esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Traditional MPPT Techniques
### Perturb and Observe Algorithm  
The Perturb and Observe (P&O) MPPT algorithm is a widely used technique for optimizing the power output of PV systems. It operates by periodically perturbing the operating voltage of the PV system and observing the change in power output [40]. If the power increases, the algorithm continues perturbing in the same direction; if the power decreases, it reverses the perturbation direction. The main advantage of P&O is its simplicity and ease of implementation, making it suitable for a wide range of applications. However, its disadvantages include potential inefficiencies in the presence of partial shading and its tendency to oscillate around the MPP [41; 42]. The flowchart of P&O is shown in Figure 5, where \(V(t)\) is the module voltage, \(I(t)\) is the module current, \(P(t)\) is the module output power, \(D(t)\) is the duty cycle of the DC-DC converter, and \(\Delta D\) is the variation in the duty cycle.  
Figure 5: Flowchart of the P&O MPPT.
## 5 Results
### Results on the MOT17 Det Dataset  
On this dataset there were 2 notable experiments:  
* MOT_1, which uses the weights of YOLOv4 that were obtained after training the algorithm on the MobilityAids dataset (Pretrain_Hospital1);
* MOT_2, which uses the weight of YOLOv4 pre-trained on COCO dataset.  
Since MOT17 Det only provided ground truth data on the train set, the final train-test split was the following:  
* train set: MOT17-02, MOT17-05, MOT17-10, MOT17-13 (2841 images)
* test set: MOT17-09, MOT17-11 (1425 images)  
This split was chosen based on:  
* the dynamic state of the camera (stationary or mobile): \(\odot\) stationary: MOT17-02, MOT17-04, MOT17-09; \(\odot\) mobile: MOT17-05, MOT17-10, MOT17-11, MOT17-13.
* the location of the scenes: \(\odot\) indoors: MOT17-09, MOT17-11; \(\odot\) outdoors: MOT17-02, MOT17-05, MOT17-10, MOT17-13.
* the average number of people in an image (the occluded people were removed from the annotations).  
As such, the scenes recorded with a mobile camera in an outdoor environment were preferred to be used in the train set, as they offer more diversity in terms of background and illumination. Because of this, MOT17-09 was considered a good candidate for the test set. MOT17-11 was chosen for the test set due to the higher population density (compared to MOT17-09) and because it was recorded using a mobile platform. Out of the seven sequences of images, MOT17-04 had a different perspective (aerial view) and it was not used at all.  
As can be seen in Figure 14, in both experiments the number of false positives decreased, while the number of false negatives increased as the model trained, which led to a decreasing mAP. This was caused by the small size of the dataset and the high instance of partially occluded people, which the algorithm had difficulty detecting (see Figure A4).  
In terms of experiment comparison, MOT_2 had a higher maximum mAP than MOT_1 because in the MobilityAids dataset, on which the algorithm had been previously trained in the case of MOT_1, people were being differentiated based on their physical handicap, which was not relevant for this dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Backbone** & **[EMAIL_ADDRESS]** & **Inference Time (ms)** \\ \hline Fast R-CNN [47] & ResNet-50 & 64.98 & 43 \\ Fast R-CNN [63] & ResNet-50 & 79.16 & 43 \\ Faster R-CNN Centroid [63] & ResNet-50 & 96.52 & 110 \\ Proposed Method & CSPDarknet53 & 95.00 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy ([EMAIL_ADDRESS]) comparison between this paper’s method and those of [47; 63].
# Intelligent Environment Monitoring System for University Laboratories
## 2 System Design and Implementation
### System Function Introduction  
Based on the current situation of university laboratory management and Internet of things technology, we propose a monitoring system that integrates sensor technology, wireless communications and other important technologies. This proposed system achieves many functions, such as monitoring indoor light off, temperature and humidity detection, anti-theft, and fire prevention. The details of the system functions are as follows:  
1. Special voice prompts for the current function that the proposed system will achieve and the operation that the proposed system will complete.
2. Testing the concentration of combustible gas in the laboratory and investigating fire risk 24 h a day.
3. Achieving the multiple-point measurement of humidity and temperature, and obtaining feedback from the measured temperature and humidity accurately.
4. Monitoring whether there are illegal persons entering the laboratory during the unused period.
5. Monitoring the light intensity of the laboratory in the unused period and judging the situation of lighting in the laboratory.
6. Displaying the monitoring data in mobile terminals by which administrators can realize remote monitoring.
7. Achieving wireless data transmission in time. When a disaster occurs, the system starts a buzzer alarm and a remote dial-up alarm to inform administrators with the SMS signal in time.
# Contribution of Atmospheric Depositions to Inventory of Nutrients in the Coastal Waters of Crimea
## 2 Materials and Methods
### Calculation of Nitrogen Concentrations Using the Multiple Nonlinear Regression Equation  
For the period of 2009-2014, when atmospheric precipitation sampling was not provided, the multiple nonlinear regression Equation (2) [31] was used to calculate nitrogen concentrations in atmospheric depositions. This equation accounted for variations in meteorological conditions.  
\[\text{InC}=4.55\cdot 1.0509\cdot\text{exp}^{-0.065\text{Ri}}\cdot 0.96\text{d }^{0.0449}\cdot(0.0009\text{f}+0.9282)\cdot(0.00006\text{Vy}^{3}+0.0015\text{Vy} ^{2}+0.032\text{Vy}+0.9963)\cdot \tag{2}\] \[(-0.0004\text{Vx}^{3}+0.0003\text{Vx}^{2}-0.0009\text{Vx}+0.9946) \cdot(0.0004\text{Ta}^{2}-0.0109\text{Ta}+1.0518)\]  
where  
Ri--number of precipitations, mm;  
d--number of days without precipitations;  
f--relative air humidity, %  
Vy, Vx--wind direction vectors;  
Ta--air temperature, \({}^{\circ}\)C.  
The number of atmospheric precipitations is the most important meteorological parameter [32], and its variations determine 85% of variations in nitrogen concentrations [21]. This has allowed us to simplify calculations for this work and to limit accounted meteorological parameters to only number of precipitations. Precipitation data for the period of 2009-2014 have been retrieved from long-term observations at MGS. This equation of multiple nonlinear regression has also been used to estimate the inorganic nitrogen input with atmospheric precipitations in some other coastal cities of Crimea (Yevpatoria and Kerch). Meteorological parameters for these cities have been taken from www.rp5.ru. (accessed on 27 February 2023) by (LLC) \"Schedule Weather\".  
Both measured in precipitations and calculated from data on meteorological parameters and precipitation from www.rp5.ru values of concentrations are available for Sevastopol. These data have been used to assess the consistency of the results of calculation with analytically determined values (for the open sampler). Our results have shown that the difference is about 14%. For example, the measured annual flux of inorganic nitrogen with atmospheric precipitation in 2019 was 23.43 mmol m\({}^{-2}\) year\({}^{-1}\), and the calculated value using meteorological data from the archive www.rp5.ru was 26.60 mmol m\({}^{-2}\) year\({}^{-1}\). The difference in these values is probably the result of the difference in the measured and reported amount of precipitation in Sevastopol in 2019: it is 333 mm according to the website and 308 mm for onsite monitoring. Therefore, data obtained using a multiple nonlinear regression equation can serve as a good approximation of nitrogen deposition to estimate the influence of atmospheric deposition on the input of nutrients to coastal waters with an error of under 15%.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 2 Description of the System  
Figure 1 shows the overall structure diagram of the system. Firstly, the main circuit topology involves a single-phase H6 inverter with a LC filter. The H6 inverter consists of six power IGETs (S1-S6) and two freewheeling diodes (D1 and D2), and its output is connected to the grid through the inductors \(L_{1}\) and \(L_{2}\) respectively, which is used to filter out the high-order harmonic current caused by the switching action. The SPWM drive logic can be expressed as follows. Switches S1-S4 operate at high frequency diagonally according to the polarity of the grid voltage. In the power transfer  
stage, current flows through switches S1, S6 and S4 during positive grid voltage and switches S3, S5 and S2 while grid voltage is negative. In the freewheeling stage, current will flow through S6, D1 during forward freewheeling and S5, D2 in negative freewheeling period. The topology uses the newly-added S5, S6, D1, and D2 for freewheeling, so that the power generation equipment such as the photovoltaic panel is separated from the grid in the freewheeling stage to achieve electrical isolation and suppression of the DC component in grid-connected current. This paper assumes that the input of the inverter is a fixed DC voltage source, so it is unnecessary to design a voltage loop to regulate the DC bus voltage. Hence, the given current amplitude \(I_{ref}\) combined with the grid voltage phase angle \(\theta\) extracted by a second-order generalized integrator-based phase-locked loop (SOGI-based PLL) can generate the grid current reference \(i_{ref}\). Then, the current controller applies the PMQR-type repetitive control strategy, so that the detected instantaneous grid current \(i_{g}\) can track the grid current reference \(i_{ref}\) with zero steady-state error and low THD.  
In this paper, the PMQR-type repetitive control scheme is analysed in a single-phase inverter system. And repetitive control is used for harmonic suppression. Certainly, the control strategy can also be employed in a three-phase system in a stationary coordinate frame.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Geographical Areas and Weather Data
#### 2.1.3 BOLAM Weather Forecasts  
Weather forecasts for temperature, relative humidity, precipitation, wind speed, and global solar radiation are produced by the ARPAS Meteoclimatic Department using the BOLOG Limited Area Model (BOLAM) hydrostatic model simulations [15; 16; 17; 18]. The forecasts are made on a grid of 1922 nodes, arranged with a horizontal resolution of 0.04\({}^{\circ}\), corresponding to a spacing of approximately 4.5 km (Figure 2, right). For each meteorological parameter, hourly predictions are provided, covering a 2-day period. Overall, the BOLAM time series received daily at CRS4 contain nearly half a million forecast values.
# Railway Track Stress-Strain Analysis Using High-Precision Accelerometers
## 2 State of the Art
Modeling of a stress-strain state is a separate research area in the railways industry [42; 43; 44]. The results of simulation and modeling can be used to determine the characteristics of the railway track and its elements [45; 46], in addition to the calculations and real experiments. The analysis of stresses in the railway elements from the dynamic interference between its parts and operational loads is carried out on basis of the results of finite element modeling [47; 48]. The dynamical theory of elasticity enables describing of the dynamic equilibrium of strains of the railway track layers. The model of the stress-strain state for the railway track consists of a combination of the geometric equations of the outline of a part in the system's space involved in interaction at a given time, and the equations of the dynamic equilibrium of its deformation. The modeling process is also used to study the influence of the installation temperature difference on the operational lifetime of the rail. It is especially noted that the track's substructure is a vital and essential part of the railway system which supports the track, and its properties have a strong influence on the overall track behavior [49]. Analysis of the dynamic interaction between vehicle and railway track considers the complexity of the modeling of the ballast bed, rail pads, under sleeper pads, and their effect on the determination of the overall track stiffness. As an analytical tool, in this study we implemented the finite element method, which is widely used in academia and industry [50; 51; 52]. Some examples of the finite element method being used to analyze complex mechanical systems are presented in [53; 54; 55]. The basic premise of the method is that a solution region can be analytically modeled or approximated by replacing it with an assemblage of discrete elements. Since these elements can be put together in a variety of ways, they can be used to represent exceedingly complex shapes. The finite element method provides stress-strain analysis of a railway track considering the initial and boundary conditions, and also allows taking into account the nonlinear properties of the ballast layer and subgrade soils, while maintaining the calculation accuracy, limited only by the quality of the finite element mesh [56]. With the help of modern mathematical methods, it is possible to monitor the states of all track elements during loading; to study deformations and displacements at any point in the model, which makes it possible to estimate the real rail deflections under the influence of rolling stock wheels; and to study the changes in the rail slope under significant lateral forces.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## Design of the Improved MPPT Control Method
### Estimation of the Equivalent Wind Speed  
The estimation of the equivalent wind speed is a process to solve the nonlinear Equation (34):  
\[f(\hat{v})=n\hat{T}_{m}^{\prime}-\frac{1}{2}\rho\pi R^{3}\hat{v}^{2}C_{\mathrm{ T}}(\frac{\hat{\omega}_{\mathrm{r}}^{\prime}R}{n\hat{v}},\beta)=0 \tag{34}\]  
The Jacobian of function \(f(\hat{v})\) is:  
\[J_{\mathrm{k}}=\frac{\partial f(\hat{v})}{\partial\hat{v}}=\frac{1}{2}\rho \pi R^{4}\omega_{\mathrm{r}}\frac{\partial C_{\mathrm{T}}}{\partial\lambda}- \rho\pi R^{3}\partial C_{\mathrm{T}}(\frac{\hat{\omega}_{\mathrm{r}}^{\prime}R }{n\hat{v}},\beta) \tag{35}\]  
The Newton-Raphson method is utilized to solve Equation (34). The procedure is [22]:  
1. Initialize the wind speed \(v_{0}\).
2. Calculate the increment \(\Delta\hat{v}(k)=J_{\mathrm{k}}^{-1}f(\hat{v}(k))\).
3. Calculate the wind speed in the next step \(\hat{v}(k+1)=\hat{v}(k)-\Delta\hat{v}(k)\), \(k=k+1\).
4. If \(\left|\Delta\hat{v}(k)\right|\leq\delta\), stop. Else get back to step 2. where \(\delta\) is the threshold for estimation.  
The instantaneous equivalent TSR is:  
\[\hat{\lambda}=\frac{\hat{\omega}_{\mathrm{r}}^{\prime}R}{n\hat{v}} \tag{36}\]
## 3 Proposed Method
### Optimization Objectives  
#### 3.1.1 Divisibility  
We use the NISD to measure separability between different categories, and the larger NISD means that the two samples are less similar and easily separated [35]. Prioritizing categories that can be easily classified for classification can reduce the accumulation of errors, thereby increasing classification accuracy. The calculation method is as follows.  
First, the center vector of class \(c_{i}\) can be calculated by using Equation (16):  
\[\overline{x}=\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{16}\]The weights assigned to the samples \(x_{1}^{i},x_{2}^{i},\cdots x_{n_{i}}^{i}\) in \(c_{i}\) according to the Euclidean distance from the center vector, can be calculated by using Equation (17):  
\[1/d\left(x_{1}^{i},\overline{x}\right),1/d\left(x_{2}^{i},\overline{x}\right),\cdots 1/d\left(x_{n_{i}}^{i},\overline{x}\right) \tag{17}\]  
The intra-class similarity direction of \(c_{i}\) can be calculated by using Equation (18):  
\[M_{i}=\left(x_{1}^{i}-\overline{x}\right)/d\left(x_{1}^{i},\overline{x} \right)+\left(x_{2}^{i}-\overline{x}\right)/d\left(x_{2}^{i},\overline{x} \right)+\cdots\left(x_{n_{i}}^{i}-\overline{x}\right)/d\left(x_{n_{i}}^{i}, \overline{x}\right) \tag{18}\]  
Then, the inter-class similarity direction between \(c_{i}\) and \(c_{j}\) can be calculated by using Equation (19):  
\[P_{i,j}=q_{i}+q_{j} \tag{19}\]  
where \(q_{i}=\cos\left(M_{i},d_{ij}\right)=\left(M_{i},d_{ij}\right)/\left(\|M_{i}\| \bullet\|d_{ij}\|\right)\), \(q_{j}=\cos\left(M_{j},d_{ji}\right)=\left(M_{j},d_{ji}\right)/\left(\|M_{j}\| \bullet\|d_{ji}\|\right)\), \(\|d_{ij}\|\) and \(\|d_{ij}\|\) are the vector norm from the center of class \(c_{i}\) to the center of class \(c_{j}\).  
Finally, the NISD between class \(c_{i}\) and \(c_{j}\) can be calculated by using Equation (20):  
\[\widetilde{P}_{i,j}=\left|d_{i,j}\right|-\left(q_{i}r_{i}+q_{j}r_{j}\right) \tag{20}\]  
where \(r_{i}\) and \(r_{j}\) represent the radius of category \(c_{i}\) and category \(c_{j}\), respectively. Thus, the first objective of optimization model is to maximize the \(\widetilde{P}_{i,j}\).
# A Fair Cooperative MAC Protocol in IEEE 802.11 WLAN
## 3 The Proposed FC-MAC Protocol
The relay node is removed from _CoopTable_ when \(CL_{i}=0\). However, the removed node can be added to _CoopTable_ after a timeout. 4. The mechanism of sending control packets and data packets between the source, relay(s), and the destination is according to the Finite State Machine (FSM) of Figure 2. There exist extra states (e.g., _WAITHTS_, _WAITDATA1_, and _WAITDATA2_) which modify the Distributed Coordination Function (DCF) procedure. Moreover, two new data packets and three control packets are defined for exchanging between source, relay, and destination. The numbering 1 and 2 in Figure 2 refers to two phases of cooperation: 1 = source to relay and 2 = relay to destination. 5. After sending a modified RTS (called _CoopRTS_) packet via the source node containing the addresses of the destination and the intended relay node, a new control packet called _Cooperative Helper-To-Send (CoopHTS)_ is sent by the relay node if it is ready to cooperate. 6. After the destination receives the modified _CoopRTS_ and _CoopHTS_ packets, it sends a _CoopCTS_ packet to indicate to the source node to start data transmission. 7. By receiving the _CoopCTS_ packet via the source node, the data packet is transmitted from source to relay (_DATA1_) and then forwarded from relay to destination (_DATA2_), respectively. 8. When the data packet of the second part (_DATA2_) is received successfully by the destination, it sends an \(ACK\) packet to the source node. 9. During the cooperation phase, an expired timeout of control packets initiates the non-cooperation phase. In this case, the original RTS and CTS packets are employed to accomplish the transmission. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(R_{Sri}\) (Mbps) & **11** & **11** & **5.5** & **11** & **2** & **5.5** & **5.5** & **2** \\ \hline \(R_{iID}\) (Mbps) & 11 & 5.5 & 11 & 2 & 11 & 5.5 & 2 & 5.5 \\ \(CG_{i}\) & 5.5 & 3.67 & 3.67 & 1.69 & 1.69 & 2.75 & 1.47 & 1.47 \\ \(CL_{i}\) & 4 & 2 & 2 & 1 & 1 & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FC-MAC scenarios in IEEE 802.11b (R\({}_{SD}=1\) Mbps). ```
1:Let \(CG_{i}\) be the cooperation gain of relay node (\(R_{i}\))
2:\(CG_{i-init}\leftarrow\) Minimum of \(CG_{i}\)
3:while\(CG_{i}>1\)
4:\(CL_{i}=CG_{i}/CG_{i-init}\)
5:if Packet transmission fails then
6:\(CL_{i}\gets CL_{i}-1\)
7:if\(CL_{i}>0\) then
9: Send \(CL_{i}\) number of packets by \(R_{i}\)
10:elseif\(CL_{i}<0\) then
11: Remove \(R_{i}\) from CoopTable
12:End while
```  
**Algorithm 1**Algorithm of relay selection in the FC-MAC protocol  
Figure 2: Finite State Machine of the FC-MAC protocol.
## 2 Development History of Traffic Prediction
### Traditional Machine Learning Methods  
Traditional machine learning methods in traffic prediction practice mainly fall into three categories: feature-based models, Gaussian process models, and state space models. Feature-based models, particularly suitable for traffic prediction [14], rely on regression models constructed from statistical and traffic features. The challenge with such methods lies in the manual construction of regression models, with their effectiveness largely dependent on the accuracy of regression analysis. [PERSON] et al. [15] proposed a feature selection-based method to explore the performance of machine learning models, such as Support Vector Regression (SVR) and K-Nearest Neighbor (K-NN), in predicting traffic speed across various feature selections. On the other hand, Gaussian process models involve complex manipulation of the spatiotemporal attributes of traffic data. While accurate, scaling to large datasets is difficult due to its high computational resource requirements [16; 17]. [PERSON] et al. [18] proposed a method for a mixture of variational approximate Gaussian processes, which extends the single Gaussian process regression model. [PERSON] et al. [19] established a fourth-order Gaussian process dynamical model for traffic flow prediction based on K-NN, achieving significant improvements. State space models operate by simulating the uncertainty of the system through Markovian hidden states. While they demonstrate specific capabilities in simulating complex systems and dynamic traffic flows, their application is limited when dealing with more intricate traffic simulations and flow dynamics [20]. [PERSON] et al. [21] utilized a Markov chain with velocity constraints to stochastically generate velocity trajectories for traffic speed prediction. [PERSON] et al. [22] chose the hidden Markov model to represent the dynamic transition process of traffic states and used it to estimate traffic states.
# Error Modeling of Fiber Optic Gyroscope Universal Time Measurement
###### Abstract  
Since the fiber optic gyroscope (FOG) is rigidly strapped down to the earth's crust, there are various errors that affect the universal time (UT1) measurements. In this paper, the errors caused by various physical factors and mechanisms are analyzed in detail, with precession and nutation errors being taken into account, and modeling of the observation equations based on precession and nutation error correction is proposed. The mapping relationship with UT1 is established based on this observation equation; after the corresponding error correction and VLBI calibration, the high-accuracy solution of UT1 is finally completed. Through 14-day measurement experiments under a room temperature environment without any vibration isolation and magnetic shielding devices, the error variation of UT1 solution compared with the earth orientation parameter (EOP) 14 C04 data is calculated at less than 3.57 ms, with UT1 solution accuracy improved by 56% compared with the traditional method. These results indicate that this work facilitates the study of giant FOG error modeling and correction, advancing our understanding of errors in giant FOG measurements and improving the accuracy of UT1 solution.  
fiber optic gyroscope; error modeling; universal time measurement Article