esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Energy-Sensitive X-ray and Gamma-ray Imaging in Wide FoV
### Effective Area, Geometric Factor  
The effective area of the device as X-ray and Gamma-ray imager is given by the area of the radiation sensitive sensor of the pixel detector coupled to the effective area of the optics collimator. The Timepix detector provides a sensitive matrix of 256 \(\times\) 256 pixels for a total area of 14.08 mm \(\times\) 14.08 mm = 1.98 cm\({}^{2}\). The effective area of the collimator/optics element consists of four quadrant regions, each of 7 mm \(\times\) 7 mm, for total effective area 14 cm \(\times\) 14 cm = 4 cm\({}^{2}\). The resulting geometric area for the assembled imager is thus the same as the effective area of the detector (1.98 cm\({}^{2}\)).  
Figure 9: Directional response and FoV tests with an X-ray unit scanned in wide angular range from \(-35\) to \(+60\) degrees. Results are shown along the X- and Y- directions with step 5 degrees (see the array of black & white detector frames. Measurements along the diagonal direction at selected positions are also included. One frame, at the central position, i.e., at the fully perpendicular direction, is shown in detail (color frame shown at top left)—see also Figure 10.
###### Abstract  
Road scene segmentation is an integral part of the Intelligent Transport System (ITS) for precise interpretation of the environment and safer vehicle navigation. Traditional segmentation methods have faced difficulties in meeting the requirements of unstructured and complex image segmentation. Therefore, the Deep-Neural Network (DNN) plays a significant role in effectively segmenting images with multiple classes in an unstructured environment. In this work, semantic segmentation models such as U-net, LinkNet, FPN, and PSPNet are updated to use classification networks such as VGG19, Resnet50, Efficiently7, MobilenetV2, and Inception V3 as pre-trained backbone architectures, and the performance of each updated model is compared with the unstructured Indian Driving-Lite (IDD-Lite) dataset. In order to improve segmentation performance, a stacking ensemble approach is proposed to combine the predictions of a semantic segmentation model across different backbone architectures using a simple grid search method. Thus, four ensemble models are formed and analyzed on the IDD-Lite dataset. The two metrics Intersection over Union (IoU or Jaccard index) and Dice coefficient (F1 score) are used to assess the segmentation performance of each ensemble model. The results show that an ensemble of U-net with different backbone architectures is more efficient than other ensemble models. This model has achieved 73.12% and 76.67%, respectively, in IoU and F1 scores.  
s _applied_  
15 October 2022  
Accepted: 21 November 2022  
Published: 29 November 2022  
Published: **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 7 Research Directions
### Network Anomaly Detection  
Authentication and encryption techniques protect end-to-end communications. However, adversaries can perform various types of networks attacks, such as SQL injection, Cross-Site Scripting, Malware injection in payloads, Denial of Service, and Distributed Denial of Service attacks, from inside the network and the Internet. These types of wireless attacks can be prevented by deploying Intrusion Prevention Systems (IPS) [193; 194] to the IoT systems. It is also possible to detect the wireless attacks by using Intrusion Detection Systems (IDS) [193; 194]. The IPS and IDS capture network traffic and perform deep packet  
Figure 27: A PUF-based scheme for storing credentials securely.  
analysis to identify network anomalies. An IoT network can achieve a higher degree of security against network attacks by increasing control and monitoring on the network packets. However, the increase in the inspection of network packets can be a threat to the privacy of the user and sensor information. As a result, there are opportunities to research anomaly detection systems that apply an optimal level of security control on the network traffic and can identify network intrusions effectively without compromising information privacy.  
Moreover, the IoT application domains are still emerging. New smart applications and services can introduce unseen and unpredictable vulnerabilities, threats, and attacks in IoT systems. Therefore, research can be done to enable anomaly-detection services to identify and mitigate emerging network attacks based on the knowledge base of existing threats.  
Machine learning techniques can be adopted to identify new and emerging network anomalies. Figure 28 shows an overview of an adaptive security model to detect current and emerging network attacks. The IPS and IDS receive incoming and outgoing packets and inspect the payloads to find a match with anomaly signatures or rules. The IPS and IDS generate alter when they find matches. However, the database for signatures and rules needs to be updated periodically to detect most recent attacks. An attack has to be seen first to identify its signatures. Machine learning models can be developed that inspect network packets, predict new attacks, and create signatures for alerts. As shown in Figure 28, a Network Monitoring System (NMS) [195] captures all the network traffic, parses it to generate session information (such as protocols, headers, and payloads), and stores the session information in a database. A machine learning model uses the session information or metadata to identify unusual or suspicious communications and messages and then creates signatures using the metadata. Hence, new rules can be added to the signature database.
# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 1 Introduction
The network is integrated into a multiscale structure by combining dilated convolution modules of different sizes to increase network responsiveness to different frequency components and increase the robustness of network features. At the same time, inspired by the autoencoder structure and the N-BEATS network [20], our network is divided into a feedback branch and a prediction branch. The prediction branch is used to output the prediction result, while the feedback branch is used to fit the input sequence. This approach stabilizes features within hidden layers. 2. Our model outperformed four other models when challenged with the Traffic public dataset, delivering superior results in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Correlation coefficient (CORR). The role played by different model components was studied via ablation experiments.
## 4 Examples of Critical Constructability Features in Facade Panels of the Orchard Commons Project
### Feature 1: Panel Thickness  
**Constructability Constraints and Experts' Knowledge:** The thicker the concrete gets, the more eccentricity appears in its vertical loading on the concrete slab. Therefore, it is necessary to reduce the weight of the panels as much as possible. For this reason, the thickness of the panels has a crucial role, in addition to its other dimensions which are dictated by the architectural design. With the consultation of construction experts, the computational designer decided to set this constraint to 9 inches. This includes three different panel layers, i.e., inner wythe, insulation, and outer wythe, as shown in Figure 3. This decision was mainly based on the panel layers' composition as well as the depth limitation of the concrete panels' formwork.  
**Constructability Feature and Computational Implementation:** The described thickness feature is an example of constructability features that address the \"dimensional constructability constraints\". Dimensional constructability constraints are prevalent and influence not only the fabrication process of a component but also its transportation, storage, and installation. In computational design, to capture the dimensional constructability constraints for a component, it is necessary either to directly define its corner points (\(p_{(1-n)}\)), or indirectly determine its length (\(l_{(d)}\)), width (\(w_{(d)}\)), and height (\(h_{(d)}\)) with a point of origin (\(p_{(x,y,z)}\)). This origin point is essential for calculating corner points and spatial placement. Figure 4 shows the implementation of this feature in the Grasshopper environment for the OC project.  
Such constraints were also taken into account when developing features in the OC project, like the example above concerning panels' thickness. Further features developed for dimensional constructability constraints in this case study include: \"fixed panel height\", corresponding to the floor-to-floor height (2625 mm); and \"varying panel widths\", stemming from a sustainability goal to achieve a 60/40 ratio between the opaque and glazed surfaces.  
Figure 4: Implementation of dimensional constructability feature for panel thickness in Grasshopper environment.  
Figure 3: Shop drawing of panel layers (**left**); a prefabricated facade panel in preparation for lifting (**right**).
## 2 Materials and Methods
### Wavelets Based on Higher Derivatives of the Logistic Function  
Similarly, as in the previous section we define a wavelet \(\psi_{n}(x)\) to be the \(n\)th (\(n=3,4,\ldots\)) derivative of the logistic function \(u(x)=\frac{1}{1+\exp(-x)}\). Thus, (6) gives  
\[u^{(n)}(x)=(-1)^{n}\sum\limits_{k=0}^{n-1}\genfrac{\langle}{\rangle}{0.0 pt}{}{n}{ k}u^{k+1}(u-1)^{n-k},\]  
and then \(\psi_{n}(x)\) can be explicitly expressed as  
\[\psi_{n}(x)=(-1)^{n}\sum\limits_{k=0}^{n-1}\genfrac{\langle}{\rangle}{0.0 pt}{}{n }{k}\Big{(}\frac{1}{1+e^{-x}}\Big{)}^{k+1}\big{(}\frac{1}{1+e^{-x}}-1\big{)}^{n -k}=\frac{(-1)^{n}\sum\limits_{k=0}^{n-1}\genfrac{\langle}{\rangle}{0.0 pt}{}{ n}{k}(-e^{-x})^{n-k}}{(1+e^{-x})^{n+1}}. \tag{17}\]  
By definition, the function \(\psi_{n}(x)\) is an even function for odd n and an odd function when n is even. The numerator of the expression (17) is a polynomial of degree \(n\) of the variable \(e^{-x}\), while the denominator of degree \(n+1\). Therefore, for any polynomial \(p(x)\) we have \(\lim\limits_{x\rightarrow-\infty}p(x)\psi_{n}(x)=0\). Since \(\psi_{n}(x)\) has the symmetry property then also \(\lim\limits_{x\rightarrow\infty}p(x)\psi_{n}(x)=0\). The last conclusion results also from multiplying the numerator and the denominator of (17) by \(e^{(n+1)x}\). From this and from the fact that \(\psi_{k+1}(x)=\psi_{k}^{\prime}(x)\) for any integer \(k\geq 2\) it follows that \(\psi_{n}(x)\in S(\mathbb{R})\), (\(n=2,3,\ldots\)).  
By (13) we have  
\[\hat{\psi}_{n}(\xi)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}\psi_{n}(x)e^{ -i\xi x}dx=\sqrt{\frac{\pi}{2}}\frac{(i\xi)^{n-1}\xi}{\sinh(\pi\xi)}. \tag{18}\]  
Now, using (18) and once again formula (15) we can calculate the integral of the admissibility condition (14) as follows:  
\[2\pi\int_{-\infty}^{\infty}|\xi|^{-1}|\hat{\psi}_{n}(\xi)|^{2}d\xi =\pi^{2}\int_{-\infty}^{\infty}\frac{|\xi|^{2n-1}}{(\sinh(\pi\xi) )^{2}}d\xi=\pi^{2}\frac{2\Gamma(2n)}{2^{2n-2}\pi^{2n}}\xi(2n-1)\] \[=\frac{(2n-1)!}{2^{2n-3}\pi^{2n-2}\xi(2n-1)}. \tag{19}\]  
As usually we generate a doubly-indexed family of wavelets from \(\psi_{n}\) by dilating and translating,  
\[\psi_{n}^{a,b}(x)=\frac{1}{\sqrt{a}}\psi_{n}\Big{(}\frac{x-b}{a}\Big{)},\]  
where \(a,b\in\mathbb{R},\ a>0,\ n=2,3,\ldots\).
## 1 Introduction
As stated in [18; 19], the growing interest in these methods is due partly to their high versatility, particularly in the case of high-dimensional problems. Conventional grid-based approaches such as finite difference (FDM) [20] and finite-element (fem) [21] methods have inherent problems, namely a mesh generation requirement. Mesh generation is not a simple job, particularly for domains with irregular and complex geometries requiring additional mathematical transformations that can be as costly as solving the problem. Meshfree avoid this problem since the conventional definition of the concept does not include the meshing procedure. This makes it easy to understand the behavior of complex solids, structures and stress and strain fields arbitrarily distributed in the domain without any constraints. Moreover, we do not need to consider the connectivity between the nodes while applying the meshfree process. For a survey of various meshless approaches and underlying findings, we refer the reader to [22; 23; 24; 25]. The paper describes a meshless process by which the elastic strain of a series of Bragg-edge strain measurements can be reconstructed tomographically. Because of the attractive properties of radial basis functions (rbf), the approach has some advantages over previous approaches. Especially its ability to represent extremely spatially varying functions accurately. The proposed algorithm is evaluated on a two-dimensional simulated beam data for which a thorough comparison of the fem and rbf methods is shown. It is shown to be able to reconstruct a tensor field, after [26; 27] imposed equilibrium conditions. The framework is then extended to real experiment measurements for the steel geometry of an offset ring-and-plug and a crushed ring. The paper has the following structure: Section 2 discusses strain measurement through the Longitudinal Ray Transformation. Section 3 offers an overview and algorithm of the meshless method for strain field reconstruction and presents the proposed algorithm structure and the finite-element approach. Section 4 addresses the validation of the algorithm using a cantilevered beam and the key difference between the fem and the solution being suggested. Finally, the experimental findings with the ring-and-plug and crushed ring steel samples are addressed in Section 5. Section 6 summarizes the work in this paper and lists the work to be done in the future.
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 2 Bibliometric Study and Typical Aerial Camera-to-Ground Target Localization Algorithm
### Target Location Algorithm Based on Digital Elevation Model  
In an area with complex terrain, it is impossible to acquire the geodetic elevation information of the target in advance. In this situation, the positioning algorithm of the ground target based on the earth's ellipsoid model is shown in Figure 7a. It can be seen that the geodetic height error of the target will cause huge positioning errors. To solve this problem, the digital elevation model (DEM) is used to replace the earth ellipsoid model, as figured in Figure 7b.  
The accuracy of DEM model is the core of this algorithm. There are mainly three ways to obtain DEM data: (1) Measuring on the ground by a hand-held GPS measuring instrument or total station; (2) Obtaining remote sensing images through photoelectric equipment and remote sensing technology, while carrying out three-dimensional reconstruction; (3) Using high-precision equipment such as radar and laser scanner to obtain elevation information like the 3D point cloud [65; 66; 67; 68; 69; 70; 71].  
This paper gives the basic model of the DEM-based iterative target location algorithm. The positioning principle is contained in Figure 8, and the specific process can be divided into five steps:  
1. [label=(0)]
2. Estimate the geographical location of the target and extract the digital elevation information of the target area from DEM data;
3. Initialize the number of iterations and calculate the maximum height \(H_{i}=H_{\text{max}}\) of the target area;
4. The localization method based on the earth ellipsoid model initializes the localization of the target, and the localization result is \([\varphi_{i},\lambda_{i},H_{i}]\);
5. Find the geodetic height information with longitude and latitude \([\varphi_{i},\lambda_{i}]\) through DEM data and record it as \(h_{i}\);
6. Compare \(h_{i}\) and \(H_{i}\), if \(h_{i}<H_{i}\), continue the iteration and let \(H_{i}=H_{i-i}-\varepsilon_{h}\), if \(h_{i}>H_{i}\), output the target positioning result \([\varphi_{i},\lambda_{i},H_{i}]\).  
Without the control points, terrain undulation is always one of the difficult problems of remote target location. The terrain model represented by DEM is introduced into the target location algorithm to make up for this deficiency. When the load and carrier conditions permit, the positioning accuracy in mountainous areas is greatly improved, and the use direction of DEM model is expanded.  
Figure 8: Schematic of target location model based on DEM.  
Figure 7: Schematic diagram of target positioning results in areas with complex terrain. **(a)** Positioning results using the earth ellipsoid model; **(b)** Positioning results using DEM model.
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 3 Preliminary Objective Tests  
### Choice of Compressor Time Constant Settings  
In preparation for perceptual listening experiments, work was conducted to ascertain appropriate time constant settings for use in the experiments. The 1176 has continuously variable attack and release controls. Thus, a large amount of possible combinations are available. However, it would not be practical to use all of these in listening experiments as a large number of stimuli is known to cause listener fatigue [29]. Thus, content analysis [30] was conducted on 1176 vocal compression settings, created by professional engineers for the 1176 UAD plugin. This work was conducted to discover the most popular settings, which could then be used in the creation of stimuli for the listening experiments. The results revealed that specific combinations of attack and release settings were regularly used, with release positions between five and seven and attack positions between one and three being most common. Additionally, it was noted that the 4:1 ratio was often implemented for general vocal settings and the all-buttons mode (a popular \"special mode\" achieved by depressing  
Figure 1: Results of clustering using the Ward method. The descriptors are split into five subsets (brown), four subsets (green), three subsets (blue) and two subsets (grey).  
all ratio buttons simultaneously) was employed for highly coloured processing. Table 3 shows how frequently particular settings are used in the vocal presets. The bottom two rows of the table show that positions between one and four are most common for attack and positions between five and seven most common for release. Anecdotal evidence by the author supports this result as they have observed many professional audio engineers setting the 1176 compressor with these time constant settings.  
Based on these findings, the following attack and release combinations were used in the following listening experiment (attack is abbreviated to A and release is abbreviated to R): A3R7, A1R7, A3R5, A1R5. The combinations were used in both the 4:1 and all-buttons ratio modes. More general research of content on the 1176 [31] showed the A3R7 combination to be a popular setting for a range of instrument sources. Therefore, the settings used in the experiment are considered by the author to be representative of real working scenarios. It is also worth bearing in mind the attack control on the 1176 is quoted as ranging between 20-800 microseconds and critical listening by the author revealed very little difference in sound quality between any attack time between positions one and four. Additionally, the reader should consider that the attack and release controls on the 1176 work counterclockwise, meaning attack and release position seven is the fastest and one the slowest.
## 3 Results and Discussion
### Analysis on Energy Consumption and Energy Flows  
The summary of energy flows in each major unit is shown in Figure 4, in which the negative heat duties of the two reactors indicate that an external heat supply is required for maintaining the required operating temperature. However, the required heat duty of the mild HDO reactor is less than that of the deep stage because the feedstock streams have been partially preheated by the product streams through the heat exchangers.  
In total, three heat exchangers are employed for heat recovery. The heat exchange rate in HEATX2 is the highest, because the amount of heat required for bio-oil preheating is large due to high mass flow rate. The optimal reaction temperature for the mild stage was selected as 280 \({}^{\circ}\)C, because the product yields at the two operating temperatures are similar but the energy required for 280 \({}^{\circ}\)C is less than that for 300 \({}^{\circ}\)C.  
Figure 3: The mass flow at different temperatures of the mild HDO stage. (**a**) PRODUCT (model compound that represents the final product of bio-fuel, with same properties of gasoline, CH\({}_{1.71}\)); (**b**) ODF (oil-phase organic compounds of mild stage, CH\({}_{1.47}\)O\({}_{0.11}\)); (**c**) COKES (soluble coke deposit, C\({}_{8}\)); and (**d**) COKEIS (insoluble coke deposit, C\({}_{\text{is}}\)).
There is a lack of published information on the use of metaheuristic algorithms to estimate important biometric parameters of livestock. Most of the works deal with issues of resource optimization. For example, in [47], the study attempted to posit a suitable strategy for optimal production with maximum resilience and sustainability in industrial dairy farms. Another type of application was given in [48], where a metaheuristic approach was presented for selecting optimal clusters in wireless body area networks to achieve an energy-efficient routing protocol for livestock health and behavior monitoring. In [37], two metaheuristic algorithms, MOPSO and MICA, were used to balance the broiler production process's cost, time, and quality. Currently, in the livestock field, there is interest in monitoring parameters through embedded systems, for example, in [49], the implementation of an electronic system specifically developed for real-time monitoring of feeding patterns in dairy cows was shown. In [50], an embedded system was presented that could track individual cows using ultra-wideband technology; in addition, social interactions between individuals around the feeding area were analyzed with a computer vision module. This work aims to develop a model based on biometric parameters to estimate the BW, using the GA and the CSA through a polynomial model with a variable degree adjusted by MAs; these biometric parameters can be acquired through a CV system that is suitable for implementation on embedded systems. Therefore, given many variables and taking advantage of the available information, a polynomial model is proposed, which has shown promising results in other areas [51; 52]. Two algorithms are used to verify that the efficiency of the GA is not limited to a local optimum. Unlike the works described above, the proposal of this work uses the evolutionary computation branch to estimate the BW. This technique has an advantage over intelligent techniques based on Artificial Neural Networks (ANNs), which present a polynomial model and not a black box one, as in an ANN. The results show a Root-Mean-Squared Error (RMSE) of 7.68% for the GA and 7.55% for the CSA.