esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition
## 3 Overview of IndRNN  
Independently recurrent neural network (IndRNN) was proposed in [10] as a basic RNN component. It follows  
\[\mathbf{h}_{t}=\sigma(\mathbf{W}\mathbf{x}_{t}+\mathbf{u}\odot\mathbf{h}_{t-1 }+\mathbf{b}) \tag{1}\]  
where \(\mathbf{x}_{t}\in\mathbb{R}^{M}\) and \(\mathbf{h}_{t}\in\mathbb{R}^{N}\) are the input and hidden state at time step \(t\), respectively. \(\mathbf{W}\in\mathbb{R}^{N\times M}\), \(\mathbf{u}\in\mathbb{R}^{N}\) and \(\mathbf{b}\in\mathbb{R}^{N}\) are the weights for the current input, the recurrent input, and the bias of the neurons. \(\odot\) represents the Hadamard product (element-wise multiplication). \(\sigma\) is an element-wise activation function of the neurons, which is the ReLU (rectified linear unit) in this paper, and \(N\) is the number of neurons in this IndRNN layer. Each neuron in one layer is independent from others as shown in Figure 1 and the correlation among neurons is explored by stacking two or more layers of IndRNNs. IndRNN solves the gradient vanishing and exploding problems, and can be used to process long sequences and construct deeper networks. Better results than the existing RNN networks on various tasks, including skeleton-based action recognition, were reported in [10].  
Since neurons in one IndRNN layer are independent from each other, the gradient backpropagation through time can be calculated for each neuron individually. For the \(n\)-th neuron \(h_{n,t}=\sigma(\mathbf{w}_{n}\mathbf{x}_{t}+u_{n}h_{n,t-1})\), where the bias is ignored, suppose the objective trying to minimize at time step \(T\) is \(J_{n,T}\). Then the gradient backpropagated to the time step \(t\) is  
\[\frac{\partial J_{n,T}}{\partial h_{n,t}}=\frac{\partial J_{n,T}}{\partial h_{ n,T}}u_{n}^{T-t}\prod_{k=t}^{T-1}\sigma^{\prime}{}_{n,k+1} \tag{2}\]  
where \(\sigma^{\prime}{}_{n,k+1}\) is the derivative of the element-wise activation function. For more details on the above derivation, please refer to [10; 11].
## 3 Results and Discussions
### Ground-Based Validation
#### 3.2.1 Gobabeb Station  
For the desert station Gobabeb mono-window LSTs met the LSA SAF target accuracy requirement (RMSD \(\geq\) 2 K and bias \(<\) 1 K; Table 3 and Figures 3a and 4).  
Due to the exceptionally wet January/February and October/November 2010, the presented analysis included a large number of observations for a wide range of atmospheric conditions, including also rather moist atmospheres (Figure 3a). Despite the overall good model performances, the two single-channel models had a distinct positive bias for dry atmospheres compared to the LSA SAF dataset (0.8-1.2 K _versus_ 0.1-0.2 K, respectively; Figure 3a). This single-channel bias is close to zero during nighttime, but is greater than 1 K during daytime (Figure 4). Other studies (e.g., [8]) also report that the largest single-channel biases occur at LST values greater than 310 K, which is in line with our observations. The observed daytime LST bias likely demonstrates the implicit sensitivity of the mono-channel models  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**TCWV \(\leq\) 45 mm**} & \multicolumn{2}{c}{**GSW**} & \multicolumn{2}{c}{**PMW**} & \multicolumn{2}{c}{**SMW**} \\ \cline{2-7}  & **RMSD (K)** & **BIAS (K)** & **RMSD (K)** & **BIAS (K)** & **RMSD (K)** & **BIAS (K)** \\ \cline{2-7}  & 1.5 & 0.4 & 1.8 & 0.8 & 2.0 & 0.9 \\ Evora & 2.0 & 1.2 & 1.9 & 0.7 & 2.5 & 1.4 \\ Dahra & 2.3 & \(-\)0.8 & 2.6 & \(-\)1.2 & 2.4 & 0.4 \\ RMZ & 1.9 & \(-\)0.5 & 1.9 & \(-\)0.8 & 1.7 & \(-\)0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics for the comparison of LST between _in situ_ measurements and the operational LSA SAF dataset for dry to medium-moist atmospheres (TCWV \(\leq\) 45 mm).  
Figure 4: Comparison between _in situ_ LST (KIT Gobabeb station, 2010) and Meteosat LST from the PMW for TCWVs \(<\) 35 mm: (**a**) Nighttime; (**b**) daytime.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**TCWV \(>\) 45 mm**} & \multicolumn{2}{c}{**GSW**} & \multicolumn{2}{c}{**PMW**} & \multicolumn{2}{c}{**SMW**} \\ \cline{2-7}  & **RMSD (K)** & **BIAS (K)** & **RMSD (K)** & **BIAS (K)** & **RMSD (K)** & **BIAS (K)** \\ \cline{2-7}  & 3.4 & \(-\)2.2 & 6.3 & \(-\)4.3 & 5.3 & \(-\)3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics for the comparison of LST between _in situ_ measurements and the operational LSA SAF dataset for very moist atmospheres (TCWV \(>\) 45 mm) experienced at the KIT ‘Dahra’ station.  
on NWP errors. The GSW model, which is less dependent on accurate NWP input, does not show a significant daytime bias in Gobabeb.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments
### Performance Analysis
#### 4.3.3 Validation of Scalability  
To validate the upwards and downwards scale-solving ability of the GDNN-PPO trained at a specific instance scale, we use GDNN-PPO-X, where X is the number of instance tasks used for training, to solve 50 test instances with different task scales and calculate the ASP. Results are shown in Table 10, and the gap between the ASP of GDNN-PPO-X and the optimal ASP is shown in Figure 10.  
Because the conflict degree of VTWs increases with the task scale, the decision neural networks trained by instances with specific scales can learn different strategies. All GDNN-PPOs are shown to calculate a solution with a small difference from optimal solutions, highlighting the scalability of this method. Among these four models, GDNN-PPO-40 has  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task Scale** & **40** & **60** & **80** & **100** \\ \hline GDNN-PPO-40 & **190.62** & **278.62** & **367.82** & 448.24 \\ GDNN-PPO-60 & 188.90 & 277.58 & 366.74 & 449.14 \\ GDNN-PPO-60 & 188.36 & 277.42 & 365.74 & **449.70** \\ GDNN-PPO-100 & 186.96 & 277.42 & 366.00 & 447.02 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental results of scalability in different instances.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Instance Scale** & **40** & **60** & **80** & **100** \\ \hline \multirow{4}{*}{GAT-PPO} & ASP & 197.0 & 272.0 & 380.0 & 454.0 \\  & AST (S) & 0.270 & 0.568 & 1.162 & 1.676 \\  & PSP (\%) & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \multirow{4}{*}{GRILS} & ASP & **201.5** & **281.4** & **397.8** & **487.4** \\  & AST (S) & 73.452 & 224.986 & 370.964 & 600.308 \\  & PSP (\%) & 2.23 & 3.33 & 4.47 & 6.86 \\ \hline \multirow{4}{*}{SDE} & ASP & 159.0 & 215.7 & 294.0 & 348.8 \\  & AST (S) & 58.760 & 162.508 & 342.463 & 618.514 \\  & PSP (\%) & 23.90 & 26.10 & 29.27 & 30.17 \\ \hline \multirow{4}{*}{SGA} & ASP & 154.5 & 194.6 & 294.1 & 336.1 \\  & AST (S) & 24.170 & 32.661 & 45.607 & 57.583 \\ \cline{1-1}  & PSP (\%) & 27.54 & 39.80 & 29.19 & 35.09 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experimental results of meta-heuristics in different instances.  
Figure 9: AST of GDNN-PPO and high-quality competitors.  
better scalability and can achieve optimal performance in solving the AEOSSP with 40, 60 and 80 task scales.
# Virtual Power Plant Optimization in Smart Grids: A Narrative Review
###### Abstract  
Virtual power plants (VPPs) are promising solutions to address the decarbonization and energy efficiency goals in the smart energy grid. They assume the coordination of local energy resources such as energy generation, storage, and consumption. They are used to tackle problems brought by the stochastic nature of renewable energy, lack of energy storage devices, or insufficient local energy flexibility on the demand side. VPP modeling, management, and optimization are open to research problems that should consider, on one side, the local constraints in the operation of the energy resources and power flows and the energy grid's sustainability objectives on the other side. There are multiple goals to create a VPP, such as to deliver energy services on a market or to the grid operator, to operate a microgrid in autonomy decoupled from the main grid, or to sustain local energy communities. In this paper, we present the results of a narrative review carried out on the domain of VPP optimization for the local energy grid integration. We have defined a search strategy that considers highly rated international databases (i.e., Elsevier, IEEE, and MDPI) in a six-year timeframe and applied objective inclusion/exclusion criteria for selecting articles and publications for the review; 95 articles have been analyzed and classified according to their objectives and solutions proposed for optimizing VPP integration in smart grids. The results of the study show that VPP concepts and applications are well addressed in the research literature, however, there is still work to be done on: engaging prosumers and citizens in such a virtual organization, developing heuristics to consider a wider range of local and global constraints and non-energy vectors, and to decentralize and make transparent the services delivery and financial settlement towards community members. This study can help researchers to understand the current directions for VPP integration in smart grids. As a next step we plan to further analyze the open research directions related to this problem and target the development of innovative solutions to allow the integration of multi-energy assets and management of cross energy sector services in energy communities.  
virtual power plants; smart grid optimization; energy efficiency; narrative review +
Footnote †: journal: _Future Internet_
# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 4 Emerging Fraud Detection Techniques
### Proposed Conceptual Model
The performance results of the card fraud model trials are shown in Table 6.  
When the models presented above are examined, it is seen that the training model with the highest f1-score value is the DDQN model. This model is defined as the champion model and the deployment process is started based on this model. Through the model deployment methods, fraud/legal patterns are recorded on the model template with learning agents. Python libraries such as pickle and mflow can be used during the model saving process. After the saving process, the model template can be loaded and made ready for scoring. With the model template, real-time transactions can be evaluated, and instant and effective decisions can be made. Once running the deployment system, Q values, which are the function output values, are obtained from the DDQN model developed. When the DDQN model detects transactions containing fraudulent information, the Q value increases through deviating from the average. At the same time, risk groups have begun to be created using Q values and actual fraud flag. In this section, the Q value has been subjected to binning and divided into risk groups. In particular, for card fraud prevention process, the creation of example risk groups is shown in Table 7). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Partition** & **Recall** & **Precision** & **GMean** & **F1-Score** \\ \hline DDQN Model & Train & 1.000000 & 0.987671 & 0.993816 & 0.993797 \\ DDQN Model & Test & 1.000000 & 0.987617 & 0.993789 & 0.993770 \\ SVM Model & Train & 0.993123 & 0.991848 & 0.992485 & 0.992485 \\ SVM Model & Test & 0.993450 & 0.991721 & 0.992585 & 0.992585 \\ RF Model & Train & 0.979660 & 0.998757 & 0.989162 & 0.989116 \\ RF Model & Test & 0.978902 & 0.998480 & 0.988643 & 0.988594 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performances of the models developed using the IL and FS methods. Figure 2: Proposed conceptual RL model in detecting credit card fraud. While the actual fraud rate of risk group 1 is lower compared to the other groups, the actual fraud rate of risk group 5 is higher compared to the other groups. In SL approaches, labelling card transactions as fraudulent or legitimate can lead to Type 1, Type 2, and Type 3 errors. Instead, determining the risk group according to the Q value and applying security protocols specific to the risk group can benefit for card fraud processes in banking. Different security protocols at the transaction can benefit bank processes in fraud management. For example, for those with a risk group assignment of 4, 5, or 6, mobile application approvals, personal inquiries, and additional passwords may be requested. In this way, the management of risks encountered as a result of incorrect label assignments of models (Type 1 (_FP_--not assigning fraud label to an action that is actually fraud) or Type 2 (_FN_--assigning fraud label to an action that is not actually fraud)) will be facilitated.
## 3 Radar Micro-Doppler Analysis of Vibrating Structures
### Radar Micro-Doppler from Gear Shifts
Manual transmissions, where gear shifts are more deliberate and involve more direct mechanical movement (clutch, gearbox engagement), might produce more noticeable vibrations during gear shifts, but even here, the vibrations may still be subtle. While radar micro-Doppler could theoretically indicate gear shifts in an idling vehicle by detecting changes in vibrations and mechanical movements associated with gear engagement, it would likely require advanced radar technology and high-resolution sensors to accurately identify these shifts. The vibrations produced by gear changes are typically subtle, especially in modern vehicles with automatic transmissions, making it difficult for basic radar systems to consistently detect them. For practical applications, radar micro-Doppler might be able to detect general shifts in the drivetrain's behavior (e.g., from Neutral to Drive), but it would not necessarily offer precise or reliable information about every gear shift in a typical idling vehicle. However, with the right technology, it could be an additional tool for monitoring vehicle behavior and diagnosing issues related to the transmission or engine performance.
## 3 Results
### Regression Analysis of UD and USD on Sustainability  
Table 8 shows the results of the regression analysis of UD or USD on sustainability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**USD Guidelines**} & \multicolumn{4}{c}{**USD**} & \multirow{2}{*}{**Mean of UD**} \\ \cline{2-2} \cline{4-6}  & **S2** & **S3** & **S4** & **S5** & **S6** \\ \hline S1 Physical support & 0.577 * & 0.635 * & 0.615 * & 0.627 * & 0.552 * & 0.843 * \\ S2 Flexibility & & 0.594 * & 0.597 * & 0.637 * & 0.561 * & 0.746 * \\ S3 Accessibility & & & 0.690 * & 0.654 * & 0.591 * & 0.876 * \\ S4 Safety and health & & & & 0.639 * & 0.697 * & 0.812 * \\ S5 Inclusion & & & & & 0.598 * & 0.789 * \\ S6 Sustainability & & & & & 0.693 * \\ \hline \hline \end{tabular}
* Significant at significance level 0.01.  
\end{table}
Table 6: Correlation coefficients between USD and UD guidelines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**USD Guidelines**} & \multicolumn{2}{c}{**UD (\(y\)) = f(\(x_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\))**} & \multicolumn{2}{c}{**UD (\(y\)) = f(\(x_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\), \(x_{5}\))**} \\ \cline{2-2} \cline{4-6}  & **B** & \(p\) & **B** \\ \hline (Constant) & 0.161 & 0.087 & 0.155 & 0.076 \\ Physical support (\(x_{1}\)) & 0.240 & 0.001 * & 0.218 & 0.001 * \\ Flexibility (\(x_{2}\)) & 0.184 & 0.001 * & 0.155 & 0.001 * \\ Accessibility (\(x_{3}\)) & 0.362 & 0.001 * & 0.345 & 0.001 * \\ Safety and health (\(x_{4}\)) & 0.184 & 0.001 * & 0.152 & 0.001 * \\ Inclusion (\(x_{5}\)) & & & 0.101 & 0.001 * \\ Regression model & \(y=0.161+0.240x_{1}+0.184x_{2}+\) & \(y=0.155+0.218x_{1}+0.155x_{2}+\) \\  & \(\begin{array}{c}0.362x_{3}+0.184x_{4}\\ \text{Statistics for model}\\ R^{2}\end{array}\) & \(\begin{array}{c}0.345x_{3}+0.152x_{4}+0.101x_{5}\\ \text{F}=815.3\text{, }p<0.001\text{ *}\\ 0.952\end{array}\) & \(\begin{array}{c}0.345x_{3}+0.152x_{4}+0.101x_{5}\\ \text{F}=762.1\text{, }p<0.001\text{ *}\\ 0.959\end{array}\) \\ \hline \hline \end{tabular}  
* Significant at significance level 0.01.  
\end{table}
Table 7: Regression analysis of USD guidelines on the mean of UD.  
The regression equation of UD on sustainability shows a low explanatory power (\(R^{2}\) = 0.478). On the other hand, the regression equation of USD on sustainability shows a relatively high explanatory power (\(R^{2}\) = 0.675). It presents that USD guidelines increase the design concept of sustainability.
# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 4 Simulation Results
Moreover, the aforementioned nodes as they are already at the boundaries of the simulation area, they only move to the inner sections of the testbed as time passes. Their final position is based on the random walk properties and as a result the most possible outcome is to be found in lower SF zone after the initialization of the simulation. Being a protocol for IoT applications with moving nodes and limited energy resources, in Figure 8 we calculate the expected lifetime of the network by comparing all the protocols presented in this work. MOTILO, due to the elimination of the empty slots in comparison to the other two approaches, manages to offer almost 3 times more days of operation until the energy exhaustion of the nodes. We calculated the mean energy consumption of the nodes and we projected the life time of them based on a 1200 mAh battery as shown in Table 3. This metric is based on a 10 s cycle for all the protocols and as expected, for lighter load conditions MOTILO outperforms the other solutions and keeps that advantage for  
Figure 7: Mean data latency under different simulation scenarios of MOTILO algorithm. Values illustrated with red color corresponds to the average packet loss. loads up to 70%. After that load, nodes in MOTILO consume higher amounts of energy in comparison to the TDMA-PL, primarily due to the second GPS measurement along with the addition of phase one where all nodes need to open their circuitry in order to inform about their intention for transmission. For 100% load, MOTILO and TDMA-2M offer the same network lifetime, while the TDMA-PL offers almost 28% more. However, one should recall that TDMA-PL does not offer lost packet protection while the other two protocols offer increased protections against lost packets due to ED motion.
## 3 SWIPT-Based Multi-Hop Uneven Clustering Routing Protocol Design for RF EH-CRSNs
### Network Model
#### 3.2.4 Data Transmission Stage
If \(E_{res}(j)\) exceeds \(E_{intra}(j)\), then all CMs are required only to transmit information; otherwise, each CM \(k\) must calculate the energy \(E_{supply}(k)\) supplemented to its CH through SWIPT for intra-cluster data processing, as indicated in Equation (13). \[E_{supply}(k)=\left(P_{thresh}+\frac{E_{intra}(j)}{n_{CM}(j)}\times\frac{1}{ \eta}\right)\times d_{CM(k)\to CH}^{2} \tag{13}\]  
where \(P_{thresh}\) is the minimum threshold for SWIPT information transmission, set at 6.8 nW or \(-\)51.66 dBm; and \(n_{CM}(j)\) is the number of CMs belonging to the cluster of CH \(j\). CM \(k\) decides whether to perform intra-cluster SWIPT based on its remaining energy. If \(E_{res}(k)-E_{self-data}(k)\) exceeds \(E_{supply}(k)\), then CM \(k\) transfers energy \(E_{supply}(k)\) to CH \(j\) while transmitting data using the SWIPT mechanism. In this case, the effective energy received by CH \(j\) is \(E_{intra}(j)/n_{CM(j)}\). \(E_{self-data}(k)\) is the energy consumption of CM \(k\) for transmitting its own data, as shown in Equation (14). If \(E_{res}(k)-E_{self-data}(k)\) is less than or equal to \(E_{supply}(k)\), then the CM only transmits data. \[E_{self-data}(k)=\left(E_{elec}+E_{fs}\times d_{CM(k)\to CH}^{2}\right) \times L_{2} \tag{14}\]  
2. Prior to inter-cluster data transmission, following the established routing path, node \(j\) assesses whether the residual energy \(E_{res}(s)\) of the next hop \(s\) exceeds the energy required to forward its data \(E_{relay}(s)\), as presented in Equation (15). If \(E_{res}(s)\) is greater than \(E_{relay}(s)\), node \(j\) transmits data directly to the next hop \(s\), which relays the data packet. Otherwise, an assessment must be made according to Equation (16) to see if \(E_{res}(j)\) surpasses the combined requirements of the intra-cluster data processing energy \(E_{intra}(j)\), the inter-cluster supplemented energy \(E_{S-supply}(j)\) for the next hop to relay data packets, and the dormancy threshold \(E_{dormancy}(j)\). If it does, node \(j\) supplements the residual energy of the next hop while transmitting data using inter-cluster SWIPT. \[E_{relay}(s)=\left(2E_{elec}+E_{fs}\times d_{CH(r(s))\to CH(r(s)+1)}^{2} \right)\times L_{2}, \tag{15}\]  
where \(d_{CH(r(s))\to CH(r(s)+1)}\) represents the average relay distance from the CHs in the ring where relay \(s\) resides to their next hops. \[E_{S-supply}(j)=\left(\frac{\left|E_{res}(s)-\left(2E_{elec}+E_{fs}\times d_{ CH(r(s))\to CH(r(s)+1)}^{2}\right)\times L_{2}\right|}{\eta}+P_{thresh}\right)\times d_{ CH(j)\to s}^{2} \tag{16}\]
# Towards Adaptive Multipath Managing:
## 1 Introduction  
In the past several years, wireless communications systems and networks have experienced a dramatic development [1]. These dramatic advances in wireless communications and networking technologies provide ubiquitous Internet connectivity to a mobile user [2]. Moreover, promoted by the increasing popularity of various wireless networks, mobile devices (i.e., smart phones, laptop computers, and netbooks) are already embedded with multiple Wireless Network Interface Cards (WNICs) and enabled multiple IP features [3, 4]. With the multiple IP features (also known as multihoming), the devices can connect to the Internet via multiple IP addresses to provide enhanced and reliable Internet connectivity; they also can simultaneously use several IP addresses to spread data across several end-to-end IP paths. For example, the Apple iOS based products (i.e., iPhone and iPad) [5] and Samsung Galaxy mobile phones (i.e., S9 and S9 Plus) [6] can use both the Wi-Fi and cellular network links to download large files. Such multihomed devices can speed up the transmissiondata rate and increase their throughput performance by making use of multiple wireless network links simultaneously, enabled by the emerging Multipath TCP (MPTCP) technology [7].  
The MPTCP is an extension to traditional single path TCP that enables a multihomed device to allocate data traffic across multiple independent end-to-end paths concurrently through multiple network interfaces [8]. Figure 1 presents a typical MPTCP based multipath communication scenario that involves two multihomed Mobile Devices (MD3 and MD4), which communicate with each other by using both the Wi-Fi and 4G mobile links simultaneously. Such a multipathing feature would be beneficial to the mobile devices to aggregate bandwidth, increase throughput, and enhance communication robustness [9]. Furthermore, MPTCP inherits the standard socket Application Programming Interfaces (APIs) from TCP, which means an MPTCP based device can initiate and establish an MPTCP connection with other devices without making any modifications to today's Internet applications [10]. With its multipath transmission and backwards compatibility features, the MPTCP is being considered as a preferred transmission technology for data delivery over the current and future Internet [11].