esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 2 Acquisition Scheme
### Sensors and Actuators  
Before the laser beam signal is detected, the absolute pointing accuracy of the telescope depends on the star trackers and telescope pointing angle sensors. The accuracy of the star trackers whose use has been suggested in the GWs detection mission is about 1 arcsec (3\(\sigma\)) and its field-of-view (FOV) is about 0.26 rad [24]. The read-out noise introduced by the telescope pointing angle sensor can be represented as white noise with a constant power spectral density equal to 10 \(\mathrm{rad}/\mathrm{Hz}^{1/2}\)[24]. When the laser beam signal is detected, the acquisition sensor can be used as an attitude sensor to provide the relative attitude angle knowledge between two satellites. The acquisition sensor suggested in this paper is an array detector, which can replace the traditional scheme of combining Charged Coupled Device (CCD) and Quadrant Photodetector (QPD) to detect the weak laser signals from the sending satellite. The accuracy of the array detector we suggested is about 10 \(\mathrm{nrad}/\mathrm{Hz}^{1/2}\)[40]. Additionally, the obvious difference between the traditional satellite and the GWs detection satellite is that the GWs detection satellite has two cubic test mass for inertial reference. The test mass and its measurement system can be used as a sensor to measure the relative position and attitude between satellite and test mass with an extremely high accuracy. In this paper, it is considered as a sensor for high-precision attitude control in acquisition phase with an accuracy about \(2\times 10^{-9}\)\(\mathrm{rad}/\mathrm{Hz}^{1/2}\)[41].  
The actuators used in the acquisition phase include the micro-propulsion system and the telescope pointing actuator system. The micro-propulsion system is used to provide force and torque for the position and attitude control of the satellite. The thrust range of the micro-propulsion varies from 1 \(\mathrm{\SIUnitSymbolMicro N}\) to 120 \(\mathrm{\SIUnitSymbolMicro N}\) with a noise about 0.1 \(\mathrm{\SIUnitSymbolMicro N}/\mathrm{Hz}^{1/2}\)[42].  
Figure 3: Configuration of the satellite platform for the GWs detection mission.  
Figure 2: Control of the telescope pointing DOFs. (**a**) Three attitude DOFs and one telescope DOF; (**b**) Each telescope has two DOFs.  
The telescope pointing actuator system is a two-axis mechanism which is used to provide force for the two-direction rotation control of the telescope. It will perform the scanning and compensate for the angle change between the telescope which is caused by the orbital perturbation. The telescope pointing actuation disturbance noise introduced by the actuator can be represented as white noise with a constant power spectral density equal to 60 \(\mathrm{nN/Hz^{1/2}}\)[24].
## 4 Relay Scheme based on FAHP
### Measurement of Relative Importance
#### 4.2.3 Criterion 3: Number of Neighbors
This can be calculated by multiplying each \(\widetilde{g}_{i}\) value with the reciprocal value of the total mean, yielding the values in Table 7. For example, for the distance criterion, its TFN weight \(\widetilde{w}\) would be calculated as follows:  
\[V_{sphere}=4\ \pi\ d_{TX}^{3} \tag{26}\]0 \begin{table}
\begin{tabular}{l c} \hline \hline
**Criterion** & \(\widetilde{g}\) \\ \hline
**Distance** & (2.924, 3.3019, 3.6593) \\
**No. Hops** & (0.8939, 1, 1.1187) \\
**No. Neighbors** & (0.2733, 0.3029, 0.3420) \\ \hline
**Total Mean (\(\sum_{i=1}^{3}\widetilde{g}_{i}\))** & (4.0912, 4.6048, 5.12) \\
**Reciprocal of Total Mean** & (0.1953, 0.2171, 0.2444) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Geometric mean of criteria from (11). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Pairwise** & **Distance** & **No. Hops** & **No. Neighbors** \\ \hline
**Distance** & 1 & 6 * & 6 * \\
**No. Hops** & \(1/6\) & 1 & 6 * \\
**No. Neighbors** & \(1/6\) & \(1/6\) & \(1\) \\ \hline \hline \end{tabular}  
* 6: between 5 (strong significance) and 7 (very strong significance).  
\end{table} 
Table 4: Pairwise comparison matrix of criteria. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Pairwise** & **Distance** & **No. Hops** & **No. Neighbors** \\ \hline
**Distance** & \((1,1,1)\) & \((5,6,7)\) & \((5,6,7)\) \\
**No. Hops** & \((1/7,1/6,1/5)\) & \((1,1,1)\) & \((5,6,7)\) \\
**No. Neighbors** & \((1/7,1/6,1/5)\) & \((1/7,1/6,1/5)\) & \((1,1,1)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pairwise comparison matrix of criteria as TFNs. From Table 7, it is possible to obtain the non-fuzzy weights \(q_{i}\) from (14) and their corresponding normalized values \(l_{i}\) from (15), whose values are presented in Table 8. As an example, in the case of the distance criterion, its weight \(q_{i}\) is calculated as \((0.5711+0.7171+0.8944)/3=0.7275\), and its corresponding normalized value is \(0.7275/(0.7275+0.2217+0.0676)=0.7155\). Note that the summation of the normalized weights \(l_{i}\) is equal to 1. At this point, we have the normalized weights (\(l_{i}\)) corresponding to every criterion, which will be applied to every candidate node to select the best one. In order to make the final decision, is necessary to carry out a similar comparison process by means of a matrix between every pair of candidate nodes (alternatives) under every criterion considered. The result of this process is a new set of normalized weights \(l_{i}^{k}\) for every alternative \(i\) under the criterion \(k\). The whole process in an example case is given in Appendix A. A valid definition for the final score of every alternative is given by the following rank:  
\[Rank_{i}=\sum_{k=1}^{3}l_{k}\cdot l_{i}^{k} \tag{32}\]  
where \(l_{k}\) is the local weight of criterion \(k\) (from Table 8) and \(l_{i}^{k}\) is the weight of the criterion \(k\) calculated for the alternative (node) \(i\). The selected alternative (node) will be the higher-ranked of the four alternatives considered.
## 5 Numerical Examples  
### Data Information  
Dataset #1 (DS1), presented in Table 2, was reported by [PERSON] [25] based on software failure data from a real time command and control system (RTC&CS), and represents the failures that were observed during system testing (25 hours of CPU time). The number of test object instructions delivered for this system, which was developed by Bell Laboratories, was 21,700.  
Dataset #2 (DS2), as shown in Table 3, is the second of three releases of software failure data collected from three different releases of a large medical record system (MRS) [26], consisting of 188 software components. Each component contains several files. Initially, the software consisted of 173 software components. All three releases added new functionality to the product. Between three and seven new components were added in each of the three releases, for a total of 15 new components. Many other components were modified during each of the three releases as a side effect of the added functionality. Detailed information of the dataset can be obtained in the report by [PERSON] and [PERSON] [26].  
Dataset #3 (DS3), as shown in Table 4, is from one of four major releases of software products at Tandom Computers (TDC) [27]. There are 100 failures that are observed within testing CPU hours. Detailed information of the dataset can be obtained t  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hour Index** & **Failures** & **Cumulative Failures** & **Hour Index** & **Failures** & **Cumulative Failures** \\ \hline
1 & 27 & 27 & 14 & 5 & 111 \\
2 & 16 & 43 & 15 & 5 & 116 \\
3 & 11 & 54 & 16 & 6 & 122 \\
4 & 10 & 64 & 17 & 0 & 122 \\
5 & 11 & 75 & 18 & 5 & 127 \\
6 & 7 & 83 & 19 & 1 & 128 \\
7 & 2 & 84 & 20 & 1 & 129 \\
8 & 5 & 89 & 21 & 2 & 131 \\
9 & 3 & 92 & 22 & 1 & 132 \\
10 & 1 & 93 & 23 & 2 & 134 \\
11 & 4 & 97 & 24 & 1 & 135 \\
12 & 7 & 104 & 25 & 1 & 136 \\
13 & 2 & 106 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset #1 (DS1) : real time command and control system (RTC&CS) data set.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 3 Deep Learning Surface Defect Detection Methods for Industrial Products  
Deep learning has become increasingly popular in the field of defect detection due to its rapid development. This section summarizes the state of research on inspection of industrial products for detecting surface defects. Learning-based approaches are classified as supervised, semi-supervised, and unsupervised. The performance of learning-based methods is best optimized when large datasets are provided. In particular, supervised techniques perform well when there are sufficient examples of each class in the dataset.
# Plasma-Activated Water Affects the Antioxidant Contents in Water Spinach
## 2 Materials and Method
### PAW Treatments  
#### 2.3.1 Phase-1: Pot Experiment in Soil with Pb/Cd Contaminants  
Water spinach (_Ipomoea aquatica_) seeds were procured from Known-You Seed Co., Ltd. (Kaohsiung City, Taiwan). First, 15 seeds were randomly selected and placed in a beaker containing 200 mL of RO water. A single APPJ electrode was immersed in the water, and the power was set at 60 W for plasma generation. The seeds were treated for 7 min (Figure 1B). For the untreated (control) group, 15 seeds were placed in the RO water for 7 min without applying a plasma jet. For seedling preparation, the non-treated and PAW-treated seeds were separately placed on a wet filter paper in a Petri dish for a 5-day germination. Six of the best-grown seeds were chosen and moved to pots for growing under PAW or RO water (control) irrigation. Planting pots with 13 cm height and 15 cm diameter were used for plantation. Cultivation (control) soil and man-made contaminated soil with added metals, such as Pb (Pb acetate trihydrate, 99-102.5%, Sigma-Aldrich Co., St. Louis, MO, USA) and Cd (Cd II acetate anhydrous, 99.995%, Sigma-Aldrich Co., St. Louis, MO, USA), were prepared in advance according to our previous study [41]. Each planting pot contained 550 g of either control or contaminated soil (Figure 2).  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Nitrate (mg/L)** & **Nitrite (mg/L)** & **pH** & **Conductivity (\#S/cm)** & **ORP (mV)** \\ \hline
42.7 \(\pm\) 0.70 & 14.7 \(\pm\) 0.58 & 3.17 \(\pm\) 0.06 & 311.7 \(\pm\) 12.01 & 554 \(\pm\) 2.65 \\ \hline \end{tabular}
\end{table}
Table 1: The physiochemical properties of PAW.  
Figure 1: (**A**) The PAW generation system consisting of two plasma jet electrodes, RO water supply, power supply, and air supply. (**B**) The PAW treatment of seeds.  
The PAW was applied for the seeds and irrigation, resulting in the following four treatment combinations: Group 1, non-treated seeds and irrigation (NTS + NTW); Group 2, non-treated seeds but PAW was used for irrigation (NTS + PAW); Group 3, plasma-treated seeds but none for irrigation (PTS + NTW), and Group 4, plasma-treated seeds and irrigation (PTS + PAW). Each group was planted indoors in triplicate, and each pot contained six seedlings. Planting was performed under neutral day conditions (photoperiod: 13 h light/11 h dark between 07:00 and 20:00 h) using a T9 fluorescent lamp (6500 K, 18 W). Each pot of water spinach was watered with 150 mL of either PAW (experimental group) or RO water (control group) three times weekly for 5 weeks.
## 3 Results and Discussion
### NO\({}_{x}\) Storage/Reduction Cycles
Quantitative analysis of the lean/rich cycle at 300 \({}^{\circ}\)C for Ba6/Cu2 CZ is reported in Table 1 in terms of selectivity to reduction products (i.e., NO, N\({}_{2}\), NH\({}_{3}\)) and N-balance evaluated as N-species adsorbed versus N-products evolved. As it appears, the catalyst exhibits almost the same behavior during all the cycles; indeed, the NO\({}_{\rm x}\) amounts stored is always near 250 umol/gcat, while the selectivity to N\({}_{2}\) is in the range of 26-34%. These results confirm the large stability of this catalyst during the seven cycles tested. At the end of the lean-rich sequences, a desorption under the programmed temperature in inert atmosphere is carried out in order to completely remove the remaining superficial species. Indeed, as shown in Table 1, the N-balance results slightly higher than 1, suggesting that small amounts of NO\({}_{\rm x}\) remain adsorbed on the surface. As soon as the temperature increases, a release of NO is observed, with the maximum at 375 \({}^{\circ}\)C, followed by the subsequent emission of N\({}_{2}\), with the maximum at 500 \({}^{\circ}\)C. This is properly explained in the next section.
## 5 Simulation Setup
### Detectable Space for the Stereo Camera  
From Appendix A, Table A3, the angle of view in width and height for both lenses in the stereo camera is \(\alpha=86.05^{\circ}\), and \(\beta=55.35^{\circ}\). The detectable space for each lens can be modeled as the inner area of a cone with angles \(\alpha\) and \(\beta\). And the overlapped space is the detectable space for the stereo camera. The model is shown below (Figure 15). Point C is the center of the baseline whose length is denoted as \(b\) or the center of the camera system. The overlap area is also a cone with angles of vertex \(\alpha\) and \(\beta\), with offset \(d\) from the baseline. Point Q is the vertex of the cone.  
\[d=\frac{b/2}{\tan(\frac{\phi}{2})}=0.064\text{ m} \tag{62}\]  
There is an upper bound of the distance between the object to the camera center; if the object is too far away from the camera center, the dimensions of the projected images are too small to be measured. Suppose to have a clear image of the fiducial markers (circle shapes),  
Figure 15: The detectable space of the stereo camera.  
it is required that the diameter of the projected image should take at least 5-pixel numbers in the image frame:  
\[N_{H}\ \geq 5\ \text{resolution} \tag{63}\]  
where \(N_{H}\) is the pixel number in the image frame.  
Select the Zed camera's mode so that its resolution is 1920 \(\times\) 1082, and the image sensor size is 5.23 mm \(\times\) 2.94 mm. Then, the number resolution in unit length is:  
\[n=367\ \text{resolution/mm} \tag{64}\]  
Then, the range of the markers' diameter on the image frame is:  
\[d_{H}=\frac{N_{H}}{n}\geq 0.0136\ \text{mm} \tag{65}\]  
Also, assume in the inertial frame, the diameter of the attached fiducial marks is:  
\[D_{H}=12\ \text{mm} \tag{66}\]  
Then, from the pinhole model of the camera, the range of distance between the markers and camera center \(Z\) is:  
\[Z=\frac{f_{u}D_{H}}{d_{H}}\leq 2.47\ \text{m}=Z_{max} \tag{67}\]  
where \(Z_{max}\) is the maximum depth of the camera to detect the markers. This parameter defines the height of the cone in Figure 15. The detectable space (abiding by the second geometric constraint) forms an elliptic cone with different angles of the vertex.
## 4 Experiments and Evaluation
### Predictive Maintenance: Comparative Analysis for Anomaly Detection  
In this section, we compare the performance of three anomaly detection candidates. Since we are using the same artificial outliers and pseudo target datasets for all of the algorithms, we can directly compare their performance through the calculated error rates and the training/prediction times. We use the most optimal parameter sets decided for each algorithm in Section 3.3. The results are shown in Table 2.  
Although the kernel PCA achieves a relatively low error rate, it consumes considerable amount of time even when trained with only 1/8 of the full dataset as opposed to the full size used in training the other two algorithms. Therefore, it is not suitable for real-time applications. As for \(k\)-NN, despite its reasonable error rate, it suffers from over-fitting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Error Rate (\%)** & **Training Time (s)** & **Predicting Time for Pseudo Target Data (s)** \\ \hline OCSVM & 2.919 & 2.232 & 0.735 \\ \hline \(k\)-NN & 5.932 & 4.514 & 4.349 \\ \hline kernel PCA & 4.722 & 74.616 & 1.513 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance comparison of OCSVM, \(k\)-NN and kernel PCA.  
Figure 8: The experiment setup.  
under the current parameter combination. As it can also be seen from Figure 4 that OCSVM provides a more reliable decision boundary for our application. It not only has the lowest error rate, but also spans the shortest training and prediction time.
## 2 Materials and Methods
### Spectral Reflectance Measurements  
Spectral indices closely related to _Sphagnum_ primary productivity and water content were calculated and compared to flux measurement data [31; 34; 35; 36]. Changes in spectral water content (wetness index WI, floating water band index FWBI) and photosynthetic capacity (chlorophyll index CI, normalized difference vegetation index NDVI) were measured using an ASD Fieldspec 3 spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) under clear, low haze conditions as previously described in detail [31; 36]. The spectroradiometer was held 30 cm above the surface of the center of each sphagnocosm, and was white referenced every ten minutes to adjust for changes in sun angle and sky conditions.
# Quantum Game Application to Recovery Problem in Mobile Database
## 4 Discussion  
### Simulation Setup  
The simulation is designed to evaluate the proposed model for MD recovery based on game theory. In this respect, we implemented the prototype NS2 program using two software packages: Matlab and NS2. By modeling discrete occurrences, the NS2 simulation software enables developers to improve their businesses in real time. Additionally, it supports a variety of protocols such as TCP, routing, and multicast across wired and wireless networks and runs on a variety of platforms including Linux and Windows [38; 39]. As a result, we use this software to implement the stage of collecting data on the work environment at various levels in order to mimic changes in the work environment. Then, Matlab software is used to construct a game theory in order to assess various recovery procedures by inputting the output values from the simulation stage in order to find the optimal decision. For our solution, we used mobile log files of various sizes that included the process data that each method would obtain.  
The settings for the NS2 simulation are summarized in Table 4. The MAC layer protocol of IEEE 802.11 for wireless large area networks is utilized here. A movement file provides the mobile client node's motions. The mobile client node transmission range is 250 m. Each cell has one base station. A random waypoint (RWP) model determines the starting node position and movement. The RWP model is based on random locations, speeds, and halt durations. The prototype was built in modules and tested on a Dell Inspiron N5110 laptop from Dell Computer Corporation in Texas. Processor: Intel(R) Core(TM) i5-2410M, 4.00 GB RAM, Windows 7 64-bit. The proposed model's efficiency is assessed using execution time and recovery probability. See [24; 27] for more details. The results are the average of several repeated experiments for different initial location and movements of the nodes due to the using of RWP model. The most suitable values for the collection of protocol's factors (strategies) were picked from the literature, based on the assessment of selected recovery protocols, to represent the protocol's performance in a variety of settings.
# Evaluation of Operator and Patient Doses after Irradiation with Handheld X-ray Devices
###### Abstract  
This study aimed to evaluate radiation doses from handheld X-ray devices, specifically NOMAD Pro 2 TM (DvcN), Restar X (DvcRX), and Dixo 602 (DvcD), targeting operator and patient's critical organs and tissues. Calibrated TLD-100H dosimeters were placed on a mannequin and phantom head, focusing on the eyes, thyroid, gonads, hands, and salivary glands. Using a TLD reader, absorbed equivalent doses post irradiation were assessed. Conventional systems yielded higher radiation doses than phosphor plates and digital systems. Notably, implementing protective measures resulted in significant (\(p<0.05\)) dose reductions to the operator. Peak measurements without protection included: gonad 24.4 (DvcN) uGy; thyroid 30.5 (DvcN) uGy; right eye 31.9 (DvcN) uGy; left eye 27.9 (DvcN) uGy; right hand 111.6 (DvcRX) uGy; and left hand 71.7 (DvcD) uGy. Radiation dose reductions ranged from 11.49% to 93.25%, depending on the region and device. It is imperative to adhere to radiological protection protocols, particularly when employing handheld X-ray devices; optimally, these should be used with digital systems.  
handheld; X-ray; dental; radiation dose; radiation protection Article
# Intelligent Decision Forest Models for Customer Churn Prediction
## 3 Methodology
### Telecommunication Customer Churn Datasets  
For the experimentation phase of this research work, two CCP datasets with diverse characteristics were used for training and testing the CCP models. The first dataset (hereafter referred to as Dataset 1) was obtained from the Kaggle ML repository [59; 60; 61], and the second dataset (hereafter referred to as Dataset 2) was downloaded from the UCI ML repository [59; 62]. The selected CCP datasets are publicly available and are regularly utilized in existing CCP studies [14; 17; 34; 59; 62]. Dataset 1 is primarily derived from the IBM business analytics community, which describes information about a telecommunication company that provided voice and internet services for customers. Specifically, Dataset 1 consists of 3333 instances, out of which 2850 are non-churners (NC), and 483 are churners (C) with 21 features. Dataset 1 has a churn rate of 14.49% and an imbalance ratio (IR) (NC/C) of 5.9. Similarly, Dataset 2 has 5000 instances, out of which 4493 are non-churners while 507 are churners, meaning that Dataset 2 has a churn rate of 10.14% and an IR of 8.86. Further description of Dataset 1 and Dataset 2 is presented in Table 2.
# Design of a Broadband Transition from a Coaxial Cable to a Reduced-Height Rectangular Waveguide
## 4 Conclusions  
We have proposed a design method for a broadband transition between a coaxial cable and a rectangular waveguide with a height-to-width ratio ranging from 0.125 to 0.375. We adopted the disk probe structure of [PERSON] and coworkers and the method of [PERSON] to increase the ratio bandwidth from the previous value of 2.0 to a new value of 2.4 or greater. With the use of symmetrical structures, the excitation of the higher-order mode was suppressed below \(-\)57 dB for a height-to-width ratio from 0.125 to 0.375. The bandwidth performance of the transition presented in this paper is the largest or a record-breaking one compared with previous works for the transition structure presented in this communication. The proposed technique may also be applied to coaxial-to-rectangular waveguide transitions with a height-to-width ratio ranging from 0.08 to 0.12. One may apply the proposed method to extending the bandwidth of the transition between a coaxial cable and other types of waveguides, such as circular, single-, and double-ridge waveguides. These topics will be investigated in the future.  
Conceptualization, C.-S.L. and B.-C.A.; methodology, B.D., S.X., J.H. and B.-C.A.; validation, B.D., S.X., J.H., C.-S.L. and B.-C.A.; formal analysis, C.-S.L. and B.-C.A.; formal data curation, B.D. and B.-C.A.; writing--original draft, B.D., S.X. and J.H.; writing--review and editing, C.-S.L. and B.-C.A. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Commercial half-height waveguide adapter with an SMA(f) connector by Fei Teng Wireless Co. [12].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work (Year)** & **Experiments** & **\(b/a\)** & **Frequency** & **Reflection** & **Bandwidth** & **Complexity** \\ \cline{1-1} \cline{3-6} \cline{6-6} \cline{7-  
**Funding:** This work was supported by funding from an academic research program of Chungbuk National University in 2022.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.