esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calculation Results and Partial Discussion
\(\Delta\)_f_ = 12.5 GHz); (**b**) Option 9 (proposition—Figure 8)—analog transport of IF over optical DWDM/UDWDM link (min. \(\Delta\)_f_ = 1.5625 GHz); (**c**) analog transport of baseband over optical DWDM link (128 antennas per sector)—extension of Option 8 (proposition—Figure 8). In the case of the need to use a higher radio carrier (>10 GHz), the RFoF method becomes ineffective. Here, it is worth using the intermediate frequency (intermediate-frequency-over-fiber (IFoF) technique) around 3 GHz (Figure 12b)--we assume that the DWDM system scope will be exceeded due to the lack of the need for optical line amplifiers--short optical paths. The introduction of a much denser DWDM grid (evolution towards EON/UDWDM) will further increase the efficiency of optical path utilization. However, only if we use a lower intermediate frequency (IF) or use only basebands (BBoF technique) directed to individual antenna modules. In Figure 12c, we see the cumulative OWS, but for zero RF carrier. In this case, the A-RoF mode was only used for BB signals, where a single BB signal occupies one optical channel with a width resulting from DSB modulation. The calculations were performed for the optical bandwidths of channels lying outside the standard DWDM grid [25]--Figure 12c. In a situation where one classic fiber is insufficient, it is recommended to introduce more fibers connected to one gNB-DU/RRH or use multi-core fiber in proportion to the number of sectors [50]. It can be noticed that the analysis of the D-RoF and A-RoF interfaces is slightly different, because in the case of digitized solutions, the bit rate is the starting point, while in analog solutions we operate on the frequency band occupancy. Optical links based on the DWDM/UWDM network can be used to transport signals of different formats. It is only important that signals at the nodes of this network are correctly recognized due to the photonic band they occupy in the core of the optical fiber.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Hybrid Detection Position Sensorless Method  
In recent years, a variety of position-free control strategies have been mixed to form a method for full speed range estimation. Such methods combine multiple magnetic features and use different methods for position estimation at different velocity ranges to meet performance.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
###### Abstract  
This paper presents the concept of a passive bistatic radar (PBR) system using existing phased array radar (PAR) as the source of illumination. Different from PBR based on common civil illuminators of opportunity, we develop an experimental PBR system using an high-power air surveillance PAR with abundant signal modulation forms as the transmitter. After the introduction of the PBR system and PAR signals, it can be concluded that the agility of the waveform parameters of PAR signal brings two problems to the signal processing of the PBR systems, which are not discussed in conventional PBR systems. The first problem is the time and frequency synchronization of the system, so we propose a direct wave parameter estimation method based on template matching to estimate the parameters of the transmitted signal in real time to achieve time and frequency synchronization of the system. The second problem is the coherent integration for moving target detection and weak target detection, so we propose a coherent integration method based on Radon-Nonuniform Fast Fourier Transform (Radon-NUFFT) to deal with the problems introduced by the agile waveform parameters. Preliminary results from the field experiment demonstrate the feasibility of the PBR system based on PAR signals, and the effectiveness of the proposed methods is verified.  
Keywords:passive bistatic radar; phased array radar; parameter estimation; coherent integration; aircraft surveillance +
Footnote †: journal: Electronics Letters
###### Abstract  
Diosgenin, a crucial precursor for steroidal drug production, has poorly understood regulatory pathways. Diosgenin is the primary active component of _Dioscorea zingiberensis_. Notably, _D. zingiberensis_ also possesses the highest diosgenin content among _Dioscorea_ species, reaching up to 16.15% of dry weight. This study identified DZHDZ32 as a potential regulator of diosgenin biosynthesis in _D. zingiberensis_ through transient overexpression. To validate its function, we developed an optimized genetic transformation method for _D. zingiberensis_ and generated two _DZHDZ32_-overexpressing lines. The DZHDZ32 transcription factor belongs to the HD-ZIP I subfamily and is localized to the nucleus. Notably, overexpression of _DZHDZ32_ resulted in a significant increase in its transcript levels in leaves (264.59- and 666.93-fold), leading to elevated levels of diosgenin and its biosynthetic intermediates, including cholesterol and \(\beta\)-sitosterol. Specifically, diosgenin content increased by 41.68% and 68.07%, cholesterol by 10.29% and 16.03%, and \(\beta\)-sitosterol by 12.33% and 19.49% in leaves compared to wild-type plants. Yeast one-hybrid and dual-luciferase assays demonstrated that DZHDZ32 directly binds to the promoters of _ACAT_ and _GPPS1_, consistent with the significant upregulation of _ACAT_ and _GPPS1_ expression (3.69- and 4.87-fold and 4.75- and 6.53-fold, respectively) in the overexpression lines. This study established an optimized genetic transformation method for _D. zingiberensis_ and identified DZHDZ32 as a key regulator of diosgenin biosynthesis. The discovery of DZHDZ32 has significant implications for enhancing diosgenin production and advancing steroidal drug development.  
Dioscorea zingiberensis; diosgenin; DZHDZ32; HD-ZIP I; regulation Article
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 6 Conclusions  
This paper introduced the QESIF, a Quantum-Enhanced Secure IoT Framework designed to safeguard smart city infrastructures by integrating QKD with classical IoT protocols and a quantum-aware intrusion detection system (Q-IDS). The framework dynamically adapts between quantum-secured and classical communication modes through a hybrid protocol stack, enabling resilience in diverse environmental and network conditions. The simulation results highlight the QESIF's performance, with an Attack Detection Rate of up to 98.7%, average latency as low as 20.3 ms, throughput peaking at 868 kbit/s, and minimal energy consumption of 13.4 mJ. Additionally, the QESIF effectively discarded over 95% of the quantum keys under high QBER in eavesdropped scenarios, ensuring strong forward secrecy. Future enhancements to the QESIF will focus on expanding its security and scalability through integration with satellite-assisted QKD networks. This extension would enable secure key exchanges across geographically distributed smart city zones, overcoming the range limitations of terrestrial QKD links. Additionally, incorporating blockchain-based auditing mechanisms can provide immutable logging of quantum key usage and anomaly detection, thereby strengthening trust, transparency, and accountability in multistakeholder smart city environments.  
**Author Contributions:** Conceptualization, A.R. and O.A.; methodology, A.R.; software, A.R.; validation, A.R. and O.A.; formal analysis, A.R.; investigation, A.R.; resources, A.R.; data curation, A.R.; writing--original draft preparation, A.R.; writing--review and editing, A.R. and O.A.; visualization, A.R.; supervision, O.A.; project administration, O.A.; funding acquisition, O.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research endeavor was generously supported by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University, which provided funding for this project under the designation number (R-2025-1878). The backing from the deanship has been instrumental in facilitating the comprehensive exploration and analysis undertaken in this study.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The author extends appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2025-1878).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Procedures  
Recruited subjects were divided into three groups, depending on the gait training protocol, as follows: 8 subjects underwent gait training with an end-effector t-RAGT (Geo-System, Reha Technology AG; Olten, CH)--Geo Group (GG); 8 subjects underwent gait training with an exoskeleton o-RAGT (Ekso(tm), Ekso Bionics, Richmond, California, USA)--Ekso Group (EG); 10 subjects underwent conventional gait training--Conventional Group (CG).  
Subjects in GG conducted \(15\pm 2\) sessions (3 days/week for 6 weeks) of t-RAGT using the G-EO System (Reha Technology AG, Olten, Switzerland) device. The practice consisted of robot-assisted walking, at variable speeds, for 60 min, exploiting BWS. In the first session, all participants started with 30-40% BWS, at a speed of 1.5 km/h; afterwards, speed was progressively increased to reach 2.2-2.5 km/h maximum, while BWS was progressively decreased, up to 20%, according to subject's tolerance.  
Subjects in EG conducted \(15\pm 2\) sessions (3 days/week for 6 weeks) of o-RAGT, using the Ekso(tm) (Ekso Bionics, Richmond, California, USA) wearable powered exoskeleton. Before starting the training period, a trained physiotherapist checked for correct alignment of the subject's joints with the robot and for areas of increased skin pressure, adjusting the fit accordingly. The exoskeleton gait parameters were fine-tuned using surface electromyography, as detailed in [PERSON] et al. [24]. Each single session of gait training lasted 60 min. ProStep Plus(tm) and Bilateral Max Assist were used as device settings: i.e., each subject's step was triggered by the user's lateral weight shift, and the amount of power contribution to legs during walking was totally provided by the robot. No strength was required by the subject, only proper balance and weight shifts were required to walk.  
Subjects in CG conducted \(15\pm 2\) sessions of CGT, including: muscle strengthening exercises of the lower limb; static and dynamic exercises for the recovery of balance in supine and standing positions using assistive devices; training gait exercises with parallel bars or in open spaces performed both with and without assistive devices; training to climb up and down stairs; exercises to improve proprioception in supine, sitting and standing positions, using a proprioceptive footboard; exercises to improve the trunk control.  
In all groups, the gait training was combined with daily conventional therapy including physical therapy (e.g., upper limb rehabilitation, functional task practice, muscle strengthening), speech therapy, and occupational therapy.
## 3 Experiments and Results
### Empirical Properties of the PINN
#### 3.3.2. Effect of Number of Samples on the Prediction Error and Training Time  
In this section, we investigate the influence of the number of samples on the training time and prediction error of the neural network. We test various number of samples. With respect to the size of the neural network, we use 2 hidden layers with 60 neurons in each layer.  
Table 4 displays the training time, RMSEs of predictive results as compared to the analytical solution, and the practical epochs for this experiment. We can see that all combinations' loss functions converge below the cut-off threshold within the maximum epoch. The Xavier initialization function sets different initial values for the weight matrix of the model, which makes the training results slightly different each time. Consequently, we can see that the training time corresponding to 1000 points in Table 4 is slightly different from the training results of the neural network with 2 hidden layers and 60 neurons in each layer in Tables 2 and 3.  
Finally, we observe that too few sample points, such as 10, converge the fastest while resulting in a high prediction error. Other than this option, the convergence speed increases as the sampling number goes up at the beginning; however, when it reaches a certain level, the improvement of the convergence speed stagnates or even becomes worse.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**WidthDepth** & **1** & **2** & **4** & **6** \\ \hline
5 & 20,000 & 8539 & 5126 & 5193 \\
10 & 13,120 & 4832 & 2634 & 3046 \\
20 & 8791 & 2239 & 1430 & 1591 \\
30 & 7493 & 1993 & 1146 & 1584 \\
60 & 5958 & 1161 & 1343 & 2936 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The practical epoch for different neural network sizes.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**WidthDepth** & **1** & **2** & **4** & **6** \\ \hline
5 & 1129 & 547 & 410 & 497 \\
10 & 739 & 311 & 211 & 293 \\
20 & 495 & 144 & 115 & 153 \\
30 & 422 & 128 & 92 & 152 \\
60 & 335 & 75 & 108 & 286 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The training time (s) for different neural network sizes.
## 3 Proposed Method
### Activity Recognition Model
```
1:\(S^{\prime\prime}=\{S_{i,t}:i=1\ldots n,t=1\ldots m\}\), \(A^{\prime\prime}=\{a_{t}:t=1\ldots m\}\), \(M\), \(i^{*}\)
2:\(D=\{d_{i,t}:i=1\ldots n,t=1\ldots m\}\)
3:for\(t=1\ldots m\)do
4:\(found=\) false
5:\(k=0\)
6:repeat
7:\(X=\) set of all \(k\)-element subsets of \(\{\{1,2,\ldots,n\}\setminus\{i^{*}\}\}\)
8:repeat
9:\(Z=\) random element from \(X\)
10:\(X=X\setminus Z\)
11:\(Z=Z\cup\{i^{*}\}\)
12:\(S_{t}=\{S_{i,t}:i\in Z\}\)
13:if\(R(S_{t},M)=a_{t}\)then
14:\(found=\)true
15:endif
16:until\(found\)or\(X=\emptyset\)
17:\(k=k+1\)
18:until\(found\)or\(k=n-1\)
19:for\(i=1\ldots n\)do
20:if\(found\)and\(i\
otin Z\)then
21:\(d_{i,t}=0\)
22:else
23:\(d_{i,t}=1\)
24:endif
25:endfor
26:endfor
```  
**Algorithm 3** Data preparation for training of models \(M_{i}\)  
Algorithm 3 searches for the smallest subset of sensor nodes that enables correct recognition of the human activity. This search is repeated for each time step covered in the sample data set. Dominant operation in this algorithm is the test of activity recognition for a selected subset of nodes (line 11). Each considered subset of sensor nodes has to include the cluster head node, thus the total number of possible subsets equals \(2^{n-1}-1\). In the optimistic case, data collected by the cluster head are sufficient for correct recognition, and only one test is performed. The pessimistic case is that the test is executed for all possible subsets. On average, \((2^{n-1}-1)/2\) tests are required. It means that the average time complexity of the algorithm is \(O(m(2^{n-2}-0.5))\). It should be kept in mind that this algorithm is used at the training stage, and its execution time does not impact the operation of sensor network.
## 4 Solution Method  
This paper proposes a novel heuristic known as MCGRH, the purpose of which is to solve the MCFLPD problem. This guarantees that all constraints in Table 1 are satisfied. At the outset, the idea of the algorithm is to select a set of facilities \(S\) to be opened, with size \(p\), from the set of potential facilities \(J\) that maximizes \(\sum_{i\;\in\;I}\;\sum_{j\;\in\;S}\;y_{j}\;c_{ij}w_{i}\) (i.e., maximizing the total demand of patients that it is possible to serve). In turn, the algorithm assigns each potential patient to the nearest open facility \(j\in S\) with the available capacity \(U_{j}\). Any available drones are then assigned to the opened facilities according to the highest unserved demand weights \(\sum_{i\;\in\;I}\;w_{i}\;a_{ij}\), where \(a_{ij}=1\) and \(j\in S\). Finally, patients are assigned to drones (in each opened facility) according to the lowest battery consumed \(b_{ij}\) followed by the highest demand weight \(w_{i}\) of all unserved patients of a specific facility \(j\), such that for each drone \(k\), the total battery consumed by the drone in serving the demand is less than the drone's total battery capacity (that is, \(\sum_{i\;\in\;I}\;b_{ij}\;\leq B_{k}\), where \(j\;\in\;S\).)  
The proposed method proceeds in three steps: (i) select the number of facilities to be opened from the potential facilities, (ii) assign patients to the opened facilities, and (iii) assign drones to the opened facilities and assign patients to those drones. Algorithms 1-3 present the details of these steps. An explanation of each algorithm is provided below.
## 3 Electromagnetic Performances Comparison and Analysis  
### Optimization Design  
As mentioned in (36), four feasible stator/rotor pole pair combinations, i.e., the \(12/7\), \(12/8\), \(12/10\), and \(12/11\) have been designed for comparison. Four models are optimized independently by the nondominated sorted genetic algorithm-II (NSGA-II). The common design parameters of all models are listed in Table 2 and the variables to be optimized are listed in Table 3. The optimization goals are to maximize the output torque density \(K_{T}\) and minimize the maximum torque ripple ratio \(T_{rip}\) with the armature current density of \(6\) A/mm\({}^{2}\). \(K_{T}\) and \(T_{rip}\) are defined as follows:  
\[K_{T}=\frac{T_{avg}}{V} \tag{37}\]  
\[T_{rip}=\frac{\max(T)-\min(T)}{T_{avg}}\cdot 100\% \tag{38}\]  
where \(T_{avg}\) is the average torque of the outer rotor, and \(V\) is the volume of the electromagnetic part of the machine.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Item** & **12/7** & **12/8** & **12/10** & **12/11** \\ \hline Phase & & & 3 & \\ Number of slots & & & 12 & \\ Number of modulator segments & & & 12 & \\ Pole pair number of armature windings & 7 & 8 & 10 & 11 \\ Coil pitch (Slot) & 1 & 1 & 2 & 5 \\ Pole pair number of the rotor PMs & 7 & 8 & 10 & 11 \\ Outer diameter of the stator (mm) & & & 190 & \\ Axial length (mm) & & & 50 & \\ Length of the inner airgap (mm) & & & 0.7 & \\ Length of the outer airgap (mm) & & & 0.5 & \\ Rated armature current density (A/mm\({}^{2}\)) & & & 6 & \\ Rated speed (rpm) & & & 500 & \\ \hline \end{tabular}
\end{table}
Table 2: The common design parameters of the optimized models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Item** & **Notation** & & & & \\ \cline{3-5} \multicolumn{1}{c}{Width of the PMs (mm)} & \(L_{pm}\) & **12/7** & **12/8** & **12/10** & **12/11** \\ \hline Width of the PMs (mm) & \(L_{pm}\) & [10, 20] & [10, 20] & [10, 20] & [10, 20] \\ Width of the modulator segments (\({}^{\circ}\)) & \(\theta_{m}\) & [7.5, 22.5] & [7.5, 22.5] & [7.5, 22.5] & [7.5, 22.5] \\ Width of the stator teeth (mm) & \(b_{1}\) & [10, 20] & [10, 20] & [10, 20] & [10, 20] \\ Thickness of the PMs (mm) & \(h_{pm}\) & [3, 8] & [3, 7] & [3, 5] & [3, 5] \\ Thickness of the modulator segments (mm) & \(h_{m}\) & [5, 10] & [5, 10] & [5, 10] & [5, 10] \\ Height of stator slots (mm) & \(h_{s}\) & [10, 18] & [10, 18] & [10, 18] & [10, 18] \\ Height of stator yoke (mm) & \(h_{gg}\) & [8, 20] & [8, 20] & [8, 20] & [8, 20] \\ \hline \end{tabular}
\end{table}
Table 3: The optimizing variables of models.  
The optimization process flow chart is shown in Figure 8. The optimization in this paper has 20 generations, with 100 designs in each generation. The Pareto fronts of four designs are presented in Figure 9. The designs indicated by the red arrow in Figure 9 are the final optimized designs. All final designs have the highest torque density when the torque ripple is below 10%.
## 2 Methodology
### EnGS-DGR Model
#### 2.2.1 Dynamic Graph Reconfiguration Algorithm
Then all subspace representations of each view are combined by \(Q=\frac{1}{v}\sum\limits_{v=1}^{V}\Big{|}Z^{(v)}\Big{|}+\Big{|}Z^{(v)}{}^{T} \Big{|}\), where the resulting matrix Q is the required affinity matrix, \(Z^{(v)}\) is the coefficient matrix of \(v\)th view, \(V\) is the number of views. The resulting affinity matrix Q is expressed as:  
\[Q=\left( \begin{array}{cccc}1&q_{12}&\cdots&\cdots&q_{1N}\\ q_{21}&1&&&q_{2N}\\ \vdots&&\ddots&&\vdots\\ \vdots&&&\ddots&\vdots\\ q_{N1}&q_{N2}&\cdots&\cdots&1\end{array} \right) \tag{5}\]  
Figure 5: The flow of low-rank tensor-constrained multiview subspace representation algorithm. where \(q_{ij}\) represents the quantized degree of correlation between the nodes \(i\) and \(j\). The greater the element value of matrix \(Q_{ij}\), the stronger the correlation between the nodes \(i\) and \(j\). Based on the scale of traffic flow forecasting, an appropriate threshold is selected. Setting the appropriate threshold as \(q^{H}\), a subgraph is constructed by selecting the traffic nodes with higher correlation degree than \(q^{H}\), which is described as:  
\[G_{sub}=(V_{sub},E_{sub},A_{sub}) \tag{6}\]  
where \(V_{sub}\) is the node set consisted of selected nodes; \(E_{sub}\) is the edge set composed of the road segments connected by the selected nodes. \(A_{sub}\in\mathbb{R}^{|E_{sub}|\times|E_{sub}|}\) is the adjacency matrix of the constructed subgraph, where \(|E_{sub}|\) denotes the cardinality of edge set of \(E\). In details, \(A_{sub}(i,j)=1/d_{ij}\) represents the weight between the selected nodes \(v_{i}\) to \(v_{j}\) along the driving direction with the road section length \(d_{ij}\).
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## Appendix A.2.2 Bodies of Influence (BOIs)  
Bodies of Influence (BOIs) are auxiliary geometries defined around the wing wall that enhance mesh control and improve cell quality within the volume surrounding the wing. To align with the objectives of this framework, every generated geometry must be meshed, ensuring that the mesh design accommodates simulations across various angles of attack. By strategically defining BOIs around the wing wall, the mesh can be refined in critical regions, such as the wake and upstream flow areas, thereby enabling the use of a \"universal mesh per geometry\", which improves efficiency.  
Considering the shape of the far-field defined in the previous subsection, the _BOIs_ method generates two trapezoidal frustums, as auxiliary geometries for mesh refinement. As shown in Figure 11, the frustums are strategically oriented with their larger bases positioned towards the wing's wake, where greater refinement is essential, particularly at higher angles of attack. The dimensions of each frustum are also parametrized with the MAC and the wing's semispan (\(b_{2}\)).