail signals, d2, d3, and d4, the following steps are developed for searching the candidate extreme points.  
Step 1: Find the extreme detail signals, d2', d3', and d4', consisting of all the positive or negative extreme points which have sudden changes in slope in d2, d3, and d4, respectively.  
Figure 5: Illustrations of common QRS complexes and their high-frequency detail signals, d1, d2, d3, and d4. N: Normal beat; V: Premature ventricular contraction beat; L: Left bundle branch block beat; Q: Unclassifiable beat.  
Figure 6: Illustrations of the extreme detail signals including only the extreme points of the detail signals, named d1′, d2′, d3′, and d4′ according to d1, d2, d3, and d4, respectively, in Figure 5.  
Step 2: Divide the extreme detail signals with a length of 4096 samples into four subsections with lengths of 1024 samples and then calculate the positive and negative thresholds, \(TH+\) and \(TH-\), respectively, as follows.  
\[TH+=\frac{1}{4}\times\frac{(M_{Max,1}+M_{Max,2}+M_{Max,3}+M_{Max,4})}{4} \tag{6}\]  
\[TH-=\frac{1}{4}\times\frac{(M_{Min,1}+M_{Min,2}+M_{Min,3}+M_{Min,4})}{4} \tag{7}\]  
where \(M_{Max,i}\) and \(M_{Min,~{}i}\), \(i=1,~{}2,~{}3,~{}4\), denote the largest values of the positive extreme points and the smallest values of the negative extreme points for the four subsections. Each subsection is about 2.84 s at a sampling rate of 360 Hz and, hence, can at least include one QRS peak if the heartrate is greater than 21 bps. Before the detection of QRS peaks, it is easy to find the largest and smallest values of the extreme points produced by the QRS peaks in each subsection. The thresholds are defined by one fourth of the average values of four extreme values. The use of four extreme values is to avoid the thresholds being dominated by an extreme value that is too large or too small.  
Step 3: Search the candidate extreme points with values larger than the positive threshold, \(TH+\), or smaller than the negative threshold, \(TH-\). A too-large positive or a too-small negative threshold may lose the extreme points produced by the true QRS peaks with smaller amplitudes. On the contrary, a too-small positive or a too-large negative threshold may include the extreme points produced by the noise peaks. The proposed \(TH+\) and \(TH-\) are a compromise of the two.
## 3 Distributed Drive Vehicles Based on MCSRCKF
### Algorithm Complexity  
Computational resources are crucial for real-time applications. In order to analyze the relationship between the computational cost and the size of the state and measurement vectors, this section analyzes the complexity of CKF, SCKF, and MCSRCKF based on equivalent floating-point operations [39]. The computational complexity of the equations is shown in Table 1.  
The complexity of the algorithm for CKF is given in [39]:  
\[S_{CKF}=\frac{20}{3}n^{3}+10n^{2}+10n^{2}m+8 nm^{2}+2 nm+m^{3}+3m^{2}+m \tag{55}\]  
The basic equations of SCKF include Equations (23), (34), (36)-(38). The algorithm complexity is given in Table 1.  
\[S_{SCKF}=14n^{3}+2n^{2}+8n^{2}m+6 nm^{2}+\frac{10}{3}m^{3}+m \tag{56}\]  
Before the simulation experiment, the initial values for process noise and measurement noise in the estimator need to be continuously adjusted through extensive simulation experiments because the covariance matrix of the algorithm in the observer and the sensor noise characteristics are closely related. In real vehicle operation, the sensor noise is considered non-Gaussian, particularly heavy-tailed noise with large outliers, because large outliers frequently cause the measurement signal to be disturbed. In this paper,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Parameter Name** & **Symbol** & **Value** \\ \hline Vehicle mass & \(m\) & 1418 kg \\ Distance between the front axle and the mass center & \(a\) & 1.016 m \\ Distance between the rear axle and the mass center & \(b\) & 1.896 m \\ Wheelbase of front wheels & \(T_{f}\) & 1.675 m \\ Wheelbase of rear wheels & \(T_{r}\) & 1.675 m \\ Yaw moment of inertia & \(I_{z}\) & 1556 kg m\({}^{2}\) \\ Wheel rolling radius & \(R_{0}\) & 0.325 m \\ Centroid height & \(h_{g}\) & 0.54 m \\ Maximum motor power & \(p_{max}\) & 68 kw \\ Maximum motor torque & \(T_{max}\) & 140 N\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter configuration of the simulation vehicle.  
Figure 2: MCSRCKF flow chart.  
it is assumed that the process noise and the measurement noise both consist of heavy-tailed non-Gaussian noise. In order to more closely approach the situation where the measurement effect of the sensor is greatly affected at high speed, select low-cost and low-accuracy sensors installed on distributed drive vehicles and set up large measurement noise. Therefore, the covariance matrix is as follows:  
Set the system noise and observation noise to:  
\[q_{i}\sim 0.8N(0,0.001)+0.2N(0,0.1) \tag{58}\]  
\[r_{i}\sim 0.8N(0,10)+0.2N(0,1000) \tag{59}\]  
where, \(i=1,2,\ldots,7\), representing system noise and observation noise respectively.
## 4 Calibration of Electrical Inertia Simulation Accuracy
### Calibration of Electrical Inertia  
In the standard inertia calibration test, the test result of 400 kg\(\cdot\)m\({}^{2}\) inertia flywheel set at a 1:2.36 transmission ratio is 2232.05 kg\(\cdot\)m\({}^{2}\). As the basic inertia is known to be 523.96 kg\(\cdot\)m\({}^{2}\), the inertia to be simulated by the electric motor is 1708.09 kg\(\cdot\)m\({}^{2}\). After calibrating in the same way as the standard inertia calibration, the experimental results are shown in Figure 22. The system inertia is 2211.09 kg\(\cdot\)m\({}^{2}\) and the percentage deviation of the electrical inertia simulation is 0.95%.  
With the same method, the equivalent inertia of 750 kg\(\cdot\)m\({}^{2}\) and 250 kg\(\cdot\)m\({}^{2}\) in the standard inertia calibration test is 4157.69 kg\(\cdot\)m\({}^{2}\) and 10,522.34 kg\(\cdot\)m\({}^{2}\) respectively. Through the calibration shown below, the inertia and percentage deviation of the two groups are 4183.43 kg\(\cdot\)m\({}^{2}\) (0.61%) and 10,603.38 kg\(\cdot\)m\({}^{2}\) (0.76%), respectively, shown in Figure 23.  
Figure 21: Test results of basic inertia calibration.  
Figure 22: Test results under equivalent inertia of 2232.05 kg\(\cdot\)m\({}^{2}\).  
Figure 20: Structure of a basic inertia calibration test.  
The variation of electrical inertia simulation deviation with the increase of equivalent inertia is shown in Figure 24. It can be found that in the range of inertia of 1000-30,000 kg\(\cdot\)m\({}^{2}\), the deviation is less than 1%. The trend of simulation deviation of electrical inertia is the same as that of standard inertia.
## 2 Materials and Methods
### Synthesis and Morphology of Selenium Nanoparticles  
To obtain spherical SeNPs, the laser ablation in liquid was used. The process of irradiation of a polished selenium plate was carried out in a flow cell. The cell with a selenium plate was filled with deionized water. The volume of deionized water used was about 40 mL. A fiber ytterbium laser with a wavelength of 1064 nm, pulse duration of 100 ns, frequency of 20 kHz, average power of 20 W and pulse energy of 1 mJ was used as a laser radiation source. In the same way as it was described above in the case of selenium nanorods, laser radiation traveled over the target surface. The described approach is necessary to maximize the production rate of selenium nanoparticles during ablation. The typical time of laser ablation was about 20-25 min.  
Using TEM images, it was found that the obtained nanoparticles have a spherical shape; the size of the SeNPs is approximately 90-150 nm (Figure 2A,B). The nanoparticle size data obtained from TEM are consistent with the size distributions obtained on the CPS analytical centrifuge and DLS particle analyzer. The largest number of SeNP in the distribution has a size of about 130 nm (Figure 2C). The largest share in the volume distribution falls on particles with a diameter of 110 mm (Figure 2D). The aggregation of the nanoparticles did not occur within a few days of their receipt. The zeta potential of the Se nanoparticles obtained by laser ablation was approximately \(-30\) mV.  
Figure 2: Shape and size distribution of Se nanoparticles. **(A)** TEM image of a 130 nm spherical selenium nanoparticle (scale mark size: 100 nm). **(B)** TEM image of a single SeNP (scale mark size: 1 μm). **(C)** SeNPs number distribution by sizes. **(D)** SeNPs volume distribution by sizes. Disc centrifuge and a DLS particle size analyzer were used to obtain data. PDI = 0.27 \(\pm\) 0.5; (_n_ = 3).
# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 2 Materials and Methods  
### ECG Dataset Description  
We constructed a large ECG-signal dataset that includes 28,308 lead-II ECGs collected from the Korea University Anam Hospital in South Korea. The collected data are meaningful in that they are not refined, and they include various types of actual data measured in hospitals. The data consist of the following 7 categories:  
- Normal sinus rhythm (Normal)  
- Atrial fibrillation (AF)  
- Atrial flutter (AFL)  
- First-degree atrioventricular block (FAB)  
- Sinus bradycardia (SB)  
- Sinus tachycardia (ST)  
- Premature ventricular contraction (PVC)  
Cardiologists in the Korea University Anam Hospital in South Korea annotated the labels for these 7 classes. In this study, our model was designed to classify seven rhythm classes (Normal/AF /AFL/FAB/SB/ST/PVC) from raw single-lead ECG data. The data ratios for each sector were 34.48 (Normal), 33.86 (AF), 6.17 (AFL), 6.90 (FAB), 6.87 (SB), 6.20 (ST), and 5.53% (PVC).  
The ECG data were measured for 10 s at a frequency of 200 Hz. The data we used were based on lead-II ECG data taken from the 12-lead ECG data. In addition, the range of data values was adjusted to enable the smooth learning of deep-learning models with min-max normalization.
## 3 Results and Discussion
### Mix Design Study of Asphalt Mixtures with SSA Resulting from the Preliminary Study
#### 3.4.1 Particle Size Adjustment of the Selected Asphalt Mixtures with 75% SSA  
The Marshall mix design study begins with the grading curve adjustment of each asphalt mixture (AC 14 and AC 20) with 75% SSA to the corresponding standard envelope after combining different amounts of natural aggregates and SSA (Table 7). The final grading curves adjusted for these mixtures with 75% SSA are presented below (Figure 7) to compare with curves of equivalent conventional mixtures with natural aggregates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{3}{c}{**Indirect Tensile Strength (kPa)**} & \multirow{2}{*}{**ITSR (\%)**} \\ \cline{2-2} \cline{4-4}  & **ITSd** & **ITSw** & \\ \hline AC 14 (50\% SSA) & 3100 & 2776 & 90 \\ AC 14 (80\% SSA) & 3026 & 2865 & 95 \\ AC 20 (50\% SSA) & 2928 & 2247 & 77 \\ AC 20 (95\% SSA) & 2557 & 2445 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Water sensitivity test results of the preliminary asphalt mixtures with SSA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mixture** & **SSA Incorporation** & **MD (kg/m3)** & **Bulk Density (kg/m3)** & **Va (\%)** \\ \hline \multirow{3}{*}{AC14} & 50\% & 2717 & 2609 & 3.9\% \\  & 80\% & 2925 & 2739 & 6.4\% \\ \cline{1-1}  & 50\% & 2754 & 2553 & 7.3\% \\ \cline{1-1}  & 95\% & 3077 & 2719 & 11.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Volumetric characterization of the mixtures evaluated in the preliminary study.  
The main fraction of natural aggregates added to the mixtures incorporating 75% SSA was the 0/4 fraction because the SSA does not have enough particles of this size to meet the specified grading envelope. Moreover, these mixtures with SSA also need higher amounts of commercial filler than usual in conventional asphalt mixtures.
## 7 MT-SOTA Optimization Techniques
### Memory Reduction--Usage of Different Hash Algorithm for Non-Leaf Nodes  
According to our study results, the larger the hash output size is, the more data flash and RAM memory are required. Some ECUs may have restrictions on the available memory in their devices. To reduce the amount of memory used for the Merkle tree without compromising the algorithm, it suffices to store the number of bytes correspondent with the sufficient security level in the leaf nodes and then modify the Merkle tree to use different hash algorithms with smaller output sizes in the non-leaf nodes versus the leaf nodes. This can save many bytes per non-leaf node because each node of the tree is a single hash value, thus reducing the overall memory used by the Merkle tree. This optimization requires only changes in OemR and EcuX, where the Merkle tree scheme is implemented, without any impact on ImgR. When using SHA3-512 or other hash algorithms with 64-byte hash values for hashing the software clusters, we can reduce the memory used by 1 Kbyte for 32 nodes, and by 2 Kbytes for 64 nodes and 4 Kbytes for 128 nodes if we use SHA3-256 or other hash algorithms with 32-byte hash values for non-leaf nodes. For the case of using SHA2-256 or other hash algorithms with 32-byte hash values for hashing the software clusters, we can reduce the memory by 0.5 kbytes for 32 nodes, and by 1 Kbyte for 64 nodes and by 2 Kbytes for 128 nodes if we use SHA3-256 or other hash algorithms with 16-byte hash values for non-leaf nodes.
# Using Social Media & Sentiment Analysis to Make Investment Decisions
## 3 Methodology and Implementation
### Design
#### 3.2.3 System Models  
Once a high-level visualization of the system had been designed, we then created a class diagram to understand the web application models and the relationships they have with one another (See Figure 8).  
Figure 8: Class diagram.  
Figure 7: Web application diagram.  
As seen in this diagram, there is a reference to ASP.net in the Identity Table, which was the framework used to implement the system, and it will be discussed later in this section. Additionally, this diagram has a simple structure with only 3 models (excluding the ASP.net model):  
**Application User:** A model which contains all the details of the user such as their email and password.  
**Assets:** Has a many to many relationships with the Application User model. This structure allows users to add and remove existing assets without deleting the assets themselves. This was a design choice made while implementing the application for two reasons:  
Preventing users from adding the same assets to the system, which as a result, makes it easier for the deep learning system to generate all the asset metrics (SA metrics).  
Allowing us to have complete control over what assets can be added to the system was important because the whole approach was dependent on large volumes of relevant SM data. Utilizing this, we only added big companies (selected S&P500 Stocks) which would guarantee large quantities of relevant SM data, and overall provide more useful insight when testing our approach.  
Asset Metrics: Has a many to one relationship with Asset and includes a date field, a sentiment score field and a range of tweets that have been \"JSONified\".  
The **sentiment score** is a number between 0 and 1 and is the average sentiment of all the tweets of a particular day for that specific asset.  
If the score is less than, 0.5 it's negative; and if it's greater than 0.5, it's positive.  
This way of quantifying the sentiment was a design choice as it allowed the sentiment to be easily plotted on a graph in the UI. That way, the trend of sentiment could be visualized and compared to the stock market performance of each Asset, to test the proposed approach effectively.  
This model is mapped to an SQL table where the Deep Learning System saves the daily metric results to.
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 4 Discussion
As hypothesized, variations in the anatomical proportions of the phantom, despite similar overall dimensions, and the presence of plastic support structures, which were not accounted for in the simulations, could introduce additional losses and reflections, thereby affecting the measured radiation characteristics. Despite these minor variations, the comparable maximum gain values between the simulation and measurements enable confidence in the antenna's performance in a real-world scenario. The findings of this study have several important implications for the development of wearable technology. The successful integration of a dual-band antenna with glasses demonstrates a discreet and potentially comfortable solution for enabling seamless connectivity for both current and emerging wireless communication standards. The consistent performance under on-body conditions, particularly in the lower 5G band, highlights the potential for reliable communication in various usage scenarios. While the higher Wi-Fi band exhibited minor detuning, the maintained bandwidth still allows for robust operation. Future research directions could focus on further miniaturization of the antenna's structure to enhance its aesthetic appeal and integration with various eyewear designs. Investigating the impact of different user postures on the antenna's performance would also be beneficial for real-world deployment. Furthermore, exploring techniques to mitigate the minor impedance detuning observed in the higher band when in close proximity to the head could lead to even more robust performance. This study provides a foundational step towards realizing unobtrusive and efficient wearable antennas for next-generation wireless communication systems.
## 3 Results  
### Conditioning the NIR Irradiation Time  
The irradiation time was modulated to increase the irradiation intensity from 1 to 5 J/cm\({}^{2}\), based on previous studies [17]. We examined cell viability, cytoplasmic transmembrane potential (CMP), mitochondrial transmembrane potential (MMP), and intracellular ROS levels, immediately after NIR irradiation. Cell viability was assessed using the WST-1 assay, which was used for quantification of DNA synthesis by colorimetry. Cell viability was not affected when irradiation was below 4 J/cm\({}^{2}\) and was slightly reduced at 5 J/cm\({}^{2}\) (Figure 1b). The CMP was assessed with FLIPR dye, which indicates hyperpolarization as the dye bleaches and depolarizes as the dye intensifies. The cells subjected to 2-4 J/cm\({}^{2}\) showed hyperpolarization compared to the control (Figure 1c). MMP was assessed with JC-1 dye, which aggregates in the inner mitochondrial membrane to exhibit red fluorescence in polarized mitochondria and green fluorescence in other places. The ratio of red to green fluorescence intensity was used to determine the degree of MMP. Cells subjected to 1-2 J/cm\({}^{2}\) showed hyperpolarization as compared to the control (Figure 1d). ROS production was not linearly dependent on the NIR dose. The cells subjected to NIR irradiation of 2 J/cm\({}^{2}\) exhibited increased amounts of ROS, while those subjected to NIR irradiation of 3-5 J/cm\({}^{2}\) exhibited a reduction in ROS as compared to the control (Figure 1e). We chose 3 J/cm\({}^{2}\) NIR irradiation for subsequent experiments with IRE, which had no harmful effects on proliferation and induced CMP hyperpolarization, induced no effects on MMP, and reduced ROS levels.
## 2 System Methodology for Coexisting Public Safety Networks
### Details of the Channel Model  
The channel model was constructed as seen in Equation (5), calculating path loss, shadowing, and fast-fading from antenna gain:  
\[H_{u,j}(dB)=AG_{u,j}-PL_{u,j}-Shd_{u,j}-F_{u,j} \tag{5}\]  
where channel \(H_{u,j}(dB)\) encompasses the effect of pathloss \(PL_{u,j}\), shadowing \(Shd_{u,j}\), and antenna gains \(AG_{u,j}\) and it also has a fast fading \(F_{u,j}\) component.  
In this paper, the rural macro model is used as a path loss model for eNB-connected users [24] as shown in Equation (6):  
\[\begin{split} PL=69.55+26.16 log_{10}(f)-26.16 log_{10}(D_{b})+[44. 9-6.55 log_{10}(D_{b})]log_{10}(L)\\ -4.78(log_{10}(f))^{2}+18.33 log_{10}(f)-40.94\end{split} \tag{6}\]  
where \(PL\) is the distance between the BS and user in km, \(f\) is the carrier frequency in MHz, and \(D_{b}\) is the base station antenna height above ground in meters. The details of (6) are given in [24]. Shadowing and fast fading are considered in this paper [25; 26].  
The UL per-user signal-to-interference-plus-noise ratio (SINR) of user u associated with BS \(j\) on a non-blank (n) RB is as follows:  
\[SINR_{u,j}^{(n)}=\frac{P_{u,j}\big{|}H_{u,j}\big{|}^{2}}{\sum_{l\in B,\ l\
eq(M \cup j)}\rho_{l}^{(b)}P_{u,l}\big{|}H_{u,l}\big{|}^{2}+\sigma^{2}},\ \ \ \ \forall_{j}\ \in B \tag{7}\]  
where \(P_{u,j}\) is UL-transmitting power of user \(u\) associated with BS \(j\).  
The UL per-user SINR is as follows:  
\[SINR_{u,j}^{(n)}=\left(\begin{array}{cc}\frac{P_{u,j}\big{|}H_{u,j}\big{|}^ {2}}{\sum_{l\in B,\ l\
eq(M\cup j)}\rho_{l}^{(b)}P_{u,l}\big{|}H_{u,l}\big{|}^ {2}+\sigma^{2}},&\forall_{j}\ \in B,\ j\
eq M,\\ 0,&\forall_{j}\ \in M,\ \delta_{l,u}^{(b)}=0\end{array}\right. \tag{8}\]  
where \(P_{u,j}\) is the UL-transmitting power of user u associated with BS \(j\). \(\delta_{l,u}^{(b)}=\{0,\,1\}\) is the priority indicator, and is chosen depending on whether the user is in the CRE to access the blank (b) RB area.  
We utilize the minimum path loss-based and PS priority-aware user association using the following [13]:  
\[\psi_{u,j}^{k}=\arg max_{k\in[1,2,\ldots,K]}\delta_{u,j}^{k}\Big{(}PL_{min_{n,j}}^{k}+\mathit{bias}_{j}\Big{)} \tag{9}\]  
The details about Equation (9) are given in [13].