esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Section
### Impact Analysis for the ADG Scheme
#### 5.4.2 Change of Node Movement  
ADG gathers the meta-data and adaptively selects the proxy nodes for the data gathering. So we changed the paths of network nodes at the middle of the simulation (at the15 th epoch), and studied its impact on the amount of gathered data. Figure 12 depicts the average number of total messages and amount of gathered data at each epoch in 10 rounds of simulation. We could see that the number of gathered messages is small (less than 700 messages) at initial epochs, _i.e._, from 1 to 5 epochs. This is because at initial epochs nodes exchange meta-data with other nodes through opportunistic communications. The proxy nodes has not been selected, and the only way \(MC\) gathers data is that it would pick up data from its neighboring nodes when it moves within the field following a random waypoint mode. Yet after some epochs it gathers enough metadata and the proxy nodes are selected. During these epochs, nodes send their messages to proxy nodes, and \(MC\) visit the proxy nodes to gather the data. So both the total messages and the gathered data would increase. \(MC\) would gather about 1306 messages of sensed data at each round on average. When the node change their paths at epoch 15, we could see a sharp decline to about 521 messages on the number of gathered data. This is because \(MC\) would not encounter the proxy nodes when it visits the predicted location, since the proxy nodes have changed their paths and would not appear there. However, the proxy nodes would broadcast its new locations to neighbors; and after some epochs of meta-data gathering, new proxy nodes would be selected, and \(MC\) would acquire the new data gathering points of the proxy nodes. In this way, ADG adaptively updates the data gathering points and \(MC\) would again gather normal amount of sensed data from the proxy nodes. The data coverage returns to about 70.8%, which is almost the same as the case when the movement of nodes do not change.
###### Abstract  
The Internet of Things (IoT) is empowering various sectors and aspects of daily life. Green IoT systems typically involve Low-Power and Lossy Networks (LLNs) with resource-constrained nodes. Lightweight routing protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL), are increasingly being applied for efficient communication in LLNs. However, RPL is susceptible to various attacks, such as the black hole attack, which compromises network security. The existing black hole attack detection methods in Green IoT rely on static thresholds and unreliable metrics to compute trust scores. This results in increasing false positive rates, especially in resource-constrained IoT environments. To overcome these limitations, we propose a delta-threshold-based trust model called the Optimized Reporting Module (ORM) to mitigate black hole attacks in Green IoT systems. The proposed scheme comprises both direct trust and indirect trust and utilizes a forgetting curve. Direct trust is derived from performance metrics, including honesty, dishonesty, energy, and unselfishness. Indirect trust requires the use of similarity. The forgetting curve provides a mechanism to consider the most significant and recent feedback from direct and indirect trust. To assess the efficacy of the proposed scheme, we compare it with the well-known trust-based attack detection scheme. Simulation results demonstrate that the proposed scheme has a higher detection rate and low false positive alarms compared to the existing scheme, confirming the applicability of the proposed scheme in green IoT systems.  
green internet of things; black hole attack; RPL; low-power and lossy networks (LLNs); trust-based +
Footnote †: journal: _Sensors_**2024**, 24, 1775. [[https://doi.org/10.3390/s24061775](https://doi.org/10.3390/s24061775)]([https://doi.org/10.3390/s24061775](https://doi.org/10.3390/s24061775))
###### Abstract  
The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method for predicting the RUL of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP) was proposed here. First, the random forest algorithm was used to evaluate the importance of historical monitoring parameters of the engine, selecting the key features that significantly impact the engine's lifetime operation cycle. Then, the single exponent smoothing (SES) algorithm was introduced for smoothing the extracted features to reduce the interference of original noise. Next, an MLP-based RUL prediction model was established using a neural network. The Bayes' online parameter updating formula was used to solve the objective function and return the optimal parameters of the MLP training model and the minimum value of the evaluation index RMSE. Finally, the probability density function of the predicted RUL value of the aircraft engine was calculated to obtain the RUL prediction results.The effectiveness of the proposed method was verified and analyzed using the C-MAPSS dataset for turbofan engines. Experimental results show that, compared with several other methods, the RMSE of the proposed method in the FD001 test set decreases by 6.1%, demonstrating that the method can effectively improve the accuracy of RUL prediction for aircraft engines.  
a _Applied_  
_sciences_
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 3 Qualitative Benchmark for Analysis
### Criteria for Requirement Mapping for Extensibility  
The criterion for requirement EL1 for extensibility is met, which allows the handover provision at various levels of the network, utilization of MEC, NFV, and SDN platform required for the proficient implementation of requirement 9 mentioned in Table 1. At the time of considering the MM mechanism, the criteria for the requirement mapping for fulfilling the extensibility parameters EL2, EL3, and EL5 congruently are met in order to assist in achieving requirements 1 and 10 of mentioned in Table 1. Finally, when the criterion for parameter EL4 is satisfied, requirement 6 of Table 1 is achieved.
## 4 Integration of Biosensors
### Power Consumption and Energy Efficiency  
Efficient management of power consumption is a critical aspect of integrating biosensors into multi-hop IoT systems. This section explores key considerations and strategies to enhance energy efficiency in biosensor nodes within a multi-hop communications framework.  
Biosensor nodes are configured with low-power design principles to minimize energy consumption during sensing, data processing, and communication tasks. This involves optimizing hardware components, employing energy-efficient microcontrollers, and utilizing low-power sensing technologies [98].  
To conserve power during periods of inactivity, biosensor nodes need to implement sleep modes. By transitioning into low-power states when not actively sensing or communicating, the nodes reduce overall energy consumption, extending the operational lifespan of the device [99].  
Biosensors are configured with adaptive sampling rates based on contextual factors. Adjusting the sensing frequency in response to variations in environmental conditions or user requirements ensures that energy is allocated efficiently, avoiding unnecessary data collection.  
To supplement power sources, biosensor nodes may integrate energy harvesting mechanisms like solar panels or kinetic energy harvesters. Renewable energy sources contribute to prolonged operational lifetimes, especially in remote or off-grid deployment scenarios [100].  
Multi-hop communications introduces the concept of duty cycling, where biosensor nodes selectively activate their communication modules at scheduled intervals. This intermittent communication strategy balances the need for timely data transmission with energy conservation, particularly in scenarios with low data rate requirements. Moreover, energy-efficient routing algorithms are employed in multi-hop communications to minimize the distance and number of hops between biosensor nodes and the gateway. This reduces the energy expended during data relay, contributing to overall energy efficiency in the network [101].  
Furthermore, biosensor nodes employ dynamic power management techniques to adapt to varying operational conditions. This includes adjusting the transmission power of communication modules based on the proximity of neighboring nodes, and optimizing energy usage for reliable data transmission [102]. In addition, biosensor nodes incorporate mechanisms for estimating the remaining battery life or energy reserves. This information is valuable for proactive maintenance, allowing system administrators to replace or recharge batteries before nodes become non-operational.  
In summary, the integration of biosensors with multi-hop IoT systems requires meticulous attention to power consumption and energy efficiency. By implementing low-power design, sleep modes, adaptive sampling rates, energy harvesting, duty cycling, optimized routing algorithms, dynamic power management, and lifetime estimation, biosensor nodes can operate efficiently within the constraints on energy resources, ensuring prolonged and reliable functionality in diverse IoT applications.
# Soybean Germination Response to Algae Extract and a Static Magnetic Field Treatment
###### Abstract  
The present study examines the separate and synergistic effects of macroalgal extract and static magnetic field (SMF) on the germination of soybean seeds (_Glycine max_ (L.) Merrill), cv. Abelina, seedling growth, chlorophyll, and carotenoids content in leaves. Algal extract was produced from freshwater green macroalga (_Cladophora glomerata_) using ultrasound-assisted extraction. The germination tests were conducted in two stages. Firstly, different concentrations of extracts, 20%, 40%, 60%, 80%, and 100%, were applied to a paper substrate. The best results (taking into account germination percentage, seedlings length and weight, and pigments content in leaves) were observed for 20% and 80% extracts. At the same stage, germination of seeds exposed to SMF (exposure times for 3 and 12 min and magnetic inductions of 250 and 500 mT) was studied. The best developed seedlings were determined for the group treated at 3 min with a magnetic induction of 250 mT. In the final step, the simultaneous effects of 20% and 80% algal extracts and treatment with 3 min at 250 mT SMF were tested. Taking into account all the parameters, the simultaneous use of 20% extract and 3 min of 250 mT magnetic induction is recommended.  
Soybean seeds; _Cladophora glomerata_; algal extract; static magnetic field; germination
## 3 Methodology
### Membrane Desorber/Condenser Module  
The experimental membrane module was constructed with two support plates, a hydrophobic membrane, a cooling plate, seal gaskets, and several bolts and screws. Support walls were composed of polymeric material with 25.4 mm thickness, and the gaskets were composed of neoprene and thermal-resistant silicon with 1 and 3 mm thickness, respectively. These gaskets were used to maintain a constant thickness of the flow channels and the air gap. A PTFE hydrophobic porous membrane with a 0.22 um pore size and with a reported contact angle of 138\({}^{\circ}\)[26] was used. A metallic mesh was placed between the membrane and the air gap to reduce the membrane deformation caused by the LiBr solution flow. Figure 2 shows the components of the membrane desorber/condenser module. The silicon gasket formed a space between the support plate and the hydrophobic membrane, which was the H\({}_{2}\)O/LiBr solution flow channel; it was 80 mm wide, 3 mm deep, and 180 mm long. An air gap with 3 mm of thickness was between the other side of the hydrophobic membrane, two neoprene gaskets, the metallic mesh, one silicon gasket, and one side of the condensing plate. Finally, the cooling water flows in the space between the other side of the condensing plate and the support plate. Previous studies described the experimental membrane desorber in detail [15; 16; 17].  
A 316 L SS plate heat exchanger (by Alfa Laval) was used to heat the H\({}_{2}\)O/LiBr solution. A Cole-Parmer Polystat digital heating circulating bath with a 6 L capacity and 1000 W electrical power was used as the thermal source. A Halo CoolMax chiller (DCA504 GUBB01 model) was used to chill water. Two storage tanks with 500 L capacities were used: one for storage of inlet cooling water for the AGMD module and the other for storing the cooling water after removal of the heat load by the condensation process inside the AGMD module. A Coriolis (by Micromotion) mass flow meter was used to measure the H\({}_{2}\)O/LiBr solution mass flow rate. Two analogical flowmeters at the inlet of the plate heat exchanger and the membrane desorber were used to measure the volumetric flow rates of the heating fluid and the cooling water. A rubber septum was placed in the LiBr solution pipe, and a liquid sample of H\({}_{2}\)O/LiBr was drawn out for each experimental test with a syringe. An ABBEMAT 200 (by [PERSON]) refractometer was used and, with the refractive index correlation reported previously for H\({}_{2}\)O/LiBr solution, the LiBr concentration was calculated; the uncertainty of this correlation was \(\pm\)0.15% _w/w_[27]. Two 32 W gear pumps were used to pump the solution and the heating fluid. The amount of distilled water was measured with an electronic balance. RTD pt100 temperature sensors were placed at the input and output ports of the membrane desorber and the plate heat exchanger (PHE). An Agilent data acquisition unit was used to record the temperature and the solution mass flow data. Figure 3 describes the experimental setup.  
Figure 2: Membrane desorber/condenser module.
## 4 Experiments and Results  
The proposed GAN model was trained for a total of 2000 epochs. Figure 8 shows sample output signals generated at different epochs. As can be observed, as the epochs increase, the signals present a more realistic form. In this work, the EWT filter is a post-processing stage that is applied to the synthetic signal generated with 2000 training epochs, in order to reduce the noise level of the generator output. Therefore, the EWT filter is not part of the training loop for the GAN. This number of epochs was determined after observing the synthetic signals obtained at different training points (from 100 epochs to 12,000 epochs). It was observed that from 2000 epochs, the synthetic signal has a shape very similar to a natural signal, but with a relatively high noise level, as shown in Figure 8E. Therefore, it was decided to generate the synthetic signals up to 2000 epochs, and subsequently apply the proposed EWT algorithm. Figure 8F,G shows the results of a synthetic signal generated with 12,000 training epochs without applying an EWT algorithm or a natural signal, respectively.  
Figure 8: _Cont._In this work, a comparison is made between the proposed method and a mathematical model proposed in [21], in order to determine which method generates a cardiac signal realistic enough to be used by a classification model. The mathematical method [21] was inspired by a dynamic model that generates a synthetic electrocardiographic signal, as described [45]. Therefore, this model is based on the morphology of the phonocardiographic signal, and has been used as a reference in other proposed methods [23]. The equation for the reference model [21] is described below:  
\[\dot{z}=-\sum_{i\in[S1^{-}\ S1^{+}S2^{-}S2^{+}]}\left(\frac{\alpha_{i}}{\alpha_ {i}}(\theta-\mu_{i})e^{(\frac{(\theta-\mu_{i})^{2}}{2\sigma_{i}^{2}})}\cos(2 \pi f_{i}\theta-\varphi_{i})+2\pi\alpha_{i}f_{i}e^{(\frac{(\theta-\mu_{i})^{2} }{2\sigma_{i}^{2}})}\sin(2\pi f_{i}\theta-\varphi_{i})\right) \tag{2}\]  
where \(\alpha_{i}\), \(\mu_{i}\), and \(\sigma_{i}\) are the parameters of amplitude, center, and width of the Gaussian terms, respectively; \(f_{i}\) and \(\varphi_{i}\) are the frequency and the phase shift of the sinusoidal terms, respectively; and \(\theta\) is an independent parameter in radians that varies in the range -\(\pi\), \(\pi\) for each beat. The parameters used by the authors in [21] are summarized in Table 3.  
The ordinary differential equation \(\dot{z}\) was solved using the numerical Runge-Kutta method of fourth order, using the Matlab software. Using the values in Table 3, we obtain the graph in Figure 9A, which represents the S1 and S2 sounds of a cardiac cycle. Figure 9B shows a natural signal of heart sound.  
Figure 8: (**A**) Synthetic signal with 100 epochs, (**B**) synthetic signal with 500 epochs, (**C**) synthetic signal with 1000 epochs, (**D**) synthetic signal with 2000 epochs, (**E**) synthetic signal with 2000 epochs + EWT, (**F**) synthetic signal with 12,000 epochs, and (**G**) natural signal of heart sound.
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 4 Suspension Time-Delay Control Parameter Optimization
### Time-Delay Control Parameter Optimization Based on Adaptive Weighted Particle Swarm Algorithm  
Particle Swarm Optimization (PSO) is an optimization algorithm based on the iterative operation. The system is initialized as a set of random solutions. It searches for the optimal value by iteration. An information sharing mechanism exists among particles. Pbest(individual extremum) or Gbest (global optimal solution) passes information to other particles. The particle search updates in the reciprocal flow follow the optimal solution and converge quickly. However, the PSO algorithm has the problem of \"precocious\" convergence and easily falls into the local best in the optimization process. To solve the problem of particle swarm, it combines the objective function characteristics established by Equation (19). In this paper, an adaptive inertia-weighted particle swarm optimization algorithm is used to optimize the delay feedback control parameters. The adaptive inertia weight particle swarm optimization (PSO) adjusts the inertia weight by using a nonlinear decreasing strategy. By balancing the global and local search capabilities of the particle swarm optimization algorithm, it can reduce the number of invalid iterations and reflect the advantages of fast convergence speed in the process of optimization [43; 44; 45].
# Stability of Reinforced Retaining Wall under Seismic Loads
## 2 Assumptions and Formulation of the Horizontal Slice Method (HSM)  
### Basic Assumptions of HSM  
In the HSM approach, the sliding wedge is divided into horizontal slices with rigid-plastic behavior. The seismic force is considered as a pseudostatic force acting at the center of gravity of each slice [23]. The geotechnical interaction between the reinforcing structure and the surrounding soil is ignored. The assumptions are as follows:  
1. The log spiral slip surface line of the reinforced retaining wall passes through the toe of the wall.
2. The sliding body of the retaining wall is divided into several horizontal slices, the number of which is equal to that of reinforcement layers, and each slice contains reinforcement [32].
3. The horizontal shear force between reinforcements is ignored.
4. The safety factor is assumed to be equal for various slices [23].
5. Both horizontal and vertical seismic actions are applied to the center of gravity of the slice.
6. The analysis is done by the limited equilibrium method.
7. The anchoring strength of the reinforcement and the resistance of the reinforcement against pulling out are not taken into account.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 7 Discussion on the Review of CNN-Based Approaches for Fruit Image Processing
### Challenges and Future Research Directions  
In the present study, we have found that DL-based models, especially CNNs, are very efficient approaches to address important tasks on fruit image processing for the agro-food industry. However, CNN-based approaches should still face important challenges in order to apply them in real-world scenarios. In our opinion, these limitations indicate the main future research directions and are the following:  
1. Size of the datasets--the dataset must be sufficient large and well labeled to train CNN, address overfitting problems, and to perform the assigned task efficiently. Therefore, the process of preparing the dataset is one of the activities that require more time and effort in the application of CNN. Although there is a wide variety of databases proposed by the authors, not all are available, for this reason, the reproducibility of all studies is not entirely guaranteed. In addition, in many cases, the databases are collected depending on the task at hand.
2. Search of CNN parameters: the number of layers and filters when proposing a CNN architecture for a specific problem, as well as determining the parameters and hyperparameters of the model, remains a relevant problem commonly solve by trial-and-error tuning until getting the best settings, which is very time-consuming for very deep models. At this point, pre-trained CNN models represent a great help since they can be taken as the basic design of other CNNs. Besides,  
Figure 9: Overall distribution of training-testing (Tr-Ts) and training-validation-test splits (Tr-V-Ts) applied in CNN models.  
other recent approaches, such as Multi-layer Extreme Learning Machine [98], could be evaluated aiming to reduce the computation time for tuning network parameters and the amount of data for training purposes.
3. Multi-fruit classification--in fruit classification studies, we found that no evaluation has been carried out with multiple types of fruit in the same image, limiting themselves to images with a single kind of fruit, either individually or grouped. Thus, the challenge is to design a CNN model for multi-detection and classification of different kinds of fruit at the same time.
4. Pre-processing of fruit images for quality control--almost all the quality control works were carried out under laboratory conditions by using sensors that are not ready for real conditions. Hence, extensive pre-processing procedures are required in all cases, making them very hard to implement efficiently in real-world scenarios.