esa-sceva/satcom-chunk-collection huggingface dataset:
Recent reports, noted that the digestive fluid of a fish lacks an alginate-specific gradation enzyme [15; 16]. Because of the aforementioned stability, alginates have even been utilized to adopt exogeneous digestive enzymes or probiotics to increase the fishes' digestive ability [17; 18; 19]. Clays are also inert toward enzymes, and may be considered an important ingredient that can be used in this process. Because of their inertness and stability toward enzymes, clay minerals are often utilized to immobilize enzymes much like lipases [20; 21; 22]. On the other hand, both alginate and clays are fairly sensitive to the pH conditions since their surface charge, swelling property and hydrolysis are dependent upon the proton concentration. In this regard, our organ-simulation was carried out with pH adjusted media. Therefore, we focused on storage under gastric condition and release under intestinal conditions for the main experiments. The alginate that forms cross-linked hydrogel beads under the existence of Ca\({}^{2+}\)[23; 24] was selected as the major encapsulating material. Alginate has been widely known to be a biocompatible polymer, and it can be utilized as a drug reservoir with controlled release [25; 26; 27; 28]. The alginate beads were shown to easily swell in neutral pH by absorbing water. On the other hand, the mannuronic side chains became hydrophobic at a low pH, prohibiting swelling in an acidic condition. In this work, two biocompatible ingredients, kaolinite (KA) and layered double hydroxide (LDH) were selected to control the encapsulation efficiency and intestinal delivery rate. Both of these ingredients are clay nanoparticles that are registered in the European pharmacopoeia. Notably, they are similar in terms of having a layered structure and plate-like morphology [29; 30], but they are different in their surface charge. KA and LDH are negatively and positively charged, respectively, under a neutral pH. We hypothesized that the interface interaction between the clay nanoparticles and pathogenic microorganisms would modify the encapsulation efficiency and release behaviors of the alginate beads. We selected _Streptococcus parauberis_ as the model antigen for starry flounder (_Platichthys stellatus_). Inthis case, the microbe cells were inactivated to obtain formalin-killed cells (FKC). Both the FKC and FKC + clay mixtures were thoroughly characterized before and after the alginate bead encapsulation. The encapsulation efficiency and release at different simulated biological fluids will be discussed in further detail later in this report.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
###### Abstract  
In this work, we propose an improved network, RSCS6D, for 6D pose estimation from RGB-D images by extracting keypoint-based point clouds. Our key insight is that keypoint cloud can reduce data redundancy in 3D point clouds and accelerate the convergence of convolutional neural networks. First, we employ a semantic segmentation network on the RGB image to obtain mask images containing positional information and per-pixel labels. Next, we introduce a novel keypoint cloud extraction algorithm that combines RGB and depth images to detect 2D keypoints and convert them into 3D keypoints. Specifically, we convert the RGB image to grayscale and use the Sobel edge detection operator to identify 2D edge keypoints. Additionally, we compute the Curvature matrix from the depth image and apply the Sobel operator to extract keypoints critical for 6D pose estimation. Finally, the extracted 3D keypoint cloud is fed into the 6D pose estimation network to predict both translation and rotation.  
6D pose estimation; keypoint extraction; convolutional neural network; Sobel operator
## 4 Experiment Results and Discussion
### Discussion  
Presently, there are many fields which contain big data, for example, finance, health care, stock, banking, etc. To analyze these datasets, it requires spending more effort with the different methods are used. It becomes a challenge with huge data with high dimensional features. A good feature selection technique helps us to analyze and evaluate to choose the important features in big data that ensure without losing important information. Besides, a good method helps reduce the effort for data analysis about time and cost. The proposed method can be applied to other fields which have big data to generate the best feature subset supporting further prediction model or using the result of the proposed method for other activities such as statistic, prediction, etc. In other words, this method is a low-cost design which helps data analyst can make a quick and accurate decision about what features are important and effect and then keeping them for supporting another further purpose.  
In the proposed scheme, SFSDT's goal is to generate and find the best feature subset from the complete feature set. The result of the proposed method on ISCX data are different in each day because their valuable data are different, even though each day the dataset has the same 15 original features. Hence, the obtained result of the proposed depends on the values of features. Therefore, when there is suspicious traffic with the different feature sets, the proposed model can accurately recognize it, to generate feature subsets which contain this different feature and then evaluate these subsets are the best feature or not. Besides, the proposed SFSDT goal is a feature selection based on the learning model. This proposed model can solve the high-dimensional data leading to the curse of dimensionality phenomenon in big data. The results of experiment 3 show that the proposed method can find the best feature subset in short time and small memory used.  
Furthermore, the variant RNNs applied to the best feature subset can reduce the amount of required computation as well as improve performance accuracies on each attack classification and the average accuracy of classification IDS model. In particular, this work compares the proposed model to previous models on two criteria including detection of attack types and intrusion detection accuracies on both IDS datasets.  
First, Tables 6 and 7 show the comparison accuracies of detecting attack types between the proposed models to in advance IDS models on NSL-KDD dataset and ISCX dataset, respectively. Based on the results obtained, LSTM and GRU models outperform than others. In particular, the accurate detection of U2R and R2L attacks are improved significantly on LSTM and GRU models.
# Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches
## 2 Material and Methods
### Vessel Segmentation and Reconstruction  
OCT images were used to reconstruct patient-specific artery models with and without the side branches. The 3D reconstruction process of OCT-derived artery models is shown in Figure 2. Segmentation of OCT images was performed using MATLAB image processing toolbox (2020b. The MathWorks, Inc., Natick, MA, USA). First, the lumen of the main vessel was segmented, and the extracted lumen contours from each frame were lofted along their centers to construct the 3D vessel geometry without side branches. Then, the lumen of each side branch vessel (near ostitium) was manually segmented, and each side branch was separately constructed by lofting the corresponding lumen contours. This process could successfully reconstruct the segment of the side branch near its ostitium, as shown in Figure 2b. The reconstructed geometries of the main vessel and the side branches in STL format were imported to ANSYS SpaceClaim (ANSYS Inc., Canonsburg, PA, USA), where they were combined to create the model with side branches. Here, the geometry was also modified by extending the side branches to improve the numerical convergence of the CFD solution.  
Figure 1: Geometric representation of the idealized stenosed artery model: (**a**) Model without side branch. (**b**) Profile of the stenosis. (**c**) Model with side branch.  
Figure 3 shows the 3D reconstructed vessel tree models of the two patients with corresponding angiography views. The severely stenosed regions are enclosed with dashed circles. In patient 1's vessel tree geometry, the diffused stenosed region was located after three side branches and before a side branch with a much smaller diameter (<1 mm). In contrast to patient 1's geometry, patient 2's geometry had a more focal stenosed region located before the side branches and another stenosed region in the distal region of the vessel.  
Figure 3: Reconstructed vessel tree models with corresponding angiography views of (**a**) patient 1 and (**b**) patient 2. Regions with significant stenosis are enclosed with dashed circles.  
Figure 2: The 3D reconstruction of OCT-derived vessel tree geometry: (**a**) Segmentation of the main lumen and side branch lumen cross sections; (**b**) reconstructed geometry of the main vessel and side branches separately; (**c**) final vessel tree model generated by combining main vessel and side branches.
# Nearest Embedded and Embedding Self-Nested Trees
## 4 Approximation Algorithms
### Definitions
#### 4.1.4. NEST and NeST  
In view of the foregoing, we consider the set of inserting and deleting operations that fulfill the below requirements.  
Adding operations (see Figure 7)* Internal nodes (AI): adding \(w\) as a child of \(v\) making \(w\) the parent of the child \(c\) of \(v\) can be done only if \(\mathcal{H}(\tau[c])+1<\mathcal{H}(\tau[v])\).
* Subtrees (AS): adding \(t\) as a child of \(v\) can be done only if \(\mathcal{H}(t)+1\leq\mathcal{H}(\tau[v])\).  
Deleting operations (see Figure 8)  
* Internal nodes (DI): deleting \(v\in\mathcal{C}_{\tau}(u)\) (making the unique child \(w\) of \(v\) a child of \(u\)) can be done only if there exists \(v^{\prime}\in\mathcal{C}_{\tau}(u)\), \(v\
eq v^{\prime}\), such that \(\mathcal{H}(\tau[v^{\prime}])\geq\mathcal{H}(\tau[v])\).
* Subtrees (DS): deleting the subtree \(\tau[w]\), \(w\in\mathcal{C}_{\tau}(v)\), of \(\tau\) can be done if there exists \(w^{\prime}\in\mathcal{C}_{\tau}(v)\), \(w^{\prime}\
eq w\), such that \(\mathcal{H}(\tau[w^{\prime}])+1=\mathcal{H}(\tau[v])\).  
**Proposition 5**: _The editing operations AI and AS (DI and DS, respectively) are the only inserting (deleting, respectively) operations that ensure that (i) the induced mapping is a constrained mapping and that (ii) the height of all the pre-existing nodes is unchanged._  
Proof.: This result is a direct corollary of Lemmas 2-6.  
The NEST (the NeST, respectively) of a tree \(\tau\) is the self-nested tree obtained by the set of inserting operations AI and AS (of deleting operations DI and DS, respectively) of minimal cost, the cost of inserting a subtree being its number of nodes. Existence and uniqueness of the NEST are not obvious at this stage. The NeST exists because the (self-nested) tree composed of a unique root can be easily obtained by deleting operations from any tree, but its uniqueness is not evident.
###### Abstract  
Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.  
optimized EEMD; 2D-MUSIC; composite structure; impact localization +
Footnote †: journal: Applied Mechanics and Physics Letters
## 4 Descriptors of the Second Criteria and Experimental Results
### Data Volume  
This subsection analyzes the amount of data stored in the blockchain database after each transaction. In Table 3, we specify the addStudData function, which consumes no more than 550,000 gas. This is possible due to the amount of information. Let us take as a basis a bachelor student who studies up to six courses per semester, i.e., in one transaction, we need to put a record of six courses along with grades. Course data are specially serialized in the JSON format, which contains only critical data (course ID, grade, number of credits, semester and type of control). All data are digital. Our calculation shows that such a format of information for up to six records weighs no more than 1 KB. Table 5 shows the approximate amounts of data.  
Figure 8: Average transaction execution time (blockchain transaction) when executing 1000 transactions.  
In the article titled \"Blockchain and smart contracts for registering higher education in Brazil,\" the authors proposed a formula for memory increment as a result of the modification of the MTP tree, which looks as follows:  
\[S_{mpt}(n)=s_{b}\bigg{(}\frac{\log(an)}{2}-1\bigg{)}+s_{1}+s_{a}+s^{\prime} \tag{4}\]  
where \(s_{b}\) is the size of the filled branch, \(s_{1}\) is the size of the end node, \(s_{a}\) is the storage space occupied by the account state and \(s^{\prime}\) is the sum of storage trees for each account. If we include here the size of the \(S_{t}\) transaction itself, then the final formula becomes  
\[S_{sum_{g}}(n)=nS_{t}+\sum_{i=0}^{n}S_{mpt}(n) \tag{5}\]  
From the experimental results of the authors, we find that, under the following conditions, when one Ethereum node is used without a network, only one transaction is packed into a block: one million transactions are randomly made, and the values of the parameters are \(S_{b}=564\) B, \(S_{l}=96\) B, \(S_{a}=102\) B and \(S^{\prime}=0\). Here, the dimensions are not given in terms of the amount of memory in the disk, but in the form of the sum of the compressed key and the LevelDB value.  
In our work, relatively, the same conditions are met, so we can use these parameter values in our calculations. Based on the fact that the total amount of information in one transaction does not exceed 1 KB, we can assume that the value of the parameter is \(S_{t}=8000\) B.  
As a result, we can obtain Formula (6) for calculating the increase in the total storage volume of the blockchain database:  
\[S_{predict}(n)=564*\bigg{(}\frac{\log(n)}{2}-1\bigg{)}+96+102 \tag{6}\]  
The proposed data format is the right option for our solution in terms of optimizing transaction costs and increasing the efficiency of the platform in terms of transaction execution speed and minimizing data storage.
## 5 Experimental Validation and Benchmarks
### Self-Organizing Networks (SONs)  
Self-organizing networks (SONs) leverage AI to enable autonomous network configuration, fault management, and performance optimization [94]. By integrating machine learning algorithms, SONs can dynamically monitor network conditions, detect anomalies, and make real-time decisions about network adjustments without the need for human intervention [91]. This autonomy allows for faster response times to network issues, minimizing downtime and enhancing the reliability of communication networks.  
SONs are capable of adapting to network changes and reconfiguring themselves to accommodate varying traffic demands, topology changes, or even hardware failures. For example, when a network component experiences a failure or degradation in performance, SONs can automatically reroute traffic, reallocate resources, or activate backup systems to maintain uninterrupted service. This self-healing ability ensures that networks remain resilient and operational under diverse and often unpredictable conditions [94].  
Moreover, SONs optimize network performance by continuously learning from past experiences and adjusting network configurations to improve efficiency. AI algorithms can analyze performance metrics such as signal strength, load distribution, and throughput,allowing SONs to fine-tune parameters and ensure that resources are being utilized optimally. This results in improved Quality of Service (QoS), reduced operational costs, and enhanced user experience [125].  
Through the integration of AI, SONs provide a level of autonomy and intelligence that traditional networks cannot match, making them ideal for modern, complex communication environments where rapid adaptability and continuous optimization are key to maintaining high-performance standards.
# Removal of the Previous Root Canal Filling Material for Retreatment: Implications and Techniques
## 1 Introduction
### Supplementary Approaches
The phenomena of acoustic streaming and cavitation are dependent on the free displacement of the ultrasonically activated instrument [122; 123; 127]. Many studies have addressed the effects of ultrasonics in improving disinfection by NaOCI after preparation, and the results are conflicting [128; 129; 130; 131]. Ultrasonics have also been used to enhance the removal of the filling material during retreatment and studies have shown significant improvement [50; 132; 133]. For instance, a study revealed that passive ultrasonic irrigation reduced, on average, 43% of the volume of the remaining filling material [50]. However, other studies showed no significant improvement [89; 94; 134; 135]. Laser has also been proposed for a supplementary step of irrigant activation during canal treatment and retreatment, to improve disinfection and also to enhance filling material removal. A study used the Er:YAG laser, Er:YAG laser-based photon-induced photoacoustic streaming (PIPS), and Nd:YAG laser and reported a significant improvement in material removal; a comparison between them showed better results for the Er:YAG laser [136]. PIPS was significantly better than sonic and ultrasonic approaches in removing filling remnants from oval canals [137]. The additional cleaning effects of PIPS seem not to be significantly affected by the type of filling material [138]. However, a study reported that the PIPS method did not have a significant effect on the removal of filling remnants in comparison with conventional needle irrigation [139]. When the Er:YAG laser-initiated shockwave-enhanced emission of photoacoustic streaming (SWEEPS) was used in curved canals, it improved the removal of filling remnants and performed better than PUI when no solvent was used [140]. The superior cleaning effects of SWEEPS over PUI were confirmed by another study [141]. A study compared PUI and super short pulse (SSP) and SWEEPS modes of Er:YAG laser-activated irrigation (LAI) with two different laser tips for removing filling materials in curved canals and concluded that all of them were significantly effective; the LAI/SSP showed better results than the others [142].
## 2 Generation of Precision Timing Information
### System Setup  
There are two time and frequency laboratories under NMC in Singapore. They are located at 8 Clean Tech Loop (CT3) and Fusionopolis 2 (FP2). The lab at CT3 is the main lab traceable to _UTC_ and the lab at FP2 is the backup lab traceable to _UTC_(\(SG\)). The results discussed in this paper are with respect to the backup lab at FP2, where most of the R&D works are carried out. The hardware that we have at FP2 are shown in Figure 2. Here at the FP2 facility, we have three atomic clocks, which include two cesium clocks, and one hydrogen maser. We have one multi-channel measurement system (MMS), and one steering device (AOG-110, Microchip, Chandler, AZ, USA). We have higher-end GNSS equipment from Septentrio. This GNSS equipment has the option to be steered by an external clock as well.  
National Metrology Institutes (NMIs), like NMC, typically operate multiple atomic clocks, forming an ensemble [18] that contributes to the derivation of the _UTC_(\(k\)) for a specific NMI, denoted as \(k\). Ensemble timing capitalizes on the advantages offered by each clock within the ensemble. _AT1_ is a very famous method to generate the ensemble timing that has been adopted by many NMIs [19]. ALGOS and Kalman filter-based methods are a few other techniques to generate the ensemble timings [20; 21]. The next section will describe the time-scale algorithm that has been implemented for this paper.  
Figure 2: Hardware at Time and Frequency Lab of Singapore [located at Fusionopolis 2 (FP2)].
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## The APD-TDMA
### Transmission Phase
#### Scheduling to Avoid Collisions at the SN
Finally, we can determine \(\Delta w_{2}^{j+1}\) as follows:  
\[\Delta w_{2}^{j+1}=T_{Drx,2}^{j+1}\cdot start-(T_{CYC}^{j}+\delta_{2}(T_{ Brx,2}^{j+1})+\tau_{B}+\delta_{2}^{j+1}\cdot up). \tag{15}\]  
Similarly, we can compute \(\Delta w_{i}^{j+1}\) for all UNs by the above recursive procedure. If the SN calculates all of the UN's waiting time in the \((j+1)\)th cycle, the beacon packet is broadcasted to all UNs in the \((j+1)\)th cycle. The transmission phase of the APD-TDMA is summarized in Figure 5. In this scheduling procedure, the local time of UNs are not involved. Therefore, time synchronization is not necessary in the proposed APD-TDMA.
## 2 Materials and Methods
### Modeling the CV Data  
As mentioned earlier, this study attempted to explicitly deal with zero WTP in analyzing the DC CV data. To this end, the spike model given in [PERSON] [40], [PERSON] [41] and [PERSON] [42] was applied. [PERSON]'s [43] approach to modeling DC CV data was also used. Therefore, the CV data model used in this study was the 1.5B DC spike model. The 1.5B DC spike model can explicitly reflect zero WTP as well as positive WTP responses. The mean WTP estimate obtained from analyzing the 1.5B DC spike model using a total of 1000 observations was considered reasonable in its use for information about the benefits ensuing from the reduction. For the application of this model, the cumulative distribution function (cdf) of WTP had to first be defined. This study adopted a logistic function that is almost always applied in the spike model. Thus, the cdf, \(M_{E}(\cdot)\), can be specified as:  
\[M_{E}(E;\tau_{0},\tau_{1})=\begin{cases}\left[1+\exp(\tau_{0}-\tau_{1}E)\right] ^{-1}&if\ E\geq 0\\ 0&if\ E<0\end{cases} \tag{2}\]where \(\tau_{0}\) and \(\tau_{1}\) are parameters of \(M_{E}(\cdot)\). If \(E=0\), the equation in the first line on the right side becomes the spike. Thus, the spike means \(\Pr(E=0)\). \(E\) is a bid presented to respondents and \(\Pr(\cdot)\) means a probability.  
Concerning the model, the log-likelihood function can be specified as:  
\[\begin{array}{ll}\ln L=&\sum\limits_{q=1}^{1000}\left(\left(I_{q}^{YY}+I_{q} ^{Y}\right)\ln\left[1-M_{E}\left(E_{q}^{H},\tau_{0},\tau_{1}\right)\right]\right. \\ &+\left(I_{q}^{YN}+I_{q}^{NY}\right)\ln\left[M_{E}\left(E_{q}^{H},\tau_{0}, \tau_{1}\right)-M_{E}\left(E_{q}^{L},\tau_{0},\tau_{1}\right)\right]\\ &+\left(I_{q}^{NY}+I_{q}^{NNNY}\right)\ln\left[M_{E}\left(E_{q}^{L};\tau_{0}, \tau_{1}\right)-M_{E}\left(0;\tau_{0},\tau_{1}\right)\right]\\ &+\left(I_{q}^{NN}+I_{q}^{NNNN}\right)\ln M_{E}\left(0;\tau_{0},\tau_{1} \right)\end{array} \tag{3}\]  
Maximum likelihood (ML) estimation method relates to obtaining parameter estimates that maximize the log-likelihood function. This study employed the ML estimation method. Thus, the estimates for \(\tau_{0}\) and were obtained by maximizing Equation (3). In addition, the mean WTP was derived from Equation (2) as [44; 45]:  
\[\left(1/\tau_{1}\right)\ln\left[1+\exp(\tau_{0})\right] \tag{4}\]