esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 4 Result Analysis
### Comparison with GEOV1 FCover Product
#### 4.1.2 Temporal Consistency

Table 3: The number of pixels to calculate the mean values and standard deviationsacross different biomes for 12 months. Figure 6: _Cont._  
Figure 6. Temporal profiles of the TRAGL and GEOV1 FCover values for several sites with different vegetation types: (**a**) Grassland and Cereal Crops; (**b**) Broadleaf crops; (**c**) Broadleaf forests; (**d**) Needleleaf forests; and (**e**) Savannahs and (**f**) Shrubs. Figure 6d shows the temporal FCover trajectories for needleleaf forest biome sites. For the Sonian and Rovaniemi site, the temporal FCover trajectories of TRAGL and GEOV1 show good agreement, although some FCover values are missing in the GEOV1 FCover product. However, the TRAGL FCover values are between 0.1 and 0.31 higher than those of GEOV1 at the Larose site. For the Larose and Rovaniemi sites, the TRAGL FCover values demonstrated excellent agreement with the mean values of the high-resolution FCover maps, but at the Sonian site, the TRAGL FCover values were slightly underestimated with values of 0.12 compared with the mean values of the high-resolution FCover maps. The temporal FCover profiles for the Hombori and Wankama sites with savannah biome type are shown in Figure 6e. There are large discrepancies for the Hombori site. Many GEOV1 FCover values were missing at the beginning of the growing season. During the non-growing season, the GEOV1 FCover values, at nearly zero, are almost 0.05 lower than those of TRAGL. For the Wankama site, the TRAGL FCover values are in generally good agreement with the GEOV1 FCover values, in terms of seasonal patterns. Similarly to the Hombori site, the GEOV1 FCover values are slightly lower than those of TRAGL during the non-growing season. However, the GEOV1 FCover values are slightly higher than those of TRAGL during the growing season. The temporal FCover trajectories for Turco site with shrub biome type in 2001 and 2003 are shown in Figure 6f. The TRAGL and GEOV1 FCover products show very low values as expected, with little seasonality. The FCover values for both FCover products were less than 0.1 during these years. TRAGL FCover shows slightly higher values than GEOV1 FCover, especially during the non-growing season. At this site, the TRAGL FCover values were slightly underestimated with values of 0.051 in 2001 and slightly overestimated by 0.036 in 2003 when compared to the mean values of the high-resolution FCover maps.
## 5 Model Experiment
### Impact of Key Parameters on Model Performance  
Based on our experimental design and literature review, we identified the most influential parameters for each model architecture in Table 6. These parameter choices significantly impacted model accuracy.  
For the ViT-based facial expression model, the SGD optimizer with a relatively high learning rate (\(1\times 10^{-3}\)) was crucial for the transformer architecture to efficiently learn attention patterns across facial regions. In contrast, the CvT-based facial expression model required a much lower learning rate (\(1\times 10^{-5}\)) with the Adam optimizer to fine-tune the convolutional features that captured the subtle facial expressions characteristic of children with autism.  
In the CvT-based Mel spectrogram model, an intermediate learning rate (\(1\times 10^{-4}\)) balanced efficient convergence with feature sensitivity, as spectrograms displayed more distinct emotional patterns compared to facial expressions. For the Bi-LSTM model processing sequential MFCCs data, the MSE loss function and disabled random shuffling were key parameters that preserved temporal dependencies in speech emotion patterns, though the model's performance remained suboptimal despite these considerations.  
The multimodal feature fusion model's performance was most influenced by its significantly reduced learning rate (\(1\times 10^{-6}\)) and smaller batch size (8), which were essential for the attention mechanism to properly weight complementary information between modalities without overlooking subtle cross-modal relationships.  
The convergence patterns in Figures 10 and 11 confirm the effectiveness of these parameter selections, with the CvT models achieving a substantially lower loss values (0.3 for facial and 0.1 for spectrogram data) compared to alternative architectures, demonstrating their parameter configurations were better suited for emotion recognition in children with autism.  
Figure 10: Train loss curves of different models.The BiLSTM model (red) shows a distinctive pattern with a rapid initial descent (point A), followed by early convergence (point B) at a loss value of approximately 0.18. The CvT-based Mel spectrogram model (green) demonstrates superior performance, reaching optimal convergence at point C with a loss value of approximately 0.1. The CvT facial expression model (orange) exhibits steady improvement throughout training, converging to approximately 0.3 (point D), while the ViT model (blue) converges more slowly to a higher final loss value of around 1.0 (point E). These convergence patterns illustrate the varying effectiveness of different network architectures and input representations for emotion recognition tasks in children with autism.
# Deep Q-Network Algorithm-Based Cyclic Air Braking Strategy for Heavy-Haul Trains
###### Abstract  
Cyclic air braking is a key element for ensuring safe train operation when running on a long and steep downhill railway section. In reality, the cyclic braking performance of a train is affected by its operating environment, speed and air-refilling time. Existing optimization algorithms have the problem of low learning efficiency. To solve this problem, an intelligent control method based on the deep Q-network (DQN) algorithm for heavy-haul trains running on long and steep downhill railway sections is proposed. Firstly, the environment of heavy-haul train operation is designed by considering the line characteristics, speed limits and constraints of the train pipe's air-refilling time. Secondly, the control process of heavy-haul trains running on long and steep downhill sections is described as the reinforcement learning (RL) of a Markov decision process. By designing the critical elements of RL, a cyclic braking strategy for heavy-haul trains is established based on the reinforcement learning algorithm. Thirdly, the deep neural network and Q-learning are combined to design a neural network for approximating the action value function so that the algorithm can achieve the optimal action value function faster. Finally, simulation experiments are conducted on the actual track data pertaining to the Shuozhou-Huanghua line in China to compare the performance of the Q-learning algorithm against the DQN algorithm. Our findings revealed that the DQN-based intelligent control strategy decreased the air braking distance by 2.1% and enhanced the overall average speed by more than 7%. These experiments unequivocally demonstrate the efficacy and superiority of the DQN-based intelligent control strategy.  
heavy-haul train; long and steep downhill; cyclic air braking; DQN; intelligent control
## 3 Results and Discussion
### Content Analysis
#### 3.3.5 Final Remarks: Implications, Limitations, and Future Research Directions
Endless applications and practices are revealed as a result of connecting all the devices of a network in real time. This also provides benefits in terms of cost reduction, efficiency, and effectiveness in business processes. With the help of the technological advancements in all devices and analytical capabilities, the Internet of Things reshapes and redefines the business environment by providing best-value propositions to the stakeholders. Despite the contributions to the Internet of Things research field in relation to Business Model Innovation, several limitations could be found in the nature of research work. The Internet of Things and Business Model Innovation domains are relatively recent subjects for the combined research area; most articles have been published since 2015. In addition, there is a lack of empirical evidence and a systematic review of the current knowledge domain from a state-of-the-art perspective. First, the bibliometric approach supports the minimization of subjectivity in the review process. We focused on conducting a non-biased review. However, this would not be completely free of limitations to keep out of subjectivity to some degree. Second, although our study is comprehensive, we utilized the WoS Core Collection database to search relevant keywords and conducted the analyses based on Cowword (keyword co-occurrences) and Thematic Content Analysis (clustering the publication dataset). Therefore, we depended on the software results in a co-occurrence network. Third, the paper concentrates on discovering the topics' relations and trends rather than the bibliometrics' descriptive style. Further studies would focus more on the country's performance, author, institutions, citation/co-citation patterns, and other perspectives. Fourth, articles in languages other than English were not included. Finally, the bibliometric study does not present and test the significant statistical relevance. In addition, some constraints could be found that would impact any quantitative studies. The research domains are prominent, and since 2016, there has been an upward trend in publication records, suggesting that interest in them is expanding. As previously discussed, the word dynamics shows the trends and potential research areas to be continued in further studies. The main themes, as illustrated in Table 1, and Figures 1 and 2, are the popular research themes \"internet of things (IoT)\", \"innovation\", \"model/models\" \"business model innovation\", \"industry 4.0\", and \"digitalization\". The new research directions could focus more on content-specific themes with various research approaches such as reviews (state-of-the-art, systematic literature review), empirical studies, industry-related cases, and comparison studies. Since technology advances in more of a digital context, the opportunities available for scholarly publications are within those themes.
## 3 The Proposed Encoder-Decoder Network
### The Original CTC Decoding  
CTC sums over all possible alignments upon conditional independent assumptions. Firstly, given a training sample of \(T\) frames:  
\[\mathbf{x}=(x_{1},\cdots,x_{T}) \tag{1}\]  
where \(t\) is the index of \(T\) frames and \(x_{t}\) is a vector of frame features.  
According to the conditional independence assumption (CIA) from the original CTC formulation [7], the probability of path \(\mathbf{\pi}=(\pi_{1},\cdots,\pi_{T})\), \(\pi_{t}\in\mathcal{A}\) is the stepwise product of the network output softmax activation \(y_{t}(\pi_{t})\) of \(\pi_{t}\) at each frame \(t\):  
\[P(\mathbf{\pi}|\mathbf{x})\xRightarrow{\rm CIA}\prod_{t=1}^{T}y_{t}(\pi_{t}) \tag{2}\]  
where \(y_{t}(k)\) is the probability of the network outputting action \(k\) at time \(t\), given input \(\mathbf{x}\), \(k\in\mathcal{A}\) (\(\mathcal{A}\) is the collective set of all possible actions).  
We refer to \(\mathbf{\pi}\) over \(\mathcal{A}\) as _paths_, to be distinguished from the action order \(I\), which is naturally produced from path \(\mathbf{\pi}\) by applying the operator that removes repetitions \(\mathcal{B}(\mathbf{\pi})\):  
\[\mathcal{B}(\mathbf{\pi})=\mathbf{I} \tag{3}\]  
The probability of \(I\) sums over all paths consistent with \(I\):  
\[P(\mathbf{I}|\mathbf{x})=\sum_{\mathbf{\pi}\in\mathcal{B}^{-1}(I)}P(\mathbf{\pi}|\mathbf{x}) \xRightarrow{\rm Equation~{}\eqref{eq:P(\mathbf{\pi})=I}}\sum_{\mathcal{B}(\mathbf{\pi} )=I}\prod_{t=1}^{T}y_{t}(\pi_{t}) \tag{4}\]  
Finally, the negative log likelihood of observing \(\mathbf{I}\):  
\[\mathcal{J}=-\ln P(\mathbf{I}|\mathbf{x}) \tag{5}\]
## 4 Mechanisms by Which Sprouted Grains Improve Insulin Sensitivity
### Reduction of Postprandial Glycemic Response  
One of the key mechanisms by which sprouted grains exert antidiabetic effects is through the attenuation of postprandial glycemic response, which is closely associated with improved insulin sensitivity [75]. This effect is primarily mediated by the inhibition of carbohydrate-digesting enzymes (e.g., \(\alpha\)-amylase and \(\alpha\)-glucosidase), which hydrolyze complex carbohydrates into glucose, thereby regulating the rate of glucose absorption in the small intestine [76]. Recent findings demonstrate that sprouted barnyard millet (_Echinochloa esculenta_, cv. 'CO (KV)2') significantly inhibits these enzymes, outperforming acarbose, a commonly used pharmacological inhibitor. Specifically, barnyard millet sprouts inhibited \(\alpha\)-amylase activity by 87.2% during the intestinal phase, while microgreens showed 81.2% inhibition in the gastric phase. In \(\alpha\)-glucosidase assays, enzyme activity was reduced by approximately 78-80% following treatment with digested extracts of sprouted samples, underscoring the role of polyphenols--particularly flavonoids--in enzymatic suppression [66]. Germination enhanced \(\alpha\)-amylase and \(\alpha\)-glucosidase inhibition across all grain types studied, with black wheat (_Tritcium aestivum_, cv. 'NABIMG-11-Black') and barnyard millet (_Echinochloa fumentacea_, cv. 'VL172') exhibiting particularly strong activity while maintaining a low predicted GI (pGI \(<\) 55), even after processing [22]. In contrast, brown sorghum (_Sorghum bicolor_, cv. 'VL Bhat 201') required soaking followed by 48 h of germination to achieve optimal metabolic properties, including an _in vitro_ protein digestibility (IVPD) of 68.7%, a GI of 51.03, and trypsin inhibitory activity of 23.17 TIU/mg [22]. Sprouting also enhances glycemic regulation at the systemic level. In a high-fat diet-induced mouse model of IR, supplementation with pre-GBR reversed hyperglycemia and improved insulin signaling in liver and skeletal muscle tissues by upregulating key components such as insulin receptor, insulin receptor substrate-1 (IRS-1), phosphoinositide 3-kinase (PI3K), protein kinase B (Akt), AMP-activated protein kinase (AMPK), glucose transporter (GLUT)-1 and GLUT-4, as well as glucokinase (GCK) and peroxisome proliferatorator-activated receptor gamma (PPAR-\(\gamma\)) [67]. Likewise, dietary intervention with GBR significantly reduced fasting blood glucose levels, with an effective response rate of 62% in a human trial [68].
## 2 Materials and Methods
### Methods
#### 2.3.2 Mann-Kendall Significant Test (M-K Test)  
The M-K test method can indeed examine the trend of a time series and also detect whether a mutation has occurred [46; 47]. Because the method does not require the sample information to obey a certain distribution, with the advantage of less influence of outliers [48]. Therefore, it has become an effective method to test the trend of time series elements. The formula is as follows:  
\[Z_{c}=\begin{cases}\frac{W-1}{\sqrt{var(W)}}&W>0\\ 0&W=0\\ \frac{W+1}{\sqrt{var}}&W<0\end{cases} \tag{6}\]  
\[\rm S=\sum_{k=1}^{n-1}\sum_{j=k+1}^{n}sign(W_{k}-W_{j}) \tag{7}\]  
\[\rm var(S)=\frac{n(n-1)(2n+5)-\sum_{k=1}^{n}t_{k}(t_{k}-1)\ (2t_{k}+5)}{18} \tag{8}\]  
\[\rm sign(W_{k}-W_{j})=\left\{\begin{array}{c}1\ W_{k}-W_{j}>0\\ 0\ W_{k}-W_{j}=0\\ -1\ W_{k}-W_{j}<0\end{array}\right. \tag{9}\]  
where \(W_{k}\) and \(W_{j}\) denote the WUE in year \(k\) and year \(j\), respectively. \(\alpha\) represents the significance level, when \(\mid Z_{c}\mid>Z_{1-\alpha/2}\), it means that the sequence changes significantly at the \(\alpha\) level. In this research was mainly used for significance analysis of WUE.
# Improving the Maritime Traffic Evaluation with the Course and Speed Model
## 1 Introduction  
According to the statistical data from analytical agencies specializing in the maritime transportation market (Clarkson, Alpha Liner, Drury, etc.) and UNCTAD's Review, the volume of cargo on container ships, bulk carriers, and tankers has been increasing annually [1]. This upward trend is expected to persist in the future. Furthermore, there is a strong emphasis on developing technologies for smart and autonomous ships that utilize the Internet of Things, big data, artificial intelligence, and other cutting-edge innovations. The imminent commercialization of autonomous ships is anticipated to further bolster maritime traffic.  
Maritime transport continues to dominate the global cargo movement. As the prevalence of autonomous ships rises, ensuring safe and efficient maritime traffic management will become increasingly crucial. These studies encompass methods to assess maritime traffic risks and predict maritime accidents based on the increasing volume of maritime activity. The vessel traffic service (VTS) systems currently in place rely on human resources for maritime traffic management.  
Unlike road traffic, which operates on designated roads, maritime traffic enjoys autonomy in navigation, as long as seaworthiness is ensured. To facilitate safe navigation,ship operators use historically used navigation patterns or established routes. The use of historically used navigation patterns or conventional routes is because they have been tested and verified for a long time, and the accumulated data of actual ship passage prove it and have reliability. Also, they refer to routes recommended by publications, such as the International Maritime Organization's (IMO) \"Routes for Ships\", or recommended routes provided by services, such as Weather Routing (WRI), Applied Weather Technology (AWT), and Weather News (WNI) based on climate forecasts and ship navigation databases. As a result, a certain level of consistent navigation flow is observed on open waters as well as on designated navigation passages.  
Within this context, this study introduces a method that quantitatively assesses the smoothness of maritime traffic flow based on the existing traffic flows generated by a large number of sailing vessels. This method is crucial for determining the smoothness of maritime traffic flow and thus predicting the traffic flow and secondary behavior of unmanned and smart ships. Additionally, they can provide valuable insights to identify and enhance the current traffic flow configurations.  
The paper is organized as follows. Section 2 describes the literature review and research methodology, and Section 3 presents the improving marine traffic flow assessment. Section 4 presents the theory and results of the evaluation model. Finally, Section 5 concludes the paper by discussing the evaluation results, limitations and suggesting future work directions.
# Simulation of a Low Concentrator Photovoltaic System Using COMSOL
## 4 Conclusions  
Simulation of a low concentrator photovoltaic (LCPV) system (V-trough) was performed for two geometrical designs: double and partial coverage, using the COMSOL Multiphysics package of ray optics. The LCPV system was presumed to be non-tilted, to be suitable for flat top surfaces, and with no tracking system. After investigating the effect of the inclination angles of both mirrors on a CPV system, the optimum angles of the mirrors for obtaining maximum incident irradiance on the PV panel were determined. The concentration ratio (CR) to test the performance of the LCPV was calculated, and it was found that attaching mirrors in a V-trough shape increased the amount of irradiance falling on the PV panel. For instance, in Jeddah, the CR improved by 171% for double coverage and 131% for partial coverage. Furthermore, in this work, the effect of the number of movements on changing the mirrors' inclination on the total amount of irradiance falling on the PV panel was studied. A comparison between fixed mirrors; between 13 and 3 movements per day; and between the morning, noon, and afternoon periods was conducted. The reduction in CR values was found to be between 1.2% and 14%, depending on the geographical location of the LCPV, when movements were reduced to three per day. Such analysis will assist in  
Figure 9: The CR of a V-trough system at five geographical locations for double coverage.  
conducting a cost-effectiveness analysis of LCPV systems. The use of COMSOL as a design tool integrated with the laws of physics can allow for the improvement of other forms of CPV systems and for the measurement of its CR. This will further assist in designing and testing models prior to manufacturing.  
Conceptualization, R.M.A.; Data curation, M.M.A.; Formal analysis, M.M.A.; Funding acquisition, E.A.G. and R.M.A.; Investigation, M.M.A., E.A.G. and R.M.A.; Methodology, M.M.A.; Project administration, R.M.A.; Resources, E.A.G. and R.M.A.; Software, M.M.A.; Supervision, E.A.G. and R.M.A.; Validation, E.A.G. and R.M.A.; Visualization, M.M.A.; Writing--original draft, M.M.A.; Writing--review & editing, E.A.G. and R.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The simulation graphs and data files used to support the findings of this study are available from the corresponding author upon request (corresponding author: [PERSON], [EMAIL_ADDRESS]).  
The authors declare that there is no conflict of interest regarding the publication of this paper.
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Storage and Transport
#### 2.2.6 Display/Retail Coolers and Freezers
The investigations comprise comparison of the case with PCM and the reference case without. The results show the potential of PCM to keep cabinet air temperatures low, specifically to provide cooling for several hours. However, they also identified problems, for example if the PCM supercools and low heat transfer between PCM and air. Interesting is that all the literature sources showed R&D only on applications with a vertical display. Here, no commercial solution seems to exist yet, probably due to the complexity of PCM integration. The reason to focus on vertical display might be that horizontal display at a first glance is less interesting, because in horizontal display cooling with ice is a simple and still common solution, e.g., for seafood. However, ice does not work for cooling at temperatures below 0 \({}^{\circ}\)C. And here commercial solutions exist for display/retail coolers and freezers, not being mentioned and discussed in previous publications. First, the commercial solution from PLUSS Advanced Technologies Ltd., previously discussed in the section on Section 2.2.5, is not only available for households but also for retail. Descriptions are available e.g., in [53; 54; 55]. Figure 9 shows the commercial solution again, now for display/retail with transparent lid on top. Further on, there is another commercial solution for horizontal display. Ref. [56] describes the commercial use of PCM as thermal backup for retail freezers (Figure 9). According to the source, the problem in this application is that when cooling ice cream, during the warm season there is an increased cooling demand that can cause compression coolers to fail. Consequences are cost for replacement of the cooler, and the risk of losing the ice cream if it cannot be stored cold elsewhere. The solution offered are plates filled  
Figure 9: Display/retail freezer/cooler (source: PLUSS Advanced Technologies Ltd.) and PCM-based cold battery as thermal backup for retail freezers (source: RGEES, Arden, NC, USA). with PCM changing phase at \(-26\)\({}^{\circ}\)C, which are cooled in a separate freezer, and then placed horizontally within the air circulation chamber under the ice cream pans. The plates with PCM provide thermal backup to maintain ice cream frozen even on cooler failure, and during normal operation reduce the load on the cooler which reduces energy consumption, maintenance, and the risk of failure, all related to cost savings. Ref. [56] also has some interesting customer testimonials, mentioning that otherwise, meaning without the PCM solution, dry ice is commonly used.