esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Upper-Level Multiple Resources Coordinated Optimization Modeling and Solving
### Mathematical Model for Multiple Resources Coordinated Optimization  
#### 3.2.1 Objective Function  
The objective of EFC is to minimize the total control cost of maintaining the frequency stability of the receiving-end power grid and can be expressed as  
\[\text{min}F=\lambda_{\text{dc}}\sum_{i=1}^{N_{\text{dc}}}c_{\text{dc},i}P_{ \text{dc},i}+\lambda_{\text{tl}}\sum_{j=1}^{N_{\text{tl}}}c_{\text{tl},j}P_{ \text{tl},j}+\lambda_{\text{VPP}}\sum_{k=1}^{N_{\text{VPP}}}c_{\text{VPP},k}P_ {\text{VPP},k} \tag{4}\]  
where \(\lambda_{\text{dc}}\lambda_{\text{tl}}\), and \(\lambda_{\text{VPP}}\) are the weight coefficients of the HVDC emergency power support, ELS, and VEPR, respectively. The weight coefficients are determined by the dispatcher of the grid dispatching center. \(P_{\text{dc},i}\), \(P_{\text{tl},j}\), and \(P_{\text{VPP},k}\) are control amounts of the \(i\)th HVDC, \(j\)th load shedding, and \(k\)th VPP. \(c_{\text{dc},i}c_{\text{tl},j}\), and \(c_{\text{VPP},k}\) are control cost factors of the \(i\)th HVDC, \(j\)th load shedding, and \(k\)th VPP.
# A Low-Profile Dielectric Resonator Antenna Array for OAM Waves Generation at 5G NR Bands
## 3 Results and Discussion  
The successful generation of OAM vortex waves was verified by analyzing its spiral phase distribution [27; 28; 29], a donut-shaped intensity distribution [32], a null in the center of the radiation pattern [31], and the purity percentage of the chosen mode should be higher than the other modes carried by the OAM waves.
# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 5 Conclusions  
VANETs play a crucial role in ensuring the safety, security, and efficiency of transportation systems. However, the interconnected and dynamic nature of VANETs presents significant challenges and risks that require rigorous assessment and mitigation. In this study, we addressed the problem of intrusion detection in VANETs by proposing a model based on mutual information for feature selection and utilizing the SMOTE technique to handle class imbalance. Our experimental evaluations on multiple datasets demonstrated the effectiveness of the proposed model in enhancing the security of VANETs. The model achieved commendable performance in terms of accuracy, precision, and F1 score, highlighting its potential for intrusion detection in VANETs. Additionally, we compared our model with other machine learning techniques, further validating its superiority. However, it is important to acknowledge certain limitations. The fast mobility of nodes in VANETs and the dynamic changes in network typology pose challenges for intrusion detection. The rapid movement of nodes and dynamic network conditions require adaptive and dynamic intrusion detection mechanisms to ensure accurate and up-to-date threat detection. In conclusion, our study contributes to the field of VANET security by proposing an effective intrusion detection model. The use of mutual information and the SMOTE technique demonstrated promising results. Looking ahead, future research should focus on developing adaptive intrusion detection approaches that can address the challenges posed by the fast mobility of nodes and the dynamic network typology in VANETs. By doing so, we can enhance the security and resilience of VANETs, ensuring safer and more reliable transportation systems in the future.  
Conceptualization, S.A. and A.G.; methodology M.M.N., H.F. and A.G.; software, M.A.; validation, S.B., S.B.A.K. and A.G.; formal analysis, M.M.N.; investigation, M.A.; resources, A.G.; data curation, S.A.; writing--original draft preparation, S.A.; writing--review and editing, A.G., S.B., M.A. and M.M.N. visualization, S.B.A.K. and H.F.; supervision, S.B.; project administration, S.B.; funding acquisition, M.M.N. All authors have read and agreed to the published version of the manuscript.  
This study was not funded and did not receive financial support. We did this research work as academic researchers of computer science at the university.  
Not applicable.  
Available on request.  
The authors would like to acknowledge Cadi Ayyad University, Moulay Ismail University of Meknes, Prince Sultan University (PSU) and Smart Systems Engineering lab for their valuable support. Additionally, the authors would like to acknowledge the support of PSU for paying the Article Processing Charges (APC) of this publication.  
The authors declare no conflict of interest.
# Rheological Properties of Lunar Mortars
## 2 Research and Scope
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Stage** & \multicolumn{2}{c}{**Performed Tests**} & \multicolumn{2}{c}{**Obtained Effects**} \\ \hline \multirow{4}{*}{Stage 1: Micro-fillers (LRS and QP) characterization} & \(\bullet\) & Density (Le Chatelier) & \multirow{4}{*}{Characterization of LRS Selection of QP of similar granulometry (subject of substitution with LRS)} \\  & \(\bullet\) & Particle size distribution & & \\  & \(\bullet\) & Specific surface area & & \\  & \(\bullet\) & Morphology (micrographs) & & \\ \hline \hline \end{tabular}
\begin{tabular}{l l l l} \hline \hline
**Stage 2: Initial determination of the content of micro-filler in the mortar aggregate & \multirow{4}{*}{Flow table test} & \multirow{4}{*}{Determining the effect of max. share of QP in aggregate while maintaining w/c ratio and satisfactory workability of the mortar: 20\% of aggregate mass (using large dose of superplasticizer)} \\ \hline \hline
**Stage 3: “Lunar micro-mortars”** & \(\bullet\) & Standard consistency in Vicat apparatus Modified flow table test & \multirow{4}{*}{
\begin{tabular}{l} Determining the effect of LRS on the “lunar micro-mortar” consistence (2 series: w/c = 0.265 and w/c = 0.500) \\ \end{tabular} } \\  & \(\bullet\) & Flow table test & & \\ \hline \hline
**Stage 4: “Lunar mortars” mechanical properties & \multirow{4}{*}{\
\begin{tabular}{l} Comparative strength \\ \(\bullet\) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{l} Additional characterization of basic mechanical properties of selected “lunar mortars” and comparison to standard mortars properties \\ \end{tabular} } \\  & \(\bullet\) & Flexural strength & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stages of the experimental program (LRS—lunar regolith simulant; QP—quartz powder). The research on the evaluation of workability parameters in 3D concrete printing showed that the \"flow test table was more consistent than the other methods and printability window for the printing system was found between flow values of 18 and 24 cm\" [30]. In other research [31], it was stated that the \"pumpability\" of the cementitious mix is suitable for 3D printing if the flow value is between 150 and 190 mm and such a mix provides a smooth surface and high \"buildability.\"
# A Novel Method for Field Measurement of Ankle Joint Stiffness in Hopping
## 2 Materials and Methods  
### Participants, Procedures, and Equipment  
Twelve male participants (mean \(\pm\) SD age of 23.33 \(\pm\) 3.23 year; mass 77.04 \(\pm\) 12.56 kg; height 174.46 \(\pm\) 7.16 cm;) gave their informed, written consent to participate in this study, following the approval by the research ethics committee of the University of Tehran (IR.UT.SPORT.REC.1397.019). Four reflective markers, as shown in Figure 1, were placed on greater trochanter (GT), lateral femoral epicondyle (LFE), lateral malleolus (LM), and the fifth metatarsophalangeal joint (5 thMTP). The length of thigh (distance between GT and LFE), shank (distance between LFE and LM), and foot (distance between LM and 5 thMTP) were measured. After walking five minutes, as a warm-up, each participant performedtwo-legged continuous vertical controlled hops (without shoes and keeping hands on the waist) with frequency of 2.2 Hz [26] that was controlled through synchronization with a metronome. Each participant then performed 15 hops continuously in each trial and repeated it for three times, (data of the five middle hops in each trial were used). The hops were performed on a force platform (AMTI, Watertown, NY, USA; 1000 Hz) and a motion analysis system (Motion Analysis Co., Rohnert Park, CA, USA; 200 Hz) with six high speed cameras recorded the movement. Kinematic and ground reaction force data obtained after post processing (filling gaps, smoothing, filtering) via Cortex software.
## 4 Results and Discussion
### Experiment Settings
#### 4.1.3 Evaluation Metrics  
To evaluate the model's performance more objectively and rationally, this paper adopts three common indicators for time series forecasting, as well as a special indicator in the field of PV power prediction. The three common indicators include Mean Absolute Error (MAE) [65], Root Mean Square Error (RMSE) [62], and R Squared index (CORR) [66], which are represented in Equation (8) below:  
\[\left\{\begin{array}{l}\text{MAE}=&\left(\sum\limits_{n=1}^{\text{N}}\left| \text{P}_{n}-\hat{\text{P}}_{n}\right|\right)/\text{N}\\ \text{RMSE}=&\sqrt{\left(\sum\limits_{n=1}^{\text{N}}\left|\text{P}_{n}-\hat{ \text{P}}_{n}\right|^{2}\right)/\text{N}}\\ \text{CORR}=&1-\left(\sum\limits_{n=1}^{\text{N}}\left|\text{P}_{n}-\hat{ \text{P}}_{n}\right|^{2}\right)/\left(\sum\limits_{n=1}^{\text{N}}\left|\text {P}^{-}-\text{P}_{n}\right|^{2}\right)\end{array}\right. \tag{14}\]  
where N represents the length of the wind power series, P\({}_{n}\) is the wind power at the n-th point, \(\hat{\text{P}}_{n}\) is the predicted wind power at the n-th point, and \(\bar{\text{P}}\) is the average value of the wind power series. To express the advancement of the proposed model more intuitively, the promoting percentages of MAE and RMSE are also calculated as follows:  
\[\left\{\begin{array}{l}\text{P}_{\text{MAE}}=\left(\text{MAE}_{1}-\text{ MAE}_{2}\right)/\text{MAE}_{1}\times 100\%\\ \text{P}_{\text{RMSE}}=\left(\text{RMSE}_{1}-\text{RMSE}_{2}\right)/\text{ RMSE}_{1}\times 100\%\end{array}\right. \tag{15}\]  
where p\({}_{\text{MAE}}\) and p\({}_{\text{RMSE}}\) represent the promoting percentages of MAE and RMSE, respectively.
# Classification of Malaria Using Object Detection Models
### Literature Review
The paper sheds light on how the YOLO algorithm was born to reframe the object detection problem as a regression problem, and it was carried out by a single neural network. [PERSON] et al. (2020) reported blast cell detection for acute lymphoblastic leukemia diagnosis using object detection models YOLOv4 [17]. The authors incorporated the use of transfer learning to understand the impact of different variables in the detection process. They also proposed methods for the real-time detection of leukemia. [PERSON] et al. (2021) developed an automated assessment of knee alignment from full leg X-rays by training a YOLOv4 model to locate regions of interest in full leg radiographs for the hip, knee, and ankle joints. Residual neural networks were then trained to regress landmark coordinates for each of the regions of interest to then determine the knee alignment [18]. [PERSON] et al. (2021) used YOLOv5 for bone marrow cell recognition. They reported bone marrow cell recognition based on a novel loss function. The loss function proved to boost the model's performance. The models can be used to train custom datasets [19]. Another recent approach was outlined in the paper \"Image analysis and machine learning -based malaria assessment system\" by the authors [PERSON] et al. [20]. The authors used image segmentation techniques to segment the infected (or uninfected) red blood cells to the region of interest before making a prediction based on a convolutional neural network. The model achieved an overall testing accuracy of 91.0%. [PERSON] et al. (2021) focused on identifying malaria cell images using the transfer learning method [21]. The research used pre-trained Resnet50 model and a fully connected dense layer that utilizes backpropagation for learning. The paper reported a training accuracy of 95.91%. [PERSON] et al. (2021) reported an algorithm for the detection of malaria implemented by deep learning using PyTorch. Pretrained models of DenseNet121 and Resnet50 are repurposed with transfer learning to fit the task of malaria detection in segmented single-cell images [22]. The authors evaluated the performance of the two deep learning models and reported an accuracy of 94.43% for the DenseNet121 model while the Resnet50 model achieved an accuracy of 91.72%.
## 3 Experiments and Results
### Experimental Verification of Theoretical Analysis of Steady-State Performance  
The steady-state performance of the GMACC algorithm under different step sizes and noise distributions were simulated. The noise in the simulation was a uniform noise distributed in [\(-\)m, m]. When the step size and noise variance were changed, the other parameters were kept unchanged, that is, the kernel width was set to 0.5, and the shape parameter was set to 2. For each simulation under a certain parameter setting, 100 Monte Carlo runs were executed to get the average simulation result of the EMSE. When calculating the steady-state results of the EMSE, the mean value of the EMSE of the last 500 iterations was taken as its steady-state value. In order to ensure that the algorithm could converge to the steady state in the last few hundred iterations, 5000 iterations were set in each Monte Carlo simulation. Considering that too small a step size leads to too slow a convergence of the algorithm, the step size should be adjusted appropriately to avoid too small a step size. The comparison between theoretical analysis results and experimental results is shown in Figures 3 and 4.  
It can be observed that: (1) when the step size and noise variance increase, the steady-state EMSE also increases and (2) the theoretical analysis shows the relationship between the steady-state performance of the algorithm and the step size and noise, and the theoretical analysis results agree well with the experimental results.  
Figure 3: Theoretical and simulated steady-state EMSEs with different step sizes.
## 5 Simulation and Discussion
In addition, comparing Figure 13a,b, we can see that the smaller the duty ratio is, the larger error the APD method leads to, which is caused by the assumption of a uniform distribution of errors within a code word. Figure 11: An example of the time-domain waveforms of the pulsed disturbance when \(\sigma_{l}=1\), \(\sigma_{h}=10\), \(\rho=0.1\), and the repeated cycle is \(T_{p}=20Q\), \(T_{p}=1.5Q\), \(T_{p}=0.1Q\), respectively. Figure 10: Test bench employed in the simulation. Figure 14: Simulation for the dynamic MI-based model, IEEE802.16 LDPC code (2/3 rate), 16 QAM under different duty ratio (**a**) \(\rho\) = 0.1; (**b**) \(\rho\) = 0.9. Figure 12: APD and PDD plots of the pulsed disturbances shown in Figure 11: **(a)** the same APD of all disturbances; **(b)** the PDD when \(T_{p}=20Q\), \(\rho\) = 0.1; **(c)** the PDD when \(T_{p}=1.5Q\), \(\rho\) = 0.1; and **(d)** the PDD when \(T_{p}=0.1Q\), \(\rho\) = 0.1. Figure 13: Simulation for the dynamic MI-based model, Long-Term Evolution (LTE) Turbo code (2/3 rate), 16 QAM under different duty ratio (**a**) \(\rho\) = 0.1; **(b)** \(\rho\) = 0.9. We can see that estimated \(BLER\) curves based on the MI-based model are close to the actual decoding error rate. As shown in Figure 13a, the maximum SINR errors at 1% \(BLER\) with different \(Tp\) are 1.2, 0.8 and 0.05 dB respectively. That is to say, as for the RBIRL-based model, the evaluation error tends to decrease as the \(Tp\) increases, because the pulsed interference is more likely to be approximated as two independent AWGN parts, which reduces the accidental and incidental effect of the burst interferences.
## 2 Theoretical Analysis and Fabrication of Lens and Heatsink for the Seaport Luminaire
### Fabrication of Seaport luminaire  
The prototype of the seaport luminaire with a rated power of 500 W consists of two LED modules with an optimized lens and heatsink (i.e., lens type of D and heatsink type D), as shown in Figure 6a,b. The total lighting output of this luminaire was 59,162 lm with an input power of 493 W, which means that the luminous efficacy of this luminaire is about 120 lm/W. To verify the feasibility of this seaport luminaire, the asymmetric characteristics of light distribution were measured using a gonio-photometer, as shown in Figure 6c. Then, light distribution was simulated using Relux software considering 150 m \(\times\) 150 m of the illuminated area, as shown in Figure 6d. The results of simulation show the calculated average illuminance of 92.0 lx, minimum illuminance of 53.3 k, and thus, overall uniformity of 0.71, which is a value that meets the required overall uniformity of 0.70 in seaport lighting application.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measured Point**} & \multicolumn{4}{c}{**Type of Heatsink**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** \\ \hline point 1 & 77.2 \({}^{\circ}\)C & 70.6 \({}^{\circ}\)C & 62.9 \({}^{\circ}\)C & 61.9 \({}^{\circ}\)C \\ point 2 & 76.4 \({}^{\circ}\)C & 71.9 \({}^{\circ}\)C & 62.2 \({}^{\circ}\)C & 60.2 \({}^{\circ}\)C \\ point 3 & 77.5 \({}^{\circ}\)C & 76.6 \({}^{\circ}\)C & 64.9 \({}^{\circ}\)C & 61.9 \({}^{\circ}\)C \\ point 4 & 60.0 \({}^{\circ}\)C & 58.8 \({}^{\circ}\)C & 52.1 \({}^{\circ}\)C & 50.1 \({}^{\circ}\)C \\ point 5 & 55.7 \({}^{\circ}\)C & 55.3 \({}^{\circ}\)C & 49.6 \({}^{\circ}\)C & 48.6 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of actual thermal measurement of four types of heatsink.  
Figure 6: Fabricated seaport luminaire with a rated power of 500 W using an optimized lens and heatsink: top side view (**a**) and back side view (**b**), measured actual light distribution of single luminaire (**c**), and simulated light distribution considering the real dimensions (150 m \(\times\) 150 m) of the seaport installation (**d**).  
Comparison on Light Distribution Characteristics of Fabricated Prototype LED Seaport Luminaire with Conventional HID Seaport Luminaires
## 5 Conclusions
#### Opportunities for Future Research  
Despite these challenges, HIL simulation offers many opportunities for future research in cities, building, and systems. One area of future research is integrating HIL simulation with urban planning tools. Urban planning tools, such as geographic information systems (GIS), could be used to create more accurate and comprehensive simulations of smart city systems. Moreover, another interesting integration is HIL simulation with building information modelling (BIM). BIM is a digital representation of a building's physical and functional characteristics, and integrating HIL simulation with BIM could enable more accurate and comprehensive testing of building systems.  
Another area of future research is in the application of HIL simulation to emerging smart city technologies, such as autonomous vehicles and smart grids. HIL simulation could be used to optimize the performance of these technologies and identify opportunities for further innovation, and HIL could be applied to emerging building energy systems, such as integrated renewable energy systems. HIL simulation could be used to optimize the performance of these systems and identify opportunities for further energy savings.  
Conceptualization, L.F.C. and D.V.; methodology, M.T.; software, M.T.; validation, D.V.; formal analysis, L.F.C.; investigation, M.T. and D.V.; resources, L.F.C.; data curation, L.F.C.; writing--original draft preparation, D.V. and M.T.; writing--review and editing, L.F.C.; visualization, D.V. and M.T.; supervision, L.F.C.; project administration, L.F.C.; funding acquisition, L.F.C. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the Ministerio de Ciencia e Innovacion--Agencia Estatal de Investigacion (PID2021-123511 OB-C31--MCIN/AEI/10.13039/501100011033/FEDER, EU and RED2022-134219-T).  
Not applicable.  
Data Availability Statement: Data are available upon request to the corresponding author.  
The authors thank the Generalitat de Catalunya for the quality accreditation granted to the GREiA research group (2021 SGR 01615) and the GRIHO research group (2021 SGR 01620). GREiA is a TECNIO-certified agent in the category of technological development of the Gobierno de Catalunya. This work is partially supported by ICREA inside the program ICREA Academia.  
The authors declare no conflict of interest.
# Scheduling for Media Function Virtualization
## 5 Evaluation
### Impact of Formats  
Here, we report the impact of media formats on the performance of the scheduling heuristic with BFS traversal. The format of media streams affects resource utilization efficiency. Figure 7 compares the average number of cores used per node for three different video formats. With no VMF-FG decomposition (\(M=1\)), the average core utilization decreases with higher quality of media formats. The network bandwidth increasingly becomes a bottleneck with an increase in media quality; thus, fewer VMFs are placed at a node even if free cores are available. The average core utilization for DFS is not reported as it remains same as BFS.  
It can also be observed that VMF-FG decomposition improves the average core utilization per node. By decomposing VMF-FG, bandwidth requirement per virtual link decreases, thus increasing VMF consolidation per node.
## 5 Simulations and Results
### Simulation Setup  
The reference tracking performance of the LQI controller and the proposed RLCA are evaluated in the time domain through customized simulations under nominal conditions, sudden wind gusts, constant wind disturbance, cruise altitude adjustments, and aerodynamics parameter variations. The control application is implemented in MATLAB/Simulink R2020b and executed on a 64-bit embedded computer (2.1 GHz CPU, 12 GB RAM). The sampling frequency is set at 250 Hz. As outlined in Section 2, the longitudinal motion dynamics of a small UAV, Bluebird, are considered to validate the proposed controller's effectiveness in tracking the reference trajectory while rejecting the bounded exogenous disturbances. The band-limited white noise is incorporated into the control input to simulate the effects of moderate turbulence and typical sensor noise encountered in UAV operations. In MATLAB/Simulink, the said noise block is configured with a noise power of \(10^{-3}\) and a sampling time of 4.0 msec, ensuring a realistic representation of sensor disturbances in the system. To prevent the elevator's actuator saturation, the control input signal \(u(t)\) is restricted between 0 and 10 V.