esa-sceva/satcom-chunk-collection huggingface dataset:
# SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
## 4 Experiments
### Experimental Procedure and Evaluation Metrics  
The experimental process used stereo image pairs as the input to calculate the center coordinates of the objects in each image through object detection and segmentation. For the object pairs in Image 1 and Image 2, pairs were formed among objects with the same class and similar positions, prioritizing those with the smallest rotation angle, while maintaining the same order of classes between the two images. For the determined object pairs, image rotation, padding, and translation were performed to align the y-coordinates of the object pair's center points in both images. Subsequently, to verify the effectiveness of the method proposed in the paper, metrics such as the fundamental matrix, Y-disparity, X-disparity entropy, SSIM [36], feature matching accuracy, and area-based correlation were used. Additionally, feature point matching was visualized, and a disparity map was generated for verification.  
Typically, when image rectification is performed, the fundamental matrix satisfies the following form:  
\[\mathbf{F}=\begin{bmatrix}0&0&0\\ 0&0&-1\\ 0&1&0\end{bmatrix} \tag{18}\]  
Looking at Equation (18), the first row and column, the center has a value of 0, which means that displacements in the x-axis and y-axis directions do not affect the epipolar constraint. And, in the 2 \(\times\) 2 submatrix in the lower right corner, it has values of \(\begin{bmatrix}0&-1\\ 1&0\end{bmatrix}\), showing opposite signs. This indicates a rotation matrix and means that the epipolar lines have become horizontal. The last value may also appear as one, which is a normal phenomenon that can occur due to coordinate system normalization or numerical stability reasons.  
Y-disparity measures the difference in the y-coordinates of the corresponding points in stereo image pairs. If rectification is successful, corresponding points should exist at the same y-coordinates, so the Y-disparity value should be close to 0. X-disparity entropy measures the difference in the x-coordinates of the corresponding points in stereo image pairs. If rectification is well performed, the disparity distribution becomes more concentrated, resulting in a lower entropy value. SSIM represents the structural similarity between stereo images, with values closer to one indicating perfect structural similarity. Feature matching accuracy measures the quality of matches between feature points extracted from both images. A value closer to one is better, indicating that more feature points were accurately matched. Area-based correlation is an indicator calculated by dividing the images into small regions and then computing the correlation between the two images. A value closer to one is better, signifying high local similarity between the two images. In summary, if the image rectification method proposed in this paper is effective, theY-disparity and X-disparity entropy should decrease, while the SSIM, feature matching accuracy, and area-based correlation should increase.
## 6 Stage 1: TS Modeling
### Model Parameter Determination
#### 6.2.3 AIC-Based Candidate Model Evaluation  
The AIC values were examined for more insight into the candidate models. Since segmentation operation was conducted, which caused the AIC value to vary from one segment to another, the average AIC values were computed from all segments of each model. In addition, the datasets were split into training and test sets and the average AIC values of each model were computed for both sets. To explore the influence of overlapping in the SSND for data augmentation, the average AIC values were also evaluated for both Non-OL and OL patterns. However, OL was only performed on the training data.  
Table 3 shows that a sliding window with OL slightly improved the average AIC, which demonstrates the effectiveness of using OL for data augmentation. In addition, the values from the test set were close and even slightly better than those from the training set. This observation indicates the stable fitting performance and the generalization of these TS models. It is also shown that the ARIMA(6,1,6) and the ARIMA(7,1,6) achieved similar average AIC values with the latter slightly lower (algebraically), and both models had significantly lower values than that of the simpler ARI(7,1) model. This observation reveals that more complex models fit the data better than simpler ones.  
Figure 10: BIC values for the sub-models computed on four sample TS segments.
## 2 Materials and Methods
### Tower Data  
Data from the FLUXNET2015 archive of eddy-covariance tower measurements ([[http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/)]([http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/))) are used to create an in situ \(\tau\) validation dataset by computing the ratio between transpiration and potential evaporation at each daily time step for each tower using daytime values. The measured latent heat at each tower is partitioned into transpiration and soil evaporation using the simple approach introduced by [PERSON] et al. [1], where the ratio of T to total evaporation is a function of land cover type and leaf area index (LAI)--the latter is obtained from the Global Inventory Modeling and Mapping Studies (GIMMS) [68; 69]. Using the empirical equations provided by [PERSON] et al. [1], International Geosphere-Biosphere Programme (IGBP) classifications are regrouped into six categories, and the ratio of T to total evaporation is calculated for each time step using empirical LAI regression functions for each land cover type. This ratio is then multiplied by the latent heat at the tower to obtain the flux of transpiration. The E\({}_{\text{p}}\) from the towers is calculated using the simple available energy formulation by [PERSON] [64] using tower R\({}_{\text{n}}\) and G, with an \(\alpha\) value calibrated per individual tower using a subset of unstressed days, as explained by [PERSON] et al. [63]. A series of quality checks are applied to eliminate towers in which representativeness issues may arise (i.e., lack of observations, large fraction of open water, etc.); a full description of these checks is available in the supplemental information. After all quality checks, a total of 25 FLUXNET towers remain for comparisons, as illustrated in Figure S1 and listed in Table S2.
## 4 Materials and Methods
### Vineyard Sites  
The experiment was carried out during 2017, 2018 and 2019, in two different commercial vineyards of Ribolla Gialla, located in two different DOC districts of the FVG region. The first vineyard chosen is located in Corno di Rosazzo at the foot of the hills, sited in the Friuli Colli Orientali and Ramandolo (FCO) district (46\({}^{\circ}\)00\({}^{\prime}\)19.1\({}^{\prime\prime}\) North; 13\({}^{\circ}\)26\({}^{\prime}\)30.6\({}^{\prime\prime}\) East; elevation 94 m a.s.l.), with a planting density of 3367 plants/ha (2.7 m between the rows and 1.1 m within the row). The vineyard soil was classified as Eutric Cambisols and was characterized by a silt-clay-loam texture with no coarse. In such soil, the root penetration is limited to a depth of about 100-150 cm due to the lack of oxygen during the rainy periods of the growing season, as there is inadequate water drainage. During the summer season, the vines suffer from periods of water stress, as there is no possibility of irrigation. The second vineyard was located south of the town of Casarsa della Delizia, on the plain of Friuli Grave (FG) district (45\({}^{\circ}\)55\({}^{\prime}\)21.9\({}^{\prime\prime}\) North; 12\({}^{\circ}\)50\({}^{\prime}\)54.2\({}^{\prime\prime}\) East, 35 m a.s.l.). The planting density was 3086 plants/ha (2.7 m between rows and 1.2 m within the row). In this area, the typical soil was classified as Cutanic Luvisols. It was characterized by a silt-clay-loam texture with a low presence of skeleton. Unlike the first vineyard site, root deepening in this soil was not limited since there is adequate water drainage, even at-depth. Therefore, the conditions of anoxia are easily avoided. In addition, the water stress situations were resolved during the summer season thanks to using a drip irrigation system. In both vineyards, the clone used was the VCR 100 (Vivai Cooperativi Rauscedo, Italy) grafted onto the Kober 5 BB rootstock. The training system adopted is a single arched Guyot.
## 3 Review of WBASN MAC Protocols for IEEE 802.15.4 and IEEE 802.15.6
### MAC-Layer-Based and Parameter-Tuning-Based Approaches  
These approaches recommend that minimum modification should be made to the standard so that appropriate benefits can be achieved from the strengths of the standard. In this context, according to the requirements of the application, parameters should be tuned properly. The performance of the slotted CSMA/CA mainly depends on four parameters, namely, the minimum backoff exponent (macMinBE), the maximum backoff exponent (macMaxBE), the contention window (CW) value and the maximum number of backoffs (macMaxCSMAbackoffs). The advantage of this approach is that it does not require a complex modification of the existing protocol. The drawback of this approach is that this optimisation may only be specific to some applications. The details of such optimisations, based on parameter tuning, can be found in [70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83]. Overall, the higher the number of retransmissions, the better the reliability of the data transmission. Retransmission occurs when a transmitting device does not receive an acknowledgement from the receiver. There are different reasons for not receiving the acknowledgement, i.e., data packet loss due to a collision on receiving side, acknowledgement loss and late reception of the acknowledgement, etc. A high number of retransmissions assures reliability, but, at the same time, it causes a delay in network performance as retransmissions will involve channel access to the same packet; it also requires bandwidth, which will affect the performance of other nodes. The traditional profile of IEEE 802.15.4 and IEEE 802.15.6 imposes restrictions by giving default values for these MAC layer parameters.  
The discussion above concludes that IEEE 802.15.4/ IEEE 802.15.6 standards are capable of supporting time-critical applications by tuning the MAC parameters to more suitable values. There are few performance trade-offs between reliability and latency and, similarly, between delay and energy consumption. Hence, it can be observed that these MAC parameters have a significance impact on network performance. There is a need to understand the appropriate setting of these parameters to optimise the performance of biomedical applications that require specific data rates and latencies.
## 4 Simulation Results  
### Optimal System Architecture  
Results from the simulation suggest significant environmental and financial benefits are to be gained in installing a VRB as an integration technology for remote wind/diesel power systems. HOMER calculated the following optimal system architecture from the input data (Table 4):  
As can be seen, the rated power output of the optimal VRB (1,000 kW) matches the peak load of the system suggesting favourable use of the VRB over the alternative of supplementing wind power with diesel generation. Furthermore, the very large optimal storage capacity of 18,000 kWh also suggests HOMER favours the use of the VRB over the diesel generators in times of minimal wind turbine output.  
Good use is made of the VRB throughout the year, especially in times of little or no wind. Figures 7-9 indicate the output of power from the wind and VRB for each month of the calendar year. As is evident from the diagrams, in times of little or no wind power output, the VRB is used, demonstrated by the low state-of-charge (SOC) at these times. The frequency histogram (Figure 9) demonstrates that the VRB is in a minimal state of charge for  
A significant reduction in the use of the diesel generators and the reliance on diesel fuel is observed with the inclusion of a VRB in the wind/diesel system. This is made evident by a comparison of identical systems with and without a VRB installed. Figures 10 and 11 graphically displayed the use of the 600 kW diesel generator in these systems.  
Figure 8: Vanadium Redox Battery (VRB) state of charge variations over a twelve month period.  
Figure 10: 600 kW diesel generator use in wind/diesel/VRB system over 12 month period.  
Figure 9: VRB state-of-charge frequency histogram.  
As these figures demonstrate, there is far greater reliance on the diesel generators in the absence of a VRB. With the inclusion of a VRB the role of the diesel generators shift from the primary base source of power to a necessary supplement in the event of sustained periods of little or no wind.
## 2 Result
### Analysis of Ka and Ks Substitution Patterns  
The patterns of synonymous (Ks) and nonsynonymous (Ka) nucleotide substitutions offer critical insights into the evolutionary mechanisms of genes. The Ka/Ks ratio, a fundamental metric in genetics, is employed to gauge the selective pressures on protein-coding genes and evaluate their diversification rates. A Ka/Ks ratio less than 1 signifies purifying selection, ratios greater than 1 indicate positive selection, and a ratio of 1 represents neutral selection [29]. To assess selective pressures, the Ka/Ks ratios for the _CaCA_ gene family across four _Rosa_ species were calculated. Genes with a Ka or Ks value of zero were excluded from the analysis. The average Ka/Ks ratio was 0.409, demonstrating that all genes examined were subject to purifying selection, with individual values ranging from 0.092 to 0.982. These results illustrate that the _RrcCaCA_ genes have experienced substantial purifying selection (Figure 5).
# Narrowband Internet of Things (NB-IoT):
## 4 Narrowband-IoT Possible Deployment Strategies
### Asynchronous NB-IoT Deployment in Small Cells and LTE in Macrocells  
In this strategy, NB-IoT uses separate PRBs between small cells and macro cells. This means that one or more PRBs are used for small cells and different PRB(s) for the macrocells. If the PRBs are not well planned, NB-IoT users from adjacent cells (using the same resource units) may suffer from interference. Also, LTE users that are using the same resource elements may interfere with small cell or macro cell UEs. Different transmit power control configurations may be used to control interference.  
The choice of the deployment strategy depends on several factors such as use-case requirements, environmental conditions, equipment quality, etc. It is imperative to implement better interference estimation, mitigation or management techniques that will ensure better performance and spectral efficiency for the massive NB-IoT deployment in coexistence with other technologies.  
**Summary:** This section has presented the possible NB-IoT deployment strategies by considering the NB-IoT support for small cells in coexistence with legacy LTE in HetNet scenario.  
The following section presents the open research challenges to motivate future research directions.
## 3 Results and Discussion
### The Results Compared with Other Approaches
One possible explanation is that the model is optimized primarily for shorter segments, leading to a saturation effect when applied to longer durations. Moreover, neural networks typically learn complex representations from training data, which may not generalize as effectively to longer signals if the training set is biased toward shorter durations.
# Assessment of Mechanical Properties of Corroded Prestressing Strands
## 2 Investigation of Corrosion in External Tendons  
### Bridge Description  
Two post-tensioned segmental box girder bridges, referred to as \"A\" and \"B\", located in an urban arterial road and that were known to have corrosion in their external tendons were examined. Bridge A used 19 seven-wire strands with a nominal diameter of 15.2 mm, while Bridge B used 27 seven-wire  
Figure 1: Observed corroded strands in existing bridges: (**a**) corroded tendon; (**b**) corroded strand.  
strands with a nominal diameter of 12.7 mm. A fracture in one external tendon of Bridge A was found 18 years after the bridge construction was completed. The external tendons were covered by a duct filled with grout. To decide on the repair or replacement of the corroded tendons, the ducts were uncovered, and a detailed inspection was conducted. Severe local section losses were found on the corroded strands. The investigation concluded that the causes were poorly treated air vents and joints in the duct resulting in an infiltration of chloride, presumed to be from de-icing chemicals, and rainwater. In addition, voids not filled by the grout were found inside the duct, and most of the corrosion was located in those sections. In the tendons that were selected for replacement, the corroded strands were cut and moved to a lab to evaluate the corrosion. Tensile tests were conducted on 100 corroded strands.
## 3 Methodology  
### Proposed Framework  
This study introduces a hybrid CNN-transformer model, namely Wet-ConViT, for wetland classification from satellite data. The architecture of Wet-ConViT includes patch embedding, transformer, and convolutional blocks (Figure 2). The model processes 14 layer inputs, comprising 10 from Sentinel-2 and 4 from Sentinel-1, using a patch size of 16 (determined through an experimental analysis detailed in Section 4.8). These inputs are initially passed through two convolutional layers with output sizes of 32 and 64, respectively. Following the initial processing, patch embedding is applied to capture multi-scale contextual information. The embedded data are then directed through multiple convolu  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Polygons** & **Samples** \\ \hline Bog & 98 & 42,148 \\ Fen & 113 & 29,648 \\ Swamp & 116 & 15,424 \\ Marsh & 49 & 3445 \\ Water & 68 & 24,615 \\ Forests & 103 & 35,452 \\ Shrublands & 37 & 6400 \\ Grassland & 126 & 17,624 \\ Pasttures & 47 & 19,745 \\ Barren & 24 & 1789 \\ Urban & 48 & 25,308 \\ \hline Total & 829 & 221,598 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of ground truth land cover polygons and samples per class.  
tional and local transformer blocks, enabling the extraction of both short- and long-range features. Accordingly, the data flow proceeds through a convolutional block with an output size of 96. The model then progresses through two consecutive blocks, each comprising patch embedding, convolutional block, and transformer block. The first block outputs 256 features, while the second block outputs 1024 features from the transformer blocks.  
Within these convolutional and local transformer blocks, specialized modules, namely the modified multi-head self-attention (MMHSA), multi-head convolutional attention (MHCA), and local feed-forward network (LFFN), are employed to derive the desired features. MMHSA focuses on extracting low-frequency features, MHCA attends to local relationships among data tokens, and LFFN preserves locality in the feed-forward framework. This strategic integration ensures that Wet-ConViT can effectively capture both short-range spatial details and long-range dependencies inherent in wetland classification tasks.  
After the feature extraction stages, the derived information is passed through batch normalization to stabilize and accelerate training, followed by average pooling to aggregate spatial information. Finally, a fully connected layer with an output size equal to the number of wetland classes (11 in this case) performs the classification for each pixel. Detailed information regarding the modules and blocks is summarized in the following subsections.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Rate of Failure for DRFTSA Using Large Dataset by Varying Number of VMs  
In this section, we precisely present the generated rate of failure for DRFTSA for a large dataset by varying the number of VMs. The numbers of tasks considered in this dataset are 700, 800, 900, and 1000. To 700, 800, 900, and 1000 tasks are assigned 50, 60, 70, and 80 VMs, respectively. Generated rate of failure for PSO with 700, 800, 900, 1000 tasks is 56.24, 53.14, 58.19, 62.15, respectively. Generated rate of failure for GA with 700, 800, 900, 1000 tasks is 52.15, 58.06, 63.17, 65.87, respectively. Generated rate of failure for ACO with 700, 800, 900, 1000 tasks is 58.08, 61.43, 65.86, 71.36, respectively. Generated rate of failure for DRFTSA with 700, 800, 900, 1000 tasks is 42.14, 34.23, 26.87, 27.62, respectively.  
From Table 15 and Figure 15, it is evident that the rate of failure of DRFTSA compared to various approaches is greatly minimized with the considered dataset and VMs.
## 3 Application of Diagnosis
### Feature Extraction of Sensor Data  
To extract the features of the fault and normal operation signals during the MAB operation, the statistical feature values listed in Table 1 were extracted at intervals of 30 min. Figure 8a shows a split state for extracting features at regular time intervals from 23 sensor data histories. The extracted data features can be represented as time series data, with a total of 138 features for 23 sensors: 23 (sensors) times 6 (features). Figure 8b shows some of the extracted features as time series data.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 4 Key Control Logic Design
### Ethernet Communication Logic Design  
The wireless communication module has successfully transmitted the data to the back-end test bench. In order to achieve further data processing, storage, and interaction with external devices, the system integrates the Ethernet communication module to fulfill this function. The logic block diagram of the UDP top-level module is shown in Figure 11, which includes a UDP data processing module, a clock CLK module, and a data buffer  
Figure 10: State diagram of encoding and decoding logic.  
FIFO module. The data processing module is mainly responsible for handling upper-layer network protocols, including data segmentation and recombination. The clock module is used to synchronize data processing operations. By adjusting the phase of the clock signal, it ensures clock synchronization during high-speed data transmission. The data buffer module temporarily stores the data packets received by the UDP module. The data processing module consists of several key sub-modules. The UDP sending module manages the data sending process. It uses the state-machine transition method to package the valid data in the Ethernet frame format and send it back. The UDP receiving module handles data reception. It extracts data packets from the Ethernet and checks whether the MAC address and IP address are consistent with those of the target board. The CRC data verification module performs a cyclic redundancy check to ensure the integrity and accuracy of the data [29].