esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 2 System Model
### Wind Generation Model
Such limitation has been taken into consideration while deploying it for wind support and reserve service. It is assumed that the ESS cannot discharge and will remain idle if its \(SOC\) reaches the minimum limit, \(SOC_{min}\). The ESS charges from the surplus wind energy or from the grid supply during the off-peak hours. When the ESS charges through the grid, it accumulates charge at its full charging rate, \(ES_{CP}\). The charging rate is often reduced and highly variable when the ESS is charged by wind power. The ESS stops charging and remains idle if the charging conditions are not favorable or if the ESS is fully charged until a discharging need arises. An ESS in a charging mode is an additional load to the system. The ESS can, however, stop charging and discharge to support the load in response to a contingency event. Such a response of an ESS virtually enhances the system reserve. Hence, it can be modeled as a dispatchable load during grid charge operation. The \(SOC\) of an ESS at a given interval \(i\), of the \(k^{th}\) UCP, \(SOC_{k,i}\), is continuously changed depending on its mode of operation. It is chronologically updated for each interval \(dt\), using Equations (3) and (4) when respectively charging from the grid and from wind power. The \(SOC\) remains unchanged, as shown in Equation (5), in the idle mode. Equation (6) is used to update the \(SOC\) following a discharge mode of operation. \[SOC_{k,i}=SOC_{k,i-1}+ES_{CP}*dt*\eta_{c} \tag{3}\]  
\[SOC_{k,i}=SOC_{k,i-1}+\sum_{w=1}^{\text{\tiny ESS}}\min\Bigl{(}ES_{CP},\; \left(W_{k,w}^{P}-WC_{k}\right)\Bigr{)}*dt*\eta_{c} \tag{4}\]  
\[SOC_{k,i}=SOC_{k,i-1} \tag{5}\]  
\[SOC_{k,i}=SOC_{k,i-1}-\frac{\sum_{w=1}^{\text{\tiny ESS}}\min\Bigl{(}ES_{DP}, \;\left(WC_{k}-W_{k,w}^{P}-Res_{k,i}\right)\Bigr{)}*dt}{\eta_{d}} \tag{6}\]The contribution of an ESS to the system operational adequacy and other benefits accrued over a specified evaluation period will also depend on its \(SOC\) at the start of the evaluation period. The \(SOC\) at a given time is modeled to be normally distributed. Since the operational adequacy evaluation is carried out well in advance of the actual system operation, the operator has the flexibility to adjust the expected magnitude of the \(SOC\) at the start of the evaluation period. For example, an ESS may be represented by a normally distributed \(SOC\) model with a mean \(SOC\) of 50% at the start of the evaluation period.
# 0.5-V High-Order Universal Filter for Bio-Signal Processing Applications
## 2 Proposed Circuit
### Proposed High-Order Universal Filter
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Filtering Function**} & **Input** & **Transfer Function** \\ \hline \multirow{2}{*}{HPF} & Non-inverting & \(V_{in41}=V_{in}\) & \(H(s)=\frac{s^{3}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in42}=V_{in}\) & \(H(s)=\frac{-s^{3}}{D(s)}\) \\ \hline \multirow{2}{*}{BPF1} & Non-inverting & \(V_{in31}=V_{in}\) & \(H(s)=\frac{s^{2}\frac{s_{m2}}{\text{C}_{2}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in32}=V_{in}\) & \(H(s)=\frac{-s^{2}\frac{s_{m2}}{\text{C}_{2}}}{D(s)}\) \\ \hline \multirow{2}{*}{BPF2} & Non-inverting & \(V_{in21}=V_{in}\) & \(H(s)=\frac{s\frac{s_{m1}s_{m2}}{\text{C}_{1}\text{C}_{2}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in22}=V_{in}\) & \(H(s)=\frac{-s\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}}}{D(s)}\) \\ \hline \multirow{2}{*}{LPF} & Non-inverting & \(V_{in11}=V_{in}\) & \(H(s)=\frac{\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}\text{C}_{3}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in12}=V_{in}\) & \(H(s)=\frac{-\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}\text{C}_{3}}}{D(s)}\) \\ \hline \multirow{2}{*}{APF} & Non-inverting & \(V_{in41}=V_{in32}=V_{in21}=V_{in12}=V_{in12}=V_{in}\) & \(H(s)=\frac{s^{3}-s^{2}\frac{s_{m2}}{\text{C}_{2}}+s\frac{s_{m1}s_{m2}}{\text{ C}_{1}\text{C}_{2}}-\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}\text{C}_{3}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in42}=V_{in31}=V_{in22}=V_{in11}=V_{in}\) & \(H(s)=\frac{-s^{3}+s^{2}\frac{s_{m2}}{\text{C}_{2}}-s\frac{s_{m1}s_{m2}}{\text{ C}_{1}\text{C}_{2}}+\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}\text{C}_{3}}}{D(s)}\) \\ \cline{2-4}  & & & \(\text{Note: }D(s)=s^{3}+s^{2}\frac{s_{m2}}{\text{C}_{2}}+s\frac{s_{m1}s_{m2}}{ \text{C}_{1}\text{C}_{2}}+\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2} \text{C}_{3}}\), \(H(s)=\frac{V_{out(3 rd)}}{V_{in}}\). \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Obtaining variant filtering functions of the third-order universal filter. Figure 8: Third-order universal filter based on n-order filter. Letting \(s^{3}=-s\omega_{o}^{2}\) the parameters \(\omega_{o}\) and \(Q\) of filters be given, respectively, by  
\[\omega_{o}=\sqrt{\frac{S_{m1}S_{m3}}{C_{1}C_{3}}} \tag{23}\]  
\[Q=\frac{\sqrt{\frac{C_{1}C_{3}}{S_{m1}S_{m3}}}}{\frac{C_{3}}{S_{m3}}}-\frac{C_{2 }}{S_{m2}} \tag{24}\]
## 2 Results and Discussion
### Method Application--GC-Ms  
The applicability of the optimized method was evaluated based on the analysis of six urine samples collected from healthy individuals. The comparison of the samples was not the aim of this study, therefore the urine samples were not normalized. However, for the application of the method in metabolomics studies in the future, the normalization will be necessary [35]. The analysis of urine with more analytical techniques can increase the number of metabolites of different physicochemical properties, and consequently may provide more information about the metabolic state of the body [36]. Therefore, in the present study, we decided to conduct the analysis of the samples using both GC-MS and \({}^{1}\)H-NMR spectroscopy, with the aim of detecting greater numbers of peaks corresponding to individual compounds from a single extract.  
The GC-MS analysis resulted in a total number of 400 individual deconvoluted peaks representing different compounds, detected in chromatographs, present in at least 50% of samples. The number of VCs detected in each sample is presented in Table 3. A comparison between the optimized method and SPME method is presented in Figure 1. The extraction of VCs from another individual run using both LLE and SPME of a headspace above the urine from the same individual showed a greater number of deconvoluted peaks detected using our optimized method (328 \(\pm\) 5.66 vs. 234 \(\pm\) 4.24, for LLE and SPME, respectively). As can be seen in Figure 1, the optimized LLE method results in more peaks at longer retention times, corresponding to heavier molecules, when compared to the SPME method, which is more efficient for small mass VCs. A comparison of the number of peaks detected by our method and those in the literature using SPME fiber pre-concentration technology with urine shows there is a significant improvement using LLE. The extraction using SPME with Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber and acidic conditions resulted in the detection of 75 VCs in urine in a single run [30]. In another study, a total number of 147 VCs was detected in urine using SPME with CAR/PDMS fiber; however, this number represents a sum of VCs obtained in acidic, basified and neutral pH samples [22]. On the other hand, in the study of [PERSON] et al. [37], GCxGC-TOFMS analysis allowed for the detection of approximately 700 compounds, of which 294 were tentatively identified, however it resulted from using a very high-cost and complex chromatographic system. Previous attempts at the application of LLE with diethyl ether using 450 mL of urine for VCs analysis resulted in the detection of 300 VCs, 40 of which were identified [28]. The results obtained proved that the optimized method is applicable for the GC-MS profiling of VCs in the urine.
## 4 Devices' Vulnerabilities and Requirements
### IoT Attacks
#### 4.5.3 Attacks Based on Transmitting Data  
IoT devices employ sensors to detect and collect information about their surroundings. These sensors are susceptible to a number of concerns, including injecting fake sensor patterns or initiating commands to launch malware that has been placed in the device of a victim [75]. Attackers can leverage unintended communication channels between device peripherals to change important sensor settings (e.g., device mobility, temperature, pressure, light intensity, magnetic field, etc.) or send malicious instructions.  
* Transmitting via Light Sensors: One of the methods to compromise signals and send malevolent signals is light sensors which makes it simple to transmit data packets by turning on and off a light source. The difference of light intensity will act as a source of the device's data flow. An adversary can activate malware by delivering a trigger message by regulating light intensity, i.e., adjusting the voltage of a light source.
* Transmitting via Magnetic Sensors: The functionality of a magnetic sensor depends on the magnetic fields of the surroundings of the device. It will be affected if there is a change in the magnetic fields of the device's peripherals. To send data using magnetic sensor, permission from users is not required and can be silently run in the background [76]. An attacker can make forged data of the magnetic sensor by shifting the magnetic field of the atmosphere of the device. This action can ultimately trigger messages of malware. Triggering signals encoded by an electromagnet can be communicated to an IoT device, and this message will cause certain variations in the device's magnetic sensed data. The triggering message can be derived from this electromagnetic signal by calculating differences.
* Transmitting via Audio Sensors: Malware in IoT devices can also be activated using audio sensors. Microphones in modern IoT devices may identify audio signals with a frequency relatively lower than the audible frequency range, and triggering messages can be sent using this type of audio signal to get over the device's security measures.
# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 4 Experiment on Outlier Detection and Imputation for Mining Ventilation Sensor Data
### Missing Value Imputation
#### 4.2.3 Experimental Results  
To verify the applicability of the SGAIN algorithm for missing data imputation in mining ventilation wind speed sensors, the dataset described in Section 4.1.1 (noisy dataset) was used for imputation. First, outliers and missing values in the dataset were removed to create blank missing points, resulting in a sample dataset that required imputation. The dataset was then input into the SGAIN model, and multiple experiments were conducted to ensure the accuracy of the model. To evaluate the superiority of the SGAIN model, other imputation algorithms--random forest (RF), generative adversarial imputation network  
Figure 10: RMSE and MAE vary with the learning rate.  
Figure 9: Changes of RMSE and MAE with the number of iterations when the learning rate was \(1\times 10^{-2}\).  
model, although the RF model improved imputation speed by 25.9%, the SGAIN model outperformed RF in terms of imputation accuracy, with RMSE and MAE values reduced by 36.2% and 41.5%, respectively. Although the DAE model was faster by 15.47% than SGAIN, it showed a significant reduction in imputation accuracy, with RMSE and MAE values increasing by 87.7% and 88.8%, respectively. Considering the high accuracy requirements for mine ventilation wind speed data, the SGAIN model is more suitable for use in the mining ventilation field.  
The ventilation wind speed sensor data from mines exhibit inherent characteristics such as temporal sequence, periodicity, spatial correlation, volatility, and gradual change. Reference [21] discusses other cleaning models applied in the mining ventilation field, including the adaptive KF algorithm and the SDAE model. A comparison with the results in reference [21] shows that the KF algorithm had RMSE and MAE values of 0.355 and 0.325, respectively, which are higher than those of the method proposed in this paper. Therefore, the method proposed in this paper outperforms the adaptive KF algorithm. The SDAE model had RMSE and MAE values of 0.056 and 0.018, respectively, which are similar to those of the method proposed in this paper. However, the SDAE model's repair effectiveness was more dependent on the inherent features of the data samples. For the mine ventilation wind speed sensor data characteristics, the generative adversarial structure of the SGAIN model is better suited to handle the inherent features of the mining ventilation data compared to the SDAE model.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 4 Case Study of Transmission Line Insulator Defect Detection
### Result Analysis  
#### 4.3.1 Detection Results with Different Proportion of Samples  
In order to analyze the influence under different proportion of insulator image samples on the detection accuracy, the proportions of the training set (including the validation set) and the test set were taken as 5:5, 6:4, 7:3, 8:2, and 9:1, and 10% of the samples in the training set were used as the validation set, as shown in Table 2. The performance indexes including \(P\), \(R\), \(AP\), and \(mAP\) of the test results under different sample proportions are shown in Figure 10.  
It can be seen from the Figure 10 that the values of \(P\), \(R\), \(AP\), and \(mAP\) show upward trends with the increased proportion of training set to test set, and the proportion has a greater influence on insulator defect targets. When the proportion is 9:1, all the performance indexes have the maximum values, which are above 90%. The \(mAP\) value can reach 93.81%. Since the model has the highest detection accuracy, the proportion of 9:1 is used in the following analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **First Stage** & **Second Stage** \\ \hline Batchsize & 8 & 2 \\ Initial learning rate & \(1\times 10^{-3}\) & \(1\times 10^{-4}\) \\ Training epochs & 50 & 50 \\ \hline IoU threshold & 0.5 & \\ NMS threshold & 0.3 & \\ Label smoothing value & 0.01 & \\ Minimum learning rate of cosine annealing & \(1\times 10^{-6}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter setting.  
Figure 10: Performance indexes under different proportions of sample.
## 2 Object Fields and Methods
### Diagnosis of Fruit Tree Vigor Using Optical Sensing
#### 2.3.1 Thermal Image Acquisition of Mandarin Orange Leaves  
A series of laboratory scale experiments was carried out to determine the emissivity of mandarin orange leaves [5]. The thermal image of each mandarin orange leaf was taken by a Thermoshort thermography camera (Nippon Avionics Co., Ltd., Tokyo, Japan) from two trees with greatly different tree vigor under the same cultivation environment selected in the orange grove (Figure 1a,b). This thermography camera can measure from \(-20\) to \(100\)\({}^{\circ}\)C and its resolution is \(0.1\)\({}^{\circ}\)C. The spectral range of this camera is from 8 to 13 \(\upmu\)m, and its thermal image pixels are \(160\times 120\) pixels in size. The control area was marked on the thermal image using aluminum tape of which the emissivity greatly differed in order to facilitate the comparison between the visible and thermal images (Figure 1c).  
The cultivation environment is shown in Figure 2. The emissivity determined in the laboratory (0.95) was applied in this experiment. A leaf template is needed to make a precise comparison of the temperature distribution between two leaves. Ten leaves with different shapes shown in Figure 3 were sampled in the orange grove and the average shape of the Satsuma Mandarin leaf was determined using  
Figure 1: Aluminum as the control point in the field experiment: (**a**) Tree whose vigor strength is good; (**b**) Tree whose vigor strength is bad; (**c**) The usage of aluminum tape.  
the shape analytical method based on r-\(\theta\)-\(\phi\) coordinate system, which we have already developed [13]. This shape analysis was applied to the Satsuma Mandarin leaf.  
Shape analysis was performed by the projection of a binary image of a leaf on tangent coordinate system. The origin was set on the center of gravity. A vector from the origin to a point on the orbital was auxiliary drawn as an arrow. Let r, \(\theta\) and \(\phi\) be the vector, angle of the vector and angle between the vector and tangent line at the point along the orbital, respectively. The orbital can then be projected into the tangent coordinate system with parameters \(\theta\) and \(\phi\). Each obtained thermal image was then mapped to the template leaf and a comparison of temperature distribution was made between the strong leaf and weak one.  
Figure 3: Sampled leaves used in this study.  
Figure 2: Cultivation condition of orange trees.
# Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues
## 3 Problem Modeling and Algorithm Modeling
### Waiting Time Analysis  
In SSA-BMMFPQ, the waiting time of ships of the \(k\)-th class consists of two parts: one is the workload of ships with a priority no lower than that of the \(k\)-th class ships already existing in the system at the arrival time; the other is the workload brought by newly arrived ships of the \((k{+}1)\)-th priority class during the waiting process. Among them, the joint probability that the system workload is 0 and the background process state is \(S_{\mathrm{i}}\) is stored by \(\alpha^{(k{+})}(t)=[\alpha_{i}^{(k{+})}(t)]\), and its elements are expressed as  
\[\frac{\partial}{\partial t}w^{(k{+})}(t,x)+\frac{\partial}{\partial x}w^{(k{ +})}(t,x)(\frac{1}{d}R^{(k{+})}-\mathbf{I})=w^{(k{+})}(t,x)Q \tag{12}\]  
According to the above formula derivation, the boundary condition is  
\[\frac{d}{dt}\alpha_{i}^{(k{+})}(t)=\sum_{j\
u_{j}^{(k{+})}\leq d}\alpha_{j}^{ (k{+})}(t)q_{ji}-w_{i}^{(k{+})}(t,0)\left(\frac{r_{i}^{(k{+})}}{d}-1\right) \tag{13}\]  
and \(\alpha_{i}^{(k{+})}(t)=0\) (for \(i\): \(r_{i}^{(k{+})}\leq d\)). Through the above formulas, the stationary density \(w^{(k{+})}(x)=\lim_{t\to\infty}w^{(k{+})}(t,x)\) and the probability mass at \(0\)\(\alpha^{(k{+})}=\lim_{t\to\infty}\alpha^{(k{+})}(t)\) are given in matrix-exponential forms. When ships of the \(k\)-th class arrive at the queue, they face the workload of existing ships of the \((k{+}1)\)-th class in the system, and there may be an increase in the workload of ships of the \((k{+}1)\)-th class during the waiting process. By combining this with \(Q\) of SSA-BMMFPQ, constructing the dynamic equilibrium equation of the fluid queue, and deriving it through the LST, we get  
\[f_{\mathcal{T}}^{*}(s)=\hat{\kappa}^{(k)}\Phi^{*}(s)\mathrm{l} \tag{14}\]  
According to the mathematical relationship between the LST and matrices, taking the m-th derivative of \(f_{\mathcal{T}}^{*}(s)\) and substituting \(s=0\), the \(m\)-th moment can be obtained:  
\[E(\mathcal{T}^{m})=(-1)^{m}\hat{\kappa}^{(k)}\Phi^{(m)}\mathrm{l},k>0 \tag{15}\]  
An approximation method is adopted to construct the waiting time distribution function \(F_{\mathcal{T}}^{(n)}(t)\). Combining the initial state and the dynamic cumulative effect, we can get  
\[F_{\mathcal{T}}^{(n)}(t)=\hat{\alpha}^{(k)}\mathrm{l}+\hat{\kappa}^{(k)}\Phi_{ n}(t)\mathrm{l} \tag{16}\]  
In terms of the arrival rate, if the arrival rate of a certain type of ship increases while the service rate at the anchorage remains unchanged, according to the relationship between the arrival rate and the workload in SSA-BMMFPQ, the workload in the system when this type of ship arrives will increase. It can be seen from Formulas (14)-(16) that this will lead to an increase in the waiting time. By following these steps to understand the situation of ships at the anchorage, the data can be used to better manage ship traffic. The pseudocode for the entire ship anchorage algorithm design process is presented below, as shown in Algorithm 1.
# Adaptive Clustering of Users in Power Domain NOMA
## 6 Results and Discussions  
In the experiments, users are randomly distributed in a rectangular region around the BS. Figure 5 illustrates the user clustering based on their channel gains using the proposed clustering method.  
The simulation parameters are shown in Table 1. The same values are typically found in the literature [23; 25; 33].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of antennas in the BS & 1 \\ Number of antennas in the EU & 1 \\ Number of available resource blocks (w) & 100 \\ Resource block bandwidth (B) & 180 kHz \\ Downlink transmission power budget (\(P_{T}\)) & 46 dBm \\ Detection threshold at SIC receiver (\(P_{tol}\)) & 10 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for downlink NOMA.  
Figure 5: Distribution of users in space.  
Users are considered to be randomly distributed in a region with distances from the BS ranging from 1 to 20 km, following typical average values. We also considered perfect knowledge of the channel state information in the experiments, using gain values defined by Equation (4) in 10,000-time stamps. The parameters used by the equation are presented in Table 2.  
Table 3 presents the values adopted for the parameters of the proposed clustering algorithm and power allocation. These parameters were adjusted through several tests to maximize the performance of the proposed method.
## 5 Simulation Study
### Sensitivity Analysis
#### 5.3.4. Variable Virtual Waiting Zone Length Analysis  
The variable virtual waiting zone makes it possible for CAVs to enter the crossing early during a red light in a nonstop manner, which shortens the CAV passing time. To analyse the effect of variable virtual waiting zone length variation on how well the control model performs, this study sets the size of the left-turn virtual waiting zone and straight virtual waiting zone to 50%, 75%, and 100% of the maximum length under the premise of ensuring traffic safety. The passing zone is 300 m long. Four traffic demands of 1 veh/s, 2 veh/s, 2.5 veh/s, and 3.75 veh/s are used for comparison, and the corresponding demand coefficients of the four traffic demands are 1, 2, 2.5, and 3.75. The comparison in Figure 24 shows the performance difference between the control model based on VVWL and the control model based on SPDL in coping with the variable virtual waiting zone length variation for when the vehicles arrive in an evenly distributed manner with different levels of traffic demand. The variable virtual waiting zone length variation has no effect on the control performance of the control model based on SPDL, and has a greater effect on the control performance of the control model based on VVWL, and the longer the variable virtual waiting zone, the smaller the vehicle delay. Comparing Figure 24a,d, it can be further seen that the higher the pass saturation is, the greater the advantage of the variable virtual waiting zone in reducing vehicle delays. Similar results are obtained in Figure 25 for a Poisson vehicle arrival distribution. There are two reasons for these results: (1) The proposed variable virtual waiting zone allows CAVs to cross the intersection stop line at a relatively high speed. CAV trajectory planning can be delayed inside the variable virtual waiting zone at the intersection, resulting in a relatively high CAV speed in the passing zone, which in turn reduces the delay time. This is shown in Figure 16. (2) The variable virtual waiting zone is equivalent to extending the passing zone of the dedicated CAV lane, providing more space for vehicle speed changes.  
Figure 23. Effect of left-turn proportions on vehicle delay when the distribution of vehicle arrivals is Poisson.  
Figure 24: A uniform distribution of vehicles arriving on time and the effect of a varied virtual waiting zone duration on vehicle delay.  
Figure 25: A Poisson distribution of vehicles arriving on time and the effect of a varied virtual waiting zone duration on vehicle delay.