esa-sceva/satcom-chunk-collection huggingface dataset:
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
###### Abstract  
Arthropod-borne infectious diseases cause many deaths and a major economic burden worldwide. Repellens play an important role in protecting people from infectious biting arthropods. The repellency of veratraldehyde, a known food additive, and the WJ-1041 formulation containing 10% veratraldehyde was tested against _Aedes albopictus_ and _Culex pipiens pallens_ females and _Haemphysalis longiornis_ nymphs using arm-in-case, indoor or filter paper tests. Veratraldehyde exhibited repellency similar to or lower than that of \(n\),_n_-diethyl-_meta_-toluanide (DEET) against _A. albopictus_, but in _H. longicornis_, the activity of veratraldehyde was better than that of DEET. The repellency of the 10% veratraldehyde solution was comparable to that of 20% DEET against the two mosquitoes. When comparing repellency between the WJ-1041 formulation (10% veratraldehyde) and 10% DEET against _C. pipiens pallens_, _A. Alopictus_ and _H. longicornis_, the two showed similar repellency and complete protection time (CPT) values. However, there was a small difference depending on the tested insects. The absorption of veratraldehyde via skin was minimal, if at all. The pharmacokinetic parameters (Cmax and Tmax) of veratraldehyde in blood samples of rats were not different from those of the control group. Based on these results, veratraldehyde has high potential to be commercialized as a repellent agent against infectious disease-borne pests in the near future.  
Aedes albopictus; Culex pipiens pallens; Haemphysalis longicornis; repellency; veratraldehyde +
Footnote †: journal: Applied Sciences
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology  
The performance of the sensor is closely related to its sensitive material, manufacturing process, and application environment [18; 19; 20; 21], so a single performance indicator cannot fully evaluate the contribution of a sensor to the E-nose. Therefore, we should choose multiple indicators to build a multi-dimensional evaluation model. In this paper, we propose the following model-building approach: Firstly, evaluate each sensor according to four indicators: sensitivity, selectivity, correlation, and repeatability. Subsequently, analyze the contribution of each indicator to the E-nose and calculate the weight value of each indicator based on EWM algorithm. Finally, establish the multi-dimensional comprehensive evaluation model for optimizing the sensor array based on the EWM-TOPSIS algorithm. The details of the model building process are as follows.
## 5 Proposed MIARO
### Computational Complexity Evaluation  
The algorithm's complexity depends on the maximal iteration count (\(T\)), the problem dimensionality (\(D\)), and the population size (\(S\)). Analyzing the MIARO algorithm, the overall computational complexity is \(O(MIARO)=O(initialization)+O(function evaluation)\)  
Figure 3: Archimedean spiral mutation.  
\(+\)\(O(\)_detour foraging\()\) + \(O(\)random hiding\()\) + \(O(\)Archimedean spiral mutation\()\)_. The complexity associated with the initialization process is \(O(S)\), function evaluation has \(O(S\times T)\), and both detour foraging and random hiding phases exhibit a complexity measure of \(O(\frac{1}{2}\times S\times T\times D)\), and the computational complexity of the Archimedean spiral mutation is \(O(S\times T\times D)\). Hence, \(O(MIARO)=O(S)+O(ST)+O(\frac{1}{2}STD)+O(\frac{1}{2}STD)+O(STD)=O(S(1+T+2 TD))\).  
The pseudo-code for MIARO along with its flowchart are presented in Algorithm 1 and Figure 4.  
Figure 4: Flowchart of MIARO.  
```
1:Initialize search agents \(\mathbf{x}_{i}\) (i = 1, 2, 3,..., N), dimension \(d\), iteration number \(Max\_Iter\).
2:Calculate each individual's fitness value to identify the top-performing agent \(\mathbf{x}_{best}\).
3:while\(t<Max\_Iter\)do
4:for\(i=1:N\)do
5: Energy factor calculation \(A\) using Equation (17)
6: Compute the new running operator R' using Equation (21)
7:if\(A>1\)then
8: Randomly select an individual from other individuals
9: Perform detour foraging operations using Equation (5)
10: Updating individual locations using the greedy strategy of Equation (16)
11:else
12: Get the random probability \(rand\) between (0, 1)
13:if\(rand>0.5\)then
14: Perform random hide operations
15:else
16: Calculate the golden mean coefficient \(c_{1},c_{2}\)
17: Perform the golden sine operation us
18:endif
19: Updating individual locations using the greedy strategy of Equation (16)
20:endif
21: Perform Archimedean spiral mutation using Equation (26)
22: Calculate the fitness
23: Updating individual locations using the greedy strategy of Equation (16)
24:endfor
25: Best solution \(\mathbf{x}_{best}\) update.
26:\(t=t+1\)
27:endwhile
28:Output: Preferred solution and its fitness score.
```  
**Algorithm 1** Pseudo algorithm of MIARO.
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime  
The launch and utilization of mega-constellations has highlighted several deficiencies within the current international space law framework, presenting challenges that the existing legal regime has yet to effectively address. Notably, the mechanisms established to prevent interference have proven inadequate, as evidenced by reports of astronomical interference, collisions, and the proliferation of space debris. Moreover, there are concerns about the potential violation of environmental sustainability laws. Presently, there are no stringent international rules governing how operators manage constellations in orbit, despite the rapid expansion of mega-constellations.
## 4 Experimental Results and Analysis
### Experimental Evaluation Criteria  
Precision (P) represents the proportion of items found that are relevant. Recall (R) is the proportion of relevant items that are found. F1-measure (F1) is a score that combines P and R, and is defined as the summed average of P and R.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Model name (model) & Skip-Gram \\ Training dimension & (100, 150, 200, 250, 300) \\ Window size (window) & 10 \\ Minimum word frequency (min count) & 1 \\ Learning rate (alpha) & 0.05 \\ Random downsampling threshold (sample) & \(1\times 10^{-4}\) \\ Iterations (ITER) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pre-trained word vector parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Environment** & **Similarity** & **Internet** & **Similarity** & **Politics** & **Similarity** \\ \hline Environmental & 0.9009 & \begin{tabular}{c} Network \\ service \\ \end{tabular} & 0.8214 & \begin{tabular}{c} International \\ relations \\ \end{tabular} & 0.8854 \\ \hline Contaminated & 0.8583 & \begin{tabular}{c} The Beacon \\ Tower \\ \end{tabular} & 0.7460 & \begin{tabular}{c} Political \\ economy \\ \end{tabular} & 0.8824 \\ \hline Agricultural & 0.8526 & \begin{tabular}{c} Science and \\ technology \\ \end{tabular} & 0.7449 & \begin{tabular}{c} Social \\ transformation \\ \end{tabular} & 0.8746 \\ \hline Water & 0.8488 & \
\begin{tabular}{c} Computer \\ network \\ \end{tabular} & 0.7020 & Politicians & 0.8530 \\ \hline Resources & 0.8446 &
\begin{tabular}{c} Industrial \\ Revolution \\ \end{tabular} & 0.6724 & Those in power & 0.8471 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of similarity of pre-trained word vectors.  
Where TP (true positive) means that the relevant item is correctly identified as relevant, FP (false positive) means that the irrelevant item is incorrectly identified as relevant, and FN (false negative) means that the relevant item is incorrectly identified as irrelevant.
## 1 Introduction
They reported 6.4% and 2.1% bandwidths using a tri-layered structure. However, for a structure consisting of more than two metallic layers, the fabrication process becomes complex, requiring alignment to be perfectly matched. Furthermore, the operating band for polarization conversion is narrow and loses stability with variation in the incident waves' angles. Therefore, wide operational bands, simple design and fabrication, and stability over a wide range of oblique incidences are key requirements for dual-band polarizers. In this work, a dual-band polarizer converter was developed using a unique design strategy that can be used in communication systems to generate dual-band CP waves from linearly polarized antennas in wide frequency bands. The proposed structure has wide operational bands, is uni-layered and polarization-diverse, and remains stable over a wide range of oblique incidences of linearly polarized waves. This communication is structured as follows. In Section 2, the design principle for the metasurface-based structure is described. The simulation and detailed physical analyses are described in Section 3. A discussion of the experimental results is carried out in Section 4, along with a comparison of the performance with other state-of-the-art designs. Finally, a conclusion is presented in Section 5.
## 1 Introduction
For instance, [PERSON] and [PERSON], [PERSON] et al., and [PERSON] et al. (2020) [10; 15; 16] optimized the parameters of 2-, 3-, 4-, and 5-DOF models using the gradient-based MATLAB function, called \"fmincon\". [PERSON] et al. [17] introduced a reference vector-guided evolutionary algorithm to obtain the parameters of a 7-DOF human model, whereas [PERSON] et al. [18] explored multi-objective and genetic algorithms (GAs) for parameter identification of a 5-DOF model. From these studies, it was found that the separate or hybrid application of these optimization techniques is usually associated with trade-offs that include an exhaustive search for initial values, randomly generated optimal solutions, high computation time, and generation of estimated parameters that do not reflect the dynamic response of the human body to WBV [6; 19]. Therefore, there is still a gap in research to develop model parameter estimation algorithms that are both reliable and computationally efficient. In this study, we developed a lumped parameter seven-degree-of-freedom (7-DOF) biodynamic model for a seated human under vertical vibrations, including rotational degrees-of-freedom (DOF) and seat (and backrest) modeled as massless torsional and translational spring and damper. We designed a vibration experiment to obtain the transmissibility of different body segments of a seated subject under random base excitation in the vertical direction. A new hybrid optimization method (HOM) that combines gradient-based and genetic algorithms in some systematically defined steps was developed to estimate unknown stiffness and damping parameters of the model. We quantitatively evaluate the model fitting performances of the optimization methods using goodness-of-fit criteria, objective function value at the final solution, and computation speed. The sensitivities of the estimated parameters to the appearance and magnitude of the resonance frequencies were studied based on the relationship between the increment of the excitation magnitudes (0.5 to 2.0 m/s\({}^{2}\) rms) and using the transmissibility of different body segments in the optimization problem. Important findings on the key parameters affecting the region of resonance and how vibration is transmitted to each body segment were discussed. Ultimately, the development of the 7-DOF biodynamic model and HOM advances research on the optimization of comfort and safety for individuals when riding in vehicles.
# Hyperledger Fabric Access Control for Industrial Internet of Things
## 3 System Architecture
### Hyperledger-IIOT System Flow
#### 3.3.2 One-Time URL  
In this research, a blockchain-based controller manages identity and access control policies and acts as a tamper-proof log of access events, and uses one-time URLs to secure data access only once. A one-time URL has the following characteristics: (1) Once a URL is used, it cannot be used again. (2) The URL will expire after a certain period. (3) The administrator can revoke a valid URL, and if the user tries to use this URL again, an error message will be seen. As shown in Figure 9, the data owner first uploads the industrial IoT data to the cloud, and the cloud will store the data uploaded by the data owner individually, and then upload the original URL to the blockchain. After the data consumer sends a request to the blockchain, the identity will be verified. If the identity is eligible, the access record between the data owner and the data consumer (manufacturer) will be saved in the blockchain and transmitted once through the device contract Sexual URLs to data consumers.  
The sequence diagram of the access situation is shown in Figure 10. First, the data consumer will register with the administrator, and the administrator will then add permissions to the data consumer. The data owner will upload the data to the cloud, and the device contract in the smart contract obtains the original URL and stores it in the state database in the system. When a data consumer makes an access request to the system, the system will confirm from the policy contract and the access contract whether they have permission to access. If it is allowed, the system will return a one-time URL to the data consumer, otherwise, it will return an Error.  
Figure 8: Example of project alliances data sharing.  
Figure 9: One-time URL context sequence diagram.
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information  
This section introduces the steps necessary to obtain the path planning for an autonomous robot (or team of robots) using the current location of the robots, satellite imagery, and cadastral information. For this purpose, we identified two main stages.  
To allow the interaction among all the modules and sub-modules, proper control architecture is needed. In particular, we used _Agriture_[23] to integrate all the modules into the system and to enhance the interoperability.
# Incorporating Clinical Imaging into the Delivery of Microbeam Radiation Therapy
## 2 Materials and Methods
### Image Registration for Alignment  
To align the target volume present within the CT data set to the now defined centre of the treatment beam, images acquired within each frame of reference must be co-registered. To achieve this, the image guided radiotherapy system, SyncMRT developed specifically for the DynMRT stage in IMBL Hutch 2B is implemented. SyncMRT provides a user-friendly interface to import CT DICOM and planar image sets allowing a user to manually select points common to both data sets. SyncMRT calculates the optimum 6D rigid body transformation to co-register the two sets of manually selected points, the solution is then reduced to a 4D solution accounting for the lack of rotational freedom about the transverse and longitudinal axis. SyncMRT allows for 3 or more pairs of markers to be identified to align the two data-sets and determine the tumour position. In our work, we found that 6 well-described anatomical landmarks provided more consistent results than using the minimum 3 markers. SyncMRT allows a clinical treatment plan to be used to identify tumour and beam positions; however, this is not required in this work as the exact tumour locations can be determined directly from the CT datasets, and skeletal landmarks visible in both the CT dataset and planar X-ray images provides adequate detail for alignment.  
Figure 2 below shows a radiograph of the treatment phantom mounted on the DynMRT stage with overlays of the output of the rigid body co-registration process. The location of the red cross indicates the calculated location of the ball bearing as recorded on the CT scan showing good agreement to the visible location of the ball bearing from the conventional radiograph image. Further details of the image co-registration and translation calculations can be found in [PERSON], 2018 [29]. The tumour of a patient would not be visible on this radiograph and so was ignored for alignment purposes, serving only as a validation.  
Figure 2 also identifies the six prominent skeletal landmarks used for registration between the DRR and conventional radiograph. They are (A) the distal tip of the median line, (B) posterior tip of the occipital bone, (C, D) left and right zygomatic process of the squamosal bone and (E, F) the left and right anterolateral corner of the frontal bone.  
The DynMRT stage was translated to place the inferred position of the ball bearing (red cross) at the centre of the treatment field. The stage was then rotated 32.7\({}^{\circ}\) to face the synchrotron treatment beam at normal incidence and treated using an 8 mm \(\times\) 8 mm square microbeam field.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 3 The Narrowband-IoT Technology
### Random Access Procedure  
The random access (RA) procedure of a NB-IoT UE, which is necessary to allow an uplink packet transmission, is composed of multiple steps. To start with, the UE scans the channels for the synchronization signals. When it gets correctly synchronized to the base station, the UE obtains first the Master and then a number of Secondary Information Blocks (MIB and SIBs, respectively), containing all the relevant information about the network, the cell and its resource allocations. Then, to connect to the cell, the RA procedure starts by sending a preamble (or Msg1) during one of the periodic random access windows (NPRACH). After sending Msg1, the UE waits for Msg2 from the eNB. Note that, at this stage, the network is not aware of possible overlapping among UEs' transmissions, which may happen if more than one UE chooses the same random preamble. With Msg2, the eNB notifies that it has received the specific preamble sequence and allocates NPUSCH resources for Msg3 for UEs which transmitted that specific sequence. In Msg3, each of the UEs sends different ID data and, in this case, collisions may actually happen, since UEs which used the same preamble sequence will use the same resources to send their Msg3. If the eNB can correctly receive at least one of the Msg3 packets, it will respond with Msg4 to complete the procedure. If the access procedure is carried out correctly by an UE, the resources for the UL and downlink (DL) transmissions are scheduled by the cell eNB with data integrity insured through the Hybrid Automatic Repeat Request (HARQ) processes.
## 3 Materials and Methods
### Landsat 8 LST Data Products and Time-Series Processing
Water body removal: Lakes, rivers, and other water bodies were identified using Sentinel-2A data with a Normalized Difference Vegetation Index (NDVI) threshold of less than 0. Pixels dominated by clouds, water, or snow were excluded to minimize their influence on temperature data and to improve the spatial accuracy of LST distribution. 2. Altitude correction: To minimize the impact of elevation differences on LST, the study area was divided into 30 elevation intervals, and the temperature distribution within each interval was analyzed. A regression equation was derived by analyzing the relationship between elevation and LST:  
\[\text{Landsat LST}=92.12-0.0168\times\text{Altitude} \tag{2}\]  
Here, Landsat LST refers to land surface temperature derived from Landsat 8 data (\({}^{\circ}\)C), while Altitude indicates elevation from the SRTM DEM, measured in meters above sea level. The regression model yields a coefficient of determination (R\({}^{2}\)) of 0.86. Based on this relationship, an altitude correction formula was developed to normalize LST values to a reference elevation of 4300 m [25; 51]:  
\[\text{Landsat LST corrected}=\text{Landsat LST}+0.0168\times(4300-\text{Altitude}) \tag{3}\]  
The calibration formula effectively removes the altitude effect, resulting in a clearer geothermal anomaly signal. 3. Local Block Threshold Method: After removing water bodies and applying altitude correction, the study area was divided into 12 \(\times\) 9 subregions based on the multi-year average LST image. The high-temperature threshold for each subregion was defined as the mean temperature plus one standard deviation, enabling independent identification of thermal anomalies in each area.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### LEO Constellation Patterns
#### 2.1.2 3d-Lfc  
A 3D-LFC is defined by six integer parameters and six continuous ones [32]. Two of the six integer parameters, \(N_{O}\) and \(N_{SO}\), are of the same meanings as those defined for 2D-LFC, and the other four integer parameters include the number of unique orbits (with different arguments of perigee) on each plane (\(N_{W}\)) and the three phasing parameters (\(N_{C}^{1},N_{C}^{2},N_{C}^{3}\)). Each satellite has the same semi-major axis, eccentricity, and inclination. This results in the most important characteristic of a 3D-LFC, which is the constellation symmetry. In other words, all the satellites in the elliptical orbits are distributed uniformly. The expression to obtain the orbital elements of the satellites in a 3D-LFC reads [32]:  
\[\left[\begin{array}{ccc}N_{O}&0&0\\ N_{C}^{3}&N_{W}&0\\ N_{C}^{1}&N_{C}^{2}&N_{SO}^{\prime}\end{array}\right]\left[\begin{array}{c} \Delta\Omega_{ijk}\\ \Delta\omega_{ijk}\\ \Delta M_{ijk}\end{array}\right]=2\pi\left[\begin{array}{c}i-1\\ k-1\\ j-1\end{array}\right] \tag{2}\]  
where \(i=1,\ldots,N_{O}\), \(j=1,\ldots,N_{SO}^{\prime}\), \(k=1,\ldots,N_{W}\), \(N_{C}^{1}\in[1,N_{O}]\), \(N_{C}^{2}\in[1,N_{W}]\), and \(N_{C}^{3}\in[1,N_{O}]\); \(\Delta\Omega_{ijk}\), \(\Delta\omega_{ijk}\) and \(\Delta M_{ijk}\) are the values of \(\Omega\), \(M\) and \(\omega\) of the \(j\)-th satellite on the \(k\)-th orbits of the \(i\)-th orbital plane with respect to the reference satellite, respectively. The variation ranges of the constellation parameters for the 3D-LFC in the simulation are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Variation Range** \\ \hline \(i\) & [1\({}^{\circ}\),100\({}^{\circ}\)] \\ \(\mathrm{e}\) & [0.01,0.05] \\ \(\mathrm{orbit\ altitude}\) & \([\frac{300\ \mathrm{km}}{1-\epsilon},\frac{1000\ \mathrm{km}}{1+\epsilon}]\) \\ \(N_{O}\) & \(N_{O}\in\left\{f(N)^{\star}\right\}\) \\ \(N_{W}\) & \(N_{W}\in\left\{g(N)^{\star}\right\}\) \\ \(N_{SO}^{\prime}\) & \(N_{SO}^{\prime}=\frac{N}{N_{SO}N_{W}}\) \\ \(N_{C}^{\star}\) & \([1,N_{O}]\) \\ \(N_{C}^{\star}\) & \([1,N_{W}]\) \\ \(N_{C}^{\star}\) & \([1,N_{O}]\) \\ \hline \end{tabular} \({}^{\star}\)\(f(N)\) and \(g(N)\) denotes the divisors of \(N\) which satisfy that \(f(N)\cdot g(N)\) is also the divisor of \(N\).  
\end{table}
Table 2: The variation range of the parameters defining 3D-LFC composed of \(N\) satellites.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Variation Range** \\ \hline \(i\) & [1\({}^{\circ}\),100\({}^{\circ}\)] \\ \(\mathrm{e}\) & [0.01,0.05] \\ \(\mathrm{orbit\ altitude}\) & \([\frac{300\ \mathrm{km}}{1-\epsilon},\frac{1000\ \mathrm{km}}{1+\epsilon}]\) \\ \(N_{O}\) & \(N_{O}\in\left\{f(N)^{\star}\right\}\) \\ \(N_{W}\) & \(N_{W}\in\left\{g(N)^{\star}\right\}\) \\ \(N_{SO}^{\prime}\) & \(N_{SO}^{\prime}=\frac{N}{N_{SO}N_{W}}\) \\ \(N_{C}^{\star}\) & \([1,N_{O}]\) \\ \(N_{C}^{\star}\) & \([1,N_{W}]\) \\ \(N_{C}^{\star}\) & \([1,N_{O}]\) \\ \hline