esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics
## 3 SGP Motion Model Implementation  
To develop the proposed approach of fitting a kinematic model to the sGP motion model, we need to consider the following cases.  
* If the derived kinematic model is in accordance with real measurements, the machine learning model will be able to represent the corresponding physical behavior.
* However, if the kinematic model does not rely on data acquired from real-life measurements, the machine learning model might identify incoherences on the sample data that would prevent it from making the appropriate decisions.  
Taking this into account, we relied on the work presented in [8] and derived an sGP-based movement model from a data sample. To this end, we used wheel joint movement and simulated ground truth data (X, Y and \(\theta\) values). To fit the movement models we used sGP regression to estimate a probabilistic function \(f:\Delta\Phi_{t}\mapsto\Delta\vec{x}_{t+1}\) where \(\Delta\Phi_{j}=\left(\Delta\Phi_{t,1}\quad\ldots\quad\Delta\phi_{r,n}\right)^ {T}\) includes the wheel joint movements from the time slot \(j-1\) to \(j\), describing \(\Delta\vec{x}_{j}\) as the state change from \(j-1\) to \(j\).  
In addition, the vehicle states are described using \(\vec{x}_{j}=\left(x\quad y\quad\theta\right)_{j}^{T}\). \(x\) and \(y\) describe the position in a 2D map and \(\theta\) denotes the orientation of the vehicle at time \(j\).  
Considering that in this work we focus on estimating vehicle parameters instead of improving localization, we decided to use a simplified model as opposed to the one defined above. With the simplified model each movement prediction is based on independent sGP (see Equation (5))  
\[\begin{pmatrix}\mathcal{N}(\mu_{x},\sigma_{x}^{2})\\ \mathcal{N}(\mu_{y},\sigma_{y}^{2})\\ \mathcal{N}(\mu_{\theta},\sigma_{\theta}^{2})\end{pmatrix}_{t}=\begin{pmatrix} sGP_{x}\\ sGP_{y}\\ sGP_{\theta}\end{pmatrix}_{t} \tag{5}\]  
where each \(sGP_{j}\) is a sparsed Gaussian process, mapping wheel movement and current pose to the new vehicle pose. Note that the vehicle parameters are latent variables in the sGP models and affect each state prediction. To explicitly model the parameters, an additional node can be added to the graphical model (see \(\Theta\) in Figure 3).  
To perform the analysis, we need to measure the effect of the vehicle parameters rather than the drift. Thus, we perform a virtual vehicle movement using the sGP models, starting from the known vehicle pose \(\vec{x}_{0}=\left(0\quad 0\quad 0\right)^{T}\). Since \(\vec{x}_{0}\) is no more a random variable, we can be sure that we measure \(\Theta\) rather than the drift or noise. In this procedure (known as clamping), \(\vec{x}_{t}\) becomes independent from the vehicle parameters (Figure 3b).
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
###### Abstract
The main contributions are as follows:  
* This study adapted the implementations of BBRv2 and BBRv3 for integration into the ns-3 simulator and presents results confirming their intended behavior for single-flow scenarios. This study has released the simulation framework as open-source to encourage further experimentation and refinement within the research community. * This study performed a comprehensive evaluation of all versions of BBR, thus analyzing network performance metrics, including throughput, loss ratio, and intra- and interprotocol fairness, in scenarios with various distributions of RTTs, including a representative distribution of RTTs at the central link, as well as a mix of varying RTT ratios for different links. * This study conducted simulations on a 100 Mbps network, with buffer sizes ranging from 0.1 to 10 times BDP. The interprotocol fairness was analyzed in comparison to CUBIC CC. Routers implemented a drop-tail strategy; neither ECN nor AQM was evaluated. * Through detailed analysis, this study demonstrates that BBRv3 addresses certain limitations observed in its predecessors. However, issues persist regarding its fairness towards loss-based flows and those with varying RTT characteristics. Additionally, this study examines whether streams with longer RTT consistently achieve greater throughput. The remainder of this paper is organized as follows: the related work is reviewed in Section 2. Section 3 provides details of BBR behavior and the distinctions among different versions of BBR. The simulation framework is described in Section 4, followed by the presentation of evaluation results in Section 5. Section 6 concludes and summarizes the findings of this evaluation.
## 5 Case Studies
### Proportional-Integral Speed Controller for DC Motor
#### 5.2.1 Mathematical Formulation  
To simplify the mathematical description, the torque control loop is modeled as the first-order system:  
\[G_{\text{DC }TCL}(s)=\frac{T_{e}(s)}{T_{e}^{\text{ref}}(s)}=\frac{K_{t}}{ \tau_{\text{CCL}}s+1} \tag{40}\]  
where \(T_{e}(s)\) and \(T_{e}^{ref}(s)\) are the torque generated by the motor and the reference value, respectively, \(\tau_{\text{CCL}}\) is the time constant of the current control loop, and \(K_{t}\) is the torque constant of the DC motor. Next, the mechanical equation can be modeled using a moment of inertia (\(J_{m}\)) and a viscous friction (\(B_{m}\)):  
\[G_{\text{DC }m}(s)=\frac{\Omega_{m}(s)}{T_{e}(s)}=\frac{1}{J_{m}s+B_{m}} \tag{41}\]  
Figure 5: Results of hysteresis PID correction coefficients for the proposed objective function with different values of the \(\alpha\) coefficient.  
where \(\Omega_{m}(s)\) is the angular speed of the motor. Finally, the plant can be written as  
\[G_{\text{DC}}(s)=\frac{\Omega_{m}(s)}{T_{e}^{\text{ref}}(s)}=G_{\text{DC \ TCL}}(s)\cdot G_{\text{DC \ }m}(s) \tag{42}\]  
As mentioned earlier, the PI controller will be used to control the speed loop of the DC motor. Assuming that the system operates in a linear range, the transfer function of the PI controller is as follows:  
\[G_{\text{PI}}(s)=\frac{T_{e}^{\text{ref}}(s)}{\Omega_{m}^{\text{ref}}(s)- \Omega_{m}(s)}=K_{p}+\frac{1}{T_{i}s} \tag{43}\]  
where \(K_{p}\) and \(T_{i}\) are parameters of the PI controller that will be optimized to minimize the objective function. The block diagram of the control system is presented in Figure 6, while the parameters used are listed in Table 4.
## 6 Simulation Results  
The proposed DYMO-Auction and SSI auction both converge to satisfy the quality requirement for the tasks with an increase in the number of tasks, as shown in Figures 8 and 9. Tables 3 and 4 show that the average quality requirements of the tasks that are executed by the high-quality robots deviate in a negative direction, which indicates that the robot performs tasks that require a quality lower than that of the robot. Alternately, the opposite occurs with the robot with low quality when its tasks' average quality requirements deviate in a positive direction, meaning that the robot performs tasks with higher quality requirements. These deviations become smaller with an increase in the number of tasks because there will be more tasks of every quality level. Noteworthy, we feed SSI with all information about the upcoming tasks and their quality requirements, while DYMO gets task details as they emerge.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of robots & 3, 6, 9, 12 \\ Number of tasks & (12, 24, 36, 48) \\ Robot’s quality (\(q_{R}\)) & (2, 5, 8) \\ Task’s required quality (\(q_{T}\)) & (1, 2, 3, 4, 5, 6, 8, 9) \\ Area size & 50 m \(\times\) 50 m \\ Task inter-arrival mean time (\(1/\lambda\)) & 80 s \\ Task execution mean time (\(1/\mu\)) & 140 s \\ Task’s required resources & (1, 2, 3, 4, 5, 6, 8, 9, 10) \\ PID parameters \(K_{p},K_{i},K_{d}\) & (2, 0.01, 0.04) \\ \(W_{d},W_{q},W_{l}\) & 0.46, 0.21, 0.33 \\ Number of Iterations & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 7: Network schematic diagram of the simulation experiments.  
The load-balancing objective has been met with a deviation from the optimal (the load for all robots is equal) by up to \(\pm\)2.5% for the SSI auction and up to \(\pm\)5% for DYMO-Auction as shown in Figures 10 and 11. The deviation in both methods is due to the competition between the load-balancing objective and the other two objectives, which may compromise the load objective. However, the SSI auction deviates less because it has a full view of all tasks ahead, which is not the case for DYMO-Auction.  
In terms of the total traveled distance per robot, this objective has a significant weight in the cost function \((Wd=0.46)\). Robots also attempt to select the minimum route to visit/execute all assigned tasks, which further decreases the total traveled distance. The total traveled distance increases by 20% using DYMO-Auction compared to an SSI auction, as shown in Figure 12. An SSI auction requires global  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline High & \(-\)5.2438 & \(-\)2.488 & \(-\)1.4275 & \(-\)1.7900 \\ Medium & \(+\)1.3240 & \(-\)1.0440 & \(+\)2.7080 & \(+\)0.4400 \\ Low & \(+\)41.8550 & \(+\)32.8350 & \(+\)15.9600 & \(+\)17.2350 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage deviation from the quality satisfaction using SSI.  
Figure 8: Quality satisfaction using a distributed multi-objective task allocation approach (DYMO-Auction).  
Figure 9: Quality satisfaction using an sequential single-item approach (SSI) auction.
## 5 Results
### Results from the Simulation Dataset
#### 5.2.1 Analysis for SNR = 1000
It can be seen from the figure that the samples with incorrect classification are located in the area of mixed pixels, and in the five result diagrams, a large number of mixed pixels are classified as vegetation, which can prove that the mixed spectra of the three types of artificial objects are very similar to the vegetation spectra. Compared with GA-BPSO, IOIF and Partitioned Relief-F, LBI-BPSO has better performance for the classification of mixed vectors. Especially in the edge area of ground objects, that is, for mixed pixels with low abundance values, the result of LBI-BPSO has a more obvious dividing line, and the noise in the mixed pixel area is smaller. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**\#**} & \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**GA-BPSO**} & \multicolumn{2}{c}{**IOIF**} & \multicolumn{2}{c}{**Partitioned**} & \multicolumn{2}{c}{**LBI-BPSO**} \\ \cline{3-11}  & & & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** \\ \hline
1 & C1 & Vegetation & 94.87 & 0.79 & 75.35 & 1.88 & 77.94 & 0.64 & 80.07 & 1.86 & **81.57** & \(1.35\) \\
2 & C2 & Green coating & 41.08 & 2.54 & 66.57 & 3.61 & 66.83 & 1.88 & 70.25 & 2.15 & **70.50** & \(1.00\) \\
3 & C3 & Artificial leaf & 23.93 & 7.96 & 65.09 & 2.03 & 64.01 & 1.97 & **66.44** & 2.77 & 65.57 & \(1.85\) \\
4 & C4 & Camouflage cloth & 96.64 & 0.70 & 96.16 & 1.55 & 96.12 & 0.58 & **98.71** & 0.96 & 98.28 & 0.64 \\ \hline  & Dimension & 301 & 39 & & & & & & & & \\ \hline  & OA (\%) & 69.24 & 1.62 & 75.79 & 0.68 & 76.60 & 0.14 & 79.09 & 0.55 & **79.50** & 0.16 \\  & AA (\%) & 64.12 & 1.88 & 75.79 & 0.79 & 76.23 & 0.18 & 78.86 & 0.52 & **78.98** & 0.39 \\  & Kappa (\%) & 55.83 & 2.28 & 66.63 & 0.98 & 67.66 & 0.16 & 71.16 & 0.73 & **71.57** & 0.29 \\ \hline  & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 3: Experimental results of simulation data set (SNR = 1000).
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 2 Materials and Methods
### Method
#### 2.3.5 Cross-Validation  
Cross-validation [65; 66], also known as rotation estimation, this theory was proposed by [PERSON] [67]. It is a statistically practical method of cutting data samples into smaller subsets. Typically, it is used as an accuracy test and hyperparameter screening method when the amount of sample data is small. This study used 10-fold cross-validation to determine the best parameters of the model to obtain a model with higher accuracy. By dividing the dataset into 10 parts, 9 of them were used as training data in turn, and the remaining one was used as test data. Each test yielded a corresponding accuracy. The average error rate of 10 results was used to estimate the accuracy of the algorithm. The advantage of this method is that the randomly generated sub samples could be repeatedly used for training and verification, and the results could be verified each time.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 3 Architecture Design  
This section outlines the key principles and components of the proposed architecture for smart transportation edge computing. Details of the prototype implementation based on this architecture are presented in the succeeding sections. Our envisioned system continuously collects and stores sensor data from urban areas for efficient processing and response to user requests. It demands scalability, real-time functionality, and fault tolerance, aligning perfectly with the capabilities of the Elixir language, as discussed in the previous section.  
The proposed architecture comprises multiple interacting components, designed for specific functionalities. Asynchronous message passing with MQTT for event transport and Elixir for event processing facilitates concurrent execution and fault isolation. Specifically, Elixir's lightweight concurrency and distributed processing handle asynchronous events in a scalable manner (Section 3.2), while MQTT's publish-subscribe messaging distributes events across service components (Section 3.3).
# Farm Monitoring System with Drones and Optical Camera Communication
## 4 Computer Simulation
### Simulation Results
#### 4.2.2 Total Travel Time  
Figure 7 shows the total travel time to receive all the data from the sensor nodes where the field size was \(50\times 50\) m. The points and error bars represent the average and the maximum/minimum values. The total travel time increased as the filming time, i.e., data size of the sensor nodes, increased. As the filming time increased from 0 to 3 s, the total travel time for all algorithms increased proportionally. The shortest path consistently produced the highest total travel time over the entire range of filming times. On the other hand, the proposed algorithm achieved significantly lower total travel times. This is because the sensor nodes were clustered so that the receiver drone simultaneously received data from multiple sensor nodes. Among the clustering algorithms, the k-means algorithm typically showed the lowest total travel time, closely followed by the group average and [PERSON]'s methods. We confirmed that the proposed algorithm outperformed the shortest path  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Altitude of receiver camera \(h\) & 5 m \\ Speed of drone & 3 m/s \\ Horizontal angle of view \(\phi_{h}\) & \(77^{\circ}\) \\ Vertical angle of view \(\phi_{v}\) & \(40^{\circ}\) \\ Elevation angle of camera \(\theta\) & \(20^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
algorithm regardless of the conditions and clustering algorithms. Although the expected performance was almost the same with the clustering algorithms employed, the minimum value of the total travel time was slightly shorter with the group average method.  
Figure 6: Total trajectory length.  
Figure 7: Total travel time.
## 3 Materials and Methods
### Statistical Empirical Mode Decomposition (SEMD)  
The method of SEMD introduced by [53] uses smoothing instead of cubic spline interpolation for the purpose of extracting the first mode. The smoothing technique is somehow more useful than cubic spline interpolation, specifically when the signal is corrupted with noise. The step-by-step procedure of this method is outlined in the following lines.  
Step 1: Make \(K\) test datasets \(T_{1}\),..., \(T_{K}\),..., \(T_{K}\) from a signal \(x(t)\).  
Figure 1: Flowchart of the EMD algorithm.  
Step 2: To generate \(\widetilde{T}_{k}\) impute the \(k^{th}\) test dataset using the local average of two nearby points.  
Step 3: Apply the SEMD technique to break down the composite signal \(T_{1}\),..., \(T_{k-1}\), \(\widetilde{T}_{k}\), \(T_{k+1}\),..., \(T_{k}\) into an \(h_{1,\lambda}\), and the remaining signal \(r_{\lambda}\) with the specified smoothing parameter.  
Step 4: Determine the forecasted values for the remaining signal that was assessed at the \(k^{th}\) segment, namely \(r_{\lambda}^{k}(t)\).  
Step 5: Steps (ii) through (iv) should be repeated for \(k\) = 1,..., \(K\), and the prediction error is computed as  
\[PE(\lambda)=\frac{1}{n}\sum_{i=1}^{n}\Bigl{\{}x(t)-r_{\lambda}^{k}(t)\Bigr{\}}^ {2} \tag{6}\]
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Mobility Models
#### 3.1.3 The Smooth-Turn Model  
The ST mobility model is proposed in [3, 4]. The idea is as follows. An MN randomly chooses a point along the line perpendicular to its direction and circles around that point until it chooses another center. The perpendicularity ensures the smoothness of the trajectories. In a nutshell, the MN continuously moves in circular arcs. The choice of the centers is random, as well as the choice of the duration of the movement along the corresponding circular arc. As proposed in [3, 4], the duration distribution is exponential. For the selection of the centers, the inverse length of the turning radius follows a Gaussian distribution in order to ensure that straight lines and slight turns are favored over very sharp curvy turns. At the boundary of the movement area, a \"reflection\" strategy is used. This strategy consists of mirroring the out-of-region trajectory against that boundary. Figure 3 shows a UAV simulation trajectory using the above-described ST mobility model.  
Figure 3: Trajectory of a UAV under the smooth-turn (ST) model.
## 3 The PFAC Measurements and the FRM Uncertainty Analysis  
In-situ measurements used for external calibration and validation of Earth observation have to follow and be traceable to internationally agreed reference standards. In addition, the uncertainty analysis of these measurements has to accompany the respective Cal/Val results. This uncertainty should be available to allow altimetry operators to evaluate the credibility and suitability of the Cal/Val results for efficient monitoring of the altimeter's performance. A strategy for assuring a uniform and absolute standardization for calibrating satellite altimeters is given in [24]. The main uncertainty constituents for sea-surface and transponder Cal/Val techniques have been identified in [9] and quantified in [14].  
Accurate determination of the absolute geodetic coordinates for a Cal/Val site constitutes one of the key uncertainty components in both sea-surface and transponder calibration. Particularly, the height component is of utmost importance as it controls both the sea-surface height at the sea-surface Cal/Val site and the distance between the transponder and altimeter phase center. The following section presents the strategy followed at the PFAC to determine the absolute coordinates of the Cal/Val sites  
Figure 3: An intersection of the Sentinel-3A Pass No. 14 and Jason Pass No. 109 occurs about 20 km south of Gavdos, Crete, Greece. This location is used to perform crossover analysis and determine the relative bias between the two altimeters.  
and their uncertainty. Later, the remaining constituents of uncertainty contributing to each calibration method are examined separately.
## 2 General Sensor Specifications
### Embedding the Sensor into an Energy-Autonomous Sensor Node  
The new measurement approach with the low cost ams AS7341 as a PAR and light quality-sensor makes it possible to integrate all required hardware and software into a small mobile sensor node, together with other useful sensors, such as for temperature and humidity. This system could be deployed at different canopy levels. In fact, our team has  
Figure 1: Spectral responsivity according to the AS7341 datasheet [23] (graph redrawn with permission). The eight channels show the whole PAR spectrum. Not shown is a channel with a sensitivity in the near infrared at 910 nm.  
Figure 2: For all tests, the ams AS7341 is put into a housing formed to achieve cosine correction for incoming light. As the optical filters are integrated in the AS7341, there is no need for additional parts in the housing other than a diffuser and in our case an optional glass dome.  
already built a prototype of a wireless sensor node that fulfills the following properties: Firstly, it is attachable to smaller branches, e.g., with a zip tie as the prototype weights only 39 g with dimensions below a typical matchbox as shown in Figure 3. Second, it is self-sustaining. The sensor-node is powered with an array of high-performance solar cell and should be able to run for periods longer than several years without the need to change any primary batteries. The low energy consumption even allows the prototype to work in very poor light conditions as low as 5.5 W h m\({}^{-2}\) per day. Third, it provides an easy deployment. The prototype uses the wireless communication technology LoRa-WAN that offers a free-field range of several kilometers at a low power consumption. When there is no public network available, own gateways can be deployed in the forest to receive the data from hundreds of nodes. A remote configuration is also possible due to the bidirectional communication protocol.