esa-sceva/satcom-chunk-collection huggingface dataset:
# Genipin Attachment of Conjugated Gold Nanoparticles to a Decellularized Tissue Scaffold
## 3 Results  
### Neutron Activation Analysis  
NAA was performed to measure the concentration of gold attached to the tissue scaffold. As shown in Table 1, the number of nanoparticles increased with the amount of crosslinking time with or without the use of genipin. For all time points, the scaffold with genipin had a higher concentration of gold, but the difference was only significant at the 15 min time point.
# Multi-Branch Deep Residual Network for Single Image Super-Resolution
## 4 Experiments
### Comparisons with State-of-the-Art Methods  
In this section, the qualitative and quantitative comparisons with other state-of-the-art SR approaches will be introduced. We mainly compare the performance of MRSR with results from Bicubic, A+ [21], SRCNN, DRCN, SRResNet, EDSR, and the most recent work WSD [42]. Benchmark, the public code for these algorithms and the same technique to obtain the LR images from HR images were used in experiments. For a good visual comparison, we also adopt same method to deal with the luminance components.  
Besides, compared with EDSR, the new ResNet structure uses fewer filters and parameters. The proposed model only uses 128 filters that account for 50% of EDSR. Not only that, the parameters has been reduced to about 12 M, which is 0.28 times compared with EDSR, as shown in Table 1. Consequently, the GPU memory usage and the training difficulty can be dramatically reduced. Moreover, the optimized ResNet architecture exhibits better performance with less computation.  
The summary quantitative results on several datasets are presented in Table 2. As can be seen from the table, our model exhibits superior performance than existing methods in all datasets and scale factors in terms of PSNR and SSIM. In addition, visual comparisons of the super-resolution images are shown in Figures 3-5. It can be seen intuitively from the figures that our reconstructed images show higher quality regardless of details or textures and exhibit more realistic outputs compared with the previous works. In Figure 6  
Figure 4: The results of “Image007” (Set14) on \(\times\)4 super-resolution. MRSR shows a better performance in the qualitative comparison with other methods.  
Figure 5: The results of “Image0824” (DIV2K) on \(\times\)4 super-resolution. MRSR shows a better performance in the qualitative comparison with other methods.  
Figure 3: The results of “Image0362” (DIV2K) on \(\times\)4 super-resolution. MRSR shows a better performance in the qualitative comparison with other methods.  
Figure 6: The qualitative super-resolution comparison between our work and DRCN with an upscaling factor of 4. Whether PSNR or SSIM, the SR results have been improved greatly.
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 4 Proposed Methodology-TAEO
### Scheduling  
Data submission to the DF node or to sink is TDMA based, in which a time slot is assigned to each node, during which it can submit the recorded information to the DF. If a node has no data to transmit during the assigned time slot, it declares itself as an idle node and moves to a standby state to save power.
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 3 Network Model
If the decision is No, the UAV attempts to form a new cluster. If the decision is Yes, the process proceeds to the next steps. * Condition: CNRF Comparison and Cluster Joining: UAVs compare CNRF values and proceed with the cluster joining process. * Cluster Joining Process: Nodes join the appropriate cluster based on the comparison results. * Node Move-in Process: Nodes that successfully join a cluster go through the move-in process to integrate into the cluster. * Route Discovery: The protocol initiates the route discovery process to establish communication paths. Data Routing and Adjustment: Based on the discovered routes, data routing and necessary adjustments are made. If the data routing is successful (Yes), the protocol continues with data transmission using alternative routes as needed. If the data routing fails (No), the node initiates the move-out process. * Node Move-out Process: Nodes that cannot successfully route data will leave the cluster and the protocol ends for those nodes.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 3 NDC Transmission and NRS Overview
### Overview of the NDC Transmission Process
In addition, the ICN router can use the late-binding technology [13], and if the current replica node cannot retrieve the cache of the NDC, the replica node can initiate a query to the NRS and forward its REQ packet to another replica node until the REQ packet reaches the replica node that caches its requested NDC. Figure 1 is a schematic diagram of the NDC transmission process. Due to the support of the late-binding technology, when the client acquires an NDC, it can directly send an REQ packet to the edge node without performing name resolution. After receiving the first REQ packet, the edge node will request a network address (NA) list of replica nodes from the NRS, and use the replica-selection algorithm to select a replica node RN3 (in Figure 1) to forward the REQ packet. After receiving the REQ, RN3 replies with the DATA packet according to the REQ. The DATA packet will be forwarded to the client. It should be noted that the client can obtain the size of the NDC only after receiving the first DATA packet.
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
## 4 CNN for Material Classification  
In a preliminary work [19], the CNN was used for pigment classification. Samples made from polyethene (PE) were chosen, since PE is opaque to THz waves, and different pigments could be detected in the material. The proposed preprocessing method uses the WSD algorithm for data ordering and measurement transformation. Lower frequencies are expected to have more impact on the material classification, since the amplitude of the detected photocurrents are several orders of magnitude higher than those seen at higher frequencies, as shown in Figure 3. However, the WSD algorithm shows promise in mitigating the THz-FDS system's drawback with uncertainties in frequency tuning. Because of the nature of the WSD algorithm, it can be used to determine the appropriate bandwidth and frequency resolution with the greatest sensitivity on the measured spectral image of the target material. This section presents preprocessing, the WSD algorithm, the CNN structure and occlusion sensitivity used for frequency range optimization.  
Figure 4: Principle of proposed Gauss Spotlight Filter (GSF). The filter moves through the spectrogram on every vertical position or every target frequency.
# Airborne GNSS Reflectometry for Water Body Detection
## 5 Flight Experimentation  
### Flight Information  
The flight took place in the North of France and started at 14:45 UTC, the 19 October 2020 and ended at 15:30 of the same day lasting for 45 min. We show in Figure 10 the gyrocopter that was used for the flight experimentation with the different sensors embedded on it. The gyrocopter (MTO Sport 2017) is a light aircraft with an approximate weight of 245 kg and size of 5.1 m \(\times\) 1.9 m \(\times\) 2.7 m. It can carry a load up to double its weight. The maximum speed that the gyrocopter can fly at is 195 km/hr. It can cover a distance up to 700 km with 6.7 h loft within a single flight.  
An RHCP antenna is fixed on the nose of the gyrocopter pointing towards the zenith and an LHCP antenna is mounted on the bottom of the gyrocopter pointing towards the nadir. A drone board sensor records the receiver's attitude, altitude and position with respect to the GPS time. In addition to the on-board sensors, the gyrocopter was loaded with the necessary setup that constitute the GNSS-R receiver hardware for data collection. This receiver is composed of a delay line, combiners/splitters and a Syntony L1-L5 bit grabber for digitizing the composite signal.  
The gyrocopter took off from Calais-Dunkerque Airport located in Marck, 7 km east-northeast of Calais, in the Hauts-de-France region. We scanned a large zone that borders the English Channel over a trajectory of \(\sim\)71 km between Calais, Escalles, and Ardes. This study area was selected for experimentation because it contains a number of different landforms. The topography of the study area, especially the region between Guines and Ardes, does not vary a lot as it only contains plain land, water bodies, and some forests with no landscapes that might hugely affect the topography of the surface (such as mountains or big hills). This specific region holds over 50 different water body surfaces (such as lakes, ponds, rivers, swamps, etc.) of different nature and sizes. The effects of the study area surface roughness on the reflectivity measurements are not taken into consideration since we aim for the detection and localization of in-land water body surfaces using the variations of the measurements as we obtain reflections from different landforms along the trajectory rather than its estimated value. The flight trajectory and study area are shown in Figure 11.  
Figure 10: The gyrocopter that was used during the flight with the sensors embedded on it.  
During the flight, the gyrocopter maintained a low altitude of approximately 315 m above the ground with an average speed of 95 km/h. The wind speed was approximately at 25 km/h. Table 1 presents an overview of the flight information.
## 8 Experiments and Results
### Performance Metrics  
Assessments of the models used in this work were based on root mean squared error (RMSE), mean average percentage error (MAPE), and accuracy. The formula for calculating RMSE is expressed in Equation (5):  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\widehat{y}_{i})}^{2}}} \tag{5}\]  
* \(n\) is the total number of observations in the test sample;
* \(y_{i}\) represents the actual value at position \(i\);
* \(\widehat{y}_{i}\) represents the predicted value at position \(i\).  
The RMSE is useful because it provides a measure of error that is in the same unit as the original data, thus facilitating direct interpretation. The lower the RMSE value, the better the model performance, indicating a closer agreement between the predicted values and the actual values [31].  
The calculation of MAPE is straightforward and intuitive. For each data point in the test sample, by calculating the absolute percentage difference between the predicted value and the actual value, the average of these differences is obtained [36]. The formula for calculating MAPE is given by the following:  
\[MAPE=\frac{1}{n}\times\sum\limits_{i=1}^{n}{\left|\frac{y_{i}-\hat{y}_{i}}{y_ {i}}\right|}\times 100\% \tag{6}\]  
where:  
* \(n\) is the total number of observations in the test sample;
* \(y_{i}\) represents the actual value at position \(i\);
* \(\widehat{y}_{i}\) represents the predicted value at position \(i\).  
Finally, accuracy is a fundamental metric for evaluating the overall quality of prediction models [37]. Mathematically, accuracy is calculated as follows:  
\[Accuracy=\frac{TP+TN}{n}\times 100\% \tag{7}\]  
where:  
* \(TP\) is the number of correct predictions for samples in the class;
* \(TN\) is the number of correct predictions for samples out of the class;
* \(n\) is the total number of observations in the test sample.  
A high accuracy indicates that the model is making accurate predictions and is correct most of the time, while a low accuracy suggests that the model is struggling to predict observations correctly.  
LSTM-ReLU  
The performance of the LSTM--ReLU model was the one that showed the best results, as can be seen in Figures 7 and 8.  
Figure 8 shows the graph of the predicted and actual amplitude values of the belt failure component. The green color represents the actual amplitudes of the belt failure component, while the red color represents the predicted values. The proposed model accurately predicted the normal values and followed the degradation trend, which was also efficiently predicted.  
In addition, a forward-looking forecast was generated, predicting the next days based on the previous data. Multiple scenarios were illustrated, as depicted in Figure 9, including optimistic and pessimistic fault trajectories, providing a comprehensive view of potential outcomes.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Cement Slurries for Primary Cementing through Fractured Formations
#### 5.3.2. Thixotropic Cement Slurry  
A thixotropic cement slurry is based on a Portland cement slurry to which thixotropic additives are added. A thixotropic cement slurry has the following specific properties: shear thinning, high apparent viscosity, and very rapid development of static shear force under static conditions ([PERSON] et al., 2014). Thixotropic properties allow for the rapid gelling of cement slurries immediately after interruption of circulation. Furthermore, the hardening time of a thixotropic cement slurry in set cement is very fast and the drilling process can be continued in a short time after the cement plug setting ([PERSON] et al., 2014). Due to the previously mentioned characteristics, thixotropic cement slurries are mainly used as cement plugs which are placed in the loss zone.  
Thixotropic slurries develop good gel and compressive strength, but they remain liquid during placement, which permits the slurry to be lost prior to developing enough gel strength for effective loss prevention ([PERSON] et al., 2014).  
[PERSON] et al. (2019) described the first application of the new special-purpose thixotropic cement slurry, during September 2016, for the mitigation of partial and total losses of drilling fluid within the Volga-Urals region. The advantages observed using this slurry were the following: 50% reduced drilling NPT which is equivalent to two days per one well, minimized mud losses and associated mud costs, and improved conditions for primary cementing operations. In addition, the advantages of the thixotropic cement slurry used for lost circulation control include the fact that it mixes easily, can be pumped through BHA with telemetry tools and a drill bit, it features high gel strength and a relatively high compressive strength when set, and it has low content of abrasive particles ([PERSON] et al., 2014).
## 2 Related Work
### Imbalanced Learning
#### 2.1.2 Reweighting  
Reweighting adjusts the loss function to enhance the sensitivity of the classifier to the infrequent classes [23]. Focal Loss [24] modifies weights in accordance with the difficulty of classifying a sample and the degree of certainty associated with the classifier's prediction, thus concentrating on difficult-to-classify instances. Building on this, CB Focal Loss [25] introduces a class-balance factor to this method, recalibrating the model's focus according to actual class distribution in the dataset. Adaptive Class Sample Loss (ACSL) [26] varies weights based on the complexity of classifying a sample, rather than just frequency, recognizing the differing impacts of various misclassifications.  
Innovations like Drop Loss and Seesaw Loss offer new mechanisms for adjusting classification errors, particularly for minority classes. Drop Loss [27] employs a probabilistic approach to diminish foreground errors while supporting minority classes in the background. Seesaw Loss [28] adjusts weights based on prediction confidence, penalizing overconfidence in minority class errors. Equalization Loss [29] addresses the disparity in learning between frequent and infrequent classes by equalizing gradient contributions, ensuring a balanced learning process. PML Loss [30] proposes a boundary-aware weighting mechanism that places greater emphasis on samples near decision boundaries, crucial for accurate class delineation. However, these methods generally assume single-label scenarios, presenting challenges for optimizing multi-label datasets with long tails. Table 1 summarizes the predominant techniques explored in this study for addressing class imbalance, providing a comparative analysis of each method's pros and cons. Resampling methods enhance minority class representation, reduce overfitting, and balance the dataset. However, these techniques might introduce noise, particularly in high-dimensional spaces and near decision boundaries. Reweighting strategies improve learning from underrepresented classes, but they require meticulous hyperparameter tuning and effective label noise management.  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Methods** & **Advantage** & **Limitation** \\ \hline Resampling SMOTE [18] & It can reduce overfitting and increase minority class instances to balance class distribution. & It may introduce noise and be misleading in high-dimensional spaces, thus reducing the performance of the classifier. \\ Borderline-SMOTE [19] & Creates a borderline between and within majority and minority samples. & Does not identify samples near decision boundary. \\ Safe-level-SMOTE [20] & Generates samples in safe areas. & Far from decision boundary and neglected by classifier. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the references.
## 3 Materials and Methods
### Threshold Search  
The threshold search framework proposed in this paper achieves the optimization of the model's decision boundary through systematic parameter space exploration and quantitative analysis, significantly improving the accuracy and reliability of the error correction system. The scientific value of this method is reflected in three core aspects: (1) a confidence-driven dynamic decision-making mechanism. When making character-level predictions, the model generates a softmax probability distribution. Traditional methods directly use argmax decision-making (implicit threshold 0). This study introduces an explicit confidence threshold \(\tau\) and establishes a dynamic decision function: \(\hat{y}\) = {argmax(\(p\)) if max(\(p\)) \(\geq\)\(\tau\), {x_orig} otherwise. This mechanism gives the model the ability to \"know what it does not know\": when the prediction confidence is insufficient, the original characters are retained to avoid overcorrection. (2) Threshold optimization based on grid search, using equidistant grid search in the continuous space \(\tau\) \(\in\) [0.01, 0.5] to systematically evaluate model performance. For each candidate threshold \(\tau\)_i, perform a complete inference process on the validation set, calculate the confidence mask: M = I(max(softmax(logits)) \(\geq\)\(\tau\)_i); generate the final prediction: \(\hat{y}\) = M \(\odot\) argmax(logits) + (1 - M) \(\odot\) x_input; calculate the accuracy (Acc), recall (Recall), and F1 score triplet. By traversing 50 equidistant points (\(\Delta\tau\) = 0.01), a high-resolution \(\tau\)-F1 response surface is constructed. (3) Visual representation of multidimensional sensitivity analysis, innovatively constructing a threshold sensitivity map (Figure 1) to reveal the multidimensional impact of threshold selection. (4) Generalization verification of the optimal threshold: Apply the optimal threshold \(\tau^{*}\) determined by the verification set to the test set. The figure below shows the threshold sensitivity map.  
As can be seen, the optimal performance range is within the threshold of 0.0-0.2, where the F1 score remains at a high level of 0.93-0.94, indicating that the model's decision-making corrections are stable and reliable within this range. The 0.05 threshold is located within this high-performance zone. When the threshold exceeds 0.2, all metrics begin to decline significantly, with a sharp drop observed in the 0.2-0.3 range (F1 score decreases from 0.93 to 0.92). A second major decline occurs after 0.4, resulting in the severe degradation of model performance. Therefore, the F1 score (the core metric) is most stable in the low-threshold zone, serving as the basis for selecting the 0.05 threshold. Accuracy increases slowly with increasing threshold (green dashed line), indicating that higher thresholds  
Figure 1: Confidence threshold search.  
reduce false corrections, while recall is most sensitive to the threshold (red dotted line), showing a sharp decline when the threshold exceeds 0.2.
## 4 Pseudo-Dynamic Test Overview and Result Analysis
### Experiment Results and Analysis
#### 4.5.2 Maximum Seismic Response Load and Displacement  
Table 11 compares the experiment results for the maximum response loads and displacements of the PD-RC specimen under the input ground motion of 200 cm/s\({}^{2}\) and the PD-LLFD-V specimen under 200-500 cm/s\({}^{2}\) with the failure modes and seismic damage scales. According to the pseudo-dynamic test results for the PD-RC specimen, a maximum seismic response value of 251.4 kN (displacement: 67.3 mm) was observed under the input ground motion of 200 cm/s\({}^{2}\), and the target frame showed shear failure at approximately 5.35 s when the maximum seismic response occurred. The seismic damage scale was determined to be the collapse level according to [PERSON] [48] and [PERSON] et al. [49].  
Meanwhile, the PD-LLFD-V specimen exhibited a maximum seismic response shear force of 439.7 kN (displacement: 9.6 mm) under 200 cm/s\({}^{2}\). Consequently, the specimen retrofitted with the LLFD V-bracing system showed minor seismic damage. Under 300 cm/s\({}^{2}\), which caused a maximum seismic response of 610.9 kN (displacement: 15.7 mm), a low degree of damage occurred, according to [PERSON] [48] and [PERSON] et al. [49]. Compared to the non-reinforced frame that showed the shear failure mode, the failure mode was changed from shear failure to bending failure, confirming that the developed LLFD vibration control system has an excellent seismic energy dissipation capacity. In the cases of 400 and 500 cm/s\({}^{2}\), which assumed a large earthquake, maximum seismic response shear forces of 756.6 kN (maximum displacement: 27.1 mm) and 883.6 kN (maximum displacement: 36.3 mm) were observed. The degree of seismic damage was judged to be  
Figure 21: Cracks and final scene of LLFD-strengthened frame specimen (500 cm/s\({}^{2}\)).  
at the medium level, with the validity of the seismic retrofitting effect verified for large earthquakes at the 2400-year return period level.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 8 Lipase-Catalyzed Synthesis of Sustainable Polyesters and Polyamides from Biobased Monomers
### Biobased Furan Polyesters and Furan Polyamides
The enzymatic polymerization resulted in poly(octamethylene furanamide) (PA 8,F) with a very high \(\overline{M_{\text{w}}}\) of up to 54,000 g/mol. This is the first time that FDCA-based polyamides are successfully produced via enzymatic polymerization; and the molecular weights of the obtained PA 8,F are much higher than those produced via melt-polycondensation, the primarily synthesis approach for semi-aromatic polyamides, at elevated temperatures usually above 200 \({}^{\circ}\)C. Moreover, the obtained PA 8,F possesses a similar \(T_{\text{g}}\) and similar crystal structures, a comparable \(T_{\text{d}}\), but a lower \(T_{\text{m}}\), compared to its petrol-based counterpart, poly(octamethylene terephthalamide) (PA 8,T).