esa-sceva/satcom-chunk-collection huggingface dataset:
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 2 Background
### Execution Model  
In this work, we apply an execution model to reduce the interference caused by memory sharing in avionics systems. Several methods have been proposed for reducing memory sharing interference. However, the software verification cost for the system and service in avionics systems is very high [27]. Therefore, we employed a model that eliminated interference without modifying the software.  
Execution models are methods of defining the execution sequences of tasks. In this section, we describe two execution models that are applied to multicore avionics systems, as shown in Figure 4. Avionics systems set the execution order and time of partitions and processes before runtime, as defined in ARINC-653. Therefore, it is possible statically to design and verify the execution model.  
TDMA [14; 28] executes only one core at a time, as shown in Figure 4a. Consequently, even if resources are shared, execution interference does not occur among the cores. However, TDMA uses only one core at a time, thereby reducing the overall throughput.  
In addition, AER [9; 29; 30] allows the parallel execution of tasks in each core to improve the limitations of TDMA, as shown in Figure 4b. Tasks are divided into three phases: acquisition, execution, and restitution (denoted by A, E, and R, respectively). The A phase brings data from the shared memory to the local memory, such as a private cache. The E phase performs operations with the data read from the previous phase, and the R phase stores the result in the shared memory. Thus, the E phase can execute concurrently with the A or R phases of other cores by accessing only the local memory. Theoretically, in the AER model, if the execution time of the E phase of one core is longer than the A and R phases of the other core, up to four cores can run simultaneously. However, legacy code may not be split into the A-E-R phases of the AER model.  
The major time frames of the TDMA and AER models in a partitioning system are shown in Figure 5. Since AER has higher utilization than TDMA, it has a shorter major time frame. If the major time frame is shortened when performing the same task, the execution safety margin of the partition can be set up. Table 1 summarizes the TDMA and AER execution models.  
Figure 4: Execution models. (**a**) Time-division multiple access (TDMA). (**b**) Acquisition execution  
Figure 3: Memory sharing on multicore partitioning systems.
## 2 Development of the Pre-Tuning Machine
### Software Control Architecture  
To enable full automation of the pre-tuning machine, a dedicated software control system was developed using LabVIEW 2013. The software architecture integrates with two synchronized interfaces, one for the measurement area and another for the tuning area, to coordinate testing and adjustment processes seamlessly, as is shown in Figure 7.  
Figure 6: Pre-tuning clamp assembly.  
Figure 7: The pre-tuning software interface.  
The measurement area interface manages real-time data acquisition and analysis. It communicates with the network analyzer via GPIB protocol to acquire S21 parameters and synchronizes with the bead-pull system's stepper motor to control bead movement along the cavity axis. Perturbation theory-based algorithms process phase-frequency shifts to generate axial electric field profiles. Simultaneously, laser displacement sensors provide coaxial data into the system. All measurement results are compiled into tuning prescription files that define target frequency corrections and field flatness thresholds for individual cells [21].  
In the tuning area interface, these prescription files drive closed-loop mechanical adjustments. The software controls multiple stepper motors through RS-485 communication, executing micron-level clamp displacements predicted by a nonlinear displacement-frequency model. Safety protocols include real-time monitoring of piezoelectric force sensors and optical encoders. If clamping forces or displacements exceed the thresholds, the system initiates an emergency retraction sequence and logs fault codes for diagnostics To enhance operational flexibility, the software retains manual tuning functionalities, enabling users to perform fine adjustments or calibration when necessary.  
A state machine architecture governs the interaction between the two areas, ensuring no conflicting operations occur. During operation, the measurement area completes field characterization and locks the cavity in a stationary state before transferring control to the tuning area. A unified dashboard displays real-time progress metrics, including field flatness improvements, frequency convergence trends, and system health indicators. This architecture reduces manual intervention, initial parameter configuration, and final validation, which greatly shortens the adjustment cycle compared to manual methods during experimental testing.
## 2 Materials and Methods
### WRF-Chem Model
#### 2.3.2 WRF-Chem Model Configuration  
In this study, WRF-Chem version 3.9.1 was used to simulate dust storms. Two domains were defined for the model. The first domain was defined in the area with the number of grid points being \(90\times 100\) and the spacing of 11.2 km. The second domain was defined in the area showed in Figure 2. Initial and boundary conditions were obtained from the National Oceanic and Atmospheric Administration Global Forecast System (NOAA GFS) with horizontal resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) United States Geological Survey (USGS) data were used for static geographic data such as altitude, soil properties, vegetation fraction and land use. Table 3 lists the parameterization schemes used to implement the WRF-Chem model.  
Additionally, the distribution of dust particle diameters in WRF-Chem model is shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline WRF Single-Moment 5-class & Micro-scale physics \\ RRTM & Long wave radiation \\ Goddard shortwave & Shortwave radiation \\ Noah Land Surface Model & Surface Physics \\ YSU & Boundary layer \\ Grell 3D & Cumulus Convection \\ \hline \end{tabular}
\end{table}
Table 3: Lists the parameterization schemes used to implement the Weather Research and Forecasting model with chemistry (WRF-Chem) model.  
To simulate the investigated storms, the model was run starting 24 h before the storm, and the first 3 h of each run were considered as spin-up time. The time interval between networks was 180 s. GFS data with a 6-h time interval was used for initial and boundary conditions. The model output was also saved every 3 h.
## 1 Introduction  
Embedded software plays a critical role in various application domains, including medical devices and defense, where requirements traceability has become an indispensable aspect of software development and management processes [1, 2]. In particular, ensuring traceability between requirements and design is crucial for guaranteeing the reliability and safety of software. Requirements traceability aids in effectively managing the changes and updates throughout the software lifecycle, contributing to project flexibility.  
Embedded software must operate in real time under constrained hardware resources, necessitating close integration between software and hardware. In such complex environments, clear traceability between requirements and design becomes even more critical. However, the process of maintaining traceability can be resource intensive, and its importance is often overlooked during development [3]. Requirements traceability offers numerous benefits even in general software development, leading to the proposal of methods that can perform traceability after development. Notably, post-development traceability is often less effective than methods that integrate traceability throughout the developmental lifecycle. Moreover, unlike general-purpose software, embedded software with hardware dependencies requires specialized traceability methods that reflect these characteristics.  
This study demonstrates that the structured feature-based component architecture design method can apply structured features to trace requirements in component design. Structured features serve as tools for bridging the gap between requirements analysis and design, effectively reflecting the requirements in the component architecture [4]. We have analyzed how structured features can support traceability not only between functional requirements and components but also between hardware elements affected by embedded software and components.  
This paper proposes a method for applying structured features to ensure traceability within a component-based embedded software development process. The proposed method supports the component-based design process by enabling traceability between requirement analysis and component design, thereby ensuring traceability among functional requirements, components, and hardware elements affected by embedded software.  
The rest of this paper is structured as follows: Section 2 discusses the background of requirements traceability analysis, covering component-based software architecture design processes, structured features, and the structured feature-based design process; Section 3 presents a review of existing requirements traceability methods; Section 4 analyzes the application of structured features at each step of the design process, helping to visualize the relationships between artifacts; Section 5 explains the traceability of structured features through a case study showing the intermediate outputs that reflect the requirements in the design; finally, Sections 6 and 7 summarize the findings, discuss their limitations, present our conclusions, and propose future research directions.
Some may also seek to verify data from manufacturers, for which independent methods for collecting such data would be useful. High-precision global navigation satellite system (GNSS) receivers located in successive vehicles could theoretically be used for this purpose [19; 20]. Some areas, however, have road tunnels and topographical features where GNSS-based data collection methods would be subject to signal blockages [12; 20]. External aftermarket radar sensors may circumvent the problem, provided they are adequately practical and accurate. Truck platooning research is often publicly funded, so solutions should preferably be low-cost. Since the output of aftermarket radar sensors would not be used for operative vehicle control, they do not need to be as capable, nor provide the same level of reliability as automotive-grade distance sensors, both of which drive cost and complexity. They may also be more flexible, in terms of allowing for custom placement and user adaptations. Many researchers have focused on perception, functional safety and operative control for truck platoons [21]. These studies often include cameras and radars. However, for studying the effects of truck platooning from the standpoint of roads authorities, these methods are more computationally complex than they need to be, and simple methods for estimating inter-vehicle distances from truck platooning would be useful. The current study investigated the feasibility of using Anteral universal radar (uRAD) sensors for Raspberry Pi [22] to measure inter-vehicle distances in a truck platoon on rural roads. This application represents a novel use case for this type of sensor. We propose a multi-faceted approach for collecting inter-vehicle distance data from truck platooning field trials. It aims to provide technical details and best-practices on data collection, synchronization, filtering and analysis. Dashboard cameras in each truck filmed the driving scene. This footage was used to log the timestamps of specific, recurring events, allowing for exploring sensor operation in different driving conditions. The paper addresses the research question: How can low-cost radar sensors and action cameras be used to investigate inter-vehicle distances in real-world truck platooning?
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 4 Proposed Method
### Dynamic Algorithm for Online Label Distribution Feature Selection
#### 4.3.3 Redundancy Analysis
1: Use Equation (5) to calculate weight matrix \(W(L)\);
2: Initialize reduct \(\leftarrow\mathcal{O}\);
3: If new features \(F_{i}\) arrive, calculate \(\gamma_{F_{i}}(D)\) according to Equation (16);
4:\(/significance analysis/\)
5:if\(\text{red}=\emptyset\)then
6: Calculate \(\text{SIG}\big{(}F_{i},S_{t_{i-1}},D\big{)}=\gamma_{F_{i}\cup S_{t_{i-1}}}(D) -\gamma_{S_{t_{i-1}}}(D)\);
7:if\(\text{SIG}\big{(}F_{i},S_{t_{i-1}},D\big{)}<0\)then
8: discard \(F_{i}\), to \(\mathbf{5}\)
9:else
10:\(\text{reduct}=\text{reduct}\cup F_{i}\), to \(\mathbf{5}\);
11:endif
12:\(/\)importance Analysis/
13:else
14: According to Equation (11), calculate \(\text{SIG}^{*}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}\);
15:endif
16:if\(\text{SIG}^{*}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}>0\)then
17:\(\text{reduct}=\text{reduct}\cup F_{i}\), to \(\mathbf{5}\);
18:else
19:\(/\)Redundancy Analysis/
20:while\(\exists F_{k}\in\text{reduct}\)do
21:if\(\text{SIG}\big{(}F_{i},F_{k},D\big{)}=0\) and \(\text{SIG}^{*}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}\geq\text{SIG}^{*}\big{(} F_{k},S_{t_{i-1}},D^{L}\big{)}\)then
22: discard \(F_{i}\), to \(5\);
23:else if\(\text{SIG}^{*}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}<\text{SIG}^{*}\big{(}F_{k},S_{t_{i-1}},D^{L} \big{)}\)then
24:\(\text{reduct}=\text{reduct}\cup F_{i}\), to \(\mathbf{5}\)
25:endif
26:endwhile
27:endif
28:No new features \(F_{i}\) arrive;
29:Output reduced set reduct. ```  
**Algorithm 2** Dynamic online label distribution feature selection (DLILC-LDL). The feature calculation performed by Algorithm 2 is taken from Method 1. Hence, the time complexity of a single feature selection is \(O\big{(}|L|^{2}+|U|\log|U|\big{)}\). However, in most cases, Algorithm 2 is neither simple nor optimistic, and it requires online updates. Because the time complexity of the update depends on the calculation of feature dependencies, in the worst case, it is necessary to traverse all selected features for processing, and hence the time complexity in the worst case is \(O\big{(}|L|^{2}+|S_{t-1}||U|\log|U|\big{)}\).
## 3 Numerical Simulation and Experimental Analysis
### Crack Mode  
#### 3.1.1 Numerical Analysis  
Substituting the physical properties as well as other parameters into Equation (3), the transfer matrix can be derived. It can be noticed that the matrix is an exponential one which means the analytical solution of the eigenvalues cannot be obtained. Thus, the MATLAB software is used in this paper to gain the numerical solutions of the eigenvalues. The propagation characteristics of the elastic wave can then be derived from the eigenvalues and be shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Location of the Crack (\(x_{c}\))** & **Depth of the Crack (\(a\))** & **RPM (Hz)** \\ \hline Part 1 & \(0.3L\) & \(0.5R_{c}\) & 50 \\ Part 2 & \(0.3L\) & \(0.1R_{c}\), \(0.2R_{c}\), \(0.3R_{c}\), \(0.4R_{c}\), \(0.5R_{c}\) & 50 \\ Part 3 & \(0.1L\), \(0.2L\), \(0.3L\), \(0.4L\), \(0.5L\), & \(0.5R_{c}\) & 50 \\  & \(0.6L\), \(0.7L\), \(0.8L\), \(0.9L\) & \(0.5R_{c}\) & 50 \\ Part 4 & \(0.3L\) & \(0.5R_{c}\) & 0, 10, 20, 30, 40, 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The numerical and experiment scheme.  
Figure 6: _Cont._  
Figure 6a-c are the numerical propagation constants derived with Mode I, II and III, respectively. There are three stopbands for each mode, and which are known as the first, the second and the third stopband, respectively. The upper part of each figure shows the attenuation factor varying with time, and the lower part indicates the phase angle varying with time. The stop bands are identified in the figure, according to the definition that the band with phase angle equals 0 or \(\pm\pi\) is a stopband. All the stopbands are listed in Table 2 for easy comparison.  
\begin{table}
\begin{tabular}{c c} \hline
**Crack Mode** & **Stopbands (Hz)** \\ \hline Mode I & [106; 138], [492; 546], [1100; 1261] \\ Mode II & [110; 141], [499; 564], [1157; 1261] \\ Mode III & [34; 124], [346; 516], [826; 1261] \\ \hline \end{tabular}
\end{table}
Table 2: Numerical stopbands derived from different crack modes.  
Figure 6: Numerical propagation constants derived from three different modes, (**a**) is derived with Mode I, (**b**) is derived with Mode II and (**c**) is derived with Mode III.  
It can be known from the above table that different mode will result in different stopbands. The results for Mode I and Mode II are similar, but they are obviously different with the results for Mode III. Thus, it is of great importance to choose the most suitable mode. Furthermore, related experiments will be performed to choose out the most suitable one for the transverse crack in the non-uniform shaft. A very simple linear variation form of the shaft is chosen to be studied as an example here, other variation cases can be studied this way too.
## 3 Results  
### Selection of Studies  
The PRISMA statement has been used to guide the SR. A total of 2998 published studies were classified from different search engines: 121 from PubMed, 1188 from Scopus, 1620 from Science Direct, 20 from EBSCOhost and 49 from Google Scholar after confirming by two individual reviewers. After removing duplicates, 198 studies were selected for screening. A total of 177 articles were excluded due to study design (Either case reports or series of cases reports). The remaining 21 studies with full text were evaluated for inclusion in this SR and 15 were excluded due to inclusion criteria. the gray literature has been searched; however, it did not change the number of selected studies. In the end, six articles were included in the SR (Table 2) and three articles (bottom 3 from Table 2) were selected for MA. All included articles for this study compared the cephalometric measurement between Camouflage and surgery groups. However, two [27; 28] among six articles assessed only the pre-treatment radiograph; whereas others evaluated both pre-treatment and post-treatment radiographs and compared between both treatment groups.
## 3 Results
### Volatile Components of NCS
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **RI 1** & **Compound** & **120 \({}^{\circ}\)C** & **130 \({}^{\circ}\)C** & **140 \({}^{\circ}\)C** \\ \hline
1 & 810 & 2-Methylpropanal & 0.13 \(\pm\) 0.05 b 2 & 0.28 \(\pm\) 0.05 b & 0.49 \(\pm\) 0.09 a \\
2 & 911 & 2-Methylbutanal & 0.14 \(\pm\) 0.02 c & 0.48 \(\pm\) 0.03 b & 0.83 \(\pm\) 0.12 a \\
3 & 914 & 3-Methylbutanal & 0.30 \(\pm\) 0.03 b & 0.95 \(\pm\) 0.09 a & 1.08 \(\pm\) 0.21 a \\
4 & 972 & 2,3-Butanedione & 0.11 \(\pm\) 0.02 b & 0.15 \(\pm\) 0.01 b & 0.24 \(\pm\) 0.03 a \\
5 & 1053 & 3-Hexanone & 0.03 \(\pm\) 0.01 c & 0.11 \(\pm\) 0.01 b & 0.25 \(\pm\) 0.03 a \\
6 & 1077 & Hexanal & 0.04 \(\pm\) 0.01 & tr & tr \\
7 & 1181 & Heptanal & 0.09 \(\pm\) 0.01 a & 0.05 \(\pm\) 0.01 b & tr \\
8 & 1208 & Pyrazine & 0.03 \(\pm\) 0.02 c & 0.12 \(\pm\) 0.01 b & 0.24 \(\pm\) 0.02 a \\
9 & 1229 & 2-Pentyfuran & 0.07 \(\pm\) 0.03 a & 0.05 \(\pm\) 0.01 a & tr \\
10 & 1259 & Dihydro-2-methyl-3(2H)-furanone & 0.03 \(\pm\) 0.02 c & 0.10 \(\pm\) 0.01 b & 0.35 \(\pm\) 0.05 a \\
11 & 1263 & 2-Methylpyrazine & 0.82 \(\pm\) 0.21 c & 3.31 \(\pm\) 0.14 b & 6.95 \(\pm\) 0.48 a \\
12 & 1280 & Acetoin & 0.03 \(\pm\) 0.01 c & 0.06 \(\pm\) 0.01 b & 0.18 \(\pm\) 0.01 a \\
13 & 1320 & 2,5-Dimethylpyrazine & 1.26 \(\pm\) 0.17 b & 2.67 \(\pm\) 0.07 a & 3.23 \(\pm\) 0.43 a \\
14 & 1326 & 2,6-Dimethylpyrazine & 1.68 \(\pm\) 0.17 c & 3.77 \(\pm\) 0.15 b & 4.77 \(\pm\) 0.43 a \\
15 & 1332 & 2-Ethlypyrazine & 0.12 \(\pm\) 0.03 c & 0.54 \(\pm\) 0.02 b & 0.98 \(\pm\) 0.10 a \\
16 & 1343 & 2,3-Dimethylpyrazine & 0.38 \(\pm\) 0.06 c & 0.59 \(\pm\) 0.03 b & 1.57 \(\pm\) 0.10 a \\
17 & 1384 & 2-Ethlyly-6-methylpyrazine & 0.08 \(\pm\) 0.01 c & 0.48 \(\pm\) 0.04 b & 0.77 \(\pm\) 0.09 a \\
18 & 1390 & 2-Ethly-5-methylpyrazine & 0.24 \(\pm\) 0.03 b & 0.37 \(\pm\) 0.07 b & 0.57 \(\pm\) 0.05 a \\
19 & 1403 & 2,3,5-Trimethylpyrazine & 0.09 \(\pm\) 0.01 c & 0.43 \(\pm\) 0.04 b & 1.17 \(\pm\) 0.08 a \\
20 & 1434 & 2-Ethenylypyrazine & tr & 0.10 \(\pm\) 0.01 b & 0.22 \(\pm\) 0.02 a \\
21 & 1444 & 3-Ethly-2,5-dimethylpyrazine & 0.18 \(\pm\) 0.01 b & 0.41 \(\pm\) 0.03 a & 0.22 \(\pm\) 0.05 b \\
22 & 1447 & Acetic acid & 1.42 \(\pm\) 0.13 c & 2.76 \(\pm\) 0.23 b & 6.43 \(\pm\) 0.48 a \\
23 & 1458 & Furfural & 0.52 \(\pm\) 0.07 b & 3.11 \(\pm\) 0.48 b & 14.92 \(\pm\) 2.38 a \\
24 & 1486 & 2-Ethenyl-6-methylpyrazine & 0.20 \(\pm\) 0.02 b & 0.56 \(\pm\) 0.01 a & 0.44 \(\pm\) 0.08 a \\ \hline \end{tabular}
\end{table} 
Table 1: Volatile components of NCS obtained at different evaporation temperatures (\(\upmu\)g/100 g).
## 5 Study A--Without Tetralegia  
A total of 10 participants without tetraplegia were recruited for study A. The recruited participants consisted of 1 female and 9 males within the age range of 19-34, with the average age being 25 years. None of them had any connections to the departments of the respective authors.  
The participants in study A did not have tetraplegia and were therefore asked to relax both their hand and arm entirely when using the system. This was performed to replicate the intended use case of the system, where an individual with tetraplegia would control the exoskeleton. Furthermore, electromyography (EMG) was recorded at all times during the study to ensure that the participant did not move their hand or arm independently of the exoskeleton by accident. The EMG was recorded using a Myo armband [37] placed on the right upper arm of the participant. After mounting the Myo armband, the participants were asked to repeatedly flex their biceps. These measurements served as a reference for the maximum muscle activation that the participant was capable of. Anytime the measured EMG of a participant would exceed just 20% of the measured maximum muscle activation, the participant would be instructed to relax and possibly repeat any ongoing task.  
All participants had received three days of training in using the ITCI for controlling the exoskeleton 4-5 weeks prior to the study, as previous studies on the ITCI have shown that long resting periods are beneficial when learning to use the tongue-based interface [38]. Besides the prior training in using the ITCI, study A consisted of two consecutive days where the first day was used to train using the three different control schemes and refresh how the tongue-based interface worked. In the last day, the participants used the three different control schemes to complete the four test scenarios described earlier, where they had to grasp and lift either a bottle or a strawberry. This was repeated six times to counteract outliers, as also described earlier in Section 4.4.  
The ordering of the test scenarios was completely randomized, and the used control scheme was randomized such that the same control scheme could not appear more than twice in a row before using another control scheme. This was performed to avoid having large concentrations of a specific control scheme at the start or end of the study which could skew the data.