esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 1 Introduction
### Brief Review of Quantum Computing Essentials
#### 1.1.2 Quantum Circuit Model  
The quantum circuit model is widely used in Quantum Computing to represent quantum algorithms as a series of \"quantum gate\" operations acting on one or more qubits. Each of the qubits is represented by a horizontal solid line in quantum circuit diagrams, with a vertical arrangement of the multiple qubits in the register. The number of qubits and, therefore, the number of parallel lines defines the _circuit width_. Quantum gates are designed to either work on a single qubit (\"single-qubit gates\") or work on multiple qubits simultaneously (\"multi-qubit gates\"). The quantum computation progresses step-by-step in the horizontal direction, starting from the initial qubit register state defined on the left-hand side. The diagram in the left-hand side of Figure 1 shows a simple two-qubit quantum circuit with a single two-qubit gate acting on the state of qubits \(|q1|q0\rangle\). The example shows a controlled-\(NOT\) (\(CNOT\)) operation, where the \(NOT\) (also termed \(X\) in quantum computing) is performed on qubit \(|q0\rangle\) conditional on qubit \(|q1\rangle=|1\rangle\). This operation represent a single time instance. The \(X\) gate (\(X\equiv\begin{bmatrix}0&1\\ 1&0\end{bmatrix}\)) has the effect of flipping a qubit in the computational basis, such that \(X|0\rangle=|1\rangle\), \(X|1\rangle=|0\rangle\), by swapping of the qubit's probability amplitudes. This is the analogue of the classical NOT gate. In more complex circuits, a series of such quantum gate operations is performed step-by-step. For illustration, the right-hand side of Figure 1 shows how a general single-qubit gate \(G\) with a negative control (here \(|q1\rangle=|0\rangle\)) can be implemented using a sequence of three gate operations. The number of time slices required to run the circuit (i.e., circuit slices with gates that affect mutually exclusive qubits) defines the _circuit depth_. Since a quantum circuit is composed of unitary operations, every quantum circuit has an inverse. The inverse of a quantum circuit can simply be composed of the inverse of all its constituent quantum gates, applied in reverse order. The operation of applying the inverse of a circuit after \"moving\" useful data from the circuit is common in quantum computing, and is termed _uncomputation_. This is typically used to return qubits allocated to create workspace to their initial state, so that these can be re-used in subsequent parts of the circuit.  
The quantum circuits considered in the current work typically involve tens or hundreds of qubits, with a quantum circuit depth of tens to thousands of gate operation steps. Simulating quantum circuits with this number of qubits clearly represents a major computational challenge.
## 3 Materials and Methods
### The Isolation Forest  
The Isolation Forest (iForest) method was selected as the basic unsupervised machine-learning technique for anomaly detection. This approach demands minimal data preprocessing, is robust against missing data and varying data dimensions, is invariant to feature scaling, and does not require parameter tuning. It has linear time complexity and low memory usage, making it more efficient than most other algorithms, particularly for analyzing streaming data.  
The iForest method is based on the construction of a set of several disjoint, undirected binary decision trees (or isolation trees, iTree), otherwise a decision tree ensemble.  
A decision tree is a graph where there is only one route between any pair of vertices, and it does not contain a single loop. The decision tree is a connected acyclic graph (Figure 5).  
Isolation Forest algorithm [16], as shown in Algorithms 1 and 2 below:  
```
1:Inputs: X--dataset, \(d\)--number of trees, \(\psi\)--number of subsample instances
2:Output: set of \(d\) iTree trees
3:Initialize Forest
4:set tree height limit \(l=ceiling(log_{2}\ \psi)\)
5:for\(i=1\) to \(d\)do
6:\(X^{\prime}\leftarrow\)sample \((X,\ \psi)\)
7:\(Forest\gets\)Forest \(\cup\)iTree \((X^{\prime},\ 0,\ l)\)
8:end for
9:return Forest
```  
**Algorithm 1**_iForest(X, \(d\), \(\psi\))_  
Figure 5: Schematic representation of the Isolation Forest method [20].  
The developers of the Isolation Forest algorithm [16; 21; 22] propose the following estimation of anomaly (2):  
\[\mathrm{s}(x,\psi)=2^{-\frac{E(h(x))}{c(q)}} \tag{2}\]  
where \(\psi\)--number of instances in the subsample;  
\(h(x)\)--length of the path to the observation \(x\), defined by the number of edges starting from the root of the tree;  
\(E(h(x))=\frac{\sum_{i=1}^{d}h_{j}(x)}{d}\)--mean \(h(x)\) from iTree ensemble;  
\(c(\psi)\)--average path length of unsuccessful search in iTree.  
Marginal data refer to the anomaly estimation of an instance according to the following (2):  
1. If \(E(h(x))\ \to 0\), _then_\(s\to 1\);
2. If \(E(h(x))\ \to\psi-1\), _then_\(s\to 0\);
3. If \(E(h(x))\ \to c(\psi)\), \(s\to 0.5\).  
The criteria for recognizing an instance as abnormal are as follows:  
1. If \(s\to 1\), then an instance of the class can be considered anomalous with certainty;
2. If \(s<0.5\), then an instance of the class can be considered normal;
3. If anomaly estimation yields \(\mathrm{s}\approx 0.5\) for all instances, then the sample has no anomalies.  
In other words, the class instance normality measure is the arithmetic mean of the trees' depths. Anomalous specimens are located in the leaves, at a shallow tree depth [20; 23].
## 3 Discrete Formulation of Topology Optimization Problem Statements and Sensitivities
### Sensitivity Analysis  
The gradient of the cost function can be defined as:  
\[\bigtriangledown_{\rho}J=\frac{\partial J}{\partial\rho}+\frac{\partial J}{ \partial u}\frac{\partial u}{\partial\rho}. \tag{38}\]  
In order to find \(\frac{\partial J}{\partial u}\), it is possible to reorganize the state equation, multiply it for a generic vector \(v\), and derive the expression, leading to:  
\[v^{T}\bigg{[}\frac{\partial K(\rho)}{\partial\rho}u(\rho)-\frac{\partial F( \rho)}{\partial\rho}+K(\rho)\frac{\partial u(\rho)}{\partial\rho}\bigg{]}=0; \tag{39}\]  
therefore:  
\[-\bigg{(}\frac{\partial u(\rho)}{\partial\rho}\bigg{)}^{T}K^{T}(\rho)\ v=v^{t} \bigg{[}\frac{\partial K(\rho)}{\partial\rho}u-\frac{\partial F(\rho)}{ \partial\rho}\bigg{]}. \tag{40}\]  
As stated in Section 2, solving Equation (40) for all values of \(\rho\) would be too expensive, motivating the use of the adjoint method. Defining the adjoint variable as \(\lambda=v\) such that \(K^{T}(\rho)\lambda=-\frac{\partial J}{\partial u}\) leads to:  
\[-\bigg{(}\frac{\partial u(\rho)}{\partial\rho}\bigg{)}^{T}K^{T}(\rho)\lambda= \bigg{(}\frac{\partial u}{\partial\rho}\bigg{)}^{T}\frac{\partial J}{ \partial u}=\lambda^{T}\bigg{[}\frac{\partial K(\rho)}{\partial\rho}u-\frac{ \partial F(\rho)}{\partial\rho}\bigg{]}. \tag{41}\]  
Thus, the gradient can finally be rewritten as:  
\[\bigtriangledown_{\rho}J=\frac{\partial J}{\partial\rho}+\frac{\partial J}{ \partial u}\frac{\partial u}{\partial\rho}=\frac{\partial J}{\partial\rho}+ \lambda^{T}\bigg{[}\frac{\partial F(\rho)}{\partial\rho}-\frac{\partial K( \rho)}{\partial\rho}u\bigg{]}. \tag{42}\]  
With this information, the equivalent three-step process described in Section 2.3 can be rewritten as:  
* Find \(u\) such that: \(K(\rho)u=F(\rho)\);
* Find \(\lambda\) such that: \(K(\rho)\lambda=-\frac{\partial J}{\partial u}\);
* Compute: \(\bigtriangledown_{\rho}J=\frac{\partial J}{\partial\rho}+\lambda^{T}\bigg{[} \frac{\partial F(\rho)}{\partial\rho}-\frac{\partial K(\rho)}{\partial\rho}u \bigg{]}.\)
## 3 The Selection Parameters of the MPPT Algorithms
### Cost  
The cost is the financial expense involved in implementing the MPPT algorithm, which includes both hardware and software requirements. More complex algorithms may require more expensive components or higher computational resources. Digital implementations may require specialized processors, sensors, and converters, driving up costs. Analog circuits are cheaper but may be less flexible or efficient. The cost also includes power losses due to less efficient tracking.
## 3 Results and Discussion
### Characterization of the Resins
#### 3.1.3 Optical Microscopy  
In the Na\({}^{+}\)-form, the resin showed spherical forms on a microscopic view (100\(\times\) magnification), with average particle diameters of 140 \(\pm\) 12 \(\upmu\)m (\(n\) = 100, Figure 6). The spheres show almost no fissures, with a 1% ratio of fissures on the sampled resin particles. For the H-form, the average diameter of the particles was 155 \(\pm\) 19 \(\upmu\)m, showing no damage in the particles observed. This implies that the H-form has a bigger variation in size and shows better stability regarding the form; on the contrary, the Na\({}^{+}\)-form particles show better stability concerning the particle size but show imperfections in the formation of the particle. When comparing both resins, the variation in size, however minor, may facilitate the adsorption of the toxin or even its passing through the bivalve's digestive system, indicating that the Na\({}^{+}\)-form properties could be more promising to promote the depruation. The particles of both types of resin are translucent, showing a yellow colour to the naked eye that is not visible under a microscope.  
Figure 6: Microscopic image with DIC filter from particles of the Na+-form, showing the only deformation observed and two samples of the measurement taken from the particles.
## 4 Sca  
Optimisation is a process or mathematical procedure of obtaining the best values of the parameters of a system in the search space. SCA is a population-based algorithm that starts with a set of random solutions, stochastically searches optima and works in two phases: exploration and exploitation. In the exploration phase, the algorithm finds promising areas in the search space by producing a set of results that contain random outcomes unexpectedly with high-rate randomness. In the exploitation phase, the algorithm mostly emphasises the gradual changes in random solutions [36]. The following equation represents the changing positions for both phases:  
\[X_{i,j}^{t+1} =X_{i,t}+r_{1}^{t}\times\sin r_{2,j}\times\Big{|}r_{3,j}Q_{d,j}^{t }-X_{i,j}^{t}\Big{|}, \tag{50}\] \[X_{i,j}^{t+1} =X_{j,t}+r_{1}^{t}\times\cos r_{2,j}\times\Big{|}r_{3,j}Q_{d,j}^{t }-X_{i,j}^{t}\Big{|},\] (51) \[X_{i,j}^{t+1} =\left\{\begin{array}{ll}X_{i,t}+r_{1}^{t}\times\sin r_{2,j} \times\Big{|}r_{3,j}Q_{d,j}^{t}-X_{i,j}^{t}\Big{|},&r_{4,j}<0.5\\ X_{j,t}+r_{1}^{t}\times\cos r_{2,j}\times\Big{|}r_{3,j}Q_{d,j}^{t}-X_{i,j}^{t} \Big{|},&r_{4,j}\geq 0.5\end{array}\right., \tag{52}\]  
where \(Q_{d,j}^{t}\) is the position of destination in the \(j\)th dimension, \(X_{i,t}\) is the current solution at the \(t\)th iteration, \(r_{1}^{t}\) is a random number that determines the region of position, \(r_{2,j}\) is a random number that defines the direction outwards or towards the station point, \(r_{3,j}\) is the random number importance at the station, \(r_{4,j}\) a random number ranging from zero to one, and \(\mid\mid\) shows an absolute value. To obtain a favourable region in a given domain and converge it to the universal optima, the SCA algorithm provides a balance during exploitation and exploration by changing the sine and cosine ranges using Equation (53), which is expressed as follows:  
\[r_{1}^{t} =a-t\frac{a}{T}, \tag{53}\]  
where \(t\) is the current iteration, \(a\) is a constant number, and \(T\) is the maximum number of iterations. The effects of sine and cosine on Equations (50) and (51) are two-dimensionally demonstrated in Figure 4 and can be extended to higher dimensions. These equations describe a domain between two results in a given search domain, and their patterns allow a solution to reposition and ensure exploitation of a region between two results. The optimisation technique should search outside the space that has subsequent destinations to control the exploration in a given search domain. The achieved changing amplitude range of cosine sine functions is shown Figure 5. Random number \(r_{2,j}\) that has a range in \((0,2\pi)\) decides whether the place is outside or inside. The pseudocode of the SCA algorithm is presented in Table 1.  
Figure 4: Effects of sine cosine in Equations (50) and (51) on the upcoming locality.  
Figure 5: Cosine and sine with an amplitude scale of \((-2,2)\) and period range of \(\{0,2\pi(6.283185)\}\).
## 5 Conclusions  
This work highlighted the limitations, performance, and the great contribution of using medium-resolution SAR images for various geotechnical engineering related phenomena. The study demonstrated the applicability of using shared Sentinel-1 SAR data provided by ESA under the COPERNICUS Programme processed with open-source software packages, SNAP and StaMPS, employing the conventional and advanced interferometric processing. Two well-defined processing workflows were presented and tested in four geotechnical engineering applications namely DEM generation, earthquake- and tunneling-induced ground deformations, and rainfall-induced landslide in the Korean Peninsula. The major limitations of SAR interferometry were also discussed, which will serve as important pointers in future projects involving continuous deformation monitoring and landslide early detection with improved satellites features.  
Overall, the challenges that the authors have encountered on using open data and open-source software tools have created and promoted a conducive and scientific learning environment between remote sensing and geotechnical engineering fields. The experience gained through this work opens new opportunities to explore in addressing timely social and environmental impacts of various geohazards.  
Study design, R.R. and T.-H.K.; methodology, R.R.; formal analysis, R.R. and T.-H.K.; data curation, R.R.; writing--Original draft preparation, R.R.; writing--Review and editing, R.R., T.-H.K. and S.-R.L.; project administration, S.-R.L.; funding acquisition, S.-R.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Research Laboratory Program through the National Research Foundation of Korea (NRF) funded by the MST (NRF-2018R1A4A1025765).  
The authors would like to thank the European Space Agency (ESA) for freely supplying Sentinel-1 SAR data under the COPERNICUS Programme ([[https://schub.copernicus.eu](https://schub.copernicus.eu)]([https://schub.copernicus.eu](https://schub.copernicus.eu))). The authors would also like to thank the United States Geological Survey (USGS) in cooperation with the National Aeronautics and Space Administration (NASA) for providing the SRTM DEM ([[https://eartheexplorer.usgs.gov](https://eartheexplorer.usgs.gov)]([https://eartheexplorer.usgs.gov](https://eartheexplorer.usgs.gov))). SNAP (by ESA), StaMPS (by [PERSON]), and TRAIN (by [PERSON]) software packages are freely distributed under GNU-GPL license.  
The authors declare no conflict of interest.
## 2 The Building, Temporary Instrumentation and Methodology
### Parametric Analysis Procedure
#### 2.3.2 Projection of Blocks (\(\mathcal{P}_{i}\))  
The projection \(\mathcal{P}_{i}\) is defined by the orthogonal projection matrix of the SSI method and is determined as the product between the observability matrix \(\mathcal{O}_{i}\) and the estimated sequence of states of the Kalman filter \(\hat{\mathbf{X}}_{i}\). This procedure is depicted in Figure 4. The orthogonal projection of the future outputs \(\mathbf{Y}_{f}\) onto past outputs \(\mathbf{Y}_{p}\) determines the forward state sequence \(\hat{\mathbf{X}}_{i}\). Conversely, the orthogonal projection of past outputs \(\mathbf{Y}_{p}\) onto the future outputs \(\mathbf{Y}_{f}\) determines the backward state sequence \(\hat{\mathbf{Z}}_{i}\)[32]. Figure 4 also presents the variables \(\Delta_{i}^{c}\) and \(\mathbf{\mathcal{B}}_{i}\), which correspond to a controllability matrix and the projection of past outputs onto the future outputs, respectively However, these variables are not part of the procedure since they serve only to define and understand the origin of the projection of blocks \(\mathbf{\mathcal{P}}_{i}\).
# Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
###### Abstract
Adaptive PSO, which adjusts weights by evaluating the swarm's state in real-time, demonstrates greater stability in noisy environments and for dynamic objectives [27]. Furthermore, the developed ADIWACO algorithm integrates dynamic inertia weights with adaptive acceleration coefficients, verifying its superiority in high-dimensional, complex problems through multi-objective test functions [28]. In this study, a novel hybrid model DIWPSO-SVM is presented, which combines the strengths of DIWPSO and SVM for midspan deflection prediction. The hybrid model is enhanced by incorporating wavelet transform to separate and analyze the deflection signals under temperature and vehicle loads. The proposed model is validated using real-world data from a long-span cable-stayed bridge, and its performance is compared with traditional SVM and PSO-SVM models.
# Technical Interoperability for Machine Connectivity on the Shop Floor
## 3 Method and Project Description
### Connectivity Infrastructure  
The project infrastructure is distributed over three Swedish cities: Gothenburg, Lund, and Stockholm. See Figure 3 for an overview of the setup. In Gothenburg, the SKF factory is connected to Ericsson's facilities through a radio-link. The Gothenburg site is connected to Ericsson's core network and linked to facilities in Lund, which is a data center that hosts a private cloud in terms of processing and storage. The gathered data is also distributed to the data analytics site in Stockholm. On the shop floor, the closest thing to a 5G network was deployed, which is a dedicated LTE network, TDD (Time Division Duplex) on Band 40, with 5G enabling technologies. Devices are connected to the network through either embedded modem (e.g., LTE enabled mobile phones) or using external USB modems, and the received data rates were measured to 70 Mbps downlink and 17 Mbps uplink.  
Figure 2: The artifacts described in the results section are the enabling technologies connecting the grinding machine to the data consumers on top of the basic connectivity infrastructure.
## 4 Lignin Biodegradation and Ligninolytic Enzymes
### Versatile Peroxidase (VP)  
Versatile peroxidase (VP: EC 1.11.1.16) is the third member of the extracellular oxidase II family used for lignin degradation [17; 98; 97]. The VP activity has been reported from Pleurotus [113], but a limited number of VPs have been identified and characterized, mainly from the white fungal Basidiomycetes Pleurotus, Bjerkandera, and a few other fungi genera [99; 114; 115; 116; 113; 117; 118]. Recently, the comparative genomics for wood-decayed fungi showed that other fungi could harbor VP coding genes [98; 117; 118]. Indeed, fungal VPs may have evolutionarily split from a common ancestral peroxidase origin and then acquired the ability to catalyze nonphenolic compounds [117]. VPs have similar biochemical characteristics to MnPs. They are secreted glycoproteins with approximately 37-55 kDa in size and with a pI of 3.5-4.5. VPs display multifunctional properties typical of both LiP and MnP: they have a catalytic function similar to LiPs toward nonphenolic substances, such as veratryl alcohol, but these oxidases are capable of oxidizing Mn\({}^{2+}\) to Mn\({}^{3+}\) via a mechanism similar to that of MnPs [119; 120; 121]. The versatility of VPs allows it to directly oxidize low- and high-redox potential substrates because it contains two additional active sites [116; 122]: one site is exposed to the heme edge to provide VPs with the capability to oxidize substrates directly without Mn\({}^{2+}\); and the other site is the exposed tryptophan radical (Trp164 residue in VPs of Pleurotus strains), which is exposed on the surface of VPs, and is involved in the direct oxidation of low- and high-redox potential substrates in the presence of redox mediators [119; 120; 121]. On the basis of the Trp residue in the active site, VP has a higher redox potential (E\({}_{\text{G}^{\prime}}>\) 1.4V versus SHE at pH 3) to catalyze nonphenolic compounds without a mediator like LiPs. Therefore, this high-redox potential peroxidase could be used for catalyzing the degradation of xenobiotics, depolymerization, or polymerization of functional monomers or macromolecules, through enzymatic oxidoreduction [123; 124; 125; 105].
## 2 Materials and Methods
### Absorbed Dose Measurements  
An absorbed dose to water measurement involves using an ionization chamber with absorbed dose to water calibration factor in water phantom according to the reference conditions recommended in TRS-398, TG-51, and DIN 6800-2 dosimetry protocols. This includes determining the radiation beam quality index (\(Q\)) to determine the photon beam quality factor required along with the ionization chamber calibration factor to convert the ion chamber reading into the absorbed dose to water, according to Equations 1, 3, and 4 [1,2,5].  
For absorbed dose to water measurements, the cylindrical chamber was placed with its axis at reference depth of measurements in water phantom (\(Z_{ref}\)), which equaled 5 cm (for 4 and 6 MV) and 10 cm (for 10 and 20 MV) in all protocols. Measurements were taken at the chamber reference point of measurements, which is at the chamber axis at the center of the cavity volume in IAEA TRS-398 and AAPM TG-51. In DIN 6800-2, measurements are taken at the effective point of measurement (Peff) shifted toward the radiation source by approximately 0.5 \(r_{\text{cyl}}\). For a parallel-plate chamber, the reference point of measurements is located at its center on the inner surface of the front window.  
Using the _PDD_, all doses were then converted to doses at a depth of the dose maximum \(d_{max}\).  
\[D_{w,Q}(d_{max})=100\cdot D_{w,Q}\left(Z_{ref.}\right)/PDD\left(Z_{ref}\right) \tag{11}\]
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
###### Abstract  
This paper proposes a distributed data-driven adaptive iterative learning control (DAILC) method to address the challenges of modeling and voltage regulation in isolated AC microgrids subject to disturbances and sensor saturation. Firstly, by utilizing the input-output data from the microgrid, a time-varying linear data microgrid model is developed for the distributed renewable energy generation unit (DREGU) that is independent of the microgrid's physical information. Subsequently, the DAILC algorithm is developed from the microgrid data model, which only uses the input data and the corresponding saturated outputs from each DREGU, along with embedded measurement disturbances. Additionally, we verify the convergence of this algorithm, demonstrating that it can ensure the error in voltage restoration converges to a small neighborhood around the origin, even under conditions of sensor saturation and disturbances. Finally, through a simulation involving four DREGU nodes, we confirm the effectiveness of the proposed distributed DAILC method.  
sensor saturation; iterative learning; AC microgrids; voltage control; data-driven 2025