esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 1 Introduction
ABC optimization can be used to optimize routing parameters or adapt routing decisions dynamically based on network conditions through optimal routes by considering factors like energy efficiency, load balancing, and path reliability [11]. The clustering process operates based on specific criteria, each one defined by its application domain and functionality. The cluster network comprises cluster members (CMs) and cluster heads (CHs). Data are then transmitted through intra-cluster and inter-cluster processes [12]. The selection of cluster heads significantly influences network optimization. CHs facilitate high-speed data transfer, benefiting communication by enhancing the transmission capabilities and cellular interfaces of nodes under them. Thus, selecting CHs becomes a challenging task [9]. Addressing optimal clustering falls into the realm of NP-hard problems, often tackled with metaheuristic algorithms, as traditional clustering methods struggle to adapt across diverse communication scenarios. Clustering in VANETs encounters challenges when handling dynamic network parameters, which can limit support for a wide range of vehicle routing applications. In our research, we believe we have achieved a significant milestone by introducing the dynamic aware TR-PED technique, effectively addressing the limitations of conventional swarm-based routing protocols. The key contributions of this paper are as follows:  
* This study introduced DA-TRPED, a heuristic-driven clustering method that improves network connectivity and data delivery in heterogeneous environments (V2V and V2I) by incorporating dynamic transmission ranges into ACO. It also addresses the local search space problem through constraint modeling, promoting efficient collaboration and adaptability to changing topologies while reducing routing exploration time and congestion. * We implemented the proposed DA-TRPED model to Network Simulator (NS-2) to facilitate the observation, tracking, and enhancement of the transmission process in comparison to traditional VANET models. The remaining sections are organized as follows: in Section 2, we provide a comprehensive assessment of existing methods, highlighting their strengths and weaknesses. Section 3 outlines the research methodology, our proposed model and simulation criteria, offering insights into practical solutions for addressing the research challenges. Section 4 demonstrates the results of comparative simulated analysis to validate the effectiveness of the proposed communication model. Finally, in Section 5, we present the conclusions drawn from the proposed model and provide recommendations based on our findings.
## 2 Related Works
They also develop a decision-making process to identify the energy-efficient MPTCP setup for a node streaming with a constant bit rate. An energy consumption model for smartphones with MPTCP or standard TCP is derived in [36] based on real-life measurements by considering four multimedia services such as video streaming, voice over IP, web browsing, and file download. In [37], the authors perform energy consumption measurements of a specific mobile device with handover in different modes of operation. The authors of [38] perform a hardware-based comparative analysis for the energy consumption of three MPTCP congestion control algorithms. Thus, the previously mentioned research works addressing MPTCP energy consumption mainly aim at changing the current MPTCP Linux kernel implementations by modifying the scheduler [28; 29; 30; 31; 32; 33; 34] or the congestion control algorithm [13; 14]. On the other hand, the works combining MPTCP and SDN do not address energy consumption. A comparative summary of the above-mentioned related works is presented in Table 1.  
To the best of our knowledge, no research work in the literature experimentally introduce SDN combined with DNN to manage the energy consumption of multiconnectivity wireless nodes running MPTCP, which avoids the need to make any changes in the MPTCP Linux kernel implementation.
# Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
## 4 Prediction of Frost Heave Ratio Using the Artificial Neural Network Model
### Establishment of an Artificial Neural Network
In Equation (24), the predicted value is obtained by variables that contain input signal, weight, biases, transfer function, and the expected value. Therefore, the cost function can be rewritten as follows. \[C(W,b,f(y),y,x,\exp) \tag{25}\]  
Secondly, the backward pass computes the loss function and updates the weight matrix. This process is repeated until the sum squared error over all epochs is minimized. With every training iteration, a new weight \(W^{+}\) can be obtained based on the cost function and current weight \(W\). \[W^{+}=W-\eta\
abla C \tag{26}\]  
where \(\eta\) is learning rate, which is usually a small constant. \(\
abla C\) is the gradient of the cost function with respect to the weight and can be estimated as follows. \[\
abla C=\big{[}\ \partial C/\partial w_{1}\ \ \partial C/\partial w_{2}\ \ \cdots\ \ \partial C/\partial w_{n}\ \big{]}^{T} \tag{27}\]  
One of the most frequently encountered problems is overfitting, which occurs during training procedures. Overfitting occurs when ANN model is overly trained with training data and fails to evaluate the testing data. In this study, we have investigated the effect of Bayesian regularization and Levenberg Marquardt. The Bayesian regularization technique can be applied to guarantee the efficiency of the ANN training process. This study also applied the Bayesian regularization technique. The training process reduces the sum of squared errors, which can be denoted \(F=F_{D}\). However, the Bayesian regularization adds some terms to construct the objective function as follows. \[F=\beta E_{D}+\alpha E_{W} \tag{28}\]  
Figure 7: Artificial neural network model for estimating the frost heave ratio. where \(E_{D}\) is the sum of squared errors, \(E_{w}\) is the sum of the square of the weight matrix of the ANN model. \(\alpha\) and \(\beta\) are the objective function parameters. Both objective function parameters can be obtained via the Gauss-Newton Approximation method. The Levenberg Marquardt technique is used to solve the non-linear least squares problem that is combined the Gaussian-Newton method and the Steepest Decent method. The new weights are calculated using the following equation  
\[W_{t+1}=W_{t}-\left(\mathbf{J}^{T}\mathbf{J}+\mu\mathbf{I}\right)^{-1} \mathbf{J}^{T}\mathbf{E}_{i} \tag{29}\]  
where \(\mathbf{I}\) is identity unit matrix, \(\mu\) is a learning parameter. \(\mathbf{J}\) is the Jacobian matrix and \(\mathbf{E}\) is cumulative error vector which is determined as following [51]. For the learning rate of \(\mu=0\), the Gauss-Newton method is adapted while the Steepest Decent is applied within larger learning rate. The learning rate \(\mu\) is automatically adjusted at each iteration. The disadvantage of Levenberg Marquardt requires the high computational cost to compute the large Jacobians and inverting matrixes.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 3 Proposed Method
### Procedure of ECL-INS-LMOA  
The procedure of ECL-INS-LMOA is shown in Figure 4. Firstly, some evolutionary parameters are set to prepare for the running of ECL-INS-LMOA. The evolutionary parameters of ECL-INS-LMOA include the population size \(N\), the maximum evaluations \(Evaluation_{\max}\), the evolutionary strategy parameters, and the evolution process adjustment coefficient \(K\). Then, the initial population with the size of \(N\) is generated through randomization. Next, the evolution process is divided into two stages through the parameter \(K\): the early and later stages. In the early stages of evolution, ECLUS is run first, followed by NSGA-II-SDE, and only NSGA-II-SDE is performed in the later stage of evolution. Finally, the final population is returned, that is, the satellite imaging mission planning scheme is returned.  
Figure 3: The selection of the winner, (**a**) Shift-based density estimation, (**b**) Crowding distance.  
The proposed ECL-INS-LMOA has the following characteristics: Firstly, an individual update strategy ECLUS with mixed decision variables and flight times is proposed to improve the convergence speed. Secondly, ECLUS and NSGA-II-SDE are run alternately to achieve complementary advantages. That is, ECLUS is used to overcome the slow convergence speed of NSGA-II-SDE in large-scale multi-objective optimization problems, and NSGA-II-SDE is used to avoid the problem of ECLUS easily falling into local optima. Thirdly, to further improve the convergence efficiency of the algorithm, SDE is used for the selection of dominant individuals in both ECLUS and NSGA-II simultaneously. Fourthly, to cater to the different evolutionary needs of the different stages of progress, ECL-INS-LMOA adopts the idea of running in two stages. ECL-INS-LMOA focuses on fast convergence while also considering global optimization in the early stage, but focuses on global optimization while also considering algorithm convergence in the later stage. ECL-INS-LMOA continuously searches for optimal solutions by combining alternating optimization with only single optimization and balancing fast convergence with global optimization in different stages until a reliable solution that meets the conditions is obtained.
## 2 Materials and Methods
### Flux Measurements  
Ecosystem CO\({}_{2}\) fluxes and additional environmental variables were obtained using eddy covariance and meteorological observations taken in the 2015-2020 period. The instruments used for flux measurements were mounted on the top of a 42 m tower. Eddy covariance system included a 3D sonic anemometer WindMaster Pro (Gill Instruments, Lymington, UK) and enclosed infrared CO\({}_{2}\)/H\({}_{2}\)O gas analyzer LI-7200 A (LI-COR Inc., Lincoln, NE, USA). Global radiation was measured by a 4-component radiometer CNR4 (Kipp & Zonen B.V., Delft, The Netherlands) at the height of 41 m. Additionally, the humidity and temperature probe HMP155 (Vaisala Inc., Vantaa, Finland) was mounted at the same height to obtain air temperature and humidity data. Precipitation measurements were carried out using two TR-525M (Texas Electronics Inc., Dallas, TX, USA) rain gauges installed at heights of 2 m and 30 m, respectively. The soil temperature and water content (SWC) were measured using 3 Stevens Hydroprobe II (Stevens Water monitoring Systems Inc., Portland, OR, USA) reflectometers at a depth of 5 cm. Flux data were stored on a flash drive using an LI-7550 (LI-COR Inc., USA) data logger every 0.1 s, while the meteorological data were collected by the LI-COR Biomet system 103 (LI-COR Inc., USA) every 1 min, respectively. The time in Moscow (UTC+3) was used for the data storage.
## 3 Case Study  
### LHTS System Description  
The LHTS system considered in this study is a vertical shell-and-tube type, with a height of 1.5 m. Each tube has an inner diameter of 19.05 mm (6/8\({}^{\prime\prime}\)) and is equipped with 16 longitudinal fins whose extension is 30 mm (thickness: 1 mm). Instead, the interaxial distance between each tube is assumed to be 91 mm. The PCM is characterized by an average phase change temperature of 70 \({}^{\circ}\)C, as reported in Table 1. The HTF inlet temperature is assumed to be 75 \({}^{\circ}\)C during the charging phase and 48 \({}^{\circ}\)C during the discharge. These values were selected analyzing the working temperatures of the heating system of the building described in Section 3.2. The HTF mass flow rate is 0.168 kg/s per tube. This value was obtained fixing an average HTF velocity of 0.6 m/s. Finally, the size of the LHTS system (in terms of stored energy) is not fixed a priori. As a matter of fact, the LHTS can be conceived as a modular system composed by the aggregation of several tubes (\(n_{tubes}\)) and their associated PCM depending on the considered application. Each unit (tube + PCM) is able to store 2637.2 kJ, considering both the sensible and latent heat content of the PCM (at the selected temperatures).
# A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype
## 5 Experimentation and Results
### Analysis of Results
#### 5.3.6 Problem P6  
The MMF14_a problem has three decision variables and three objective functions, making it a many-objective problem. As explained above, in this problem the number of evaluations was modified to 20,000 and the parameters of the three algorithms were tuned in order to improve the quality of the solutions. Note that this problem has two global surfaces of PSs that correspond to a single concave global PF (Figures 12 and 13). Additionally, the generated PFs converge to the real PF, also covering the two surfaces that make up the PSs. The most populated front was obtained by MOHS3, followed by MOHS2 and MOHSg. The best error ratio corresponds to MOHS3 with 34.95%, while for MOHS2 and MOHSg the error ratios were 47% and 50%, respectively, as indicated in Table 8. The front closest to the real PF was also obtained from the MOHS3 algorithm, while the sets closest to the real PS were obtained by MOHSg. In the case of the uniform distribution of the PF, the best mean value corresponds to \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **MOHS2** & **MOHS3** & **MOHSg** \\ \hline \multirow{4}{*}{**Error ratio**} & Average & \(2.725\times 10^{-2}\) & \(2.175\times 10^{-2}\) & \(\mathbf{1.375\times 10^{-2}}\) \\  & Best & \(5.000\times 10^{-3}\) & \(5.000\times 10^{-3}\) & \(\mathbf{0.000}\) \\  & Worst & \(4.500\times 10^{-2}\) & \(3.500\times 10^{-2}\) & \(\mathbf{3.000\times 10^{-2}}\) \\  & Std. Dev. & \(1.118\times 10^{-2}\) & \(\mathbf{7.993\times 10^{-3}}\) & \(8.867\times 10^{-3}\) \\ \hline \multirow{4}{*}{_GDf_} & Average & \(3.280\times 10^{-3}\) & \(\mathbf{2.643\times 10^{-3}}\) & \(3.203\times 10^{-3}\) \\  & Best & \(3.036\times 10^{-3}\) & \(\mathbf{2.502\times 10^{-3}}\) & \(2.683\times 10^{-3}\) \\  & Worst & \(3.586\times 10^{-3}\) & \(\mathbf{2.903\times 10^{-3}}\) & \(4.612\times 10^{-3}\) \\  & Std. Dev. & \(1.570\times 10^{-4}\) & \(\mathbf{1.010\times 10^{-4}}\) & \(5.389\times 10^{-4}\) \\ \hline \multirow{4}{*}{_GDx_} & Average & \(7.234\times 10^{-4}\) & \(5.239\times 10^{-4}\) & \(6.242\times 10^{-4}\) \\  & Best & \(6.314\times 10^{-4}\) & \(\mathbf{4.633\times 10^{-4}}\) & \(5.058\times 10^{-4}\) \\  & Worst & \(9.297\times 10^{-4}\) & \(6.222\times 10^{-4}\) & \(7.374\times 10^{-4}\) \\  & Std. Dev. & \(7.805\times 10^{-5}\) & \(\mathbf{3.946\times 10^{-5}}\) & \(6.383\times 10^{-5}\) \\ \hline \multirow{4}{*}{**Spacing**} & Average & \(5.168\times 10^{-3}\) & \(\mathbf{4.420\times 10^{-3}}\) & \(4.479\times 10^{-3}\) \\  & Best & \(4.811\times 10^{-3}\) & \(3.359\times 10^{-3}\) & \(3.555\times 10^{-3}\) \\ \cline{1-1}  & Worst & \(\mathbf{5.547\times 10^{-3}}\) & \(6.183\times 10^{-3}\) & \(6.416\times 10^{-3}\) \\ \cline{1-1}  & Std. Dev. & \(\mathbf{2.198\times 10^{-4}}\) & \(8.600\times 10^{-4}\) & \(7.011\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statisticalanalysis for problem P4.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 3 Data and Methods
### Data  
#### 3.2.1 Climatological Data  
The AEMET 5 km dataset [60; 61], obtained from the Spanish Meteorological Agency, was chosen for this study because it offers extensive temporal coverage with suitable temporal resolution (daily) and a spatial resolution of 5 km of the climatic variables (precipitation and temperature). This long-term series can be useful to perform analyses of drought propagation and their statistics [62; 63; 64].  
Due to the coarse spatial resolution of this dataset, a distributed analysis of the climatic variables was not feasible; therefore, we conducted a lumped one. Precipitation analysis was performed for the entire domain of the mountain ranges, while temperature analysis was conducted within the common elevation range for the Iberian mountains (1600-2300 m.a.s.l.) to facilitate proper comparison, considering the strong elevation influence on temperature at both regional [65] and local scales [66; 67]. Precipitation and temperature series were aggregated using a weighted average, conducted by the Thiessen polygon method [68]. In this method, each point within the considered elevation range was assigned a weight based on its relative area in relation to the total area.
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 4 Experiment  
### Infrared Aerial Dataset  
At present, most public datasets, such as ImageNet [12], VOC, and COCO, are not suitable for the application scene requirements of infrared aerial pedestrian detection. Therefore, we built an infrared aerial dataset, named the AIR-pedestrian. The class of the target is person.  
An infrared camera is mounted on the four-rotor drone to capture pedestrian images in different scenes. The acquisition device is FLIR's Vue Pro 336, which takes pictures of pedestrians at 5, 6, and 7 m at different flight altitudes to obtain infrared thermal images of different scales of pedestrians. The frame rate of captured video is 25 FPS, saved in AVI format. The resolution of a single frame is \(336\times 256\). Due to the high frame rate, images are obtained every five frames to contribute to the dataset.  
Furthermore, in order to expand data and increase the diversity of data samples, some samples were introduced from the famous OTCBVS Benchmark Dataset via [[http://viciplokstate.org/pbys/bench/](http://viciplokstate.org/pbys/bench/)]([http://viciplokstate.org/pbys/bench/](http://viciplokstate.org/pbys/bench/)) (accessed: 24 July 2021), without any other data augmentation. In addition to directly augmenting the dataset, transfer learning and pretraining weights can also be used, which can be understood as a form of indirectly augmenting the data [5; 43].  
Figure 3: Schematic diagram of channel pruning.  
The AIR-pedestrian dataset includes different ages and gender of pedestrians, different numbers of pedestrians, and different occlusion degrees of pedestrians in different scenes, as shown in Figure 4.  
The infrared dataset contains 2500 infrared aerial images and is divided into the training set \(\mathcal{D}_{train}\) and validation set \(\mathcal{D}_{val}\). \(\mathcal{D}_{train}\) is used for the training detection model, \(\mathcal{D}_{val}\) is used for evaluating the model during training, and they do not intersect with each other. In the AIR-pedestrian dataset, 2000 images are used as \(\mathcal{D}_{train}\), and 500 images are used as \(\mathcal{D}_{val}\). Table 1 shows the scene distribution of the infrared aerial dataset.
## 5 Results and Analysis
### Comparative Analysis  
Various works have been published in the area of STLF. Most of the works have focused on individual methods and the improvement of Individual methods. This paper, instead of focusing on individual methods, focused on numerous methods and on understanding the merit and demerit of each method. In [34], the focus is completely on ANN. ANN is efficient when the data set is large; it is also affected by data sensitivity but it is important to understand that not all load data sets might give consistent patterns, and also there might be data scarcity. So, ANN might not be the best method for specific data sets. The algorithm used in [35; 36] is Recurrent Neural Network (RNN). The issue with RNN is the selection of an activation  
Figure 15: Tuned hyperparameters Graphs of Tree using Grid Search.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MSE** & **Leaf Size** \\ \hline Bayesian Optimization & 0.0070 & 3 \\ Grid Search & 0.0067 & 6 \\ Random Search & 0.0069 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter tuning results (Decision Tree).  
function and the system can be quite slow in producing results. Load data with long sequences might not be processed easily. Since load data are highly dynamic in nature and vary according to geographical location, RNN might not produce the best results. Since RNN follows a deep learning architecture, its operational cost is also quite high. So, as compared to ANN used in [34], and compared to deep learning methods used in [35; 36], the methodology used in this paper tries to behave as per the nature of the data set and produce an efficient algorithm. Some of the algorithms produced in this area are highly individualistic in nature; Reference [12; 14] focused only on a single method whereas this paper focuses on crucial regression methods. The methodology used in [15] is close to that of this paper but lacks a comparative analysis of important hyperparameter tuning methods, whereas this paper gives a broad comparative analysis of hyperparameter tuning methods.
## 2 Model Principles and Methods
### GRU Principle  
The GRU (gate recurrent unit) is a type of recurrent neural network introduced to address the issue of exploding gradients in RNNs, similar to LSTM (long short-term memory). The GRU model introduces two gates on top of the basic RNN architecture: namely, the update gate and the reset gate. The structure is depicted in Figure 3.  
In the diagram, \(z_{t}\) and \(r_{t}\) represent the update gate and reset gate, respectively. Their mathematical expressions are as follows:  
\[\begin{array}{l}r_{t}=\sigma(\omega_{r}[h_{t-1},x_{t}])\\ z_{t}=\sigma(\omega_{r}[h_{t-1},x_{t}])\\ \widetilde{h_{t}}=\text{tanh}(\omega_{\widetilde{h}}[r_{t}h_{t-1},x_{t}])\\ h_{t}=(1-z_{t})h_{t-1}+z_{t}\widetilde{h_{t}}\\ y_{t}=\sigma(\omega_{0}\cdot h_{t})\end{array} \tag{13}\]  
In the equations, \(\omega_{r}\) represents the weight for the reset gate, \(\omega_{z}\) represents the weight for the update gate, tanh denotes the hyperbolic tangent function, and \(\sigma\) represents the sigmoid function. The parameters \(\omega_{z}\), \(\omega_{r}\), \(\omega_{\widetilde{h}}\) and \(\omega_{0}\) are trainable parameters in the model [29; 30].  
Figure 3: GRU unit structure diagram.
## 4 Hardware Implementation of the RNCS
### D&F Relay Node Implementation
PDSCH is used to transmit the Downlink Shared Channel, which acts as the transport channel used for transmitting downlink data (a transport block). The resultant signal at the output of scheme in Figure 10, is modulated through the OFDM Modulation RF transmit chains in the MIMO NI-USRP 2944R platform.