esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
###### Abstract  
There are many sensor fusion frameworks proposed in the literature using different sensors and fusion methods combinations and configurations. More focus has been on improving the accuracy performance; however, the implementation feasibility of these frameworks in an autonomous vehicle is less explored. Some fusion architectures can perform very well in lab conditions using powerful computational resources; however, in real-world applications, they cannot be implemented in an embedded edge computer due to their high cost and computational need. We propose a new hybrid multi-sensor fusion pipeline configuration that performs environment perception for autonomous vehicles such as road segmentation, obstacle detection, and tracking. This fusion framework uses a proposed encoder-decoder based Fully Convolutional Neural Network (FCNx) and a traditional Extended Kalman Filter (EKF) nonlinear state estimator method. It also uses a configuration of camera, LiDAR, and radar sensors that are best suited for each fusion method. The goal of this hybrid framework is to provide a cost-effective, lightweight, modular, and robust (in case of a sensor failure) fusion system solution. It uses FCNx algorithm that improve road detection accuracy compared to benchmark models while maintaining real-time efficiency that can be used in an autonomous vehicle embedded computer. Tested on over 3K road scenes, our fusion algorithm shows better performance in various environment scenarios compared to baseline benchmark networks. Moreover, the algorithm is implemented in a vehicle and tested using actual sensor data collected from a vehicle, performing real-time environment perception.  
Keywords:environment perception; sensor fusion; LiDAR; road segmentation; fully convolutional network; object detection and tracking; extended Kalman filter; autonomous vehicle +
Footnote †: journal: Sensors
# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 2 Theoretical Foundation
### Machine Learning  
Machine learning is a critical branch of artificial intelligence that has seen extensive application in recent years, including our daily lives. In recent times, machine learning has also gained considerable attention and application in the industrial sector. According to retrospective studies [22], it can be used for equipment anomaly detection, equipment monitoring, early prevention of equipment failures, reducing downtime, lowering maintenance costs, and improving production efficiency, all of which hold significant importance. The core objective of machine learning is to address complex problems, build models capable of learning from large volumes of data, and discover patterns within the data. It can be broadly categorized into four main types: (1) supervised learning; supervised learning primarily involves training models using labeled training data. The model learns the underlying relationship between inputs and outputs, enabling it to predict the desired output from input data. (2) Unsupervised learning; in contrast to supervised learning, unsupervised learning does not require labeled training data. It classifies and clusters data based on relationships or similarities among data points. (3) Reinforcement learning; reinforcement learning allows models to learn through interaction with their environment. Models adjust their behavior based on rewards or penalties received for their actions. This type of learning is suitable for scenarios where models need to learn through trial and error, closely resembling human learning. (4) Semi-supervised learning; semi-supervised learning is a combination of supervised and unsupervised learning. It involves training on both labeled and unlabeled data to improve prediction accuracy.  
Different machine learning methods have their strengths, weaknesses, and suitable application scenarios. The choice of which method to use should be based on the task objectives and context assessment.
# Virtualized MME Design for IoT Support in 5G Systems
## 7 Evaluation
### Results  
#### 7.2.1 Dimensioning  
We carried out the dimensioning at each tier of the vMME versus \(N_{MBB}\) for all of the schemes considered (see Figure 10) by using the theoretical framework described in Section 6 and assuming \(\overline{D}_{j}=1\,ms\,\forall\,j\).  
Notably, to simplify the comparisons in the Traffic separated Scheme (TS), we set the same response time budget for MBB and IM2M traffic classes; however, this scheme enables one to set different budgets for each class. Note additionally that, for the TS scheme, the required number of SL instances \(m_{SL}\) is the sum of the required number of SL instances for each type of traffic, which are depicted in Figure 10d. Additionally, we computed the cost per hour for each scheme considered (see Figure 11).  
As was expected, the Overdimensioned Scheme (OS) demands the greatest amount of resources, being the most expensive scheme. Conversely, the Baseline Scheme (BS) is the least expensive one. The Traffic separated Scheme (TS) and the traffic Shaper Scheme (SS), which have a similar cost, achieve a noticeable reduction in cost in comparison with OS. This is mainly thanks to the isolation between traffic types in the TS case and the limitation imposed by the traffic shaper on the mM2M traffic arrival rate in the SS case. In such cases, the dimensioning at the SL and SDB tiers can be performed without considering \(\lambda_{mM2M}^{peak}\), which is around 12.47-times greater than \(\lambda_{IM2M}^{peak}\) in our experimental setup. However, both the TS and SS schemes are designed to satisfy the delay constraint for IM2M traffic.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cost** & **Configuration** & **Calculation** \\ \hline \(C_{tipyc}(k)\) & \(m3.xlarge\) instance rental (0.266 \$/h) & 0.266/3600 \\ \hline \(C_{c_{idync}}(k)\) & Local storage (10 GB/month) and optimized data access (0.025 \$/h). & 10 \(\cdot\) 0.10 \(\cdot\) 0.025/3600 \\ \hline \multirow{4}{*}{\(C_{c_{film}}(k)\)} & \multirow{4}{*}{Data sent from the data center, (\(\lambda\) (message/s) \(\cdot\) 200 (byte/message))} & 0.000 \(\$  
Figure 11: Dimensioning costs comparison per evaluated scheme.  
Figure 10: Dimensioning at each tier of the vMME model (ten M2M devices per MBB UE). **(a)** FE dimensioning; **(b)** SDB dimensioning; **(c)** SL dimensioning; **(d)** Detailed SL TS dimensioning.  
Additionally, Table 6 summarizes the estimation of the memory consumption for each tier of the vMME. It assumes the same number of MBB UEs and M2M devices than the simulation setup. We use the number of SL instances from Figure 10c. We utilize 16,545 packets queued in the system. This number is the worst case of packets queued of the traffic separated scheme proposed, calculated from the results obtained in the next subsection.
## 2 Methods
### Multistep Prediction  
Currently, multistep prediction tasks can be performed either by explicitly training a direct model to predict the overstep, called the direct method, or by repeating the one-step overstep prediction until the desired horizon, called the iterative method [34]. The results show that the direct method requires a much higher training data volume and computational effort than the iterative method [35; 36]. In this paper, the prediction of speed and trajectory is chosen for the iterative method; furthermore, to reduce the cumulative error caused by the iterative method, the driving intention is identified for each prediction step.  
As an example of predicting a future 2-step trajectory, with the input data of historical 3-step and current moment trajectory information, as well as predicted future 1-step trajectory information, after extracting the corresponding feature parameters, the probability that the set of data belongs to different driving intentions is identified by the FCM algorithm. The trajectory data and the probability of driving intention data are input in the LSTM trajectory predictor, and the driving intention probability data are used as weight parameters, which are combined with the trajectory prediction results calculated based on different driving intentions to finally predict the car's trajectory ahead of the next two steps.
# The Role of Microorganisms and Their Antibacterial Compounds in Food Biopreservation
## 3 Antimicrobial Compounds Produced by Bacteria
### Lipopeptides  
Lipopeptides (LPs) are natural antimicrobial agents demonstrating wide-ranging inhibitory properties against bacteria (Gram-positive and Gram-negative strains), fungi, molds, and viruses. They are cyclic peptides mostly synthesized by _Bacillus_ spp. divided into three families: surfactin, iturin, and fengycin [82]. The main antimicrobial mechanism of LPs is the destabilization of sensitive microorganisms by the impairment of their cell membrane, resulting in the leakage of cytoplasm, which in turn leads to cell death [83]. LPs, due to high antimicrobial effectiveness and low toxicity, were employed as natural bioppreservatives for the conservation of post-harvested perishable vegetables and different fruits (lemon, strawberry, mulberry, table grape, tomato, orange fruits, grapes, and Chinese cabbage), resulting in the extension of the shelf life. Another possible field of the food industry for LP implementation is the biopreservation of aquatic products and marine foods [83]. They were successfully applied by [PERSON] et al. in the winemaking process for the prevention of fungal contamination [84]. The first discovered lipoproteins were ituring (1950), produced by _B. subtilis_ and named after the Ituri Congo region where they were isolated from the soil [85,86].  
Iturin effectively inhibits fungi and bacteria and has been commonly applied for fresh fruits preservation (berries, pome fruits). It was demonstrated that iturin reduced the pathogenic _Pseudomonas_ and _Vibrio_ viability in fish slices and effectively suppressed the growth of spoilage microorganisms in large yellow croaker [87,88].  
Fengycin is produced by _Bacillus subtilis_. It has a strong antagonistic effect on filamentous fungi but the activity against bacteria and yeast is weak [89]. Fengycin can be used for the food preservation of fruits (cherries, apples, and peaches) and aquatic products [90].  
Surfactin has been shown to inhibit the growth of the microorganisms present in rice and bread, prolonging the product's shelf life [91].  
Reports are showing enhanced effectiveness of LPs used in mixtures, i.e., iturin, surfactin, and fengycin. Such combinations were demonstrated to be more potent than single lipoprotein in the preservation of fruits, dairy and aquatic food, which is the result of the widening of the antimicrobial spectrum and a possible synergistic effect [83].
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 4 Conclusions  
This study proposes an early smoke detection method using image from video sequences, which is based on detecting the motion, color and texture properties, estimated in cascade to delimit the candidate smoke region, where the texture is analyzed using the LBP and LBPV operators. Finally, if the candidate regions maintain an upward expansion, the presence of smoke is determined and an alarm is activated. The evaluation results show that the proposed system provides a good detection rate of smoke, with enough robustness against several scenarios. Evaluation results show that the proposed system provides fairly good detection results: when the input videos contained smoke, the alarm was turned on in all cases, while, in the videos without smoke, the alarm was not turned on. Some factors that affected detection were low quality videos and the distance to the target, i.e., the viewing distance to the place in which smoke was produced. On the other hand, the main goal is its application in places such as offices, houses, and streets as well as in surveillance; however, its main drawback is the distance between the camera and smoke, because, in the case that the fire event occurred far from the camera, the methodology proposed is not accurate enough to detect it, thus this method cannot be applied in a forest fire. Even though the algorithm provides good results in the environments described above, the performance of the proposed algorithm must be analyzed in the cases of dust or aerosols as well as when there are changes in lighting, especially at night. These issues are under study and will be reported as part of future work.  
All authors contributed equally to this work.  
This research received no external funding.  
This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 700326 (website: [[http://ramses2020.eu](http://ramses2020.eu)]([http://ramses2020.eu](http://ramses2020.eu))). In addition, the authors thank the National Science and Technology Council of Mexico (CONACyT), and the Instituto Politecnico Nacional for the financial support for this research.  
The authors declare no conflict of interest.
## 1 Introduction
### Contribution and Organization  
In this paper, we concentrate on the IoV-based IVI in the vehicle-mounted scenarios and then propose the practical IVI solutions for the MaaS ecosystem. The contributions of this paper are three-fold:  
* Firstly, we investigate the evolution of IVI carriers from the birth of vehicles to date in terms of human audiovisual senses and multi-sensory services. More importantly, we point out the characteristics and restrictions of the vehicle-mounted media scenarios, which could provide guidelines to construct and flourish the IVI in the future.
* Secondly, using the research methods of case study, field research and survey, we present the cutting-edge progress of IVI solutions from both academia and industry perspectives to reveal their pros and cons in vehicle-mounted scenarios. Moreover, the megatrends and characteristics of IoV-based IVI are summarized in 3G and 4G eras. It shows that the IVI functions and applications are continuously enriched with higher performance gains of IoV. The information island is gradually interconnected and meanwhile the demands of IVI users become more diverse.
* Thirdly, we construct two featured vehicle-mounted media scenarios to extend the IVI functionalities, usage scopes, and user engagements of MaaS in 5G/B5G. (1) Linkage Display mode connects interior and exterior environments utilizing the proposed car body on-screen display (OSD) system. (2) IVI Collaborative mode consists of mobile centers for media and social, and resource complementarity among intelligent connected vehicles. Exploiting the potential of vehicle-mounted media scenarios, vehicles' value will be increased.  
As shown in Figure 1, the rest of this paper is organized as follows: Section 2 presents the characteristics of vehicle-mounted scenarios and the IVI evolution from the senses of hearing, vision, and multi-sensory services. Section 3 illustrates the main characteristics and limitations of the IoV-based IVI in the era of 3G/4G. Section 4 constructs IVI media scenarios as an indispensable part of the MaaS ecosystem in 5G/B5G. The linkage display mode and IVI collaborative mode are illustrated. Finally, Section 5 concludes this paper.
# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 5 Numerical Case of Interval Prediction
### Interval Prediction Model Based on FIG-Elman  
This paper innovatively combines the advantages of the FIG and Elman neural network, including short-term memory function, high sensitivity and dynamic modeling ability. Compared with other WPF methods, the main outstanding points are the point forecasting results, and the interval forecasting results can be obtained simultaneously with quick calculating speed and high working efficiency. Figure 5 is the flow chart of the WPF model, based on FIG-Elman.  
The triangular granulation function is used to calculate the content in this paper, with the expression as follows:  
\[A(x,a,m,b)=\left\{\begin{array}{c}0,x<a\\ (x-a)/(m-a),a\leq x\leq m\\ (b-x)/(b-m),m<x\leq b\\ 0\end{array}\right. \tag{15}\]
## 2 Scenarios and Setup
### Simulation Scenario and Setup in VLC Bands  
The VLC channel characteristics are investigated based on the ray tracing features of Zemax(r) [18] due to the lack of a channel sounding platform for VLC, which is limited by the narrow \(-3\) dB bandwidth (typically around a few MHz) of optical transmitters (LEDs) [19]. VLC channel models developed by this ray tracing method were accepted as reference channel models in IEEE 802.15.13 and IEEE 802.11 bb [20]. This channel modeling approach has been demonstrated by experimental results in [21]. In this work, we adopt this well-established realistic channel modeling approach [22]. First, a 3D simulation environment is created, as shown in Figure 2(a), where the geometry of the simulation environment can be specified accurately based on the conference room in Figure 1(a). Moreover, the CAD models, e.g., tables, desks, and display screen, designed according to the actual measured size in the conference room (No. 510), are imported into the software. The map of this simulation environment and the 16 detector positions are shown in Figure 2(b). The wavelength-dependent spectral reflection characteristics of the surface materials (i.e., ceiling, floor, walls, furniture, etc.) are given in Figure 4, which are from [23]. Moreover, we consider the mixed reflections including diffuse and specular reflections by adjusting the scatter fraction (SF) parameter. This parameter changes between 0 and 1 such that zero indicates the purely specular reflections and unity indicates the purely diffuse case. The coating materials of objects in this environment and SF setting are given in Table 2. The specifications of the light sources are shown in Figure 5. These are commercially available light sources (PAR20) with 20\({}^{\circ}\) half viewing angle [24], i.e., 40\({}^{\circ}\) viewing angle. Considering the 360\({}^{\circ}\) and 40\({}^{\circ}\) HPBW in azimuth and elevation of mmWave antennas, we use nine LEDs (PAR20) with different pointing from 0\({}^{\circ}\) to 360\({}^{\circ}\) (interval of 40\({}^{\circ}\)) at TX. Therefore, the same coverage as mmWave antennas can be achieved in azimuth (360\({}^{\circ}\)) and elevation (40\({}^{\circ}\)), as illustrated in Figure 4(a). Figure 4(b),c illustrate the intensity profiles and the normalized optical spectrum of LED, respectively. Moreover, a spherical PD, as in Figure 2(a), called detector polar in Zemax(r), is adopted as the RX antenna. This spherical PD is used in order to collect all the MPCs arriving at the PD, as mmWave antenna does. The photosensitive area of PD can be changed by the radial size (RS), i.e., the radius of detector polar in our simulation. Note that the LEDs and detector are positioned at the same height of 1.68 m as the mmWave antennas at RX. Figure 3: Simulation scenario and map for this scenario, in which there are 16 detector positions in total. Figure 4: Spectral reflectance of various materials. Figure 5: The specifications of the light sources, including the emission pattern of LEDs, relative intensity distribution, and relative spectral power distribution.
## 2 Materials and Methods
### Effective Augmentation to Gastric Dataset
#### 2.2.2 Cut and Paste Using Window Sliding Algorithm  
To accurately identify the lesion, a gastroenterology specialist manually confirmed its location and size and labeled the area. The extracted bounding box contained information regarding the size and position of the lesion. The X- and Y-coordinates of the bounding box were calculated to extract only the lesion. To perform augmentation with limited data, a method for pasting the created lesion image into a normal gastric image was used. Variances in the RGB channels of the cut lesion images were calculated to select the appropriate location for pasting. The sliding window algorithm moved normal images along the X- and Y-directions at equal intervals. While moving the virtual window at equal intervals, the lesion image was pasted onto the corresponding region by finding the point where the difference between the image variance of the corresponding region and that of the lesion image was minimized. The following equation was used to calculate the variance of lesion images:  
The formula for calculating \(T_{cut}\), the average of the cut lesions images' RGB channels, is as follows. The size of the lesion image is \(w\times h\), and \(i\) and \(j\) are the pixel coordinates.  
\[T_{cut}=\frac{1}{wh}\sum_{i=1}^{w}\sum_{j=1}^{h}I_{cut}(i,j) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **EGC** & **NOR** \\ \hline \multirow{3}{*}{People} & Training & 28 & 28 \\  & Validation & 10 & 10 \\  & Test & 10 & 10 \\ \hline \multicolumn{2}{c}{People Total} & 48 & 48 \\ \hline \multirow{3}{*}{Images} & Training & 180 & 180 \\  & Validation & 60 & 60 \\  & Test & 60 & 60 \\ \hline \multicolumn{2}{c}{Image Total} & 300 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Original dataset of EGC and normal gastric endoscopy.  
The formula for calculating the variance of a cut image is as follows.  
\[\sigma^{2}_{cut}=\frac{1}{wh}\sum_{i=1}^{w}\sum_{j=1}^{h}\bigl{(}I_{cut}(i,j)- \overline{I}_{cut}\bigr{)}^{2} \tag{2}\]  
The sliding window algorithm calculates the variance while sequentially moving along the \((x,y)\) area by dividing a normal gastric image of size \(w\times h\) into \(k\) equal lengths horizontally and vertically.  
\[\overline{I}_{slide}(x,y)=\frac{k^{2}}{wh}\sum_{i=x}^{xw/k}\sum_{j=y}^{y/k}I_{slide }(i,j) \tag{3}\]  
The formula for calculating the variance of each region is as follows.  
\[\sigma^{2}_{slide}(x,y)=\frac{k^{2}}{wh}\sum_{i=x}^{xw/k}\sum_{j=y}^{y/k}\bigl{(} I_{slide}(i,j)-\overline{I}_{slide}(x,y)\bigr{)}^{2} \tag{4}\]  
It sequentially moves the position to determine the \((x,y)\) positions, where the difference between the variances of the area located at \((x,y)\) and the lesion image is the smallest.  
\[(x,y)=arg\text{min}\Bigl{(}\left|\sigma^{2}_{slide}(x,y)-\sigma^{2}_{cut} \right|\Bigr{)} \tag{5}\]  
This method was augmented by pasting the lesion image at the \((x,y)\) position. Figure 2 shows an example of a cut-and-pasted image obtained using the sliding window algorithm.