esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 2 Related Works
A Fully Convolutional Network (FCN) [41] generated face proposals by the heat map of facial parts which are scored by a new facial parts responses method by their spatial structure and arrangement. As described in Section 1, cascaded structures are more advantageous than single networks in the task of face detection, and thus have been widely used in face detection, CascadedCNN [42] is a cascade framework built on CNNs and a CNN-based calibration stage was introduced to adjust the position of the detection window. [PERSON] et al. [43] proposed joint training to achieve end-to-endoptimization for CNN cascade and showed back propagation used in training a single CNN can be naturally used in training the cascade CNN structure. Coarse-to-Fine Auto-encoder Network (CFAN) [44] cascades a few Stacked Auto-encoder Networks to accomplish different tasks including face detection. Multitask Cascaded Convolutional Networks (MTCNN) [45] is a multi-task cascade network to detect faces by three stages in a coarse-to-fine detection structure. [PERSON] et al. [46] proposed a multi-scale and multi-channel shallow convolutional network (MMSC) for real-time face detection after the pre-identified method detecting faces in the images based on a traditional pedestrian detection method. [PERSON] et al. [47] proposed a multi-task detector with hybrid resolutions (HR), which detects different face scales from multiple layers of a single neural network. The methods mentioned above have good performance in face detection when images are of high resolution and the faces are big, but all of these traditional methods and region-based single deep CNNs cannot keep a high accuracy in tiny-face detection, the reason is that the effective information of tiny faces is very limited, as explained in Section 1. Inspired by [46], we designed a novel structure to reduce the unnecessary input of the face detection order to improve the effectiveness and propose a new pre-identification mechanism.
## 3 Results and Discussions
The separation between both the back to the left signal and the back to the left framework is significant through the BP adaptive NN-RA algorithm, and the distinction among the frameworks in another algorithm still seems to be reasonably large, as demonstrated in Figure 6. There is also a significant overlap of messages in these 50 datasets, with a 98 percent match rate. Besides being an effective evaluation method, the NN-RA algorithm is simple to implement. The responsive BP NN-RA algorithm proposed in this study is a fuzzy input-matching algorithm that primarily addresses the issue of whether data inputs are active. Figure 5: F1 data categories of the BP neural network with three varying inputs for the vocational course. Table 1 displays the results from an analysis of the use of several algorithms in the BP neural network-based instruction of agriculturally focused advanced placement courses. Pilot research was carried out using multimedia to compare the several algorithms that can help teachers achieve their goals. Precision, recall, F1 score, and accuracy were calculated using a neural network-based recognition algorithm for data sequences involving motion (83.2%), time-series features (89%), and combined features (82%). The purpose of this study was to investigate several ways that academic champions in agricultural higher education might obtain practical experience and none their craft at different universities. Table 2 below is an assessment and comparison of current AI-assisted practical agriculture learning systems. The automation of the teaching, learning, and grading processes is made possible with the use of artificial intelligence and backpropagation neural networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Neural Network-Based Recognition Algorithm**} \\ \cline{2-4}
**Dataset** & \begin{tabular}{c} **Data Movement** \\ **Data Sequences (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Time-Series Feature** \\ **Sequences (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Combined Feature** \\ **Sequences (\%)** \\ \end{tabular} \\ \hline Precision & 86.1 & 85.8 & 93 \\ Recall & 93.1 & 99.2 & 83 \\ F1 & 92.1 & 94 & 99.5 \\ Accuracy & 83.2 & 89 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall Analysis for the teaching of agricultural higher vocational courses based on the BP neural network. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Overall Training** \\ **Result Analysis** \\ \end{tabular} & \
\begin{tabular}{c} **Overall Testing** \\ **Result Analysis** \\ \end{tabular} &
\begin{tabular}{c} **Overall Accuracy** \\ **Analysis** \\ \end{tabular} \\ \hline BP neural & & & & \\ network-based & 97.89\% & 98.56\% & 99.12\% \\ recognition algorithm & & & \\ Existing algorithm: & 95.425 & 94.12\% & 96.34\% \\ Fuzzy logic algorithm & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall result analysis for the existing system. Figure 6: Model metrics for the teaching of agricultural higher vocational courses based on the BP neural network.
## 6 FWA Transport Network Solutions
### Wired Solution
#### 6.2.1 Dedicated Fiber Solution  
To start with, it has been observed that optical fiber is predominant and will not relent to be the prime transport solution [48]. Moreover, dedicated fiber is one of the attractive transport solutions being employed for various applications. It can be an appealing option in scenarios where operators have a huge fiber being deployed or where it is more economical to lease fiber than installing optical transport elements. However, as it is a PtP direct connection, it does not have the demanded network protection. This makes it not an appropriate option for supporting 5G use cases like uRLLC services where considerable reliability is demanded. To address the challenge, mechanisms for the implementation of operations and maintenance as well as protection can be adopted. For instance, dark fiber with physical ring topology can be deployed. Moreover, dedicated fiber application requires a high volume of fiber resources. 5G systems are envisaged to support ultra-dense networks in which an additional amount of fiber will be required, making it even more demanding. Consequently, the existing fiber resources may be insufficient to support mIoT devices and other envisioned multimedia devices. As a result of this and the related deployment cost, dedicated fiber applications may be limited. The limitation can be attended to with different WDM and Ethernet solutions employment [384, 385, 386, 370, 371, 372, 387, 388, 389, 47, 370, 371, 380, 381, 382, 383, 384].
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion  
This section presents the detailed parametric analysis of the fractals, SRR loading and reduced ground, FSS gap variations, miniaturization of the antenna, antenna prototype, reflection coefficients, radiation patterns, and gain validation. It also explains the current density distribution of the antenna at the resonating frequency and compares the proposed antenna with similar, recently published antennas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbolic Designation** & **Description** & **Optimized Dimensions (mm)** \\ \hline \(\mathrm{W_{S}}\) & Substrate width & 35.94 \\ \hline \(\mathrm{L_{S}}\) & Substrate length & 38.97 \\ \hline \(\mathrm{a_{e}}\) & Patch radius & 11.98 \\ \hline \(\mathrm{W_{F}}\) & Feed width & 3.0 \\ \hline \(\mathrm{L_{F}=\frac{\lambda_{F}}{4}}\) & Feed length & 10.30 \\ \hline \(\mathrm{W_{G}}\) & Ground width & 35.94 \\ \hline \(\mathrm{L_{G}}\) & Ground length & 9.7 \\ \hline \(\mathrm{R_{out}}\) & Outer ring radius of SRR & 3.1 \\ \hline \(\mathrm{R_{in}}\) & Inner ring radius of SRR & 2.35 \\ \hline \(\mathrm{g}\) & SRR gap & 0.50 \\ \hline \(\mathrm{W}\) & SRR width & 0.25 \\ \hline \(\mathrm{S}\) & Gap between SRR rings & 0.28 \\ \hline \(\mathrm{K}\) & Koch curve dimension & 16.94 \\ \hline \(\mathrm{K_{0}=\frac{K}{3}}\) & Iteration 0 & 16.94 \\ \hline \(\mathrm{K_{1}=\frac{K_{c}}{3}}\) & Iteration 1 & 7.98 \\ \hline \(\mathrm{K_{2}=\frac{K_{c}}{3}}\) & Iteration 2 & 1.882 \\ \hline \(\mathrm{L_{FSS}}\) & Length of FSS & 33.96 \\ \hline \(\mathrm{W_{FSS}}\) & Width of FSS & 35.94 \\ \hline \(\mathrm{x}\) & FSS square edge length & 5 \\ \hline \(\mathrm{y}\) & Gap between two squares of FSS & 10 \\ \hline \(\mathrm{h}\) & Height of substrates & 1.6 \\ \hline \(\mathrm{H}\) & Gap between substrates & 7.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the proposed Koch fractal antenna.  
Figure 7: Split ring resonator (SRR) structure designed at 3.5 GHz.
# IPv6 Convergence for IoT Cyber-Physical Systems
## 3 IP-Convergence Model
### 6 LoWPAN  
6 LoWPAN is an adaptation header format. It enables the use of IPv6 over low-power wireless links. It features IPv6 and UDP header compression. The format was initially defined in RFC4944 and updated by RFC6282. It originally was designed for low-power wireless personal area networks (LoWPANs) comprising devices that conformed to the IEEE 802.15.4-2003 standard. The main characteristics of 6 LoWPAN are that it supports small packet sizes, 16-bit short or IEEE 64-bit extended media access control addresses and low-to-medium bandwidth (250/40/20 kbps). Star and mesh network topologies are supported; networks are generally ad hoc, and devices have limited accessibility and user interfaces. The intended use cases predominately feature devices requiring low-power battery-operated modes and are relatively low cost. It was also designed for networks that are inherently unreliable due to nature of devices in the wireless medium.  
Figure 5: Multiple interconnecting IP and Non-IP networks supporting one or more services.
# On Ontology-Based Tourist Knowledge Representation and Recommendation
## 3 Ontology-Based Tourist Knowledge Representation and Recommendation Method Development
### Tourist Attraction Recommendation
#### 3.4.4 Tourist Attraction Sorting  
Based on Section 3.4.2, attraction reasoning, and Section 3.4.3, tour planning, there may be one or more than one results. The tourist attraction sorting ranks all the analytical results based on their distances. The nearest destination is ranked first and will be recommended to travelers. The method for tourist attraction sorting is explained as follows:  
Section 3.3.3 showed how to measure the distance between tourist attractions \(X_{i}\) and \(X_{j}\). The relation matrix thus becomes a distance matrix. (\(X_{i}\), \(X_{j}\)) is used to represent the distance between tourist attractions, as displayed in Figure 17.  
As Algorithm 2 showed, the results of tourist attraction reasoning are (\(X_{2}\), \(X_{4}\)), (\(X_{2}\), \(X_{5}\)), (\(X_{3}\), \(X_{6}\)), and (\(X_{3}\), \(X_{7}\)). The distance matrix reveals the distance between points. The distance of (\(X_{2}\), \(X_{4}\)) is 4, the distance of (\(X_{2}\), \(X_{5}\)) is 8, the distance of (\(X_{3}\), \(X_{6}\)) is 64, and the distance of (\(X_{3}\), \(X_{7}\)) is 23. These are then ranked by distance, and the one with the nearest distance is ranked first. The first recommended result is thus (\(X_{2}\), \(X_{4}\)), followed by (\(X_{2}\), \(X_{5}\)), (\(X_{3}\), \(X_{7}\)), and (\(X_{3}\), \(X_{6}\)). In order to automatize the tourist attraction sorting, this study developed an algorithm for tourist attraction sorting, as shown in Algorithm 3.  
```
input DistanceMatrix [\(X_{i}\)] [\(X_{j}\)]//The users input the Distance Matrix, \(X_{i}\) represents the tourist attraction name in the X axis. \(X_{j}\) represents the tourist attraction name in the Y axis. main()| while (\(X_{i}!=\) null&\(\&X_{j}!\)= null)//When the value of (\(X_{i}\), \(X_{j}\)) does not equal to 0 in the Distance Matrix, attractionList. Add (distanceMatrix[\(X_{i}\)][\(X_{j}\)]);//add \(X_{i}\) and \(X_{j}\) to the attractionList. while-end//When the value of (\(X_{i}\), \(X_{j}\)) equals 0 in the Distance Matrix, the process is terminated.  sorting (attractionList);//Sorting function loads the value of attractionList.  sorting (attractionList)| for\(m=0\) to (attractionList.length - 1) swap = false;//If there is no change of location, it is marked as false. for\(n=0\) to(attractionList.length - 1) //If the value of current location is bigger than the value of the next location. temp = attractionList[\(n\)]; it executes the swap program, as explained above. attractionList[\(n\)] = attractionList[\(n\)+1];  attractionList[\(n\)+1] = temp; swap= true;//If there is a change of location, it is marked as true. if-end for-end if(swap == false) //There is no change of location break;//Exit for loop if-end for-end outputattractionList//It outputs all the values in attractionList. }
```  
**Algorithm 3** Algorithm for tourist attraction sorting.
## 3 Materials and Methods  
### Dataset  
We collected 338 retinal fundus images from 169 Caucasian patients enrolled in an optometry and contact lens office in Northern Italy. An optometrist evaluated all patients who visited the office for a regular eye exam. Patient history, primary complaints, distance visual acuity (both unaided and aided), static retinoscopy and subjective refraction, slit lamp, and fundus photography were performed. All eye fundus pictures were captured by the same well trained optometrist using the Optopol REVO FC 80(r) (Optopol Technology; Zawiercie, Poland; software version 11.5.0), a non-mydriatic 12.3-megapixel fundus camera with a 45 \(\pm\) 5\({}^{\circ}\) angle of view. The device was placed in a dark room, with the camera set to central fundus photo mode, automatic flash, and gain levels adjustment. The images, in PNG format and RGB color mode, were downloaded from the FC's computer software (version 11.5.0) and used to train and build the model with YOLOv8. Only good-quality pictures were included in this study: well centered, clear, and free of artifacts. These images were selected and stored in a dedicated folder. The optometrist analyzed each image, ensuring that key structures such as the macula, optic nerve, and retinal vasculature were in focus. Blurry, shaky, or dark images were excluded. An example of a good-quality image versus a poor-quality one is shown in Figure 1.  
Figure 1: Example of good- (**left**) and poor-quality (**right**) retinal fundus image.  
The selected images were first anonymized and divided into two separate datasets according to each eye's subjective spherical equivalent refraction (SER), calculated using the following formula:  
\[\text{SER}=\text{SF}+\left(\frac{\text{CYL}}{2}\right) \tag{1}\]  
where SF is the spherical power and CYL is the cylindrical power of the lens.  
According to the International Myopia Institute (IMI), myopia is defined as a condition where the SER of an eye is \(\leq\)\(-\)0.50 diotpers (D) [1]. Eyes with an SER \(\leq\)\(-\)0.50 D were classified as myopic and assigned to the myopic dataset, while those with an SER of \(>\)\(-\)0.50 D were classified as non-myopic and assigned to the non-myopic dataset.  
One hundred twenty-four patients were diagnosed as myopic (74 female; mean age \(\pm\) standard deviation 26.74 \(\pm\) 13.78), and 45 were diagnosed as non-myopic (27 female; mean age 40.33 \(\pm\) 17.74).  
After an image quality assessment and SER-based dataset allocation, we obtained 238 eligible fundus images from the myopic dataset and 86 from the non-myopic dataset, including both right and left eyes. The mean SER for the myopic eyes was \(-\)3.29 D (SD = 3.13), while the mean SER for the non-myopic eyes was +0.92 D (SD = 1.32). Informed consent was obtained from all participants or their relatives before participation, and the study was conducted in accordance with the principles of the Declaration of Helsinki. A summary of the two datasets is provided in Table 1.
# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 3 Results and Discussion
### Parametric Study  
Three parameters of the YOLO_v2 using'resnet18' (as the feature extractor) were studied, (1) Influence of the training epochs on detection results; (2) Influence of the feature extraction layer on detection results; (3) Influence of the testing image size on detection results.  
(1) A case study for the maximum epoch was therefore performed on a training set with 891 training images to determine the optimal epoch for the training. The \(AP\) value and computing time were recorded in Figure 4, 22-30 epochs corresponded to the most stable \(AP\) value, meanwhile, the computational cost increased with the increase of the epoch number. Therefore, an optimal epoch of 22 was chosen for the best balance between speed and precision.  
By studying the influence of epoch on detection results, the optimal detection precision could be achieved with fewer epochs. Because once the detection precision was stable, with the increase of epoch number, the computational cost would increase, which was not necessary.  
(2) Eight layers (L1-L8 in Figure 5) of the'resnet18' were employed as the feature extraction layers of the YOLO_v2 respectively. These networks were trained with the training data, the computational cost was recorded, and then the testing data were input into the eight YOLO_v2 detectors. The results are illustrated in Table 4. The \(AP\) value increased first, and then decreased with the increase of the layer depth; 'L6' achieved the best detection results. However, the computational time increased gradually by selecting a deeper layer as the feature extraction layer.  
Figure 4: The \(AP\) value and computational cost of different epochs. (**a**) \(AP\) value; (**b**) computational cost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Detection Precision (\(AP\))** & **Computational Cost (s)** \\ \hline alexnet & 0 & 2,266 \\ googlenet & 0.84 & 4,451 \\ mobilenetv2 & 0.87 & 8,763 \\ inceptionv3 & 0.16 & 9,720 \\ squeezenet & 0.60 & 2,873 \\ resnet18 & 0.89 & 3,299 \\ resnet50 & 0.02 & 10,321 \\ resnet101 & 0.73 & 17,457 \\ vgg16 & 0 & 13,580 \\ vgg19 & 0.23 & 15,719 \\ inceptionresnet2 & 0.86 & 22,523 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of different feature extractors in locating cracks.  
(3) According to the optimal network obtained above (the YOLO_v2 with'resnet18', and 'L6' as the feature extraction layer), seven testing image sets (Section 2.3) with different sizes were input into the YOLO_v2 respectively to evaluate the influence of image resolution (image size) on detection results (Table 5). The results indicated that: with the increase of resolution, the \(AP\) value increased gradually and tended to become stable. However, The FPS (frames per second), which evaluates the testing speed, decreased gradually with the increase of resolution. Therefore, the image with an appropriate resolution could achieve the optimal combination of precision and computing speed.
###### Abstract  
This paper presents a logistics problem, related to the transport of goods, which can be applied in practice, for example, in postal or courier services. Two mathematical models are presented as problems occurring in a logistics network. The main objective of the optimisation problem presented is to minimise capital resources (Capex), such as cars or containers. Three methods are proposed to solve this problem. The first is a method based on mixed integer programming (MIP) and available through the CPLEX solver. This method is the reference method for us because, as an exact method, it is guaranteed to find the optimal solution as long as the problem is not too large. However, the logistic problem under consideration belongs to the class of NP-complete problems and therefore, for larger problems, i.e., for networks of large size, the MIP method does not find any integer solution in a reasonable computational time. Therefore, two nature-inspired heuristic methods have been proposed. The first is the evolutionary algorithm and the second is the artificial bee colony algorithm. Results indicate that the heuristics methods solve instances of large size, giving suboptimal solutions and therefore, enabling their application to real-life scenarios.  
metaheuristics; evolutionary algorithm; bees algorithm; mixed integer and integer linear programming; combinatorial optimization; logistic problems Article
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 4 Results
### Legislative Status
A related requirement is that owners or operators must submit a law enforcement interaction plan to the department of motor vehicles. The plan must describe how to communicate with a fleet support specialist, how to safely remove the vehicle from the roadway, and the steps needed to safely tow the vehicle. A frequent stipulation for vehicles involved in an accident is that it shall remain on the scene while the owner or a representative reports the incident to law \begin{table}
\begin{tabular}{c c} \hline \hline
**State and Status** & **Special Conditions** \\ \hline Montana (MT) & AVs cannot operate on public highways until the Department of Transportation completes \\ H 339 (2023): Failed & specific rulemaking. \\ Human: Required & \\ \hline Nevada (NV) & Does not provide specific conditions related to AV operations on public roads. Primarily \\ S 182 (2023): Enacted & addresses regulatory aspects concerning the manufacturing and selling of AVs. \\ Human: Unclear & \\ \hline New Mexico (NM) & An operator must be able to monitor vehicle performance, intervene, operate the vehicle, \\ H 378 (2023): Failed & and turn off the engine. \\ Human: Required & \\ \hline Oregon (OR) & The operator must possess the proper class of driver’s license for the type of vehicle. The \\ H 4063 (2018): Enacted & owner must obtain a general liability insurance policy that covers no less than USD 5 \\ Human: Required & million for death or bodily injury for each person covered and for property damage. \\ \hline Utah (UT) & The AV must be capable of achieving a minimal risk condition if it malfunctions or it must \\ H 101 (2019): Enacted & request human intervention. Owner or operator must report accidents and provide \\ Human: Optional & registration and insurance information. \\ \hline Washington (WA) & The AV must be capable of achieving a minimal risk condition if it malfunctions. If involved \\ S 5594 (2023): Pending & in an accident, the AV shall remain on the scene. The owner must report the accident. \\ Human: Optional & Municipalities can require crash data sharing. \\ \hline Wyoming (WY) & \\ S 16 (2022): Failed & Clearly marked to indicate that it is operating autonomously. \\ Human: Optional & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._enforcement. A few of the legislations require the system to have a designated or remote operator to assume control if needed. Other special conditions require the owners to obtain a certificate of safety and performance compliance from the department of motor vehicles. Related requirements stipulate that AVs must have the capability to capture and store data in case of a crash, and that municipalities can require crash data sharing. A related requirement for some legislation is that manufacturers must provide a written disclosure of information that the technology collects.