esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Remote Sensing Solutions and Future Applications
#### 3.2.2 New Freely Available RS Data to Improve Monitoring Agricultural Crop Status
Recent advances in remotely sensed-based approaches to estimate photosynthesis relied on the flux of chlorophyll fluorescence emitted by the canopy, which has provided opportunities to develop many satellite retrieval algorithms [217; 218; 219]. Recently, [PERSON] et al. [220] provided a framework to correlate solar-induced fluorescence (SIF) retrievals and crop yield. Crop productivity was estimated for 2007-2012 using spaceborne SIF retrieval from the Global Ozone Monitoring Experiment-2 satellite in the United States. Besides the more accurate and efficient measurement of crop productivity compared to traditional crop monitoring approaches, the SIF was able to capture information on the impact of environmental stresses on carbon use efficiency and autotrophic respiration, with considerable sensitivity of both to high temperatures. These outcomes revealed new opportunities to increase the accuracy of crop yield forecasting and increase understanding of crop yield responses to different climatic conditions in ASA regions.
## 2 Materials and Methods
### Model validation  
The Model (1)-(3) performance was assessed using the most common in the solar field statistical parameters, described by [35]:  
Root mean square error (RMSE)  
\[\text{RMSE}=\sqrt{\frac{1}{n}\Sigma_{i=1}^{i=n}\left(H_{i,c}-H_{i,m}\right)^{ 2}} \tag{4}\]  
Mean absolute bias error (MABE)  
\[\mathit{MABE}=\frac{1}{n}\Sigma_{i=1}^{i=n}\left(H_{i,c}-H_{i,m}\right) \tag{5}\]  
Mean absolute percentage error (MAPE)  
\[\mathit{MAPE}=\frac{1}{n}\Sigma_{i=1}^{i=n}\bigg{(}\bigg{|}\frac{H_{i,c}-H_{i, m}}{H_{i,m}}\bigg{|}\bigg{)}.100\% \tag{6}\]  
where:  
\(H_{m}\)--measured long-term monthly average daily global solar radiation (from the years selected at random),  
\(H_{c}\)--calculated monthly average daily global solar radiation determined for so-called average day of the month, which simplifies the calculation procedure for the user. The most representative days for each month of a year are the following: 17, 47, 75, 105, 135, 162, 198, 228, 258, 288, 318, and 344 [36].  
The annual series of measured and calculated mean monthly daily global solar radiation were plotted using R software [37].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Station** & **Model** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **R2** \\ \hline \multirow{4}{*}{Gdynia} & 1 & 1.06 & 19.53 & 10.71 & 2.25 & & & & 0.96 \\  & 2 & 10.39 & \(-\)9.87 & 10.57 & & & & & 0.96 \\  & 3 & 10.79 & \(-\)9.11 & 1.05 & \(-\)4.08 & \(-\)5.54 & 1.03 & 5.20 & 0.96 \\  & 1 & 1.34 & 19.69 & 10.39 & 2.15 & & & & 0.96 \\ Gorzów & 2 & 10.91 & \(-\)9.91 & 10.35 & & & & & 0.96 \\  & 3 & 11.23 & \(-\)10.05 & 1.04 & \(-\)4.25 & \(-\)3.87 & 1.03 & 4.94 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of models (1)–(3) for analyzed stations.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### Reinforcement Learning Decision and Strategy Mechanism  
In order to achieve adaptive optimization of edge detection models in multi-view target detection tasks, a category migration decision mechanism based on reinforcement learning is designed. The Deep Q-Network (DQN) is used as the core decision model. The state-driven method is used to determine whether a certain category performs local migration operations, thereby achieving the adaptation of edge detection models under limited resources.
## 4 Experiments and Results
### The Impact of Different Densities on the Energy Requirements of Sink Nodes  
This experiment compares the impact of different densities on the energy requirements of sink nodes, as shown in Figure 14. This experiment deploys four kinds of sink node density. The first case is 4C0S (C = Close, S = Separate) representing four sink nodes that have overlapping coverage. The second case is 2C2S representing two sink nodes that have overlapping coverage (Sink 1, Sink 2) and two isolated sink nodes (Sink 3, Sink 4). The third case is 3C1S represents three sink nodes that have overlapping coverage (Sink 1, 2, 3) and one isolated sink node (Sink 4). The fourth case is 0C4S, which means four sink nodes are isolated. The positions of sensor nodes are randomly deployed in the coverage of sink nodes. The experimental results found that the average energy requirements of sink nodes is almost the same in 0C4S and 4C0S situation, as shown in Figure 12. In the case of sink nodes that have overlapping coverage, sink nodes can share the processing of sensor nodes in the same coverage, and so the energy requirements of sink nodes will be shared and reduced. Therefore, the average energy requirements of sink nodes are the same as the four isolated sink nodes. In 2C2S and 3C1S situation, some sink nodes have overlapping coverage and some sink nodes are isolated. As isolated sink nodes have no other sink nodes in the coverage to help process the data from sensor nodes in the coverage, this causes an increase in the energy requirements of sink nodes, as shown in Figure 14.  
Figure 13: The energy consumption and supplementary of sink nodes with different sink rates.
## 1 Introduction
Deep learning (DL) techniques have also been applied to improve traditional communication performance, such as channel estimation [10; 11], channel equalization [12], and signal decoding [13]. Furthermore, DL technology can enhance system performance through the joint optimization of system modules, such as joint channel coding and source coding [14]. [PERSON] et al. employed a deep learning scheme based on autoencoders (AE) to reduce low-frequency noise in PAM-based VLC systems [15]. Additionally, some studies have used neural networks to predict and optimize link blockage in VLC/RF systems [16]. Traditional analytical channel models are nondifferentiable, making it impossible to calculate gradients during the backpropagation process in neural networks, which prevents the updating of end-to-end systems. Consequently, conventional analytical channel models cannot be directly cascaded into end-to-end systems. Therefore, it is necessary to establish a differentiable channel model for Visible Light Communication to meet the requirements of end-to-end systems. Meanwhile, DCO-OFDM is crucial for providing high-capacity access services in future VLC systems, and the construction of end-to-end systems should not be limited to PAM systems. This paper aims to investigate channel models suitable for end-to-end VLC systems. In existing studies, many channel models are based on theoretical analysis or simulation, which, while providing significant references for VLC system research, often fail to fully reflect the channel characteristics in real environments. To establish an accurate channel model, we comprehensively consider factors such as ambient light interference, multi-path effects, and scattering. We collect a large amount of channel response data and use various channel modeling algorithms to accurately model the channels of the DCO-OFDM VLC system. To validate the effectiveness of the model in end-to-end VLC systems, we conduct a comparative analysis of the bit error rate (BER) performance between traditional channel modeling schemes, existing DL-based channel estimation schemes, and the end-to-end channel model optimization scheme proposed in this paper.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Model Performance Evaluation
These results indicate that ADDAEIL can adapt to various forms of drift while maintaining strong window-level detection accuracy for both non-stationary and relatively stationary data streams. The hybrid drift detection enables ADDAEIL to combine the strengths of distribution-based and structure-based approaches, while its performance evaluation and proportional updating help avoid unnecessary model changes when no drift is present.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 1 Introduction
### Major Challenges in an Anomaly-Based IDS
This alone can make deep inspection of the traffic unfeasible, so only metadata such as connection type and timing are often exploited. Even in that case, the number of records can be large: the IDS developed by Cisco reports more than 10 billion analyzed requests daily [13]. IDSs are frequently built with a complex layered architecture, decreasing with each layer the amount of processed data and increasing with each layer algorithmic complexity. * Low quality training datasets: This is best illustrated by the fact that a synthetic dataset knowledge discovery and data (KDD) Cup 99 [14] created in the year 1999 is still widely used by the research community as shown in an extensive survey [15]. This is despite its age and serious flaws that have been discovered over time [15]. Recently, [PERSON] and [PERSON] presented the problem of KDD 99 datasets [16]. [PERSON] et al. [17] presented a review of the issues of current datasets and the lack of the representative attacks. Their main finding results conclude that the benchmark datasets lack real world properties and fail to cope with constant changes in attacks and network architectures. Also, high-quality training datasets for IDS development are typically very expensive to construct. Due to the cost, complexity of the task and large data volume, the results may not be very reliable. Another option is to use real-world or simulated background network traffic and add malicious traffic gathered from infected virtual machines. The biggest hurdle to publishing datasets in the security domain is privacy concerns. Data may be sanitized by removing or anonymizing potentially sensitive information.
## 8 Conclusions  
Our MATLAB implementation allowed us to successfully extract vital blood pressure-related features from the PPG's shape and the time shift concerning the ECG, as well as SBP and DBP values derived from the ABP waveform. Analysis of the performed CNN revealed that the main source of variance within the dataset originated from the subjects themselves, indicating that the subject's identity was the most significant factor contributing to the variance. There was no clear predominance of variance attributed to, or explained by, any specific feature; therefore, no dimensionality reduction was performed based on these features.  
Our estimation models were developed using a stepwise multivariate linear regression approach, which facilitated the elimination of certain features based on their statistical significance in the regression. As expected, models trained with data from a single subject tended to overestimate their performance when applied to samples from the same subject. The results of our cross-validation demonstrated improved MAE and STD values compared to those reported in the literature, primarily due to the more limited dimensions of our dataset. This led to results with smaller MAE and STD; however, these outcomes were  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Works**} & \multicolumn{2}{c}{**Systolic Blood Pressure (mmHg)**} & \multicolumn{2}{c}{**Diastolic Blood Pressure (mmHg)**} \\ \cline{2-7}  & **MAE** & **STD** & **RMSE** & **MAE** & **STD** & **RMSE** \\ \hline CNN-BiLSTM in [31] & 7.849 & NP & 11.50 & 4.418 & NP & 6.52 \\ \hline GRNN in [41] & 5.36 & NP & 5.54 & 3.28 & NP & 3.45 \\ \hline KNN in [42] & 6.52 & NP & NP & NP & 5.20 & NP \\ \hline ML in [43] & 8.64/9.45 & NP & NP & 18.20/8.13 & NP & NP \\ \hline Proposed model (S1 and S2) & 4.70 & 7.60 & 7.51 & 2.01 & 3.90 & 3.90 \\ \hline S1 only & 3.30 & 3.08 & 4.52 & 1.10 & 1.12 & 1.56 \\ \hline S2 only & 6.56 & 8.4 & 10.71 & 3.48 & 4.80 & 5.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross-validation of the linear models with the calculated RMSEs, standard deviation (STD), and MAEs. Compared with other works. Subject (S). Not provided (NP).  
somewhat overfitted to our specific pair of subjects. The limited amount of interpretable data at our disposal raises questions regarding the generalizability of our models to data from other subjects. On a positive note, we have confidence in the adaptability of our preprocessing and feature extraction pipeline to different datasets.  
[PERSON] initiated the idea of using photoplethysmogram to derive BP, proposed the preprocessing pipeline, and supervised and revised the whole manuscript. [PERSON] conducted the experiment, proposed the methods, and wrote the manuscript. [PERSON] revised the manuscript and helped to optimize the model as well as improve results. All authors have read and agreed to the published version of the manuscript.  
Shanghai Engineering Research Center of Interventional Medical Device (No. 18 DZ2250900).  
Not applicable.
## 2 System Arrangement
### Formulation of System Arrangement
#### 2.4.2 Optimal Arrangement Formulation  
In the optimal arrangement, assuming the fixed number \(q\) of nodes needs to be deployed at each tower, the objective function of cost is shown below:  
\[\mathbf{Minimize}:f_{1}(q)=c\cdot q\cdot N, \tag{1}\]  
which computes the total fixed cost for purchase and deployment of WSNs. Another consideration is the reliability of the network during natural disasters. The objective function of reliability considering network connectivity is shown in Equation (2):  
\[\mathbf{Maximize}:f_{2}(q,x_{i,k})=\frac{\sum\limits_{k=1}^{NR}p_{\mathrm{e}}^ {n_{k}}}{NR}=\frac{\sum\limits_{k=1}^{NR}p_{\mathrm{e}}^{\sum\limits_{k=1}^{N }x_{i,k}}}{NR}, \tag{2}\]  
where \(p_{\mathrm{e}}=1-(1-p)^{q}\) indicates the contingency of equivalent nodes' normal working state. Considering the two objectives, a comprehensive objective function is constructed in Equation (3):  
\[\mathbf{Maximize}:f(q,x_{i,k})=\frac{f_{2}(q,x_{i,k})}{f_{1}(q)}. \tag{3}\]  
The comprehensive objective indicates the maximum reliability of the network per unit cost.  
Constraints in Equations (4)-(9) ensure the connectivity of route and the requirement of end-to-end delay for design. Since the \(k\)th solution of these constraints corresponds to the \(k\)th route, the following constraints neglect variable \(k\) in expression for better readability:\[\sum\limits_{i}^{N}\sum\limits_{j=i+1}^{i+m-1}l_{ij}=\sum\limits_{i=0}^{N+1}x_{i }-1 \tag{4}\] \[\forall i\in[0,N+1],\forall j\in[1,N],j-i\in(0,m],\]  
\[x_{0}=1,x_{N+1}=1, \tag{5}\] \[\sum\limits_{t=i-m}^{i-1}l_{ti}=x_{i},\forall i\in[1,N-1],\] (6) \[\sum\limits_{k=i+1}^{i+m}l_{ik}=x_{i},\forall k\in[2,N+1],\] (7) \[t(\sum\limits_{i=0}^{N+1}x_{i}-1)\leq D,\forall i\in[0,N+1],\] (8) \[x_{i},l_{ij}\in\{0,1\},\forall i,j\in[0,N+1]. \tag{9}\]  
Equations (4) and (5) maintain that substations are sources of protection data and one route is exactly selected for the data flow. Equations (6) and (7) ensure that if the \(i\)th tower serves as a relay node, then there are two links intersecting at tower \(i\). Equation (8) restricts the end-to-end delay for design. If multiple latencies between the \(i\)th tower and the \(j\)th tower are imposed considering different performance of nodes, \(t\) can be replaced by \(t_{ij}\). The last constraint in Equation (9) ensures the binary variables.
## 3 Research Design
### Relationship Strength Quantification Methods
#### 3.4.2 Visual Representation  
The visualization module overlays a connection network on the original stimulus image to represent the relative strength between AOI pairs. The width of each connection line dynamically adjusts according to the normalized fixation count, with thicker lines indicating stronger connections and thinner lines representing weaker ones. The normalization is performed by dividing the fixation count between each AOI pair by the maximum fixation count observed across all pairs, yielding a value between 0 and 1. This approach ensures consistent visual scaling and facilitates the comparative interpretation of spatial relationships across regions.  
This method was developed in this study to highlight the relative intensity of spatial co-occurrence, rather than to indicate directional gaze transitions or attentional shifts. Specifically, line width is mapped using the following function:  
\[W(s)\ =\ 0.5+4.5\times\frac{s-s_{\mathrm{min}}}{s_{\mathrm{max}}-s_{\mathrm{ min}}} \tag{6}\]  
where \(s\) is the relationship strength value, and \(s_{\mathrm{min}}\) and \(s_{\mathrm{max}}\) are the boundary values of the strength range. This mapping ensures that the line width remains within a reasonable range of 0.5 to 5.0, providing good visual differentiation while maintaining overall visual harmony. The final visual representation is shown in Figure 9a-c.  
Figure 8: Calculationof fixation relationship strength.
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 1 Introduction
In [18], the range bins corresponding to fast time in the raw data were summed to obtain time-series data for a slow time, which were then input into a deep convolutional neural network to detect falls. In [19], a method was proposed to detect falls by converting a spectrogram into a binary scale. A binary image was generated through k-means clustering, median filtering, and a morphological opening operation. The resulting image was input into a deep convolutional neural network, achieving 98.37% precision and 97.82% specificity in distinguishing between falls and non-falls. In [20], a time-frequency image was generated using the STFT operation and input into a CNN-based Capsule Network, achieving 94.22% accuracy, 95.66% precision, 93.99% sensitivity, and 94.55% specificity. In [21], FFT and singular value decomposition (SVD) filtering were used to extract frequency-domain and time-domain feature images. An adaptive channel selection algorithm was then proposed to distinguish activity from background. This output was then input into a deep convolutional neural network to classify three types of fall actions. In [13; 22], a Convolutional Long Short-Term Memory Model was used to extract spatial and temporal features for fall detection. However, these deep-learning algorithms have large model sizes and many parameters that require a significant amount of memory. This can be a limitation in terms of size and cost of fall detection systems that need to be installed throughout the home in the form of IoT devices. To solve these problems, we propose a lightweight fall detection method that utilizes a CW radar sensor and a BNN. Unlike other types of radar, CW radar does not compress pulses or modulate frequencies when generating transmission waves, making it cheaper. BNN has the same structure as CNN, but unlike CNN, where features and parameters are multi-bit floating-point, it uses \(+1\) and \(-1\). Since CNN uses multi-bit floating-point, it requires considerable memory to store features and parameters. On the other hand, BNN only uses \(+1\) and \(-1\), so it can express data as 1 bit, simplifying the computation process and reducing memory usage. In addition, when implemented as a very-large-scale integrated circuit (VLSI), matrix multiplication, which corresponds to the operations of the convolutional and fully-connected layers that compose the CNN-based architecture, can be parallelized to reduce the inference time. Therefore, both methods are suitable for fall detection systems in home environments that require lightweight solutions. The remainder of this paper is organized as follows: Section 2 describes the experimental environment for measuring data and dataset. Section 3 explains the structure of the proposed method. Section 4 evaluates the performance of the proposed method and verifies its utility through a comparison with previous studies. Finally, Section 5 presents the conclusions of this study.
# A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm
## 3 Dynamic Generalized Opposition-Based Learning Grey Wolf Optimizer (DOGWO)
### Dynamic Generalized Opposition-Based Learning Strategy (DGOBL)  
#### 3.3.1 The Concept of DGOBL  
The focus of dynamic generalized opposition-based learning strategy (DGOBL) is to transform the fixed search space of individuals to a dynamic search space which can provide more chances to find solutions closer to the optimum. The DGOBL can be explained as follows:  
Let x be an individual in current search space S, where x \(\in\) [lb, ub]. The opposite point x* in the transformed space S* can then be defined by:  
\[\text{x*}=\text{R}(\text{lb}_{\text{j}}+\text{ub}_{\text{j}})-\text{x} \tag{12}\]  
where R is the transforming factor which is a random number in the range [0, 1].  
According to the definition, x \(\in\) [lb, ub], then x* \(\in\) [R(lb + ub) \(-\) lb, R(lb + ub) \(-\) ub]. The center of search space will transform from a fixed position \(\frac{\text{lb}+\text{ub}}{2}\) to a random position in range [\(-\frac{\text{lb}+\text{ub}}{2}\), \(\frac{\text{lb}+\text{ub}}{2}\)].  
If the current population number is N and dimension of individual is D, for an individual Xi = (Xi1, Xi2,..., XiD), the generalized opposition-based individual can be described as Xi* = (Xi1*, Xi2*,..., XiD*) which can be calculated by:  
\[\text{X}_{\text{ij}}{}^{*}=\text{R}[\text{lb}_{\text{j}}(\text{t})+\text{ub} _{\text{j}}(\text{t})]-\text{X}_{\text{ij}},\text{i}=1,2,\,\ldots,\,\text{N };\text{j}=1,2,\,\ldots,\,\text{D} \tag{13}\]  
where R is a random number in range [0, 1], t indicates the iteration, and lb(t), ubj(t) are dynamic boundaries which can be obtained by the following equation:  
\[\text{lb}_{\text{j}}(\text{t})=\text{min}(\text{X}_{\text{ij}});\,\text{ub} _{\text{j}}(\text{t})=\text{max}(\text{X}_{\text{ij}}) \tag{14}\]  
It may be possible that the transformed individual Xij* jumps out of the boundary [Xmin, Xmax]. In this case, the transformed individual will be reset to be a random value in the interval as follows:  
\[\text{X}_{\text{ij}}{}^{*}=\text{rand}(\text{lb}_{\text{j}}(\text{t}),\,\text{ ub}_{\text{j}}(\text{t})),\,\text{if}\,\text{X}_{\text{ij}}{}^{*}<\text{X}_{\text{min}} \,\text{or}\,\text{X}_{\text{ij}}{}^{*}>\text{X}_{\text{max}} \tag{15}\]