esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Seehive Monitoring System for Early Detection of the Varroa Mite
## 3 Proposed Varroa Detection System Implementation
### End-Node Device Functionality and Modes of Operation  
There are two modes of operation that the end-node device can be used: Offline and online. The operation modes can be selected using a selection switch placed at the bottom of the MCU casing. The switch is connected to an MCU GPIO pin digital input, and by setting it to LOW or HIGH, it switches between the two modes of operation accordingly. The two end-node modes operate as follows:  
* **Online mode:** In this mode of operation, the process of Varroa detection is performed over the cloud. For this purpose, appropriate cloud service and API using HTTP requests have been implemented. The API is capable of image data transmission from the uploaded by the end-node MCU using HTTP protocol PUT requests. Also, an HTTP JSON POST request can be sent to the cloud API, including an API key and a beehive id, and the API returns as part of a JSON object. The Varroa detection results for this beehive, including the base64 encoded images Regions Of Interest (ROIs), where Varroa mite has been detected. The online mode of operation requires using the beehive concentrator, which is responsible for the node data transmissions over the internet over HTTP. It acts as an intermediate gateway among the end nodes and the cloud application service. The concentrator can upload images with an overall bandwidth capability that varies from 1-7/10-57 Mbps, depending on the gateway distance from the beehive [39]. If the distance is 20-30 m, it is limited by the LTE technology used.  
Figure 1: Proposed Varroa mite detection system end-node device and device components.  
* **Offline mode:** In places of limited Internet connectivity and cellular coverage, the end-node offline mode can be used. The offline mode does not require the use of the concentrator device. In this mode, a micro-service that includes a version of the detection algorithm inside the end-node device is used for the process of executing the Varroa detection algorithm locally (as presented in Figure 2). Then, the final CSV output is transmitted using the MCU BLE transponder to the farmers' mobile phones. A BLE service and two read characteristics can be used for the CSV output, and Varroa detected ROI image acquisition accordingly. The beekeeper can check the status of each one of his beehives by moving close to the beehive and pairing with each one accordingly, performing the BLE read from his mobile phone. The drawbacks of the offline mode are that it offers 20-25% less end-node energy consumption and no communication provider costs. Nevertheless, it has difficulties with BLE pairing, especially if many BLE devices are close-by and there are difficulties on characteristic reads of imagery base64 encoded data [40,41]. For this reason, only one Varroa mite ROI is available (the last one detected) via that BLE characteristic.  
The following section describes the Varroa mite detection algorithm used by the end-node devices in offline or online operation modes.
## 3 Results and Discussion
### ITCs Reduce the Toxicity of S. dysenteriae  
The most harmful effect during _S. dysenteriae_ infection is caused by production of the Shiga toxin, and thus not only growth inhibition but also toxin synthesis downregulation would be expected from a potential antimicrobial compound. Since the eukaryotic cell lines: human HeLa and simian Vero are sensitive to Shiga toxin [33], they can serve as good indicators for bacterial virulence. Thus, we tested viability of these cells when exposed to the ITC-treated or untreated bacterial lysates. We observed that the treatment of _S. dysenteriae_ strains with PEITC and SFN at the concentrations of \(\frac{1}{4}\) MIC (which effectively impaired bacterial growth) resulted in a statistically significant decrease in toxicity of bacterial lysates, as tested by the MTT assay (Figure 2). Alleviation of bacterial toxicity was similar for both ITCs tested. The results are in concert with those previously reported by us for the EHEC strain, where PEITC-treated cultures showed a notably lower virulence against the Vero and HeLa cells [19].  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Strain**} & \multicolumn{2}{c}{**MIC (MBC [mM]**} \\ \cline{2-3}  & **PEITC** & **SFN** \\ \hline _S. dysenteriae_ SZ2855/97 Japan & 0.50 (1.00) & 0.25 (0.50) \\ _S. dysenteriae_ Bonn/76 & 0.25 (0.25) & 0.06 (0.125) \\ _S. dysenteriae_ 156-36/88 Osaka & 0.50 (0.50) & 0.25 (0.50) \\ _S. sonnei_ & 1.00 (2.00) & 2.00 (2.00) \\ _S. flexneri_ & 4.00 (ND 1) & 4.00 (ND) \\ \hline \hline \end{tabular} \({}^{1}\) not determined.  
\end{table}
Table 1: Susceptibility of _Shigella_ species to PEITC and SFN.  
Figure 1: SFN and PEITC affect bacterial growth. _S. dysenteriae_ isolates (indicated above graphs) were cultured in the presence of (**a**) SFN and (**b**) PEITC at indicated concentrations related to the MIC determined previously (Table 1).  
Figure 2: ITC reduces the toxicity of _S. dysenteriae_ lysates. The effect of SFN on viability of HeLa and Vero cells treated with _S. dysenteriae_ lysate was assessed after 74 h in the absence or presence of SFN at concentrations indicated. Untreated culture was used as a negative control (black bar) and a culture treated with bacterial lysate was used as a positive control (white bar). SFN (green bar) and PEITC (red bar) treated groups were assessed against positive control using Student’s _t_-test. Statistical significance is marked with asterisks: ***_p_\(<\)0.001; **_p_\(<\)0.01.  
and SFN are used alone at the concentrations employed for this study, they do not have a detrimental effect on the cell lines' viability.
## 3 Results and Discussion
### The Visited and the Revisited Coverages Obtained by the Optimal Constellations  
The visited and the revisited coverages obtained by the optimal 8-, 24-, 60- and 120-satellite 2D-LFCs during one day are shown in Figure 2. As stated in Section 2.2, the length of the time for 90% visited coverage defines the fitness function in this study. The revisit coverage means the percentage of all the \(1^{\circ}\times 1^{\circ}\) cells that have been visited twice, and the temporal resolution of GNSS-R observations are usually evaluated by the length of the time corresponding to certain revisit coverage.  
From Figure 2, it can be seen that for each of the four optimal constellations, the goal of 90% visited coverage was achieved within 24 hours, and the optimal 8-satellite LEO constellation took the longest time to achieve this goal. What needs to be noticed is that although the optimal 120-satellite configuration took the shortest time to achieve 90% coverage among the four optimal constellations, its improvement was not significant when compared with the optimal 24- and 60-satellite ones. Furthermore, the largest visited and revisited coverages of the optimal 24- and 60-satellite configurations all reached 100%, while the largest visited and revisited coverages of the optimal 120-satellite configuration were both 98%. This is due to the fact that no reflection data were obtained over the polar areas by this LEO constellation with sun-synchronous orbits. On the other hand, the launch cost was quite high for the optimal 8-satellite 2D-LFC, considering that eight launches were needed to deploy the satellites in eight different orbit planes. Taking into consideration the above issues, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nsat** & **I (\({}^{\circ}\))** & \(N_{o}\) & \(N_{so}\) & \(N_{c}\) & **Fitness Value (h)** \\ \hline
8 & 95.505 & 8 & 1 & 1 & 9.2575 \\
24 & 86.627 & 8 & 3 & 2 & 3.0839 \\
60 & 84.428 & 10 & 6 & 1 & 1.5376 \\
120 & 97.035 & 10 & 12 & 1 & 1.1475 \\ \hline \end{tabular} Note: Nsat, \(N_{so}\), and \(N_{c}\) denotes the total number of satellites, the number of satellites per orbit, and the phasing parameter of the constellation, respectively.  
\end{table}
Table 1: The configurations for the optimal 8-, 24-, 60-, and 120-satellite 2D-lattice flower constellations.
## 3 Analysis of Strain Transfer Influential Factors
### The Influence of Stacking Sequences  
Stacking sequences describe the arrangement order of carbon-fiber fabric layers in CFRP laminates. To analyze the influence of the stacking sequences, the average strain transfer ratios of CFRP laminates [0\({}_{8}\)/90\({}_{8}\)], [0/90\({}_{8}\)], [90/0]\({}_{8}\), and [90\({}_{8}\)/0\({}_{8}\)] are calculated according to (21) and (24) as theoretical results, which are 87.1%, 85.1%, 84.6%, and 82.9%, respectively. The stacking numbers and stacking directions of the four CFRP laminates are the same. However, CFRP laminates whose 90\({}^{\circ}\) fabric layers are closer to the adhesive layer tend to have lower average strain transfer ratios. To verify the conclusion, the measurement structure was simulated in the ANSYS 15.0 software as shown in Figure 6a. The simulation parameters were set according to Table 1. The same strain value was applied to all of the four CFRP laminates by setting displacement boundary conditions. The average strain transfer ratios according to the simulation analysis are obtained as simulation results. The simulation results are 87.3%, 85.2%, 84.5%, and 83.0% for CFRP laminates [0\({}_{8}\)/90\({}_{8}\)], [0/90]\({}_{8}\), [90/0]\({}_{8}\), and [90\({}_{8}\)/0\({}_{8}\)], respectively. The average strain transfer ratios according to both the theory model and the simulation analysis are shown in Figure 6b. The errors between theoretical results and simulation results are less than 0.3%, which indicates a good consistency.  
Figure 5: Average strain transfer ratios of stacking units [0/0/0/0], [45\(/\)\(-\)45/45\(/\)\(-\)45], and [90/90/90/90] under different stacking numbers.  
Figure 6: Finite element simulation of strain transfer. (**a**) Measurement structure simulated in the ANSYS 15.0 software; (**b**) Comparison of average strain transfer ratios between theoretical results and simulation results.  
The strain fields of measurement structures with stacking sequences [0\({}_{8}\)/90\({}_{8}\)], [0/90]\({}_{8}\), [90/0]\({}_{8}\), and [90\({}_{8}\)/0\({}_{8}\)] are exhibited from Figure 7a-d, respectively. It shows that the adhesive layers have a reaction to CFRP laminates. The reaction reduces the strain field intensity, which is more distinct from Figure 7a-d. The \(x\)-direction stiffness in the surfaces of the CFRP laminates declines when more 90\({}^{\circ}\) fabrics stack in the upper layers. The reaction effect is more significant with relatively low surface stiffness, and the average strain transfer ratios become lower. Therefore, the stacking sequences influence the strain transfer by changing the surface stiffness of CFRP laminates.
## 3 Adaptive Time-Frequency Analysis
### Variational Mode Decomposition and Optimization Models
#### 3.2.4 Adaptive Variational Mode Decomposition
For instance, in the analysis of hydropower station signals, AVMD demonstrates greater robustness compared to traditional methods (such as EMD and EWT), accurately extracting effective modes. Furthermore, AVMD typically possesses efficient computational capabilities. By utilizing optimization algorithms (such as fast iterative operators and energy entropy), it shortens convergence time and enhances computational efficiency. For example, when performing fault diagnosis on wind turbine gearboxes, AVMD combined with the firefly algorithm optimizes the number of modes and penalty factors, significantly reducing computation time. Previously mentioned methods, including empirical mode decomposition (EMD) and the subsequently discussed empirical wavelet transform (EWT), suffer from error accumulation due to recursive decomposition and sensitivity to noise and sampling frequency, respectively. In contrast, AVMD performs well when dealing with harmonic signals with similar frequencies. Ref. [107] verifies the effectiveness of AVMD through simulated signals and actual hydropower station measurement signals, highlighting its ability to accurately separate each harmonic component, a feat not achieved by EMD or EWT. However, AVMD also has its disadvantages. Issues unresolved by classic models have been improved but not completely eliminated. First, parameter selection still poses a certain level of complexity. Although AVMD reduces the need for predefined parameters, the choice of its internal parameters (such as the penalty factor and convergence criteria) still significantly impacts the decomposition results. Improper parameter settings can lead to the over- or under-decomposition of modes, affecting signal reconstruction accuracy. Similarly, the issue of local optimal solutions requires further research. In some complex signal processing scenarios, AVMD may fall into local optimal solutions, resulting in unsatisfactory decomposition effects. Additionally, AVMD may experience mode aliasing when processing modal components with similar frequencies. Finally, although AVMD improves computational efficiency compared to VMD, its computational complexity remains high when processing large-scale data or high-dimensional signals. In real-time structural health monitoring, AVMD may not meet the requirements for real-time processing. Therefore, it may be necessary to reduce the computational complexity of AVMD through GPU acceleration or distributed computing technologies to satisfy real-time processing demands.
## 4 Results and Discussions
### Sem  
The study used the SEM analysis method, employing partial least square (PLS) techniques through smartPLS. The aim was to highlight the correlation between alignment and intersection with road safety. The study was conducted on 80 hotspot locations along Jordan's Desert Highway.
_applied_  
_sciences_  
_Article_  
**Temperature Drift Characteristics Analysis of GMM-FBG**  
**Current Sensor Based on Finite-Element**  
**Multi-Physics Simulations**  
**[PERSON] 1, [PERSON] 2,*(c) and [PERSON] 1,3,*  
Key Laboratory of Engineering Dielectrics and Its Application, School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Ministry of Education, Harbin 150080, China  
2 SP Group-NTU Joint Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore  
3 The MOE Key Laboratory of Special Machine and High Voltage Apparatus, Shenyang University of Technology, Shenyang 110870, China  
*Correspondence: [EMAIL_ADDRESS] (W.-F.S.); [EMAIL_ADDRESS] (W.Z.)  
**Abstract:** A composite current sensor is designed with soft ferrite as the magneizer in combination with giant magnetostrictive material (GMM) and fiber Bragg grating (FBG). The temperature drift characteristics of the GMM, detecting performances under thermal strain caused by temperature variation in the GMM rod, are investigated by simulating the coupled fields of magnetostriction and thermal expansion with the finite-element multi-physics method to explore the temperature-drift mechanism of the ferrite-GMM current detector. The sensing characteristics of the GMM-FBG current sensor under quasi-static current excitation at various work temperatures are evaluated by simulating thermal stress between the GMM and FBG to analyze the temperature drift mechanism of the Bragg wavelength signal modulated by FBG. Even though temperature elevation suppresses GMM magnetization and thereby reduces the slopes of stress-strain curves, the steering magnetization of magnetic domains in the GMM rod tends to saturation without appreciable thermal inhibition in the high-stress region of large current excitation, while the magnetostrictive strain is still abated by the reduction in magnetic flux density caused by the thermal expansion of GMM rods. The temperature elevation can also produce thermal stress between the GMM and FBG, which will decrease the detection sensitivity and testing range of the GMM-FBG current sensor. The temperature drift characteristics of the GMM-FBG are generalized into a formula by fitting the wavelength shifting as a function of thermal strain, which will significantly facilitate designing the scale calibration for various ambient temperatures. The present researchers provide a theoretical basis and experimental guidance for developing GMM-FBG current sensors with high sensitivity and stability.  
**Keywords:** magnetostrictive material; fiber current sensor; multi-physics coupling; temperature drift
# Comprehensive Analysis of the Acute Toxicity of Ionic Liquids Using Microtax(r) Bioassays
## 3 Results and Discussion
As mentioned earlier, two toxicity classifications were employed to categorize the selected ILs. Table 5 shows the classification of the 30 ILs, revealing that none of them falls under the categories of highly toxic or extremely toxic, based on both criteria. These findings confirm that both criteria show similar conclusions, and thus, protic ILs exhibit broadly lower toxicity in comparison with aprotic ones, and non-aromatic ILs are less toxic than aromatic ones. The alkyl chain length plays a fundamental role in the toxicity of the ILs, with six carbons being the critical size to mark the transition from non-toxic to toxic in many cases, although further studies in this line should be performed to verify this conclusion. Figure 4: EC50 values at 15 min of exposure for the different studied ions: (**a**) FAP anion-based, (**b**) ammonium cation-based, (**c**) Nitrate anion-based, (**d**) pyrrolidinium cation-based, (**e**) TFSI anion-based, and (**f**) imidazolium cation-based.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 2 Theoretical Background  
The GNSS-R signals received by the CYGNSS satellite constellation have their origin in the L-band signals that are transmitted by the GPS satellite constellation and subsequently forward-scattered from Earth's surface in the specular direction of the CYGNSS receiver. Thus, the combination of GPS transmitter and CYGNSS receiver can be considered an L-band bistatic radar that receives the forward scatter associated with the scattering target. This forward-scattered signal carries information relevant to the properties of the surface. This section defines the basic measurements obtained by CYGNSS and their sensitivity to land surface inundation.
## 2 Materials and Methods
### The Problem Formulation  
CVRP is defined on a complete directed graph \(G=(V,E)\), where \(V=\{0,1,\ldots,n\}\) is a set of nodes and \(E=\{(i,j)\in V\times V\), \(i\
eq j\}\) is a set of arcs. The node \(0\) represents the depot for a fleet of \(p\) vehicles with the identical capacity \(Q\), and the remaining \(n\) nodes represent customers, each with the demand \(d_{i}\), \(i\in V-\{0\}\). Every arc \((i,j)\in E\) is associated with the positive travel cost \(c_{ij}\). We assume that the cost matrix is symmetric, i.e., \(c_{ij}=c_{ji}\) for all \(i,j\in V\), \(i\
eq j\), and it satisfies the triangular inequality, \(c_{ij}+c_{jl}\geq c_{il}\) for all \(i,j,l\in V\). The aim is to determine a minimum-cost set of \(p\) routes in order to satisfy the demands of all customers under the following constraints [23]:  
1. Each route starts and ends at the depot,
2. Each customer is visited exactly once,
3. The total demand of all customers on any route must not exceed the vehicle capacity.  
An illustrative example of a feasible CVRP solution is shown in Figure 1, where 7 customers (their demands are displayed next to the nodes) are served by three vehicles of the capacity 50. The arc labels correspond to travel costs between the nodes, so the total transport cost is 30.  
The following mathematical model is taken from [24]. Two-index binary variables \(x_{ij}\) are used as decision variables equal to \(1\) if arc \((i,j)\) belongs to the optimal solution and \(0\) otherwise. Additional continuous variables \(y_{i}\) are defined for all \(i\in V-\{0\}\), which model the load in the vehicle after it visits customer \(i\). The mixed linear programming model of the CVRP can be stated as:  
**MILP model of the CVRP**  
\[\text{minimize}\ \sum_{(i,j)\in E}c_{ij}\,x_{ij}, \tag{1}\]subject to  
eq j\end{subarray}}x_{ij} =1, \forall i\in V-\{0\}, \tag{2}\] \[\sum_{\begin{subarray}{c}j\in V\\ i\
eq j\end{subarray}}x_{ji} =1, \forall i\in V-\{0\},\] (3) \[\sum_{j\in V-\{0\}}x_{0j} =p,\] (4) \[\sum_{i\in V-\{0\}}x_{i0} =p,\] (5) \[y_{i}+d_{j}-y_{j}\leq(1-x_{ij})\,Q,\quad\forall i,j\in V-\{0\},i \
eq j,\] (6) \[d_{i}\leq y_{i}\leq Q, \forall i\in V-\{0\},\] (7) \[x_{ij}\in\{0,1\}, \forall(i,j)\in E. \tag{8}\]  
The objective function (1), to be minimized, is the total transport cost. The constraints (2)-(5) are the degree constraints for the depot and customers, respectively. The constraints (6) impose both the capacity and connectivity of the feasible routes and also guarantee that sub-tours are avoided. The constraints given in (7) restrict the upper and lower bounds of \(y_{i}\). Lastly, the obligatory constraints (8) express the binary requirements on the variables.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## Exact Solution: OptTs
### Complexity Analysis of OptTs  
The computational complexity of OptTs, as shown in Equation (29), is related to the exploration of all search space, which is given by the values of \(N\) and \(T_{s}^{k}\). Equation (29) shows that an increase in \(N\) leads to a non-linear growth in complexity; however, the impact due to the exclusive action of \(T_{s}^{k}\) is not evident. The first derivative of the linearized version of Equation (29) is evaluated with respect to \(N\) in order to analyze the contribution of \(T_{s}^{k}\) in the growth of the problem, as shown in Figure 7. Subsequently, the obtained results allow for us to verify that an increase in the value of \(T_{s}^{k}\) also causes  
Figure 6: Flow chart of the exact scheduling algorithm.  
a non-linear growth of the search space. Thus, the increase in \(N\) or \(T_{s}^{k}\) leads a non-linear growth of complexity, which can be reflected in greater use of computational resources or an increase in the execution time. The main (Figure 6) and secondary (Figure 3) algorithms contribute to the increase of complexity; however, the major contribution comes from the three first steps of the main algorithm. The growth rate of OptTs as a function of \(N\) can be expressed as:  
\[f(N)=N+(2\times N\times T_{s}+N)+(2\times T_{s}+1)^{N} \tag{30}\]  
where, discarding low-order terms, the last term in Equation (30) reveals that OptTs has an exponential complexity with an order of growth \(\mathcal{O}(2^{N})\), according to the Big-O notation. The complexity of OptTs indicates the limits and drawbacks for applicability to large values of \(N\) and \(T_{s}^{k}\) and the computational capacity (in terms of processing and memory) needed for its execution.