esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 1 Introduction
Our study proposes a problem-solution approach using blockchain to build reliable AI models and reduce processing times through faster transactions. To address these challenges, this study investigates the potential of hashgraph--an advanced distributed ledger technology designed for fast, scalable, and secure transaction processing [10]. Unlike traditional blockchains, hashgraph uses a directed acyclic graph (DAG) structure, allowing for asynchronous and parallel transaction processing [11]. This architecture eliminates sequential bottlenecks and significantly increases throughput, enabling the network to process thousands of transactions per second while maintaining robust security. Additionally, hashgraph's consensus mechanism offers low-latency finality, rendering it particularly well suited for applications requiring rapid data validation. As each participant maintains their own transaction history, hashgraph is also advantageous for collaborative AI model development, including ensemble and federated learning. These features position hashgraph as a promising solution for managing the vast and continuously growing datasets required for training AI models in autonomous vehicles. This study proposes a secure and efficient training data management framework for autonomous vehicles using hashgraph. The framework collects training data from autonomous vehicles--including road conditions, traffic signals, and environmental features--and transmits them to both a central server and the hashgraph network. After processing, refined AI models are distributed back to the vehicles to improve real-world performance. To evaluate the feasibility of the proposed approach, we utilized Hedera, the most widely adopted implementation of hashgraph, and conducted simulations using CARLA, an open-source autonomous driving research platform. The results demonstrate that integrating hashgraph into the autonomous vehicle data pipeline ensures secure data storage, efficient verification, and reliable AI training. Leveraging Hedera's high throughput and tamper-resistant storage capabilities, our proposed system enhances the trustworthiness of AI-driven transportation technologies, contributing to the development of safer and more reliable autonomous mobility solutions.
# Zero Average Dynamic Controller for Speed Control of DC Motor
## 4 Conclusions  
In this research, a ZAD technique was applied to a buck converter in order to control a permanent DC motor. In the first simulations, the discrete-time model was considered with parameters \(K_{S1}=K_{S2}=2\), \(K_{S3}=40\), and neither time delay nor quantization effects. The resulting system is two times faster than that previously reported in the literature, and it exhibits low steady-state error for the tested references. Additionally, the system is not saturated and remains in the steady-state operation at values between zero and one; this means that the system has a fixed switching frequency. Therefore, the system is very stable as there is no saturation in the duty cycle; in addition, it exhibits neither subharmonics, unwanted noise, nor chattering. This is confirmed by the lack of sudden changes in the steady-state values of the input voltage. Additionally, robust speed tracking performance of the DC motor was obtained under reference signal and controller parameter changes and disturbances in the load torque.  
Figure 10: Simulation of the closed-loop system under varying load torque: (**a**) trajectory of the output, (**b**) trajectory of the output tracking error, (**c**) trajectory of the \(T_{L}\), (**d**) trajectory of the \(d\), (**e**) trajectory of the \(V_{a}\), (**f**) trajectory of the \(i_{a}\).  
In the second discrete-time simulation and experimental tests, the same model was considered, with parameters \(K_{S1}=K_{S2}=2\), \(K_{S3}=40\), one-period time delay, and quantization effects. As a result, the control regulates the speed \(W_{m}\) properly, thus allowing researchers to impose the desired rotation by properly defining the reference signal \(W_{mref}\). The speed control error is lower than 2% for all \(K_{S3}\) values within its variation range, with \(K_{S1}=K_{S2}=2\), when there is a one-period delay time, which is noticed in the bifurcation diagrams. Regarding the fast-prototyping tool DS1104, the system is adaptable for programming control approaches, which is challenging in analog form. In addition, it is possible to synchronize variables with any external or internal trigger signal and measure state variables with a resolution of up to 16 bits.  
Conceptualization, F.E.H.; methodology, F.E.H.; validation, F.E.H. and J.E.C.-B.; formal analysis, A.R., F.E.H. and J.E.C.-B.; investigation, F.E.H.; writing--original draft preparation, A.R., F.E.H. and J.E.C.-B.; writing--review and editing, A.R., F.E.H. and J.E.C.-B.; visualization, A.R., F.E.H. and J.E.C.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Universidad Nacional de Colombia, Sede Medellin, under the project HERMES-45142. [PERSON] and [PERSON] thank the School of Physics and the Department of Electrical Energy and Automation. The work of [PERSON] was supported by Universidad Catolica de Manizales.  
The authors declare no conflict of interest.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 5 Experimental Results
### RQ3: Which Mobility Model Stows the Highest PDR Performance in the Reconnaissance Scenario?
The RDPZ model maintains uniform reconnaissance of the operational area, so it is not affected much by the GCS location. **Findings. ** the GCS location affects the performance of the mobility model. It is recommended to locate GCS at the center of the reconnaissance area.
## 5 Conclusions  
The point-wise acceleration approach was proposed to recover the gravity field model based on satellite accelerations directly estimated from the GOCE's onboard carrier phase observations. The satellite accelerations derived by the carrier phase differentiation method had a slightly better quality in terms of time-domain than those derived by the kinematic orbit differentiation method, respectively. A static gravity field model up to degree and order 130 was compiled from 71 days and 2.5 years of GOCE SST-hl data by the carrier phase differentiation method. Additionally, the gravity models were estimated based on the conventional orbit differentiation method from the ESA's, Graz's, and SGG's PKI orbits. In comparison with the reference models (EIGEN-6C4 and DGM-1S) in accordance with DE-RMS, the 71-day carrier phase solution was slightly better than the SGG-orbit solution in the entire frequency band; however, it showed a worse performance in the low frequency part than the ESA-orbit solution and Graz-orbit solution. Furthermore, the 2.5-year carrier phase solution had the best accuracy for the degrees greater than 70, and its cumulative geoid height error up to d/o 130 was the lowest, which indicates that the proposed approach in this paper shows very good performance for gravity field modeling from the SST-hl observations.  
The empirical variance-covariance matrices used in this paper were constructed directly by using the residual accelerations. In the future, the variance-covariance matrix can be derived by using the error propagation method from the prior carrier phase and orbital variance-covariance information.  
**Author Contributions:** Resources, J.L.; Funding acquisition, J.L. and X.X.; Conceptualization, X.X.; Data curation, H.W. and K.K.; Investigation, T.W.; Methodology, X.X. and H.W.; Validation, T.W. and H.W.; Writing--original draft, T.W. and X.X.; Writing--review & editing, J.L., H.W., K.K., and Y.Z.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 41574019, 41774020,41804020), the German Academic Exchange Service (DAAD) Thematic Network Project (Grant No. 57421148), The Major Project of High Resolution Earth Observation System, the Civil Space Technology Pre-research Project \"Satellite Gradiometry Technology\", and the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (Grant No. 16-02-08,17-01-10,18-02-04).  
**Acknowledgments:** The numerical calculations in this paper were carried out on the supercomputing system in the Supercomputing Center of Wuhan University. We gratefully acknowledge the European Space Agency for providing the GOCE data and the International GNSS Service for providing the GPS data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Challenges and Pitfalls of AI  
### Data Size and Quality  
ML and DL algorithms require a large amount of training samples, which grows rapidly with the dimensionality of data (the curse of dimensionality). An unappropriated data size will lead to a reduction in the certainty of the prediction, considering that many ML applications will always deliver a result, disregard the size and quality of the data set [100]. Unfortunately, a proper metric to evaluate sample size and power for ML and DL is missing.  
Frequently, datasets used for training AI have a small number of samples with respect to the dimensionality of data and of the desired tasks [101], to the point that, frequently, there are more features per subject than subjects in the entire dataset [102]. Under these circumstances, overfitting, a condition where models are more sensitive to noise in the data than to their patterns, and instability occur, making the model poorly reproducible and generalizable, meaning that it will perform poorly on unseen datasets [103].  
Feature selection algorithms, such as stepwise feature selection [104], the minimum redundancy maximum relevance (mRMR) [105], and RELIEF (relevance in estimating features) [106,107], can be applied to reduce overfitting by selecting a non-redundant subset of variables best suited to predict the outcome.  
To reduce overfitting in DL, data augmentation (e.g., by the affine transformation of the images) during training is commonly implemented [10], and layers in the networks are specialized in reducing overfitting, such as dropout layers [108]. On the other side, DL suffers from other sources of uncertainties (e.g., the presence of many local minima in the loss function and the stochastic nature of training algorithms), so that repeating model training multiple times does not necessarily produce the same model [2]. Besides, the class imbalance problem, in which some classes have a significantly higher number of samples, is detrimental for ML performance, if not properly accounted for [109,110]. For overcoming class imbalance, under-sampling or over-sampling can be applied; the latter has been proven to be more effective [110].  
Other biases in the training datasets, e.g., age, gender, and race, or in the diagnostic or therapeutical approach, e.g., technologies use for imaging or radiotherapy, may result in biased models, which may lead to poor performance for minority groups who are poorly represented in the training dataset. This could potentially aggravate healthcare disparities [103].  
Another source of unreliability stems from the constant evolving of the patterns of clinical practice over time due to the introduction of new treatment approaches, technologies, or gradual changes in patient population (e.g., percentage of patients with a given histological subtype). This may result in increased unreliability of the AI system's recommendations or prediction over time [30]. The \"half-life\" of the relevance of clinical data used for training is thought to be typical of 4 months [111].
# Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
## 3 Proposed HAR System
### Data Pre-Processing  
The data pre-processing performed in the microprocessor included both parallel translation and normalization processes. Normalizing point-cloud data ensures that all points are distributed within a certain range without information loss [24]. This facilitates gradient descent learning and prevents bias toward specific values. In addition, by performing a parallel translation before normalization--to move the center of the point cloud to the origin--the point cloud was evenly distributed around the origin when normalization was performed.  
In this study, parallel translation was performed to move the center of the maximum and minimum values along the \((x,y,z)\) axis to the origin for the points included in each frame. Subsequently, data pre-processing was performed to normalize the \((x,y,z)\) coordinates of all points by dividing the \((x,y,z)\) coordinates of all points by the largest absolute value.
# Speeding up Statistical Tolerance Analysis to Real Time
## 5 Methods: Detailing the Approach to Speeding Up Tolerance Simulations  
The core of this work is the execution of the statistical tolerance analysis and the statistical tolerance synthesis considering different programming and architecture approaches as well as a varying sample size for the underlying Monte Carlo simulation. First, the procedure (Section 5.1) as well as the implementation (Section 5.2) of the statistical tolerance analysis are presented. The following sections focus on the procedure (Section 5.3) and the implementation of the statistical tolerance synthesis using numerical optimiza  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dimension** & **Nominal (in mm)** & **Tolerance \(T_{i}\) (in mm)** & **Distribution** \\ \hline \(M_{1}\) & 7.5 & \(\pm\)0.05 & Gaussian (\(\pm\)3\(\sigma\)) \\ \(M_{2}\) & 5.1 & \(\pm\)0.1 & Uniform \\ \(M_{3}\) & 17.5 & \(\pm\)0.05 & Gaussian (\(\pm\)3\(\sigma\)) \\ \(M_{4}\) & 5.1 & \(\pm\)0.1 & Uniform \\ \(M_{5}\) & 5.05 & \(\pm\)0.1 & Gaussian (\(\pm\)3\(\sigma\)) \\ \(M_{6}\) & 12.5 & \(\pm\)0.05 & Gaussian (\(\pm\)3\(\sigma\)) \\ \(M_{7}\) & 5.1 & \(\pm\)0.1 & Uniform \\ \hline \end{tabular}
\end{table}
Table 2: Dimensions and corresponding tolerance specifications.  
Figure 3: Two assembly scenarios occurring due to the overconstraint design of the door hinge assembly.  
tion (Section 5.4). This is followed by details on the implementations and hardware used (Sections 5.5 and 5.6).
## 3 Results
### Measurements of Vegetation Canopy
aureus_ were placed under the horizontal center (origin shown in Figure 12) of the hemispherical sensor FOV at first with 1.0 m vertical distance between the sensor and the top of the canopy, and was moved away from the center at a 0.2 m step. Figure 12 shows the variations of SIF, reflectance at 760 nm and NDVI with the distance between _E. aureus_ and the center. When _E. aureus_ was moved away from the FOV center, SIF, reflectance at 760 nm, and NDVI all dramatically decreased with an increase of distance. Once the distance was larger than one meter, the decreases of the three parameters with distance were slackened. Reflectance at 760 nm remained invariant since the distance reached 1.4 m, while SIF and NDVI kept slowly decreasing until SIF became negative and NDVI turned to be steady around 0.05  
Figure 9: Diurnal variations of integration time of different systems of (**A–D**) hemi-con and (**E–H**) bi-hemi configurations (SIV of hemi-con configuration as a reference) while the upwelling sensors pointing toward vegetation with almost same field of view (different viewer height). The right volume specifically shows the integration times (ITs) of SI and SII. when _E. aureus_ was pulled 2.0 m away from the FOV center. The underlying picture indicates the vegetation canopy and the transparency of the rings represents the contributions of different places of the canopy to the SIF signal captured by the cosine corrector. The theoretical radius of the footprint of the cosine corrector simulated by [PERSON] et al. (2019) should be 2.7 m above the canopy here. Our findings suggested that the measured SIF signal was limited at 2.0 m the same as NDVI behaved. This result also indicates the far-red SIF signal of vegetation measured by the spectrometer is beyond the range expressed by reflectance at 760 nm when the reflectance was already invariant before that distance. Figure 10: Irradiance, radiance and reflectance measured by the splitter (SI) and prism systems (SII) of (**A**,**B**) hemi-con and (**C**,**D**) bi-hemi configurations while the upwelling sensors pointing toward vegetation.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 2 Architecture of Digital Twin Monitoring System
### The Cutting Behavior Process of the Roadheader  
Taking the EBZ55 roadheader as the research object, it is assumed that when the roadheader is on horizontal ground, the direction from the rear support to the cutting head and parallel to the horizontal plane is the front, the direction perpendicular to the ground and upward is the upper direction, and the left and right directions can be determined, as shown in Figure 3.  
It is assumed that the initial state of the roadheader before operation is as follows: the slewing platform is in the middle position, and the rear support and the shovel plate are both at the positions where the strokes of their corresponding hydraulic cylinders are the smallest, as shown in Figure 4.  
Figure 3: Schematic diagram of direction marking of roadheader.  
Figure 2: Operation process of DTMSR.  
The cutting arm is in the horizontal position, as shown in Figure 5, and the roadheader is at a certain distance from the cutting surface. The ideal roadway profile is selected as a semi-circular arch type. The main cutting process action steps of the roadheader are decomposed as follows:  
1. Start the rotation of the cutting head, and the star wheel and the transfer part rotate forward.
2. The traveling part drives the fuselage forward, and the cutting head drills into the coal wall for an appropriate distance.
3. The shovel plate swings downward, and the rear support swings downward to lift the fuselage.
4. Adjust the cutting arm so that the cutting head is at the lower left corner of the ideal contour of the cutting surface.
5. Control the cutting head to cut in an S-shaped route, and then perform wall brushing and floor cutting to complete the cutting cycle. The cutting path and the ideal roadway profile are shown in Figure 6.
6. The cutting head drops and waits for the support operation.  
Repeat the above action steps to complete the tunneling of the roadway.  
Figure 4: Side view of the initial state of the roadheader.  
Figure 5: Top view of the initial state of the roadheader.  
Figure 6: The cutting path and the ideal roadway profile.
## 3 Results and Discussion
### Suitability of MSM Extraction Procedure  
Measurements were performed on two parallel hens' feed mixture samples (S1 and S2) by using the MSM. The obtained results are shown in Table 3. For both samples, the obtained values of recovery were acceptable (sample 1 (S1) 95.68% and sample 2 (S2) 98.38%). Therefore, we concluded that the MSM is suitable for determining the concentration of lutein in real samples.  
\begin{table}
\begin{tabular}{c c} \hline
**Solvent/Solvent Mixture** & **mg of Lutein/kg of Feed Mixture** \\ \hline methanol:acetone (1:1, \(v/v\)) & 4.572 \(\pm\) 0.28 \({}^{\text{a}}\) \\ \hline methanol & 3.499 \(\pm\) 0.17 \({}^{\text{b}}\) \\ \hline ethanol & 3.201 \(\pm\) 0.13 \({}^{\text{b}}\) \\ \hline acetone & 1.214 \(\pm\) 0.04 \({}^{\text{c}}\) \\ \hline
2-propanol & 0.543 \(\pm\) 0.09 \({}^{\text{d}}\) \\ \hline \(p\)-value & \textless{0.01} \\ \hline \(\overline{\tau}\) = mean; sd = standard deviation; \({}^{\text{a,b,c,d}}\) values within a column with different letters differ significantly at \(p<0.001\). \\ \end{tabular}
\end{table}
Table 1: Comparison of lutein content extracted from samples of hens’ feed mixture obtained by using chosen solvents (\(\overline{\tau}\pm\) sd).  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **mg of Lutein/kg of Feed Mixture** \\ \hline SM & 12.753 \(\pm\) 0.715 \({}^{\text{b}}\) \\ \hline MSM & 19.410 \(\pm\) 1.337 \({}^{\text{a}}\) \\ \hline \(p\)-value & \textless{0.001} \\ \hline \(\overline{\tau}\) = mean; sd = standard deviation; \({}^{\text{a,b}}\) values within a column with different letters differ significantly at \(p<0.001\). \\ \end{tabular}
\end{table}
Table 2: Comparison of lutein content obtained by standard method and modified standard method (\(\overline{\tau}\pm\) sd).  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **mg of Lutein/kg of Feed Mixture** \\ \hline SM & 12.753 \(\pm\) 0.715 \({}^{\text{b}}\) \\ \hline MSM & 19.410 \(\pm\) 1.337 \({}^{\text{a}}\) \\ \hline \(p\)-value & \textless{0.001} \\ \hline \(\overline{\tau}\) = mean; sd = standard deviation; \({}^{\text{a,b}}\) values within a column with different letters differ significantly at \(p<0.001\). \\ \end{tabular}
\end{table}
Table 3: Concentrations of lutein obtained from samples used for determination of suitability of MSM extraction procedure.
## 4 Discussion
### Sentinel-1 PS Density in Alexandria  
The overall density of the PS-InSAR points in the images from the ascending orbit was 191 points per square kilometer within the Alexandria study area, increasing to 345 points/km\({}^{2}\) in the denser parts of the city. In images from the descending orbit, the point density was 184 points/km\({}^{2}\) within the study area increasing up to 319 points/km\({}^{2}\) in denser built-up areas in comparison to the few attempts in the literature to use the PS-InSAR technique in Alexandria [8; 17], this density of extracted PS-INSAR points in our study is significantly higher than any previous study. Only 14 points per square kilometer were obtained in [8], by utilizing 40 Advanced Synthetic Aperture Radar (ASAR) images between 2003-2010. In addition, our findings in results of vertical motion decomposition is considered much precise than the estimated surface deformation in the descending line-of-sight concluded in previous PS-InSAR studies in Alexandria [8; 17]. Because, InSAR measurements from one particular imaging geometry (ascending or descending), needs to be interpreted with caution, as vertical motion, horizontal displacement, and tilt can introduce large errors in the final deformation results.
## 3 Methods
### Research Methods
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Characteristic** & **Question** \\ \hline \multirow{2}{*}{Appropriateness of Stage Division} & Is the division into four stages (Image Recognition, Application of Image Recognition, Image Segmentation and Object Detection, Video Analysis) appropriate? \\ \hline \multirow{2}{*}{Validity of Computer Vision Elements} & Are the computer vision elements presented at each stage (e.g., image classification, object recognition) suitable for that stage? \\ \hline \multirow{2}{*}{Connection with Data Science Elements} & Are the data science elements appropriately connected with each stage of computer vision education? \\ \hline \multirow{2}{*}{Suitability of Coding Education Level} & Are the coding education level presented at each stage (e.g., no-code platforms, block coding, Python coding) appropriate for learners’ levels and educational goals? \\ \hline \multirow{2}{*}{Relevance of Computational Thinking Elements} & Do the computational thinking elements presented at each stage relate to the learning content of that stage? \\ \hline \multirow{2}{*}{Suitability of Learning Activities Examples} & Are the learning activities examples presented at each stage appropriate for achieving the learning objectives of that stage? \\ \hline \multirow{2}{*}{Connection Between Stages} & Is each stage designed to progressively deepen based on the learning content of the previous stage? \\ \hline \end{tabular}
\end{table} 
Table 5: Validity assessment questions for the staged approach and criteria (Round 1).