esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Dust Explosions. A Brief Review
## 1 Brief Review of Dust Explosion Basics
### Combustion of Dust Clouds in Air
#### 1.4.3 Influence of Dust Chemistry, Dust Particle Size, and Dust Moisture Content  
As discussed by [PERSON] [1] dust chemistry and dust particle size influence both the ignition sensitivity and explosion violence of explosive dust clouds. The heat of combustion of a solid material per mole of oxygen consumed is a useful parameter for comparing the potential explosion powers of various dust materials. Mg and Al top the list, whereas natural organic materials have much lower values. This is why flames of burning clouds in air of Mg and Al dusts are almost white, whereas those of burning clouds air of natural organic materials are orange.  
Figure 2: The range of explosive dust concentrations in air at normal temperature and atmospheric pressure for a typical natural organic dust (maize starch). A comparison with typical range of maximum permissible dust concentrations in the context of industrial hygiene, and a typical density of deposits of natural organic dusts. From [PERSON] [1; 2; 3]. Copyright (2019), with permission from Elsevier.  
For most dust materials, both ignition sensitivity and explosion violence of explosive dust clouds increase with decreasing dust particle size down to about 1 \(\upmu\)m. Moisture in the dust reduces both dust cloud ignition sensitivity and explosion violence. Several mechanisms contribute to this [1].
## 4 Experiments and Evaluation  
### Experiments Setup of Explosion Simulation  
We used two balloons with coffee beans installed between them to simulate the explosion fragments. The reason why we installed coffee beans between two balloons is because the broken shell's initial velocity is slow. With around 0.1 g of coffee beans, a high speed scattered motion can be created. Similar to the regular explosion presented in the introduction, our explosion followed these four similar stages:  
1. The balloons are inflated in the inner shell and the inflation behavior is mainly radial;
2. The elastic limit causes breakage of the balloons;
3. Because of the elastic contraction of the balloon, the pressure difference between the inside and outside is constructed at the same time;
4. The shells of the balloons are randomly broken, and the coffee beans are distributed randomly during the inflation. After the balloons are broken, the coffee beans between these two balloons are scattered with a high initial velocity due to the elasticity and pressure difference.  
We used two Emergent Vision Technology (EVT) HFR cameras (10 GigE, HT-2000). When using these two cameras for experiments, the frame-rate can be set from 1080 FPS to 91 FPS, where the resolution of these cameras are from 512 \(\times\) 320 to 2048 \(\times\) 1088, respectively. HFR cameras captured the scattered motion of the explosion. We also deployed a monitoring module composed of two long-wavelength infrared (LWIR) cameras and one large FOV visible camera under the regular frame-rate (60 FPS). The covering FOV of this module is 120 degrees in both the visible band and LWIR band. The monitoring module helped us (1) monitorthe explosion's environment for safety concerns, (2) control the stop time of the HFR cameras to save storage space, and (3) capture the possible landing position of each fragment for further research. Our instruments and constructed balloons are shown in Figure 5.
## 4 Backend Algorithms of VizCycSemantics
### Textualization
#### 4.2.2 Moving Feature Textualization  
As a restriction of cycling, the conditions of the road network strongly influence the cyclist's movement, thus indicating the moving topics of cycling. In this sense, we textualized the cyclists' fine-grained moving features on roads from the large-scale GPS trajectories to convey the moving semantics of cycling on the roads.  
As shown in Figure 3b, there are \(n\) trajectories going through a road segment. For \(m\) sampling points in each trajectory, we calculated three moving attributes, including the _velocity_, _acceleration_ and _turning angle_. Generally, each moving feature at a point can be viewed as a word, and \(m\) words constitute a sentence, e.g., \(v\_Sentence\). As a result, each trajectory is represented by three sentences with regard to three kinds of moving attributes. Finally, a road segment can be represented as three documents with \(3\times n\) sentences, namely, \(v\_Doc\), \(a\_Doc\) and \(angle\_Doc\). By performing the textualization for all road segments, we could obtain three corpora regarding the cycling moving attributes, i.e., \(C\_segments\). The details on the computation of the three moving attributes are as follows.  
Each trajectory provides a start time \(t_{s}\), end time \(t_{e}\), and a sequence of GPS points with latitudes and longitudes (i.e., \(P_{1}\), \(P_{2}\), \(P_{3}\), \(\dots\), \(P_{n}\)). The Euclidean distance \(d_{i}\) between \(P_{i}\) and \(P_{i+1}\) and the time interval of the trajectory are computed as:  
\[d_{i}=\text{dist}(P_{i},P_{i+1}) \tag{1}\]  
\[\Delta t=t_{e}-t_{s}/(n-1) \tag{2}\]  
Figure 3: The processes of trajectory data transformation. (**a**) Textualization for trajectories to street names. (**b**) Textualization for moving features.  
Based on that, the velocity \(v_{i}\) of point \(P_{i}\) is approximated as the mean velocity between \(P_{i}\) and \(P_{i+1}\), and the acceleration \(a_{i}\) is similar. The equations are as follows:  
\[v_{i}=d_{i}/\Delta t \tag{3}\]  
\[a_{i}=(v_{i+1}-v_{i})/\Delta t \tag{4}\]  
where \(v_{i}\) is the velocity of point \(P_{i}\) and \(v_{i+1}\) is the velocity of point \(P_{i+1}\).  
As for the turning angle, it can be obtained by calculating the difference between continuous heading directions [40]. Figure 4 displays three continuous GPS points. The heading direction \((heading_{P_{i}})\) of \(P_{i}\) is the angle between the moving direction (denoted by the solid arrow) and the fundamental direction (denoted by the dotted arrow). Hence, the turning angle at a point \(P_{i}\) can be computed according to Equations (5) and (6):  
\[\text{turnAng}_{P_{i}}=\mid\text{ heading }_{P_{i-1}}-\text{ heading }_{P_{i}}\mid \tag{5}\]  
where \(\mid\text{ heading }_{P_{i-1}}-\text{ heading }_{P_{i}}\mid\leq 180^{\circ}\).  
\[\text{turnAng}_{P_{i}}=\mid\text{ heading }_{P_{i-1}}-\text{ heading }_{P_{i}}\mid-180^{\circ} \tag{6}\]  
where \(\mid\text{ heading }_{P_{i-1}}-\text{ heading }_{P_{i}}\mid>180^{\circ}\).
## 3 Results and Discussion
### Comparing with Real Axicon  
The real axicon was manufactured with mechanical methods, which will generate inevitable fabrication errors. In order to compare the quality of Bessel beams produced by these two methods, we chose a real axicon (\(r=12.7\) mm, and \(\theta=0.008\) rad) and generated the equivalent holograms (\(d=13\) pixels, \(h=0.0012\) mm). Figure 7 presents the images of the Bessel beams on different \(z\) planes. We can see that the Bessel beams generated by the SLM + CGHs remained in perfect form with the whole range. However, the Bessel beams generated by the real axicon could not stay in the same form, and deformation appeared when the distance was far away from the axicon, as shown in Figure 7, when \(z\geq 930\) mm. Therefore, using CGHs is not only a flexible way to generate the Bessel beams but can also avoid the fabrication errors.  
At the same time, we compared the axial intensity distribution of the Bessel beams generated by these two methods. Some other parameters of the holograms and the incident beams are given by \(n_{1}\) = 1.51637, \(\omega=6\) mm, \(\lambda=650\) nm, and \(I_{0}=1\). Table 1 presents axial intensity distributions of Bessel beams of theory (calculated by Equation (20)), generated by the SLM and the real axicon within the \(z_{\text{max}}\), and their varying trends and fitting curves are shown in Figure 8. From Table 1 and Figure 8, we can see that the axial intensity distributions of the SLM are well matched with the theoretical analysis. The distributions of the real axicon are consistent with the theoretical analysis in the near distance,  
Figure 6: Bessel beams generated by the holograms with different periods: (**a**) \(d=15\), (**b**) \(d=20\), (**c**) \(d=25\) pixels.  
but it began to deviate from the theoretical value after the peek. The reason for the deviation is the deformation of the Bessel beams due to the manufacture errors. This also proved that the axial intensity distributions are affected by the manufacture precision of the axicon.  
The results show that both the axicon and SLM satisfy the principles that the intensity first increases, and, after reaching a peak, it begins to decrease with the extension of the transmission distance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(z\)**Plane (mm)**} & \multicolumn{3}{c}{**Axial Intensity (a.u.)**} \\ \cline{2-4}  & **Theory** & **SLM** & **Axicon** \\ \hline
240 & 92.52 & 88.47 & 93.20 \\
370 & 132.53 & 120.47 & 137.73 \\
470 & 155.58 & 147.43 & 147.59 \\
580 & 172.19 & 166.32 & 158.47 \\
700 & 179.74 & 173.31 & 170.70 \\
810 & 177.76 & 179.48 & 172.27 \\
930 & 167.50 & 176.57 & 178.30 \\
1040 & 152.58 & 167.73 & 163.02 \\
1160 & 132.54 & 112.73 & 63.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Axial-intensity distributions of Bessel beams of theory generated by the SLM and the real axicon.  
Figure 8: The axial-intensity distributions of Bessel beams generated by theoretical analysis, the SLM, and the axicon.  
Figure 7: Bessel beams generated by the spatial light modulator (SLM) and the real axicon at different \(z\) planes.
# Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning
## 3 Problem Formulation
### Collision Avoidance Constraint  
To prevent the drones from colliding, it is necessary to maintain a minimum distance between them at each time step.  
\[\mathbf{p}_{i}^{t}-\mathbf{p}_{j}^{t}\geq R\ \forall i,j\ i\
eq j,\forall t \tag{31}\]  
where \(R\) is the collision threshold distance. Equation (6) is linearized as follows:  
\[\mathbf{p}_{i}^{t,q}-\mathbf{p}_{j}^{t,q}+\frac{\left(\mathbf{p}_{i}^{t,q}- \mathbf{p}_{j}^{t,q}\right)^{T}}{\mathbf{p}_{i}^{t,q}-\mathbf{p}_{j}^{t,q}} \left(\mathbf{p}_{i}^{t}-\mathbf{p}_{j}^{t}\right)\geq R \tag{32}\]  
where the superscript \(\mathbf{p}_{i}^{t,q}\) denotes the position vector of agent '\(i\)' at time step '\(k\)' in the previous outer-loop iteration \(q\).  
The Collision Avoidance Constraint can be derived as follows:  
\[A_{collision}X\leq b_{collision} \tag{33}\]  
By combining Equations (28) and (31), we obtain the following inequality:  
\[AX\leq b \tag{34}\]  
An optimization problem can be solved in two phases. In the first step, we disregard the collision avoidance constraint and construct our quadratic programming (QP) problem, relying on the position, velocity, acceleration, and jerk constraints. Consequently, a single agent's total inequality coefficient and residual matrices are obtained by vertically stacking all the coefficients and residual matrices. The result \(\hat{X}\) is subsequently employed to commence the second phase. Starting from the second iteration, we can apply the collision avoidance constraints because we now have a previous solution that serves as a reference to simplify the collision avoidance constraint.  
(1) We found an initial value by using the feasibility constraints.  
\[\underset{X}{\text{min}}f_{0}\]  
which is subjected to the following:  
\[A_{eq}X =b_{eq}\] \[A_{feasibility}X \leq b_{feasibility}\]  
which will determine \(\hat{X}\).  
(2) We utilized the estimated value \(\hat{X}\) to create new inequality matrices A and b.  
\(\min_{X}f_{0}\) which is subjected to \(A_{eq}X=b_{eq}\)  
\(AX\leq b\) During the second step, an iterative approach is used, where the most recent estimation of \(\mathbf{X}\left(\tilde{\alpha}\right)\) is applied in each iteration, assuming that \(f_{threshold}\) is a specified threshold. Step two is repeated if \(f_{0}-(f_{0})_{\text{prev}}>=f_{threshold}\).  
Because all the constraints are either convex or convexified, quadratic programming tools can be used to solve the optimization problem. The following is the pseudocode of this algorithm. \(A_{1,feasibility}\) represents the value in the first iteration of \(A_{feasibility}\) and \(b_{1,feasibility}\) represents the value in the first iteration of \(b_{feasibility}\).
## 3 Threat Model and Security Requirements
### Specific Attack Scenarios  
We define six critical attack scenarios based on real-world threat analysis, each characterized by distinct objectives, methods, detection challenges, and CVSS severity scores. Figure 3 presents our comprehensive classification of these attack vectors organized by mechanism and severity of impact.  
As detailed in Figure 3, Stealth Timing Manipulation gradually alters motor timing to induce instability through sub-threshold timing errors that accumulate over time, remaining within normal tolerance ranges to evade detection (CVSS 6.8 Medium severity, see Table 1 for details). Desynchronization attacks disrupt coordination between multiple motor channels by phase-shifting selected channels to create asymmetric thrust while individual channels appear normal (CVSS 9.0 Critical severity).  
The practical implementation of desynchronization detection leverages the cross-channel correlation capabilities of our Feature Extraction Engine (Figure 4). Specifically, the system performs real-time phase relationship monitoring through: (1) sliding window correlation analysis across all motor channels with 50 us temporal resolution, (2) statistical deviation detection when inter-channel phase differences exceed \(\pm\)15\({}^{\circ}\) thresholds (derived from our 16-scenario simulation framework), (3) time-domain pattern recognition to identify asymmetric thrust signatures characteristic of coordinated attacks, and (4) temporal consistency verification across multiple DShot frame periods to distinguish attacks from environmental noise. The Neural Network Engine processes these correlation features using the same mathematical framework described in Section 4.2.1, enabling detection within the 10 ms real-time constraint while maintaining the 2.3% false positive rate demonstrated in our evaluation.  
Burst Interference Attacks overwhelm error detection during critical maneuvers using high-intensity bursts timed to flight control actions, with sporadic nature evading pattern detection (CVSS 5.4 Medium severity). Replay attacks inject previously captured commands at inappropriate times by storing and replaying valid frames with modified timing, leveraging cryptographically valid commands to avoid detection (CVSS 7.1 High severity).  
Coordinated Multi-Motor Attacks synchronize manipulation across all motor channels creating specific flight patterns, with system-wide coordination masking individual anomalies (CVSS 8.3 High severity). Persistent attacks at the firmware level establish permanent control through compromise of the ESC firmware, modifying the firmware to accept covert control channels operating below the protocol monitoring level (CVSS 7.2 High severity).
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 2 Wireless Communication Technologies for Agriculture
### SigFox Protocol  
SigFox is an ultra-narrowband wireless cellular network [43] with low data rate applications, thereby making this technology appropriate for IoT and machine-type communications systems. SigFox has been used in different applications, including telephone, security, mobile, broadband, and television. The SigFox network was used in [46; 47] to construct a geolocation system that localizes animals in mountain pastures throughout the summer. A system was proposed in [46] to help farmers locate their cattle and enhance their production. The importance of power consumption analyses was highlighted in [47], especially when the cattle are located in high mountainous areas.
## 3 Cycle Optimization Using a Genetic Algorithm  
For the current study, four parameters were considered for the optimization process, i.e., \(T_{1}\), \(P_{1}\), \(Pr\), and \(x\). The most frequently adopted optimization algorithm in the literature are genetic algorithms [34; 35; 36; 37; 38; 39; 40] and particle swarm optimizations [41; 42; 43]. Both genetic algorithms (G.A.s) and particle swarm optimization (PSO) are iteration-based and start with a pool of initial values and heuristic algorithms; however, the former uses discrete data and the latter uses continuous data. It is reported in the literature that both algorithms are extremely accurate, but PSO offers a relatively higher efficiency. On the other hand, G.A.s are more useful for constraint satisfaction problems. Therefore, the method opted for optimizing the present problem is a multiobjective genetic algorithm (MOGA). The design variables mentioned above with their high and low limits are displayed in Table 3. The well-known genetic algorithm has frequently been adopted in the literature [34; 35; 36; 37; 38; 39; 40] to work out global maximization and minimization problems. MOGA works on the principle known as survival of the fittest. Usually, MOGA is provided by several combinations of the design variables (Table 3) that are generated randomly, known as populations. The population size can be decided on the basis of the best practices found in the literature [41]. The MOGA tested each individual from the populations through a fitness function table. Usually, the top 1-5% individuals in the population are moved to the next generation unchanged, while the remaining population is generated through mutations and crossovers as displayed in the Figure 3. The process continues unless stopping criteria are met. The Matlab environment and a population size of 50 were used for the current optimization problem, as recommended in the literature [18; 29] for five or fewer design variables. The rank and stochastic uniform of built functions were used for the scaling and selection. Elite size, crossover, and mutation fractions were used as 5%, 80%, and 15%, respectively. A forward migration factor of 0.2 was used and augmented. The Lagrangian function was used as a nonlinear constraint algorithm. All simulations were computed on a system with a Xeon processor and 128 GB Ram. Computation time consumed by an optimization iteration was found to be around 7 hours.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variable** & **Lower Bounds** & **Upper Bounds** \\ \hline \hline \multicolumn{1}{c}{Compressor inlet Temperature (\(T_{1}\)) [K]} & 305 & 330 \\ \multicolumn{1}{c}{Compressor inlet pressure (\(P_{1}\)) [kPa]} & 7400 & 9000 \\ \multicolumn{1}{c}{Cycle pressure ratio (\(Pr\))} & 2.0 & 3.6 \\ \multicolumn{1}{c}{Split mass fraction (\(x\))} & 0.5 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design variables utilized for the present work.
# Nearshore Benthic Mapping in the Great Lakes:
## 3 Results
### Classification Performance Analysis
#### 3.1.2. Biotic Component Classification Results
Figure 19: Tier 1 biotic component classification results using satellite data combinations for the north-central section of the study area: (**a**) S2 MS + S2 DII + S2 SDB, (**b**) S2 MS + S2 DII, and (**c**) S2 SDB + S2 DII + S2 MS.
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Safety and Confidentiality  
The growing prevalence of connected devices in smart buildings presents substantial security and privacy issues, making the safeguarding of sensitive user data essential. Every connected device constitutes a potential vulnerability for cyberattacks, requiring robust security protocols that can adapt to the changing threat environment. Moreover, IoT devices often accumulate extensive personal data, and guaranteeing the safe storage and processing of this information is a considerable difficulty. Compliance with data protection requirements, such as GDPR, complicates the development and implementation of IoT devices. Neglecting to resolve these security and privacy concerns may lead to data breaches, erosion of customer confidence, and even legal consequences that compromise the entire integrity of IoT solutions.
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 2 Numerical Tools
Three approaches are used in this work to investigate the influence of aerodynamic interference between the propeller and the wing when considering wing-flutter mechanisms. An overview is given in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Structure** & & **Aerodynamics** & \\  & & **Propeller** & **Wing** & **Interaction** \\ \hline SDBox & Beam & Derivatives & DLM & NO \\ \hline \multirow{3}{*}{Hybrid} & MBDyn: & MBDyn: & DUST: & NO \\  & Beam & Strip Theory & Surface Panels & \\  & & + Inflow Model & & \\ \hline \multirow{2}{*}{Fully Coupled} & MBDyn: & DUST: & DUST: & YES \\  & Beam & Lifting Line & Surface Panels & via particles \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of analysis approaches. The first approach relies on SDBox to establish an initial flutter prediction. Starting from these results, a velocity range of interest close to the flutter point is set for the time-marching simulations. Subsequently, two distinct time-domain simulations are conducted using a \"Hybrid\" and a \"Fully Coupled\" approach in MBDyn-DUST. The \"Hybrid\" approach overlooks the aerodynamic interference between the wing and propeller, whereas the \"Fully Coupled\" approach takes it into account. The proposed workflow allows the user to \"turn off\" or \"turn on\" the aerodynamic interference between the propeller and the wing within the same simulation environment, an option that standard commercial tools are not able to supply. The comparison of the respective flutter analysis results provides a statement about the influence of the aerodynamic interaction on aeroelastic stability. As might be expected, frequency-domain simulations in SDBox provide faster results by solving an eigenvalue problem at distinct free stream velocities to determine mode shapes, frequencies, and damping ratios. On the other hand, the coupled MBDyn-DUST approach offers high accuracy and flexibility, particularly when a workflow for fluid-structure interaction analysis is established. However, it requires more computational power and time due to the complexity of the calculations.