esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper describes the unique antireflection (AR) layer of vertically arranged ZnO nanorods (NRs) on crystalline silicon (c-Si) solar cells and studies the charge transport and photovoltaic properties by simulation. The vertically arranged ZnO NRs were deposited on ZnO-seeded c-Si wafers by a simple low-temperature solution process. The lengths of the ZnO NRs were optimized by changing the reaction times. Highly dense and vertically arranged ZnO NRs were obtained over the c-Si wafer when the reaction time was 5 h. The deposited ZnO NRs on the c-Si wafers exhibited the lowest reflectance of ~7.5% at 838 nm, having a reasonable average reflectance of ~9.5% in the whole wavelength range (400-1000 nm). Using PC1D software, the charge transport and photovoltaic properties of c-Si solar cells were explored by considering the lengths of the ZnO NRs and the reflectance values. The 1.1 \(\upmu\)m length of the ZnO NRs and a minimum average reflectance of 9.5% appeared to be the optimum values for achieving the highest power conversion efficiency of 14.88%. The simulation study for the vertically arranged ZnO NRs AR layers clearly reflects that the low-temperature deposited ZnO NRs on c-Si solar cells could pose a greater prospect in the manufacturing of low-cost c-Si solar cells.  
zinc oxide; thin film; silicon solar cells; antireflection layer; power conversion efficiency; PC1D simulation +
Footnote †: journal: Applied Physics Letters
## 4 Results and Discussions
### Pulse in Time and Frequency Domain  
Figures 4 and 5 show a typical single PD pulse and its fast Fourier transform (FFT) in SF\({}_{6}\) and g3 in the POC and the POE, respectively. Parameters in terms of rising time (time period from 10% to 90% of peak magnitude), falling time (time period from 90% to 10% of peak magnitude), and pulse width (time interval between 50% of peak magnitude) in SF\({}_{6}\) and g3 were compared. A summary of the comparison of parameters is shown in Table 2, which were the average values extracted from 20 pulses for each case. It can be seen that pulses in the g3 gas had a relatively longer rising time and pulse width compared with the rapider rising time and shorter pulse width in SF\({}_{6}\). In addition, although the frequency ranges of pulses in both gases were similar, the relative amplitudes of FFT in g3 gas were much greater that those in SF\({}_{6}\). Possible reasons for this phenomenon were the remarkable corona stabilization effect due to the electronegativity of SF\({}_{6}\) and the great effective ionization coefficient of NOVEC(tm) gas, in which discharge was suppressed and developed easily, respectively [(20; 27)].  
Figure 4: PD pulse and its fast Fourier transform (FFT) in SF\({}_{6}\) and g3 in the POC. (**a**) In time domain; (**b**) In frequency domain.  
Figure 3: Partial discharge inception voltages (PDIVs) of SF\({}_{6}\) and g3 in the POC and POE at different pressures.
## 2 WENO-ZQ Schemes
Choose the big spatial stencil \(T_{1}=\{I_{i-2},I_{i-1},I_{i},I_{i+1},I_{i+2}\}\) and the other three equidistant stencils \(T_{1}^{\text{js}}=\{I_{i-2},I_{i-1},I_{i}\}\),\(T_{2}^{\text{js}}=\{I_{i-1},I_{i},I_{i+1}\}\), and \(T_{3}^{\text{js}}=\{I_{i},I_{i+1},I_{i+1}\}\) to reconstruct the polynomials \(p_{1},p_{1}^{\text{js}},p_{2}^{\text{js}}\) and \(p_{3}^{\text{js}}\). 2. Compute the linear weights based on the polynomials \(p_{1},p_{1}^{\text{js}},p_{2}^{\text{js}}\) and \(p_{3}^{\text{js}}\) as follows: \[p_{1}=\gamma_{1}^{\text{js}}p_{1}^{\text{js}}+\gamma_{2}^{\text{js}}p_{2}^{ \text{js}}+\gamma_{3}^{\text{js}}p_{3}^{\text{js}},\]\(\gamma_{l}^{js},l=1,2,3\) are linear weights in the WENO-JS5 scheme. In a uniform grid, \(\gamma_{1}^{js}=0.3,\gamma_{2}^{js}=0.6,\text{and}\ \gamma_{3}^{js}=0.1\). 3. Compute the smoothness indicators \(\beta_{l},l=1,2,3\) based on Formula (4):
4. Calculate the nonlinear weights based on the linear weights and the smoothness indicators. \[\alpha_{l}=\frac{\gamma_{l}^{js}}{(\varepsilon+\beta_{l})^{2}},\overline{ \omega}_{l}^{js}=\frac{\alpha_{k}}{\sum\limits_{l=0}^{r-1}\alpha_{l}},l=1,2,3\]
5. The final reconstruction formulation of conservative values \(u(x,\ t)\) at the point \(x_{i+1/2}\) of the target cell \(l_{i}\) is given by \[u_{i+\frac{1}{2}}^{L}=\overline{\omega}_{1}^{js}p_{1}^{js}+\overline{\omega}_ {2}^{js}p_{2}^{js}+\overline{\omega}_{3}^{js}p_{3}^{js}.\] (6)  
In the comparison of the calculation procedures, WENO-ZQ5 does not need to solve the linear weights and does not need to care about the situation of negative linear weights, which increases the robustness of the calculation and reduces the computational cost. Its linear weights are not dependent on a grid and are convenient to apply to non-uniform meshes and adaptive meshes. For the the NUAA-Turbo2.0 three-dimensional compressible solver using WENO-JS5, only appropriate deletion is needed to achieve its application. Although the new scheme proposed by [PERSON] and [PERSON] is of fifth-order accuracy, it can be extended to third-order accuracy by only replacing the polynomial and smoothing factor of the third-order big central spatial stencil, which is denoted as WENO-ZQ3.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation  
To verify the performance optimization of the observation matrix by the ABA, this paper conducted two groups of experiments: Signal reconstruction experiment and the selection of transform bases for the ABA to optimize the performance of the observation matrix. All simulation experiments were carried out in MATLAB environment.
## 2 Materials and Methods  
### Raw Materials  
As mentioned in the Section 1, to take advantage of the shape change originating from the magnetic field induced strain (MFIS), which is driven by applying an external magnetic field, it is necessary to impose a 5M martensite phase in the Ni-Mn-Ga alloy at an operation temperature of room temperature (RT, i.e., \(\sim\)296 K). The chemical composition of a Ni-Mn-Ga ingot was therefore determined to be Ni\({}_{50}\)Mn\({}_{28}\)Ga\({}_{22}\) (at.%), which shows a single 5M martensite phase according to the diagram of phase stability [37]. The Ni\({}_{50}\)Mn\({}_{28}\)Ga\({}_{22}\) (at.%) alloy is abbreviated as \"Ni-Mn-Ga\" alloy throughout this article, unless otherwise mentioned. The high-purity nickel spheres (Ni; purity \(>\) 99.99%), manganese flakes (Mn; purity \(>\) 99.9%), and gallium spheres (Ga; purity \(>\) 99.9999%) were utilized for the preparations of the ingots. Some pre-treatments were carried out on the Ni spheres and Mn flakes, such as mechanical polishing and etching (e.g., etching the Mn flakes by using a 10 vol.% HNOsolution at RT) to remove the surface contamination and thin oxidation layers. The Ga spheres were used directly without any pre-treatments.
## 4 Discussion
### Automatic Gain Control Using PGA Amplifier  
The main problem with PGA systems is the constant gain with step changes, which do not allow the input signal to be amplified to the desired signal level. As a result, the input range of the ADC is not used, and the accuracy is reduced. This limits its ability to measure the minimum current >1 A with high resolution. PGA amplifiers should be chosen with a rail-to-rail input range. The error in these systems is determined by the following relationship: System error gain = reference tolerance + gain error.
## 4 Experiments
### Experiment Analysis
#### 4.3.2 Performance Comparison w.r.t Layers of HGNNs  
In order to study the influence of the number of layers of HGNN in the HGNN-based feature-semantic interaction module, we take the number of layers of HGNN as (1, 2, 3, 4), which are denoted as HGAFMN-1, HGAFMN-2, HGAFMN-3, HGAFMN-4. In addition, we also replace HGNN with GNN for performance comparison, where the number of layers of GNN is three, and it is denoted as HGAFMN-G.  
The experimental results are shown in Figure 10. It can be seen that the prediction performance of HGAFMN-2 is better than that of HGAFMN-1, and the prediction performance of HGAFMN-3 is significantly better than that of HGAFMN-2. Furthermore, the prediction performance of HGAFMN-4 is slightly worse than that of HGAFMN-3. Experiments show that we obtain the best results when the number of layers of HGNN is three layers. Moreover, the prediction performance of HGAFMN-G is significantly worse than that of HGAFMN. It shows that in the feature-semantic interaction module of the molecular structure-odor descriptors, the model based on the hypergraph neural network performs better than the model based on the graph neural network. It also illustrates that it is very reasonable to model the odor molecular structure-odor descriptor relationship data as a hypergraph structure that can better encode high-order data correlations.  
Figure 9: (**a**) Experimental results of model variants on F1-score; (**b**) experimental results of model variants on AUROC.
# Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP
## 3 The Proposed Method
### Movement Mismatches Compensation for the Dual-Frequency Unwrapping Method  
With Equation (15), the phase maps of the two frequencies can be obtained. However, because of the object movement, position mismatches also exist between the phase maps. Assuming that a point \((x,y)\) is moved to \((u,v)\), the wrapped phase values of the two frequencies are:  
\[\Phi^{h}(x,y)=\phi^{h}(x,y)+\phi^{h}(x,y) \tag{16}\]  
\[\Phi^{l}(u,v)=\phi^{l}(u,v)+\phi^{l}(u,v) \tag{17}\]  
When the dual-frequency phase unwrapping method is implemented directly, the position between the two phase maps is mismatched, and the preset relationship between \(\Phi^{h}(x,y)\) and \(\Phi^{l}(u,v)\) is violated, leading to the failure of unwrapping. Therefore, the position of the object should be moved back firstly. The object position in the low frequency is moved to the same position of high frequency as follows:  
\[\Phi^{l\prime}(x,y)=\Phi^{l}(f^{hl}(x,y),g^{hl}(x,y)) \tag{18}\]  
where \(\Phi^{l\prime}(x,y)\) is the phase distribution of low frequency after position alignment, and \((u=f^{hl}(x,y),v=g^{hl}(x,y))\) is the relationship of the positions before movement and after movement, which is described by the rotation matrix and translation vector. The position of the object is same between the high frequency and low frequency with Equation (18).  
As the movement also introduces the phase shift, therefore the phase value in \(\Phi^{l^{\prime}}(x,y)\) cannot be used directly. The phase shift caused by the movement between the two frequencies also needs to be compensated by Equation (19) [6]:  
\[\Phi^{l^{\prime\prime}}(x,y)=\Phi^{l^{\prime}}(x,y)+\phi^{l}(x,y)-\phi^{l}(u,v) \tag{19}\]  
where \(\Phi^{l^{\prime\prime}}(x,y)\) is the phase value with movement influence compensation. With the phase map after position alignment and phase compensation, the dual-frequency phase unwrapping method can be applied as follows:  
\[\Phi^{h\_unwrap}(x,y)=\Phi^{h}(x,y)+2\pi n(x,y) \tag{20}\]  
\[n(x,y)=INT\bigg{[}\frac{k\Phi^{l^{\prime\prime}unwrap}(x,y)-\Phi^{h}(x,y)}{2 \pi}\bigg{]} \tag{21}\]  
where \(\Phi^{l^{\prime\prime}unwrap}(x,y)\) and \(\Phi^{h\_unwrap}(x,y)\) are the unwrapped phase map of the low frequency and high frequency, and \(n(x,y)\) is the fringe order of the high frequency.
# When Maritime Meets Aviation: The Safety of Seaplanes on the Water
###### Abstract  
The water environment is a dynamic domain critical to global transportation and commerce, where seaplanes operate during take-offs, landings, and ground operations, often near maritime traffic. Canada's vast remote regions and unique geography increase reliance on seaplanes, especially for private and recreational purposes. This article examines the intersection of aviation and maritime operations through a mixed-methods approach, analyzing seaplane safety on waterways using quantitative and qualitative methods. First, data from 1005 General Aviation (GA) seaplane accidents in Canada (1990-2022) are analyzed, revealing 179 fatalities, 401 injuries, and 118 destroyed aircraft--significant given that seaplanes comprise under 5% of GA aircraft. Of these, 50.35% occurred while the seaplane was not airborne. Second, insights from interviews, focus groups, and questionnaires involving 136 participants are explored through thematic and content analysis. These capture pilot concerns that are not evident in accident data, such as hazards from jet ski interactions and disruptive boat wakes. The findings highlight risks like limited visibility and maneuverability during waterborne take-offs, worsened by seaplanes' lack of priority over maritime vessels in shared spaces. This article concludes with recommendations for both the seaplane and maritime communities, including increasing awareness among boaters about the presence and operations of seaplanes, as well as regulatory adjustments, particularly considering the right of way.  
seaplane; safety; take-off; landing; collision; water operations; maritime safety; aviation safety 2025155808  
1
## 2 Literature Review
### Communication Technologies for IoT Healthcare
#### 2.3.3 Advances in Low-Power Communication Protocols  
The primary objective of communication protocol advancements has been to utilize less energy. Emerging protocols such as NB-IoT and Zigbee optimize data transmission efficiency while minimizing power consumption. Low-power communication protocols such as BLE and LoRaWAN offer extended battery life while ensuring reliable data transmission. The treNch protocol is capable of lowering power consumption by one to two orders of magnitude in comparison to various BLE modes in a variety of scenarios while maintaining an equivalent level of service quality [38; 41].
## 3 Alternative Splicing Variants: Psoriasis Isoforms
The S100A7A/A15 (Koebnerisin) protein is overexpressed in psoriasis and is one of the most important markers in psoriasis [65; 66]. The human S100A7A/A15 gene has two alternatively spliced mRNA isoforms, a short (0.5 kb), S100A15-S, and a long (4.4 kb), S100A15-L [66]. The long isoform is specific to lesional psoriatic skin, where it is highly upregulated compared to non-lesional skin [64]. S100A7A long form is not expressed in normal skin, while short is weakly expressed in normal epidermis [65; 67]. The expression levels of these two isoforms indicates differential transcription stability for the hS100A15 mRNA isoforms in psoriasis and chronic atopic [65]. Thus, S100A7A is good example for the transcript-specific regulation of inflammation and disease progression [64]. Another example is S100A8, which is also a well-known psoriasis-inducing gene belonging to the same family of genes [68]. Two splicing variants of S100A8 (ENST00000368733 and ENST00000368732) were significantly upregulated in lesional skin [64]. Another splicing variant upregulated in lesional skin samples was the IL36 RN gene, belonging to the IL-1 cytokine family. Caspase recruitment domain family member 14 (CARD14) is an activator of NF&B within the epidermis. Alternative splicing of CARMA2/CARD14 transcripts generates protein variants with differential effect on NF&B activation [69]. [PERSON] et al. identified 15 missense variants in CARD14 in seven cohorts (including 6000 cases and 4000 controls). More variants were found in cases compared to controls, and these variants increased transcriptional activation of NF&B and enhanced expression of psoriasis specific transcripts [70; 71]. TRAF3 interacting protein 3 (TRAF3 IP2) mutations are often associated with psoriasis and PsA. The TRAF3 IP2 gene encodes for proteins involved in I-17 signalling. It is reported that the exon-2-excluded isoform of TRAF3 IP2, by alternative splicing, negatively regulates IL-17 signalling, resulting in overproduction of cytokines IL-17 and IL-22 leading to perturbed inflammatory cascade [11; 63; 72]. The expression of fibronectin (FN) isoforms, including extra domain A (EDA), has been reported to be overexpressed in psoriatic uninvolved epidermis sensitizes keratinocytes to mitogenic signals [73]. Hacats--immortalized keratinocyte cell lines--expressed a higher ratio of EDA\({}^{+}\) mRNA and EDA\({}^{+}\) isoform of fibronectin [74]. Hence, it is important to investigate splicing variants of a gene and its gene expression, as compared to single gene analysis, to identify potential new candidates for disease treatment of psoriasis. When combined with relevant tools, such as RNA-seq, this provides more information to design individualised treatment options for a better outcome of the disease.
## 3 Results
### Test Setup
#### 3.1.3. PERFORM Case Study III: Survey Area  
Case Study III analyzes a survey task using a lawnmower pattern (as shown in Figure 10), common for seafloor mapping operations. Different parameters are used to reflect the desired evaluation attributes. Due to mapping operations relying on swath widths based on depth to minimize gaps in coverage, path percent error, the percent error between the actual route the vehicle takes compared to the desired lawnmower pattern, is one of the parameters measured. The second parameter is average vehicle speed. A consistent vehicle speed within the optimal range of the sonar is also important for high quality data.  
The rule base for Case III is constructed to reflect a narrower region for acceptable performance and shown in Table 4. To receive a higher performance score, the vehicle's average speed should remain in the \"good\" range window while maintaining low error over the path taken.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Linguistic Term** & **Very Close** & **Close** & **Adequate** & **Far** & **Very Far** \\ \hline
**Very Short** & Poor & Fair & Good & Very Good & Good \\ \hline
**Short** & Poor & Fair & Good & Very Good & Good \\ \hline
**Medium** & Poor & Poor & Fair & Good & Fair \\ \hline
**Long** & Very Poor & Very Poor & Poor & Fair & Poor \\ \hline
**Very Long** & Very Poor & Very Poor & Poor & Fair & Poor \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of rules used for Case Study II.  
Figure 10: Testbed setup for Case Study III: A lawnmower pattern to perform seafloor mapping operations.
## 3 The CsrA-Containing Ribonucleoprotein Complexes
### Mechanism of Action of CsrA on mRNAs Targets  
CsrA regulates translation of its target mRNAs, either positively or negatively. mRNA targets contain a variable number of CsrA binding sites. In general, they are located in the untranslated leader sequence and overlap the Shine-Dalgarno (SD) sequence. The CsrA-binding consensus sequence closely matches the canonical AAGGAGGU SD sequence [42]. The number of CsrA binding sites may vary from 1 to 6, depending on the target. For example, 4 and 6 CsrA binding sites have been identified on the _glgCAP_ and _pgaABCD_ mRNA targets, respectively [17; 80]. In the case of negative regulation, binding of CsrA prevents translation initiation and generally leads to mRNA degradation [9]. Some variations in the molecular mechanisms have been shown. As an example, negative regulation of _sdiA_ occurs by CsrA binding to two sites located within the early coding region of the _sdiA_ mRNA, without binding to or occluding the SD sequence [81].  
Positive regulation appears to be less common. Up to now, two positively regulated targets have been described. In the case of _flhDC_, two binding sites are located in the untranslated leader region of the transcript. CsrA binding to these sites stabilizes the transcript by inhibiting the 5'-end degradation mediated by RNaseE [18; 19]. The second example involves the regulation of the _moa4_ gene, which is involved in molybdenum cofactor (MOCO) synthesis. MOCO serves as a redox center in enzymes of anaerobic metabolism. The _moa_ mRNA untranslated leader sequence contains two CsrA-binding sites and constitutes a MOCO-sensing riboswitch. This is the first example of a riboswitch aptamer that interacts with two regulatory factors, a low-molecular-weight ligand and an RNA binding protein [82].
## 4 Experiments and Analysis  
In this section, we conduct experiments to evaluate the proposed method for cluster discovery of illegal websites. Our experimental environment is a workstation equipped with Intel(R) Xeon(R) Gold 6230R CPU, 256 GB memory, and Nvidia Geforce RTX4090 with 24 GB video memory.  
**Datasets.** The dataset used in this study was obtained from the asset data available on the cyberspace mapping platform. Over 3.7 million data entries were downloaded, each containing 14 attributes. A detailed description of these attributes can be found in Section 4.3. To address efficiency concerns and considering that many illegal websites primarily utilize images for webpage construction, only the'screen' attribute from the website asset data was used in this research. Text information was extracted from webpage screenshots using OCR technology. The dataset comprises a random selection of data downloaded from the surveying and mapping platform, encompassing both normal and illegal websites.  
**Evaluation metrics.** In this paper, we use ACC (Accuracy), NMI (Normalized Mutual Information) and ARI (Adjusted Rand index) to evaluate the performance of the clustering algorithm. The specific formula is as follows:  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN} \tag{6}\]  
\(TP\) is the true positive, \(TN\) is the true negative, \(FP\) is the false positive, \(FN\) is the false negative.  
\[NMI=2\times\frac{I(Y,C)}{H(Y)+H(C)} \tag{7}\]  
where \(Y\) represents the clustering result, \(C\) represents the real clustering label, \(I(Y,C)\) represents the mutual information of \(Y\) and \(C\), and \(H(Y)\) and \(H(C)\) represent the entropy of \(Y\) and \(C\), respectively. The higher the value of \(NMI\) is, the closer the clustering result is to the real label, and the value range is \([0,1]\).  
\[ARI=\frac{RI-E[RI]}{\max(RI)-E[RI]} \tag{8}\]where \(R\) is the Rand index, \(E[RI]\) is the expected Rand index, and \(\max(RI)\) is the maximum possible Rand index. The value range of \(ARI\) is \([-1,1]\), and the larger the value, the more consistent the clustering result is with the real result.
## 2 Research Area and Datasets
### Dataset  
Himawari-8 represents the third generation of geostationary satellites, launched by the Japan Meteorological Agency in 2014. It primarily serves the regions of East Asia and Australia. The satellite is outfitted with the Advanced Himawari Imager (AHI), which captures data across sixteen spectral bands, comprising six reflectance bands and ten brightness temperature bands. The spatial resolution of Himawari-8 varies between 0.5 and 2 km, and it offers a temporal resolution of 10 min, rendering it particularly effective for the real-time monitoring of forest fires [34]. Although geostationary satellites typically exhibit lower spatial resolution compared to polar orbiting satellites, their extensive coverage, synchronized data collection capabilities, and fixed observational position mitigate this drawback.  
In the present study, six spectral bands pertinent to forest fire analysis were selected from the Himawari-8 satellite data. These included three reflectance bands (bands 3, 4, and 6) and three brightness temperature bands (bands 7, 14, and 15) [35]. Specifically, bands 3 and 4 were indicative of vegetation cover, bands 7 and 14 were associated with fire detection, and bands 6 and 15 served to filter out cloud interference. Collectively, these bands furnished critical information essential for fire detection, such as thermal radiation and reflectance in areas affected by fire, thereby enhancing the accuracy and real-time efficacy of fire point detection.