esa-sceva/satcom-chunk-collection huggingface dataset:
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 3 Proposed Approach
### Preprocessing  
Image processing refers to improving image data (properties) by removing unnecessary artifacts and enhancing certain essential image features so that the deep learning methods can take advantage of this improved data. In this paper, preprocessing is the first step of the Traditional Bangladeshi Sports Video Classification framework. However, the preprocessing step consists of three segments: resizing and scaling, augmentation, and normalization. Images/frames are resized to 128 \(\times\) 128 pixels to reduce computational cost and time, which leads to the boosted performance of the model. Moreover, the original images consist of RGB coefficients in the 0-255 range that will be too high to be processed. Therefore, we have converted the pixel values to the [0, 1] interval by scaling with a 1/255.0 factor.  
On the other hand, augmentation refers to generating more training instances from the prevailing ones through some specified transformations for raising up the generalization power of the deep learning models. In this work, online, i.e., real-time augmentation, has been used on resized and rescaled training images, which creates transformed images at each epoch of training. In this regard, we have utilized four different transformation methods: horizontal flip, width shift, height shift, and rotation. This mechanism leads to a better generalization of the model.  
Normalization is added to standardize raw input pixels, which will transform input pixels by making mean 0 and standard deviation of 1. If data are not normalized, there may be some numerical data points in our data set that might be very high and others that might be very low. In unnormalized data, thus naturally, large values become dominant according to relatively small values during training, as the importance of each input is not equally distributed. When we have normalized our training data, however, we have put all of our data on the same scale, which makes convergence faster while training the network. Effect of normalization on the performance of the model is briefly explained in this paper.
# Nozzle Design of Plug-and-Play Passive Pre-Chamber Ignition Systems for Natural Gas Engines
## 2 Methodology and Specification  
### Test Cases  
The nozzles connecting the PC and MC affect the scavenging and ejection processes significantly. The critical parameters of the nozzle include orifice diameter, number, and lateral/vertical angle. The orifice diameter and number together affect the ratio of the total orifice cross-sectional area to the PC volume, i.e., the area-volume ratio. Previous studies have shown that the scavenging and flow characteristics of PCs with the same area-volume ratio are similar. An obvious inference of this is that, no matter whether the parameter to be changed is the orifice diameter or the orifice number, the goal is to search for the optimal area-volume ratio. Therefore, the orifice number constant remains while the effects of the orifice diameter and other vital parameters are investigated.  
Figure 1 is a structural diagram of the PC. The PC has a cylinder with an inner diameter of 9.6 mm and a volume of 1500 mm\({}^{3}\), which is threaded to fit a standard spark plug. To facilitate rapid replacement without modifying the cylinder head, the plug-and-play PC is designed to match the cavity originally intended for spark plugs, which enables seamless integration between the PC and the combustion chamber. The nozzle adopts a five-orifice design, including four circumferential orifices and one central orifice. The nozzle design parameters include orifice diameter d, vertical angle \(\alpha\), and lateral angle \(\beta\). The test cases are shown in Table 1. Based on Case 1, with the best economy, the other six cases are also designed to study the effects of d, \(\alpha\), and \(\beta\) on scavenging, ejection, and combustion performance. The vertical angle is within the range of 120-140\({}^{\circ}\) to ensure the entrance of  
the jet flame into the MC at an appropriate angle. The lateral angle is between 0 and 20\({}^{\circ}\) to ensure acceptable scavenging. The orifice diameter is between 1.0 and 1.6 mm, which is recommended by the ratio of the total orifice area to pre-chamber volume.
## 2 Numerical Simulation of Armature Electromagnetic Force
### Governing Equation  
The Maxwell equations, the constitutive equation, and the \(A\)-\(\varphi\) potential function are combined while considering the influence of the armature movement, and then the Coulomb specification is introduced. The electromagnetic field governing equation [19; 20] described in stationary reference frame is obtained  
\[\left\{\begin{array}{l}\
abla\times\left(\frac{1}{\mu}\
abla\times\mathbf{A} \right)+\sigma\
abla\phi+\sigma\frac{\partial\mathbf{A}}{\partial t}-\sigma\mathbf{v} \times\
abla\times\mathbf{A}=0\\ \
abla\times\left(\sigma\
abla\phi+\sigma\frac{\partial\mathbf{A}}{\partial t}- \sigma\mathbf{v}\times\
abla\times\mathbf{A}\right)=0\end{array}\right. \tag{1}\]  
In the formula, \(\mathbf{A}\) is the magnetic vector potential, \(\varphi\) is the electrical scalar potential, \(\mu\), \(\sigma\), and \(v\) denote a partition function, namely, \(\mu\) is the permeability, \(\sigma\) is the electrical conductivity, and \(v\) is the velocity, respectively. In the armature area: \(\mu=\mu_{\alpha}\), \(\sigma=\sigma_{\alpha}\), and \(v\
eq 0\); in the rail area: \(\mu=\mu_{\mathrm{r}}\), \(\sigma=\sigma_{\mathrm{r}}\), and \(v=0\); in the air area: \(\mu=\mu_{0}=4\pi\times 10^{-7}\) H/m, \(\sigma=\sigma_{0}\), and \(v=0\).  
Then, the armature electromagnetic force \(\mathbf{F}_{\mathrm{em}}\) is  
\[\mathbf{F}_{\mathrm{em}}=\iiint_{\Omega}\mathbf{J}\times\mathbf{B}\mathbf{d}V \tag{2}\]  
In the formula, \(\mathbf{J}\) is the current density; \(\mathbf{B}\) is the magnetic flux density. Under the combined action of the armature electromagnetic force \(\mathbf{F}_{\mathrm{em}}\), the armature rail friction \(\mathbf{F}_{\mathrm{f}}\), and the air resistance \(\mathbf{F}_{\mathrm{air}}\), the kinetic equation [21] are  
\[\begin{split} m_{\mathbf{a}}\mathbf{a}&=\mathbf{F}_{\mathrm{em} }-\mathbf{F}_{\mathrm{f}}-\mathbf{F}_{\mathrm{air}}\\ &=\iiint_{\Omega}\mathbf{J}\times\mathbf{B}\mathbf{d}V-\mu_{f}\left(\mathbf{F}_{ \mathrm{N,em}}+\mathbf{F}_{\mathrm{N,p}}\right)-\frac{\gamma+1}{2}\rho_{0}\left( \mathcal{S}\mathbf{v}^{2}+S\mathbf{x}\mathbf{a}+\frac{C_{f}\mathbf{L}\mathbf{v}^{2}\mathbf{x}}{2} \right)\end{split} \tag{3}\]  
In the formula, \(m_{\mathbf{a}}\), \(\mathbf{a}\), and \(\mathbf{x}\) are the mass, acceleration, and displacement of armature, respectively; \(\mu_{f}\) is the friction coefficient; \(\mathbf{F}_{\mathrm{N,em}}\) is the electromagnetic contact pressure; \(\mathbf{F}_{\mathrm{N,p}}\) is the mechanical preloading pressure; \(\gamma\) is the specific heat ratio of the air; \(\rho_{0}\) is the initial air density; \(S\) is the armature cross-sectional area; \(L\) is the perimeter of armature section; and \(C_{f}\) is the viscous friction coefficient.
# Robust Distributed Rendezvous Using Multiple Robots with Variable Range Radars
## 5 Simulation Results
### Monte-Carlo Simulations  
Table 1 shows the comparison results. In this table, \([P]\) presents the proposed rendezvous algorithm. \([R]\) presents the original circumcenter rendezvous algorithms in [13; 14; 15]. In MATLAB simulations, the radar sensing noise (2) uses the noise parameter as \(D_{R}=0.01\) km. The motion model (10) uses the process noise parameter as \(D=0.05\) km.  
At each MC simulation, we check whether the rendezvous succeeds or not. Considering the \(j\)-th MC simulation (\(j\in\{1,2,...,J\}\)), let \(S(j)\) denote whether the rendezvous succeeds or not. \(S(j)=1\) when the rendezvous succeeds at the \(j\)-th MC simulation. In addition, \(S(j)=0\) when the rendezvous does not succeed at the \(j\)-th MC simulation. Let \(m(S)=1/J*\sum_{j=1}^{J}S(j)\).  
At each MC simulation, we check how much time it takes to let all of the robots rendezvous. Considering the \(j\)-th MC simulation (\(j\in\{1,2,...,J\}\)), let \(T_{r}(j)\) denote the time it takes to let all robots rendezvous. In the case where the rendezvous is not achieved in the \(j\)-th MC simulation, \(T_{r}(j)\) is set as \(T\). Let \(m(T_{r})=1/J*\sum_{j=1}^{J}T_{r}(j)\).  
Figure 4: Considering the case where \(N=10\), a blue circle plots \(S_{i}\) (\(i\in\{1,2,...,N\}\)), the sensing range of each robot, when the rendezvous is done. The sensing range of a robot can be distinct from that of another robot.  
Figure 3: Considering the case where \(N=10\), a blue asterisk plots the final position of every robot using the proposed rendezvous control. See that all robots are positioned close to each other since multi-robot rendezvous is achieved.  
Furthermore, we check how much time (in seconds) it takes to run each MC simulation under MATLAB. Considering the \(j\)-th MC simulation (\(j\in\{1,2,...,J\}\)), let \(T_{c}(j)\) denote the computational time of the \(j\)-th MC simulation. Let \(m(T_{c})=1/J*\sum_{j=1}^{J}T_{c}(j)\).  
Table 1 shows that increasing \(N\) (the number of robots) increases both the rendezvous time \(m(T_{r})\) and the computational load \(m(T_{c})\). Table 1 shows that only the proposed rendezvous algorithms can succeed in the rendezvous process. Under the proposed control \([P]\), \(m(S)=1\) regardless of \(N\). This is due to the fact that \(S_{i}=5\) (km) is rather short for network connectivity, considering random deployment in the workspace. Original circumcenter rendezvous algorithms \([R]\) cannot handle the case where the initial connectivity assumption does not hold.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 6 Conclusions  
We have analyzed the expectation of energy dissipation in a round as a function of \(k\) and \(R\) to obtain joint optimization values of \((k_{opt},R_{opt})\). We have analyzed the amount of energy dissipation of CH nodes and non-CH nodes to derive the expectation of energy consumption in a round as a function of system design parameters \((k,R)\) with given network parameters including the number of sensor nodes \(N\) and the radius \(L\) of the sensor field. Our analyzed energy function is computationally tractable for derivative-free optimization algorithms, among which we adopt Nelder-Mead (N-M) simplex algorithm in our proposed scheme. The optimization procedure converges quickly enough for the BS or management center to run the optimization algorithm in real time. Our proposed method outperforms existing non-optimized LEACH-based schemes in terms of average node lifespan by saving the energy consumption by several tens of percent. Our optimization approach is expected to help existing cluster-based algorithms to further improve their energy efficiency.  
Figure 5: Number of nodes alive (\(N=200,L=400\) m).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{5}{c}{**Time to \%-Node-Death (rounds)**} \\ \cline{2-6}  & & **1\%** & **10\%** & **50\%** & **90\%** \\ \hline \multirow{2}{*}{LEACH [3]} & Non-opt. & 54 & 74 & 108 & 166 \\  & **With opt.** & **68** & **97** & **174** & **296** \\ \hline \multirow{2}{*}{LEACH-DT [22]} & Non-opt. & 47 & 73 & 146 & 205 \\  & **With opt.** & **51** & **98** & **286** & **488** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time to percent-node-death.  
**Funding:** This work was supported by the 2015 Research Fund of the University of Seoul.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 5 Simulation Verification
### Independent Action of Damping Control and Inertia Control  
To optimize the process of oscillation suppression, firstly, the inertia and damping are no longer fixed during the whole oscillation process. In this section, the D and P controller parameters are separately designed according to the proposed controller parameter design method, so that the inertia or the damping can be flexibly adjusted online according to the specific stage of the grid frequency response. Figure 9 shows the simulation results of the grid frequency and the electromagnetic power when the D and P controller parameters act independently.  
Figure 8: Effects of ESS controller parameters on system dynamics. (**a**) Frequency with D controller; (**b**) frequency with P controller; (**c**) SG power with D controller; (**d**) SG power with P controller.  
Figure 9: _Cont._  
Compared with the case of fixed controller parameters, the parameter online optimization can improve the performance of oscillation suppression, but it still cannot achieve a fast and stable response. As shown in Figure 9a,c, when the inertia is adjusted, the recovery time of the system is shortened, but the maximum oscillation amplitude is slightly increased. In Figure 9b,d, when the damping effect is adjustable, the recovery rate of the grid frequency changes at different stages. However, the overall recovery speed of the system slows down, and the suppression performance has not been significantly improved. Therefore, it is necessary to combine the inertia control with the damping control, and simultaneously adjust the inertia and damping effects according to the specific stage of the oscillation to further accelerate the recovery process of the power system.
## 4 Numerical Calculations  
### Model Establishment  
Numerical calculations are commonly used to study engineering problems [38, 39, 40]. Based on the failure characteristics of soft rock roadways, we established a simple roadway excavation support model using FLAC\({}^{\text{3D}}\) based on certain assumptions; an appropriate constitutive model was also selected according to the surrounding rock conditions. From the results of laboratory tests and on-site measurements, the model parameters were calibrated to study the general laws of failure of the surrounding rock and support system and propose effective support plans.  
The FLAC\({}^{\text{3D}}\) model was established according to the actual roadway design (Figure 4). To simplify the model and reduce the boundary influence, a boundary (length = 30 m) was set around the roadway. The model was 4 m long, 64.3 m wide, and 64.6 m high. A 4.3 \(\times\) 3.37 m arc arch roadway was adopted, and the beam unit was used to simulate the U-shaped steel frame shed support. A partial excavation method was used to simulate the fall of the shed to support the filling body. The model consists of 28,875 gridpoints  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Lithology** & **Number** & **Density (kg/m\({}^{\text{3}}\))** & \multicolumn{2}{c}{**Compressive Strength**} & \multirow{2}{*}{**Shear Strength (MPa)**} & \multicolumn{2}{c}{**Tensile Strength (MPa)**} \\  & 1 & 2433 & 15.74 & & 3.73 & & 0.32 & \\ Sandy & 2 & 2437 & 2413 & 15.00 & 15.49 & 2.56 & 3.13 & 1.23 & 0.72 \\ mudstone & 3 & 2368 & & 15.75 & & 3.10 & & 0.6 & \\ \hline \multirow{3}{*}{Yellow mudstone} & 1 & 2115 & & & 2.56 & & 0.34 & \\  & 2 & 2097 & 2072 & / & 1.75 & 1.98 & 0.56 & 0.42 \\ \cline{1-1}  & 3 & 2003 & & & 1.63 & & 0.36 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of physical and mechanical parameters.  
Figure 3: Fracture patterns of surrounding rocks from borehole television observation (**a**) Drilling in the roof strata; (**b**) Drilling in the right rib.  
and 26,700 zones. An overburden rock weight, with an average bulk density of 2.5 was applied to the model. The model was fixed around the model and the horizontal pressure coefficient was 1.
## 2 Results and Discussion
### Catalytic Performance of Nb/HMS and Nb/MCS Catalysts  
#### 2.2.1 Catalysts Screening  
The glucose conversion into HMF was studied over HMS and MCS with different Nb loading levels as shown in Table 2. The possible reaction pathway for catalytic conversion of glucose into HMF in the present study is illustrated in Scheme 1. The glucose conversion increased around 10% over 10-Nb/HMS and 10-Nb/MCS catalysts due to an increased amount of total acid sites (Table 1). Besides, MCS and 10-Nb/MCS not only exhibited higher HMF yield and selectivity but also a lower humins yield than HMS and 10-Nb/HMS. This result was in accord with the previous work, which indicated the beneficial effect of catalyst hydrophobicity on the HMF formation [14]. [PERSON] et al. [36] reported that HMF was rehydrated to 2,5-dioxo-6-hydroxyhexanal (DHH), which was thermodynamically unstable intermediate in the reaction system consisting of water and acid catalyst. Therefore, the hydrophobicity of MCS retarded the condensation of DHH as a monomer for humins growth [37; 38].  
When the Nb concentration on MCS was increased from 10 to 20 and 30 wt.%, the glucose conversion and HMF yield were slightly decreased due to a decreased acidity (Table 1) [13]. [PERSON] et al. [5] reported that, at a high B/L ratio (0.9-1.7), the HMF yield was decreased since the humins formation was promoted over Bronsted acid sites via aldol addition and condensation of HMF [39]. Over the catalyst with a high Lewis acidity, the formation of humins was facilitated via polymerization of glucose, fructose, and HMF [40]. Thus, a balance of B/L ratio was necessary for selective conversion of glucose into HMF. In addition, the Nb/MCS catalysts provided a low furfural yield (less than 1%). It was demonstrated that the glucose molecules tended to convert to HMF, rather than pentose as an intermediate for furfural formation, via dehydration under the combined catalysis of Lewis and Bronsted acid sites [41]. The highest HMF yield of 35.0% was obtained over 10-Nb/MCS at 78.9% glucose conversion at 170 \({}^{\circ}\)C for 1 h. Hereafter, 10-Nb/MCS as a suitable catalyst was used for optimizing the reaction conditions for HMF synthesis.  
Figure 5: Py-FTIR spectra of **(a)** HMS, **(b)** 10-Nb/HMS, **(c)** MCS, **(d)** 10-Nb/MCS, **(e)** 20-Nb/MCS, and **(f)** 30-Nb/MCS catalysts.
## 4 Integration of FamiWare and INTER-TRUST  
As explained, FamiWare is a family of middlewares specially designed and implemented for dealing with the heterogeneity of WSNs devices. It also provides a mechanism to be reconfigured at runtime when the context changes. However, it does not focus on providing security. Therefore, if we want to extend FamiWare with the benefits provided by the INTER-TRUST framework, the first thing to do is to integrate INTER-TRUST into the FamiWare family. As shown in Figure 4, this implies several tasks that have been organized into two main steps. The first step consists of the integration of the INTER-TRUST modules and the INTER-TRUST security concerns into the FamiWare product line. This implies the extension of both the variability model and the base model of FamiWare. This step allows the generation of different FamiWare middleware configurations, customized for the necessities of each device, including the customization of the security modules and concerns. In the second step, the security policies specified in INTER-TRUST and the security concerns (to be deployed for enforcing those security policies) are taken as input to define and implement the reconfiguration actions. These reconfiguration actions need to be executed in order to generate a new configuration adapted to the new security context. Therefore, these actions must be part of the reconfiguration plans that FamiWare generates at the implementation level to switch from the current FamiWare configuration to the next one. This step enables the security requirements to be adapted at runtime.  
In the rest of this section, we explain these steps in more detail. The first step is detailed in Section 4.1 and the second step in Section 4.2. Finally, Section 4.3 shows different configurations of the FamiWare middleware with security for different devices of our ITS case study.  
Figure 4: Steps for the integration of INTER-TRUST into the FamiWare family of middlewares.
## Materials and Methods
### Characterization  
The microstructure, morphology and thickness were characterized by field emission scanning electron microscopy (FESEM, Merlin compact, Berlin, Germany). The four-point resistance of printed tracks was measured with a source meter ([PERSON], 2400). The resistivity \(\varrho\) was calculated using \(\varrho\) = RS/L, where R is the measured four-point resistance, S is the cross-section area and L is the length of the electrodes. Resistivity of various forms of carbon fillers were measured by four-point method after pressing them into 10 mm \(\times\) 10 mm \(\times\) 1 mm pieces. The results show that AC, CNT and G have a resistivity of \(10^{-2}\), \(10^{-4}\) and \(10^{-7}\)\(\Omega\)-m, respectively. The 20 mm electrodes were used for bending tests with a homemade bending device. Bending frequency was 15 cycles/min and bending degree was 50%, which is the ratio of the bended length to original electrode length.
## 1 Introduction
This may be problematic in creating proper methodologies of their analyses and could lead to poor policy decisions [5; 6; 7]. The risk assessment in port areas is a process wherein all possible hazards should be taken into account [8; 9]. Several complex methodologies of risk assessment have indeed been developed [10; 11] but there is no detailed solution for risk assessment in regard to mooring ships with dangerous cargo, except for problems related to excessive waves generated by passing ships [12]. The studies made by [13] showed that port infrastructure facilities significantly affect vessel exposure to risk. In addition, studies made by [14] and an earlier one, made by [15], stressed this problem and revealed that FSA methodology might be insufficient given constantly changing port infrastructure. Moreover, the authors demonstrate in this paper that the problem has a complicated spatiotemporal nature due to variability in quay occupation, which makes the situation even more complex. In the presented study, the so-called potentially catastrophic scenario (PCS) is introduced to find the risk of a ship colliding with a moored ship due to technical failure. The PCS is developed here by the combination of several analytical methods involving simulation of the impact energy. There is a potential possibility of a scenario with even higher negative outcomes, but the probability of such accidents in port has been assessed as extremely low, because of low approaching speeds, and it is not taken into consideration. Two accidents are described in this paper, neither of which caused significant consequences, but are used to demonstrate the ability of the proposed methodology to evaluate the risk of in-port manoeuvring operations. The first is a collision between the ship _Altamar_ and the bunker ship _Palica_, moored side by side, into the 250 passengers Ro-Pax ferry _Baltivia_ in Swinoujscie Ferry Terminal, Poland. The second is the collision between the container ship UASC MADINAH and the cruise ship Thomson Spirit Valletta in the port of Koper, Slovenia, Figure 1. Even though these accidents occurred between different types of ships and in different ports, the risk evaluation approach may be the same and the implementation of the RCOs transferred from one country or port to another. Luckily, neither leakage of bunker liquids nor fire resulted from these accidents. The damage to the _Palica_ tanker was around half of a square meter located on the forecastle a safe distance from the bunker ship tanks. The case of the container ship colliding with the cruiser caused more damage to the hull, but the load line area was not damaged.