esa-sceva/satcom-chunk-collection huggingface dataset:
# Perspective on Double Pulsed Gas Metal Arc Welding
## 3 Advantages of DP-GMAW
### Reduced Porosity Incidence  
DP-GMAW features the reduced porosity incidence due to the weld pool stir effect. [PERSON] et al. [38] and [PERSON] et al. [39] stated that porosity content in welds could be reduced using DP-GMAW. [PERSON] et al. [19] conducted experiments on aluminum alloy 6061 by CP-GMAW and DP-GMAW. The results indicated that both welds contained a certain amount of hydrogen-included pores, but DP-GMAW minimized the porosity incidence. [PERSON] et al. [22] found that the weld by CP-GMAW contained several pores, and fracture occurred at three continuous pores. Both the pore volume and number were reduced using DP-GMAW. [PERSON] et al. [16; 40] systematically investigated the porosity incidence by DP-GMAW. Here, porosity incidence is defined as the number  
Figure 7: Grain growth direction of 5052 aluminum alloy by DP-GMAW during the thermal base phase. (**a**) Experimental result. (**b**) Calculated result.  
of pores per 10 mm length of welds. Figure 8 illustrates the porosity incidence versus the thermal frequency of DP-GMAW with different welding speeds [40]. DP-GMAW with the thermal frequency of 0 Hz is equal CP-GMAW. Welding current and arc voltage are the same for all experiments.  
The following conclusions are drawn: (1) DP-GMAW achieves a reduced porosity incidence compared with CP-GMAW, and is an efficient method for the suppression of porosity. (2) The porosity incidence decreases with higher TF to 20 Hz and subsequently increases with higher TF once the TF exceeds 20 Hz. (3) The optimized TF for the suppression of porosity incidence ranges from 10-25 Hz. (4) The porosity incidence exhibits an upward trend with a high welding speed. The weld pool stir effect originated from the oscillation of arc, and the droplet pressure improves the fluidity of the weld pool [18]. With improved fluidity of the weld pool, a larger number of hydrogen bubbles have a chance to float out and be released from the weld pool, and fewer hydrogen bubbles are trapped in the solidification wall of the weld pool. Therefore, the reduced porosity incidence is observed in the experiments.
## 3 Novel Approach EKF Tuning  
The proposed EKF tuning method for INS/GNSS integration in both direct and indirect modes is based on the Monte Carlo technique, using ideal data from a predetermined trajectory and applying the inertial sensor error model to it. The method presented in this paper involves generating multiple trajectory measurements from a reference trajectory, with each measurement incorporating different random values of the accelerometer and gyroscope error model coefficients. These simulations are used to pre-calculate the variance matrices \(\mathbf{Q}\) for both EKF modes.  
Figure 1 illustrates the flow of the Monte Carlo simulation process. The process begins with the use of the **true specific force and angular rate** from a reference trajectory, which are then combined with **IMU errors** to generate **IMU measurements**. Next, the **INS mechanization** is performed to estimate the navigation states. During the INS loop, the **navigation variances** and **IMU variances** are calculated and saved. The process is repeated 200 times, and the final variances produced yield the **navigation variances** for the DEKF and the **IMU variances** for the IEKF.  
The color coding is as follows: orange blocks represent inputs, blue blocks represent the main computational steps of the Monte Carlo simulation, which include the INS loop, and green blocks represent the output variance matrices for both EKF modes.  
Figure 1: Flow diagram of the Monte Carlo simulation algorithm.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy
### Types of IFAs
#### 3.1.2 Unsatisfiable Interests  
Unsatisfiable interests involve sending interest packets that are inherently unresolvable due to specific manipulations. Attackers may set fields such as the _Exclude_ filter to exclude all existing content or use random values in the _PublisherPublicKeyDigest_ field, ensuring that no matching data packets can satisfy these interests. These malicious interests persist in the PIT until they expire, consuming memory resources and potentially leading to PIT overflow [26]. The deliberate use of unsatisfiable interests aims to degrade network performance and disrupt legitimate communications. Countermeasures include enhancing routers' ability to detect and discard such interests promptly and implementing stricter validation of interest packet fields. However, the dynamic nature of content naming and the legitimate use of exclusion filters complicates the identification of malicious patterns [22].
# Experimental and Empirical Study for Prediction of Blast Loads
## 2 Materials and Methods
### Predictive Models  
This section deals with predictive calculation methods for individual shock wave parameters. Based on many literature sources, there are a large number of computational relationships for individual parameters of blast. To verify their applicability to the case of small near-field charges, the most appropriate ones have been selected.  
The concept of reducing the distance is based on the complexity of determining the values of shock wave effects. In the case of one type of explosive, it would be necessary to measure the parameters for all explosive masses at different distances in the experiment, which would be very difficult for large charges, for example. It can therefore be assumed that, at the same reduced distance, the magnitude of a particular shock wave parameter induced by a particular type of explosive is the same for all explosive masses. The reduced distance makes it possible to determine the correlation between the different distances  
Figure 3: Detonation sequence of a 300 g Semtex 10-SE spherical charge with shock wave propagation marked.  
and masses of explosives and to simplify the possibilities of determining their explosion parameters [22; 23].  
Thus, the reduced distance is a basic calculation relationship that is used to derive shock wave parameters based on the knowledge of the explosive mass and the distance between the point of exposure to the shock wave and the epicentre of the explosion, as given in Relation (1).  
\[Z=\frac{R}{\sqrt[3]{W}}, \tag{1}\]  
In this relationship, \(Z\) represents the reduced distance in m\(\cdot\)kg\({}^{-1/3}\), \(R\) is the actual distance of the charge in m, and \(W\) is the mass of the charge, usually the equivalent mass of TNT in kilograms.
## 2 Materials and Methods
### Traffic Data  
The travel time of the \(i\)-th vehicle (of those that count for \(p_{1}\)) is \(t_{i}=t_{i}^{out}-t_{i}^{in}\), where \(t_{i}^{out}\) is the timestamp (in seconds, the time starts as the simulation does) when the \(i\)-th vehicle leaves the avenue, and \(t_{i}^{in}\) is the timestamp when the \(i\)-th vehicle enters the avenue. The expected travel time (\(t^{sp}\)) is calculated using Equation (1). As \(sp_{1}\) is assumed to be the legal speed limit, it is also the maximum speed to travel along the distance \(d\). Therefore, \(t^{sp}\) is the minimum time (without any delay) to traverse \(d\).  
\[t^{sp}=d/sp_{1} \tag{1}\]  
The mean extra travel time (\(T\)) is calculated with Equation (2).  
\[T=\sum_{i=1}^{N}(t_{i}-t^{sp})/N \tag{2}\]  
where \(i=1\)... \(N\) is the vehicle index, and \(N\) is the total number of fast vehicles during the simulation. Table 2 shows the \(T\) of the simulations with modeling purposes, so \(T_{sp_{1}}^{p_{2}}=T_{60}^{10}=0.232\)\(\times\)\(10^{-3}\) s is the mean extra travel time from a simulation with \(p_{2}=10\)% and \(sp_{1}=60\) km/h.  
Extra travel time variability is calculated with Equation (3), similar as in the study presented in [37].  
\[V=\frac{1}{N}\sum_{i=1}^{N}|t_{i}^{e}-T| \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fast Vehicles’** & **Slow Vehicles’** & **Fast Vehicles’** & **Slow Vehicles’** & **Time Lapse** & **between Vehicles** \\
**Desired Speed** & **Desired Speed** & **Percentage (\%)** & **Percentage (\%)** & **per Lane (s)** \\
**(km/h)** & **(km/h)** & **(km/h)** & **(km/h)** & **(km/h)** \\ \hline \hline A value of the set & A value of the set & A value of the set & A value of the set & \\
**(60,65,... 85,90)** & **(30,32.5,... 42.5,45)** & **(90,80,... 30,20)** & **(10,20,... 70,80)** & **6 s** \\ \hline \end{tabular}
\end{table}
Table 1: Simulation settings.  
In Equation (3), \(V\) is the extra travel time variability and \(t_{i}^{a}=t_{i}-t^{sp}\) is the extra travel time of the \(i\)-th vehicle. The calculated \(V\) values in Table 3, where \(V_{sp_{1}}^{P2}=V_{90}^{80}=1.105068\) s, correspond to a simulation with \(p_{2}=80\%\) and \(sp_{1}=90\) km/h.
## 4 Test Results and Discussion
### Surface Roughness Degradation Anisotropy
#### 4.2.2 Analysis of the Roughness Degradation Perpendicular to the Shear Direction  
Compared with the 90\({}^{\circ}\) and 270\({}^{\circ}\) analyzed directions, the 0\({}^{\circ}\) and 180\({}^{\circ}\) directions were the least affected by the cyclic shear. Figure 15 shows that the roughness degradation perpendicular to the shear direction was also consistent with the RDF, but the value of the residual roughness \(c\) was generally larger than that along the shear direction.  
Based on the RDF, considering the positive x axis to be 0\({}^{\circ}\), the values of the residual roughness \(c\) in each analyzed direction between 0\({}^{\circ}\) and 360\({}^{\circ}\) at intervals of 30\({}^{\circ}\) (counter-clockwise) could be calculated (Figure 16b). The results show that the roughness degradation degrees of the different analyzed directions in the cyclic shearing were inconsistent due to the joint surface roughness anisotropy. The roughness degradation degree was the most serious near the cyclic shear analyzed directions (240\({}^{\circ}\), 270\({}^{\circ}\), and 300\({}^{\circ}\)), and the influence was the smallest in the 0\({}^{\circ}\), 30\({}^{\circ}\), 150\({}^{\circ}\), and 180\({}^{\circ}\) analyzed directions. A possible linear negative correlation was predicted between the residual roughness \(c\) and the normal stress variation based on the fact that almost all of the analyzed directions decreased as the normal stress increased.  
Figure 14: Curves of parameter c vs. normal stress along the 90\({}^{\circ}\) and 270\({}^{\circ}\) analyzed directions.  
Figure 15: Roughness parameter change process along cyclic shear directions under different normal stresses: (**a**) 0\({}^{\circ}\) and (**b**) 180\({}^{\circ}\) analyzed directions.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 3 Improved Sparrow Search Algorithm
### Improved Sparrow Search Algorithm
#### 3.2.4 Dynamically Selected Adaptive \(t\)-Distribution Disturbance  
The sparrow search algorithm should have a divergence trend in the initial iteration of the population to obtain a better global development ability, and, with the increase in the iteration times, sparrows gradually gather, so it should enhance the local exploration ability of individuals and improve the convergence speed. The iteration number _iter_ is taken as the degree of freedom of the adaptive _t_-distribution, and it can be seen from the morphological characteristics of the _t_-distribution curve that the degree of freedom is small and the curve is relatively flat at the early stage of the iteration, and that the individuals in the population enter the wide search mode at this time [39]. Otherwise, the curve is close to the normal distribution curve, and the algorithm can obtain faster convergence speeds. The position perturbation of the adaptive _t_-distribution is as follows:  
\[X_{i,j}^{iter+1}=X_{i,j}^{iter}+X_{i,j}^{iter}\cdot t(iter) \tag{29}\]  
where _t_(_iter_) represents the adaptive _t_-distribution function with the current iteration number _iter_ as the degree of freedom parameter. If the positions of all individuals in the population are disturbed indiscriminately, a lot of computing resources will be wasted, and the calculation time will be prolonged. Therefore, the dynamic selection rule is set to randomly select some individual positions for disturbance. The basis of the rule is as follows: in the early stage of iteration, the probability of disturbance is larger to avoid falling into the local optimality too early and thus affecting the global search. The late iteration perturbation probability is small, so as to give full play to the local search performance of the algorithm and improve the convergence speed. Accordingly, the probability \(p\) of disturbance is set as follows:  
\[p=w_{1}-w_{2}\times(iter_{\max}-iter)/iter_{\max} \tag{30}\]  
Among it, \(w\)1 and \(w\)2 are the upper limit and change amplitude of the selection probability, respectively. According to reference [40], \(w\)1 = 0.5 and \(w\)2 = 0.1 can be set.
## 3 Multi-Modal Distribution of RSSI
### Summary  
The situations where RSSI obeys a Gaussian distribution or irregular multi-modal distributions are summarized in Figure 5. On the top level, a distinction is made between signals and noise, where signals refer to human-generated electromagnetic waves and noise refers to any uncontrollable disturbance in the RF system. Next, signals are divided based on whether the number of modulated carriers is constant or not. Note that the single-carrier modulated signal is considered as a special case of signals with a constant amount of carriers. Then, the signals modulated with constant amount of carriers are further divided based on whether the transmission is continuous. The conclusion is that RSSI forms a Gaussian distribution when the signal is not present or it is modulated with a constant amount of carriers and continuously transmitted; in all other cases, a bimodal or multi-modal distribution is observed instead.
## 2 Mathematical Model Description  
### Governing Equations  
The internal flow of a steam ejector is governed by the compressible steady-state axisymmetric conservation equations of fluid flow. The Navier-Stokes equations are more suitable for variable-density flows. The total energy equation, including viscous dissipation, was considered and coupled with the ideal gas law. The governing continuity equation, momentum equation, and energy equation can be written as follows:continuity equation:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{1}\]  
momentum equation:  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}(\rho u_ {i}u_{j})=-\frac{\partial P}{\partial x_{i}}+\frac{\partial\tau_{ij}}{\partial x _{j}} \tag{2}\]  
energy equation:  
\[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{i}}(u_{i}( \rho E+P))=\overset{\rightarrow}{\
abla}\cdot(\alpha_{eff}\frac{\partial T}{ \partial x_{i}})+\overset{\rightarrow}{\
abla}\cdot(u_{j}(\tau_{ij})) \tag{3}\]  
where  
\[\tau_{ij}=\mu_{eff}(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j }}{\partial x_{i}})-\frac{2}{3}\mu_{eff}\frac{\partial u_{k}}{\partial x_{k}} \delta_{ij} \tag{4}\]  
In addition, gas state equation:  
\[\rho=\frac{P}{RT} \tag{5}\]  
conservation equations for the working fluid:  
\[\sum_{i=1}^{n}Y_{i}=1 \tag{6}\]  
\[Y_{i}=\frac{m_{i}}{m_{total}} \tag{7}\]  
where \(u_{i}\), \(u_{j}\), and \(u_{k}\) are components of velocity, \(\tau_{ij}\) is the stress tensor of the fluid, \(E\) is the fluid energy, \(\alpha_{eff}\) is the effective thermal conductivity, \(\mu_{eff}\) is the effective viscosity of the fluid, \(\delta_{ij}\) is the unit tensor, \(n\) is the total number of gases, \(Y_{i}\) is the mass fraction of \(i\)-th species in the mixture, and \(m_{i}\) is the mass of each species. In this study, air and steam were treated as non-reactive species in the flow field, so the species transport equations could be written as follows:  
\[\frac{\partial}{\partial t}(\rho Y_{i})+\
abla\cdot(\rho\overset{ \rightarrow}{v}\cdot Y_{i})=-\
abla\cdot\overset{\rightarrow}{j_{i}}+S_{i} \tag{8}\]  
\[\overset{\rightarrow}{j_{i}}=-(\rho D_{i,m}+\frac{\mu_{t}}{Sc_{t}})\
abla Y_ {i}-D_{T,i}\frac{\
abla T}{T} \tag{9}\]  
where \(S_{i}\) is the rate of creation by addition from the dispersed phase, \(J_{i}\) is the diffusion flux of species \(i\). \(D_{i,m}\) is the mass diffusion coefficient for species \(i\) in the mixture, \(D_{T,i}\) is the turbulent diffusivity for species \(i\) in the mixture, \(\mu_{t}\) is the turbulent viscosity, and \(Sc_{t}\) is the thermal diffusion coefficient.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 5 Fire Detection Results
### Classification Evaluation  
In this context of object classification, the most commonly utilized metric is the average accuracy (AP), as it provides an overarching evaluation of model performance. This metric gauged the model's accuracy for a specific label or class. Specifically, we focused on calculating the precision and recall for each of the classes \"fire\", \"non-fire\" and \"smoke\" across all the models. From the precision and recall for each class, the macro-average precision and macro average recall were calculated as the averages over the classes' values for these two metrics, respectively. At the model level, three metrics were considered, namely the F1-score, the arithmetic average class accuracy (arithmetic mean for short), and the harmonic average-class accuracy (harmonic mean for short).  
Precision stands as a fundamental measure disclosing the accuracy of our model within a specific class. It was calculated, as shown in Equation (1), as the ratio of _TP_ to the sum of _TP_ and _FP_ for each level of the target, that is, fire, non-fire, and smoke.  
\[Precision(l)=\frac{TP(l)}{TP(l)+FP(l)} \tag{1}\]  
The precision indicated how confident we could be that a detected region predicted to have the positive target level (fire, non-fire, smoke) actually had the positive target level.  
Recall, also known as sensitivity or the true positive rate (TPR), indicates how confident we could be that all the detected regions with the positive target level (fire, non-fire, smoke) were found. It was defined as the ratio of _TP_ to the sum of _TP_ and _FN_, as shown in Equation (2).  
\[Recall(l)=\frac{TP(l)}{TP(l)+FN(l)} \tag{2}\]  
The mean average precision at an intersection over union (IOU) threshold of 0.5 (mAP50) was also used to assess the performance of the detection. Using mAP50 meant that model's predictions were considered correct if they had at least a 50% overlap with the ground truth bounding boxes.  
For the overall performance of the model, three metrics were considered, namely the F1_score, the arithmetic average class accuracy (arithmetic mean for short), and the harmonic average class accuracy (harmonic mean for short). They were calculated as given by the following equations.  
\[F1_{score}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]  
\[arithmetic\_average\_class\_accuracy=\frac{1}{3}\frac{3}{l=1}Recall(l) \tag{4}\]  
\[harmonic\_average\_class\_accuracy=\frac{1}{\frac{1}{3}\sum_{l=1}^{3}\frac{1}{ recall(l)}} \tag{5}\]
# Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method
## 2 Methods
### Modeling Approach
#### 2.2.5. DBSCAN (Density-Based Spatial Clustering of Application with Noise) Clustering Algorithm  
In this paper, the spatial density clustering (DBSCAN) algorithm is used to find the hot spots of users' vehicle parking based on the iterative parameter search theory of the K-dist graph, which provides technical support for scientific planning of centralized parking areas for shared bicycles. DBSCAN is a typical density clustering algorithm that was proposed by [PERSON] et al. [37]. Compared with other clustering algorithms, the DBSCAN algorithm clusters according to the density of data distribution. It can find clusters of any shape, effectively deal with abnormal data, and is suitable for irregular shape clustering problems. The closeness of the sample distribution is characterized by a set of 'neighborhood' parameters \((\epsilon,M_{p})\), where \(\epsilon\) is the neighborhood radius, and \(M_{p}\) represents the MinPts value, which is the number of points that must be included at least in the neighborhood. For the DBSCAN algorithm, the data set \(D=\{x_{1},x_{2},\ldots,x_{m}\}\) is given. The \(\epsilon\) neighborhood of an arbitrary point \(x\) is defined as follows:  
\[N_{\epsilon}(x_{i})=\big{\{}\,x_{j}\in D\big{|}d\big{(}x_{i},x_{j}\big{)}< \epsilon\big{\}} \tag{13}\]  
Here, \(d\big{(}x_{i},x_{j}\big{)}\) is the distance between point \(x_{i}\) and \(x_{j}\).  
If the \(\epsilon\)-neighborhood of a point contains at least \(M_{p}\) points, that is, \(|N_{\epsilon}(x_{i})|\geq M_{p}\), then \(x_{i}\) is a core point, and the points that do not satisfy this condition are non-core points. Based on the concept of core point, the clustering process of DBSCAN algorithm is described as follows:  
1. According to the set \(\epsilon\) and \(M_{p}\) parameters, find the \(\epsilon\)-neighborhood of each sample, and put the core point into the new set \(\Omega\).
2. Randomly select a core point in \(\Omega\), find all the samples with suitable density, and form the first cluster \(C_{1}\).
3. Remove the core point contained in \(C_{1}\) from \(\Omega\): \(\Omega=\Omega/C_{1}\), and then randomly select a core point from the new set \(\Omega\) to generate the next cluster \(C_{2}\).
4. The above steps 2 and 3 are repeated until \(\Omega\) is an empty set, generating n clusters: \(C_{1}\); \(C_{2}\); \(\ldots\); \(C_{n}\).  
This study mainly uses this method to cluster origin-destination (OD) points for shared bicycle travel, so as to grasp the distribution of origin-destination (OD) points for travel with bike sharing and provide data support for the planning and scheduling of shared bicycles.