esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 6 Shard Recovery and Reconfiguration
The algorithm of shard recovery is presented in Algorithm 3. The input is \(cTx^{c}\)s, \(chainA^{\prime}\) is provided by a requester, and \(chainA\) is retrieved from shard \(A\). ```
0:\(cTx^{c}\)s, \(chainA^{\prime}\), \(chainA\)
0: Recovery of Shard \(A\)
0: For a node within shard \(A\)
1: The node sends \(chainA^{\prime}\) to shard \(D\) and \(E\) to accuse shard \(A\) of being corrupted
1: For shards \(A\), \(D\) and \(E\)
2: According to \(cTx^{c}\)s, the lastest trusted block, \(ltBlock\), is confirmed through the majority principle
3: For both shards \(D\) and \(E\)
3:if\(ltBlock\) is agreed by shard \(A\)then
4: Calculate the reputation value of shard \(A\), \(shRep\), according to (9)
5:if\(shRep<th2\)then
6: Verify transactions after \(ltBlock\) in \(chainA\)
7:if any false transaction found in \(chainA\)then
8: Shard \(A\) is reconfigured
9:\(ltBlock\) is the latest block of the chain in shard \(A\)
10:else
11: Shard \(A\) is reconfigured
12:\(ltBlock\) is the latest block of the chain in shard \(A\)
```  
**Algorithm 3** Recovery of Shard \(A\) by shard \(D\) and \(E\). Figure 6: According to (9), \(\lambda=10\) and \(\epsilon=0.5\). The lists of \(r\) of nodes in shard1, shard2 and shard3 are \(\{0,0,0.5,0.5\}\), \(\{0.5,0.5,0,0\}\) and \(\{0.1,0.2,0.3,0.4\}\), respectively. Hence, \(T=4\) and \(u=0.25\). The reputation values of shard1, shard2 and shard3 are presented with the increase of nodes in the shards. Moreover, an adaptive shard reconfiguration is devised. Compared to a fixed epoch widely used in traditional shard reconfiguration, e.g., Elastico [26] and Monoxide [27], the dynamic shard reconfiguration is based on the shard reputation in (9). That is, when the reputation value of a shard is less than \(th3\), \(the3<th2\), the shard is reconfigured by the shuffling of nodes. The nodes with more events included in the blacklist are removed from the shard.
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 4 Metabolic Regulation of Secondary Metabolites
### Metabolic Regulation by Biotic Stresses  
#### 4.2.1 Metabolic Regulation by Bacteria  
The biosynthesis of secondary metabolites can be regulated by either pathogenic and non-pathogenic bacteria or their extracts and components.  
If rice leaves were infected with the bacterial pathogen _Xanthomonas oryzae_, phenylamides were induced. They were identified as \(N\)-feruloylagmatine (**244**), \(N\)-feruloylputrescine (**245**), \(N\)-benzoylserotonin (**249**), \(N\)-benzoytryptamine (**250**), \(N\)-benzoyltryptamine (**251**), \(N\)-_trans_-cinnamoylserotonin (**252**), \(N\)-_trans_-cinnamoyltryptamine (**254**), \(N\)-\(p\)-coumaroylserotonin (**255**), and \(N\)-feruloylserotonin (**256**) [116].  
The bacterial phytotoxin coronatine was isolated from a _Pseudomonas syringae_ pv. _atropurpurea_ culture broth as a chlorosis-inducing compound in the leaves of Italian ryegrass (_Lolium multiflorum_). The structure of coronatine, an amide of coronafacic acid and coronamic acid, was somewhat related to that of JA. This phytotoxin could induce the accumulation of sakuraneetin (**81**) and momilactone A (**148**) in rice leaves. Coronatine-inducible sakuraneetin production is under the control of kinetin and ascorbicacid, as observed with JA. The similarity of the structures and elicitation manner of coronatine and JA suggests that they have a similar action mechanism leading to rice phytoalexin production [129].
## 4 Discussion  
### Methods of Deseasonalization  
Section 3.1 showed that the seasonality/periodicity seriously impacted the qualification of the relationship between SIF and the climatic factors. We eliminated the seasonality disturbance through the STL method. In fact, differencing and classical decomposition methods were also widely used to deseasonalize the seasonality of the time series [54], and these methods can be implemented by computer languages such as Python and R. However, both of them have weaknesses. Differencing refers to using the next cycle minus the time series of this cycle, which will reduce a cycle of the observations. For the data of this study, a cycle is a year, and the difference method will thus reduce the length of the original time series by one year. For classical decomposition methods, there are two forms of classical decomposition: an additive decomposition and a multiplicative decomposition [55]. They assume that the seasonal component repeats from year to year, which means they are unable to capture these seasonal changes over time. Classical decomposition methods are also unable to handle outliers properly. The STL method does not have these problems, and it can be robust to outliers. Although the STL method does not handle trading day or calendar variation automatically and only provides facilitation for additive decompositions, it does not affect the extraction of the seasonality of SIF and the four factors [52]. We believe that the STL method can play a greater role in the field of natural sciences.
# Marine Gelatine from Rest Raw Materials
## 4 Opportunities for Novel Applications of Fish Gelatine and Collagen
Such hydrolysates, consisting of various peptides, are relatively cheap and easy to produce, and many have shown to possess proven health and functional (antioxidant, antihypertensive, immunomododulatory and antimicrobial) benefits. Bioactive peptides from food proteins offer great potential for incorporation into functional foods and nutraceuticals [15; 89]. Some of these products, such as sardine muscle hydrolysate, have already been approved by FDA and EFSA for use in human nutrition [15]. [PERSON] et al. [90] have investigated angiotensin I converting enzyme (ACE I) inhibitory properties of tuna frame hydrolysates obtained by several proteolytic enzymes (alcalase, neutrale, pepsin, papain, \(\alpha\)-chymotrypsin and trypsin). Their results showed that peptic hydrolysate exhibited the highest ACE-I inhibitory activity, and a potent ACE-I inhibitory peptide composed of 21 amino acids was subsequently isolated. Antioxidant activity of a hydrolysate from Nile tilapia (_Oreochromis niloticus_) skin gelatine was examined by [PERSON] et al. [91]. Hydrolysates obtained by several enzymes (bromelain, papain, trypsin, flavourzyme, alcalase and neutrale) showed varying levels of antioxidant (ABTS radical scavenging, reducing power, ferrous ion chelating activity, inhibition of linoleic acid oxidation) activity and also a significant degree of ACE-I inhibitory activity. Beside their health benefits, fish gelatine hydrolysates also exhibit many useful techno-functional properties which may be utilized by the food industry. Hydrolysate of shark skin gelatine was tested as a cryoprotectant on surimi subjected to different freeze-thaw cycles by [PERSON] et al. [92], and the results indicated that gelatine hydrolysates with 10% degree hydrolysis was able to prevent the denaturation of surimi protein compared to a commercial cryoprotectant. [PERSON] et al. [93] reported that a tetrapeptide isolated from Amur surgeon skin gelatine showed antioxidative and cryoprotective effects in Japanese sea bass mince subjected to repeated freeze-thawing cycles. Such properties of gelatine hydrolysates have excellent potential for use by the food industry for improving shelf-life and oxidative stability of food products and commodities. Antimicrobial activity of fish gelatine hydrolysates has also been demonstrated by [PERSON] et al. [94]. Alcalase-derived glycosylated hydrolysates of fish gelatine had antioxidative and antimicrobial activity when incubated with Escherichia coli and Bacillus subtilis, indicating its potential for use as an antimicrobial agent.
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 3 User-Based Collaborative Filtering and Trust-Based Social Networks  
### User-Based Collaborative Filtering  
The core idea of user-based collaborative filtering is to analyze and mine user historical ratings to calculate similarity between users. Then, based on these user similarities, it predicts ratings to make recommendations.  
Methods for calculating similarity mainly include Euclidean distance, the Pearson correlation coefficient, cosine similarity, and modified cosine similarity. In this context, we use the Pearson correlation coefficient to calculate user similarity:  
\[sim(u,v)=\frac{\sum_{i\in I_{uv}}\left(\left(R_{ui}-\overline{R}_{u}\right) \left(R_{vi}-\overline{R}_{v}\right)\right)}{\sqrt{\sum_{i\in I_{uv}}\left(R _{ui}-\overline{R}_{u}\right)^{2}\sum_{i\in I_{uv}}\left(R_{vi}-\overline{R}_ {v}\right)^{2}}} \tag{1}\]  
where \(R\) represents the user-item rating matrix and \(R_{ui}\) and \(R_{vi}\) denote the ratings given by user \(u\) and user \(v\) to item \(i\), respectively. \(\overline{R}_{u}\) and \(\overline{R}_{v}\) represent the average ratings of user \(u\) and user \(v\). \(I_{uv}\) is the set of items that have been rated by both user \(u\) and user \(v\), and \(|I_{uv}|\) is the number of items that have been commonly rated by both users \(u\) and \(v\).  
After calculating the similarity between users, the principle for predicting the ratings for each user \(u\) and each item \(i\) is as follows. Select the top \(u\) users who are most similar to the user for whom the prediction is being made. The predicted ratings for the user are then calculated based on the similarity between these selected users and the predicting user, as well as their ratings for item \(i\). The formula for this calculation is:  
\[\hat{p}_{ui}=\frac{\sum_{v\in N(u)}sim(u,v)\cdot r_{vi}}{\sum_{v\in N(u)}|sim (u,v)|\cdot I(r_{vi}>0)} \tag{2}\]  
where \(N(u)\) represents the set of users most similar to user \(u\), \(sim(u,v)\) is the similarity between users \(u\) and \(v\), and \(r_{vi}\) denotes the rating given by user \(v\) to item \(i\). \(I(r_{vi}>0)\) is an indicator function, signifying whether user \(v\) has rated item \(i\) (i.e., whether the rating is greater than 0). If user \(v\) has not rated item \(i\), then user \(v\) is not included in the calculation of the predicted rating for item \(i\).
## 6 Results
### Computational Efficiency  
Figure 4 illustrates the average prediction time (in milliseconds) for five models--ConvLSTM, LSTM, Extended LSTM, Transformer, and XGBoost--across networks of increasing size (25, 50, 100, 200, 300 nodes). The graph shows that ConvLSTM's prediction time scales significantly as the number of nodes grows, surpassing 0.25 ms at 300 nodes. In contrast, LSTM, Extended LSTM, Transformer, and XGBoost remain relatively stable and low in prediction time, indicating they handle larger networks more efficiently. This highlights the importance of computational overhead when selecting a model for real-time or large-scale scenarios.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 2 Methodology
### Hierarchical Grouping Solution
To address this, regularization methods such as ridge estimation [65] or Tikhonov regularization [66] could be introduced. From Equations (12) and (24), the batch solution incurs a time complexity of approximately \(\mathcal{O}(n^{3})\) and a space complexity of \(\mathcal{O}(n^{2})\)[67, 68], where \(n\) denotes the total number of points in the cloud. This high cost stems from the need to simultaneously process all pairwise correspondences by constructing and solving a large-scale matrix equation. In contrast, the HiGoReg alleviates computational burden through a grouping strategy combined with recursive estimation. The point cloud is divided into \(m\) groups, each containing a subset of the data, and processed in sequence. This reduces the time complexity to \(\mathcal{O}\left(\frac{n^{3}}{m^{2}}\right)\), indicating that a larger number of groups leads to smaller per-iteration data volumes and, thus, lower time costs. The space complexity is similarly reduced to \(\mathcal{O}\left(\frac{n^{2}}{m}\right)\), reflecting decreased memory usage per group. However, an excessively large \(m\) results in too few points per group, causing instability in matrix operations and potentially increasing the total computation time. Moreover, a greater number of iterations may raise memory demands due to intermediate result storage. Therefore, in practice, it is critical to select an appropriate number of groups. A well-balanced \(m\) can optimize the trade-off between per-iteration complexity and the total number of iterations, thereby maximizing the computational efficiency of HiGoReg.
## 4 Wireless Transmission in WBANs
### WBAN Channel Models
Furthermore, various environmental models should be established, such as family chambers, hospitals and outdoors, which have different impacts on path loss and fade. Therefore, the environment in which WBAN communication occurs is highly dynamic and unstable, and a specific communication channel must be considered to guarantee good communication performance  
Figure 3: Possible communication links for body area networking. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Description** & **Frequency Band** & **Channel Model** \\ \hline S1 & Implant to Implant & 420-405 MHz & CM1 \\ S2 & Implant to Body Surface & 420-405 MHz & CM2 \\  & Body surface to & 13.5, 50, 400, 600, 900 MHz & \\ S3 & Body Surface & 2.4, 3.1–10.6 GHz & CM3 \\ S4 & Body Surface to External & 900 MHz, 2.4, 3.1–10.6 GHz & CM4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Descriptions of IEEE 802.15.6 channel models. Data from Ref. [82]. in a highly dynamic and unstable environment [83]. The IEEE standard channel models are only static, having no time varying effects and correlation features. Several dynamic WBAN channel models have been proposed, as shown in Table 10.
# Automated Diatom Classification (Part B): A Deep Learning Approach
## 4 Results and Discussion
### Original Dataset
The following list stated some species that are misclassified and the respective species with whom they are confused:  
* _Encyonema reichardtii_ (29): _Achnantthidium atonoides_ (2), _Achnantthidium cararelense_ (3), _Achnantthidium eutrophilium_ (6), _Gomphonema minutum_ (52). * _Achnanthidium rivulare_ (9): _Achnanthes subhudsonis_ (1), _Achnantthidium atomoides_ (2), _Achnanthidium eutrophilium_ (6), _Eolinna ninina_ (34), _Eolinna rhomelliptica_ (35). * _Nitzschia fossilis_ (71): _Nitzschia amphibia_ (66), _Nitzschia tropica_ (74). Even though the images are slightly different compared to the previous datasets, the classes that are more difficult to classify are similar. Finally, when the number of samples per species is over 1000, only two pairs of diatoms have some perceptible error. They are:  
* _Encyonema alpina_ (32) with _Encyonopsis minuta_ (33). * _Nitzschia amphibia_ (66) with _Nitzschia fossilis_ (71). Figures 16 and 17 show a summary of the most frequently pairs of species that are misclassified as mentioned above. \begin{table}
\begin{tabular}{l c c c} \hline
**Dataset** & **Samples per Class** & **Mean Accuracy (\%)** & **Standard Deviation** \\ \hline Normalized & 300 & 93.23 & 0.26 \\ Normalized & 700 & 97.55 & 0.12 \\ Normalized & 1000 & 98.84 & 0.15 \\ \hline \end{tabular}
\end{table} 
Table 6: Deep Learning experiments with the normalized database. Figure 15: Confusion matrix heat map for the normalized dataset with 1000 samples per class. Figure 16: Normalized dataset main confusions (part 1). Each row shows an example of the true species, a missclassified sample between two species and a sample of the second one are shown.
# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
###### Abstract
The chosen method and grouping procedure will directly influence the results. Cluster analysis is frequently used in all scientific fields such as pharmacy, medicine, energy, economy, biological issues, etc. Our research presents the differences between the results obtained by using two well-known clustering methods for the evaluation of financial performance taking into account three main financial ratios (Return on Sales--ROS, Return on Assets--ROA and Return on Equity--ROE). This study answers the question of whether it is worthwhile to process databases with large and variable data using cluster analysis with K-Mean or with K-Medoid in the field of financial performance. Moreover, it compares the results obtained using these two clustering methods.
## 2 Experimental Section
### Test Methods  
#### 2.2.1 Mechanical Strength Tests  
To investigate the modifications in strength characteristics of specimens additionally cured in a 30% strontium nitrate solution for 1, 3, and 7 days, compared to those cured in deionized water for the same periods, tests for compressive and flexural strength were conducted. The compressive strength test was performed on cube specimens in accordance with ASTM C109 [30]. For this test, a Universal Testing Machine (UTM) (HD-201) (Hyundai Precision Industry Co., Ltd., Seoul, Republic of Korea) was used as the load application device. The specimens were loaded at a rate of 900 N/sec until failure, and the load history was automatically recorded through a connected computer system. The maximum load recorded was then divided by the cross-sectional area to calculate the compressive strength. For the flexural strength, the three-point loading test method, as per ASTM C293 [31], was employed. The span length between supports was set at 150 mm, and the test was conducted using a UTM at a displacement rate of 0.005 mm/min. Since the load at fracture in the flexural strength test is not very high, a precise measurement was ensured in this study by using a separate load cell (DBHS-1t) to record load history. The flexural strength (\(f\)) was calculated using the load at the point of fracture with the following formula:  
\[f=\frac{3 PL}{2 bd^{2}} \tag{1}\]  
where \(f\) = flexural strength (MPa); \(P\) = applied load (N); \(L\) = span length (mm); \(b\) = width of specimen (mm); and \(d\) = depth of specimen (mm).
## Data and Methods
### Development of the Prediction Models  
An RF machine learning algorithm was used as the regression approach for this study. The RF is an efficient bagging-based ensemble learning method developed for improving the regression and classification tree by combining multiple decision trees [57].  
RF regression was implemented through Scikit-learn packages [58] in Python 3. The input sets were the single bands and VIs of each sensor and the AGB and Corg samples separated as follows: the samples collected at each point with a 0.25 m\({}^{2}\) sampler were proportionally estimated for plots of varying sizes (Group 1, Table 3). These were subjected to the normalized natural logarithmic transformation--NL statistical treatment (Group 2, Table 3). The plot sizes were defined based on the most commonly used sizes in studies estimating biomass and aboveground carbon in wetlands using remote sensing. These sizes include sample size, sensor pixel size, or resized size, and this was determined following an extensive literature review.  
In this manner, we generated six AGB and Corg models per sensor to compare accuracy in relation to treatments and plot sizes. Due to the characteristics of whole AGB and Corg values, the NL transformation was applied to strengthen the relationship with spectral data [13; 59]. For each decision tree in RF, we utilized the bootstrapping method (random sampling with replacement) to select the original dataset. At each bootstrap resampling step, 2/3 of the data (in-bag) were selected to build the decision tree without pruning. The other 1/3 of the data (out-of-bag--OOB) were used as evaluation data and to calculate the OOB error as an unbiased estimate of prediction error [60].  
The RF estimates the importance of predictors by calculating the total reduction in impurity (heterogeneity) brought about by these predictors. It is also known as the Gini importance. These variable importance values are then used to rank the predictors in terms of the strength of their relationship with the dependent variables [57; 58] of AGB and Corg.  
Finding the best combination of parameters is critical to the optimization of the model. To find the number of trees (Ntree) that best predicts AGB and Corg in each model, the Ntree parameter was optimized based on lower RMSE [18; 28]. The test with the lowest RMSE and the lowest number of trees provides the optimal number of decision trees in the RF for a good compromise between accuracy and computational time [61]. The Ntree values were tested from 50 to 1000 with an interval of 50, while the number of predictor variables (bands and VIs) tested at each node (Mtry) and the standard node size were accepted throughout the analysis, which allowed the regression trees to grow to their maximum size without pruning, based on the selection of predictor vectors that reduce the impurity of each node. The final regression model was based on the average value of all the results from the individual trees.
## 4 Our Proposed ADDGCN System
### Down-Sampling Dynamic Graph Convolution Module Based on Feature Augmentation  
#### 4.1.1 Feature Augmentation  
Due to the correlation among multiple features of traffic data, different features have varying effects on predicting traffic flow. In order to obtain a thorough comprehension of the inherent properties of time series data, this study introduces a feature augmentation layer utilizing a channel attention mechanism [33]. This layer consists of two submodules: temporal attention and channel attention. The channel attention submodule employs two multi-layer perceptrons to compress the input feature map, thereby rescaling features with a lower-dimensional distribution. Individual activation processes and self-threshold mechanisms are utilized to analyze the influence of each channel, determining the importance of each channel. Finally, the compressed feature maps are fused through an element-wise product and the ReLU activation function to obtain enhanced channel attention features. The temporal attention [34] submodule performs temporal feature fusion by using two 1D convolutional layers. Similar to the submodule for channel attention, this submodule determines the importance of each time point through the element product and sigmoid activation function, uses the convolution kernel to fuse the temporal features, and finally acquires the enhanced temporal attention features. The configuration of the feature enhancement module can be observed in Figure 3.  
Assuming a given input feature map is \(X_{1}\in R^{\mathbb{C}\times N\times T}\), the channel attention submodule can be expressed as:  
\[X_{2}=\sigma\Big{(}W_{2}RuLU\Big{(}W_{1}X_{1}+b_{1}\Big{)}+b_{2}\Big{)}\odot X _{1} \tag{2}\]  
where \(\odot\) stands for the multiplication of elements, \(\sigma\) represents the sigmoid function, and \(W_{1},W_{2}\) and \(b_{1},b_{2}\) represent the fully linked layer's weight parameters and biases. The input feature map \(X_{2}\in R^{\mathbb{C}\times N\times T}\) is processed through the channel attention submodule. The temporal attention submodule can be expressed as:  
\[X_{3}=\sigma(\Theta_{1,k}\times RuLU(\Theta_{1,k}\times X_{2}(t)))\odot X_{2}(t) \tag{3}\]  
where \(\Theta_{1,k}\) is the convolution kernel. After \(X_{3}\in R^{\mathbb{C}\times N\times T}\) is preprocessed, the weighted feature graph is used as the feature enhancement module's result for subsequent model training.  
The feature enhancement layer is based on the \"squeeze excitation\" structure [35]. It not only combines characteristics from varying scales but also automatically learns the importance of different features, enhancing the representation of feature maps.  
Figure 3: Feature augmentation module structure diagram.  
Figure 2: ADDGCN overall structure diagram.