esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 3 Method
### Spatial-Angle Joint Transform Module  
To effectively reduce redundant information in light field images, a SAJTM is proposed to reduce redundancy by modeling the intrinsic correlation between spatial and complete angle features. The proposed SAJTM takes spatial feature \(f_{S}\) and complete angle feature \(f_{A^{*}}\) as inputs, and the outputs are the compact spatial feature \(f_{S}^{c_{2}}\) and compact angle feature \(f_{A^{*}}^{c_{2}}\).  
As shown in Figure 1, the proposed SAJTM is composed of two correlation learning-based joint transform blocks (CL-JTBs). Two CL-JTBs are sequentially connected to reduce redundant information in \(f_{S}\) and \(f_{A^{*}}\). Taking a CL-JTB as an example, its architecture is illustrated in Figure 3. To obtain a more compact spatial feature, the inputs \(f_{S}^{in}\) and \(f_{A^{*}}^{in}\) are first processed through a residual block, two convolution layers, and an activation layer to generate spatial feature \(f_{S}^{r_{1}}\) and angle feature \(f_{A^{*}}^{r_{1}}\) with the same size, respectively. Subsequently, the spatial predictive feature \(f_{p}^{s}\), which is the predicted redundant information that can be obtained through prediction, is obtained by modeling the intrinsic correlation between \(f_{S}^{r_{1}}\) and \(f_{A^{*}}^{r_{1}}\). Finally, \(f_{p}^{s}\) is subtracted from \(f_{S}^{r_{1}}\) to obtain the more compact spatial feature \(f_{S}^{out}\). The above process can be expressed as  
\[f_{p}^{s}=F_{c}(R_{S}(f_{S}^{in})\oplus R_{A}(f_{A^{*}}^{in})), \tag{7}\]  
\[f_{S}^{out}=f_{S}^{r_{1}}-f_{p}^{s}, \tag{8}\]  
where \(R_{S}(\cdot)\) and \(R_{A}(\cdot)\) both consist of a residual block layer, an activation layer, and two convolution layers. \(F_{c}(\cdot)\) is composed of an activation layer and a convolution layer, and \(\oplus\) denotes the channel-wise concatenation.  
The process of obtaining the compact angle feature is similar to the above description. The specific process can be expressed as  
\[f_{p}^{a}=F_{c}(R_{S}(f_{S}^{out})\oplus R_{A}(f_{A^{*}}^{in})), \tag{9}\]  
\[f_{A^{*}}^{out}=f_{A^{*}}^{r_{2}}-f_{p}^{a}. \tag{10}\]  
As mentioned above, after the input spatial feature \(f_{S}\) and complete angle feature \(f_{A^{*}}\), pass through two CL-JTBs, compact spatial feature \(f_{S}^{c_{2}}\) and compact angle feature \(f_{A^{*}}^{c_{2}}\) are obtained. In this way,the proposed SAJTM effectively reduces redundant information by utilizing the inherent correlation among features, thus improving compression efficiency.  
Figure 3: Illustration of correlation learning-based joint transform block.
## 2 BiCA-LSTM-Informer Framework
### Model Architecture  
We propose a multi-task deep learning model that integrates both LSTM and Informer networks, leveraging the short-term temporal memory of LSTM and the long-range dependency modeling capabilities of Informer. Furthermore, we introduce a bidirectional cross-attention mechanism for dynamic multi-scale feature fusion and incorporate uncertainty-aware loss weighting to design the bidirectional cross-attention LSTM-Informer uncertainty multi-task learning framework (BiCA-LI). The overall framework is illustrated in Figure 1.  
The proposed model primarily comprises three key components: a dual-branch encoder, a cross-fusion module, and a multi-task output module. The dual-branch encoder is composed of a bidirectional LSTM encoder and an Informer encoder that operate in parallel. The bidirectional LSTM encoder employs gating mechanisms to automatically filter salient temporal features, focusing on capturing short-term dependencies within the sequence. In contrast, the Informer encoder leverages the ProbSparse attention mechanism to efficiently model long-range dependencies.  
To mitigate feature bias inherent in single-path architectures, we introduce a bidirectional cross-attention mechanism. Each encoder generates attention weights by using its own outputs as query and the other encoder's outputs as key/value. This process computes bidirectionally fused attention features, enabling the model to capture both long-term and short-term dependencies in the sequence. Based on these fused representations and  
Figure 1: BiCA-LI Net architecture diagram.  
integrated with a Bayesian regression network, the model separately computes outputs for the regression and classification tasks.  
Compared to conventional single-architecture models based on either LSTM or Informer, the proposed framework incorporates a dual-path temporal modeling strategy along with cross-modal feature fusion. By jointly extracting local and global features in parallel, the model effectively improves its adaptability and robustness in complex temporal scenarios, thereby enhancing performance in both multi-task classification and regression tasks. Additionally, the model employs a task-aware dynamic weighting strategy that automatically adjusts the loss weights for each task based on shared gradient information, thereby alleviating task competition and gradient conflicts in multi-task learning. This design enables adaptive optimization of model performance and significantly enhances its generalization ability.
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 3 Proposed Method
### Plused Training and Loss Functions  
As shown in Figure 2, the proposed framework contains two parallel branches, which estimate global and local blur feature from the input, respectively. To constrain the two branches to extract local and global blur feature, we use a branch loss function and the task-specific training data to constrain the branch network. There are 2 task-specific training sets involved in our phased training scheme, a global blur dataset and a local blur dataset, namely the GoPro dataset [18]. The details of these two datasets are described in Section 4.1. The training process is divided into 3 phases.  
Phase 1: Train the global blur branch with the global blur dataset and a global loss function.  
Phase 2: Train the local blur branch with the GoPro [18] training dataset and a local loss function.  
Phase 3: Fix the network parameters of the global blur feature extraction branch, ensure that the global blur branch extracts the global blur feature, and then train the overall network. Therefore, the loss function of the network at this stage computed with the GoPro dataset.  
We train the model by minimizing the dual-branch loss and an image content loss. The branch loss functions are shown in follows:  
\[L_{G}(x)=\left\|\Psi_{G}(x)-G_{GT}\right\|_{2}^{2}, \tag{2}\]  
\[L_{L}(x)=\left\|\Psi_{L}(x)-G_{GT}\right\|_{2}^{2}, \tag{3}\]  
where \(x\) represents the input blurry image, and \(G_{GT}\) and \(L_{GT}\) represent the ground truth corresponding to the global and local blur branch outputs, respectively. \(\Psi_{G}(\cdot)\) and \(\Psi_{L}(\cdot)\) represent global and local blur branch networks, respectively. \(L_{G}(\cdot)\) and \(L_{L}(\cdot)\) represent global and local blur branch loss functions.  
The content loss function is used to calculate the mean square error loss (MSE) between the output deblurred image and the corresponding clear image, that is, the \(L_{2}\) loss:  
\[L_{\phi}(x)=\left\|\phi(x)-X_{GT}\right\|_{2}^{2}, \tag{4}\]  
where \(x\) and \(X_{GT}\) represent the input blurry image and its corresponding clear image, respectively. \(\phi(\cdot)\) represents the overall network of the proposed method, which contains the feature information of the dynamic fusion of the two branches.
## 5 Hydrothermal Pre-Treatment: Energy and Value-Added Products Recovery
### Hydrochar  
Hydrochar is a high value-added carbonaceous matter obtained from hydrothermal pre-treatment of biomass at temperature ranges between 150-350 \({}^{\circ}\)C [77]. It is used for soil amendments, as a catalyst and in energy storage application. In the field of energy storage, hydrochar is processed to be used as electrodes in Li-ion batteries [78]. [PERSON] et al. [79], studied the hydrothermal carbonisation (HTC) of Miscanthus for the production of hydrochar. The treatment temperature of 190, 225, and 260 \({}^{\circ}\)C, reaction time of 5, 15, and 30 min and substrate to water ratio of 1:6 and 1:12 was varied. It was observed that the reaction temperature was the most important factor controlling the biomass properties. The hydrochar produced at a hydrothermal carbonisation of 260 \({}^{\circ}\)C gave the maximum energy density of 26-30 MJ/kg. [PERSON] et al. [80] studied the HTC of cellulose, and observed that the pre-treatmentcontributed in high carbon content and high calorific value. HTC at 220 \({}^{\circ}\)C led to an increase in fixed carbon content of cellulose increased from 6.1% to 35.0%, which implies that decomposition of cellulose began at this temperature. Along with the fixed carbon, the calorific value of cellulose also was enhanced at temperatures of 180, 200, 220, 280 \({}^{\circ}\)C showing an increase from 16.5 to 18.9, 23.1, 26.5 and 27.7 MJ/kg, respectively. The effect of HTC on the quality of biochar obtained from the lignocellulosic biomass can be calculated by increasing the calorific value and this effect can be due to the pyrolysis of cellulose during HTC reactions leading to hydrolysis, chemical dehydration, and decarboxylation reactions. Thus, HTC which is a combination of high pressure and temperature uses biomass as an energy resource for the production of hydrochar, having energy density in the range of 25-30 MJ/kg. Through HTC, biomass with a low calorific value and high water content can be upgraded to a valuable carbon-rich-solid, lignite-like fuel [80].
# Blockchain Applications in Agribbusiness: A Systematic Review
## 3 Results and Discussion
### A Qualitative Systematic Review
#### 3.2.1 Blockchain Application for Finance in Agribusiness  
Blockchain is considered to be one of the most promising technologies for secure financial transactions for various economic activities, including agribusiness. In agribusiness in particular, it has the potential to revolutionize support for financial transactions for agriculture and improve the credit system. Blockchain allows the reduction of information asymmetry, establishing a fluent channel for transmitting information, increasing the reliability of transactions, improving efficiency, and reducing the costs of agricultural financing [37; 38]. The mechanisms for recording, storing, validating, and protecting data are aimed at solving financing problems between all actors in agricultural supply chains. This includes farmers, development agencies, banks, insurance companies, and other financial institutions [37; 38]. The AgriDigital and OlivaCoin platforms, both enabled for blockchain, are cited as examples of faster and safer payment opportunities in rural credit management [7; 39]. [PERSON] and [PERSON] [40] state that an integrated payment solution has been launched to increase liquidity in private bonds and global payments using distributed ledger technology (DLT) applied in financing and agricultural insurance through smart contracts.  
[PERSON] et al. [41] mention the study applied to delay-tolerant payment that focused on remote rural villages in India, which have a community-run station, such as Nokia Kuha, connected to the public internet through unreliable satellite links. [PERSON] et al. [42] implemented a credit assessment system, adopting blockchain to strengthen the supervision and management of traders in the food supply chain where the entire processing flow and logistics are provided by smart contracts. The smart contracts are programs that combine computer protocols from the user interface to execute the terms of a contract. Since, with blockchain, the whole process becomes more simplified, no longer needing intermediaries involved in asset contracts, it controls the damage of the properties, tangible or intangible, by sharing the access data [43].
# Explainable AI in Education: Techniques and Qualitative Assessment
## 2 Theoretical Background
### Feature Importance  
The impact of each feature or variable on the predictive ability of the model is represented by feature importance, which is a key concept in ML. Feature importance assists in determining what inputs are most pertinent to the prediction task by measuring the degree to which each feature affects the model's decision-making process. By eliminating less important elements from models, this insight might help simplify them or better understand the behavior of complicated models in datasets. Algorithms differ in how they determine feature importance; for example, DTs use Gini importance, random forests use permutation importance, and more complicated models like neural networks use LIME or SHAP values to explain their findings [19]. Understanding the significance of features is essential for analyzing model outputs and improving their interpretability or performance in practical applications.
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 5 Selection of Design Solutions  
The _IIS design spectrum_ offers the possibility of selecting alternative design solutions, characterized by different dynamic behaviour. Four solutions designated in Figure 3b as B, C, D, E, are chosen and explored (Figure 5a). Solution B corresponds to the minimum response, thus it can be considered as the _optimal_ solution, according to the modelling approach and analysis methods adopted herein (3 DOF model, linear RSA). Solution C also falls into zone 2, where a non-negligible response reduction is expected, while Solution D is in the transition zone; finally, the Solution E corresponds to almost perfect isolation, with values of the response parameters very close to those of the LS in the as-is configuration. A conventional vertical extension, consisting of US without an isolation system, designated as Solution A in Figure 3b, is also considered for comparison.  
Both the isolation system and the upper structure of each structural solutions have been designed. The upper structure, a steel structure with braced frames, remains the same in all solutions A, B, C, D, E. Characterized by K = 1.28 and \(T_{US}\) = 0.14 s with \(T_{US}\) < \(T_{ISO}\)/3, it has been designed to avoid higher mode coupling with the lower structure and to be sufficiently stiffer than the isolation system. The isolation system, which governs the dynamic behaviour of the extended building, is made by High Damping Rubber Bearings (HDRBs), with an equivalent viscous damping ratio \(\delta_{ISO}\) = 0.15. It has been designed to  
Figure 5: (**a**) Front view of IIS building, (**b**) 3D FEM of the IIS, (**c**) exploded axonometric view of IIS.  
provide a global secant stiffness \(k_{ISO}\) such that the selected values of \(T_{ISO}\) (\(T_{ISO}\) = 0.5 s; 1.0 s; 1.5 s; 5.0 s) are obtained as: \(T_{ISO}\) = 2\(\pi\)(\(M_{ISO}\)/\(k_{ISO}\))\({}^{0.5}\), with \(M_{ISO}\) = 940 kNs\({}^{2}\)/m the total isolated mass of the steel superstructure plus the isolation system.  
The schematic representation of the isolation system, composed by 10 HDRBs placed at the intersection of orthogonal masonry walls, is provided in Figure 5b,c, respectively in elevation and in plan.
## 4 Discussion
### Integration of UAVs and Airborne LiDAR to Quantifying Landslide Volume  
The study area belongs to the high mountain valley and, therefore, was hindered by the high-risk of data collection and uniform control point establishment. The accuracy of the DEMs could be improved, which facilitated confirmation with the local relief and volume calculation. Central Geological Survey, Ministry of Economic Affairs of Taiwan has also applied a similar method (geomorphometric analysis), using multi-phase LiDAR DEM data and DEM data from other image sources to analysis the landslide volume after adjustment, and using the LiDAR image before and after the typhoon season to calculate the sediment yield [40,48] and to evaluate topography changes [56,57]. The study focuses on the application of UAV photogrammetry on change in the landscape by considering LiDAR DEM with relatively precise 3D geoinformation. LiDAR DEM provides GCPs that cannot be measured on-site. Therefore, LiDAR DEM can also provide the benchmarks for comparison of surface change of the survey DEMs. We refer to the smallest relative vertical bias between LiDAR DEM and survey DEMs instead of absolute vertical bias in this study. When the volume of the landslide sediment or the sediment yield in the river channel needs to be calculated, the information can be quickly applied to the planning design and cost analysis of the landslide control project or river dredging project for post-disaster recovery. This study uses multiple images, including aerial images in grid spacing size of 2 m, LiDAR in grid spacing size of 1 m and UAS images in grid spacing size of cm-scale, all of which have been calibrated based on LiDAR, which is sufficient for the analysis of landslide disasters in Taiwan. In addition to analyzing the information of the affected area after the typhoon, the same technique can be used in land monitoring in meter-scale accuracy [55]. A large landslide material caused by Typhoon Morakot in the Laishe River catchment has remained in place. Heavy rain caused significant downstream sediment transport and the narrow upstream river channel easily  
Figure 5: (**a**) Longitudinal profile of the Laishe River. (**b**) Longitudinal profile of the Laishe River midstream. Location of outlet marked in Figure 3. (**c**) Cross section of the Laishe River in profile B-B’. Location of profile B-B’ marked in Figure 4c. Colored lines denote different periods.  
formed a landslide dam, which may cause a future disaster such as the historic case of the Xiaolin village [41; 57; 58]. Therefore, monitoring the changes in the river channel and landslides with the DEMs by regular UAV surveys, not only allows for the understanding of the sediment migration [55], but also ensures the safety of life and property in the downstream areas. Based on this case study, it is suggested that the UAV-acquired DEMs integrated with airborne LiDAR, can play an important role in quantifying and characterizing typhoon-derived landslide volumes and the potential topographic impact to a mountainous river.
# Yeast Diversity during Spontaneous Fermentations and
## 3 Results and Discussion
### Genetic Diversity of _S. cerevisiae_ by Interdelta Polymorphism Fingerprinting
(**A**): Interdelta sequence profiles of the 33 distinctive genotypes. (**B**): UPGMA dendrogram generated from Interdelta fingerprinting patterns of 33 genotypes. C, Cinsault; CG, Cabernet Gernischet; CS, Cabernet Sauvignon; M, Merlot; PN, Pinot Noir; R, Riesling; S, Semillon; and Y, Yan73. between any two of the grape varieties was within 500 m, insect vectors are likely to homogenise the yeast populations [53], and this might explain why NX1, NX13 and NX16 were observed in fermentations with multiple grape varieties. The impact of fermentation progress on the distribution of _S. cerevisiae_ populations was further analysed. NX4 and NX20 were the only two biotypes found from the grape must whilst a number of 15 and 18 genotypes were encountered at the middle and end of fermentation, respectively (Table 1). Among those genotypes seen at the middle of fermentation, 10 genotypes, including NX1, NX2, NX3, NX4, NX5, NX12, NX13, NX16, NX23 and NX27, could also be found towards the end of fermentation (Table 1). In contrast, NX8, NX10, NX14, NX21 and NX24 were only detected at the middle stage (Table 1). Nonetheless, ANOSIM analysis did not show any significant genetic differentiation of the temporal distribution of _S. cerevisiae_ genotypes (R = \(-\)0.07591, \(p\) = 0.8964). Genetic divergence of indigenous _S. cerevisiae_ can also be weakly but not significantly altered by SO\({}_{2}\) treatment, further confirmed by ANOSIM analysis (R = \(-\)0.02188, \(p\) = 0.6857). The number of _S. cerevisiae_ genotypes in most SO\({}_{2}\)-free groups were reduced compared to the corresponding SO\({}_{2}\)-added groups (Table S4). Notably, some novel genotypes were seen in SO\({}_{2}\)-treated fermentations compared to the SO\({}_{2}\)-free groups, for example, NX18 and NX33 (Table 1), suggesting that SO\({}_{2}\) addition may impact the evolution of _S. cerevisiae_ during the course of fermentation. To further investigate the clonal relationships between genotypes, the Interdelta data was used to build a dendrogram from the Euclidean distance, shown in Figure 3B. A total number of 12 groups were identified from the 33 genotypes at 2.22 Euclidean distance, among which six major groups (I-VI) was composed of at least two genotypes. The dendrogram showed that clustering of the genotypes was influenced by grape varieties (Figure 3B). Group I gathered 10 genotypes mainly originated from Cabernet Gernischet fermentations whilst Group III clustered genotypes solely isolated from Pinot Noir fermentation. Further investigation is required to determine whether these genotypes can be considered as potential microbial signatures of local Cabernet Gernischet and Pinot Noir wines [31]. Groups II, IV, V and VI harboured genotypes from two or more grape varieties.
## 4 Experiment and Security Analysis
### Comparison with the Current State-of-the-Art Works
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Images** & **Method** & **Proposed** & [18] & [20] & [22] & [24] & [25] & [26] & [30] & [31] \\ \hline Barbara & PSNR* (dB) & 50.348 & 39.89 & 46.85 & 49.12 & 48.71 & 54.27 & 47.47 & 48.72 & 48.72 \\  & ER (bpp) & 1.161 & 1.5314 & 1.16 & 1.00 & 1.2413 & 0.1867 & 1.25 & 1.1274 & 1.1274 \\  & EE & 1.935 & 0.2296 & 0.8637 & 1.2558 & 1.4167 & 0.7676 & 1.0736 & 1.2912 & 1.2912 \\ Boat & PSNR* (dB) & 50.352 & 39.89 & 46.84 & 49.03 & 48.71 & - & 47.46 & 48.72 & 43.04 \\  & ER (bpp) & 1.161 & 1.531 & 1.16 & 1.00 & 1.2419 & - & 1.25 & 1.1271 & 1.6666 \\  & EE & 1.935 & 0.2296 & 0.8617 & 1.23 & 1.4174 & - & 1.0711 & 1.2909 & 0.5161 \\ Goldhill & PSNR* (dB) & 50.353 & 39.90 & 46.85 & 49.13 & 48.72 & 53.73 & 47.46 & 48.72 & 43.03 \\  & ER (bpp) & 1.161 & 1.531 & 1.16 & 1.00 & 1.2412 & 0.1867 & 1.25 & 1.1274 & 1.6659 \\  & EE & 1.935 & 0.2301 & 0.8637 & 1.2587 & 1.4199 & 0.6778 & 1.0711 & 1.2912 & 0.5147 \\ [PERSON] & PSNR* (dB) & 50.351 & 39.89 & 46.84 & 49.13 & 48.71 & 53.96 & 47.47 & 48.72 & 43.04 \\  & ER (bpp) & 1.161 & 1.5314 & 1.16 & 1.00 & 1.2405 & 0.1867 & 1.25 & 1.1274 & 1.6676 \\  & EE & 1.935 & 0.2296 & 0.8617 & 1.2587 & 1.4158 & 0.7147 & 1.0736 & 1.2912 & 0.5164 \\ [PERSON] & PSNR* (dB) & 50.349 & 39.94 & 46.42 & 49.09 & 48.71 & 53.94 & 47.46 & 48.72 & 43.03 \\  & ER (bpp) & 1.161 & 1.5253 & 1.16 & 1.00 & 1.1971 & 0.1867 & 1.25 & 1.1273 & 1.6673 \\  & EE & 1.935 & 0.2314 & 0.7823 & 1.2472 & 1.3679 & 0.7114 & 1.0711 & 1.2911 & 0.5151 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison results in terms of PSNR, embedding rate, and embedding efficiency. Figure 13: The visual inspection results of four types of attack and the corresponding restored image. much larger embedding efficiency than [PERSON] et al. 's work [25]. The embedding ratio of [PERSON] et al. [26] is higher than ours, but the average PSNR is 47.46 dB. Thus, the comparison in terms of the embedding efficiency confirms that our method is better than [26]. The embedding ratio of the proposed method is close to [PERSON] and [PERSON] [30], but our scheme has higher PSNR (e.g., 48.72 vs. 50.35 in the Lena test image). The average PSNR value of [30] is 48.72 dB, lower than ours (50.35 dB). Finally, the embedding ratio of [PERSON] et al. [31] is higher than ours, but the average PSNR just reaches 43.04 dB, much lower than that of our scheme. The embedding efficiency demonstrates our scheme is superior to [31]. We conclude that the overall comparison confirms that our scheme outperforms all eight competitors.