esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design
### Battery State of Charge  
The state of charge of the battery system is determined by the Ordinary Differential Equation (ODE) (Equation (15)) [32].  
\[\dot{SOC}=\frac{P_{bat}}{V_{bat}Q} \tag{15}\]  
where \(Q=n_{b}q\) is the nominal discharge capacity of the battery system with \(n_{b}\) being the number of batteries and \(q\) being the discharge capacity of each battery. The dependence of the battery voltage is on the SOC and also on the temperature, which is exponential, as shown by Equation (16).  
\[V_{bat}(SOC)=\left(3+\frac{e^{SOC}-1}{e-1}\right)\left(2-e^{\lambda\frac{T-T_ {0}}{T_{0}}}\right) \tag{16}\]  
where \(T_{0}\) is the reference temperature, and \(\lambda\) is the temperature decay coefficient. At any given time, the battery power is the sum of the loads, given by Equation (17).  
\[P_{bat}=P_{sol}-P_{adcs}-P_{comm}-P_{0} \tag{17}\]  
where \(P_{sol}\) is the power generated by the solar panels, \(P_{adcs}\) is the power consumed by the ADCS system, \(P_{comm}\) is the communication power, and \(P_{0}\) is the power consumed during the nominal operation of the CubeSat and the payload (camera).
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 6 Conclusions
In addition, the outlier rejection part in the proposed method can work not only with the four RANSAC-based algorithms, but also with other methods, including yet other algorithms from the RANSAC family and a robust graph transformation matching (GTM) [36] with possible modifications. Finding the method that best supports the satellite operation is an interesting future topic. The authors thank USGS for the open data policy of Landsat-8 mission, [PERSON] from Nagoya University for her helpful comments on satellite attitude determination, Advanced Engineering Services Co., Ltd. (AES) for technical assistance, [PERSON] from Wakayama University for his great contribution to UnIFORM-1 operation, and [PERSON] from Wakayama University for managing UNIFORM project. The authors also thank the anonymous reviewers for providing valuable comments, in particular suggestions for the importance of satellite position and the robust estimation part for the proposed method. This study was supported in part by the New Energy and Industrial Technology Development Organization (NEDO), Japan, and JSPS KAKENHI 26730130 and 15K17767. T.K. and A.K. conceived and designed the study, performed the experiments, analyzed the data, and wrote the paper; T.K., S.K., and R.N. designed geometric fitting; T.K., S.K., and T.F. contributed materials; and T.K., A.K., and N.I. conceived error evaluation. The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Using Waste in Producing Bio-Composite Mycelium Bricks
## 3 Results and Discussion
### Compressive Strength  
The strength test results of the mycelium-induced bricks and regular bricks were obtained. For the compressive test, four specimens of each design mix were tested. From this test, all the mycelium-induced brick specimens achieved the 3.5 MPa minimum compressive strength for masonry bricks [12] as seen in Figure 6. Moreover, the majority of the average values of the compressive strength for bricks without mycelium attained the minimum requirement with the exception of the RB specimens. The highest compressive strength test results can be seen on the C and CM design mixes. In the results, it can also be extracted that there was a 38.5% increase in compressive strength from RB to RBM mixes. Then for the SD to SDM substrates, there was an increase of 31.0% in compressive strength. The presence of mycelium can be inferred to be the prevailing substance that caused the increase in strength of the bricks. It acted as a fibrous-binding material or a bio-composite binder or stabilizer which effectively increased the capacity of the mycelium-induced bricks. The mycelium-induced bricks also appeared to be more ductile as compared to the regular substrate bricks without mycelium. It can be seen particularly in the RBM and SDM bricks that particles did not entirely collapse or had been crushed, and it remained intact with very minimal cracks after the test.  
Figure 7 shows the box and whisker plots of the six design mixes of the brick specimens' compressive strength results. It shows from top to bottom, the maximum value as the top whisker, first quartile as the top of box, median as the circle, third quartile as the bottom of box, and the minimum value as bottom whisker. The design mixes were arranged left to right from least to greatest median. It can be observed in the figure that the variation of RB, RBM, SD, and SDM is small (less than 2 MPa) compared to CM and C. The greatest variation is seen in CM, since there were minimal mycelium present, an average of 12.25 g at 34 days, leading to few and inconsistent mycelium fiber networks present in achieving cohesive and better compressive results.
### Motivation  
Despite the promising results obtained in the studies summarized in Table 1, most studies have not considered geometry-based channel models. Distance-dependent path loss channel models are based on media such as free space, rain, fog, or gas. They are not realistic because ATG channels depend on the elevation angle as well [38]. The elevation angle plays a more important role in high-frequency communications, such as mmWave, because the antenna beamwidth is narrow. Standard stochastic channel models address multipath fading and typically do not capture the spatial variation in coverage between different environments, such as urban or rural [29]. These models are generic and not intended for a specific realization. Neither the path loss nor the standard stochastic channel model require prior knowledge of environmental geometry. Because of the geometry, a UT can receive LoS, strong NLoS, and multiple reflected components that cause multipath fading [32]. Standard stochastic channel models used for sub-6 GHz assume two-dimensional (2D) propagation; such an assumption is not valid for mmWave [39]. Therefore, 3D geometry-based stochastic channel models are essential for next-generation IoT systems and smart cities, which will be enabled by the mmWave and 3D networks. The use of such channels in UAV-based IoT networks, such as in smart cities and highly populated urban areas, needs to be sought.  
Geometry-based spatial channel models, whether deterministic or stochastic, typically define scatterers that reflect signals transmitted to a receiver. They are developed to better represent beamforming and multiple-input-multiple-output (MIMO) links since other channel models do not account for array geometries and array responses. Among the spatial channel models is the ray tracing model, in which the locations of scatterers are specified using building location information. In spatial models, waves are considered simple particles; hence, reflection and scattering are approximated using a simple geometry. Because the geometry of the environment is known in practice, spatial channel models can provide more accurate results as compared with general channel models. While a simple two-ray channel model is considered in [30], such a model is specific and limited to half-space regions, such as over-the-sea communications. While there are several channel models illustrated in Table 2 for 5G and beyond, the 3 GPP is widely adopted [33]. It identifies 5G requirements, supports a relatively wide frequency range, and covers a variety of scenarios.
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 4 System Optimization Model Extraction Method
### XML-Based Optimization Information Extraction  
In order to implement the extraction of the SysML optimization model, specific rules for extracting each type of element can be defined. Taking the extraction rule Var_Rule for optimization variables as an example, the pseudo-code of the implementation algorithm is shown in Algorithm 1. In Algorithm 1, \(\mathit{ov}\) denotes optimization variables, \(\mathit{cv}\) denotes continuous variables, \(\mathit{av}\) denotes aleatory variables, and \(\mathit{ev}\) denotes cognitive variables. The algorithm can extract the information of variables contained in the SysML model by traversing each block of the SysML model.  
```
01: Input: BS = (Blocks of SysML model)
02: boolean ErrorFound \(\leftarrow\) False
03: optimization problem P \(\leftarrow\)\(\O\)
04: OptimizationVariable cv \(\leftarrow\)\(\O\)
05: For b in BS
06: cv \(\leftarrow\) b.GetVariable ()
07: If(cv \(\
eq\)\(\O\))
08: Switch(ov.GetType())
09: case cv:
10: P.AddNumVar(cv.enum)
11: P.AddValue(cv. min,max)
12: case av:
13: P.AddNumVar(av.enum)
14: P.AddValueVar(av. mean,variance,distribution)
15: case ev:
16: P.AddNumVar(ev.enum)
17: P.AddValueVar(ev.. min,max)
18: default:
19: ErrorFound \(\leftarrow\) True
20: Else
21: AT \(\leftarrow\) b.GetRealAttrList()
22: For at in
23: cv \(\leftarrow\) at.GetVariability()
24: If (ov.GetType() = cvv)
25: Switch(at.GetType())
26: case real:
27: P.AddRealVar(cv.min,cv.max)
28: case integer:
29: P.AddIntVar(cv.min,cv.max)
30: Endif
31: Endfor
32: Endif
33: Endfor
34: Return P,ErrorFound
```  
**Algorithm 1** Pseudo-code of the optimization variable extraction rule algorithm  
The process of optimization can be summarized and abstracted from the actual model extraction activities to obtain a set of modeling elements, and the activity diagram of optimized model extraction can be constructed visually, as shown in Figure 10, which can support designers to carve out the mapping relationships between SysML models and XML in model transformation by defining extraction rules.
## 3 Results
### Cross-Site Evaluation  
#### 3.4.1 Evaluation at Jena University Hospital  
Our goal was to map each JUH internal code from a few example value sets from intensive care medicine and laboratory diagnostics to a LOINC or SNOMED CT code. To achieve this, internal codes were inserted into a CSV file that was uploaded to the tool. The desired LOINC or SNOMED CT codes could be inserted either directly as the numerical code or by searching for, e.g., the SNOMED fully specified name and then choosing the desired code. The extraction of internal codes and the mapping was performed by a semantic interoperability specialist at JUH's Data Integration Center. The inserted codes were previously independently validated by a physician from JUH's Department of Anesthesiology and Intensive Care Medicine.  
Using the export function, the internal codes, each now annotated with a validated international code, were exported into a.csv file. In the example shown in Table 2, eight JUH internal codes relating to ECMO and cardiac support procedures were annotated, validated, and exported. The experience with the tool gained at JUH led to systematic feedback to the developers with questions, problem fixes, and feature requests, e.g., on the provenance of internal codes, the categorizing, grouping, and commenting on annotations, exporting in the FHIR ConceptMap format, etc.  
In summary, with the test at JUH, the basic functionality and usability of the CoAT was successfully and independently verified. In addition, input from this evaluation contributed to further improvements to CoAT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Description** & **Success Rate** \\ \hline
1. & Import data & 2/3 \\
2. & Annotate data using SNOMED CT & 3/3 \\
3. & Annotate data using LOINC & 3/3 \\
4. & Annotate data using ATC & 3/3 \\
5. & Delete a medical data item & 3/3 \\
6. & Vide an annotation & 2/3 \\
7. & Restore an older working state of an annotation & 1/3 \\
8. & Use the collaborative search feature & 2/3 \\
9. & Use the cross mappings feature & 2/3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Usability evaluation results. Three users with different backgrounds performed nine tasks in the CoAT as part of think-aloud usability tests and were able to solve most tasks. The challenges encountered by the users were taken as input to improve the CoAT’s usability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Internal Code** & **SNOMED CT** \\ \hline
60\_va\_ECMO & 786451004 \\
61\_vv\_ECMO & 786453001 \\
161\_IABP\_1:1 & 399217008 \\
66\_PECLA & 233574002 \\
109\_vv\_ECMO & 786453001 \\
109\_ECMO & 233573008 \\
109\_va\_ECMO & 786451004 \\
109\_vva-ECMO & 786451004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-site evaluation at Jena. Example mapping of internal codes for ECMO and heart support procedures at Jena University Hospital to SNOMED CT as received in the export from the Collaborative Annotation Tool.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 4 Geoacoustic Inversion Framework Based on DQN
### Inversion Workflow  
The inversion workflow based on the DQN framework is expressed in Algorithm 2:  
```
1:Initializing the observed data, geoacoustic model, and original search bounds.
2:Executing Action 0 with the constrain of the original bounds and initializing the environment state as \(\mathbf{S}^{1}=[E_{norm}/E_{norm},E_{mean}/E_{norm},E_{std}/E_{norm},-1,-1,-1]\).
3:repeat
4: Obtaining the current environment state \(\mathbf{S}^{i}\).
5: Passing \(\mathbf{S}^{i}\) into the network of the DQN framework and collecting the suggested action.
6: Executing the suggested action and passing the \(\mathbf{k}\) sets of parameters into the environment.
7:until the termination criteria shown in Equation (7) are satisfied.
8: Inversion result \(\mathbf{m}^{\text{end}}\).
```  
**Algorithm 2** Inversion workflow based on the DQN framework.  
In the initialized environment state \(\mathbf{S}^{1}\), \(E_{norm}\), \(E_{mean}\), and \(E_{std}\) are the minimum, mean, and standard deviations of the misfit values (with a dimension that depends on the inverse problem) among the \(\mathbf{k}\) selected sets. Specifically, \(E_{norm}\) involves the normalization factor, and each item of the environment state is a percentage.
## 3 Results and Discussion
### Quality Evaluation
#### 3.1.4 VIFF  
VIFF is a quality metric of fused image using visual information fidelity. Fused image and two source images are decomposed into different scales using Laplacian Pyramid. At each scale, the image is divided into different blocks. Then, evaluate the visual information of each block with and without distortion. Evaluate the VIFF for each scale. Finally, calculate the overall metric based on the above result [25].
###### Abstract  
The output voltage of inverters is influenced by nonlinear factors such as dead time and voltage drops, injecting low-order harmonics. This results in fifth and seventh harmonic distortions in the stator current, causing periodic torque ripples and significantly affecting the control precision of Permanent Magnet Synchronous Motors (PMSMs). To address this issue, this paper proposes a control strategy named quasi-proportional-resonant sliding mode control (QPR-SMC). Initially, sliding mode control is employed as the current controller to enhance disturbance rejection capability and provide a rapid dynamic response. Subsequently, a quasi-proportional-resonant controller is introduced to extract the sixth harmonic component from the current, which is then used as a compensation term for the sliding mode control surface. Finally, the current tracking error and the compensation term are combined as inputs to the sliding mode control law, forming a current error-proportional resonant-sliding mode control surface. This approach enhances the harmonic suppression capability of the system. The results demonstrate that the proposed method effectively reduces the fifth and seventh harmonic components in the three-phase current and mitigates motor jitter by suppressing the sixth harmonic in the d-q coordinate system.  
permanent magnet synchronous motor; harmonic suppression; sliding mode controller; quasi-proportional-resonant controller
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Business Level in the CTE Model
#### 3.4.2 \"Sales and Customer Care\" Area  
This area and related items will analyze existing standard sales and customer care channels, as well as new sales and customer access channels. An example of an item regarding the \"Quality of the distribution of outlets in the country\" is presented by the following equation:  
\[QoSP=\left(\frac{QoSP_{UA1}\cdot F_{UA1}}{QoSP_{Ref/UA1}}+\frac{QoSP_{UA2}\cdot F _{UA2}}{QoSP_{Ref/UA2}}+\frac{QoSP_{UA3}\cdot F_{UA3}}{QoSP_{Ref/UA3}}+\frac{QoSP _{UA4}\cdot F_{UA4}}{QoSP_{Ref/UA4}}+\frac{QoSP_{UA4}\cdot F_{UA4}}{QoSP_{Ref/UA 4}}\right).0.1\]  
This equation is divided into five parts. These parts analyze different urban areas (UA1-UA4 and UAA). The CTE model defines all details and definitions related to the measurement and calculation of reference values and factors in this item. The value of this item gives us the value of the total quality of the distribution of points of sale. Also, its parts give us the quality of the distribution of points of sale according to individual urban areas.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
###### Abstract  
For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle's own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand-eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information.  
ego-motion estimation; hand-eye calibration; IMU; lidar odometry; sensor fusion Article
## 3 Proposed Method
### Algorithm for Stepwise Linear Approximation  
The proposed method consists of two main steps: Step 1 involves dividing the stages, and Step 2 involves predicting RUL. In Step 1, the goal is to find a data point where a consistent increase in the slope of the linear regression occurs over four consecutive points. This point will be referred to as the stage criterion. Due to noise in the measurement data, the slope may fluctuate between an increase and decrease. A continuous increase in the slope could indicate a change in degradation characteristics, i.e., a stage change. Therefore, finding the stage criterion based on a continuous increase in the slope is the key algorithm of Step 1, which proceeds as follows:  
++  
concept of how to divide the stages. In Figure 2a, the slopes increase consecutively four times from \(b_{13}\) to \(b_{17}\), identifying \(k=17\) as the stage criterion for the minor growth stage. For the moderate growth stage, with \(k_{0}=17\), the slopes increase consecutively from \(b_{23}\) to \(b_{27}\), establishing \(k=27\) as the stage criterion for the moderate growth stage and marking the end of Step 1.  
Once the stages are divided, the remaining process for predicting RUL becomes much simpler. The goal of Step 2 is to predict RUL, and it proceeds as follows:  
Step 2. Predicting RUL  
1. Set the parameters : \(-k_{0}\) is the last \(k\) at \(s=2\). \(-y_{thres}\) is defined by a user. 2. Fork \(=k_{0}\) to \(n\) : \(-\)Estimate \(a_{k}\) and \(b_{k}\) from Equation (1) using \(\mathbf{y}_{k-2k}\) and \(\mathbf{t}_{k-2k}\). \(-\)Solve the following equation for \(t_{\text{EOL}}\) : \(y_{thres}=a_{k}+b_{k}t_{\text{EOL}}\) \(-\)Obtain RUL : \(\text{RUL}_{k}=t_{\text{EOL}}-t_{k}\)  
where \(y_{thres}\) is the degradation threshold defined by the user. The process is based on linear regression, as in Step 2, but with some key differences: both model parameters, \(a_{k}\) and \(b_{k}\), are required, and they are estimated using only three data points, \(\mathbf{y}_{k-2k}\) and \(\mathbf{t}_{k-2k}\). It was found that using three data points is optimal for capturing the nonlinearity with the linear model in the accelerated growth stage. Using two data points to estimate the two unknown parameters is unstable, while using four data points yields lower accuracy than using three. Once the two parameters are estimated from the most recent three data points, the end of life (EOL)--the time at which degradation reaches the threshold \(y_{thres}\)--can be calculated using the same linear model as in Equation (1). Consequently, the RUL at \(k\) is obtained by subtracting the current time \(t_{k}\) from the EOL.
# CoSimulating Communication Networks and Electrical System for Performance Evaluation in Smart Grid
## 3 OOCoSim Design and Implementation
### OPNET and OpenDSS
Since most of smart grid applications are executed in distribution and customer domain [6], and OpenDSS is designed to meet future needs of the domains [22; 26], it is an appropriate simulator for the cosimulation. If legacy simulation tools which do not have simulation functionality of future system are employed, many parts of simulation modeling should be done in application model, which can cause unrealistic simplification and assumption. For these reasons, OOCoSim utilizes OpenDSS as a power system simulator.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 1 Introduction  
With the rapid developments in wireless communication technology, the research and development of 6G has also started. The interconnection of all things, such as smart cities, intelligent transportation systems, smart homes, and modern agriculture, has raised the requirements for communication. In addition, the spectrum resources used in mobile communication systems are becoming increasingly scarce. In the face of new technical challenges, researchers have proposed various technical solutions to solve spectrum resource shortage. As a critical factor affecting the performance of wireless communication systems, multiple access technology has become one of the hot research topics. In contrast to traditional orthogonal multiple access (OMA), power domain non-orthogonal multiple access (NOMA) supports more users [1, 2], has higher spectral efficiency [3], and achieves higher throughput [4, 5]. NOMA is expected to become a candidate multiple access technology in the next generation of mobile communication [6]. NOMA systems realize multi-users multiplexed in the power domain [2]. Different users have different transmit powers, and the receiver separates user signals according to the power, so that the data from multiple users can be transmitted on a subchannel. The principle of NOMA is to allocate more power to users with poor channel conditions and less power to users with good channel conditions. In the downlink of a multicarrier NOMA system, the base station (BS) transmits the signals of some users on the same time-frequency resource through superposition coding technology. Successive interference cancellation (SIC) technology at the receivers demodulates and separates the superimposed signal on the same subcarrier. Resourceallocation is a critical step affecting the performance of NOMA systems, containing user grouping and power allocation [7].