esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Episodic Control
## 3 Hierarchical Episodic Control
### Hierarchical Implicit Memory Planning  
Episodic memory utilizes hierarchical implicit memory-based planning to leverage the analogical reasoning capacity of parameterized memories, estimating the value associated with the best possible rollout for each state-option-action pair. The update of hierarchical episodic memory is based on implicit memory planning to estimate the optimal rolloutvalue for each state-action pair. At each step, the best cumulative reward along the trajectory up to that point is compared with the value obtained from the memory module, and the maximum of the two is taken. The memory module associated with an option includes an option value memory module and an option internal memory module, and the value to choose between them is determined by the termination equation. \(M_{\theta}\) and \(M_{\Omega,\alpha}\) are induced from similar experiences, representing value estimations for counterfactual trajectories related to options. This process recursively forms an implicit planning scheme in the episodic memory, aggregating experience along and across trajectories. The entire backpropagation process can be expressed in the form of Equation (6). Figure 1 depicts an instance of hierarchical implicit memory planning, showcasing two trajectories that represent different values of \(R_{t}\) based on various termination function values, and the red-colored optimal path among other trajectories represents the best trajectory stored in memory, while the green-colored optimal path among the trajectory signifies the current trajectory's optimal policy.  
\[R_{t}=\left\{\begin{array}{ll}r_{t}+\gamma\max(R_{t+1},M_{\theta}(s_{t+1}, \omega_{t+1},a_{t+1}))&\text{if }t<T,\beta_{\omega,\eta}(s_{t+1})=0\\ r_{t}+\gamma\max(R_{t+1},M_{\Omega,\alpha}(s_{t+1},\omega))&\text{if }t<T,\beta_{ \omega,\eta}(s_{t+1})=1\\ r_{t}&\text{if }t=T\end{array}\right. \tag{6}\]  
where \(t\) denotes the step along the trajectory and \(T\) represents the episode length. The back-propagation process in Equation (6) can be expanded and rewritten as Equation (7).  
\[V_{t,h} =\left\{\begin{array}{ll}r_{t}+\gamma V_{t+1,h-1}&\text{if }h>0\\ M_{\Omega,\alpha}(s_{t+1},\omega)&\text{if }h=0,\beta=1\\ M_{\theta}(s_{t},\omega_{t},a_{t})&\text{if }h=0,\beta=0\end{array}\right. \tag{7}\] \[R_{t} =V_{t,h^{*}}\]  
where \(h^{*}=\underset{h>0}{\arg\max}V_{t,h}\).
## 5 Discussion
### Significance of Hydrocarbon Exploration
#### 5.2.5 Favorable Exploration Zones  
In the Xisha area, the distribution of effective source rocks, migration channels, and the spatial distribution of biological reefs and carbonate reservoirs are the three most important factors controlling the formation of oil and gas reservoirs in reef facies. The reefs of the Huangliu and Yinggehai-Ledong Formations are mainly distributed in the islands and reefs above the Xisha Uplift. This area is far from the source rock and has not yet been sealed, making it impossible to form oil and gas reservoirs. The drilling data of Well XY-1, Well XC-1, and Well XK-1 provide the most powerful reliable evidence. The biological reefs developed in the Huaguang Sag of the Qiongdongnan Basin and the platform margins of the northern Zhongjiannan Basin are abundant and of different types and scales. The area of a single-point reef is within 1-2 km\({}^{2}\), and the thickness is almost 50 m. The area of the reef edge of the platform can reach 150 km\({}^{2}\), and the thickness is more than 200 m. These reefs are mainly developed on the hanging wall of the fault, and the thicker (lower) Oligocene Yacheng Formation in the lower part is the main interval for the development of source rocks. In addition, during the separation of the Xisha block from the South China block, a large number of northeast-oriented faults developed in this area during the Oligocene [40]. These faults connect the Paleogene and Neogene and are migration conduits for hydrocarbon migration [41]. These geological conditions of oil and gas accumulation in this area resulted in the spatial configuration of source rocks, migration of hydrocarbons, and formation of biological reefs and carbonate reservoirs. The abovementioned results indicate that the Huaguang Sag in the Qiongdongnan Basin and the northern Zhongjiannan Basin are two favorable exploration zones for the biological reefs and carbonate platforms in the Xisha sea area.
## 4 Proposed Method
### DNC Method
#### 4.2.3 Example Analysis  
We use the karate network [36] to illustrate the execution process of the DNC algorithm. The karate network is made up of social contacts between Zachary Karate Club members, as shown in Figure 1, and it is a network with 34 nodes and 78 edges. We initiate the demonstration of the DNC by selecting node 8 as an example to showcase its execution process. Subsequently, we compute the top ten nodes identified by DNC and perform a comparative analysis with the baseline methods.  
First, we calculate the degree of node \(k_{8}\), which is 5. Next, we identify the neighboring nodes of node 8, which are its closest neighbors 0, 2, 30, 32, and 33.  
Next, we calculate the local clustering coefficient (LCC) for each neighboring node. Subsequently, we compute the \(slc\) of node 8. The LCC for the neighboring nodes of node 8 are as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Node & 0 & 2 & 30 & 32 & 33 \\ \hline LCC & 0.15 & 0.24 & 0.50 & 0.20 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The LCC of the first-order neighbors of node 8 in the Karate network.  
Figure 1: Karate network. The numbers in the figure indicate the node numbers.  
Finally, we obtain the DNC value of this node by adding its degree and the \(slc\).  
\[DNC(8)=k_{8}+\sum_{j\in N_{8}}c_{j}=5+(0.15+0.24+0.50+0.20+0.11)=6.2 \tag{12}\]  
We present the top ten ranked nodes using various methods, as shown in Table 2. The first row represents the method names.  
From the table above, we can observe that the top three nodes ranked by DNC are identical to those of LGM and LGC. There are slight variations in the ordering of the top five nodes between DNC, CI, LGM, and LGC. In the following sections, we will use Kendall's Tau (\(\tau\)) to quantify the associations between the sequences generated by different methods.
## 3 Methods
### Risk Factor Extraction
#### 3.3.1 TRBERT Pretraining  
BERT employs a transformer encoder with 12 layers as its core architecture, capturing contextual information from surrounding text to produce word embeddings [39]. In addition, BERT leverages the masked language model (MLM) approach to learn contextual semantic information from the corpus. To improve the performance of the pretrained model in terms of accident risk understanding, we followed the training process of the BERT model based on the transformer architecture and developed the pretrained model TRBERT using a specific accident-risk corpus. In a previous study, [PERSON] et al. [34] developed a specific pretraining model for the maritime domain, and we followed the same practice in the continuous pretraining of BERT. Specifically, given a character sequence \(\mathbf{c}=\{c_{1},c_{2},\dots c_{T}\}\), 15% of the input characters are randomly selected for processing; 80% of these characters are randomly replaced for [MASK] tokens, 10% are replaced with any random token, and the remaining 10% remain the original tokens [39]. Given the hidden outputs of the last layer \(\left\{\mathbf{h}_{1}^{L},\mathbf{h}_{2}^{L},\dots,\mathbf{h}_{T}^{L}\right\}\), for each mask character \(c_{t}\) in a character sequence, the predicted probability \(p(c_{t}|\mathbf{c}_{<t}\cup\mathbf{c}_{>t})\) of the MLM is calculated as follows [49]:  
\[p(c_{t}|\mathbf{c}_{<t}\cup\mathbf{c}_{>t})=\frac{\exp\left(\mathbf{E}_{c}^{T}[c_{t}] \mathbf{h}_{t}^{L}+b_{c}\right)}{\sum_{\mathbf{c}\in V}\exp\left(\mathbf{E}_{c}^{ T}[c]\mathbf{h}_{t}^{L}+b_{c}\right)} \tag{1}\]  
where \(\mathbf{E}_{c}\) is the character embedding lookup table and \(V\) is the character vocabulary.  
The domain-specific corpus originated from accident investigation reports, the Chinese Emergency Corpus (CEC), and law and regulation documents, totaling 546 MB and approx  
Figure 2: Risk factor identification framework for traffic accident.  
imately 600,000 sentences, as shown in Table 1. Notably, to avoid contamination of the NER dataset, the accident cause section in the report was excluded from the pretraining corpus.
# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis
### Validating MODIS Product  
#### 4.3.1 Spatial Comparison  
The histograms of MODIS FAPAR were compared to the upscaled ASTER data, as shown in Figure 12. The distribution of MODIS FAPAR values in general coincided well with the upscaled ASTER FAPAR values. Especially on 15 June, the distribution of data between the pair of reference and MODIS FAPAR were very similar. The only less coincident distribution was observed on 10 July. The upscaled ASTER FAPAR values were more concentrated around 0.75, while the MODIS FAPAR values were more uniformly distributed in the range 0.6-0.8. This might be partially attributed to the tendency of MODIS FAPAR product to derive less high estimates at coarse spatial resolution, which was also observed in [16] that MODIS values rarely exceed FAPAR = 0.6. However, in general, the spatial comparison verified the high spatial agreement between the MODIS FAPAR product and the upscaled ASTER FAPAR data.  
The statistics of differences between the MODIS and the upscaled ASTER FAPAR also revealed a high degree of agreement (Table 5). Except on 10 July, the mean absolute and the STD of the differences were less than 0.04 and 0.015. The MODIS FAPAR values on 10 July were less consistent and concentrated, with a higher value of 0.072 and 0.034 for the mean absolute and the STD of the differences. Except on 30 May, the MODIS product showed slight underestimates compared to the reference FAPAR as the mean values were all negative from 15 June to 10 July. In general the relative errors were all very small.  
Figure 11: (a–d) ASTER FAPAR derived from ASTER NDVI and _in-situ_ FAPAR at four dates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dates** & **Mean** & **Mean Absolute Difference** & **Standard Deviation of Difference** \\ \hline
30 May 2012 & 0.015 & 0.031 & 0.015 \\
15 June 2012 & \(-\)0.025 & 0.039 & 0.011 \\
24 June 2012 & \(-\)0.004 & 0.035 & 0.013 \\
30 July 2012 & \(-\)0.022 & 0.072 & 0.034 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of difference between MODIS and upscaled ASTER FAPAR.  
Figure 12: (a–d) Histograms of FAPAR values of upscaled ASTER and MODIS product at four dates.  
To further quantify the consistency of MODIS FAPAR with the upscaled ASTER FAPAR, the scatter diagrams were analyzed (Figure 13). The MODIS FAPAR values agreed well with the truth values at 1:1 line and almost all values were concentrated within the 0.1 envelope of the 1:1 line. This evidently confirmed the high consistency of MODIS FAPAR with the truth reference data. The overall RMSE of MODIS was 0.054, which reinforced the good performance of the MODIS FAPAR product.
## 5 Performance Evaluation
### Simulation Environment  
We consider a network area of \(10\,m\times 10\,m\), in which 50 nodes are deployed in uniform random distribution, and the \(BC\) is placed in the center of the network. In the simulation, nodes send data to the \(BC\) through multi-hop communication. The simulation program has been developed in C++.  
Table 1 describes the simulation parameters. We obtain the values for tissue properties and dielectric characteristics from [26, 27]. Initially, the temperature of all the sensors are set to 37 \({}^{\circ}\)C. Among the \(50\) implant nodes, traffic classes are distributed as follows: \(5\) nodes are assigned \(Cr\) traffic, \(10\) nodes are assigned \(Dc\) traffic, \(Rc\) traffic is assigned to \(15\) nodes and the remaining \(20\) nodes are assigned to \(Rg\) traffic. Nodes are chosen randomly during the distribution. We implement the basic functionalities of non-beacon enabled mode of IEEE 802.15.4 MAC protocol with the default values [28]. As benchmark protocols, we choose TARA, ALTR and TMQoS to compare with TLQoS. TARA and ALTR are chosen as both the protocols employ hop-by-hop approach in making routing decision, while TMQoS is the only protocol that provides QoS provisioning with end-to-end route maintenance. Each simulation has been performed for 1000 s and we averaged the value obtained over 10 random runs.
## 4 Feature Representation of Mobile Video  
The essence of mobile video features is a data representation of video content in feature dimension. A mobile video has many characteristics, such as wide variety, single content, and short duration, which makes it difficult to form an effective video representation if the visual features such as color, texture, and shape are used directly. Although there is many researches on video feature extraction, the traditional video features for video recommendation are always neither flexible nor satisfactory enough. We proposed a deep learning architecture called ELU-3 DCNN to extract deep features for mobile videos in our previous work [21], in which ELU-3 DCNN indeed processed a superior learning ability compared with ReLU-3 DNN. In addition, the ELU-3 DCNN not only outperforms ReLU-3 DCNN on training accuracy and training loss, but also reaches a higher training efficiency. Meanwhile, the validation accuracy of ELU-3 DCNN is also better than ReLU-3 DCNN. Moreover, the final test accuracy of ELU-3 DCNN is 84.5%, which outperforms the test accuracy of ReLU-3 DCNN by 2.2%.
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Optical Activity in Liquid Crystals  
Figure 10 shows the transmission Mueller matrix of the ChGLC sample. A circular Bragg resonance that transmits right-handed circularly polarized light with strong circular dichroism is observed in the elements \(m_{41}\) and \(m_{14}\), which are very close to each other. The resonance is centered at 409 nm with a width at half maximum of \(\approx\)120 nm. The corresponding circular birefringence is found in the four central elements analogous to the quartz sample data in Figure 4. A major difference is that the quartz sample did not exhibit any circular dichroism, whereas this was the case for the ChGLC sample due to the Bragg resonance.  
Furthermore, the circular dichroism is so strong that it masks the circular birefringence in the Bragg window. This is an effect of that large values of \(C_{\mathrm{D}}\) that cause sech \(C_{\mathrm{D}}\) to be close to zero and, thereby, down-weight the \(C_{\mathrm{B}}\) contributions as found by inspection of Equation (4). This effect also causes the damping of the oscillations in the four central elements for wavelengths close to but outside the Bragg window when sech \(C_{\mathrm{D}}\) decreases from unity far from the resonance to zero in the Bragg resonance. The elements \(m_{12}\), \(m_{13}\), \(m_{21}\), \(m_{24}\), \(m_{31}\), \(m_{34}\), \(m_{42}\), and \(m_{43}\) are close to zero indicating a very small linear anisotropy.  
Figure 10: Transmission Mueller matrix of the ChGLC sample (solid curve) and the best fit obtained using Equation (12) (dashed curves).  
Figure 9: Model function with two Gaussian resonances for the chirality parameter \(\kappa\) of the elytron from _C. aurata_ (sample CA1).
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 2 Principle
### Calculation Flow Chart  
The flowchart presented in Figure 2 outlines the process of the inverse cross-correlation digital holographic imaging of multiplanar objects with large depth of field.  
The entire process can be divided into four main parts: diffraction reconstruction, cross-correlation, multiplanar reconstruction, and image fusion.  
In digital holographic imaging, diffraction reconstruction is a crucial step in realizing three-dimensional image reproduction. Prior to diffraction reconstruction, a hologram must first be processed through an a + 1 order frequency domain filter; this is an indispensable process for all off-axis holograms.  
**Diffraction reconstruction.** The filtered hologram involves two reconstructions that use the angular spectrum reconstruction algorithm. Each reconstruction generates a reconstructed image, and the purpose of the two reconstructions is to generate two images that can be computed in correlation with each other. It is necessary to note that the diffraction distance \(z\) differs between the two reconstructions, thus leading to a change in the transfer function \(H\). As a result, two different reconstructed images are obtained, named Reconstructed Image 1 and Reconstructed Image 2, respectively.  
**Cross-correlation.** In this process, Reconstructed Image 1 and Reconstructed Image 2 are subjected to an image cross-correlation operation, as described in Equation (3), to obtain the inverse cross-correlation matrix. Subsequently, the image edges are divided into multiple labeled regions, named Area 1 to Area \(N\), by setting a threshold.  
**Multi-plane reconstruction.** Depth estimation is performed separately for each delineated area, and the area is reconstructed according to the position of the extreme point in the estimation curve. This step contributes to a more accurate reconstruction of the structures and characteristics of objects within each distinct area.  
**Image fusion.** Ultimately, the images obtained from the reconstruction are integrated based on their respective regional positions. This step involves amalgamating the reconstructed information from various regions to generate a comprehensive composite image.  
Figure 2: Flowchart of inverse cross-correlation digital holographic imaging.  
This sequence of processes enables large-depth-of-field reconstruction for multiplanar objects in digital holographic imaging. Through synergizing multiple steps, including filtering, reconstruction, cross-correlation, depth estimation, and image fusion, the obstacles encountered when imaging multiplanar objects can be effectively overcome, thus leading to enhanced image quality and reconstruction precision. This method holds potential applications in the field of digital holographic imaging, and it provides a new way of realizing finer and more realistic object reconstruction.
# Personalizing Environmental Awareness through Smartphones Using AHP and PROMETHE II
## 5 Evaluation
### Data Analysis and Discussion
It is worth mentioning that, after using the application, participants mainly preferred to use cold water instead of hot when they could, putting less pressure on electricity grids (\"every time\": from 7.50% to 25.83%, \"often\": from 13.33% to 34.17%). Comparing pre-test and post-test students' awareness on energy saving using the paired-sample t-test, the findings revealed a significant improvement of their consciousness level related to energy saving (pre-test awareness = 53.97%, post-test awareness = 73.83%, difference = \(-\)19.868, t = \(-\)12.676, \(p<0.05\)). The presented mobile application was developed in order to engage users on environmental sustainability. The system introduces a novel approach to raise awareness of environmental issues through the delivery of personalized advice. The individuals' advice emerges from the use model and the surroundings. The results of this study illustrate a high user acceptance regarding the use of the mobile eco-app. Users indicated in their responses to the system's usefulness survey that the application provided a user-friendly and pleasant interface, while the advice received was pertinent, enjoyable, and useful. Moreover, the vast majority of users stated that they intend to use the application in future and recommend it to a friend. The feasibility and suitability of mobile eco-apps for improving users' environmental consciousness is also attested by [8; 10; 49]. These studies have highlighted the importance of exploiting mobile technologies to expose the community to environmental protection. Moreover, they have also confirmed that the use of mobile eco-apps had positive effect on users' awareness of sustainability issues and users enjoyed using such applications. Regarding the personalization of the content provided in a mobile eco-app, our evaluation results reveal that combining a user model with context features (such as location, time, and season) can significantly improve users' environmental awareness. In particular, users' awareness on all the three environmental dimensions involved in the survey, namely conscious consuming, reducing waste/recycling, and energy saving, were increased around 20 units, reaching a percentage higher than 70%. Triggering content at the right time and place and pushing notifications in terms of advisory service can increase the chances of engaging users in behavior change [50]. The findings are congruent with the research of [51; 52], which shows that personalized eco-feedback and interventions toward environmental issues using location tracking can promote environmental behavior and alter users' attitudes toward the protection of the ecosystem.
## 3 Results
For each sample, PCA-LDA calculated the group-associated discriminant values and samples were then attributed to the group with the highest discriminant value. The confusion matrix yielded by the PCA-LDA analysis is shown in Figure 5b. Among the 22 urine samples in the control group, PCA-LDA classified correctly 21 samples, corresponding to a specificity of 95%. In the case of the breast cancer group, PCA-LDA classified correctly 43 out of 53 samples. Therefore, the sensitivity of this test was 81%. In total, the test classified correctly 64 out of 75 samples, corresponding to an overall accuracy of 88%.
# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 3 Decision Trees and Gradient-Boosting Machines (GBMs)
### Decision Trees  
Decision trees are popular supervised learning algorithms for classification and regression tasks. They work by repeatedly splitting the input data into smaller subsets based on specific conditions applied to feature values. During each step, the algorithm selects a feature and a threshold that best separates the data, creating branches that lead to different outcomes. This process continues until the data are divided into distinct groups, forming a hierarchical tree-like structure. In this structure, internal nodes act as decision points where the model evaluates a specific feature to determine how to split the data. Each branch represents a possible decision outcome, leading to further splits or final results, while the leaf nodes contain the model's final predictions.  
Decision trees are highly valued for their interpretability, flexibility, and ability to effectively handle both numerical and categorical data [29]. Decision trees are considered intrinsically interpretable in model explainability, as their hierarchical structure allows for straightforward visualisation and understanding of the decision-making process [10].  
A classification and regression tree (CART) is a specific decision tree algorithm designed for classification and regression tasks [29]. While general decision trees use various splitting criteria such as entropy (ID3) [32] or chi-square statistics (CHAID) [33], CART employs \(Gini\) impurity for classification and variance reduction for regression. The Gini index is a standard impurity measure used in decision trees to evaluate the quality of a split. It quantifies the likelihood that a randomly chosen element would be incorrectly labelled if it were randomly labelled according to the distribution of labels in the subset.  
A key characteristic of CART is its exclusive use of binary splits, which simplifies the tree structure and enhances interpretability. CART applies cost-complexity pruning to avoid overfitting, balancing model complexity and generalisation performance. By recursively partitioning the feature space into binary subspaces, CART effectively builds structured decision rules while maintaining robustness through pruning. Interested readers can find a more detailed description of CART in [29].