esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations
### Impact of Parameters on Performance  
In SCR, there are three parameters: \(\omega\), \(\mu\) and \(n\). The parameter \(\omega\) is the threshold of social relationship strength for cluster members, and to each node, the size of its local cluster will increase with the decreasing of \(\omega\). The parameter \(\mu\) is the referring encounter times for the measurement of social relationship between nodes. In a given condition, the bigger \(\mu\) has the smaller value of social relationship between two given nodes. For local cluster constructing, \(\omega\) and \(\mu\) have a similar effect. On the one hand, with fixed \(\omega\), we can change \(\mu\) to let a node join the cluster. On the other hand, to a given \(\mu\), we can change the threshold \(\omega\) to have a node join the cluster. It is obvious that changing the value of the social relationship has the same effect as changing the threshold of the cluster for cluster constructing. Consequently, we herein only analyse the impact of \(\omega\) on performance. The parameter \(n\) is the number of self-decreasing times in the time period \(T\), which is for the social relationship to self-decrease to \(\omega\), as is shown in Equation (8). Given that the simulation time is three days, the performance of SCR in \(Infocom5\) dataset is shown in Figure 10, where \(\omega\) is denoted as \"Omega\".  
As is shown in Figure 10, the smaller \(\omega\) has the higher packet delivery ratio, the lower transmission delay, and the higher routing overhead ratio. This is consistent with theoretical analysis: the increasing size of the cluster generates more copies of the message forwarded by cluster members, which improves the success of packet transmission and thus decreases the transmission delay, but more copies of the message raises the routing overhead ratio. Considering the tradeoff of packet delivery ratio, transmission delay and routing overhead, we suggest that the parameter \(\omega\) should be 0.4, which is why we set up \(\omega=0.4\) in the above simulation.  
From simulation results, the \(n\) has almost no impact on packet delivery ratio and transmission delay. In our opinion, the \(n\) has a relationship with the speed of an outdated node dropping from the cluster, but no relationship with the key nodes that can forward messages to destinations successfully. As far as routing overhead is concerned, the smaller \(n\) leads to more useless nodes staying in the cluster, and more copies of the message forwarded to the network. Therefore, routing overhead ratio increases, as is shown in the lower right part of Figure 10.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithms** & **PRoPHTEv2** & **DRAFT** & **BUBBLE** \\ \hline Infocom5 & 30.7 & 9.3 & 19.2 \\ Infocom6 & 18.5 & 15.3 & 3.3 \\ Cambridge & 11.2 & 3.5 & 15.8 \\ Intel & 13.6 & 9.4 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ROR decrease of SCR compared with the other three algorithms (%).
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 2 Materials and Methods
### Proposed Segmentation System
#### 2.2.2 Note Detection Algorithm  
Our algorithm to detect note areas uses the premise that music is a sequence of tones that need minimum stability and duration to be noticed as such. Compared with a state-of-the-art algorithm like Tony [27], our method is much simpler, but, as we do not have any annotated data, we needed to devise a method that required the minimum supervision.  
The first task is to map the pitch curve to cent scale [28; 29] taking as origin the lowest note that we consider may appear in the recordings. We use expression (2) to do the mapping:  
\[f_{0c}=1200 log_{2}(\frac{f_{o}}{f_{ref}})+5800, \tag{2}\]  
where \(f_{ref}\) is 440 Hz, the frequency of \(A_{4}\) note.  
In a cent scale, a smoothing is applied to the pitch curve to neutralise vibrato variation within the notes. The smoothed curve is created averaging interpolation curves of local maxima and minima. Then, the smoothed curve is discretised to the closest ideal semitone as shown in Figure 3. As a result of this process, the real smoothed \(f_{0}\) curve (in blue) is substituted by the discretised curve (in orange).  
Figure 2: Structure of the proposed speech/singing voice segmentation system.  
To detect notes in the discretised curve, we apply an algorithm that uses subsequence search techniques [30; 31]. We search sequences in the discrete curve that fulfil the minimum conditions of length and stability defined in expressions (3) and (4):  
\[Len(s)\geq L, \tag{3}\]  
\[max(s)-min(s)\leq R, \tag{4}\]  
where \(s\) is the semitone subsequence, R is the maximum amplitude range and L is the minimum length. The detailed steps of the proposed algorithm are defined in Algorithm 22.  
In Figure 4, we can see how a note detected by the algorithm would look (green line) and how the rest of the voice sequence is split into two smaller sequences. These two new sequences will be added to the sequence list to be analysed recursively if they meet the duration requirements.  
Figure 4: Detected musical note and new split sequences.  
Figure 3: Discretisation of a \(f_{0}\) curve.
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 3 Collective Perception Mechanism for Autonomous Vehicles
### Network and System Model  
We consider a CAV scenario composed of \(n\) autonomous vehicles (nodes) moving on a multi-lane urban or highway area, where each vehicle has an individual identity (\(i\in[1,n]\)). These vehicles are represented in a dynamic graph \(G(V,E)\), where the vertices \(V=\{v_{1},\cdots,v_{n}\}\) represent a finite set of vehicles, and edges \(E=\{e_{1},\cdots,e_{m}\}\) build a finite set of asymmetric wireless links between neighbor vehicles (\(v_{i}\)). Each vehicle (\(v_{i}\)) moves toward a certain direction (\(\overrightarrow{dir_{i}}\)) following a travel path \(P_{i}\) (a set of roads connected by intersections) with speed \(s_{i}\) ranging between a minimum (\(s_{min}\)), and a maximum (\(s_{max}\)) speed limit. Vehicles periodically broadcast beacon messages on the network via V2X communication, where VILE includes extra information in the beacon, i.e., vehicle position, speed, and direction. In this sense, VILE takes advantage of such beacon transmissions to acquire information and build the dynamic graph \(G(V,E)\), avoiding extra overhead. In this sense, RSUs collects such information and builds the knowledge necessary for its decision making (i.e., selection and perception phases). Furthermore, the RSU divides the scenario in a set of hexagonal regions \(H(L,S)\), where \(L_{h}\) represents the center position of the hexagonal region \(h\) with a \(S_{h}\) size.  
At each time interval \(t\), the CAV \(v_{i}\) detects a set of road objects \(O_{vi,t}\) that are inside the sensor FoV. Specifically, an object \(o_{j}\in O_{vi,t}\) is the state space representation of a physically perceived entity inside the sensor's perceptual range (i.e., its FoV). The sensors detect all objects that are not shadowed by buildings or other vehicles using a simple ray-tracing-like approach. The perception information is represented by each object's position, type, size, and dynamic state (i.e., speed, acceleration, and direction), which can be estimated by analyzing the vehicle's own sensor measurements. Sensor noise or other inaccuracies are not modeled. In this way, it is possible to extract the set of perception information from each object \(o_{j}\in O_{vi,t}\) and build the local perception of the CAV. The local perception will be included into the CPM, using the ETSI proposed format, and shared with the other CAVs. The size of the CPM is dynamically calculated based on the number of objects included in each CPM. Each object included in the message has an average size of about 30 bytes [32]. For the VILE works properly, the following assumptions are made:  
* Every CAV \(v_{i}\) can detect and share a object that is inside the sensor FoV. The sensors detect all objects not covered by buildings or other vehicles;
* Other errors, sensor noise, and processing latency are not modeled;
* Each CAV \(v_{i}\) is aware of its own data by means of orientation and positioning system, such as global positioning systems (GPS) and inertial measurement unit (IMU);
## 2 Results and Discussion
### Optimization of UPLC-HRMS Conditions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & **Retention** & **Molecular** & **Ionization** & **Theoretical** & **Experiment** & **Mass** \\  & **time (min)** & **Formula** & **Mode** & **Value of Exact** & **Value of Exact** & **Deviation** \\  & & & & & **Mass (Da)** & **Mass (ppm)** \\ \hline
**Carbonazin** & 1.97 & C\({}_{9}\)H\({}_{8}\)N\({}_{5}\)O\({}_{2}\) & [M + HI]\({}^{*}\) & 192.0/675 & 192.0/632 & \(-\)2.23 \\
**Pyrimethanail** & 4.67 & C\({}_{12}\)H\({}_{13}\)N\({}_{3}\) & [M + HI]\({}^{*}\) & 200.11822 & 200.11786 & \(-\)1.80 \\
**Thiopanate-methyl** & 4.70 & C\({}_{12}\)H\({}_{14}\)N\({}_{4}\)O\({}_{4}\)S\({}_{2}\) & [M + HI]\({}^{*}\) & 343.05292 & 343.05240 & \(-\)1.52 \\
**Metalaxyl** & 5.13 & C\({}_{15}\)H\({}_{21}\)NO\({}_{4}\) & [M + HI]\({}^{*}\) & 280.15433 & 280.15393 & \(-\)1.43 \\
**Dimethomorph** & 5.50 & C\({}_{21}\)H\({}_{22}\)C\({}_{10}\)A & [M + HI]\({}^{*}\) & 388.13101 & 388.13052 & \(-\)1.26 \\
**Prochloraz** & 5.51 & C\({}_{15}\)H\({}_{16}\)Cl\({}_{8}\)N\({}_{3}\)O\({}_{2}\) & [M + HI]\({}^{*}\) & 376.03809 & 376.03702 & \(-\)2.85 \\
**Triadimeron** & 5.94 & C\({}_{14}\)H\({}_{6}\)Cl\({}_{8}\)N\({}_{2}\)O\({}_{2}\) & [M + HI]\({}^{*}\) & 294.10038 & 294.09973 & \(-\)2.21 \\
**Procymidone** & 6.28 & C\({}_{13}\)H\({}_{11}\)Cl\({}_{8}\)N\({}_{2}\)O\({}_{2}\) & [M + HI]\({}^{*}\) & 284.02396 & 284.02338 & \(-\)2.04 \\
**Difenoconazole** & 6.50 & C\({}_{19}\)H\({}_{17}\)Cl\({}_{2}\)N\({}_{3}\)O\({}_{3}\) & [M + HI]\({}^{*}\) & 406.07197 & 406.07132 & \(-\)1.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The molecular formula, exact mass, mass deviation, and retention time of nine target fungicides. Figure 3: SIM chromatogram of nine target fungicides at 10.0 \(\upmu\)g/L. NL: normalized level, FTMS: Fourier-transformed mass spectrometry, ESI: electrospray ionization, MS: mass spectrometry, DDMS: data-dependent mass spectrometry.
# Mini-Review: Potential of Diatom-Derived Silica for Biomedical Applications
## 7 Future Perspectives
Massive diatom culturing can lead to the combined production of biosilica for biomedical applications, and highly valuable organic compounds such as PUFAs and carotenoids. In summary, we believe that further studies are required to exclude any acute and long-term toxicity of diatom biosilica--as suggested by [PERSON] and [PERSON] [PERSON] and [PERSON] (2016)--and this will pave the way for clinical trials of biosilica transplantation. However, assessing the best processes to minimize costs and wastes for the concomitant production of biosilica and highly valuable products is mandatory to lay the foundations for this new industrial application. A.S. conceptualization and original draft preparation, IO. support in draft preparation, figure preparation, support in literature research, S.B. and L.B. editing and critical review, G.R. editing and supervision. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors did not need any signed informed consent. Not applicable. The authors are grateful to [PERSON] and [PERSON] (SZN, Marine Biotechnology Department) and [PERSON] (SZN, Research Infrastructures for MArine biological Resources, RIMAR) for graphical support. Thanks are due also to [PERSON] for editing and reviewing the English language. The authors declare no conflict of interest. Figure 3: Schematic representation of a hypothetic diatom-based biorefinery for the whole exploitation of microalgal mass. Diatoms are cultured in open ponds or photobioreactors; after harvesting, the remaining water is still rich in nutrients and can be partially reused as medium for new culture inocula or serve as agricultural fertiliser. Four major valuable products, lipids, proteins, carbohydrates, and biosilica can be obtained from the harvested biomass.
## 4 Techniques Used in Academia to Improve Performance Metrics
### Mechanisms to Improve Latency and Real-Time Capability
#### 4.2.1 MAC Protocols to Improve Latency  
Generally, mechanisms used in MAC protocols can be categorized into two classes: (i) contention-free (scheduled communication) and (ii) contention-based. The contention-free protocols are presented first, after which a discussion of contention-based protocols will follow.
# Wheel-Based MDM-PON System Incorporating OCDMA for Secure Network Resiliency
## 1 Introduction
In wheel-based TWDM-PON architecture, an OLT is located in the fiber ring center linked with an '_n_' number of ONUs to generate a closed-loop access ring structure. In contrast to ring topology, OLT directly links the entire connected remote nodes (RNs) or ONUs by assigning two directed optical fiber links. This topology is employed to manage the optical and wireless resources with separate integration of each bandwidth allocation in PON [1].
## 3 Experiments and Evaluation
### Limitation and Discussion  
In this work, dense oblique point clouds are used to acquire a direct observation of the building's outer wall. This ensures the precision of extracted boundaries that are consistent with normative requirements. However, due to the limited viewing angles, occlusion, and noise, the wall points in the raw point clouds may be incomplete. Moreover, the definition of the cadastral survey, in theory, includes not only the extraction of building and courtyard boundaries but also the separation of substructures like independent columns and single-column porches. Half-areas of those substructures will be also calculated in the final statistical results, which are difficult to separate due to the limited size and various types. We only have some trails on the porches that are consistent with the rooftop, and many more types need to be considered. As a result, some further interactions and additions are still required in real applications. In Figure 8, some examples of the limitations are given and we summarize the situations in the following aspects:  
1. The non-closed walls caused by shared or occluded walls. As shown in location 2 of Figure 8, since our method detects the vector boundaries mainly based on the wall points and building roof from UAV-based point clouds, it will fail when buildings are adjacent or even connected, where no wall points are available. In such situations, the boundaries will be merged, which may require further manual additions in real applications to separate them.
2. For the gates and ancillary structures of the compound wall, i.e., in location 3 of Figure 8, since only one wall plane passes through the boundaries and polygons are detracted, our methods simply produce linear detection results, and the small polygons and the wall width are not considered. Further operations are still needed to meet the industry requirements of the cadastral survey.
3. Due to the influence of noise, the occlusion of vegetation, and various other factors, spurious edges may still appear and require further checking. In location 1 of Figure 8, it can be seen that a large amount of debris is usually piled up along the yard walls of rural houses; this may cause occlusion or spurious edges and require further checks in the process of actual production application.
###### Abstract  
Vegetation phenology in temperate grasslands is highly sensitive to climate change. However, it is still unclear how the timing of vegetation phenology events (especially for autumn phenology) is altered in response to climate change across different grassland types. In this study, we investigated variations of the growing season start (SOS) and end (EOS), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000-2016), for meadow steppe, typical steppe, and desert steppe in the Inner Mongolian grassland of Northern China. Using gridded climate data (2000-2015), we further analyzed correlations between SOS/EOS and pre-season average air temperature and total precipitation (defined as 90-day period prior to SOS/EOS, i.e., pre-SOS/EOS) in each grid. The results showed that both SOS and EOS occurred later in desert steppe (day of year (day) 114 and 312) than in meadow steppe (day 109 and 305) and typical steppe (day 111 and 307); namely, desert steppe has a relatively late growing season than meadow steppe and typical steppe. For all three grasslands, SOS was mainly controlled by pre-SOS precipitation with the sensitivity being largest in desert steppe. EOS was closely connected with pre-EOS air temperature in meadow steppe and typical steppe, but more closely related to pre-EOS precipitation in desert steppe. During 2000-2015, SOS in typical steppe and desert steppe has significantly advanced by 2.2 days and 10.6 days due to a significant increase of pre-SOS precipitation. In addition, EOS of desert steppe has also significantly advanced by 6.8 days, likely as a result from the combined effects of elevated preseason temperature and precipitation. Our study highlights the diverse responses in the timing of spring and autumn phenology to preceding temperature and precipitation in different grassland types. Results from this study can help to guide grazing systems and to develop policy frameworks for grasslands protection.  
growing season start; growing season end; meadow steppe; typical steppe; desert steppe; MODIS 201810, 17; doi:10.3390/rs10010017
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 3 Results and Discussion
### Adsorption Studies
#### 3.4.2 Effect of pH  
Figure 7 illustrates the adsorption of Ag\({}^{+}\) ions onto the studied biochars (CB, RHB, and ARHB) at varying pH levels. pH plays a critical role in the adsorption process of metal ions like Ag\({}^{+}\), as it influences both the surface charge of the adsorbent and the availability of the ions for adsorption. From Figure 7, it is evident that the adsorption of Ag\({}^{+}\) increases with pH up to a certain point, with the maximum adsorption occurring in the pH range of 3.5 to 5.5. At lower pH levels, the increased concentration of H\({}^{+}\) ions causes competition between protons and Ag\({}^{+}\) ions for adsorption sites on the surface of the biochars. This leads to a reduced uptake of Ag\({}^{+}\) ions, as the presence of H\({}^{+}\) ions can hinder the adsorption of Ag\({}^{+}\) through competitive ion exchange mechanisms.  
Interestingly, ARHB shows a more pronounced increase in Ag\({}^{+}\) adsorption with pH, continuing to rise at higher pH values (above 5.5), which could be due to the greater deprotonation of surface functional groups, increasing the negative surface charge and enhancing electrostatic attraction to Ag\({}^{+}\) ions. In contrast, RHB and CB show a plateau or  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Sample**} & \multicolumn{3}{c}{**Pseudo-First-**} & \multicolumn{3}{c}{**Pseudo-Second-**} & \multicolumn{3}{c}{**Intraparticle**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Order Constants**} & \multicolumn{3}{c}{**Order Constants**} & \multicolumn{3}{c}{**Diffusion Constants**} \\ \cline{2-10}  & **Q\({}_{e}\)** & **k\({}_{1}\)** & **r\({}^{2}\)** & **Q\({}_{e}\)** & **k\({}_{2}\)** & **r\({}^{2}\)** & **k\({}_{id}\)** & **C** & **r\({}^{2}\)** \\ \cline{2-10}  & \multicolumn{3}{c}{**(mg g\({}^{-1}\)) (min\({}^{-1}\))**} & \multicolumn{3}{c}{**(mg g\({}^{-1}\)) (g mg\({}^{-1}\) min\({}^{-1}\))**} & \multicolumn{3}{c}{**(mg g\({}^{-1}\) min\({}^{-1/2}\)) (mg g\({}^{-1}\))**} \\ \hline CB & 0.63 & 0.008 & 0.7740 & 4.07 & 0.66 & 0.9999 & 3.18 & 0.090 & 0.6637 \\ \hline RHB & 0.29 & 0.005 & 0.8213 & 3.35 & 0.99 & 0.9998 & 2.99 & 0.034 & 0.7852 \\ \hline ARHB & 1.90 & 0.009 & 0.8978 & 5.26 & 0.28 & 0.9996 & 3.26 & 0.192 & 0.8093 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated values of the kinetic parameters.  
Figure 7: Dependence between Ag\({}^{+}\) ions uptake and the pH.  
slight decrease in Ag\({}^{+}\) adsorption beyond pH 4. This suggests that the surface characteristics of these biochars limit their ability to further adsorb Ag\({}^{+}\) ions at higher pH values.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 2 Principle of the Converter
### LC Filter in the Converter
#### 2.4.1 Design of the Output Capacitor  
The relationship between current and voltage of capacitor \(C_{o}\) is described as:  
\[i_{c}=i_{2}-I_{o}=C_{o}\frac{dv_{c}}{dt} \tag{10}\]  
where \(i_{c}\) and \(v_{c}\) are the current and voltage of output capacitor \(C_{o}\), respectively. \(i_{2}\) is the rectifier output current. \(I_{o}\) is the output current of the converter.  
According to Figure 4, Equation (10) can be expressed as:  
\[\frac{V_{\text{C2}}-V_{c}}{L_{lk}N}t-I_{o}=C_{o}\frac{dv_{c}}{dt}\;0\leq t\leq (1-D)T_{s} \tag{11}\]  
where \(V_{C}\) is the average voltage of the output capacitor, \(T_{s}\). is the switching period.  
\[\Delta v_{c}=\int_{0}^{(1-D)T_{s}}\frac{1}{C_{o}}\left(\frac{\frac{V_{I_{o}}}{ 2(1-D)}-V_{c}}{L_{lk}N}-I_{o}\right)dt \tag{12}\]  
where \(\Delta v_{c}\) is the increment voltage of the output capacitor during \((1-D)T_{s}\).  
Equation (12) is sorted out to be:  
\[\Delta v_{c}=\frac{1}{C_{o}}\bigg{\{}(1-D)\bigg{(}\frac{T_{s}^{2}V_{in}}{4L_{ lk}N}-I_{o}T_{s}\bigg{)}-\frac{V_{C}}{2L_{lk}N}[(1-D)T_{s}]^{2}\bigg{\}} \tag{13}\]  
The maximum value of \(\Delta v_{c}\) is obtained from (13):  
\[\Delta v_{cmax}=\frac{\left[\frac{T_{s}^{2}V_{in}}{4L_{lk}N}-I_{o}T_{s}\right] ^{2}}{\frac{2V_{c}T_{s}^{2}}{L_{lk}N}}\frac{1}{C_{o}} \tag{14}\]  
The capacitor voltage ripple coefficient \(\alpha_{v}\) is defined as \(\alpha_{v}=\Delta v_{cmax}/V_{\text{C}}\). The minimum value of output capacitor \(C_{min}\) is obtained as:  
\[C_{min}=\frac{\left[\frac{T_{s}^{2}V_{in}}{4L_{lk}N}-I_{o}T_{s}\right]^{2}}{ \frac{2V_{c}T_{s}^{2}}{L_{lk}N}\alpha_{v}V_{C}} \tag{15}\]  
As \(\alpha_{v}\) is increased, the output capacitance is reduced. That is benefit for LVRT. However, large voltage ripple on the capacitor will lead to high-voltage stress on the semiconductors. As the switching frequency increased, such as applying a SiC-based semiconductor, the capacitance is dramatically reduced.  
Figure 6: Converter topology with LC filter.
## 2 Hybrid Wind-PV System Modeling
### Wind Turbine System Modeling
#### 2.1.1 Turbine Model  
The turbine's aerodynamic power is expressed by Equation (1).  
\[\mathrm{P_{T}}=\frac{1}{2}.\mathrm{\rho.A.v^{3}.C_{p}(\lambda,\beta), \tag{1}\]  
Figure 1: Schematic of the control strategy for Wind-PV system using MISO converter.  
Figure 2: The wind system.  
where \(\rm P_{T}\) is the turbine aerodynamic power (W), \(\rm\rho\) is the air density (kg/m\({}^{3}\)), A is the area swept by the rotor blades (m\({}^{2}\)), \(\rm v\) is the wind speed (m/s), \(\rm C_{P}\) is power coefficient, \(\rm\beta\) is the pitch angle (rd) and \(\rm\lambda\) is the Tip-Speed Ratio (TSR).  
The TSR is the ratio of the linear speed of the blades to wind speed, and it is represented by Equation (2).  
\[\rm\lambda=\frac{\rm\Omega_{g}R}{v}, \tag{2}\]  
The gear box is used between the turbine and generator in order to amplify the rotation speed. Its equation is given by Equation (3).  
\[\rm G=\frac{\rm\Omega_{g}}{\rm\Omega_{T}}=\frac{T_{T}}{T_{m}}, \tag{3}\]  
In the literature, several equations are available to evaluate the \(\rm C_{P}\). In our case, we assume that the angle \(\rm\beta\) = 0 and the \(\rm C_{P}\) is given by the following equation:  
\[\rm C_{P}=-0.7317\lambda^{3}+0.2953\lambda^{2}+0.8760\lambda, \tag{4}\]  
The mechanical equation is expressed as  
\[\rm\frac{d\Omega}{dt}=-\frac{1}{J}(T_{m}-|T_{em}|-f.\Omega), \tag{5}\]  
where \(\rm T_{em}\) is the torque of the generator.  
Figure 3 presents the interconnection between the scheme of Equations (1)-(5).