esa-sceva/satcom-chunk-collection huggingface dataset:
# A Convex Optimization Algorithm for Electricity Pricing of Charging Stations
## 5 Conclusions  
In this paper, a convex optimization algorithm was proposed to solve the problem of power planning for charging station. Second-order conic relaxation and Karush-Kuhn-Tucker optimality conditions were proposed to transform the model into a convex optimization problem. Further, a polyhedral approximation method was applied to construct a MILP and the model was solved iteratively to obtain the Pareto front according to the scalarization basic theorem. The simulation results of IEEE 33-bus test system show that some conclusions can be drawn as follows:  
1. The proposed second-order conic relaxation is accurate, which can transform the original model into a convex optimization model in order to obtain the global optimal solution. At the same time, the original variables can be easily restored.
2. The proposed polyhedral approximation method can speed up the solution of the model. Compared with other algorithms, the polyhedral approximation algorithm can get the global optimal solution with the minimum CPU time.
3. The proposed scaling algorithm can obtain the Pareto front by solving the problem iteratively, which can preserve the multi-solution of the model and obtain the equilibrium solution.  
However, this study ignores the competitive relationship between charging stations, which may lead to an unfair market. Therefore, the game between charging stations needs further consideration, which is the focus of our future work.  
Conceptualization, J.Z. and T.L.; Methodology, X.Z. and S.H.; Validation, L.J. and Y.Z.; Formal Analysis, J.Y. and X.D.  
This research was funded by Open Fund of Beijing Engineering Technology Research Center of Electric Vehicle Charging/Battery Swap, China Electric Power Research Institute Co., Ltd. (YD83-19-003).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Adsorption Isotherms  
Adsorption isotherm models investigated the interactions between dyes and adsorbents. For MB removal, the BET isotherm model provided a best fit to the experimental data for the GO absorbent, achieving the highest R\({}^{2}\) (R\({}^{2}_{\text{MB}}\) = 0.966), compared to the Langmuir (R\({}^{2}_{\text{MB}}\) = 0.840), Temkin (R\({}^{2}_{\text{MB}}\) = 0.879), and Freundlich (R\({}^{2}_{\text{MB}}\) = 0.911) models (Table 3). [PERSON] et al. (2023) also reported a best fit for MB removal with the Freundlich isotherm for a modified GO material, which further suggests that the MB molecules formed a multilayer coverage on the surface of GO [21, 28]. Conversely, the Langmuir isotherm model was the best fit for MB adsorption on GAC and NaY, indicating a monolayer coverage of MB on these adsorbents due to the homogeneous surface-active sites (Table 3). These findings are consistent with previous studies [12, 29]. For RhB removal, the adsorption isotherm analysis revealed that the Freundlich model was the most suitable for GO, with the highest (R\({}^{2}_{\text{RhB}}\) = 0.942) compared to Langmuir (R\({}^{2}_{\text{RhB}}\) = 0.854), Temkin (R\({}^{2}_{\text{RhB}}\) =0.854), and BET (R\({}^{2}_{\text{RhB}}\) = 0.919) (Table 4), indicating multilayer adsorption behaviour. The 1/n value in the Freundlich model reveals a higher adsorption intensity and implies a more heterogenous adsorbent surface as it approaches 0 [29]. The 1/n value of 0.452 in this study for RhB removal with GO indicates a higher-intensity adsorption process and the possibility of a more heterogenous GO surface (Table 4). The BET isotherm model best fit the adsorption of RhB with GAC (R\({}^{2}_{\text{RhB}}\) = 0.969) compared to Langmuir (R\({}^{2}_{\text{RhB}}\) = 0.719), Freundlich (R\({}^{2}_{\text{RhB}}\) = 0.816) and Temkin (R\({}^{2}_{\text{RhB}}\) = 0.625) (Table 4). In contrast, NaY did not demonstrate notable removal of RhB during the removal efficiency experiments, which is attributed to the large molecular size of RhB in relation to the smaller pore size of NaY. Therefore, isotherm analyses with RhB and NaY were not conducted. Figure 5: (a) Effect of different initial concentrations of MB and (b) RhB (4 to 100 mg/L) on removal efficiency of a fixed amount of 5 mg of adsorbents GO, NaY, and GAC under 50 rpm. The superior fitting of the [PERSON] and BET isotherm models for the adsorption of synthetic dyes with GO compared to GAC and NaY suggests a multilayer adsorption mechanism, which accounts for the higher removal efficiencies and adsorption capacities of GO. In contrast, GAC and NaY exhibited monolayer adsorption behaviour for the removal of MB. The comparison of adsorbents for removal of synthetic dyes highlighted GO as a potential alternative to GAC and NaY. GO demonstrated a high capacity for removal of two cationic dyes with different molecular sizes and structures under slow mixing conditions
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Media Access Control Layer
#### 3.2.1 Radio Resource Allocation
It could be better to use real-time channel parameters or MCS or BLER value to schedule the respective downlink channels to the devices. This is because, within the same distances, devices may experience different signal attenuation due to different factors such as fading, non-line of sight, line of sight, indoor placement, outdoor placement, underground placement, etc. Therefore, there is still an open space for practical deployment to analyze the effectiveness of the different downlink andNB-IoT scheduling schemes which considers the device's simplicity, modulation schemes, channel conditions, and delay requirement for specific use cases. In [71], the paper proposed a new resource allocation technique by extending the paging resource that will be specific for paging traffic offload. The authors noted that the new paging PRB could lower power consumption which is mostly used to load and offload the paging load. Also, the work proposed the selection scheme based on UE identity (ID) that is used to balance the load between the paging resource blocks. The simulation results show that power consumption reduction and resource optimal usage are of 80% and 30.5%, respectively. This work considered adding other PRBs for paging monitoring; however, the authors do not demonstrate the trade-off between the newly introduced scheme and the UE complexity requirements. In [83], the authors proposed an enhanced access reservation protocol (ARP) that allows the device to transmit a fraction of a preamble sequence by providing an analytical model that captures the performance of ARP in terms of the false alarm, misdetection, and collision probabilities. They mathematically analyze the trade-off between the misdetection and the collision probabilities. The drawback of this protocol is that with massive NB-IoT deployment, altering the configuration of the protocol may result in detection performance degradation which can lead to huge packet loss. In [72], the authors analyzed the impact of interference when the 15 kHz LTE system coexists with a guard-band NB-IoT system with 3.75 kHz subcarrier separation. Their simulation results demonstrated that it is desirable that the scheduler of the LTE system empties the neighboring RBs of the NB-IoT system and allocates resources if possible. The authors then proposed an NB-IoT scheduling method for the LTE system to improve the performance of the studied NB-IoT system. Their results showed that if emptying is not done, at 10\({}^{3}\) BER there is 1 dB drop of SNR as compared to when emptying of RBs is done.
## 4 High SNR Analysis and Secrecy Throughput
### High SNR Analysis
: _The asymptotic SOP of the MRC-ZFB/MRT-SC-ZFB scenario under \(\overline{\gamma}_{B2}\rightarrow\infty\) and \(\overline{\gamma}_{E2}\rightarrow\infty\) is given by_  
\[p_{out}^{MRC-ZFB/MRT-SC-ZFB}\left(R_{S}\right)\approx 1-\left(1-F_{\gamma_{1}^{MRC- ZFB}}\left(R_{S}\right)\right)\left(1-F_{\gamma_{2}^{MRT-SC-ZFB}}\left(R_{S} \right)\right), \tag{56}\]  
_where \(F_{\gamma_{1}^{MRC-ZFB}}\left(R_{S}\right)\) and \(F_{\gamma_{2}^{MRT-SC-ZFB}}\left(R_{S}\right)\) are respectively expressed as_  
\[F_{\gamma_{1}^{MRC-ZFB}}\left(R_{S}\right) \approx 1-\sum_{k=0}^{N_{B}-1}\frac{2^{kR_{S}}\Gamma\left(k+1\right) }{k!}\left(\frac{\bar{\gamma}_{E2}}{\bar{\gamma}_{B2}\bar{\gamma}_{I}}\right)^{ k}\left(1-\exp\left(-\frac{\mu}{\lambda_{AP}}\right)+\Gamma\left(1,\frac{\mu}{ \lambda_{AP}}\right)\right) \tag{57}\] \[\times\left[\left(N_{B}-2\right)\Psi\left(k+1,3+k-N_{B},\frac{2^{R_ {S}}\bar{\gamma}_{E2}+\bar{\gamma}_{B2}}{\bar{\gamma}_{B2}\bar{\gamma}_{I}} \right)+\frac{1}{\bar{\gamma}_{I}}\right.\] \[\times\left.\Psi\left(k+1,4+k-N_{B},\frac{2^{R_{S}}\bar{\gamma}_{ E2}+\bar{\gamma}_{B2}}{\bar{\gamma}_{B2}\bar{\gamma}_{I}}\right)\right],\]_and_  
\[ \begin{split} F_{\gamma_{2}^{MRT-SC-ZFB}}\left(R_{S}\right)& \approx\sum_{i=0}^{N_{B}}\left(\begin{array}{c}N_{B}\\ i\end{array}\right)(-1)^{i}\Theta_{N_{R},i}\left(\frac{2^{R_{S}}\eta\tilde{ \gamma}_{E2}}{\eta\tilde{\gamma}_{B2}\tilde{\gamma}_{I}}\right)^{\phi}\Gamma \left(\phi+1\right)\\ &\times\left[\left(N_{B}-2\right)\Psi\left(\phi+1,\phi+3-N_{B}, \frac{i2^{R_{S}}\eta\tilde{\gamma}_{E2}+\overline{\epsilon}\overline{\gamma}_{ B2}}{\epsilon\overline{\gamma}_{B2}\tilde{\gamma}_{I}}\right)\right.\\ &\left.+\frac{1}{\tilde{\gamma}_{I}}\Psi\left(\phi+1,\phi+4-N_{B}, \frac{i2^{R_{S}}\eta\tilde{\gamma}_{E2}+\epsilon\tilde{\gamma}_{B2}}{ \epsilon\overline{\gamma}_{B2}\tilde{\gamma}_{I}}\right)\right].\end{split}  \tag{58}\]  
Proof. : By using (44) and (46), when \(\overline{\gamma}_{B2}\rightarrow\infty\) and \(\overline{\gamma}_{E2}\rightarrow\infty\), \(F_{\gamma_{1}^{MRC-ZFB}}\left(R_{S}\right)\) and \(F_{\gamma_{2}^{MRT-SC-ZFB}}\left(R_{S}\right)\) can be easily derived, respectively. Thus, substituting (57) and (58) into (56), the final result of MRC-ZFB/MRT-SC-ZFB scenario is given. Remark 2.: _In contrast to Scenario I, when \(\overline{\gamma}_{B2}\rightarrow\infty\) and \(\overline{\gamma}_{E2}\rightarrow\infty\), two scenarios appear to be the secrecy outage floor that unable to obtain secrecy diversity. Thus, the secrecy performance of the system can only be improved by the secrecy coding gain._
# Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry
###### Abstract  
The interaction mechanism between the cavitation bubble and a solid wall is a basic problem in bubble collapse prevention and application. In particular, when bubble collapse occurs near solid walls with arbitrarily complex geometries, it is difficult to efficiently establish a model and quantitatively explore the interaction mechanism between bubbles and solid walls. Based on the advantages of the lattice Boltzmann method, a model for cavitation bubble collapse close to a solid wall was established using the pseudopotential multi-relaxation-time lattice Boltzmann model. Solid walls with arbitrarily complex geometries were introduced in the computational domain, and the fractal dimension was used to quantify the complexity of the solid wall. Furthermore, owing to the lack of periodicity, symmetry, spatial uniformity and obvious correlation in this process, the Minkowski functionals-based morphological analysis method was introduced to quantitatively describe the temporal evolution of collapsing bubble profiles and acquire effective information from the process. The interaction mechanism between the bubble and solid wall was investigated using evolutions of physical fields. In addition, the influences of the solid walls' surface conditions and the position parameter on collapsing bubbles were discussed. These achievements provide an efficient tool for quantifying the morphological changes of the collapsing bubble.  
Keywords: cavitation bubble; lattice Boltzmann method; solid wall with complex geometry; morphological analysis; Minkowski functionals +
Footnote †: journal: Applied Sciences
## 3 Classification of Attacks on RPL-Based IoT
### Wireless Sensor Network-Inherited Attacks
#### 3.1.5 Hello Flood Attacks  
In a Hello Flood attack, a malicious node exploits the network entry process, typically initiated with a \"HELLO\" message or DIO message in the case of RPL. The attacker sends deceptive DIO messages with robust routing metrics or a strong signal, then abruptly disappears or lowers its transmission power. It can interrupt the delivery of packets from legitimate nodes, causing depletion of their resources and escalating network congestion due to increased control overhead [38].  
Figure 1: RPL-based IoT network with black hole attack. Malicious node 2 drops all received packets from nodes 5 and 6.
## 3 Results
### Use of HSDM Formulation for Interpreting Dynamic Adsorption
#### 3.4.1 Dynamic Adsorption Simulation
The numerical value of the diffusion coefficient obtained for each column study of element is provided in Table 4.  
To summarize, the present work permitted us to simulate, in a single system, the adsorption dynamics of the ions Cs\({}^{+}\), Rb\({}^{+}\), Co\({}^{2+}\), and Sr\({}^{2+}\) on alkali-activated brick. Breakthrough curves of cationic adsorption experiments were successfully described from the HSDM model. The simulated values of diffusion parameters (k\({}_{\text{f}}\) and D\({}_{\text{s}}\)) obtained from linear driving force approximation together with the Langmuir isotherm constant (K\({}_{\text{L}}\)) and maximum adsorption capacity (Q\({}_{\text{AC}}\)) agreed well with those obtained experimentally under static (batch) operational conditions. The goodness-of-fit parameter then validated the successful prediction of experimental breakthrough curves and evidenced the predominance of surface diffusion for the studied cations. To support this, in the next paragraph, we calculate the different column dimensionless parameters which defined the shape of the breakthrough curve via adsorption dynamics, and use them to verify the importance of surface diffusion phenomena along the packed-brick pellets column. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**HSDM Modelling**} & **Cs\({}^{*}\)** & **Rb\({}^{+}\)** & **Co\({}^{*+}\)** & **Sr\({}^{2+}\)** \\ \hline \hline \multirow{7}{*}{**Adsorbent characteristics**} & Bed volume (BV, mL) & 9.5072 & 9.5072 & 9.5072 & 9.5072 \\ \cline{2-6}  & Empty bed contact time (EBCT, s) & 114.09 & 114.09 & 114.09 & 114.09 \\ \cline{2-6}  & Particle-specific mass (\(\rho_{\text{pr}}\) g-cm\({}^{-3}\)) & 1.8135 & 1.8135 & 1.8135 & 1.8135 \\ \cline{2-6}  & Bed porosity & 0.42 & 0.42 & 0.42 & 0.42 \\ \cline{2-6}  & Bed-specific mass (\(\rho_{\text{L}}\) g-cm\({}^{-3}\)) & 1.0518 & 1.0518 & 1.0518 & 1.0518 \\ \cline{2-6}  & Averaged particle diameter (d\({}_{\text{p}}\), mm) & 0.85 & 0.85 & 0.85 & 0.85 \\ \hline \multirow{7}{*}{**Equilibrium and kinetics parameters**} & Adsorption capacity (Q\({}_{\text{AC}}\), mg g-1) & 10.2 & 10.5 & 5.5 & 18 \\ \cline{2-6}  & Langmuir constant (K\({}_{\text{L}}\), L mg-1) & 0.025 & 0.0593 & 0.554 & 0.0763 \\ \cline{2-6}  & Film diffusion coefficient (k\({}_{\text{x}}\), m s-1) & 0.005 & 0.0025 & 0.0019 & 0.0042 \\ \cline{2-6}  & Surface diffusion coefficient (D\({}_{\text{s}}\), m\({}^{2}\)-s\({}^{-1}\)) & 6.66 \(\times\) 10\({}^{-12}\) & 3.00 \(\times\) 10\({}^{-12}\) & 5.04 \(\times\) 10\({}^{-12}\) & 2 \(\times\) 10\({}^{-12}\) \\ \hline \multirow{7}{*}{**Dimensionless parameters**} & Solute distribution parameter (D\({}_{\text{g}}\)) & 162 & 256 & 203 & 241 \\ \cline{2-6}  & Biot number (B\({}_{\text{s}}\)) & 2725 & 2291 & 1091 & 5237 \\ \cline{1-1} \cline{2-6}  & Stanton number (S\({}_{\text{s}}\)) & 778 & 389 & 296 & 669 \\ \cline{1-1} \cline{2-6}  & The ratio: S\({}_{\text{s}}\)/B\({}_{\text{s}}\) (Ed) & 0.286 & 0.170 & 0.271 & 0.128 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 4 Decoding Performance Analysis and Degree Profile Design  
In this section, we optimize the output degree profile of the Raptor code used by each user to further improve the achieved sum throughput of the system. Note that we assume that each user is not aware of the instantaneous CSI. Hence, it can only be assigned one optimized output degree profile, which is used to generate Raptor code during all rounds of transmission. Therefore, the objective is to find a universally good output degree profile in an average sense for each user under all possible  
Figure 3: Flow chart of the decoding procedure.  
channel states, with the knowledge of CSI statistics, i.e., the probability distribution of the channel state for each link.
## 3 Results and Discussion
Figure 7: Effects of time and temperature on biocrude yield (a), and influence of time and flow rate on product yield (b). Figure 8 shows the effects of temperature and time on bioc crude yield in the plug-flow tube HTL reactor. It shows that as the time increased from 15 to 60 min, the amount of bioc crude oil in the reactor increased from 23% to 42% at a constant flow rate of 6 kg/h. As the reaction progressed, the amount of gaseous phase also increased, and the liquid phase showed an increasing trend up to about 52 min and a decreasing trend after 52 min. The yield of bioc crude oil also increased with the increasing temperature, which is a key factor affecting the rate of biomass conversion in the tubular reactor. In the temperature range of 150 \({}^{\circ}\)C to 250 \({}^{\circ}\)C, depolymerization reactions took place. For the 250-350 \({}^{\circ}\)C temperature range, decomposition reactions predominated, where the decomposition reaction time could be similar to the depolymerization reaction time but could be longer for more complex organic compounds.
## 3 Results
### Turning Point Year of NPP  
#### 3.4.1 Turning Point Year of NPP at National and Regional Scale  
To clarify the characteristics of the NPP trends, the averaged trends of the NPP and the turning point year at national and regional levels were calculated over the study period (Figure 5). At the pixel scale, 87.26% of the total pixels showed abrupt changes in inter-annual NPP variation (\(\Delta\)AIC \(<-2\); Figure S3b), most of which appeared at year 2004 or year 2010 (which may also have been caused by criterion we set up in the calculation), accounting for 29.36% and 31.78%, respectively (Figure S3a). At a national scale, the linear regression model suggested an increasing trend of 0.0007 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.3118) from 2001 to 2014 (Figure 5a). However, two distinct periods were clearly identified by the piecewise linear regression model: a trend change from a marginal significant increasing trend of 0.0104 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.0746) before 2004 to a non-significant increasing trend of 0.0012 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.2775) after 2004 (Figure 5a). The information criterion of the piecewise regression model was less than that of the linear regression model (\(\Delta\)AIC \(=-3.1797\)). At a regional scale, a marginal significant increasing trend of 0.0025 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.0509) in Northern China was found, and the turning point year occurred in 2010 (\(\Delta\)AIC \(<-2\)). However, the increasing trends were all non-significant before and after 2010, with a rate of 0.0006 and 0.0068 kg C m\({}^{-2}\) a\({}^{-1}\), respectively (Figure 5b). In Southern China, a rate of \(-0.0007\) kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) = 0.6559) was found for annual NPP variation, and a turning point year occurred in 2010 (\(\Delta\)AIC \(<-2\)). However, the variation trend was opposite (Figure 5c). Although a significant increasing trend of 0.0016 kg C m\({}^{-2}\) a\({}^{-1}\) (\(p\) < 0.01) was found in the Tibetan Plateau over the past 14 years (Figure 5d), the increasing trend slowed down after 2006 (\(\Delta\)AIC \(<-2\)).  
Figure 5: Inter-annual variations of Net Primary Productivity (NPP) in China at national level **(a)** and regional level (Northern China **(b)**; Southern China **(c)**; and the Tibetan Plateau **(d)**). Trends estimated by the least-squares linear regression are shown. The blue and pink lines indicate the linear fit before and after the turning year, respectively. The red line indicates linear fit during the period 2001–2014. TP represents the turning point year.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 9 Multidimensional Evaluation
### Average Latency for Delivered Messages  
Latency is used to measure the amount of time taken for a message to be delivered. The graph (Figure 8) shows the average amount of time, considering all delivered messages, between being sent and received. A lower latency is preferable as it means that messages are delivered faster.
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 2 Related Work
Many studies discussed industry 4.0 in several field including economic and business [32], information sharing [33], technologies and applications [34], future trends [35] and industry 4.0 in the wood industry [36] ([[https://awfsfair.org/2019/04/industry-4-0-in-the-wood-industry-beyond-the-buzz/](https://awfsfair.org/2019/04/industry-4-0-in-the-wood-industry-beyond-the-buzz/)]([https://awfsfair.org/2019/04/industry-4-0-in-the-wood-industry-beyond-the-buzz/](https://awfsfair.org/2019/04/industry-4-0-in-the-wood-industry-beyond-the-buzz/))). However, many studies have been done in connecting industry 4.0 and forestry 4.0 with the help of advanced technologies such as IoT, Artificial intelligence, robots, vehicles and etc. [37], industry 4.0 application to forestry [30], wood processing [31], digitization in wood supply [38]. The authors of [39] discussed Internet of improving the forest sustainability. Moreover, the authors of [40] introduced the framework of industry 4.0 from the prospective of forest supply chain. The framework components were included system intelligence, digital technologies, communication network infrastructure, collaborative supply chain of forest. Forest fire accident represents a common hazard that destroys the forest. Therefore, giant trees were reduced drastically, which led to an unhealthy environment for human beings and animals. The authors of [41] introduced IoT devices and cloud to produce forest fire alert in case the fire is detected. Therefore, detecting fire is necessary to avoid fire hazards in the forest and benefit from distributing IoT devices in forest areas. The authors of [37] introduced optical remote sensing for early fire alert systems. The proposed system architecture included spaceborne, airborne, and terrestrial to detect fire with a high accuracy level. Smart IoT devices are implemented in smart systems to measure CO\({}_{2}\) emissions of different forest fire sources [42]. Furthermore, IoT devices are used for designing efficient forest fires detection [43]. The summary of the related research work has been presented in Table 1.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 3 Stray Capacitance of Rogowski Coils  
To extract the stray capacitance of Rogowski coils, we regard each turn of the winding as a single coil represented by lumped elements, and present a network model of the equivalent circuit of Rogowski coils in Figure 4, where \(R_{i}\) and \(L_{i}\) are the internal resistance and self-inductance of the \(i\)-th turn of the winding, respectively. \(M_{i}\) represents the mutual inductance of the \(i\)-th turn and the current-carrying conductor, and \(M_{i,k}\) denotes the mutual inductance of the \(i\)-th and \(k\)-th turns. Similarly, \(C_{i,0}\) is the capacitance of the \(i\)-th turn and the shielding shell, while \(C_{i,k}\) is the capacitance of the \(i\)-th and \(k\)-th turns of the winding.