esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
## 5 Case Studies
### Single-Machine, Single-Load System
Furthermore, once the analytical boundary is given, it can provide quantitative analysis results for problems of uncertainty. Moreover, to investigate the adaptive mechanism of the algorithm in the face of harsh working condition, random disturbance is artificially applied to the system voltage to simulate the unpredictable operation conditions of a power system, and the SVSRB constructed by a different method under disturbed conditions is shown in Figure 13.  
Both types of algorithms can construct the SVSRB under disturbed working conditions, as shown in Figure 13. In terms of conservative algorithms, taking the analytical boundary derived from reference [25] as the standard, the original method is more conservative compared with the proposed method, while the proposed algorithm has a good robust behavior in dealing with such working conditions. Generally speaking, the distance between the predicted value and the true value will greatly affect the number of iterations for solving the nonlinear equation in the correction stage. The number of iterations of the conventional tracking method proposed in reference [25] and the method presented in this paper in the correction stage of searching each boundary point are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperplane** & **Analytical Expressions** & \(\mathbf{Q}_{2}\) \\ \hline \(H_{1}\) & \(1.600\mathrm{Q}_{2}+P_{2}+0.088=0\) & \(0.180<\mathrm{Q}_{2}<0.205\) \\ \(H_{2}\) & \(2.001\mathrm{Q}_{2}+P_{2}+0.260=0\) &  
It can be seen from Figure 14 that, in most cases, the number of iterations of the proposed method is smaller than that of the conventional tracking method, indicating that the proposed algorithm has high robustness compared with the conventional method. Especially at \(S_{0}\), \(S_{1}\), \(S_{3}\) and \(S_{6}\), due to the curve extrapolation method, the distance between the predicted point and the exacted point is relatively close, which makes the number of iterations in the correction process less.
# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 4 Implementation and Results
### LSTM Model for SOC Inference  
RMSE (root mean square error) and MAE were used as indicators to check the performance of the LSTM model used in this paper. Here, RMSE was used to determine how accurate the model's prediction was. The lower the value, the higher the model's prediction performance. The calculation formula is as shown in Equation (9) below.  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(SOC_{i}-SOC_{i}^{*})}^{2}} \tag{9}\]  
Here, \(n\) is the total number of data points, and \(SOC_{i}\) is the SOC value of the \(i\)th actual observed data point. \(SOC_{i}^{*}\) is the SOC value of the \(i\)th data point predicted by the model. For each observation, calculate the difference between the actual value \(SOC_{i}\) and  
Figure 7: Graph of training and validation loss.  
\begin{table}
\begin{tabular}{c c c} \hline
**Epoch** & **Actual SOC (\%)** & **Validation Loss (\%)** \\ \hline Epoch 10 & 2.5654 & 1.5072 \\ Epoch 20 & 1.6975 & 0.7171 \\ Epoch 30 & 1.4155 & 0.4085 \\ Epoch 40 & 1.3254 & 0.3496 \\ Epoch 50 & 1.1692 & 0.3253 \\ Epoch 60 & 1.1306 & 0.3192 \\ \hline \end{tabular}
\end{table}
Table 3: Actual SOC values versus verification losses over epochs.  
the predicted value \(SOC_{i}^{*}\). This difference is squared to avoid negative values. Because the square emphasizes the size of the error, larger errors are given greater weight, and all calculated squared errors are added together. Calculate the mean square error by dividing that sum by the number of data points \(n\). Finally, we take the square root of this average value to obtain the RMSE value. The RMSE can be directly compared to actual values with the same units, helping to intuitively understand the size of the model's prediction error. MAE is a measure of mean absolute error, which is the average of the absolute differences between predicted and actual values. MAE is an indicator of how incorrectly the size of the error was predicted on average, and its calculation formula is as shown in Equation (10) below.  
\[MAE=\frac{1}{n}\sum_{n-1}^{n}|SOC_{i}-SOC_{i}^{*}| \tag{10}\]  
where \(n\) is the total number of data points. \(SOC_{i}\) is the SOC value of the \(i\)th actual observed data point. \(SOC_{i}^{*}\) is the SOC value of the \(i\)th data point predicted by the model. For each observation, calculate the difference between the actual value \(SOC_{i}\) and the predicted value \(SOC_{i}^{*}\). Here, we take the absolute value of each difference and only consider the magnitude, regardless of the direction (positive or negative) of the error. After adding up all of these absolute errors, divide the sum by the number of data points \(n\) to obtain the average. MAE represents the average size of the error, and like RMSE, the lower the value, the better the model's prediction performance.
## 4 Discussion
### Effects of Different Treatments on Soil Organic Carbon Components
Particulate organic carbon (POC) refers to organic carbon that is associated with soil sand components. It primarily originates from the residues of plants and animals, as well as from humidified organic matter [70]. The addition of exogenous organic matter, such as straw, promotes the formation of POC. Soil particulate organic carbon content was observed to be the lowest at 90 days, suggesting an acceleration in the mineralization process due to changes in soil hydrothermal conditions [71]. This study found that the dynamic changes in soil dissolved organic carbon (DOC) and microbial biomass carbon (MBC) contents were similar, with the highest levels recorded at 90 days. Redundancy analysis indicated a correlation between soil DOC and MBC. After 90 days, favorable soil hydrothermal conditions and strong photosynthesis led to the transfer of carbon from crops to roots, which then entered the soil through root secretions and exfoliations. Some of this carbon served as a source of soil soluble organic carbon, while microbial activity converted some soil rhizosphere active carbon into microbial biomass carbon. Additionally, the conducive hydrothermal conditions promoted an increase in soil microbial activity and the decomposition of organic matter by microorganisms, thereby impacting the levels of soil DOC and MBC [72]. The results of this study demonstrated that incorporating manure into straw strips significantly enhanced the presence of active organic carbon in the soil. Over time, the levels of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and readily oxidizable carbon (ROC) initially increased, followed by a decrease before stabilizing. This pattern suggests that as the easily decomposable carbon from the straw was fully broken down, the active substances in the soil decreased and eventually reached a stable state during the later stages of the study.
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 3 BOC Acquisition Based on Band-Pass Sampling
### Band-Pass Sampling Technique  
As mentioned before, the secondary peaks and the high sampling frequency of high-order BOC signals result in high complexity. Band-pass sampling technique can be used to remove the secondary peaks and reduce computational complexity.  
For a single-band signal, the range of \(f_{\rm s}\) is given by [12]:  
\[\frac{2f_{U}}{n}\leq f_{\rm s}\leq\frac{2f_{L}}{n-1} \tag{6}\]  
where \(f_{L}\) and \(f_{U}\) are the lower and upper frequencies, respectively; \(n\) is an positive integer.  
Figure 1: The normalized squared ACF and power spectrum density. (**a**) Squared ACFs; (**b**) Power spectrum density of BOC(15,2.5).  
The frequency of the sampled signal is [17]:  
\[f^{\prime}_{IF}=\left\{\begin{array}{ll}\text{rem}(f_{IF},f_{s}),&\text{fix} \Big{(}\frac{f_{IF}}{f_{s}/2}\Big{)}\text{ \ is even}\\ f_{s}-\text{rem}(f_{IF},f_{s}),&\text{fix}\Big{(}\frac{f_{IF}}{f_{s}/2}\Big{)} \text{ \ is odd}\end{array}\right. \tag{7}\]  
where \(f_{IF}\) is the intermediate frequency of the continuous signal; fix(\(a\)) is the truncated portion of argument \(a\) and rem(\(a\),\(b\)) is the remainder after division of \(a\) by \(b\).  
For a dual-sideband signal, the range of \(f_{s}\) is:  
\[f_{s}\in\left[\frac{2f_{t1}}{n_{1}},\frac{2f_{t1}}{n_{1}-1}\right]\cap\left[ \frac{2f_{t2}}{n_{2}},\frac{2f_{t2}}{n_{2}-1}\right] \tag{8}\]  
The signal bands must not overlap in the frequency spectrum of the resultant sampled bandwidth. This can be expressed mathematically for two signals as [17]:  
\[\left|f^{\prime}_{IF1}-f^{\prime}_{IF2}\right|>\frac{B_{1}+B_{2}}{2} \tag{9}\]  
where \(f^{\prime}_{IF1}\) and \(f^{\prime}_{IF2}\) are the center frequencies of the sampled signals; \(B_{1}\) and \(B_{2}\) are the bandwidth of the two signals, respectively.
## 5 Discussion
### Burn-Type Differentiation  
When evaluating the efficiency of our algorithm for detecting agricultural burns, the disparity between permit-provided and NLCD-based burn types can be explained by the uncertainties in NLCD data and the location and burn area of permit data. Although NLCD updates the land cover data every three years, there is still a two-year gap between each update. The changes in Florida's cropland percentage and spatial extent are relatively large, as indicated by [PERSON] et al. [56]. Meanwhile, NLCD, which is derived from multiple satellite imageries, has uncertainties in land type classifications. [PERSON] et al. [57] evaluated the uncertainty of NLCD 2016 and reported an 86.4% and 90.6% overall accuracy for level II and level I data, respectively. For reported permit data, the land cover type may not be accurate if the recorded location is not accurate. Additionally, the square burned area assumed in the algorithm could include other land types than the actual burned area, which would change the dominant land cover type for the fire.  
In the wildfire detection algorithm, the disparity between the WFIGS and our algorithm can be explained for several reasons. First, WFIGS reports 9460 wildfires (without filtering out small wildfires) in the southeastern United States, which is much lower than  
Figure 10: A comparison between permit and adjusted FINN-prescribed burning emissions in matched grid cells under a 4 km grid definition. The black line is a 1:1 line and the red line is the regression line. Uncertainty of the linear regression parameters was reported with a 95% confidence interval.  
the number of fires (130, 780) reported in the national statistics of wildfires provided by the National Interagency Fire Center [58], indicating that WFIGS is not a complete dataset for wildfires. Meanwhile, prescribed burns in neighboring lands conducted on consecutive days can be misclassified as wildfires by the wildfire detection algorithm. Additionally, some wildfires are missed in FINN due to cloud cover or other obstacles, such as the tree canopy [59]. FINN without burn-type differentiation only matches 53.21% of wildfires in WFIGS. Additionally, the fires that cannot be retrieved by FINN may affect the estimation of fire duration, leading to misclassification. For example, a three-day wildfire detected on the first and last days will be falsely classified as two separate prescribed burns.
## 3 Distributed PV Storage Virtual Synchronous Generator System
### Structure of System  
The object of this paper is the PV-storage system. There are two common topologies including the common DC (direct current) bus type and the common AC bus type. In the PV storage system with common AC bus, the distributed photovoltaic power supply and the energy storage unit are connected to the alternating current bus through the bidirectional converter respectively. The alternating current bus is connected to the power grid through the point of common coupling (PCC). In this scheme, the capacity of each power conversion device is relatively small, which is beneficial for improving the reliability while the control is more difficult. In a common DC bus-type PV storage system, renewable distributed and energy storage systems are connected in parallel to the DC bus via power electronics and then connected to the grid through a bidirectional converter. The PV storage system set up by this scheme facilitates system expansion and reduces the number of converters used, which saves costs. In particular, once a fault occurs in the system, the distributed units and energy storage units can be individually removed without affecting the overall operation of the power system. This paper adopted the common DC bus topology of PV storage system.  
In this paper, the PV storage system was constructed as shown in Figure 9. The system consists of a PV array, an energy storage system, a boost chopper, a bidirectional chopper, a converter, and its filter. Among them, the boost chopper is used for achieving voltage boost of the PV array while achieving maximum power point tracking (MPPT) in order to ensure the maximum use of photovoltaic energy. Since photovoltaic power generation is affected by external factors such as light intensity and temperature randomly, when the power generated by the converter is unbalanced with the required power, the energy storage system ought to achieve a power balance through the two-way DC-DC circuit so as to ensure the stability and reliability of PV storage system's output power. In addition, the DC-AC converter can achieve energy bidirectional flow when heavy load as a inverter and a light load as a rectifier. The bidirectional DC-DC chopper and the DC-AC converter are key devices for the entire PV storage system and were modeled separately below.  
Figure 9: Structure of PV storage system.
# High-Speed Photodetectors for Microwave Photonics
## 4 High-Power Photodiodes on Si
### Ge-on-Si Photodiode Arrays
As shown in Figure 14b, the RF power increases with reverse voltage and begins to saturate above  
Figure 13: (**a**) Optical image and schematic of the PD array, and (**b**) I-V characteristics. \(-5\) V bias. The RF power and the saturation current at 1-dB compression are 7 dBm and 19 mA, respectively, under \(-7\) V at 15 GHz. In order to demonstrate the applicability of a Ge-on-Si PD array in a microwave photonics application, a balanced PD pair integrated with a Mach-Zehnder delay line interferometer (MZ DLI) was used to demodulate and detect a phase-modulated signal in an analog photonic link. The optical image and the schematic of the integrated receiver are shown in Figure 15. The optical input signal is fed through one of the vertical grating couplers and then split by a Y-junction. One arm includes a 1.4 mm-long silicon waveguide to delay the optical signal by 25 ps to allow for interferometric demodulation of the phase modulated signal. The demodulator worked at the quadrature point by adjusting the laser wavelength. The RF gain of the phase-modulated link was measured using the setup shown in Figure 16a. A commercial optical phase modulator with a V\({}_{\pi}\) of 7 V was driven by a signal generator to modulate the phase of the optical carrier from the 1550 nm laser. The demodulated signal was recorded from a spectrum analyzer. The RF link gain versus frequency at different photocurrents is shown in Figure 16b. Also shown in the figure is the calculated link gain versus frequency according to [52]. The measurement agrees well with the theory once the 3-dB bandwidth of the balanced PD pair of  
Figure 14: (**a**) Frequency responses of PD array at different photocurrents. (**b**) RF output power and compression of the PD array at 15 GHz. Figure 15: Optical image and schematic of the interferometric demodulator. 20 GHz was taken into account (blue curve). At 7 mA photocurrent per PD, the difference between the measurement (black curve) and the ideal model (red curve) increases, which indicates a reduction of the bandwidth of the balanced PDs due to saturation. A higher RF gain can be expected from traveling wave balanced PD arrays.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 3 SWE-Based Acquisition Chain
### SOS Server  
The Sensor Observation Service (SOS) acts as a server for storing and managing both sensor data and metadata. Due to its data archiving and metadata management roles, as well as its ability to interface with further services, it is one of the core components of the proposed SWE-based cyber-infrastructure, vital to interact with further processes.  
In the proposed acquisition chain the 52\({}^{\circ}\) North's open source SOS implementation running on a PostgreSQL database is used [35]. For ingesting metadata about a sensor into an SOS server (registeringa new sensor) the transactional _InsertSensor_ operation of the SOS interface is used. The upload of data to the SOS server is achieved through the so called _ResultHandling_ operations of the SOS standard (_InsertResultTemplate_ and _InsertResult_). To ensure an efficient data transfer the server supports the insertion of EXI-encoded O&M files.  
The SOS Server also provides a standardized interface for further process to query and access sensor data archived in the SOS database. SWE services can connect to the SOS server to query for archived sensor data and related metadata.
## 2 System Modeling
### Total System  
Defining \(\mathbf{x}=[x_{1},x_{2},x_{3}]^{T}:=[\theta,\dot{\theta},D_{m}P_{L}/J]^{T}\) as the state vector, based on (4)-(7) and (10), the total system dynamics can be derived in a strict feedback form as  
\[\dot{x}_{1} =x_{2} \tag{11}\] \[\dot{x}_{2} =x_{3}+f_{1}(\mathbf{x})+d_{1}(\mathbf{x},t)\] \[\dot{x}_{3} =f_{2}(\mathbf{x})+g_{2}(\mathbf{x},u)u+d_{2}(\mathbf{x},t)\]  
where \(f_{1}(\mathbf{x})\), \(d_{1}(\mathbf{x},t)\), \(f_{2}(\mathbf{x})\), \(g_{2}(\mathbf{x},u)\), \(g_{2}(\mathbf{x},u)\) and \(d_{2}(\mathbf{x},t)\) are given by  
\[f_{1}(\mathbf{x})=-\frac{B}{J}x_{2}-\frac{1}{J}\bigg{(}\tau_{ \text{c0}}+\tau_{\text{s0}}\exp\left(-\frac{|x_{2}|}{\omega_{s}}\right)\bigg{)} \tanh\Big{(}c_{f}x_{2}\Big{)} \tag{12}\] \[d_{1}(\mathbf{x},t)=-\frac{\Delta\tau_{f}+\tau_{d}}{J}\] \[f_{2}(\mathbf{x})=-\frac{4\beta_{c}D_{m}^{2}}{JV_{t}}x_{2}- \frac{4\beta_{c}}{V_{t}}\bigg{(}c_{1}\frac{J^{2}}{D_{m}^{2}}x_{3}^{2}+c_{2} \frac{J}{D_{m}}x_{3}+c_{3}\bigg{)}\] \[g_{2}(\mathbf{x},u)=\frac{4\beta_{c}k_{t}}{V_{t}}\sqrt{P_{s}- \tanh(c_{v}u)\frac{J}{D_{m}}x_{3}}\] \[d_{2}(\mathbf{x},t)=\frac{4\beta_{c}}{V_{t}}(\Delta Q_{L}+q(t))\]  
To facilitate the controller design, some reasonable assumptions are made as follows:  
**Assumption 1**.: _The desired trajectory \(x_{1d}(t)\) is sufficiently smooth, and its derivatives up to order three are bounded._  
**Assumption 2**.: _The pressures of the actuator chambers \(P_{1}\) and \(P_{2}\) are bounded by the supply pressure \(P_{s}\). The absolute value of the load pressure \(P_{L}\) is adequately smaller than the supply pressure \(P_{s}\) to ensure the function \(g_{2}(\mathbf{x},u)\) is distant from zero._  
**Assumption 3**.: _The mismatched and matched lumped disturbances \(d_{1}(\mathbf{x},t)\) and \(d_{2}(\mathbf{x},t)\) are smooth enough and their first-order derivatives are bounded by positive constants; i.e., \(\left|\dot{d}_{1}(\mathbf{x},t)\right|\leq\delta_{1}\) and \(\left|d_{2}(\mathbf{x},t)\right|\leq\delta_{2}\)._
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 3 Compact Data Structures  
Compact data structures offer a fascinating approach in substitution, data compression and efficient retrieval of information. In this paper, we used two well-known compact data structuresmethods: Kd-tree and wavelet tree for the compression, substitution and permutations of the SPIHT bit stream.
## 2 Background
### Industry 4.0  
The Industry 4.0 initiative was devised in 2011 as part of a German government strategy to maintain and develop its position as a global leader in the manufacturing sector and it was defined as _\"the technical integration of CPS into manufacturing and logistics and the use of the Internet of Things and Services in industrial processes\"_[18]. However, as research into this topic has evolved, more comprehensive definitions have been presented, based on what individual authors or institutions perceive to be the essential components of Industry 4.0. One such example of a comprehensive definition is _\"the Fourth Industrial Revolution can be best described as a shift in the manufacturing logic towards an increasingly decentralised, self-regulating approach of value creation, enabled by concepts and technologies such as CPS, IoT, IoS, cloud computing or additive manufacturing and smart factories, so as to  
Figure 1: GAMP 5 V-Model.  
help companies meet future production requirements_\" proposed by [19]. Although there is no single definition for Industry 4.0, it can be generally described as the application of enabling technologies, such as the Internet of Things (IoT) devices, Artificial Intelligence (AI) and simulation and modelling techniques to transform manufacturing systems into Cyber Physical Systems (CPS) and enhance processes throughout the value chain within an organisation [20]. Equipment design for Industry 4.0 is defined by four key design principles [21]:  
Interconnection  
Information transparency  
Technical assistance  
Decentralised decisions  
Implementation of these technologies can enable organisations to develop new digital business models, reduce operating costs, improve equipment efficiency, enhance product quality, and enable flexible manufacturing operations [22; 23].