esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
## 2 Optimization Design of the Primary Mirror
### Initial Model Design of Primary Design
The actual mirror surface accuracy index can be obtained by root mean square of mirror surface accuracy in mechanical stability tolerance and processing and assembly tolerance, but there is overlap between them. Therefore, in the actual design and development \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **E (Gpa)** & **p (10\({}^{-9}\) \%/mm\({}^{3}\))** & **\#** \\ \hline RB-SiC & 330 & 3.05 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RB-SiC Material Property. Figure 2: Primary mirror assembly showing the lateral support, axial support, mirror body, the back support holes, and basic dimensions. process, referring to the optical design tolerance allocation requirements, the RMS values of the surface shape error of the mirror parallel to the \(X\) and \(Z\) axes under gravity should not exceed \(\lambda/60\) nm (where \(\lambda\) = 632.8 nm represents the wavelength of visible light). In addition, to ensure the stability of the primary mirror assembly, the fundamental frequency should not be lower than 2100 Hz. Given the mass limitation of balloon and launch costs, the mass of the primary mirror assembly does not exceed 100 kg. For primary mirror assembly, including primary mirror, flexure supports, and primary mirror chamber, the mass of the primary mirror should be less than 32.5 kg. The design requirements of the primary mirror are shown in Table 2.
## 3 IoT Traffic Analysis with Two-Stage Clustering
### Experimental Conditions
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Device Category**} & \multicolumn{1}{c}{**IoT Device**} \\ \hline Smart speakers & Amazon Echo \\  & Smart Things \\ \hline \multirow{3}{*}{Smart sensors} & Belkin Wemo Motion Sensor \\  & Netatmo Weather Station \\  & NEST Protect Smoke Alarm \\ \hline \multirow{3}{*}{Smart plugs} & Belkin Wemo Switch \\  & TP-Link Smart Plug \\  & iHome \\  & Light Bulbs LiFX Smart Bulb \\ \hline \multirow{3}{*}{Healthcare devices} & Blipcare Blood Pressure Meter \\  & Withings Smart Scale \\  & Withings Aura Smart Sleep Sensor \\ \hline \multirow{6}{*}{Smart cameras} & Dropcam \\  & Insteon Camera \\ \cline{1-1}  & Netatmo Welcome \\ \cline{1-1}  & Samsung Smart Camera \\ \cline{1-1}  & Withings Smart Baby Monitor \\ \cline{1-1}  & TP-Link Day Night Cloud Camera \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IoT devices and their categories in the dataset used in this paper [23]. The dataset consists of 21 IoT traffic datasets and six device categories: two smart speakers, three smart sensors, four smart plugs, three healthcare devices, six smart cameras, and three smart gadgets.
## 2 Transmission Mode Analysis and System Modeling
### Simulation Model Establishment  
ITI is one of the leading software and engineering companies in the field of system simulation. ITI's SimulationX is a standard tool for evaluating component interactions within technical systems. It fully supports the Modelica language and allows users to incorporate the required equations into the model according to their needs, so secondary development is easy [17].  
The Power Transmission library of SimulationX software has its own engine model, as shown in Figure 9. This model is an engine crankshaft angle control model based on theory. Users are unable to modify the internal characteristics of the engine. Therefore, the model is not suitable for the engine model based on the data measured from the bench.  
Figure 8: Engine starting resistance moment model.  
According to the modeling method used for the experimental data, an engine model suitable for the measured model was established in this paper, as is shown in Figure 10.  
The shunt mechanism is composed of a planetary gear model. We used the Planetary gearbox model from the Transmission Elements library in the SimulationX software, as shown in Figure 11. Among them, \"ctr5\" denotes the planetary gear mechanism's sun gear, \"ctrPC\" represents its planetary carrier, and \"ctrR\" signifies its ring gear.  
The clutch model in this paper is a wet clutch. The Disc Clutch model in the Power Transmission library in SimulationX software was adopted, as shown in Figure 12.  
We adopted a variable pump-quantitative motor system to achieve stepless speed regulation. Its design was simple and it had only a small temperature rise. This study used a smaller load torque of 100 N\(\cdot\)m, a smaller main oil pressure of 3.5 MPa, and a larger speed control valve flow rate of 5 L/min. The purpose of this was to reduce the impact of hydraulic systems on the results. The hydraulic speed control module is shown in Figure 13.  
The clutch hydraulic control system module consists of a hydraulic system, as shown in Figure 14. This can be used to control the timing of the engagement and separation of Disc Clutch models. The clutch can essentially be reduced to an oil cylinder with a specified mass, and the piston inside the cylinder moves under the combined action of the oil pressure and the return spring. The oil pressure valve of the main oil circuit controls the oil filling pressure, the relief valve of each branch regulates the oil filling flow to ensure the  
Figure 11: Planetary gear model.  
Figure 12: Disc Clutch model.  
Figure 9: Combustion engine model.  
oil filling time, and the check valve is responsible for rapid oil discharge by controlling the solenoid valve on and off to control the clutch connection and separation operation.  
Optimization of Switching Process from Hydraulic Mode to Mechanical Hydraulic Compound Transmission Mode
## 3 Performance Analysis
### Short Takeoff Performance  
The utilization of a thrust vector can effectively shorten the takeoff distance. During the takeoff, the takeoff velocity can be efficiently reduced by controlling the thrust vector to provide part of the lift in cooperation with the lift fan. Meanwhile, the reduction in the support force of the rear wheel on the ground reduces the friction resistance and increases the taxiing acceleration so that the takeoff velocity can be achieved in a shorter time and improve the takeoff performance. However, only a part of the thrust vector produces thrust, and this reduces the takeoff acceleration of the vehicle. Therefore, it is necessary to optimize the position of the thrust vector to achieve the shortest liftoff distance.  
An airplane takes off in three stages: (1) from the home point to the nose wheel lifting; (2) from lifting its nose wheel until the aircraft lifts off the ground; and (3) lifting off the ground to a safe height. Only two processes (1) and (2) are considered in this section, and the problem is transformed into how to adjust the thrust of the lift fan and the vector deflection angle of the 3 BSD to minimize the distance between the home point and the departure point under the condition that the certain takeoff angle is 6\({}^{\circ}\) and the 3 BSD is working at maximum power.  
In this process, the vehicle is subject to aerodynamic force, \(F_{a}\), the lift fan, \(T_{f}\), the thrust vector, \(T_{N}\), gravity, \(G\), and friction, \(F_{f}\), and when calculating the takeoff distance through the real-time attack angle,\(\alpha\), the real-time aerodynamic force can be calculated by interpolating the drag coefficient, \(C_{D}\), and the lift coefficient, \(C_{L}\). The lift fan and the deflection angle of the 3 BSD, \(\delta_{N}\), remain constant values for the whole run process, and these two constants need to be determined. Gravity remains the same value. The ground friction coefficient was chosen to be the rolling friction coefficient of a cement floor, and the friction coefficient was chosen to be 0.03. The angle of attack, \(\alpha\), remains a certain value of 0.33 degrees in stage 1. During stage 2, the changing rate of the attack angle was chosen to be 2 degrees/s according to the bandwidth of the pitching angle rate loop, which is about 1 rad/s.  
The lift fan was chosen to be 30%, 40%, 50%, 60%, and 70% of the maximum thrust, and the aircraft was trimmed to find the deflection angle of the 3 BSD, \(\delta_{N}\), and the takeoff velocity at the maximum thrust vector,\(T_{N\text{max}}\). The trimming curve is shown below in Figure 3.  
After trimming processing, the variation curves of the deflection angle of the 3 BSD, the throttle of the lift fan, and the trimming velocity are shown below in Figure 4.  
According to the Table 2, the shortest running distance appears when the size of the lift fan ranges between 0.5 and 0.7.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Ontology-Based Stock Selection
#### 4.2.2 Role of Financial Ontology in Asset Selection  
The financial ontology serves as the foundational framework for asset selection, focusing on identifying stocks with higher potential for returns based on their financial profiles. Unlike traditional models that integrate ontology directly with predictive algorithms, this study employs the ontology as a filtering mechanism to narrow down the pool of assets for further analysis.  
The selection process begins with the classification of stocks using fundamental financial indicators such as liquidity, profitability, debt, and growth. These indicators are hierarchically organized in a lattice graph, enabling pattern identification and consistent classification. The classification process involves the following steps:  
1. Data Mapping: Financial data from each company is mapped into the ontology, associating metrics such as current ratio (liquidity), return on equity (profitability), debt-to-equity ratio (debt), and revenue growth (growth) to their respective nodes in the lattice graph.
2. Threshold Application: Each metric is assessed against dynamically adjusted thresholds, calculated from the quartiles of the financial data distribution. This approach ensures that companies meeting the revised performance criteria and aligned with market trends are retained.
3. Hierarchical Filtering: The ontology leverages its hierarchical structure to identify patterns across multiple metrics. Stocks that exhibit strong performance in liquidity, profitability, and growth while maintaining manageable debt levels are classified as high-potential assets.
4. Subset Selection: Based on the filtering process, a refined subset of stocks is created, aligning with the investment criteria and ensuring that subsequent predictive models are applied to a reduced pool of high-quality candidates.  
This dual-purpose approach not only simplifies the computational complexity by reducing the universe of analyzed stocks but also enhances the interpretability of the selection process by clearly associating decisions with financial metrics.  
The selected stocks are then passed to the subsequent stages of the pipeline, including market state prediction and trading strategy optimization. This ensures that the framework focuses on assets with a higher likelihood of generating returns, aligning with the study's goal of enhancing investment performance through a multimodal framework.  
Figure 5: The diagram illustrates the ontology-based stock selection process, including data ingestion, processing, and ontology management. Each step outlines the flow from gathering financial data to storing ontology updates in OWL format.
## 4 Implementation and Evaluation
### Functional Evaluation  
#### 4.2.1 Time Synchronization  
The overall time synchronization procedure is described in Figure 4 and Section 3.2. At first, we just considered synchronizing the date, time, and the software time tick, but the variation in the initial synchronization error was between 0 and 300 us, which was quite large. We found that it was due to the ignorance of the alignment of the TAR and TACCR0 registers. Then we improved the time synchronization algorithm to take into account the alignment of those registers as well as the software time tick as explained in Figure 5. Finally, we reduced the variation of the initial time synchronization error to under several tens of microseconds.  
The evaluation procedure consists of two phases: a software time tick and interrupt timing. Firstly, to make sure that the software time tick value is synchronized, T-Sensor nodes are asked to send a Ping_Res packet with their current software time tick values for a Ping_Req broadcast packet from the T-Sink node. We verified that T-Sensor nodes were synchronized using the aforementioned method, for they replied with the same tick values to the T-Sink node for the Ping_Req packet. Secondly, to evaluate the designed receiver-to-receiver time synchronization scheme in the timing offset level of a single timer interrupt, we needed a little more complex method using an oscilloscope. The local software time tick increased by 1 every 320 us in the ISR for the Timer_A interrupt as described in Section 3.2. For every 10 cycles of the timer interrupt, we cleared a General Purpose Input Output (GPIO) pin for the first nine cycles and set the GPIO pin for the remaining 1 cycle in the ISR.  
The GPIO pins from two T-Sensor nodes are probed by an oscilloscope (Agilent 5000A) as in Figure 8. Ignoring the software time tick value, Figure 9(a) shows the misalignment of registers  
Figure 9: TAR, TACCR0 register alignment for synchronized periodic timer interrupt in two different T-Sensor nodes.  
relevant to timers of two T-Sensor nodes. Through the time synchronization explained in Section 3.2, the offset between two T-Sensor nodes are aligned as Figure 9(b). The measurement result for the drift of the offset between two T-Sensor nodes after the initial synchronization is presented in Figure 10. For the target operation time of 6 hours of PES, the drift is under 300 \(\upmu\)s.
## Appendix B
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \# of Criteria & Algorithm & GD (StdDev) & S (StdDev) &
\begin{tabular}{c} **FrontSize** \\ **(StdDev)** \\ \end{tabular} \\ \hline \multirow{3}{*}{4} & IMGAMO & 0.002 (0.001) & 0.1 (0.01) & 56.1 (8.27) \\  & NSGAII & 0.14 (0.039) & 0.12 (0.01) & 100 (0) \\  & SPEA2 & 0.4 (0.1889) & 0.11 (0.02) & 100 (0) \\ \hline \multirow{3}{*}{5} & IMGAMO & 0.002 (0.002) & 0.29 (0.07) & 30.4 (5.77) \\  & NSGAII & 3.61 (0.9) & 0.39 (0.04) & 100 (0) \\  & SPEA2 & 3.82 (1.5821) & 0.17 (0.05) & 100 (0) \\ \hline \multirow{3}{*}{6} & IMGAMO & 0.02 (0.03) & 0.47 (0.1) & 17.6 (3) \\  & NSGAII & 5.89 (0.4) & 0.54 (0.02) & 100 (0) \\  & SPEA2 & 6.01 (0.7382) & 0.25 (0.02) & 100 (0) \\ \hline \multirow{3}{*}{7} & IMGAMO & 0.02 (0.03) & 0.56 (0.28) & 9 (2.41) \\  & NSGAII & 4.88 (0.27) & 0.61 (0.03) & 100 (0) \\  & SPEA2 & 5.14 (0.23) & 0.24 (0.02) & 100 (0) \\ \hline \multirow{3}{*}{5} & IMGAMO & 0.01 (0.01) & 0.26 (0.06) & 21.5 (3.11) \\  & NSGAII & 45.52 (1.2) & 0.08 (0.03) & 100 (0) \\ \cline{1-1}  & SPEA2 & 22.55 (30.84) & 0.27 (0.24) & 100 (0) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of results for DTLZ5 problem. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# of Criteria & Algorithm & GD (StdDev) & S (StdDev) &
\begin{tabular}{c} **FrontSize** \\ **(StdDev)** \\ \end{tabular} \\ \hline \multirow{3}{*}{4} & IMGAMO & 0.002 (0.001) & 0.1 (0.01) & 56.1 (8.27) \\  & NSGAII & 0.14 (0.039) & 0.12 (0.01) & 100 (0) \\  & SPEA2 & 0.4 (0.1889) & 0.11 (0.02) & 100 (0) \\ \hline \multirow{3}{*}{5} & IMGAMO & 0.002 (0.002) & 0.29 (0.07) & 30.4 (5.77) \\  & NSGAII & 3.61 (0.9) & 0.39 (0.04) & 100 (0) \\  & SPEA2 & 3.82 (1.5821) & 0.17 (0.05) & 100 (0) \\ \hline \multirow{3}{*}{6} & IMGAMO & 0.02 (0.03) & 0.47 (0.1) & 17.6 (3) \\  & NSGAII & 5.89 (0.4) & 0.54 (0.02) & 100 (0) \\  & SPEA2 & 6.01 (0.7382) & 0.25 (0.02) & 100 (0) \\ \hline \multirow{3}{*}{7} & IMGAMO & 0.02 (0.03) & 0.56 (0.28) & 9 (2.41) \\  & NSGAII & 4.88 (0.27) & 0.61 (0.03) & 100 (0) \\  & SPEA2 & 5.14 (0.23) & 0.24 (0.02) & 100 (0) \\ \hline \multirow{3}{*}{5} & IMGAMO & 0.01 (0.01) & 0.26 (0.06) & 21.5 (3.11) \\  & NSGAII & 45.52 (1.2) & 0.08 (0.03) & 100 (0) \\ \cline{1-1}  & SPEA2 & 22.55 (30.84) & 0.27 (0.24) & 100 (0) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of results for DTLZ5 problem. For some cases, IMGAMO found a less numerous set of the Pareto optimal solutions (FrontSize), but this factor has significant importance if solutions are close to the true Pareto front (GD) and are well distributed (S). For the cases where IMGAMO found smaller Frontsizes than NSGA II and SPEA2, values of metric's GD and S were significantly worse. That means that much more numerous FrontSize could be a local front or solutions are not well distributed. The algorithm showed its scalability in decision variables' space. In problems DTLZ2 and DTLZA, the number of variables was increased to 50. The IMGAMO algorithm, without any problems, found the front sought.
## 4 Sensing-Based SPS Scheduling with Lookahead  
### Motivation  
We notice that one reason the current SPS algorithm produces significant packet collisions [33; 35] is the _uncertainty_ about the next resource location a UE will (re)select. In fact, there is no provision in the current specification of the sensing-based SPS with which a UE can convey the information about its selected location for the next streak in the time-frequency plane. Although randomness is necessary to spread the resource selections thus reduce collisions, there is randomness cost to it--the collision probability cannot be minimized below a certain limit. Although the sensing provides some information about the likely locations to be used in the selection window, it is insufficient information to deterministically prevent a collision. For example, a sensed use of a resource location \((c,m)\) by a UE does not mean that \((c,m+RRI)\) will be occupied, where \(C\) is a subchannel and \(m\) is a subframe. Because the UE-internal variable RC is not advertised to other nodes ([38], Section 5.4.3.1.2), such newly occurring vacancies cannot be exploited. Worse yet, a resource location in the selection window that a UE \(V\) selected when its RC counter reaches 0 at subframe \(n\) may be selected by other UEs in \([n-(T2-T1),n]\). A UE does not have any means to avoid such probabilistically arising collisions, because it does not know other UEs' RC values. It is why [PERSON] et al. [32] proposes to piggyback the internal RC counter value on the broadcast packet, using which the UEs with the simultaneously expiring RCs try to reduce collision probabilities by intentionally changing the RC values and realigning their selection windows. However, realigning the entire selection windows is costly, as significant realignment cannot be made under the latency requirement (\(T2\) is set according to the requirement). Compared with the wide window size as large as 100 subframes, the realignment through the change of RC values is limited by the RC range, which can be as small as five (subframes). The realignment may further be blocked by many potentially colliding UEs when the congestion level is high. Even if we can overcome all these hurdles, if selection windows overlap, the resource selection will still be probabilistic.
## 5 Connected Vehicles
### Technological Developments
#### 5.3.1 eCall  
The eCall is an alarm system initiated in Europe that can automatically provide assistance information to the emergency service station after a traffic accident or a crash [104; 105]. The in-vehicle eCall using E112 wireless communication technology can be triggered manually by in-vehicle passengers pressing a button or independently after the sensor senses a collision, and then communication between the vehicle passenger and the nearest professional operator of the emergency service station will be established. Meanwhile, a data package rich in information about the accident will be automatically sent to the operator, including the time, precise location, vehicle plate, eCall triggered mode, and information about the relevant emergency service providers. Since January 2015, the I_HeERO project has been launched in Europe to construct the required Public Safety Answering Points (PSAP) facilities to introduce pan-European eCalls [106]. The project is hosted by over 10 EU member states and over 100 relevant partnerships. Its primary contents include:  
* Promoting investment in PSAP infrastructure and service interaction projects in EU member states;
* Advancing the preparation process for the universalization of eCall for vehicles with a high danger ratio, including chemical goods vehicles, long-term passenger carriers, and Powered Two-Wheeled machines (PTWs) [106];
* Promoting the development of data management and E112 call technologies and assessing the operational feasibility of PSAP infrastructure. In particular, Luxembourg achieved the criteria of feasibility assessment in 2017 and became the first member state to implement eCall.  
The EU required its member states to establish the necessary PSAPs infrastructure to handle eCalls by 1 October 2017, and all the new car and light-duty truck models in Europe must be equipped with eCall devices from 31 March 2018 [107]. Researchers claim that the eCall system can reduce emergency response time by 40% within cities and 50% within rural regions.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Position Optimization  
Optimizing the antenna position of an LAA plays a vital role in improving the signal quality, coverage area, system capacity, energy efficiency, and cost reduction. Therefore, the positions (\(x_{n}\)) are determined using the MAOA algorithm considering the uniform current amplitudes (\(\mathrm{I_{n}}=1\)) and phase (\(\mathrm{\Theta_{n}}=0\)) for \(n=1\) to N, and the comparison has been made with other intelligent metaheuristic optimization algorithms. The spacing between the elements of the antenna ranges from 0.25 \(\lambda\) to 0.50 \(\lambda\). The array factor for position optimization is mentioned in Equation (3). In designs 1 and 2, the positions of the array elements are optimized for PSLL reduction and CSLL minimization, respectively, while side lobe power minimization without any condition on FNBW is discussed in design 3. Side lobe suppression with null positioning is discussed in design 4, whereas design 5 illustrates the side lobe average power minimization with a fixed FNBW constraint.