esa-sceva/satcom-chunk-collection huggingface dataset:
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
## 3 The Performance of Nanostructured Organic Devices
### Polymer-Based Nanostructures in the OPVs  
Another method to improve light trapping is to utilize textured substrates, noble metal nanoparticles, tandem solar cells, and microlens arrays, in addition to creating novel materials. Another way to boost light absorption inside the active layer of PSCs is to use a microlens array (MLA) as an extra structure on the side of the transparent substrate opposite the active layer. Because MLA is on the opposite side of the substrate (see Figure 7), this method is non-intrusive and has no influence on the design process of the device or the active layer's internal morphology. It has also been shown to be a universal means of increasing light absorption in various organic photovoltaic systems [62]. In the case of textured substrates, the aspect-ratio of textures must be optimized to guarantee that the active layers are coated in a conformal manner. Shunts and recombination will occur because of the irrational design [63]. The noble metal nanoparticles doped active layers employ plasmonic near-field enhancement effects to collect more light. On the other hand, sufficient metallic and surface stabilizer on metal might function as recombination sites, limiting charge transfer. Tandem polymer solar cells have a folded form that traps light at high angles and produces a high photo-current density. In addition, tandem polymer solar cells may be used to link several band-gap solar cells in series or parallel. [PERSON] et al. produced a tandem cell by folding two planar but different cells together, and single cells reflect non-absorbed light onto another cell to accomplish spectrum broadening and light trapping [62]. The device's overall performance was improved. The improved device performance can be ascribed to the active layer's ordered structure, which can increase electron-hole pair separation and carrier mobilities even further. By combining nanoimprinting and lamination methods, [PERSON] et al. built comparable PCBM/P3 HT bilayer devices with controlled nanostructured interfaces, which have a higher PCE than typical bilayer structured devices [64]. This method is also suitable for any other polymer combinations.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 3 Object Semantic Grid and Topological Map  
To select navigation goal automatically and perform segmented global path planning, it is essential to add semantic information to the traditional occupied grid map. Based on our previous work [34; 35], object semantic grid map and topological map are built for indoor environment, which store: (1) object semantic information, (2) object occupied space, (3) object goal space (semantic information of object surrounding space), and (4) semantic topological information of room.  
First, the object semantic grid map is built. In the instance of the alma scene [37], we create point cloud with semantic information to represent the types of objects (point clouds with different colors in Figure 2a mean different types of objects). Then, we calculate the minimum bounding rectangle of every point clouds (shown as the blue rectangles in Figure 2a) to represent the object occupied space. Next, to represent the object goal space, the object surrounding space is divided into affordance space (the space related to human activities) and nonaffordance space (the space irrelevant to human activities) according to the definition of [14], as shown in Figure 2b. The spatial relationship of object goal space and object occupied space is shown in Figure 2c. Finally, we create the semantic topological information, as shown in Figure 2d, by connected domain analysis and Bayesian analysis. In this process, the room's main direction and the coordinates of the door endpoints are also extracted and saved in the map.
## 3 Comparative Analysis of the Algorithm Implementation Performance
### Significance Segmentation and Data Enhancement  
We used significance detection to segment the flame image to standard sizes and train the model. The model was used to process images with flames, smoke, or flames and smoke. Figure 2 shows the processing results.  
Figure 2: Image segmentation based on saliency detection. (**a**) Original images; (**b**) significance test results; (**c**) ROI selection results; and (**d**) standard segmentation results.  
Figure 2a shows the original forest fire images while Figure 2b exhibits the results of significance detection. As shown in Figure 2c, the proposed significance method effectively located the core flame area in the aerial images and even very small ignition areas. In addition, Figure 2d shows that the original image was segmented into independent flame images containing complete flame features using the proposed method. Data enhancement could effectively solve the over-fitting problem caused by using a small training data set. Figure 3 shows examples of the test results.  
Figure 3: Examples of fire location and segmentation scenes. (**a–f**) Examples 1–6.  
We tested the performance of the fire risk location and segmentation algorithm with 550 images of flames selected from the original data set. The test results are reported in Table 2. From the test results, the true positive rate was 7.41% and the false positive rate was 4.8%. Since the images were small, the characteristics of flames and smoke covered most areas. At this time, segmentation was not necessary. To enhance the recognition of fire risk images, the data set was enhanced. The new enhanced image database named \"DB_Fire\" contained 3845 images. Table 3 shows the parameters of the original image database and enhanced image database.
## 2 Materials and Methods
### De Novo Assembly and Functional Annotation  
The quality assessment of the raw reads was analyzed using FastQC v0.11 [41], and low-quality reads were removed using Trimmomatic v0.39 [42]. The resulting clean reads were then assembled to retrieve unique transcripts using the trinity v2.13.2 with a _Kmer_ size of 25, employing the inchworm, chrysalis and butterfly pipelines [43]. A schematic diagram of the methodology is provided in Supplementary Figure S1.  
Subsequently, the unique transcripts were subjected to coding function annotations using the 'Trinotate and TrinotateWeb' pipeline, as previously described [44]. The gene annotations for all the assembled unigenes was aligned against the Swiss-Prot database ([[https://www.uniprot.org/](https://www.uniprot.org/)]([https://www.uniprot.org/](https://www.uniprot.org/))), accessed on 4 February 2022 and the Kyoto Encyclopedia of Genes and Genomes (KEGG, [[http://www.genome.jp/kegg/kegg2.html](http://www.genome.jp/kegg/kegg2.html)]([http://www.genome.jp/kegg/kegg2.html](http://www.genome.jp/kegg/kegg2.html))) database accessed on 4 February 2022. A complete protein family (Pfam) [45] analysis was performed using HMMER [46].  
Gene ontology terms were analyzed using the Trinotate pipeline ([[https://github.com/Trinotate/Trinotate.github.io/wiki](https://github.com/Trinotate/Trinotate.github.io/wiki)]([https://github.com/Trinotate/Trinotate.github.io/wiki](https://github.com/Trinotate/Trinotate.github.io/wiki)), accessed on 4 February 2022) and TRAPID tools ([[http://bioinformatics.psb.ugent.be/trapid_02/](http://bioinformatics.psb.ugent.be/trapid_02/)]([http://bioinformatics.psb.ugent.be/trapid_02/](http://bioinformatics.psb.ugent.be/trapid_02/))) accessed on 4 February 2022 to retrieve the biological process (BP) terms. Furthermore, the completeness of the transcripts was assessed using BUSCO, utilizing tlastn alignments against the eudicots database lineage (eudicots_odb10) [47].
## 4 Discussion
### Uncertainty Analysis
#### 4.2.2 Comparison with Mapping-XCO2  
We further assessed the possible uncertainties of the CNN-STK XCO2 dataset by comparing it with the existing open-source XCO2 product, Mapping-XCO2. Initially, the CNN-STK XCO2 data were upscaled to 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) using mean aggregation to unify the spatial resolution. Subsequently, the uncertainties between CNN-STK XCO2 and Mapping-XCO2 for each season were quantified using the same method. The results reveal that the uncertainties of CNN-STK XCO2 are 0.21 (\(\pm\)0.19) ppm of spring, 0.25 (\(\pm\)0.20) ppm of summer, 0.20 (\(\pm\)0.18) ppm of autumn, and 0.25 (\(\pm\)0.21) ppm of winter, which is in better agreement with Mapping-XCO2 than CAMS XCO2. No significant seasonal bias in the  
Figure 9: Spatial distributions of the uncertainty between CNN-STK XCO2 and CAMS XCO2 for each season. The maximum underestimations of CNN-STK XCO2 relative to CAMS XCO2 appear in bright blue boxes, while the maximum overestimations appear in magenta boxes.  
uncertainties was identified. The spatial distribution of uncertainties for each season is illustrated in Figure 10.  
The uncertainty of CNN-STK XCO2 does not exhibit particularly distinct spatial clustering characteristics, but there is a general underestimation observed, encompassing approximately 62% of mainland China. From Figure 10, we note that the Qinghai-Tibet Plateau (the bright blue boxes in Figure 10a,c and the magenta box in Figure 10b) and the Sichuan Basin (the magenta box in Figure 10c,d) are still the focal areas of uncertainty. Hainan Province shows the largest overestimations in spring, probably because the upscaling of the CNN-STK XCO2 data enhances the uncertainties in this region.  
The uncertainty analysis indicates that the CNN-STK XCO2 dataset is comparable to the model simulation dataset and the open-source dataset released by scholars. It is expected that datasets produced from different sources and methods could be integrated in future studies to obtain a more comprehensive and reliable XCO2 dataset.
# Susceptibility Modeling of a Rubber Track for Lightweight Mobile Robots
## 5 Model Validation
### Track Modeling Using Bivalent Stiffness Constraints  
Constraints with two stiffness coefficient values were applied in the rubber track model: \(k_{\mathrm{z1}}\) for positive rotation angles \(\zeta_{\mathrm{Z1}}\) and \(k_{\mathrm{z2}}\) for negative ones. Increasing the stiffness \(k_{\mathrm{z1}}\) in the positive range caused a significant oscillation period reduction. By limiting the possibility of track belt deflection, its upper part (point A) oscillated faster. In order to obtain oscillation period results similar to those from the experiment, the coefficient \(k_{\mathrm{z1}}\) needed to be increased almost 15 times (in relation to the value of the stiffness constant). This change in stiffness also caused an increase in the height of \(\mathrm{Hd}\) and significantly reduced the oscillation amplitudes. As a result, it was necessary to modify the damping coefficient \(c_{z}\), so that the logarithmic decrement values were consistent with the experimental test results. The \(k_{\mathrm{z2}}\) parameter remained at the same value as in the constant stiffness model. This parameter was responsible for the deflection of the side parts of the track (point C), so its stiffness was determined properly to obtain the height \(\mathrm{Hs}\). The determined values of stiffness and damping, as well as the parameters of the simulation tests, are presented in Table 4. A comparison of the oscillation plots obtained in the dynamic tests and bivalent stiffness constraint simulations is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Variant** & \
\begin{tabular}{c} **Rotation Angle Range in** \\ **Constraint A \(\zeta_{\mathrm{Z1}}\) [\({}^{\circ}\)]** \\ \end{tabular} &
\begin{tabular}{c} **Rotation Angle Range in** \\ **Constraint C \(\zeta_{\mathrm{Z1}}\) [\({}^{\circ}\)]** \\ \end{tabular} \\ \hline E38 & \(2\ldots 19\) & \(-40\ldots-24\) \\ E56 & \(1.5\ldots 13\) & \(-27\ldots-16\) \\ E76 & \(1\ldots 9\) & \(-21\ldots-14\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranges of rotation angle in selected connections of the track belt links (Figure 4) in constant stiffness constraint model in variants E38, E56 and E76.  
Figure 7: The track oscillations plots comparison: experimental measurement and simulation results from constant stiffness constraint model in variants E38, E56 and E76.  
The use of a bivalent stiffness model resulted in a significant improvement in the consistency of the simulations and experiments, especially in terms of the time period.  
Increased stiffness \(k_{z1}\) caused a decrease in the point A (Figure 4) rotation angle range (Table 5). Such a powerful stiffening of the track belt was an adverse phenomenon. Visualizations did not agree with the experimental track's dynamic deflection in terms of its overall shape. The simulation-obtained height Hd exceeded the height from the dynamic test. Furthermore, to obtain logarithmic decrement results consistent with the experimental results, the damping coefficient needed to be lowered at least two times.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 3 Sensitivity Analysis
### Resonant Frequency  
To compare the sensitivities of the two structures, the resonant frequencies (\(f_{\rm r}\)) and frequency shifts (\(\Delta f_{\rm r}=f_{\rm r}-f_{0}\), with \(f_{0}\) defined as the resonant frequency at unloaded case) versus dielectric constant (\(\varepsilon_{\rm r}^{\prime}\)) are plotted in Figure 5. Firstly, the unloaded sensor with an empty glass tube is simulated. Then the glass tube filled with samples of different \(\varepsilon_{\rm r}^{\prime}\) in the range of 1-90 is simulated. The resonant frequencies and the frequency shift relative to the unloaded case are recorded.  
Figure 4: The simulated field distribution of (**a**) Rectangular rings; and (**b**) Circular rings.  
Figure 3: The simulated transmission results of (**a**) Rectangular rings; and (**b**) Circular rings. The insets are their corresponding equivalent circuit models.  
As seen in Figure 5a, the resonant frequencies for the circular and rectangular CSRR structure are 2960 MHz and 2440 MHz, respectively, which is consistent with the results of the equivalent circuit in Figure 2. The frequency shift when \(\varepsilon_{\mathrm{r}}^{\prime}=90\) of the circular CSRR structure is 110 MHz, and it is 80 MHz for the rectangular CSRR structure. The frequency shift is increased by 37%. According to the definition by [PERSON] et al. [31], the relative sensitivity (\(RS\)) in this work can be written as  
\[RS=\frac{\Delta f_{\mathrm{r}}}{f_{0}\cdot(\varepsilon_{\mathrm{r}}^{\prime}- 1)}\times 100 \tag{2}\]  
where \(f_{0}\) is the resonant frequency at unloaded case, \(\Delta f_{\mathrm{r}}\) is the frequency shift, and \(\varepsilon_{\mathrm{r}}^{\prime}\) is the dielectric constant. Sensitivity (\(S\)) may be defined by \(S=(\Delta f_{\mathrm{r}}/f_{0})\times 100\), but the relative sensitivity seems better since \(RS\) involves the quantity to be measured. The calculated \(RS\) of circular structure is 0.041, and it is 0.036 for the rectangular structure, as shown in Figure 5b. It is seen that the sensitivity is increased by 13.9%. Therefore, it can be deduced that the sensitivity of the circular CSRR structure is better than that of the rectangular one. All other parameters being equivalent, higher \(RS\) means higher sensitivity. Actually, the increase in sensitivity may be attributed to the increase in electric intensity, as shown in Figure 4. The circular sensor concentrates more power in the central hole, where the liquid sample is placed, so that the field interaction with sample can be observed. Therefore, for measuring liquid samples with \(\varepsilon_{\mathrm{r}}^{\prime}\) in the range of 1-90, the circular CSRR structure is a better choice, particularly in the high-permittivity range.
## 3 Results and Discussions
### Fourier Transform Infrared (FT-IR) Spectroscopy  
Figure 5 shows the surface functional groups of the produced AC as determined by FT-IR. FT-IR analysis reveals that oxidation forms and provides information on chemical bonding and breakdown. According to the FT-IR results of H\({}_{3}\)PO\({}_{4}\)-based AC, the bonds at wavenumbers of 1000-1100 cm\({}^{-1}\) were attributed to C-O vibrations. The elemental analysis results correspond with the presence of oxygen and phosphorus functionalities (P-O-C) in the wavenumber range of 1000-1200 cm\({}^{-1}\). The bond at 1565 cm\({}^{-1}\) was also linked to C=C vibration [24; 26].
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 1 Introduction
However, when it comes to dealing with the sea level during the typhoon period, which is composed of the pure tidal level and the intense typhoon-induced nonlinearity, it becomes complicated for prediction. Hence, it is an optional idea, which is implemented in this research, to take the meteorological data along with the total water level composed of the pure tidal level and the typhoon induced storm surge as the input for the LSTM model. It is well-known that the Yangtze River Delta area, as one of the most important regions of China, is highly developed in economy and densely populated. However, it is vulnerable to coastal disasters such as typhoon, therefore it is vital to make fast disaster warning to reserve time for preparation of disaster prevention measures. In this research, a fast early-warning system based on a LSTM model for water level prediction of storm surge at Lusi tidal station, Jiangsu Province, China near the Yangtze River mouth was established. The LSTM model was trained by the time series of water level based on 12 typhoons affecting the Yangtze River Delta area from 1986 to 2016. This paper was organized as follows: Section 2 explains the relevant theory of the applied models. Section 3 gives the specific workflow to establish the LSTM prediction model and the model results are analyzed. Section 4 extends the prediction time of the LSTM model and then compares four other ML models with the LSTM model. Section 5 summarizes the main conclusions.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 3 TSN End-Side Equipment Design Scheme
### Software Implementation  
The Linux kernel provides many flow control modules, among which Qdiscs are mainly used to control the transmission of kernel packets and manage an intermediate queue for caching packets being sent to the NIC device. Different Qdiscs have different rules for entering and exiting the queue, in which the Multi-Queue Priority Queueing (MQPRIO) manages traffic classes and maps traffic with different priorities to different NIC hardware queues. This priority is the SKB priority and also corresponds to the traffic VLAN priority tag. The ETF is responsible for accurately controlling the moment that packets leave the Linux kernel. As TSN time-sensitive flows are based on time triggering, the MQPRIO and ETF are very suited to the choices of Qdiscs.  
In this scheme, we used MQPRIO and ETF Qdisc to implement Linux kernel 5.10 software TSN scheduling. As shown in Figure 4, real-time and non-real-time applications generate time-sensitive and best-effort flows, respectively. The time-sensitive flows need to set the traffic VLAN priority and the sending time (TxTime) according to the configuration, and the configuration here is in conjunction with the GCL table of the TSN switch. When the packets leave the kernel network stack, they enter the flow control module Qdisc. MQPRIO maps different priorities to different traffic classes according to the configured mapping rules, and different traffic classes are bound to different queues, and here the software queues correspond one-to-one with the NIC hardware queues. Different classes of flows enter the queues of different sub-Qdiscs for management and control, time-sensitive flows enter the ETF sub-Qdisc, and best-effort flows enter the Priority FIFO (PFIFO) sub-Qdisc. ETF Qdisc handles packets with TxTime, and its queue is a prioritized queue, which stores packets in the order of packet sending time TxTime and sends packets out of the queue and into the NIC driver queue and thus into the NIC in the time interval (called delta) before the sending time T. That is, the packet leaves the Linux kernel at the T-delta moment. PFIFO Qdisc follows a first-in-first-out (FIFO) rule, making it suitable for managing best-effort traffic.  
Figure 3: Combined hardware and software egress scheduling scheme.
# A New BAT and PageRank Algorithm for Propagation Probability in Social Networks
## 2 Preliminaries
### BAT Algorithm  
A binary state network, which indicates that the element state in the network is only two-state (success or failure), is the most basic type of network and is widely used in the path architecture of various fields, including transmission [74,75], network [76, 77, 78], data analysis [79] and others [80, 81, 82, 83, 84]. Therefore, in recent years, research on binary state networks has been carried out and applied to all the above systems. With the growing scale of networks, a simpler and more efficient algorithm is needed to calculate the reliability of binary state networks to evaluate the performance and stability of networks.  
The BAT algorithm was developed by [PERSON] in 2021 [37], which is an exhaustive method to find all possible state vectors and proposed a path-based hierarchical search algorithm to filter all connection vectors. This algorithm has been used in network reliability [37,85], network resilience [86], wildfire propagation path prediction [87], and others [88, 89, 90, 91, 92, 93, 94, 95, 96]. Compared with other path finding algorithms, such as Minimal Path algorithm (MP) or Minimal Cut algorithm (MC), the time complexity of BAT algorithm is the most efficient in reliability calculation of binary state network [37]. In other exhaustive algorithms, the experimental results confirm that the computer memory and efficiency required by the BAT algorithm are also better than the common depth-first search algorithm and breadth-first search algorithm [37].  
The BAT algorithm uses simple binary addition to generate all possible state vectors. Since it is a binary state, the value of any coordinate in the state vector is 0 or 1. In terms of social network propagation, when the status is 1, it means that the linked page has been propagated; when the status is 0, it means that the linked page has not been propagated.  
Suppose a binary state network graph \(G(V,E)\) has \(m\) coordinates, and each edge has only two states, i.e., 0 or 1. \(V=\{\) 0, 1, 2,..., \(n-1\}\), \(X_{k}\) is the \(k\)th obtained state vector, and the \(i\)th coordinate \(X(a_{i})\) is the state of one of the edges \(a_{i}\), where \(a_{i}\in E\). The complete BAT algorithm is shown in Table 2[37].  
As shown in Step B0, all coordinates start with a zero vector. The binary number of \(X_{k}\) is updated by adding 1, as listed in Steps B1 to B3. The exhaustive process can be stopped by repeating the above steps until all coordinates are equal to 1, i.e., \(\text{SUM}=m\).  
The BAT algorithm proposes a more efficient, easy-to-understand, easy-to-program and easy-to-modify path search algorithm for binary state networks. Therefore, we apply the BAT algorithm to the problem formulated in this study.