esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Calculation and Gridding of Emissions  
Emissions along the trajectories are estimated and converted to a numerical grid with GRIDLAB by DLR. Based on the reduced emission profiles resulting from the TCM, three-dimensional emission inventories are generated for each flight of the described traffic sample defined by origin, destination, aircraft type, flight level and intermediate stop airport candidate. For this purpose, the best-fitting reduced emission profile in terms of mission length is picked from the before-mentioned trajectory database for every flight. Afterwards, every trajectory is adjusted to the exact great circle distance between the two connected airports by extending or truncating the trajectory in the cruise phase. Airport elevation is also regarded by modifying the respective climb and descent profile within its phases with speed curtailments.  
Emissions from taxiing and take-off are calculated following the standardised ICAO landing take-off cycle, assuming reference emission indices from the ICAO engine emission database in both idle and take-off mode and the respective reference fuel flow from the same database [29]. Before take-off, on-ground emissions of engine running in idle mode for 19 min are added for taxiing out, followed by 42 s in engine take-off mode. After landing, emissions of 7 min in idle mode are attached, representing taxiing in.  
In-flight emissions are calculated as follows before they are projected on the great circle between the connected airports and distributed spatially on a numerical grid:  
* \(\mathrm{CO_{2}}\) and \(\mathrm{H_{2}O}\) emissions are assumed to be linear to fuel burn [28].
* \(\mathrm{NO_{x}}\) emissions are simulated with the non-proportional fuel flow correlation method by DLR [28] with the reference emission indices for engines obtained from the ICAO engine emission database [29].  
Figure 2: Global distribution of considered airports.  
* The sulphur dioxides (SO\({}_{2}\)) emission index varies regionally according to fuel sulphur content [35].
* Hydrocarbons (HC) and carbon monoxide (CO) emissions are derived with the Boeing fuel flow correlation method [36] with reference emission indices for engines as provided by ICAO engine emission database [29].
* Soot emission calculations are based on the ICAO smoke number using reference values from the engine emission database [29], applying a method by DLR [37] to derive the soot emission index. An extrapolation beyond the landing take-off cycle to the cruise phase is performed with the P3-T3 correlation [38].  
Resolution of the resulting grid per mission is 0.25\({}^{\circ}\) both latitudinally and longitudinally and 1000 ft vertically. An exemplary resulting fuel consumption inventory of this study's reference case and the fuel-optimised ISO scenario is displayed in Figure 3.
# XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection
## 1 Introduction
[26] proposed an unsupervised learning where Long-Short Term Memory (LSTM) cell Recurrent Neural Networks (RNN) is used for network traffic anomaly detection by utilizing a public dataset ([[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) (accessed 30 September 2021)). However, no research has provided an in-depth analysis of machine learnings' applicability in the field of host-based intrusion detection for IoT environments [27]. From the literature, it is clear that machine learning-based techniques, being used widely in the IoT security domain, are proven to be effective for developing workable models for detecting IoT threats. As most of the attacks happen in real-time in the IoT environment, therefore a fast attack detection algorithm is required while maintaining higher accuracy. This could be possible by using fewer features that reduce the system complexity and consequently, it should execute faster. This could be very helpful in real-time attack detection. To solve this issue, we propose a machine learning-based approach for intrusion detection in IoT systems where the random forest (RF) algorithm is used to select inevitable features from the N-BaIoT dataset to boost detection accuracy. Then, the eXtreme Gradient Boosting algorithm (XGB) is utilized to characterize and detect malicious attacks. This novelty is very essential for IoT-based intrusion detection as here we have to deal with a big dataset where both time and accuracy are very crucial. However, when RFE and RFECV are used, attributes are added recursively in a one-by-one fashion and check whether that attribute improves the performance or not. If it doesn't improve the classification performance, then that attribute is discarded. In addition, this process continues until all the attributes are checked. On the other hand, RF ranks the features according to information gain rather than checking the feature importance of each feature one-by-one basis. After that, the proposed system is compared with some other state-of-the-art algorithms applied on the same N-BaIoT dataset, where our system achieves much better performance in every case. The key contributions of this paper are as follows:* A hybrid machine learning algorithm has been proposed, for the first time, named XGB-RF where the prominent attributes are selected using RF algorithm and then classified using XGB algorithm. * The proposed algorithm has also been compared with other state-of-the-art machine learning algorithms.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 3 Non-Learning-Based Approaches
### 3D-Information-Based Approaches
#### 3.2.3 Conclusions  
In this part, we introduced 3D-information-based approaches and classified the approaches into two categories: matching-based approaches and local descriptor approaches. Matching-based approaches are more computationally expensive; however, local descriptor approaches require some preprocessing offline.  
The biggest weakness for approaches based on 3D information is that they do not perform well when the object is reflective, which is due to how the approaches work. When measuring the 6D pose of reflective objects, these approaches are not able to obtain the depth information (or accurate point clouds) of the object. Additionally, another weakness is that the efficiency of this approach is relatively low, because point clouds and depth images include a lot of data, causing computational burden.
## 2 Mathematical and Physical Model  
Considering the influence of the furnace structure on sinter particle accumulation, a discrete element method (DEM)-computational fluid dynamics (CFD) coupled model was used to describe the gas-solid heat transfer behavior. The motion and heat transfer between particles were solved using the Hertz-Medellin (no-slip) model and the heat conduction model under the Lagrangian framework [16]. The flow and heat transfer of the fluid phase between the particle phases were calculated using the continuity equation, the Navier-Stokes equation, and the energy conservation equation based on local average variables. The drag force was considered the main force between the particle phase and the fluid phase. In this study, [PERSON]'s and [PERSON]'s drag model [17] was used to describe the drag force on particles. The convective heat transfer between the particle phase and the fluid phase is described by the Ranz-Marshall model, and the accuracy of this model for the calculation of gas-solid heat transfer has been verified [18; 19; 20]. The specific expression in the above model is detailed in Refs. [14; 15]. Ref. [21] indicated that the cooling gas in the vertical cooling furnace exhibited turbulent flow. The standard k-\(\epsilon\) turbulence model is widely used in engineering calculations, and this algorithm has the characteristics of high computational accuracy, simplicity, and stability. Therefore, this study also used the standard k-\(\epsilon\) turbulence model to calculate the turbulent flow of gas in the vertical cooling furnace. The commercial software EDEM 2020 and ANSYS Fluent 2020 were used for the simulation calculation.  
The Meishan Steel vertical cooling furnace is composed of six bins, and its overall and single-bin structures are shown in Figure 1a,b. In this study, the cuboid furnace cavity was transformed into a Venturi furnace cavity. First, a contraction structure was added at the lower part of the original furnace cavity. Then, the center vent cowl was raised to correspond to the contraction structure of the side wall to form an expansion structure. The existence of the contraction structure and expansion structure made the channel width of the furnace cavity change in space, forming a contraction-vertical-expansion Venturi structure, as shown in Figure 1c. At this time, the Venturi furnace type involves four structural parameters: the width \(w\) and length \(l\) of the vertical part, the contraction angle of the contraction part \(\beta\), and the expansion angle of the expansion part \(\alpha\), as shown in Figure 1d. In order to improve the calculation efficiency, the buffer bin, distribution pipe,crisscross beam, and other structures were ignored. At the same time, a slice was cut from the center of the furnace cavity, and half of the region was selected as the calculation domain. The thickness of the slice was 5 times the maximum particle size (750 mm) to eliminate the effect of front- and rear-wall friction on particle motion, as shown in Figure 1e.
# Hybrid NOMA Protocol with Relay Adaptive AF/DF Collaboration and Its Modeling Analysis in NB-IoT
## 3 Performance Modeling Analysis of H-NOMA Protocol  
Due to the characteristics of narrowband IoT with wide coverage, large connection, and low energy consumption, it is difficult to use a single OMA or NOMA to meet its applicable IoT scenarios, and considering the limitations of AF relay and DF relay, this section obtains the reachable rate when the user is in NOMA or OMA state respectively based on the five transmission cases described above after introducing the corresponding specific transmission mechanisms, and analyzes the probability of each state of the model by establishing a discrete-time Markov chain, and then obtains a closed expression for the system outage probability and throughput.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 3 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)
#### 3.3.1 Objective Function Zero (OF0)  
The Objective Function Zero (OF0) in the RPL guides nodes in selecting and optimizing routes within an RPL instance by using available information objects. Instead of providing specific instructions, OF0 offers a general concept to calculate a node's rank by adding a normalized scalar to the rank of its preferred parent \(R_{p}\). OF0 encodes rank in units of 256, allowing for hop ranges from 28 (worst) to 255 (best). In RPL, all parents are feasible successors for upward traffic, and nodes can consider parents in subsequent DODAG versions as potential successors. To compute the current node's rank \(R_{N}\), the step of rank \(S_{p}\) is multiplied by the rank factor \(R_{f}\) and adjusted with a stretch factor \(S_{r}\) that is constrained by the configured rank stretch. This rank is then added to the preferred parent's rank to determine the node's overall rank. By calculating the rank, OF0 enables a node to participate in a DODAG version that provides satisfactory connectivity, with the minimal hop rank increase defining the lowest rank increase to any potential parent. OF0 does not guarantee optimization according to any specific metric; connectivity validation is specific to implementation and link type, which falls outside OF0's scope [32].
## 2 Materials and Methods
### Experimental Setup  
Tensile tests were performed using a UTM (MTS--Measure Test Simulate(r) [18]), by applying a displacement rate of 0.5 mm/min. During each tensile test, two types of fracture progressions were distinguished: brittle-like and ductile-like fracture progressions [16]. They differ in the rate of deformation of a material--the former being elastic and fast, the latter being plastic and slow--as well as in terms of the type of released energy.  
A DIC Stingray_F-504 camera taking 5 frames/s, was used to visually detect the progression of fracture and monitor it over time, allowing to measure its length in millimeters [19].  
AE allowed to detect the energy released by the fracture. In this study, two sensors (frequency operating range = 100-900 kHz), manufactured by Vallen Systeme [20], were used: sensor S5 was placed closer to the 20 mm pre-crack and sensor S6 was positioned farther from the pre-crack (Figure 1b). The position of the AE sensors was chosen taking both the geometrical constraint of samples and the necessity to avoid spurious signals due to clamps into account. In this way, S6 was not affected by the background noise of the UTM, whereas S5 on the other hand was allowed to have a signal backup. The AE technique provided the following three parameters:  
* Amplitude (A): is the peak in decibel during an acoustic emission event; it shows the disturbance level and the response of the sensor after power/energy loss. The set threshold value was 40 dB.
* Counts (C): it shows the number of times a signal exceeds a set threshold value. Real events have high counts.
* Energy (E): is the elastic energy released during an acoustic emission event, measured in arbitrary units (or energy units, e.u. = 10\({}^{-14}\)V\({}^{2}\)s). The AE energy can be determined using Equation (1), integrating the absolute or the squared values of the signal's voltage curve over time [21]. Real events have high energy units.  
\[E=\int_{t_{0}}^{t}U^{2}(t)dt \tag{1}\]  
Figure 1: (**a**) Simil-CT samples cut from Scots pine (_Pinus sylvestris_ L.) beam, naturally dried out. (**b**) Experimental setup: universal testing machine (UTM) for tensile fracture tests, digital image correlation (DIC) for evaluating the elongation of the fracture, and two sensors, indicated by code S5 and S6, for the acoustic emissions (AE) to detect amplitude (A), counts (C), and energy (E) released by the fracture.  
Raw data collected through the experimental setup were organized in order to determine the synchronicity between the fracture event (UTM), its elongation (DIC) and the associated AE signals. The analysis was focused on brittle fractures, because under these circumstances, the energy released by the material and detected by the AE is mainly elastic.
# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
## 2 Methodology  
Given an RGB image, the main purpose of the 6D pose estimation is to predict a rotation matrix \(R\in SO(3)\) and a translation vector \(\mathbf{t}\in\mathbb{R}^{3}\) from the objects' coordinate system to the camera coordinate system. When the pose is accurately detected, the transformation between the industrial robot and the object can be easily inferred for further action such as object grasping or assembling. In fact, the 6D pose estimation problem can be divided into two subtasks: (1) Find out the target objects from the image. (2) Calculate the poses for all the target objects. Most of the existing works [9; 25; 26; 32] solve the two problems in a unified framework for boosting the performance on commonly used open-evaluation datasets such as LINEMOD, Occlusion LINEMOD, and YCB Video. However, all of these datasets only contain a single target for each of the classes in one frame. When there are plenty of targets of the same type, the model cannot handle the situation well. Therefore, in this paper, we propose a two-stage framework to separately solve the 6D pose estimation problem for multi-instance environments.
Based on the phase delay effect and stress wave propagation intervals, a porous superposition method is used to derive an analytical solution for the stress in the surrounding rock of adjacent tunnels under blasting. Theoretical formulas are employed to calculate stress magnitude and propagation patterns of radial particles in adjacent tunnels. These results are then validated through field stress measurements and numerical simulations. The findings provide a theoretical basis for calculating dynamic stress in surrounding rock, understanding the propagation of internal cracks and fissures, and improving surrounding rock reinforcement and blast control.
## 6 Implementation Setup and Evaluation Criteria
### Performance Evaluation Criteria  
The following performance evaluation criteria were used to assess the effectiveness of the proposed solution to improve the policy decision point. First, the confusion matrix was defined in Table 2 using the decision-making results. \(D_{\text{AA}}\) defines the quantity of samples that can be accessed correctly, \(D_{\text{AR}}\) = the number of samples incorrectly denied access, \(D_{\text{RA}}\) = the quantity of samples incorrectly allowed access samples, and \(D_{\text{RR}}\) = the number of correctly refused access samples. Each access decision case (access attempts) will be discussed separately in the following :  
* Positive prediction of denied access attempts: indicates denied access request correctly classified as denied access decision.
* Positive prediction of allowed access attempts: indicates allowed access request correctly classified as allowed access decision.
* Negative prediction of allowed access attempts: indicates allowed access request incorrectly classified as denied access decision.  
1. **Accuracy Metric:** used to evaluate the effectiveness of policy decision points in access control strategies in all scenarios. The percentage is calculated by dividing the number of accurate estimates by the total number. The following formula is used to calculate Accuracy (CM) Using the confusion matrix:  
\[Accuracy=\frac{D_{\text{AA}}+D_{\text{RR}}}{D_{\text{AA}}+D_{\text{AR}}+D_{ \text{RA}}+D_{\text{RR}}} \tag{13}\]
2. **Precision Metric:** is computed by dividing the total number of positive samples by the number of positive samples correctly identified (either correct or not). This is determined by the measure of the total correct samples that were allowed to the expected number of samples that were allowed. It was determined by employing the following formula: \[Precision=\frac{D_{\text{AA}}}{D_{\text{AA}}+D_{\text{RA}}}\] (14)
3. **Recall Metric:** measured as the proportion of positive samples accurately categorised as actual relative to the number of allowed accurate samples. The Recall metric examines the model's identification of samples. Recall increases the actual sample size. The Recall is only concerned with accurate sample categorization. This holds actual whether or not faulty samples are categorised for Precision. Even if the model incorrectly labels all incorrect samples as accurate, the Recall will be 100%. Negative samples, regardless of their classification, should be disregarded. The Recall is computed using only accurate samples. \[Recall=\frac{D_{\text{AA}}}{D_{\text{AA}}+D_{\text{AR}}}\] (15)
4. **F1-score Metric**: the F1-score is a harmonic average of Precision and Recall, the two most significant components. F1-score is effectively unbalanced data. The harmonic average differs from the arithmetic mean. This formula can be used to compute the F1-score \[F1-score=\frac{Precision*Recall}{Precision+Recall}\] (16)
## 4 Model Development
### Input Correlations  
The input relationship analysis computed the correlation between the model input variables. A pairwise comparison analysis was carried out between each pair of input variables. The absolute value of the strength relationship was expressed up to a value of one, while no relationship was expressed as a value of zero. Thus, the closer the relationship was to zero, the weaker the relationship was. Table 8 summarizes the input variables' correlations between the model inputs. The weakest correlation was 0.015 between \"type of school\" and \"AC capacity.\" The maximum correlation was 0.95 between the variables \"number of classrooms\" and \"total air-conditioned area (sqm).\" It should be noted that a positive value indicates a positive relationship, which means an increase in one variable leads to an increase in the other. On the other hand, a negative value indicates a negative relationship, which means an increase in one variable will lead to a decrease in the other. It is interesting to note that the age of the building had a weak correlation with AC capacity and total air-conditioned area. This could be an indication of good school conditions due to an excellent maintenance plan. The city variable had a weak correlation with all other factors. This was because all schools considered in this  
Figure 3: Scatter charts of different factors.  
study were located in Eastern Province in the same climatic zone. This could be different if the study covered all of Saudi Arabia, with its four climatic zones.
# Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics
###### Abstract
This is why we propose in this work a new multi-fidelity strategy for RDO that drastically reduces the required number of function calls by leveraging an inexpensive (but low-accuracy) first-order method of moments (MoM) and a Bayesian GEK [56]. Using gradient-based solvers allows reducing per se the number of function solves in the UQ step and to mitigate the curse of dimensionality. Here, the two models are fused together by using a methodology similar to [57] to generate a surrogate model for the MOGA optimization that combines the efficiency of MoM and the accuracy of GEK. The surrogate is enriched based on the expected improvement criterion during MOGA convergence, as in [9]. The required gradient information is obtained by using either continuous or discrete adjoint formulations. The new MF-RDO strategy is applied to an inexpensive test problem, i.e., the stochastic inverse design of a supersonic quasi-1D nozzle. The results show that a few GEK UQ solves are sufficient to correct the MoM solution, thus reducing the computational cost of the RDO by approximately one order of magnitude with respect to the nested BK strategy. Computational gains are expected to be even more substantial for costly industrial CFD problems. The paper is organized as follows. In Section 2, we present the RDO problem and the test problem. The UQ methods considered in the study are described in Section 3. Section 4 presents the surrogate-based and multi-fidelity RDO strategies. In Section 5, we first apply various UQ methods to the test configuration and compare their accuracy and computational costs; afterwards, such methods are combined with a SMOGA or MF-SMOGA, and their efficiency in solving the RDO problem is assessed. Conclusions and final remarks are reported in Section 6.