esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 2 Materials and Methods  
### Steepest Ascent Hill Climbing  
HC is one of the most widely used optimization algorithms [13; 14; 15]. The algorithm aims to maximize or minimize an objective function. This paper assumes a minimization problem; therefore, the goal of the algorithm is to find the location of the \(S\) search space, where the value of the \(f\) fitness function (also known as cost function) is minimal. The method is quite simple compared to the other metaheuristics and is as follows: It maintains the position of one candidate solution (\(p\)). After a random initialization, an iterative process tries to improve the position of \(p\). This is carried out by evaluating the neighboring solutions and moving to the one that provides a better outcome for the objective function. The process continues until no better neighbors are found.  
The base idea has several variants, like Stochastic Hill Climbing and Steepest Ascent Hill Climbing. Algorithm 1 presents the main steps of the latter.  
1. Selecting a random starting point from the entire search space (\(5\)). This will be the position of the candidate solution (\(p\)).  
Evaluating the \(\epsilon\) neighborhood of \(p\). These are the \(x\) points of the \(S\) search space, where the distance between the \(p\) and \(x\) points (\(|p-x|\)) equals to \(\epsilon\). The steepest ascent variant of the method selects the best candidate (\(q\)) solution from this neighborhood.
3. If the \(q\) position represents a better solution than the previously presumed best solution (\(p\)), then \(p\) moves to the \(q\) position, and the iteration restarts.
4. If the \(q\) position does not represent a better solution than \(p\), it means that the algorithm is stuck in a local minima. The result of the function is the \(p\) itself. It is recommended to start an entire new search from another random starting point.  
As it is known, the algorithm has several limitations, especially for large search spaces. In general, it is not the right tool to explore the whole search space; it is more applicable to exploit the best candidate in a smaller area. To compensate for this disadvantage, it is fast enough to start the search from different random points consecutively. However, the general opinion is that Hill Climbing is a typical local search method.  
```
1:functionHillClimbingSteepestAscent(\(S,f,\epsilon\))
2:\(p\xrightarrow{\text{md}}S\)
3:\(stuck\leftarrow\)false
4:while\(\rightarrow\)stuck do
5:\(q\xleftarrow{\operatorname*{argmin}(\epsilon)}\{x\in S\mid|x-p|=\epsilon\}\)
6:if\(f(q)<f(p)\)then
7:\(p\gets q\)
8:else
9:\(stuck\leftarrow\)false
10:endif
11:endwhile
12:return\(p\)
13:endfunction
```  
**Algorithm 1** Hill Climbing Steepest Ascent algorithm
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 3 Method
### Dilated Frequency-Aware Channel Correlation
\tag{5}\]where \(y\) represents the submaps obtained from Equation (2), DCT\((\cdot)\) denotes the discrete cosine transform previously mentioned, and \(h(\cdot)\) represents a linear transformation function. The frequency processing function, \(g(\cdot)\), is dedicated to enhancing the representation of multifrequency features with improved high-frequency feature representation, and channel fusion can be implemented through a \(1\times 1\) convolution.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Types of Sensors
#### Magnetometers  
These sensors measure the magnetic field around them in order to perceive big metallic objects, e.g., vehicles [13]. For instance, sensors used to collect information in San Francisco, allow to establish a demand profile for parking lots, giving more importance to occupancy on parking lots that are being monitored compared to the ones that are not [70].  
Magnetic loop detectors maintain a resemblance with magnetometers; the difference being that a change occurs in the loop when an object passes through it, the magnetic field is altered, and the detection is performed [41]. These are presented as a feasible option to retrieve parking lot information, and in the presented solution it comes along with RFID so an on-street parking reservation can be performed, allowing the sensor to recognize the slot occupancy and confirms the vehicle identity using the RFID information [46].  
Like the previous sensors, magnetometers offer the same functions and also help to know that the object placed near the sensor is metallic, it helps to differentiate common objects from cars.
## 4 Numerical Results and Discussion
### Grid Study and Validation  
A non-uniform square mesh is generated throughout the domain with a more density around the obstacles surfaces. A schematic view of this mesh with a near zone around the obstacles is disclosed in Figure 3. Various mesh sizes are explored to certify the sensitivity of the numerical outputs to the mesh resolution. The results of this test for the mean Nusselt number on the top wall of the duct at \(Re\) = 100, \(S/D\) = 1.5, \(d_{p}\) = 30 nm, and \(\Phi\) = 0.01 are presented in Table 1. Note that \(\delta/D\) in this table indicates the ratio of smallest cell size (\(\delta\)) to side of the obstacle (\(D\)). It is observed that the difference in the mean Nusselt number between Cases 3 and 4 is 0.23%. Hence, the cell number of Case 3 is selected for the subsequent calculations.  
To examine the accuracy of the numerical results, the numerical outputs are benchmarked with experimental results obtained by [PERSON] et al. [36]. The case for validation is aluminum oxide-water nanofluid flow with \(dp\) = 40 nm and \(\Phi\) = 0.01 in a straight tube. The results of comparison between the experimental and numerical results are presented in Figure 4 for the variations of pressure drop ratio with Reynolds number. The pressure drop ratio is defined as the ratio of pressure drop of nanofluid to that of pure water. This comparison showed good agreement between the experimental and numerical results with a relative error about 5%.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Grid Number** & \(\delta/D\) & \(\left\langle\overline{\text{Nu}}\right\rangle\) & **Percentage Difference** \\ \hline
1 & 2375 & 0.05 & 3.342 & 1.34\% \\
2 & 4750 & 0.033 & 3.387 & 1.01\% \\
3 & 9500 & 0.025 & 3.421 & 0.23\% \\
4 & 19,000 & 0.02 & 3.429 & — \\ \hline \end{tabular}
\end{table}
Table 1: The grid study at \(Re\) = 100, \(S/D\) = 1.5, \(d_{p}\) = 30 nm, and \(\Phi\) = 0.01.  
Figure 4: The ratio of pressure drop for Al\({}_{2}\)O\({}_{3}\)-water nanofluid versus Reynolds numbers at \(dp\) = 40 nm and \(\Phi\) = 0.01.  
Figure 3: Mesh distribution (**a**) whole domain (**b**) near the obstacles.
## 3 Proposed Approach
### Riota  
The concept of RIOTA was introduced in a previous work [1]. The idea behind RIOTA is for it to be a generalized application capable of recognizing and distinguishing individual objects among others of the same class. This concept comprises four important characteristics: (i) a non-invasive property; (ii) a tagless approach; (iii) zero added info; and (iv) zero added finishing. The developed algorithm uses a local feature detector and extractor entitled Oriented Fast and Rotated BRIEF [11] combined with a brute force matcher and RANSAC (Random Sample Consensus) [40] for outliers removal. The procedure for finding a correspondence in the cork database is shown in Algorithm 1.  
```
1:procedurefindMatch(\(queryImage,imagesDatabase,decisionThreshold\))
2:\(queryKeypoints\gets detectKeypoints(queryImage)\)\(\triangleright\) ORB Detection
3:\(queryDescriptors\gets extractDescriptors(queryKeypoints)\)\(\triangleright\) ORB Extraction
4:for\(img\)in\(imagesDatabase\)do
5:\(matches1\gets knnMatch(queryDescriptors,img.Descriptors)\)
6:\(matches2\gets knnMatch(img.Descriptors,queryDescriptors)\)
7:\(matches1\gets ratioTest(matches1)\)
8:\(matches2\gets ratioTest(matches2)\)
9:\(symmetricMatches\leftarrow symmetryTest(matches1,matches2)\)
10:\(numberOfMatches\leftarrow\)\(ransacTest(queryKeypoints,img.Keypoints,symmetricMatches)\)
11:if\(numberOfMatches>decisionThreshold\)then
12:return\(True\)\(\triangleright\) Found a match in the database return\(False\)\(\triangleright\) No match found in the database
```  
**Algorithm 1** RIOTA's Implementation  
The algorithm takes as arguments the query image, the database of images, and the threshold needed to decide if the query image has an homologous counterpart in database or not. The ORB keypoint detection task of the algorithm was tuned to detect up to 1500 keypoints. Then, ORB extracts binary descriptors of the keypoints. For each image in the database, the matching process of the resultant binary descriptors use a brute force approach. The \(knnMatch\) tries to match the \(k\) with the best nearest neighbors for each binary descriptor (in this work \(k\) was tuned to 2). Next, the ratio test proposed in Reference [41] is applied. At this point, two different sets of matches exist. These sets are merged using a symmetry test. This test only retains the common matched descriptors in the two sets. For the symmetric matches, the homography is calculated using RANSAC [40] which serve as outlier removal (the re-projection error was tuned to 5, which considering the resolution of the images in the Cork Database is adequate to reject false matched descriptors). Finally, if the resultant number of matches is greater than a predefined threshold, the query image has a match in the database and the procedure returns the true value. Otherwise, the value false is returned.  
Figure 2: Two photos taken using the illumination setup: (**a**) image of the top surface of an agglomerated cork stopper; (**b**) image of the top surface of a natural cork stopper.
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 4 Simulation
### Scenario 2
Figure 9 shows the signal pattern calculated for each model as a space-time diagram. It shows the signal patterns of each intersection and the trajectories of vehicles accordingly. In the case of the fixed-time model, the average travel time per vehicle was 250 s, while the comparison model decreased by about 60 s to 190 s. The proposed model decreased by about 30 s to 220 s. However, for the comparison model, the phase duration was irregular and short. Figure 7: Measure of effectiveness by intersections: (**a**) Cumulative delay; (**b**) Cumulative number of stops. Figure 10 shows the traffic situation when the proposed model and the comparison model were applied to each intersection. The color of the road indicates the average speed of the vehicles. In the comparison model, traffic congestion occurred at intersection 5. On the other hand, the proposed model can be seen as a solution to the congestion at intersection 5. However, some congested sections occurred between intersection 3 and intersection 4. During the entire simulation, the comparison model showed 13% shorter delay and 17% fewer stops than the proposed model. Although the comparison model had the best performance, the proposed model also showed sufficiently ideal results. In addition, the comparative model calculated an irregular signal pattern, while the proposed model calculates a realistic signal pattern. Therefore, the proposed model would be the best in terms of applying it to real-world intersections. Figure 8: Measure of effectiveness: (**a**) Average delay per vehicle; (**b**) Average number of stops per vehicle. Figure 9: Time-space diagram: (**a**) Fixed-time model; (**b**) Comparison model; and (**c**) Proposed model.
## 3 Test Results and Discussion
### Damaged Process and Mechanism
#### 3.1.2 Bending-Shear Failure  
Figure 9 shows the crack patterns of HPLW800-1 at the failure stage. The crack pattern in this wall was very similar to that of the HPLW500-1; however, the inclined cracks formed to the top of the wall and the angle of inclined cracks was steeper than that in HPLW500-1. The first yielding of the boundary longitudinal reinforcement was observed at drift ratios of 0.86% and \(-\)1.14%. Peak lateral loads of 385.2 and 561.7 kN were reached at the same drift ratio of 2.70% for both positive and negative loading directions. At the end of testing, the wall panel was almost separated into two parts by the diagonal cracks. During the drift ratio of \(-\)2.91%, the bottom corners of concrete were crushed, and boundary longitudinal reinforcements buckled seriously under flexural compressive stresses. Obviously, HPLW800-1 failed with a combined shear and flexure mode.  
Figure 7: Arrangements of strain gauges of reinforcement in the flange.
## 2 Piezoelectric-Driven FTS System
### Dynamic Modeling of FTS System  
The piezoelectric-driven FTS system is a typical electromechanical coupled system. From an electrical perspective, the piezoelectric ceramic actuator is regarded as a capacitor, and the voltage amplifier is viewed as a constant amplifier. From mechanical analysis, the flexible actuator and the tool holder are considered as a whole, taking into account the elastic contact issue of the flexible actuator, and it is regarded as a mass-spring-damping system. Therefore, the dynamic model of the FTS system is shown in Figure 2.  
In Figure 2, \(m\) represents the quality coefficient of the flexible actuator. \(k_{c}\) represents the damping coefficient of the flexible actuator, and \(k_{f}\) represents the spring coefficient of the flexible actuator. \(k_{b}\) represents the gain of the piezoelectric amplifier, \(u\) represents  
Figure 1: The prototype of the FTS system: (**a**) Machining principle of FTS system; (**b**) FTS system architecture.  
Figure 2: Electromechanical model of the FTS system.  
the system control voltage, \(y\) represents the tool output displacement of the FTS system, and \(F_{PZT}\) represents the force generated by the deformation of the piezoelectric ceramic driver. \(x\) represents the deformation amount of the piezoelectric ceramic actuator, and \(T_{em}\) represents the electromechanical conversion efficiency of the piezoelectric ceramics. \(H(t)\), \(F_{c}(t)\), and \(Q(t)\) represent the system's nonlinear hysteresis, cutting force disturbance, model uncertainty, and other external disturbance term.  
Based on [PERSON]'s voltage law, the voltage applied to the piezoelectric ceramic is  
\[u_{pzt}=k_{b}u \tag{1}\]  
\[F_{pzt}=T_{em}\cdot u_{pzt} \tag{2}\]  
According to [PERSON]'s law [20]  
\[m\ddot{y}(t)+k_{c}\dot{y}(t)+k_{f}y(t)=F_{pzt}(t)-H(t)-F_{c}(t)-Q(t) \tag{3}\]  
To facilitate the controller design, the actual system can be further represented by the state variables of \(x_{1}\) and \(x_{2}\) as follows  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}=\dot{y}\\ \dot{x}_{2}=-a_{1}x_{1}-a_{2}x_{2}+bu+f\end{array}\right. \tag{4}\]  
where \(\frac{k_{f}}{m}=a_{1}\), \(\frac{k_{b}}{m}=a_{2}\), \(\frac{k_{b}\cdot T_{em}}{m}=b\), \(f=\frac{H(t)+F_{c}(t)+Q(t)}{m}\). The related problem we need to solve is how to achieve an accurate tracking of the desired motion trajectory in the presence of equivalent disturbances \(f\).
## 2 Case Study
### Load Classification
For each of the four classes of the load classification task we then proceed to take about 1000 images of common loads that belong to such class. It is worth noticing that that number of images represents quite a realistic estimate of the number of images to be collected for a preliminary analysis on the application of AI to commercial appliances: the image collection and labelling process is a costly and time-consuming one, and companies typically aim at reducing its costs to the bare minimum. Since input diversity ease the avoiding of overfitting issues for NNs, we aim at providing as much diversity as possible inside each class images. Images were collected having a 256 \(\times\) 256 pixels resolution, following the OpenMV board settings. The collected dataset represents an intellectual property of Electrolux Professional. Therefore, the collected images can not be made publicly available and no further information can be disclosed regarding the collected dataset. However, the relevance of the presented case study should not be affected by the non-disclosure agreement in place. Up to our knowledge, our work represents the first one combining implementation of AI techniques on a hyper-constrained embedded device with the proposal of a relevant and realistic load-classification task. A comparison between the collected dataset and commonly-used image-classification datasets is presented in Table 3. Thanks to its specificity--i.e., image content--and to the low number of classes involved, the proposed load-classification dataset suits the scenario of AI application to low-end commercial appliances more realistically than the other datasets. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset Name** & **Image Content** & **\# Classes** & **\# Images** & **Image Resolution** & **Dimensions** \\ \hline Ours & Dishwasher & 4 & 4K & 256 \(\times\) 256 & 51 MB \\  & Loads & & & & \\ MNIST [33] & Handwritten & 10 & 70K & 28 \(\times\) 28 & 21 MB \\  & Digits & & & & \\ Fashion-MNIST [34] & Clothes & 10 & 70K & 28 \(\times\) 28 & 36 MB \\ CIFAR-10 [30] & General & 10 & 60K & 32 \(\times\) 32 & 162 MB \\ CIFAR-100 [30] & General & 10 & 60K & 32 \(\times\) 32 & 161 MB \\ EMNIST [35] & Handwritten & 62 & 814K & 28 \(\times\) 28 & 535 MB \\  & Characters & & & & \\ Caltech-101 [36] & General & 101 & 9K & variable & 132 MB \\ TF-Flowers 1  & Flowers & 5 & 4K & variable & 221 MB \\ Stanford-Dogs [37] & Dogs & 120 & 20K & variable & 778 MB \\ Food-101 [38] & Food & 101 & 101K & variable & 4.65 GB \\ ImageNet [29] & General & 20,000 & 14M & variable & 155.8 GB \\ \hline \hline \end{tabular}  
\end{table} 
Table 3: Image classification datasets comparison.
## 1 Introduction
Employing carrier frequencies of around 60 GHz, as compared to centimeter-wave communications (e.g., 3 GHz), mm-Wave presents the advantage of having much higher channel coherence bandwidth but experiencing much higher path loss. Moreover, the very low wavelength of mm-Wave facilitates the implementation of m-MIMO, both due to the low antenna sizes and due to the low distance between MIMO antenna elements (typically 3 to 4 wavelengths for the signals in adjacent antennas to be uncorrelated). mm-Wave is specially well fitted for Vehicle-to-Everything (V2X) communications (This includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Pedestrian (V2P), where the link distance is low and there are demanding requirements in terms of latency, reliability and amount of data being exchanged. Intersymbol Interference (ISI) represents the greater limitation to allow sending higher throughputs [13]. Block transmission techniques, such as OFDM or Single-Carrier with Frequency-Domain Equalization (SC-FDE) are widely employed to mitigate it. Due to the lower Peak-to-Average Power Ratio, SC-FDE tends to be a better solution [14]. MIMO  
Figure 1: Groups of use cases of 5G that support Network Slicing. systems is another technique that combats ISI. The combination of SC-FDE with m-MIMO results in a system that makes use of the available spectrum in a much more efficient manner. MIMO receivers are normally associated with a high level of complexity and processing requirements, being even more demanding in case of m-MIMO. Zero Forcing (ZF) receivers require the inversion of the channel matrix for each frequency component of the channel [15]. MRC is a receiver that can be employed to reduce the complexity, by avoiding the need to compute the inversion of the channel matrix, for each frequency component of the channel [15; 16; 17]. With the aim of providing a potential solution for the absence of spectrum to serve extremely high number of IoT devices supported by 5G, this article focuses on the association of NOMA and Cooperative NOMA with SC-FDE and associates it with m-MIMO, using a low complexity iterative receiver. The ZF and MRC iterative receiver is utilized in this article, optimized for m-MIMO and using SC-FDE signals, as in [17]. This article is organized as follows: Section 2 describes the system and signal characterization for m-MIMO using SC-FDE transmissions; Section 3 deals with the receiver design for NOMA; Section 4 analyses the performance results and Section 5 concludes the article.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 3 Results
### Comparison Results in a Single-Room Environment  
The first experiment was carried out in a separate room. Figure 9 shows the experimental results.  
Figure 9a shows the experimental environment, which was a 5 m \(\times\) 7 m office with some tables and chairs.  
Figure 9b shows the grid map of the mobile robot. The size of each grid was 20 cm \(\times\) 20 cm. The turquoise points are the BLANK grids, the cyan points are the OBSTA-CLE grids, and the blue points are the UNKNOWN grids.  
Figure 9c shows the line segments obtained using the proposed RM-Line.  
As shown in Figure 9d, before running CannyLine, the grid map was converted to a grayscale image, where the OBSTACLE grids had a gray value of 100 (white pixels in Figure 9d), and the BLANK grids and UNKNOWN grids had a gray value of 0 (black background in Figure 9d). Figure 9e shows the edge points obtained using CannyLine.  
Figure 9f shows the line segment obtained using CannyLine.  
Table 1 shows the comparison of the two algorithms.  
Figure 8: Distance from grids to lines.  
Figure 9: The experiment in a single room: (**a**) the environment; (**b**) grid map; (**c**) line segments obtained using RM-Line; (**d**) Canny-Line using grayscale image as input; (**e**) edge map obtained using Canny-Line; (**f**) line segments obtained using Canny-Line.  
As shown in Table 1, RM-Line consumed less time than Canny, thanks to the use of ray models. Furthermore, RM-Line extracted 37 lines from the grid map, and CannyLine extracted 79 lines. When these lines were used for map compression, the proposed RM-Line saved 53% more storage space than CannyLine. More importantly, the straight lines obtained using CannyLine were distributed on both sides of the obstacle grid, which could not accurately describe the obstacle information in the map. Using them for map compression or map optimization would produce unexpectedly bad results. In contrast, the straight line obtained using RM-Line could accurately describe the obstacle information in the grid map, thereby meeting the needs of tasks such as map compression and map optimization of mobile robots.