esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Corporate Business Intelligence Impact: A Case Study in the Financial Sector
## 2 Proposed Research Framework, Methodology, and Case Study
### Credit Registry Bureau (CRB) Cheque Report  
It is a service that instantly queries cheques accepted for collateral or collection purposes. This service is provided with data, such as the cheque's bank code, branch code, cheque account number, and cheque sequence number, and provides information about whether the cheque is in circulation. It is a report that aims to provide more comprehensive information about the validity and potential fraud of a cheque by verifying the identity of the cheque owner.  
With the cheque query made via the CRB Cheque Report, a record is added to the relevant tables below after each query. Reporting is made based on the records added to the tables after each query process.  
* CEKSOR Table: This is the table that holds the information about the queries that are recorded with a different ID as a result of each query made via the CRB Cheque Report screen.
* CEKICMAL Table: A SORGUNO is created in the CEKICMAL table for each ID field in the CEKSOR table. This table summarizes information about the number of cheques, payment status, and cheque status.
* CEKBANKA Table: A SORGUNO is created in the CEKBANKA table for each ID field in the CEKSOR table. This is the table that stores information about the bank name and bank code of the bank.
* CEKOZET Table: A SORGUNO is created in the CEKOZET table for each ID field in the CEKSOR table. This is the table that stores information about the maturity, amount, and status of the cheque.
* CEKKESIDECI Table: A SORGUNO is created in the CEKESIDECI table for each ID field in the CEKSPR table. This is the table that stores information about the average cheque amount and the bank.
* CEKMUHABIR Table: A SORGUNO is created in the CEKKESIDECI table for each ID field in the CEKSPR table. In case of an error regarding the cheque, this is the table where the error code and error description information are kept.
* CEKOSRONUC Table: A SORGUNO is created in the CEKESIDECI table for each ID field in the CEKSPR table. This is the table where detailed information such as the cheque reference number and transaction result is kept.
* CEKKESIDECITUARI Table: A SORGUNO is created in the CEKESIDECI table for each ID field in the CEKSPR table. This is the table where the year, type of cheque, and minimum and maximum amount of information regarding the cheque are kept.  
The main table is the CEKSCR table. The ID correspondence formed by the CEKSOR table is created in the other tables as SORGUNO, and the matches between the tables are made through these fields.  
Figure 1: Implementation steps aimed at the study.  
The cheque tables in Table 1, which contain millions of records, demonstrate the functionality and value of big data analytics. These large data sets are too complex to be processed by traditional methods, and big data analytics analyzes these data to make strategic decisions, increase operational efficiency, and gain competitive advantage. Therefore, big data studies allow businesses to operate more effectively and efficiently.
## 5 Multi-Drone Guidance Law  
We consider a high-speed target whose goal is to hit a ship. The target heads towards its goal (ship) at least in the terminal phase. Otherwise, it is impossible to make a target hit the ship.  
Therefore, we let multiple drones form a planar grid formation, whose center lies on the line segment connecting the target and the ship. Moreover, the planar grid formation is generated to be perpendicular to the line segment connecting the target and the formation center. The grid formation can be considered as a \"net\" structure for capturing the incoming target. By maximizing the grid formation size, we can increase the capture rate, even when there exists error in the prediction of the target's position. Since the target is guided to hit its goal (the ship), the drones can effectively block the target using this grid formation.  
The proposed multi-drone guidance law is summarized as follows. At every sample-index, the ship measures the 3D Cartesian coordinates of the incoming target. Thereafter, we run the Kalman filter to predict the target's 3D Cartesian coordinates after one sample-index in the future. See Section 5.1 for the prediction of the target's position after one sample-index.  
Based on the predicted target pose, the virtual agent is guided to remain in the lineState. See Section 5.2 for the guidance law of the virtual agent. In addition, each drone is guided to generate a grid formation centered at the virtual agent. See Section 5.3 for the guidance law of a drone. Figure 2 shows the block diagram of the proposed multi-drone guidance law.  
Figure 1: The case where the ship moves as the time index changes from \(k\) to \(k+1\). The ship positions are indicated by crosses. In this figure, \(L_{k}\), \(\bar{L}_{k+1}\), and \(\mathbf{c}_{k}\) are depicted.
## 4 Results and Discussion
### Development of Predictive Models for Estimating Bolme Abrasion Value (BAV)
#### 4.2.3 Artificial Neural Networks (ANN)  
ANN offers distinct advantages, including a straightforward feedforward backpropagation structure and faster training capabilities. These strengths enable ANNs to effectively solve a wide range of problems. In this study, a novel ANN model was developed in the MATLAB (R2021b) environment. Before loading the datasets into the software, they were normalized between \(-1\) and \(1\) to mitigate overfitting. The normalization was conducted using Equation (7). The input parameters were \(\rho_{d}\), \(w_{a}\), and \(V_{p}\), and various ANN architectures were attempted to develop the optimal predictive model with a simplified structure. As a result, one of the best ANN-based predictive models was found to have a 3-4-1 ANN architecture, indicating that the three input parameters (\(\rho_{d}\), \(w_{a}\), and \(V_{p}\)) were processed through four hidden layers to estimate the output (BAV).  
\[x_{norm}=2\times\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}}-1 \tag{7}\]  
Figure 6: Developed M5P model for evaluating BAV.  
where \(\rm x_{min}\) and \(\rm x_{max}\) are the minimum and maximum values in the relevant datasets, respectively.  
The mathematical formulations of the developed ANN model were derived using the deterministic approach proposed by [PERSON] [40]. Specifically, the weights and biases obtained from the MATLAB environment were used to reveal the mathematical expressions of the ANN model, which are listed in Equations (8)-(15).  
\[BAV=45.807\rm tanh\left(\sum\limits_{i=1}^{4}A_{i}+2.5158\right)+47.112 \tag{8}\]  
\[A_{1}=-4.0751\rm tanh\big{(}1.8599{\rho_{d}}^{n}+4.7668{w_{a}}^{n}+8.3054{V_{p} }^{n}+0.6765\big{)} \tag{9}\]  
\[A_{2}=3.7966\rm tanh\big{(}-0.0546{\rho_{d}}^{n}+0.7594{w_{a}}^{n}-0.81760{V_{p} }^{n}-1.5816\big{)} \tag{10}\]  
\[A_{3}=0.3332\rm tanh\big{(}-0.8108{\rho_{d}}^{n}-9.3598{w_{a}}^{n}-5.3047{V_{p} }^{n}-3.1726\big{)} \tag{11}\]  
\[A_{4}=-4.1126\rm tanh\big{(}-1.3518{\rho_{d}}^{n}-6.5916{w_{a}}^{n}-8.7894{V_{ p}}^{n}-2.467\big{)} \tag{12}\]  
Normalization functions  
\[{\rho_{d}}^{n}= 1.0101{\rho_{d}}-2.0101 \tag{13}\] \[{w_{a}}^{n}= 0.0509{w_{a}}-1.0015\] (14) \[{V_{p}}^{n}= 0.3263{V_{p}}-1.4013 \tag{15}\]
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 5 Flexible Chain Segment Toughening Method
### Current Research
#### 5.2.1 Toughening Epoxy Resins with Flexible Chain Segments in Curing Agents  
Flexible curing agents, due to the free rotation of their molecular chain segments, exhibit high flexibility and adaptability. During the curing process, these flexible chains bond to the dense crosslinked network of the epoxy resin, forming a more relaxed crosslinked structure [91]. Under applied stress, the molecular chains have more room to move, absorbing impact energy through deformation and reorientation of the segments, which effectively prevents crack propagation [92; 93].  
[PERSON] et al. [19] employed a thiol-ene click reaction to introduce flexible chain segments into a polymer network, thus toughening a flexible and hydrophobic epoxy resin (DGEB-DBP). The study found that the flexibility of the epoxy resin increased with the addition of DGEBDBP (Figure 7A). When the mass fractions of DGEBDBP and E44 were 75% and 25%, respectively (EP0.75), the composite achieved the highest elongation at break, reaching 77.8%, which is nine times that of pure epoxy resin. Additionally, the compressive strength also peaked at 112.8 MPa (Figure 7B).  
[PERSON] et al. [91] synthesized a novel amine with flexible poly(propylene oxide) side chains (AFPE) as a curing agent for bisphenol A diglycidyl ether (DGEBA), resulting in a high-toughness epoxy resin. The study showed that the epoxy resin modified with AFPE 40-C achieved a fracture elongation of 22.3%, which is an increase of 150.5% compared to pure epoxy resin, and the impact resistance improved by 46.2%.
# SOPRENE: Assessment of the Spanish Armada's Predictive Maintenance Tool for Naval Assets
## 4 Unified Architecture
### Failure Diagnose
#### 4.3.1 Artificial Failure Mode Generator  
The FMECA document collects the nominal thresholds and range caps of the variables that make up each failure mode. By automatically analyzing this document, the system is able to obtain this information. The nominal value of a variable for a given failure mode is the usual value over which it oscillates without the failure mode occurring. The upper range values, however, are those minimum and maximum thresholds from which a sensor is in an abnormal state, which can result in the failure mode in question. The ranges of operating values of each variable, therefore, depend on the rest of the variables at each moment.  
Figure 2: Autoencoder output (red) versus the original value to be reconstructed (blue) and the associated reconstruction error (green) for three attributes of the diesel engine for propulsion. \(x\)-axis represents normalized data coming from a BAM-class (Maritime Action Vessels) ship; \(y\)-axis contains an ordered index. Reconstruction error rises when the autoencoder is unable to reproduce the signals.  
Since there are no labeled real data of each failure mode to train the models, based on the values described in the FMECA, the system is capable of generating artificial data associated with each failure mode. The variables that do not intervene in the failure mode will present values that oscillate above their corresponding nominal values, while the values of the variables that intervene in the failure mode will deviate beyond the upper range values associated with that failure mode. In order to avoid generating datasets that are too homogeneous, the system uses different kinds of distributions to introduce some variability in the sensors. For example, given an artificially generated dataset, the values taken by a variable that do not intervene in the generated failure mode will follow a normal distribution centered on the nominal value without reaching the upper range values.
## 3 Results and Discussion
### Gradient Boosting Regression  
Another approach to improve prediction performance is gradient boosting, which can be applied to many machine learning methods [29]. The major difference of this approach is that instead of training the model concurrently, as is done in decision tree and random forest methods, it trains it sequentially. Specifically, the first decision tree is fitted to the training data set, the second is fitted using the residuals of the previous one, and then this new tree is added to the fitted function in order to update the residuals. This procedure is followed by fitting rather small trees -few number of terminal nodes- to the residuals, and that way we improve the model in areas where it does not perform as well. An additional shrinkage parameter \(\lambda\) exists that allows more and different trees to be fitted in certain areas where higher residuals appear.  
We selected to fit our gradient boosting regressor model to our data set by using a tree number -iterations- of 5000 and a learning rate of 0.05. The model performed sufficiently and was comparable to the RF by achieving the value R\({}^{2}\) = 0.941 and an RMSE = 7.71. In Figure 9, the model improvement is illustrated in terms of predictive accuracy as compared to the boosting iterations, which in the case of regression problems equals the number of trees, since each iteration uses a single tree.  
Impurity-based feature importance can be misleading for features with many unique values. As an alternative, we can compute the permutation importance. Permutation feature importance is a model inspection technique that can be used for any fitted estimator when the data is tabular, something very useful for non-linear estimators. The permutation feature importance is defined as the decrease in a model score when we randomly shuffle the value of a single feature [41]. This procedure breaks the relationship between the feature and the target; thus, the drop in the model score is indicative of how much the model depends on the feature. This technique benefits from being model agnostic and can be calculated many times with different permutations of the feature. For the current test subset, both the impurity-based and permutation methods -as a box-plot figure- identify the air temperature as the most significant predictive feature, whereas the rest of the ranking differs, as shown in Figure 10.  
Figure 9: Training and testing MSE as n trees are added to the GBM algorithm.
## 2 Methodology of DLR Measurements  
### Theoretical Analysis  
Using the laser ranging link equation, the number of laser echo photons can be estimated from the parameters of the measuring system and the size of targets, which provides a reference for practical laser ranging. The link equation is written as [11].  
\[n_{0}=\frac{\lambda\eta_{q}}{hc}\times\frac{E_{t}A_{r}\rho S\cos\theta}{\pi \theta_{t}R^{4}}\times T^{2}\times\eta_{t}\times\eta_{r}\times\alpha \tag{1}\]  
where \(n_{0}\) is the average number of photoelectrons produced by the photodetector, \(\lambda\) is the wavelength of the laser, \(\eta_{q}\) is the quantum efficiency of the photodetector at the wavelength \(\lambda\), \(E_{t}\) is the single-pulse energy, \(A_{r}\) is the effective receiving area of the tele scope, \(S\) is the cross-section of the debris targets, and \(\rho\) is the reflection rate of the debris targets. It is supposed that the pieces of space debris are spherical, such that \(\cos\theta=1\). \(h=6.6260693\times 10^{-34}\) J-s is the [PERSON]'s constant, \(c=3\times 10^{8}\) m/s is the speed of light, \(\theta_{t}\) is the divergence angle of the laser beam, \(R\) is the distance from the ground station to the target, \(T\) is the one-way atmospheric transmittance, \(\eta_{t}\) and \(\eta_{r}\) are respectively the efficiencies of the transmitting and receiving optics units, and \(\alpha\) is the attenuation factor. Taking into consideration the background noise (\(n_{1}\)) and detector dark noise (\(n_{2}\)), the total detecting probability of the laser signal is given by:  
\[\xi=e^{-(n_{1}+n_{2})}\big{(}1-e^{-n_{0}}\big{)}=e^{-(n_{1}+n_{2})}(1-e^{- \frac{-\frac{\lambda\eta_{t}}{\lambda\kappa}\times\frac{\xi_{t}A_{\mu}\xi\cos \theta}{\pi\theta_{t}R^{4}}\times T^{2}\times\eta_{t}\times\eta_{r}\times \alpha}}) \tag{2}\]  
Considering the power (\(P\)) and repetition rate (\(f\)) of the laser system, the number of echoes (\(D\)) from targets per second is calculated as:  
\[D=fe^{-(n_{1}+n_{2})}(1-e^{-\frac{\lambda\eta_{t}}{\lambda\kappa}\times\frac{ \eta_{t}\rho\xi\cos\theta}{\pi\theta_{t}R^{4}}\times T^{2}\times\eta_{t}\times \eta_{r}\times\alpha}) \tag{3}\]  
In Equation (3), the number of echoes is inversely proportional to the fourth power of the distance and proportional to the cross-section, which is taken as the radar cross-section (RCS). Increasing the measuring ability requires the development of DLR technology mainly focusing on the laser system, receiving performance, detection ability, and the number of noise levels required to be decreased. Laser systems have been developed from a low repetition rate to a high repetition rate and from nanoseconds to picoseconds in terms of the pulse width. Optical and electronic developments have resulted in excellent detectors and improvements to DLR technology (i.e., a detector with a large chip and high efficiency, a low-noise avalanche photo-diode detector and a superconducting detector with super low dark noise). Additionally, the signal receiving system uses large-aperture telescopes and telescope arrays.
## 3 Results and Discussion
### Evaluation of Visual Images and Selection of Quantification Method  
A set of images at 254 nm were obtained and visionCATS (CAMAG) software was used to interpret each of the images. Percent (%) recovery and correlation coefficient (\(r^{2}\)) values were found to be consistent for all four fat-soluble vitamins at 254 nm over the entire concentration ranges of their respective calibration curves. For the quantification of vitamins D\({}_{2}\) and D\({}_{3}\), absorbance peak area versus concentration of their calibration curves were found to produce the best results with high levels of reproducibility, whereas standard curves using peak height versus concentration were found to be most suited to quantify vitamins E and K\({}_{1}\) (Figure S1).
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 2 The Proposed Method
### Dropout-Based Ensemble for Testing  
Once the SSD-SAR-BS training process has been completed, we can employ the well-trained MSDNet to achieve despeckling for a given speckled SAR image \(\mathbf{Y}^{\prime}\). As described in Section 2.1, the input of the MSDNet is the Bernoulli-sampled speckled SAR image \(\hat{\mathbf{Y}}^{\prime}\) rather than the original full one \(\mathbf{Y}^{\prime}\). The recovered despeckled SAR image may lead to  
Figure 5: Architecture of the DRB.  
a problem in the testing process: some pixels may be lost. However, this problem can be solved by using the dropout-based [39; 45] ensemble, as presented in Figure 2. Firstly, we generated a set of Bernoulli-sampled images (i.e., \(\hat{\mathbf{Y}}^{(1)},\hat{\mathbf{Y}}^{(2)},\cdots,\hat{\mathbf{Y}}^{(K)}\)) for the same speckled SAR image \(\mathbf{Y}^{\prime}\), according to (3), (4), and (5). Here, the lost pixels of the used Bernoulli-sampled images may be different in every sampling. We introduced a dropout layer before each convolution of each DB, as presented in Table 1. Some units of each convolution were randomly ignored in each forward pass. Due to the independent randomness of dropout, we can view the well-trained MSDNet as a set of despeckling networks (i.e., \(f_{\theta}^{(1)}(\cdot)\), \(f_{\theta}^{(2)}(\cdot)\), \(\cdots\), \(f_{\theta}^{(K)}(\cdot)\)). Then, these Bernoulli-sampled speckled SAR images are fed to the corresponding despeckling networks to generate the corresponding output results (i.e., \(f_{\theta}^{(1)}(\hat{\mathbf{Y}}^{(1)}),f_{\theta}^{(2)}(\hat{\mathbf{Y}}^{ (2)}),\cdots,f_{\theta}^{(K)}(\hat{\mathbf{Y}}^{(K)})\)). Finally, the predicted despeckled SAR image \(\mathbf{X}^{\prime}\) is obtained by averaging all output results, which can be described as:  
\[\mathbf{X}^{\prime}_{w,h}=\frac{1}{K}\sum_{k=1}^{K}f_{\theta}^{(k)}(\hat{ \mathbf{Y}}^{\prime(k)})_{w,h^{\prime}}1\leq w\leq W,1\leq h\leq H. \tag{16}\]  
where \(K\) is the number of average times (i.e., the number of the generated Bernoulli-sampled images) in the dropout-based ensemble. \(W\) and \(H\) denote the image size. \(\mathbf{X}^{\prime}_{w,h}\) and \(f_{\theta}^{(k)}(\hat{\mathbf{Y}}^{(k)})_{w,h}\) denote the pixel values in \(\mathbf{X}^{\prime}\) and \(f_{\theta}^{(k)}(\hat{\mathbf{Y}}^{(k)})\), respectively. Because the Bernoulli sampling and the dropout layers both have independent randomness, all pixels can be considered when the average number of times is sufficiently large. Hence, the problem of pixels being lost can be solved, and the despeckling performance can be effectively boosted.
## 5 Experiment and Discussion  
### Experiment Platform  
A test rig has been constructed in order to validate the proposed control strategy. Figure 7 briefly depicts the schematic of the hydraulic system of the test rig. It consists of a position tracking system and a loading system. The position tracking system consists of cylinder 5, servo valve3.1 and 3.2, pressure sensor4, relief valve2.1 and 2.2, and fixed displacement pump1.1 and 1.2. The cylinder 5 is controlled by two servo valves, respectively. The max system pressure is determined by the crack pressure of the relief valve. Pressure sensors are attached to both cylinder chambers. The cylinder is equipped with a displacement sensor. The loading system is used to maintain a constant load force that is applied to cylinder 5. The direction of the loading force can be controlled by directional valve 9, and the magnitude of the loading force can be regulated by relief valve 2.2. Table 1 provides the main specifications of the test rig. Figure 8 shows a photo of the test rig.  
Figure 8: The photo of the test rig.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Item** & **Value** & **Unit** \\ \hline Pump displacement (1.1 and 1.2) & 40 & mL/r \\ Motor speed & 1500 & rpm \\ Servo valve rated flow (3.1) & 63 & L/min \\ Servo valve rated flow (3.2) & 38 & L/min \\ Piston diameter (5 and 8) & 63 & mm \\ Rod diameter (5 and 8) & 36 & mm \\ Weight of moving part & 100 & Kg \\ Stroke (5 and 8) & 280 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Specifications of the Test Rig.  
Figure 7: The schematic of the hydraulic system for the test rig: 1.1,1.2-fixed displacement pump; 2.1,2.1-relief valve; 3.1,3.2-servo valve; 4—pressure sensor; 5—testing cylinder; 6—displacement sensor; 7—mass block; 8—loading cylinder; 9—directional valve; and, 10—controller.
# Globally Optimizing QAOA Circuit Depth for Constrained Optimization Problems
## 2 Materials and Methods
### Optimizing Global Variable Substitutions for 3-Sat
#### 2.6.3 Heuristic Approximation  
The previous integer programming approach will provide an optimal solution. However, depending on the 3-SAT instance, it might be a very difficult problem to solve. As an alternative, we have developed a heuristic to approximate it. Greedy algorithms are a common way to approximate large scale set covering problems [26]. Generally, this involves a greedy selection of elements that cover the greatest amount of sets until all sets are covered. We have chosen to use a similar approach as the degree of the graph derived using GVS is directly impacted by the number of substitutions made. Therefore, we expect by selecting the coverings with the highest degree in the covering graph, we will be able to minimize the total number of substitutions made and obtain a locally minimal solution. Let \(U\) be the set of uncovered clauses. Let \(K\) be the set of covered clauses. At the beginning of the iteration, \(K\) is empty and \(U=C\), where \(C\) is the set of all clauses in the 3-SAT instance. The greedy algorithm is described in Algorithm 1. Often in step 2, multiple \(s_{i,j}\) pairs may cover the same number of clauses. To break these ties, we randomly select a pair \(s_{i,j}\) of the current largest degree in the covering graph.  
```
while\(|U|>0\)do  select \(s_{i,j}\in S\) which covers the the greatest number of \(u\in U\)  add all covered \(u\) to \(K\)  remove all covered \(u\) from \(U\)  remove \(s_{i,j}\) from \(S\)
```  
**Algorithm 1** Greedy by Covering Heuristic