esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Furthermore, the values set to adjust input features, neuron activation functions, and network topology/training parameters were optimized and selected using a Genetic Algorithm. The experiments achieved 95% accuracy as the best system performance results. [PERSON] [24] proposed a model called \"DenseNet-GA\" that combines artificial deep learning and machine learning models with genetic algorithm for actions detection of drivers from input images. Two dense layers, K nearest neighbor, random forest, support vector machine, and extreme boost algorithms have been used as classifiers. The DenseNet-GA model was developed with the use of a state farm dataset that contains information of one safe driving class and nine dangerous behaviors. Experimental results show that the classification accuracy of the DenseNet-GA is 99.80% when using the state farm dataset. However, it is highly recommend that the proposed model is evaluated using another dataset. **Detection using U-net Model:** One of the most efficient CNN approaches is U-Net, which is mainly used for medical image segmentation. U-Net contains a shrinking path for deriving information and a corresponding expansion path for restoration and has high accuracy for image segmentation [25]. Many researchers have investigated U-Net because of its popularity and efficiency in image segmentation in several domains such as cytology [5], geology [26], and microorganisms [27]. Although the original U-Net has a strong versatility in achieving good results in image segmentation, many variant network structures have been proposed to improve the segmentation effect [28]. To the best of our knowledge, no research has previously investigated the U-Net to address the problem of detecting and classifying the driver's state of distraction while driving, which might be used to alert the driver. This paper introduces an improved version of U-Net model that uses deep feature maps, dense layers, more convolution, max pooling, and dropout layers to add more spatial information and context to the classifier, which enhances the accuracy of the classification results. There is a similar attempt that extends the U-Net architecture for volumetric medical image segmentation tasks called V-Net, which is a 3D CNN architecture that uses a similar encoding-decoding structure to U-Net but incorporates 3D convolutional layers instead of 2D convolutional layers. V-Net also uses residual connections to improve the gradient flow and speed up the convergence during training [29]. Table 1 summarizes the related work in terms of the proposed method, architecture, dataset, and resulted accuracy.
## 2 Preliminaries and Problem Formulation
### Preliminaries
#### 2.1.3 Low-Cost Underwater Drone Modeling  
From the field of guidance, navigation and control of underwater vehicles, the 6 degrees of freedom (DoF) dynamic model of UUVs in body frame [26], can be written in the following form:  
\[\dot{\eta}_{i}=J\left(\eta_{i}\right)\mathbf{v}_{i} \tag{2}\] \[M_{i}\dot{\mathbf{v}}_{i}+C_{i}\left(\mathbf{v}_{i}\right) \mathbf{v}_{i}+D_{i}\left(\mathbf{v}_{i}\right)\mathbf{v}_{i}+g_{i}\left(\eta _{i}\right) =\tau_{iAct}+\tau_{iDist} \tag{3}\]  
where: \(\eta_{i}=\left(x_{i},y_{i},z_{i},\varphi_{i},\theta_{i},\psi_{i}\right)^{T}\) is the position (NED: North, East, and Down) and the orientation (Euler RPY: Roll, Pitch, Yaw angles) of \(i^{th}\) UUV; \(\mathbf{v}_{i}=\left[u_{i},v_{i},w_{i},p_{i},q_{i},r_{i}\right]^{T}\) is the velocity and angular velocity; \(J\left(\eta\right)\) is the _Jacobian_ matrix typed \(6\times 6\) for \(\eta\); \(M=M_{RB}+M_{A}\) is a matrix, which denotes the \(6\times 6\) system inertia matrix containing \(M_{RB}\)--the generalized constant inertia matrix, and \(M_{A}\)--the added mass inertia matrix; \(C\left(\mathbf{v}_{i}\right)=C_{RB}\left(\mathbf{v}_{i}\right)+C_{A}\left( \mathbf{v}_{i}\right)\) is the \(6\times 6\) Coriolis and centripetal forces matrix, including added mass; linear and nonlinear hydrodynamics damping are contained within the \(6\times 6\) matrix \(D\left(\mathbf{v}_{i}\right)=D+D_{n}\left(\mathbf{v}_{i}\right)\), \(D\) contains the linear damping terms, and \(D_{n}\left(\mathbf{v}_{i}\right)\) contains the nonlinear damping terms; \(g\left(\eta\right)\) is the \(6\times 1\) vector of gravitational and buoyancy effects; \(\tau_{Act}\) is the \(6\times 1\) control input forces and torques; and finally, \(\tau_{Dist}\) is the \(6\times 1\) vector external disturbances caused by wind, waves, and ocean current.
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 6 Results and Discussion (Case Study 2)
### Case Study 2: Data Analytics (Load Forecasting)
#### 6.1.2 Model Evaluation  
After training a model, the next step is scoring the model to evaluating the proposed model. This evaluation, conducted by computing various statistics such as the Mean Absolute Percentage Error (MAPE), was calculated using the formula shown in Equation (6), where an MAPE value (<10) means highly accurate forecasting, MAPE between (10 and 20) means good forecasting, MAPE value between 20 and 50 reasonable forecasting, and MAPE higher than (50) mean weak forecasting [35]. In addition, a Mean Absolute Error (MAE) to measure inaccuracy in the data was used. The difference between actual values and accurate values is known as the absolute error, and the average of these absolute errors is known as the mean absolute error [66; 67]. It can be calculated using the formula shown in Equation (7). Moreover, the Mean Squared Error (MSE) was used to measure the difference between prediction and the actual value, the average of the squared absolute errors. It is calculated by first squaring the absolute error and then taking their average [66]. The formula is shown in Equation (8). Additionally, Root Mean Squared Error (RMSE) is used to measure the error between the actual data and the model of estimation [54; 55; 56]. It is calculated by simply taking the root of MSE. It can be calculated using the formula shown in Equation (9).  
\[\text{MAPE}=\Sigma\bigg{[}\frac{(pi-ri)}{pi}\bigg{]}\times\frac{100\%}{n} \tag{6}\]  
\[\text{MAE}=\frac{\Sigma(pi-ri)}{n} \tag{7}\]  
\[\text{MSE}=\frac{\Sigma(pi-ri)^{2}}{n} \tag{8}\]  
\[\text{RMSE}=\sqrt[2]{\frac{\Sigma(pi-ri)^{2}}{n}} \tag{9}\]  
where:  
\(n\) = number of times the summation iteration happens.  
\(pi\) = actual value.  
\(ri\) = forecast value.  
For a comprehensive study, the same dataset is trained and predicted using the ARIMA model and GBTL independently. Table 8 shows the three-model evaluation, i.e., the Proposed Hybrid Model, ARIMA, and GBTL where MAPE, MAE, and RMSE were used to evaluate the process. The results show that our proposed model gives better results where lower MAPE is obtained. It can be concluded that the proposed methods have the highest accuracy. Additionally, very low values can be observed in MAE and RMSE to indicate a high forecasting accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  &
\begin{tabular}{c} **Proposed Model** \\ **(ARIMA-GBTL)** \\ \end{tabular} & **ARIMA** & **GBTL** \\ \hline Cluster 0 & & & \\ \hline MAPE & 0.298241254 & 1.595470053 & 2.102847728 \\ \hline MAE & 0.000802411 & 0.010943998 & 0.009823649 \\ \hline RMSE & 0.001040184 & 0.013007015 & 0.012507055 \\ \hline Cluster 1 & & & \\ \hline MAPE & 1.268934292 & 4.565898212 & 3.116166309 \\ \hline MAE & 0.001681791 & 0.010046462 & 0.01249306 \\ \hline RMSE & 0.002270159 & 0.012239579 & 0.014918207 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The Model Evaluation.
## 4 Discussion
### Main Advantages  
It is worth noting that coherent superposition has been used by some previous methods [15; 22; 24], but these superposition methods have not used multiple light beams as used in this study, thus suffering low energy efficiency usages. Our method, in principle, can synthesize at least four beams simultaneously, furthermore, nine beams, sixteen beams, twenty-five beams are also feasible, especially with the VCSEL & SPAD technology, where multiple lasers can be made possible in a small footprint place. The laser combination technique used in this study greatly improved the power of optical decryption images and, thus, significantly improved the image reconstruction quality, especially for remote sensing applications. With regard to RGB image encryption, different from the previous method, where typically an image is decomposed to three-color channels and then encrypting them respectively [25]. In contrast to previous approach, in this study, the color image is converted to QR code before encryption and converted back to color image using QR code following decryption therefore to address the color image encryption problem with minimum amount of hardware investment. In terms of encryption and decryption time, the scheme is a hybrid optoelectronic system; thus, it can take advantages of utilizing existing digital encryption methods [30; 31; 32; 33] but also optical encryption schemes as well [10; 11; 12; 13; 14]. Time efficient multiplication and addition are used in the digital encryption process. Meanwhile, the operation of optical encryption is a parallel operation process. Therefore, the encryption speed is more efficient than the pure digital image encryption schemes such as chaotic encryption and elliptic curve cryptography [18].
## 3 Methodology and Algorithms
### Theoretical Foundation
#### 3.1.2 Inverse Kinematics (IK) for Motion Modeling  
Inverse kinematics (IK) is a fundamental technique in robotics and motion synthesis that focuses on determining a set of joint configurations that achieve a desired end-effector position or trajectory. In contrast to forward kinematics where the pose of a skeleton is calculated by successively applying joint transformations from root to end-effector IK solves the inverse problem: given the target positions of certain parts (e.g., fingertip, hand, foot), it computes the internal joint angles that satisfy kinematic constraints while maintaining anatomical plausibility.  
Mathematically, IK is often formulated as a constrained nonlinear optimization problem. Let \(\theta\in\mathbb{R}^{n}\) represent the vector of joint parameters (e.g., angles or positions), and let \(f(\theta)\in\mathbb{R}^{3}\) be the forward kinematics function that maps joint parameters to the Cartesian position of an end-effector. Given a desired target position \(\mathbf{x}_{\text{target}}\in\mathbb{R}^{3}\), IK seeks a solution to the following system:  
\[f(\theta)=\mathbf{x}_{\text{target}}. \tag{1}\]  
Due to redundancy or nonlinearity in articulated systems, an exact solution may not exist or may not be unique. Thus, the problem is commonly relaxed to minimizing the squared positional error:  
\[\min_{\theta}\left\lVert f(\theta)-\mathbf{x}_{\text{target}}\right\rVert^{2}, \tag{2}\]  
subject to joint limits:  
\[\theta_{\min}\leq\theta\leq\theta_{\max}. \tag{3}\]In practice, iterative methods are used to approximate solutions. One widely used approach involves the Jacobian matrix \(J(\theta)\in\mathbb{R}^{3\times n}\), defined as:  
\[J(\theta)=\frac{\partial f(\theta)}{\partial\theta}. \tag{4}\]  
A linear approximation of the end-effector motion is given by:  
\[\Delta x\approx J(\theta)\Delta\theta, \tag{5}\]  
and the corresponding joint update can be computed via the Jacobian pseudoinverse:  
\[\Delta\theta=J(\theta)^{+}\Delta x, \tag{6}\]  
where \(J^{+}\) denotes the Moore-Penrose pseudoinverse of the Jacobian.  
Alternative real-time solvers such as Cyclic Coordinate Descent (CCD) optimize each joint sequentially and are computationally efficient for low-DOF systems like fingers.  
In the domain of motion modeling and gesture synthesis, IK ensures physical realism and structural coherence. For instance, fingertip trajectories captured from video can be converted into full-hand joint configurations using IK, even when some joints are occluded or missing. This enables realistic hand articulation from sparse data.  
Moreover, IK systems are often integrated with animation engines or physics-based simulators to dynamically adjust to constraints such as obstacle avoidance or balance preservation during complex transitions.  
In summary, IK serves as a bridge between sparse high-level control signals and full-body anatomically plausible movements. Its integration is crucial in gesture reconstruction, animation blending, and physics-informed motion generation pipelines.
## 3 Results and Discussion
### Mechanical Properties of the 3D Nanofibrous Scaffolds  
The mechanical strength of the scaffolds is expected to improve upon the incorporation of nanofibers. To test this hypothesis, compression tests were conducted, and the results are presented in Figure 4. After addition of the nanofibers, the compressive modulus of the 3D nanofibrous scaffolds increased significantly compared to the pure chitosan/RHC scaffolds as the compressive moduli of the 1%-0.5 mL and 1%-1 mL scaffolds were 66 and 157 kPa, respectively, while the 1% chitosan/RHC scaffolds showed compressive moduli as low as 43 kPa. Moreover, increasing the concentration of the chitosan/RHC solution further reinforced the strength of the scaffolds as the compressive modulus of the 2%-1 mL increased to 196 kPa. A typical stress-strain curve showed three discrete regions: a linear elastic regime (0-10% strain) caused by internal pore bending, a collapse plateau regime (10-60% strain) produced by pore buckling or collapse, and a densification regime (60-100% strain) via complete pore collapse throughout the materials [37]. As shown in Figure 4A, it was difficult to distinguish the elastic regime from the collapse plateau regime in the pure chitosan/RHC and 1%-0.5 mL scaffolds, and the curves were shown linear from strains lower than 60%. These phenomena might reflect the low mechanical strength of these scaffolds. By adding 1 mL of short nanofibers solution and increasing the chitosan/RHC solution to 2%, the strength of the scaffolds improved significantly, and their elastic and collapse plateau regimes were easily differentiated (Figure 4A). The compressive strengths of the 1%-1 mL and 2%-1 mL scaffolds were 34 and 82 kPa, respectively. The collapse strainsof the 1%-1 mL and 2%-1 mL scaffolds were 12% and 20%, respectively. Adding nanofibers and increasing the amount of the chitosan/RHC solution resulted in a higher density and lower porosity in fabricated porous scaffolds, improving the mechanical strength significantly. Furthermore, it is well known that the mechanical strength of the porous scaffolds can be improved by chemical crosslinking [38]. Thus, the compressive modulus of the uncrosslinked 1%-1 mL scaffold (125 kPa) was much lower than those of the crosslinked scaffolds (\(p<0.05\)). All these results demonstrated that increasing the number of nanofibers or the concentration of the chitosan/RHC solution is sufficient to improve the mechanical strength of the scaffolds.
# Adaptive Beamforming Applied to OFDM Systems
## 5 Conclusions  
This paper proposed a novel beamforming scheme for OFDM systems, with the pre-FFT adaptive algorithm. This adaptive beamforming allows a very low guard interval of the OFDM system, and it enables one to work with more bandwidth efficiency. This strategy used a simple, fast and efficient solution for the OFDM receiver compared to pre-FFT structures found in the literature. The results suggest the feasibility of implementing the proposed scheme in different OFDM receivers, such as for digital TV and wireless LANs standards. Another important aspect concerns the simplicity of the adaptive structure, which does not change the conventional algorithms, reinforcing the feasibility of the proposed scheme.  
All the authors have contributed to various degrees to ensure the quality of this work (e.g., [PERSON] and [PERSON] conceived of the idea and experiments; [PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper).  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)-Finance Code 001.  
The authors wish to acknowledge the financial support of the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) for their financial support.  
The authors declare no conflict of interest.  
Figure 8: BER performance curve for Channel 3 (see Table 3).
###### Abstract  
The main objective of the present research activity was to investigate the effect of very fast composition transitions of the engine exhaust typical in real-world driving operating conditions, as fuel cutoff phases or engine misfire, on the aftertreatment devices, which are generally very sensitive to these changes. This phenomenon is particularly evident when dealing with engines powered by natural gas, which requires the use of a three-way catalyst (TWC). Indeed, some deviations from the stoichiometric lambda value can interfere with the catalytic converter efficiency. In this work, a numerical \"quasi-steady\" model was developed to simulate the chemical and transport phenomena of a specific TWC for a compressed natural gas (CNG) heavy-duty engine. A dedicated experimental campaign was performed in order to evaluate the catalyst response to a defined \(\lambda\) variation pattern of the engine exhaust stream, thus providing the data necessary for the numerical model validation. Tests were carried out to reproduce oxygen storage phenomena that make catalyst behavior different from the classic steady-state operating conditions. A surface reaction kinetic mechanism concerning CH\({}_{4}\), CO, H\({}_{2}\), oxidation and NO reduction has been appropriately calibrated at different \(\lambda\) values with a step-by-step procedure, both in steady-state conditions of the engine work plan and during transient conditions, through cyclical and consecutive transitions of variable frequency between rich and lean phases. The activity also includes a proper calibration of the reactions involving cerium inside the catalyst in order to reproduce oxygen storage and release dynamics. Sensitivity analysis and continuous control of the reaction rate allowed evaluating the impact of each of them on the exhaust composition in several operating conditions. The proposed model predicts tailpipe conversion/formation of the main chemical species, starting from experimental engine-out data, and provides a useful tool to evaluate the catalyst's performance.  
TWC (three-way catalyst); natural gas; kinetic scheme; aftertreatment; modeling; emission; internal combustion engines 2019 9 4610; doi:10.3390/app9214610
###### Abstract
These include morphological analysis [3], transportation network improvement and tolling strategies methods [4], as well as graph theory methods [41], which are partially adopted for transportation, energy saving and environmental impact assessment. The literature presents methods for time and/or cost evaluation in road transportation of performance measures for two-lane intercity presented by [PERSON] et al. (2015) [22]. An accessibility comparative analysis was developed by [PERSON] et al. [24] for a road network based on separate factors. Methods for calculating costs and time taken by transportation via railway, for example, employ a mixed integer linear programming method, which is presented by [PERSON] et al. (2020) [6], and railway transportation problems and solutions, presented by [PERSON], [PERSON] (2013) [7], can be used for the same factor calculations, but it is very complicated to calculate all the main factors. A number of methods for the different factor calculations in waterborne transport [8; 9; 36], port and logistics [18; 26; 29; 37], presented in the literature, mainly analyzed typical situations. At the same time, it is very important in complex evaluation and comparison, including energy saving and environmental impact assessment. Energy saving and environmental impact assessment from transport modes, are analyzed for road transport in research papers [12; 17; 19], for railway transport in research papers [7; 17; 19], for waterborne transport in research papers [11; 14; 33; 34; 39] and for transport and logistics processes in research papers [29; 37] among others, but mainly based on the typical transportation conditions (Table 2). The research analysis of different factors for different types of transport has shown that individual factors such as transport costs and time have been studied widely enough. At the same time, such factors of transport and logistics processes as environmental impact, energy consumption, complex studies and assessment of all factors are insufficient or difficult to apply in practice. For the research and practical tasks, it is very important to develop methodic and calculated modules, which can cover all main factors, including transportation costs, time and energy savings, which can show comparative transport processes in different transport roads or corridors. This is the main aim of the article. Theoretical Basis for the Energy Saving by Cargo Transportation Modes and Its Combinations, Methods
## 1 Introduction  
Herbal immunostimulants are generally categorized as natural compounds that can enhance immune responses by activating immune cells, such as macrophages [1; 2]. By promoting the phagocytosis of macrophages, immunostimulants induce the secretion of cytokines, such as tumor necrosis factor-alpha (TNF-\(\alpha\)), interleukin-1 beta (IL-1\(\beta\)), and interleukin-6 (IL-6), thereby improving immune function [3].  
Nuclear factor-kappa B (NF-\(\kappa\)B)-mediated cellular changes are known to be closely associated with both the innate and adaptive immune responses [4; 5]. In innate immune cells, such as macrophages, NF-\(\kappa\)B activation is initiated by the degradation of inhibitory kappa B alpha (I\(\kappa\)B) proteins, following which the free NF-\(\kappa\)B enters the nucleus to perform various downstream functions, including immune regulation and proliferation [4; 6]. Generally, immunostimulants activate the immune cell function through specific binding with receptors [7]. Moreover, immunostimulants trigger several downstream signals, such as the NF-\(\kappa\)B pathway, p38 mitogen-activated protein kinase (MAPK), and the c-Jun N-terminal kinase (JNK) pathway, increasing the secretion of nitric oxide (NO), as well as TNF-\(\alpha\), IL-1\(\beta\), and IL-6 [1; 7].  
The spleen plays a role in the immune system by protecting against blood-derived antigens and removing aged red blood cells and damaged cells [8]. The populations of cells in the spleen are mainly composed of various immune lymphocytes, including T cell, B cell, and macrophages [9], which are widely used for immunomodulatory research.  
_Platycodon grandiflora_ (PG) has a long history of being widely used as a traditional medicine [10]. It has been reported, inter alia, to reduce inflammation and improve liver function [8; 10]. Saponins, which are part of single compounds of PG, have also been studied actively, mainly focusing on their anti-cancer properties [10; 11]. Among them, platycodin D showed immunoassay activity in ovalbumin-immunized mice [12] and increased serum cytokine levels in h22 hepatocellular carcinoma-bearing mice [11], providing the evidence of immune enhancing effect. Red PG, which is made by steaming and drying PG several times, increases the saponin content during processing when compared to raw (white) PG [8]. Red PG with a high content of platycodin D might have potential as a herbal immunostimulant, however no study has been conducted on the pharmacologic effects and molecular mechanisms of red PG in the immune-enhancing response. In this study, we aimed to evaluate the immune-enhancing effects of red PG extract (RPGE) in RAW 264.7 macrophage cells and mouse splenocytes. We also investigated its effects on the NF-kB and MAPK signaling pathways to determine the underlying mechanisms of the immune-enhancing effects of RPGE in immune cells. Thus, the results of our study provide novel insights regarding the immunomodulatory effects and the mechanism action of RPGE.