esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ocean Data Assimilation and Ocean Model Simulation  
Ocean models and historical sample data from satellites and _in situ_ observations provide convenience for the construction of ocean environment models. Based on the knowledge of ocean environments and sample data from vehicles, many kinds of methods such as optimal interpolation, variation method and EnKF are utilized in data assimilation. We compare the assimilation results, and select the rapid data assimilation method for near real time observations. Figure 5 gives the processing flow scheme of data assimilation. There are appropriate and mature ocean circulation models (POM, ROMS, HYCOM) for ocean observations. In order to progressively enhance the temporal and spatial resolution of ocean simulation results, the sample data of observing region is refined level by level from 15' \(\times\) 15', to 5' \(\times\) 5', and then 2' \(\times\) 2'. The low resolution simulation results provide the initial conditions and boundaries for the high resolution of ocean model simulations. Numerical simulations of regional observations include the following two steps: first, the ocean model starts up with the forcing field of climatology. When the ocean model becomes stable, we can get the climatological annual cycle. With these basic analyses, the driven factors, such as wind, sea surface heat flux, and freshwater flux, are added to get a NRT ocean environment simulation. Numerical modeling and simulation of regional ocean areas can provide ocean dynamic simulation environments, which are useful for verification of the correctness and effectiveness of the relevant sample data. On the other side, the simulation results can be applied to construct the background for the data assimilation.  
With the help of historical sample data and numerical model simulation results (for ocean regional areas that lack observation data), we select a suitable observation mode, and different kinds of marine vehicles to sample the relative region, and then construct the ocean feature environment model. The sample data collected by vehicles are the original data, and can be fused with the historical data for ocean background construction. The data assimilation results are evaluated on various aspects, such as demographic characteristics of the sample data, mean of the sample and RMS (root mean square) errors. The core concept of data assimilation is the definition of error and error estimation, including the background error (ocean dynamic model error) and observation error. The introduction of a background error covariance matrix that is suitable for the actual situation is important and difficult if there is less background information. Here, the recursive filter method and sequential three-dimensional  
Figure 5: Ocean model and data assimilation.  
variational analysis (Sequential 3 DVAR) are utilized to construct the background error covariance matrix. With the consideration of the effectiveness and efficiency of data assimilation methods, the rapid data assimilation methods, that suitable for the marine vehicles' observation system are ultimately determined.
## 3 Results and Discussion
### Characterization of the N-TiO\({}_{2}\) Powder
#### 3.1.3 XPS Analysis  
An examination of the chemical state of the elements that are present on the surface of the N-TiO\({}_{2}\) synthesized powder was conducted via XPS analysis. The results are presented in Figure 3.  
Figure 3a displays the wide spectrum survey of N-TiO\({}_{2}\), where all the peaks that correspond to the chemical synthesis followed, i.e., the Ti2p (Ti\({}^{4+}\) in TiO\({}_{2}\) chemical state, Figure 3b in high resolution with binding energy of Ti2p3/2 at 458.8 \(\pm\) 0.1 eV), deconvoluted O1s (lattice oxygen Ti-O in TiO\({}_{2}\) state with binding energy 530.0 \(\pm\) 0.1 eV and hydroxides and/or adsorbed water on the surface with binding energy 531.7 \(\pm\) 0.1 eV, Figure 3c), and C1s (hydrocarbon state) are ascertained, which is in compliance with the corresponding relative publications [33; 46]. The N1s element, assigned to N-Ti bonds, is presented in high resolution in Figure 3d and pinpointed at \(\sim\) 400 \(\pm\) 0.1 eV [47]. The surface atomic concentration of N1s peak is 0.3 \(\pm\) 0.1%, whereas that of the Ti2p peak is 20.8 \(\pm\) 0.1%.
## 2 Methods
### Betweenness Centrality (BC)  
Betweenness centrality measures a node's importance based on how often it appears on the shortest paths between pairs of other nodes in a network [25]. Nodes with high betweenness centrality serve as bridges, potentially controlling the flow of information, resources, or activity between different parts of the network. The formula for calculating betweenness centrality is given in Equation (4).  
\[BC(v)=\sum_{u\
eq v\
eq w}\frac{\sigma_{uw}(v)}{\sigma_{uw}} \tag{4}\]  
where \(BC(v)\) is the betweenness centrality of node \(v\), \(\sigma_{uw}\) is the total number of shortest paths from node \(u\) to node \(w\), and \(\sigma_{uw}(v)\) is the number of those paths that pass through node \(v\).
## 4 Discussion  
### Path-Tracking Accuracy  
The accuracy of path tracking was evaluated with three indexes: mean deviation, maximum deviation, and steady-state deviation. As shown in Figure 8 and Table 2, the average deviations of path tracking for the variable look-ahead distance for three different scenarios (three initial states) were 12.5 cm, 20.8 cm and 25.7 cm, and the average value of deviation of the three scenarios was 19.7 cm. The steady-state deviations were 2.0 cm, 3.4 cm, and 3.8 cm, respectively, and the average value of the three scenarios was 3.1 cm. The maximum deviations were 100 cm, 106.8 cm, and 100.9 cm, respectively. The above-mentioned values were less than the corresponding value of the fixed look-ahead distance in the control group. These results show that the path-tracking accuracy of the presented method was higher than that of the traditional pure tracking model with a fixed look-ahead distance. This is because when the initial deviation was large, the fuzzy controller provided a small look-ahead distance so that the lateral deviation of the vehicle body was quickly reduced, while when the vehicle body had a large look-ahead distance, it adopted a small  
Figure 9: Locally enlarged trajectories of Figure 8. (**a**) Locally enlarged trajectories of Figure 8a; (**b**) locally enlarged trajectories of Figure 8b; (**c**) locally enlarged trajectories of Figure 8c.  
deviation that effectively reduced the over adjustment of the lateral deviation control. Thus, selecting the appropriate look-ahead distance in real time according to the current deviation state could effectively control and stably control the actual tracking path of the prototype vehicle closer to the desired path.
## 2 Background Information
### Generation Mechanism of Hydrodynamic Noise
#### 2.1.3. Cavitation Noise
Consequently, cavitation noise can serve not only as a means to monitor the state of the cavitation process, but also as a crucial instrument for cavitation detection.
## 2 Materials and Methods
### Spectral Vegetation Indices  
Numerous spectral vegetation indices have been reported in the literature and shown to be correlated with various vegetation parameters including plant structure, leaf pigments, biochemical composition and physiological parameters [17; 18]. Their performance and sensitivity to external factors, such as illumination conditions, observation geometry or soil background, were tested in extensive studies [19]. Due to the effect of glyphosate on healthy green vegetation, eight spectral indices (Table 1) specifically designed to detect leaf pigments, plant water content, biochemical components and radiation use efficiency have been selected for this study. In addition, the relevance of other spectral indices was statistically tested (e.g., MTVI2--modified triangulation vegetation index; PSRI--plant senescence reflectance index). As they were less specific to build-up the plants' glyphosate response, the data are not shown in the results section.
# Bio-Based Polymers with Potential for Biodegradability
## 1 Introduction
By definition, biodegradability consists in the breakdown of matter as the result of the activity of microorganisms, such as fungi and bacteria, which typically secrete enzymes that cleave specific chemical bonds or perform very specific chemical reactions, leading to lower molecular weight products that can then be used in other processes, by other organisms [19]. For the same reasons previously exposed, one would expect regular thermosetting polymers to be less biodegradable than thermoplastics. However, because enzymes act on very specific chemical bonds, it is possible to design polymeric materials that can be broken down by microorganisms. Indeed, in the US and Canada, microorganisms can be genetically modified in order to produce enzymes tailored to cleave desired chemical bonds in existing polymers. Such an approach is currently prohibited in Europe, where only transflutaminases can be used. There is an intrinsic expectation that novel polymers designed from renewable, and often biodegradable resources, are also biodegradable. Although this is a logical assumption, there is no guarantee that such polymers can be fully biodegradable. Changes in functional groups, crosslink density, and copolymerization with non-biodegradable co-monomers can lead to materials that do not necessarily exhibit significant or relevant biodegradability. Therefore, biodegradability studies are crucial in order to assess the long-term environmental impact of bio-based materials. A study comparing the degradability time of different bio-based systems would be of great interest to a broad audience, and such a research endeavor should definitely be undertaken in a consistent manner in the near future, as bio-based plastics become increasingly popular. Currently, however, the lack of consistency in biodegradability studies, with varying conditions and protocols being applied to different systems, makes it extremely hard and controversial to compare the biodegradability of plastics in general. As an example, 84% mineralization of poly(lactic acid) (PLA) was observed after 58 days under simulated compost conditions [20], whereas poly(ethylene terephthalate) (PET) takes approximately one year to break down into monomers and oligomers when exposed to intense UV irradiation in the environment. The scope of this review is on bio-based materials developed over the past decade for which biodegradability information is available. The review starts by covering different classes of bio-based polymers, with a strong focus on vegetable oil-derived resins due to their versatility and the authors' field of expertise. Secondly, an entire section is dedicated to PLA. PLA is one of the most popular bio-based plastics and finds wide industrial use nowadays. Finally, the available literature on the biodegradability of bio-based systems is discussed in detail.
## 3 Methodology Description
### Long Short-Term Modeling
#### 3.2.1 Structure of the LSTM  
All RNNs are in the form of a chain of repeating modules of neural networks. In standard RNNs, this repeating module has a basic structure, such as a single tanh layer. However, LSTMs have a chain-like structure (called a carousel) with different repeating modules of the RNN. In the LSTM model, the repeating module has three different types of gates, namely, the forget gate, fg(t), the input gate, ip(t) \(\times\) Gi(t), and the output gate, O(t), as shown in Figure 1.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 2 Related Work
### Pansharpening Method  
Since the advancement of image processing technology, the field of multispectral fusion methods could be categorized into four main methods: CS based method, MRA based method, VO based method, and DL based method [13]. As a typical branch of model driven algorithms, the CS based approach is the most traditional fusion algorithm [14; 15; 16]. The MRA algorithm decomposes PAN and MSI and subsequently merges them [17; 18; 19], potentially leading to different levels of spectral distortion or spatial artifacts. Since the 1990s, there has been a proliferation of variational optimization (VO) approaches, leading to the proposal of numerous VO-based fusion methods. As a result, the overall fusion system has significantly improved [20; 21]. Recently, the field of remote sensing image fusion has benefited from the rapid progress of deep learning, namely the application of Deep Neural Network (DNN). This is because DNN possesses strong abilities in extracting features and self-learning. [PERSON] et al. [22] were the first to combine DNN with sparse denoising autocoding to develop a Pansharpening technique using deep learning. This algorithm yielded successful fusion results. The CNN is a prominent component of deep learning and has found extensive use in image processing. Currently, numerous scientists have put forth a variety of sophisticated Pansharpening networks [23; 24; 25], including deep residual networks and generative adversarial networks.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### Expense Factor Analysis
#### 5.1.4 Total Interpretation Variance  
The cumulative variance of the first two factors was 72.798%, suggesting most of the observed variables were fully represented (Table 7). Therefore, the common factors F1 and F2 were selected.  
Figure 2 shows a screen plot. The horizontal axis shows the component numbers, while the vertical axis shows the eigenvalues. The eigenvalues for the first two common factors 1 and 2 were greater than 1, which meant they were suitable for analysis.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Initial Value** & **Extraction Value** \\ \hline Total fee receivable for the month & 1 & 0.857 \\ Fixed monthly cost & 1 & 0.878 \\ Local fee & 1 & 0.682 \\ Roaming fee & 1 & 0.578 \\ Unicom network fee & 1 & 0.590 \\ Fee with China Mobile & 1 & 0.838 \\ Fixed line fee & 1 & 0.672 \\ \hline Principal component analysis applied to extract values. & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Common factor variance results—adapted from Zhang [6].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Eigenvalues Starting Value**} & \multicolumn{4}{c}{**Squared Sum Extraction Loading**} & \multicolumn{4}{c}{**Square Sum Rotation Loading**} \\ \cline{2-9}  & **Sum** & **Variance \%** & **Accumulative \%** & **Sum** & **Variance \%** & **Accumulative \%** & **Sum** & **Variance \%** & **Accumulative \%** \\ \hline
1 & 4.086 & 58.369 & 58.369 & 4.086 & 58.369 & 58.369 & 4.054 & 57.910 & 57.910 \\
2 & 1.010 & 14.429 & 72.798 & 1.010 & 14.429 & 72.798 & 1.042 & 14.888 & 72.798 \\
3 & 0.912 & 13.024 & 85.822 & & & & & \\
4 & 0.473 & 6.756 & 92.578 & & & & & \\
5 & 0.298 & 4.263 & 96.841 & & & & & \\
6 & 0.171 & 2.447 & 99.288 & & & & & \\
7 & 0.050 & 0.712 & 100.000 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total interpretation variance.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Test of KMO and Bartlett** \\ \hline KMO measures of sampling adequacy & & 0.599 \\  & Value of Chi-square & 22,244.842 \\ Bartlett testing of sphericity & Value of df & 21 \\  & Value of Sig. & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: KMO and Bartlett tests.
## 2 Methodology
### Architectural Appearance Generation Module
```
Deep sparse and text fusion generative adversarial network: Adam optimizer with \((\beta_{1},\beta_{2})=(0.5,0.999)\), \(lr\_G=0.0001\), \(lr\_D=0.0003\)
1: Initialize discriminator parameters \(\theta_{D}\) and generator parameters \(\theta_{G}\)
2:for number of training epoch do
3: sampling real data pairs \(\{(x_{1},Text_{1}),\ldots,(x_{m},Text_{m})\}\), latent variables \(\{z_{1},\ldots,z_{m}\}\)
4: extract semantics: \(T_{i}=\text{text\_encoder}\)\((Text_{i})\)
5: calculate loss function: \(\mathcal{L}_{D}^{i}=\frac{1}{2}D_{\theta_{D}}\big{(}G_{\theta_{G}}(z_{i},\ T_{i}),T_{i}\big{)}+\frac{1}{2}D_{\theta_{D}}\big{(}x_{i},\ T_{j}\big{)}-D_{\theta_{ D}}(x_{i},\ T_{i})+k\big{(}\|\
abla_{x_{i}}D(x_{i},\ T_{i})\|+\big{\|}\
abla_{T_{i}}D(x_{i},\ T_{i})\|\big{)}^{p}\)
6: update the discriminator parameters: \(\theta_{D}\gets [PERSON]\Big{(}\
abla_{m}\frac{1}{m}\sum_{i=1}^{m}\mathcal{L}_ {D}^{i},\theta_{D},\beta_{1},\beta_{2},lr_{G},lr_{D}\Big{)}\)
7: sampling latent variables \(\{z_{1},\ldots,z_{m}\}\)
8: calculate loss function: \(\mathcal{L}_{G}^{i}=-D_{\theta_{G}}\big{(}G_{\theta_{G}}(z_{i},\ T_{i}),\ T_{i} \big{)}\)
9: update the discriminator: \(\theta_{G}\gets Adam\Big{(}\
abla_{m}\frac{1}{m}\sum_{i=1}^{m}\mathcal{L}_ {G}^{i},\theta_{G},\beta_{1},\beta_{2},lr_{G},lr_{D}\Big{)}\)
10:endfor
#text_encoder_ is pre-trained from the Contrastive text-image matching model
```  
**Algorithm 2**Pseudocode for the training of DSTF-GAN. The PR module leverages the image-to-image capabilities of the pre-trained latent stable diffusion model [27] to re-create the generated conceptual sketches as follows:  
\[\widetilde{\mathbf{x}}=F_{LDM}(\mathbf{x}) \tag{21}\]  
where \(\mathbf{x}\) and \(\widetilde{\mathbf{x}}\) denote the input conceptual sketch and the generated high-rise building image, respectively, and \(F_{LDM}\) is the pre-trained latent stable diffusion model. Additionally, by integrating the pre-trained text encoder, DSTF-GAN, and the _stable-diffusion-v1-5_ model, we have developed a user-friendly work interface within the Python framework. This interface facilitates the seamless transition from input text prompts to high-rise building renderings. Detailed descriptions of the interface and workflow are provided in Appendix A, as illustrated in Figure A1.
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 5 Calculation Algorithm Possibilities in Commercial Treatment Planning Systems
### Dosimetric Performance of the Commercial Algorithms
The accuracy of RaySearch's PBA and MC algorithms for proton therapy and clinical validation data on a phantom reproducing animal lung tissue was presented by [PERSON] et al. [126]. The RayStation MC algorithm resulted in more accurate dose calculations than the PBA algorithm for lung targets. The AcurosPT performance, evaluated by [PERSON] et al. [105], demonstrated acceptable dose distributions for clinical proton beams, within 2 mm/2%, although the use of caution was recommended at very distal depths.
# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 3 The Proposed Network
### The Overall Architecture  
In response to the loss of semantic information caused by noise interference, we propose a semantic feature-complementary fusion strategy based on spatial feature reconstruction. It extracts semantic features from RGB and depth modalities, respectively, and supplements the missing information through a complementary fusion mechanism to improve the detection accuracy of the model under noisy conditions and reduce misidentification. At the same time, in order to solve the problem of insufficient feature information of negative obstacles such as potholes and cracks in the fusion process, we propose a data enhancement method. This method enriches the dataset with geometric transformations such as scaling, rotation, and affine transformation to increase data diversity and balance. By capturing small-scale features at various scales and viewpoints, this method enhances the model's ability to detect negative obstacle features and improves edge segmentation accuracy in complex environments.  
As shown in Figure 1, the entire network adopts a dual-stream multimodal fusion architecture, with RGB and depth as its two input data streams. The RGB image is processed by an encoder to extract color and texture information, providing rich surface details. Meanwhile, the depth image captures depth and distance information about potholes and cracks in the 3D space, enabling the model to better perceive spatial changes. Each branch undergoes multiple layers of convolution, with a dilated convolution module applied at each level to extract multi-scale features. This design ensures the capture of global semantic information while preserving local detail features.  
In the decoder stage, the model recovers high-resolution feature maps in two independent data streams through progressive upsampling and skip connections. RGB and depth features are passed along their respective data streams and processed by the feature enhancement module at each stage to further improve feature expression capabilities. Subsequently, these enhanced features are deeply fused through the multimodal attention fusion module to finally generate accurate segmentation results for labeling negative obstacles in the road. This multimodal and multi-scale feature fusion method significantly improves the model's detection and segmentation accuracy for negative obstacles and effectively copes with noise interference and detail loss in complex scenes.  
Figure 1: The overall network architecture of dual-branch multimodal fusion network (NROSegNet).
# Quantum Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
## 6 Conclusions  
SNOW-V is a new stream cipher, designed for high-speed encryption in virtualized environments, to provide confidentiality and integrity for 5G communications. Up to now, there have been no TMDTO attacks published on this stream cipher. This paper analyzes the security of SNOW-V against TMDTO attacks and quantum TMDTO attacks. The results show that SNOW-V is secure against BS-TMDTO attacks, while the attacker can mount a BG-TMDTO attack, where none of the online time complexity, the memory complexity and the offline time complexity is bigger than \(2^{256}\), if the keystream sequences generated by the secret key, together with different IVs, are provided to the attacker. Furthermore, we analyze the security of SNOW-V against quantum TMDTO attacks, and the results show that a quantum TMDTO attack offers, strictly, better online time complexity than [PERSON]'s algorithm, when the available memory space is bigger than \(2^{170,67}\). The results of this paper are helpful in evaluating the security of SNOW-V against (quantum) TMDTO attacks, and we look forward to further work in evaluating SNOW-V against other kinds of cryptanalytic attacks.  
Conceptualization, L.D. and S.L.; methodology, Z.L. and Z.W. ([PERSON]); validation, L.D. and Z.W. ([PERSON]); writing--original draft preparation, S.L., Z.L., Z.W. ([PERSON]), and Z.W. ([PERSON]); writing--review and editing, L.D.; funding acquisition, L.D. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant Nos. 61602514, 61802437, and 61902428.  
Not applicable.  
The authors declare no conflict of interest.