esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 5 Experiments
### Ablation Study  
To further investigate the effectiveness of each core module in the DSTSAN model, modifications were made to the core modules, and comparative experiments were conducted between the modified variants and the original DSTSAN model. Specifically, this research performed the following variant experiments:  
1. Variant FA: removal of the feature enhancement layer;
2. Variant Mh: spatial self-attention module using only the \(\mathbf{\mathcal{M}_{hop}}\) mask matrix, considering only local spatial dependencies;
3. Variant Ms: spatial self-attention module using only the \(\mathbf{\mathcal{M}_{sem}}\) mask matrix, considering only global spatial dependencies;
4. Variant TSA: removal of the temporal self-attention module.  
By comparing these variant models with the original DSTSAN model, this method can assess the performance differences and evaluate the contributions of each core module to the model's performance.  
This experiment conducted comparative experiments between DSTSAN and its variants on the PEMS04 and PEMS07 datasets, as shown in Figure 7:  
1. The absence of any core module in the DSTSAN leads to a decrease in the model's predictive performance, indicating that all core modules in the model capture key information to some extent;
2. The model exhibits the most significant decrease in predictive performance when only considering global spatial dependencies, indicating that local spatial dependencies have the greatest impact on the model's predictive performance. Furthermore, this suggests that the traffic conditions at a road node are most influenced by the traffic conditions at its adjacent road nodes, aligning with the real-life observations;In addition to local spatial correlations, global spatial correlations and dynamic temporal correlations are also crucial for influencing traffic flow.
# The Characteristics of Intrinsic Fluorescence of Type I Collagen Influenced by Collagen I
## 3 Results and Discussions  
### Fluorescence Spectroscopy Analysis  
Due to a lack of phenylalanine and tryptophan [19; 20], the intrinsic fluorescence of type I collagen is presumed to be dominated by tyrosine. Compared to traditional fluorescence technology, synchronous fluorescence technology can scan monochromators of excitation and emission simultaneously. Therefore, synchronous spectroscopy has the more obvious advantages of simplifying spectroscopy, increasing sensitivity and reducing interference [21]. Figure 1 shows the changes of intrinsic florescence intensity of type I collagen influenced by collagenase concentration and time. It reveals that the fluorescence intensity and the synchronous fluorescence intensity increased with increasing collagenase usage. As presented in Figure 1a,c, the peaks at 310 nm and 370 nm represent the existence of tyrosine and tryptophan, respectively [22]. After adding collagenase I, the intensity of the tyrosine peak increased with rising time and concentration. It illustrates that type I collagen aggregation was cleaved by collagenase I, which resulted in exposure of tyrosines wrapped in collagen, and thus the intrinsic fluorescence of type I collagen increased with stronger binding to tyrosine. From Figure 1b,d, it is seen that the intensity of the peak at 280 nm increased with the addition of collagenase I, which represented an increase in the number of tyrosine residues.  
The reaction mechanism can be further illustrated by Figure 2. When type I collagen was dissolved in acetic acid solution, it would form aggregates by self-assembly [23]. A large number of tyrosine residues of type I collagen were wrapped in polypeptide chains, which reduced intrinsic fluorescence intensity. Owing to the cleavage specificity of collagenase I [24] in the sequence of Gly-Pro-X of collagen [25], the aggregated collagen could be degraded into small fragments and the tyrosine residues were exposed for further binding. This enhanced the intrinsic fluorescence intensity of type I collagen.  
Figure 1: The fluorescence and synchronous fluorescence spectroscopy of type I collagen. (**a**) Changes of tyrosine residues influenced by time with fluorescence spectroscopy. (**b**) Changes of tyrosine residues influenced by time with synchronous fluorescence spectroscopy. (**c**) Changes of tyrosine residues influenced by collagenase I concentration with fluorescence spectroscopy. (**d**) Changes of tyrosine residues influenced by collagenase I concentration with synchronous fluorescence spectroscopy.
## 2 Dynamic Modeling of Trans-Medium Process
### Dynamic Modeling
#### 2.3.3 Restoring Force and Moment  
The amount of buoyancy equal to the weight of water expelled by the HAUV is calculated based on Archimedes' principle:  
\[F_{bw}=k_{s}\rho Vg \tag{8}\]  
where \(\rho\), \(V\), and \(g\) represent the density of water, the volume of the watertight chamber, and the acceleration of gravity, respectively. It is worth noting that as the HAUV crosses the water-air boundary, we approximately assume that the change \(V\) is still linear.  
Since the origin of the body coordinate system is defined at the center of mass of the HAUV, the heavy moment is not considered. If the center coordinate of buoyancy is defined as \(\left[x_{b}\quad y_{b}\quad z_{b}\right]^{\text{T}}\), then the buoyancy torque can be expressed as follows:  
\[M_{bw}=-F_{bw}\begin{bmatrix}z_{b}s\phi c\theta-y_{b}c\phi c\theta\\ x_{b}c\phi c\theta+z_{b}s\theta\\ -y_{b}s\theta-x_{b}s\phi c\theta\end{bmatrix} \tag{9}\]  
**Remark 3**.: _To improve the maneuverability of the coaxial HAUV, it is designed to be slightly less buoyant than gravity when not equipped with a float ball. In this mode of operation, the coaxial HAUV can maneuver under the water. In addition, according to the basic knowledge about the initial stability of a floating body using the ship principle, the stability of a floating body is closely related to its initial metacentric position. Meanwhile, the position of the metacenter is related to the positions of the center of buoyancy and the center of gravity, which makes it difficult to calculate the recovery moment accurately. In order to further simplify the dynamic model, the recovery torque generated by the buoyancy force is regarded as an external interference, and its calculation is not considered._
## 4 Results and Analysis
### Packet Delivery Ratio (PDR) (\(\%\))  
Figure 5 shows the PDR of AODV, LID-AODV, HPSO-GA, and DDBG. The PDR of DDBG is the highest recorded (97\(\%\)) because, after the detection of malicious nodes, packets are easily delivered more quickly to the destination node. As the malicious nodes increase, indeed they will cover most of the network and will disturb the communication by sending fake replies and not delivering data packets to the destination properly. However, after the deployment of our proposed technique, it was observed that the PDR is slightly increased, as it blacklists the malicious nodes with status packet queries in a short amount of time. Nevertheless, the PDR is much improved when compared to the other three protocols. Moreover, result analysis of packet delivery shown in Table 5 indicates that the proposed method outperforms AODV, LID-AODV and HPSO-GA.
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 4 Discussion
### Proposed Methodological Approach
The use of a spatial co-registration algorithm should minimize the effect of spatial misalignment of different acquisitions [46], which is critical when working on metric-scale processes at a spatial resolution of a few meters, possibly reducing biases in the subsequent processing steps for time series analysis. The use of percentiles to clean spurious pixels allows us to remove neighboring cropland fields with very high soil moisture, which may result in pixels with high PWC. Generally speaking, water content modifies the spectral signature of soil targets, affecting mapping products also generated using many other techniques. The methodology presented here allows near-real-time monitoring at a meter scale of both climate-related phenomena and hydromorphological processes. These two processes often co-occur, making it difficult to single out the effect of an individual phenomenon on the river dynamics. The three proposed statistics integrate each other, since PWC and MWFA are more suitable for the detection of climate-related phenomena (e.g., the reduction in the river width in July 2022 compared to July 2018, and the July 2022 anomaly computed against the long-term average, Figure 5) and year-by-year changes in river morphology (Figures 7 and 9), while DPM allows the detection of short-term dynamics (Figure 6), thereby providing an effective tool for the near-real-time monitoring of river hydromorphological processes. The combined use of satellite data and metrics generated from in situ river discharge measurements (i.e., the CV and the SDI) allows a more comprehensive understanding of the effects of extreme events on river dynamics, as demonstrated in the case study of the record-breaking Po River shrinking during the severe 2022 drought event. A global assessment of water dynamics at the whole river mainstem scale (as in Figures 7 and 8) allows us to clearly depict anomalies over time and to identify hotspots, as the illustratedcase studies, where climate-related phenomena combine with geomorphic singularities resulting in unique river dynamics. While the proposed approach has been tested on the Po River, it can be used to assess a variety of rivers worldwide since it only requires satellite optical multispectral acquisitions and discharge data.
# Tomato Maturity Estimation Using Deep Neural Network
## 4 Results and Discussion
### Maturity Estimation
The green and red bars indicate the relative frequencies for hue value and estimated maturity, respectively, while the brown bars indicate the intersection of the two methods. The Gaussian fitted distributions appeared to be similar between the two methods in the green stage (i.e., immature), when green almost fully occupied the tomato. For the turning stage, there was a slight difference compared with that of the green stage, but the maximum probability of maturity was similar (around 0.4). In the case of the pink and red stages, they had more differences in variance than the green and turning stages, although they showed similar mean maturities. In the DNN-based maturity estimation, the pink stage showed a narrower range distribution, compared with that of the hue value, whereas the opposite was observed in the red stage. It seems that the illuminance may affect the feature representation in DNNs, whereas the hue value is only related to color, with saturation (s) and value (v) having been separated. This is one of the reasons why tomato images have color variance, which might have affected the high variance in the maturity of the red stage, which was even higher than that in the pink stage. Figure 6: Tomato maturity estimations expressed as classification (**left**) and distribution (**right**). The comparison between the DNN-based estimated maturity and hue values was analyzed statistically by maturity class, as detailed in Table 2. Each value is represented as the average and standard deviation of the samples, with respect to each maturity class. The means were similar between the two methods, and their differences were in the range 0.1-0.6, with a relatively similar 10% level of their means. There were no differences between the two methods at the 0.05 significance level (except in the pink stage) and the similarity between the two groups was highest in the red stage. From this result, it can be concluded that our method can efficiently estimate tomato maturity from images, further enabling assessment in terms of a continuous value, and the performance of our method indicates that the estimated values are significant enough to represent the tomato growth when compared with the hue value, which is correlated with tomato growth. Figure 7: Comparisons of histograms and fitted Gaussian distributions between the estimated maturity and hue value from tomato images: (**a**) green, (**b**) turning, (**c**) pink, and (**d**) red.
## 9 Discussion
### Challenges
Here, distributed ledger technologies such as blockchain can ensure security and privacy in a decentralized and scalable manner. **Challenge 4--Long vehicle lifespan:** The typical life cycle of vehicles far outlasts the rapid evolution of cyber threats, posing a unique challenge. **Proposed solution:** Over-the-air (OTA) updates can be instrumental in enabling vehicles to continually update their security systems to counter new threats. Ensuring backward compatibility of these updates is a critical aspect of this solution. **Challenge 5--legislation and standards:** The accelerated pace of autonomous vehicle technology development often surpasses the existing legislation and security standards. **Proposed solution:** A cooperative effort involving vehicle manufacturers, cyber security experts, and policymakers is necessary to establish and regularly update regulations and standards specifically tailored to autonomous vehicles. Although these challenges are significant, they are not unsolvable as long as there is continual research, innovation, and a concerted effort to collaborate across the numerous industries that are contributing to the development and deployment of autonomous vehicles.
## 3 Results and Discussion  
### Optimization of SPME Experimental Conditions  
Various fiber coatings and exposure times were explored prior to analysis of volatile compositions of pasteurized milk. PDMS-coated fiber originated lower area counts of individual volatile compounds than that of PDMS/DVB or CAR/PDMS coatings (Figure 1). Moreover, PDMS/DVB and CAR/PDMS fibers revealed, correspondingly, 14 and 21 peaks on their respective chromatograms and showed adsorbed distinctive compounds. Consistent with the manufacturer, CAR/PDMS coating is more suitable for low molecular weight compounds detection (e.g., 2-butanone or acetone), while PDMS/DVB absorbs higher molecular weight compounds (e.g., nonanal or decanal) [18]. In the present work, the lowest molecular weight compound detected was 3-methylbutanal, and the highest one was heptanal. PDMS/DVB coated fiber proved to be superior for analysis of volatiles in pasteurized milk samples.  
Regarding the SPME fiber exposure time to the headspace of the milk sample, it was noticed that, after 30 and 45 min, most of the volatile compounds were detected with PDMS/DVB fiber. Other compounds, for example heptanal, were only detectable after 60 min-exposure and 90 min-exposure time was suitable for SPME analysis of a milk sample, as the area count of most of the compounds (e.g., heptanal, 3-heptanone, 2-heptanone, and 2-pentylfuran) revealed small changes. Because the area count of hexanal or 1-octen-3-ol remained affected by exposure time after 90 min, this time was prudently controlled throughout the experiment in order to restrain variability among replications.
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 2 Fundamentals
### Relevance of I* in IoT Requirements Analysis  
The i* framework offers several advantages for the analysis of IoT requirements. First, it effectively captures stakeholder intentions by systematically modeling the diverse objectives of users, developers, and service providers involved in IoT systems. Second, it handles the inherent complexity of IoT requirements, including functional and non-functional requirements, like energy efficiency, security, and real-time constraints, thereby facilitating their analysis and balance. Third, the framework supports sustainability considerations by incorporating sustainability goals and constraints during the initial requirement analysis, thus helping to optimize resource consumption. Finally, i* models the interdependencies among IoT components, providing a holistic view of the system's needs and ensuring proper representation of actor dependencies.  
Given the inherent complexities associated with the analysis of IoT requirements, the adoption of a comprehensive and well-defined RE framework specifically tailored to the IoT domain is imperative. The integration of GORE and the i* framework offers a structured methodological approach that significantly enhances the efficiency, reliability, and sustainability of IoT systems. By organizing the requirements analysis process around goal decomposition, formal requirement traceability, and sustainability constraints, the i* framework facilitates the development of IoT systems that emphasize energy efficiency, computational optimization, and adaptability. For a more detailed examination of iStar 2.0, refer to [23]. This study contributes to the establishment of a standardized RE framework for IoT systems, ensuring that IoT applications are not only functional and scalable, but that they are also resource-efficient and environmentally sustainable.
## 4 Conclusions  
This paper proposed an integrated image fusion framework based on online dictionary learning. Compared with traditional image fusion methods, the integrated framework had two major improvements. First, it introduced a local density-based clustering method in sparse representation, which had high performance in clustering without any apriori knowledge. Second, an online dictionary learning algorithm was used to extract discriminative features that enhanced the efficiency of image fusion. The proposed method was compared with seven existing algorithms LE, DWT, DT-CWT, CVT, NSCT, SR-DCT, SR-KSVD, and SR-SCC using four source image pairs and objective metrics. Experimental results demonstrated that the proposed method was significantly superior to other methods in terms of subjective and objective evaluation. This means that the fused images of the proposed method had better quality than other methods. Compared with other sparse-representation based methods, the proposed method had high efficiency in generating fused images.  
Although the proposed solution had a good performance in image fusion, many optimizations are still worth doing in the follow-up research. The parallel processing and the use of multiple graphics processing units (GPUs) will be considered to improve the efficiency of proposed solution. Denoising techniques will also be applied to the proposed solution to enhance the quality of the fused image.  
**Acknowledgments:** We would like to thank the supports by National Natural Science Foundation of China (Nos.61374135, 61203321,61302041), China Central Universities Foundation (106112013 CDJZRI170005), Chongqing Special Funding in Postdoctoral Scientific Research Project (XM2013007) and Chongqing Funding in Postgraduate Research Innovation Project (CYB14023).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] provided technical support and revised the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: Computation Time Comparison.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Hot-Air Balloon** & **Leopard** & **Lab** & **Bottle** \\ \hline K-SVD & 46.49 & 45.02 & 47.72 & 42.72 \\ SCC & **3.56** & **3.60** & **3.72** & **3.12** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time Consumption Comparison.
## 2 Related Works
### CNN-Based MRI Super-Resolution  
A CNN-based SR approach aims to learn an end-to-end mapping \(F\) between the low-resolution image \(y\) and high-resolution image \(x\). The \(F\) is decomposed into a sequence of convolutional layers, which are combined of rectified linear unit (ReLU) layers. The \(l\)th convolutional layer convolves the image by filters \(f_{l}\times f_{l}\). The output of the \(l\)th layer is a set of feature maps, which is formulated as:  
\[F_{l}(y_{l})=max(0,W_{l}*y_{l-1}+B_{l}) \tag{3}\]  
where \(W_{l}\) denotes the convolutional weight vectors and \(B_{l}\) is the biases of the \(l\)th layer. \(*\) represents the convolutional operations. \(y_{l-1}\) denotes the input data, which is the output of the previous \(l-1\)th layer. \(y_{l}\) is the output of the convolution. \(y_{0}\) is the input LR images \(y\).  
In summary, CNN-based approaches attempt to learn a mapping function \(\hat{x}=F(y;\Theta)\) that is parameterized by \(\Theta\), where \(\Theta\) contains all parameters of \(W_{l}\) and \(B_{l}\). To estimate \(\Theta\), the mean squared error (MSE) between the reconstruction image and the ground-truth image is often applied as a loss function, which is defined as \(\mathcal{L}=\|x-F(y;\Theta)\|_{2}^{2}\).  
Suppose a T2-weighted (T2w) low-resolution MRI image is denoted as \(y_{T2}\), the CNN-based MRI super-resolution aims to learn an end-to-end mapping \(\hat{x}_{T2}=F(y_{T2},\Theta)\) from label data. The objective of network is to generate a corresponding T2w high-resolution image \(\hat{x}_{T2}\) that is similar quality to the ground-truth \(x_{T2}\) MRI image.  
The residual network structure [32] was wildly adopted in CNN-based approaches for image super-resolution [18; 21]. As illustrated in Figure 2a, the conventional residual block (Resblock) has two convolutional layers and a shortcut connection, and the result of Resblock is the addition of the input and output.
## 2 Materials and Methods
### Human Electrocardiogram (ECG) Data Collection and Processing
#### 2.3.2 ECG Signal Recording Protocol  
ECG recordings were carried out at the Juana Portaceli Health Center of the Universitat Politecnica de Valencia on twenty volunteers: 17 males and three females with ages between 20 and 70 years old (mean \(\pm\) standard deviation: \(36\pm 14.1\)) and body mass indices between 19 and 33 Kg/m\({}^{2}\) (mean \(\pm\) standard deviation: \(25.1\pm 3.2\) Kg/m\({}^{2}\)) who attended routine check-ups, some of them healthy and others with cardiac pathologies. The study was approved by the institutional Ethics Committee (project identification code P4_20_02_19) and adheres to the Declaration of Helsinki. Volunteers were informed of the aim of the study, briefed on the recording protocol, and signed the informed consent forms.  
Recordings were conducted with volunteers at rest, lying on a stretcher in a supine position. The sensor node was placed at CMV1 (location comparable to conventional precordial V1, 4 th intercostal space to the right of the sternum) for each patient as shown in Figure 3. This location was chosen to make picking up electrical signals from both atria and ventricle easier since the atrial activity is noticeably weaker than that of the ventricle. Previously, to reduce contact impedance, the skin area was slightly exfoliated (Nuprep, Weaver and Company, Aurora, CO, USA) and, in the case of male volunteers, shaved. Five minutes of bipolar ECG signals were recorded for each volunteer.  
Figure 2: Bilayer design of the multi-ring concentric electrode (conductors and dielectric).