esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
_Optimization of FEC Framework for Seamless Video Transmission System_: This study proposes an FEC mechanism that robustly adjusts redundancy levels in response to real-time network conditions. This method minimizes the impact of packet loss and channel noise while optimizing bandwidth usage without requiring retransmissions. 2. _Improvement of Objective Video Quality Metrics_: The proposed framework achieves measurable enhancements in PSNR and PSNR\({}_{611}\) by integrating the proposed FEC framework with H.264/AVC video coding. The framework effectively addresses the challenges posed by variable video characteristics, such as motion, structure, and resolution. 3. _Integration of Selective Encryption and Error Correction within the Video Coding Process_: The proposed technique combines the FEC mechanism with H.264/AVC syntax-basedselective encryption and decryption algorithms using specific syntax elements of the video to ensure confidential and reliable video transmission system. * _Adaptiveness to Various Video Resolutions_: This method is not limited to videos with a specific resolution and can be deployed on multiple video resolutions. Its working efficiency is tested on video sequences of CIF and HD resolution in our work and the results prove its adaptiveness to the resolution flexibility of videos. * _Effectiveness on Different Quality Perception Values on Video Quality_: The proposed framework is implemented at different QP values to assess the effect on video quality. It shows that perception quality is inversely related to video quality. The rest of this paper is structured as follows. Section 2 reviews the related work on this topic. Section 3 describes the materials and methods of the proposed framework. Section 4 illustrates the results of the proposed framework on different test video sequences. Section 5 provides a discussion and suggestions related to the outcomes of this work. Section 6 concludes the paper by providing an overview of this work and its adaptiveness for quality enhancement due to error-prone channels in modern communication.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
###### Abstract  
Proton exchange membrane fuel cell (PEMFC) hybrid vehicles offer a long driving range but are heavily dependent on energy management strategies (EMS). Traditional EMS methods, such as rule-based approaches and optimization-based methods like model predictive control (MPC), either lack flexibility or are computationally complex and rely on prior driving experience. To overcome these limitations, this study proposes a semi-empirical approach that combines state machine (SM) and MPC in a novel hybrid EMS (SM-MPC) to optimize power distribution in a 100 kW PEMFC hybrid vehicle. The SM-MPC strategy uses SM to handle fast power fluctuations and MPC to manage slow variations, balancing real-time adaptability and efficiency. Simulation results based on the NEDC and HWFET driving cycles show that compared to the traditional MPC method, SM-MPC significantly reduces hydrogen consumption by 7.11 g (NEDC) and 1.89 g (HWFET). Additionally, the proposed method effectively maintains the state of charge (SOC) of the lithium-ion battery using a PID controller and ensures the PEMFC stack temperature remains within \(\pm\)5.8 \({}^{\circ}\)C. Overall, the SM-MPC strategy improves energy efficiency, reduces fuel consumption, and enhances the stability of the hybrid power system, offering a promising solution for real-time energy optimization in fuel cell vehicles.  
PEMFC; hybrid power system; energy management strategy; Modelica; simulation +
Footnote †: journal:  
0000-0001-5307-2201][PERSON]  
0000-0001-5307-2201][PERSON]  
0000-0002-1828-2201][PERSON]
## 3 Tracking Framework for Lobe Fissure Based on Modified ACO Algorithm
### Design of the Modified ACO Method  
The modified ant colony optimization (ACO) algorithm is used to track the lobe fissures in lung CT images. The idea of ACO is that the ants will find a best path to walk by their pheromone. The procedure of modified ACO algorithm is listed in Figure 4, where \(R\) is the number of path-tracking rounds and \(N\) is the number of ants. First, the input images are transferred to the sagittal slices from each axial CT cases. Second, we marked two points to be the start and end points on the boundary of lung; the ants can use the initial pheromone table to search and create the candidate paths. In each round, the pheromone table will be updated by the updating rules and the local optimal path will also be found. Last, the lobe fissure is the final optimal path through \(R\) rounds and \(N\) ants. Figure 5 shows the flowchart of proposed system.  
Figure 4: The procedure of modified ACO algorithm.
# A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
## 2 Literature Review  
In recent years, many researchers have proposed AI models for PCOS diagnosis, with datasets consisting of clinical parameters and vital signs. [PERSON] et al. [24] provided a detailed analysis of various clinical features and their contribution to a patient having PCOS. They used an open-source Kaggle PCOS dataset [25], with records of 541 patients and 44 features whose target label was PCOS diagnosis. The dataset is multicentric and was collected from 10 different hospitals in Kerala. The architecture consisted of machine learning classifiers and the Pearson correlation technique for feature selection. They concluded, the most critical features were: the average left and right follicle size, number of follicles in the left, hair growth, and prolactin levels. The SVM Radial Basis Function (RBF)kernel, and Multilayer Perceptron (MLP) obtained an accuracy of 93%. [PERSON] et al. (2018) used the same dataset, adopting the CatBoost classifier with K-fold validation evaluating the classifier performance, achieving 82.5% for the invasive methods and 90.1% accuracy for the non-invasive clinical parameters. [PERSON] et al. (2017) suggested a CatBoost model and a voting hard and voting soft classifier. They extracted the top 13 features by univariate feature selection followed by a hold-out and cross-validation for the data splitting. The soft voting classifier performed with an accuracy of 91.12%. [PERSON] et al. (2018) developed an ML based Smart PCOS diagnostic system. The architecture compared the performance of various classifiers such as SVM, LR, RF, AdaBoost, DT, KNN, Gradient Boosting, XgBoost, CatBoost, Linear Discriminant Analysis, and Quadratic Discriminant Analysis. They used an open-source dataset and obtained an overall accuracy of 93.25%. [PERSON] et al. (2019) developed an ensemble random forest classifier model and trained features selected by embedded feature selection methods. The model performed with accuracy and sensitivity of 98.89% and 100%, respectively. [PERSON] et al. (2019) proposed an ML model architecture with classifiers such as RF, LR, Hybrid Random Forest- Logistic regression (RFLR), and gradient boosting. RFLR gave the best performance with an accuracy of 91%. [PERSON] et al. (2019) proposed a BorutaShap method and sequentially trained a random forest model to identify the most relevant and significant clinical markers. A dataset comprised 72 PCOS patients and 73 healthy women. Fifty-eight features were ranked according to their relevance and significance. The model was able to obtain an overall accuracy of 86%.
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
## 3 Solution Algorithms
### HGA-VNS Algorithm  
The GA is a metaheuristic algorithm that emulates the genetic and evolutionary processes observed in biological evolution. It iteratively evolves and searches for the optimal solution to a problem. GA possesses several advantages, including robust search  
Figure 3: Mutation operator.  
capabilities, adaptability, parallelization, and straightforward implementation. As a result, it is highly effective in tackling NP-hard problems [35]. The HGA-VNS algorithm leverages the robust search capability of GA and the local search technique of VNS to achieve optimal results. Algorithm 2 provides the pseudocode for the HGA-VNS approach.  
```
Step 1: Initialization Step 1.1: Set parameters: population size (\(P\)), maximum iterations (\(Max\_iter\)), crossover rate (\(P_{c}\)), mutation rate (\(P_{m}\)) Step 1.2: Generate individuals using the coding mechanism to form the initial population Step 2: Calculation of individual fitness values () Step 3: Select operation () Step 4: Crossover operation () Step 5: Mutation operation () Step 6: Repairing operation () Step 7: VNS local search () Step 8: Repeat steps 2 to 7 until the maximum iterations of the GA are reached Step 9: Final solution ()
```  
**Algorithm 2**Pseudocode of HGA-VNS
# Modal Identification of Train Passenger Seats Based on Dynamic Tests and Output-Only Techniques
## 3 Case Study: Alfa Pendular
### Results
#### 3.2.2 EFDD Method
That was composed of an accelerometer placed on the floor and one tri-axial accelerometer on the seat surface. Therefore, the author obtained the transmissibility curves but could not perform the modal identification. The new foams and restricting covers justify the slight difference between results. Figure 9: Experimental modal parameters for the comfort seat. Mode 5, with a frequency equal to 4.88 Hz, is associated with a coupled mode involving structural movement of the carbody, as stated by the quite visible support vertical movement, structural movement of the seat frame, demonstrated by bending movement, and vertical foam movements. In conclusion, resonance modes 1 and 5 are connected with the seat frame and foam natural frequencies and, therefore, amplified movements. Mode 2 and the antiresonance valley, Mode 4, are strictly connected with seat frame movements and the generation of those movements transitions. Figure 11 illustrates the first two curves of the average normalised singular values of the spectral density matrices of the S1 standard seat test, where it is possible to observe the peaks corresponding to the identified frequencies and mode shapes. The seat movements derived from the vibration modes are demonstrated in Figure 12, where \(f\) represents the average value of the natural frequency. As previously mentioned, standard and comfort seats differ mainly in terms of dimensions. Therefore, it was anticipated that both seats face a similar structural behaviour. Due to the foam thickness difference, a distinct foam vibration range absorption was expected, as observed on transmissibility curves. To confirm the similar structural behaviour of both seats, mode pairing was conducted through the MAC parameter. Mode 2 presents an MAC value of 0.96, followed by Mode 1 (MAC equal to 0.93) and Mode 5 (0.86). Indeed, the modal analysis demonstrated that standard and comfort seats present the same seat frame movements occurring at different frequencies. Mode 1 is the exception, as  
Figure 11: EFDD method: average normalised singular values of the spectral density matrices regarding AP standard seat. Figure 10: Demonstration of seat bending movement. it occurs on the same frequency for both seats. Standard seat Mode 2 matches Mode 2 of the comfort seat. Modes 4 and 5, characterised by higher deformations, occur at slightly, but not significant, lower frequencies for the standard seat. The standard seat foam demonstrates the capacity to absorb vibrations within the 1.37-3.91 Hz frequency range. This parameter represents the main difference regarding both seats, as the comfort foam presents a larger vibration range absorption (0.59-4.88 Hz).
## 2 Overview of the Study Area
### Methodology  
The basic methodology aimed at assessing landslide susceptibility of the study area is shown in Figure 2. Firstly, to create a landslide inventory, we collected available data of 550 landslide points and 1670 non-landslide points. To create a landslide susceptibility map for the study area, the most significant landslide predisposing factors were selected in the light of multicollinearity analysis, relevant literature review, and evaluation of the local geological environment. Then, XGBoost algorithm was used to classify landslide-prone areas, and landslide susceptibility maps were generated. Finally, validation was conducted by using performance indicators such as receiver operating characteristic curve area under (ROC-AUC), sensitivity, specificity, accuracy, precision, recall, and kappa coefficient.  
Figure 1: Elevation distribution of Anhua County, Taojiang County, and Heshan District of Yiyang City.  
Figure 2: Methodology flowchart of the research.
## 1 Introduction
Analytical and numerical studies [19; 20] have shown that the relation between the optimal diameter and height is not evident. The optimal diameter can be larger than the bale height [19] (Figure 6.5b), [20] (Figure 11c) or can be smaller than the bale height [19] (Figure 6.5d), [20] (Figure 11a), depending on the bale volume as well as film and wrapping parameters. But the optimal bale diameter can also be equal to its height [19] (Figure 6.5a,c), [20] (Figure 11b). The last case corresponds to typical large bales of 1.2 m diameter and height [10; 24; 25]. However, in agricultural practice typical bales are 1.2 to 1.3 m diameter and height [6; 10; 19], where diameter is equal [26; 27; 28; 29; 30] or almost equal [9; 31; 32], but not much bigger, than bale height. Other dimensions of large round bales, for example 1.2 m \(\times\) 1.6 m \(\mathcal{O}\)[33], or 1.2 m \(\times\) 1.5 m \(\mathcal{O}\)[34] or 1.5 m \(\mathcal{O}\)[35], are used less frequently. Although round bales of the height greater than the diameter are also being investigated, for example 1.2 m \(\times\) 0.9 m \(\mathcal{O}\)[36], they are much less common in agriculture practice [37]. Standard 3D bale wrappers are designed to wrap cylindrical bales up to 1.6 m in diameter and up to 1.2 m in height [22], or up to 1.5 m diameter and 1.2 m height [21; 23]. Therefore, the question becomes how to choose such film and wrapping process parameters for a round bale with a given volume wrapped using the 3D method so that the bale diameter minimizing film consumption is equal to or almost equal to its height. The aim of the paper is to solve the problem of the selection of the film and wrapping parameters (overlaps, numbers of bottom and upper film layers, etc.) that for a given bale volume, i.e., a given bale weight, its diameter and height are equal or near-equal and, at the same time, they minimize the film consumption. It is, in essence, the inverse problem in which the film and wrapping parameters are determined that the equilibria of optimal dimensions are guaranteed, while in the direct problem considered in [20] the optimal dimensions for a given parameters are sought. The thesis is that for any given bale volume and number of global film layers there exist film and wrapping parameters for which the optimal bale dimensions are equal or near-equal, with the pre-assumed proportions diameter/height. To prove the above, the necessary and sufficient conditions of equilibrium and near-equilibrium of the optimal bale dimensions were derived in the form of algebraic equations. The equilibrium conditions are dichotomous, as some depend on the given bale volume and others apply to any bale volume. Then, the problems of selecting such bale wrapping parameters for which the optimal bale dimensions are equal or near-equal were formulated and solved, separately, for a given and an arbitrary bale volume. Suitable design algorithms were proposed and numerically verified for large bales.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 4 Experiments and Results
### Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Test Set I**} & \multicolumn{6}{c}{**Test Set II**} & \multirow{2}{*}{**Avg. (mm)**} \\ \cline{2-2} \cline{5-12}  & **kf1** & **kf2** & **kf3** & **kf4** & **kf5** & **kf1** & **kf2** & **kf3** & **kf4** & **kf5** \\ \hline [PERSON] [14] & 5.70 & 7.18 & 6.98 & 8.66 & 5.13 & 13.8 & 6.85 & 13.1 & 5.70 & 7.73 & 8.08 \\ [PERSON] [14] & 7.91 & 2.97 & 1.71 & 2.52 & 2.91 & 5.39 & 1.67 & 4.34 & 3.18 & 2.79 & 3.54 \\ [PERSON] [14] & 6.30 & 2.15 & 3.41 & 3.86 & 4.80 & 6.57 & 2.56 & 6.72 & 4.34 & 1.19 & 4.19 \\ [PERSON] [14] & 8.25 & 3.36 & 2.21 & 2.03 & 1.33 & 8.26 & 2.29 & 7.04 & 2.22 & 0.42 & 3.74 \\ D.P.1 [14] & 7.73 & 2.07 & 1.94 & 2.63 & 0.62 & 4.85 & 0.65 & 1.62 & 0.77 & 0.41 & 2.33 \\ D.P.2 [14] & 7.41 & 2.03 & 1.92 & 2.75 & 0.65 & 4.78 & 1.19 & 3.34 & 1.82 & 0.36 & 2.63 \\ [PERSON]. [PERSON] [14] & 7.61 & 2.41 & 1.84 & 2.48 & 0.99 & 4.33 & 1.10 & 3.65 & 1.69 & 0.48 & 2.66 \\ \hline STTR [28] & 9.24 & 4.42 & 2.67 & 2.03 & 2.36 & 7.42 & 7.40 & 3.95 & 7.83 & 2.93 & 5.03 \\ HybridStereoNet [10] & 7.96 & 2.31 & 2.23 & 3.03 & 1.01 & 4.57 & 1.39 & 3.06 & 2.21 & 0.52 & 2.83 \\ \hline [PERSON] [15] & 8.62 & 2.69 & 2.36 & 2.29 & 2.51 & 6.06 & 0.95 & 2.97 & 0.86 & 1.23 & 3.05 \\ PASMenet [21] & 8.99 & 2.53 & 1.93 & 2.93 & 1.31 & 5.11 & 1.52 & 3.71 & 2.04 & 0.83 & 3.09 \\ Ours & 8.80 & 2.55 & 1.65 & 2.21 & 1.11 & 4.23 & 1.13 & 3.06 & 1.61 & 0.62 & 2.70 \\ \hline \hline \end{tabular} The data are measured in millimeters.  
\end{table} 
Table 4: Comparison to reported methods on _SCARED_. The data represent the average depth errors. The top section of the table shows the performance of leading models in the MICCAI SCARED sub-challenge, all of which are supervised. The models in the middle section are also trained in a supervised manner, but they are not exposed to the _SCARED_ train set. They rely on excellent generalization to be directly evaluated on the _SCARED_ test set. The bottom section consists of self-supervised models. Our model achieved a fourth place performance and was in close proximity to that of the top-ranked supervised model. And our model surpassed the well-generalized models and self-supervised ones. The results of STTR and HybridStereoNet were reported in [10]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **EPE\(\downarrow\)** & **D1\(\downarrow\)** & **D3\(\downarrow\)** & **MAE\(\downarrow\)** \\ \hline HybridStereoNet [10] & 1.569 & 6.273 & 4.750 & 1.438 \\ PASMenet [21] & 2.279 & 14.640 & 12.779 & 2.242 \\ Ours & **1.515** & **5.834** & **4.401** & **1.370** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative results on keyframes. Our model outperforms the baseline model by over 23% in all metrics, especially in the \(t\)-pixel error rate, where the improvement exceeds 100%. Additionally, our model demonstrates advantages over HybridStereoNet in all metrics.
## 2 Materials and Methods
### Design of the Experiment (DOE)  
The spectroscopic measurements were carried out on pellets prepared by mixing specific amounts of saccharides with PET powder at a mass ratio to obtain the highest optical absorbance within the dynamic range (\(DR\)) of the THz-TDS set-up [2]. The highest absorbance \((\alpha d)_{max}\) that can be measured by the THz-TDS set-up is computed by using Equation (1) [2]:  
\[(\alpha d)_{max}=2\ \ln\biggl{(}DR\frac{4n}{(n+1)^{2}}\biggr{)}. \tag{1}\]  
where \(n\) is the refractive index of the sample. The graphs of \(DR\) and \((\alpha d)_{max}\) as a function of the frequency are shown in the Supplementary Materials. To estimate the mass ratios of saccharides to PET in the pellets that give the highest absorbance, the absorption coefficient of the main THz peaks of the saccharides investigated in this work, \(\alpha_{s}^{M}\), together with their refractive indexes in the THz range, were obtained from the preliminary THz-TDS experiments.  
The absorption coefficient of the pellets (\(\alpha_{p}^{M}\)) corresponding to the main saccharide peaks can be expressed as  
\[\alpha_{p}^{M}=\alpha_{s}^{M}\
u_{s}+\alpha_{PET}^{M}(1-\
u_{s}) \tag{2}\]  
where \(\
u_{s}\) is the volume fraction of saccharides, and \(\alpha_{PET}^{M}\) is the absorption coefficient of PET at the frequency of the main saccharide peaks. Thus, the maximum thickness of the pellets is given by  
\[d_{max}=\frac{(\alpha d)_{max}}{\alpha_{p}^{M}}. \tag{3}\]  
In practice, to avoid having a THz signal transmitted through the pellets at the same level as the noise floor of the THz-TDS set-up, a pellet thickness of \(d=d_{max}/3\) was chosen. Then, the masses of saccharides (m\({}_{s}\)) and PET (m\({}_{PET}\)) in the pellets can be calculated as follows:  
\[m_{s}=\rho_{s}V_{p}\
u_{s} \tag{4}\]and  
\[m_{PET}=\rho_{PET}V_{p}(1-v_{s}) \tag{5}\]  
where \(V_{p}=\pi r^{2}d\) is the total volume of the pellet with radius \(r\) (6.5 mm in our case) and the chosen thickness \(d\), and \(\rho_{s}\) and \(\rho_{PET}\) are the mass densities of the saccharides and PET, respectively (values used in the calculations are shown in the Supplementary Materials). The calculated values of the saccharides and PET mass ratios varied from approximately 1:10 to approximately 1:5.
## 4 Results and Discussions
### Optimization
#### 4.2.2 Activation Function and Optimizer  
Next, the activation function and optimizer are to be selected through this optimization process. Sigmoid, Tanh, ReLU, and SELU are compared for the activation-function optimization while RMSProp, AdaGrad, Adam, and Adamax are compared for the optimizer performance. Their characteristics and advantages/disadvantages are summarized in Table 4[25; 26; 27; 28; 29; 30; 31; 32].  
As shown in Figure 9, the Sigmoid function, one of the most classic activation function, provides the best results than any other activation functions. This indicates that the Sigmoid function operates nicely as a classifier for the present goal. As for optimizer, Adamax outperforms other optimizers. In this regard, Sigmoid and Adamax are selected as the activation function and optimizer.  
Figure 7: Time histories of lateral and vertical displacements of the tunnel at its mid-length (numerical sensor No. 5) in two different mooring-failures in the opposite sides.
## Materials and Methods
### Magnetic Field (MF) Application  
An MF can be generated by the circulation of an electric current through a conductor. Hence, coils using enameled copper wire (0.8 mm diameter) were specifically designed and created to generate a homogenous transverse MF in the region of interest (ROI), i.e., in the volume of the PBR. To ensure this homogeneity, two coils (with 400 turns) were used on each side of the flasks, as shown in Figure 1A. A schematic diagram is provided in Figure 2. Each coil was connected to a power source that could provide an adjustable electrical current (in this study, a 3 A current was used). The MF intensity was mapped using an FH 51 gaussmeter (Magnet-Physik) equipped with an HS-TB51 sensor in the ROI. This mapping was performed by measuring the MF intensity at 11 positions along the x-axis (width), 5 positions along the y-axis (thickness) and 5 positions along the z-axis (height) (Figure 3).  
Figure 3: MF intensity mapping at a 7 cm height (middle of the culture vessel). Raw values are available in the Supplementary Materials.  
Figure 2: Schematic diagram of the experimental device used for the _L. indica_ PCC8005 cultivation with magnetic field application through coils. Red arrows indicate the transverse magnetic field.  
Microalgae cultures were exposed to the MF effects for 24 and 1 h per day and they were compared to the control culture (Cc), where no MF was applied and the same conditions of temperature, illumination, aeration and nutrients were used. These application times were chosen in order to evaluate the _L. indica_ behavior under the same conditions as in previous studies on other strains [5; 23; 26; 27] in order to obtain an effective comparison of the results.