esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
###### Abstract  
This paper deals with the problem of detection and direction of arrival (DOA) estimation of slowly moving targets against clutter in multichannel mobile passive radar. A dual cancelled channel space-time adaptive processing (STAP) scheme is proposed, aiming at reducing the system computational complexity, as well as the amount of required training data, compared to a conventional full array solution. The proposed scheme is shown to yield comparable target detection capability and DOA estimation accuracy with respect to the corresponding full array solution, despite the lower computational cost required. Moreover, it offers increased robustness against adaptivity losses, operating effectively even in the presence of a limited set of training data, as often available in the highly non-homogeneous clutter scenarios experienced in bistatic passive radar. The effectiveness of the proposed scheme and its suitability for passive GMTI are demonstrated against both simulated and experimental data collected by a DVB-T-based multichannel mobile passive radar.  
passive radar; GMTI; AB-STAP; DOA estimation +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Untpaired and Unlabeled Cine to Tagged Image Transformation
#### 2.1.3 Deep Learning Based _Cine_ to Tagged Data Transformation  
An unpaired image-to-image transformation translation neural network (i.e., CycleGAN) was trained using an unlabeled and unpaired collection of _cine_ and tagged images. The model consists of four sub-networks, two generators and two discriminators. We modeled both the transformation from _cine_ to tagged and vice versa are modeled (Figure 3). This architecture was chosen for its cycle-consistent optimization loss, ensuring that transformed images are optimized not to displace inherent cardiac structures [11]. In our work, the myocardium segmentation mask was transferred from its original _cine_ image to the newly generated tagged equivalent. A CycleGAN as described in the original paper by [PERSON] et al. (2020) was trained for 200 epochs on the aforementioned dataset containing 2440 _cine_ and 4539 unlabeled tagged images. As the model is trained on images through acquisition time (full cycle, from ED to ES to ED), it is theoretically able to deform taglines according to the myocardial contraction found in the images from the tagged domain.
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.3 Glass Data Set  
According to the steps of optimized BT-MTS, fitness function values can be obtained, as shown in Table 10, from which it can be seen that the classification sequence of optimized BT-MTS was category 7, 2, 3, 5, 1, and 6. The thresholds for the MTS1-MTS5 are 5.6075, 5.1663, 29.8456, 14.2530, and 10.1904. The feature selection results are shown in Table 11. It should be noted that the number of samples in category 6 is small, and after the random division of training and testing samples, the number of samples involved in training is even smaller and insufficient for calculation, so this paper directly places it in the last position for classification.  
The classification order of BT-MTS is category 5, 2, 7, 1, 3, and 6, and the thresholds of MTS1-MTS5 are 2.5646, 2.2468, 2.7533, 1.9409, and 2.7274, respectively. The corresponding feature selection results are shown in Table 12, and the DRR of the two MTS methods can be obtained, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\) & \(x_{8}\) \\ \hline MTS1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ MTS2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ MTS3 & 1 & 0 & 0 & 0 & 0 & 0 & 1 \\ MTS4 & 0 & 1 & 1 & 0 & 1 & 0 & 0 \\ MTS5 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\ \hline \end{tabular} Note. “1”means using the variable, “0” means not using the variable.  
\end{table}
Table 11: Feature variable selection results of optimized BT-MTS of the glass data set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\) & \(x_{8}\) \\ \hline MTS1 & 1 & 0 & 1 & 1 & 1 & 1 & 0 \\ MTS2 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 \\ MTS3 & 0 & 1 & 1 & 1 & 0 & 1 & 1 & 0 \\ MTS4 & 1 & 1 & 0 & 1 & 1 & 1 & 0 & 1 \\ MTS5 & 0 & 1 & 1 & 1 & 0 & 1 & 1 & 0 \\ \hline \end{tabular} Note. “1”means using the variable, “0” means not using the variable.  
\end{table}
Table 12: Feature variable selection results of BT-MTS of the glass data set.
## 6 Limitations and Future Research Directions  
This study has limitations that open new avenues for research. While the CR-ICC incorporates technical and contextual aspects, it does not consider how interactions between players and opponents influence individual performance, an essential topic for future studies. The data analyzed focused exclusively on women's matches, limiting its generalizability; therefore, it is necessary to include men's competitions to explore potential gender differences.  
Additionally, the data were derived from a single event, highlighting the need to validate the model across multiple competitions. Psychological factors such as anxiety, which impact performance under pressure, were also not addressed and could enrich future analyses. Finally, exploring advanced tools such as multi-criteria decision analysis (MCDA) and machine learning could complement the CR-ICC by integrating more criteria and dynamic patterns, improving its predictive precision and applicability.
## 3 The Self-Triggered Controller Design
### Event-Triggered Control Algorithm  
The self-triggered control is divided into two phrases. The first phrase is about parameter design, and the second phrase is online computing of triggering interval \(\sigma(x(t_{k}))\). First, for a given power grid dynamic model (4) and \(Q\), the \(\tau_{min}\) is calculated. The best value of \(\theta\) is designed by Theorem 4. Optimal searching algorithms consider both state and communication cost in cost function. If the feedback with gain \(K\) does not have a solution with given \(\gamma\) and \(\beta\), \(\beta\) and \(\gamma\) should decrease and increase respectively until there is a solution of \(K\). After \(\theta\), \(\tau_{min}\) and \(\tau_{max}\) are selected, the approximation error \(\varepsilon_{1},\varepsilon_{2}\) and \(\varepsilon_{3}\) are calculated. Meanwhile, the two-point Taylor expansion of \(\Phi\), \(\psi_{1}\) and \(\psi_{2}\) can be obtained by software such as Mathematica.  
Second, the triggering interval is calculated online. The power system obtains new system state \(x_{k}\) from sensors, when a self-triggered time is up. Then, the time \(t_{k+1}=t_{k}+\sigma(x(t_{k}))\) is calculated for the next triggering. For a system state \(x_{k}\), the coefficient of polynomial respect to \(z\) is derived by \(x^{T}H(z)x\). Then \(z_{c}\) is solved using Equation (18) by inverse iteration. Therefore, the triggering interval can be obtained by \(z_{c}\) and (17). The new control output \(u=-Kx(t_{k})\) is calculated and applied to actuators. At last, the control output is updated at the next triggering time \(t_{k+1}\).
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 4 Experiment and Discussion
### Result and Discussion  
After sliding the dynamic measurement 10 times, the measurement data are processed by static correction and the proposed GA-SA dynamic compensation method, respectively. The results of the raw data and compensated data by the two methods are shown in Figure 20a. To further verify the accuracy, the pre-calibrated geomagnetic field values are subtracted from the data in Figure 20a and are shown in Figure 20b. During the compensation process, the vertical component is converted from the east-north-up coordinate system to the north-east-down coordinate system, so the raw data of the vertical component is multiplied by \(-1\) to facilitate comparison.  
The comparison of the average standard deviations of the 10-time measurements is shown in Table 6. Bx is the north component of the geomagnetic field; By is the east component of the geomagnetic field.  
It is clear from the comparison results of Figure 20 that the measurement stability of the dynamic compensation data by the proposed method is much better than raw data and static compensation data. Meanwhile, it is clear from Figure 20b that the accuracy of the dynamic compensation data is best since it is almost entirely close to zero after subtracting the field calibration. Furthermore, in Table 6, the mean standard deviations of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Bx** & **By** & **Bz** \\ \hline \multirow{3}{*}{Standard deviation/nT} & Before compensation & 95.30 & 277.35 & 108.51 \\ \cline{2-5}  & Static compensation & 62.90 & 107.96 & 79.95 \\ \cline{2-5}  & Dynamic compensation & 3.41 & 2.36 & 2.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the mean standard deviations of 10-time measurements.  
Figure 19: Real-time curve display on the upper computer.  
north/east/vertical components decrease from 95.30/277.35/108.51 nT to 3.41/2.36/2.75 nT. The comparison of the mean standard deviations of 10-time measurements shows the accuracy and stability of the proposed method. Thus, the results and comparison of the geomagnetic measurement experiment verify the effectiveness of the proposed method in a practical application.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 6 Performance Analysis
### Communication Cost Analysis
Thus, the total cost of vehicle, fog node, and cloud server is 5312 bits. In [PERSON] et al. 's protocol [21], a smart device transmitted \(\{CID_{s},RV_{2},C_{sm},T_{1}\}\), so we calculated the communication cost of the vehicle as 864 bits. A fog node transmitted \(\{CID_{s},CID_{f},C_{sm},C_{f},F_{c},FULID_{i},RV_{2},FV_{2},T_{1},T_{2}\}\) and \(\{F_{sm},T_{4},FCSUID_{i},T_{3},CV_{2}\}\), so we calculated the communication cost of the fog node as 3136 bits. The cloud server transmitted \(\{CV_{2},T_{3}\}\), so we calculated the communication cost of the cloud server as 352 bits. Thus, the total cost of vehicle, fog node, and cloud server is 4352 bits. In [PERSON] et al. 's protocol [22], we calculated the computational costs by combining those of the fog nodes and the RSUs. A vehicle transmitted \(M_{1}=\{TID_{U_{i}},R_{1},a\}\), so we calculated the communication cost of the vehicle as 704 bits. A fog node transmitted \(M_{2}=\{M_{1},TID_{RSU_{k}},R_{2},\beta\}\), \(M_{3}=\{M_{2},TID_{FN_{j}},R_{3},\gamma\}\), \(M_{5}=\{R_{10},X_{k}\}\), and \(M_{6}=\{R_{9},X_{i}\}\), so we calculated the communication cost of the fog node as 4672 bits. The cloud server transmitted \(M_{4}=\{R_{7},R_{8},R_{9},X_{j}\}\), so we calculated the communication cost of the cloud server as 1344 bits. Thus, the total cost of vehicle, fog node, and cloud server is 6592 bits. In our proposed protocol, a vehicle transmitted \(D_{1}=\{R_{1},RID_{i},Q_{i},ID_{j}\}\), so we calculated the communication cost of the vehicle as 960 bits. A fog node transmitted \(D_{2}=\{R_{1},RID_{i},R_{2},r_{2}R_{1},L_{j},ID_{j}\}\) and \(D_{4}=\{K_{ij},r_{2}R_{3},Auth_{i}\}\), so we calculated the communication cost of the fog node as 2432 bits. The cloud server transmitted \(D_{3}=\{K_{ij},R_{3},r_{3}R_{1},Auth_{j}\}\), so we calculated the communication cost of the cloud server as 1152 bits. Thus, the total cost of vehicle, fog node, and cloud server is 4544 bits. We show the comparison results of communication cost for our proposed protocol and other related protocols in Table 6 and Figure 7. In Table 6 and Figure 7, the results show that our proposed protocol has the lowest total communicational cost among other related protocols. Therefore, we can state that our proposed protocol has relatively higher communication efficiency than other related protocols.
## 2 Materials and Methods
### Methods
#### 2.3.3 Hurst Exponent Analysis  
The R/S method is a classic and widely recognized approach for calculating the Hurst exponent [38]. The main steps involved in the calculation are as follows [39]:  
\[\left\langle\xi\right\rangle_{\tau}=\ \frac{1}{\tau}\sum_{t=1}^{\tau}x(t),\ \ \tau=1,\ 2,\ldots,\ n \tag{8}\]  
\[X(t,\ \tau)=\sum_{u=1}^{t}\big{(}\xi(\mu)-\left\langle\xi\right\rangle_{\tau} \big{)},\ 1\leq t\leq\tau \tag{9}\]  
\[\text{R}(\tau)=\max_{1\leq t\leq\tau}X(t,\ \tau)-\min_{1\leq t\leq\tau}X(t,\ \tau),\ \tau=1,\ 2, \ldots,\ n \tag{10}\]  
\[\text{S}(\tau)=\bigg{(}\frac{1}{\tau}\sum_{t=1}^{\tau}(\xi(t)-\left\langle\xi \right\rangle_{\tau})^{2}\bigg{)}^{1/2},\ \ \tau=1,\ 2,\ldots,\ n \tag{11}\]  
\[\frac{R(\tau)}{S(\tau)}=\left(c\tau\right)^{H} \tag{12}\]  
Based on the work of [PERSON] (H) [40] and [PERSON] [41], the H ranges from 0 to 1. When H is equal to 0.5, it signifies that the time series follows a random walk, with no persistence, meaning that future trends are independent of past trends. A value greater than 0.5 suggests positive persistence, where future trends are likely to align with past trends, with the degree of persistence increasing as the value rises. Conversely, a value below 0.5 signifies negative persistence (anti-persistence), implying that upcoming patterns tend to oppose past trends, with the opposition becoming stronger as the value decreases.
## 4 Experimental Results and Discussions
### Flexural Performance of Concrete Pedestrian Decks
#### 4.2.2 Ductility Evaluation
The required amounts of steel fibers to satisfy the minimum value of 0.39 were calculated to be 0.98%, 1.08%, and 1.10% for the 3D, 4D, and 5D hooked-end fibers, respectively. In general, for reinforced-concrete beams, DI is calculated based on deflections at the steel bar yielding and peak points [29; 30]. However, because no conventional steel rebar was used for the SFRC decks tested in this study, the deflection at the steel bar yielding point was replaced by the cracking deflection (\(\Delta_{cr}\)), as given in Equation (6). The calculated DIs are shown in Figure 10b. A higher DI was generally obtained with a higher amount of steel fibers, similar to the results of the \(P_{u}/P_{cr}\) ratio. The highest DI was found to be 5.5 for the specimen with 1.0 vol% of 3D hooked-end steel fibers, and the specimen with 1.0 vol% of 4D hooked-end fibers exhibited the second highest DI of 4.2. Identical to the results of the \(P_{u}/P_{cr}\) ratio and DI, at the high volume fraction of 1.0%, the DI was reduced by increasing the number of hook ends. This is caused by the enhanced post-cracking flexural stiffness as shown in Figure 7c. The specimen with 3D hooked-end fibers exhibited a gradual and continuous  
Figure 9: Effects of hooked steel fiber geometry and volume fraction on cracking load and peak load ratio. increase in load after the cracking point, while the specimen with 5D hooked-end fibers provided a steep increase in load after cracking, quickly reaching the maximum point, followed by a gradual load decrease. Thus, the higher cracking load and post-cracking flexural stiffness of the specimen with 5D hooked-end fibers resulted in a lower DI. On the other hand, the DI increased by increasing the number of hook ends at the moderate volume fraction of 0.6%. Thus, the highest DI of 3.8 was found in the case of 5D hooked-end fibers at the volume fraction of 0.6%. Consequently, the steel fiber with a lower number of hook ends led to the steepest improvement in ductility of concrete pedestrian decks and the 3D hooked-end steel fiber is the most effective in enhancing the ductility of decks at a high fiber volume fraction of 1%.
## 5 Conclusions  
This study comprehensively evaluates the occupant kinematics and injury responses in a zero-gravity seat under low-, medium-, and high-impact speeds with different seat belt systems. The THUMS-based finite element model assembly of occupant restraint systems consisted of a conventional three-point seat belt and a seat model in the regular position. The simulation model was verified through the PMHS test, and good agreements were obtained for both the kinematic postures at different collision times and time-history curves of head acceleration, head rotation, and T1 acceleration. As the impact speed increased, the occupant in the zero-gravity seat with the three-point seat belt showed different kinematic and injury responses. When the impact speed was at a low or medium level, the occupant could be restrained by the seatback and the three-point seat belt system. The head injury metrics, including the BrIC and CSDM, neck injury metrics, including the cervical spine injury metric, N\({}_{\text{ij}}\), N\({}_{\text{km}}\), and NIC, and the cervical ligament injury metrics ALL, PLL, CL, LF, and ISLs were all below their thresholds. As the speed increased to 56 km/h, the maximal seatback rotation angle increased to 13.1\({}^{\circ}\), and the occupant's head moved out of the headrest and collided with the headrest at 100 ms. The collision led to a surge in the contact force between the head and the headrest and of all the head injury metrics and neck injuries, most of which surpassed their corresponding thresholds. In terms of the four-point seat belt, the out-of-position posture of the occupant's head can be avoided, as well as the following collision between the neck and the headrest. The head injury indices BrIC and CSDM (0.25) were reduced by 66% and 70%, respectively, and the neck injury indices N\({}_{\text{ij}}\), N\({}_{\text{km}}\), and NIC were reduced by 22%, 52%, and 39% respectively. The cervical ligament injury metrics ALL, PLL, CL, LF, and ISL were reduced by 37%, 52%, 57%, 22%, and 46%, respectively.  
Conceptualization, W.T., X.Z. and X.Y.; methodology, P.Z., X.Y. and W.T.; validation, P.Z.; investigation, P.Z., J.Z. and Y.L.; writing--original draft preparation, P.Z. and Y.L.; writing--review and editing, P.Z., W.T. and X.Z.; visualization, P.Z. and J.Z. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support of the Jiangsu University Faculty Startup Fund under grant [5501120014] and the Jiangsu Shuangchuang Doctor Program under grant [1711120022].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Weichai Power Co., Ltd. Author [PERSON] was employed by the company YA Engineering Services, LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# PharmiTech: Addressing Polypharmacy Challenges through AI-Driven Solutions
## 4 Case Study
### Herb-Drug Interaction Detection  
The pharmacist selects the \"List all possible interactions\" option to investigate any potential HDI. When using this option, it is important to provide the name of the drug to be analyzed, along with relevant patient information such as age, gender, and any associated medical conditions. Considering this use case, the patient has 66 years old, female, and suffers from both atrial fibrillation and asthma. Thus, the pharmacist enters this information in the system. Under the pathologies section, he needs to select \"Cardiovascular\" and \"Respiratory\" options, as demonstrated in Figure 7.  
Upon clicking the \"Click here\" option, a pop-window with a table is displayed, presenting a list of herbs that interact with the drug warfarin. The table provides essential information to the pharmacist, including the scientific and common names of the herbs, the clinical outcome resulting from the interaction, the significance value, and the level of interaction. Figure 8 provides a visual representation of this information available to the pharmacist. The system has detected three different HDIs. These interactions involve the  
Figure 6: ForPharmacy Decision Support System Use Case.  
Figure 7: Selection of the attributes needed to evaluate the interaction.  
herbs Goji berry, Chamomille, and Korean red ginseng. Simultaneous use of warfarin and Matricaria Chamomilla herb is linked to a 50% hazard percentage, indicating a moderate level of interaction. This interaction may lead to potential adverse effects like elevated International Normalised Ratio (INR) levels and an increased risk of bleeding. The INR blood test gauges an individual's blood coagulation rate. When an individual's INR test result surpasses their designated target range, it signifies that their blood is coagulating at a slower pace, heightening the likelihood of bleeding [32; 33; 34].  
Additionally, the interaction with Goji berry is of significant concern as it presents a high risk, leading to symptoms such as bruising, echymosis, pistaxis, hematochezia, and an elevation in INR. Although this HDI has a significance value of 0% due to its limited documentation in the literature (reported only once), it is important to inform the patient about it given the potential symptoms associated with it. On the other hand, the literature reports no effect or hazard level associated with the interaction between Korean red ginseng and the drug warfarin. Considering the available information, the pharmacist provides Mrs. [PERSON] with clear and straightforward guidance on the precautions she should take.
## 3 Results
### Impact of Frequency  
Regarding the impact of the resonant frequency on the induced electric field, the scatter plots in Figure 8 present the correlation of the W values for each path of the CIEDs inside the model of Duke, at LD\({}_{3}\), as were estimated at 1.5 T and 3 T. When the power of each coil was adjusted to generate B\({}_{1}\)\({}^{+}\)RMS = 2 \(\upmu\)T at the center of the coil, the electric field was significantly lower at 1.5 T than that at 3 T (Figure 8a), while, for the SAR fixed at the normal operation limiting values, the difference between the results of the two resonant frequencies was much lower. Figure 9 displays the resulting distribution in dB of the deviation between the electric field at both resonant frequencies for all numerical models. Assuming constant B\({}_{1}\)\({}^{+}\)RMS, the electric field was higher at 3 T along the cardiac paths than at 1.5 T from 1.5 to 5.7 dB. On the other hand, fixing SAR resulted in differences in the range of \(-1.7\) to 1.6 dB, demonstrating that there are paths with a higher induced electric field at 64 MHz than at 128 MHz.  
Figure 6: Distribution of the magnitude of the tangential electric field at 128 MHz inside the Duke model over all imaging positions. The input power of the coil was adjusted to generate (a) B\({}_{1}\)\({}^{+}\)RMS = 2 \(\upmu\)T at the iso-center and (b) the limiting value of the global SAR. The blue and the red dotted lines present the mean values of W for the right and left positioning of the IPG, respectively.  
Figure 7: Distribution of the magnitude of the tangential electric field at 128 MHz inside the Duke model over the dimensions of the coils at landmark LD3. The input power of the coil was adjusted to generate (a) B\({}_{1}\)\({}^{+}\)RMS = 2 \(\upmu\)T at the iso-center and (b) the limiting value of the global SAR. The blue and the red dotted lines present the mean values of W for the right and left positioning of the IPG, respectively.