esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The EOB and DOB-Based Indirect Feedforward Control
### The Additional FOG-Based DOB  
EOB can apparently enhance the low-frequency TP and ADA. It is adequate to complete the tracking because the main frequencies of the target signal are low. However, the frequencies of external disturbance distribute in a wide band. In addition to the low-frequency sway, the MOTS could also be greatly affected by relatively high-frequency mechanical vibration and electromagnetic interference. Therefore, to further improve the ADA and release the potential of the FOG only used in feedback, the FOG-based DOB was added into the velocity loop. FOG had a high bandwidth over 100 Hz, while at the low frequency, its signal was susceptible to drift and noise. Hence, the DOB could extract an accurate disturbance at the relatively high frequency, which can promote the ADA a lot in the higher band. The structure combining EOB with DOB is presented in Figure 6.  
The closed-loop velocity is given as follows  
\[v=uG_{v}+sD \tag{8}\]  
\[u=(v_{ref}-v)C_{v}-(v-u\widetilde{G}_{v})Q_{2} \tag{9}\]  
After calculation,  
\[v=\frac{C_{v}G_{v}}{1+C_{v}G_{v}+Q_{2}(G_{v}-\widetilde{G}_{v})}v_{ref}+\frac{( 1-Q_{2}\widetilde{G}_{v})s}{1+C_{v}G_{v}+Q_{2}(G_{v}-\widetilde{G}_{v})}D \tag{10}\]  
Since \(E=R-\frac{1}{s}v\) and \(v_{ref}=E\cdot e^{-\tau s}C^{\prime}{}_{p}\), we could get  
\[\begin{array}{rl}\hat{S}_{R}=\frac{E}{R}&=\frac{1-Q_{1}e^{-xs}}{1-Q_{1}e^{-xs }+\frac{1-Q_{1}e^{-xs}}{1+C_{v}G_{v}+Q_{2}(G_{v}-G_{v})}(C_{p}+SQ_{1})e^{-xs} \frac{1}{s}}\\ &\approx\frac{1-Q_{1}e^{-xs}}{1-Q_{1}e^{-xs}+(C_{p}+SQ_{1})e^{-xs}\frac{1}{s}} \\ &\approx\frac{1-Q_{1}e^{-xs}}{1+C_{v}G_{v}}\frac{(1-Q_{1}e^{-xs})}{1+C_{p}e^{ -xs}\frac{1}{s}}\end{array} \tag{11}\]  
\[\begin{array}{rl}\hat{S}_{D}=\frac{E}{D}&=\frac{(1-Q_{2}\widetilde{G}_{v})} {1+C_{v}G_{v}+Q_{2}(G_{v}-G_{v})}\cdot\frac{(1-Q_{1}e^{-xs})}{1-Q_{1}e^{-xs}+ \frac{(1-Q_{1}e^{-xs})}{1+C_{v}G_{v}+Q_{2}(G_{v}-G_{v})}(C_{p}+SQ_{1})e^{-xs} \frac{1}{s}}\\ &\approx\frac{(1-Q_{2}\widetilde{G}_{v})}{1+C_{v}G_{v}+Q_{2}(G_{v}-G_{v})} \cdot\frac{(1-Q_{1}e^{-xs})}{1-Q_{1}e^{-xs}+(C_{p}+SQ_{1})e^{-xs}\frac{1}{s} }\\ &\approx\frac{(1-Q_{2}\widetilde{G}_{v})}{1+C_{v}G_{v}}\cdot\frac{(1-Q_{1}e^ {-xs})}{1+C_{p}e^{-xs}\frac{1}{s}}\end{array} \tag{12}\]  
Equation (11) is approximately equal to Equation (6), which means that the FOG-based DOB could not enhance the TP, but slightly affect the stability of the system. Comparing Equation (12) with Equation (7), it could be obviously found that the ADA was continuously improved. Then, at the low frequency \(1-Q_{1}e^{-xs}\) was close to 0 and at the relatively high frequency \(1-Q_{2}\widetilde{G}_{v}\) was close to 0, which means that the CCD-based EOB and FOG-based DOB could complement each other and a system with strong ADA in a wide band was acquired. The design of \(Q_{1}\) and \(Q_{2}\) will be discussed in the following section.  
Figure 6: The VPDC structure enhanced by EOB and DOB. \(\widetilde{G}_{v}\) refers to the approximate velocity model of the platform and \(\widetilde{G}_{v}\approx G_{v}\). \(Q_{2}\) refers to the DOB controller.
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 2 Preliminaries and Problem Formulation
### Graph Theory  
In the distributed control architecture of AC microgrids, each distributed generator (DG) is equipped with a local controller that not only performs control tasks independently but also communicates with the surrounding controllers. Each DG's controller is depicted as a node, and the communication network of an islanded microgrid with \(N\) DGs is modeled using a directed graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\). Here, \(\mathcal{V}=(v_{1},v_{2},\ldots,v_{N})\) denotes the set of \(N\) nodes, while \(\mathcal{E}=\mathcal{V}\times\mathcal{V}\) represents the communication links. The adjacency matrix \(\mathcal{A}=\left[a_{ij}\right]\in\mathbb{R}^{N\times N}\) is established such that \(a_{ij}=a_{ji}=1\) if nodes \(i\) and \(j\) are connected by a communication link, signifying that nodes \(i\) and \(j\) are adjacent. Otherwise, \(a_{ij}=a_{ji}=0\). Furthermore, we define a diagonal matrix \(\mathbf{O}\), defined as \(\mathbf{O}=\text{diag}(o_{1},\ldots,o_{N})\in\mathbb{R}^{N\times N}\). If \(o_{i}>0\), this implies direct communication between the leader and agent \(i\). The neighborhood set for a node \(i\) is defined as \(\mathbf{N}_{i}=\left\{j\mid(v_{j},v_{i})\in\mathcal{E}\right\}\). The in-degree matrix \(\mathbf{D}=\text{diag}(d_{i})\in\mathbb{R}^{N\times N}\) is constructed such that \(d_{i}=\sum_{j\in\mathbf{N}_{i}}a_{ij}\).
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 5 Standards-Based Universal Acquisition Middleware  
### Background  
In the previous section an approach how to organize sensor metadata into standardized SensorML-encoded SDF has been discussed. However, in order to provide a plug and play framework, a middleware capable of automatically retrieving this SDF, interpret it and configure an acquisition the process is required.  
Some marine observation platforms are deployed in long-term missions in remote and inaccessible places (i.e., underwater gliders and profilers). Therefore, these platforms present severe power and communications constrains. Taking into account these constraints and aiming to achieve a highly interoperable and versatile software component, the following design requirements where formulated for such a middleware:  
* **Plug and play sensor discovery**: The middleware shall be able discover and communicate with sensors connected on-the-fly, without any prior knowledge about these sensors.  
Figure 10: Metadata mapping from Sensor Deployment Files (SDF) to the Sensor Observation Service (SOS) transactional operations _InsertSensor_ (**left**) and _InsertResultTemplate_ (**right**). The blue elements correspond to Sensor Instance elements while the green elements correspond to Sensor Mission elements. Dashed lines indicate optional elements and thick lines indicate multiple elements.  
* **Standards-based configuration**: The middleware shall be able to interpret SDFs and setup an acquisition process based on the information contained in these files.
* **Cross-platform design**: The middleware shall be deployable in a maximum number of platforms, regardless of their particular hardware and software architecture.
* **Minimum resource requirements**: Due to the intrinsic constraints of some observation platforms, the usage of hardware and software resources has to be reduced as much as possible (RAM usage, bandwidth, etc.).
* **Standard compliance**: Such a middleware shall be described through SensorML files to allow systems to automatically understand its role and capabilities.  
The Sensor Web Enablement Bridge (SWE Bridge) is a middleware component designed to fulfil these previously mentioned requirements. This middleware is aimed to be used as a universal driver for any sensor providing a RS232 or Ethernet interface. Its cross-platform and hardware abstraction design makes it suitable to be deployed in the majority of observation platforms, whether they are fixed or mobile.
# EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure
## 2 Theoretical Background  
### Empirical Mode Decomposition (EMD)  
Proposed by [PERSON] et al. [59], the EMD method is a suitable SPS to analyze time signals with NL, transient, and NS properties. EMD can decompose a time signal in a set of frequency bands known as intrinsic mode functions (IMFs). To obtain an IMF, it must satisfy two conditions: a) the number of zero crossings and extrema into the time signal have to be either equal or different at most by one, and b) the average value of the upper and the lower envelopes must be equal zero.  
In order to obtain each IMF, a method known as the \"sifting process\" is carried out; this method is described below:  
1. Detect the extrema points (local maximum and minimum points) of the original time signal \(x(t)\).
2. Connect the points estimated in (1), employing cubic-splines in order to obtain an upper- and lower-envelope. The average of both envelopes is assigned as \(m_{1}\), and it is subtracted from the original time signal \(x(t)\) to obtain a new time signal \(h_{1}(t)\) as follows: \[h_{1}(t)=x(t)-m_{1}(t)\] (1) If \(h_{1}(t)\) does not satisfy the previous conditions (a) and (b), steps (1) and (2) are repeated until \(h_{k}(t)\) satisfies both conditions; then, \(h_{k}(t)\) is considered as the first IMF, defined as: \[c_{1}(t)=h_{k}(t)=IMF_{1}\] (2)Once the first frequency band or \(IMF_{1}\) has been obtained, the signal \(c_{1}(t)\) is subtracted from the original time signal \(x(t)\) to calculate the residue signal \(r_{1}(t)\) as follows: \[r_{1}(t)=x(t)-c_{1}(t)\] (3)
4. Check if \(r_{1}(t)\) is a monotonic function, which would indicate that no more IMFs can be extracted. If \(r_{1}(t)\) is not a monotonic function, it has to be treated as the original time signal and repeat the steps (1) to (3) in order to estimate the other IMFs. The process is finished when \(r_{1}(t)\) becomes a monotonic function.
5. Once the process is stopped, the original time signal \(x(t)\) is decomposed into \(N\) intrinsic modes, IMFs, and the last residue \(r_{N}(t)\) as follows: \[x(t)=\sum_{n=1}^{N}c_{n}(t)+r_{N}(t)\] (4)
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
###### Abstract  
Machine-type communication (MTC) is an emerging communication trend where intelligent machines are capable of communicating with each other without human intervention. Mobile cellular networks, with their wide range, high data rates, and continuously decreasing costs, offer a good infrastructure for implementing them. However, power consumption is a great issue, which has recently been addressed by 3 GPP (3 rd Generation Partnership Project) by defining power-saving mechanisms. In this paper, we address the problem of modeling these power-saving mechanisms. Currently existing modeling schemes do not consider the full range of states in the discontinuous reception (DRX) mechanism in LTE-A networks. We propose a semi-Markov based analytical model, which closes this gap and shows very good results in terms of predicting performance evaluation metrics, such as the power-saving factor and wake-up latency of MTC devices compared to simulation experiments. Furthermore, we offer an evaluation of the DRX parameters and their impact on power consumption of MTC devices.  
machine-type communication; power-saving mechanisms; semi-Markov chain; power-saving factor; wake-up latency +
Footnote †: journal: Sensors
## 4 Case Study: MIMO Magnetic Levitation System  
Motion control of magnetic levitation system has become an increasingly important area of research for many engineering systems. Some of them are dual axis motion control [16], rotary table with 6-DOF [17], precision-positioning stages [18], maglev trains, and the reader can refer to the papers in References [19; 20] for more applications. The proposed non-sequential design in Section 3 is illustrated by means of a challenging and benchmark magnetic levitation system [21]. The magnetic levitation system (refer to Figure 4a) is set up as the MIMO system (\(2\times 2\)) with two control loops: Channel-11 (Control loop-1) that consists of the lower magnet and coil. Input to this control loop is the voltage applied to lower coil to levitate the lower magnet and the output is displacement of lower magnet. Channel-22 (Control loop-2) consists of the upper magnet and coil. Input to this control loop is the voltage applied to upper coil to levitate the upper magnet and the output is the displacement of upper magnet. The displacement of the magnets produces interaction between the loops.  
The objective was to design a \"non-interacting\" stable control system which satisfies the model matching specifications. The uncertain plant sets for MIMO configuration of a magnetic levitation system [21] are:  
\[\begin{array}{ll}p_{11}(s)=\frac{-A\bar{s}^{2}-B}{s^{4}+C\bar{s}^{2}+D},&p_{ 12}(s)=\frac{-E}{s^{4}+C\bar{s}^{2}+D}\\ p_{21}(s)=\frac{F}{s^{4}+C\bar{s}^{2}+D},&p_{22}(s)=\frac{H\bar{s}^{2}+I}{s^{4 }+C\bar{s}^{2}+D}\end{array}\]  
where, \(A\) = [1445; 1565], \(B\) = [944,000; 1,036,000], \(C\) = [1271; 1289], \(D\) = [391,800; 401,200], \(E\) = [205,600; 241,000], \(H\) = [2604; 2819], \(F\) = [214,100; 251,000], \(I\) = [1,608,000; 1,767,000].  
The nominal plant values were \(A_{0}\) = 1565; \(B_{0}\) = 1,036,000; \(C_{0}\) =1289; \(D_{0}\) = 401,200; \(E_{0}\) = 241,000; \(H_{0}\) = 2819; \(F_{0}\) = 251,000; \(I_{0}\) = 1,767,000.
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 3 TA Modules from Pathogenic Bacteria
### Gram-Positive Bacteria
faecalis_ is a human commensal bacterium that causes endocarditis, septicaemia, urinary tract infections, and meningitis while inhabiting the human gastrointestinal tract [134; 144]. This bacterium is known to have type II TA pairs including two MazEF, one Phd-Doc, and two RelBE (see Supplementary Table S1). In addition, the par addiction operon of the plasmid pAD1 of _E. faecalis_ encodes the hydrophobic Fst toxin consistingof 34 residues and a small antisense RNA that complementarily binds to the toxin mRNA, which is characteristic of type I TA system. Fst has neither haemolytic nor antimicrobial activities [145], strengthening the possibility that it has negative effects on chromosome segregation and cell division. _S. aureus_ commonly causes skin and respiratory diseases such as abscesses, impetigo, sinusitis, and pneumonia [146]. The emergence of methicillin-resistant _S. aureus_ (MRSA) has become a worldwide problem in the 21 st century [147]. The _S. aureus_ genome contains three main type II TA operons, namely _mazEF_, _yefM-yoE_, and \(\omega\)-\(\varepsilon\)-\(\zeta\), among which _mazEF_ has been most extensively studied [5]. Multiple _Bacillus_ species are considered important Gram-positive human pathogens; for example, _B. anthrac_ causes anthrax, and _B. cereus_ causes food poisoning. The TA database has suggested that _B. anthrac_ and _B. cereus_ have two and three MazzEF TA systems, respectively. Non-pathogenic _B. subtilis_ has proved an excellent model for molecular and cellular biology research [134]. The SpoIISA-SpoIISB operon encoded on _B. subtilis_ chromosome was initially shown to be a novel TA system in a screen for _B. subtilis_ mutants that are defective in sporulation after the formation of the polar septum [148]. The SpoIISA toxin inhibits the formation of cell envelopes after asymmetric septation.
## 2 Study Area and Data  
### Study Area  
In Figure 1, the study area is located in Linze County in Zhangye, Gansu province, China. The central position is 39.250 43\({}^{\circ}\) N, 100.005 871\({}^{\circ}\) E, and the altitude is 1385 m. The land cover map is obtained from GLC30 [50].  
The region is in a temperate drought desert climate zone in the middle reach of Heihe River Basin with a flat terrain, which belongs to the lower plain part of the corridor. The average annual rainfall is 121.5 mm with an annual potential evaporation exceeding 2340 mm. The dry index is 15.9. The average temperature and annual sunshine hours are 7.1 \({}^{\circ}\)C and 3045 h, respectively. The ground features are mainly bare land and farmland with a small amount of sparse sedge. There is approximately 16.7% sandy soil, 74.8% sand, and 8.5% clay in the sand texture [46].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Data Source** & **Model Used** &
\begin{tabular}{c} **Surface Roughness** \\ **Parameterization** \\ \end{tabular} \\ \hline Zribi (2003) [40] & SIRC/X-SAR, ERASME, RADARSAT & IEM & \(Zs=s^{2}/l\) \\ [PERSON] (2010) [46] & ASAR & AIEM & \(Zs=s^{2}/l^{2}\) \\ [PERSON] (2014) [47] & ENVISAT-ASAR & CIEM & \(Zs=s^{2}/l^{2}\) \\ [PERSON] (2017) [41] & ALOS/PALSAR, RADARSAT-2 & IEM & \(Zs=s^{23}/l^{3}\) \\ [PERSON] (2017) [48] & ASAR & AIEM & \(Zs=s^{3}/l^{3}\) \\ [PERSON] (2018) [45] & Radarsat-2 & AIEM & \(Zs=s^{3}/l\) \\ [PERSON] (2019) [44] & RADARSAT-2 & AIEM & \(Zs=s^{3}/l^{2}\) \\ [PERSON] (2019) [49] & Sentinel-1 & AIEM & \(Zs=\sqrt{3}s/l\) \\ This Study & ASAR & CIEM & \(SJ_{\text{Jpt}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the approaches in related papers.  
Figure 1: _Cont._
## 3 Results and Discussion
### Color Analysis  
The statistical analysis was carried out using the ANOVA method, taking into account [PERSON]'s test; the results are mapped in Tables 1 and 2. The data in Tables 1 and 2 refer to the Lab color space model of currant powders. The Lab color space model allows the determination of three basic color components such as brightness (L), red green (a) and yellow blue (b) [35; 36; 37]. In the context of the [PERSON] test, subclasses were obtained for currant powders with 30% and 50% carrier. This means that some samples of currant powders are similar to each other, while others differ [37]. In Table 1, it was observed that the color component responsible for brightness showed that currant powder with 50% milk whey protein (W50) statistically finds similarity with inulin (IN50) and fiber (F50). In the case of component (a), currant powders with 50% of the carrier show statistically the greatest similarity among themselves, including milk whey protein, inulin, microcrystalline cellulose and fiber. In the case of component (b), currant powders with 50% milk whey protein (W50) were found to have statistically significant similarity with maltodextrin (MD50). On the other hand, Table 2 shows the analysis of variance for different samples with the lowest proportion of carrier in the currant solution. It turns out that the L component statistically shows significant differences between the currant powder samples [37]. In the case of components (a) and (b), there was a statistically significant similarity between fiber (F70) and microcrystalline cellulose (C70) in currant powders with 30% carrier. A comparison, mentioned above, of the results related to moisture and water activity made it possible to identify how the proportion of the carrier, i.e., inulin, is statistically significantly affected [23]. In the case of color when sampling currant powders due to the components of the Lab color space model, inulin becomes one of those carriers that determine a significant statistical similarity.  
Figure 3: Water activity of powders containing 50% and 70% of blackcurrant concentrate solids obtained with gum arabic (GA), inulin (IN), maltodextrin (MD), microcrystalline cellulose (C), fiber (F) and whey milk proteins (W) by dehumidified air-assisted spray drying.
# Comprehensive Method to Test the Stability of High Bedding Rock Slope Subjected to Atomized Rain
## 5 Case Study
### Slope Model Design  
According to the field-testing scale, experimental precision requirements, and engineering characteristics of high bedding rock slope, we selected the model geometric ratio \(C_{L}\) = 200, deformation modulus ratio \(C_{E}\) = 200, volume-to-weight ratio \(C_{\gamma}\) = 1, and strain ratio \(C_{c}\) = 1. The model size was 2.0 m \(\times\) 2.65 m (vertical direction \(\times\) transverse direction), which was equivalent to a 400 m \(\times\) 530 m range in the original project.  
During the modeling process, the four rock classifications (II, III\({}_{1}\), III\({}_{2}\), and IV) were simulated through different materials from their geometric characteristics and spatial locations relative to each other. This test focused on the simulation of six weak structural planes, namely, LS\({}_{337}\), C\({}_{3-1}\), J\({}_{110}\), J\({}_{136}\), f\({}_{101}\), and J\({}_{108}\), in the I\({}_{5}\) section. The model adopted two-dimensional cross control along the river and elevation directions. The two-dimensional geomechanical model of slope is presented in Figure 7.  
According to substantial engineering experience and test data, the shear strength of the structural planes decreases by about 20% under rainy conditions. Therefore, this research mainly reduced the mechanical parameters of the low-angle structural planes LS\({}_{337}\) and C\({}_{3-1}\) and developed strength reduction materials, which were mainly composed of barite powder, machine oil, and fusible polymer. The increased sliding forces could be simulated by the steel frame lifting technique.  
The displacement transformers were installed to monitor the displacements, and their data were collected by digital displacement measuring instruments (UCAM-8 BL, UCAM-70A) and displacement meters [26], which included the slope surface displacements and relative displacements of structural planes. Additionally, a temperature monitoring system was attached to monitor the change in temperature during the strength reduction stage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structural Plane** & **Occurrence** & **Thickness (cm)** & **Deformation Modulus (GPa)** & **f\({}^{\prime}\)** & **c\({}^{\prime}\)(MPa)** \\ \hline \multirow{2}{*}{C\({}_{3-1}\)} & N44\({}^{\circ}\)-55\({}^{\circ}\)E, & \multirow{2}{*}{10–40} & \multirow{2}{*}{0.20} & \multirow{2}{*}{0.35} & \multirow{2}{*}{0.04} \\  & SE\(\text{L}\text{1}^{35}\)–17\({}^{\circ}\) & & & \\ \multirow{2}{*}{LS\({}_{337}\)} & N20\({}^{\circ}\)–45\({}^{\circ}\)E, & & & \\  & SE\(\text{1}^{8}\)–30\({}^{\circ}\) & & & \\ \multirow{2}{*}{f\({}_{101}\)} & N20\({}^{\circ}\)–25\({}^{\circ}\)W, & & & \\  & NE\(\text{.}\)\(\text{.