esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Growth Performance and Nutrient Digestibility  
In the current study, the effect of the dietary inclusion of VBP supplements on growing-finishing pigs' growth performance and nutrient digestibility is illustrated in Tables 3 and 4. There was no significant difference (\(p>0.05\)) observed in body weight, ADG, ADFI, and G:F ratio among treatments over the entire experimental period. In addition, no significant differences were observed (\(p>0.05\)) in dry matter and nitrogen digestibility among treatments during the phase I and phase II feeding trials. However, there was a tendency for the apparent total tract digestibility (ATTD) of dry matter to linearly increase (\(0.05<p<0.10\)) during phase III.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{4}{c}{**VBP, g/kg**} & \multirow{2}{*}{**SEM**} & \multicolumn{3}{c}{**\(p\)-Value**} \\ \cline{2-2} \cline{5-8}  & **0** & **10** & **20** & **30** & & **Linear** & **Quadratic** \\ \hline \multicolumn{8}{c}{Body weight, kg} \\ Initial & 26.06 & 26.05 & 26.05 & 26.05 & 0.528 & 0.984 & 0.992 \\ Phase I & 45.53 & 45.74 & 46.23 & 45.92 & 0.955 & 0.698 & 0.789 \\ Phase II & 66.30 & 68.01 & 67.94 & 67.19 & 1.335 & 0.666 & 0.365 \\ Phase III & 95.80 & 97.92 & 97.77 & 97.17 & 1.805 & 0.628 & 0.459 \\ \multicolumn{8}{c}{Phase I} \\ Average daily gain, g & 695 & 703 & 720 & 710 & 17.85 & 0.447 & 0.608 \\ Average daily feed intake, g & 1704 & 1731 & 1753 & 1734 & 45.48 & 0.591 & 0.624 \\ Gain:feed ratio & 0.408 & 0.406 & 0.412 & 0.409 & 0.003 & 0.443 & 0.829 \\ \multicolumn{8}{c}{Phase II} \\ Average daily gain, g & 742 & 753 & 776 & 759 & 18.04 & 0.359 & 0.459 \\ Average daily feed intake, g & 2039 & 2035 & 2072 & 2063 & 51.23 & 0.642 & 0.967 \\ Gain:feed ratio & 0.363 & 0.370 & 0.376 & 0.368 & 0.01 & 0.439 & 0.235 \\ \multicolumn{8}{c}{Phase III} \\ Average daily gain, g & 843 & 843 & 852 & 857 & 16.41 & 0.497 & 0.904 \\ Average daily feed intake, g & 2437 & 2470 & 2413 & 2435 & 44.37 & 0.747 & 0.902 \\ Gain:feed ratio & 0.391 & 0.383 & 0.399 & 0.399 & 0.006 & 0.130 & 0.414 \\ \multicolumn{8}{c}{Overall} \\ Average daily gain, g & 766 & 773 & 788 & 782 & 15.20 & 0.379 & 0.683 \\ Average daily feed intake, g & 2142 & 2158 & 2156 & 2159 & 34.39 & 0.759 & 0.857 \\ Gain:feed ratio & 0.357 & 0.358 & 0.366 & 0.362 & 0.003 & 0.161 & 0.569 \\ \hline \hline \end{tabular} \({}^{1}\) Abbreviations: VBP, by-products obtained after the extraction of vitamin B\({}_{2}\) from fermented soybean.  
\end{table}
Table 3: Effect of dietary vitamin B\({}_{2}\) from fermented soybean VBP supplementation on growth performance in growing-finishing pigs \({}^{1}\).
# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 2 Methods
### Weighted Joint Loss Function  
Considering the imbalanced distribution of background and vessel pixels in retinal fundus images, a weighted joint loss function composed of weighted binary cross-entropy loss and dice loss is used to train the network and it is defined as follows:  
\[L=L_{WBCE}+\alpha L_{Dice} \tag{10}\]  
\[L_{WBCE}=-\frac{1}{N}\sum_{i=1}^{N}\left(\beta y_{i}log(p_{i})+(1-y_{i})log(1-p _{i})\right) \tag{11}\]  
\[L_{Dice}=1-\frac{2|X\cap Y|}{|X|+|Y|} \tag{12}\]  
To address the imbalanced problem, a combination of the weighted binary cross-entropy loss for pixel-wise classification and the dice loss for crossing regions is employed. In Equation (10), the hyperparameter \(\alpha\) is introduced to balance these two losses and set it to 0.9 in the experiment. Equation (11) involves the number of pixels \(N\) in each training image and the ground truth \(y_{i}\) and predicted probability \(p_{i}\) of pixel \(i\), where \(\beta\) is a balance factor used to measure the false positive rate and false negative rate. When \(\beta<1\), the false positive rate decreases while the false negative rate increases. Contrarily, when \(\beta>1\), the false negative rate decreases while the false positive rate increases. In the context of retinal vascular segmentation, false positives may result in overtreatment, while false negatives may lead to missed diagnosis or inadequate treatment, increasing the risk of disease deterioration and visual loss. Therefore, it is crucial to minimize the false negative rate. In this study \(\beta=2\). In Equation (12), \(X\) represents the magnitude of the segmentation result, and \(Y\) represents the magnitude of the ground truth. \(|X\cap Y|\) represents the common elements between \(X\) and \(Y\). This formula is a loss function widely employed for image segmentation tasks.
## 3 Results
### Multinomial Naive Bayes  
Using dataset version A and character-level tokenization, the results of the hyperparameter sweep on vocabulary and tokenization are presented in Figure 5. The tokenization approaches included either CountVectorizer or TF-IDF vectorization and explored unigrams, unigrams/bigrams, and unigrams/bigrams/trigrams. The results were similar for CountVectorizer (a) and TF-IDF vectorization (b) methods, where the highest performance was found using the full 5888-token vocabulary. By a small margin, the best character-level model used TF-IDF, unigrams/bigrams, and possessed 78.2% accuracy.  
For word-level tokenization, the hyperparameter sweep was repeated and the results are presented in Figure 6. The approach differed from the character-level tokenization by exploring the full vocabulary of the 167,506 tokens that resulted from applying the jieba method. Again, the results were similar for CountVectorizer (a) and TF-IDF vectorization (b) methods. The best model used the CountVectorizer, unigrams with an 84,025-token vocabulary and possessed 82.8% accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} batch size = 32 \\ \end{tabular} & 80.8 & 80.8 & 80.8 & 80.8 & 80.8 & 80.8 & 80.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy (%) on the test/holdout dataset for data preparation steps A-F and a variety of algorithms and encodings. Columns B\(-\)F also include the difference from the baseline column A. HP: hyperparameter; TF-IDF: term frequency-inverse document frequency; NN: neural network.  
Figure 5: Multinomial Naive Bayes algorithm: character-level model accuracy vs. character vocabulary for CountVectorizer (**a**) and TF-IDF vectorizer (**b**). Using the test dataset, unigram performance is shown in blue, unigram/bigram accuracy in orange, and unigram/bigram/trigram in green.
## 4 Numerical Methodology and Simulation Setup
### Spray-Wall Impingement  
As pointed out above, in this work the applied spray-wall interaction model is based on [PERSON] [35] in which the spray regimes are defined by means of two dimensionless parameters, T* and K, describing the wall temperature and the incident droplet characteristics, respectively:  
\[\mathrm{T^{*}}\equiv\frac{\mathrm{T_{W}}}{\mathrm{T_{sat}}} \tag{8}\]  
where T* represents the dimensional temperature, TW is the wall temperature and Tsat the saturation/boiling point temperature of liquid:  
\[\mathrm{K}=\;\mathrm{We}^{\frac{\delta}{\mathrm{S}}}\mathrm{La}^{\frac{1}{ \delta}},\;\mathrm{We}=\frac{\rho\mathrm{V_{n}^{2}d}}{\sigma}\;,\;\mathrm{La}= \frac{\rho\sigma d}{\mu^{2}} \tag{9}\]  
where K represents the kinematic parameter of droplet as function of Weber- and Laplace number, with \(\rho,\;\sigma\) and \(\mu\) being the droplet density, surface tension and viscosity, respectively. Vn is the droplet velocity normal to the wall with diameter d. This way, various regimes such as rebound, splashing, deposition and thermal breakup can be defined in a K \(-\) T* diagram, as suggested by [PERSON] [35].
# Underwater Target Recognition Based on Multi-Decision
### Contributions  
The main contributions of this paper are summarized as follows:  
1. In contrast to the traditional algorithm, we use the decomposition algorithm based on resonance signal to preprocess the signal. Based on the multi-step decision algorithm with the line spectrum characteristic cost function [4], this paper proposes the specific calculation method of double threshold. In the purpose, this algorithm not only retains the continuous spectrum information in the original LOFAR spectrum, but also merges the extracted line spectrum with the original LOFAR spectrum. Finally, the breakpoint completion of the LOFAR spectrum is realized.
2. To further improve the recognition rate of underwater targets, we adopt the enhanced LOFAR spectrum as the input of CNN and develop a LOFAR-based CNN (LOFAR-CNN) for online recognition. Taking advantage of the powerful capability of CNN in feature extraction, the proposed LOFAR-CNN can further improve the recognition accuracy.
3. Simulation results demonstrate that when testing on the ShipsEar database [5], our proposed LOFAR-CNN method can achieve a recognition accuracy of 95.22% which outperforms the state-of-the-art methods.
## 3 Results and Discussion
### Answering RQ3 to RQ5
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Ref.** & **Programming Language** & **Functionalities Library/SDK** & **3D Content Creation** \\ \hline
2020 & [126] & C++ & N/A & N/A \\ \hline
2020 & [139] & C\# & YOLO, Unity3D, Vuforia Engine & Autodesk Inventor \\ \hline
2020 & [150] & C\# & Unity3D & N/A \\ \hline
2020 & [151] & N/A & Holo Toolkit & N/A \\ \hline
2020 & [162] & C\# & Unity3D, ARCore SDK & Catia \\ \hline
2020 & [163] & N/A & ROS, Hololens & N/A \\ \hline
2020 & [164] & N/A & Unity3D & N/A \\ \hline
2020 & [182] & C\# & Unity3D, Vuforia & N/A \\ \hline
2020 & [192] & C\# & ARToolkit, Vuforia, Wikitude, EasyAR & N/A \\ \hline
2020 & [194] & N/A & Hololens & Siemens NX, Blender \\ \hline
2020 & [203] & N/A & Unity 3D, MicrosoftMixed Reality Toolkit & N/A \\ \hline
2020 & [210] & C++ & ROS, OpenCV, PCL &
\begin{tabular}{c} Based on 3D point cloud extraction and algorithm \\ \end{tabular} \\ \hline
2020 & [211] & N/A & Optitrack, Oculus Rift DK2 & SolidWorks \\ \hline
2020 & [222] & C\# & Hololens, Unity3D, Holotoolkit & N/A \\ \hline
2020 & [235] & C++, C\# & OpenCV, Unity3D & N/A \\ \hline
2019 & [8] & N/A & Unity3D, Hololens, MicrosoftMixed Reality Toolkit, Vuforia & N/A \\ \hline
2019 & [64] & C++ &
\begin{tabular}{c} Visual Studio, OpenCV, Eigen 3.2.8 for OST, \\ VST calibration \\ \end{tabular} & N/A \\ \hline
2019 & [73] & C++ & OpenCV & N/A \\ \hline
2019 & [103] & C\# & Unity3D, Vuforia & N/A \\ \hline
2019 & [104] & N/A & Vuforia, Catia, PiXYZ software, Unity3D & N/A \\ \hline
2019 & [112] & C++ & Microsoft Visual Studio, OpenCV & Coin3 Ds \\ \hline
2019 & [130] & C\# & Unity3D & N/A \\ \hline
2019 & [140] & PHP & Matlab, Unity, Vuforia, Optitrack & N/A \\ \hline
2019 & [141] & C\# & Hololens, Unity3D, Vuforia & 3 Ds Max or Blender, Catia \\ \hline
2019 & [154] & C++ & ViSP library (Visual Servoing Platform), OpenCV & Ogre3D \\ \hline
2019 & [155] & C\# & Unity3D, MixedRealityToolkit & N/A \\ \hline
2019 & [157] & C\# & Unity3D, Vuforia & N/A \\ \hline
2019 & [165] & Visual Basic & Unity3D, Vuforia & N/A \\ \hline
2019 & [174] & N/A &
\begin{tabular}{c} ROS, Unity3D, \\ Microsoft Mixed Reality Toolkit \\ \end{tabular} & N/A \\ \hline
2019 & [175] & C\# & Unity3D, HoloToolKit, Vuforia & N/A \\ \hline
2019 & [183] & C\# & Unity3D, Vuforia & N/A \\ \hline
2019 & [187] & C\# & Unity, Vuforia & 3 ds Max \\ \hline
2019 & [195] & N/A & Google Project Tango SDK (instead of Vuforia), Unity3D & N/A \\ \hline
2019 & [196] & N/A & Hololens, Unity, Visual Studio, Vuforia Engine, MixedRealityToolkit & Blender \\ \hline \hline \end{tabular}
\end{table} 
Table 16: _Cont._
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 5 Opportunities in the 5G Era
### AR-Enabled Fire Evacuation  
Augmented Reality (AR) is becoming one of the most promising technologies to facilitate evacuation safer, quicker, and easier. Unlike Virtual Reality (VR), AR overlays digital or computer-generated information over a real-time environment. Thus, AR supplements reality rather than completely replacing it. In building fire evacuation, with the use of asmartphone or head-up display (HUD), the virtual indicators can be superimposed onto the real objects in buildings, such as exits, stairwells, and hazardous areas. In this way, occupants can be alerted and find evacuation routes immediately.  
The application of AR facilitating fire evacuation has been studied a lot. In the study of [PERSON] and [PERSON] [85], AR technologies, route planning, and three-dimensional graphics technologies were integrated to provide evacuation route guidance by giving virtual indicators on the escape route in the context of a radioactive accident. An AR-based building fire evacuation system was developed by [PERSON] et al. [86]. The system applied a Microsoft HoloLens augmented/mixed reality device to provide a visual representation of a building in 3D space and allowed the occupants to see where exits were in the building. Moreover, AR technologies were applied to help firefighters search and rescue in fire emergencies. The C-Thru helmet from Qwake Technologies was developed to help firefighters' fire-ground operations by showing the silhouettes of otherwise indiscernible objects and people on the fire-ground [87]. However, limited by the mobile communications previously used that possess low speed and high latency, the application of AR is not to scale. Using AR in fire evacuation is still being explored.  
In the 5G era, this situation will change. The high bandwidth and ultra-low latency that the 5G network provides will bring more new experiences to AR-enabled fire evacuation. In the future, occupants can use AR to identify the risks in surroundings during the evacuation, such as falling ceilings, thick gas layers, or even collapsing walls. In this case, it is necessary to enable real-time video streaming via the 5G network because the captured video must be transmitted to a local or cloud processor for visual-based data processing in real-time due to the limitation of the computing power on the end device. Moreover, with the edge computing work with 5G, the network latency will be highly reduced by processing data closer to the end-user. Low wireless latency improves the immersion of the users, helping evacuees receive feedback from the processor more quickly and receive better information acquisition from the interaction with the environment.
## 5 Analysis and Verification
### Comparison with Field Measured Ddata  
The comparison results of the analytical solution, FDM-DEM coupling, and field measured data are shown in Figure 9. The results indicate that the analytical solution proposed in this study and the numerical solution based on FDM-DEM coupling are both in good agreement with the field measured data. The numerical solution based on FDM-DEM coupling is slightly larger than the measured data when analyzing the surface settlement on the left side of the tunnel. The displacement of surface settlement trough at x = \(-\)30 m is \(-\)1.893 mm, which is larger than \(-\)0.459 mm calculated by analytical solution. The reason for this phenomenon is that in the FDM-DEM coupling calculation, the calculation mode of the continuum medium needs to be adjusted to the'model largestrain' mode. The results of soil displacement are more significantly affected by the parameters, and the calculation results may be larger than the measured data. This finding clearly indicates that when isolation piles are pre-installed on one side of the tunnel, surface settlement tends to develop more on the opposite side. In the meantime, the surface settlement trough could be discontinuous while passing through the position of isolation pile in the actual situation.  
Figure 8: Results of FDM-DEM Coupling.  
Figure 9: Comparison with field measured data.
# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.2 Traffic Sign and Light Recognition  
One of the most important aspects of a safe and better decision-making process for automotive driving system was traffic sign and light identification by regulating traffic, monitoring and avoiding accidents through warning the drivers. Traffic sign and light recognition systems follow a double-step process, detection and classification, where detection denotes correctly spotting the geometric position in the image and classification means identification of the category in which the detected sign or light signal appears [28; 188]. A bio-mechanism inspired novel architecture named Branch Convolutional Neural Network (BNN) was proposed by [PERSON] et al. for traffic sign recognition [46]. To improve the recognition machine speed and accuracy, a branch-output mechanism which was placed between pooling and convolutional layer and added to the framework. Furthermore, instead of the initial output layer, the sign in the preceding branch was projected by the BCNN that results perfect prediction in partial visibility of road signs with 98.52%accuracy based on German Traffic Sign Recognition Benchmark (GTSRB). For complex visual scenarios, BCNN based approach worked very well for traffic sign recognition. [PERSON] et al. trained the 16 different forms of Korean traffic sign with LeNet-5 CNN architecture for a real-time traffic sign recognition system where the training set had 25,000 positive and 78,000 false samples [47]. The method obtained up to 99.95% within a fast-processing time. The applied color property-based CNN approach could be very efficient for lightweight traffic sign detectors for AVS as well as achieving the highest accuracy. An improved traffic sign recognition algorithm was demonstrated by [PERSON] et al. for an intelligent driving system [48]. For accurate detection spatial threshold segmentation, the HSV color space was utilized, and traffic signs were identified accurately depending on shape features and processed with LeNet-5 CNN architecture with Gabor kernel, which was the primary convolutional kernel, and batch normalization was applied after the pooling layer. The Adam model was also implemented as the optimizer algorithm. The proposed methodology was applied to the German Traffic Sign Recognition Benchmark and obtained 99.75% accuracy with 5.4 ms per frame on average, which was higher in both sectors than [189, 190], where the accuracies were 98.54% in 22 ms and 95.90 in 5.4 ms, respectively, adopting HoG + PCA and multilayer perceptions methods.
## 7 Conclusions and Future Work  
In this paper, we presented an approach for classifying requests for public administration documents in Moroccan dialect (Darija). The proposed method is based on machine learning algorithms which include supervised learning, deep learning, and large language models. The solution also includes the potential for implementing the appropriate model on a chatbot. First, we collected a dataset of 3000 rows and three classes: birthday certificate, national identity card and passport. Then, we applied a new data augmentation technique based on ChatGPT, called 'AugGPT', to cope with the overfitting problem arising from the small size of the initial dataset. Then, we conducted a comparative study of various supervised learning, deep learning, and LLM algorithms. Findings from the experimental study showed that deep learning algorithms improved the performance of the classification system compared to supervised learning and LLMs algorithms. RNN performed best among deep learning-based algorithms, with an accuracy of 93%. Ultimately, the optimal model will be deployed in a chatbot designed to generate responses in Moroccan dialect for users requesting documents from the public administration, provided that requests remain short, not exceeding two or three sentences, to ensure high accuracy.  
For future research, we will focus on expanding and refining the dataset to facilitate the integration of LLMs, given the current advancements in chatbot development. Additionally, we plan to convert the text dataset into an audio dataset, establishing the first Moroccan dialect audio dataset dedicated to treating administrative document requests. This will pave the way to the development of a voice assistant designed to help illiterate individuals formulate requests more fluently and effectively.  
Conceptualization, H.Z.; Data Curation, H.Z.; Formal Analysis, R.K.; Investigation, A.M. and M.O.; Methodology, R.K. and A.M.; Project Administration, R.K. and A.M.; Resources, H.Z.; Software, H.Z.; Supervision, A.M. and M.O.; Validation, A.M. and M.O.; Writing--Original Draft, H.Z.; Writing--Review and Editing, R.K., A.M. and M.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study did not require ethical approval.  
Not applicable.  
Data Availability Statement: Data will be made available upon request.  
We would like to express our sincere gratitude to [PERSON], Founder and Chief Strategist of Tawjeeh Consulting, for his support in sharing the data collection link with his Instagram community. His contribution played a significant role in the successful completion of this project.  
The authors declare no conflicts of interest.  
Figure 24: Proposal of Mou3 in chatbot answering voice request.
# Blockchain Technology and Its Potential to Benefit Public Services Provision: A Short Survey
## 5 Applications
### Digital Identity Management  
As the name already suggests, digital identity is nothing more than a digital representation of a person's physical identity, which enables access to services once it has been verified. According to [62; 74], public areas of potential blockchain application involve decentralized systems for personal identification, digital signature, and access authorization, with clear advantages in terms of disintermediation, process simplification, trust, and decreased costs. Ref. [19] states that digital identity could be transformed into a more efficient and accessible public service thanks to blockchain adoption. For example, governments may save large sums of money by employing physical office spaces for verification and call centers [55]. As mentioned in [30], blockchain technology can take it one step further, enabling a decentralized digital identity referred to as self-sovereign identity (SSI) through a public-private key pair for enhanced security, which allows citizens to exert full control over their personal information, overcoming the limitations of traditional centralized infrastructures. As discussed in [54; 56], centralized storage solutions pose challenges in terms of a single point of failure and raise concerns regarding privacy and government control over everyone's data. SSI could prevent surveillance and misuse from happening thanks to the possibility of tracking access to someone's information and the validity of credentials. The Italian Ministry of Economy and Finance, in [28], acknowledges that the EU has well understood the potential of blockchain in this context, deciding to launch a pan-European project called Poseidon that aims to build a platform for the management and protection of personal data. The platform, initially accessible to public administration employees through the use of smart contracts and blockchain to implement the principles of transparency and encryption, provides them with more control over their digital identity by enabling them to determine who can process their data, grant or revoke permissions, and decide on the removal of their personal information from the platform. This EU-wide initiative has paved the way for several initiatives at the local level. In [64], identity management is mentioned as one of the key use cases that could benefit from blockchain adoption, and the government of Catalunya is presented as a municipality that deployed a solution to offer students discounts on utilities using their digital identity. Refs. [19; 26; 63] introduce Estonia's e-residency program as a good example of how blockchain could change the traditional way in which citizens andpublic sector bodies interact, diminishing costs required to promote public services and reducing the administrative burden on both the state and citizens.