esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Evaluation
### Performance Evaluation
#### 5.2.2 Processing Time by Data Verification  
Figure 5 shows the average delays to evaluate how a data token query function of the SC can be successfully handled by the client as \(N_{m}\) increases from 5 to 200. Regarding a larger \(N_{m}\), the query token procedure of the SC needs more computational resources to process the data on the distributed ledger. Thus, the delays of querying a data token on both platforms scale linearly with \(N_{m}\) with the same gain. Due to different computational resources, the processing time of the data token query on the Rpis is almost double that on the desktops.  
Figure 6 shows the computational overheads by validating token the data on the client side as \(N_{m}\) changes. The data token data validation requires reconstructing the binary Merkle tree of the ordered list of Swarm hashes, which results in a traversal complexity of \(\mathcal{O}(N_{m})\). Then, the root hash can be used as the fingerprint for all the meta-data to check for inconsistencies, which requires a computational complexity of \(\mathcal{O}(1)\). Finally, the computational overheads incurred by verifying the token data are scale linearly with \(N_{m}\). Computing the root hash of the binary Merkle tree demands intensive hash operations such that the computational power of the client machines dominates the performance of the data token validation. Therefore, a larger \(N_{m}\) in the data token validation brings more delays on the Rpis than the desktops. However, the impact was almost marginal in our test scenarios such that \(N_{m}\leq 200\).  
Figure 7 shows the processing time of verifying the meta-data on the client side as \(N_{m}\) increases. In the meta-data verification stage, a client uses the Swarm hash list in the data token to sequentially retrieve \(N_{m}\) meta-data from the DDS, which results in a communication complexity of \(\mathcal{O}(N_{m})\). Regarding the fixed bandwidth of the test network, increasing \(N_{m}\) allows for a larger round-trip time (RTT) and more computational resources in meta-data transmission. As a result, the delays of verifying a batch of meta-data are scale linearly with \(N_{m}\). Unlike the desktops, the Rpis have limited computational resource to handle each data transmission. Therefore, the Rpis take a longer time to verify the same amount of meta-data than the desktops do.  
Figure 4: Gas usage in data authorization.  
Figure 5: Latency by data token query on different platforms.  
Figure 6: Processing time by data token validation on different platforms.  
Figure 7: Processing time by meta-data verification on different platforms.
## 4 Simulation Results
In these cases, regardless of the grid voltage drops, the grid-connected ESS controls the reactive current as rating current. Figure 13: Simulation results of the PCC voltage depending on the phase of the reactive current when the ESS operates under the charging condition. (**a**) PCC voltage; (**b**) Synchronous reference frame \(d\)-axis current and reference current; (**c**) Synchronous reference frame \(q\)-axis current and reference current; (**d**) Three-phase grid currents. Figure 14: Simulation results of the active and reactive currents of the grid-connected ESS depending on the state of charge (SOC) under discharging condition. (**a**) SOC; (**b**) Voltage-level of the three-phase grid; (**c**) Synchronous reference frame \(d\)-axis current and reference current; (**d**) Synchronous reference frame \(q\)-axis current and reference current; (**e**) Three-phase grid currents. Figure 15: Simulation results of the active and reactive currents of the grid-connected ESS depending on the SOC under charging condition. (**a**) SOC; (**b**) Voltage-level of the three-phase grid; (**c**) Synchronous reference frame d-axis current and reference current; (**d**) Synchronous reference frame q-axis current and reference current; (**e**) Three-phase grid currents.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 4 FESS Applications
### Military  
In the military, a recent trend has been towards the inclusion of electricity in military applications, such as in ships and other ground vehicles, as well as for weapons, navigation, communications, and their associated intelligent systems. This use of electric energy at different rates and different power levels requires energy storage to respond rapidly and reliably to this variable energy demand [8]. Hybrid electric power is essential for future combat vehicles, based on their planned electricallypowered applications. Flywheels appear as an appropriate energy storage technology for these applications. They are combined with supercapacitors to provide power for high speed systems requiring power in less than 10 \(\upmu\)s.  
Flywheels are also likely to find applications in the launching of aircraft from carriers. Currently, these systems are driven by steam accumulators to store the energy; however, flywheels could replace these accumulators to reduce the size of the power generating systems that would otherwise be sized for the peak power load [8]. A FESS is integrated into a microgrid serving the US Marine Corp in California, to provide energy storage applications throughout the entire distributed generation at the base [65]. The purpose of the project is to provide energy security to military facilities using renewable energy. It is a network of interconnected smaller microgrids that are nested into a 1.1 MW bigger-scale microgrid, that include solar PV systems, diesel generators, batteries, and 60 kW, 120 kWh FESS [87]. The flywheel storage is intended to decrease the dependency on diesel generators by about 40% and provide peak shaving applications by mainly supplying high power loads such as elevators. In addition to extending the lifespan of the batteries, the FESS is estimated to work for 50,000 cycles and have a lifespan of 25 years [87].
## 4 Mathematical Model
### Cummins Time Domain Equation  
The estimation of external forces like wind or wave forces play a major part in determining designs of ships or marine structures. With the force from the mooring connection, the dynamic responses of the floating body are usually calculated by solving motion equations based on the Cummins time domain equation [28], which can be written as below:  
\[(M+A_{\infty})\ddot{\zeta}=-\int_{0}^{t}I(t-\tau)\dot{\zeta}(\tau)d\tau+F_{ext }+F_{R}+F_{B}+F_{m}, \tag{10}\]  
where \(A_{\infty}\) is the added mass matrix at the infinite frequency; \(\ddot{\zeta}\) and \(\dot{\zeta}\) represent the acceleration vector and velocity vector of the floating body in translational or rotational motions, respectively. \(M\) is indicated as the mass matrix; \(I\) the impulse response function; \(F_{ext}\) the wave excitation force; \(F_{R}\) resultant force from wave radiation in the sinusoidal form, consisting of added-mass force and radiation damping components; \(F_{B}\) the linear hydrostatic properties of restoring buoyancy force; and \(F_{m}\) the mooring load.  
By using linear quasi-static mooring stiffness, the mooring force \(F_{m}\) can be represented by  
\[F_{m}=-K_{m}\zeta-C_{m}\dot{\zeta}, \tag{11}\]  
where \(\zeta\) is defined as the displacement vector of the floating body in the translational or rotational modes. \(K_{m}\) and \(C_{m}\) indicate the stiffness and damping matrices of the mooring system.  
In order to solve 6-DOF motion responses in the time domain, the Runge-Kutta method is used for numerical simulation. The equation can be written as:  
\[u_{i+1}=u_{i}+\sum_{j=1}^{n}w_{j}hk_{j},\ i=0,\ 1,\ 2,\ldots,\ N-1, \tag{12}\]  
where \(u\) is an unknown function of time \(t\); \(w_{j}\) is denoted as the weighting factor; h the interval size of time \(t\); \(k_{j}\) the increment based on the slope; \(N\) the total data length.  
Let the step size \(h>0\) and define \(n=4\), \(u_{i+1}\) is the approximation of fourth-order Runge-Kutta method and expressed as below:  
\[u_{i+1}=u_{i}+\frac{1}{6}[k_{1}+2k_{2}+2k_{3}+k_{4}],\ i=0,\ 1,\ 2,\ldots,\ N-1, \tag{13}\]  
where  
\[t_{i+1}-t_{i}=h \tag{14}\]  
\[k_{1}=\mathit{h}\mathit{f}(t_{i},u_{i}) \tag{15}\]  
\[k_{2}=\mathit{h}\mathit{f}(u_{i}+\frac{h}{2},u_{i}+\frac{1}{2}k_{1}) \tag{16}\]  
\[k_{3}=\mathit{h}\mathit{f}(t_{i}+\frac{h}{2},u_{i}+\frac{1}{2}k_{2}) \tag{17}\]  
\[k_{4}=\mathit{h}\mathit{f}(t_{i}+h,u_{i}+k_{3}). \tag{18}\]
## Separation Process and Microstructure-Chemical Composition Relationship of Cenospheres from
###### Abstract  
The cenosphere is one becoming a focus of the power plant in terms of value addition and ash management. This study presents a systematic investigation and characterization of physical properties, morphological structures, and chemical composition of cenospheres separated from fly ash produced from the Mae Moh coal-fired power plant, Thailand. To our knowledge, this is the first report on cenospheres separation from Mae Moh class C fly ash, with high calcium content \(\sim\)24 wt.%, by adopting the traditional wet separation method (using water as the medium) to separate the lightweight cenospheres. Various effects of process parameters (fly ash-to-water ratio, stirring method, ultrasonication, and size classification) were designed to examine the cenosphere recovery yield in comparison. The result has revealed the limit of physical stirring-settling effect associated with the cenospheres content by nature governing the percent recovery. The bulk cenospheres were subject to size sieving into different sized fractions, with the structure-chemical composition relationship established for more insight. The particle diameter/shell thickness ratio revealed its significant correlation with the aluminosilicate glass composition, with the relating cenosphere shell structures (single-ring and porous) mapped to compare for a better elucidation of their structure-property relationship. The phase composition was also studied.  
cenospheres; fly ash; wet separation; chemical composition; aluminosilicate; SiO\({}_{2}\); Al\({}_{2}\)O\({}_{3}\); power plant
## 4 Experimental Application of the Model
### Reusability, Expandability, Flexibility, and Complexity of the General Model  
Whatever technique we choose for modeling complex self-organizing systems, we find ourselves up against a number of methodological problems that not yet been fully resolved [8]. There are four main features of general SP and meta-pattern model that can help to solve CSS problems (described in the Section 3): (1) reusability: SP and meta-patterns can be reused for creating new SP, also new spatial meta-patterns can arise based on the existing meta-patterns; (2) expandability: after the practical SP and meta-patterns experiment, knowing the ranges of measure values, we can describe the whole new set of SP and meta-patterns and cover many different geographical models, urban and spatial phenomena and problems using the general SP model and the same principles (UML class model and algorithms); (3) flexibility: existing SP, meta-patterns and algorithms can be improved by using another measures or another SP values, together with the different spatial forms and structures, to refine spatial problems better if the new insights arise while working with SPDIAM; and (4) complexity: SPDIAM algorithm lets us use the different spatial and topological structures (such as Grid, Segment, Convex, and Visuality Graph) for the same SP, using the strengths of each structure. The other spatial analysis methods described in Reference [2], when using them as stand-alone methods, lack these features and need a lot of work and additional IT artefacts to have them.  
Cellular Automata, Agent-Based Modeling, Fractal Analysis, and Space Syntax all can be used for creating the simulation models, but the difference is that Cellular Automata, Agent-Based Modeling, and Fractal Analysis show how the process is spreading over time, whereas Space Syntax shows the whole process compressed into one moment of time, as well as the potential of that process; this is the most valuable property of this method. Another important feature is that SPDIAM lets us use different models of spatial entity to follow CSS development over time and to predict changes that can happen in the future (based on Reference [41]). SPDIAM allows the users to create their own SP, their measures, and interpretations of measure values. In addition, SPDIAM can be used to describe other environmental phenomena with a structure, e.g., to identify natural phenomena (natural analysis), to evaluate building interior, etc.
## 4 Methodology
### Feature Selection  
Feature selection is important to obtain a model that can capture precisely the dynamics of an HVAC system. Data-driven models rely heavily on data to identify patterns and make predictions; however, generally, it is almost impossible to interpret their decision-making process. Modeling an HVAC system is fundamentally a thermodynamics problem, necessitating that the model resemble the behavior of heat transfer equations. To guide the model in understanding the processes occurring within the system, the thermal interactions are first described to facilitate proper feature selection. The focus is on detailing which thermal interactions occur and then choosing features that represent or significantly impact the system based on their similarity to variables in thermodynamic equations.  
According to thermodynamics, heat transfer can take place in the form of conduction, convection, or radiation. Radiation involves the emission of electromagnetic waves by all objects at temperatures above absolute zero. However, radiation loss becomes significant only at high temperatures. Considering a typical HVAC temperature range of 8 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, this interaction can be neglected.  
Convection, the transfer of energy through fluid movement, significantly impacts room temperature, as hot air circulated by a fan coil heats the entire space. The objective is not to predict the temperature at every point in the room, but rather to establish a single representative temperature for each HVAC element. Consequently, detailed spatial temperature predictions are not considered in this process.  
Conduction is the process by which heat is transferred from a region of higher temperature to a region of lower temperature within an object. HVAC systems typically focus on interactions involving fluids, such as air and water, rather than solid objects. To simplify the analysis, the system can be modeled by considering the HVAC unit and the acclimatized room as contiguous blocks, with the exterior environment acting as a third block. In this context, thermal interactions are governed exclusively by conduction. Analyzing [PERSON]'s law of cooling (Equation (2)) provides valuable insights into the importance of the variables involved:  
\[q=h\Delta T \tag{2}\]  
Here, \(q\) represents the heat flux transferred into or out of the body, \(h\) denotes the heat transfer coefficient, and \(\Delta T\) signifies the temperature difference between the two objects. This equation highlights the importance of the heat sources and the temperatures of the interacting objects.  
Based on this information, a study must be conducted for each case to identify the heat sources that significantly impact the temperature system (e.g., the number of people, operating electrical devices, server racks, etc).
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Model
#### 3.4.2 Deep Learning Model Selection  
The most commonly used DL algorithms for traffic classification in SDN are CNN, SAE, MLP, and LSTM [12; 38]. The dataset used in this study exhibits temporal evolution in communication patterns over time. RNN algorithms perform better when dealing with time series problems and sequences, such as anomaly detection and network traffic classification. However, an RNN has different characteristics than other networks like CNN; its focus is on time series data. In other words, an RNN has directional connections that allow it to calculate the next step based on the previous steps [39]. Nevertheless, a simple RNN needs help to learn long-term relationships in data sequences. Therefore, for the development of the traffic classifier, four RNN-derived algorithms were employed: LSTM, BiLSTM, GRU, and BiGRU. These algorithms are designed to learn meaningful connections in longer sequences.  
* GRU: These are variants of LSTM networks that incorporate a forgetting gate. Unlike LSTMs, GRUs have fewer parameters due to the absence of an output gate. Despite this difference, GRUs offer similar accuracy to LSTMs [40]. In addition, it has been observed that GRUs can offer computationally more efficient performance in certain scenarios. Figure 3 shows the architecture of the GRU model.
* BiGRU is a model that adds a future layer in the opposite direction of the data sequence. This network uses two hidden layers to extract past and future information connected to a single output layer. This bidirectional structure aids an RNN in removing  
* LSTM: It is a variant of RNNs that can perform computations based on time sequences, allowing the processing of recent data and data that occurred at distant time steps in the series. Unlike a simple RNN algorithm, LSTMs can assess the significance of data, even when there are many time steps between them, making them ideal for learning long-term relationships [42]. Figure 5 shows the architecture of the LSTM model.
* BiLSTM: This model employs two LSTMs, one of which processes the sequence forward and the other in reverse. Through this bidirectional process, BiLSTM increases the amount of information available to the network, which can be valuable in traffic classification problems where comprehending relationships between various traffic features is essential [43]. Figure 6 shows the architecture of the BiLSTM model.
## 2 System Description and Its Mathematical Modeling
### Small-Signal Model of the Inverter under the Differential Peak Current Mode Control
Therefore, the small-signal model of the system under CMC with current reference can be written as follows  
\[\frac{d\hat{\mathbf{x}}}{dt}=\mathbf{A}\hat{\mathbf{x}}+\mathbf{B}\hat{i}_{ \text{ref}}^{\intercal} \tag{21}\]  
where \(\mathbf{A}\) and \(\mathbf{B}\) can be computed from the following expression  
\[\mathbf{A}=\left.\frac{\partial f}{\partial\mathbf{\overline{x}}}\right|_{d=D} +\left.\frac{\partial f}{\partial d}\right|_{\mathbf{x}=\mathbf{X}}\times \frac{\partial d}{\partial\mathbf{\overline{x}}}\right|_{\mathbf{x}=\mathbf{X }},\quad\mathbf{B}=\left.\frac{\partial f}{\partial d}\right|_{d=D}\times \left.\frac{\partial d}{\partial\mathbf{\  
Figure 7a shows the simulated inverter frequency response obtained from the derived model. The figure shows that the magnitude and phase profiles remain the same while the duty cycle varies although they slightly change their curvatures. Therefore the design for a specific value of the duty cycle could be valid for all the range of duty cycle values. Figure 7b shows the simulated inverter frequency response for the extreme operating values of duty cycle \(D=0.28\) and \(D=0.72\) obtained using a straightforward cycle-by-cycle simulation of the switching circuit implemented in PSIM\({}^{\copyright\textregistered}\)[33] version 12.0.3.0.264. This was carried out by modulating the reference current by a sine wave signal with frequency going from 100 Hz to 25 kHz (half switching frequency) and obtaining the resulting small signal at the output grid current while fixing the system parameters as depicted in Table 1 and the value of reference current to \(i_{\text{ref}}\) according to (20). The running time was few orders of magnitudes longer than the simulation obtained by the derived small signal model. The comparison between the results reveals from one hand that the derived linear model is valid for the entire considered range of the duty cycle because the frequency response profile do not significantly change within the entire considered range of the duty cycle. The comparison with the results obtained from PSIM\({}^{\copyright\textregistered}\) software demonstrates the validity and accuracy of the proposed average model of the differential boost inverter with peak differential CMC when used in a grid-tied PV system. Figure 6: Pole-zero plot of the differential boost inverter showing likely pole-zero pair cancellation when duty cycle \(D\) varies in the range (0.28, 0.72) for \(S=1000\) W\(/\)m\({}^{2}\) and \(\Theta=25\)\({}^{\circ}\)C. The zero at high frequency is invariant under the duty cycle variation. (**a**) Overall view when \(D\) is varied, (**b**) Close view in the vicinity of the imaginary axis. Figure 7: Frequency response of the differential boost inverter when duty cycle varies in the range (0.28, 0.72) for \(S=\)1000 W\(/\)m\({}^{2}\) and \(\Theta=25\)\({}^{\circ}\)C.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions
### UAV-Assisted Networks
\begin{table}
\begin{tabular}{c c c} \hline
**Phase** & **Approaches** & **Strategies/Models** & **Advantages/Findings** \\ \hline  & & Optimization of the UAV-mounted base stations (MISs) placement, setting forth a & - The correct deployment of MIISs can cover a set of \(k\) nodes with a minimum number of \(k\) nodes of a given circular surface with radius \(r\). \\  & Geometric Disk Cover (GIC) algorithm, which costs with all ground terminals (GIS) in an inward spiral manner [(5)]. & - The computational complexity may be significantly reduced when coverage starts from the perimeter of the area boundary. \\  & &, By introducing a Correlation Factor (CP), which relies on the UAV altitude, the large-scale fading and the PI, for the sake channel will be accurately characterized. \\  & &, in urban contexts, it increases with motivation distance. In the case of rural zones, PI is irrelevant to UAV heights, albeit it approximates for free-space topologies models at height around \(0.0\). \\  & & UAV-based networks face a large amount of neighboring interference due to the \(k\)-th client antenna pattern of cellular networks. Moreover, the coverage behavior will be affected beneath this scheme. \\  & &, By correctly selecting the UAV controller and then performing network hargaining, the aerial base station could top of a more reachable improvement on the throughput, SINR per UE capacity in the order of \(6.3\)-v and minimal delays and error rates. \\  & Improvement of coverage and capacity for future SC configurations of aerial networks beneath two algorithmic approaches, entropy-based network formation [(10)] and latency-minimal 3D cell association scheme [(91)]. \\  & &, Using 30 configuration for aerial cellular networks, a yield of reducing up to \(46\)% in the average total latency without dynamic spectral efficiency. \\  & &, The optimization problem converges in a non-convex approach to find high-quality cooperative trajectory solutions. \\  & &, Geometry delay-sensitive rates and SNR requirements restrict the target communication performance. \\  & &, UAV’s mission completion time may guarantee an efficient method for checking the strategy's feasibility. \\  & &, in the case of ultra-dense networks, the approach efficiently broadcasts the common issues such as sparse and low-quality coverage and then-steady aerial links. \\  & &, The rapidly unfolding of UAV carries out in the non-dependency of geographical constraints and implies system performance lifting by establishing LOS communication links in most scenarios. \\  & &, Among other advantages-at the top of cooperative distributed UAV networks— are the distributed gateway-selection algorithms used and stability-control regimes. \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._