esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stare/Stare** & **Stare/Scan** & **Scan/Stare** & **Scan/Scan** \\ \hline LDA \textgreater{} UCA & LDA \textless{} UCA & LDA \textgreater{} UCA & LDA \textless{} UCA \\ UCA \textless{} FOV & UCA \textless{} FOV & UCA \textgreater{} FOV & UCA \textgreater{} FOV \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The relationship among LDA, UCA and FOV for the four acquisition modes. a predefined-time attitude stabilization control for the receiver aircraft in aerial refueling. The establishment of the inter-satellite laser link also requires stringent time response constraints. When the master satellite performs the scanning with guidance law step-by-step, the telescope is expected to point a certain direction within a certain time so that the salve satellite has enough time to respond and the laser beam can be guaranteed to fully cover the uncertainty cone. To the best of our knowledge, the predefined-time stability control theory has not been applied in the acquisition phase for the establishment of the inter-satellite laser link. In view of above results, we investigated the laser acquisition problem for space-based GWs detection programs. The main contributions in this paper are:  
1. We proposed a new type of satellite equipped with two two-degree-of-freedom-telescopes. This configuration allows two laser links to be established at the same time, which means that the acquisition time will be drastically reduced;
2. We proposed a predefined-time controller for the acquisition phase. This control method can guarantee that the telescope will point to the desired direction within the predefined time, which will greatly improve the probability of acquisition. The remaining parts of this paper are Section 2 introduces the acquisition scheme. Section 3 develops the dynamic model for the platform. Section 4 designs the predefined-time controller. Section 5 is the simulation and Section 6 is the conclusions.
## 3 Data and Methodology
### Integrated Geophysical Exploration Method
#### 3.3.1. ERT Survey  
ERT generates an artificial electric field by applying electrical energy to electrodes inserted into the subsurface. It leverages the electrical disparities among rock layers to derive the apparent resistivity distribution of the subsurface medium, facilitating a dependable interpretation of the subsurface geological structure. Field measurements necessitate the simultaneous placement of multiple electrodes along the measurement line. These electrodes are subsequently linked to a programmable multielectrode converter, which is controlled by a program to enable rapid switching of a variety of pole distances, measuring points, and arrangements. This process allows for the collection of a substantial volume of data in a single operation [23,24].  
The ERT analyses were conducted using a WDA-1 super digital DC resistivity meter from Chongqing Pentium Company, featuring 10 m spacing and 90 road stakes selected for the survey. As shown in Figure 7, survey line positions were determined using handheld GPS devices, covering a total mapping profile length of 15.99 km. To ensure an effective survey depth, the long profile underwent a mobile superposition measurement. Field-collected data underwent several operations, including bad point removal, format conversion, and inversion calculations. The ERT inversion software RES2 DINV (5.0) was used to generate resistivity contour maps in this study. These ERT results, combined with geological survey data, were used for geological interpretation, and the interpretation map of the physical exploration results was created using ArcGIS (10.8.1) software.
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 6 Case Study  
### Design Example  
To verify the correctness of the proposed method, a down-scale three-phase MMC design case is given. Key system parameters and design requirement were as shown in Table 2. The maximum SM voltage ripple \(V_{rip}\) should be limited within 10% of the SM DC-bias, while the allowable SM voltage \(V_{th}\) is selected 25% higher than the SM DC-bias. Two common grid fault conditions are selected for SM capacitor sizing: single-line-to-ground fault and three-phase short-circuit fault. Both positive- and negative-sequence currents will be injected into the grid when the grid fault is detected.  
Figure 8: Design flowchart of SM capacitors.  
The reactive current injection strategy follows the German grid code, as shown in [40]. Thus, the MMC operating points during the normal state and grid fault conditions are shown in Table 3.  
By putting the design parameters above into Formulas (33) and (35), the SM capacitance demand for energy storage requirement and ripple voltage requirement can be calculated as: \(C_{sm\_enj}=250.8\) uF, \(C_{sm\_ripj}=857.7\) uF. According to Equations (38)-(39), the relationship between SM capacitance \(C_{sm}\) and the maximum transient SM voltage stress \(V_{tmax}\) can be calculated and shown in Figure 9. To keep the SM voltage in a safety range during both grid fault conditions, \(C_{sm\_ij}\) is selected as 1.14 mF. With a 20% extra design margin (\(\eta=1.2\)), the final SM capacitor is chosen as 1.36 mF, formed by two 680 uF capacitors connecting in parallel. By setting the rated voltage of the SM capacitor at 100 V, the information of the selected product is given in Table 4. By taking the ESR value into Formula (43), the power losses of the SM capacitor is around 0.1 W, which is relatively small. Thus, the hot-spot temperature of the SM capacitor is considered as equal to the ambient temperature in this case study. Assuming the operating ambient temperature is 60 \({}^{\circ}\)C, the SM capacitor lifetime can be estimated using Equation (51) as \(2.7\times 10^{5}\) h.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**System Parameters**} \\ \hline SM number in each arm (\(N\)) & 3 \\ Arm Inductance (\(L_{a}\)) & 5 mH \\ Grid frequency & 50 Hz \\ Switching frequency & 8 kHz \\ DC-bus voltage & 120 V \\ \(K_{op}\) & 10 \\ \(K_{i}\) & 60 \\ \(K_{cp}\) & 5 \\ \(K_{r}\) & 35 \\ \hline \multicolumn{2}{c}{**Design Requirement**} \\ \hline Peak-to-peak SM voltage ripple (\(V_{ripj}\)) & 4 V \\ Maximum SM voltage threshold (\(V_{th}\)) & 50 V \\ Capacitance redundancy factor (\(\eta\)) & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters and design requirement.  
Figure 9: The maximum SM voltage under different grid faults: (**a**) single-line-to-ground condition and (**b**) three-phase short-circuit condition.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 2 Types of Common LPBF Part Defects  
The LPBF printing process is prone to defects even when process parameters are optimized. It is virtually impossible to produce a completely flawless LPBF part. The complexity of this process is influenced by many processes and phenomena that need further efforts to be understood and controlled. Significant research activities have been made to comprehend these phenomena and to reduce or eliminate the likelihood of defects [3]. The standard method to mitigate these defects involves evaluating and reevaluating operating parameters and data collected using in situ and post-fabrication sensors. Common defects reported for LPBF are geometrical/dimensional, surface quality, microstructure, and mechanical defects. The majority of defects are interrelated or interdependent. The following subsections briefly define the most common defects in LPBF metal printing, which are commonly predicted and/or detected using numerical modeling and coupled AI-sensing technology detection methods [3].
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Covariance Matrix Regularization
#### 2.3.2 Shrunk Covariance Estimation  
The shrinkage covariance estimator creates a better-conditioned inversion matrix problem and generally performs better when applied to unseen data. The estimators for the covariance and inverse covariance are given by:  
\[\hat{C}_{\alpha}=(1-\alpha)\hat{C}_{\text{emp}}+\alpha\frac{\text{Tr}(\hat{C }_{\text{emp}})}{cs}\mathbb{I} \tag{8}\]  
\[\widehat{C^{-1}}_{\alpha}=\hat{C}_{\alpha}^{+} \tag{9}\]  
with \(0<\alpha<1\). Analogous to L2 regularization of the beamforming problem, shrinkage reduces the ratio between the smallest and largest eigenvalues of the covariance matrix by strengthening the diagonal. Figure 1c,d show examples of the shrunk estimator of the covariance and the inverse covariance matrices, respectively.  
Earlier work [23] applied shrinkage regularization to ERP decoding with the spatiotemporal beamformer and showed competitive performance compared to other state-of-the-art decoding techniques such as stepwise LDA or SVM. The abovementioned researchers chose the shrinkage coefficient \(\alpha\) as a fixed hyperparameter. However, its optimal value depends on the number of training epochs, the covariance matrix's dimensionality, and the independence and variance of the data, which can vary across evaluation settings and per session. The optimal value for \(\alpha\) can be found with a line search using cross-validation method, but this can be a costly procedure. Methods exist to estimate an optimal shrinkage value directly from the data. Most notable among these are the Ledoit-Wolf procedure [32], the Rao-Blackwell Ledoit-Wolf method [33], and the oracle approximating shrinkage method [33]. A more recent estimation method [34] emulates a leave-one-out cross-validation (LOOCV) scheme expressed by the data-driven closed-form estimate:  
\[\alpha=1-\frac{\frac{n}{n-1}\text{Tr}(\hat{\mathcal{C}}_{\text{emp}}^{2})- \frac{2}{cs}\big{[}\text{Tr}(\hat{\mathcal{C}}_{\text{emp}})\big{]}^{2}+\frac{ 1}{cs}\text{Tr}(\hat{\mathcal{C}}_{\text{emp}}^{2})-\frac{1}{n(n-1)}\sum_{i=1} ^{n}||\mathbf{x}_{i}||_{2}^{4}}{\frac{n^{2}-2n}{(n-1)^{2}}\text{Tr}(\hat{ \mathcal{C}}_{\text{emp}}^{2})-\frac{2}{cs}\big{[}\text{Tr}(\hat{\mathcal{C}}_ {\text{emp}})\big{]}^{2}+\frac{1}{cs}\text{Tr}(\hat{\mathcal{C}}_{\text{emp}}^ {2})+\frac{1}{n(n-1)}\sum_{i=1}^{n}||\mathbf{x}_{i}||_{2}^{4}} \tag{10}\]  
Herein, we opt for the LOOCV shrinkage estimator because it avoids some of the assumptions made by [32; 33] and because it generalizes to structured covariance estimations, as described in Section 2.3.3.
## 3 Experimental Results
### Evaluation Criteria  
We used quantitative metrics similar to the criteria used in other works for a fair comparison. We define the following metrics using the temporal overlap between the predicted summary \(A\) and the ground truth summary \(B\):  
\[Precision\ (P)=\frac{overlap(A,B)}{length(A)} \tag{7}\]  
\[Recall\ (R)=\frac{overlap(A,B)}{length(B)} \tag{8}\]  
\[F-measure\ (F)=\frac{2P\times R}{P+R}\times 100 \tag{9}\]  
To put these metrics into words: precision (\(P\)) is the percentage of true positive predictions over all positive predictions, recall (\(R\)) is the percentage of true positive predictions over the ground truth, and the F-score (\(F\)) is the harmonic mean between them.
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 6 Study Area: NEOM Smart City
### Meteorological Information  
According to the data provided by Saudi National Center for Meteorology (NCM), the NEOM climate is generally mild in the winter and hot in summer. In this study, we investigated the effect of extreme weather conditions on the FSO link in winter (Decemberto February) and summer (May to August) when the temperature approaches the minimum and maximum values, respectively, in the region. It is considered that owing to the moderate weather conditions in the other two seasons (spring and autumn), the FSO system will not encounter severe challenges in realizing optimal functionality. In the winter season, the monthly average temperature ranges from 16.56 \({}^{\circ}\)C to 21.70 \({}^{\circ}\)C, and the total average temperature is 18.88 \({}^{\circ}\)C. The summer season starts with a monthly average temperature of 30.15 \({}^{\circ}\)C, with an average temperature of up to 32.49 \({}^{\circ}\)C in the subsequent months, and the observed total average temperature is 31.79 \({}^{\circ}\)C, as shown in Figure 3a. Moreover, at the start of the winter season, the average humidity is 37.85%, which increases to up to 46.4% in the subsequent months. The overall average humidity is 41.9%. The humidity does not deviate significantly in the summer season: the average value ranges from 33.14% to 49.26%, and the overall average humidity is 41.49%, as shown in Figure 3b. In winter, the wind speed ranges from 4 m/s to 5 m/s, and the average value is 4.6 m/s. The oscillation of the wind speed is minimal in the summer season, and it ranges from 3.5 m/s to 3.8 m/s, with an average value of 3.8 m/s, as shown in Figure 3c. In general, the climate of NEOM is mostly stable throughout the year, unlike the dry and extremely hot weather experienced in the rest of Saudi Arabia. In particular, the overall temperature of this area is approximately 10 \({}^{\circ}\)C lower than the rest of the country [6].
# A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps
###### Abstract  
This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher's parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.  
hardware implementation; haze removal; hazy particle map; modified hybrid median filter; Batcher's parallel sort +
Footnote †: journal: Applied Sciences
## 3 Peak-Shaving Costs and Economic Dispatch Model
### Modeling Peak-Shaving Costs
#### 3.1.4 Environmental Cost  
Environmental pollution is also serious in the DPSC period. On the one hand, the desulfurization efficiency is decreased while the sulfur content of pollutants increased; on the other hand, the emission of NOX will be raised during low load level. Thermal power plants may be fined for excessive emissions of pollutants, and the corresponding environmental cost of UPI could be described as  
\[W_{i,t}^{ep-p}=1.6\times 10^{6}f_{i}(P_{i,t})\delta_{s}\Delta\eta_{s}W_{s}+ \big{[}1.63\times 10^{6}\delta_{N}\Delta\eta_{N}+1530\big{]}f_{i}(P_{i,t})W_{N}. \tag{10}\]For UOI, oil injection will bring oil pollutant and the UOI environmental cost is  
\[\begin{array}{c}W_{i,t}^{ev-o}=\Big{[}1.6\times 10^{6}f_{i}(P_{i,t})\delta_{s} \Delta\eta_{s}+2\times 10^{6}o_{i,t}\Big{]}W_{s}\\ +\Big{[}1.63\times 10^{6}\delta_{N}\Delta\eta_{N}+1530\Big{]}W_{N}[f_{i}(P_{i,t})+o _{i,t}]\end{array}. \tag{11}\]  
Based on the four parts of costs analyzed above, the total peak-shaving costs of coal-fired units with UPI can be piece-wisely represented as Equation (12). In RPS, the coal consumption cost accounts for the main part; in DPSW period, the peak-shaving costs are composed of the coal consumption cost and the wear-and-tear cost; while in DPSC period, the combustion supporting cost and the environmental cost should be extra considered in the peak-shaving costs.  
\[F_{i}(P_{i,t})=\left\{\begin{array}{cc}f_{i}(P_{i,t})&P_{a}<P_{i,t}\leq P_{ \max}\\ f_{i}(P_{i,t})+W_{\cos t}(P_{i,t})&P_{b}<P_{i,t}\leq P_{a}\\ f_{i}(P_{i,t})+W_{\cos t}(P_{i,t})+W_{i,t}^{pila}+W_{i,t}^{ev-p}&P_{c}<P_{i,t} \leq P_{b}\end{array}\right. \tag{12}\]  
The peak-shaving costs model of coal-fired units with UOI can be similarly summarized for the RPS, DPSW, and DPSC periods, respectively, as Equation (13).  
\[F_{i}(P_{i,t})=\left\{\begin{array}{cc}f_{i}(P_{i,t})&P_{a}<P_{i,t}\leq P_{ \max}\\ f_{i}(P_{i,t})+W_{\cos t}(P_{i,t})&P_{b}<P_{i,t}\leq P_{a}\\ f_{i}(P_{i,t})+W_{\cos t}(P_{i,t})+W_{i,t}^{oil}+W_{i,t}^{ev-o}&P_{c}<P_{i,t} \leq P_{b}\end{array}\right. \tag{13}\]  
According to Formulas (12) and (13), the peak-shaving costs of coal-fired units with UOI and UPI could be visually shown in Figure 2.  
Although the start/stop cost is not considered in the peak-shaving, it is used to calculate the marginal price \(V_{m}\) of coal-fired units for evaluating its operation economy.  
\[V_{m}=\frac{\sum\limits_{t=1}^{T}\sum\limits_{i=1}^{N}F_{i}(P_{i,t})+\sum \limits_{t=1}^{T}\sum\limits_{i=1}^{N}\Big{[}U_{i,t}(1-U_{i,t-1})S_{iup}+U_{i,t-1}(1-U_{i,t})S_{idown}\Big{]}}{E_{c}} \tag{14}\]  
where \(U_{i,t}=0\) for the OFF state and \(U_{i,t}=1\) for the ON state.  
Figure 2: Peak-shaving costs model of coal-fired units with unit with oil ignition (UOI) and unit with plasma ignition (UPI).
## 4 Results and Discussion
### End-to-End Performance  
The HieD DLRI for optical satellite image is formed when all stages are attached end to end. The conventional metrics are insufficient for analyzing the multiple stages necessary to evaluate the HieD approach. Therefore, falseNegative2 and falsePositive2  
\begin{table}
\begin{tabular}{c c  
terms are defined, and their meanings are clarified in Table 11. While HieD offers several flexibilities in designing the stages, it introduces the error cycle due to the serial connection. We deduced the following outcomes when we evaluated the output metrics in Table 11.  
* The detection stage has the same results as the individual because it is the first stage to stay out of the error cycle. Only two images are missing, and each of them have one ship. The results of the detection phase are very satisfactory, except for the false positives.
* The localization stage is also not affected by the error cycle. Sixteen false true images detected in the previous stage are eliminated in this stage with true negative labeling. Thus, the results are very close to the individual evaluation. The number of missed ships is a bit high, which lowers the recall values of the following stages. On the other hand, a low false positive value increases the precision value of the next stages.
* The recognition starts with 274 errors from the previous two stages. For this reason, the accuracy and recall values are lower than they are in the individual evaluation.
* The identification evaluation metrics are a bit higher than 50 percent. Even though this may seem low, the relatively low number of ships belonging to navy classes, the compulsory samples in the dataset, and the below-average recognition performance (falseNegative and falsePositive2) of the navy class generated these results.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 3 Performance Prediction Model
### Step 2: Hypothesis Testing and Model Selection  
In the prepared dataset, the initial five columns \((N_{macro},\ N_{pico},R_{macro},\ R_{pico},P_{TD})\), as shown in Table 2, represent the input attributes, and the remaining two are the model target variables (CP and ASE). The dataset was analyzed to evaluate the best fit hypothesis for our problem, stating the statistical relation between the input attributes and the target variables. The null hypothesis for the given problem was set as, \"The input attributions are not linearly related with the target\". The existence of a null hypothesis was tested using correlation analysis. Correlation is a measure of the strength and direction of the relationship between variables ranging between \(-1\) to \(1\). The null hypothesis was established if the correlation coefficient was closely bound to zero, indicating no relation between the variables. Table 3 shows the correlation of the input attributes with the targets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range of Values** \\ \hline \(N_{macro}\) & 50–200 \\ \(R_{macro}\) & 10–40 \\ \(N_{pico}\) & 8–20 \\ \(R_{pico}\) & 4–8 \\ \(P_{TD}\) & 5–20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of values of all input parameters used in the simulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(N_{macro}\) & \(R_{macro}\) & \(N_{pico}\) & \(R_{pico}\) & \(P_{TD}\) & \(CP\) & \(ASE\) \\ \hline
50 & 40 & 20 & 8 & 10 & 0.5786 & 46.8879 \\
50 & 40 & 20 & 8 & 20 & 0.8596 & 42.4943 \\
100 & 10 & 8 & 4 & 5 & 0.4207 & 60.0825 \\
100 & 10 & 8 & 4 & 15 & 0.5017 & 73.2641 \\
150 & 10 & 16 & 4 & 5 & 0.4763 & 74.8983 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial five rows of the prepared dataset.  
Figure 2: Topology of a simulated two-tier typical network.  
As the correlation coefficients were not close to zero for any of the inputs, there was sufficient evidence to reject the null hypothesis and to suggest that the PMs (either CP or ASE) showed a linear relationship with the network parameters. Hence, two multivariate linear regression models were inferred to approximate the relations of the network performance metrics (PMs) with the network parameters that will predict the network performance of an arbitrary set of network parameter values outside the training set. Hence, the hypothesis to approximate to an unknown stochastic function is given by the following: [28],  
\[\hat{f}=h_{\beta}(m)=\beta^{T}m. \tag{16}\]  
Here, \(m\) is the independent input attribute vector given by \([1,N_{macro},\ R_{macro},\ N_{pico},R_{pico},\ P_{TD}]^{T}\) and \(\beta=[\beta_{0},\beta_{1},\ldots,\beta_{5}]^{T}\) is the tunable regression parameter vector.
# Bacteriotherapy in Breast Cancer
## 1 Introduction
Bacillus Calmette-Guerin (BCG) is an attenuated strain of _Mycobacterium bovis_ and it is the only bacterial agent that has been approved by the FDA for the treatment of non-muscle invasive bladder cancer (NMIBC), since the late 1970s. BCG has been used as a standard and is the most effective treatment for high-risk NMIBC [22; 23]. Although clinical application of cancer bacteriotherapy is not very useful and routine, this approach can be promising in the future. Recently, advanced genetic engineering has increased the ability to change bacterial strains, which can help the production of non-hazardous bacteria that can treat cancer. Most bacteria produce antitumor effects by reducing the nutrients required for the metabolism of cancer cells [24]. Obligate or facultative anaerobic bacteria find favorable niches within the hypoxic and necrotic regions of the tumor. The systemic administration of bacteria can lead to their entry in the tumor tissue, proliferation, and the formation of a necrotic region by reducing oxygen and the nutrient supply. Thus, it causes the tumor cells in the center of the solid tumor to die from starvation and suffocation [24]. Additionally, the bacteria, along with several other mechanisms have the ability to fight cancer cells, the most important of which include: (i) enhancing human immunity, (ii) as a carrier for cancer therapeutic agents, (iii) releasing substances (iv) forming biofilms, and (v) invading and colonizing the solid tumor [6]. In this article, we review the studies using bacteriotherapy (alone or combined with other methods) for the treatment of breast cancer, and critically discuss the impact of bacterial products (including, a bacterial peptide, bacteriocins, and toxins) that have the potential for anti-breast cancer treatment and mechanisms of bacteria-cancer-cell interaction.