esa-sceva/satcom-chunk-collection huggingface dataset:
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
###### Abstract  
A DataMatrix (DM) code is an automatic identification barcode based on a combination of coding and image processing. Traditional DM code sampling methods are mostly based on simple segmentation and sampling of a DM code. However, the obtained DM code images often have problems such as wear, corrosion, geometric distortion, and strong background interference in practical scenarios. To improve decoding ability in complex environments, a DM code recognition method based on coarse positioning of images is proposed. The two-dimensional barcode is first converted into a one-dimensional waveform using a projection algorithm. Then, the spacing between segmentation lines is predicted and corrected using an exponential weighted moving average model for adaptive grid division. Finally, the local outlier factor algorithm and local weighted linear regression algorithm are applied to predict and binarize the gray level values, converting the DM code image into a data matrix. The experimental results show that this method effectively handles problems like blurring, wear, corrosion, distortion, and background interference. Compared to popular DM decoding libraries like libdmtx and xing, it demonstrates better resolution, noise resistance, and distortion tolerance.  
DataMatrix code; recognition method; exponential weighted moving average; local outlier factor; local weighted linear regression; adaptive sampling
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 4 Limitations and Extension Approaches for the IFC Schema
### Limitations of the IFC Schema
Consequently, information entered into _IfcClassification_ is not manifested in these receiving applications, leading to critical omissions in data exchanges. Incomplete implementation of domain information needs:  
Throughout their development, the IFCs have progressively added new specialties into their domain layers to meet the growing needs of the industry. For instance, early on, they added a structural element and an analysis domain, followed by domains for MEP disciplines, and most recently for infrastructure sectors (e.g., road and rail, etc.) (Figure 6). Unfortunately, even officially released domains and their relevant entities are, in many cases, not sufficient to properly represent the needed semantics and attributes required in that specialty. An illustrative case is the _IfcStructuralLoad_ entity in the structural analysis domain. The entity is designated to house the output values of structural analysis results [34]. However, existing studies show that this entity's specifications are not comprehensive enough to encapsulate the various structural analysis-related parameters routinely used in practice [30]. The incomplete representation of such needs limits its use and thus stipulates the need for custom IFC extensions. Figure 6: Newly added domains in IFC 4.3.
## 2 Verification with Finite Element Model
### Model Validation  
Figure 5 shows the load displacement curve comparison between the test results of concrete-filled steel tubular short columns and the finite element results. It can be seen that the curves are in good agreement as a whole, and the elastic stiffness of the curve obtained by the finite element is greater than the test results, which is due to the fact that the influence of initial defects, residual stress and other factors of the member are not considered in the process of finite element analysis. The ratio of the axial compression bearing capacity obtained from the finite element and the test is 1.011, and the difference of the bearing capacity is less than 2%, which is in good agreement on the whole.  
Figure 6 show the load transverse strain curve and load longitudinal strain curve of concrete-filled steel tubular short columns under test and finite element analysis, respectively. According to Figure 6, the ratio of the finite element to the lateral limit strain on the left side of the test specimen is 1.012, the ratio of the lateral limit strain on the right side is 1.008, and the average value is 1.01. The match of the curve is good and reliable. Similarly, according to Figure 6, the ratio of the finite element to the longitudinal limit strain on the left side of the test specimen is 1.013, the ratio of the longitudinal limit strain on the right side is 1.011, and the average value is 1.012.  
Figure 5: Load-displacement curve.  
Figure 6: Load transverse strain curve and load longitudinal strain curve.  
In general, the finite element results are in good agreement with the test results and the errors are within a reasonable range, showing good reliability. It seems that the constitutive relationship and parameter setting adopted in this paper can accurately simulate the concrete-filled steel tubular structure, which can be the base for the following simulation.
## 4 Results and Discussion
### Experimental Result
The CC-LT model activates more nodes, primarily within tightly connected groups, as shown in Figure 20c. However, it struggles to activate nodes across different communities. Inversely, the SH-LT model (Figure 20d) demonstrates a more distributed diffusion pattern, with information spreading across multiple communities due to the role of structural hole nodes. The UPI-LT model, as shown in Figure 20f, achieves the most widespread activation, effectively reaching nodes across the entire network. This indicates that integrating multiple factors, such as PageRank, structural holes, and clustering, provides a more robust and comprehensive approach to information diffusion. Analysis of the experimental results on the Wiki-Vote dataset reveals that certain characteristics limit the full potential of the UPI-LT model. The high homogeneity among nodes reduces the effectiveness of the homophily ratio, as there is limited variability to exploit for influence propagation. Additionally, the static nature of the Wiki-Vote network minimizes the benefits of dynamic thresholds, which are intended to adapt to changing conditions but have limited impact in this stable setting. In contrast, the Facebook dataset presents a heterogeneous and dynamic network environment, which highlights the strengths of the UPI-LT model more effectively. The homophily ratio plays a larger role here due to the diversity of user attributes, allowing the model to better target influential nodes. The dynamic thresholds also shine in this context, enabling the model to adjust activation criteria in response to the frequent changes in user relationships, which is crucial for maintaining effective influence propagation. Moreover, the combination of PageRank scores, structural holes, and local clustering coefficients in the UPI-LT model allows it to comprehensively capture both global and local dynamics of influence. This approach facilitates broader and more efficient information spread,  
Figure 20: Visualization of propagation models on the Facebook dataset. especially in the dynamic and community-diverse Facebook network. The UPI-LT model's adaptability and multifaceted strategy make it particularly effective for complex and evolving social networks, while its advantages are less pronounced in more homogeneous and stable environments like Wiki-Vote. The experimental results from both the Wiki-Vote and Facebook datasets clearly show that the UPI-LT model excels in both node activation and runtime efficiency. By combining multiple node attributes, the UPI-LT model achieves broader and more efficient information dissemination compared to models that focus on single factors. These results validate the effectiveness of integrating the PageRank, clustering coefficient, and structural hole metrics into a single model.
## 3 Results
### Rotation Analysis  
In the rotation analysis, the initial rotation angle at Stage 1 ranged from \(-0.004^{\circ}\) in Group 0 to \(-0.033^{\circ}\) in Group 3, with the absolute value of rotation increasing with the increase in beam width. Across all groups, the absolute value of the rotation angle progressively increased with each simulation stage. At Stage 50, the final rotation angles ranged from \(-0.515^{\circ}\) (Group 0) to \(-0.480^{\circ}\) (Group 3) (Figure 4). The final rotation angles, in terms of absolute value, were as follows: Group 0 (\(-0.515^{\circ}\)) > Group 1 (\(-0.501^{\circ}\)) > Group 2 (\(-0.494^{\circ}\)) > Group 3 (\(-0.480^{\circ}\)). These results indicate that Group 3, which incorporated a beam of 3 mm width, was the most effective at controlling unwanted rotational movement of the canine.
# Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter
## 7 Results
### Experimental Results  
The experimental tests were carried out in indoor unknown environment; however, since the localization of the system is estimated using only onboard sensors, it can also navigate in outdoor conditions. It is preferable that the scene includes sufficient objects (features) to be seen by the camera in a range of 0-7 m. The weights were initialized with the last value of the simulation. Since the Ar.Drone 2.0 driver for ROS was used, the parameters should be close to the real system. The data were received at 30 Hz (the time interval of each iteration was 1/30 s). Figure 11 shows the camera view and mapping output of the experiment. In Figure 12 are the results of controlling the quadrotor using the MLP using online training. As shown, oscillations and overshot were eliminated.  
For the yaw angle \(\psi\), in contrast with the other three modules, the weights were initialized randomly. The fourth MLP module was trained online to control the yaw angle, and the results are shown in Figure 13. As can be seen, the neural network adapts its weights to follow the reference.  
Figure 11: Camera view and map generated with PTAM for experimental tests.  
Figure 10: Parallel Tracking and Mapping (PTAM) output for the first trajectory. \(x\), \(y\) and \(z\) axes are denoted red, green and white respectively. It can be seen that the quadrotor follows the reference correctly.  
For a second experiment, a mass is added to the system during the flight in order to test its adaptability; the mass has a value of 45 g, which represents, approximately, 1/3 of the maximum payload of the Ar.Drone 2.0. The results of this experiment are shown in Figure 14. As can be seen, at iteration 750 approximately, the mass is added on the \(x\) axis of the quadrotor. After some iterations, the oscillations are eliminated.  
Figure 12: Experimental results for position of the UAV. One MLP module for each axis is used and they are trained online. The desired value is represented by the dashed line and the output of the system with the solid line.  
Figure 13: Experimental results for yaw angle of the UAV. Dashed line represents the reference and the solid line represents the system output.
## 3 Proposed Approach  
The goal of the proposed lower confidence bounding approach based on the entropy weight algorithm (EW-LCB) is to obtain an optimal solution with less computational burden through a sequential process. In EW-LCB, a new-weight factor is developed, which can allocate factors to balance global exploration and local exploitation by quantifying the degree of variation of the predictedvalue and variance from the Kriging model, respectively. In detail, the entropy theory is adopted to evaluate the relative discrepancy between the predicted value and uncertainty of the Kriging model. The framework of the EW-LCB is shown in Figure 1, which is composed of six steps.  
To demonstrate the proposed EW-LCB approach more intuitively and detailed, a one- dimensional toy example is utilized. The test function is adopted from [33], which can be expressed as  
\[y=0.5\sin(4\pi\sin(x+0.5))+\frac{1}{3}(x+0.5)^{2};\quad x\in[0,1] \tag{18}\]  
The objective is to obtain the minimum value of Equation (18). Meanwhile, this function has a local optimal value \(y=-0.0445\) at \(x=0\) and a global optimal value \(y=-0.1341\) at \(x=0.5312\).  
The details of the steps are elaborated as follows:
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 3 Fault Detection
### Proposed Fault Detection Method  
Considering NN classification and the problem of open-set recognition and its impact on neural networks, the proposed method uses the OSR paradigm to detect a worst-case disturbance in microgrids that cannot be detected by most conventional protection systems--a high-impedance fault. In this work, the focus is on lines connecting constant current passive loads, as this load type represents the majority in practical scenarios. The idea behind the method is to identify normal operating states of a microgrid line that connects passive load: its steady states and transients, which denote the change between the steady states, i.e., load changes. Other disturbances, in this case destructive HIFs, which should be removed from the system in the shortest time possible, are detected using an NN. However, the OSR paradigm allows these to be detected as outliers without having to model them and include them in the training dataset. Since HIFs behave differently depending on the different conditions (surface material and humidity, voltage levels, etc.), this method helps significantly in their detection [53].  
The proposed HIF detection method is examined for the simplest form of NNs, the FFNN. The classifier is first trained using a standard hyperparameter tuning procedure, where the architecture of the FFNN is varied until satisfactory results are achieved and the optimizer parameters are adjusted for an efficient learning procedure. Then, the classifier is trained using the same architecture but with additional steps, as proposed in [50]. Finally, the OSR performance of the method is investigated.  
The current is used as the input as transients can be detected from its waveform. It should be noted that the predominant component of DC current is the 0 Hz component, which is not useful indicator since it changes in case of both load change and HIF events. However, the difference becomes apparent when the signal is decomposed in the frequency domain using the fast Fourier transform (FFT) algorithm. The FFT algorithm is used as it is a fast solution for obtaining harmonic decomposition, especially when computation time is a crucial aspect [54]. The HIF event contains many more high-frequency components than the load change event. Hence, frequencies higher than 0 Hz are used as indicators to distinguish between the events [5]. The method should ideally distinguish between a static operating point (no transients) and load changes, flagging HIFs as outliers. The diagram of the method is provided in Figure 3.
## 4 Results and Analysis  
This paper designs three sets of experiments based on two baseline object detection models (YOLO v8 and Faster RCNN) to demonstrate the accuracy enhancement effect of the proposed method on pavement distress detection. (1) A comparison with the traditional transfer learning fine-tuning method verifies the proposed framework's ability to enhance cross-scene distress detection accuracy. (2) An ablation study is conducted to assess the individual contributions of the three modules--viewpoint transformation, foreground focus, and adaptive normalization--toward accuracy improvement. (3) The superiority of the proposed framework is further validated through comparisons with current methods, evaluating multiple dimensions such as accuracy improvement, deployment complexity, and computational efficiency. Across all experiments, changes in training loss are used to assess model convergence, while metrics such as F1-score, Precision, Recall, and mAP (introduced in detail in Section 3.2) are employed to evaluate detection accuracy.
# Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers
## 4 Results
### Experimental Results  
Figure 3 present the performance results for both adaptive equalizers to be compared (UCGA and RLS) for (a) 6-tap urban, (b) 12-tap urban, and (c) 6-tap hilly-terrain channel models, respectively. Each time, a variety of Doppler shift (\(f_{d}\)) values are considered. The measure points at the SNR scale are distributed every 5 dB, which, in the authors' opinion, is enough to display the trend and the curves' locations to each other. The 95% confidence intervals are presented at data points to prove accuracy of the simulation outcomes; they are obtained according to the method presented in [17].  
Figure 3: BER vs. SNR performance for the considered adaptive equalizers: GA and RLS under different Doppler shift (\(f_{d}\) = 0, 3, 15, 50, and 200 Hz) over: (**a**) typical urban area 6-tap channel; (**b**) typical urban area 12-tap channel; (**c**) typical hilly-terrain 6-tap channel.  
Figure 4 compares different population initialization methods (mentioned in Section 3): fully random initialization, in which all chromosomes in the initial population in the 1 st signaling interval are generated randomly, and single-1 initialization. The channel model used in this part of the experiment is the typical urban-area 6-tap model.  
The training-phase convergence of both the UCGA-AE and the RLS-AE is also studied for the same channel model, i.e., the curves in Figure 5 represent the MSE vs. iteration number, given SNR = 35 dB and \(f_{d}=0\) Hz for the considered transmission of a single data frame. More reliable data regarding the training phase convergence are presented in the form of a box plot [18] for every 20 th iteration (signaling interval). The box plot, shown in Figure 6, delivers MSE statistics based on the results collected in 500 trial runs (in every trial run only the training sequence is transmitted). The red lines on the boxes  
Figure 4: Comparison of different population initialization methods: fully-random initialization and single-1 initialization; assumed typical urban area 6-tap channel model with specific Doppler shifts: (**a**) \(f_{d}=0\) Hz; (**b**) \(f_{d}=3\) Hz; (**c**) \(f_{d}=15\) Hz; (**d**) \(f_{d}=50\) Hz (red curves) and \(f_{d}=200\) Hz (blue curves).  
represent the expected values of the median in the considered iteration, the notches delimit 95% confidence intervals of the median. The bottom and the top boxes' ends have the meaning of the 1 st and the 3 rd MSE quartile, respectively. The whiskers, set to be 1.5 times the interquartile range, cover as much as ca. 99.3% (\"almost all\") entries. The remaining outliers are shown by + markers.
# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 3 Materials and Methods
### Data Collection and Sample Preparation  
In this study, a dataset comprising 172 images of vineyard flora was collected using a Sony RGB Camera DSC-RX100M2 (sensor: type 1.0 (13.2 \(\times\) 8.8 mm) Exmor R\({}^{\oplus}\) CMOS sensor; number of pixels approx. 20.2 Megapixels; lens: ZEIS\({}^{\text{\textregistered}}\) Vario-Sonnar\({}^{\text{\textregistered}}\) T\({}^{*}\) Lens; optical zoom 3.6\(\times\); angle of view (35 mm format equivalent) 75\({}^{\circ}\)-24\({}^{\circ}\) (28-100 mm); focal length (F=) f = 10.4-37.1 mm). The images were taken during the spring and autumn seasons in the Douro Natural Park. The images covered five weed classes: _Centaurea melitensis, Echium plantagineum, Erodium moschatum, Lolium rigidium_, and _Ornithopus compressus_. Figure 1 shows examples of the species used to test the architectures available for the classification task in the PyTorch framework.  
Figure 1: Representation of the five species in the image classification task.  
The initial dataset was divided into training (85% of images, 145 images) and validation (15% of images, 27 images) sets following established protocols [33].  
The distribution of images for each species in both the training (represented in blue) and validation (represented in orange) sets is shown in Figure 2.
## 3 Results
### Evaluation Metrics  
The LUBE interval prediction method is used to make up for the inaccuracy of point prediction. It considers the prediction error caused by uncertain factors and obtains the upper and lower bounds under a given confidence level, providing effective information for decision makers. Therefore, this paper explores the effect of combining LUBE with a neural network in precipitation interval prediction. The influence of interval prediction can frequently be estimated from two facets, named the PI coverage probability (PICP) and PI normalized root-mean-square bandwidth (PINRW).  
In addition to the index of interval prediction, we also use root-mean-square error (RMSE) and mean absolute percentage error (MAPE) as evaluation indicators. The evaluation indicators of RMSE and MAPE are defined as follows:  
\[MSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-q_{i})^{2}} \tag{33}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-q_{i}}{q_{i}}\right|\times 100\% \tag{34}\]  
where \(q_{i}\) is the predicted value, and \(y_{i}\) represents the value of real runoff observation data.
## 2 Materials and Methods
### BPNN-MOPSO Parameter Optimization of the Seed Metering Device  
#### 2.7.1 Principle and Flow of the MOPSO Algorithm  
The MOPSO principle is the same as that of PSO and originates from the group behavior of foraging birds. When a bird finds food, others gather around to search for food. PSO searches for the extremum of a single optimization objective. Unlike PSO, MOPSO searches for a Pareto optimal set consisting of non-dominated solutions for multiple optimization objectives.  
The main flow of the MOPSO algorithm is as follows:  
1. Define a fitness function based on optimization objectives, determine the dimensions and constraints for each input variable, and set MOPSO algorithm parameters;
2. Randomly generate position and velocity vectors for the initial population; then, calculate and record the fitness value of each particle as the individual optimal value;
3. Based on Pareto dominance, check the dominance relationship of all particles, record all non-dominated solutions, and select one as the global optimal value;
4. Update the velocity and position vectors of the population, recalculate each particle's fitness value, recheck the domination relationship, and update the non-domination solution library;
5. Update individual and global optimal values;
6. Check whether the maximum number of iterations is reached. If so, the algorithm terminates; otherwise, return to Step 4;
7. Output the Pareto optimal set and the Pareto optimal front.
## 2 SF-PPP Methodology
### GRAPHIC Model  
Considering that the ionospheric delay of the pseudorange and carrier phase measurements have the same magnitude but opposite signs when ignoring the high-order terms, the GRAPHIC model can be formed as a linear combination by single-frequency of the pseudorange and carrier phase:  
\[G_{r,f}^{s,O}=\frac{P_{r,f}^{s,O}+L_{r,f}^{s,O}}{2}. \tag{3}\]  
When implementing the GRAPHIC model, the carrier-phase observation in Equation (1) is replaced by \(G_{r,f}^{s,O}\) in Equation (3). For the GRAPHIC model, an a priori ionosphere background model is utilized for correcting the pseudorange observations. However, some mis-corrected ionospheric errors remain in pseudorange observations because of the accuracy limitation of the priori ionospheric model. It is worth noting that the noise level of the \(G_{r,f}^{s,O}\) observation in GRAPHIC approach is mainly affected by the pseudorange measurement precision, which may degrade the positioning accuracy and affect the convergence speed [34]. Thus, it is significant in the GRAPHIC model to improve the pseudorange measurement accuracy, which suffers mostly from ionospheric delay.