esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Skip Connection in Semantic Segmentation Applications  
Skip connection was initially introduced by ResNet [40] to address the problem of gradient vanishing during the training of deep neural networks. The original residual block consists of two convolutional layers along with a skip connection. Building upon ResNet, the Fully Convolutional Network (FCN) proposes a mechanism for long skip connections that link feature maps in shallower layers of the network to those in deeper layers. The FCN utilizes these long skip connections to effectively integrate detailed information from the shallower layers with the semantic information derived from the deeper layers, thereby enabling the generation of more precise representative features for semantic segmentation. U-Net adopts this long skip connection mechanism between its encoder and decoder. Specifically, U-Net employs long skip connections to merge local information from the encoder with contextual information from the decoder, resulting in feature maps that possess a higher degree of information richness. This enhancement significantly improves the performance of object detection and pixel segmentation tasks. As a result, U-Net has emerged as a widely used architecture in the field of medical image segmentation.  
Models such as SegNet [41], DiSegNet [42], Unet++, Unet3+, and RED-Net [43] integrate a short skip connection structure within the U-Net framework. For instance, Unet3+ introduces a short skip connection mechanism, termed the Dense Skip Connection, between each long skip connection in the decoder. Missformer incorporates a Feed-Forward Network (FFN) into the skip connection, which allows the model to extract precise features from the feature map at the skip connection, thereby preventing the introduction of invalid information into the decoder. However, due to the four stages of the encoder, the dimensions of the processed feature maps vary. Utilizing a convolutional kernel of uniform size to process feature maps of different dimensions can result in the skip connection filtering out information pertinent to small organs. This project addresses this issue by employing the Demodulate Bridge.
## 3 Results and Discussion
### Spring Effect of Laminated Composites  
Consistency for the laminated composite to retain the original shape without any spring-forward or spring-back after curing is highly sought by composite manufacturers. A significantly large amount of residual stresses in laminated composites are likely to develop during the curing process using complex-shaped molds with blunt corners and lack of draft angle. Figure 9 illustrates the traces of manufactured laminates relative to that of the mold. Figure 9a, c and e shows concave-cornered laminates produced using SVB, MSVB and DVB techniques, respectively. Whereas, Figure 9b,d,f illustrates convex cornering using SVB, MSVB, and DVB techniques, respectively. Spring effect is measured by the measuring the corner angle of final consolidated part. Spring-back effect occurs when the edges of the final laminates are shrunk inwards. In this case, the final corner angle tends to reduce compared to that of intended. Whereas, spring-forward effect occurs when the edges of the final laminates are widened outwards. In this case, the final corner angle tends to increase when compared to that of intended. Please note that there was no visible spring-back or spring-forward behavior. The coefficient of determination (R\({}^{2}\)) was calculated for all concave- and convex-cornered composites relative to that of the mold and the results are plotted in Figure 9. It was found that the samples shape showed a very close agreement with that of the mold. An insignificant variation in the inclined flange for concave-cornered laminated composites is observed, which can be explained by the relaxation of very low residual stresses accumulated at the corners to ultimately cause deviation. Whereas, the flat flange showed a remarkably close agreement in all bagging techniques and curing profiles. Furthermore, convex-cornered laminated composites showed excellent agreement in both inclined and flat flange regions; however, a slight decrease in the coefficient of correlation is subjected to corner thinning effect which causes data offset during observation.  
The overall investigation of spring effect (forward and back) in complex shapes showed local curvature angles with adequate corner rounding i.e., 45\({}^{\circ}\) with a 6.35 mm radius, decreases the potential for deformation after curing. The results reveal fundamental information associated with complex-shaped laminated composites and provide a basis for developing effective manufacturing guidelines. This research highlights the potential of complications with the increase in part size and complexity such as curvature angle and corner radii. Therefore, the results presented in this work must be combined with further research on other processing parameters affecting the shape conformation of prepreg processing.  
Figure 8: Thickness across the length of laminates with concave corner produced using (**a**) SVB, (**b**) MSVB, and (**c**) DVB techniques.
###### Abstract  
In this report, we present a study of the quinhydrone/methanol (QHY/MeOH) organic passivation technique for a silicon (Si) surface. The roles of p-benzoquinone (BQ) and hydroquinone (HQ), which make up QHY, in controlling the uniformity and coverage of the passivation layer as well as the minority carrier lifetime (\(\tau_{\text{eff}}\)) of Si were investigated. The uniformity and coverage of the passivation layer after treatment with diverse mixture ratios of BQ and HQ in MeOH were studied with two different atomic force microscope (AFM) techniques, namely tunneling mode (TUNA) and high-resolution tapping mode AFM (HR-AFM). In addition, the'ref and surface potential voltages (SPV) of passivated surfaces were measured to clarify the relationship between the morphologies of the passivation layers and degrees of surface band bending. The molecular interactions between BQ and HQ in MeOH were also analyzed using Fourier-transform infrared spectroscopy (FT-IR). In our study, we successfully demonstrated the role of each molecule for effective Si surface passivation with BQ working as a passivation agent and HQ contributing as a proton (H\({}^{+}\)) donator to BQ for accelerating the passivation rate. However, our study also clearly revealed that HQ could also hinder the formation of a conformal passivation layer, which raises an issue for passivation over complex surface geometry, especially a nanostructured surface.  
**Keywords:**\(p\)-benzoquinone; hydroquinone; passivation; atomic force microscope; minority carrier lifetime
## 3 Methodology  
To systematically evaluate the trade-offs between transportation-induced carbon emissions and processing-related costs in the context of steel slag utilization for road base applications, a machine learning-based multi-objective optimization (MOO) framework was developed. The entire methodological sequence is summarized schematically in Figure 1, illustrating the integrated progression from model training to optimization and final decision support.  
First, predictive models were developed for two dependent variables--total unit cost (USD/m\({}^{3}\)) and CO\({}_{2}\)-equivalent emissions (kg/m\({}^{3}\))--using four tree-based ensemble learning algorithms: Random Forest Regressor (RFR), Extremely Randomized Trees (ERT), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR). These models were trained on a dataset of 482 engineered instances, with hyperparameters optimized via Bayesian search and model performance evaluated using statistical indicators such as Pearson correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and the a20 accuracy index.  
Following model selection, the interpretability of the chosen regressor was enhanced through the SHapley Additive exPlanations (SHAP) framework. SHAP analysis enabled the quantification of the marginal contribution of each input variable to the model output, both globally (across the dataset) and locally (at the level of individual predictions), thus supporting the transparent evaluation of variable influence and enabling a better formulation of optimization constraints.  
A bi-objective optimization problem was then formulated to simultaneously minimize the predicted cost and emissions. The five input features--transport distance, energy intensity, moisture content, gradation level, and grid CO2 emission factor--formed the decision vector, while the trained GBR model served as a surrogate objective function. Domain-specific constraints, including variable bounds, thermodynamic feasibility, and volumetric consistency, were incorporated to ensure the physical realism of solution space. This optimization problem was solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), which iteratively evolves populations of candidate solutions using crossover, mutation, and elitist selection to approximate the Pareto front of non-dominated trade-offs between cost and emissions.  
Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to support actionable decision making. Each point on the Pareto front was scored based on its Euclidean proximity to an ideal solution (minimum cost and minimum emission), and the alternative with the highest closeness coefficient was identified as the recommended configuration.
## 6 Discussion
### Challenges
#### 6.2.1 Processing of Large Volumes of Data  
Knowledge-based networks gather vast amounts of data, also referred to as big/massive data, for making inferences for network decisions. This enormous amount of data may be safely and trustworthily preserved inside the blockchain through the adoption of blockchain technology, protecting data from unauthorized modification and deletion. This information may be prepared to be used in ML model learning or making inferences from already trained models. Due to their capability to acquire knowledge from vast volumes of data through hierarchical learning, deep learning models are typically employed to learn from or make inferences from massive data. Processing large volumes of raw data can be a significant challenge, as raw data can be heterogeneous and originate from different sources. Even if self-executing contracts are deployed for automating the data preliminary processing, categorizing, and labeling, users may have a challenge in defining conditions and statements in the smart contracts for the diverse data that big data represent. Furthermore, it is challenging to process large datasets using knowledge generation models due to the high computational demand required to process them. Dispersed storage of big amounts of data is likewise difficult on blockchain, as there can be issues with how to provide incentives for users to share the data.
## 3 Results
The disc stress at the instrumented segments (L2-L3 and L3-L4) decreased in comparison to the INT model in flexion (maximum decrease of 16% in AwC-TSF and TSF), extension (maximum decrease of 58% in TSF) and left lateral bending (maximum decrease of 61% in AwC-TSF-S). However, in contrast, the disc stress increased at the adjacent levels (L1-L2 and L4-L5), as shown in Figure 4. The disc stress in the AwC model was similar to the INT model at each level. Figure 3: Normalized stiffness of each model at L2–L4 segment in all implanted models. Figure 5 shows a clear reduction in stress on the endplate at the instrumented segments (AwC-TSF, AwC-TSF-S and TSF), in comparison to the INT model in all motion conditions. The maximum reduction in endplate stress (over 50% reduction) occurred at the instrumented segments (AwC-TSF, AwC-TSF-S and TSF) in lateral bending. The adjacent levels in AwC-TSF, AwC-TSF-S and TSF demonstrated a considerable increase in stress, with the maximum increase (over 200% increase over the INT model) occurring at the adjacent level (L5) of the instrumented models in flexion and extension. The endplate stress in the AwC model was similar to the INT model at each level. Figure 4: (a) Normalized maximum von Mises stress on the disc at each level in all implanted models. (b) von Mises stress distribution of the disc annulus under flexion for the INT and AwC-TSF-S models. _Appl. Sci._**2020**, _10_, 2133  
Figure 5: Cont. In the instrumented models, the maximum von Mises stress on the pedicle screws occurred at L2 in the TSF model in torsion (443 MPa), while the minimum stress occurred at L2 in the AwC-TSF-S model in extension (123 MPa), as shown in Figure 6.  
Figure 5: Normalized maximum von Mises stress on the endplate at each level in (**a**) flexion, (**b**) extension, (**c**) lateral bending, and (**d**) torsion in all implanted models.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
###### Abstract  
Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential Delay-Doppler Maps (PN-D), to distinguish between sea ice and sea water using differential Delay-Doppler Maps (dDDMs). PS-D and PN-D make use of power-summation and pixel-number of dDDMs, respectively, to measure the degree of difference between two DDMs so as to determine the transition state (water-water, water-ice, ice-ice and ice-water) and hence ice and water are detected. Moreover, an adaptive incoherent averaging of DDMs is employed to improve the computational efficiency. A large number of DDMs recorded by UK TechDemoSat-1 (TDS-1) over the Arctic region are used to test the proposed sea ice detection methods. Through evaluating against ground-truth measurements from the Ocean Sea Ice SAF, the proposed PS-D and PN-D methods achieve a probability of detection of 99.72% and 99.69% respectively, while the probability of false detection is 0.28% and 0.31% respectively.  
sea ice; GNSS-R; Delay-Doppler Map (DDM); differential DDM (dDDM); UK TechDemoSat-1 (TDS-1)  
1
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 3 Results
### Novobiocin-Induced Relaxation of DNA Affects the Activity of _rmB_ P1 In Vivo  
Next, by RT-qPCR we monitored the response of _rmB_ P1 and _Pveg_ to novobiocin treatment, using the same conditions as in the previous experiment. We grew cells carrying the appropriate fusions (_rmB_ P1-_lacZ_ (LK134) and _Pveg-lacZ_ (LK135)) to early-exponential phase (OD\({}_{600}\)\(\sim\) 0.3) and either treated them with novobiocin or mock-treated them. In the case of _rmB_ P1, the promoter activity decreased after novobiocin treatment (as opposed to mock treatment), but in the case of _Pveg_, the promoter activity displayed the same moderate decline regardless of the novobiocin treatment, suggesting that _rmB_ P1 is more sensitive to changes in DNA topology (Figure 2B,C).  
We also measured the GTP levels in novobiocin treated cells. We observed that novobiocin-induced relaxation resulted in a massive increase in the GTP level in cell (Figure 2D). The levels of ATP increased only slightly (Supplementary Figure S2). Thus, the activity of _rmB_ P1 and the level of GTP became uncoupled. These experiments suggested that DNA topology might affect the activity rRNA promoters, but it was also possible that unknown, secondary effects of the novobiocin treatment could be the cause.  
Figure 2: Effect of novobiocin-induced relaxation of chromosome on total RNA synthesis, selected promoter activities, and GTP level. (**A–D**) Cells were grown to early exponential phase (OD\({}_{600}\)\(\sim\)0.3), and at time 5 min they were treated with novobiocin (5 \(\mu\)g/mL). (**A**) Total RNA synthesis after novobiocin treatment. After \({}^{3}\)H-uridine had been added (time 0), the culture was divided into three flasks. At time 5 min the cells were treated with novobiocin (blue line) or with rifampicin (red line) as a control, or left untreated (black line). The amount of radiolabeled RNA at 5 min was set as 1. Black circles, mock-treated; blue triangles, treated with novobiocin; red squares, treated with rifampicin. The values are averages of three independent experiments \(\pm\)SD. (**B**,**C**) The relative activities of _rmB_ P1 and _Pveg_ promoters after novobiocin treatment. Cells were grown and at 5 min treated with novobiocin or not. RNA was extracted and determination of promoter activity was done by RT-qPCR. Promoter activities were set as 1 at time 5 min. Blue lines are novobiocin-treated samples, black lines are untreated samples. The experiment was performed three times. The error bars show \(\pm\)SD. (**D**) GTP concentration after novobiocin treatment. Cells were grown in the presence of [\({}^{32}\)P] H\({}_{3}\)PO\({}_{4}\) and treated with novobiocin. Levels of GTP were determined by TLC. The GTP level at 5 min was set as 1. Results are averages from two measurements. The error bars show the range.
## 4 Discussion  
### Analysis of Involution Failing to Converge in the Validation Set  
Since the performance was limited due to the hardware facilities available, the experiments in this paper did not include large batches of data operations at the same time, and the experiments on the Rednet-50 [41] network were conducted with batch sizes of 6 and 12. Loading the trained weights of the Rednet-50 model after 30,000 iterations revealed the presence of bad points in some of the images generated by the network, as shown in Figure 15.  
Figure 15: (a) Input RGB image; (b) ground truth for the input image; and (c) dead segmentation image produced by Rednet-50.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Pavement Materials**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Asphal**} & \multicolumn{2}{c}{**Concrete**} & \multicolumn{2}{c}{**Gravel**} \\ \cline{2-7}  & **MIOU** & **ms** & **MIOU** & **ms** & **MIOU** & **ms** \\ \hline BiseNet & 0.5887 & 3.54 & 0.5609 & 3.87 & 0.6790 & 3.32 \\ Segnet & 0.6610 & 18.65 & 0.6484 & 19.12 & 0.6751 & 18.26 \\ DeepLabV3+ & 0.6419 & 19.21 & 0.6219 & 19.83 & 0.7170 & 18.14 \\ PSPnet & 0.5641 & 11.09 & 0.5275 & 12.64 & 0.6738 & 10.53 \\ U-Net & 0.7015 & 26.14 & 0.7288 & 27.85 & 0.6949 & 25.43 \\ UNet++ & 0.6232 & 18.54 & 0.6224 & 18.03 & 0.6982 & 19.65 \\ Deepcrack & 0.6744 & 8.65 & 0.6908 & 9.46 & 0.6416 & 8.13 \\ RIIAnet (proposed model) & 0.7222 & 4.57 & 0.7252 & 4.96 & 0.7289 & 4.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance under different pavement materials.  
Dead images such as this cause the loss functions for the validation set to fail to reach convergence. We found that the involution operator was more sensitive to the input data than conventional convolution, and the reasons for this are likely to be as follows:  
* Batch normalization is calculated differently during training and validation. When small batches are used for training, the small-batch mean and variance are calculated each time and are used in the backpropagation of the gradient. However, in testing, the mean and variance precalculated for the training set via a sliding average are used for forwarding inference. When the distributions of the training and test data are significantly different, the precalculated mean and variance at training time do not effectively represent the distribution of test data that differ from the expected value.
* The involution operator performs parameter updating by quadratic optimization. When the distribution of the training data differs from the test data, the correlation between the weight W after batch normalization and the data obtained by spreading without normalization decreases. This results in the differences being continuously magnified, and bad points in the subsequent output of the deep network because of the decreasing resolution of the feature maps.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 6 Results and Discussion
### The Effect of the Congestion Control Algorithm  
The congestion control algorithm used in the transport protocol plays a major role in the performance of HTTP protocols. For this, we evaluated the performance of HTTP with the Cubic and BBR algorithms with multi-object web pages in the satellite link. The web page structure was fixed to the uniform multi-object where the total page size was set according to Table 3.  
The average PLT of multi-object web pages downloaded with HTTP over the direct LAN link and the satellite link is shown in Figure 9. The solid lines are for Cubic while the dashed lines represent BBR. The results indicate that BBR outperforms Cubic regardless of the type of HTTP protocol. This is because BBR allows up to \(2\times BDP\) packets in-flight during its start-up phase in addition to \(BDP\) packets queued in the buffer. As a result, the number of packets in-flight particularly in satellite link will be large enough, due the large RTT, to produce better channel utilization than Cubic. Although Cubic manages to utilize the channel in the direct LAN link, it fails to do so in the larger RTT satellite link because it only updates its transmission rate every RTT, implying slower growth in such high latency links.  
Moreover, the evaluation results revealed that Cubic with HTTP/3 also suffers in the direct LAN link. This is because HTTP/3 is able to transfer many more packets compared to HTTP/1.1 and HTTP/2 hence producing more protocol overhead in the link. Table 6 shows the overhead percentage generated from transmitting different web pages in the three HTTP protocols. This is not a problem with BBR since it quickly utilizes the channel as discussed earlier, thus overcoming the effect of the HTTP/3 overhead.  
Figure 9: Average PLT of uniform multi-object web page with Cubic vs. BBR. (**a**) Direct LAN link (RTT\(=1.08\) ms). (**b**) Satellite link (RTT\(=530\) ms).