esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 3 Description and Formulation of BACASP
### Mathematical Formulation
\begin{table}
\begin{tabular}{c c c} \hline
**Decision Variables** & **Definition** & **Unit** \\ \hline \(x_{i}\) & Berthing position of vessel i, \(\forall i\in V\) & m \\ \(\text{y}_{\text{i}}\) & Berthing time of vessel i, \(\forall\text{i}\in\text{V}\) & h \\ \(\text{g}_{\text{i}}\) & Delay incurred in processing vessel i with respect & h \\  & to its desired departure time, \(\forall\text{i}\in\text{V}\) & \\ \(\text{r}_{\text{i}}\) & Deviation of vessel i from its desired berthing & m \\  & position x\({}_{\text{i}}^{*}\), \(\forall\text{i}\in\text{V}\) & h \\ \(\text{t}_{\text{i}}\) & The index of the first cranes assigned to vessel i, & unitless \\ \(\text{n}_{\text{i}}\) & \(\forall\text{i}\in\text{V}\) & unitless \\ \(\text{h}_{\text{iq}}\) & Equals 1, if q QCs serve vessel i, 0 otherwise, & unitless \\  & \(\forall\text{i}\in\text{V}\) & unitless \\  & Equals 1, if vessel i and j are served at the same & \\ \(\sigma_{\text{ij}}\) & time and vessel i to the left of vessel j, 0 otherwise, & unitless \\  & \(\forall\text{i},\text{j}\in\text{V}\) & \\ \(\delta_{\text{ij}}\) & Equals 1, if vessel j starts its processing after vessel & unitless \\  & i departs, 0 otherwise, \(\forall\text{i},\text{j}\in\text{V}\) & unitless \\ \hline \end{tabular}
\end{table} 
Table 2: The decision variables of BACASP. abandonment strategy. Section 4.1 describes the encoding and decoding strategies of IMOCS for solving BACASP and proposes a constructive heuristic to assign specific QCs to vessels. Section 4.2 discusses the initialization of the population, in which greedy and tent mapping strategies are used to increase the diversity of the initial population. To address the problem that the original MOCS algorithm is slow to converge and easily falls into local optima during the iteration process. Section 4.3 introduces two adaptive mechanisms to improve it. One is the elite-guided adaptive tangent flight, which selects elite individuals based on the non-dominated ordering and crowding degree distance; the other is the information-enhanced abandonment process, which aims to enhance the algorithm's local search performance in the late iteration.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Simulation Parameters  
Table 2 shows the simulation environment that was prepared to create different scenarios that were used to obtain the best possible results. The parameters are set up to represent low, medium, and high density (fixed size with varying nodes) traffic. The sensors' capacity in terms of transmit distances, power sustainability, and speed of transmission are considered in setting these parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value(s)** \\ \hline Simulator & NetSim Standard v12.1 \\ \hline Application protocols & OLSR \\ \hline Grid length & 1000 m \(\times\) 1000 m \\ \hline Simulation time & 100 s \\ \hline Traffic type & Video \\ \hline QoS class & rtPS (real-time polling service) \\ \hline Node movement model & Random waypoint \\ \hline Trajectory & Random \\ \hline Transport protocol & UDP \\ \hline Speed & 50 km/h \\ \hline Refresh interval & 2 s \\ \hline Encryption algorithm & Advanced Encryption Standard \\ \hline Node density & 16, 49, 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 8: Pathrater algorithm.  
The QoS measures used to ensure that quality is optimized in the network are throughput, delay, and jitter.  
Throughout: It represents the number of bits forwarded from the MAC to higher layers in all nodes in the network; it is measured in bits per second. The throughput may also be referred to as the average number of packets successfully transmitted or received per second. This work focused on the application throughput which is the total user data sent to the intended destination per second. The formula to calculate throughput is given as:  
\[\text{Throughput}=\text{Pd}/\text{T} \tag{1}\]  
where Pd is the number of packets delivered and T is the time in seconds.  
Delay: This is normally the time taken for one packet to be transmitted from the source node to the destination node. This performance metric evaluates the routing protocol's effectiveness in the use of network resources. Delay may be caused by several obstacles including transfer time, buffering during discovery latency, interference queue, and propagation. The formula to calculate delay is given as:  
\[\text{Delay}=\text{Trx}-\text{Tst} \tag{2}\]  
where Trx is the time the packet is received and Tst is the time the packet is sent.  
Jitter: This is the variation in time between route changes and data packets arriving. The variation may be caused by internal sources, such as data transmission errors, the presence of a malicious node, and network congestion. It usually affects the audio quality of the video if its level is high. The formula to calculate jitter is given as:  
\[\text{Jitter}=\text{Dt}-\text{Dp} \tag{3}\]  
where Dt is the transmission delay of the current packet and Dp is the transmission delay of the previous packet.
## 2 System Model and Dynamic Optimization Problem  
### Network Model  
As shown in Figure 1, this study considers a cellular vehicle-to-everything (C-V2X) scenario consisting of a macrocell and several platoons of vehicles on a freeway [31], where the V2I communication coexists with the V2V communication. The platoons share the channels with the macrocell users (MUs), i.e., the vehicle network underlies the cellular communication. It is assumed that the vehicles of a platoon have the same mobility patterns. This network uses Mode 4 (autonomously scheduled) for V2V communications to execute the resource allocation, i.e., the VUs determine the resource allocation themselves. Each platoon has a leader that gathers channel state information and allocates the channels to the platoon members. In the platoon, it is assumed that each vehicle communicates with the following one and sends the channel state information directly to the leader.  
There is no signal transmission among different platoons. Therefore, the channel allocations are independent in the different platoons. To mitigate the interference among the members of a platoon, the leader allocates orthogonal channels to the members. In the macrocell, the users are distributed along the road in the coverage of the base station; the MUs in the same macrocell base station (MBS) are assigned orthogonal channels. The downlink of the network is considered.  
The platoons are modeled as a one-dimensional Poisson point process (1D PPP) \(\Phi\) with a density \(\lambda\) (platoons/m), which is considered suitable for a freeway scenario [22; 23]. The set of vehicles in a platoon is denoted as \(\mathcal{N}\), where the cardinality of \(\mathcal{N}\) is denoted as \(N\). The number of MUs and platoons are denoted as \(N^{M}\) and \(N^{P}\), respectively. White noise is neglected at the receivers. Let \(\mathcal{K}=\{1,2,\ldots,K\}\) be the set of orthogonal channels. The threshold of the signal-to-interference ratio (SIR) is denoted as \(\theta\); that is, the packet transmission is successful only if the SIR is larger than \(\theta\). Let \(P^{M}\) and \(P^{V}\) denote the transmit power of the MBS and the VU, respectively, and let \(\alpha\) denote the factor of the path loss. In this study, the small-scale fading of each channel denoted as \(g\) is distributed independently and identically and has an exponential distribution with a unitary mean [22; 23], i.e., \(g\sim\exp(1)\).
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 5 Results and Analysis
### Reflection Coefficient  
For resonant frequencies of 28 GHz and 40 GHz, a reflection coefficient/return loss graph that shows the ratio of the amplitude of the reflected wave to the incident wave was simulated, yielding the results shown in Figure 6. As seen from the figure, before  
Figure 5: A 4 \(\times\) 4 RMS integrated with PIN diodes.  
optimization, the unit cell resonated at 25.6 GHz and 39.85 GHz with a magnitude of \(-14.80\) dB and \(-13.11\) dB, respectively. With the use of the CST optimizer, the unit cell resonated at 28.3 GHz and 40.3 GHz with a reflection coefficient of \(-18.57\) dB and \(-22.0\) dB, respectively. The bandwidth of the unit cell before optimization was 4.65 GHz and 0.67 GHz and, after optimization, as depicted at the \(-10\) dB mark in Figure 6, it was 6.8 GHz and 1.50 GHz. When the unit cell was optimized, it resonated at a frequency closer to the desired operating frequencies (i.e., 28 GHz and 40 GHz) with less reflection than before. As a result, the optimized return loss results of the unit cell were used to analyze a 4 \(\times\) 4 frequency RMS antenna structure shown in Figure 5. The student version simulation software was not licensed for hundreds of elements, therefore an array characterized by 16 elements was chosen.  
Figure 7 depicts the return loss results for a 4 \(\times\) 4 metasurface antenna with and without adding PIN diodes from the lumped elements section in CST MWS, which enable frequency reconfigurability. The results are further numerically summarized in Table 3. It was observed that when diodes were added, the resonating frequency decreased as the capacitance increased. From the table, the bandwidth of the 16 element metasurface antenna without diodes was relatively the same as when they were added. Furthermore, it can be observed from this table and figure that the proposed metasurface antenna array was capable of frequency reconfiguration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Capacitance (pF)** & **Resonating Frequency (GHz)** & **Bandwidth (GHz)** \\ \hline Without diodes & 26.90–31.39, 32.26, and 40.20 & 4.46, 0.66, and 1.36 \\ \hline
0.03 & 26.61–31.49, 32.49, and 40.28 & 4.88, 0.42, and 1.41 \\ \hline
0.035 & 23.60, 26.72–31.47, 32.48, and 40.28 & 1.17, 4.73, 0.43, and 1.41 \\ \hline
0.04 & 23.08, 26.79–31.46, 32.48, and 40.24 & 1.17, 4.68, 0.44, and 1.39 \\ \hline
0.045 & 22.72, 26.81–31.44, 32.48, and 40.24 & 1.05, 4.63, 0.45, and 1.38 \\ \hline
0.05 & 22.48, 26.89–31.43, 32.44, and 40.24 & 0.98, 4.58, 0.46, and 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of return loss and bandwidth results.  
Figure 6: Return loss results of metasurface unit cell before and after optimization.
###### Abstract  
The contact voltage (CV) method, which can detect miniature failures, has been tested under laboratory conditions to monitor the condition of bearings. In this study, the bearing materials for marine diesel engines, aluminium and copper alloy, were tested on a bearing fatigue wear test bench in the boundary lubrication state, which was found through tests of the different parameters. The frictional torque, the oil film thickness and the bearing temperature were measured, as well as the CV signals. The possibility of using the CV technique to monitor the condition of the bearings was also assessed by evaluating the tribological properties. After 10 h of the test, the aluminium alloy bearing was worn to the alloy layer. Then, the wear-reducing layer on the surface of the bearing slowly peeled off, and the wear was intensified. Due to its higher wear-resisting property, the amount of wear on the copper alloy bearing increased slowly. After 20 h of the fatigue wear test, the aluminium alloy bearing became severely worn, the CV characteristic was up to 81% of the initial value, the bearing temperature increased by 6.3%, and the torque value increased by 32%. This indicates that the CV method is more sensitive to wear failure. Due to better wear resistance, the copper alloy bearing showed only slightly wear and a small increase in its CV value. The main contribution is that the CV method is useful for monitoring the lubricated condition and for evaluating the tribological properties of bearings. This research has laid technical foundations for the engineering of the sliding bearing wear monitoring system based on the CV method.  
contact voltage method; tribological properties; bearing; monitoring  
* 2021 25 July 2021 24 August 2021 25 August 2021
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 3 System Functionality and Motivation
### System Functionality  
The functionality at an edge server involves the query, collection, and extraction of distributed sensing information at vehicles for extending the capacity of sensing and perception. We call the system the extended sensing-perception information system. By overcoming the sensing and recognition constraints of an edge server and individual vehicles, the extended sensing-perception information system facilitates the identification and perception of conditions, and the status of a V2X network to enable, for example, the appropriate broadcast configuration, dynamic channel selection, and maneuver coordination.  
An application example of the extended sensing-perception information system is to establish an appropriate data rate or transmission time in a broadcast according to the collection of RSSI information at various vehicles. Broadcasting is an important activity in V2X. However, a broadcast lacks reliability, since there is no specified individual destination, and packet delivery can fail. Since the broadcast destination is a number of vehicles, it is important to know the RSSI features of the object vehicles. One coordination method at the BS/AP is to select an appropriate data rate or a suitable broadcast time according to the minimum RSSI (MinRSSI) among the sensing results of vehicles. This method allows the BS/AP to take account of the worst case for a reliable broadcast transmission.
## 2 System Model
### PV Power Management  
In this stage, the PV panels generate electricity during daylight hours. This power is used to meet local demand and to charge the hydrogen storage system. Under consideration, the polycrystalline silicon PV panel is manufactured by Jinko Solar, and its model number is JKM215P-60B. The alphanumeric code \"60B\" indicates both the number of solar cells and their configuration. A significant amount of solar energy can be captured as photons and converted into electricity using thin-film semiconductors. By utilizing **\"direct band gap semiconductors\"** instead of **\"indirect band gap silicon material\"**, the cost of photo-absorbing materials can be reduced. Any surplus power is exported to the grid or used to power non-critical loads. The input for solar power management is defined by the solar Irradiation (I) and ambient temperature (\(T_{amb}\)) that is obtained from the National Solar Radiation Database NSRDB. At the beginning of solar power, the cell temperature (\(T_{C}\)) is calculated using (1) [33; 34].  
\[\mathrm{T_{C}(t)=T_{amb}(t)+I(t)(\frac{NOCT-20}{0.8})} \tag{1}\]  
Here, NOCT is the nominal operating cell temperature of the solar cells, and the output power of a solar panel is calculated using (2):  
\[P_{PV\ unit}\ (t)=\Big{(}R_{f}\Big{)}(\eta_{PV})(A_{PV})(I(t))\bigg{(}1\ -\frac{K_{P}}{100\ (T_{C}(t)-25)}\bigg{)} \tag{2}\]  
Here, \(R_{f}\) is the reduction factor for dust accretion, whereas \(\eta_{PV}\) is the conversion efficiency of the solar cell, \(A_{PV}\) denotes area of solar, and \(K_{P}\) indicates the temperature coefficient. Consequently, (3) expresses the total power produced by a complete solar panel:  
\[P_{PV}\ (t)=(N_{PV})(P_{PV\ unit}\ (t)) \tag{3}\]  
where \(P_{PV}\) (t) presents the output power of solar in watts and \(N_{PV}\) is the number of cells for PV. The increase in temperature may sacrifice its efficiency. Figure 4 shows clearly the characteristics of voltage and power for the solar system.  
Figure 4: Characteristic of the solar system [35].  
A slight change in the temperature of solar panels can lead to a reduction in power output, affecting both current and voltage. In this proposed study, the optimized EMS considers the cost of solar power per kilowatt and enhances the quality of power delivered to the system.
## 5 Energy-Harvesting Applications
### Cognitive Radio for Wireless Sensor Networks
#### 5.1.2 Spectrum Sensing and Management  
Finding and classifying available spectrum bands that secondary users can use is known as \"spectrum sensing.\" Maximizing spectrum use and avoiding interference with primary users depend on effective spectrum sensing. The essential methods are as follows:  
* Energy Detection: Measures the energy of the received signal to determine the presence of primary users.
* Matched Filtering: Uses known characteristics of primary user's signals to detect their presence.
* Cyclo-stationary Feature Detection: Exploits the periodicity of signals to detect primary users.
* Spectrum Allocation: Assigning spectrum bands to users based on their requirements and availability.
* Spectrum Sharing: Allowing multiple users to share the same spectrum band using different access strategies.
## 3 Results and Discussion
### Performance of the Models
On mobile devices and single-board computers, MobileNetV3 outperformed NasNet-A and EfficientNetB0 in terms of speed. NasNet-A mobile (23 MB) and EfficientNetB0 (29 MB) had substantially larger models than MobileNetV3 (1.99 MB), although MobileNetV3's detection speed (27 ms/image) was quicker than NasNet-A mobile (38 ms/image) and EfficientNetB0 (43 ms/image). Hence, the classification of the images by MobileNetV3 took the least time among the compared models, making it the most effective model. The model, however, was assessed as a Keras model. Thereafter, the model was enhanced using post-training quantisation and transformed to a TensorFlow Lite model in order for it to be used in edge computing applications like Android mobile and Raspberry Pi 4.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development
### Empirical Model
#### 3.1.1 Linear Regression Methods  
Pseudo-first-order rate expressions: Since O\({}_{2}\) and H\({}_{2}\)O are excessive in the SCWO system, their reaction orders are often assumed to be 0, thus simplifying the reaction kinetic equations into the pseudo-first-order rate expressions. As the simplest form of the empirical models, the pseudo-first-order kinetic equations have been widely used to describe the degradation of organics and complex pollutants in the SCWO process. [PERSON] et al. established the pseudo-first-order kinetic equation for SCWO of methanol. The model indicated the induction period of methanol SCWO reaction was shortened with the increase of temperature, but the product distribution was almost independent of temperature. Additionally, the type of reactor and the experimental operation process also affected the establishment of the kinetic equation. Both [PERSON]'s group [83] and [PERSON]'s group [84] established the pseudo-first-order kinetic equation for SCWO of methanol at 400-500 \({}^{\circ}\)C and 25 MPa, but the conversion rates from the two equations were an order of magnitude different.  
Multiple linear regression expressions: Multiple linear regression expressions considered the effect of oxygen on the COD removal rate. To use the multiple linear regression expression to solve the power exponent equation, the exponential regression was used to get the function relationship of [COD] with the residence time, as shown in the Equation (22):  
\[[\text{COD}]=[\text{COD}]_{0}e^{-m\tau} \tag{22}\]  
[PERSON] et al. [84] studied the SCWO kinetics of acetic acid, methanol and phenol using multiple linear regressions. Although the oxygen content in the linear regression data was lower or higher than the stoichiometry and the data for pseudo-first-order rate expressions had an oxygen excess of one order of magnitude regarding the stoichiometry of oxidation reaction, the kinetic consistency of the two methods validated that the rates determining steps of oxidation reaction were similar for 0.4-1.4 oxygen coefficient. [PERSON] et al. [85] compared the kinetics between subcritical and supercritical water oxidation of phenol. An exponential function was necessary to fit the evolution of the normalized phenol concentration ([phenol]/[phenol]\({}_{0}\)) versus residence time. The phenol order was 0.95 \(\pm\) 0.41, which was in agreement with the literature, while the oxygen order was \(-\)0.03 \(\pm\) 0.26, which was smaller than those obtained in the literature due to the great oxygen excess present in all experiments.
## 4 Proposed Method for Estimating the Tail Probability of the MMPP/D/1 Queue
### Is Estimation for Tail Probability
#### 4.3.2 IS Estimation  
Let us consider Equation (18). In this equation, the parameters \(\theta_{0}^{*}\) and \(\theta_{1}^{*}\) are determined and calculated in the above sections based on the input data \(\lambda_{0},\lambda_{1},r_{01},r_{10}\). However, the parameters \(y,j,i\) must be calculated in real time. At the dequeue event, we can determine the queue length and compute the difference queue length \(y\) between successive dequeue events. However, determining the state of the MMPP in real time is very difficult. Nevertheless, to facilitate the calculation of the weighting function in real time, we removed the part \(e^{-\theta_{1}^{*}(j-i)}\), and then (18) becomes  
\[W_{t_{k}}^{*}=\prod_{m_{k}=1}^{t_{k}}e^{-\theta_{0}^{*}y} \tag{19}\]  
Why we can remove it? To explain this, we investigated the events \(\{j>i\}\) and \(\{j<i\}\) during the simulation (in case of \(\{j=i\}\), the value of \(e^{-\theta_{1}^{*}(j-i)}=1\); hence, for each of the time slots that the state of the MMPP does not change, the value of the weighting function \(W_{t_{k}}^{*}\) is not also changed). In fact, in the two-state MMPP/D/1 queue, if the state of the MMPP changes from state 0 to state 1, then it will be changed again from state 1 to state 0. This means that the number of transitions from state 0 to state 1 and the number of transitions from state 1 to state 0 are equivalent. On the other hand, the number of events \(\{j>i\}\) and the number of events \(\{j<i\}\) are equivalent. Therefore, the part \(e^{-\theta_{1}^{*}(j-i)}\) can be removed from the weighting function without much impact on the final result. In the next section, we will examine and contrast the effectiveness of the proposed method with those of the traditional MC and IS methods.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the IMM-MCCKF Algorithm  
The IMM algorithm features adaptive adjustments that enable real-time probability modifications for each submodel. Using the probability transition matrix, the output invariably favors the submodel with the minimum error [33]. Although the MCCKF can effectively suppress non-Gaussian heavy-tailed noise, a fixed-noise matrix reduces estimation accuracy when the system model undergoes sudden noise changes. Consequently, this paper integrates the IMM with MCCKF to develop the IMM-MCCKF algorithm, addressing the decreased accuracy in MCCKF during sudden model noise fluctuations. Each submodel employs identical state and measurement vectors, with the MCCKF employed to construct the filters, thereby minimizing the impact of uncertainty from prior noise statistical data. The specific derivation process is outlined below:  
\[p=\left[\begin{array}{ccc}p_{11}&\cdots&p_{1r}\\ \cdots&\cdots&\cdots\\ p_{r1}&\cdots&p_{rr}\end{array}\right] \tag{53}\]  
where \(p\) represents the Markov probability transition matrix.  
Figure 2 shows the iterative process of the IMM-MCCKF.