esa-sceva/satcom-chunk-collection huggingface dataset:
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 4 Experiments and Results
### Datasets and Metrics  
We used the _SCARED_ dataset [14] for training and testing. The _SCARED_ dataset was released at the MICCAI Endovis Challenge and was captured using a da Vinci Xi surgical robot, Intuitive Surgical, Sunnyvale, CA, USA. It consisted of nine subsets with the content of pig enterocoolia. Each subset contained five keyframes from different perspectives, and included four to five video segments starting with these keyframes. The keyframes had accurate point cloud labels. However, labels of subsequent frames in the video were created by reprojection and interpolation of the keyframe depth maps using the kinematic information from the da Vinci robot, and consequently, had a misalignment to the RGB data. Therefore, during the ablation experiment, we utilized only the keyframes and the subsequent 19 frames of each video in the test set to calculate disparity-related metrics, including the end-point error (EPE), \(t\)-pixel error rate (D\(t\)), and depth-related mean absolute error (MAE) for reducing the effect of cumulative errors. In the testing phase, we used the official method to evaluate the depth average error on all images in the test set. In addition, we tested our model only for keyframes and computed the structural similarity (SSIM) [31] metric for all test images. In total, we had 22,590 image pairs for training, 162 images pairs for validating, and 5907 image pairs for testing. We rectified all image pairs using OpenCV 4.6.0 and retained the boundary occlusion from the correction. For the testing set, we additionally transferred the ground truths to rectified coordinates using the provided scene points and camera parameters.  
The EPE metric can be formulated as  
\[\text{EPE}=\frac{1}{|W|}\sum_{(x,y)\in W}|\mathbf{D}_{pred}(x,y)-\mathbf{D}_{ gt}(x,y)|, \tag{11}\]where \(W\) represents the set of valid pixels, where pixels with a ground truth disparity not exceeding 0 are typically excluded from the calculation; \(|W|\) denotes the number of valid pixels; \(D_{pred}\) represents the predicted disparity map; \((x,y)\) denotes the pixel coordinate; \(D_{gt}\) represents the ground truth disparity. The \(t\)-pixel error rate can be expressed as follows:  
\[\text{D}t=\frac{1}{|W|}\sum_{(x,y)\in W}[|\mathbf{D}_{pred}(x,y)-\mathbf{D}_{ gt}(x,y)|>\max(t,0.05*\mathbf{D}_{gt}(x,y))], \tag{12}\]  
where \(t\) typically takes the values 1 or 3, denoted, respectively, as D1 and D3. The MAE can be expressed as follows:  
\[\text{MAE}=\frac{1}{|V|}\sum_{(x,y)\in V}|\mathbf{Z}_{pred}(x,y)-\mathbf{Z}_{ gt}(x,y)|, \tag{13}\]  
where \(V\) represents the set of valid pixels, where pixels with a ground truth depth not exceeding 0 are typically excluded from the calculation; \(|V|\) denotes the number of valid pixels; \(V_{pred}\) and \(V_{gt}\) represent the predicted and the ground truth depth maps, respectively.
## 2 Materials and Methods
### Procedures  
All testing procedures were conducted across three primary locations, each serving specific research purposes. The primary research center was established at the Faculty of Sports Sciences of the University of Murcia, specifically, within the laboratory of the Research Group E0A1-06 BIOVETMED & SPORTSCI in San Javier (Murcia, Spain). Thisfacility served as the main center for data processing and research team meetings, equipped with necessary computational resources and analysis software.  
The algorithm development and mathematical analysis were conducted at the Higher Technical School of Engineering of Roads, Canals and Ports and Engineering of the Polytechnic University of Cartagena. This facility provided the necessary technical infrastructure for developing and refining the mathematical algorithms used in the study.  
Field testing and data collection were performed at the Sports Technification Center in Los Narejos (Los Alcazares, Murcia, Spain), specifically, on the beach area. Testing sessions were conducted under controlled environmental conditions (wind speed < 8 km/h, water temperature 18-22 \({}^{\circ}\)C) to ensure consistency in data collection. Each testing session followed a standardized protocol beginning with a 10 min device setup period, including calibration and secure placement of the WIMU PRO(tm) device on the kayak.  
The WIMU PRO(tm) device was consistently positioned 40 cm from the bow of each came (Figure 1), securely attached using a waterproof adjustable bracket with vibration-dampening properties. This standardized position was selected to optimize data collection of forward motion parameters while minimizing interference from water splashing. The placement was identical across all participants and trials to ensure measurement consistency. Preliminary testing confirmed that this specific location provided reliable velocity and acceleration readings with minimal signal noise due to water contact or paddle stroke vibrations.  
Athletes completed a structured 15 min warm-up protocol, including course familiarization and equipment adjustment. The main testing protocol consisted of three maximal effort trials over 200 m, with 10 min recovery periods between trials. GPS markers were placed at start and finish points to ensure accurate distance measurement. During each trial, continuous recording of position data was maintained, with real-time monitoring of signal quality and a backup recording system for redundancy.
## 3 Results
### Soil Tillage Detection  
Based on the technical validations above, we made the following choices for solving the soil tillage classification problem: (a) We use both SAR and MSI images; (b) we use RF as the classifier observed to be the most robust one; and (c) we use GKDE as computationally efficient and accurate representation. We use \(B=50\) for MSI and \(B=30\) for SAR within the range of \(-30\ldots 0\) dB in alignment with [48; 49; 50]. Motivation for these choices is illustrated in Figure 6.  
We now use all six classes described in Section 2.1.1: _plughed, conservation tillage, autumn crop, grass, stubble_ and _stubble with companion crop_. We train the model in total on 43,299 parcels with the number of samples per class ranging from 169 to 15,885, and evaluate the accuracy on 10,666 parcels not used for training. Together these form the full set of parcels we find at the intersecting area of the SAR and MSI images in our data. The overall classification accuracy, evaluated on the test parcels, was 70% and Figure 11 shows the confusion matrix for the test parcels. The largest classes _plughed_, _grass_ and _stubble_ are classified with high accuracy, whereas the smaller classes _conservation tillage_, _autumn crop_ and _stubble with companion crop_ are more difficult to classify correctly.  
Figure 10: Data integration vs single-source classification on a Random Forest classifier. The integrated solution clearly outperforms both MSI and SAR alone for all training set sizes.
## 4 Case Study and Results
### Bayesian Updating  
It was assumed that total of 20,000 cubic yards of soil needed to be moved from the cut areas to the fill area. As data could be obtained from three trucks (out of five), it was also assumed that the project was run by only three trucks instead of five. The initial simulation predicted the project will be completed in 16 days. To match the project duration with the collected data, the activity durations were divided into 16 sub-sections, representing data from each day of the project.  
It was decided to update the model every 2 days using the most recent durations. To update the input parameters (i.e., activity durations), the Bayesian technique was used to combine the initial estimates and actual sample data as described in the Methodology section. Figure 14 shows the comparison of prior and posterior distribution updated on day 2 for the loading time. We see that the prior was a normal distribution with a mean of 2.49 and a standard deviation of 0.23, which was updated to a posterior with a mean of 2.31 and a standard deviation of 0.19. This calculation was done for each of the four activities. So, the next prediction of the DES model was obtained using these updated input parameters. This process was continued every 2 days to get an updated prediction from the simulation model.  
Figure 15 shows the percent completion of the project using a static prediction at the beginning (i.e., at day zero) of the project and dynamic prediction using the updated (at every 2 days) cycle times from the Bayesian update. The static simulation shows that the total project completion time was 16 days. However, when the cycle times were updated using the Bayesian updating technique and used as inputs in the simulation model, the project completion time was extended to 18 days. Figure 16 shows how cycle times changed throughout the project compared to the initial estimates. Following the updated cycle time, the productivity also changed, and we see how the productivity decreased with increased cycle time in Figure 16.  
Figure 13: DES model of the earthmoving operations.  
Figure 14: Prior and posterior normal distribution of the loading time after day 2.  
Figure 15: Percent completion of the project with initial vs. updated prediction.  
Figures 15 and 16 show that simulations conducted during the construction might provide more accurate and reliable predictions as they used the actual data from the site compared to the initial prediction. These results show that the Bayesian updating technique was successfully used to update the distribution of the input parameters. Moreover, it leads to the conclusion that updating the simulation model frequently with actual data collected from the field can improve the prediction of the project performance by eliminating uncertainty in the original assumption.
## 3 Results and Discussion  
### Characterization of the Synthesized Silver Nanowires  
The silver nanowires used in the study are shown in Figure 2b. The average diameter of the nanomaterials was 80-120 nm, while the length varied between 10 and 30 \(\upmu\)m. It is also possible to observe inclusions in the silver crystallites with a morphology that differs from that of the expected 1D objects. However, the practical effect of such objects on electrical conductivity, based on the authors' observations, is negligible.  
X-ray diffraction (XRD) analysis was used to evaluate the structure of the selected sample of AgNWs. Figure 5a shows a typical XRD spectrum for the manufactured AgNWs. Diffraction peaks occurring at 2\(\Theta\) = 38.10, 44.25 and 64.35\({}^{\circ}\) are in line with the peaks which describe bulk silver (ICSD card No. 01-071-6549). They can be attributed to the crystallographic (111), (200), and (220) planes of face-centered cubic silver crystals. The observed (111) to (200) intensity of the reflexes ratio amounts to 3.7 (Figure 5a), and it surpasses the theoretical value of 2.2 (ICSD sheet No. 01-071-6549). The value indicates a significant quantity of the (111) crystal planes in the sample analyzed and a favored direction of growth of one-dimensional silver nanostructures [38].  
Figure 4: Schematic diagram of the electrical system for hybrid paper sensor research.  
Figure 5b shows the UV-Vis spectrogram obtained for the AgNPs dispersion used to manufacture the sensors. The observed maximum at around 385 nm and the shoulder peak at 351 nm correspond to the transverse surface plasmon resonance of AgNPs. The absence of a maximum for wavelengths above 400 nm indicates the absence of nanoparticles in the suspension studied [39].
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 2 Methods
### Loss Function  
For vessel segmentation, both the precision and the recall of the segmentation need to be taken into account [42].  
Dice Loss can more accurately measure the similarity between predicted results and the ground truth, allowing the model to pay more attention to the coverage of the vascular region during the training process, thus increasing the recall of the segmentation. However, Dice Loss only considers the similarity between the predicted results and the ground truth, and there is no penalty mechanism for misclassification to fully optimize the accuracy of the model.  
Binary Cross-Entropy (BCE) Loss is primarily used for binary classification tasks. It enables training of classifiers by computing the difference between the predicted results and the ground truth, exhibiting excellent performance in penalizing classification errors. However, for the retinal vessel segmentation task, the vessel region tends to occupy a smaller proportion of the whole image, and the model may tend to predict more vessel as background. To minimize the effect of category imbalance, category weights are introduced to adjust the loss function. The weights are adjusted to control the contribution of positive samples to the loss function, so that the model can better focus on vessel information.  
PAM-UNet uses Dice Loss and BCE Loss in combination to fully utilize their respective advantages, thus improving the model performance. Dice Loss, BCE Loss, and the final joint loss function are shown in Equations (7)-(9).  
\[L_{Dice}=1-\frac{2\sum_{i=1}^{N}y_{i}p_{i}}{\sum_{i=1}^{N}y_{i}+\sum_{i=1}^{N }p_{i}} \tag{7}\]  
\[L_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}\left[w_{i}y_{i}\log(p_{i})+(1-y_{i})\log(1 -p_{i})\right] \tag{8}\]  
\[Loss=L_{Dice}+L_{BCE} \tag{9}\]  
where \(N\) is the number of pixel points, \(y_{i}\) is the true category, \(p_{i}\) is the predicted category, and \(w_{i}\) is the positive sample weight.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using SPSS software package (IBM SPSS statistics version 24). This statistical software package was suitable for multivariate analysis of the environmental data [16; 35; 36; 37; 38; 39]. Shapiro-Wilk and Kolmogorov-Smirnov tests used to study whether the data followed normal distribution with a value of \(p>0.05\) indicated normal distribution. As no variable of the dataset was normally distributed, the Mann-Whitney test for 2 independents was employed to carry out if there was a statistically significant difference. A value of \(p<0.05\) (95% confidence level) was considered to indicate a significant difference in the statistical analysis of the data. Principal Component Analysis (PCA) was used for the investigation of any possible associations and source apportionment among PAHs, PCDD/Fs, and PCBs. PCA consists of eigenvalue decomposition of the covariance matrix of Gaussian distributed random variables. However, in environmental studies, PCA is used as a tool for data compression, dimension reduction, or even filtering method for non-Gaussian (non-normal) distributed and/or nonlinear data. Application of PCA for source apportionment purposes has been performed in many POPs' studies [16; 23; 39].
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 4 System Evaluation
### Transmission Delay Analysis  
During the experiment, operation at different stages will cause some delay, such as the encoding delays, transmission delay, decoding delay, etc., so we analyzed the delay caused in these stages. We recorded the time before and after different operations of the video frame, including operations such as reading, transmitting, encoding, and decoding. These timing points are then used to calculate the duration of different operations accurately. We summed up the time needed for various operations of all frames. Finally, we calculated the average time required for each frame after multiple measurements. The delay distribution is shown in Table 3:  
In Table 3, all delays are average delays. The delays caused by the reading operation are consistent; the replication-based transmission method does not have any encoding delay; the scheme proposed in this paper requires two milliseconds less encoding time than the RLNC-based scheme. During the transmission process, the RLNC-based scheme has the most prolonged delay. During the decoding process, the replication-based method takes the least time, which only requires 9 ms; our proposed scheme requires 11.5 ms; the RLNC-based scheme takes 11.1 ms. In general, the replication-based transmission method has the shortest delay, while the RLNC-based transmission method involves the most prolonged delay. Compared with the traditional RLNC-based scheme, our proposed scheme can reduce codec delay by about 8%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation** & **Replication-Based** & **RLNC-Based** & **Proposed Scheme** \\ \hline Reading & 10 ms & 10 ms & 10 ms \\ Encoding & 0 ms & 10.2 ms & 8.2 ms \\ Transmitting & 2.2 ms & 2.5 ms & 2.2 ms \\ Decoding & 9 ms & 11.1 ms & 11.5 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Delay distribution.  
Figure 9: Analysis of video quality.
## 3 Results and Discussion
### Comparison Between R245 fa/Propane and Butane/Propane Mixtures
#### 3.3.2 Second Law Efficiency at Optimal Conditions  
Figure 20a,b show the effects of hot and cold source temperatures on the second law cycle efficiency at optimal conditions (Table 8) for the two working fluid mixtures. Similarly to the cycle efficiency (Figure 19a,b), the second law cycle efficiency is directly proportional to the hot temperature for both mixtures. In general, butane/propane (0.8/0.2) has a higher second law efficiency than R245 fa/propane. Both of them have the highest second law cycle efficiency at the hot temperature 95 \({}^{\circ}\)C and the cold temperature 20 \({}^{\circ}\)C. Unlike the cycle efficiency, the difference in second law cycle efficiency is higher at lower hot temperatures and is almost identical at higher hot temperatures.
# Detection of Malicious Websites Using Symbolic Classifier
## 3 Results
### Best Symbolic Expressions  
Based on obtained results in both cases the symbolic expressions with the best classification accuracy were obtained with the dataset that was balanced with the KMeansSMOTE method. The procedure necessary steps to use presented symbolic expression can be divided into the following steps:  
* Data preparation--the procedure is described in the Materials and Methods section;
* Providing input variables to the symbolic expression and calculating numerical output;
* The obtained numerical output is provided as an argument of Sigmoid decision function (Equation (17)), and rounding the output of Sigmoid function to obtain 0/1 value. In the case of GPSC with random hyperparameter search and train/test split in ratio 70:30 the best symbolic expression obtained on the dataset balanced with the KMeansSMOTE method can be written as: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Type**} & \(\overline{\mathbf{ACC}}\) & \(\overline{\mathbf{AUC}}\) & \(\overline{\mathbf{Precision}}\) & \(\overline{\mathbf{Recall}}\) & \(\overline{\mathbf{F1-score}}\) & **Average CPU Time** \\  & \(\mathbf{pmSD(ACC)}\) & \(\mathbf{\pm SD(AUC)}\) & \(\mathbf{\pm SD(Precision)}\) & \(\mathbf{\pm SD(Recall)}\) & \(\mathbf{\pm SD(F1-score)}\) & **per Simulation [min]** \\ \hline Original & 0.976 & 0.97627 & 0.9768 & 0.9756 & 0.9762 & \\ Dataset & \(\pm 2.75\times 10^{-4}\) & \(\pm 2.75\times 10^{-4}\) & \(\pm 2.9\times 10^{-4}\) & \(\pm 2.8\times 10^{-4}\) & \(\pm 2.9\times 10^{-4}\) & 320 \\ \hline Random & 0.992 & 0.9924 & 0.9956 & 0.9891 & 0.9924 & \\ undersampling & \(\pm 1.9\times 10^{-4}\) & \(\pm 2.2\times 10^{-4}\) & \(\pm 3.6\times 10^{-4}\) & \(\pm 3.6\times 10^{-4}\) & \(\pm 2.5\times 10^{-4}\) & 140 \\ \hline Random & 0.997 & 0.9978 & 0.9983 & 0.9972 & 0.9978 & \\ oversampling & \(\pm 1.79\times 10^{-5}\) & \(\pm 4.11\times 10^{-5}\) & \(\pm 1.6\times 10^{-5}\) & \(\pm 7.9\times 10^{-5}\) & \(\pm 4.1\times 10^{-5}\) & 400 \\ \hline ADASYN & 0.9883 & 0.9883 & 0.9915 & 0.985 & 0.9883 & \\  & \(\pm 1.71\times 10^{-5}\) & \(\pm 1.49\times 10^{-5}\) & \(\pm 8.26\times 10^{-5}\) & \(\pm 2.2\times 10^{-5}\) & \(\pm 1.19\times 10^{-5}\) & 400 \\ \hline SMOTE & 0.9971 & 0.9971 & 0.9977 & 0.9939 & 0.9971 & \\  & \(\pm 2.47\times 10^{-5}\) & \(\pm 3.1\times 10^{-5}\) & \(\pm 9\times 10^{-5}\) & \(\pm 8.3\times 10^{-5}\) & \(\pm 2.6\times 10^{-5}\) & 400 \\ \hline Borderline & 0.9741 & 0.9741 & 0.9773 & 0.9735 & 0.9741 & \\ SMOTE & \(\pm 1.69\times 10^{-5}\) & \(\pm 1.6\times 10^{-5}\) & \(\pm 3\times 10^{-4}\) & \(\pm 1.56\times 10^{-5}\) & \(\pm 1.6\times 10^{-5}\) & 400 \\ \hline Kmeans & 0.9994 & 0.9994 & 1.0 & 0.9988 & 0.9994 & \\ SMOTE & \(\pm 1.13\times 10^{-5}\) & \(\pm 1.2\times 10^{-5}\) & \(\pm 0\) & \(\pm 2.4\times 10^{-5}\) & \(\pm 1.2\times 10^{-5}\) & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The mean and standard deviation of evaluation metric values used in GPSC with random hyperparameter search with 5-fold cross-validation.
# Current Uncertainties and Future Challenges Regarding NAD+ Boosting Strategies
## 2 Increasing NAD+ Levels  
Tryptophan, nicotinic acid (pyridine-3 carboxylic acid), nicotinamide (nicotinic acid amide), nicotinamide mononucleotide (NMN) and nicotinamide riboside (NR) are precursors of NAD+ in mammals. NAD+ can be produced and reused by three pathways in humans (Figure 1): (1) de novo synthesis (from L-tryptophan), (2) Preiss-Handler pathway (from nicotinic acid or nicotinic acid ribose), and (3) salvage pathway (from nicotinamide mononucleotide, niacinimide/nicotinamide (NAM) and nicotinamide riboside) [53; 54; 55; 56]. De novo synthesis from tryptophan is an eight-step pathway; there are three steps from NA, while two steps are required for its synthesis from Nam or NR [57; 58]. Different cell types preferentially use different NAD+ biosynthesis pathways under non-stress conditions [59; 60]. For example, liver cells depend largely on de novo biosynthesis, while kidney cells use a combination of the Preiss-Handler and de novo pathways, and the salvage pathway is generally used to recycle NAM generated by NAD+-consuming reactions. Pharmacologic restoration of NAD+ is at the moment intensively investigated and approaches include NAD+-replacement therapy with NAD+ precursors such as NR, NMN, and NA supplementation [4; 22] or by halting NAD+ utilization by poly-ADP-ribose polymerase (PARP) and CD 38 inhibitors [61; 62; 63; 64; 65; 66]. Recently, a reduced form of nicotinamide riboside NRH (1-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl]-4H-pyridine-3-carboxamide) was discovered to function as an orally bioavailable NAD+ precursor with the potential to significantly increase NAD+ levels and to overcome the degradability of NR [67].