esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Discussion  
In this paper, we propose a fast slope-curvature hybrid reconstruction algorithm (FA) and compare it with conventional wavefront reconstruction methods. Through simulation experiments, we find that the FA algorithm has higher reconstruction accuracy in the ideal case and stronger noise immunity in the presence of noise. Compared with the traditional slope-curvature hybrid algorithm and the multi-directional slope-curvature hybrid algorithm, the FA algorithm has a significant advantage in reconstruction accuracy. By introducing slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions with smaller truncation errors, the FA algorithm can provide more accurate reconstruction results. Compared with hybrid reconstruction algorithms that directly introduce information about the diagonal direction, the FA algorithm has a significant accuracy advantage for some reconstructed surface types and provides reconstruction accuracy comparable to existing algorithms for other surface types. In addition, when measurement noise exists, the FA algorithm has smaller reconstruction errors and stronger noise immunity compared to conventional algorithms. In the case of higher-order aberrations, the FA algorithm provides more accurate reconstruction results and introduces more integration loop points to reduce the effect of noise. The null test experiment based on the FHAM-WS has verified the effectiveness of the FA algorithm in practical measurements.  
In summary, the FA algorithm proposed in this paper provides higher reconstruction accuracy and stronger noise immunity compared to traditional wavefront reconstruction methods. This algorithm is of great practical significance for wavefront reconstruction and other optical imaging applications.  
Figure 11: The reconstruction results of 10 groups of FHAM-WS measurements produced by the two algorithms: (**a**) reconstruction accuracy, (**b**) reconstruction time, (**c**) reconstructed wavefronts under FA algorithm.  
**Author Contributions:** Conceptualization, S.L.; methodology, S.L.; software, H.Z. and S.L.; validation, H.Z. and S.L.; formal analysis, S.L.; investigation, H.Z. and S.L.; resources, Y.L. and K.L.; data curation, K.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L., H.Z. and K.L.; visualization, S.L.; supervision, K.L.; project administration, K.L.; funding acquisition, Y.L. and K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (61975013, 62175014); National Science and Technology Major Project (2017 ZX02101006-001); Major Scientific Instrument Development Project of National Natural Science Foundation of China (11627808).  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Besides dielectric metasurfaces, magnetic metasurfaces have also been created for enhancing the RF magnetic field [30]. In this case, the aim is to increase the equivalent magnetic permeability [31]. In both the dielectric and magnetic versions, metasurfaces have been proved to be an effective medium to improve the magnetic field distribution and thus, to guarantee significant image quality improvement [10; 11; 32]. However, to date, a severe limitation of metasurfaces employed in MRI relies on the operating frequency. Indeed, only single frequency metasurfaces have been developed by preventing this promising technology from being extended for multi-nuclei MRI systems. Therefore, to overcome the actual literature limits and, for the first time to the best of the authors' knowledge, this paper presents an innovative dual-tuned metasurface able to guarantee dual-nuclei imaging and to enhance the magnetic field distribution for 1.5 T MRI scanners. In particular, the work aims to explore the possibility to improve both the transmission efficiency and the signal-to-noise ratio for \({}^{1}\)H and \({}^{23}\)Na MRI imaging with a single metasurface, which is able to simultaneously perform at both related Larmorfrequencies. This can be translated into the reduction in scanning time, instrumentation complexity, and overall system costs. From a design perspective, the proposed metasurface comprises a 5 \(\times\) 5 passive array, in which each unit-cell is properly loaded with a capacitor to control the flowing current by resorting to an opportune analytical model [32; 33]. To achieve a double-frequency operation, each unit-cell consists of two concentric spiral coils; the internal one resonates at the proton Larmor frequency (64 MHz), whereas the external one is at the sodium Larmor frequency (17 MHz). By interposing the passive metasurface between a standard MRI RF loop and the biological tissue, the overall transmission efficiency \(T_{x}\) and signal-to-noise ratio improvements are evaluated at both the operating frequencies. In particular, theoretical and numerical results are supported by workbench measurements acquired on fabricated PCB prototypes. The remainder of the paper is organized as follows. Section 2 describes the imaging system design and the analytical model behind the proposed solution. Further, the numerical and experimental test-cases are also reported in Section 2, while Section 3 is devoted to presenting the numerical and experimental results. Finally, conclusions and future developments are discussed.
## 4 Data-Driven CIM Construction  
With the rapid advancement of urban digitization, CIM is transitioning from static models to dynamic, data-driven intelligent systems. By harnessing multi-source data collection, intelligent analytics, and advanced visualization technologies, CIM enables the precise perception, predictive modeling, and optimization of urban operations, thereby enhancing the decision-making efficiency and resource allocation capabilities. In domains such as smart city management, transportation optimization, and energy distribution, data-driven CIM provides robust support for sustainable urban development.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 7 LoRaWAN Improvements
### Synchronization and Scheduling for LoRaWAN
The synchronization mechanism consists of end node asking for the synchronization and scheduling of time slots. The synchronization request can happen in band or in a separate channel reserved for synchronization traffic only. The synchronization and scheduling entity will reply with the current time slot, current time offset in the current time slot, as well as all the future time slots where end node can transmit. The information about scheduled time slots is compressed using probabilistic data structure, making it possible to fit in one packet payload. Based on the reply, the end node can determine the current time and future time slots when it can transmit. Synchronization can happen in-frequently and considers clock drifts of end node. It is shown that PDR of synchronized network is increased by 30% for networks using SF12, using practically the full network capacity. Authors of [108] showed only implementation for single SF deployments. For multi-SF deployments, SNR based scheduling need to be performed. Another approach is to use slotted-Aloha for LoRaWAN [109]. An ACK based time synchronization algorithm is proposed in [109]. It is shown that, by using slotted-Aloha, collision probability is decreased three times compared to pure Aloha.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 3 Double-Layer CS Framework-Based DOA Estimation
### Double-Layer CS Framework
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Single-Layer CS**} & \multicolumn{2}{c}{**Double-Layer CS**} \\ \hline
**Packet Size** & \(\mu\left(\mathbf{A}\right)\) & \(\mu_{av}\left(\mathbf{A}\right)\) & \(\mu\left(\mathbf{A}\right)\) & \(\mu_{av}\left(\mathbf{A}\right)\) \\ \hline
1 & 0.2859 & 0.2297 & 0.2825 & 0.2306 \\
8 & 0.5911 & 0.3328 & 0.2905 & 0.2269 \\
16 & 0.7489 & 0.4077 & 0.2920 & 0.2311 \\
32 & 0.8866 & 0.5118 & 0.2927 & 0.2296 \\
64 & 0.9745 & 0.5616 & 0.2929 & 0.2308 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mutual coherence of the equivalent measurement matrix under the single-layer CS and double-layer CS framework when \(N=512\) and \(M=64\). Figure 7: Histograms of the absolute off-diagonal elements of the corresponding Gram matrix to the equivalent measurement matrices under the single-layer CS and double-layer CS framework when \(N=512\) and \(M=64\).
## 6 Conclusions  
A NAM on the inductance identification method of DDPMSM with SWF is proposed herein, in which the spatial position of shorted turns in the ITSCF is considered. The inductance analytical expressions of the faulty coil are derived in detail. The accuracy of the proposed method is validated by the comparison with the results obtained by the FEM, NAM, and SAM.  
Based on the proposed analytical method, the inductances of the faulty coil of the branch are calculated at different fault conditions and the fault current is also studied. The inductances and fault currents calculated by the NAM are compared with the ones obtained by the FEM and SAM,  
Figure 17: The waveforms of the fault current \(i_{f}\) when there are six turns shorted in layer 2 of the faulty coil: (**a**) the shorted turns are the turns from no. 13 to no. 18; (**b**) the shorted turns are the turns from no. 16 to no. 21; (**c**) the shorted turns are the turns from no. 19 to no. 24.  
Figure 18: The waveforms of the fault current \(i_{f}\) when there are 10 turns shorted in the different layers of the faulty coil: (**a**) the shorted turns are the turns from no. 8 to no. 17; (**b**) the shorted turns are the turns from no. 19 to no. 28; (**c**) the shorted turns are the turns from no. 32 to no. 41.  
which shows that the accuracy of the proposed method is higher than that of the SAM. Additionally, it can be concluded that, as the fault position changes from the bottom of the slot to the slot opening, the self-inductance of the faulty part gradually decreases while the self-inductance of the healthy part tends to increase, the mutual inductance between the faulty and healthy parts gradually decreases and the fault current of the faulty coil is reduced.  
The present work has laid an important foundation for the fault detection, modeling, and fault-tolerant control of the DDPMSM.  
Conceptualization: C.G.; data curation: M.G. and Y.H.; formal analysis: M.G.; funding acquisition: C.G. and J.S.; methodology: C.G., M.G., J.S., and Y.H.; resources: P.S.; software: M.G. and P.S.; writing-original draft: M.G.  
This work is supported by the National Natural Science Foundation of China under grant no. 5177060 and no. 51277054, and the Key Science and Technology National Program of Henan Province, no. 192102210230.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Network Design  
The network design is explained in Figure 4. The communication process between objects or incubators uses the MQTT protocol and a WiFi network to transmit sensor data. Communication using the MQTT protocol involves the terms subscribe and publish. An IoT broker functions to receive messages from publishers and then distribute them to subscribers who have subscribed to relevant topics [29].  
Subsequently, the data on the broker are subscribed to by clients and then displayed in the web application or stored in the database. In this research, the IoT broker is installed on a Raspberry Pi mini-computer, which also functions as an Apache server for the web application [17]. Mosquito is used as the MQTT broker service for the IoT broker [30]. Several configurations on the IoT broker, such as port settings, authentication, and host settings, must be configured in advance.
## 4 Discussion
### Modulus of Elasticity  
The UCS test provided important information about each soil type's load-bearing capacity prior to failure, while the ME test complemented this analysis by describing the stiffness of the soil when the material was still within the elastic range.  
As shown in Figure 15, the silt sample exhibits a concave curve, which indicates a nonlinear relationship between ME and storage time. The maximum stiffness was reached after 28 days.  
The coefficient of determination (R\({}^{2}\)) for this correlation is 0.8838, indicating a strong relationship between the variables and Equation (7).  
\[y=0.003831x+0.0003984\ x^{2}-0.000008213x^{3}+0.08845 \tag{7}\]  
where \(x\) is time (days).  
Figure 14: Correlation equation based on test results and elapsed time for unconfined compressive strength-organic soil.  
Similarly, the organic soil sample presented a concave curve, where the critical point was at 28 d, with a maximum ME and breakage of the curve, with a coefficient of determination (R\({}^{2}\)) of 0.9123 and Equation (8), as shown in Figure 16.  
\[y=-0.00002647x^{2}+0.001654x+0.01033 \tag{8}\]  
where \(x\) is time (days).  
Figure 16: Correlation equation based on test results and elapsed time for the modulus of elasticity-organic soil.  
Figure 15: Correlation equation based on test results and elapsed time for the modulus of elasticity-silt.  
Considering that the two samples presented a similar trend, the organic soil had a higher coefficient of determination than the silt sample, implying that the relationship between ME and storage time had a higher correlation between the variables.
###### Abstract  
Terpenoids are toxic compounds produced by plants as a defense strategy against insect herbivores. We tested the effect of _Origanum vulgare_ terpenoids on the generalist herbivore _Spodoptera littoralis_ and the response of the plant to herbivory. Terpenoids were analyzed by GC-FID and GC-MS and quantitative gene expression (qPCR) was evaluated on selected plant genes involved in both terpene biosynthesis. The insect detoxification response to terpenes was evaluated by monitoring antioxidant enzymes activity and expression of insect genes involved in terpene detoxification. _O. vulgare_ terpenoid biosynthesis and gene expression was modulated by _S. littoralis_ feeding. The herbivore-induced increased level of terpenoids (particularly carvacrol and \(p\)-cymene) interacted with the herbivore by decreasing larval survival and growth rate. The assimilation by _S. littoralis_ of more than 50% of ingested terpenes correlated with the possible toxic effects of _O. vulgare_ terpenoids. In choice test experiments, carvacrol and \(\gamma\)-terpinene mediated the larval feeding preferences, whereas the prolonged feeding on _O. vulgare_ terpenoids (particularly on \(\gamma\)-terpinene) exerted relevant antinutritional effects on larvae. _S. littoralis_ was found to react to _O. vulgare_ terpenoids by increasing its antioxidant enzymes activities and gene expression, although this was not sufficient to sustain the toxicity of _O. vulgare_ terpenoids.  
Origanum vulgare; Spodoptera littoralis; terpenoid biosynthesis; larval survival; antioxidant enzyme activity and gene expression; toxicity Article
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Airborne LiDAR Point-Cloud and UAV Images  
The survey was carried out under the condition of light breeze in the afternoon to prevent changes in the natural environment affecting the flight status and data quality of UAV. The LiAir VH2 UAV-LiDAR system, which has integrated light and small LiDAR systems, a global navigation satellite system, and a digital camera, was installed on a DJI M300 (Figure 2a). This system was equipped with the Livox Avia LiDAR and Sony A5100 camera. This LiDAR exhibited a wavelength of 905 nm, a maximum scanning frequency of 240 kHz/s, and a data acquisition rate of 720,000 points/s under the three-echo observation at a scanning frequency of 160 kHz/s. The field angles were 70.4\({}^{\circ}\) and 4.5\({}^{\circ}\). The Sony A5100 camera was equipped with a 16 mm fixed-focus lens with an effective pixel count of 24.3 million. The above parameters are listed in Table 1.  
In this study, a ground-based flight route planning method was adopted (Figure 2b) to avoid problems (e.g., low flight efficiency, lack of images, data redundancy, image distortion, and low overlap) caused by changes in topography relief [35]. A DEM with a resolution of 12.5 m served as the basic data. The flight altitude was set at 100 m relative to the surface, the flight speed was 8 m/s, the airborne LiDAR side overlap was 35%, the forward overlap was 90%, and an area of 1.2 km\({}^{2}\) was scanned twice through ortho-view and three-echo observation (Figure 2c). In addition, the scanning point density was 213.82 points/m\({}^{2}\), and 2240 images were obtained. Moreover, the ground base station data with the coordinate system CGCS 2000 was acquired in real time using the Qianxun SR3 Pro RTK equipment (Figure 2d), which provided an accurate coordinate reference for LiDAR data interpretation. The device exhibited static horizontal accuracy of \(\pm\)8 mm + 1 ppm and elevation accuracy of \(\pm\)5 mm + 1 ppm. Table 2 lists the UAV flight parameters and collected data statistics.  
Figure 1: (**a**) The location of the study area, (**b**) the satellite image of the annular structure, and (**c**) the complex pit rim geomorphology.  
Figure 2: (**a**) The LiAir VH2 LiDAR system, (**b**) the UAV flight trajectory, (**c**) the ground-based flight route, (**d**) GNSS base station, (**e**) GCPs and checkpoints layout, and (**f**) GNSS measurements.
## 4 Model Validation
### Comparison with Traditional Models
#### 4.1.3 Comparison of the Models  
As can be observed from Figure 4, there was good agreement between the mass flux calculated by the current and traditional models, with high positive correlations between the proposed model and Civan model (\(R^{2}\) = 0.98) as well as between the proposed model and the DGM model (\(R^{2}\) = 0.97). In addition, the error percentages between the mass flux calculated by the current and traditional models are small. The error percentages between the proposed model and the Civan model, as well as between the proposed model and the DGM model, range from 2.26% to 5.73% (avg. 3.75%) and 0.39% to 6.61% (avg. 1.83%), validating the suitability of the current model for evaluating bulk phase gas flow through shale reservoirs. However, at reservoir pressures of <10 MPa, the mass flux calculated by the proposed model was slightly higher than that of the traditional models. This is because the proposed model considered multiple effects, while the DGM model only considered viscous flow and Knudsen diffusion. The Civan model with empirical coefficients may have led to differences between the results. The mass flux curve obtained by the proposed model had a rebound trend at low pressures because the mass flux in free molecular regime within nanoscale pores was significant at low pressures, which increased the mass flux of the bulk phase gas.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 3 System Overview
### Complexity Estimation of Function Call Sequence
#### 3.1.3. Reached Seeds Scheduling  
After generating seeds that can reach the target point, if these cannot trigger the bug in the target site, then the goal of DGF is to generate seeds that can trigger the bug as soon as possible. At this point, we need to consider two situations: the expected function call sequences that can expose the vulnerability that exists in the seeds that have reached the target site, and the other is when the vulnerability cannot be exposed. Since we do not know the exact expected function call sequences, and thus, do not know what situation the fuzzer is in, we start with the easier one, i.e, the expected function call sequences that can expose the vulnerability exist in the seeds that have reached the target site. We deal with the other situation in Section 3.2.  
We design the scheduling algorithm as shown in Algorithm 2. The main idea is for those seeds that have arrived at the target site to allocate the energy according to their complexity. A higher complexity means more code space for exploration, and therefore, we assign more energy to seeds of higher complexity. Lines 1-11 calculate the complexityof seeds \(S\to C_{SEQ}\) according to the seeds' function call sequence \(Q_{S}\) and store the total complexity to \(\mathbb{P}_{R^{*}}\). In lines 12-23, we choose roulette-wheel selection to assign more energy to seeds of higher complexity. After the seed \(S^{*}[0]\) is chosen in line 17, to explore the function space \(F\_set\) executed by this seed, we collect the seeds reaching the seed pool and execute functions contained in \(F\_set\) to fuzz.  
```
Input:\(\mathbb{P}_{R^{*}}\leftarrow\) The reached seed pool \(\mathbb{P}_{R}\leftarrow\) The reaching seed pool \(D\leftarrow\) Function complexity dictionary Output:\(S^{*}\leftarrow\) The next seeds to fuzz
1/* Calculate the complexity of reached seeds. */
2\(\mathbb{C}_{\mathbb{P}_{R^{*}}}=0\) for\(S\)in\(\mathbb{P}_{R^{*}}\)do
3\(c=0\) \(Q_{S}[F_{1},F_{2},...,F_{N}]=getFuncSeqFromSeed(S)\) for\(F\)in\(Q_{S_{N}}\)do
4\(c=c+Dict(F)\)
5 end for
6\(S\to C_{SEQ}=c\) \(C_{\mathbb{P}_{R^{*}}}=C_{\mathbb{P}_{R^{*}}}+c\)
7 end for
8/* Allocate seed energy and select the next seed. */
9\(Prob=[\ ]\), \(S^{*}=[\ ]\), for\(S\)in\(\mathbb{P}_{R^{*}}\)do
10\(Prob.append(S,\frac{S\to C_{SEQ}}{C_{\mathbb{P}_{R^{*}}}})\)
11 end for
12\(S^{*}[0]=RouletteWheelSelection(Prob)\) \(F\_set=set(getFuncSeqFromSeed(S^{*}[0]))\) for\(S\)in\(\mathbb{P}_{R}\)do
13 if\(set(getFuncSeqFromSeed(S))\subset F\_set\)then
14\(S^{*}.append(S)\)
15 end for
16
17 end for return\(S^{*}\)
```  
**Algorithm 2**Reached Seed Scheduling
## 2 Materials and Methods
### Selection Criteria  
After duplicates were removed, two reviewers independently screened for inclusion based on the title and abstract of all of the potentially relevant studies that had been identified. In case of disagreement, a consensus was achieved by the decision of a third reviewer. Full-text studies were subsequently retrieved by the same two reviewers and were independently screened for inclusion. If consensus was not reached through discussion between them, disagreements were solved by the decision of a third reviewer. Studies were considered eligible if they were able to answer the questions that have been defined according to the following PICO model:  
1. (P) Participants: PwMS
2. (I) Intervention: Rehabilitation training with robotic-assisted device for upper limbs, with or without conventional therapy.  
3. (C) Comparator: Conventional rehabilitation.
4. (O) Outcome measures: Safety of robotic rehabilitation, the feasibility of robotic rehabilitation, upper limb strength, functioning, independence in activity of daily living (ADL), and Health-related Quality of Life (HRQoL).  
We included systematic reviews, randomized controlled trials (RCTs), observational analytic studies, and crossover and case series. The exclusion criteria were (1) papers involving animals; (2) papers that had been written in language other than English; (3) a case report design; (4) participants with neurologic disabilities other than MS; and (5) robotic-assisted rehabilitation combined with other advanced technologies such as non-invasive brain stimulation (NIBS) compared to robot-assisted therapy alone.