esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 3 Erosion Mechanism in Cavitating Jets
### Effect of Angle of Attack  
For hydrodynamic cleaning applications, it is important to consider the angle of attack of the nozzle on the target surface which depends on the industrial application. For example, the optimal nozzle angle for cleaning the concrete plug, which occupies full pipe diameter, is different from the angle for cleaning the thin layer of scale on the walls.  
A series of tests were performed in order to investigate the influence of the angle of attack on the behavior of the cavitating jets and on the erosion process in [52]. The jet impact angle was defined as the angle between the axis of the nozzle and the target surface. The same type of nozzle was used for these experiments. Experimental conditions for the attack angle tests were defined as follows: P\({}_{1}\) = 14.6 \(\pm\) 0.1 MPa, P\({}_{2}\) = 0.321 \(\pm\) 0.001 Mpa, V = 113.6 \(\pm\) 0.5 m/s, \(\sigma\) = 0.00342 \(\pm\) 0.001, T = 25 \({}^{\circ}\)C. The experimental results are presented in Figure 10. It can be seen that the angle of attack significantly affects the erosion rate and the mass loss. Due to the inclination, the distance between the nozzle and the target varied along the sample. In the upper part of the sample, the longer distance allowed more bubbles to be produced before they collapsed along the surface. Therefore, most of the bubbles will collapse on the upper region of the target (Figure 11), leading to more pronounced damage in that region. The optimal angle that results in a higher erosion rate could be related to the influence on the two most important cavitation erosion parameters: the shape of the bubbles and the pressure distribution around them.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review
### Kitchen Fire
#### 2.2.1 Three Steps for Extinguishing a Grease Fire  
Many fire accidents start from the kitchen, and if such fires are not extinguished immediately, they result in irreparable harm, perhaps even tragedy. A common cause for kitchen fires is excessively hot oil catching on fire, which could be caused by overheating cooking oil or stew. Many people intuitively pour water onto the oil to put out the fire, but doing so causes the oil to splatter, making the situation more dangerous.  
The boiling point of oil is generally higher than 100 \({}^{\circ}\)C, at which point water vaporizes. When water enters the oil that has caught on fire and vaporizes, the water vapor carries burning oil droplets into the air and spreads the fire. The correct three steps for extinguishing a grease fire are as follows [14]:  
1. When the fire is only within the pot or pan, turn off the gas stove.
2. Cover the pot that is on fire with a lid because this puts out the fire by blocking its access to air.
3. If the fire has been put out but the temperature in the pot remains high, directly opening the lid may cause the fire to reignite. Therefore, leave the pot alone and let it cool down naturally.  
Every kitchen should be equipped with a dry-powder fire extinguisher for emergency use. Installing home smoke alarms can enable residents to become aware of the fire early and respond to it in a timely manner.
## 4 VoLTE Scenario and Simulation Report
### Simulation Parameters and Setup  
Our simulation setup emulates the data plane of the LTE RAN and EPC in frequency-division duplexing (FDD) mode with portable eNBs, utilizing omnidirectional antennas and realistic channel models, MAC, and resource scheduling in both directions. In this simulation, each UE establishes VoLTE calls through the eNBs. To replicate real-world scenarios, all UEs initiate VoLTE calls simultaneously, spanning 60 s, thus generating a burst of call setup requests. We adopt the G.711 codec, renowned for its high-fidelity audio transmission [17]. Unlike the native VoLTE AMR codec, the G.711 is supported by Asterisk V18.10 software to implement VoIP and VoLTE calls, therefore suiting our system, as its high-quality communication can enhance comprehensible communication in a search and rescue scenario, although the use of different codecs is feasible. To adhere to the specifications of the G.711 codec, we transmit voice data in packets of 214 bytes at 20 ms intervals where 160 bytes represent the raw voice data (data rate of 64 kbps) and 54 bytes of protocol overhead [18].  
To replicate real-world wireless propagation and account for signal attenuation caused by obstacles and terrain variations, characteristic of forest environments, we integrate the log shadow path loss model featured on OMNeT++ into our simulation. This model has been used in research to represent the fluctuating signal strength due to factors such as forest vegetation [19; 20; 21]. The log path model features Equation (1), where we choose the path loss exponent (n) to be 2, showing only a few obstacles between UEs and eNBs when the standard deviation for \(X\) is set to 8 dB, meaning that shadowing effects may very likely take place, as in a mildly dense forest.  
\[PL(d)=L(d\_0)+10n\text{ log}\_\{10\}(d/d\_0)+X. \tag{1}\]  
Table 3 presents the RF parameters of our VoLTE simulation scenarios. We use one channel with a center frequency of 2.69 GHz as in our SDR-based system for a single eNB scenario, and we shifted one eNB to 2.71 GHz for the dual eNB scenario.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 2 Taxonomy of Malware in the Smart Home Network
### Attacks Based on the Smart Home Architecture
#### 2.1.2 Network Layer  
A network consists of a set of computers or other interconnected devices sharing resources, information, and services. This layer, which is responsible for connecting the IoT infrastructure [15], collects data from the lower layer as a perception layer and transmits the communication up to the upper layer of the smart home architecture. The communication medium may be wireless or wired, and the different technologies used can be Bluetooth, ZigBee, 3G, WiFi, and others [20; 21]. Occurring on the network layer are diverse types of attacks typically affecting the information sharing among network devices. It can be classified as a passive attack, such as traffic analysis, monitor and eavesdropping and others, or as active attack, such as routing attack, denial of service node malfunction, and much more.  
* Denial of service attack: in this type of attack, a hacker denies a service to authorize the user or even creates delays through resources, generating a large amount of data. This classification presents the impact of DoS on the victim's network or bandwidth resources. In such attacks, the attacker aims to consume the victim's limited available resources [17].
* Sybil attack: in this kind of attack, a single attacker can actually take over the networking, and multiple identities in the network are presented to the victim's node, which allows the victim's node to perform multiple operations, thus defeating the purpose of redundancy [22; 23].
* In the sinkhole attack, a compound node attracts the flow of data from nearby nodes used by hackers [23; 24; 25]. The system is tricked into thinking that the data have reached their destination [24]. In a wireless sensor network, the attacker can use the malicious node to attract network traffic, and then the sensor data can be arbitrarily operated.
* Man-in-the-middle (MITM) attack is also a kind of attack wherein communication occurs between victim nodes when the attacker gains access to it, as well as the trust of the two nodes and obtains information of different nodes [26; 27; 28].
## Appendix A  
**Assumption A1**.: _Define \(H\) as the contraction operator, the following formula is easily obtained:_  
\[HQ(s,a)=R(s,a)+\gamma\mathbb{E}_{s_{t+1}\sim p(\cdot|s,a)}[\max_{a_{t+1}} \!\!Q(s_{t+1},a_{t+1})].\] (A1)  
_Assuming that the optimal value function \(Q^{*}\) is a fixed point of \(H\), it means:_  
\[Q^{*}=HQ^{*}.\] (A2)  
_That is, \(Q^{*}\) remains at its original value after any multiplication with the \(H\) operator._  
\[(Hq)(s,a)=\sum_{s_{t+1}\in\mathcal{S}}p_{a}(s,s_{t+1})[r(s,a,s_{t+1})+\gamma \max_{a_{t+1}\in\mathcal{A}}\!\!q(s_{t+1},a_{t+1})].\] (A3)  
**Lemma A1**.: _[PERSON]'s convergence Test: The necessary and sufficient condition for the convergence of a sequence is that for any positive number \(\varepsilon\), there must be a positive integer \(N\) that satisfies the following equation under the condition of \(n>m\geqslant N\):_  
\[|x_{n}-x_{m}|<\varepsilon.\] (A4)  
_We refer to \(\{x_{n}\}\) that satisfies this condition as a Cauchy sequence. So the theorem can be expressed as: the necessary and sufficient condition for the convergence of sequence \(\{x_{n}\}\) is that the sequence is a Cauchy sequence._  
**Theorem A1**.: _In the \(Q\)-learning algorithm, \(Q(s,a)\) can converge to \(Q^{*}\) when the agent accesses any action in any state multiple times._  
Proof.: The convergence of the \(Q\) function is proved:  
\[||Hq_{1}-Hq_{2}||_{\infty} =\max_{s,a}\gamma|\sum_{s_{t+1}\in\mathcal{S}}p_{a}(s,s_{t+1})[ \max_{a_{t+1}\in\mathcal{A}}\!\!q_{1}(s_{t+1},a_{t+1})-\max_{a_{t+1}\in \mathcal{A}}\!\!q_{2}(s_{t+1},a_{t+1})]|\] (A5) \[\leq\max_{s,a}\gamma\sum_{s_{t+1}\in\mathcal{S}}p_{a}(s,s_{t+1})[ \max_{a_{t+1}\in\mathcal{A}}\!\!q_{1}(s_{t+1},a_{t+1})-\max_{a_{t+1}\in \mathcal{A}}\!\!q_{2}(s_{t+1},a_{t+1})]\] \[\leq\max_{s,a}\gamma\sum_{s_{t+1}\in\mathcal{S}}p_{a}(s,s_{t+1}) \max_{a_{t+1}\in\mathcal{A}}\!\!|q_{1}(s_{t+1},a_{t+1})-q_{2}(s_{t+1},a_{t+1})|\] \[=\gamma||q_{1}-q_{2}||_{\infty}.\]  
According to the [PERSON]'s convergence Test, any \(q_{1}\) function can ultimately converge to \(Q^{*}\) when \(q_{2}\) is the optimal function of \(Q^{*}\).
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 2 Materials and Methods
### The Update Step of the TC RTK/INS Integration
#### 2.3.2 The Covariance Matrix of the Observation Noise  
A low-elevation satellite can provide measurements with a lot of noise. We adopt the elevation-dependent weight model to determine the noise density for GNSS measurements, as Equation (15) shows [76]:  
\[\begin{split}\sigma_{\text{ZD},\rho}^{2}&=\alpha_{ \rho}^{2}+\frac{\beta_{\rho}^{2}}{(sin\gamma)^{2}},\\ \sigma_{\text{ZD},\phi}^{2}&=\alpha_{\phi}^{2}+ \frac{\beta_{\phi}^{2}}{(sin\gamma)^{2}}.\end{split} \tag{15}\]  
In Equation (15), the subscript \"ZD\" means the measurements are undifferenced. \(\gamma\) denotes the satellite elevation. \(\alpha\) and \(\beta\) with different subscripts refer to the empirical coefficients for pseudo range and carrier phase, respectively. In general, the coefficients for the pseudo range should be bigger than those for the carrier phase. Assuming all of the undifferenced measurements are independent, we can obtain the noise covariance matrix for the undifferenced measurements, as Equation (16) shows:  
\[\mathbf{R}_{\text{ZD}}=\begin{bmatrix}\sigma_{\text{ZD},\rho,\varsigma 1}^{2}&\dots\\ &\sigma_{\text{ZD},\rho,\varsigma 2}^{2}&\\ &&&\sigma_{\text{ZD},\phi,\varsigma 1}^{2}\\ &&&\dots\\ &&&\sigma_{\text{ZD},\phi,\varsigma n}^{2}\end{bmatrix}, \tag{16}\]  
where \(\mathbf{R}_{\text{ZD}}\) is a diagonal matrix.  
The SD measurements eliminate the satellite clock bias at the cost of increased noise. When it comes to the short-line differential positioning, we can set the noise variance of the SD measurements to twice the noise variance of the undifferenced measurements of the user receiver, as Equation (17) shows:  
\[\begin{split}\mathbf{R}_{\text{SD}}&=\begin{bmatrix}\mathbf{R}_{ \text{SD},\rho}&\\ &\mathbf{R}_{\text{SD},\phi}\end{bmatrix}\\ &=\begin{bmatrix}2\sigma_{\text{ZD},\rho,\varsigma 1}^{2}&\\ &\dots\\ &&2\sigma_{\text{ZD},\phi,\varsigma n}^{2}\end{bmatrix},\end{split} \tag{17}\]  
where \(\mathbf{R}_{\text{SD}}\) is a diagonal matrix.  
The DD measurements suffer more noise when compared to the SD measurements. The noise covariance matrix for the DD measurements, also known as the observation noise covariance matrix of the TC RTK/INS integration, can be formulated as Equation (18) shows:\[\mathbf{R}=\begin{bmatrix}\mathbf{DR}_{\text{SD}\
ot\!\!{\mathbf{R}}}\mathbf{D}^{\text{T}}&\mathbf{O}_ {(m-1)\times(m-1)}\\ \mathbf{O}_{(m-1)\times(m-1)}&\mathbf{DR}_{\text{SD}\
ot\!\!{\mathbf{R}}}\mathbf{D}^{\text{T}} \end{bmatrix}. \tag{18}\]  
Notably, \(\mathbf{R}\) is no longer a diagonal matrix, as the different DD measurements are correlated.
## 4 Experimental Results
Most V2X applications are time-sensitive and require immediate responses, necessitating a continuous exchange of vehicle data with minimal intervals between vehicles and infrastructure. Therefore, the number of received data packets containing DTC codes over a specific distance is crucial for ensuring both the reliability and stability of the V2X communication architecture. Utilizing the extensive cellular infrastructure present across Mexican territory, which offers broad coverage in all states, the system demonstrates that real-time data transmission with significant range is not only feasible but also highly efficient. Finally, the length of the test section is based on the cellular coverage area, however, it is of no importance if the test space changes because the proposed method is not related to the signal latency between the developed device and the cell tower. Due to this, there is a significant gap between the proposed method and the proposed method. \begin{table}
\begin{tabular}{l c c} \hline  & **Total of DTC Codes Sent** & **Total of DTC Codes Received** \\ \hline with 2G infrastructure & 85 & 67 \\ with 5G infrastructure & 85 & 0 \\ \hline \end{tabular}
\end{table} 
Table 10: Total sent and received DTC codes. Figure 14: Route taken by the vehicle for testing with 5G infrastructure (green color corresponds only to 5G coverage). Figure 13: Route taken by the vehicle for testing with 2G infrastructure (green color corresponds only to 2G coverage). no external factor that causes interference in the execution and installation of the proposed system. This research will allow us to further explore the data loss that is still present in this work, as long as there is 2G coverage during the vehicle's journey there will be no factor that will prevent the data from reaching its destination. The evolution of 1G to 5G technologies has had important changes at the level of architecture and access network between one generation to the next. In these different generations (1G, 2G, 3G, 4G and 5G), three main parameters are fundamental: performance, bandwidth and latency. In this implemented system, the following values were obtained: performance was in a range of 5 Mbps to 50 Mbps, bandwidth in a range of 25 MHz to 30 MHz and latency in a range of 150 ms to 500 ms. These parameters were with the use of the 2G cellular technology infrastructure. 3G, 4G and 5G technologies were not considered because there is no coverage during the vehicle's trajectory.
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country/Region** & **Frequency Band** & **Frequency** & **Auction Status** \\ \hline \multirow{4}{*}{China} & n41 & 2.515–2.675 GHz & Auctioned \\  & n78 & 3.4–3.6 GHz & Auctioned \\  & n79 & 4.8–4.9 GHz & Auctioned \\  & n258 & 24.75–27.5 GHz & Upcoming \\ \hline \multirow{2}{*}{Finland} & n78 & 3.41–3.8 GHz & Auctioned \\  & n258 & 25.1–27.5 GHz & Auctioned \\ \hline \multirow{2}{*}{France} & n78 & 3.4–3.8 GHz & Auctioned \\  & n257 & 26.5–27.5 GHz & Upcoming \\ \hline \multirow{2}{*}{Germany} & n78 & 3.4–3.7 GHz & Auctioned \\  & n258 & 24.25–27.5 GHz & Upcoming \\ \hline \multirow{2}{*}{Ireland} & n78 & 3.4–3.8 GHz & Auctioned \\  & n258 & 26 GHz & Upcoming \\ \hline \multirow{2}{*}{Italy} & n78 & 3.6–3.8 GHz & Auctioned \\  & n258 & 26.5–27.5 GHz & Auctioned \\  & - & 700 MHz & Auctioned \\ \hline \multirow{2}{*}{Russia} & n40 & 2.3–2.4 GHz & Upcoming \\  & n79 & 4.4–4.99 & Auctioned \\  & n248 & 24.25–27.5 GHz & Upcoming \\ \hline \multirow{2}{*}{Spain} & n78 & 3.4–3.6 GHz & Auctioned \\  & n78 & 3.6–3.8 GHz & Upcoming \\ \hline \multirow{2}{*}{United Kingdom} & n78 & 3.4–3.8 GHz & Auctioned \\  & n258 & 24.25–27.5 GHz & Upcoming \\ \hline \multirow{2}{*}{USA} & n258 & 27.5–28.35 GHz & Auctioned \\  & n258 & 24–47 GHz & Auctioned \\ \hline \multirow{2}{*}{Japan} & n78 & 3.6–3.8 GHz & Auctioned \\  & n79 & 4.4–4.9 GHz & Auctioned \\  & n258 & 27.5–29.5 GHz & Upcoming \\ \hline \multirow{2}{*}{Republic of Korea} & n78 & 3.4–3.7 GHz & Auctioned \\  & n258 & 26.5–29.5 GHz & Upcoming \\ \cline{1-1}  & n79 & 4.8–5.0 GHz & Auctioned \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 5G spectrum allocation in major European countries [6; 7].
## 4 Case Studies
For the two-stage stochastic scheduling model in Case 4, the transferred active and reactive power by the VSC facilities under different scenarios are shown in Figure 8, and the voltage profiles under different wind power scenarios at hour 17 are shown in Figure 9. From Figure 8, it can be seen that the deviation of transferred power by VSC1 among different wind power scenarios is less than that by VSC2 and VSC3. The reason is that the location nodes of VSC2 and VSC3 are very close to wind generators, the power uncertainty of which has a direct impact on the transmission power of VSC. Combining Figures 8 and 9, it can be found that the node 12 has the highest voltage value for the DC nodes from 8 to 18. The reason is that during the whole scheduling period, SVC2 transferred a large amount of active power to the DC distribution network through node 12, which can be regarded as the DC power supply. Furthermore, the storage capacity curves by ESS in Cases 2 and 4 when the power load increased by 12.5% are shown in Figure 10. It can be seen that: (1) For the two-stage stochastic scheduling model in Case 4, the charging and discharging periods of ESS are consistent during the whole scheduling period while the charging and discharging power changed with different wind power scenarios; and (2) the charging/discharging period of ESS1 and ESS2 for the deterministic scheduling model in Case 2  
Figure 6: The transferred active and reactive power by VSC in Case 2. Figure 7: The charging/discharging power of energy storage system in Case 2.  
is shorter compared to that in Case 4. For instance, ESS1 is not in use at hours 19 to 22 and ESS2 is not in use at hours 16 to 20 in Case 2 while the above ESSs are in the discharging/charging mode at these hours. In addition, to verify the performance of the proposed model, the charging/discharging state of ESSs obtained by the deterministic scheduling model in Case 2 is implemented for the two-stage stochastic scheduling model in Case 4. The simulated results indicate that the power purchasing cost and power loss cost are similar to the corresponding values while the power fluctuation penalty cost has increased by $36.56 compared to the corresponding value shown in Table 7. It can be drawn that for the same charging/discharging state of ESSs, the two-stage stochastic scheduling model can  
Figure 8: The transferred active and reactive power by voltage source converter in Case 4. Figure 10: Storage capacity of ESS in Cases 2 and 4. Figure 9: Voltage profiles at hour 17 in Case 4.  
make more economic decisions to reduce the power fluctuation penalty cost. On the other hand, the scheduling solution by the two-stage stochastic scheduling model has greater decision reliability under the consideration of different wind power scenarios in real time. As a result, the scheduling strategy achieved by the proposed model shows superior performance to ensure the economic and stable operation of the hybrid AC/DC distribution system considering wind power uncertainty.
###### Abstract  
In this paper, we demonstrated efficient mid-infrared generation using a low-power 1064 nm single-frequency (SF) fiber laser based on phase-matched intracavity difference frequency generation (DFG) in a continuous-wave (CW) periodically poled lithium niobate (PPLN)-based optical parametric oscillator (OPO). This is the first time that the frequency down conversion of a low-power SF light source has been achieved using intracavity difference frequency mixing. A high power 1018 nm fiber laser was firstly used for building the parametric oscillation and providing the high power resonant signal wave. To realize an efficient DFG process between the SF pump wave and the intracavity signal wave, the temperature of periodically poled lithium niobate (PPLN) crystal was properly adjusted to satisfy the phase-matching conditions. Finally, the low-power 1064 nm SF pump wave was successfully converted to a 3.7 \(\upmu\)m mid-infrared wave with a conversion efficiency of 21.6%. The conversion efficiency, to the best of our knowledge, is the highest for SF lasers in DFG processes. Meanwhile, taking advantage of SF laser pumping, a narrow linewidth of 271 pm (5.9 GHz) in the mid-infrared region was achieved without adding any etalons or devices in the cavity.  
Keywords:infrared lasers; fiber lasers; nonlinear optics; optical mixing +
Footnote †: journal: Applied Sciences
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Reactive Routing Protocols
#### 2.2.2 Ad-Hoc On-Demand Distance Vector (AODV)  
An AODV is the improved version of both the DSDV and DSR routing protocols. AODV basically inherits hop-to-hop routing from the DSR protocol and periodic updates from DSDV. Owing to its reactive nature, it discovers a route only when it is needed. AODV does not retain the routes to destinations which are not active during the communication process [31]. This routing protocol basically assigns dedicated time slots to packet transmissions in order to avoid network congestion and improve the packet delivery ratio. Routing with the AODV protocol includes three phases: (i) route discovery; (ii) packet transmitting; and (iii) route maintaining. Whenever a source UAV wishes to send a packet, it first initiates a route discovery phase to locate the position of the intended UAVs. In the packet transmitting phase, it forwards packets over a determined path without routing loops. A route maintenance phase takes place to restore link failure issues. It uses a sequence number to find an updated routing path towards the destination UAV. Moreover, the route's freshness is maximized with the help of an expiration time. In AODV, the intermediate UAVs also refresh their routing tables. However, because of the vibrant nature of the FANET system, network congestion is an issue with AODV.