esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Dynamic longitudinal information may reflect the inherent features of POAG changes from early to late stages and may infer early biomarkers for its development or progression. Hence, in this project, we report a different approach to the previous literature: we developed a convolutional long short-term memory neural network (CNN-LSTM) architecture with both spatial and temporal image features for the automated detection of POAG progression from longitudinal fundus stereophotographs collected during the OHTS. Our model was derived from a previous model our lab developed to detect the longitudinal progression of Stargardt and geographic atrophy [36]. A CNN-LSTM architecture was chosen due to its ability to capture patterns of POAG progression over several longitudinal timepoints, as noted in the previous literature [37; 38]. In recent years, the implementation of attention techniques in neural networks has become crucial for developing transparent models and visualizing the focus of their attention at each layer. Essentially, there are two categories of attention techniques regarding CNNs: trainable and non-trainable. Trainable methods allow CNNs to focus on key features of images during training and testing, similar to the way human eyes focus on specific elements in a scene [39], and have demonstrated improvements in accuracy and interpretability in biomedical image analysis [40; 41]. Non-trainable methods include post hoc attention techniques, which generate heatmaps that show which features of the input data significantly influence the model's decisions [42; 43] but do not affect the training process. As a preliminary exploration of glaucoma-associated image features, we applied a post hoc gradient-weighted class activation mapping (Grad-CAM) [44] technique for explainability after developing a CNN-LSTM model for automatically detecting the progression of POAG from longitudinal CFPs. Our goal is to advance glaucoma classification beyond subjective human interpretation by leveraging explainable AI, enabling a more objective assessment of POAG. This study aims to develop three novel CNN-LSTM models that leverage both spatial and temporal image features from longitudinal CFPs to independently detect the progression of POAG based on structural changes at the optic disc only, functional changes in the VF only, or both structural and functional changes, which were determined by the OHTS clinical endpoints.
## 2 Proposed Smart Farm Structure: Overall System--Inflatable Aeroponic Enclosed Smart Farm  
Figure 1 provides a structural overview of the smart farm system developed in this study. Additionally, Figure 2 shows the system in operation. This system takes in water 'A', nutrients 'B', and power 'C' from external sources to facilitate the growth of plants 'H'. The enclosed structure '1' creates a uniform and controllable growth environment, ensuring independence from external climate variations. Water within the system circulates through two paths, enabling efficient use of water and reuse of nutrients for hydroponic cultivation. The supplied water 'A' and nutrients 'B' are mixed in the right proportions by the Nutrient Mixer '2' to form the nutrient solution needed by the plant roots. The Misting System '3' delivers this solution as fine droplets to the roots located in the growing beds 'D'. Excess solution not absorbed by the roots is collected by the Drainage Manager '4', goes through an internal filter, and is recirculated, with the purified water 'A' being reused to make more nutrient solution. The plants use the absorbed water and nutrients in their metabolic processes, and the water vapor released through transpiration in the leaves is captured by the Air-control System 'E' and recycled back into liquid form.  
Figure 1: Schematic overview of the closed-system misting smart farming system. This schematic illustrates the key components and resource flow within the closed-system misting smart farming setup.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 4 Function-Based Methodology
### Lessons Learned  
The function-based methodology demonstrates how to model the system and evaluate its resilience in the presence of CPA. It demonstrates the inability in the power grid when there is a mismatch in power demand-supply for a certain period. In this section, we showed that an adversary's motive is to destabilize the grid. In reality, this is not always true. The primary motive of an adversary is not always to destabilize the grid, instead to increase the operating cost of the power system operator by performing slight modifications in the power demand-supply. Instead of closing most (or all) of the DERs in an area, an adversary closes a few of the DERs that will affect the power grid and increase the operational cost regarding increased power reserve, dispatch and updating DERs software. The power grid is not ready to defend against such complex attack scenarios that cause abrupt changes in the power demand and supply [10; 11; 12; 17]. Although it is challenging for power engineers to prevent such attacks where the attacker compromised a cyber system to control some physical processes, in Section 8, we discuss some countermeasures.  
The function-based methodology assists power engineers to identify various attack vectors and how they affect the power system metrics (such as frequency thresholds) when subject to cyber-attacks, but it does not discuss how to provide security to these cyber components of the SG that control the power grid functions (such as power dispatch, outage management, demand response, etc.). Providing security to such critical systems is a complex task. Power engineers should understand how a system is behaving, which functions and network components are vulnerable, what are the vulnerabilities and how does this affect the resilience of the system. In the next section, we discuss the BAGS tool,  
Figure 3: Simulation results. (**a**) Generation and load: no attack; (**b**) area frequency: no attack; (**c**) generation and load: attack; and (**d**) area frequency: attack. (**Gen** in Figure refers to Power Generation in MegaWatts).  
which can be used to compute the likelihood of the compromise of various cyber components of the SG system, answering the latter questions.
# Assessment of Frost Depth and Frost Heave in Romania
## 4 Evaluation of Frost Heave/Freeze-Thaw Cycle (FTC)
The frost-free period, denoted by the average values (LFFS), ranges from 319 days in Sulina along the Black Sea to 203 days in Miercurea Ciuc, reflecting the negative relief forms of the Inter-Carpathian Depression in the central part of Romania. Calculations based on extreme frost dates (50-year return period) indicate a reduction in the frost-free period to 268 days in Sulina and 138 days in Miercurea Ciuc.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 6 Discussion of Simulation Results
### Impact of Traffic Load and Placement of Nodes
#### 6.1.3 Average Delay and PDR Illustrated for Each Node  
Showing the average delay and PDR for each node can better illustrate the performance of our scheduler and the fact that the nodes suffering the most from congestion are the children of the root. For the simulation runs, we use placement 2 (Figure 8). During the burst, the traffic load is set to 10 pkts/minute. Before and after the burst, the traffic is set to 1 pkt/minute.  
Figure 12a illustrates the average PDR for each node in a low traffic load situation (1 pkt/minute). We can observe that both schedulers have similar performance, which is normal considering that the new implementation behaves like RBS Orchestra when the traffic is low enough to avoid congestion at the root's children. The placement of nodes used for running the simulations results in a multi-hop topology. We expect to see the highest PDR coupled with the lowest delay for the root's children. In the placement of nodes we used to run the simulations (Figure 8), nodes 2-5 are the children of the root and have the highest rank among sender nodes. In Figure 12, one can see that, in case of RBS Orchestra, the lowest PDR is for the root's children, this being a sign of congestion. In contrast, the new scheduler behaves as we expect in high traffic situations.  
Figure 12: Average PDR for each sender node in low and high traffic situations. (**a**) We observe that in normal traffic situation, the average PDR is high for both schedulers. (**b**) We observe that in a high traffic situation for RBS Orchestra, the average PDR is lowest for the root’s children, being nodes 2-5.  
Figure 13a shows the average delay when the traffic is low. One can observe that nodes 2-5, the root's children, for both schedulers, have the lowest delay. For RBS Orchestra, this is not true anymore when there is an increase in traffic and nodes become congested (Figure 13b). In this situation, the delay for the root's children becomes comparable to or higher than the delay for the rest of the nodes.
## 1 Introduction
An intercomparison of SOS retrieved using 10 satellite methods shows that the difference between individual methods can be as much as two months; and two methods were more closely related to ground observations than other methods [27]. Although validation against ground observations has been conducted for remotely sensed phenological metrics in many studies [5, 13, 14, 26, 27], the validation process is not standardized because the phenology-monitoring method usually varies among sites, and even the same dataset can be processed differently. More importantly, remotely sensed phenological metrics have not been evaluated in the context of DEMs. In order to improve the accuracy of carbon and water budgets derived from DEMs, there is still a need to define and test phenology transition periods as estimated by satellite sensors [28]. Many issues therefore need to be addressed to determine whether a phenological metric can be used as prescribed phenology in a DEM or to parameterize the embedded phenological model. For example, it should be ensured that the choice of phenology references from available ground observations, against which the remotely sensed phenological metrics would be evaluated, represent the phenology requirements in a DEM. Otherwise, even if the remotely sensed phenological metrics are able to capture some ground phenological metrics that is selected based on the needs of certain applications, they may not be the appropriate variable to be used in a DEM. Remotely sensed phenological metrics depend not only on the method, but also the data source. When the remote sensing data source changes (_i.e._, from AVHRR to MODIS), a given method may lose its validity due to the difference between sensors, such as spectral and spatial resolution. In this study, we evaluate phenological metrics derived using six satellite methods for temperate deciduous trees in the context of a DEM, Agro-IBIS (the Integrated Biosphere Simulator, agricultural version) [29, 30, 31], using the long-term phenological observations [32] and flux measurements at the Harvard Forest AmeriFlux site [33]. We aim to establish a systematic evaluation process that can be used for the parameterization of phenology models embedded in DEMs. First, we identify the reference phenological metrics from ground observations according to the definition of phenology in the Agro-IBIS model, and use them as prescribed phenology to assess how well Agro-IBIS captures the seasonal evolution of LAI and carbon cycle components. Second, we compare phenological metrics derived from remote sensing with the ground reference. Then, all phenological metrics are used to parameterize the phenology models to examine the propagation of errors during the parameterization and modeling process. Finally, the modeled phenology is used in Agro-IBIS to evaluate the sensitivity of simulated carbon cycle to phenology.
## 5 Experimental Results and Evaluation
### Baselines  
To answer RQ1 and evaluate the performance of our approach, we compared our proposed model with several baseline methods that are commonly used in document-level sentiment analysis tasks. To answer RQ2, we conducted ablation experiments to evaluate the impact of query set and dual attention mechanism.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Query Symbol** & **Query Aspects** & **Content Example** \\ \hline \(q_{1}\) & Macroeconomic Analysis & Economy, Cycle, Price, International, Finance, Currency, Policy, Politics, Macro, etc. \\ \(q_{2}\) & Industry Analysis & Industry, Market, Industry, Product, Innovation, Shipment, Sales Volume, Business, Peers, etc. \\ \(q_{3}\) & Business Analysis & Personnel, Executives, Management, Law, Litigation, Cases, Resignation, Complaints, Stepping down, etc. \\ \(q_{4}\) & Financial Analysis & Revenue, Profit, Sales, Loan, Cost, Loss, Debt, Liability, Arrears, etc. \\ \(q_{5}\) & Technical Analysis & Candlestick chart, Tangent line, Pattern, Trend, Market index, Indicator, Technical analysis, Share, Equity capital, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of expert-defined domain-specific query set for the QGDA framework.  
* HAN [50]: This method adopts a hierarchical structure with GRU based word-level and sentence-level attention mechanisms. The original paper demonstrated that deep learning-based methods can outperform lexicon-based methods.
* FISHQA [6]: A novel attention mechanism that incorporates user-specified queries to spotlight texts on different levels and provide explainability through the distribution of attention weights.
* BERT-Attention [5]: We designed a hierarchical BERT with a standard GRU attention mechanism without queries in order to evaluate the performance of BERT and the impact of queries.
* BERT-GRU-QA (BQGA): We conducted an ablation experiment by removing the dual attention mechanism from the sentence-level encoder in our full model in order to evaluate the performance of our proposed QGDA mechanism.
* FISHDQA: We conducted an ablation experiment by adding the dual attention mechanism to FISHQA in order to evaluate its performance within the hierarchical GRU structure.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 3 Proposed Method
### Analysis of Distortion
For key frames in the screen content encoding, more redundant frames are allocated for encoding in order to adapt to different characteristics of the SCC and other scenarios, thereby achieving reliability improvement under secure transmission. Then we evaluate the rate-distortion (R-D) performance with a redundant slice, which is the ratio of the changing distortion and the changing bit consumption (the distortion change per unit bit consumption) after embedding a redundant slice:  
\[\delta=\frac{\Delta D}{\Delta R} \tag{6}\]After obtaining the Lagrangian coefficient \(\lambda\) from the code rate, the quantization parameter is obtained based on the relationship between \(QP\) and \(\lambda\). \[\lambda=\alpha\cdot R^{\beta}=-\frac{\partial D}{\partial R} \tag{7}\]  
\[\lambda=(1-p)\cdot p^{m}\cdot\frac{\partial D_{\mathit{diff}}}{\partial R_{ \mathit{rad}}} \tag{8}\]  
\[QP=4.2005\ln\lambda+13.7122 \tag{9}\]  
In general, natural images can be divided into the following categories:  
1. A completely static scene, that is, the image content of the current frame and the previous frame are exactly the same. 2. A continuously changing scene, that is, there is continuity between adjacent frames in the video. The moving object or the angle of view of the shot has moved. However, for the sequence of screen contents, in addition to the above two types of images, there are images with scene mutations appearing. In the proposed solution, several cases based on the coding of the screen content are considered. Most of the intra prediction in a picture is likely to mean scene conversion. If the motion vector of all macroblocks is large, this indicates that the features in the picture may be more complicated; and if the ROI area in a picture is large, it means that a larger protected area is needed. After the current picture is encoded, the encoding parameters are updated based on the \(R-\lambda\) model [25].
## 3 Classification of the Reviewed Literature
### Evaluation Techniques  
The final stage in the development of CARS is to prove the effectiveness of a proposed system. Three evaluation strategies are used in practice for the performance evaluation of recommender systems. These are online, offline and by user studies. In a user study, subjects are asked to assess their overall satisfaction with the system, usually through ratings. It could be lab-based, in which the subjects are aware that they are part of the study or a real-world study where the subjects are not aware of the study. Even though researchers consider user studies to be the optimal evaluation method [175], the results often depend on the questions asked [176].  
Online evaluation is considered the best method for evaluating recommender systems [177], it measures the recommendation's acceptance in the real world usually through click-through  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Filtering Approach** & **Description** & **References** \\ \hline Pre-filtering & The irrelevant scores to the current context are filtered out before computing the final contextual recommendation. & [36; 37; 55; 61; 62; 68; 74; 75; 79; 80; 83; 84; 89; 92; 98; 101; 103; 105; 109; 111; 112; 114; 119; 121; 126; 127; 131; 133; 139; 160; 164; 173] & [36; 37; 55; 61; 62; 68; 74; 77; 80; 83; 84; 89; 92; 98; 101; 103; 105; 109; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 141; 166] & \\ \hline Post-filtering & In post-filtering, the irrelevant scores to the current context are filtered out after computing the final contextual recommendation. & [37; 50; 59; 94; 161; 174] & \\ \hline Contextual & In contextual approach, the identified contexts are used in the classical recommendation-generating algorithm. & [37; 82; 89; 95; 110; 130; 141; 166] & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Filtering approaches.  
Figure 6: Classification by filtering approach.  
rates (CTR). Online evaluations are very expensive to conduct and are mostly used in the field of online advertising that has access to the real-world systems. Although online evaluation is the most trustworthy experiment, it is significantly more time-consuming than any other evaluation method [177].  
Offline evaluation, on the other hand, is the easiest and the most convenient method of evaluation, as it requires no interaction with real users [177]. It measures the recommendation's accuracy based on a ground truth. Offline evaluation is aimed to identify some promising recommendation approaches, which is later evaluated in detail with respect to online or user studies.  
Figure 7 shows the classification of the reviewed papers into their evaluation approaches. From the figure, user studies are the most used evaluation approach. Depending on the evaluation approach used, certain predefined metrics are  
Figure 8 summarizes the major stages in the context-aware developmental process. Each stage must be fully represented for a framework to be effective and flexible enough for recommending tailored services to users.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 4 Proposed Model
### Transformer Components for TGAN-AD
#### 4.3.1 Generator Training Process  
As shown in Figure 4(a), \(X^{\text{train}}\) is input into the Transformer encoder to learn the hidden representation of the normal time series, which can help the generators generate the data approaching the real time series data. The hidden representation of \(X^{\text{train}}\) is fed into the Transformer decoder. To generate rich similar samples, the hidden space \(Z\) is the input of the Transformer decoder. The fake samples are generated based on the hidden representation of \(X^{\text{train}}\) and \(Z\) with Transformer.  
The goal of the generator is to continuously train the transformer to generate time series data approaching the real time series data, which makes it difficult for the discriminator to distinguish the generated data from the training data.
## 4 Comparative Analysis of Test Results
### Comparative Analysis of Numerical Simulation and Triaxial Photogrammetry
#### 4.2.1 Comparative Analysis of Stress-Strain Curves  
The deformation of the inner part of the soil sample shows inhomogeneity. As shown in Figure 15, the bias stress of soft clay obtained by the three methods reaches the peak at about 6% of axial strain, and the bias stress shows a slightly increasing trend after the peak. In contrast, the bias stress values obtained by the photogrammetric method are lower than those obtained by numerical simulation and conventional triaxial test. The photogrammetric method allows for real-time acquisition of surface information from soft clay, resulting in more precise deformation data for each section, which more accurately reflects the actual deformation. The analysis of the stress-strain curves indicates that the bias stress values obtained from the conventional triaxial test are increased by factors of 1.069, 1.039, and 1.028, resulting in an average increase of 1.045 times compared to the photogrammetric method. The bias stress values in the numerical simulation are increased by factors of 1.061, 1.046, and 1.021, resulting in an average increase of 1.043. The amplification of bias stress diminishes as the confining pressure rises, suggesting that photogrammetry achieves higher accuracy at lower confining pressures.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
\begin{tabular}{c} **Counterpart** \\ **/\%** \\ \end{tabular} \\ \hline
100 kPa & 50.78 & 50.40 & 13.21 & 13.43 & \(-\)0.38 & \(-\)0.75 & 0.22 & 1.67 \\ \hline
200 kPa & 100.01 & 100.71 & 20.76 & 21.00 & 0.70 & 0.70 & 0.24 & 1.16 \\ \hline
300 kPa & 150.42 & 151.44 & 34.28 & 34.53 & 1.02 & 0.68 & 0.25 & 0.73 \\ \hline Average value & & & & & 0.45 & 0.21 & 0.24 & 1.18 \\ \hline \hline \end{tabular} Note: CTC as the true value; absolute error equal to FEM value–CTC value, relative error equal to absolute error/true value.  
\end{table}
Table 8: Comparison of numerical simulation and conventional triaxial test results.  
Figure 15: Stress–strain graph: (**a**) 100 kPa, (**b**) 200 kPa, and (**c**) 300 kPa.