esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 4 New Approach: Parallel Vector Processing Based Algorithm
### Transpose  
Referring to the input DW vector as a virtual 2D matrix of \(\left[\left\lceil\frac{N}{4P}\right\rceil\right]\times\left[4P\right]\), the vector is accessed with constant strides defined by the number of columns \(\left[4P\right]\) as seen in Figure 4. This virtual 2D matrix is transposed such that indexes \(j+4P\) are the successors of indexes \(j\). The CEVA-XC4500 DSP best transposes blocks of \(4\times 16\) [DWs]; therefore, a block of 4 rows, each of 16 [DWs], is loaded from the memory. Figure 5 shows an example first block of the virtual 2D matrix illustrated in Figure 4. The \(i^{th}\) block to be transposed is defined by four rows of 16 consecutive DWs. The \(k^{th}\) row of block \(i\) starts at  
\[16i+k\cdot 4P;\;\;k=0,1,2,3,\;\;1\leq i\leq\left\lceil\frac{4P}{16}\right\rceil\]  
and \(\left\lceil\frac{4P}{16}\right\rceil\) is the number of blocks to be transposed. The number of stuffed rows in the last block is \(16\cdot\left\lceil\frac{4P}{16}\right\rceil-4P\).  
Figure 6 shows the same block illustrated in Figure 5. Each row is stored in a continuous manner. The next row is stored at the end of the previous row. The next block rows are stored at the end of the previous block.  
Figure 3: Bit to Word: Padding every bit with 15 zeros.  
Figure 4: Example of a virtual 2D matrix for \(N=776;P=39\).  
Figure 5: The first block of the virtual 2D matrix (highlighted in Figure 4) before transposition.  
Figure 6: Block in Figure 5 after transposition.  
Notes:
* An important part of this phase is swapping the words of odd-indexed \((1,3,5,\ldots,\)\(N-1)\) DWs, meaning that in every odd-indexed DW, the high word and the low word are swapped. This swapping implements the permutation inside the bit-couplet as required. The CEVA-XC4500 DSP can swap words while loading any DW, meaning the bit-level swapping is implemented without influencing the processing time or memory access.
* The chosen vector size and parameter sets used in simulations require transposing matrices of up to eight rows. An expansion of this algorithm for larger-sized matrices might be possible but was not simulated.
# Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models
## 3 Results
### Performance Exploration of Different Depths
In Ms. Pac-Man (Figure 11), the seven-layer network demonstrates good performance in strategy diversity and score stability. The six-layer network shows slight deficiencies in accurately recognizing complex paths, while the eight-layer network has strategy confusion due to its high complexity. In Bowling (Figure 12), although network depth varies, scores generally hover around 1, with occasional changes. As a relatively simple game requiring minimal complex strategies or decision processes, even shallower networks can achieve high scores. River Raid requires high reaction speed and precise control (Figure 13). The seven-layer network outperforms other depths in both score and stability. Although the eight-layer network can extract complex features, it performs poorly in rapidly changing environments. The six-layer network fails to adequately handle the dynamic environment, showing slower score improvements. Figure 8: Performance of different network depths in Seaquest. Figure 10: Performance of different network depths in Enduro. Figure 9: Performance of different network depths in QBert requires complex strategy adjustments due to its dynamic environment. Overall, the seven-layer network performed best in most games, showing good robustness and adaptability. This may be because the seven-layer network has achieved a good balance between feature extraction and policy learning capabilities. It is neither too shallow to capture complex patterns nor too deep to cause overfitting or increased training difficulty. However, in some games (such as Enduro and Bowling), the performance of networks of different depths is not much different. This may be because the environmental complexity of these games is relatively moderate, and the strategy requirements and reaction speed requirements do not significantly enhance the advantages of deep networks. In these scenarios, the choice of network depth does not seem to be the main bottleneck of model performance, so the network design needs to be flexibly adjusted according to specific task requirements.
## 3 Thermo-Mechanical Analysis and Further Design Improvement of the WCLL COB Segment TC Region  
Once a TC region geometric configuration able to fulfil the thermal design criterion on the Eurofer maximum temperature is found, its structural performance under the nominal and reference accidental loading conditions are investigated. In particular, the thermo-mechanical behavior of the TC region layout against the over-pressurization (OP) loading scenario, conservatively representing the loading conditions due to an in-box loss of coolant accident, is assessed and firstly presented, as it represents the design basis accident for the DEMO BB [15]. Then, if necessary, the obtained results under nominal conditions (normal operation, NO, scenario) are shown as well to demonstrate the soundness of the proposed design solution in the two main loading scenarios foreseen for the DEMO BB. An isotropic behavior is assumed for the structural materials, and linear-elastic analysis is performed.  
Regardless of the scenario, the following set of loads and boundary conditions is applied, with the proper differences in the numerical values due to the considered scenario:  
* Design pressure onto the water-wetted surfaces, equal to 17.825 MPa, given by the coolant nominal pressure increased by a factor of 1.15 [15].
* Design pressure onto the breeder-wetted surfaces, equal to 0.575 MPa (namely the breeder operational pressure times 1.15) in the NO scenario, whereas the water design pressure (17.825 MPa) is assumed to be acting onto breeder-wetted surfaces in the case of the OP scenario.
* Dead weight of structure and breeder, imposed assuming a purposely calculated Eurofer equivalent density (as a function of temperature) to take into account the weight of the breeder, not directly simulated in the mechanical analysis.
* Ferromagnetic forces spatial distribution, due to the ferromagnetic nature of the Eurofer steel [19; 20].
* Displacement field applied to the model lower surface, obtained from the analysis of the thermo-mechanical behavior of the whole WCLL COB segment [21], in order to simulate the effect of the rest of the COB segment on the investigated region. In particular, depending on the scenario, the appropriate displacement field is extracted from the corresponding COB analysis and applied to the present model. As an example, the displacement field applied for NO analysis is shown in Figure 7.  
Figure 7: Displacement field applied onto the model lower surface in NO analysis.
# Adaptive Driver Face Feature Fatigue Detection Algorithm Research
## 4 Experiment and Analysis
### The Datasets  
In this research, two primary types of datasets were used for experiments to test the efficacy of the proposed _MAX-MIN_ algorithm in the driver fatigue detection task. The model and algorithm were used to test the driver's yawning and closed eyelids. For eye and mouth detection, the first class chooses the publicly accessible YawDD video dataset; videos of many drivers behind the wheel can be found there. The movies are split into two sections: one was recorded from the camera mounted beneath the rearview mirror of the vehicle and measured the driver; the other was recorded from the camera mounted above the dashboard and faces the driver, capturing a 30-frame-per-second frontal image of the driver. Each driver had three to four videos taken. The generalizability of the model was tested using this dataset. The dataset's frontal portion was used in this paper. There are 322 films in this dataset showing drivers of various races, including men and women, wearing and not wearing eyeglasses and sunglasses. Figure 7 displays a few examples of the YawDD dataset. The second dataset is our own SBD. To collect data for our study, six healthy drivers (three men and three women, aged 20 to 40 years) were recruited to participate in the SBD real-world driving environment. Participants were required to possess a valid driver's license and log more than 1000 km annually. Each driver recorded approximately 8 min of their driving behavior while adhering to instructions that prohibited mobile phone use, smoking, conversing with passengers, yawning while dozing off, or closing their eyes. The driving conditions were sunny, with clear roads and safe traffic, providing an optimal day for data collection. The data were collected between 8:00 and 17:00. The video footage captured various realistic scenarios of driving behavior, such as yawning, conversing, and closing one's eyes. Figure 8 displays an example scenario of a driver yawning in the video. In addition to the real-world data, we also included a 30 s video of simulated fatigue as part of our experimentation for detection purposes. It is important to note that all participating drivers were in excellent physical health and had no relevant illnesses.
# A Novel Digitized Method for the Design and Additive Manufacturing of Orthodontic Space Maintainers
## 1 Introduction and Background
Furthermore, while additive manufacturing reduces geometric constraints, it may also introduce alternative issues, including the choice of appropriate materials, printing resolution, post-processing treatments, structural integrity, and cost implications. Material extrusion technologies, for example, cannot print fine details with high surface quality, while micro laser sintering technologies are expensive solutions. In addition, parts manufactured by micro laser sintering also present lower surface finishing. A smooth surface finish, instead, is desirable to minimize potential irritation or discomfort in the oral cavity. For this reason, post-processing treatments such as sanding, polishing, or coating may be required to achieve the desired surface quality [21]. The present work proposes a fully digital framework to produce customized pediatric unilateral space maintainers that address most of the above-described limitations. The procedure starts with an intraoral scanning of the dental anatomy and integrates generative 3D modeling, finite element analysis (FEA), and additive manufacturing. Generative algorithms are used to enhance flexibility in creating free-form geometries providing more freedom for designing complex shapes and structures. A finite element model has been developed to evaluate the structural behavior of the designed space maintainer. In particular, the finite element analysis has been conceived to simulate how an incorrect loading condition of the device could affect its functionality. Specifically, the analysis considered potential out-of-plane loading caused by external elements exerting pressure on the device during occlusion movement. Finally, the space maintainer has been additively manufactured by using a bio-compatible photosensitive resin and vat photopolymerization, specifically Digital Light Processing (DLP) technology. DLP 3D printers enable to produce high-resolution parts with fine details and smooth surface finishes. Also, they support a wide range of photopolymer materials specifically formulated for dental applications. These materials are biocompatible, ensuring they are safe to use in the patient's mouth. They also offer various mechanical properties, such as flexibility, strength, and durability, allowing for customization based on the specific requirements of the space maintainer. The main aim of this study is to showcase the practical feasibility of a fully digital workflow in evaluating the mechanical behavior and producing orthodontic space maintainers prior to their implementation with patients.
# Detection of Welding Defects Using the YOLOv8-ELA Algorithm
## 2 Methodology  
### YOLOv8-ELA Model Architecture  
The proposed YOLOv8 model retains the Darknet53 structure [26] as the main trunk network for feature extraction, a design that has been used since YOLOv3. However, it replaces the residual modules in the original Darknet53 with the C2f module. The proposed C2f module synthesizes the architectural characteristics of YOLOv5's C3 component through its hierarchical topology while incorporating the multi-branch connectivity paradigm from YOLOv7's ELAN architecture [27]. This hybrid configuration enhances the gradient flow propagation through residual shortcut connections and optimizes the multi-scale feature fusion via parallel computation branches, achieving balanced computational efficiency and representational capacity compared to conventional aggregation approaches. This design ensures a lightweight architecture while providing richer gradient flow information. For hierarchical feature integration, YOLOv8 maintains the architectural paradigm of PANet-based cross-scale feature aggregation, which establishes a bidirectional information flow between the feature pyramids through top-down and bottom-up pathways. In the prediction head, YOLOv8 adopts a decoupled head with separate branches for classification and regression, abandoning the anchor-based strategy in favor of an anchor-free approach. The architectural framework of the YOLOv8 network is visually represented in Figure 1.  
The architectural framework of the enhanced YOLOv8 network, demonstrating its modified topology and connectivity patterns, is visually represented in Figure 2. The primary architectural innovation involves superseding the conventional Darknet53 backbone with an ELA-HS-FPN configuration, embedding the ELA attention mechanism [28; 29] to optimize the feature representation within the hierarchical scale-aware pyramid framework. This structural refinement achieves simultaneous improvements in the model parameters by 34.1% (from 3.01 M to 1.98 M) through the implementation of depthwise separable convolutions and parameter-sharing mechanisms, while maintaining competitive performance metrics (1.3% [EMAIL_ADDRESS] increase). Furthermore, the integration of SimAM's dynamic channel-spatial attention mechanism [30; 31] introduces an adaptive receptive field adjustment, significantly enhancing the feature discriminability for sub-30-pixel objects in low-resolution scenarios through its energy-function-driven weight allocation strategy.  
Figure 1: YOLOv8 frame diagram.
## 4 Discussion
### Recommendations for Future Studies  
The findings from this study provide support for accurate and reliable measurement of FPA during gait using Opal IMUs, and are specifically appropriate for gait retraining therapy clinical scenarios. Further research should be conducted with the acquisition of the FPA using dedicated IMU FPA algorithms or the use of artificial intelligence, to make more reliable estimates. Future research on IMUs should also focus on determining optimal FPA feedback methods for participants undergoing gait retraining. Some research has already been conducted with 3D motion analysis and a proof-of-concept a haptic feedback-sensorized shoe in combination with a target FPA [22]. Clinical studies of long-term duration are needed to determine the best methods to change gait in people with knee OA.
## 3 Results and Discussion
### Diurnal Concentration and Emission Profiles
#### 3.2.2 Nm
NH\({}_{3}\) concentrations in CC, AV, and NM with manure belt systems were well controlled, and all were under the recommended threshold of 15 mg/m\({}^{3}\) to maintain a good environmental quality given by the Ministry of Agriculture and Rural Affairs of the People's Republic of China [35]. Furthermore, it was found that NH\({}_{3}\) concentrations and emission rates were sharply dropped down after manure removal. In CC, AV, and NM, NH\({}_{3}\) concentrations were reduced by 64.29%, 28.57%, and 35.71% after manure clearance, and consequently, their emissions were lowered by 67.12%, 71.36%, and 55.69%, respectively. It is suggested that the manure belt could efficiently reduce NH\({}_{3}\) concentrations and the emissions as well by thoroughly and timely removing manure outside of the laying hen houses, which was also supported by [PERSON] and [PERSON] [18] and [PERSON] et al. [36].
# Efficacy of Virtual Reality in Painting Art Exhibitions Appreciation
###### Abstract  
Virtual reality (VR) technology has been employed in a wide range of fields, from entertainment to medicine and engineering. Advances in VR also provide new opportunities in art exhibitions. This study discusses the experience of art appreciation through desktop virtual reality (Desktop VR) or head-mounted display virtual reality (HMD VR) and compares it with appreciating a physical painting. Seventy-eight university students participated in the study. According to the findings of this study, painting evaluation and the emotions expressed during the appreciation show no significant difference under these three conditions, indicating that the participants believe that paintings, regardless of whether they are viewed through VR, are similar. Owing to the limitation of the operation, the participants considered HMD VR to be a tool that hinders free appreciation of paintings. In addition, attention should be paid to the proper projected size of words and paintings for better reading and viewing. The above indicates that through digital technology, we can shorten the gap between a virtual painting and a physical one; however, we must still improve the design of object size and the interaction in the VR context so that a virtual exhibition can be as impressive as a physical one.  
virtual reality; painting appreciation; virtual exhibition; exhibition experience +
Footnote †: journal: Applied Sciences
# Impact of Chitosan-Genipin Films on Volatile Profile of Wine along Storage
## 2 Materials and Methods
### Analysis of Volatile Compounds  
#### 2.4.1 Extraction of Volatile Compounds  
The volatile compounds were extracted using headspace solid phase micro-extraction (HS-SPME) according with the methodology described by [PERSON] et al. [21]. Briefly, 3 mL of the samples (wine model solutions and white and red wines) and 0.6 g of sodium chloride were placed into vial (9 mL) and immersed in a thermostated bath at 40.0 \(\pm\) 0.1 \({}^{\circ}\)C. Then, SPME fibre was inserted into the headspace along 20 min, under continuous stirring at  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Standard** & **C\({}_{\text{vial}}\) (mg/L)** & **Sampling Time** & **MW** & **Log P** & **BP (\({}^{\circ}\)C)** \\ \hline Hexanol & 2.4 & 20, 80, 140, 260 min & 102.17 & 2.03 & 158 \\ Octan-3-one & 0.058 & 20, 80, 140, 260 min & 128.21 & 2.30 & 168 \\ Hexanal & 17.8 & 20, 80, 140, 260 min & 100.16 & 1.78 & 130 \\ Benzaldehyde & 1.0 & 20, 80, 140, 260 min & 106.12 & 1.48 & 179 \\ Ethyl acetate & 9.6 & 20, 80, 140, 260 min & 88.11 & 0.73 & 77 \\ Ethyl hexanoate & 0.11 & 20, 50, 80, 140, 200, 260 min & 144.21 & 2.83 & 167 \\ Hexanoic acid & 10.9 & 20, 50, 80, 140, 200, 260 min & 116.16 & 1.92 & 205 \\ Arabinese & 1.5 & 0, 34, 41, 49, 106, 120 days & 150.13 & & \\ Leucine & 1.31 & 0, 30, 60, 74, 91, 120 days & 131.17 & & \\ Phenylalanine & 1.65 & 0, 34, 41, 49, 106, 120 days & 165.16 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of wine model solutions in contact with chitosan-genipin films, sampling time, and compounds physico-chemical parameters: molecular weight (MW, g/mol), logarithm of the octanol/water partition coefficient (Log P), and boiling point (BP, \({}^{\circ}\)C).  
400 rpm, to cause the extraction and concentration to occur in the same step. The assays were performed in triplicate.
# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 3 Data and Methods
### Vehicle Detection and Tracking
For geospatial trajectory mapping, the vehicle tracks were identified, and the pixel coordinates were converted into corresponding geographical coordinates using a transformation matrix obtained by implementing the automatic georeferencing workflow using SIFT. The geographical coordinates of tracks across each frame were stored along with other relevant information, including the frame number, distance traveled, track ID, and class ID, in JSON format. The vehicle velocities were calculated and compared with observations taken from a speed gun using the logged information from the object tracking.
## 5 Experiments and Results
### Classification Models  
When implementing the experiments for the classification models presented in Section 4.4.2, we measured the AUC ROC metric. From Table 6, we found that the best classification performance was obtained by _Model 4 (CatBoost)_ in both experiments, with an AUC ROC of at least 0.7359, and surpassing the second-best model by 0.065 points in the worst case. However, best results were achieved in Experiment 2.  
The second-best model in Experiment 1 was the _Model 2 (MLPC)_, and the _Model 1 (LgR)_ for Experiment 2. When comparing the model performance across experiments, we observed an increased performance in Experiment 2 for the _Baseline 2 (LR)_ and _Model 2 (SVC)_ models. On the other hand, a decreased discrimination power was measured for _Model 1 (LgR)_, and _Model 3 (MLPC)_. _Model 2 (SVC)_ performed worse than a zero forecast in both experiments, but this difference was not statistically significant in Experiment 2. We hypothesize that the performance decrease in Experiment 2 for certain models can be related to the stronger class imbalance (8% event occurrence in Experiment 1 is reduced to 5% event occurrence in Experiment 2). Such imbalance influences the learning of the algorithms, and most likely affects the discrimination power of the trained models. We found the differences between results within the experiments were statistically significant, except between the _Baseline 1 (zero forecast)_ and _Model 2 (SVC)_ in Experiment 2.  
Finally, we analyzed which features were considered most informative under the mutual information criteria for each experiment. For Experiment 1 the most informative features were the readings from the pressure sensor, categorical features indicating whether the sensed pressure is below 7.14 kg/cm\({}^{2}\), or below 7.06 kg/cm\({}^{2}\), and features from _Features Group 4_ (see Table 3). This changed for Experiment 2, where the most important features were related to readings from the pressure sensor and the _Features Group 5_.
## 4 Method
### The Proposed Scheme Uses Extended Chaotic Maps
#### 4.2.1 Parameter Initialization Phase  
The registration center prepares the necessary parameters, which are the chaotic map function \(T(\cdot)\), the hash function \(h(\cdot)\), a random number \(x\), a prime number \(p\), and publishes \(x,p\), \(T(\cdot)\), \(h(\cdot)\).
## 4 Results and Analysis
### EV Penetration in Rural Network
#### 4.2.3 Case 7: 60% EV Penetration in Rural Network  
Increasing diffusion up to 60% of the total car fleet of EVs in a rural network can still be managed without any criticalities as observed in Figure 15 both for the MV/LV transformer and the most loaded feeder voltage profile. In fact, the transformer hardly works above 100% of its nominal capacity, which was overcome only in the peak hour. The voltage profile is never below 0.965 so widely within the limits, with a variation around 1% in the worst case.  
Figure 14: Comparison of rural (a) transformer loadings and (b) most loaded feeder voltage profiles with 40% EV penetration.  
Figure 13: Comparison of rural (a) transformer loadings and (b) most loaded feeder voltage profiles with 20% EV penetration.