esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### Data Routing Based on Two-Hop Feedback Mechanism
The network performance can be further improved if the data packet is processed by these nodes. To this end, if data information is lost, cannot be collected or there is no node's transmission control value that is greater than message carrier, the _node\({}_{1}\)_ will keep message duplicates in its own cache space and find active relay nodes by randomly moving to another communication area. Since the amount of data associated with transmission control value that needs to be transmitted is extremely small, the relevant data can be collected promptly, even in the dynamical topology structure of opportunistic networks.
## 3 Data Acquisition, Processing, and Analysis of Magnetotelluric Sounding
### Dimensionality and Strike Direction Analysis  
Dimensionality analysis provides an important basis for determining the inversion method (1D/2D/3D). The two-dimensional inversion must ensure that the study area has an obvious two-dimensional structure. The skewness is an important parameter reflecting the dimension of the electrical structure of the underground medium. The phase tensor is not affected by electric field distortion [31] and has a unique advantage in judging the dimensionality of the area. In this study, the phase tensor decomposition was used to analyze the regional dimensionality. Figure 4 shows the skewness of the study area. At present, it is generally accepted that when the skewness is less than 0.3, the underground structure can be approximated as a two-dimensional structure; when the skewness is greater than 0.3, a three-dimensional nature of the underground structure is more likely. The calculation results showed that the overall skewness degree is <0.3; only the local skewness degree is >0.3. As a result, we found that the underground structure of the study area has obvious two-dimensional features. This provided the basis of the 2D inversion work in this study.  
Figure 3: Typical measured point apparent resistivity (App-Res) phase curve (Off-diagonal impedance component YX and XY).  
Before the 2D inversion of the magnetotelluric data, it was necessary to determine the electrical strike direction of the study area. We used phase tensor decomposition [31] for electrical strike analysis. Figure 5 shows the electrical strike azimuth rose diagram of all measuring points in different periods. The study area has a consistent electrical strike in different period ranges, and the average angle is about 40\({}^{\circ}\). As most of the faults in the study area are in the north-east direction, the direction of electrical strike was determined to be 40\({}^{\circ}\).
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 3 Methods
### Dynamic Combined Loss Function
The weight \(\beta(n)\) at the \(n\)-th epoch is given by:  
\[\beta(n)=\beta_{\max}-\frac{e^{-3\frac{n}{N}}-1}{e^{-3}-1}(\beta_{\max}-\beta_{ \min})\in[\beta_{\min},\beta_{\max}] \tag{6}\]  
where \(N\) represents the maximum number of training epochs, and \(\beta_{\max}\) and \(\beta_{\min}\) denote the predefined maximum and minimum weight values, respectively. Under the four different preset values of \(\beta_{\max}\) and \(\beta_{\min}\), the variation trend of the dynamic weight \(\beta(n)\) is shown in Figure 5. As the number of training epochs increases, \(\beta(n)\) exhibits a nonlinear decay, gradually decreasing from \(\beta_{\max}\) to \(\beta_{\min}\), with the decay rate gradually slowing down. Figure 5: Trend of Dynamic Weight Variation. Thus, the combined loss function for the \(n\)-th training iteration is expressed as:  
\[Loss_{n}=\beta(n)\times CELoss+(1-\beta(n))\times(DiceLoss+FocaLoss) \tag{7}\]  
The combined loss function signifies that, during the early stages of model training, the cross-entropy loss function plays a primarily role, enabling the model to focus on the overall information of cracks and effectively recognize the entire area and approximate contours of the cracks. As training progresses, the weights of _DiceLoss_ and _FocalLoss_ gradually increase, guiding the model to concentrate on the challenging-to-segment edge regions of the cracks. Ultimately, this approach leads to accurate segmentation of both the overall crack and its edge details by the network.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 4 Results
### Route 2--Turn Left/Right' Direction  
As discussed earlier, navigation directions such as 'turn left/right' are generally not optimal as the interpretation of the heading can be ambiguous. However, in some cases, this type of direction can be helpful in finding the correct way. An example of such a situation is presented in Figure 11. A user approaching the ticket zone in the metro station notes three passages--the gate on the left for wheelchairs and two turnstiles--one in front of them and one more on the right. Moreover, there is no landmark that can be used for indication of the passage to be taken. The only solution is to base the direction on a 'turn left/right' message.  
In order to solve this issue, it was required to identify conditions that trigger a specific rule. In this case, the route leads through an _Interior Space_ to a _Space Connection_ of type 'ticket zone', and there are more _Space Connections_ of the same type nearby. The algorithm should attempt to distinguish them, preferably by indicating a landmark, and if there is no landmark, a 'turn left/right' direction should be provided.  
The other problem observed while testing the implementation of such a rule was the accurate determination of turns when the geometry of the path network was analysed. While the geometric properties of _Path Edges_ typically suffice to determine the angle of a turn, special consideration is needed when aligning with turnstiles or doors. This is because spatial connections for stairs or elevators tend to be more linear, whereas doors and turnstiles are represented as points. In Figure 12, at a point located at wide turnstiles on the left, the angle of the consecutive _Path Edges_ is less than 30 degrees. However, the expected  
Figure 11: Entrance to the ticket zone in the Politechnika metro station.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Decision Point**} & **Interim Directions** & **Final Directions After Generalisation in Stage 4** \\  & **(Output of Stage 3)** & **Final Directions After Generalisation in Stage 4** \\ \hline
11 & Change in the floor [down] & \\ \hline
12 & Change in the floor [elevator] & Go down with the elevator to the \(-2\) level \\ \hline
13 & Change in the floor [\(-2\)] & \\ \hline
14 & Turn right & After exiting the elevator turn right \\ \hline
15 & Turn left & - \\ \hline
15 & Go straight & Go straight into the platform \\ \hline
17 & Change in the room [platform] & \\ \hline
18 & Turn right & Get into the last car of a train on the right-hand side \\  & & (direction: ‘Mlociny’) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._direction after going through the turnstiles is 'turn right'. This problem was solved so that the _Indoor Installation_ stores information about its azimuth and, on this basis, an additional turn direction can be created.
## 1 Introduction
[14] utilized NIR-HSI combined with deep learning models to identify five common rice seed varieties, with most models achieving classification accuracies exceeding 95%. DenseNet demonstrates a higher connection density than ResNet, significant classification accuracy advantages over MobileNet, and better parameter efficiency compared to traditional CNNs, particularly in the spectral data classification. To achieve accurate screening of tea tree seeds using MIR spectroscopy, this study proposes an improved model based on DenseNet121 (ECA-DenseNet) for classifying seeds of different quality levels. Specifically, the following tasks were carried out:  
* Spectral data acquisition and preprocessing: An FTIR spectrometer was employed to gather spectral data of tea tree seeds across different states. The data were preprocessed via Savitzky-Golay (SG) filtering and wavelet transform to ensure data quality. * Model improvement targeting DenseNet121 limitations: The model was enhanced by addressing DenseNet121's shortcomings, including simplifying the architecture, replacing the convolutional kernels, adopting a new normalization method, introducing a novel module, optimizing the transition layers, and adjusting the classification strategy to improve the model performance. * Performance comparison and validation:  
The improved model was compared with DenseNet121 and other relevant models. Its advantages were evaluated using metrics such as accuracy, Kappa coefficient, Matthews correlation coefficient (MCC), and confusion matrices, verifying the effectiveness of the improvement strategies.
## 3 Materials and Methods
### Neural Network Model
#### 3.6.1 Architecture  
In detail, the architecture was composed of the following layers:  
* Masking Layer: A masking layer was used to ignore padded values in the input sequences, ensuring that padding does not influence the learning process [75].
* Convolutional Layers: Two convolutional layers (CNN) with ReLU activation functions were applied, followed by max pooling layers. These layers extracted temporal features from the input signals, enabling the network to learn spatial hierarchies and reduce the dimensionality of the data [76].
* Bidirectional LSTM Layer: A bidirectional LSTM (Long Short-Term Memory) layer was implemented to capture temporal dependencies in both forward and backward directions. This allowed the model to preserve information from both past and future time steps, crucial for processing time-series data related to human movement [75].
* Standard LSTM Layer: Following the bidirectional LSTM, a standard unidirectional LSTM layer with 256 units was employed to further process the temporal dependencies identified by the previous layer [75].
* Dense Layer: A fully connected dense layer with 256 units was included to process the output of the LSTM layers, providing the model with the capacity to learn complex representations of temporal features [77].
* Output Layer: The final output layer was a fully connected dense layer with a sigmoid activation function. This layer was responsible for binary classification, differentiating between individuals with a history of falls and those without [77].
## 3 Test Results and Discussion
### Surface Strain and Stress of Concrete Specim  
After the reinforcement is completed, the prestress at each point of the concrete is shown in Table 3.  
Although the stress-strain relationship of concrete is not completely linear elastic [38], considering that the strain of concrete during the test is within the elastic range, a simplified calculation equation within the linear elastic range is adopted herein. The magnitude of the prestress on concrete specimens in different connection methods is calculated according to Equation (2), where the elastic modulus of concrete (\(E_{Con}\)) is shown in Table 1.  
\[\sigma_{Con}=E_{Con}\times\epsilon \tag{2}\]  
Figure 14: Strain and stress on the compression side of anchor bolt holes in Fe-SMA plates: (a) strain–activation temperature curve; (b) local stress.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Anchoring Methods**} & **Average Stress on Specim Surface/MPa** & \multirow{2}{*}{\
\begin{tabular}{c} **Average Stress on Specim Side Surface/MPa** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Stress at Concrete Anchor Hole/MPa** \\ \end{tabular} } \\  & & & \\ \hline Adhesive–bolted hybrid connection & \(-19.26\) & \(-5.14\) & \(-34.78\) \\ Bolted connection & \(-13.23\) & \(-5.68\) & \(-46.28\) \\ Adhesively bonded connection & \(-15.31\) & \(-1.94\) & \(-17.84\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prestress of concrete.  
Figure 15 shows the variation in the average strain on the surface of the concrete (strain gauge 4) with the activation temperature during the activation process. Corresponding to the activation recovery stress of Fe-SMA, during the heat activation stage, the recovery stress of Fe-SMA is relatively small, and the effect of the prestress on the concrete is not significant. During the activation cooling stage, due to the greater increase in recovery stress of Fe-SMA, the average prestress on the surface of the concrete also further increases. Overall, the average prestress on the surface of the concrete is highest for the adhesive-bolted hybrid connection, reaching \(-19.3\) MPa, slightly lower for the adhesively bonded connection, reaching \(-15.3\) MPa, and lowest for the bolted connection, reaching \(-13.2\) MPa. Although the softening and slip of the adhesively bonded connection during activation lead to a decrease in the recovery stress of Fe-SMA, the adhesively bonded connection method has a better effect on the preloading stress of the concrete surface compared to the bolted connection. For engineering requirements that require prestress to be applied to the surface of concrete, such as closing cracks on the concrete surface, the use of adhesively bonded connections for structural reinforcement is more effective.
# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
## 2 Materials and Methods
### Simulation Conditions
In Equation (3), each element \(\text{p}_{\text{ij}}\) represents the probability that pixel (i,j) is part of a lane, and H and W are the height and width of the input image:  
\[\text{confidence}=\frac{1}{\text{H}\cdot\text{W}}\sum_{\text{i=1}}^{\text{H}} \sum_{\text{j=1}}^{\text{W}}\text{P}_{\text{ij}} \tag{3}\]  
On the other hand, the coverage metric (Equation (4)) evaluates the proportion of the image in which the prediction exceeds a certain confidence threshold \(\tau\), thus indicating the area detected as a traffic lane in the image, where \(\tau\) is the decision threshold and 1 (\(\text{p}_{\text{ij}}>\tau\)) = 1 if the pixel probability is higher than the threshold, otherwise 0. Both considered metrics are essential for the robust evaluation of the performance of semantic segmentation systems in autonomous applications:  
\[\text{coverage}=\frac{1}{\text{H}\cdot\text{W}}\sum_{\text{i=1}}^{\text{H}} \sum_{\text{j=1}}^{\text{W}}1\Big{(}\text{P}_{\text{ij}}>\tau\Big{)} \tag{4}\]  
The dataset was divided into training and testing sets: 80% of the dataset was used for training and 20% for testing. The training process used Adam optimizer (adaptive moment estimation--an optimization algorithm used in training DL models) and a learning rate of \(10^{-4}\)[35]. The activities related to the experimentation and validation of the proposed hybrid model, as well as the acquisition of results after training the new hybrid architecture of the proposed and developed model, were carried out by recording real traffic images usingan optical camera (Logitech web cam type c 920 pro HD (Logitech, Shanghai, China) with resolution: 1920 \(\times\) 1080, automatic focus, 78 degree field of view, and 1.2x digital zoom), the captured information was then processed on a laptop (Asus tuf gaming f15 with 12 th generation i7 processor, 2.3 GHz frequency, 16 GB of RAM, and an Nvidia GeForce RTX 3060 video card with 6 GB of RAM) using only the computer's CPU. The training was performed on images of size 512 \(\times\) 256 pixels.
## 3 Results
### Resampling  
The effect of resampling on the counts within the spheres is illustrated in Figure 2. The x-axis reports the resampling factor where a factor of 1 represents the original image matrix (256 \(\times\) 256 \(\times\) 47), while the maximum resampling resulted in a 1792 \(\times\) 1792 \(\times\) 329 matrix with a corresponding factor of 7; the y-axis reports the counts ratio between the counts obtained in the resampled image and the counts associated to the original image.  
The accuracy in gain can be appreciated especially in the smallest sphere (\"sphere 6\" blue dots in Figure 2), where the counts on the resampled image are three times the original ones.  
As can be seen in Figure 2, the counts ratio increases with increasing the resampling factor with an asymptotic behavior, which has been verified by fitting the data with the function f(x) = a \(-\) b \(\times\) c\({}^{\rm x}\) and studying the horizontal asymptote (i.e., the \(a\) parameter). Table 4 reports the asymptotes derived from the fit (with confidence level set at 95%) and the count ratio obtained with a resampling factor of six (i.e., the factor adopted in the following analysis, which corresponds to an image matrix of 1536 \(\times\) 1536 \(\times\) 282). The counts ratios associated to a resampling factor of six were very close to the asymptote \(a\) obtained from the fit. Moreover, a finer matrix of 1536 \(\times\) 1536 \(\times\) 282 respect to the original one represents a good compromise between the accuracy in the counts recovery and the increase in computation time. The increment from a resampling factor of six to seven was below the 1.5%, but the computation time became three times higher.  
Figure 1: Horizontal (x), vertical (y), and axial (z) profiles extracted from the point-like source fitted with a Gaussian function.  
Figure 2: Counts ratio normalized to the original counts at the increasing of the voxel division of the starting matrix (256 \(\times\) 256 \(\times\) 47) for each sphere, marked in different color with different markers.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 1 Introduction
### Related Work  
Dealing with weather in ATC has multiple dimensions, such as accurate weather data, on-ground and on-board systems, visualizations of weather for pilots, ATCOs and supervisors, the determination of weather effects, the rerouting of aircraft, and the monitoring of weather-affected air traffic using deviations from standard routes.  
To develop controller support systems for adverse weather conditions, in addition to meeting ATCOs' requirements from these systems, it is also necessary to understand the factors influencing pilots' decisions and their situational awareness when facing predicted or suddenly arising severe weather on their planned flight route [14]. Air traffic management decision making in thunderstorm situations is affected by the uncertainty of such situations [15; 16]. Studies have shown that pilots pursue different strategies when avoiding severe weather conditions in general [17; 18], even with onboard support functionalities that highlight unobtrusive and important weather characteristics [19; 20]. One of the outcomes of [PERSON]'s validations was the discovery that there is still a lack of actual research on the needs of Terminal Maneuvering Area (TMA) ATCOs concerning weather information and support functions [21]. Recent studies also show that air traffic controller support systems may reduce their workload in adverse weather conditions in the TMA [22; 23]. The use of decision support tools along with input data considering current and forecasted weather for a user display was also proposed by [PERSON] for the decision loop including impact analysis and mitigation plans [24].  
Although weather is one of the most important factors for aviation, ATCOs with no outside view have only selective information about the meteorological conditions in their sector, which they receive from the pilots. When available, they also use general weather information from the Internet, but this is not specifically adapted for aviation. So far, there is no integrated situation-sensitive weather view in their traffic situation display. In 2000, a detailed study on weather impact awareness with respect to the next five minutes that included 20 ATCOs showed that, in almost 40% of the used scenarios, the ATCOs were not fully able to identify the effects of weather on air traffic [25]. Since that time, comprehensive systems such as weather radar have been further developed and are now available at many ATC centers; thus, the meteorological situational awareness of ATCOs should be much greater today. However, detailed studies considering new support systems are not available.
## 4 Conclusions  
Applicability of the weakly basic anion exchangers of different matrices composition (polyarylic Amberlyst A24, polystyrene Amberlyst A21 and phenol-formaldehyde Amberlyst A23) in C.I. Direct Yellow 50 dye removal was investigated and described. Experimental parameters such as dye initial concentration, phase contact time and auxiliaries addition (Na\({}_{2}\)SO\({}_{4}\), Na\({}_{2}\)CO\({}_{3}\), SDS and TX-100) on dye removal efficiency are important parameters influencing the yield of dye removal. The largest sorption capacity for DY50 was exhibited by Amberlyst A24 (\(Q_{0}=666.5\) mg/g) and Amberlyst A21 (\(Q_{0}=610.9\) mg/g). Taking into account the composition of the matrix of anion exchange resins, the following series of sorbent usefulness can be presented:  
\[\text{polyacrylic > polystyrene > phenol-formaldehyde.}\]  
Kinetics sorption parameters were described by the pseudo-second order model and the equilibrium studies fitted well to the Langmuir equation. The resins regeneration can be performed with a yield of no more than 20% using 1 M NaOH/1 M NaCl in 50% \(\
icefrac{{\varepsilon}}{{v}}\) methanol. The anion exchangers can be considered as promising sorbents for removal of impurities such as dyes from aqueous solutions.  
Conceptualization, M.W.; Investigation, E.P.-A.; Methodology, M.W.; Software, M.W. and E.P.-A.; Writing--original draft, M.W. and E.P.-A.; Writing--review & editing, M.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available in this article. The authors declare no conflict of interest.  
Figure 12: Desorption effectiveness of DY50 from the A21, A23 and A24 anion exchangers.