esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 1 Introduction
### Our Contributions  
In this paper, we extensively study the use of TempCNNs--where convolutions are applied in the temporal domain--for the classification of high-resolution SITS. The main contributions of this paper include:  
* demonstrating the potential of TempCNNs against RFs and RNNs,
* showing the importance of temporal convolutions,
* evaluating the effect of additional hand-crafted spectral features such as vegetation indices,
* exploring the architecture of TempCNNs.  
This paper proposes the study of TempCNN for SITS classification. We provide a methodological understanding of the models and their theoretical underpinnings, as well as an experimental study that gives general guidelines about how they should be used and parameterized. For this purpose, we first compare the classification performance of TempCNNs to the one of RFs and RNNs. Then, we discuss different architecture choices including size of the convolutions, pooling layers, width and depth of the network, regularization mechanisms and batch size. To our knowledge, this work is the first extensive study of TempCNNs for remote sensing to date. This paper focuses on 1D-CNN models and does not cover the use of the spatial structure of the data: each pixel is considered as an instance and the features learnt by the network are spectro-temporal. Similarly to previous studies, we work at pixel level and not at object level, mostly because the delineations of plots of land are not available for the whole studied area. Working at object level would thus require a pre-segmentation of the data that would introduce another dimension to this study and overly complicate the analysis. An interested reader could refer to [41] for a first exploration of that topic.  
All the topics are addressed experimentally using 46 high-resolution Formosat-2 images, with training/test sets composed of one million labeled time series. This paper presents the results obtained over 2000 (79 architectures and 25 experiments for each) deep learning models. It corresponds to more than 2,000 hours of training time performed mainly on NVIDIA Tesla V100 Graphical Processing Units (GPUs).  
This paper is organized as follows: Section 2 describes our TempCNN model which is used in the experiments. Then, Section 3 is dedicated to the description of the data and the experimental settings. Section 4 is the core section of the paper that presents our experimental results. Finally, Section 5 draws the main conclusions of this work.
## 4 Experimental Results and Discussion
### CIFAR-10 Dataset
The BNN model reduces the size to 0.071 MB \begin{table}
\begin{tabular}{c c c c} \hline
**Network Type** & _Top-1 (\%)_ & _Param Size (MB)_ & _Compression_ \\ \hline \multicolumn{4}{c}{Network In Network} \\ \hline FP Model & 87.72\% & 3.7 & - \\ FP-Tensorized & 86.640\% & 1.3 & 2.642\(\times\) \\ BNN Model & 83.35\% & 0.299 & 12.37\(\times\) \\ Ours-scratch & 82.45\% & 0.113 & 32.74\(\times\) \\ Ours & 82.45\% & 0.113 & 32.74\(\times\) \\ \hline \multicolumn{4}{c}{AlexNet} \\ \hline FP Model & 87.240\% & 91.7 & - \\ FP-Tensorized & 86.68\% & 9.8 & 9.35\(\times\) \\ BNN Model & 81.79\% & 2.85 & 32.2\(\times\) \\ Ours-scratch & 77.65\% & 0.542 & 169.1\(\times\) \\ Ours & 80.91\% & 0.542 & 169.1\(\times\) \\ \hline \multicolumn{4}{c}{Resnet-20} \\ \hline FP Model & 92.60\% & 1.1 & – \\ FP-Tensorized & 90.98\% & 0.62 & 1.7\(\times\) \\ BNN Model & 81.87\% & 0.047 & 23.40\(\times\) \\ Ours & 80.92\% & 0.0342 & 32.16\(\times\) \\ \hline \multicolumn{4}{c}{Resnet-32} \\ \hline FP Model & 93.53\% & 1.9 & - \\ FP-Tensorized & 91.56\% & 1.1 & 1.72\(\times\) \\ BNN Model & 83.53\% & 0.071 & 26.760\(\times\) \\ Ours & 81.05\% & 0.054 & 35.18\(\times\) \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of different architectures on Cifar-10. with a 26.76\(\times\) compression ratio but decreases accuracy to 83.53%. Our method (Ours) achieves a 35.18\(\times\) compression, reducing the model to 0.054 MB while maintaining 81.05% accuracy, making it highly suitable for edge deployment where memory and computation resources are limited. Our proposed method achieves significantly higher compression ratios than both the full-precision and tensorized models while maintaining competitive accuracy levels. This makes our method particularly effective for scenarios where model size is a critical constraint, such as in edge computing environments. The balance between high compression (up to 169.1\(\times\) in AlexNet) and acceptable accuracy losses showcases the robustness of our approach.
## 3 Result and Discussion
### Assay Validation
#### 3.2.4 Extraction Recovery and ME  
The recovery percentage and the ME for 5-AIQ with plasma as a sample matrix was evaluated and presented in Table 3. The overall mean % recovery of 5-AIQ between LQC, MQC and HQC were 79.1% (% RSD = 4.63). This indicates that the recovery of analyte from the proposed extraction procedure was consistent and concentration independent. The mean value of matrix effects for 5-AIQ was 92.4% (RSD, 3.18%) which reflect the ion suppression effects of endogenous phospholipid on analyte response. Since these effects were within the acceptable limit of \(\pm\)15% with % RSD of \(\leq\)5%, it indicates that the developed assay will produce a negligible impact on the quantification of 5-AIQ in actual sample analysis.  
Figure 2: Representative MRM chromatograms of 5-AIQ and IS for blank plasma (**A**) and plasma spiked at LLOQ (1.0 ng/mL) concentration (**B**).
## 4 Results and Discussion
### Flexural Strength  
The effects of using various rubber sizes (R1, R5 and R10) and dosages (10%, 20% and 30%) on the flexural strength at 14, 28 and 56 days are illustrated in Figure 12. All of the results showed a CV of less than 1.2%. For all the tested mixtures with various rubber sizes and their dosages, an increased flexural strength was observed at later stages. For instance, the mixture incorporating 20% of R5 rubber showed approximately 19% and 33% higher flexural strengths at 28 and 56 days, respectively, compared to that of 14 days. This increase in flexural strength at later stages is mainly attributed to the continuous hydration process [44].  
Figure 11: Splitting tensile strength for the mixture incorporating various dosages of rubber at 28 days.  
Figure 12: _Cont._  
Figure 13 shows the results of the 28-day flexural strength for mixtures incorporating various dosages of rubber and their sizes. It was observed that the flexural strength increased for the mixture incorporating the R5 size of rubber compared to that of the R1 for all dosages of rubber (10%, 20% and 30%). For example, for the mixture incorporating 10% of rubber, an approximately 9% increase in flexural strength was observed for the mixture incorporating R5 rubber compared to that of the similar mixture with the R1 rubber size.  
Figure 12: Flexural strength results for specimens incorporating various dosages of rubber: (**a**) R1, (**b**) R5, (**c**) R10.  
Figure 13: Flexural strength for the mixture incorporating various dosages of rubber at 28 days.  
It was observed that the flexural strength was reduced for the mixture incorporating the R10 rubber size compared to that of the R1 and R5 sizes. For example, for the mixture incorporating 20% of rubber, approximately 8% and 14% decreases in flexural strength were observed for the R10 rubber size compared to that of the R1 and R5 rubber sizes, respectively. This decreasing trend is similar to the compressive and splitting tensile strengths and is mainly related to the weaker bond between concrete ingredients and rubber aggregates [45].
# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 3 Simulation Model for Object Pick-Point Evaluation
### Graphical User Interface  
A graphical user interface (GUI) was developed in MATLAB/App Designer (Figure 10), enabling a user-friendly simulation model parameter setup. The application is divided into four tabs, each containing a window for setting various model parameters. If simulations are performed for another object, the model must be reimported into ADAMS/View and re-exported. That is a minor drawback since the 3D model of the object is easily switched in ADAMS/View with the new model. However, the friction coefficients must also be altered based on the object material, yet that is possible to change from within the model GUI. Once the appropriate changes are made, a model of the object must first be imported into the GUI. Since the 3D model cannot be directly imported for further analysis, a top-down picture of the object is imported into the GUI. Using the \"Image Processing Toolbox,\" image segmentation and object detection are performed. Second, the \"Set Gripper Parameters\" tab lets the user specify two-fingered gripper stroke width, finger width, and other gripper-related parameters. Additionally, the user can perform test simulations in interactive mode to ensure everything is set up correctly before performing parallel simulations in batch mode. Third, the \"Generate input data\" tab generates input data points based on the input parameters and starts parallel simulations. In addition, the user can visualize the generated pick-point to ensure correct input parameters. Lastly, the \"Analyze results\" tab enables visualization of the pick-point and the graph of (contact) forces between the robotic gripper and the object in question. For each pick-point, three metrics are calculated, which correspond to the grasp quality. Since validation of the model is critical to ensure correct model operation, the user can export a selected number of (\(n\)) best and (\(n\)) worst pick-points in the form of an object positioning template for testing purposes. The template is also valid in the event of rotating the robotic gripper.  
Figure 9: The test object’s initial position. The blue rectangle shows robotic fingers, the green rectangle is a possible grasping area, and the red rectangle is the object’s bounding box. Source: own.  
Since animations cannot be accessed directly from the model GUI, they can be imported into ADAMS/View. First, a file containing model information (.cmd or.amd) must be loaded into ADAMS/View. Second, in ADAMS PostProcessor, the user must import the graphics file generated during the simulation. From there, it is possible to visualize the entire simulation (animation) of the pick-point evaluation.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 2 Related Work and Background
### Clustering
#### 2.1.1 Locality-Based vs. Non-Locality-Based Clustering  
In locality-based clustering, usually, a sensing network is divided into cells where a single cell naturally represents a cluster. Afterward, in each cluster a set of nodes organize themselves according to their locations. Hence, in this type of clustering, every node has its location information. In a locality-based strategy, the shape of clusters can differ greatly like cross ring-shaped clusters [12], rectangular shaped clusters [22; 23], and normal hexagonal clusters [28]. The cluster-based strategies which are not using location information are categorized as non-locality-based categories [7; 29].
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Results Grouped by the Method Used
#### 4.2.3 Lstm  
Since the performance metrics used were different in different articles, our survey analyzed them based on different metrics. The results used the average performance of the best performing models in the mentioned papers. Table 10 shows the metrics and results for the papers which used the LSTM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Metrics** & **Reference No.** & **Corresponding Value** & **Performance Metrics** & **Reference No.** & **Corresponding Value** \\ \hline  & [32] & 0.306543 & & [35] & 1.03 \\  & [35] & 347.46 & & [38] & 1.6905 \\  & [36] & 0.0151 & & [40] & 4.13 \\  & [37] & 25.90 & & [57] & 0.119 \\ RMSE & & & [10] & 1.37 \\  & & & [62] & 1.65 \\  & [38] & 0.0242 & Precision & [53] & 0.553 \\  & [39] & 1.3 & Recall & [53] & 0.129 \\  & [45] & 9.72 & Return rate & [33] & 1.0667 \\  & [46] & 4.24 (Average) & & [32] & 0.093969 \\  & [47][57] & 1-100.0015 & & [33] & 0.004854592 \\  & [65] & 0.02295 & & [59] & 0.000379 \\  & [32] & 0.21035 & & [35] & 120731.4 \\  & [35] & 262.42 & MSE & [38] & 19.7096 \\  & [37] & 0.1895 & & [39] & 0.019 \\ MAE & [38] & 0.0169 & & [40] & 0.00098 \\  & [40] & 0.023 & & [41] & 7.556 \\  & [42] & 0.01069 & & [42] & 0.00149 \\  & [41] & 1.975 & & [43] & 1.012 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The metrics and results for the papers which used the LSTM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Metrics** & **Reference No.** & **Corresponding Value** & **Performance Metrics** & **Reference No.** & **Corresponding Value** \\ \hline  & [32] & 0.306543 & & [35] & 1.03 \\  & [35] & 347.46 & & [38] & 1.6905 \\  & [36] & 0.0151 & & [40] & 4.13 \\  & [37] & 25.90 & & [57] & 0.119 \\  & [60] & 0.91 & & [10] & 1.37 \\ RMSE & & & [62] & 1.65 \\  & [38] & 0.0242 & Precision & [53] & 0.553 \\  & [39] & 1.3 & Recall & [53] & 0.129 \\  & [45] & 9.72 & Return rate & [33] & 1.0667 \\  & [46] & 4.24 (Average) & & [32] & 0.093969 \\  & [47][57] & 1-100.0015 & & [33] & 0.004854592 \\  & [65] & 0.02295 & & [59] & 0.000379 \\  & [32] & 0.21035 & & [35] & 120731.4 \\  & [35] & 262.42 & MSE & [38] & 19.7096 \\  & [37] & 0.1895 & & [39] & 0.019 \\ MAE & [38] & 0.0169 & & [40] & 0.00098 \\  & [40] & 0.023 & & [41] & 7.556 \\  & [42] & 0.01069 & & [42] & 0.00149 \\  & [41] & 1.975 & & [43] & 1.012 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The metrics and results for the papers which used the LSTM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Metrics** & **Reference No.** & **Corresponding Value** \\ \hline  & [27] & 512–530 \\ RMSE & [29] & 0.0205 \\  & [31] & 0.00165 \\ MAPE & [29] & 0.2431 \\ MAE & [31] & 0.232 \\ MAE & [29] & 0.0132 \\ Accuracy & [28] & 68.95\% \\  & [29] & 66.54\% \\ F-measure & [28] & 0.7658 \\ Recall & [28] & 0.7471 \\ Precision & [28] & 0.7855 \\ MSE & [30] & 0.057443 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The metrics and results for the papers which used the RNN model.
# Can Blockchain Strengthen the Energy Internet?
## 3 Improved Trust, Security and Privacy  
Future energy grids have envisaged seamless peer-to-peer connectivity with the autonomous operation in contrast to conventional smart grid context. This is where the Distribution System Operator (DSO) governs the ownership and authority over the management of infrastructure, certificate invocation and revocation for DER integration and supervision of energy exchange with the grid [26, 38]. Consumers liberating in the envisaged EI grids would promote microgeneration where energy is traded between two nodes of the network without the involvement of a third party, reducing the losses and additional cost incurred. A mechanism which could establish the trust factor with minimal middle-man involvement would drive the energy grids towards the expectations.  
Furthermore, EI grid operation aggregates data related to real-time energy consumption through smart meters, electrical measurements obtained by IoT sensors, bids to trade excess energy, requests to fulfil demand deficit and control signals for grid regulation. The key considerations that strengthen the future energy network would be privacy-preserving protocols to eliminate the risk of revealing individual energy usage patterns, exposing consumer identity and disclosing information to a third party without the consent of the user. Additional key considerations would be secure operations through reduced vulnerabilities of the grid towards physical attacks, software attacks, network attacks, control-related attacks and encryption attacks [39]. The increase in the number of access points connected to the network and the heterogeneity of the devices observed would have a direct impact over the management of trust, security and privacy issues of the speculated EI grids [40].  
The following challenges were identified, impeding the secure and privacy-protected operations of EI grids for which a blockchain platform would be a promising solution.
# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
###### Abstract  
Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject's heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.  
wearable sensors; PPG; bio-signal processing; normalization; physiological signals Article
## 2 Materials and Methods
### Extended Depth-of-Focus Contact Lens  
The EDOF CL used in the current study was Mylo (Mark'ennovy Personalize Care SL, Majadahonda, Madrid, Spain), which is a silicone hydrogel CL (Filcon 5B) of monthly replacement. The material has a high-water content (75%) and a low coefficient of friction (0.02). This CL can be manufactured with a great variety of radius of curvature (7.10-9.80 mm), diameter (13.50-15.50 mm) and negative optical power (0.25-15.00 D) to customize the fitting. This CL has been specifically designed for myopia control and has been approved by the Spanish Agency of Medication for such a purpose. From the optical perspective, it is based on the use of a distribution of power from the center to the periphery, which is not progressive, especially in the optical zone that includes non-monotonic and aperiodic series of variation of powers [23; 24; 28]. This complex optical surface leads to a controlled induction of high-order aberrations, especially primary (Z4\({}^{0}\)) and secondary (Z6\({}^{0}\)) spherical aberration, allowing an enlargement of the depth of focus and consequently the relative maintenance of its optical behavior under different circumstances (pupil aperture, centration, corneal optical properties). For the generation of these complex surfaces, proprietary non-linear optimization algorithms for high-order aberrations are used [23; 24; 28].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Pre-Fitting** & **CL Testing** & \multicolumn{3}{c}{**Post-Fitting Follow-Up**} \\ \hline _Visit_ & \(1\) & \(2\) & \(3\) & \(4\) & \(5\) \\ \hline  & _First visit_ & _CL testing_ & _1 day_ & _15 days_ & _30 days_ \\ \hline Objective refraction & X & X & X & X & X \\ (retinoscopy) & X & & & & \\ Subjective refraction & X & & & & \\ Over-refraction with CL & & X & X & X & X \\ Accommodative response & X & & & X & X \\ (MEM/Nott) & X & & & & X \\ Cover test (far/near) & X & & & & X \\ Fusional vergences & X & & & & \\ (far/near) & & & & & \\ Stereopsis & X & & & & \\ Photoic phenomena & X & & & & \\ (questionnaire) & & & & & \\ Slit lamp biomicroscopy & X & X & X & X & X \\ Corneal topography & X & X & X & X & X \\ (VX120+\({}^{\circ}\)) & & & & & \\ Ocular aberrometry & & & & & \\ (VX120+\({}^{\circ}\)) & & & & & \\ Optical biometry & & & & & \\ (IOL-Master\({}^{\circ}\)) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical protocol followed in the current study. Abbreviations: CL, contact lens; MEM, monocular estimation method.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Collaborative Machine Learning for Energy-Efficient Edge Networks in 6G
#### 5.3.2 Energy Efficient Computational Offloading Empowered by Federated DRL  
To train each agent to use past global information, distributed agents need to be able to exchange local information with each other, which could lead to significant signal overheads. Different cells in small cell networks are very closely linked to each other due to interference. After receiving the interference, each cell can decide on its own computational offloading and resource allocation policy. As a result, the energy consumption reduction problem can be divided into \(M\) subproblems, each of which corresponds to a cell whose consumption should be minimized. In this situation, each SBS agent develops its own strategy based on its observations, and the other agents are treated as components of the environment.  
However, training a performing DRL model for each agent might be difficult due to strict latency constraints. To solve this issue, a federated learning strategy can be used to increase training performance without requiring data to be shared centrally. The main features of each local model can be defined in the same way as the previously stated DRL algorithm, with the exception that the reward for each cell is local in this case, consisting of energy consumption and the usefulness of ensuring necessary delay in the required tasks.  
The offloading system proposed in [64] is based on the presence of multiple SBS agents executing a DDPG model, in which the actor network takes its own local observations of the environment as inputs and returns the selected action; and the critic network has local observation and action as inputs the local action, and outputs its Q value. The FL process consists of a multiple number of coordinated rounds. In each round, each SBS agent independently trains its local model based on its data, without having information on the surrounding environment. Each agent loads the weights of its model to the coordinator, who aggregates them only after these several training rounds. Finally, the coordinator delivers the averaged global model to all agents, who update their local models using it.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## Preliminaries  
### Invariance of the Histogram Shape  
In earlier works [13; 14; 15; 16; 17; 18], a great number of researchers utilized the invariance of the histogram shape to design robust image watermarking. The motivation behind using this histogram-based property can be categorized into two points: (1) modifying a portion of the pixels to embed the watermark is only related to the modification count of each grayscale, not to the position of pixels; (2) the histogram of the host image that has suffered some attacks is immune to those changed pixels in this image, including pixel position, value, and count. Therefore, the robustness of these schemes actually originates from the histogram invariance. It is well known that a histogram is a display of statistical information, the grayscale occurrence frequency of which can be calculated by splitting the pixel values into equally-sized bins. Given an image \(I=\{I(x,y)|x=1,\cdots,R,y=1,\cdots,C\}\) (here, \(R\) and \(C\) are the number of rows and columns in the image, respectively), the histogram can be described by:  
\[H=\{h(i)|i=1,\cdots,L\}, \tag{1}\]  
where \(H\) and \(L\) denote the grey-level histogram of the image and the total number of equally-sized bins, respectively, and \(h(i)\) means the number of pixels in the \(i\)-th bin, and it satisfies \(\sum\limits_{i=1}^{L}h(i)=R\times C\).