esa-sceva/satcom-chunk-collection huggingface dataset:
# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Philosophy of Model Design  
The core design principle is to harness the strengths of diverse feature representations to generate a more thorough and resilient output. By integrating the QP map and fusing features from early to deep layers of the CNN, the post-processing filter can produce a refined image that boasts enhanced visual quality and significantly reduced compression artifacts and noise. The integration of the QP map and feature fusion in convolutional layers empowers the network to develop a richer image representation, mitigates the effects of intensity variations, and yields enhanced output quality in post-processing tasks. The QP map serves as a spatial guide, indicating the compression level applied to each image region. By leveraging this map, the network can dynamically compensate for varying compression levels, thereby mitigating the effects of pixel intensity fluctuations. The QP map's spatial adaptivity enables the network to tailor its correction to the unique compression artifacts and noise patterns present in each local image area. The feature fusion process integrates the QP map with the diverse features extracted from the convolutional layers, which encode various image attributes such as edges, textures, and patterns. By merging these features, the network can generate a more holistic and detailed representation of the image, encompassing both the compression-induced distortions and the intrinsic image content. The QP map plays a crucial role in mitigating pixel intensity variations by providing a spatially adaptive representation of compression artifacts and noise. The feature fusion process takes this a step further by combining the QP map with convolutional layer features, which are inherently less sensitive to intensity fluctuations. This fusion enables the network to become more resilient to pixel intensity variations, ultimately leading to enhanced post-processing performance and improved overall image quality.
## 2 Results and Discussion
The stearopten fraction in the HS phase was observed at low concentration, with main rep  
Figure 1: Variations of color tint and number of petals in the _R. alba_ population (authors’ picture). resentatives nonadecane/nonadecene (0.56-10.95/0.21-3.23%), heptadecane,heneicosane, and tricosane (total n-alkane fraction in the range 1.19-17.32%). Similar to our results, the essential oil from _R. alba_ was found to be rich in geraniol (18.28%), followed by heneicosane (12.95%), nonadecane (10.75%), and citronellol (9.00%) [34]. The main components responsible for the aroma profile of the white oil-bearing rose are presented in Table 1, and the quantitative results for all identified VOCs in _R. alba_ blossom, together with the aroma description of each compound, are collected in Table S1. In addition, information about the Odor detection (OD) thresholds and CAS numbers of the compounds is presented in the Supplementary Information (Table S2). Figure 2.  
Our study reveals that the two studied _R. alba_ population clones with pink and white colored petals show different VOCs profiles (Figure 3). Figure 2: Representative HS-SPME-GC/MS total-ion current (TIC) chromatogram of fresh _R. alba_ flowers: (**a**) Petals mix; (**b**) Whole blossom white; (**c**) Whole blossom pink. Main components: 1. Benzyl alcohol, 2. _cis_-Rose oxide, 3. 2-Phenyl ethyl alcohol, 4. _trans_-Rose oxide, 5. \(\beta\)-Citronellol + Nerol, 6. Z-Citral (Neral), 7. Geraniol, 8. _E_-Citral (Geraniol), 9. _trans_-\(\beta\)-Caryophyllene, 10. Heptadecane, 11. Nonadecene, 12. Nonadecane. This finding is in agreement with the previous reported GC-MS analysis of the volatiles of essential oils distilled from two different \(R\). _alb_ accessions, which showed significant variation of the abundances of some of the volatiles between the two studied phenogroups [3]. Figure 3: VOCs profile (at 45 \({}^{\circ}\)C) of fresh \(R\). _alb_ blossom with pink (**a**) and white (**b**) colored petals. As seen from Figure 1 and Table 1 and Table S1, petals and whole _R. alba_ blossoms also show different VOC composition, which agrees with the observation of [PERSON] et al. [22] for different volatiles emitted from different rose tissues (buds, stems, leaves, and flowers). The effect of different extraction temperatures (30-60 \({}^{\circ}\)C) on the _R. alba_ volatiles content using whole mixed blossom samples is shown in Figure 4.  
In general, increasing the conditioning and extraction temperature reduces the concentration of low-boiling compounds, which is most noticeable by benzyl- and 2-phenyl ethyl alcohol, together with benzaldehyde, phenyl acetaldehyde, and monoterenes, while the higher temperatures used in SPME experiments increase the amount of high-boiling sesquiterpenes and n-paraffins.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.11 Knowledge (K) \(\rightarrow\) Data (D)  
This process refers to the cognitive subject's use of the completeness of Knowledge semantics to reverse-engineer or refine expressions, thereby generating Data points that support the Knowledge system. The transformation from Knowledge to Data typically involves extracting specific facts, metrics, and verification Data from structured Knowledge. This approach enables the precise representation and empirical validation of abstract Knowledge constructs, facilitating the alignment of theoretical frameworks with tangible, Data-driven evidence.  
* **Principle 8: Anti-weight** We have expanded the semantic content of this principle here, which is to achieve a solution to the problem through certain compensation methods. Therefore, in the conversion of knowledge to data, the idea of transforming complete content into consistent semantic content is often used in distributed processing. For example, in a distributed file system, to reduce the burden on a single storage system, complete content can be stored separately on each node through specific policies, which allows each node to undertake a small portion of the storage task, reducing the burden on individual nodes [81].
* **Principle 9: Preliminary Anti-Action** Using the completeness of knowledge semantics to filter out identical semantic content that does not belong to the knowledge system, in other words, the knowledge system pre-screens irrelevant data to ensure that only valuable data are collected and analyzed. This method not only improves data processing efficiency but also prevents irrelevant data from affecting the decision-making process. For example, before training an AI model, we need to preprocess the dataset in order to use our existing knowledge system to filter out data that do not match the actual situation.
* **Principle 25: Self-service** Apply existing Knowledge systems to autonomously guide Data collection and processing, reducing the need for external intervention. For instance, in the design of intelligent sensor networks, pre-set algorithms can be used to automatically identify important Data streams and prioritize them, thereby optimizing resource allocation and enhancing the system's adaptive capabilities [82; 83].
* **Principle 27: Cheap Short-living Objects** Data represent the same semantic content, and the conversion of knowledge to data through this principle represents the transformation from complex and complete to simple and lightweight. For example, in streaming media services, in order to reduce video content transmission time or bandwidth, service providers often use efficient video compression algorithms [84] to compress videos into smaller data formats while preserving semantic integrity as much as possible.
# Erosion Resistant Hydrophobic Coatings for Passive Ice Protection of Aircraft
## 4 Conclusions  
Novel surface-modifying polymers (SMPs) were designed, synthesized and formulated into the erosion resistant polyurethane coating PU-R to produce highly hydrophobic SPU coatings with superior erosion durability. The SPU coatings had high solid contents of ca. 58 wt.% (based on total resin solid) and could be easily applied by spraying or solution casting. Drying and curing of the coatings at ambient conditions produced elastomeric coatings with high water contact angles (100-115\({}^{\circ}\)), high mechanical strength (19-27 MPa), high elongation at break (640-730%) and low tensile set (20-35%). Reduced ice adhesion strength in the range of 220-400 kPa was measured for the SPU coatings using a static push rig test at \(-14\)\({}^{\circ}\)C. Repeated icing/ice removal cycles led to no substantial increase in the ice adhesion strength. The ASTM G76 and G73 standard tests demonstrated excellent erosion resistance against both high-speed solid particles and water droplets for the SPU coatings. Thus, the combination of high erosion durability, high mechanical strength, high surface hydrophobicity and reduced ice adhesion strength makes the SPU coatings attractive for ice protection of leading edge surfaces of fast moving aerodynamic structures, such as aircraft wings and helicopter rotor blades.  
Conceptualization, N.S. and A.B.; methodology, N.S. and A.B.; validation, N.S.; investigation, N.S.; resources, A.B.; writing--original draft preparation, N.S.; writing--review and editing, A.B.; project administration, A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Integrated Aerial Mobility program and the Aerospace Future Initiative Program of the National Research Council Canada, and the Canadian Department of National Defence-Royal Canadian Navy.  
Not applicable.  
Not extra supporting information is provided.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript.  
Figure 7: Water droplet erosion resistance of SPU coatings. Test conditions: 463 μm water droplets, impact velocity of 175 μm/s, impact angle of 90\({}^{\circ}\), impact frequency of 42,000 droplets per minute.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 2 Theoretical Basis for Structural Health Damage Trend Prediction  
### Variational Mode Decomposition (VMD)  
The theory of variational mode decomposition (VMD) [9] is a new non-stationary, nonlinear signal processing method proposed by [PERSON] in 2014. If the signal is decomposed into modal components, the constrained variational model is as follows:  
\[\underset{\{u_{i}\},\{w_{i}\}}{\underset{\{u_{i}\}}{\underset{\{u_{i}\}}{ \underset{\{u_{i}\}}{\underset{\{u_{i}\}}{\underset{\{u_{i}\}}{\underset{\{u_{i} \}}{  
LSTM utilizes the following equation to iteratively calculate network mapping from input sequence X to output sequence Y, from \(t=1\) to \(t=T\), which is similar to the RNN calculation. The initial \(c_{0}=0\) and \(h_{0}=0\):  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{3}\]  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}) \tag{4}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{5}\]  
\[\tilde{C}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{6}\]  
\[C_{t}=f_{t}\otimes C_{t-1}+i_{t}\otimes\tilde{C}_{t} \tag{7}\]  
\[h_{t}=o_{t}\otimes\tanh(C_{t}) \tag{8}\]  
\[\sigma=\frac{1}{1+e^{-x}} \tag{9}\]  
where \(W_{i}\), \(W_{f}\), and \(W_{o}\) represent the weight matrix of the input, forget, and output gates, respectively. The hidden layer weight matrix is characterized by \(U_{i}\), \(U_{f}\), and \(U_{o}\), while \(b_{i}\), \(b_{f}\), and \(b_{o}\) represent the corresponding gate structure bias matrix, respectively. \(\sigma\) is the nonlinear activation function, which is a logistic sigmoid; \(i_{t}\), \(f_{t}\), \(o_{t}\), and \(c_{t}\) are the input, forget, and output gates and unit state vector at time \(t\), respectively, which have the same size as the unit output vector \(h_{t}\). The element-wise multiplication of the two vectors is represented by \(\otimes\).
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 4 Results
### Distribution of Range Errors  
The distribution of range errors on the first and third frequencies is first analyzed. As shown in Figure 4, the upper subplots show the distribution of range errors for the smartphone, while the lower panel gives the results for MGEX stations. It can be observed that, for both smartphone and geodetic receivers, the standard derivation (STD) for the third frequency is smaller than the first frequency, with values of 7.3 m on P1 and 2.1 m on P5 for smartphones, and 0.5 m and 0.2 m for those of geodetic receivers, respectively, which indicates that the observation precision on the third frequency is higher. This difference may be due to the fact that the bandwidth of L5 is larger and the transmitted power of L5 is higher than the first frequency. Therefore, the anti-interference ability and anti-noise performance may also be significantly improved for L5 [31; 46; 47]. The range errors for the geodetic receivers are obeying a nearly zero-mean distribution, while for smartphones, the mean values are not zero, but rather 0.8 m and 0.6 m for the first and third frequencies, respectively. When further investigating the range errors, the STD of range errors for the geodetic receiver on P1 is 0.5 m, considering that the double-differenced measurements are formed when calculating the range errors, the original code precision should be 2 times smaller than the range errors by applying the error propagating law, which is 0.2-0.3 m. This value is consistent with the code precision used in MGEX data processing as in [48], which demonstrates the validity of the proposed range error derivation method.  
To provide a clear view of the characteristics of range errors for different constellations, Figure 5 illustrates the mean values and STDs of each constellation for smartphone and geodetic receivers. For GLONASS and BDS, there are no observations for the test smartphone data, and there are only a few GLONASS observations for ZIM2 and ZIM3. It can be concluded from Figure 5 that, among the four constellations, the GLONASS pseudorange measurements have the largest STDs, with values of 11.8 m and 0.7 m for smartphone and geodetic receivers. Therefore, in the GNSS data processing procedure, GLONASS pseudorange measurements are usually de-weighted by two times, and these results on the first frequency are similar to what has been found in [31]. With the four constellations, except for Galileo, the STDs on P5 are nearly 2 times smaller than on P1, which infers that more weight can be put on the third frequency. For this smartphone dataset, the STDs are more than ten times larger compared to the corresponding constellations for geodetic receivers, and hence, for the analysis for range errors on different frequencies of different constellations, the stochastic models of the smartphone observation noise can be adjusted accordingly in the future research.
## 2 Study Area and Data
### Research Data and Processing
#### 2.2.1 JL1 KF01A Data  
For the study area, a mosaic of high-resolution optical images from Jilin-1 Wide Field of View 01A with a resolution of 0.75 m was used. This mosaic image was obtained by merging and clipping data from December 2022. The preprocessing of optical remote-sensing images involved geometric correction, registration, orthorectification, image fusion, and mosaicking, followed by enhancement techniques including contrast adjustment, color transformation, and sharpening. The entire process used ground-control points uniformly selected from topographic maps at a scale of 1:50,000 as the reference. Jilin-1 satellite remote-sensing image data were provided by the Jilin-1 Network ([[https://www.jllmall.com/](https://www.jllmall.com/)]([https://www.jllmall.com/](https://www.jllmall.com/)) (accessed on 7 January 2025)).
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
###### Abstract
Firstly, the traffic flow time series is decomposed seasonally, and the SARIMA basic model with higher fitting goodness is screened out through seasonal and non-seasonal differences; then, two different combined prediction models are proposed based on the SARIMA model and NAR model. Finally, the average absolute percentage error (MAPE), the average absolute error (MAE), the root mean square error (RMSE), and other indicators of these two models are comprehensively compared to that of the traditional prediction model; at the same time, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination Method** & **Features** & **Methodology** & **Literature Examples** \\ \hline \multirow{4}{*}{Method of combining linear and nonlinear} & \multicolumn{2}{c}{LSSVR with the Gaussian kernel} & \multirow{4}{*}{[23]} \\  & Build predictive models & function and the linear & & \\  & considering linear and nonlinear & kernel function & \\  & characteristics of traffic flow & ARFMA-NAR & [24] \\  & & SSA-LLNF & [25] \\  & & ARIMA-GARCH & [18] \\ \hline \multirow{4}{*}{Method of assigning weight combinations} & \multirow{4}{*}{Optimize the model by assigning the best weights} & ARIMA-WNN & [7] \\  & & GM(1,1), SGM with & [26] \\  & & wavelet analysis & [27] \\  & & WND-LSTM & [28] \\  & & EnLSTM-WPEO & [28] \\ \hline \multirow{4}{*}{Method of stacking combination after decomposition} & \multirow{4}{*}{Capture different characteristics of traffic flow data through decomposition} & TVF-EMD-LSSVM & [29] \\  & & CEEMDAN-XGBoost & [30] \\ \cline{1-1}  & & \multirow{2}{*}{W-CNN-LSTM} & [31] \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous literature on combining models. the performance of the combined prediction model on traffic flow during the epidemic period is further investigated. This paper is mainly divided into the following parts: in Section 2, the construction methods of two different combined models of SARIMA, NAR, and SARIMA-NAR are described in detail; in Section 3, the prediction results of traffic flow are obtained through specific case analysis; in Section 4, the prediction results of different models are compared, and the adaptability of the combined model under the influence of the epidemic is discussed, in Section 5, some conclusions are drawn.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Channel Access and Operation Modes
#### 3.3.2 Sigfox  
The operating frequency band in Sigfox networks is 192 kHz wide. To transmit a frame, Sigfox sensors can use one of the two modes: U-procedure and B-procedure. With U-procedure, sensors send their frames in the uplink with no onward downlink frame. With B-procedure, sensors request a response to their uplink frame and thus start a bidirectional procedure. The Sigfox operator imposes a limit on the downlink transmission equal to four frames per day. It means that downlink is not intended for constant use. Therefore, we do not consider B-procedure in this paper. The sensors may send a frame once or three times, which improves transmission reliability. Sigfox rules allow for creating and transmitting one (single frame procedure) or three frames during one procedure (multiple frame procedure). In case three messages are transmitted, they must be identical, except for frame type (FT) (see Section 3.2) and convolution code.  
In any case, the first transmission attempt occurs at a random central frequency \(f_{1}\) within the uplink channel. If the sensor transmits the frame only once, it waits for \(T_{P}\geq 10\,\mathrm{ms}\) after the transmission before it can initiate a new one. Such an interval is introduced to simplify the detection and reception of uplink frames by the BS.  
Otherwise, see Figure 8, the sensor repeats the transmission twice at random central frequencies \(f_{2}\) and \(f_{3}\), each time waiting for \(10\leq T_{U}\leq 2000\,\mathrm{ms}\) after the previous transmission. The sensor completes the transmission procedure \(T_{P}\geq 10\,\mathrm{ms}\) after it finishes the third transmission.  
If the sensor needs to transmit several frames in a row, so-called block transmission, it accesses the channel independently for each frame. Moreover, the sensor may switch between one and three transmission attempts for each frame.
## 1 Introduction  
Since the invention of conductive polyacetylene in the 1970s [1], conductive polymers (CPs) have attracted great interest due to their good mechanical, optical and electrical properties. Nowadays, CPs have been widely used in optoelectronic devices [2; 3; 4; 5], chemical sensors [6; 7; 8; 9], capacitor electrodes [10; 11; 12; 13; 14; 15] and other fields [16; 17; 18; 19]. In the past few decades, many different types of CP have been synthesized using different methods, such as polypyrrole [20; 21], polythiophene [22; 23], polyaniline [24; 25] and so on [26]. Among various materials, polypyrrole (PPy) and its composite materials have attracted much attention due to the ease of manufacturing it, its variable conductivity and its environmental stability.  
PPy films can be prepared via chemical and electrochemical polymerization. The films prepared by electrochemical methods can usually achieve a uniform surface and high conductivity. However, the parameter control during the reaction process is more complicated, and any parameter changes will cause changes in the properties of the PPy film. So far, there has not been a comprehensive analysis of the influencing factors of the reaction process. In contrast, the chemical polymerization method is relatively simple in operation and can produce large amounts of thin films. However, chemical methods usually produce black PPy powders [20], which need to be pressed into a thin film which leads to poor electrical conductivity and mechanical properties. Therefore, in order to obtain high performance films, chemical preparation methods need to be improved.  
In this paper, an interfacial chemical oxidation method was used to prepare a PPy film with a smooth surface and regular interlayer arrangement. The oxidant and pyrrole monomers were dissolved in different solvents which are immiscible with each other. Then, the polymerization reaction occurs at the interface of the two solutions. This method can control the reaction to form a continuous PPy film at the interface and avoid the formation of dispersed PPy particles. The results showed that the surface of PPy film was continuous and flat and the surface roughness can reach 1 nm. More importantly, the XRD results show that the film had obvious diffraction peaks, indicating that the PPy film had a very high degree of order in the molecular chain arrangement. As far as we know, the sharp XRD diffraction peaks of PPy films have not been reported in previous papers. The conductivity of the film is about 0.3 S/cm, which can be changed by changing the synthesis conditions. The size and thickness of the film also can be changed by controlling the interface area and reaction time, respectively.