esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Visual Exploration System with Fault Tolerance Structure
## 3 Active Controller Based on Semantic Segmentation  
As is shown in Figure 1, the first module in our framework is the active controller. For robot exploration, we design a controller which is able to work without global localization and continue its motion so that fault recovery becomes feasible with respect to hardware control. Such a controller is proposed to overcome the fragility of visual localization. The pseudo-code of the active controller is given in Algorithm 1.  
```
0: Current captured image, represented as \(I\)
1: Ground segmentation from \(I\), denoted as \(G\)
2:if\(I=\emptyset\)then
3: Control command \(C\) generated from recover motion
4:else
5: Goal point searching from \(G\)
6: Control command \(C\) generated from \(G\) with a PID controller
7:endif
8:return\(C\)
```  
**Algorithm 1** Framework of active controller  
In this section, we detail our active controller based on ground recognition. The pre-trained Pascal CNN [12] is applied to perform semantic image segmentation, and a Gaussian expression of the segmented image is used for goal planning. After obtaining the goal, the PID (proportion, integral and differential) controller is used for velocity command generation.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 3 Materials and Methods
### False Detection Filter  
Due to its dielectric properties, water has a very important impact on the soil's SAR backscattered signal, as an increase in water content will result in an increase in the backscatter value [40; 41; 58]. Therefore, irrigation events and rainfall usually cause sudden spikes in the backscatter values. Moreover, the spikes in the backscatter signal can also appear as a result of the increase in surface roughness due to the plowing of agricultural parcels [39]. A false freezing detection could thus be observed following specific events (e.g., irrigation/rainfall or plowing). Assuming a plot state at date '\(d_{i}\)', that was preceded at date '\(d_{j}\)' by such an event (e.g., irrigation/rainfall or plowing), where \(\sigma_{0}\) at date \(d_{j}\) is chosen as the maximum \(\sigma_{0}\left(max\_o^{0}\left(d_{j}\right)\right)\). In such a case, and even though we consider the mean of the last three \(max\_o^{0}\left(d_{j}\right)\left(ref\_o^{0}\right)\), the difference between \(ref\_o^{0}\) and \(\sigma_{0}\) acquired at date \(d_{i}\) could be of a magnitude that causes the algorithm to detect the plot state at date \(d_{i}\) as being frozen, even if it is not. \(max\_o^{0}\left(d_{j}\right)\) could be high in comparison to \(\sigma^{0}\left(d_{i}\right)\) due to irrigation or rainfall events but also due to plowing. In addition, \(\sigma^{0}\left(d_{i}\right)\) could also be much lower in comparison to \(\sigma^{0}\left(d_{j}\right)\) due to a decrease in soil water content or a decrease in soil roughness (e.g., preparation of soil for sowing). In addition, the difference between \(ref\_o^{0}\) and \(\sigma^{0}\left(d_{i},p\right)\) could be greater than +3 dB if at date \(d_{i}\) there was an important decrease in the radar signal due to the growth cycle of certain vegetation types such as wheat [42].  
To minimize such false detections, a filter is applied at the end of the freeze detection algorithm using a temperature threshold from in situ measurements. If a given plot at a certain date \(d_{k}\) is detected as frozen, and the air temperature at the same date is higher than a certain value, the plot state will be set to unfrozen. A temperature of 0 \({}^{\circ}\)C was used as a temperature threshold to distinguish between unfrozen and frozen conditions in some studies [59]. Other studies used a more conservative threshold to improve the likelihood of the temperature to represent unfrozen/frozen conditions (e.g., 3 \({}^{\circ}\)C threshold in [PERSON] et al. [60]). In the present study, similarly to [PERSON] et al. [60], a threshold of 3 \({}^{\circ}\)C will be used to filter out the false detections.
## 5 Results and Discussion  
### Experimental Setup  
In our experiment, firstly, the training images are normalized and augmented. Then, these images are input into our proposed PHCNet. In the PHCNet, we set the number of convolution filters in the encoder to 64, 64, 128, 128, 256, 256, 512, 512, 1024, and 1024, respectively, and we set the number of convolution filters in the decoder to 512, 512, 256, 256, 128, 128, 64, and 64, respectively. In the PHCM, the size of the filters in three convolution layers was set to 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, respectively. In addition, the Swish activation function was used in all convolution layers. A Stochastic Gradient Descent [25] (SGD) optimizer was used to train the PHCNet.  
Figure 7: Some sample images of the OAD_CRACK datasets, (**a**) shows the samples of linear crack, (**b**) show the samples of circular crack, (**c**) shows the samples of void crack.  
We used the accuracy as the evaluation criterion for our experiment. The definition of accuracy is the number of correct samples divided by the number of samples in the test datasets. The formula can be expressed as follows:  
\[Acc=\frac{TP+TN}{S_{All}} \tag{7}\]  
where \(Acc\) represents the accuracy criterion, \(TP\) represents the number of correctly identified positive samples, \(TN\) represents the number of correctly identified negative samples, and \(S_{All}\) represents the number of samples in the test datasets.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 5 Performance Results
The worst performance, for both fully and sub-connected approaches, was obtained for the full load case, i.e., when the number of users was equal to the number of RF chains \(N_{RF}=U\). In Figure 4 we present results for different numbers of RF chains and two users. If the number of antennas (\(R\)) connected to each RF chain was reduced, we verified that the penalty for fully digital approach decreased. We could observe a penalty of approximately 3 dB, 2 dB, and 0 dB for \((R=8,N_{RF}=2)\), \((R=2,N_{RF}=8)\), and \((R=1,N_{RF}=16)\), respectively (BER of \(10^{-3}\)). This happened because more RF chains and consequently less antennas per chain increase the number of available degrees of freedom of the sub-connected architecture. For the extreme case of \(R=1\) (one RF chain per antenna) the curve obtained for the sub-connected approximately overlapped the one obtained for the fully connected. As seen in Figures 5 and 6, we evaluated the impact of imperfect knowledge of the average AoD at the transmitter side. To compute the analog precoders we assumed the knowledge of only a quantized version of the average AoD of each cluster, as discussed in Section 3. We presented results for \(n=[2,4,6]\) quantization bits. Figures 5 and 6 depict the results for two and eight users, respectively. As expected, increasing the number of quantization bits improved the performance of the proposed sub-connected scheme and tended to the one achieved for perfect knowledge of the average AoD (\(n=\infty\)) for both cases \(U=2,8\). When the number of bits in the quantizer was lower, the performance was worse compared to the perfect curve. In Figure 5 we can observe a performance penalty, for BER of \(10^{-3}\), of approximately 5 dB, 1.5 dB, and 0 dB, for \(n=2\), 4, and 6, respectively. This means that a very limited number of bits for the quantization of the average AoD of each cluster was enough to get a performance close to the perfect case. Since the mmW channels were usually sparse, the amount of information needed to be fed back from the BS to the UTs was small. Figure 3: Performance of the proposed hybrid sub-connected scheme for \(U\in\{2,4,8\}\). BER is bit-error-rate. Figure 4: Performance of the proposed hybrid sub-connected schemes for \(N_{RF}\in\{2,8,16\}\) and \(U=2\). Figure 5: Performance of the proposed hybrid sub-connected scheme for different numbers of quantization bits of the average AoD, \(U=2\).
###### Abstract  
Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. By analyzing these factors, the ANN captures complex relationships influencing fatigue cracking more effectively than traditional methods. A comprehensive dataset from the Long-Term Pavement Performance (LTPP) program is used for model training and validation. The ANN's ability to adapt and recognize patterns enhances its predictive accuracy, allowing for more reliable pavement condition assessments. Model performance is evaluated against real-world data, confirming its effectiveness in predicting fatigue cracking with an overall R\({}^{2}\) of 0.9. This study's findings provide valuable insights for pavement maintenance and rehabilitation planning, helping transportation agencies optimize repair schedules and reduce costs. This research highlights the growing role of AI in pavement engineering, demonstrating how machine learning can improve infrastructure management. By integrating ANN-based predictive analytics, road agencies can enhance decision-making, leading to more durable and cost-effective pavement systems for the future.  
ANN; long-term pavement performance; falling weight deflectometer; ESAL Article
# Analytic Form Fitting in Poor Triangular Meshes
## 4 Results
### Cylinder Fitting  
**Cylinder. Dihedral Angle-based Support Subset \(S\) Extraction**  
Figure 19 presents results for \(g()\) cylinder fitting when the \(g()\) support subset \(S\) has only \(C^{0}\)-continuity with the rest (\(M-S\)) of the input set \(M\). \(S\) is extracted at sharp dihedral angle EDGEs. As \(g()\) support subset \(S\) extraction is independent of \(g()\), the extraction and fitting are real-time processes (taking less than 1 s).  
**Cylinder. Fitting-based Support Subset \(S\) Extraction**  
Figure 20 displays cases in which the support subset \(S\) for \(g()\) holds \(C^{1}\)-continuity or higher with \(M-S\). In these cases, \(g()\) fitting and \(g()\) support subset \(S\) extraction take place simultaneously.  
Table 6 presents the numerical results for the test on cylinder extraction, comparing the preset parameters against the parameters obtained by the fitting. Subindex \(fit\) indicates the variable obtained via fitting, as opposed to the experimental preset value.  
An interesting result is that for the more difficult cases (fitting-based extraction), the estimated \(g()\) is more accurate than the one achieved in the simpler cases (\(C^{0}\)-continuity between \(S\) and \(M-S\)). This result shows that the algorithm discussed in Figures 13 and 14, albeit more complex, renders a better accuracy.  
Dihedral angle-based extraction cases (1-4) perform in real time, while fitting-based extraction cases (5-6) do not. The former are faster not only because extraction and fitting are independent from each other, but also because \(g()\) is fitted to small triangle subsets of \(S\). These triangle subsets are scattered to make reasonable even though fast estimations.  
Figure 19: Cylinder fit. \(S\) extraction based on dihedral angles. Pink: extracted \(S\) support subset. Yellow: fitted \(g()\).  
Figure 18: Step-wise execution of the \(S\) extraction \(g()\) fitting algorithm, applied to a cone which does not accept \(S\) extraction based on dihedral angles.
## 1 Introduction
The contribution of this study lies in its detailed exploration of improving the detection of abnormal ECG patterns potentially linked to CKD through the application of machine learning approaches, with a particular emphasis on the innovative LADTree model. By comparing the LADTree model to traditional ML models such as MLP, KNN, SVM, NB, and J48, the study sheds light on the usefulness of various CKD prediction approaches. The work ensures the selection of the most relevant and non-redundant features for improved detection of abnormal ECG patterns by meticulously collecting and preprocessing data, followed by rigorous feature selection using multiple search approaches. Additionally, this work comprehensively investigates model performance under various circumstances by using K-fold cross-validation and percentage splitting for the training and testing procedures. Both error rate and accuracy evaluation metrics are included in the analysis: MAE (mean absolute error), recall, ROC (receiver operating characteristic curve), PRC (precision-recall curve) area, KS (Kappa Statistics), and CA (classification accuracy). This thorough method offers a clear picture of the advantages and disadvantages of each model. However, the LADTree model regularly performs better than other machine learning models on a range of criteria, suggesting that it might be a top choice for abnormal ECG pattern detection. All things considered, this study improves medical diagnostics by offering a unique method using ECG signal data for abnormal ECG signal prediction and by illuminating the relative efficacy of many ML models in this domain. The subsequent sections of this paper are structured as follows: Section 2 delineates the literature review, and Section 3 outlines the research design and methodology. Section 4 elucidates the analysis and discussion of the result, whereas Section 5 encapsulates the conclusion of the study.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 2 Background
### Eeg

Table 1: Categorization of the EEG rhythms based on their frequency. \begin{table}
\begin{tabular}{l l} \hline \hline
**ERP** & **Description** \\ \hline P300 & P300 is a positive potential that arises approximately 300 ms after the onset of a stimulus as a result of internal decision-making [16]. It shows the activity of attention in working memory as well as the adjustment to responses [17]. \\ \hline N200 & N200 is a negative potential related to unfamiliarity, and reaches its peak between 200 and 350 ms after the onset of a stimulus [16]. \\ \hline LPC & The late positive component is a positive potential that arises approximately 400–800 ms after the onset of a stimulus associated with explicit recognition memory [18]. \\ \hline LPP & The late positive potential indicates enabled attention to emotional stimuli of either a positive or negative valence. This can be translated as neutral, pleasant, or unpleasant contextual stimuli. \\ \hline N400 & N400 is a negative potential related to oddness experiments such as brand names with products [10] \\ \hline FN400 & FN400 is located in the front-center of the brain related to familiarity and arises approximately 300–500 ms after the onset of a stimulus. FN400 has a more negative potential for new words than for similar and familiar words [18] \\ \hline FRN & Feedback related negativity is a front-central negative potential related to a subject’s choices. It arises in response to passively observed products 200–300 ms after the presentation of unfavorable versus favorable products [3]. \\ \hline PSW & Positive slow waves are correlated with sustained attention to visual emotional stimuli and can be detected long after the appearance of an emotional stimulus [10]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Common ERP components used in neuromarketing studies.
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 4 Discussion
### Network Layer  
In the network layer, we developed three kinds of communication middleware, OPC UA-MQTT wrapper, OPC UA-GraphQL wrapper, and OPC UA-Unity client. OPC UA allows to freely interact with high-end server applications by the cost-effective dedicated controller, such as the Raspberry Pi. Moreover, OPC UA integrates industry-specific data models from several industry trade organizations. It can transmit data from a simple state to a large amount of highly complex factory-wide information. However, its flexibility is based on complex and verbose specifications. Meanwhile, MQTT is based on the Publish/Subscribe architecture, and data is completely decoupled with the event producer and event consumer. Hence, MQTT is easily understandable and rapidly deployable. GraphQL is a query language providing a complete description of the data in one's API. We conducted the responsiveness test based on three kinds of communication middleware, and found that the average response time of each communication middleware is less than 0.6 s, which can meet the control requirement of the crane in the laboratory environment.  
Figure 12: The screenshot of the MR application in HoloLens 2.  
Moreover, the OPC UA-MQTT wrapper has the best performance with an average response time of less than 0.3 s. In our framework, the response time of the OPC UA client in Unity is constrained by the refresh rate of Unity. We set the refresh rate as 1 s for the subscription function to subscribe to the value. That is the reason why the OPC UA-GraphQL and OPC UA Client performed closely. For the OPC UA-GraphQL wrapper and OPC UA Client in Unity, we also can improve the refresh rate of Unity to increase the response time. However, it will account for a larger computing resource. Therefore, to face complex operational scenarios, the balancing point between the response time and rendering effect should be further discussed.
## 2 Measurement Setup and Technique
### Recording Setup  
The source-receiver arrangement for the RIR measurement is depicted in Figure 3, with the room's bottom-left corner considered as the coordinate origin.  
The RIRs were measured for three distinct source locations, with only one source being active during each measurement. The source locations are marked as S1, S2, and S3 in Figure 3, and their Cartesian coordinates are mentioned in Table A1. The height and  
Figure 1: ANU Recording Studio: (**a,b**) panorama view; (**c,d**) zoomed view of panels.  
Figure 2: Receivers and source used in data collection.  
direction of the sound source were set to reflect the placement of typical performers or ensembles in the recording studio. Source S1 was positioned closest to the front door, at a height of 1.2 m, reflecting a typical drum kit height. Source S2 in the middle of the room was positioned at a height of 1.4 m to reflect a singer's projection height [28]. Source S3 was positioned at a height of 0.9 m, corresponding to the height of the strings on a grand piano. In terms of source orientation, both S1 and S2 faced the control room, while S3 was pointed to the left wall. Note that these source heights are measured from the studio floor to the base of the loudspeaker. Users can refer to the loudspeaker dimensions [23] to adjust the source height with reference to the loudspeaker cone.  
Receiver locations were set up in an 8 \(\times\) 8 planar grid pattern with 1 m \(\times\) 1 m spacing. These positions are marked as circles in Figure 3. A few points in the square grid were excluded since they were inaccessible due to the placement of furnishings and the grand piano (blank space to the right side of S3). In addition to these receiver points, a denser grid with 50 cm spacing was also included in front of each active source. These are indicated by colored triangles in Figure 3 corresponding to their respective source. During the measurement, the microphone array was centered at these positions at a consistent height of 1.7 m, reflecting the average height of an Australian [28]. This height was measured from the floor to the center of the array.  
Figure 3: Diagram of the source and receiver positions. Light blue rectangular shapes indicate locations of furnishings.  
Each receiver position is assigned a code name based on its coordinates along the \(x\) and \(y\) axes. Code names of some of the receiver positions are indicated in Figure 3. For instance, \(01x00y\) is the code name of the receiver location in the second column from the left and the first row from the bottom. The Cartesian coordinates and code names of the receiver positions on the regular grid are listed in Table 14. The additional receiver points surrounding the sources are labeled differently; for example, \(e30x45y\) corresponds to the receiver associated with S1 in the fourth column, positioned between the fifth and sixth rows from the bottom. The Cartesian coordinates and code names of these source-dependent receiver positions are listed in Table 15.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 3 Link State Evaluation
### Combined Weighting Theory  
Combined weighting is used in multiattribute decision analysis to handle problems involving multiple evaluation criteria [32]. It determines the weight of each criterion by combining subjective and objective weighting methods to more accurately and comprehensively reflect the importance of each criterion. Since each individual method has its own advantages and disadvantages, using combined weighting can improve the rationality and credibility of decision making.
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## 2 Materials and Methods  
### Study Area & Sampling Layout  
The study area is located in the North of Munich, Bavaria, in Southern Germany. Two study sites at 48\({}^{\circ}\)17\({}^{\prime}\)31.25\({}^{\prime}\)7N, 11\({}^{\circ}\)42\({}^{\prime}\)21.53\({}^{\prime}\)E (field 517) and 48\({}^{\circ}\)14\({}^{\prime}\)51.46\({}^{\prime\prime}\)N, 11\({}^{\circ}\)42\({}^{\prime}\)24.10\({}^{\prime\prime}\)E (field 509) were visited regularly during two field campaigns. Both fields are part of communal farmland belonging to the city of Munich. Each was cultivated with winter wheat (triticum aestivum), representing the dominant cereal crop in the area and situated within 1.5 km distance to the Isar river. The average cloud cover in the Munich-North Isar (MNI) region was a bit higher than usual (5.68 instead of 5.44 okta). This indicates the difficulty of recording spectra on a frequent basis, since adequate clear sky conditions occurred only on few occasions. Information on the site management was provided via personal communication by the farm managers; i.e., dates of seeding, fertilization methods and quantities, harvesting dates, etc.  
During the first campaign, data were collected at almost weekly intervals from 17 April to 25 July 2014 (14 sampling dates). The second campaign period already started in autumn. Measurements were conducted from 28 November 2014, to 21 July 2015 (13 sampling dates). In this way, the complete growing cycle of the crop from seeding to harvest could be observed. One elementary sampling unit (ESU) was defined as a 10 m \(\times\) 10 m pixel size. The measurements were then related to a 3 \(\times\) 3 ESU raster with equal distances of 10 m. All nine ESUs were marked with sticks and revisited for each sampling date. The row azimuth direction of the winter wheat crops was 170\({}^{\circ}\)/350\({}^{\circ}\) for 2014 and 150\({}^{\circ}\)/330\({}^{\circ}\) for the 2014/15 season, with the angular definition of 0\({}^{\circ}\) = N. Table 1 shows the complete list of sampling dates.