esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
## 1 Introduction
Compared with traditional denoising methods, the above noise-suppression approaches avoid threshold selection and defects of the methods themselves, achieving better denoising results. Still, with the increase in noise level, restricted by their structures, neural networks are left with limited denoising performances. Other authors have offered some potential answers to this difficult problem. As an illustration, the seismic data denoising method combining U-net and STFT has been highly successful [22; 23]. However, even a simplified U-net has a much larger number of parameters than a Resnet with fewer residual blocks [24]. This may be a hindrance to the practical application of neural nets in seismic data denoising. Therefore, as a simpler and more stable denoising structure, Resnet is adopted. To further enhance neural network noise-reduction capabilities, inspired by the work of other scholars, this paper proposes a seismic signal denoising method combining wavelet transform and a neural network (DWT-Resnet). Utilizing the fact that a valid signal differs from noise after the wavelet transform, the transformed signal serves as the input of the neural network, removing noise from the signal with a clean signal as the labeled data. Experiments show that the method proposed in this paper achieves superior performance in denoising compared to the method using neural networks alone. The main contributions of this study consist of:  
* Proposing a denoising method for seismic data based on wavelet transform and residual neural network. * Analyzing the performance of residual neural networks for noisy removal under different decomposition levels of different wavelet bases
* An analysis of the number of layers of the residual neural network on the noise removal performance in the wavelet transform-based neural network denoising method is presented. The rest of the paper is organized as follows: Section 2 provides a detailed description of the method, evaluation criteria, and model. Experimental results of our approach on simulated seismic data versus real seismic data are presented and analyzed in Sections 3 and 4. Finally, Section 5 presents the conclusion, discusses this paper, and provides a discussion of future directions.
# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
## 4 GPU-Based Acceleration Using NVIDIA CUDA
### Register-Based Parallel Reduction  
This programming technique allows a thread to read a register directly from another thread within the same warp and allows them to exchange or broadcast data among each other very efficiently. The idea of parallel reduction is illustrated in Figure 3.  
Figure 3: Parallel reduction using registers.  
Where the __shfl_down() CUDA instruction calculates the source and the destination of each reduction step, so that in N steps \(2^{N}\) data elements are reduced via an associative operation (e.g., addition) within a warp, without the need for expensive explicit synchronization barriers (i.e., the maximum value of \(N\) for which this can be performed with warps of size 32 is 5). The final stage of reduction, beyond the five iterations supported by a warp, is performed less efficiently in shared or global memory for all warps belonging to the same block via explicit barrier synchronization. These two kinds of reduction are both exploited in our model to optimize the final accumulation of the results computed by each warp to generate the total CIR.
## 1 Introduction
One of the main attractions of search-engine advertising is that search engines take into account the \"current interest\" of the user and thereby can deliver the right message, ad, or product just at the right time. The enormous growth in spending going to search advertising (about $100b in the US in 2018 [33]) is a testament to the power of contextual \"interest-based\" advertising. To deal with the problem of the static weights being assigned to each item, researchers proposed several approaches [34; 35; 36; 37]. Our proposed model goes a step further, not only taking the user's interest drift into consideration, but also regarding each item in one session as \"the one and only\" item, which means each one has a unique influence on the user and therefore the user may pay different attention to each one. Figure 1: An example of Amazon recommender system. In this work, we propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to solve the next-item recommendation problem. It has been shown that the attention mechanism is able to assign different weights according to the importance of the items, and there have been works that employed the attention mechanism to form the characteristics of the user [38; 39; 40]. In order to capture the variation in user preference and generate the precise contextual user profile, we build two different RNN-based layers: one attentive layer to model the user's short-term intent, and the other one to model his long-term preference. The main contributions of this paper include:  
* We introduce the attention mechanism to investigate the variation in a user's short-term intent for session-based recommendations. * Taking the user's previous sessions into account, we model the user's long-term preference and concatenate that with short-term intent to account for more comprehensive contextual user outcomes. * We conduct empirical evaluations and validate the effectiveness of the model on three real-world datasets and two sub-datasets. * We demonstrate the effectiveness of the approach in resolving the cold-start problem.
## 5 Experimental Setup  
### Dataset  
We consider three datasets to evaluate the performance of SaSLM on the task of SPL:  
* **RealSet**: The dataset was collected from the MovieLens platform by [5]. Users were selected if they were active, since January 2015, and rated at least 25 movies. The set ratings were collected by sending emails to users. The movie sets were created by randomly selecting five movies without replacement from those they had already rated before.
* **NetEase**: This is a dataset collected from the largest music platform in China by [19]. It enables users to create song bundles or thumbs-up any bundles created by others. We use NetEase as the dataset for SPL by treating the music bundle as the item set.
* **YouShu**: This dataset was constructed by [20] from YouShu, a Chinese book review site. Similarly to NetEase, every bundle is a list of users' desired books. We treat the book list as the item set.  
The statistics of these datasets are given in Table 2. Note that among the three datasets, only RealSet is specially designed to evaluate SPL. RealSet contains explicit ratings, ranging from 0.5 to 5, for both items and sets. Although explicit ratings can well reveal the user's preferences, they are not always available in real user interactions. Taking more realistic scenarios into consideration, we also transform the RealSet under implicit settings by binarizing the ratings. To be more specific, ratings no less than three are regarded as positive, and otherwise negative. To distinguish, we use **RealSet**-explicit and **RealSet**-implicit to denote the RealSet dataset with explicit and implicit ratings, respectively.  
Since the problem of SPL is underexplored, few datasets are available. Therefore, we constructed datasets from bundle recommendation by discarding user-item interactions for training because SPL aims to predict item ratings by set ratings. In RealSet, all item-level ratings are available for evaluation. However, users just interacted with a small faction of items in the bundle in both NetEase and YouShu datasets. These non-interacted items are not necessarily negative ones but possibly unexposed items. Lacking of negative feedback in latter two datasets, we simply followed the missing-at-random assumption by treating all non-interacted items as negative [3]. Considering the partial observation of positive items, to ensure the balance between positive items and negative items, we sampled an equal number of negative items as positive ones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **\#Users** & **\#Items** & **\#Sets** & **\#Ratings** \\ \hline RealSet & 853 & 13,012 & 29,516 & 29,516 \\ YouShu & 8039 & 32,770 & 4368 & 38,977 \\ NetEase & 18,528 & 123,628 & 22,864 & 302,303 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data statistics.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Ablation Study
#### 3.3.2 Measurement Noise  
We compared different approaches for adjusting measurement noise in the KF within our method, including the original Constant method, the NSA method, and our proposed AMN method. As shown in Table 4, the results on the DanceTrack validation set clearly demonstrate that our method exhibits superior performance across all metrics compared to other methods. In complex scenarios characterized by frequent occlusions and nonlinear motion, our AMN method exhibits a distinct advantage.
###### Abstract
In this paper, MODIS and Landsat TM/OLI remote sensing images are used as data sources to extract the gross primary productivity (GPP), land surface temperature (LST), wetness, and bare soil index (SI), which uses greenness, heat, humidity, and dryness to construct the RSEI of the Meizhou ecological environment evaluation and analyze the spatiotemporal distribution and change characteristics of ecological environment quality in the study area in 2008, 2013, and 2018. Then, using the GPP and the LST extracted from the MODIS data products, followed by adding the POI data, we were able to quantitatively analyze the correlation between them and the remote sensing ecological index, and discuss the applicability of MODIS data in the evaluation of urban ecological environment quality and the impact of human activities on the ecological environment.
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 3 Nanoformulation of Peptides
### Cavitands  
Cavitands are synthetic macromolecules, which can act as stabilizers for peptides and protein formulations by binding to amino acids and preventing the recognition of peptidase. Cyclodext (CDs) and cucurbiturils (CBs) are the best example of cavitands. CDs are cyclic non-reducing oligosaccharides that are made up of glucopyranose units [104]. CDs have amphiphilic structures that allow them to form in the inclusion complex with the protein and peptide. Moreover, CDs are safe for medical administration with less side effects even after chemical modifications of their exterior parts. They possess increased bioavailability and have enhanced local tolerability to peptide formulations. Further, [PERSON] et al. (2017) formulated CD-based aqueous cyclospor A (CyA) as an eyedrop formulation. CD was selected as a solubilizer due to its great solubility effect on CyA, with its ability to form a CyA/CD complex aggregation and nanoparticles for better ocular bioavailability of drugs [105]. However, there are adverse possible consequences of cyclodextrin intake. One study reported that the administration of 2-hydroxypropyl-\(\beta\)-CD, i.e., 200 mg/kg every day for the long-term, could cause bone loss [106]. Further, [PERSON] et al. (2016) compared the toxicity and solubilizing capacity of hydroxypropyl-\(\beta\)-CD with different degree(s) of substitution (DS). The study revealed that hydroxypropyl-\(\beta\)-CD with a high DS not only exhibited weak solubilizing capacity for steroids, but also low hemolytic activity, while hydroxypropyl-\(\beta\)-CD with a medium DS demonstrated slightly higher nephrotoxicity. On the other hand, CBs are macrocyclic co-polymers of formaldehyde and glycoluril [107]. Similarly, CBs have hydrophobic voids, whereas hydrophilic parts are located in the portal area. The CB[n] family, where n is the different number of glycoluril units, possesses high affinity, high selectivity, and constrictive binding interactions, which make them great choices for the nanoformulation of peptides. Thus, CBs can bind better together to cationic guests, compared with CDs [108].  
The most ideal system among all nanoformulation is difficult to select as every system has specific advantages and limitations. For instances, SLN and NLC have the ability to improve the delivery of drugs in various ways, but the choice of lipids and surfactants can affect both the particle size and stability in the long term. Toxicity is a significant factor that needs to be considered when selecting the surfactants to prevent any adverse impacts to be included in biomedical applications. Therefore, the above-mentioned limitations must be considered when designing a novel nanoformulated peptide with enhanced stability and the drug release time for effective circulation, low toxicity, high biocompatibility, and improved biological response.
## Materials and Methods
### Statistical Analysis  
The relationship between the GPR1 model and FEA outputs, as well as between GPR2 model outputs and measured anatomical dimensions, were evaluated using Pearson correlation. The goodness of the correlations was assessed in terms of the coefficient of determination R\({}^{2}\) and root mean square error (RMSE). For a comparison of cartilage degeneration volumes based on OA severity and between the different approaches, the knees were divided into groups based on the KL-grading at the 8-year follow-up: KL0 and KL1 (healthy), KL2 (doubtful OA), and combined KL3 and KL4 (severe OA). The use of KL is based on its use in clinical assessment to evaluate the severity of osteoarthritis. The non-parametric Mann-Whitney U-test (two independent samples) was used to evaluate KL group-wise differences in the predicted degenerations based on the GPR1 and GPR1 + GPR2 models and atlas-based FEA workflows. To evaluate differences within each KL group between GPR1 and FEA workflows and GPR1 + GPR2 and FEA workflows, a non-parametric Wilcoxon signed rank test (two related samples) was used. For all statistical tests, \(p\) < 0.05 was considered the level of significance.  
To evaluate the suitability of the GPR1 and GPR1+GPR2 models in simulating the progression of KOA, ROC (receiver operating characteristic) curves and the AUC (area under ROC curve) were calculated. Instead of presenting confusion matrices between atlas-based FEA and GPR models for the accuracies of the classifiers, ROC curves are reported since they allow the evaluation of the performance of the classifier at all possible classification thresholds [26]. In the ROC analysis, we focused only on demonstrating classification power against those KL groups that were statistically different from the severe OA KL34 group. Therefore, KL0 and KL1 groups, which can be considered healthy, were merged (KL01) in the ROC analysis.
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 1 Introduction
If the flow period is longer than the multi-superframe period, bandwidth waste occurs, as some of the GTSs reserved to the flow will not be used. Moreover, the rigid structure of the multi-superframe impairs scalability [4], because in DSME the number of superframes can be set only to powers of 2 (i.e., 1, 2, 4, 16, and so on). As a result, the multi-superframe length exponentially grows, and, with the CAP reduction enabled, this results in a low CAP frequency (i.e., one CAP every multi-superframe) and, therefore, in high delays in the transmission of the aperiodic messages. A preliminary approach to  
Figure 1: DSME multi-superframe structure. cope with the DSME scalability problem is addressed in the work [14] which, however, provide neither a detailed design of the approach nor an extensive performance evaluation. In addition, the work in [14] does not address the DSME reliability. **Contributions. ** This work proposes the enhanceD-DSME (D-DSME) extensions, i.e., two novel extensions that improve the DSME scalability and reliability. The first one, named _Shareable D-DSME_, allows GTSs to be shared among multiple periodic flows when the flow period is longer than the multi-superframe period. This improves scalability, as more flows can be scheduled without increasing the multi-superframe size and, therefore, without increasing the average delays for the aperiodic messages transmitted in the CAP, as the CAP frequency obtained using the D-DSME is higher than or equal to the one in the standard DSME protocol. The second extension, named _Mirror D-DSME_, allows using the same GTSs for both message transmission and retransmissions. This feature improves reliability, as a message is retransmitted multiple times. Henceforward, we will use the D-DSME acronym to refer to both the extensions, i.e., the Shareable and the Mirror DSME. **Organization. ** The paper is organized as follows. Section 2 deals with related work. Section 3 describes the Shareable D-DSME extension, while Section 4 presents the Mirror D-DSME extension. Section 5 provides a comparative performance assessment of DSME and D-DSME. Section 6 presents a proof-of-concept implementation of D-DSME that demonstrates the feasibility of the proposed extensions. Finally, Section 7 gives conclusions and hints for future work.
## 3 Method
### Neural Network
#### 3.2.1 UNet++  
UNet++ is an improved version of the original UNet architecture [20] initially developed for medical image segmentation. The UNet architecture comprises an encoder-decoder structure with skip connections that link the encoder outputs at each level to the corresponding decoder layers. These skip connections help preserve the spatial information that is otherwise lost during downsampling in the encoder, enabling accurate region extraction. UNet++ enhanced this structure by embedding a series of nested UNets between the encoder and decoder paths. This nested architecture enables more effective transmission of features from the encoder to the decoder, thereby improving segmentation performance. The additional intermediate layers allow for better feature refinement, leading to more precise delineation of the object boundaries compared with the original UNet.
# A Numerical Study of the Behavior of Micropile Foundations under Cyclic Thermal Loading
## 3 Results and Discussion
### Thermo-Mechanical Behavior  
#### 3.3.1 Isolated Micropile and Group Unit Cell Response  
Figures 9 and 10, and Tables 3 and 4, compare the predicted cyclic thermal response of the micropiles as a function of length (30 and 15 m) and spacing (R/D = 30 and 4), with the \"outside\" surface temperature boundary condition maintained. For comparison, the free thermal expansion of the pile, \(Y_{\text{th,free}}\) (= \(\alpha\Delta\)T L) was 3.6 and 1.8 mm, for MP-20 and MP-15 respectively, and the maximum thermal stress assuming perfect axial fixity, \(\sigma_{\text{th,fixed}}\) (=\(\alpha\Delta\)T E\({}_{\
## 3 Results and Discussion
### Actomyosin Ca\({}^{2+}\)-ATPase Activity  
Myosin (combines with actin to form actomyosin) accounts for 50% of fish myofibrillar protein. The active site of Ca\({}^{2+}\)-ATPase is in the globular head of myosin [29]. Thus, the Ca\({}^{2+}\)- ATPase activity is a good indicator of the integrity of actomyosin molecule and protein freeze denaturation. As shown in Figure 3, decreases in actomyosin Ca\({}^{2+}\)-ATPase activity were observed in all samples, though the rates of reduction varied. In the control group, activity decreased rapidly at the initial stage and at a total reduction of 56.4% over six freeze-thaw cycles. The activities of the hydrolysate and SuSo groups were higher than that of the control group (\(P<0.05\)), indicating that PH and AH could preserve Ca\({}^{2+}\)-ATPase activity, similar to SuSo. For the SuSo, PH-2, PH-4, PH-6, AH-2, AH-4, and AH-6 groups, the Ca\({}^{2+}\)-ATPase activity decreased by 30.6, 29.5, 31.9, 42.3, 32.3, 43.4 and 48.7%, respectively, after six freeze-thaw cycles. PH-2 group showed the slowest decreases in the Ca\({}^{2+}\)-ATPase activity (\(P<0.05\)), which further indicated that PH-2 was possibly most effective in stabilizing protein structures during freeze-thaw treatments. The decrease in Ca\({}^{2+}\)-ATPase activity coincided with actomyosin extractability results. Additionally, the number of hydrolysates added to surimi was also important to prevent  
Figure 2: The actomyosin extractability of surimi added with the hydrolysates during freeze-thaw cycles. SuSo (surimi added with sucrose-sorbitol blend); PH-2, PH-4 and PH-6 (surimi with addition of 2, 4, and 6 g of the hydrolysate prepared by Protamex hydrolysis, respectively); AH-2, AH-4 and AH-6 (surimi with addition of 2, 4, and 6 g of the hydrolysate prepared by Alcalase hydrolysis, respectively); and control (surimi without cryoprotectant). The reported data represent mean values from three replications. Bars represent standard deviations.  
the reduction in Ca\({}^{2+}\)-ATPase activity. Interestingly, [PERSON] et al. [14] found that addition of 8% \(w/w\) of Pacific hake protein hydrolysate was sufficient to prevent the structural changes of natural actomyosin during freeze-thaw treatments, while 2% \(w/w\) of hydrolysate did not reach the minimum level required for optimal cryoprotection, an observation that could potentially be attributed to differences in the nature of the hydrolysates. In the present study, higher amounts of hydrolysates could possibly influence the formation of stable structures of surimi, leading to increase in protein-protein interaction, ultimately inducing loss of Ca\({}^{2+}\)-ATPase activity. The results also suggested that the hydrolysates may interact with surimi proteins through a mechanism different from that of SuSo.